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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b0d58e197e558a2ee3fbd0a7258438557a35fe18
| 1,652
|
py
|
Python
|
tests/test_color_scaler.py
|
mrsmith/colorgrade
|
aa71c51ab786f095ccac7c875ba9d4e29b055652
|
[
"MIT"
] | 1
|
2020-05-13T07:02:51.000Z
|
2020-05-13T07:02:51.000Z
|
tests/test_color_scaler.py
|
mrsmith/colorgrade
|
aa71c51ab786f095ccac7c875ba9d4e29b055652
|
[
"MIT"
] | 1
|
2020-05-13T07:08:16.000Z
|
2020-05-13T07:08:16.000Z
|
tests/test_color_scaler.py
|
mrsmith/colorgrade
|
aa71c51ab786f095ccac7c875ba9d4e29b055652
|
[
"MIT"
] | null | null | null |
from colorgrade import ColorScaler
from colour import Color
def color(name):
return Color(name).hex_l.replace("#", "hex")
def test_color_scaler_mid():
cs = ColorScaler(0, 0.5, 1)
assert cs.scale(-1.0) == color("lime")
assert cs.scale(0.0) == color("lime")
assert cs.scale(0.5) == color("yellow")
assert cs.scale(1.0) == color("red")
assert cs.scale(2.0) == color("red")
cs = ColorScaler(0, 0.0, 1)
assert cs.scale(-1.0) == color("yellow")
assert cs.scale(0.0) == color("yellow")
assert cs.scale(0.5) == color("#ff8000")
assert cs.scale(1.0) == color("red")
assert cs.scale(2.0) == color("red")
cs = ColorScaler(0, 1.0, 1)
assert cs.scale(-1.0) == color("lime")
assert cs.scale(0.0) == color("lime")
assert cs.scale(0.5) == color("#7fff00")
assert cs.scale(1.0) == color("yellow")
assert cs.scale(2.0) == color("yellow")
cs = ColorScaler(0, -1, 1)
assert cs.scale(-1.0) == color("yellow")
assert cs.scale(0.0) == color("yellow")
assert cs.scale(0.5) == color("#ff8000")
assert cs.scale(1.0) == color("red")
assert cs.scale(2.0) == color("red")
cs = ColorScaler(0, 2, 1)
assert cs.scale(-1.0) == color("lime")
assert cs.scale(0.0) == color("lime")
assert cs.scale(0.5) == color("#7fff00")
assert cs.scale(1.0) == color("yellow")
assert cs.scale(2.0) == color("yellow")
cs = ColorScaler(0, 0, 0)
assert cs.scale(-1.0) == color("yellow")
assert cs.scale(0.0) == color("yellow")
assert cs.scale(0.5) == color("yellow")
assert cs.scale(1.0) == color("yellow")
assert cs.scale(2.0) == color("yellow")
| 31.769231
| 48
| 0.585351
| 266
| 1,652
| 3.620301
| 0.101504
| 0.249221
| 0.404984
| 0.174455
| 0.86189
| 0.858775
| 0.858775
| 0.858775
| 0.856698
| 0.856698
| 0
| 0.071482
| 0.195521
| 1,652
| 51
| 49
| 32.392157
| 0.653123
| 0
| 0
| 0.731707
| 0
| 0
| 0.095642
| 0
| 0
| 0
| 0
| 0
| 0.731707
| 1
| 0.04878
| false
| 0
| 0.04878
| 0.02439
| 0.121951
| 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
|
7c07435b1f920c4e461ed8ec23548fe9606d2dcf
| 4,676
|
py
|
Python
|
tests/test_best_model.py
|
Vinit-source/MLOps-Web-service-for-multiclass-classification-of-digits
|
448a93a269c0ca0d91c491955ddab4049e40c526
|
[
"MIT"
] | null | null | null |
tests/test_best_model.py
|
Vinit-source/MLOps-Web-service-for-multiclass-classification-of-digits
|
448a93a269c0ca0d91c491955ddab4049e40c526
|
[
"MIT"
] | null | null | null |
tests/test_best_model.py
|
Vinit-source/MLOps-Web-service-for-multiclass-classification-of-digits
|
448a93a269c0ca0d91c491955ddab4049e40c526
|
[
"MIT"
] | null | null | null |
from sklearn.datasets import load_digits
from mnist_example import utils
from joblib import load
from sklearn.metrics import confusion_matrix
import numpy as np
# load images for test
digits = load_digits()
X, y = utils.preprocess(data=digits, rescale_factor=1)
# Load SVM model
best_model_path = "mnist_example/models/best_model_SVC/model.joblib"
clf = load(best_model_path)
def test_SVC_digit_correct_0():
i = 0
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_1():
i = 1
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_2():
i = 2
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_3():
i = 3
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_4():
i = 4
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_5():
i = 5
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_6():
i = 6
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_7():
i = 7
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_8():
i = 8
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_SVC_digit_correct_9():
i = 9
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
# load Decision Tree best model
best_model_path = "mnist_example/models/best_model_DecisionTreeClassifier/model.joblib"
clf = load(best_model_path)
def test_Tree_digit_correct_0():
i = 0
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_1():
i = 1
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_2():
i = 2
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_3():
i = 3
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_4():
i = 4
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_5():
i = 5
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_6():
i = 6
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_7():
i = 7
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_8():
i = 8
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
def test_Tree_digit_correct_9():
i = 9
idx = len(y) - np.argmax((y==i)[::-1]) - 1
# print(y[idx], idx)
prediction = clf.predict(X[idx].reshape((1, -1)))
assert(prediction==y[idx])
# def test_Tree_class_acc_0():
# use confusion_matrix
# cm.diagonal
# Reference: https://stackoverflow.com/a/50977153/15294463
| 28.864198
| 87
| 0.599444
| 759
| 4,676
| 3.555995
| 0.090909
| 0.029641
| 0.051871
| 0.066691
| 0.871804
| 0.869211
| 0.869211
| 0.869211
| 0.869211
| 0.807707
| 0
| 0.036556
| 0.192686
| 4,676
| 162
| 88
| 28.864198
| 0.678411
| 0.120616
| 0
| 0.738739
| 0
| 0
| 0.028152
| 0.028152
| 0
| 0
| 0
| 0
| 0.18018
| 1
| 0.18018
| false
| 0
| 0.045045
| 0
| 0.225225
| 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
|
b04354ecdf4e1683c1bd8d2b4413976d359d858b
| 32
|
py
|
Python
|
test/mock/orange.py
|
ryanwohara/modlib
|
8529dcf421138015c2392442d3bd8a94dbabc94c
|
[
"MIT"
] | null | null | null |
test/mock/orange.py
|
ryanwohara/modlib
|
8529dcf421138015c2392442d3bd8a94dbabc94c
|
[
"MIT"
] | 4
|
2015-09-15T20:19:12.000Z
|
2016-01-12T01:44:12.000Z
|
test/mock/orange.py
|
akerl/modlib
|
a9dcf5516f524071a0b3f4099fe0a179ecc43d9d
|
[
"MIT"
] | null | null | null |
def name():
return 'orange'
| 10.666667
| 19
| 0.59375
| 4
| 32
| 4.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 32
| 2
| 20
| 16
| 0.791667
| 0
| 0
| 0
| 0
| 0
| 0.1875
| 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
|
c680628eec07f32b3e4bce6e237bffb17726a938
| 168
|
py
|
Python
|
fem/gui/vtk_widget/vtk_graphics/interactor_styles/__init__.py
|
mjredmond/FEMApp
|
dd8cc53acf80d0a1bb83ce9c89bcfd51e85c6be8
|
[
"MIT"
] | 1
|
2019-08-03T21:40:26.000Z
|
2019-08-03T21:40:26.000Z
|
fem/gui/vtk_widget/vtk_graphics/interactor_styles/__init__.py
|
mjredmond/FEMApp
|
dd8cc53acf80d0a1bb83ce9c89bcfd51e85c6be8
|
[
"MIT"
] | null | null | null |
fem/gui/vtk_widget/vtk_graphics/interactor_styles/__init__.py
|
mjredmond/FEMApp
|
dd8cc53acf80d0a1bb83ce9c89bcfd51e85c6be8
|
[
"MIT"
] | null | null | null |
from __future__ import print_function, absolute_import
from .hovered_interactor_style import HoveredInteractorStyle
from .my_interactor_style import MyInteractorStyle
| 33.6
| 60
| 0.89881
| 19
| 168
| 7.421053
| 0.631579
| 0.212766
| 0.297872
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 168
| 4
| 61
| 42
| 0.915584
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0.333333
| 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
|
c686f866851d3f9b2e7a909674103327d5a311d1
| 3,400
|
py
|
Python
|
src/models/amplifier/simplified_tbj/voltage_divider_polarization/drawing.py
|
luanws/amplifier-circuits
|
cd19acae11eb1dc5f11efd3bee785caedbbc81b4
|
[
"Apache-2.0"
] | null | null | null |
src/models/amplifier/simplified_tbj/voltage_divider_polarization/drawing.py
|
luanws/amplifier-circuits
|
cd19acae11eb1dc5f11efd3bee785caedbbc81b4
|
[
"Apache-2.0"
] | null | null | null |
src/models/amplifier/simplified_tbj/voltage_divider_polarization/drawing.py
|
luanws/amplifier-circuits
|
cd19acae11eb1dc5f11efd3bee785caedbbc81b4
|
[
"Apache-2.0"
] | null | null | null |
from schemdraw import Drawing
from schemdraw import elements as elm
from src.utils import numerize
from .input import Input
def draw_void():
drawing = Drawing()
drawing += (transistor := elm.transistors.BjtNpn().right())
drawing.push()
drawing += elm.Resistor().up().label('Rc')
drawing += elm.Line().left().length(drawing.unit/2)
drawing.push()
drawing += elm.Dot()
drawing += elm.SourceV().up().reverse().label('Vcc')
drawing += elm.Ground().left()
drawing.pop()
drawing += elm.Line().left().length(drawing.unit/2)
drawing += elm.Resistor().down().label('R1')
drawing += elm.Line().down().length(0.24*drawing.unit)
drawing += elm.Dot()
drawing.push()
drawing += elm.Line().to(transistor.base)
drawing.pop()
drawing.push()
drawing += elm.Line().left().length(drawing.unit/4)
drawing += elm.Capacitor().left().reverse().label('C1')
drawing += elm.Line().left().length(drawing.unit/4)
drawing += elm.Dot().label('Vi')
drawing.pop()
drawing += elm.Resistor().label('R2')
drawing += elm.Ground()
drawing += elm.Resistor().label('Re').down().at(transistor.emitter).length(0.78*drawing.unit)
drawing += elm.Ground()
drawing += elm.Dot().at(transistor.emitter)
drawing += elm.Line().right().length(0.75*drawing.unit)
drawing += elm.Capacitor().label('Ce').down().length(0.78*drawing.unit)
drawing += elm.Ground()
drawing.pop()
drawing += elm.Dot()
drawing += elm.Capacitor().right().at(transistor.collector).label('C2')
drawing += elm.Dot().label('Vo')
return drawing
def draw(amplifier_input: Input):
R2 = numerize.format(amplifier_input.R2, unit='Ω')
R1 = numerize.format(amplifier_input.R1, unit='Ω')
Rc = numerize.format(amplifier_input.Rc, unit='Ω')
Re = numerize.format(amplifier_input.Re, unit='Ω')
Vcc = numerize.format(amplifier_input.Vcc, unit='V')
drawing = Drawing()
drawing += (transistor := elm.transistors.BjtNpn().right())
drawing.push()
drawing += elm.Resistor().up().label(Rc)
drawing += elm.Line().left().length(drawing.unit/2)
drawing.push()
drawing += elm.Dot()
drawing += elm.SourceV().up().reverse().label(Vcc)
drawing += elm.Ground().left()
drawing.pop()
drawing += elm.Line().left().length(drawing.unit/2)
drawing += elm.Resistor().down().label(R1)
drawing += elm.Line().down().length(0.24*drawing.unit)
drawing += elm.Dot()
drawing.push()
drawing += elm.Line().to(transistor.base)
drawing.pop()
drawing.push()
drawing += elm.Line().left().length(drawing.unit/4)
drawing += elm.Capacitor().left().reverse().label('C1')
drawing += elm.Line().left().length(drawing.unit/4)
drawing += elm.Dot().label('Vi')
drawing.pop()
drawing += elm.Resistor().label(R2)
drawing += elm.Ground()
drawing += elm.Resistor().label(Re).down().at(transistor.emitter).length(0.78*drawing.unit)
drawing += elm.Ground()
drawing += elm.Dot().at(transistor.emitter)
drawing += elm.Line().right().length(0.75*drawing.unit)
drawing += elm.Capacitor().label('Ce').down().length(0.78*drawing.unit)
drawing += elm.Ground()
drawing.pop()
drawing += elm.Dot()
drawing += elm.Capacitor().right().at(transistor.collector).label('C2')
drawing += elm.Dot().label('Vo')
return drawing
| 30.357143
| 97
| 0.630882
| 437
| 3,400
| 4.892449
| 0.135011
| 0.233863
| 0.091674
| 0.078578
| 0.846586
| 0.846586
| 0.846586
| 0.846586
| 0.846586
| 0.846586
| 0
| 0.01552
| 0.166176
| 3,400
| 111
| 98
| 30.630631
| 0.738624
| 0
| 0
| 0.746988
| 0
| 0
| 0.010588
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.024096
| false
| 0
| 0.048193
| 0
| 0.096386
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
c6b6a75be9aacaf2b44208231822111d4b005563
| 161
|
py
|
Python
|
tests/todo/kind_params_fwrap_doctest.py
|
wilsonify/fwrap
|
f2e20eb55eaa3de72905e2ef28198da00eebe262
|
[
"BSD-3-Clause"
] | 23
|
2015-02-25T00:24:15.000Z
|
2021-09-08T01:35:45.000Z
|
tests/todo/kind_params_fwrap_doctest.py
|
fwrap/fwrap
|
61a56f2d0050096b4973d88e5f11cfac2ef01a4b
|
[
"BSD-3-Clause"
] | 1
|
2021-09-08T01:45:02.000Z
|
2021-09-08T01:45:02.000Z
|
tests/todo/kind_params_fwrap_doctest.py
|
fwrap/fwrap
|
61a56f2d0050096b4973d88e5f11cfac2ef01a4b
|
[
"BSD-3-Clause"
] | 4
|
2015-03-22T01:33:39.000Z
|
2021-09-09T15:25:44.000Z
|
from kind_params_fwrap import *
def test_kind_params():
'''
>>> test_kind_params()
True
'''
return kind_params(5, 5, 5) == (16, 17.0, 18.0)
| 17.888889
| 51
| 0.583851
| 24
| 161
| 3.625
| 0.583333
| 0.45977
| 0.321839
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 0.248447
| 161
| 8
| 52
| 20.125
| 0.628099
| 0.167702
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 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
| 1
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
c6fc30fdd66fa2620c246c2022600f1823e6918f
| 155
|
py
|
Python
|
__init__.py
|
mmcelhan/stock_market_calendar
|
be393c20a4f53c207e0727bff4331e254b1fb70e
|
[
"MIT"
] | null | null | null |
__init__.py
|
mmcelhan/stock_market_calendar
|
be393c20a4f53c207e0727bff4331e254b1fb70e
|
[
"MIT"
] | null | null | null |
__init__.py
|
mmcelhan/stock_market_calendar
|
be393c20a4f53c207e0727bff4331e254b1fb70e
|
[
"MIT"
] | null | null | null |
from .market_open import strip_date
from .market_open import to_string_date
from .market_open import to_datetime_date
from .market_open import market_open
| 31
| 41
| 0.870968
| 26
| 155
| 4.807692
| 0.346154
| 0.4
| 0.448
| 0.64
| 0.608
| 0.416
| 0
| 0
| 0
| 0
| 0
| 0
| 0.103226
| 155
| 4
| 42
| 38.75
| 0.899281
| 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
|
059bc4ca01c8a2c57af96b8f1b3832ad771a3489
| 12,597
|
py
|
Python
|
src/ign-api/img_test.py
|
flyingcakes85/ImageGoNord
|
1e4413af103bbfa1f6a08f3cd70c6d56359ab3d8
|
[
"MIT"
] | 572
|
2020-08-13T18:52:07.000Z
|
2022-03-31T05:40:06.000Z
|
src/ign-api/img_test.py
|
flyingcakes85/ImageGoNord
|
1e4413af103bbfa1f6a08f3cd70c6d56359ab3d8
|
[
"MIT"
] | 56
|
2020-08-13T16:49:35.000Z
|
2022-03-04T16:52:47.000Z
|
src/ign-api/img_test.py
|
flyingcakes85/ImageGoNord
|
1e4413af103bbfa1f6a08f3cd70c6d56359ab3d8
|
[
"MIT"
] | 22
|
2020-09-11T16:59:38.000Z
|
2022-03-06T23:03:02.000Z
|
img = 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'
| 6,298.5
| 12,596
| 0.969755
| 363
| 12,597
| 33.652893
| 0.99449
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136097
| 0.000238
| 12,597
| 1
| 12,597
| 12,597
| 0.833889
| 0
| 0
| 0
| 0
| 1
| 0.999286
| 0.999286
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
05ebb47a34e0c7f018731a9afe5097355eb26cb4
| 107
|
py
|
Python
|
kklib/__init__.py
|
kastnerkyle/kklib
|
6e6c1dc7fc4f24f8c1017fdb6edaf27237165949
|
[
"BSD-3-Clause"
] | 1
|
2019-03-19T15:15:48.000Z
|
2019-03-19T15:15:48.000Z
|
kklib/__init__.py
|
kastnerkyle/kklib
|
6e6c1dc7fc4f24f8c1017fdb6edaf27237165949
|
[
"BSD-3-Clause"
] | null | null | null |
kklib/__init__.py
|
kastnerkyle/kklib
|
6e6c1dc7fc4f24f8c1017fdb6edaf27237165949
|
[
"BSD-3-Clause"
] | null | null | null |
from .core import run_loop
from .core import get_saved_model_defs
from .core import get_saved_model_config
| 26.75
| 40
| 0.859813
| 19
| 107
| 4.473684
| 0.526316
| 0.282353
| 0.494118
| 0.4
| 0.635294
| 0.635294
| 0
| 0
| 0
| 0
| 0
| 0
| 0.11215
| 107
| 3
| 41
| 35.666667
| 0.894737
| 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
|
af0de1c990e5aa737b5a546954803a2eba142852
| 138
|
py
|
Python
|
homeworks/ilya_nilov/lesson09/test_lvl02.py
|
tgrx/Z22
|
b2539682ff26c8b6d9f63a7670c8a9c6b614a8ff
|
[
"Apache-2.0"
] | null | null | null |
homeworks/ilya_nilov/lesson09/test_lvl02.py
|
tgrx/Z22
|
b2539682ff26c8b6d9f63a7670c8a9c6b614a8ff
|
[
"Apache-2.0"
] | 8
|
2019-11-15T18:15:56.000Z
|
2020-02-03T18:05:05.000Z
|
homeworks/ilya_nilov/lesson09/test_lvl02.py
|
tgrx/Z22
|
b2539682ff26c8b6d9f63a7670c8a9c6b614a8ff
|
[
"Apache-2.0"
] | null | null | null |
from homeworks.ilya_nilov.lesson09.level02 import rotate_left
def test():
assert rotate_left([1, 2, 3, 4, 5], 2) == [3, 4, 5, 1, 2]
| 23
| 61
| 0.652174
| 25
| 138
| 3.48
| 0.68
| 0.229885
| 0.068966
| 0.091954
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.132743
| 0.181159
| 138
| 5
| 62
| 27.6
| 0.637168
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 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
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
af240328ad55cc172de956207b95b67443ff77f7
| 10,386
|
py
|
Python
|
tests/b_integration/test_accounts.py
|
brianmay/python-tldap
|
8141d8d6768afb3da045099c821ba1f7e0f4d121
|
[
"BSD-3-Clause"
] | 11
|
2015-02-26T03:25:06.000Z
|
2017-06-16T09:59:25.000Z
|
tests/b_integration/test_accounts.py
|
brianmay/python-tldap
|
8141d8d6768afb3da045099c821ba1f7e0f4d121
|
[
"BSD-3-Clause"
] | 58
|
2017-05-01T00:19:53.000Z
|
2021-07-15T13:01:15.000Z
|
tests/b_integration/test_accounts.py
|
brianmay/python-tldap
|
8141d8d6768afb3da045099c821ba1f7e0f4d121
|
[
"BSD-3-Clause"
] | 2
|
2015-09-01T23:46:03.000Z
|
2015-11-26T02:00:41.000Z
|
import os
from ldap3.core import exceptions
import pytest
from pytest_bdd import scenarios, when, then, parsers
import tldap.database
from tldap import Q
import tldap.backend.base
from tldap.django.models import Counters
from tldap.exceptions import ObjectDoesNotExist
from tests.database import Account
scenarios('accounts.feature')
@pytest.fixture
def account(ldap):
account = Account({
'uid': 'tux',
'givenName': "Tux",
'sn': "Torvalds",
'cn': "Tux Torvalds",
'telephoneNumber': "000",
'mail': "tuz@example.org",
'o': "Linux Rules",
'userPassword': "silly",
'homeDirectory': "/home/tux",
'uidNumber': 10,
'gidNumber': 10,
})
yield tldap.database.insert(account)
@when(parsers.cfparse('we create a account called {name}'))
def step_create_account(ldap, name):
""" Test if we can create a account. """
account = Account({
'uid': name,
'givenName': "Tux",
'sn': "Torvalds",
'cn': "Tux Torvalds",
'telephoneNumber': "000",
'mail': "tuz@example.org",
'o': "Linux Rules",
'userPassword': "silly",
'homeDirectory': "/home/tux",
'uidNumber': 10,
'gidNumber': 10,
})
tldap.database.insert(account)
@when(parsers.cfparse('we modify a account called {name}'))
def step_modify_account(ldap, name):
""" Test if we can modify a account. """
account = tldap.database.get_one(Account, Q(uid=name))
changes = tldap.database.changeset(account, {
'sn': "Tux",
'givenName': "Super",
'cn': "Super Tux",
})
tldap.database.save(changes)
account = tldap.database.get_one(Account, Q(uid=name))
print("modify", account['cn'])
@when(parsers.cfparse('we rename a account called {name} to {new_name}'))
def step_rename_account(ldap, name, new_name):
""" Test if we can rename a account. """
account = tldap.database.get_one(Account, Q(uid=name))
tldap.database.rename(account, uid=new_name)
@when(parsers.cfparse('we move a account called {name} to {new_dn}'))
def step_move_account(ldap, name, new_dn):
""" Test if we can move a account. """
account = tldap.database.get_one(Account, Q(uid=name))
tldap.database.rename(account, new_dn)
@when(parsers.cfparse('we delete a account called {name}'))
def step_delete_account(ldap, name):
""" Test if we can delete a account. """
account = tldap.database.get_one(Account, Q(uid=name))
tldap.database.delete(account)
@then('we should be able to search for a account')
def step_search_account(ldap):
""" Test we can search. """
list(tldap.database.search(Account))
@then('we should not be able to search for a account')
def step_not_search_account(ldap):
""" Test we can search. """
with pytest.raises(exceptions.LDAPInvalidCredentialsResult):
list(tldap.database.search(Account))
@then(parsers.cfparse('we should be able to get a account called {name}'))
def step_get_account_success(ldap, context, name):
account = tldap.database.get_one(Account, Q(uid=name))
context['obj'] = account
print("get", account['cn'])
@then(parsers.cfparse('we should not be able to get a account called {name}'))
def step_get_account_not_found(ldap, name):
with pytest.raises(ObjectDoesNotExist):
tldap.database.get_one(Account, Q(uid=name))
@then(parsers.cfparse(
'we should be able to get a account at dn {dn} called {name}'))
def step_get_account_dn_success(ldap, context, name, dn):
context['obj'] = tldap.database.get_one(Account, Q(uid=name), base_dn=dn)
@then(parsers.cfparse(
'we should not be able to get a account at dn {dn} called {name}'))
def step_get_account_dn_not_found(ldap, name, dn):
with pytest.raises(ObjectDoesNotExist):
tldap.database.get_one(Account, Q(uid=name), base_dn=dn)
@then(parsers.cfparse('we should be able to find {count:d} accounts'))
def step_count_accounts(ldap, count):
assert count == len(list(tldap.database.search(Account, None)))
@pytest.mark.django_db(transaction=True)
def test_create(ldap):
""" Test create LDAP object. """
# Create the object.
account_1 = Account({
'uid': "tux1",
'givenName': "Tux",
'sn': "Torvalds",
'cn': "Tux Torvalds",
'telephoneNumber': "000",
'mail': "tuz@example.org",
'o': "Linux Rules",
'userPassword': "silly",
'homeDirectory': "/home/tux",
'gidNumber': 10,
})
account_1 = tldap.database.insert(account_1)
assert account_1['uidNumber'] == [10000]
account_2 = Account({
'uid': "tux2",
'givenName': "Tux",
'sn': "Torvalds",
'cn': "Tux Torvalds",
'telephoneNumber': "000",
'mail': "tuz@example.org",
'o': "Linux Rules",
'userPassword': "silly",
'homeDirectory': "/home/tux",
'gidNumber': 10,
})
account_2 = tldap.database.insert(account_2)
assert account_2['uidNumber'] == [10001]
account_3 = Account({
'uid': "tux3",
'givenName': "Tux",
'sn': "Torvalds",
'cn': "Tux Torvalds",
'telephoneNumber': "000",
'mail': "tuz@example.org",
'o': "Linux Rules",
'userPassword': "silly",
'homeDirectory': "/home/tux",
'gidNumber': 10,
})
account_3 = tldap.database.insert(account_3)
assert account_3['uidNumber'] == [10002]
@pytest.mark.django_db(transaction=True)
def test_create_with_reset(ldap):
""" Test create LDAP object. """
# Create the object.
account_1 = Account({
'uid': "tux1",
'givenName': "Tux",
'sn': "Torvalds",
'cn': "Tux Torvalds",
'telephoneNumber': "000",
'mail': "tuz@example.org",
'o': "Linux Rules",
'userPassword': "silly",
'homeDirectory': "/home/tux",
'gidNumber': 10,
})
account_1 = tldap.database.insert(account_1)
assert account_1['uidNumber'] == [10000]
Counters.objects.all().delete()
account_2 = Account({
'uid': "tux2",
'givenName': "Tux",
'sn': "Torvalds",
'cn': "Tux Torvalds",
'telephoneNumber': "000",
'mail': "tuz@example.org",
'o': "Linux Rules",
'userPassword': "silly",
'homeDirectory': "/home/tux",
'gidNumber': 10,
})
account_2 = tldap.database.insert(account_2)
assert account_2['uidNumber'] == [10001]
account_3 = Account({
'uid': "tux3",
'givenName': "Tux",
'sn': "Torvalds",
'cn': "Tux Torvalds",
'telephoneNumber': "000",
'mail': "tuz@example.org",
'o': "Linux Rules",
'userPassword': "silly",
'homeDirectory': "/home/tux",
'gidNumber': 10,
})
account_3 = tldap.database.insert(account_3)
assert account_3['uidNumber'] == [10002]
def test_lock_account(account, ldap: tldap.backend.base.LdapBase):
# Check account is unlocked.
assert account.get_as_single('locked') is False
assert account.get_as_single('loginShell') == "/bin/bash"
assert ldap.check_password(account.get_as_single('dn'), 'silly') is True
# Lock account.
changes = tldap.database.changeset(account, {'locked': True})
account = tldap.database.save(changes)
# Check account is locked.
assert account.get_as_single('locked') is True
assert account.get_as_single('loginShell') == "/locked/bin/bash"
account = tldap.database.get_one(Account, Q(uid='tux'))
assert account.get_as_single('locked') is True
assert account.get_as_single('loginShell') == "/locked/bin/bash"
assert ldap.check_password(account.get_as_single('dn'), 'silly') is False
# Change the login shell
changes = tldap.database.changeset(account, {'loginShell': '/bin/zsh'})
account = tldap.database.save(changes)
# Check the account is still locked.
assert account.get_as_single('locked') is True
assert account.get_as_single('loginShell') == "/locked/bin/zsh"
account = tldap.database.get_one(Account, Q(uid='tux'))
assert account.get_as_single('locked') == True
assert account.get_as_single('loginShell') == "/locked/bin/zsh"
assert ldap.check_password(account.get_as_single('dn'), 'silly') is False
# Unlock the account.
changes = tldap.database.changeset(account, {'locked': False})
account = tldap.database.save(changes)
# Check the account is now unlocked.
assert account.get_as_single('locked') is False
assert account.get_as_single('loginShell') == "/bin/zsh"
account = tldap.database.get_one(Account, Q(uid='tux'))
assert account.get_as_single('locked') is False
assert account.get_as_single('loginShell') == "/bin/zsh"
assert ldap.check_password(account.get_as_single('dn'), 'silly') is True
@pytest.mark.skipif(os.environ['LDAP_TYPE'] != 'openldap', reason="Require OpenLDAP")
def test_lock_account_openldap(account, ldap: tldap.backend.base.LdapBase):
# Check account is unlocked.
assert account.get_as_single('locked') is False
assert account.get_as_list('pwdAccountLockedTime') == []
assert ldap.check_password(account.get_as_single('dn'), 'silly') is True
# Lock account.
changes = tldap.database.changeset(account, {'locked': True})
account = tldap.database.save(changes)
# Check account is locked.
assert account.get_as_single('locked') is True
assert account.get_as_list('pwdAccountLockedTime') == ["000001010000Z"]
account = tldap.database.get_one(Account, Q(uid='tux'))
assert account.get_as_single('locked') is True
assert account.get_as_list('pwdAccountLockedTime') == ["000001010000Z"]
assert ldap.check_password(account.get_as_single('dn'), 'silly') is False
# Unlock the account.
changes = tldap.database.changeset(account, {'locked': False})
account = tldap.database.save(changes)
# Check the account is now unlocked.
assert account.get_as_single('locked') is False
assert account.get_as_list('pwdAccountLockedTime') == []
account = tldap.database.get_one(Account, Q(uid='tux'))
assert account.get_as_single('locked') is False
assert account.get_as_list('pwdAccountLockedTime') == []
assert ldap.check_password(account.get_as_single('dn'), 'silly') is True
| 31.377644
| 85
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| 0.205854
| 10,386
| 330
| 86
| 31.472727
| 0.774733
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| 0.235336
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| 0
| 0.163793
| 1
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| false
| 0.064655
| 0.043103
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| 0.116379
| 0.008621
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| null | 0
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| 0
| 0
|
0
| 8
|
af4f7517bd3c648a0f66fe014fd44c33266d9118
| 130
|
py
|
Python
|
abbot/galaxy/__init__.py
|
narfman0/abbot
|
206ed692299956fbccb457c6e845bc92682ea79f
|
[
"MIT"
] | null | null | null |
abbot/galaxy/__init__.py
|
narfman0/abbot
|
206ed692299956fbccb457c6e845bc92682ea79f
|
[
"MIT"
] | null | null | null |
abbot/galaxy/__init__.py
|
narfman0/abbot
|
206ed692299956fbccb457c6e845bc92682ea79f
|
[
"MIT"
] | null | null | null |
from abbot.galaxy.celestial_body import CelestialBody
from abbot.galaxy.chunk import Chunk
from abbot.galaxy.galaxy import Galaxy
| 32.5
| 53
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| 19
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|
0
| 7
|
bb6959b7b007fcaef3d0b8b4fdac9c7ddef9ae2b
| 37,188
|
py
|
Python
|
operators/ibmcloud-operator/python/pulumi_pulumi_kubernetes_crds_operators_ibmcloud_operator/ibmcloud/v1alpha1/outputs.py
|
pulumi/pulumi-kubernetes-crds
|
372c4c0182f6b899af82d6edaad521aa14f22150
|
[
"Apache-2.0"
] | null | null | null |
operators/ibmcloud-operator/python/pulumi_pulumi_kubernetes_crds_operators_ibmcloud_operator/ibmcloud/v1alpha1/outputs.py
|
pulumi/pulumi-kubernetes-crds
|
372c4c0182f6b899af82d6edaad521aa14f22150
|
[
"Apache-2.0"
] | 2
|
2020-09-18T17:12:23.000Z
|
2020-12-30T19:40:56.000Z
|
operators/ibmcloud-operator/python/pulumi_pulumi_kubernetes_crds_operators_ibmcloud_operator/ibmcloud/v1alpha1/outputs.py
|
pulumi/pulumi-kubernetes-crds
|
372c4c0182f6b899af82d6edaad521aa14f22150
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by crd2pulumi. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from . import outputs
__all__ = [
'BindingSpec',
'BindingSpecParameters',
'BindingSpecParametersValueFrom',
'BindingSpecParametersValueFromConfigMapKeyRef',
'BindingSpecParametersValueFromSecretKeyRef',
'BindingStatus',
'ServiceSpec',
'ServiceSpecContext',
'ServiceSpecParameters',
'ServiceSpecParametersValueFrom',
'ServiceSpecParametersValueFromConfigMapKeyRef',
'ServiceSpecParametersValueFromSecretKeyRef',
'ServiceStatus',
'ServiceStatusContext',
'ServiceStatusParameters',
'ServiceStatusParametersValueFrom',
'ServiceStatusParametersValueFromConfigMapKeyRef',
'ServiceStatusParametersValueFromSecretKeyRef',
]
@pulumi.output_type
class BindingSpec(dict):
"""
BindingSpec defines the desired state of Binding
"""
def __init__(__self__, *,
service_name: str,
alias: Optional[str] = None,
parameters: Optional[Sequence['outputs.BindingSpecParameters']] = None,
role: Optional[str] = None,
secret_name: Optional[str] = None,
service_namespace: Optional[str] = None):
"""
BindingSpec defines the desired state of Binding
"""
pulumi.set(__self__, "service_name", service_name)
if alias is not None:
pulumi.set(__self__, "alias", alias)
if parameters is not None:
pulumi.set(__self__, "parameters", parameters)
if role is not None:
pulumi.set(__self__, "role", role)
if secret_name is not None:
pulumi.set(__self__, "secret_name", secret_name)
if service_namespace is not None:
pulumi.set(__self__, "service_namespace", service_namespace)
@property
@pulumi.getter(name="serviceName")
def service_name(self) -> str:
return pulumi.get(self, "service_name")
@property
@pulumi.getter
def alias(self) -> Optional[str]:
return pulumi.get(self, "alias")
@property
@pulumi.getter
def parameters(self) -> Optional[Sequence['outputs.BindingSpecParameters']]:
return pulumi.get(self, "parameters")
@property
@pulumi.getter
def role(self) -> Optional[str]:
return pulumi.get(self, "role")
@property
@pulumi.getter(name="secretName")
def secret_name(self) -> Optional[str]:
return pulumi.get(self, "secret_name")
@property
@pulumi.getter(name="serviceNamespace")
def service_namespace(self) -> Optional[str]:
return pulumi.get(self, "service_namespace")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class BindingSpecParameters(dict):
"""
Param represents a key-value pair
"""
def __init__(__self__, *,
name: str,
attributes: Optional[Mapping[str, Mapping[str, Any]]] = None,
value: Optional[Any] = None,
value_from: Optional['outputs.BindingSpecParametersValueFrom'] = None):
"""
Param represents a key-value pair
:param str name: Name representing the key.
:param Mapping[str, Mapping[str, Any]] attributes: A parameter may have attributes (e.g. message hub topic might have partitions)
:param Any value: Defaults to null.
:param 'BindingSpecParametersValueFromArgs' value_from: Source for the value. Cannot be used if value is not empty.
"""
pulumi.set(__self__, "name", name)
if attributes is not None:
pulumi.set(__self__, "attributes", attributes)
if value is not None:
pulumi.set(__self__, "value", value)
if value_from is not None:
pulumi.set(__self__, "value_from", value_from)
@property
@pulumi.getter
def name(self) -> str:
"""
Name representing the key.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def attributes(self) -> Optional[Mapping[str, Mapping[str, Any]]]:
"""
A parameter may have attributes (e.g. message hub topic might have partitions)
"""
return pulumi.get(self, "attributes")
@property
@pulumi.getter
def value(self) -> Optional[Any]:
"""
Defaults to null.
"""
return pulumi.get(self, "value")
@property
@pulumi.getter(name="valueFrom")
def value_from(self) -> Optional['outputs.BindingSpecParametersValueFrom']:
"""
Source for the value. Cannot be used if value is not empty.
"""
return pulumi.get(self, "value_from")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class BindingSpecParametersValueFrom(dict):
"""
Source for the value. Cannot be used if value is not empty.
"""
def __init__(__self__, *,
config_map_key_ref: Optional['outputs.BindingSpecParametersValueFromConfigMapKeyRef'] = None,
secret_key_ref: Optional['outputs.BindingSpecParametersValueFromSecretKeyRef'] = None):
"""
Source for the value. Cannot be used if value is not empty.
:param 'BindingSpecParametersValueFromConfigMapKeyRefArgs' config_map_key_ref: Selects a key of a ConfigMap.
:param 'BindingSpecParametersValueFromSecretKeyRefArgs' secret_key_ref: Selects a key of a secret in the resource namespace
"""
if config_map_key_ref is not None:
pulumi.set(__self__, "config_map_key_ref", config_map_key_ref)
if secret_key_ref is not None:
pulumi.set(__self__, "secret_key_ref", secret_key_ref)
@property
@pulumi.getter(name="configMapKeyRef")
def config_map_key_ref(self) -> Optional['outputs.BindingSpecParametersValueFromConfigMapKeyRef']:
"""
Selects a key of a ConfigMap.
"""
return pulumi.get(self, "config_map_key_ref")
@property
@pulumi.getter(name="secretKeyRef")
def secret_key_ref(self) -> Optional['outputs.BindingSpecParametersValueFromSecretKeyRef']:
"""
Selects a key of a secret in the resource namespace
"""
return pulumi.get(self, "secret_key_ref")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class BindingSpecParametersValueFromConfigMapKeyRef(dict):
"""
Selects a key of a ConfigMap.
"""
def __init__(__self__, *,
key: str,
name: Optional[str] = None,
optional: Optional[bool] = None):
"""
Selects a key of a ConfigMap.
:param str key: The key to select.
:param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
:param bool optional: Specify whether the ConfigMap or its key must be defined
"""
pulumi.set(__self__, "key", key)
if name is not None:
pulumi.set(__self__, "name", name)
if optional is not None:
pulumi.set(__self__, "optional", optional)
@property
@pulumi.getter
def key(self) -> str:
"""
The key to select.
"""
return pulumi.get(self, "key")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def optional(self) -> Optional[bool]:
"""
Specify whether the ConfigMap or its key must be defined
"""
return pulumi.get(self, "optional")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class BindingSpecParametersValueFromSecretKeyRef(dict):
"""
Selects a key of a secret in the resource namespace
"""
def __init__(__self__, *,
key: str,
name: Optional[str] = None,
optional: Optional[bool] = None):
"""
Selects a key of a secret in the resource namespace
:param str key: The key of the secret to select from. Must be a valid secret key.
:param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
:param bool optional: Specify whether the Secret or its key must be defined
"""
pulumi.set(__self__, "key", key)
if name is not None:
pulumi.set(__self__, "name", name)
if optional is not None:
pulumi.set(__self__, "optional", optional)
@property
@pulumi.getter
def key(self) -> str:
"""
The key of the secret to select from. Must be a valid secret key.
"""
return pulumi.get(self, "key")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def optional(self) -> Optional[bool]:
"""
Specify whether the Secret or its key must be defined
"""
return pulumi.get(self, "optional")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class BindingStatus(dict):
"""
BindingStatus defines the observed state of Binding
"""
def __init__(__self__, *,
generation: Optional[int] = None,
instance_id: Optional[str] = None,
key_instance_id: Optional[str] = None,
message: Optional[str] = None,
secret_name: Optional[str] = None,
state: Optional[str] = None):
"""
BindingStatus defines the observed state of Binding
"""
if generation is not None:
pulumi.set(__self__, "generation", generation)
if instance_id is not None:
pulumi.set(__self__, "instance_id", instance_id)
if key_instance_id is not None:
pulumi.set(__self__, "key_instance_id", key_instance_id)
if message is not None:
pulumi.set(__self__, "message", message)
if secret_name is not None:
pulumi.set(__self__, "secret_name", secret_name)
if state is not None:
pulumi.set(__self__, "state", state)
@property
@pulumi.getter
def generation(self) -> Optional[int]:
return pulumi.get(self, "generation")
@property
@pulumi.getter(name="instanceId")
def instance_id(self) -> Optional[str]:
return pulumi.get(self, "instance_id")
@property
@pulumi.getter(name="keyInstanceId")
def key_instance_id(self) -> Optional[str]:
return pulumi.get(self, "key_instance_id")
@property
@pulumi.getter
def message(self) -> Optional[str]:
return pulumi.get(self, "message")
@property
@pulumi.getter(name="secretName")
def secret_name(self) -> Optional[str]:
return pulumi.get(self, "secret_name")
@property
@pulumi.getter
def state(self) -> Optional[str]:
return pulumi.get(self, "state")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceSpec(dict):
"""
ServiceSpec defines the desired state of Service
"""
def __init__(__self__, *,
plan: str,
service_class: str,
context: Optional['outputs.ServiceSpecContext'] = None,
external_name: Optional[str] = None,
parameters: Optional[Sequence['outputs.ServiceSpecParameters']] = None,
service_class_type: Optional[str] = None,
tags: Optional[Sequence[str]] = None):
"""
ServiceSpec defines the desired state of Service
:param 'ServiceSpecContextArgs' context: ResourceContext defines the CloudFoundry context and resource group
"""
pulumi.set(__self__, "plan", plan)
pulumi.set(__self__, "service_class", service_class)
if context is not None:
pulumi.set(__self__, "context", context)
if external_name is not None:
pulumi.set(__self__, "external_name", external_name)
if parameters is not None:
pulumi.set(__self__, "parameters", parameters)
if service_class_type is not None:
pulumi.set(__self__, "service_class_type", service_class_type)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter
def plan(self) -> str:
return pulumi.get(self, "plan")
@property
@pulumi.getter(name="serviceClass")
def service_class(self) -> str:
return pulumi.get(self, "service_class")
@property
@pulumi.getter
def context(self) -> Optional['outputs.ServiceSpecContext']:
"""
ResourceContext defines the CloudFoundry context and resource group
"""
return pulumi.get(self, "context")
@property
@pulumi.getter(name="externalName")
def external_name(self) -> Optional[str]:
return pulumi.get(self, "external_name")
@property
@pulumi.getter
def parameters(self) -> Optional[Sequence['outputs.ServiceSpecParameters']]:
return pulumi.get(self, "parameters")
@property
@pulumi.getter(name="serviceClassType")
def service_class_type(self) -> Optional[str]:
return pulumi.get(self, "service_class_type")
@property
@pulumi.getter
def tags(self) -> Optional[Sequence[str]]:
return pulumi.get(self, "tags")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceSpecContext(dict):
"""
ResourceContext defines the CloudFoundry context and resource group
"""
def __init__(__self__, *,
org: Optional[str] = None,
region: Optional[str] = None,
resourcegroup: Optional[str] = None,
resourcegroupid: Optional[str] = None,
resourcelocation: Optional[str] = None,
space: Optional[str] = None,
user: Optional[str] = None):
"""
ResourceContext defines the CloudFoundry context and resource group
"""
if org is not None:
pulumi.set(__self__, "org", org)
if region is not None:
pulumi.set(__self__, "region", region)
if resourcegroup is not None:
pulumi.set(__self__, "resourcegroup", resourcegroup)
if resourcegroupid is not None:
pulumi.set(__self__, "resourcegroupid", resourcegroupid)
if resourcelocation is not None:
pulumi.set(__self__, "resourcelocation", resourcelocation)
if space is not None:
pulumi.set(__self__, "space", space)
if user is not None:
pulumi.set(__self__, "user", user)
@property
@pulumi.getter
def org(self) -> Optional[str]:
return pulumi.get(self, "org")
@property
@pulumi.getter
def region(self) -> Optional[str]:
return pulumi.get(self, "region")
@property
@pulumi.getter
def resourcegroup(self) -> Optional[str]:
return pulumi.get(self, "resourcegroup")
@property
@pulumi.getter
def resourcegroupid(self) -> Optional[str]:
return pulumi.get(self, "resourcegroupid")
@property
@pulumi.getter
def resourcelocation(self) -> Optional[str]:
return pulumi.get(self, "resourcelocation")
@property
@pulumi.getter
def space(self) -> Optional[str]:
return pulumi.get(self, "space")
@property
@pulumi.getter
def user(self) -> Optional[str]:
return pulumi.get(self, "user")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceSpecParameters(dict):
"""
Param represents a key-value pair
"""
def __init__(__self__, *,
name: str,
attributes: Optional[Mapping[str, Mapping[str, Any]]] = None,
value: Optional[Any] = None,
value_from: Optional['outputs.ServiceSpecParametersValueFrom'] = None):
"""
Param represents a key-value pair
:param str name: Name representing the key.
:param Mapping[str, Mapping[str, Any]] attributes: A parameter may have attributes (e.g. message hub topic might have partitions)
:param Any value: Defaults to null.
:param 'ServiceSpecParametersValueFromArgs' value_from: Source for the value. Cannot be used if value is not empty.
"""
pulumi.set(__self__, "name", name)
if attributes is not None:
pulumi.set(__self__, "attributes", attributes)
if value is not None:
pulumi.set(__self__, "value", value)
if value_from is not None:
pulumi.set(__self__, "value_from", value_from)
@property
@pulumi.getter
def name(self) -> str:
"""
Name representing the key.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def attributes(self) -> Optional[Mapping[str, Mapping[str, Any]]]:
"""
A parameter may have attributes (e.g. message hub topic might have partitions)
"""
return pulumi.get(self, "attributes")
@property
@pulumi.getter
def value(self) -> Optional[Any]:
"""
Defaults to null.
"""
return pulumi.get(self, "value")
@property
@pulumi.getter(name="valueFrom")
def value_from(self) -> Optional['outputs.ServiceSpecParametersValueFrom']:
"""
Source for the value. Cannot be used if value is not empty.
"""
return pulumi.get(self, "value_from")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceSpecParametersValueFrom(dict):
"""
Source for the value. Cannot be used if value is not empty.
"""
def __init__(__self__, *,
config_map_key_ref: Optional['outputs.ServiceSpecParametersValueFromConfigMapKeyRef'] = None,
secret_key_ref: Optional['outputs.ServiceSpecParametersValueFromSecretKeyRef'] = None):
"""
Source for the value. Cannot be used if value is not empty.
:param 'ServiceSpecParametersValueFromConfigMapKeyRefArgs' config_map_key_ref: Selects a key of a ConfigMap.
:param 'ServiceSpecParametersValueFromSecretKeyRefArgs' secret_key_ref: Selects a key of a secret in the resource namespace
"""
if config_map_key_ref is not None:
pulumi.set(__self__, "config_map_key_ref", config_map_key_ref)
if secret_key_ref is not None:
pulumi.set(__self__, "secret_key_ref", secret_key_ref)
@property
@pulumi.getter(name="configMapKeyRef")
def config_map_key_ref(self) -> Optional['outputs.ServiceSpecParametersValueFromConfigMapKeyRef']:
"""
Selects a key of a ConfigMap.
"""
return pulumi.get(self, "config_map_key_ref")
@property
@pulumi.getter(name="secretKeyRef")
def secret_key_ref(self) -> Optional['outputs.ServiceSpecParametersValueFromSecretKeyRef']:
"""
Selects a key of a secret in the resource namespace
"""
return pulumi.get(self, "secret_key_ref")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceSpecParametersValueFromConfigMapKeyRef(dict):
"""
Selects a key of a ConfigMap.
"""
def __init__(__self__, *,
key: str,
name: Optional[str] = None,
optional: Optional[bool] = None):
"""
Selects a key of a ConfigMap.
:param str key: The key to select.
:param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
:param bool optional: Specify whether the ConfigMap or its key must be defined
"""
pulumi.set(__self__, "key", key)
if name is not None:
pulumi.set(__self__, "name", name)
if optional is not None:
pulumi.set(__self__, "optional", optional)
@property
@pulumi.getter
def key(self) -> str:
"""
The key to select.
"""
return pulumi.get(self, "key")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def optional(self) -> Optional[bool]:
"""
Specify whether the ConfigMap or its key must be defined
"""
return pulumi.get(self, "optional")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceSpecParametersValueFromSecretKeyRef(dict):
"""
Selects a key of a secret in the resource namespace
"""
def __init__(__self__, *,
key: str,
name: Optional[str] = None,
optional: Optional[bool] = None):
"""
Selects a key of a secret in the resource namespace
:param str key: The key of the secret to select from. Must be a valid secret key.
:param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
:param bool optional: Specify whether the Secret or its key must be defined
"""
pulumi.set(__self__, "key", key)
if name is not None:
pulumi.set(__self__, "name", name)
if optional is not None:
pulumi.set(__self__, "optional", optional)
@property
@pulumi.getter
def key(self) -> str:
"""
The key of the secret to select from. Must be a valid secret key.
"""
return pulumi.get(self, "key")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def optional(self) -> Optional[bool]:
"""
Specify whether the Secret or its key must be defined
"""
return pulumi.get(self, "optional")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceStatus(dict):
"""
ServiceStatus defines the observed state of Service
"""
def __init__(__self__, *,
plan: str,
service_class: str,
service_class_type: str,
context: Optional['outputs.ServiceStatusContext'] = None,
dashboard_url: Optional[str] = None,
external_name: Optional[str] = None,
generation: Optional[int] = None,
instance_id: Optional[str] = None,
message: Optional[str] = None,
parameters: Optional[Sequence['outputs.ServiceStatusParameters']] = None,
state: Optional[str] = None,
tags: Optional[Sequence[str]] = None):
"""
ServiceStatus defines the observed state of Service
:param 'ServiceStatusContextArgs' context: ResourceContext defines the CloudFoundry context and resource group
"""
pulumi.set(__self__, "plan", plan)
pulumi.set(__self__, "service_class", service_class)
pulumi.set(__self__, "service_class_type", service_class_type)
if context is not None:
pulumi.set(__self__, "context", context)
if dashboard_url is not None:
pulumi.set(__self__, "dashboard_url", dashboard_url)
if external_name is not None:
pulumi.set(__self__, "external_name", external_name)
if generation is not None:
pulumi.set(__self__, "generation", generation)
if instance_id is not None:
pulumi.set(__self__, "instance_id", instance_id)
if message is not None:
pulumi.set(__self__, "message", message)
if parameters is not None:
pulumi.set(__self__, "parameters", parameters)
if state is not None:
pulumi.set(__self__, "state", state)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter
def plan(self) -> str:
return pulumi.get(self, "plan")
@property
@pulumi.getter(name="serviceClass")
def service_class(self) -> str:
return pulumi.get(self, "service_class")
@property
@pulumi.getter(name="serviceClassType")
def service_class_type(self) -> str:
return pulumi.get(self, "service_class_type")
@property
@pulumi.getter
def context(self) -> Optional['outputs.ServiceStatusContext']:
"""
ResourceContext defines the CloudFoundry context and resource group
"""
return pulumi.get(self, "context")
@property
@pulumi.getter(name="dashboardURL")
def dashboard_url(self) -> Optional[str]:
return pulumi.get(self, "dashboard_url")
@property
@pulumi.getter(name="externalName")
def external_name(self) -> Optional[str]:
return pulumi.get(self, "external_name")
@property
@pulumi.getter
def generation(self) -> Optional[int]:
return pulumi.get(self, "generation")
@property
@pulumi.getter(name="instanceId")
def instance_id(self) -> Optional[str]:
return pulumi.get(self, "instance_id")
@property
@pulumi.getter
def message(self) -> Optional[str]:
return pulumi.get(self, "message")
@property
@pulumi.getter
def parameters(self) -> Optional[Sequence['outputs.ServiceStatusParameters']]:
return pulumi.get(self, "parameters")
@property
@pulumi.getter
def state(self) -> Optional[str]:
return pulumi.get(self, "state")
@property
@pulumi.getter
def tags(self) -> Optional[Sequence[str]]:
return pulumi.get(self, "tags")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceStatusContext(dict):
"""
ResourceContext defines the CloudFoundry context and resource group
"""
def __init__(__self__, *,
org: Optional[str] = None,
region: Optional[str] = None,
resourcegroup: Optional[str] = None,
resourcegroupid: Optional[str] = None,
resourcelocation: Optional[str] = None,
space: Optional[str] = None,
user: Optional[str] = None):
"""
ResourceContext defines the CloudFoundry context and resource group
"""
if org is not None:
pulumi.set(__self__, "org", org)
if region is not None:
pulumi.set(__self__, "region", region)
if resourcegroup is not None:
pulumi.set(__self__, "resourcegroup", resourcegroup)
if resourcegroupid is not None:
pulumi.set(__self__, "resourcegroupid", resourcegroupid)
if resourcelocation is not None:
pulumi.set(__self__, "resourcelocation", resourcelocation)
if space is not None:
pulumi.set(__self__, "space", space)
if user is not None:
pulumi.set(__self__, "user", user)
@property
@pulumi.getter
def org(self) -> Optional[str]:
return pulumi.get(self, "org")
@property
@pulumi.getter
def region(self) -> Optional[str]:
return pulumi.get(self, "region")
@property
@pulumi.getter
def resourcegroup(self) -> Optional[str]:
return pulumi.get(self, "resourcegroup")
@property
@pulumi.getter
def resourcegroupid(self) -> Optional[str]:
return pulumi.get(self, "resourcegroupid")
@property
@pulumi.getter
def resourcelocation(self) -> Optional[str]:
return pulumi.get(self, "resourcelocation")
@property
@pulumi.getter
def space(self) -> Optional[str]:
return pulumi.get(self, "space")
@property
@pulumi.getter
def user(self) -> Optional[str]:
return pulumi.get(self, "user")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceStatusParameters(dict):
"""
Param represents a key-value pair
"""
def __init__(__self__, *,
name: str,
attributes: Optional[Mapping[str, Mapping[str, Any]]] = None,
value: Optional[Any] = None,
value_from: Optional['outputs.ServiceStatusParametersValueFrom'] = None):
"""
Param represents a key-value pair
:param str name: Name representing the key.
:param Mapping[str, Mapping[str, Any]] attributes: A parameter may have attributes (e.g. message hub topic might have partitions)
:param Any value: Defaults to null.
:param 'ServiceStatusParametersValueFromArgs' value_from: Source for the value. Cannot be used if value is not empty.
"""
pulumi.set(__self__, "name", name)
if attributes is not None:
pulumi.set(__self__, "attributes", attributes)
if value is not None:
pulumi.set(__self__, "value", value)
if value_from is not None:
pulumi.set(__self__, "value_from", value_from)
@property
@pulumi.getter
def name(self) -> str:
"""
Name representing the key.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def attributes(self) -> Optional[Mapping[str, Mapping[str, Any]]]:
"""
A parameter may have attributes (e.g. message hub topic might have partitions)
"""
return pulumi.get(self, "attributes")
@property
@pulumi.getter
def value(self) -> Optional[Any]:
"""
Defaults to null.
"""
return pulumi.get(self, "value")
@property
@pulumi.getter(name="valueFrom")
def value_from(self) -> Optional['outputs.ServiceStatusParametersValueFrom']:
"""
Source for the value. Cannot be used if value is not empty.
"""
return pulumi.get(self, "value_from")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceStatusParametersValueFrom(dict):
"""
Source for the value. Cannot be used if value is not empty.
"""
def __init__(__self__, *,
config_map_key_ref: Optional['outputs.ServiceStatusParametersValueFromConfigMapKeyRef'] = None,
secret_key_ref: Optional['outputs.ServiceStatusParametersValueFromSecretKeyRef'] = None):
"""
Source for the value. Cannot be used if value is not empty.
:param 'ServiceStatusParametersValueFromConfigMapKeyRefArgs' config_map_key_ref: Selects a key of a ConfigMap.
:param 'ServiceStatusParametersValueFromSecretKeyRefArgs' secret_key_ref: Selects a key of a secret in the resource namespace
"""
if config_map_key_ref is not None:
pulumi.set(__self__, "config_map_key_ref", config_map_key_ref)
if secret_key_ref is not None:
pulumi.set(__self__, "secret_key_ref", secret_key_ref)
@property
@pulumi.getter(name="configMapKeyRef")
def config_map_key_ref(self) -> Optional['outputs.ServiceStatusParametersValueFromConfigMapKeyRef']:
"""
Selects a key of a ConfigMap.
"""
return pulumi.get(self, "config_map_key_ref")
@property
@pulumi.getter(name="secretKeyRef")
def secret_key_ref(self) -> Optional['outputs.ServiceStatusParametersValueFromSecretKeyRef']:
"""
Selects a key of a secret in the resource namespace
"""
return pulumi.get(self, "secret_key_ref")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceStatusParametersValueFromConfigMapKeyRef(dict):
"""
Selects a key of a ConfigMap.
"""
def __init__(__self__, *,
key: str,
name: Optional[str] = None,
optional: Optional[bool] = None):
"""
Selects a key of a ConfigMap.
:param str key: The key to select.
:param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
:param bool optional: Specify whether the ConfigMap or its key must be defined
"""
pulumi.set(__self__, "key", key)
if name is not None:
pulumi.set(__self__, "name", name)
if optional is not None:
pulumi.set(__self__, "optional", optional)
@property
@pulumi.getter
def key(self) -> str:
"""
The key to select.
"""
return pulumi.get(self, "key")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def optional(self) -> Optional[bool]:
"""
Specify whether the ConfigMap or its key must be defined
"""
return pulumi.get(self, "optional")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
@pulumi.output_type
class ServiceStatusParametersValueFromSecretKeyRef(dict):
"""
Selects a key of a secret in the resource namespace
"""
def __init__(__self__, *,
key: str,
name: Optional[str] = None,
optional: Optional[bool] = None):
"""
Selects a key of a secret in the resource namespace
:param str key: The key of the secret to select from. Must be a valid secret key.
:param str name: Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
:param bool optional: Specify whether the Secret or its key must be defined
"""
pulumi.set(__self__, "key", key)
if name is not None:
pulumi.set(__self__, "name", name)
if optional is not None:
pulumi.set(__self__, "optional", optional)
@property
@pulumi.getter
def key(self) -> str:
"""
The key of the secret to select from. Must be a valid secret key.
"""
return pulumi.get(self, "key")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
Name of the referent. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/names/#names TODO: Add other useful fields. apiVersion, kind, uid?
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def optional(self) -> Optional[bool]:
"""
Specify whether the Secret or its key must be defined
"""
return pulumi.get(self, "optional")
def _translate_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
| 34.75514
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0
| 9
|
bba0bcaf18391e0a070e0c7a0ee775c3aa6bd044
| 73,522
|
py
|
Python
|
interview/leet/282_Expression_Add_Operators.py
|
eroicaleo/LearningPython
|
297d46eddce6e43ce0c160d2660dff5f5d616800
|
[
"MIT"
] | 1
|
2020-10-12T13:33:29.000Z
|
2020-10-12T13:33:29.000Z
|
interview/leet/282_Expression_Add_Operators.py
|
eroicaleo/LearningPython
|
297d46eddce6e43ce0c160d2660dff5f5d616800
|
[
"MIT"
] | null | null | null |
interview/leet/282_Expression_Add_Operators.py
|
eroicaleo/LearningPython
|
297d46eddce6e43ce0c160d2660dff5f5d616800
|
[
"MIT"
] | 1
|
2016-11-09T07:28:45.000Z
|
2016-11-09T07:28:45.000Z
|
#!/usr/bin/env python3
# Thinking Process
# My first thought is that I first calculate the front part
# Then set the new target for the rest of string
# When the recursive is done, it should return a sequence of valid string
# to meet the current target, then I combine the current level and
# return to previous level.
# However, because it cannot have bracket, this approach doesn't work
# E.g. 2111 = 6, with bracket, I can have 2*(1+1+1) = 6, but 2*1+1+1 = 4
# Then I thought about reverse thinking, from last character to the first
# it still cannot avoid such problem
# Then I read the hints, and I realize the we might need another kind of
# reverse thinking, i.e. build solution from top down, instead of bottom up
# kind of like `301_Remove_Invalid_Parentheses.py`
# Next trick part is to treat all operator as `+`, so in this case,
# 1-2, needs to be treated as 1+(-2), and pass (-2) as the last operand to pass
# to the recursive call.
# Note that a number can contain multiple digits.
# Since the question asks us to find all of the valid expressions, we need a way to iterate over all of them. (Hint: Recursion!)
# We can keep track of the expression string and evaluate it at the very end. But that would take a lot of time. Can we keep track of the expression's value as well so as to avoid the evaluation at the very end of recursion?
# Think carefully about the multiply operator. It has a higher precedence than the addition and subtraction operators.
# 1 + 2 = 3
# 1 + 2 - 4 --> 3 - 4 --> -1
# 1 + 2 - 4 * 12 --> -1 * 12 --> -12 (WRONG!)
# 1 + 2 - 4 * 12 --> -1 - (-4) + (-4 * 12) --> 3 + (-48) --> -45 (CORRECT!)
# We simply need to keep track of the last operand in our expression and reverse it's effect on the expression's value while considering the multiply operator.
class Solution:
def addOperators(self, num, target):
l, self.res = len(num), []
def helper(ix, op, s, prev):
# print(f'ix = {ix}, op = {op}, s = {s}, prev = {prev}')
if ix >= l and s == target:
self.res.append(prev)
for i in range(ix, l):
snop = num[ix:i+1]
nop = int(snop)
if not prev:
helper(i+1, nop, s+nop, snop)
else:
helper(i+1, nop, s+nop, prev+'+'+snop)
helper(i+1, -nop, s-nop, prev+'-'+snop)
helper(i+1, op*nop, s-op+op*nop, prev+'*'+snop)
if num[ix] == '0':
break
helper(0, 0, 0, '')
return self.res
num = "123"
target = 6
num = "232"
target = 8
num = "105"
target = 5
num = "00"
target = 0
num = "3456237490"
target = 9191
num = "1000000009"
target = 9
sol = Solution()
ans = sorted(sol.addOperators(num, target))
expected = 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+0*0*0+9","10000*0+0*0+0+9","10000*0+0*0-0+9","10000*0+0+0*0+9","10000*0+0+0+0+9","10000*0+0+0-0+9","10000*0+0-0*0+9","10000*0+0-0+0+9","10000*0+0-0-0+9","10000*0-0*0*0+9","10000*0-0*0+0+9","10000*0-0*0-0+9","10000*0-0+0*0+9","10000*0-0+0+0+9","10000*0-0+0-0+9","10000*0-0-0*0+9","10000*0-0-0+0+9","10000*0-0-0-0+9","100000*0*0*0+9","100000*0*0+0+9","100000*0*0-0+9","100000*0+0*0+9","100000*0+0+0+9","100000*0+0-0+9","100000*0-0*0+9","100000*0-0+0+9","100000*0-0-0+9","1000000*0*0+9","1000000*0+0+9","1000000*0-0+9","10000000*0+9"])
print(f'{len(ans)}, {len(expected)}')
for i in range(len(ans)):
print(f'{i}: {ans[i]}, {expected[i]}')
# for i in range(2916, 3280):
# print(f'{i}: {expected[i]}')
| 896.609756
| 70,544
| 0.471097
| 31,658
| 73,522
| 1.093973
| 0.006507
| 1.231542
| 1.563191
| 1.705772
| 0.95051
| 0.948777
| 0.948344
| 0.948344
| 0.948344
| 0.948344
| 0
| 0.452602
| 0.010854
| 73,522
| 81
| 70,545
| 907.679012
| 0.023624
| 0.025299
| 0
| 0
| 0
| 0
| 0.848408
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.054054
| false
| 0
| 0
| 0
| 0.108108
| 0.054054
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 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
| 15
|
bbce4c3dae07f5f7868c85bc353b8ea3fb3450bd
| 14,355
|
py
|
Python
|
pkgs/sdk-pkg/src/genie/libs/sdk/triggers/addremove/bgp/addremove.py
|
miott/genielibs
|
6464642cdd67aa2367bdbb12561af4bb060e5e62
|
[
"Apache-2.0"
] | 94
|
2018-04-30T20:29:15.000Z
|
2022-03-29T13:40:31.000Z
|
pkgs/sdk-pkg/src/genie/libs/sdk/triggers/addremove/bgp/addremove.py
|
miott/genielibs
|
6464642cdd67aa2367bdbb12561af4bb060e5e62
|
[
"Apache-2.0"
] | 67
|
2018-12-06T21:08:09.000Z
|
2022-03-29T18:00:46.000Z
|
pkgs/sdk-pkg/src/genie/libs/sdk/triggers/addremove/bgp/addremove.py
|
miott/genielibs
|
6464642cdd67aa2367bdbb12561af4bb060e5e62
|
[
"Apache-2.0"
] | 49
|
2018-06-29T18:59:03.000Z
|
2022-03-10T02:07:59.000Z
|
'''Implementation for bgp addremove triggers'''
# Genie Libs
from genie.libs.sdk.libs.utils.mapping import Mapping
from genie.libs.sdk.triggers.addremove.addremove import TriggerAddRemove
# Which key to exclude for BGP Ops comparison
bgp_exclude = ['maker', 'bgp_session_transport', 'route_refresh',
'bgp_negotiated_capabilities', 'notifications', 'capability',
'keepalives', 'total', 'total_bytes', 'up_time', 'last_reset',
'bgp_negotiated_keepalive_timers', 'updates', 'opens',
'bgp_table_version', 'holdtime', 'keepalive_interval',
'distance_internal_as', 'distance_extern_as', 'totals',
'reset_reason', 'holdtime', 'keepalive_interval']
route_map_exclude = ['maker']
bgp_exclude_keepalive = ['maker', 'bgp_session_transport', 'route_refresh',
'bgp_negotiated_capabilities', 'notifications', 'last_reset',
'keepalives', 'total', 'total_bytes', 'up_time',
'bgp_negotiated_keepalive_timers', 'updates', 'opens',
'bgp_table_version', 'holdtime', 'keepalive_interval',
'route_reflector_client', 'capability',
'distance_internal_as', 'bgp_neighbor_counters', 'memory_usage',
'total_entries', 'routing_table_version', 'total_memory',
'path', 'prefixes', 'cluster_id']
class TriggerAddRemoveBgpNeighborRoutemapPrefixlist(TriggerAddRemove):
"""Apply the inbound dynamically learned "prefix-list" route-map
to the dynamically learned BGP neighbor(s), and remove the
added route-map configurations.
"""
__description__ = """Apply the inbound dynamically learned "prefix-list" route-map
to the dynamically learned BGP neighbor(s), and remove the
added route-map configurations.
trigger_datafile:
Mandatory:
timeout:
max_time (`int`): Maximum wait time for the trigger,
in second. Default: 180
interval (`int`): Wait time between iterations when looping is needed,
in second. Default: 15
method (`str`): Method to recover the device configuration,
Support methods:
'checkpoint': Rollback the configuration by
checkpoint (nxos),
archive file (iosxe),
load the saved running-config file on disk (iosxr)
Optional:
tgn_timeout (`int`): Maximum wait time for all traffic threads to be
restored to the reference rate,
in second. Default: 60
tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed,
in second. Default: 10
timeout_recovery:
Buffer recovery timeout make sure devices are recovered at the end
of the trigger execution. Used when previous timeouts have been exhausted.
max_time (`int`): Maximum wait time for the last step of the trigger,
in second. Default: 180
interval (`int`): Wait time between iterations when looping is needed,
in second. Default: 15
static:
The keys below are dynamically learnt by default.
However, they can also be set to a custom value when provided in the trigger datafile.
instance: `str`
vrf: `str`
address_family: `str`
neighbor: `str`
policy_definition: `str`
statements: `str`
bgp_id: `int`
bgp_table_version: `int`
(e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported)
OR
interface: 'Ethernet1/1/1' (Specific value)
steps:
1. Learn BGP Ops object and store the BGP neighbor(s) if has any,
otherwise, SKIP the trigger. Learn RoutePolicy Ops object
to store the route-map name with prefix-list configured if has any,
otherwise, SKIP the trigger
2. Save the current device configurations through "method" which user uses
3. Add the configuration of the inbound route-map (step 1) to learned
BGP neighbor(s) from step 1 with BGP Conf object
4. Verify the BGP neighbor(s) from step 3 has inbound route-map (step 1) configured
5. Remove the route-map (step 1) configurations from the learned
BGP neighbor(s) from step 1
6. Recover the device configurations to the one in step 2
7. Learn BGP Ops again and verify it is the same as the Ops in step 1
"""
mapping = Mapping(requirements={'ops.route_policy.route_policy.RoutePolicy':{
'requirements':[['info', '(?P<policy_definition>.*)',
'statements', '(?P<statements>.*)',
'conditions',
'match_prefix_list', '(?P<match_prefix_list>.*)']],
'exclude': route_map_exclude},
'ops.bgp.bgp.Bgp':{
'requirements':[['info', 'instance', '(?P<instance>.*)',
'vrf', '(?P<vrf>.*)',
'neighbor', '(?P<neighbor>.*)',
'address_family', '(?P<address_family>.*)',
'bgp_table_version', '(?P<bgp_table_version>.*)'],
['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']],
'all_keys': True,
'kwargs':{'attributes':['info']},
'exclude': bgp_exclude}},
config_info={'conf.bgp.Bgp':{
'requirements':[['device_attr', '{uut}',
'vrf_attr', '(?P<vrf>.*)',
'neighbor_attr','(?P<neighbor>.*)',
'address_family_attr', '(?P<address_family>.*)',
'nbr_af_route_map_name_in', '(?P<policy_definition>.*)']],
'verify_conf':False,
'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}},
verify_ops={'ops.bgp.bgp.Bgp':{
'requirements': [['info', 'instance', '(?P<instance>.*)',
'vrf', '(?P<vrf>.*)',
'neighbor', '(?P<neighbor>.*)',
'address_family', '(?P<address_family>.*)',
'route_map_name_in', '(?P<policy_definition>.*)']],
'kwargs':{'attributes':['info']},
'exclude': bgp_exclude}},
num_values={'policy_definition':1, 'statements':'all', 'match_prefix_list':1, 'instance':1, 'vrf':1, 'address_family':1, 'neighbor':1, 'bgp_id':1, 'bgp_table_version':1})
class TriggerAddRemoveBgpNeighborRoutemapWeight(TriggerAddRemove):
"""Apply the inbound dynamically learned "weight" route-map
to the dynamically learned BGP neighbor(s), and remove the
added route-map configurations.
"""
__description__ = """Apply the inbound dynamically learned "weight" route-map
to the dynamically learned BGP neighbor(s), and remove the
added route-map configurations.
trigger_datafile:
Mandatory:
timeout:
max_time (`int`): Maximum wait time for the trigger,
in second. Default: 180
interval (`int`): Wait time between iterations when looping is needed,
in second. Default: 15
method (`str`): Method to recover the device configuration,
Support methods:
'checkpoint': Rollback the configuration by
checkpoint (nxos),
archive file (iosxe),
load the saved running-config file on disk (iosxr)
Optional:
tgn_timeout (`int`): Maximum wait time for all traffic threads to be
restored to the reference rate,
in second. Default: 60
tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed,
in second. Default: 10
timeout_recovery:
Buffer recovery timeout when the previous timeout has been exhausted,
to make sure the devices are recovered before ending the trigger
max_time (`int`): Maximum wait time for the last step of the trigger,
in second. Default: 180
interval (`int`): Wait time between iterations when looping is needed,
in second. Default: 15
static:
The keys below are dynamically learnt by default.
However, they can also be set to a custom value when provided in the trigger datafile.
instance: `str`
vrf: `str`
address_family: `str`
neighbor: `str`
policy_definition: `str`
statements: `str`
set_weight: `int`
bgp_id: `int`
bgp_table_version: `int`
(e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported)
OR
interface: 'Ethernet1/1/1' (Specific value)
steps:
1. Learn BGP Ops object and store the BGP neighbor(s) if has any,
otherwise, SKIP the trigger. Learn RoutePolicy Ops object
to store the route-map name with weight configured if has any,
otherwise, SKIP the trigger
2. Save the current device configurations through "method" which user uses
3. Add the configuration of the inbound route-map (step 1) to learned
BGP neighbor(s) from step 1 with BGP Conf object
4. Verify the BGP neighbor(s) from step 3 has inbound route-map (step 1) configured
5. Remove the route-map (step 1) configurations from the learned BGP
neighbor(s) from step 1
6. Recover the device configurations to the one in step 2
7. Learn BGP Ops again and verify it is the same as the Ops in step 1
"""
mapping = Mapping(requirements={'ops.route_policy.route_policy.RoutePolicy':{
'requirements':[['info', '(?P<policy_definition>.*)',
'statements', '(?P<statements>.*)',
'actions',
'set_weight', '(?P<set_weight>.*)']],
'exclude': route_map_exclude},
'ops.bgp.bgp.Bgp':{
'requirements':[['info', 'instance', '(?P<instance>.*)',
'vrf', '(?P<vrf>.*)',
'neighbor', '(?P<neighbor>.*)',
'address_family', '(?P<address_family>.*)',
'bgp_table_version', '(?P<bgp_table_version>.*)'],
['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']],
'all_keys': True,
'kwargs':{'attributes':['info']},
'exclude': bgp_exclude}},
config_info={'conf.bgp.Bgp':{
'requirements':[['device_attr', '{uut}',
'vrf_attr', '(?P<vrf>.*)',
'neighbor_attr','(?P<neighbor>.*)',
'address_family_attr', '(?P<address_family>.*)',
'nbr_af_route_map_name_in', '(?P<policy_definition>.*)']],
'verify_conf':False,
'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}},
verify_ops={'ops.bgp.bgp.Bgp':{
'requirements': [['info', 'instance', '(?P<instance>.*)',
'vrf', '(?P<vrf>.*)',
'neighbor', '(?P<neighbor>.*)',
'address_family', '(?P<address_family>.*)',
'route_map_name_in', '(?P<policy_definition>.*)']],
'kwargs':{'attributes':['info']},
'exclude': bgp_exclude}},
num_values={'policy_definition':1, 'statements':'all', 'set_weight':1, 'instance':1, 'vrf':1, 'address_family':1, 'neighbor':1, 'bgp_id':1, 'bgp_table_version':1})
| 61.346154
| 192
| 0.463811
| 1,292
| 14,355
| 5.013158
| 0.178019
| 0.028408
| 0.022233
| 0.023468
| 0.862745
| 0.862745
| 0.848232
| 0.848232
| 0.848232
| 0.848232
| 0
| 0.010106
| 0.434761
| 14,355
| 233
| 193
| 61.609442
| 0.788144
| 0.027726
| 0
| 0.821782
| 0
| 0.009901
| 0.651549
| 0.054841
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.009901
| 0
| 0.039604
| 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
|
a55471c7ccae6561f948566cc86a9ab344a63ae7
| 2,731
|
py
|
Python
|
src/data/test_labels.py
|
0shimax/SE-Wavenet
|
f3cf8239175fec02565c81995e5b9f9e1bbd5eb1
|
[
"MIT"
] | null | null | null |
src/data/test_labels.py
|
0shimax/SE-Wavenet
|
f3cf8239175fec02565c81995e5b9f9e1bbd5eb1
|
[
"MIT"
] | null | null | null |
src/data/test_labels.py
|
0shimax/SE-Wavenet
|
f3cf8239175fec02565c81995e5b9f9e1bbd5eb1
|
[
"MIT"
] | null | null | null |
seq_labels = \
[
[1,2,3,4,5],
[2,3,4,5,1],
[3,4,5,1,2],
[4,5,1,2,3],
[5,1,2,3,4],
[1,2,3,4,5],
[2,3,4,5,1],
[3,4,5,1,2],
[4,5,1,2,3],
[5,1,2,3,4],
[1,2,3,4,5],
[2,3,4,5,1],
[3,4,5,1,2],
[4,5,1,2,3],
[5,1,2,3,4],
[1,2,3,4,5,1],
[1,2,3,4,5,1],
[1,2,3,4,5,1],
[1,2,3,4,5,2],
[1,2,3,4,5,2],
[1,2,3,4,5,2],
[1,2,3,4,5,3],
[1,2,3,4,5,3],
[1,2,3,4,5,3],
[1,2,3,4,5,4],
[1,2,3,4,5,4],
[1,2,3,4,5,4],
[5,1,2,3,4,5],
[5,1,2,3,4,5],
[5,1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[1,2,3,4,5],
[2,3,4,5],
[2,3,4,5],
[2,3,4,5],
[1,3,4,5],
[1,3,4,5],
[1,3,4,5],
[1,2,4,5],
[1,2,4,5],
[1,2,4,5],
[1,2,3,5],
[1,2,3,5],
[1,2,3,5],
[1,2,3,4],
[1,2,3,4],
[1,2,3,4]
]
gt_lack_labels = \
[
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[1,0,0,0,0],
[1,0,0,0,0],
[1,0,0,0,0],
[0,1,0,0,0],
[0,1,0,0,0],
[0,1,0,0,0],
[0,0,1,0,0],
[0,0,1,0,0],
[0,0,1,0,0],
[0,0,0,1,0],
[0,0,0,1,0],
[0,0,0,1,0],
[0,0,0,0,1],
[0,0,0,0,1],
[0,0,0,0,1],
[1,0,0,0,0],
[1,0,0,0,0],
[1,0,0,0,0],
[0,1,0,0,0],
[0,1,0,0,0],
[0,1,0,0,0],
[0,0,1,0,0],
[0,0,1,0,0],
[0,0,1,0,0],
[0,0,0,1,0],
[0,0,0,1,0],
[0,0,0,1,0],
[0,0,0,0,1],
[0,0,0,0,1],
[0,0,0,0,1]
]
if __name__=="__main__":
assert len(seq_labels)==60
assert len(gt_lack_labels)==60
print("finish normaly")
| 16.353293
| 34
| 0.258513
| 622
| 2,731
| 1.11254
| 0.030547
| 0.696532
| 0.919075
| 1.057803
| 0.867052
| 0.867052
| 0.867052
| 0.867052
| 0.861272
| 0.861272
| 0
| 0.384959
| 0.425485
| 2,731
| 166
| 35
| 16.451807
| 0.056087
| 0
| 0
| 0.907692
| 0
| 0
| 0.008056
| 0
| 0
| 0
| 0
| 0
| 0.015385
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.007692
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 13
|
a566f562869c3f3ff9e22fe3b93138a6f5ed5fb3
| 35,820
|
py
|
Python
|
spaic/Network/Connection.py
|
ZhejianglabNCRC/SPAIC
|
5a08328adcc5a197316cf353746bae7ab6865337
|
[
"Apache-2.0"
] | 3
|
2022-03-01T03:04:25.000Z
|
2022-03-01T03:07:15.000Z
|
spaic/Network/Connection.py
|
ZhejianglabNCRC/SPAIC
|
5a08328adcc5a197316cf353746bae7ab6865337
|
[
"Apache-2.0"
] | null | null | null |
spaic/Network/Connection.py
|
ZhejianglabNCRC/SPAIC
|
5a08328adcc5a197316cf353746bae7ab6865337
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on 2020/8/5
@project: SPAIC
@filename: Connection
@author: Hong Chaofei
@contact: hongchf@gmail.com
@description:
定义神经集群间的连接，包括记录神经元集群、连接的突触前、突触后神经元编号、连接形式（全连接、稀疏连接、卷积）、权值、延迟 以及连接产生函数、重连接函数等。
"""
from ..Network.BaseModule import BaseModule
from ..Network.Topology import Projection, Connection
from ..Network.Assembly import Assembly
# from ..Network.ConnectPolicy import ConnectPolicy
from collections import OrderedDict
from typing import Dict, List, Tuple
from abc import abstractmethod
import numpy as np
import scipy.sparse as sp
import spaic
import torch
import math
class FullConnection(Connection):
'''
each neuron in the first layer is connected to each neuron in the second layer.
Args:
pre_assembly(Assembly): The assembly which needs to be connected
post_assembly(Assembly): The assembly which needs to connect the pre_assembly
link_type(str): full
Methods:
unit_connect: define the basic connection information and add them to the connection_information.
condition_check: check whether the pre_group.type is equal to the post_group.type, only if they are equal, return flag=Ture.
'''
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=False, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(FullConnection, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.weight = kwargs.get('weight', None)
self.w_std = kwargs.get('w_std', 0.05)
self.w_mean = kwargs.get('w_mean', 0.005)
self.w_max = kwargs.get('w_max', None)
self.w_min = kwargs.get('w_min', None)
self.param_init = kwargs.get('param_init', None)
self.is_parameter = kwargs.get('is_parameter', True)
self.is_sparse = kwargs.get('is_sparse', False)
# self.flatten_on = kwargs.get('flatten', False)
def unit_connect(self, pre_group, post_group):
# The number of neurons in neuron group
pre_num = pre_group.num
post_num = post_group.num
link_num = pre_num * post_num
shape = (post_num, pre_num)
if self.weight is None:
# Connection weight
weight = self.w_std*np.random.randn(*shape) + self.w_mean
else:
assert (self.weight.shape == shape), f"The size of the given weight {self.weight.shape} does not correspond to the size of synaptic matrix {shape} "
weight = self.weight
# The name for backend variables
input_name = self.get_input_name(pre_group, post_group)
# 如果需要避开固有延迟的问题，采用strategy_build.
# 取消下面一行的注释，取用突触前神经元当前步的发放情况
input_name = input_name
weight_name = self.get_link_name(pre_group, post_group, 'weight')
target_name = self.get_post_name(post_group, self.post_var_name)
# The backend variable
# var_code = (weight_name, shape, weight, True, False, 'uniform') # (var_name, shape, value, is_parameter, is_sparse, init)
var_code = (weight_name, shape, weight, self.is_parameter, self.is_sparse, self.param_init)
# The backend basic operation
if self.max_delay > 0 :
op_code = [target_name, 'mult_sum_weight', input_name, weight_name]
else:
op_code = [target_name, 'mat_mult_weight', input_name, weight_name]
# if self.max_delay > 0 and self.flatten_on != True:
# op_code = [target_name, 'mult_sum_weight', input_name, weight_name]
# elif self.max_delay > 0 and self.flatten_on == True:
# raise ValueError("Conv_connectoin cannot do delay recently!")
# elif self.max_delay <= 0 and self.flatten_on == True:
# op_code = [target_name, 'reshape_mat_mult', input_name, weight_name]
# else:
# op_code = [target_name, 'mat_mult_weight', input_name, weight_name]
connection_information = (pre_group, post_group, link_num, var_code, op_code)
self.unit_connections.append(connection_information)
pass
def condition_check(self, pre_group, post_group):
flag = False
pre_type = pre_group.type
post_type = post_group.type
if pre_type == post_type:
flag = True
return flag
pass
Connection.register('full', FullConnection)
Connection.register('full_connection', FullConnection)
class one_to_one_sparse(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=False, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(one_to_one_sparse, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.weight = kwargs.get('weight', None)
self.w_mean = kwargs.get('w_mean', 0.05)
self.w_max = kwargs.get('w_max', None)
self.w_min = kwargs.get('w_min', None)
self.param_init = kwargs.get('param_init', None)
self.is_parameter = kwargs.get('is_parameter', False)
self.is_sparse = kwargs.get('is_sparse', True)
def unit_connect(self, pre_group, post_group):
pre_num = pre_group.num
post_num = post_group.num
try:
assert pre_num == post_num
except AssertionError:
raise ValueError('One to One connection must be defined in two groups with the same size, but the pre_num %s is not equal to the post_num %s.'%(pre_num, post_num))
link_num = pre_num * post_num
shape = (post_num, pre_num)
if self.weight is None:
# Connection weight
weight = self.w_mean * np.eye(*shape)
else:
assert (self.weight.shape == shape), f"The size of the given weight {self.weight.shape} does not correspond to the size of synaptic matrix {shape} "
weight = self.weight
# The name for backend variables
input_name = self.get_input_name(pre_group, post_group)
weight_name = self.get_link_name(pre_group, post_group, 'weight')
target_name = self.get_post_name(post_group, self.post_var_name)
# The backend variable
var_code = (weight_name, shape, weight, self.is_parameter, self.is_sparse, self.param_init) # (var_name, shape, value, is_parameter, is_sparse)
# The backend basic operation
op_code = [target_name, 'sparse_mat_mult_weight', weight_name, input_name]
connection_information = (pre_group, post_group, link_num, var_code, op_code)
self.unit_connections.append(connection_information)
pass
def condition_check(self, pre_group, post_group):
flag = False
pre_type = pre_group.type
post_type = post_group.type
if pre_type == post_type:
flag = True
return flag
pass
Connection.register('one_to_one_sparse', one_to_one_sparse)
class one_to_one_mask(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=True, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(one_to_one_mask, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.weight = kwargs.get('weight', None)
self.w_mean = kwargs.get('w_mean', 0.05)
self.w_max = kwargs.get('w_max', None)
self.w_min = kwargs.get('w_min', None)
self.param_init = kwargs.get('param_init', None)
self.is_parameter = kwargs.get('is_parameter', True)
self.is_sparse = kwargs.get('is_sparse', False)
def unit_connect(self, pre_group, post_group):
pre_num = pre_group.num
post_num = post_group.num
try:
assert pre_num == post_num
except AssertionError:
raise ValueError('One to One connection must be defined in two groups with the same size, '
'but the pre_num %s is not equal to the post_num %s.'%(pre_num, post_num))
link_num = pre_num * post_num
shape = (post_num, pre_num)
if self.weight is None:
# Connection weight
weight = self.w_mean * np.eye(*shape)
else:
assert (self.weight.shape == shape), f"The size of the given weight {self.weight.shape} does not correspond to the size of synaptic matrix {shape} "
weight = self.weight
# The name for backend variables
input_name = self.get_input_name(pre_group, post_group)
weight_name = self.get_link_name(pre_group, post_group, 'weight')
target_name = self.get_post_name(post_group, self.post_var_name)
# The backend variable
var_code = (weight_name, shape, weight, self.is_parameter, self.is_sparse, self.param_init) # (var_name, shape, value, is_parameter, is_sparse)
# The backend basic operation
op_code = [target_name, 'mat_mult_weight', input_name, weight_name]
connection_information = (pre_group, post_group, link_num, var_code, op_code)
self.unit_connections.append(connection_information)
if self.sparse_with_mask:
mask = (weight != 0)
mask_name = self.get_link_name(pre_group, post_group, 'mask')
mask_var_code = (mask_name, shape, mask)
mask_op = (weight_name, self.mask_operation, [weight_name, mask_name])
mask_information = (mask_var_code, mask_op)
self.mask_info.append(mask_information)
pass
def condition_check(self, pre_group, post_group):
flag = False
pre_type = pre_group.type
post_type = post_group.type
if pre_type == post_type:
flag = True
return flag
pass
Connection.register('one_to_one', one_to_one_mask)
class conv_connect(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=False, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(conv_connect, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
'''
do the convolution connection.
Args:
pre_assembly(Assembly): the assembly which needs to be connected
post_assembly(Assembly): the assembly which needs to connect the pre_assembly
link_type(str): Conv
Methods:
unit_connect: define the basic connection information and add them to the connection_information.
condition_check: check whether the pre_group.type is equal to the post_group.type, only if they are equal, return flag=Ture.
'''
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=False, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(conv_connect, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.out_channels = kwargs.get('out_channels', 4)
self.in_channels = kwargs.get('in_channels', 1)
self.kernel_size = kwargs.get('kernel_size', (3, 3))
self.maxpool_on = kwargs.get('maxpool_on', True)
self.maxpool_kernel_size = kwargs.get('maxpool_kernel_size', (2, 2))
self.w_std = kwargs.get('w_std', 0.05)
self.w_mean = kwargs.get('w_mean', 0.05)
self.weight = kwargs.get('weight', None)
self.mask = kwargs.get('mask', None)
self.stride = kwargs.get('stride', 1)
self.padding = kwargs.get('padding', 0)
self.dilation = kwargs.get('dilation', 1)
self.groups = kwargs.get('groups', 1)
def unit_connect(self, pre_group, post_group):
'''
set the basic parameters, for example: link_length, connection weight, connection shape, the name for backend variables, the backend variable,the backend basic operation.
Args:
pre_group(Groups): the neuron group which need to be connected in the pre_assembly.
post_group(Groups): the neuron group which need to be connected with the pre_group neuron.
'''
shape = (self.out_channels, self.in_channels, self.kernel_size[0], self.kernel_size[1])
if self.weight is None:
# Connection weight
weight = self.w_std * np.random.randn(*shape) + self.w_mean
else:
assert (self.weight.shape == shape), f"The size of the given weight {self.weight.shape} does not correspond to the size of synaptic matrix {shape} "
weight = self.weight
Hin = pre_group.shape[-2]
Win = pre_group.shape[-1]
if self.maxpool_on: # 池化
Hin = int(Hin / self.maxpool_kernel_size[0])
Win = int(Win / self.maxpool_kernel_size[1])
Ho = int((Hin + 2 * self.padding - self.kernel_size[
0]) / self.stride + 1) # Ho = (Hin + 2 * padding[0] - kernel_size[0]) / stride[0] + 1
Wo = int((Win + 2 * self.padding - self.kernel_size[
1]) / self.stride + 1) # Wo = (Win + 2 * padding[0] - kernel_size[1]) / stride[0] + 1
post_num = int(Ho * Wo * self.out_channels)
if post_group.num == None:
post_group.num = post_num
post_group.shape = (self.out_channels, Ho, Wo)
if post_group.num != None:
if post_group.num != post_num:
raise ValueError(
"The post_group num is not equal to the output num, cannot achieve the conv connection, "
"the output num is %d * %d * %d " % (self.out_channels, Ho, Wo))
else:
post_group.shape = (self.out_channels, Ho, Wo)
link_num = post_num
# The name for backend variables
input_name = self.get_pre_name(pre_group, self.pre_var_name)
weight_name = self.get_link_name(pre_group, post_group, 'weight')
target_name = self.get_post_name(post_group, self.post_var_name)
# The backend variable
var_code_weight = (weight_name, shape, weight, True)
stride = self.get_pre_name(pre_group, 'stride')
# stride = self.stride_name(pre_group)
stride_value = np.array(self.stride)
var_code_stride = (stride, stride_value.shape, stride_value)
padding = self.get_pre_name(pre_group, 'padding')
# padding = self.padding_name(pre_group)
padding_value = np.array(self.padding)
var_code_padding = (padding, padding_value.shape, padding_value)
dilation = self.get_pre_name(pre_group, 'dilation')
dilation_value = np.array(self.dilation)
var_code_dilation = (dilation, dilation_value.shape, dilation_value)
groups = self.get_pre_name(pre_group, 'groups')
groups_value = np.array(self.groups)
var_code_groups = (groups, groups_value.shape, groups_value)
maxpool_kernel_size_name = self.get_pre_name(pre_group, 'maxpool_kernel_size')
# maxpool_kernel_size_name = self.maxpool_kernel_size_name(pre_group)
maxpool_kernel_size_value = np.array(self.maxpool_kernel_size)
var_code_maxpool = (maxpool_kernel_size_name, maxpool_kernel_size_value.shape, maxpool_kernel_size_value)
# flatten_value = np.array(self.flatten)
# flatten = self.get_post_name(post_group, 'flatten')
# var_code_flatten = (flatten, flatten_value.shape, flatten_value)
if self.maxpool_on:
op_code1 = [target_name, 'conv_max_pool2d', input_name, weight_name, maxpool_kernel_size_name,
stride, padding, dilation, groups]
connection_information = (pre_group, post_group, link_num, var_code_weight, var_code_maxpool,
var_code_stride, var_code_padding, var_code_dilation, var_code_groups, op_code1)
else:
op_code1 = [target_name, 'conv_2d', input_name, weight_name, stride, padding, dilation, groups]
connection_information = (pre_group, post_group, link_num, var_code_weight,
var_code_stride, var_code_padding, var_code_dilation, var_code_groups, op_code1)
self.unit_connections.append(connection_information)
def condition_check(self, pre_group, post_group):
'''
check whether the pre_group.type is equal to the post_group.type, only if they are equal, return flag=Ture.
Args:
pre_group(Groups): the neuron group which need to be connected in the pre_assembly.
post_group(Groups): the neuron group which need to connect the pre_group in the post_assembly.
Returns: flag
'''
flag = False
pre_type = pre_group.type
post_type = post_group.type
if pre_type == post_type:
flag = True
return flag
Connection.register('conv', conv_connect)
Connection.register('conv_connection', conv_connect)
class sparse_connect_sparse(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=False, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(sparse_connect_sparse, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.weight = kwargs.get('weight', None)
self.w_std = kwargs.get('w_std', 0.05)
self.density = kwargs.get('density', 0.1)
self.w_max = kwargs.get('w_max', None)
self.w_min = kwargs.get('w_min', None)
self.param_init = kwargs.get('param_init', None)
self.is_parameter = kwargs.get('is_parameter', False)
self.is_sparse = kwargs.get('is_sparse', True)
def unit_connect(self, pre_group, post_group):
# The number of neurons in neuron group
pre_num = pre_group.num
post_num = post_group.num
link_num = pre_num * post_num
shape = (post_num, pre_num)
if self.weight is None:
# Connection weight
sparse_matrix = self.w_std * sp.rand(post_num, pre_num, density=self.density, format='csr')
weight = sparse_matrix.toarray()
else:
assert (self.weight.shape == shape), f"The size of the given weight {self.weight.shape} does not correspond to the size of synaptic matrix {shape} "
weight = self.weight
# The name for backend variables
input_name = self.get_input_name(pre_group, post_group)
weight_name = self.get_link_name(pre_group, post_group, 'weight')
target_name = self.get_post_name(post_group, self.post_var_name)
# The backend variable
var_code = (weight_name, shape, weight, self.is_parameter, self.is_sparse, self.param_init) # (var_name, shape, value, is_parameter, is_sparse)
# The backend basic operation
op_code = [target_name, 'sparse_mat_mult_weight', weight_name, input_name]
connection_information = (pre_group, post_group, link_num, var_code, op_code)
self.unit_connections.append(connection_information)
pass
def condition_check(self, pre_group, post_group):
flag = False
pre_type = pre_group.type
post_type = post_group.type
if pre_type == post_type:
flag = True
return flag
pass
Connection.register('sparse_sparse', sparse_connect_sparse)
Connection.register('sparse_connection_sparse', sparse_connect_sparse)
class sparse_connect_mask(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=True, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(sparse_connect_mask, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.weight = kwargs.get('weight', None)
self.w_std = kwargs.get('w_std', 0.05)
self.w_mean = kwargs.get('w_mean', 0.005)
self.density = kwargs.get('density', 0.1)
self.w_max = kwargs.get('w_max', None)
self.w_min = kwargs.get('w_min', None)
self.param_init = kwargs.get('param_init', None)
self.is_parameter = kwargs.get('is_parameter', True)
self.is_sparse = kwargs.get('is_sparse', False)
def unit_connect(self, pre_group, post_group):
# The number of neurons in neuron group
pre_num = pre_group.num
post_num = post_group.num
link_num = pre_num * post_num
shape = (post_num, pre_num)
if self.weight is None:
# Connection weight
sparse_matrix = self.w_std * sp.rand(post_num, pre_num, density=self.density, format='csr')
weight = sparse_matrix.toarray()
weight[weight.nonzero()] = weight[weight.nonzero()] + self.w_mean
else:
assert (self.weight.shape == shape), f"The size of the given weight {self.weight.shape} does not correspond to the size of synaptic matrix {shape} "
weight = self.weight
# The name for backend variables
input_name = self.get_input_name(pre_group, post_group)
weight_name = self.get_link_name(pre_group, post_group, 'weight')
target_name = self.get_post_name(post_group, self.post_var_name)
# The backend variable
var_code = (weight_name, shape, weight, self.is_parameter, self.is_sparse, self.param_init) # (var_name, shape, value, is_parameter, is_sparse)
# The backend basic operation
op_code = [target_name, 'mat_mult_weight', input_name, weight_name]
connection_information = (pre_group, post_group, link_num, var_code, op_code)
self.unit_connections.append(connection_information)
if self.sparse_with_mask:
mask = (weight != 0)
mask_name = self.get_link_name(pre_group, post_group, 'mask')
mask_var_code = (mask_name, shape, mask)
mask_op = (weight_name, self.mask_operation, [weight_name, mask_name])
mask_information = (mask_var_code, mask_op)
self.mask_info.append(mask_information)
pass
def condition_check(self, pre_group, post_group):
flag = False
pre_type = pre_group.type
post_type = post_group.type
if pre_type == post_type:
flag = True
return flag
pass
Connection.register('sparse', sparse_connect_mask)
Connection.register('sparse_connection', sparse_connect_mask)
class random_connect_sparse(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=False, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(random_connect_sparse, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.weight = kwargs.get('weight', None)
self.probability = kwargs.get('probability', 0.1)
self.w_max = kwargs.get('w_max', None)
self.w_min = kwargs.get('w_min', None)
self.param_init = kwargs.get('param_init', None)
self.is_parameter = kwargs.get('is_parameter', False)
self.is_sparse = kwargs.get('is_sparse', True)
def unit_connect(self, pre_group, post_group):
# The number of neurons in neuron group
pre_num = pre_group.num
post_num = post_group.num
link_num = pre_num * post_num
shape = (post_num, pre_num)
if self.weight is None:
# Link_parameters
prob_weight = np.random.rand(*shape)
diag_index = np.arange(min([pre_num, post_num]))
prob_weight[diag_index, diag_index] = 1
index = (prob_weight < self.probability)
# Connection weight
weight = np.zeros(shape)
weight[index] = prob_weight[index]
else:
assert (self.weight.shape == shape), f"The size of the given weight {self.weight.shape} does not correspond to the size of synaptic matrix {shape} "
weight = self.weight
# The name for backend variables
input_name = self.get_input_name(pre_group, post_group)
weight_name = self.get_link_name(pre_group, post_group, 'weight')
target_name = self.get_post_name(post_group, self.post_var_name)
# The backend variable
var_code = (weight_name, shape, weight, self.is_parameter, self.is_sparse, self.param_init) # (var_name, shape, value, is_parameter, is_sparse)
# The backend basic operation
op_code = [target_name, 'sparse_mat_mult_weight', weight_name, input_name]
connection_information = (pre_group, post_group, link_num, var_code, op_code)
self.unit_connections.append(connection_information)
pass
def condition_check(self, pre_group, post_group):
flag = False
pre_type = pre_group.type
post_type = post_group.type
if pre_type == post_type:
flag = True
return flag
pass
Connection.register('random_connection_sparse', random_connect_sparse)
class random_connect_mask(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv', '...'), policies=[],
max_delay=0, sparse_with_mask=True, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(random_connect_mask, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name,
link_type=link_type, policies=policies, max_delay=max_delay,
sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.weight = kwargs.get('weight', None)
self.probability = kwargs.get('probability', 0.1)
self.w_max = kwargs.get('w_max', None)
self.w_min = kwargs.get('w_min', None)
self.param_init = kwargs.get('param_init', None)
self.is_parameter = kwargs.get('is_parameter', True)
self.is_sparse = kwargs.get('is_sparse', False)
def unit_connect(self, pre_group, post_group):
# The number of neurons in neuron group
pre_num = pre_group.num
post_num = post_group.num
link_num = pre_num * post_num
shape = (post_num, pre_num)
if self.weight is None:
# Link_parameters
prob_weight = np.random.rand(*shape)
diag_index = np.arange(min([pre_num, post_num]))
prob_weight[diag_index, diag_index] = 1
index = (prob_weight < self.probability)
# Connection weight
weight = np.zeros(shape)
weight[index] = prob_weight[index]
else:
assert (self.weight.shape == shape), f"The size of the given weight {self.weight.shape} does not correspond to the size of synaptic matrix {shape} "
weight = self.weight
# The name for backend variables
input_name = self.get_input_name(pre_group, post_group)
weight_name = self.get_link_name(pre_group, post_group, 'weight')
target_name = self.get_post_name(post_group, self.post_var_name)
# The backend variable
var_code = (weight_name, shape, weight, self.is_parameter, self.is_sparse, self.param_init) # (var_name, shape, value, is_parameter, is_sparse)
# The backend basic operation
op_code = [target_name, 'mat_mult_weight', input_name, weight_name]
connection_information = (pre_group, post_group, link_num, var_code, op_code)
self.unit_connections.append(connection_information)
if self.sparse_with_mask:
mask = (weight != 0)
mask_name = self.get_link_name(pre_group, post_group, 'mask')
mask_var_code = (mask_name, shape, mask)
mask_op = (weight_name, self.mask_operation, [weight_name, mask_name])
mask_information = (mask_var_code, mask_op)
self.mask_info.append(mask_information)
pass
def condition_check(self, pre_group, post_group):
flag = False
pre_type = pre_group.type
post_type = post_group.type
if pre_type == post_type:
flag = True
return flag
pass
Connection.register('random', random_connect_mask)
Connection.register('random_connection', random_connect_mask)
class DistDepd_connect(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse', 'conv','...'), policies=[],
max_delay=0, sparse_with_mask=False, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(DistDepd_connect, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name, link_type=link_type,
policies=policies, max_delay=max_delay, sparse_with_mask=sparse_with_mask,
pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
self.distance_weight_function = kwargs.get('distance_weight_function', None)
self.zero_self = kwargs.get('zero_self', False)
if self.distance_weight_function is None:
self.distance_weight_function = self.default_dist_weight_function
self.dist_function = kwargs.get('dist_function', 'euclidean')
if self.dist_function == 'euclidean':
self.dist_function = self.euclidean_dist_function
elif self.dist_function == 'circular':
self.dist_function = self.circular_dist_function
self.dist_a = kwargs.get('dist_a', 0.2)
self.dist_b = kwargs.get('dist_b', 0.4)
self.w_amp = kwargs.get('w_amp', -0.1)
self.pos_range = kwargs.get('pos_range', 1.0)
def unit_connect(self, pre_group, post_group):
# The number of neurons in neuron group
pre_num = pre_group.num
post_num = post_group.num
assert len(pre_group.position) > 0
assert len(post_group.position) > 0
link_num = pre_num * post_num
shape = (post_num, pre_num)
weight = np.zeros(shape)
post_pos = np.expand_dims(post_group.position, axis=1)
pre_pos = np.expand_dims(pre_group.position, axis=0)
weight = self.distance_weight_function(self.dist_function(pre_pos, post_pos))
# from matplotlib import pyplot as plt
# plt.imshow(weight)
# plt.show()
# The name for backend variables
input_name = self.get_input_name(pre_group, post_group)
weight_name = self.get_weight_name(pre_group, post_group)
target_name = self.get_target_name(post_group)
# The backend variable
var_code = (weight_name, shape, weight, True, False) # (var_name, shape, value, is_parameter, is_sparse, init)
op_code = [target_name, 'mat_mult', input_name, weight_name]
connection_information = (pre_group, post_group, link_num, var_code, op_code)
self.unit_connections.append(connection_information)
def circular_dist_function(self, pre_pos, post_pos):
if not isinstance(pre_pos, torch.Tensor):
pre_pos = torch.tensor(pre_pos)
if not isinstance(post_pos, torch.Tensor):
post_pos = torch.tensor(post_pos)
z = torch.maximum(pre_pos, post_pos)
k = torch.minimum(pre_pos, post_pos)
dist = torch.minimum(z - k, self.pos_range + k - z)
dist = torch.norm(dist, p=2, dim=-1)
return dist
def euclidean_dist_function(self, pre_pos, post_pos):
if isinstance(pre_pos, torch.Tensor) and isinstance(post_pos, torch.Tensor):
diff = pre_pos - post_pos
else:
diff = torch.tensor(pre_pos-post_pos)
dist = torch.norm(diff, p=2, dim=-1)
return dist
def default_dist_weight_function(self, dist):
weights = self.w_amp * (torch.exp(-dist / self.dist_a)/self.dist_a - 0.5*torch.exp(-dist / self.dist_b)/self.dist_b)
if self.zero_self:
weights = weights * (dist!=0).float()
# import matplotlib.pyplot as plt
# plt.imshow(weights, aspect='auto')
# plt.show()
return weights
Connection.register('dist_depd', DistDepd_connect)
class reconnect(Connection):
def __init__(self, pre_assembly, post_assembly, name=None, link_type=('full', 'sparse_connect', 'conv', '...'), policies=[],
max_delay=0, sparse_with_mask=False, pre_var_name='O', post_var_name='WgtSum', **kwargs):
super(reconnect, self).__init__(pre_assembly=pre_assembly, post_assembly=post_assembly, name=name, link_type=link_type,
policies=policies, max_delay=max_delay, sparse_with_mask=sparse_with_mask, pre_var_name=pre_var_name, post_var_name=post_var_name, **kwargs)
def unit_connect(self, pre_group, post_group):
pass
def condition_check(self, pre_group, post_group):
pass
| 47.569721
| 185
| 0.643691
| 4,767
| 35,820
| 4.520034
| 0.057059
| 0.037175
| 0.027289
| 0.03866
| 0.820439
| 0.790597
| 0.76962
| 0.762334
| 0.751381
| 0.743769
| 0
| 0.004557
| 0.258794
| 35,820
| 753
| 186
| 47.569721
| 0.807006
| 0.115382
| 0
| 0.717899
| 0
| 0.01751
| 0.08557
| 0.004458
| 0
| 0
| 0
| 0
| 0.027237
| 1
| 0.064202
| false
| 0.031128
| 0.021401
| 0
| 0.126459
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
a57c06fcfd9367ddf11e6f377131424d523baced
| 25,018
|
py
|
Python
|
bacnet/bacnet.py
|
demmsnt/visiodesk-gateway
|
2adb7115f81ef8662b4339bab055e1698fcbc3aa
|
[
"Apache-2.0"
] | null | null | null |
bacnet/bacnet.py
|
demmsnt/visiodesk-gateway
|
2adb7115f81ef8662b4339bab055e1698fcbc3aa
|
[
"Apache-2.0"
] | null | null | null |
bacnet/bacnet.py
|
demmsnt/visiodesk-gateway
|
2adb7115f81ef8662b4339bab055e1698fcbc3aa
|
[
"Apache-2.0"
] | 2
|
2020-08-17T09:08:28.000Z
|
2020-09-20T15:56:04.000Z
|
import enum
class StatusFlag(enum.Enum):
IN_ALARM = 0
FAULT = 1
OVERRIDEN = 2
OUT_OF_SERVICE = 3
def id(self):
return self.value
bacnet_name_map = {
"acked-transitions": "0",
"ack-required": "1",
"action": "2",
"action-text": "3",
"active-text": "4",
"active-vt-sessions": "5",
"alarm-value": "6",
"alarm-values": "7",
"all": "8",
"all-writes-successful": "9",
"apdu-segment-timeout": "10",
"apdu-timeout": "11",
"application-software-version": "12",
"archive": "13",
"bias": "14",
"change-of-state-count": "15",
"change-of-state-time": "16",
"notification-class": "17",
"controlled-variable-reference": "19",
"controlled-variable-units": "20",
"controlled-variable-value": "21",
"cov-increment": "22",
"date-list": "23",
"daylight-savings-status": "24",
"deadband": "25",
"derivative-constant": "26",
"derivative-constant-units": "27",
"description": "28",
"description-of-halt": "29",
"device-address-binding": "30",
"device-type": "31",
"effective-period": "32",
"elapsed-active-time": "33",
"error-limit": "34",
"event-enable": "35",
"event-state": "36",
"event-type": "37",
"exception-schedule": "38",
"fault-values": "39",
"feedback-value": "40",
"file-access-method": "41",
"file-size": "42",
"file-type": "43",
"firmware-revision": "44",
"high-limit": "45",
"inactive-text": "46",
"in-process": "47",
"instance-of": "48",
"integral-constant": "49",
"integral-constant-units": "50",
"limit-enable": "52",
"list-of-group-members": "53",
"list-of-object-property-references": "54",
"local-date": "56",
"local-time": "57",
"location": "58",
"low-limit": "59",
"manipulated-variable-reference": "60",
"maximum-output": "61",
"max-apdu-length-accepted": "62",
"max-info-frames": "63",
"max-master": "64",
"max-pres-value": "65",
"minimum-off-time": "66",
"minimum-on-time": "67",
"minimum-output": "68",
"min-pres-value": "69",
"model-name": "70",
"modification-date": "71",
"notify-type": "72",
"number-of-apdu-retries": "73",
"number-of-states": "74",
"object identifier": "75",
"object-identifier": "75",
"object-list": "76",
"object-name": "77",
"object-property-reference": "77",
"object type": "79",
"object-type": "79",
"optional": "80",
"out-of-service": "81",
"output-units": "82",
"event-parameters": "83",
"polarity": "84",
"present value": "85",
"present-value": "85",
"priority": "86",
"priority-array": "87",
"priority-for-writing": "88",
"process-identifier": "89",
"program-change": "90",
"program-location": "91",
"program-state": "92",
"proportional-constant": "93",
"proportional-constant-units": "94",
"protocol-object-types-supported": "96",
"protocol-services-supported": "97",
"protocol-version": "98",
"read-only": "99",
"reason-for-halt": "100",
"recipient-list": "102",
"reliability": "103",
"relinquish-default": "104",
"required": "105",
"resolution": "106",
"segmentation-supported": "107",
"setpoint": "108",
"setpoint-reference": "109",
"state-text": "110",
"status-flags": "111",
"system-status": "112",
"time-delay": "113",
"time-of-active-time-reset": "114",
"time-of-state-count-reset": "115",
"time-synchronization-recipients": "116",
"units": "117",
"update-interval": "118",
"utc-offset": "119",
"vendor-identifier": "120",
"vendor-name": "121",
"vt-classes-supported": "122",
"weekly-schedule": "123",
"attempted-samples": "124",
"average-value": "125",
"buffer-size": "126",
"client-cov-increment": "127",
"cov-resubscription-interval": "128",
"event-time-stamps": "130",
"log-buffer": "131",
"log-device-object-property": "132",
"enable": "133",
"log-interval": "134",
"maximum-value": "135",
"minimum-value": "136",
"notification-threshold": "137",
"protocol-revision": "139",
"records-since-notification": "140",
"record-count": "141",
"start-time": "142",
"stop-time": "143",
"stop-when-full": "144",
"total-record-count": "145",
"valid-samples": "146",
"window-interval": "147",
"window-samples": "148",
"maximum-value-timestamp": "149",
"minimum-value-timestamp": "150",
"variance-value": "151",
"active-cov-subscriptions": "152",
"backup-failure-timeout": "153",
"configuration-files": "154",
"database-revision": "155",
"direct-reading": "156",
"last-restore-time": "157",
"maintenance-required": "158",
"member-of": "159",
"mode": "160",
"operation-expected": "161",
"setting": "162",
"silenced": "163",
"tracking-value": "164",
"zone-members": "165",
"life-safety-alarm-values": "166",
"max-segments-accepted": "167",
"profile-name": "168",
"auto-slave-discovery": "169",
"manual-slave-address-binding": "170",
"slave-address-binding": "171",
"slave-proxy-enable": "172",
"last-notify-record": "173",
"schedule-default": "174",
"accepted-modes": "175",
"adjust-value": "176",
"count": "177",
"count-before-change": "178",
"count-change-time": "179",
"cov-period": "180",
"input-reference": "181",
"limit-monitoring-interval": "182",
"logging-object": "183",
"logging-record": "184",
"prescale": "185",
"pulse-rate": "186",
"scale": "187",
"scale-factor": "188",
"update-time": "189",
"value-before-change": "190",
"value-set": "191",
"value-change-time": "192",
"align-intervals": "193",
"interval-offset": "195",
"last-restart-reason": "196",
"logging-type": "197",
"restart-notification-recipients": "202",
"time-of-device-restart": "203",
"time-synchronization-interval": "204",
"trigger": "205",
"utc-time-synchronization-recipients": "206",
"node-subtype": "207",
"node-type": "208",
"structured-object-list": "209",
"subordinate-annotations": "210",
"subordinate-list": "211",
"actual-shed-level": "212",
"duty-window": "213",
"expected-shed-level": "214",
"full-duty-baseline": "215",
"requested-shed-level": "218",
"shed-duration": "219",
"shed-level-descriptions": "220",
"shed-levels": "221",
"state-description": "222",
"door-alarm-state": "226",
"door-extended-pulse-time": "227",
"door-members": "228",
"door-open-too-long-time": "229",
"door-pulse-time": "230",
"door-status": "231",
"door-unlock-delay-time": "232",
"lock-status": "233",
"masked-alarm-values": "234",
"secured-status": "235",
"absentee-limit": "244",
"access-alarm-events": "245",
"access-doors": "246",
"access-event": "247",
"access-event-authentication-factor": "248",
"access-event-credential": "249",
"access-event-time": "250",
"access-transaction-events": "251",
"accompaniment": "252",
"accompaniment-time": "253",
"activation-time": "254",
"active-authentication-policy": "255",
"assigned-access-rights": "256",
"authentication-factors": "257",
"authentication-policy-list": "258",
"authentication-policy-names": "259",
"authentication-status": "260",
"authorization-mode": "261",
"belongs-to": "262",
"credential-disable": "263",
"credential-status": "264",
"credentials": "265",
"credentials-in-zone": "266",
"days-remaining": "267",
"entry-points": "268",
"exit-points": "269",
"expiry-time": "270",
"extended-time-enable": "271",
"failed-attempt-events": "272",
"failed-attempts": "273",
"failed-attempts-time": "274",
"last-access-event": "275",
"last-access-point": "276",
"last-credential-added": "277",
"last-credential-added-time": "278",
"last-credential-removed": "279",
"last-credential-removed-time": "280",
"last-use-time": "281",
"lockout": "282",
"lockout-relinquish-time": "283",
"max-failed-attempts": "285",
"members": "286",
"muster-point": "287",
"negative-access-rules": "288",
"number-of-authentication-policies": "289",
"occupancy-count": "290",
"occupancy-count-adjust": "291",
"occupancy-count-enable": "292",
"occupancy-lower-limit": "294",
"occupancy-lower-limit-enforced": "295",
"occupancy-state": "296",
"occupancy-upper-limit": "297",
"occupancy-upper-limit-enforced": "298",
"passback-mode": "300",
"passback-timeout": "301",
"positive-access-rules": "302",
"reason-for-disable": "303",
"supported-formats": "304",
"supported-format-classes": "305",
"threat-authority": "306",
"threat-level": "307",
"trace-flag": "308",
"transaction-notification-class": "309",
"user-external-identifier": "310",
"user-information-reference": "311",
"user-name": "317",
"user-type": "318",
"uses-remaining": "319",
"zone-from": "320",
"zone-to": "321",
"access-event-tag": "322",
"global-identifier": "323",
"verification-time": "326",
"base-device-security-policy": "327",
"distribution-key-revision": "328",
"do-not-hide": "329",
"key-sets": "330",
"last-key-server": "331",
"network-access-security-policies": "332",
"packet-reorder-time": "333",
"security-pdu-timeout": "334",
"security-time-window": "335",
"supported-security-algorithms": "336",
"update-key-set-timeout": "337",
"backup-and-restore-state": "338",
"backup-preparation-time": "339",
"restore-completion-time": "340",
"restore-preparation-time": "341",
"bit-mask": "342",
"bit-text": "343",
"is-utc": "344",
"group-members": "345",
"group-member-names": "346",
"member-status-flags": "347",
"requested-update-interval": "348",
"covu-period": "349",
"covu-recipients": "350",
"event-message-texts": "351",
"event-message-texts-config": "352",
"event-detection-enable": "353",
"event-algorithm-inhibit": "354",
"event-algorithm-inhibit-ref": "355",
"time-delay-normal": "356",
"reliability-evaluation-inhibit": "357",
"fault-parameters": "358",
"fault-type": "359",
"local-forwarding-only": "360",
"process-identifier-filter": "361",
"subscribed-recipients": "362",
"port-filter": "363",
"authorization-exemptions": "364",
"allow-group-delay-inhibit": "365",
"channel-number": "366",
"control-groups": "367",
"execution-delay": "368",
"last-priority": "369",
"write-status": "370",
"property-list": "371",
"serial-number": "372",
"blink-warn-enable": "373",
"default-fade-time": "374",
"default-ramp-rate": "375",
"default-step-increment": "376",
"egress-time": "377",
"in-progress": "378",
"instantaneous-power": "379",
"lighting-command": "380",
"lighting-command-default-priority": "381",
"max-actual-value": "382",
"min-actual-value": "383",
"power": "384",
"transition": "385",
"egress-active": "386"
}
class ObjectProperty(enum.Enum):
ACKED_TRANSITIONS = '0'
ACK_REQUIRED = '1'
ACTION = '2'
ACTION_TEXT = '3'
ACTIVE_TEXT = '4'
ACTIVE_VT_SESSIONS = '5'
ALARM_VALUE = '6'
ALARM_VALUES = '7'
ALL = '8'
ALL_WRITES_SUCCESSFUL = '9'
APDU_SEGMENT_TIMEOUT = '10'
APDU_TIMEOUT = '11'
APPLICATION_SOFTWARE_VERSION = '12'
ARCHIVE = '13'
BIAS = '14'
CHANGE_OF_STATE_COUNT = '15'
CHANGE_OF_STATE_TIME = '16'
NOTIFICATION_CLASS = '17'
CONTROLLED_VARIABLE_REFERENCE = '19'
CONTROLLED_VARIABLE_UNITS = '20'
CONTROLLED_VARIABLE_VALUE = '21'
COV_INCREMENT = '22'
DATE_LIST = '23'
DAYLIGHT_SAVINGS_STATUS = '24'
DEADBAND = '25'
DERIVATIVE_CONSTANT = '26'
DERIVATIVE_CONSTANT_UNITS = '27'
DESCRIPTION = '28'
DESCRIPTION_OF_HALT = '29'
DEVICE_ADDRESS_BINDING = '30'
DEVICE_TYPE = '31'
EFFECTIVE_PERIOD = '32'
ELAPSED_ACTIVE_TIME = '33'
ERROR_LIMIT = '34'
EVENT_ENABLE = '35'
EVENT_STATE = '36'
EVENT_TYPE = '37'
EXCEPTION_SCHEDULE = '38'
FAULT_VALUES = '39'
FEEDBACK_VALUE = '40'
FILE_ACCESS_METHOD = '41'
FILE_SIZE = '42'
FILE_TYPE = '43'
FIRMWARE_REVISION = '44'
HIGH_LIMIT = '45'
INACTIVE_TEXT = '46'
IN_PROCESS = '47'
INSTANCE_OF = '48'
INTEGRAL_CONSTANT = '49'
INTEGRAL_CONSTANT_UNITS = '50'
LIMIT_ENABLE = '52'
LIST_OF_GROUP_MEMBERS = '53'
LIST_OF_OBJECT_PROPERTY_REFERENCES = '54'
LOCAL_DATE = '56'
LOCAL_TIME = '57'
LOCATION = '58'
LOW_LIMIT = '59'
MANIPULATED_VARIABLE_REFERENCE = '60'
MAXIMUM_OUTPUT = '61'
MAX_APDU_LENGTH_ACCEPTED = '62'
MAX_INFO_FRAMES = '63'
MAX_MASTER = '64'
MAX_PRES_VALUE = '65'
MINIMUM_OFF_TIME = '66'
MINIMUM_ON_TIME = '67'
MINIMUM_OUTPUT = '68'
MIN_PRES_VALUE = '69'
MODEL_NAME = '70'
MODIFICATION_DATE = '71'
NOTIFY_TYPE = '72'
NUMBER_OF_APDU_RETRIES = '73'
NUMBER_OF_STATES = '74'
OBJECT_IDENTIFIER = '75'
OBJECT_LIST = '76'
OBJECT_PROPERTY_REFERENCE = '77'
OBJECT_TYPE = '79'
OPTIONAL = '80'
OUT_OF_SERVICE = '81'
OUTPUT_UNITS = '82'
EVENT_PARAMETERS = '83'
POLARITY = '84'
PRESENT_VALUE = '85'
PRIORITY = '86'
PRIORITY_ARRAY = '87'
PRIORITY_FOR_WRITING = '88'
PROCESS_IDENTIFIER = '89'
PROGRAM_CHANGE = '90'
PROGRAM_LOCATION = '91'
PROGRAM_STATE = '92'
PROPORTIONAL_CONSTANT = '93'
PROPORTIONAL_CONSTANT_UNITS = '94'
PROTOCOL_OBJECT_TYPES_SUPPORTED = '96'
PROTOCOL_SERVICES_SUPPORTED = '97'
PROTOCOL_VERSION = '98'
READ_ONLY = '99'
REASON_FOR_HALT = '100'
RECIPIENT_LIST = '102'
RELIABILITY = '103'
RELINQUISH_DEFAULT = '104'
REQUIRED = '105'
RESOLUTION = '106'
SEGMENTATION_SUPPORTED = '107'
SETPOINT = '108'
SETPOINT_REFERENCE = '109'
STATE_TEXT = '110'
STATUS_FLAGS = '111'
SYSTEM_STATUS = '112'
TIME_DELAY = '113'
TIME_OF_ACTIVE_TIME_RESET = '114'
TIME_OF_STATE_COUNT_RESET = '115'
TIME_SYNCHRONIZATION_RECIPIENTS = '116'
UNITS = '117'
UPDATE_INTERVAL = '118'
UTC_OFFSET = '119'
VENDOR_IDENTIFIER = '120'
VENDOR_NAME = '121'
VT_CLASSES_SUPPORTED = '122'
WEEKLY_SCHEDULE = '123'
ATTEMPTED_SAMPLES = '124'
AVERAGE_VALUE = '125'
BUFFER_SIZE = '126'
CLIENT_COV_INCREMENT = '127'
COV_RESUBSCRIPTION_INTERVAL = '128'
EVENT_TIME_STAMPS = '130'
LOG_BUFFER = '131'
LOG_DEVICE_OBJECT_PROPERTY = '132'
ENABLE = '133'
LOG_INTERVAL = '134'
MAXIMUM_VALUE = '135'
MINIMUM_VALUE = '136'
NOTIFICATION_THRESHOLD = '137'
PROTOCOL_REVISION = '139'
RECORDS_SINCE_NOTIFICATION = '140'
RECORD_COUNT = '141'
START_TIME = '142'
STOP_TIME = '143'
STOP_WHEN_FULL = '144'
TOTAL_RECORD_COUNT = '145'
VALID_SAMPLES = '146'
WINDOW_INTERVAL = '147'
WINDOW_SAMPLES = '148'
MAXIMUM_VALUE_TIMESTAMP = '149'
MINIMUM_VALUE_TIMESTAMP = '150'
VARIANCE_VALUE = '151'
ACTIVE_COV_SUBSCRIPTIONS = '152'
BACKUP_FAILURE_TIMEOUT = '153'
CONFIGURATION_FILES = '154'
DATABASE_REVISION = '155'
DIRECT_READING = '156'
LAST_RESTORE_TIME = '157'
MAINTENANCE_REQUIRED = '158'
MEMBER_OF = '159'
MODE = '160'
OPERATION_EXPECTED = '161'
SETTING = '162'
SILENCED = '163'
TRACKING_VALUE = '164'
ZONE_MEMBERS = '165'
LIFE_SAFETY_ALARM_VALUES = '166'
MAX_SEGMENTS_ACCEPTED = '167'
PROFILE_NAME = '168'
AUTO_SLAVE_DISCOVERY = '169'
MANUAL_SLAVE_ADDRESS_BINDING = '170'
SLAVE_ADDRESS_BINDING = '171'
SLAVE_PROXY_ENABLE = '172'
LAST_NOTIFY_RECORD = '173'
SCHEDULE_DEFAULT = '174'
ACCEPTED_MODES = '175'
ADJUST_VALUE = '176'
COUNT = '177'
COUNT_BEFORE_CHANGE = '178'
COUNT_CHANGE_TIME = '179'
COV_PERIOD = '180'
INPUT_REFERENCE = '181'
LIMIT_MONITORING_INTERVAL = '182'
LOGGING_OBJECT = '183'
LOGGING_RECORD = '184'
PRESCALE = '185'
PULSE_RATE = '186'
SCALE = '187'
SCALE_FACTOR = '188'
UPDATE_TIME = '189'
VALUE_BEFORE_CHANGE = '190'
VALUE_SET = '191'
VALUE_CHANGE_TIME = '192'
ALIGN_INTERVALS = '193'
INTERVAL_OFFSET = '195'
LAST_RESTART_REASON = '196'
LOGGING_TYPE = '197'
RESTART_NOTIFICATION_RECIPIENTS = '202'
TIME_OF_DEVICE_RESTART = '203'
TIME_SYNCHRONIZATION_INTERVAL = '204'
TRIGGER = '205'
UTC_TIME_SYNCHRONIZATION_RECIPIENTS = '206'
NODE_SUBTYPE = '207'
NODE_TYPE = '208'
STRUCTURED_OBJECT_LIST = '209'
SUBORDINATE_ANNOTATIONS = '210'
SUBORDINATE_LIST = '211'
ACTUAL_SHED_LEVEL = '212'
DUTY_WINDOW = '213'
EXPECTED_SHED_LEVEL = '214'
FULL_DUTY_BASELINE = '215'
REQUESTED_SHED_LEVEL = '218'
SHED_DURATION = '219'
SHED_LEVEL_DESCRIPTIONS = '220'
SHED_LEVELS = '221'
STATE_DESCRIPTION = '222'
DOOR_ALARM_STATE = '226'
DOOR_EXTENDED_PULSE_TIME = '227'
DOOR_MEMBERS = '228'
DOOR_OPEN_TOO_LONG_TIME = '229'
DOOR_PULSE_TIME = '230'
DOOR_STATUS = '231'
DOOR_UNLOCK_DELAY_TIME = '232'
LOCK_STATUS = '233'
MASKED_ALARM_VALUES = '234'
SECURED_STATUS = '235'
ABSENTEE_LIMIT = '244'
ACCESS_ALARM_EVENTS = '245'
ACCESS_DOORS = '246'
ACCESS_EVENT = '247'
ACCESS_EVENT_AUTHENTICATION_FACTOR = '248'
ACCESS_EVENT_CREDENTIAL = '249'
ACCESS_EVENT_TIME = '250'
ACCESS_TRANSACTION_EVENTS = '251'
ACCOMPANIMENT = '252'
ACCOMPANIMENT_TIME = '253'
ACTIVATION_TIME = '254'
ACTIVE_AUTHENTICATION_POLICY = '255'
ASSIGNED_ACCESS_RIGHTS = '256'
AUTHENTICATION_FACTORS = '257'
AUTHENTICATION_POLICY_LIST = '258'
AUTHENTICATION_POLICY_NAMES = '259'
AUTHENTICATION_STATUS = '260'
AUTHORIZATION_MODE = '261'
BELONGS_TO = '262'
CREDENTIAL_DISABLE = '263'
CREDENTIAL_STATUS = '264'
CREDENTIALS = '265'
CREDENTIALS_IN_ZONE = '266'
DAYS_REMAINING = '267'
ENTRY_POINTS = '268'
EXIT_POINTS = '269'
EXPIRY_TIME = '270'
EXTENDED_TIME_ENABLE = '271'
FAILED_ATTEMPT_EVENTS = '272'
FAILED_ATTEMPTS = '273'
FAILED_ATTEMPTS_TIME = '274'
LAST_ACCESS_EVENT = '275'
LAST_ACCESS_POINT = '276'
LAST_CREDENTIAL_ADDED = '277'
LAST_CREDENTIAL_ADDED_TIME = '278'
LAST_CREDENTIAL_REMOVED = '279'
LAST_CREDENTIAL_REMOVED_TIME = '280'
LAST_USE_TIME = '281'
LOCKOUT = '282'
LOCKOUT_RELINQUISH_TIME = '283'
MAX_FAILED_ATTEMPTS = '285'
MEMBERS = '286'
MUSTER_POINT = '287'
NEGATIVE_ACCESS_RULES = '288'
NUMBER_OF_AUTHENTICATION_POLICIES = '289'
OCCUPANCY_COUNT = '290'
OCCUPANCY_COUNT_ADJUST = '291'
OCCUPANCY_COUNT_ENABLE = '292'
OCCUPANCY_LOWER_LIMIT = '294'
OCCUPANCY_LOWER_LIMIT_ENFORCED = '295'
OCCUPANCY_STATE = '296'
OCCUPANCY_UPPER_LIMIT = '297'
OCCUPANCY_UPPER_LIMIT_ENFORCED = '298'
PASSBACK_MODE = '300'
PASSBACK_TIMEOUT = '301'
POSITIVE_ACCESS_RULES = '302'
REASON_FOR_DISABLE = '303'
SUPPORTED_FORMATS = '304'
SUPPORTED_FORMAT_CLASSES = '305'
THREAT_AUTHORITY = '306'
THREAT_LEVEL = '307'
TRACE_FLAG = '308'
TRANSACTION_NOTIFICATION_CLASS = '309'
USER_EXTERNAL_IDENTIFIER = '310'
USER_INFORMATION_REFERENCE = '311'
USER_NAME = '317'
USER_TYPE = '318'
USES_REMAINING = '319'
ZONE_FROM = '320'
ZONE_TO = '321'
ACCESS_EVENT_TAG = '322'
GLOBAL_IDENTIFIER = '323'
VERIFICATION_TIME = '326'
BASE_DEVICE_SECURITY_POLICY = '327'
DISTRIBUTION_KEY_REVISION = '328'
DO_NOT_HIDE = '329'
KEY_SETS = '330'
LAST_KEY_SERVER = '331'
NETWORK_ACCESS_SECURITY_POLICIES = '332'
PACKET_REORDER_TIME = '333'
SECURITY_PDU_TIMEOUT = '334'
SECURITY_TIME_WINDOW = '335'
SUPPORTED_SECURITY_ALGORITHMS = '336'
UPDATE_KEY_SET_TIMEOUT = '337'
BACKUP_AND_RESTORE_STATE = '338'
BACKUP_PREPARATION_TIME = '339'
RESTORE_COMPLETION_TIME = '340'
RESTORE_PREPARATION_TIME = '341'
BIT_MASK = '342'
BIT_TEXT = '343'
IS_UTC = '344'
GROUP_MEMBERS = '345'
GROUP_MEMBER_NAMES = '346'
MEMBER_STATUS_FLAGS = '347'
REQUESTED_UPDATE_INTERVAL = '348'
COVU_PERIOD = '349'
COVU_RECIPIENTS = '350'
EVENT_MESSAGE_TEXTS = '351'
EVENT_MESSAGE_TEXTS_CONFIG = '352'
EVENT_DETECTION_ENABLE = '353'
EVENT_ALGORITHM_INHIBIT = '354'
EVENT_ALGORITHM_INHIBIT_REF = '355'
TIME_DELAY_NORMAL = '356'
RELIABILITY_EVALUATION_INHIBIT = '357'
FAULT_PARAMETERS = '358'
FAULT_TYPE = '359'
LOCAL_FORWARDING_ONLY = '360'
PROCESS_IDENTIFIER_FILTER = '361'
SUBSCRIBED_RECIPIENTS = '362'
PORT_FILTER = '363'
AUTHORIZATION_EXEMPTIONS = '364'
ALLOW_GROUP_DELAY_INHIBIT = '365'
CHANNEL_NUMBER = '366'
CONTROL_GROUPS = '367'
EXECUTION_DELAY = '368'
LAST_PRIORITY = '369'
WRITE_STATUS = '370'
PROPERTY_LIST = '371'
SERIAL_NUMBER = '372'
BLINK_WARN_ENABLE = '373'
DEFAULT_FADE_TIME = '374'
DEFAULT_RAMP_RATE = '375'
DEFAULT_STEP_INCREMENT = '376'
EGRESS_TIME = '377'
IN_PROGRESS = '378'
INSTANTANEOUS_POWER = '379'
LIGHTING_COMMAND = '380'
LIGHTING_COMMAND_DEFAULT_PRIORITY = '381'
MAX_ACTUAL_VALUE = '382'
MIN_ACTUAL_VALUE = '383'
POWER = '384'
TRANSITION = '385'
EGRESS_ACTIVE = '386'
DEVICE_ID = '846'
def id(self):
return self.value
class ObjectType(enum.Enum):
ANALOG_INPUT = "analog-input", 0
ANALOG_OUTPUT = "analog-output", 1
ANALOG_VALUE = "analog-value", 2
BINARY_INPUT = "binary-input", 3
BINARY_OUTPUT = "binary-output", 4
BINARY_VALUE = "binary-value", 5
DEVICE = "device", 8
CALENDAR = "calendar", 6
COMMAND = "command", 7
EVENT_ENROLLMENT = "event-enrollment", 9
FILE = "file", 10
GROUP = "group", 11
LOOP = "loop", 12
MULTI_STATE_INPUT = "multi-state-input", 13
NOTIFICATION_CLASS = "notification-class", 15
MULTI_STATE_OUTPUT = "multi-state-output", 14
PROGRAM = "program", 16
SCHEDULE = "schedule", 17
AVERAGING = "averaging", 18
MULTI_STATE_VALUE = "multi-state-value", 19
ACCUMULATOR = "accumulator", 23
TREND_LOG = "trend-log", 20
LIFE_SAFETY_POINT = "life-safety-point", 21
LIFE_SAFETY_ZONE = "life-safety-zone", 22
PULSE_CONVERTER = "pulse-converter", 24
ACCESS_POINT = "access-point", 33
SITE = "site", -1
FOLDER = "folder", -1
TRUNK = "trunk", -1
GRAPHIC = "graphic", -1
def name(self):
return self.value[0]
def code(self):
return self.value[1]
@staticmethod
def name_to_code(name):
for _type in ObjectType:
if _type.name() == name:
return _type.code()
return None
@staticmethod
def code_to_name(code):
for _type in ObjectType:
if _type.code() == code:
return _type.name()
return None
class StatusFlags:
def __init__(self, status_flags: list = None) -> None:
self.in_alarm = False
self.fault = False
self.overridden = False
self.out_of_service = False
if status_flags is not None and type(status_flags) is list:
try:
self.in_alarm = status_flags[0]
self.fault = status_flags[1]
self.overridden = status_flags[2]
self.out_of_service = status_flags[3]
except:
pass
def __str__(self) -> str:
return str(self.as_list())
@staticmethod
def create(in_alarm=False, fault=False, overridden=False, out_of_service=False):
return [in_alarm, fault, overridden, out_of_service]
def get_in_alarm(self) -> bool:
return self.in_alarm
def get_fault(self) -> bool:
return self.fault
def get_overridden(self) -> bool:
return self.overridden
def get_out_of_service(self) -> bool:
return self.out_of_service
def set_in_alarm(self, flag: bool):
self.in_alarm = flag
def set_fault(self, flag: bool):
self.fault = flag
def set_overridden(self, flag: bool):
self.overridden = flag
def set_out_of_service(self, flag: bool):
self.out_of_service = flag
def as_list(self) -> list:
return [self.in_alarm, self.fault, self.overridden, self.out_of_service]
def is_abnormal(self) -> bool:
"""
object is abnormal if one of follow flag is enabled (IN_ALARM or FAULT or OUT_OF_SERVICE)
"""
return self.in_alarm or self.fault or self.out_of_service
def is_normal(self) -> bool:
"""
object is normal if it is not abnormal
"""
return not self.is_abnormal()
| 29.607101
| 97
| 0.612519
| 2,901
| 25,018
| 5.073768
| 0.255429
| 0.004756
| 0.011414
| 0.006522
| 0.867382
| 0.866091
| 0.85658
| 0.849922
| 0.849922
| 0.849922
| 0
| 0.101243
| 0.224998
| 25,018
| 844
| 98
| 29.64218
| 0.657899
| 0.005116
| 0
| 0.013614
| 0
| 0
| 0.326732
| 0.097452
| 0
| 0
| 0
| 0
| 0
| 1
| 0.024752
| false
| 0.006188
| 0.001238
| 0.013614
| 0.529703
| 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
|
a584802a4469f7bb6cb367566d09d8ed7daec92b
| 2,929
|
py
|
Python
|
tests/unit/engine/test_response.py
|
amenezes/discovery-client
|
9c41456d1cc14f4aab34628ad4e13423e00bc4be
|
[
"Apache-2.0"
] | 2
|
2019-07-18T22:43:49.000Z
|
2020-03-09T03:27:41.000Z
|
tests/unit/engine/test_response.py
|
amenezes/discovery-client
|
9c41456d1cc14f4aab34628ad4e13423e00bc4be
|
[
"Apache-2.0"
] | 20
|
2019-02-27T19:08:03.000Z
|
2021-06-22T16:47:32.000Z
|
tests/unit/engine/test_response.py
|
amenezes/discovery-client
|
9c41456d1cc14f4aab34628ad4e13423e00bc4be
|
[
"Apache-2.0"
] | null | null | null |
import pytest
class TestHttpxClient:
@pytest.mark.asyncio
async def test_status(self, httpx_engine):
resp = await httpx_engine.get("https://httpbin.org/status/200")
assert resp.status == 200
@pytest.mark.asyncio
async def test_url(self, httpx_engine):
resp = await httpx_engine.get("https://httpbin.org/status/200")
assert resp.url == "https://httpbin.org/status/200"
@pytest.mark.asyncio
async def test_content_type(self, httpx_engine):
resp = await httpx_engine.get("https://httpbin.org/status/200")
assert resp.content_type == "text/html; charset=utf-8"
@pytest.mark.asyncio
async def test_version(self, httpx_engine):
resp = await httpx_engine.get("https://httpbin.org/status/200")
assert resp.version == "1.1"
@pytest.mark.asyncio
async def test_json(self, httpx_engine):
resp = await httpx_engine.get("https://httpbin.org/json")
content = await resp.json()
assert isinstance(content, dict)
@pytest.mark.asyncio
async def test_text(self, httpx_engine):
resp = await httpx_engine.get("https://httpbin.org/html")
content = await resp.text()
assert isinstance(content, str)
@pytest.mark.asyncio
async def test_content(self, httpx_engine):
resp = await httpx_engine.get("https://httpbin.org/html")
content = await resp.content()
assert isinstance(content, bytes)
class TestAioHttpClient:
@pytest.mark.asyncio
async def test_status(self, aiohttp_client):
resp = await aiohttp_client.get("https://httpbin.org/status/200")
assert resp.status == 200
@pytest.mark.asyncio
async def test_url(self, aiohttp_client):
resp = await aiohttp_client.get("https://httpbin.org/status/200")
assert resp.url == "https://httpbin.org/status/200"
@pytest.mark.asyncio
async def test_content_type(self, aiohttp_client):
resp = await aiohttp_client.get("https://httpbin.org/status/200")
assert resp.content_type == "text/html"
@pytest.mark.asyncio
async def test_version(self, aiohttp_client):
resp = await aiohttp_client.get("https://httpbin.org/status/200")
assert resp.version == "1.1"
@pytest.mark.asyncio
async def test_json(self, aiohttp_client):
resp = await aiohttp_client.get("https://httpbin.org/json")
content = await resp.json()
assert isinstance(content, dict)
@pytest.mark.asyncio
async def test_text(self, aiohttp_client):
resp = await aiohttp_client.get("https://httpbin.org/html")
content = await resp.text()
assert isinstance(content, str)
@pytest.mark.asyncio
async def test_content(self, aiohttp_client):
resp = await aiohttp_client.get("https://httpbin.org/html")
content = await resp.content()
assert isinstance(content, bytes)
| 35.719512
| 73
| 0.670195
| 379
| 2,929
| 5.058047
| 0.102902
| 0.100156
| 0.125196
| 0.160668
| 0.966093
| 0.966093
| 0.966093
| 0.966093
| 0.892019
| 0.892019
| 0
| 0.017657
| 0.207238
| 2,929
| 81
| 74
| 36.160494
| 0.807924
| 0
| 0
| 0.676923
| 0
| 0
| 0.164903
| 0
| 0
| 0
| 0
| 0
| 0.215385
| 1
| 0
| false
| 0
| 0.015385
| 0
| 0.046154
| 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
|
a59cb73a105c13d1ebb29d2cd2f0339820027e26
| 13
|
py
|
Python
|
pyworkshop/2_intermediate_python/chapter4/exercise_part2_syntaxerrors.py
|
profread/python
|
0ac98fa29ed9854959b03ef6237c2ed5f5825428
|
[
"MIT"
] | 389
|
2019-02-18T05:29:30.000Z
|
2022-02-09T11:40:55.000Z
|
pyworkshop/2_intermediate_python/chapter4/exercise_part2_syntaxerrors.py
|
profread/python
|
0ac98fa29ed9854959b03ef6237c2ed5f5825428
|
[
"MIT"
] | 45
|
2019-04-16T17:29:23.000Z
|
2020-10-13T01:20:40.000Z
|
pyworkshop/2_intermediate_python/chapter4/exercise_part2_syntaxerrors.py
|
profread/python
|
0ac98fa29ed9854959b03ef6237c2ed5f5825428
|
[
"MIT"
] | 257
|
2019-03-19T14:42:31.000Z
|
2022-03-19T07:28:35.000Z
|
a = [4,
x = 5
| 6.5
| 7
| 0.307692
| 4
| 13
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 0.384615
| 13
| 2
| 8
| 6.5
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
a5b0790c3cfdd22fef665b448ef86ad93367ed53
| 46
|
py
|
Python
|
abacus_tpot/__init__.py
|
workforce-data-initiative/tpot-abacus
|
a5abf4af544693e0c58f7891785718e7bc606ed6
|
[
"Apache-2.0"
] | 1
|
2019-09-09T20:52:49.000Z
|
2019-09-09T20:52:49.000Z
|
abacus_tpot/__init__.py
|
workforce-data-initiative/tpot-abacus
|
a5abf4af544693e0c58f7891785718e7bc606ed6
|
[
"Apache-2.0"
] | 43
|
2018-02-11T11:24:18.000Z
|
2022-02-22T05:32:16.000Z
|
abacus_tpot/__init__.py
|
workforce-data-initiative/tpot-abacus
|
a5abf4af544693e0c58f7891785718e7bc606ed6
|
[
"Apache-2.0"
] | null | null | null |
from . import input_db
from . import outcomes
| 15.333333
| 22
| 0.782609
| 7
| 46
| 5
| 0.714286
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 46
| 2
| 23
| 23
| 0.921053
| 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
|
3c06d11aee8dc0b755d98f4239b304fc705064dc
| 5,250
|
py
|
Python
|
test_all.py
|
max810/nnio
|
855a0d9d24e98a284574f72a6d7f5afb601759c2
|
[
"MIT"
] | null | null | null |
test_all.py
|
max810/nnio
|
855a0d9d24e98a284574f72a6d7f5afb601759c2
|
[
"MIT"
] | 1
|
2021-06-02T00:20:28.000Z
|
2021-06-02T00:20:28.000Z
|
test_all.py
|
max810/nnio
|
855a0d9d24e98a284574f72a6d7f5afb601759c2
|
[
"MIT"
] | null | null | null |
import json
from io import StringIO
from http import HTTPStatus
from starlette.testclient import TestClient
from .main import app
client = TestClient(app)
valid_model_body = json.load(open('example_sequential.json'))
valid_model_body_2_inputs = json.load(open('example_2inputs.json'))
valid_model_body_small = json.load(open('example_sequential_small.json'))
def test_export_from_json_body_empty_body():
response = client.request('post', '/architecture/export-from-json-body?framework=keras', json=dict())
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_export_from_json_body_no_layers():
data = dict(date_created="2019-07-24 17:56:34", id="my_id_1", layers=[], name='MyModel_1')
response = client.request('post', '/architecture/export-from-json-body?framework=keras', json=data)
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_export_from_json_body_incorrect():
response = client.request('post', '/architecture/export-from-json-body?framework=keras', json=dict(foo='bar'))
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_export_from_json_body_incorrect_with_id():
response = client.request('post', '/architecture/export-from-json-body?framework=keras', json=dict(id='my_id_1'))
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_export_from_json_body_no_framework():
response = client.request('post', '/architecture/export-from-json-body', json=valid_model_body)
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_export_from_json_body_incorrect_framework():
response = client.request('post', '/architecture/export-from-json-body?framework=mock_framework',
json=valid_model_body)
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_export_from_json_body_correct():
response = client.request('post', '/architecture/export-from-json-body?framework=keras',
json=valid_model_body)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_body_correct_small():
response = client.request('post', '/architecture/export-from-json-body?framework=keras',
json=valid_model_body_small)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_body_correct_optional_params():
response = client.request(
'post',
'/architecture/export-from-json-body?framework=keras&keras_prefer_sequential=1&line_break=crlf',
json=valid_model_body
)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_body_correct_small_optional_params():
response = client.request(
'post',
'/architecture/export-from-json-body?framework=keras&keras_prefer_sequential=1&line_break=crlf',
json=valid_model_body_small
)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_body_correct_2_inputs():
response = client.request('post', '/architecture/export-from-json-body?framework=keras',
json=valid_model_body_2_inputs)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_body_correct_2_inputs_try_sequential():
# data = dict(date_created="2019-07-24 17:56:34", id="my_id_1", layers=[], name='MyModel_1')
response = client.request('post', '/architecture/export-from-json-body?framework=keras&prefer_sequential=1',
json=valid_model_body_2_inputs)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_file_empty():
response = client.request('post', '/architecture/export-from-json-file?framework=keras')
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_export_from_json_file_correct():
files = {"architecture-file": open("example_sequential.json", 'rt')}
response = client.request('post', '/architecture/export-from-json-file?framework=keras', files=files)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_file_random_order_correct():
files = {"architecture-file": open("example_reversed.json", 'rt')}
response = client.request('post', '/architecture/export-from-json-file?framework=keras', files=files)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_file_cyclic_incorrect():
files = {"architecture-file": open("example_sequential_small_cyclical.json", 'rt')}
response = client.request('post', '/architecture/export-from-json-file?framework=keras', files=files)
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
def test_export_from_json_file_complex_correct():
files = {"architecture-file": open("example_weird.json", 'rt')}
response = client.request('post', '/architecture/export-from-json-file?framework=keras', files=files)
assert response.status_code == HTTPStatus.OK
def test_export_from_json_file_incorrect_data():
files = {"architecture-file": StringIO("""{ "a": "b" }""")}
response = client.request('post', '/architecture/export-from-json-file?framework=keras', files=files)
assert response.status_code == HTTPStatus.UNPROCESSABLE_ENTITY
| 36.971831
| 117
| 0.745524
| 671
| 5,250
| 5.536513
| 0.120715
| 0.096904
| 0.135666
| 0.116285
| 0.901211
| 0.879408
| 0.820458
| 0.818573
| 0.818573
| 0.818573
| 0
| 0.009249
| 0.135048
| 5,250
| 141
| 118
| 37.234043
| 0.808853
| 0.017143
| 0
| 0.439024
| 0
| 0.02439
| 0.273168
| 0.222761
| 0
| 0
| 0
| 0
| 0.219512
| 1
| 0.219512
| false
| 0
| 0.060976
| 0
| 0.280488
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3c1afd552d15dc84b1b8fa63ce520e9ef4e874dd
| 5,389
|
py
|
Python
|
new.py
|
chiumax/blairhacks_4
|
41359d7de1853d3c86d0b340d88eebe0492d910d
|
[
"MIT"
] | null | null | null |
new.py
|
chiumax/blairhacks_4
|
41359d7de1853d3c86d0b340d88eebe0492d910d
|
[
"MIT"
] | null | null | null |
new.py
|
chiumax/blairhacks_4
|
41359d7de1853d3c86d0b340d88eebe0492d910d
|
[
"MIT"
] | null | null | null |
import spech
import image
#print(spech.tts("https://www.youtube.com/watch?v=A1onVhL9pXs","update"))
#print(image.findTextInImage("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","is"))
#print(spech.tts("https://www.youtube.com/watch?v=3GZk4rcO5wI","it"))
| 898.166667
| 5,218
| 0.950455
| 227
| 5,389
| 22.563877
| 0.942731
| 0.003905
| 0.005076
| 0.007029
| 0.014447
| 0.014447
| 0.014447
| 0.014447
| 0.014447
| 0
| 0
| 0.162393
| 0.001299
| 5,389
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| 5,219
| 1,077.8
| 0.789298
| 0.994062
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0
| 9
|
b1c1fc75e10d57e954205969db580160048c783a
| 80,945
|
py
|
Python
|
algorithm/method_dichotomy_data.py
|
Storvild/python_examples
|
52b0b486fee442bf4b49a13cf8e609cdf4e02e0b
|
[
"MIT"
] | null | null | null |
algorithm/method_dichotomy_data.py
|
Storvild/python_examples
|
52b0b486fee442bf4b49a13cf8e609cdf4e02e0b
|
[
"MIT"
] | null | null | null |
algorithm/method_dichotomy_data.py
|
Storvild/python_examples
|
52b0b486fee442bf4b49a13cf8e609cdf4e02e0b
|
[
"MIT"
] | null | null | null |
import datetime
wheellog_data = [datetime.datetime(2020, 5, 16, 8, 2, 10, 2000),datetime.datetime(2020, 5, 16, 8, 2, 10, 193000),datetime.datetime(2020, 5, 16, 8, 2, 10, 492000),datetime.datetime(2020, 5, 16, 8, 2, 10, 585000),datetime.datetime(2020, 5, 16, 8, 2, 10, 771000),datetime.datetime(2020, 5, 16, 8, 2, 11, 71000),datetime.datetime(2020, 5, 16, 8, 2, 11, 162000),datetime.datetime(2020, 5, 16, 8, 2, 11, 412000),datetime.datetime(2020, 5, 16, 8, 2, 11, 915000),datetime.datetime(2020, 5, 16, 8, 2, 12, 195000),datetime.datetime(2020, 5, 16, 8, 2, 12, 245000),datetime.datetime(2020, 5, 16, 8, 2, 12, 439000),datetime.datetime(2020, 5, 16, 8, 2, 12, 730000),datetime.datetime(2020, 5, 16, 8, 2, 12, 975000),datetime.datetime(2020, 5, 16, 8, 2, 13, 703000),datetime.datetime(2020, 5, 16, 8, 2, 14, 191000),datetime.datetime(2020, 5, 16, 8, 2, 14, 292000),datetime.datetime(2020, 5, 16, 8, 2, 14, 307000),datetime.datetime(2020, 5, 16, 8, 2, 14, 474000),datetime.datetime(2020, 5, 16, 8, 2, 14, 525000),datetime.datetime(2020, 5, 16, 8, 2, 15, 15000),datetime.datetime(2020, 5, 16, 8, 2, 15, 257000),datetime.datetime(2020, 5, 16, 8, 2, 15, 304000),datetime.datetime(2020, 5, 16, 8, 2, 15, 508000),datetime.datetime(2020, 5, 16, 8, 2, 15, 605000),datetime.datetime(2020, 5, 16, 8, 2, 15, 828000),datetime.datetime(2020, 5, 16, 8, 2, 16, 149000),datetime.datetime(2020, 5, 16, 8, 2, 16, 805000),datetime.datetime(2020, 5, 16, 8, 2, 16, 942000),datetime.datetime(2020, 5, 16, 8, 2, 17, 21000),datetime.datetime(2020, 5, 16, 8, 2, 17, 221000),datetime.datetime(2020, 5, 16, 8, 2, 17, 451000),datetime.datetime(2020, 5, 16, 8, 2, 17, 665000),datetime.datetime(2020, 5, 16, 8, 2, 17, 817000),datetime.datetime(2020, 5, 16, 8, 2, 18, 3000),datetime.datetime(2020, 5, 16, 8, 2, 18, 230000),datetime.datetime(2020, 5, 16, 8, 2, 18, 394000),datetime.datetime(2020, 5, 16, 8, 2, 18, 613000),datetime.datetime(2020, 5, 16, 8, 2, 18, 822000),datetime.datetime(2020, 5, 16, 8, 2, 19, 2000),datetime.datetime(2020, 5, 16, 8, 2, 19, 227000),datetime.datetime(2020, 5, 16, 8, 2, 19, 407000),datetime.datetime(2020, 5, 16, 8, 2, 19, 737000),datetime.datetime(2020, 5, 16, 8, 2, 19, 796000),datetime.datetime(2020, 5, 16, 8, 2, 20, 22000),datetime.datetime(2020, 5, 16, 8, 2, 20, 262000),datetime.datetime(2020, 5, 16, 8, 2, 20, 426000),datetime.datetime(2020, 5, 16, 8, 2, 20, 691000),datetime.datetime(2020, 5, 16, 8, 2, 20, 841000),datetime.datetime(2020, 5, 16, 8, 2, 21, 27000),datetime.datetime(2020, 5, 16, 8, 2, 21, 230000),datetime.datetime(2020, 5, 16, 8, 2, 21, 455000),datetime.datetime(2020, 5, 16, 8, 2, 21, 612000),datetime.datetime(2020, 5, 16, 8, 2, 21, 815000),datetime.datetime(2020, 5, 16, 8, 2, 22, 48000),datetime.datetime(2020, 5, 16, 8, 2, 22, 356000),datetime.datetime(2020, 5, 16, 8, 2, 22, 500000),datetime.datetime(2020, 5, 16, 8, 2, 22, 765000),datetime.datetime(2020, 5, 16, 8, 2, 22, 827000),datetime.datetime(2020, 5, 16, 8, 2, 23, 61000),datetime.datetime(2020, 5, 16, 8, 2, 23, 232000),datetime.datetime(2020, 5, 16, 8, 2, 23, 465000),datetime.datetime(2020, 5, 16, 8, 2, 23, 691000),datetime.datetime(2020, 5, 16, 8, 2, 23, 886000),datetime.datetime(2020, 5, 16, 8, 2, 24, 96000),datetime.datetime(2020, 5, 16, 8, 2, 24, 717000),datetime.datetime(2020, 5, 16, 8, 2, 24, 863000),datetime.datetime(2020, 5, 16, 8, 2, 24, 908000),datetime.datetime(2020, 5, 16, 8, 2, 24, 952000),datetime.datetime(2020, 5, 16, 8, 2, 25, 129000),datetime.datetime(2020, 5, 16, 8, 2, 25, 347000),datetime.datetime(2020, 5, 16, 8, 2, 25, 537000),datetime.datetime(2020, 5, 16, 8, 2, 25, 686000),datetime.datetime(2020, 5, 16, 8, 2, 25, 829000),datetime.datetime(2020, 5, 16, 8, 2, 26, 96000),datetime.datetime(2020, 5, 16, 8, 2, 26, 390000),datetime.datetime(2020, 5, 16, 8, 2, 26, 630000),datetime.datetime(2020, 5, 16, 8, 2, 26, 655000),datetime.datetime(2020, 5, 16, 8, 2, 26, 949000),datetime.datetime(2020, 5, 16, 8, 2, 27, 117000),datetime.datetime(2020, 5, 16, 8, 2, 27, 240000),datetime.datetime(2020, 5, 16, 8, 2, 27, 492000),datetime.datetime(2020, 5, 16, 8, 2, 27, 667000),datetime.datetime(2020, 5, 16, 8, 2, 27, 875000),datetime.datetime(2020, 5, 16, 8, 2, 28, 55000),datetime.datetime(2020, 5, 16, 8, 2, 28, 256000),datetime.datetime(2020, 5, 16, 8, 2, 28, 466000),datetime.datetime(2020, 5, 16, 8, 2, 28, 687000),datetime.datetime(2020, 5, 16, 8, 2, 28, 866000),datetime.datetime(2020, 5, 16, 8, 2, 29, 103000),datetime.datetime(2020, 5, 16, 8, 2, 29, 303000),datetime.datetime(2020, 5, 16, 8, 2, 29, 511000),datetime.datetime(2020, 5, 16, 8, 2, 29, 652000),datetime.datetime(2020, 5, 16, 8, 2, 29, 863000),datetime.datetime(2020, 5, 16, 8, 2, 30, 143000),datetime.datetime(2020, 5, 16, 8, 2, 30, 275000),datetime.datetime(2020, 5, 16, 8, 2, 30, 454000),datetime.datetime(2020, 5, 16, 8, 2, 30, 701000),datetime.datetime(2020, 5, 16, 8, 2, 30, 862000),datetime.datetime(2020, 5, 16, 8, 2, 31, 86000),datetime.datetime(2020, 5, 16, 8, 2, 31, 311000),datetime.datetime(2020, 5, 16, 8, 2, 31, 490000),datetime.datetime(2020, 5, 16, 8, 2, 31, 714000),datetime.datetime(2020, 5, 16, 8, 2, 31, 872000),datetime.datetime(2020, 5, 16, 8, 2, 32, 98000),datetime.datetime(2020, 5, 16, 8, 2, 32, 270000),datetime.datetime(2020, 5, 16, 8, 2, 32, 503000),datetime.datetime(2020, 5, 16, 8, 2, 32, 679000),datetime.datetime(2020, 5, 16, 8, 2, 32, 869000),datetime.datetime(2020, 5, 16, 8, 2, 33, 80000),datetime.datetime(2020, 5, 16, 8, 2, 33, 267000),datetime.datetime(2020, 5, 16, 8, 2, 33, 507000),datetime.datetime(2020, 5, 16, 8, 2, 33, 682000),datetime.datetime(2020, 5, 16, 8, 2, 33, 935000),datetime.datetime(2020, 5, 16, 8, 2, 34, 196000),datetime.datetime(2020, 5, 16, 8, 2, 34, 513000),datetime.datetime(2020, 5, 16, 8, 2, 34, 552000),datetime.datetime(2020, 5, 16, 8, 2, 34, 716000),datetime.datetime(2020, 5, 16, 8, 2, 34, 879000),datetime.datetime(2020, 5, 16, 8, 2, 35, 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249000),datetime.datetime(2020, 5, 16, 8, 7, 43, 436000),datetime.datetime(2020, 5, 16, 8, 7, 43, 641000),datetime.datetime(2020, 5, 16, 8, 7, 43, 847000),datetime.datetime(2020, 5, 16, 8, 7, 44, 61000),datetime.datetime(2020, 5, 16, 8, 7, 44, 234000),datetime.datetime(2020, 5, 16, 8, 7, 44, 440000),datetime.datetime(2020, 5, 16, 8, 7, 44, 682000),datetime.datetime(2020, 5, 16, 8, 7, 44, 902000),datetime.datetime(2020, 5, 16, 8, 7, 45, 50000),datetime.datetime(2020, 5, 16, 8, 7, 46, 54000)]
| 26,981.666667
| 80,928
| 0.671789
| 14,944
| 80,945
| 3.638718
| 0.059087
| 0.091859
| 0.610552
| 0.64108
| 0.965059
| 0.965059
| 0.965059
| 0.861136
| 0
| 0
| 0
| 0.391533
| 0.123108
| 80,945
| 3
| 80,928
| 26,981.666667
| 0.374556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
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| 0
| 0
| 0
| 1
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|
0
| 12
|
591a69e1703a1b55b49c9387c640553c8028df17
| 27,802
|
py
|
Python
|
sdk/python/pulumi_azure/apimanagement/user.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 109
|
2018-06-18T00:19:44.000Z
|
2022-02-20T05:32:57.000Z
|
sdk/python/pulumi_azure/apimanagement/user.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 663
|
2018-06-18T21:08:46.000Z
|
2022-03-31T20:10:11.000Z
|
sdk/python/pulumi_azure/apimanagement/user.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 41
|
2018-07-19T22:37:38.000Z
|
2022-03-14T10:56:26.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['UserArgs', 'User']
@pulumi.input_type
class UserArgs:
def __init__(__self__, *,
api_management_name: pulumi.Input[str],
email: pulumi.Input[str],
first_name: pulumi.Input[str],
last_name: pulumi.Input[str],
resource_group_name: pulumi.Input[str],
user_id: pulumi.Input[str],
confirmation: Optional[pulumi.Input[str]] = None,
note: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a User resource.
:param pulumi.Input[str] api_management_name: The name of the API Management Service in which the User should be created. Changing this forces a new resource to be created.
:param pulumi.Input[str] email: The email address associated with this user.
:param pulumi.Input[str] first_name: The first name for this user.
:param pulumi.Input[str] last_name: The last name for this user.
:param pulumi.Input[str] resource_group_name: The name of the Resource Group in which the API Management Service exists. Changing this forces a new resource to be created.
:param pulumi.Input[str] user_id: The Identifier for this User, which must be unique within the API Management Service. Changing this forces a new resource to be created.
:param pulumi.Input[str] confirmation: The kind of confirmation email which will be sent to this user. Possible values are `invite` and `signup`. Changing this forces a new resource to be created.
:param pulumi.Input[str] note: A note about this user.
:param pulumi.Input[str] password: The password associated with this user.
:param pulumi.Input[str] state: The state of this user. Possible values are `active`, `blocked` and `pending`.
"""
pulumi.set(__self__, "api_management_name", api_management_name)
pulumi.set(__self__, "email", email)
pulumi.set(__self__, "first_name", first_name)
pulumi.set(__self__, "last_name", last_name)
pulumi.set(__self__, "resource_group_name", resource_group_name)
pulumi.set(__self__, "user_id", user_id)
if confirmation is not None:
pulumi.set(__self__, "confirmation", confirmation)
if note is not None:
pulumi.set(__self__, "note", note)
if password is not None:
pulumi.set(__self__, "password", password)
if state is not None:
pulumi.set(__self__, "state", state)
@property
@pulumi.getter(name="apiManagementName")
def api_management_name(self) -> pulumi.Input[str]:
"""
The name of the API Management Service in which the User should be created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "api_management_name")
@api_management_name.setter
def api_management_name(self, value: pulumi.Input[str]):
pulumi.set(self, "api_management_name", value)
@property
@pulumi.getter
def email(self) -> pulumi.Input[str]:
"""
The email address associated with this user.
"""
return pulumi.get(self, "email")
@email.setter
def email(self, value: pulumi.Input[str]):
pulumi.set(self, "email", value)
@property
@pulumi.getter(name="firstName")
def first_name(self) -> pulumi.Input[str]:
"""
The first name for this user.
"""
return pulumi.get(self, "first_name")
@first_name.setter
def first_name(self, value: pulumi.Input[str]):
pulumi.set(self, "first_name", value)
@property
@pulumi.getter(name="lastName")
def last_name(self) -> pulumi.Input[str]:
"""
The last name for this user.
"""
return pulumi.get(self, "last_name")
@last_name.setter
def last_name(self, value: pulumi.Input[str]):
pulumi.set(self, "last_name", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
"""
The name of the Resource Group in which the API Management Service exists. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter(name="userId")
def user_id(self) -> pulumi.Input[str]:
"""
The Identifier for this User, which must be unique within the API Management Service. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "user_id")
@user_id.setter
def user_id(self, value: pulumi.Input[str]):
pulumi.set(self, "user_id", value)
@property
@pulumi.getter
def confirmation(self) -> Optional[pulumi.Input[str]]:
"""
The kind of confirmation email which will be sent to this user. Possible values are `invite` and `signup`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "confirmation")
@confirmation.setter
def confirmation(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "confirmation", value)
@property
@pulumi.getter
def note(self) -> Optional[pulumi.Input[str]]:
"""
A note about this user.
"""
return pulumi.get(self, "note")
@note.setter
def note(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "note", value)
@property
@pulumi.getter
def password(self) -> Optional[pulumi.Input[str]]:
"""
The password associated with this user.
"""
return pulumi.get(self, "password")
@password.setter
def password(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "password", value)
@property
@pulumi.getter
def state(self) -> Optional[pulumi.Input[str]]:
"""
The state of this user. Possible values are `active`, `blocked` and `pending`.
"""
return pulumi.get(self, "state")
@state.setter
def state(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "state", value)
@pulumi.input_type
class _UserState:
def __init__(__self__, *,
api_management_name: Optional[pulumi.Input[str]] = None,
confirmation: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
first_name: Optional[pulumi.Input[str]] = None,
last_name: Optional[pulumi.Input[str]] = None,
note: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
user_id: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering User resources.
:param pulumi.Input[str] api_management_name: The name of the API Management Service in which the User should be created. Changing this forces a new resource to be created.
:param pulumi.Input[str] confirmation: The kind of confirmation email which will be sent to this user. Possible values are `invite` and `signup`. Changing this forces a new resource to be created.
:param pulumi.Input[str] email: The email address associated with this user.
:param pulumi.Input[str] first_name: The first name for this user.
:param pulumi.Input[str] last_name: The last name for this user.
:param pulumi.Input[str] note: A note about this user.
:param pulumi.Input[str] password: The password associated with this user.
:param pulumi.Input[str] resource_group_name: The name of the Resource Group in which the API Management Service exists. Changing this forces a new resource to be created.
:param pulumi.Input[str] state: The state of this user. Possible values are `active`, `blocked` and `pending`.
:param pulumi.Input[str] user_id: The Identifier for this User, which must be unique within the API Management Service. Changing this forces a new resource to be created.
"""
if api_management_name is not None:
pulumi.set(__self__, "api_management_name", api_management_name)
if confirmation is not None:
pulumi.set(__self__, "confirmation", confirmation)
if email is not None:
pulumi.set(__self__, "email", email)
if first_name is not None:
pulumi.set(__self__, "first_name", first_name)
if last_name is not None:
pulumi.set(__self__, "last_name", last_name)
if note is not None:
pulumi.set(__self__, "note", note)
if password is not None:
pulumi.set(__self__, "password", password)
if resource_group_name is not None:
pulumi.set(__self__, "resource_group_name", resource_group_name)
if state is not None:
pulumi.set(__self__, "state", state)
if user_id is not None:
pulumi.set(__self__, "user_id", user_id)
@property
@pulumi.getter(name="apiManagementName")
def api_management_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the API Management Service in which the User should be created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "api_management_name")
@api_management_name.setter
def api_management_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "api_management_name", value)
@property
@pulumi.getter
def confirmation(self) -> Optional[pulumi.Input[str]]:
"""
The kind of confirmation email which will be sent to this user. Possible values are `invite` and `signup`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "confirmation")
@confirmation.setter
def confirmation(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "confirmation", value)
@property
@pulumi.getter
def email(self) -> Optional[pulumi.Input[str]]:
"""
The email address associated with this user.
"""
return pulumi.get(self, "email")
@email.setter
def email(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "email", value)
@property
@pulumi.getter(name="firstName")
def first_name(self) -> Optional[pulumi.Input[str]]:
"""
The first name for this user.
"""
return pulumi.get(self, "first_name")
@first_name.setter
def first_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "first_name", value)
@property
@pulumi.getter(name="lastName")
def last_name(self) -> Optional[pulumi.Input[str]]:
"""
The last name for this user.
"""
return pulumi.get(self, "last_name")
@last_name.setter
def last_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "last_name", value)
@property
@pulumi.getter
def note(self) -> Optional[pulumi.Input[str]]:
"""
A note about this user.
"""
return pulumi.get(self, "note")
@note.setter
def note(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "note", value)
@property
@pulumi.getter
def password(self) -> Optional[pulumi.Input[str]]:
"""
The password associated with this user.
"""
return pulumi.get(self, "password")
@password.setter
def password(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "password", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the Resource Group in which the API Management Service exists. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter
def state(self) -> Optional[pulumi.Input[str]]:
"""
The state of this user. Possible values are `active`, `blocked` and `pending`.
"""
return pulumi.get(self, "state")
@state.setter
def state(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "state", value)
@property
@pulumi.getter(name="userId")
def user_id(self) -> Optional[pulumi.Input[str]]:
"""
The Identifier for this User, which must be unique within the API Management Service. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "user_id")
@user_id.setter
def user_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "user_id", value)
class User(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
api_management_name: Optional[pulumi.Input[str]] = None,
confirmation: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
first_name: Optional[pulumi.Input[str]] = None,
last_name: Optional[pulumi.Input[str]] = None,
note: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
user_id: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Manages an API Management User.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_service = azure.apimanagement.Service("exampleService",
location=example_resource_group.location,
resource_group_name=example_resource_group.name,
publisher_name="My Company",
publisher_email="company@exmaple.com",
sku_name="Developer_1")
example_user = azure.apimanagement.User("exampleUser",
user_id="5931a75ae4bbd512288c680b",
api_management_name=example_service.name,
resource_group_name=example_resource_group.name,
first_name="Example",
last_name="User",
email="user@example.com",
state="active")
```
## Import
API Management Users can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:apimanagement/user:User example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/mygroup1/providers/Microsoft.ApiManagement/service/instance1/users/abc123
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] api_management_name: The name of the API Management Service in which the User should be created. Changing this forces a new resource to be created.
:param pulumi.Input[str] confirmation: The kind of confirmation email which will be sent to this user. Possible values are `invite` and `signup`. Changing this forces a new resource to be created.
:param pulumi.Input[str] email: The email address associated with this user.
:param pulumi.Input[str] first_name: The first name for this user.
:param pulumi.Input[str] last_name: The last name for this user.
:param pulumi.Input[str] note: A note about this user.
:param pulumi.Input[str] password: The password associated with this user.
:param pulumi.Input[str] resource_group_name: The name of the Resource Group in which the API Management Service exists. Changing this forces a new resource to be created.
:param pulumi.Input[str] state: The state of this user. Possible values are `active`, `blocked` and `pending`.
:param pulumi.Input[str] user_id: The Identifier for this User, which must be unique within the API Management Service. Changing this forces a new resource to be created.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: UserArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Manages an API Management User.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_service = azure.apimanagement.Service("exampleService",
location=example_resource_group.location,
resource_group_name=example_resource_group.name,
publisher_name="My Company",
publisher_email="company@exmaple.com",
sku_name="Developer_1")
example_user = azure.apimanagement.User("exampleUser",
user_id="5931a75ae4bbd512288c680b",
api_management_name=example_service.name,
resource_group_name=example_resource_group.name,
first_name="Example",
last_name="User",
email="user@example.com",
state="active")
```
## Import
API Management Users can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:apimanagement/user:User example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/mygroup1/providers/Microsoft.ApiManagement/service/instance1/users/abc123
```
:param str resource_name: The name of the resource.
:param UserArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(UserArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
api_management_name: Optional[pulumi.Input[str]] = None,
confirmation: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
first_name: Optional[pulumi.Input[str]] = None,
last_name: Optional[pulumi.Input[str]] = None,
note: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
user_id: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = UserArgs.__new__(UserArgs)
if api_management_name is None and not opts.urn:
raise TypeError("Missing required property 'api_management_name'")
__props__.__dict__["api_management_name"] = api_management_name
__props__.__dict__["confirmation"] = confirmation
if email is None and not opts.urn:
raise TypeError("Missing required property 'email'")
__props__.__dict__["email"] = email
if first_name is None and not opts.urn:
raise TypeError("Missing required property 'first_name'")
__props__.__dict__["first_name"] = first_name
if last_name is None and not opts.urn:
raise TypeError("Missing required property 'last_name'")
__props__.__dict__["last_name"] = last_name
__props__.__dict__["note"] = note
__props__.__dict__["password"] = password
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["state"] = state
if user_id is None and not opts.urn:
raise TypeError("Missing required property 'user_id'")
__props__.__dict__["user_id"] = user_id
super(User, __self__).__init__(
'azure:apimanagement/user:User',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
api_management_name: Optional[pulumi.Input[str]] = None,
confirmation: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
first_name: Optional[pulumi.Input[str]] = None,
last_name: Optional[pulumi.Input[str]] = None,
note: Optional[pulumi.Input[str]] = None,
password: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
user_id: Optional[pulumi.Input[str]] = None) -> 'User':
"""
Get an existing User resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] api_management_name: The name of the API Management Service in which the User should be created. Changing this forces a new resource to be created.
:param pulumi.Input[str] confirmation: The kind of confirmation email which will be sent to this user. Possible values are `invite` and `signup`. Changing this forces a new resource to be created.
:param pulumi.Input[str] email: The email address associated with this user.
:param pulumi.Input[str] first_name: The first name for this user.
:param pulumi.Input[str] last_name: The last name for this user.
:param pulumi.Input[str] note: A note about this user.
:param pulumi.Input[str] password: The password associated with this user.
:param pulumi.Input[str] resource_group_name: The name of the Resource Group in which the API Management Service exists. Changing this forces a new resource to be created.
:param pulumi.Input[str] state: The state of this user. Possible values are `active`, `blocked` and `pending`.
:param pulumi.Input[str] user_id: The Identifier for this User, which must be unique within the API Management Service. Changing this forces a new resource to be created.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _UserState.__new__(_UserState)
__props__.__dict__["api_management_name"] = api_management_name
__props__.__dict__["confirmation"] = confirmation
__props__.__dict__["email"] = email
__props__.__dict__["first_name"] = first_name
__props__.__dict__["last_name"] = last_name
__props__.__dict__["note"] = note
__props__.__dict__["password"] = password
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["state"] = state
__props__.__dict__["user_id"] = user_id
return User(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="apiManagementName")
def api_management_name(self) -> pulumi.Output[str]:
"""
The name of the API Management Service in which the User should be created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "api_management_name")
@property
@pulumi.getter
def confirmation(self) -> pulumi.Output[Optional[str]]:
"""
The kind of confirmation email which will be sent to this user. Possible values are `invite` and `signup`. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "confirmation")
@property
@pulumi.getter
def email(self) -> pulumi.Output[str]:
"""
The email address associated with this user.
"""
return pulumi.get(self, "email")
@property
@pulumi.getter(name="firstName")
def first_name(self) -> pulumi.Output[str]:
"""
The first name for this user.
"""
return pulumi.get(self, "first_name")
@property
@pulumi.getter(name="lastName")
def last_name(self) -> pulumi.Output[str]:
"""
The last name for this user.
"""
return pulumi.get(self, "last_name")
@property
@pulumi.getter
def note(self) -> pulumi.Output[Optional[str]]:
"""
A note about this user.
"""
return pulumi.get(self, "note")
@property
@pulumi.getter
def password(self) -> pulumi.Output[Optional[str]]:
"""
The password associated with this user.
"""
return pulumi.get(self, "password")
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Output[str]:
"""
The name of the Resource Group in which the API Management Service exists. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
@property
@pulumi.getter
def state(self) -> pulumi.Output[str]:
"""
The state of this user. Possible values are `active`, `blocked` and `pending`.
"""
return pulumi.get(self, "state")
@property
@pulumi.getter(name="userId")
def user_id(self) -> pulumi.Output[str]:
"""
The Identifier for this User, which must be unique within the API Management Service. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "user_id")
| 43.508607
| 204
| 0.641644
| 3,412
| 27,802
| 5.03517
| 0.058617
| 0.085797
| 0.107567
| 0.0922
| 0.913446
| 0.893248
| 0.867288
| 0.845343
| 0.839232
| 0.822526
| 0
| 0.005283
| 0.257823
| 27,802
| 638
| 205
| 43.576803
| 0.827324
| 0.36278
| 0
| 0.716714
| 1
| 0
| 0.092585
| 0.004406
| 0
| 0
| 0
| 0
| 0
| 1
| 0.161473
| false
| 0.067989
| 0.014164
| 0
| 0.271955
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 9
|
3ca865a4e1ddfb4a1a3b3c296caedfad98083736
| 6,484
|
py
|
Python
|
07/win.py
|
jchristman/adventofcode
|
8ac4408aa5cda12a1996a657e4d336bae5156ccb
|
[
"MIT"
] | 13
|
2015-12-08T23:13:39.000Z
|
2021-01-03T01:27:45.000Z
|
07/win.py
|
jchristman/adventofcode
|
8ac4408aa5cda12a1996a657e4d336bae5156ccb
|
[
"MIT"
] | null | null | null |
07/win.py
|
jchristman/adventofcode
|
8ac4408aa5cda12a1996a657e4d336bae5156ccb
|
[
"MIT"
] | 1
|
2015-12-16T22:29:08.000Z
|
2015-12-16T22:29:08.000Z
|
# ----------------------------------------------------------------------------------------------------------------------
# Advent of Code - Day 7
#
# The Python One-Liner challenge:
#
# Rules:
#
# - Reading input into a variable from another file or with an assignment is acceptable and does not count against
# your total for lines.
# - Your solution must take the form of 'print INSERT_CODE_HERE'
# - Formatting your print with a format string is acceptable so long as you are only substituting ONE value (multiple
# calculations are not allowed)
# - No global variables (outside of the input variable)
#
# ----------------------------------------------------------------------------------------------------------------------
import re
_input = {re.findall('(?<=-> )([a-z]+)', line)[0]: (_type, (re.findall('([0-9]+|[a-z]+)', line)[0],) if _type == 'ASSIGN' else (re.findall('([a-z]+|[0-9]+)(?= AND| OR)', line)[0], re.findall('([a-z]+|[0-9]+) (?=->)', line)[0]) if _type == 'AND' or _type == 'OR' else (re.findall('([a-z]+).*(?=->)', line)[0], re.findall('([0-9]+)', line)[0]) if _type == 'LSHIFT' or _type == 'RSHIFT' else (re.findall('([a-z]+)', line)[0],)) for _type, line in [(re.findall('([A-Z]+)', line)[0] if len(re.findall('([A-Z]+)', line)) > 0 else 'ASSIGN', line) for line in open('input', 'r').read().splitlines()]}
# ----------------------------------------------------------------------------------------------------------------------
# Part 1 (find the value assigned to a)
#
# This is definitely a weird one. Use mutual recursion to step through the dictionary built by reading input and perform
# a bunch of operations in lambda functions. Use a bit of dynamic programming to update the _input variable with the
# values of each variable as it is found to avoid having to explore a tree that is potentially 2**(12*26) branches nodes
# large.
# ----------------------------------------------------------------------------------------------------------------------
print (lambda ask: (lambda func2, ask2: func2(func2, ask2))(lambda func1, ask1: (_input.update({ ask1: ('ASSIGN', (int(_input[ask1][1][0] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][0].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][0])),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'ASSIGN' else (_input.update({ ask1: ('ASSIGN', (int(func1(func1, _input[ask1][1][0]) >> int(_input[ask1][1][1])),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'RSHIFT' else (_input.update({ ask1: ('ASSIGN', (int(func1(func1, _input[ask1][1][0]) << int(_input[ask1][1][1])),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'LSHIFT' else (_input.update({ ask1: ('ASSIGN', (int(int(_input[ask1][1][0] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][0].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][0])) | int(_input[ask1][1][1] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][1].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][1]))),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'OR' else (_input.update({ ask1: ('ASSIGN', (int(int(_input[ask1][1][0] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][0].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][0])) & int(_input[ask1][1][1] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][1].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][1]))),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'AND' else (_input.update({ ask1: ('ASSIGN', (~int(func1(func1, _input[ask1][1][0])),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'NOT' else _input[ask1], ask))('a')
# ----------------------------------------------------------------------------------------------------------------------
# Part 2 (find the value assigned to a if b is assigned a from part 1)
#
# Reset the _input variable and assign 3176 to variable b.
# ----------------------------------------------------------------------------------------------------------------------
_input = {re.findall('(?<=-> )([a-z]+)', line)[0]: (_type, (re.findall('([0-9]+|[a-z]+)', line)[0],) if _type == 'ASSIGN' else (re.findall('([a-z]+|[0-9]+)(?= AND| OR)', line)[0], re.findall('([a-z]+|[0-9]+) (?=->)', line)[0]) if _type == 'AND' or _type == 'OR' else (re.findall('([a-z]+).*(?=->)', line)[0], re.findall('([0-9]+)', line)[0]) if _type == 'LSHIFT' or _type == 'RSHIFT' else (re.findall('([a-z]+)', line)[0],)) for _type, line in [(re.findall('([A-Z]+)', line)[0] if len(re.findall('([A-Z]+)', line)) > 0 else 'ASSIGN', line) for line in open('input', 'r').read().splitlines()]}
_input['b'] = ('ASSIGN', (3176,))
print (lambda ask: (lambda func2, ask2: func2(func2, ask2))(lambda func1, ask1: (_input.update({ ask1: ('ASSIGN', (int(_input[ask1][1][0] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][0].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][0])),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'ASSIGN' else (_input.update({ ask1: ('ASSIGN', (int(func1(func1, _input[ask1][1][0]) >> int(_input[ask1][1][1])),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'RSHIFT' else (_input.update({ ask1: ('ASSIGN', (int(func1(func1, _input[ask1][1][0]) << int(_input[ask1][1][1])),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'LSHIFT' else (_input.update({ ask1: ('ASSIGN', (int(int(_input[ask1][1][0] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][0].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][0])) | int(_input[ask1][1][1] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][1].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][1]))),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'OR' else (_input.update({ ask1: ('ASSIGN', (int(int(_input[ask1][1][0] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][0].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][0])) & int(_input[ask1][1][1] if isinstance(_input[ask1][1][0], str) and _input[ask1][1][1].isdigit() or isinstance(_input[ask1][1][0], int) else func1(func1, _input[ask1][1][1]))),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'AND' else (_input.update({ ask1: ('ASSIGN', (~int(func1(func1, _input[ask1][1][0])),))}) or _input[ask1][1][0]) if _input[ask1][0] == 'NOT' else _input[ask1], ask))('a')
| 154.380952
| 1,736
| 0.560611
| 976
| 6,484
| 3.602459
| 0.134221
| 0.220137
| 0.204778
| 0.175199
| 0.784983
| 0.784983
| 0.7719
| 0.7719
| 0.7719
| 0.7719
| 0
| 0.060206
| 0.100864
| 6,484
| 41
| 1,737
| 158.146341
| 0.542882
| 0.264651
| 0
| 0.666667
| 0
| 0
| 0.096856
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.166667
| 0
| 0.166667
| 0.333333
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
3cead67b2a89ebf311ca97b31a52693b54784c50
| 66
|
py
|
Python
|
clase 4/modulos/Finanzas/operaciones.py
|
amedina14/uip-iq17-pc3
|
89f04c0670079384cee40736d5c92175a8c586a5
|
[
"MIT"
] | null | null | null |
clase 4/modulos/Finanzas/operaciones.py
|
amedina14/uip-iq17-pc3
|
89f04c0670079384cee40736d5c92175a8c586a5
|
[
"MIT"
] | null | null | null |
clase 4/modulos/Finanzas/operaciones.py
|
amedina14/uip-iq17-pc3
|
89f04c0670079384cee40736d5c92175a8c586a5
|
[
"MIT"
] | null | null | null |
def itbms(x):
return x*1.07
def irs(x):
return x*1.22
| 13.2
| 18
| 0.545455
| 14
| 66
| 2.571429
| 0.571429
| 0.388889
| 0.444444
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 0.30303
| 66
| 5
| 19
| 13.2
| 0.652174
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 7
|
a7354f0798d94841e16517d86b82f9f3c0f953ad
| 1,272
|
py
|
Python
|
src/fleets/views.py
|
danilomarto/vltour-api
|
e9881d9e7b9ea0e890bc4d1f7efd12d08542e343
|
[
"MIT"
] | null | null | null |
src/fleets/views.py
|
danilomarto/vltour-api
|
e9881d9e7b9ea0e890bc4d1f7efd12d08542e343
|
[
"MIT"
] | null | null | null |
src/fleets/views.py
|
danilomarto/vltour-api
|
e9881d9e7b9ea0e890bc4d1f7efd12d08542e343
|
[
"MIT"
] | null | null | null |
from django.views import generic as ge
class ListFleet(ge.TemplateView):
template_name = "pages/list_all_cars.html"
def get_context_data(self, **kwargs) -> dict:
context = super().get_context_data(**kwargs)
context["cars"] = "Fleets"
return context
class ListOnlyCars(ge.TemplateView):
template_name = "pages/list_all_cars.html"
def get_context_data(self, **kwargs) -> dict:
context = super().get_context_data(**kwargs)
context["cars"] = "cars"
return context
class ListOnlyBus(ge.TemplateView):
template_name = "pages/list_all_cars.html"
def get_context_data(self, **kwargs) -> dict:
context = super().get_context_data(**kwargs)
context["cars"] = "bus"
return context
class ListOnlyMicros(ge.TemplateView):
template_name = "pages/list_all_cars.html"
def get_context_data(self, **kwargs) -> dict:
context = super().get_context_data(**kwargs)
context["cars"] = "micro bus"
return context
class ListOnlyVans(ge.TemplateView):
template_name = "pages/list_all_cars.html"
def get_context_data(self, **kwargs) -> dict:
context = super().get_context_data(**kwargs)
context["cars"] = "van"
return context
| 27.06383
| 52
| 0.659591
| 153
| 1,272
| 5.254902
| 0.222222
| 0.124378
| 0.174129
| 0.161692
| 0.746269
| 0.746269
| 0.746269
| 0.746269
| 0.746269
| 0.746269
| 0
| 0
| 0.213836
| 1,272
| 46
| 53
| 27.652174
| 0.804
| 0
| 0
| 0.645161
| 0
| 0
| 0.129717
| 0.09434
| 0
| 0
| 0
| 0
| 0
| 1
| 0.16129
| false
| 0
| 0.032258
| 0
| 0.677419
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
a735a3ede2d9b40930616d285d036cda79f04dc4
| 1,411
|
py
|
Python
|
dbrlint/tests/test_dbrlint_rules.py
|
wjohnson/AzureDatabricks
|
06882df364fba7e51bf83e561bf46670aff1858a
|
[
"MIT"
] | null | null | null |
dbrlint/tests/test_dbrlint_rules.py
|
wjohnson/AzureDatabricks
|
06882df364fba7e51bf83e561bf46670aff1858a
|
[
"MIT"
] | null | null | null |
dbrlint/tests/test_dbrlint_rules.py
|
wjohnson/AzureDatabricks
|
06882df364fba7e51bf83e561bf46670aff1858a
|
[
"MIT"
] | null | null | null |
from dbrlint.dbrlinter import DBRLinter
from dbrlint.matchtypes import *
def test_parsing_rules_exact():
rules_string = "field1: sometext\nfield2: [1,2]".split('\n')
lint = DBRLinter(rules_string)
assert(lint.rules[0] == ExactMatch("field1", "sometext"))
assert(lint.rules[1] == ExactMatch("field2", [1,2]))
def test_parsing_rules_input_required():
rules_string = "field2: [\"?\"]\nfield3: ?".split('\n')
lint = DBRLinter(rules_string)
assert(lint.rules[0] == InputRequired("field2", []))
assert(lint.rules[1] == InputRequired("field3", "?"))
def test_parsing_rules_input_required_no_quotes():
rules_string = "field2: [?]\nfield3: ?".split('\n')
lint = DBRLinter(rules_string)
assert(lint.rules[0] == InputRequired("field2", []))
assert(lint.rules[1] == InputRequired("field3", "?"))
def test_parsing_rules_contains():
rules_string = "field2: [1,2,\"*\"]".split('\n')
lint = DBRLinter(rules_string)
assert(lint.rules[0] == ContainsMatch("field2", [1,2]))
def test_parsing_rules_contains_no_quotes():
rules_string = "field2: [1,2,*]".split('\n')
lint = DBRLinter(rules_string)
assert(lint.rules[0] == ContainsMatch("field2", [1,2]))
def test_parsing_rules_contains_no_quotes_middle():
rules_string = "field2: [1,*,2]".split('\n')
lint = DBRLinter(rules_string)
assert(lint.rules[0] == ContainsMatch("field2", [1,2]))
| 34.414634
| 64
| 0.669029
| 177
| 1,411
| 5.112994
| 0.186441
| 0.145856
| 0.149171
| 0.125967
| 0.807735
| 0.790055
| 0.761326
| 0.731492
| 0.731492
| 0.731492
| 0
| 0.035391
| 0.138909
| 1,411
| 41
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0
| 7
|
59643cc5b113613035b87c28dc3b16d98339a1de
| 2,556
|
py
|
Python
|
src/cmp/tools/automata.py
|
BeginnerCompilers/cool-compiler-2021
|
fe8c4dbbbaff9dc855322c2df2892dcfc5d449f9
|
[
"MIT"
] | 1
|
2022-02-11T05:12:54.000Z
|
2022-02-11T05:12:54.000Z
|
src/cmp/tools/automata.py
|
BeginnerCompilers/cool-compiler-2021
|
fe8c4dbbbaff9dc855322c2df2892dcfc5d449f9
|
[
"MIT"
] | null | null | null |
src/cmp/tools/automata.py
|
BeginnerCompilers/cool-compiler-2021
|
fe8c4dbbbaff9dc855322c2df2892dcfc5d449f9
|
[
"MIT"
] | 1
|
2022-02-11T07:30:32.000Z
|
2022-02-11T07:30:32.000Z
|
import zlib, base64
exec(zlib.decompress(base64.b64decode('eJzNWN1um0gUvucp6BWwRSjx1SrSrBRt4iR20rRJW9VCCI1hbE+LgTBDGjvKY+1r7DPtmR9gjIm3q+5KexPDzPn9zjdnDuGIEW4xVOF8SawHtMIMc15ZZ2hKNudVVVTWNcJZZm0QZTRnHOcJsQiiObeWKCO5tUIfNyVRojka44wRa4o+VjWxPiOcbyyMtrS0OEckr9ekwhz2tUBZIl6XGbFYEwGvNieWTddlUXG73KQFt8hTQkpuX8k16QckSsyYtaiKtZ2sy6DmNGON1u9FzjHNSXUPmSUZCNrvxqegk5KFHcc0pzyOXUayhQ/pcML8Bc0hbJ9DCIxyWuRM7FQcHXmgZgvRQIki9WMsgpT82ywpW4i76sFr1tcYcu0cNMuPRYLndYarDai0woYget6ePL8sisre2jS3masi8F5AFlbdU3/u+bdiy1AKKCdr5srobQCAACwP7q3vQPqcVHHseL5zlT/ijKZ2UmQZSYSeXSxsZd4Riv1QwtMonEfott3rgg9wmrpzbz+rIKUswVXqOo6nKkBKRrMijw3DRjFkyDpiuSCT7gXSxS5FRLCKN7ZdEV5X+xqhFIxCx4lASjPqzNBwdXTLCpcrGY+M5AJJDgZnBXfB2reUVsi5vnP8Na6WNEdHwbFI+kLkH+dFSlwl/048OpIYjs9WuCTIKTNgJSdPsJLhOcmQ4/jfacpX6MhfEbpcCb55uqyZT3VZG/bs1JQi588/HJsu4Ans2AROnU3FznAoWRNEQqskIxAT32TwOi+yVJrJWleKt8oiFE0EJCKyx0LiVnp/xcn475yMDzrRVkm6bKyei8fMH2u8qPoFRskuhEZeh31fq8W+PaYd6DqsFLMVSZVzTZsL3SQqUlYxe1zGHROGCKbI4gVJRYBdQgFeUpLI4td88avgvGabVJZdS7Wks/GpC23J+4m+pI/Jtbtx4TTUxCeeKJR87jcEuQjs8Uy9JRzZ345kdaGN9FQ8mbTRh76RzUko7URCA179QVeap6JBQYLBP8qs7SBJXVUkb3urhGhdPBJtZbMGUkkQgFbqzc4LPtQsQtNeZNYw33N2UDVUfqLwKOoYM20ZA/dox5Z9s0YiFTBkmdNtkwyvaL7s1LQt3QnsRCYlZdTRX8hMlaiEJFE9wUzLZKkOo3f2LBGJVMc1L9aYF3lTnA7dG3UpdbePFBDeUtSq7SC2jYyzcovSBrR+171FMsGboC5TsOnewpsO+sYyr4kkK1hd7UcpwpuhkMngWBcceHpCz3vLwMbvK5oReybcb9EsKItShjBwHxm5Dd1W26Y0F8L+gERTp4uGk0+qQE/ymrxQ3W4W4LIkuXrXqZuji/vLkyeRyBc45kWcLnAHgsg+Qc+QVYZeRyrs8pD0i8BTFtAUHclfFksqoM+uBKoT1t25hTADvQkKRRVn6ucVLIXGfNdWNwpIDG5Qj3NbX44N9uWBPG48g/iXCssEoKI5zJBq57KBeqK2L0WaS3fiNW8/nOylUpk05bnslUu9i5tLeSrQJKB5Sp4ayUs0CQtB+PYc2Em4hXj8uVxt+m/ejTFwGbx580YOXTunpFNEIiHLvkdyoTuRE5H8tk0PHLxH4moRqfv3ftJx670kk84bx3UOTHXxsY9HJpmAGekxOoa/I4SP9dT7NoWFGuFR+z6yesMnyPZmlCQ8BT0R+vMYHowZ4mVPeTSoPGqUR7vKSZj5MMihUAVYceFHBSeeR5HqWOmTfy+tuyHsw7IvFPTNA+LqKZL+WjzvT3YK9wVBBd6CJTU49qpj7urx3f4iz3gtT0wHYAfdSBTxuX5p6wJ3pDsRpfIzb7dEQPEEdHLMD5Tq6H9dqhbXFnlzltL4HfsDY7m5p7FV0BqFNq+mtqADDkYHHIz2HPw7tWua2PHg6ap7FfupCg2ehzr6T9CvjUJ03nqIHcZp+Kv9jLKvBXwfiY92gaSY8mHkqSlbxcqse+d3V8JUwPJJwnqOytIdunA0PWqBwJ1Ia/bK0FKr8VigcI1cdxYyMeSJyVKoztRnyrsiV/O1XDwXOEyEZ3caplEgvhhgbIPrm9NHIppyKsSmgfhIYj0LcuvaM2rxKZxEzQWjo/H6NREyyIhVYRveySTvhMlP7ZgfSQwlbvEa5u813eouYk4QU2Sg7v7CmLszMIgpC6KYBmtSwSeVcUf2b89hqSF7ElR9VQ9YUXPFdCo5/0PRddlP4XusqEs1fYnVlVj9ER6Ji1aHv/LUN8USpmoEA4Ten8MX3jd4mKLE6kb/nRN/COaDhbDsq505dtojE5Nj17PuptRv/y3CuXslw3tUDlT1NSRj/3oXY5Pwj9HOfxPmaBpeA+P7NBZ7X9GVHm/UnNZejbOQ+uPdgWb/hlRC6Ktlf0AhHUyAap4G7cdknxfg5KGNYro31PaC7iYeNQddef4Hf+Y/eNZffhu9yA==')))
# Created by pyminifier (https://github.com/liftoff/pyminifier)
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0
| 10
|
59756aefb5a988bd9b594fdd533688db6159eb2d
| 44,424
|
py
|
Python
|
singulation_env.py
|
Jekyll1021/MultiPointPushing
|
4f68146ee3134da71aa7e3cf7ed2c019254b2aa1
|
[
"Apache-2.0"
] | 1
|
2020-11-30T02:33:23.000Z
|
2020-11-30T02:33:23.000Z
|
singulation_env.py
|
Jekyll1021/MultiPointPushing
|
4f68146ee3134da71aa7e3cf7ed2c019254b2aa1
|
[
"Apache-2.0"
] | null | null | null |
singulation_env.py
|
Jekyll1021/MultiPointPushing
|
4f68146ee3134da71aa7e3cf7ed2c019254b2aa1
|
[
"Apache-2.0"
] | null | null | null |
import logging
import math
import time
import pygame
from pygame.locals import (QUIT, KEYDOWN, K_ESCAPE)
import numpy as np
import pickle
import Box2D
from Box2D.b2 import (world, polygonShape, circleShape, staticBody, dynamicBody, kinematicBody)
import matplotlib.pyplot as plt
import os
from helper import *
import json
import random
from policies import *
from prune import *
PPM = 100.0 # pixels per meter
TIME_STEP = 0.1
SCREEN_WIDTH, SCREEN_HEIGHT = 1200, 1200
GROUPS = [(1, 1, 15), (0.75, 1, 12), (0.5, 1, 9), (0.25, 1, 6)]
num_classes = 8
base = plt.cm.get_cmap('Accent')
Color_list = base(np.linspace(0, 1, num_classes))
Colors = [(int(255*c[0]), int(255*c[1]), int(255*c[2]), int(255*c[3])) for c in Color_list]
class Action:
def __init__(self, vector, point):
"""
action that consists of:
vector: (x, y) force vector
point: (x, y) point of contact
all relative to the local origin of polygon.
"""
self.vector = normalize(vector)
self.point = point
def __eq__(self, other):
"""
check if vector == vector, point == point
"""
return self.vector == other.vector and self.point == self.point
class Polygon:
def __init__(self, body, fixtures, vertices, color=(255, 255, 255, 255)):
"""body: polygon shape (dynamicBody)
fixture: fixture
vertices: list of relative coordinates
"""
self.body = body
self.fixtures = fixtures
self.vertices = vertices
self.color = color
self.original_pos = np.array(self.body.position)
self.bounding_circle_radius = math.sqrt(max((self.vertices)[:,0]**2 + (self.vertices)[:,1]**2))
self.disk_coverage = compute_area(self.vertices)/(self.bounding_circle_radius**2*math.pi)
def test_overlap(self, other_polygon):
"""test if the current polygon overlaps with a polygon of another polygon
with other_centroid as centroid and other_vertices as vertices(all in numpy array)"""
if self.dist(other_polygon) > 0:
return False
return True
# def test_rod_overlap(self, rod_fix):
# """works for one polygon and one circle fixture"""
# abs_vertices = (self.vertices + self.original_pos).tolist()
# for i in range(len(abs_vertices)):
# if rod_fix.TestPoint(abs_vertices[i]) \
# or rod_fix.TestPoint(((np.array(abs_vertices[i])+self.original_pos)/2).tolist())\
# or rod_fix.TestPoint(((np.array(abs_vertices[i])+np.array(abs_vertices[i-1]))/2).tolist()):
# return True
# for rod_fix in self.fixtures:
# if rod_fix.TestPoint(self.original_pos):
# return True
# return False
def dist(self, other_polygon):
"""do not work on a polygon and a rod"""
shape1 = self.fixtures[0].shape
shape2 = other_polygon.fixtures[0].shape
transform1 = Box2D.b2Transform()
pos1 = self.body.position
angle1 = self.body.angle
transform1.Set(pos1, angle1)
transform2 = Box2D.b2Transform()
pos2 = other_polygon.body.position
angle2 = other_polygon.body.angle
transform2.Set(pos2, angle2)
pointA, pointB, distance, iterations = Box2D.b2Distance(shapeA=shape1, shapeB=shape2, transformA=transform1, transformB=transform2)
return distance
def dist_rod(self, rod_fix, rod_body):
shape1 = self.fixtures[0].shape
shape2 = rod_fix.shape
transform1 = Box2D.b2Transform()
pos1 = self.body.position
angle1 = self.body.angle
transform1.Set(pos1, angle1)
transform2 = Box2D.b2Transform()
pos2 = rod_body.position
angle2 = rod_body.angle
transform2.Set(pos2, angle2)
pointA, pointB, distance, iterations = Box2D.b2Distance(shapeA=shape1, shapeB=shape2, transformA=transform1, transformB=transform2)
# print(distance)
return distance
class SingulationEnv:
def __init__(self):
self.world = world(gravity=(0, 0), doSleep=True)
self.objs = []
self.rod = None
self.rod2 = None
self.bounding_convex_hull = np.array([])
self.centroid = (0, 0)
self.bounding_circle_radius = 0
def create_random_env(self, num_objs=3, group=0, min_threshold=0.0, max_threshold=0.3):
assert num_objs >= 1
if len(self.objs) > 0:
for obj in self.objs:
for fix in obj.fixtures:
obj.body.DestroyFixture(fix)
self.world.DestroyBody(obj.body)
self.objs = []
for i in range(num_objs):
# create shape
# vertices = create_convex_hull(np.array([(np.random.uniform(-1,1),np.random.uniform(-1,1)) for i in range(9)]))
vertices = generatePolygon(GROUPS[group][0], GROUPS[group][1], GROUPS[group][2])
raw_com = np.array(compute_centroid(vertices))
vertices = (vertices - raw_com)
bounding_r = math.sqrt(max((vertices)[:,0]**2 + (vertices)[:,1]**2))
vertices = vertices / bounding_r
if len(self.objs) <= 0:
original_pos = np.array([np.random.uniform(4,8),np.random.uniform(4,8)])
body = self.world.CreateDynamicBody(position=original_pos.tolist(), allowSleep=False)
fixture = body.CreatePolygonFixture(density=1, vertices=vertices.tolist(), friction=0.5)
self.objs.append(Polygon(body, [fixture], vertices, Colors[i]))
else:
max_iter = 1000
while True:
max_iter -= 1
# original_pos = np.array([np.random.uniform(-0.5*num_objs,0.5*num_objs),np.random.uniform(-0.5*num_objs,0.5*num_objs)]) + np.array(self.objs[-1].body.position)
original_pos = np.array([np.random.uniform(-1.8,1.8),np.random.uniform(-1.8,1.8)]) + np.array(self.objs[-1].body.position)
# original_pos = np.array([np.random.uniform(1,11),np.random.uniform(1,11)])
no_overlap = True
original_pos = np.clip(original_pos, 2, 10)
body = self.world.CreateDynamicBody(position=original_pos.tolist(), allowSleep=False)
fixture = body.CreatePolygonFixture(density=1, vertices=vertices.tolist(), friction=0.5)
# curr_polygon = Polygon(body, fixture, vertices, Colors[i])
curr_polygon = Polygon(body, [fixture], vertices, Colors[i % len(Colors)])
for obj in self.objs:
if obj.test_overlap(curr_polygon) or curr_polygon.test_overlap(obj):
no_overlap = False
if no_overlap:
# adjust position to fit the range
min_dist = 1e2
min_dist_obj = -1
for i in range(len(self.objs)):
if curr_polygon.dist(self.objs[i]) < min_dist:
min_dist = curr_polygon.dist(self.objs[i])
min_dist_obj = i
if min_dist < min_threshold and min_dist > 0:
vector = np.array(original_pos) - np.array(self.objs[min_dist_obj].original_pos)
vector = vector * min_threshold / min_dist
original_pos = vector+np.array(self.objs[min_dist_obj].original_pos)
if min_dist > max_threshold and min_dist > 0:
vector = np.array(original_pos) - np.array(self.objs[min_dist_obj].original_pos)
vector = vector * max_threshold / min_dist
original_pos = vector+np.array(self.objs[min_dist_obj].original_pos)
body.DestroyFixture(fixture)
self.world.DestroyBody(body)
body = self.world.CreateDynamicBody(position=original_pos.tolist(), allowSleep=False)
fixture = body.CreatePolygonFixture(density=1, vertices=vertices.tolist(), friction=0.5)
curr_polygon = Polygon(body, [fixture], vertices, Colors[i % len(Colors)])
no_overlap = True
for obj in self.objs:
if obj.test_overlap(curr_polygon) or curr_polygon.test_overlap(obj):
no_overlap = False
if no_overlap:
self.objs.append(curr_polygon)
# prev_pos = original_pos
break
else:
continue
else:
body.DestroyFixture(fixture)
self.world.DestroyBody(body)
if max_iter <= 0:
raise Exception("max iter reaches")
self.bounding_convex_hull = create_convex_hull(np.concatenate([obj.vertices+obj.original_pos for obj in self.objs]))
self.centroid = compute_centroid(self.bounding_convex_hull.tolist())
self.bounding_circle_radius = math.sqrt(max((self.bounding_convex_hull - np.array(self.centroid))[:,0]**2 + (self.bounding_convex_hull - np.array(self.centroid))[:,1]**2))
def create_random_concave_env(self, num_objs=3):
assert num_objs >= 1
if len(self.objs) > 0:
for obj in self.objs:
for fix in obj.fixtures:
obj.body.DestroyFixture(fix)
self.world.DestroyBody(obj.body)
self.objs = []
for i in range(num_objs):
# create shape
vertices = generatePolygon()
raw_com = np.array(compute_centroid(vertices))
vertices = (vertices - raw_com)
if len(self.objs) <= 0:
original_pos = np.array([np.random.uniform(3,6),np.random.uniform(3,6)])
body = self.world.CreateDynamicBody(position=original_pos.tolist(), allowSleep=False)
fixtures = []
for j in range(len(vertices)):
fixture = body.CreatePolygonFixture(density=1, vertices=vertices[np.array([j, (j+1) % len(vertices), (j+2) % len(vertices)])].tolist(), friction=0.5)
fixtures.append(fixture)
self.objs.append(Polygon(body, fixtures, vertices, Colors[i]))
else:
max_iter = 1000
while True:
max_iter -= 1
original_pos = np.array([np.random.uniform(-1,1),np.random.uniform(-1,1)]) + np.array(self.objs[-1].body.position)
no_overlap = True
body = self.world.CreateDynamicBody(position=original_pos.tolist(), allowSleep=False)
fixtures = []
for j in range(len(vertices) - 2):
fixture = body.CreatePolygonFixture(density=1, vertices=vertices[j:3+j].tolist(), friction=0.5)
fixtures.append(fixture)
curr_polygon = Polygon(body, fixtures, vertices, Colors[i % len(Colors)])
for obj in self.objs:
if obj.test_overlap(vertices, original_pos) or curr_polygon.test_overlap(obj.vertices, np.array(obj.body.position)):
no_overlap = False
if no_overlap:
self.objs.append(curr_polygon)
break
else:
for fixture in curr_polygon.fixtures:
body.DestroyFixture(fixture)
self.world.DestroyBody(body)
if max_iter <= 0:
raise Exception("max iter reaches")
self.bounding_convex_hull = create_convex_hull(np.concatenate([obj.vertices+obj.original_pos for obj in self.objs]))
self.centroid = compute_centroid(self.bounding_convex_hull.tolist())
self.bounding_circle_radius = math.sqrt(max((self.bounding_convex_hull - np.array(self.centroid))[:,0]**2 + (self.bounding_convex_hull - np.array(self.centroid))[:,1]**2))
def load_env_convex(self, vertices_lst):
"""take absolute vertices"""
if len(self.objs) > 0:
for obj in self.objs:
for fix in obj.fixtures:
obj.body.DestroyFixture(fix)
self.world.DestroyBody(obj.body)
self.objs = []
for i in range(len(vertices_lst)):
vertices = create_convex_hull(np.array(vertices_lst[i]))
original_pos = np.array(compute_centroid(vertices))
vertices = (vertices - original_pos)
body = self.world.CreateDynamicBody(position=original_pos.tolist(), allowSleep=False)
fixture = body.CreatePolygonFixture(density=1, vertices=vertices.tolist(), friction=0.5)
self.objs.append(Polygon(body, [fixture], vertices, Colors[i]))
self.bounding_convex_hull = create_convex_hull(np.concatenate([obj.vertices+obj.original_pos for obj in self.objs]))
self.centroid = compute_centroid(self.bounding_convex_hull.tolist())
self.bounding_circle_radius = math.sqrt(max((self.bounding_convex_hull - np.array(self.centroid))[:,0]**2 + (self.bounding_convex_hull - np.array(self.centroid))[:,1]**2))
def load_env_concave(self, vertices_lst):
"""take absolute vertices"""
if len(self.objs) > 0:
for obj in self.objs:
for fix in obj.fixtures:
obj.body.DestroyFixture(fix)
self.world.DestroyBody(obj.body)
self.objs = []
for i in range(len(vertices_lst)):
vertices = create_convex_hull(np.array(vertices_lst[i]))
original_pos = np.array(compute_centroid(vertices))
vertices = (vertices - original_pos)
body = self.world.CreateDynamicBody(position=original_pos.tolist(), allowSleep=False)
fixtures = []
for j in range(len(vertices)):
fixture = body.CreatePolygonFixture(density=1, vertices=vertices[np.array([j, (j+1) % len(vertices), (j+2) % len(vertices)])].tolist(), friction=0.5)
fixtures.append(fixture)
self.objs.append(Polygon(body, fixtures, vertices, Colors[i]))
self.bounding_convex_hull = create_convex_hull(np.concatenate([obj.vertices+obj.original_pos for obj in self.objs]))
self.centroid = compute_centroid(self.bounding_convex_hull.tolist())
self.bounding_circle_radius = math.sqrt(max((self.bounding_convex_hull - np.array(self.centroid))[:,0]**2 + (self.bounding_convex_hull - np.array(self.centroid))[:,1]**2))
def step(self, start_pt, end_pt, path, display=False, check_reachable=True):
# display
if display:
self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT), 0, 32)
pygame.display.iconify()
self.screen.fill((255, 255, 255, 255))
def my_draw_polygon(polygon, body, fixture, color):
vertices = [(body.transform * v) * PPM for v in polygon.vertices]
vertices = [(v[0], SCREEN_HEIGHT - v[1]) for v in vertices]
pygame.draw.polygon(self.screen, color, vertices, 0)
pygame.draw.polygon(self.screen, (0,0,0,0), vertices, 5)
polygonShape.draw = my_draw_polygon
def my_draw_circle(circle, body, fixture, color):
position = body.transform * circle.pos * PPM
position = (position[0], SCREEN_HEIGHT - position[1])
pygame.draw.circle(self.screen, color, [int(
x) for x in position], int(circle.radius * PPM))
# Note: Python 3.x will enforce that pygame get the integers it requests,
# and it will not convert from float.
circleShape.draw = my_draw_circle
#
start_pt = np.array(start_pt)
end_pt = np.array(end_pt)
self.rod = self.world.CreateKinematicBody(position=(start_pt[0], start_pt[1]), allowSleep=False)
self.rodfix = self.rod.CreateCircleFixture(radius=0.1)
vector = np.array(normalize(end_pt - np.array(start_pt)))
vertices_lst=[(0.0, 0.1), (0.0, -0.1), (-0.3, -0.1), (-0.3, 0.1)]
testrod = self.world.CreateKinematicBody(position=(start_pt[0], start_pt[1]), allowSleep=False)
testrodfix = testrod.CreatePolygonFixture(vertices=[rotatePt(pt, vector) for pt in vertices_lst])
# reachability check
if check_reachable:
while (np.count_nonzero(np.array([o.dist_rod(testrodfix, testrod) for o in self.objs]) <= 0) > 0):
# print(start_pt, [o.dist_rod(self.rodfix, self.rod) for o in self.objs])
start_pt -= 0.1 * vector
testrod.DestroyFixture(testrodfix)
self.world.DestroyBody(testrod)
testrod = self.world.CreateKinematicBody(position=(start_pt[0], start_pt[1]), allowSleep=False)
testrodfix = testrod.CreatePolygonFixture(vertices=[rotatePt(pt, vector) for pt in vertices_lst])
testrod.DestroyFixture(testrodfix)
self.world.DestroyBody(testrod)
self.rod = self.world.CreateKinematicBody(position=(start_pt[0], start_pt[1]), allowSleep=False)
self.rodfix = self.rod.CreateCircleFixture(radius=0.1)
self.rod.linearVelocity[0] = vector[0]
self.rod.linearVelocity[1] = vector[1]
self.rod.angularVelocity = 0.0
timestamp = 0
damping_factor = 1 - ((1-0.5) / 3)
# display
if display:
for obj in self.objs:
for fix in obj.fixtures:
fix.shape.draw(obj.body, fix, obj.color)
self.rodfix.shape.draw(self.rod, self.rodfix, (0, 0, 0, 255))
pygame.image.save(self.screen, path+"start.png")
#
first_contact = -1
while (timestamp < 100):
if first_contact == -1:
for i in range(len(self.objs)):
if (self.objs[i].body.linearVelocity[0] ** 2 + self.objs[i].body.linearVelocity[1] ** 2 > 0.001):
first_contact = i
for obj in self.objs:
obj.body.linearVelocity[0] = obj.body.linearVelocity[0] * damping_factor
obj.body.linearVelocity[1] = obj.body.linearVelocity[1] * damping_factor
obj.body.angularVelocity = obj.body.angularVelocity * damping_factor
if (math.sqrt(np.sum((start_pt - np.array(self.rod.position))**2)) < 4):
vector = normalize((end_pt+1e-8) - (start_pt+1e-8))
self.rod.linearVelocity[0] = vector[0]
self.rod.linearVelocity[1] = vector[1]
else:
self.rod.linearVelocity[0] = 0
self.rod.linearVelocity[1] = 0
self.world.Step(TIME_STEP, 10, 10)
timestamp += 1
# display
if display:
self.screen.fill((255, 255, 255, 255))
for obj in self.objs:
for fix in obj.fixtures:
fix.shape.draw(obj.body, fix, obj.color)
self.rodfix.shape.draw(self.rod, self.rodfix, (0, 0, 0, 255))
pygame.image.save(self.screen, path+str(timestamp)+".png")
#
# display
if display:
pygame.display.quit()
pygame.quit()
#
return first_contact
def step_area(self, start_pt, end_pt, gripper_width, path, display=False, check_reachable=True):
# display
if display:
self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT), 0, 32)
pygame.display.iconify()
self.screen.fill((255, 255, 255, 255))
def my_draw_polygon(polygon, body, fixture, color):
vertices = [(body.transform * v) * PPM for v in polygon.vertices]
vertices = [(v[0], SCREEN_HEIGHT - v[1]) for v in vertices]
pygame.draw.polygon(self.screen, color, vertices, 0)
pygame.draw.polygon(self.screen, (0,0,0,0), vertices, 5)
polygonShape.draw = my_draw_polygon
#
start_pt = np.array(start_pt)
end_pt = np.array(end_pt)
vertices_lst=[(0.1, gripper_width/2), (-0.1, gripper_width/2), (-0.1, -gripper_width/2), (0.1, -gripper_width/2)]
vector = np.array(normalize(end_pt - start_pt))
self.rod = self.world.CreateKinematicBody(position=(start_pt[0], start_pt[1]), allowSleep=False)
self.rodfix = self.rod.CreatePolygonFixture(vertices=[rotatePt(pt, vector) for pt in vertices_lst])
# reachability check
if check_reachable:
while (np.count_nonzero(np.array([o.dist_rod(self.rodfix, self.rod) for o in self.objs]) <= 0) > 0):
# print(start_pt, [o.dist_rod(self.rodfix, self.rod) for o in self.objs])
start_pt -= 0.1 * vector
self.rod.DestroyFixture(self.rodfix)
self.world.DestroyBody(self.rod)
self.rod = self.world.CreateKinematicBody(position=(start_pt[0], start_pt[1]), allowSleep=False)
self.rodfix = self.rod.CreatePolygonFixture(vertices=[rotatePt(pt, vector) for pt in vertices_lst])
self.rod.linearVelocity[0] = vector[0]
self.rod.linearVelocity[1] = vector[1]
self.rod.angularVelocity = 0.0
timestamp = 0
damping_factor = 1 - ((1-0.5) / 3)
# display
if display:
for obj in self.objs:
for fix in obj.fixtures:
fix.shape.draw(obj.body, fix, obj.color)
self.rodfix.shape.draw(self.rod, self.rodfix, (0, 0, 0, 255))
pygame.image.save(self.screen, path+"start.png")
#
first_contact = -1
while (timestamp < 100):
if first_contact == -1:
for i in range(len(self.objs)):
if (self.objs[i].body.linearVelocity[0] ** 2 + self.objs[i].body.linearVelocity[1] ** 2 > 0.001):
first_contact = i
for obj in self.objs:
obj.body.linearVelocity[0] = obj.body.linearVelocity[0] * damping_factor
obj.body.linearVelocity[1] = obj.body.linearVelocity[1] * damping_factor
obj.body.angularVelocity = obj.body.angularVelocity * damping_factor
if (math.sqrt(np.sum((start_pt - np.array(self.rod.position))**2)) < 4):
vector = normalize((end_pt+1e-8) - (start_pt+1e-8))
self.rod.linearVelocity[0] = vector[0]
self.rod.linearVelocity[1] = vector[1]
else:
self.rod.linearVelocity[0] = 0
self.rod.linearVelocity[1] = 0
self.world.Step(TIME_STEP, 10, 10)
timestamp += 1
# display
if display:
self.screen.fill((255, 255, 255, 255))
for obj in self.objs:
for fix in obj.fixtures:
fix.shape.draw(obj.body, fix, obj.color)
self.rodfix.shape.draw(self.rod, self.rodfix, (0, 0, 0, 255))
pygame.image.save(self.screen, path+str(timestamp)+".png")
#
# display
if display:
pygame.display.quit()
pygame.quit()
#
return first_contact
def step_two_points(self, start_pt, end_pt, gripper_length, path, display=False, check_reachable=True):
# display
if display:
self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT), 0, 32)
pygame.display.iconify()
self.screen.fill((255, 255, 255, 255))
def my_draw_polygon(polygon, body, fixture, color):
vertices = [(body.transform * v) * PPM for v in polygon.vertices]
vertices = [(v[0], SCREEN_HEIGHT - v[1]) for v in vertices]
pygame.draw.polygon(self.screen, color, vertices, 0)
pygame.draw.polygon(self.screen, (0,0,0,0), vertices, 5)
polygonShape.draw = my_draw_polygon
def my_draw_circle(circle, body, fixture, color):
position = body.transform * circle.pos * PPM
position = (position[0], SCREEN_HEIGHT - position[1])
pygame.draw.circle(self.screen, color, [int(
x) for x in position], int(circle.radius * PPM))
# Note: Python 3.x will enforce that pygame get the integers it requests,
# and it will not convert from float.
circleShape.draw = my_draw_circle
#
start_pt = np.array(start_pt)
end_pt = np.array(end_pt)
vector = np.array(normalize(end_pt - start_pt))
gripper1_vector = normalize((1, -(vector[0] / (vector[1]+1e-8))))
gripper2_vector = normalize((-1, (vector[0] / (vector[1]+1e-8))))
# print(np.array(gripper1_vector), gripper_length)
gripper1_pt = start_pt + np.array(gripper1_vector) * gripper_length / 2
gripper2_pt = start_pt + np.array(gripper2_vector) * gripper_length / 2
vertices_lst=[(0.0, 0.1), (0.0, -0.1), (-0.3, -0.1), (-0.3, 0.1)]
testrod = self.world.CreateKinematicBody(position=(gripper1_pt[0], gripper1_pt[1]), allowSleep=False)
testrodfix = testrod.CreatePolygonFixture(vertices=[rotatePt(pt, vector) for pt in vertices_lst])
testrod2 = self.world.CreateKinematicBody(position=(gripper2_pt[0], gripper2_pt[1]), allowSleep=False)
testrodfix2 = testrod2.CreatePolygonFixture(vertices=[rotatePt(pt, vector) for pt in vertices_lst])
# reachability check
if check_reachable:
while (np.count_nonzero(np.array([o.dist_rod(testrodfix, testrod) for o in self.objs]) <= 0) > 0):
# print(start_pt, [o.dist_rod(self.rodfix, self.rod) for o in self.objs])
gripper1_pt -= 0.1 * vector
gripper2_pt -= 0.1 * vector
testrod.DestroyFixture(testrodfix)
self.world.DestroyBody(testrod)
testrod2.DestroyFixture(testrodfix2)
self.world.DestroyBody(testrod2)
testrod = self.world.CreateKinematicBody(position=(gripper1_pt[0], gripper1_pt[1]), allowSleep=False)
testrodfix = testrod.CreatePolygonFixture(vertices=[rotatePt(pt, vector) for pt in vertices_lst])
testrod2 = self.world.CreateKinematicBody(position=(gripper2_pt[0], gripper2_pt[1]), allowSleep=False)
testrodfix2 = testrod2.CreatePolygonFixture(vertices=[rotatePt(pt, vector) for pt in vertices_lst])
testrod.DestroyFixture(testrodfix)
self.world.DestroyBody(testrod)
testrod2.DestroyFixture(testrodfix2)
self.world.DestroyBody(testrod2)
self.rod = self.world.CreateKinematicBody(position=(gripper1_pt[0], gripper1_pt[1]), allowSleep=False)
self.rodfix = self.rod.CreateCircleFixture(radius=0.1)
self.rod2 = self.world.CreateKinematicBody(position=(gripper2_pt[0], gripper2_pt[1]), allowSleep=False)
self.rodfix2 = self.rod2.CreateCircleFixture(radius=0.1)
self.rod.linearVelocity[0] = vector[0]
self.rod.linearVelocity[1] = vector[1]
self.rod.angularVelocity = 0.0
self.rod2.linearVelocity[0] = vector[0]
self.rod2.linearVelocity[1] = vector[1]
self.rod2.angularVelocity = 0.0
timestamp = 0
damping_factor = 1 - ((1-0.5) / 3)
# display
if display:
for obj in self.objs:
for fix in obj.fixtures:
fix.shape.draw(obj.body, fix, obj.color)
self.rodfix.shape.draw(self.rod, self.rodfix, (0, 0, 0, 255))
self.rodfix2.shape.draw(self.rod2, self.rodfix2, (0, 0, 0, 255))
pygame.image.save(self.screen, path+"start.png")
#
first_contact = -1
while (timestamp < 100):
if first_contact == -1:
for i in range(len(self.objs)):
if (self.objs[i].body.linearVelocity[0] ** 2 + self.objs[i].body.linearVelocity[1] ** 2 > 0.001):
first_contact = i
for obj in self.objs:
obj.body.linearVelocity[0] = obj.body.linearVelocity[0] * damping_factor
obj.body.linearVelocity[1] = obj.body.linearVelocity[1] * damping_factor
obj.body.angularVelocity = obj.body.angularVelocity * damping_factor
if (math.sqrt(np.sum((start_pt - np.array(self.rod.position))**2)) < 4):
vector = normalize((end_pt+1e-8) - (start_pt+1e-8))
self.rod.linearVelocity[0] = vector[0]
self.rod.linearVelocity[1] = vector[1]
self.rod2.linearVelocity[0] = vector[0]
self.rod2.linearVelocity[1] = vector[1]
else:
self.rod.linearVelocity[0] = 0
self.rod.linearVelocity[1] = 0
self.rod2.linearVelocity[0] = 0
self.rod2.linearVelocity[1] = 0
self.world.Step(TIME_STEP, 10, 10)
timestamp += 1
# display
if display:
self.screen.fill((255, 255, 255, 255))
for obj in self.objs:
for fix in obj.fixtures:
fix.shape.draw(obj.body, fix, obj.color)
self.rodfix.shape.draw(self.rod, self.rodfix, (0, 0, 0, 255))
self.rodfix2.shape.draw(self.rod2, self.rodfix2, (0, 0, 0, 255))
pygame.image.save(self.screen, path+str(timestamp)+".png")
#
# display
if display:
pygame.display.quit()
pygame.quit()
#
return first_contact
def avg_centroid(self):
total_separation = 0
for i in range(len(self.objs) - 1):
for j in range(i+1, len(self.objs)):
if i != j:
total_separation += math.log(euclidean_dist(self.objs[i].body.position, self.objs[j].body.position))
return total_separation * 2/(len(self.objs) * (len(self.objs) - 1))
def avg_geometry(self):
total_separation = 0.0
for i in range(len(self.objs) - 1):
for j in range(i+1, len(self.objs)):
if i != j:
shape1 = self.objs[i].fixtures[0].shape
shape2 = self.objs[j].fixtures[0].shape
transform1 = Box2D.b2Transform()
pos1 = self.objs[i].body.position
angle1 = self.objs[i].body.angle
transform1.Set(pos1, angle1)
transform2 = Box2D.b2Transform()
pos2 = self.objs[j].body.position
angle2 = self.objs[j].body.angle
transform2.Set(pos2, angle2)
pointA, pointB, distance, iterations = Box2D.b2Distance(shapeA=shape1, shapeB=shape2, transformA=transform1, transformB=transform2)
# print(pointA, pointB, distance, iterations)
total_separation += distance
return total_separation * 2/(len(self.objs) * (len(self.objs) - 1))
def min_geometry(self):
min_dist = 1e2
for i in range(len(self.objs) - 1):
for j in range(i+1, len(self.objs)):
if i != j:
shape1 = self.objs[i].fixtures[0].shape
shape2 = self.objs[j].fixtures[0].shape
transform1 = Box2D.b2Transform()
pos1 = self.objs[i].body.position
angle1 = self.objs[i].body.angle
transform1.Set(pos1, angle1)
transform2 = Box2D.b2Transform()
pos2 = self.objs[j].body.position
angle2 = self.objs[j].body.angle
transform2.Set(pos2, angle2)
pointA, pointB, distance, iterations = Box2D.b2Distance(shapeA=shape1, shapeB=shape2, transformA=transform1, transformB=transform2)
if distance < min_dist:
min_dist = distance
return min_dist
def min_centroid(self):
min_dist = 1e2
for i in range(len(self.objs) - 1):
for j in range(i+1, len(self.objs)):
if i != j:
if min_dist > (euclidean_dist(self.objs[i].body.position, self.objs[j].body.position)):
min_dist = euclidean_dist(self.objs[i].body.position, self.objs[j].body.position)
return min_dist
def count_threshold(self, threshold=0.3):
count = 0
for i in range(len(self.objs)):
isolated = True
for j in range(len(self.objs)):
if i != j:
shape1 = self.objs[i].fixtures[0].shape
shape2 = self.objs[j].fixtures[0].shape
transform1 = Box2D.b2Transform()
pos1 = self.objs[i].body.position
angle1 = self.objs[i].body.angle
transform1.Set(pos1, angle1)
transform2 = Box2D.b2Transform()
pos2 = self.objs[j].body.position
angle2 = self.objs[j].body.angle
transform2.Set(pos2, angle2)
pointA, pointB, distance, iterations = Box2D.b2Distance(shapeA=shape1, shapeB=shape2, transformA=transform1, transformB=transform2)
if distance < threshold:
isolated = False
if isolated:
count += 1
return count
def collect_data_summary(self, start_pt, end_pt, img_path=None, sum_path=None):
summary = {}
abs_start_pt = np.array(start_pt)
abs_end_pt = np.array(end_pt)
summary["start pt"] = abs_start_pt.tolist()
summary["end pt"] = abs_end_pt.tolist()
summary["gripper_width"] = 0.0
for i in range(len(self.objs)):
summary[str(i)+" dist to pushing line"] = pointToLineDistance(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" original pos"] = np.array(self.objs[i].body.position).tolist()
# print(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" project dist"] = projectedPtToStartDistance(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" vertices"] = np.array(self.objs[i].vertices).tolist()
summary[str(i)+" disk coverage"] = self.objs[i].disk_coverage
for i in range(len(self.objs)):
for j in range(i, len(self.objs)):
summary[str(i)+" to "+str(j)+" before push"] = self.objs[i].dist(self.objs[j])
summary["avg centroid before push"] = self.avg_centroid()
summary["avg geometry before push"] = self.avg_geometry()
summary["min centroid before push"] = self.min_centroid()
summary["min geometry before push"] = self.min_geometry()
summary["count threshold before push"] = self.count_threshold()
first_contact = self.step(start_pt, end_pt, img_path)
# if first_contact == -1:
# return
for i in range(len(self.objs)):
summary[str(i)+" change of pos"] = euclidean_dist(self.objs[i].body.position, self.objs[i].original_pos)
summary["avg centroid after push"] = self.avg_centroid()
summary["avg geometry after push"] = self.avg_geometry()
summary["min centroid after push"] = self.min_centroid()
summary["min geometry after push"] = self.min_geometry()
summary["count threshold after push"] = self.count_threshold()
summary["first contact object"] = first_contact
for i in range(len(self.objs)):
for j in range(i, len(self.objs)):
summary[str(i)+" to "+str(j)+" after push"] = self.objs[i].dist(self.objs[j])
if sum_path is not None:
with open(sum_path+'summary.json', 'w') as f:
json.dump(summary, f)
return summary
def prune_best(self, prune_method, metric="count threshold", position=None, sum_path=None):
pt_lst = prune_method(self)
best_pt = None
# if metric == "avg centroid" or metric == "avg geometry":
best_sep = -1e2
for pts in pt_lst:
if position is None:
self.reset()
else:
self.load_position(position)
# summary = self.collect_data_summary_old(pts[0], pts[1], None)
summary = self.collect_data_summary(pts[0], pts[1], None)
if summary[metric +" after push"] - summary[metric + " before push"] >= best_sep:
best_pt = pts
if position is None:
self.reset()
else:
self.load_position(position)
best_sep = summary[metric +" after push"] - summary[metric + " before push"]
if best_pt is not None:
self.reset()
return self.collect_data_summary(best_pt[0], best_pt[1], "/", sum_path=sum_path)
return best_pt
def prune_best_summary_all(self, prune_method, folder_path, ind_num, metrics=["avg centroid"]):
pt_lst = prune_method(self)
best_pt = []
# if metric == "avg centroid" or metric == "avg geometry":
best_sep = []
for i in range(len(metrics)):
best_sep.append(-1e2)
best_pt.append(None)
for pts in pt_lst:
self.reset()
# print(pts)
summary = self.collect_data_summary(pts[0], pts[1], "/")
for i in range(len(metrics)):
if summary[metrics[i] +" after push"] - summary[metrics[i] + " before push"] >= best_sep[i]:
best_pt[i] = pts
self.reset()
best_sep[i] = summary[metrics[i] +" after push"] - summary[metrics[i] + " before push"]
# best_sep = 1e2
# for pts in pt_lst:
# self.reset()
# summary = self.collect_data_summary(pts[0], pts[1], "/")
# if summary[metric +" after push"] - summary[metric + " before push"] < best_sep:
# best_pt = pts
# self.reset()
# best_sep = summary[metric +" after push"] - summary[metric + " before push"]
best_sum = []
for i in range(len(metrics)):
if best_pt is not None:
self.reset()
best_sum.append(self.collect_data_summary(best_pt[i][0], best_pt[i][1], "/", sum_path=folder_path+"/"+metrics[i]+"/"+ind_num))
# print(best_sep)
return best_sum
def collect_data_area_summary(self, start_pt, end_pt, gripper_width, img_path=None, sum_path=None):
summary = {}
abs_start_pt = np.array(start_pt)
abs_end_pt = np.array(end_pt)
summary["start pt"] = abs_start_pt.tolist()
summary["end pt"] = abs_end_pt.tolist()
summary["gripper_width"] = gripper_width
for i in range(len(self.objs)):
summary[str(i)+" dist to pushing line"] = pointToLineDistance(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" original pos"] = np.array(self.objs[i].body.position).tolist()
# print(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" project dist"] = projectedPtToStartDistance(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" vertices"] = np.array(self.objs[i].vertices).tolist()
summary[str(i)+" disk coverage"] = self.objs[i].disk_coverage
for i in range(len(self.objs)):
for j in range(i, len(self.objs)):
summary[str(i)+" to "+str(j)+" before push"] = self.objs[i].dist(self.objs[j])
summary["avg centroid before push"] = self.avg_centroid()
summary["avg geometry before push"] = self.avg_geometry()
summary["min centroid before push"] = self.min_centroid()
summary["min geometry before push"] = self.min_geometry()
summary["count threshold before push"] = self.count_threshold()
first_contact = self.step_area(start_pt, end_pt, gripper_width, img_path)
# if first_contact == -1:
# return
for i in range(len(self.objs)):
summary[str(i)+" change of pos"] = euclidean_dist(self.objs[i].body.position, self.objs[i].original_pos)
summary["avg centroid after push"] = self.avg_centroid()
summary["avg geometry after push"] = self.avg_geometry()
summary["min centroid after push"] = self.min_centroid()
summary["min geometry after push"] = self.min_geometry()
summary["count threshold after push"] = self.count_threshold()
summary["first contact object"] = first_contact
for i in range(len(self.objs)):
for j in range(i, len(self.objs)):
summary[str(i)+" to "+str(j)+" after push"] = self.objs[i].dist(self.objs[j])
if sum_path is not None:
with open(sum_path+'summary.json', 'w') as f:
json.dump(summary, f)
return summary
def collect_data_two_points_summary(self, start_pt, end_pt, gripper_length, img_path=None, sum_path=None):
summary = {}
abs_start_pt = np.array(start_pt)
abs_end_pt = np.array(end_pt)
summary["start pt"] = abs_start_pt.tolist()
summary["end pt"] = abs_end_pt.tolist()
summary["gripper_length"] = gripper_length
for i in range(len(self.objs)):
summary[str(i)+" dist to pushing line"] = pointToLineDistance(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" original pos"] = np.array(self.objs[i].body.position).tolist()
# print(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" project dist"] = projectedPtToStartDistance(abs_start_pt, abs_end_pt, self.objs[i].body.position)
summary[str(i)+" vertices"] = np.array(self.objs[i].vertices).tolist()
summary[str(i)+" disk coverage"] = self.objs[i].disk_coverage
for i in range(len(self.objs)):
for j in range(i, len(self.objs)):
summary[str(i)+" to "+str(j)+" before push"] = self.objs[i].dist(self.objs[j])
summary["avg centroid before push"] = self.avg_centroid()
summary["avg geometry before push"] = self.avg_geometry()
summary["min centroid before push"] = self.min_centroid()
summary["min geometry before push"] = self.min_geometry()
summary["count threshold before push"] = self.count_threshold()
first_contact = self.step_two_points(start_pt, end_pt, gripper_length, img_path)
# if first_contact == -1:
# return
for i in range(len(self.objs)):
summary[str(i)+" change of pos"] = euclidean_dist(self.objs[i].body.position, self.objs[i].original_pos)
summary["avg centroid after push"] = self.avg_centroid()
summary["avg geometry after push"] = self.avg_geometry()
summary["min centroid after push"] = self.min_centroid()
summary["min geometry after push"] = self.min_geometry()
summary["count threshold after push"] = self.count_threshold()
summary["first contact object"] = first_contact
for i in range(len(self.objs)):
for j in range(i, len(self.objs)):
summary[str(i)+" to "+str(j)+" after push"] = self.objs[i].dist(self.objs[j])
if sum_path is not None:
with open(sum_path+'summary.json', 'w') as f:
json.dump(summary, f)
return summary
def reset(self):
if self.rod:
self.rod.DestroyFixture(self.rodfix)
self.world.DestroyBody(self.rod)
self.rod = None
if self.rod2:
self.rod2.DestroyFixture(self.rodfix2)
self.world.DestroyBody(self.rod2)
self.rod2 = None
for obj in self.objs:
obj.body.position[0] = obj.original_pos[0]
obj.body.position[1] = obj.original_pos[1]
obj.body.angle = 0.0
obj.body.linearVelocity[0] = 0.0
obj.body.linearVelocity[1] = 0.0
obj.body.angularVelocity = 0.0
def visualize(self, path):
self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT), 0, 32)
pygame.display.iconify()
self.screen.fill((0, 0, 0, 0))
def my_draw_polygon(polygon, body, fixture, color):
vertices = [(body.transform * v) * PPM for v in polygon.vertices]
vertices = [(v[0], SCREEN_HEIGHT - v[1]) for v in vertices]
pygame.draw.polygon(self.screen, color, vertices, 0)
polygonShape.draw = my_draw_polygon
for obj in self.objs:
obj.fixture.shape.draw(obj.body, obj.fixture, obj.color)
self.rodfix.shape.draw(self.rod, self.rodfix, (100, 100, 100, 255))
pygame.image.save(self.screen, path+"debug.png")
pygame.display.quit()
pygame.quit()
def save_curr_position(self):
position = {}
for i in range(len(self.objs)):
position[i] = [self.objs[i].body.position[0], self.objs[i].body.position[1], self.objs[i].body.angle]
return position
def load_position(self, position):
if self.rod:
self.rod.DestroyFixture(self.rodfix)
self.world.DestroyBody(self.rod)
self.rod = None
if self.rod2:
self.rod2.DestroyFixture(self.rodfix2)
self.world.DestroyBody(self.rod2)
self.rod2 = None
for i in range(len(self.objs)):
self.objs[i].body.position[0] = position[i][0]
self.objs[i].body.position[1] = position[i][1]
self.objs[i].body.angle = position[i][2]
def load_env(self, dic):
assert (len(dic) - 14) % 6 == 0
num_obj = (len(dic) - 14) // 6
# assert (len(dic) - 5) % 6 == 0
# num_obj = (len(dic) - 5) // 6
for i in range(num_obj):
original_pos = np.array(dic[str(i)+" original pos"])# + np.array([-1, 3])
vertices = np.array(dic[str(i)+" vertices"])
body = self.world.CreateDynamicBody(position=original_pos.tolist(), allowSleep=False)
fixture = body.CreatePolygonFixture(density=1, vertices=vertices.tolist(), friction=0.5)
self.objs.append(Polygon(body, [fixture], vertices, Colors[i%len(Colors)]))
def sequential_prune_planning(self, prune_method, max_step=5, metric="count threshold", sum_path=None):
data_sum = []
for i in range(max_step):
curr_pos = self.save_curr_position()
best_pts = self.prune_best(prune_method, metric, curr_pos)
self.load_position(curr_pos)
curr_sum = self.collect_data_summary(best_pts[0], best_pts[1], None)
data_sum.append(curr_sum)
print(curr_sum[metric +" after push"], curr_sum[metric + " before push"])
# return data_sum[-1][metric +" after push"], data_sum[-1][metric + " before push"]
if sum_path is not None:
with open(sum_path+'.json', 'w') as f:
json.dump(data_sum, f)
return data_sum
def sequential_policy_planning(self, policy, max_step=5, metric="count threshold", sum_path=None):
data_sum = []
for i in range(max_step):
curr_pos = self.save_curr_position()
best_pts = policy(self)
self.load_position(curr_pos)
curr_sum = self.collect_data_summary(best_pts[0], best_pts[1], None)
data_sum.append(curr_sum)
print(curr_sum[metric +" after push"], curr_sum[metric + " before push"])
# return data_sum[-1][metric +" after push"], data_sum[-1][metric + " before push"]
if sum_path is not None:
with open(sum_path+'.json', 'w') as f:
json.dump(data_sum, f)
return data_sum
if __name__ == "__main__":
# path = "/nfs/diskstation/zdong/cluster_push_final/"
path = "testing/"
for num_obj in range(3, 4):
if not os.path.exists(path+str(num_obj)):
os.makedirs(path+str(num_obj))
for g in range(3, 4):
print(num_obj, g)
for i in range(0, 1):
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/avg_centroid"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/avg_centroid")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/count_threshold"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/count_threshold")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/quasi_random"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/quasi_random")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/quasi_random_area"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/quasi_random_area")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/quasi_random_two"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/quasi_random_two")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/center_object_removal"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/center_object_removal")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/center_object_removal_area"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/center_object_removal_area")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/center_object_removal_two"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/center_object_removal_two")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/min_contact_range"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/min_contact_range")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/min_contact_range_area"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/min_contact_range_area")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/min_contact_range_two"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/min_contact_range_two")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/two_cluster_separation"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/two_cluster_separation")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/two_cluster_separation_area"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/two_cluster_separation_area")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/two_cluster_separation_two"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/two_cluster_separation_two")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/min_overlap"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/min_overlap")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/min_overlap_area"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/min_overlap_area")
if not os.path.exists(path+str(num_obj)+"/"+str(g)+"/min_overlap_two"):
os.makedirs(path+str(num_obj)+"/"+str(g)+"/min_overlap_two")
print(i)
test = SingulationEnv()
while True:
try:
test.create_random_env(num_obj, g)
break
except:
print("retry")
test.reset()
summary = test.prune_best(no_prune, metric="avg centroid", sum_path=path+str(num_obj)+"/"+str(g)+"/avg_centroid/"+str(i))
test.reset()
summary = test.prune_best(no_prune, metric="count threshold", sum_path=path+str(num_obj)+"/"+str(g)+"/count_threshold/"+str(i))
# quasi random
test.reset()
push_pts = quasi_random(test)
test.collect_data_summary(push_pts[0], push_pts[1], sum_path=path+str(num_obj)+"/"+str(g)+"/quasi_random/"+str(i))
test.reset()
test.collect_data_area_summary(push_pts[0], push_pts[1], 0.3, sum_path=path+str(num_obj)+"/"+str(g)+"/quasi_random_area/"+str(i))
test.reset()
test.collect_data_two_points_summary(push_pts[0], push_pts[1], 0.6, sum_path=path+str(num_obj)+"/"+str(g)+"/quasi_random_two/"+str(i))
| 37.299748
| 173
| 0.689515
| 6,689
| 44,424
| 4.442667
| 0.050082
| 0.044419
| 0.01696
| 0.017936
| 0.865565
| 0.848605
| 0.831006
| 0.811051
| 0.789615
| 0.774338
| 0
| 0.025348
| 0.158135
| 44,424
| 1,190
| 174
| 37.331092
| 0.769245
| 0.074712
| 0
| 0.718009
| 0
| 0
| 0.058946
| 0.00967
| 0
| 0
| 0
| 0
| 0.003555
| 1
| 0.043839
| false
| 0
| 0.018957
| 0
| 0.092417
| 0.005924
| 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
|
59c19eb39b618ac129df480f97fd29297708366e
| 116
|
py
|
Python
|
server/index.py
|
hoangcaobao/Projectube_Recommendation_System
|
e97008dcbb0b3e1d40d6a630c0b5495a1d1c17d6
|
[
"MIT"
] | 5
|
2021-07-26T14:28:24.000Z
|
2022-02-04T00:39:16.000Z
|
server/index.py
|
hoangcaobao/Projectube_Recommendation_System
|
e97008dcbb0b3e1d40d6a630c0b5495a1d1c17d6
|
[
"MIT"
] | null | null | null |
server/index.py
|
hoangcaobao/Projectube_Recommendation_System
|
e97008dcbb0b3e1d40d6a630c0b5495a1d1c17d6
|
[
"MIT"
] | null | null | null |
from server import app
from server import cross_origin
@app.route('/')
@cross_origin()
def index():
return 'hello'
| 19.333333
| 31
| 0.741379
| 17
| 116
| 4.941176
| 0.647059
| 0.238095
| 0.380952
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12931
| 116
| 6
| 32
| 19.333333
| 0.831683
| 0
| 0
| 0
| 0
| 0
| 0.051282
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| true
| 0
| 0.333333
| 0.166667
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 8
|
ab762e728c146d7c7944d3b5e69d592b494d0fbe
| 180
|
py
|
Python
|
src/super_calc_hacettepe/add_subtract.py
|
AtahanCelebi/super_calc_ata
|
353aa9854775502d3247ef6d9525fb28e3c918f3
|
[
"MIT"
] | null | null | null |
src/super_calc_hacettepe/add_subtract.py
|
AtahanCelebi/super_calc_ata
|
353aa9854775502d3247ef6d9525fb28e3c918f3
|
[
"MIT"
] | null | null | null |
src/super_calc_hacettepe/add_subtract.py
|
AtahanCelebi/super_calc_ata
|
353aa9854775502d3247ef6d9525fb28e3c918f3
|
[
"MIT"
] | null | null | null |
def add_two_numbers(num1, num2):
"add two numbers"
return num1 + num2
def subtract_two_numbers(num1, num2):
"subtract_two_numbers two numbers"
return num1 - num2
| 20
| 38
| 0.711111
| 26
| 180
| 4.692308
| 0.307692
| 0.409836
| 0.213115
| 0.295082
| 0.393443
| 0
| 0
| 0
| 0
| 0
| 0
| 0.056338
| 0.211111
| 180
| 8
| 39
| 22.5
| 0.802817
| 0.266667
| 0
| 0
| 0
| 0
| 0.26257
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
abcac3917ab1dcdeae4a000f6c30bb983a55f5e4
| 8,375
|
py
|
Python
|
clients/python-flask/generated/openapi_server/test/test_pipeline_execution_controller.py
|
shinesolutions/cloudmanager-api-clients
|
d73a25878f6cc57af954362ba8dccc90d54e6131
|
[
"Apache-2.0"
] | 3
|
2020-06-23T05:31:52.000Z
|
2020-11-26T05:34:57.000Z
|
clients/python-flask/generated/openapi_server/test/test_pipeline_execution_controller.py
|
shinesolutions/cloudmanager-api-clients
|
d73a25878f6cc57af954362ba8dccc90d54e6131
|
[
"Apache-2.0"
] | 2
|
2021-01-21T01:19:54.000Z
|
2021-12-09T22:30:22.000Z
|
clients/python-flask/generated/openapi_server/test/test_pipeline_execution_controller.py
|
shinesolutions/cloudmanager-api-clients
|
d73a25878f6cc57af954362ba8dccc90d54e6131
|
[
"Apache-2.0"
] | 1
|
2020-11-18T11:48:13.000Z
|
2020-11-18T11:48:13.000Z
|
# coding: utf-8
from __future__ import absolute_import
import unittest
from flask import json
from six import BytesIO
from openapi_server.models.pipeline_execution import PipelineExecution # noqa: E501
from openapi_server.models.pipeline_execution_list_representation import PipelineExecutionListRepresentation # noqa: E501
from openapi_server.models.pipeline_execution_step_state import PipelineExecutionStepState # noqa: E501
from openapi_server.models.pipeline_step_metrics import PipelineStepMetrics # noqa: E501
from openapi_server.test import BaseTestCase
class TestPipelineExecutionController(BaseTestCase):
"""PipelineExecutionController integration test stubs"""
def test_advance_pipeline_execution(self):
"""Test case for advance_pipeline_execution
Advance
"""
body = None
headers = {
'Content-Type': 'application/json',
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
'content_type': 'content_type_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/execution/{execution_id}/phase/{phase_id}/step/{step_id}/advance'.format(program_id='program_id_example', pipeline_id='pipeline_id_example', execution_id='execution_id_example', phase_id='phase_id_example', step_id='step_id_example'),
method='PUT',
headers=headers,
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_cancel_pipeline_execution_step(self):
"""Test case for cancel_pipeline_execution_step
Cancel
"""
body = None
headers = {
'Content-Type': 'application/json',
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
'content_type': 'content_type_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/execution/{execution_id}/phase/{phase_id}/step/{step_id}/cancel'.format(program_id='program_id_example', pipeline_id='pipeline_id_example', execution_id='execution_id_example', phase_id='phase_id_example', step_id='step_id_example'),
method='PUT',
headers=headers,
data=json.dumps(body),
content_type='application/json')
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_get_current_execution(self):
"""Test case for get_current_execution
Get current pipeline execution
"""
headers = {
'Accept': 'application/json',
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/execution'.format(program_id='program_id_example', pipeline_id='pipeline_id_example'),
method='GET',
headers=headers)
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_get_execution(self):
"""Test case for get_execution
Get pipeline execution
"""
headers = {
'Accept': 'application/json',
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/execution/{execution_id}'.format(program_id='program_id_example', pipeline_id='pipeline_id_example', execution_id='execution_id_example'),
method='GET',
headers=headers)
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_get_executions(self):
"""Test case for get_executions
List Executions
"""
query_string = [('start', 'start_example'),
('limit', 56)]
headers = {
'Accept': 'application/json',
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/executions'.format(program_id='program_id_example', pipeline_id='pipeline_id_example'),
method='GET',
headers=headers,
query_string=query_string)
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_get_step_logs(self):
"""Test case for get_step_logs
Get logs
"""
query_string = [('file', 'file_example')]
headers = {
'accept': 'accept_example',
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/execution/{execution_id}/phase/{phase_id}/step/{step_id}/logs'.format(program_id='program_id_example', pipeline_id='pipeline_id_example', execution_id='execution_id_example', phase_id='phase_id_example', step_id='step_id_example'),
method='GET',
headers=headers,
query_string=query_string)
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_start_pipeline(self):
"""Test case for start_pipeline
Start the pipeline
"""
headers = {
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
'content_type': 'content_type_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/execution'.format(program_id='program_id_example', pipeline_id='pipeline_id_example'),
method='PUT',
headers=headers)
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_step_metric(self):
"""Test case for step_metric
Get step metrics
"""
headers = {
'Accept': 'application/json',
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/execution/{execution_id}/phase/{phase_id}/step/{step_id}/metrics'.format(program_id='program_id_example', pipeline_id='pipeline_id_example', execution_id='execution_id_example', phase_id='phase_id_example', step_id='step_id_example'),
method='GET',
headers=headers)
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
def test_step_state(self):
"""Test case for step_state
Get step state
"""
headers = {
'Accept': 'application/json',
'x_gw_ims_org_id': 'x_gw_ims_org_id_example',
'authorization': 'authorization_example',
'x_api_key': 'x_api_key_example',
}
response = self.client.open(
'/api/program/{program_id}/pipeline/{pipeline_id}/execution/{execution_id}/phase/{phase_id}/step/{step_id}'.format(program_id='program_id_example', pipeline_id='pipeline_id_example', execution_id='execution_id_example', phase_id='phase_id_example', step_id='step_id_example'),
method='GET',
headers=headers)
self.assert200(response,
'Response body is : ' + response.data.decode('utf-8'))
if __name__ == '__main__':
unittest.main()
| 42.51269
| 296
| 0.627104
| 950
| 8,375
| 5.154737
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| 0.06338
| 1
| 0.06338
| false
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|
0
| 7
|
abf9dfff4cfdb51b302e7a54496ca11fba3975bf
| 146,342
|
py
|
Python
|
python/ned_client/apis/individuals_api.py
|
PLOS/ned-client
|
df6a58ac59c0c4730ee74de67d24a56319f2cbae
|
[
"MIT"
] | 1
|
2018-10-30T20:51:31.000Z
|
2018-10-30T20:51:31.000Z
|
python/ned_client/apis/individuals_api.py
|
PLOS/ned-client
|
df6a58ac59c0c4730ee74de67d24a56319f2cbae
|
[
"MIT"
] | 5
|
2020-03-04T23:26:59.000Z
|
2021-01-20T23:52:43.000Z
|
python/ned_client/apis/individuals_api.py
|
PLOS/ned-client
|
df6a58ac59c0c4730ee74de67d24a56319f2cbae
|
[
"MIT"
] | 1
|
2017-02-10T00:30:10.000Z
|
2017-02-10T00:30:10.000Z
|
# coding: utf-8
"""
IndividualsApi.py
Copyright 2015 SmartBear Software
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import absolute_import
import sys
import os
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
class IndividualsApi(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):
config = Configuration()
if api_client:
self.api_client = api_client
else:
if not config.api_client:
config.api_client = ApiClient()
self.api_client = config.api_client
def find_individuals(self, **kwargs):
"""
Find individual matching specified attribute.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.find_individuals(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str entity:
:param str attribute:
:param str value:
:return: list[IndividualComposite]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity', 'attribute', 'value']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method find_individuals" % key
)
params[key] = val
del params['kwargs']
resource_path = '/individuals'.replace('{format}', 'json')
method = 'GET'
path_params = {}
query_params = {}
if 'entity' in params:
query_params['entity'] = params['entity']
if 'attribute' in params:
query_params['attribute'] = params['attribute']
if 'value' in params:
query_params['value'] = params['value']
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[IndividualComposite]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def create_individual(self, **kwargs):
"""
Create individual
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_individual(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param IndividualComposite body:
:param str authorization:
:return: IndividualComposite
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_individual" % key
)
params[key] = val
del params['kwargs']
resource_path = '/individuals'.replace('{format}', 'json')
method = 'POST'
path_params = {}
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='IndividualComposite',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def read_individual_by_cas_id(self, cas_id, **kwargs):
"""
Read individual by CAS ID
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.read_individual_by_cas_id(cas_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str cas_id: (required)
:return: IndividualComposite
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['cas_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method read_individual_by_cas_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'cas_id' is set
if ('cas_id' not in params) or (params['cas_id'] is None):
raise ValueError("Missing the required parameter `cas_id` when calling `read_individual_by_cas_id`")
resource_path = '/individuals/CAS/{casId}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'cas_id' in params:
path_params['casId'] = params['cas_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='IndividualComposite',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def read_individual(self, ned_id, **kwargs):
"""
Read individual by Ned ID
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.read_individual(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: IndividualComposite
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method read_individual" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `read_individual`")
resource_path = '/individuals/{nedId}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='IndividualComposite',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_individual(self, ned_id, **kwargs):
"""
Delete individual
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_individual(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: IndividualComposite
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_individual" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `delete_individual`")
resource_path = '/individuals/{nedId}'.replace('{format}', 'json')
method = 'DELETE'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='IndividualComposite',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_addresses(self, ned_id, **kwargs):
"""
List addresses
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_addresses(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Address]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_addresses" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_addresses`")
resource_path = '/individuals/{nedId}/addresses'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Address]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def create_address(self, ned_id, **kwargs):
"""
Create address
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_address(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param Address body:
:param str authorization:
:return: Address
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_address" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `create_address`")
resource_path = '/individuals/{nedId}/addresses'.replace('{format}', 'json')
method = 'POST'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Address',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_address(self, ned_id, address_id, **kwargs):
"""
Read address
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_address(ned_id, address_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int address_id: (required)
:return: Address
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'address_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_address" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_address`")
# verify the required parameter 'address_id' is set
if ('address_id' not in params) or (params['address_id'] is None):
raise ValueError("Missing the required parameter `address_id` when calling `get_address`")
resource_path = '/individuals/{nedId}/addresses/{addressId}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'address_id' in params:
path_params['addressId'] = params['address_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Address',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_address(self, ned_id, address_id, **kwargs):
"""
Update address
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_address(ned_id, address_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int address_id: (required)
:param Address body:
:param str authorization:
:return: Address
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'address_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_address" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `update_address`")
# verify the required parameter 'address_id' is set
if ('address_id' not in params) or (params['address_id'] is None):
raise ValueError("Missing the required parameter `address_id` when calling `update_address`")
resource_path = '/individuals/{nedId}/addresses/{addressId}'.replace('{format}', 'json')
method = 'PUT'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'address_id' in params:
path_params['addressId'] = params['address_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Address',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_address(self, ned_id, address_id, **kwargs):
"""
Delete address
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_address(ned_id, address_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int address_id: (required)
:param str authorization:
:return: list[Address]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'address_id', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_address" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `delete_address`")
# verify the required parameter 'address_id' is set
if ('address_id' not in params) or (params['address_id'] is None):
raise ValueError("Missing the required parameter `address_id` when calling `delete_address`")
resource_path = '/individuals/{nedId}/addresses/{addressId}'.replace('{format}', 'json')
method = 'DELETE'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'address_id' in params:
path_params['addressId'] = params['address_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Address]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_alerts(self, ned_id, **kwargs):
"""
List alerts
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_alerts(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Alert]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_alerts" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_alerts`")
resource_path = '/individuals/{nedId}/alerts'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Alert]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def create_alert(self, ned_id, **kwargs):
"""
Create alert
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_alert(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param Alert body:
:param str authorization:
:return: Alert
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_alert" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `create_alert`")
resource_path = '/individuals/{nedId}/alerts'.replace('{format}', 'json')
method = 'POST'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Alert',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_alert(self, ned_id, alert_id, **kwargs):
"""
Read group
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_alert(ned_id, alert_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int alert_id: (required)
:return: Alert
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'alert_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_alert" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_alert`")
# verify the required parameter 'alert_id' is set
if ('alert_id' not in params) or (params['alert_id'] is None):
raise ValueError("Missing the required parameter `alert_id` when calling `get_alert`")
resource_path = '/individuals/{nedId}/alerts/{alertId}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'alert_id' in params:
path_params['alertId'] = params['alert_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Alert',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_alert(self, ned_id, alert_id, **kwargs):
"""
Update alert
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_alert(ned_id, alert_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int alert_id: (required)
:param str authorization:
:param Alert body:
:return: Alert
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'alert_id', 'authorization', 'body']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_alert" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `update_alert`")
# verify the required parameter 'alert_id' is set
if ('alert_id' not in params) or (params['alert_id'] is None):
raise ValueError("Missing the required parameter `alert_id` when calling `update_alert`")
resource_path = '/individuals/{nedId}/alerts/{alertId}'.replace('{format}', 'json')
method = 'PUT'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'alert_id' in params:
path_params['alertId'] = params['alert_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Alert',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_alert(self, ned_id, alert_id, **kwargs):
"""
Delete alert
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_alert(ned_id, alert_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int alert_id: (required)
:param str authorization:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'alert_id', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_alert" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `delete_alert`")
# verify the required parameter 'alert_id' is set
if ('alert_id' not in params) or (params['alert_id'] is None):
raise ValueError("Missing the required parameter `alert_id` when calling `delete_alert`")
resource_path = '/individuals/{nedId}/alerts/{alertId}'.replace('{format}', 'json')
method = 'DELETE'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'alert_id' in params:
path_params['alertId'] = params['alert_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type=None,
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_auth_record(self, ned_id, **kwargs):
"""
List auth record(s)
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_auth_record(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Auth]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_auth_record" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_auth_record`")
resource_path = '/individuals/{nedId}/auth'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Auth]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def check_password(self, ned_id, **kwargs):
"""
Validate password
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.check_password(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param Auth body:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'body']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method check_password" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `check_password`")
resource_path = '/individuals/{nedId}/auth/checkpassword'.replace('{format}', 'json')
method = 'POST'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type=None,
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_auth_record(self, ned_id, auth_id, **kwargs):
"""
Update auth record
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_auth_record(ned_id, auth_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int auth_id: (required)
:param str authorization:
:param Auth body:
:return: Auth
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'auth_id', 'authorization', 'body']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_auth_record" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `update_auth_record`")
# verify the required parameter 'auth_id' is set
if ('auth_id' not in params) or (params['auth_id'] is None):
raise ValueError("Missing the required parameter `auth_id` when calling `update_auth_record`")
resource_path = '/individuals/{nedId}/auth/{authId}'.replace('{format}', 'json')
method = 'PUT'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'auth_id' in params:
path_params['authId'] = params['auth_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Auth',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_degrees(self, ned_id, **kwargs):
"""
List degrees
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_degrees(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Degree]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_degrees" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_degrees`")
resource_path = '/individuals/{nedId}/degrees'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Degree]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_emails(self, ned_id, **kwargs):
"""
List emails
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_emails(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Email]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_emails" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_emails`")
resource_path = '/individuals/{nedId}/emails'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Email]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def create_email(self, ned_id, **kwargs):
"""
Create email
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_email(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param Email body:
:param str authorization:
:return: Email
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_email" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `create_email`")
resource_path = '/individuals/{nedId}/emails'.replace('{format}', 'json')
method = 'POST'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Email',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_email(self, ned_id, email_id, **kwargs):
"""
Read email
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_email(ned_id, email_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int email_id: (required)
:return: Email
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'email_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_email" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_email`")
# verify the required parameter 'email_id' is set
if ('email_id' not in params) or (params['email_id'] is None):
raise ValueError("Missing the required parameter `email_id` when calling `get_email`")
resource_path = '/individuals/{nedId}/emails/{emailId}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'email_id' in params:
path_params['emailId'] = params['email_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Email',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_email(self, ned_id, email_id, **kwargs):
"""
Update email
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_email(ned_id, email_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int email_id: (required)
:param Email body:
:param str authorization:
:return: Email
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'email_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_email" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `update_email`")
# verify the required parameter 'email_id' is set
if ('email_id' not in params) or (params['email_id'] is None):
raise ValueError("Missing the required parameter `email_id` when calling `update_email`")
resource_path = '/individuals/{nedId}/emails/{emailId}'.replace('{format}', 'json')
method = 'PUT'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'email_id' in params:
path_params['emailId'] = params['email_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Email',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_email(self, ned_id, email_id, **kwargs):
"""
Delete email
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_email(ned_id, email_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int email_id: (required)
:param str authorization:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'email_id', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_email" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `delete_email`")
# verify the required parameter 'email_id' is set
if ('email_id' not in params) or (params['email_id'] is None):
raise ValueError("Missing the required parameter `email_id` when calling `delete_email`")
resource_path = '/individuals/{nedId}/emails/{emailId}'.replace('{format}', 'json')
method = 'DELETE'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'email_id' in params:
path_params['emailId'] = params['email_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type=None,
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_groups(self, ned_id, **kwargs):
"""
List groups
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_groups(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Group]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_groups" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_groups`")
resource_path = '/individuals/{nedId}/groups'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Group]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def create_group(self, ned_id, **kwargs):
"""
Create group
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_group(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param Group body:
:param str authorization:
:return: Group
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_group" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `create_group`")
resource_path = '/individuals/{nedId}/groups'.replace('{format}', 'json')
method = 'POST'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Group',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_group(self, ned_id, group_id, **kwargs):
"""
Read group
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_group(ned_id, group_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int group_id: (required)
:return: Group
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'group_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_group" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_group`")
# verify the required parameter 'group_id' is set
if ('group_id' not in params) or (params['group_id'] is None):
raise ValueError("Missing the required parameter `group_id` when calling `get_group`")
resource_path = '/individuals/{nedId}/groups/{groupId}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'group_id' in params:
path_params['groupId'] = params['group_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Group',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_group(self, ned_id, group_id, **kwargs):
"""
Update group
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_group(ned_id, group_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int group_id: (required)
:param str authorization:
:param Group body:
:return: Group
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'group_id', 'authorization', 'body']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_group" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `update_group`")
# verify the required parameter 'group_id' is set
if ('group_id' not in params) or (params['group_id'] is None):
raise ValueError("Missing the required parameter `group_id` when calling `update_group`")
resource_path = '/individuals/{nedId}/groups/{groupId}'.replace('{format}', 'json')
method = 'PUT'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'group_id' in params:
path_params['groupId'] = params['group_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Group',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_group(self, ned_id, group_id, **kwargs):
"""
Delete group
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_group(ned_id, group_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int group_id: (required)
:param str authorization:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'group_id', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_group" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `delete_group`")
# verify the required parameter 'group_id' is set
if ('group_id' not in params) or (params['group_id'] is None):
raise ValueError("Missing the required parameter `group_id` when calling `delete_group`")
resource_path = '/individuals/{nedId}/groups/{groupId}'.replace('{format}', 'json')
method = 'DELETE'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'group_id' in params:
path_params['groupId'] = params['group_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type=None,
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_profiles(self, ned_id, **kwargs):
"""
List profiles
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_profiles(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Individualprofile]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_profiles" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_profiles`")
resource_path = '/individuals/{nedId}/individualprofiles'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Individualprofile]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def add_profile(self, ned_id, **kwargs):
"""
Add profile
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.add_profile(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param Individualprofile body:
:param str authorization:
:return: Individualprofile
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method add_profile" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `add_profile`")
resource_path = '/individuals/{nedId}/individualprofiles'.replace('{format}', 'json')
method = 'POST'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Individualprofile',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_profile(self, ned_id, profile_id, **kwargs):
"""
Read profile
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_profile(ned_id, profile_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int profile_id: (required)
:return: Individualprofile
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'profile_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_profile" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_profile`")
# verify the required parameter 'profile_id' is set
if ('profile_id' not in params) or (params['profile_id'] is None):
raise ValueError("Missing the required parameter `profile_id` when calling `get_profile`")
resource_path = '/individuals/{nedId}/individualprofiles/{profileId}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'profile_id' in params:
path_params['profileId'] = params['profile_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Individualprofile',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_profile(self, ned_id, profile_id, **kwargs):
"""
Update a profile
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_profile(ned_id, profile_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int profile_id: (required)
:param str authorization:
:param Individualprofile body:
:return: Individualprofile
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'profile_id', 'authorization', 'body']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_profile" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `update_profile`")
# verify the required parameter 'profile_id' is set
if ('profile_id' not in params) or (params['profile_id'] is None):
raise ValueError("Missing the required parameter `profile_id` when calling `update_profile`")
resource_path = '/individuals/{nedId}/individualprofiles/{profileId}'.replace('{format}', 'json')
method = 'PUT'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'profile_id' in params:
path_params['profileId'] = params['profile_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Individualprofile',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_profile(self, ned_id, profile_id, **kwargs):
"""
Delete a profile
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_profile(ned_id, profile_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int profile_id: (required)
:param str authorization:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'profile_id', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_profile" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `delete_profile`")
# verify the required parameter 'profile_id' is set
if ('profile_id' not in params) or (params['profile_id'] is None):
raise ValueError("Missing the required parameter `profile_id` when calling `delete_profile`")
resource_path = '/individuals/{nedId}/individualprofiles/{profileId}'.replace('{format}', 'json')
method = 'DELETE'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'profile_id' in params:
path_params['profileId'] = params['profile_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type=None,
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_phonenumbers(self, ned_id, **kwargs):
"""
List phone numbers
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_phonenumbers(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Phonenumber]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_phonenumbers" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_phonenumbers`")
resource_path = '/individuals/{nedId}/phonenumbers'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Phonenumber]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_relationships(self, ned_id, **kwargs):
"""
List relationships
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_relationships(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Relationship]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_relationships" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_relationships`")
resource_path = '/individuals/{nedId}/relationships'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Relationship]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def create_relationship(self, ned_id, **kwargs):
"""
Create relationship
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_relationship(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param Relationship body:
:param str authorization:
:return: Relationship
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_relationship" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `create_relationship`")
resource_path = '/individuals/{nedId}/relationships'.replace('{format}', 'json')
method = 'POST'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Relationship',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_relationship(self, ned_id, relationship_id, **kwargs):
"""
Read relationship
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_relationship(ned_id, relationship_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int relationship_id: (required)
:return: Relationship
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'relationship_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_relationship" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_relationship`")
# verify the required parameter 'relationship_id' is set
if ('relationship_id' not in params) or (params['relationship_id'] is None):
raise ValueError("Missing the required parameter `relationship_id` when calling `get_relationship`")
resource_path = '/individuals/{nedId}/relationships/{relationshipId}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'relationship_id' in params:
path_params['relationshipId'] = params['relationship_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Relationship',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_relationship(self, ned_id, relationship_id, **kwargs):
"""
Update relationship
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_relationship(ned_id, relationship_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int relationship_id: (required)
:param str authorization:
:param Relationship body:
:return: Relationship
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'relationship_id', 'authorization', 'body']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_relationship" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `update_relationship`")
# verify the required parameter 'relationship_id' is set
if ('relationship_id' not in params) or (params['relationship_id'] is None):
raise ValueError("Missing the required parameter `relationship_id` when calling `update_relationship`")
resource_path = '/individuals/{nedId}/relationships/{relationshipId}'.replace('{format}', 'json')
method = 'PUT'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'relationship_id' in params:
path_params['relationshipId'] = params['relationship_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Relationship',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_relationship(self, ned_id, relationship_id, **kwargs):
"""
Delete relationship
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_relationship(ned_id, relationship_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int relationship_id: (required)
:param str authorization:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'relationship_id', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_relationship" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `delete_relationship`")
# verify the required parameter 'relationship_id' is set
if ('relationship_id' not in params) or (params['relationship_id'] is None):
raise ValueError("Missing the required parameter `relationship_id` when calling `delete_relationship`")
resource_path = '/individuals/{nedId}/relationships/{relationshipId}'.replace('{format}', 'json')
method = 'DELETE'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'relationship_id' in params:
path_params['relationshipId'] = params['relationship_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type=None,
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_uids(self, ned_id, **kwargs):
"""
List UIDs
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_uids(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:return: list[Uniqueidentifier]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_uids" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_uids`")
resource_path = '/individuals/{nedId}/uids'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='list[Uniqueidentifier]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def create_uid(self, ned_id, **kwargs):
"""
Create UID
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_uid(ned_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param Uniqueidentifier body:
:param str authorization:
:return: Uniqueidentifier
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'body', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_uid" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `create_uid`")
resource_path = '/individuals/{nedId}/uids'.replace('{format}', 'json')
method = 'POST'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Uniqueidentifier',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_uid(self, ned_id, id, **kwargs):
"""
Read uid
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_uid(ned_id, id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int id: (required)
:return: Uniqueidentifier
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_uid" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `get_uid`")
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_uid`")
resource_path = '/individuals/{nedId}/uids/{id}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Uniqueidentifier',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_uid(self, ned_id, id, **kwargs):
"""
Update UID
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_uid(ned_id, id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int id: (required)
:param str authorization:
:param Uniqueidentifier body:
:return: Uniqueidentifier
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'id', 'authorization', 'body']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_uid" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `update_uid`")
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `update_uid`")
resource_path = '/individuals/{nedId}/uids/{id}'.replace('{format}', 'json')
method = 'PUT'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
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'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='Uniqueidentifier',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_uid(self, ned_id, id, **kwargs):
"""
Delete UID
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_uid(ned_id, id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int ned_id: (required)
:param int id: (required)
:param str authorization:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['ned_id', 'id', 'authorization']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_uid" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'ned_id' is set
if ('ned_id' not in params) or (params['ned_id'] is None):
raise ValueError("Missing the required parameter `ned_id` when calling `delete_uid`")
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `delete_uid`")
resource_path = '/individuals/{nedId}/uids/{id}'.replace('{format}', 'json')
method = 'DELETE'
path_params = {}
if 'ned_id' in params:
path_params['nedId'] = params['ned_id']
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
header_params = {}
if 'authorization' in params:
header_params['Authorization'] = params['authorization']
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type=None,
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def read_individual_by_uid(self, uid_type, uid_value, **kwargs):
"""
Read individual by UID
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.read_individual_by_uid(uid_type, uid_value, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str uid_type: (required)
:param str uid_value: (required)
:return: IndividualComposite
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uid_type', 'uid_value']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method read_individual_by_uid" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uid_type' is set
if ('uid_type' not in params) or (params['uid_type'] is None):
raise ValueError("Missing the required parameter `uid_type` when calling `read_individual_by_uid`")
# verify the required parameter 'uid_value' is set
if ('uid_value' not in params) or (params['uid_value'] is None):
raise ValueError("Missing the required parameter `uid_value` when calling `read_individual_by_uid`")
resource_path = '/individuals/{uidType}/{uidValue}'.replace('{format}', 'json')
method = 'GET'
path_params = {}
if 'uid_type' in params:
path_params['uidType'] = params['uid_type']
if 'uid_value' in params:
path_params['uidValue'] = params['uid_value']
query_params = {}
header_params = {}
form_params = {}
files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# Authentication setting
auth_settings = ['basic']
response = self.api_client.call_api(resource_path, method,
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=files,
response_type='IndividualComposite',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
| 37.180386
| 115
| 0.524258
| 14,110
| 146,342
| 5.252091
| 0.016726
| 0.028405
| 0.024735
| 0.023588
| 0.963836
| 0.951098
| 0.946294
| 0.937563
| 0.936403
| 0.925689
| 0
| 0.000123
| 0.387763
| 146,342
| 3,935
| 116
| 37.189835
| 0.827001
| 0.237307
| 0
| 0.86632
| 0
| 0
| 0.17336
| 0.017066
| 0
| 0
| 0
| 0
| 0
| 1
| 0.022201
| false
| 0.001889
| 0.002834
| 0
| 0.047237
| 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
|
e607e680af355a5feff75e2c96b7c53aeb99e9cd
| 38,793
|
py
|
Python
|
benchmark/mysql_benchmarks.py
|
AlonFischer/SpatialDatabaseBench
|
1fe933bd4196ba17c687f04c37cb5a34acc6d824
|
[
"Apache-2.0"
] | 1
|
2020-11-17T22:56:56.000Z
|
2020-11-17T22:56:56.000Z
|
benchmark/mysql_benchmarks.py
|
AlonFischer/SpatialDatabaseBench
|
1fe933bd4196ba17c687f04c37cb5a34acc6d824
|
[
"Apache-2.0"
] | null | null | null |
benchmark/mysql_benchmarks.py
|
AlonFischer/SpatialDatabaseBench
|
1fe933bd4196ba17c687f04c37cb5a34acc6d824
|
[
"Apache-2.0"
] | null | null | null |
"""
Mysql benchmark classes
"""
from mysqlutils.mysqladapter import MySQLAdapter
from gdal.gdaldockerwrapper import GdalDockerWrapper
from benchmark.mysql_benchmark import MysqlBenchmark
from util.coordinate_transform import transform_4326_to_3857
from util.create_geometry import create_polygon, create_point, create_linestring
from util.misc import convert_decimals_to_ints_in_tuples, convert_none_to_null_in_tuples, tuple_to_str
import docker
import logging
DATABASE_NAME = "SpatialDatasets"
GEORGIA_BOUNDING_BOX = [(30.3575, -85.6082), (34.9996, -85.6082),
(34.9996, -80.696), (30.3575, -80.696), (30.3575, -85.6082)]
GEORGIA_BB_4326 = create_polygon(GEORGIA_BOUNDING_BOX)
GEORGIA_BB_3857 = create_polygon(
[transform_4326_to_3857(point) for point in GEORGIA_BOUNDING_BOX])
ATLANTA_COORDS = (33.7483, -84.3911)
ATLANTA_LOC_4326 = create_point(ATLANTA_COORDS)
ATLANTA_LOC_3857 = create_point(transform_4326_to_3857(ATLANTA_COORDS))
SAMPLE_ROUTE = [(33.6290830738968, -84.4350692100728),
(36.1369671135132, -86.6847761162769)]
ROUTE_4326 = create_linestring(SAMPLE_ROUTE)
ROUTE_3857 = create_linestring(
[transform_4326_to_3857(point) for point in SAMPLE_ROUTE])
def create_mysql_adapter():
return MySQLAdapter('root', 'root-password')
class LoadAirspaces(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Load Airspaces"
_table_name = "airspaces"
def __init__(self, with_index=True):
super().__init__(create_mysql_adapter(), LoadAirspaces._title)
docker_client = docker.from_env()
self.gdal_docker_wrapper = GdalDockerWrapper(docker_client)
self.with_index = with_index
def execute(self):
self.gdal_docker_wrapper.import_to_mysql(
"airspace/Class_Airspace.shp", LoadAirspaces._table_name, create_spatial_index=self.with_index)
def cleanup(self):
self.adapter.execute(
f"DROP TABLE {DATABASE_NAME}.{LoadAirspaces._table_name}")
class LoadAirports(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Load Airports"
_table_name = "airports"
def __init__(self, with_index=True):
super().__init__(create_mysql_adapter(), LoadAirports._title)
docker_client = docker.from_env()
self.gdal_docker_wrapper = GdalDockerWrapper(docker_client)
self.with_index = with_index
def execute(self):
self.gdal_docker_wrapper.import_to_mysql(
"airports/Airports.shp", LoadAirports._table_name, create_spatial_index=self.with_index)
def cleanup(self):
self.adapter.execute(
f"DROP TABLE {DATABASE_NAME}.{LoadAirports._table_name}")
class LoadRoutes(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Load Routes"
_table_name = "routes"
def __init__(self, with_index=True):
super().__init__(create_mysql_adapter(), LoadRoutes._title)
docker_client = docker.from_env()
self.gdal_docker_wrapper = GdalDockerWrapper(docker_client)
self.with_index = with_index
def execute(self):
self.gdal_docker_wrapper.import_to_mysql(
"routes/ATS_Route.shp", LoadRoutes._table_name, create_spatial_index=self.with_index)
def cleanup(self):
self.adapter.execute(
f"DROP TABLE {DATABASE_NAME}.{LoadRoutes._table_name}")
class PointEqualsPoint(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Point Equals Point"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PointEqualsPoint._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(A1.OBJECTID, {subsampling_factor}) = 0 AND MOD(A2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT A1.OBJECTID, A2.OBJECTID
FROM {DATABASE_NAME}.airports{self.dataset_suffix} A1, {DATABASE_NAME}.airports{self.dataset_suffix} A2
WHERE {self.subsampling_condition} st_equals(A1.SHAPE, A2.SHAPE)
;"""
PointEqualsPoint._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PointIntersectsLine(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Point Intersects Line"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PointIntersectsLine._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(A1.OBJECTID, {subsampling_factor}) = 0 AND MOD(R2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT A1.OBJECTID, R2.OBJECTID
FROM {DATABASE_NAME}.airports{self.dataset_suffix} A1, {DATABASE_NAME}.routes{self.dataset_suffix} R2
WHERE {self.subsampling_condition} st_intersects(A1.SHAPE, R2.SHAPE)
;"""
PointIntersectsLine._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PointWithinPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Point Within Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PointWithinPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(A1.OBJECTID, {subsampling_factor}) = 0 AND MOD(AS2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT A1.OBJECTID, AS2.OBJECTID
FROM {DATABASE_NAME}.airports{self.dataset_suffix} A1, {DATABASE_NAME}.airspaces{self.dataset_suffix} AS2
WHERE {self.subsampling_condition} st_within(A1.SHAPE, AS2.SHAPE)
;"""
PointWithinPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class LineIntersectsPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Line Intersects Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
LineIntersectsPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(R1.OBJECTID, {subsampling_factor}) = 0 AND MOD(AS2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT R1.OBJECTID, AS2.OBJECTID
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R1, {DATABASE_NAME}.airspaces{self.dataset_suffix} AS2
WHERE {self.subsampling_condition} st_intersects(R1.SHAPE, AS2.SHAPE)
;"""
LineIntersectsPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class LineWithinPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Line Within Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
LineWithinPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(R1.OBJECTID, {subsampling_factor}) = 0 AND MOD(AS2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT R1.OBJECTID, AS2.OBJECTID
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R1, {DATABASE_NAME}.airspaces{self.dataset_suffix} AS2
WHERE {self.subsampling_condition} st_within(R1.SHAPE, AS2.SHAPE)
;"""
LineWithinPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class LineIntersectsLine(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Line Intersects Line"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(), LineIntersectsLine._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(R1.OBJECTID, {subsampling_factor}) = 0 AND MOD(R2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT R1.OBJECTID, R2.OBJECTID
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R1, {DATABASE_NAME}.routes{self.dataset_suffix} R2
WHERE {self.subsampling_condition} st_intersects(R1.SHAPE, R2.SHAPE)
;"""
LineIntersectsLine._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PolygonEqualsPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Polygon Equals Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(), PolygonEqualsPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND MOD(AS2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID, AS2.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1, {DATABASE_NAME}.airspaces{self.dataset_suffix} AS2
WHERE {self.subsampling_condition} st_equals(AS1.SHAPE, AS2.SHAPE)
;"""
PolygonEqualsPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PolygonDisjointPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Polygon Disjoint Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PolygonDisjointPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND MOD(AS2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID, AS2.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1, {DATABASE_NAME}.airspaces{self.dataset_suffix} AS2
WHERE {self.subsampling_condition} st_disjoint(AS1.SHAPE, AS2.SHAPE)
;"""
PolygonDisjointPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PolygonIntersectsPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Polygon Intersects Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PolygonIntersectsPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND MOD(AS2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID, AS2.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1, {DATABASE_NAME}.airspaces{self.dataset_suffix} AS2
WHERE {self.subsampling_condition} st_intersects(AS1.SHAPE, AS2.SHAPE)
;"""
PolygonIntersectsPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PolygonWithinPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Polygon Within Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PolygonWithinPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND MOD(AS2.OBJECTID, {subsampling_factor}) = 0 AND"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID, AS2.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1, {DATABASE_NAME}.airspaces{self.dataset_suffix} AS2
WHERE {self.subsampling_condition} st_within(AS1.SHAPE, AS2.SHAPE)
;"""
PolygonWithinPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class RetrievePoints(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Retrieve Points"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
RetrievePoints._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(A.OBJECTID, {subsampling_factor}) = 0 AND"
self.bounding_box = f"{GEORGIA_BB_3857}, 3857"
if not use_projected_crs:
self.bounding_box = f"{GEORGIA_BB_4326}, 4326"
def execute(self):
cmd = f"""SELECT A.OBJECTID
FROM {DATABASE_NAME}.airports{self.dataset_suffix} A
WHERE {self.subsampling_condition} st_within(A.SHAPE, ST_GeomFromText({self.bounding_box}))
;"""
RetrievePoints._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class LongestLine(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Longest Line"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
LongestLine._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"WHERE MOD(R.OBJECTID, {subsampling_factor}) = 0"
def execute(self):
cmd = f"""SELECT MAX(ST_LENGTH(R.SHAPE))
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R
{self.subsampling_condition}
;"""
LongestLine._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class TotalLength(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Total Length"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
TotalLength._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"WHERE MOD(R.OBJECTID, {subsampling_factor}) = 0"
def execute(self):
cmd = f"""SELECT SUM(ST_LENGTH(R.SHAPE))
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R
{self.subsampling_condition}
;"""
TotalLength._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class RetrieveLines(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Retrieve Lines"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
RetrieveLines._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(R.OBJECTID, {subsampling_factor}) = 0 AND"
self.bounding_box = f"{GEORGIA_BB_3857}, 3857"
if not use_projected_crs:
self.bounding_box = f"{GEORGIA_BB_4326}, 4326"
def execute(self):
cmd = f"""SELECT R.OBJECTID
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R
WHERE {self.subsampling_condition} st_within(R.SHAPE, ST_GeomFromText({self.bounding_box}))
;"""
RetrieveLines._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class LargestArea(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Largest Area"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
LargestArea._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"WHERE MOD(AS1.OBJECTID, {subsampling_factor}) = 0"
def execute(self):
cmd = f"""SELECT MAX(ST_AREA(AS1.SHAPE))
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1
{self.subsampling_condition}
;"""
LargestArea._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class TotalArea(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Total Area"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
TotalArea._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"WHERE MOD(AS1.OBJECTID, {subsampling_factor}) = 0"
def execute(self):
cmd = f"""SELECT SUM(ST_AREA(AS1.SHAPE))
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1
{self.subsampling_condition}
;"""
TotalArea._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class RetrievePolygons(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Retrieve Polygons"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
RetrievePolygons._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND "
self.bounding_box = f"{GEORGIA_BB_3857}, 3857"
if not use_projected_crs:
self.bounding_box = f"{GEORGIA_BB_4326}, 4326"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1
WHERE {self.subsampling_condition} st_within(AS1.SHAPE, ST_GeomFromText({self.bounding_box}))
;"""
RetrievePolygons._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PointNearPoint(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Point Near Point"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PointNearPoint._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(A.OBJECTID, {subsampling_factor}) = 0 AND "
self.location = f"{ATLANTA_LOC_3857}, 3857"
if not use_projected_crs:
self.location = f"{ATLANTA_LOC_4326}, 4326"
def execute(self):
cmd = f"""SELECT A.OBJECTID
FROM {DATABASE_NAME}.airports{self.dataset_suffix} A
WHERE {self.subsampling_condition} st_distance(A.SHAPE, ST_GeomFromText({self.location}), 'metre') < 50000
;"""
PointNearPoint._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PointNearPoint2(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Point Near Point 2"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PointNearPoint2._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"WHERE MOD(A.OBJECTID, {subsampling_factor}) = 0"
self.location = f"{ATLANTA_LOC_3857}, 3857"
if not use_projected_crs:
self.location = f"{ATLANTA_LOC_4326}, 4326"
def execute(self):
cmd = f"""SELECT A.OBJECTID, st_distance(A.SHAPE, ST_GeomFromText({self.location}), 'metre') AS dist
FROM {DATABASE_NAME}.airports{self.dataset_suffix} A
{self.subsampling_condition}
ORDER BY dist
LIMIT 1
;"""
PointNearPoint2._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PointNearLine(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Point Near Line"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PointNearLine._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(R.OBJECTID, {subsampling_factor}) = 0 AND "
self.location = f"{ATLANTA_LOC_3857}, 3857"
if not use_projected_crs:
self.location = f"{ATLANTA_LOC_4326}, 4326"
def execute(self):
cmd = f"""SELECT R.OBJECTID
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R
WHERE {self.subsampling_condition} st_distance(R.SHAPE, ST_GeomFromText({self.location}), 'metre') < 500000
;"""
PointNearLine._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PointNearLine2(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Point Near Line 2"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PointNearLine2._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"WHERE MOD(R.OBJECTID, {subsampling_factor}) = 0"
self.location = f"{ATLANTA_LOC_3857}, 3857"
if not use_projected_crs:
self.location = f"{ATLANTA_LOC_4326}, 4326"
def execute(self):
cmd = f"""SELECT R.OBJECTID, st_distance(R.SHAPE, ST_GeomFromText({self.location}), 'metre') AS dist
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R
{self.subsampling_condition}
ORDER BY dist
LIMIT 1
;"""
PointNearLine2._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class PointNearPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Point Near Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
PointNearPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND "
self.location = f"{ATLANTA_LOC_3857}, 3857"
if not use_projected_crs:
self.location = f"{ATLANTA_LOC_4326}, 4326"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1
WHERE {self.subsampling_condition} st_distance(AS1.SHAPE, ST_GeomFromText({self.location}), 'metre') < 500000
;"""
PointNearPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class SinglePointWithinPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Single Point Within Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
SinglePointWithinPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND "
self.location = f"{ATLANTA_LOC_3857}, 3857"
if not use_projected_crs:
self.location = f"{ATLANTA_LOC_4326}, 4326"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1
WHERE {self.subsampling_condition} st_contains(AS1.SHAPE, ST_GeomFromText({self.location}))
;"""
SinglePointWithinPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class LineNearPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Line Near Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
LineNearPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND "
self.line = f"{ROUTE_3857}, 3857"
if not use_projected_crs:
self.line = f"{ROUTE_4326}, 4326"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1
WHERE {self.subsampling_condition} st_distance(AS1.SHAPE, ST_GeomFromText({self.line}), 'metre') < 500000
;"""
LineNearPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class SingleLineIntersectsPolygon(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Line Intersects Polygon"
def __init__(self, use_projected_crs=True, subsampling_factor=1):
super().__init__(create_mysql_adapter(),
SingleLineIntersectsPolygon._title, repeat_count=7)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
self.subsampling_condition = ""
if subsampling_factor > 1:
self.subsampling_condition = f"MOD(AS1.OBJECTID, {subsampling_factor}) = 0 AND "
self.line = f"{ROUTE_3857}, 3857"
if not use_projected_crs:
self.line = f"{ROUTE_4326}, 4326"
def execute(self):
cmd = f"""SELECT AS1.OBJECTID
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1
WHERE {self.subsampling_condition} st_intersects(AS1.SHAPE, ST_GeomFromText({self.line}))
;"""
SingleLineIntersectsPolygon._logger.info(f"Query: {cmd}")
return self.adapter.execute(cmd)
class InsertNewPoints(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Insert New Points"
def __init__(self, use_projected_crs=True):
super().__init__(create_mysql_adapter(),
InsertNewPoints._title, repeat_count=7)
self.dataset_suffix = ""
srid = 4326
if use_projected_crs:
self.dataset_suffix = "_3857"
srid = 3857
data_to_insert = self.adapter.execute(f"""SELECT ST_AsText(SHAPE), global_id, ident, name, latitude, longitude, elevation, icao_id, type_code, servcity, state, country, operstatus, privateuse, iapexists, dodhiflip, far91, far93, mil_code, airanal, us_high, us_low, ak_high, ak_low, us_area, pacific
FROM {DATABASE_NAME}.airports{self.dataset_suffix} A
WHERE A.OBJECTID <= 1000
;""")
data_to_insert = convert_decimals_to_ints_in_tuples(data_to_insert)
# Remove OBJECTID from returned tuples
data_to_insert = [f"({50000+idx}, ST_GeomFromText('{t[0]}', {srid}), {tuple_to_str(t[1:])[1:]}"
for idx, t in enumerate(data_to_insert)]
self.new_tuples = ', '.join([t for t in data_to_insert])
def execute(self):
cmd = f"""INSERT INTO {DATABASE_NAME}.airports{self.dataset_suffix} (objectid, SHAPE, global_id, ident, name, latitude, longitude, elevation, icao_id, type_code, servcity, state, country, operstatus, privateuse, iapexists, dodhiflip, far91, far93, mil_code, airanal, us_high, us_low, ak_high, ak_low, us_area, pacific)
VALUES {self.new_tuples}
;"""
# InsertNewPoints._logger.info(f"Query: {cmd}")
self.adapter.execute(cmd)
self.adapter.commit()
return
def cleanup(self):
cmd = f"""DELETE FROM {DATABASE_NAME}.airports{self.dataset_suffix}
WHERE OBJECTID >= 50000
;"""
InsertNewPoints._logger.info(f"Query: {cmd}")
self.adapter.execute(cmd)
self.adapter.commit()
return
class InsertNewLines(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Insert New Lines"
def __init__(self, use_projected_crs=True):
super().__init__(create_mysql_adapter(),
InsertNewLines._title, repeat_count=7)
self.dataset_suffix = ""
srid = 4326
if use_projected_crs:
self.dataset_suffix = "_3857"
srid = 3857
data_to_insert = self.adapter.execute(f"""SELECT ST_AsText(SHAPE), global_id, ident, level_, wkhr_code, wkhr_rmk, maa_val, maa_uom, mea_e_val, mea_e_uom, mea_w_val, mea_w_uom, gmea_e_val, gmea_e_uom, gmea_w_val, gmea_w_uom, dmea_val, dmea_uom, moca_val, moca_uom, meagap, truetrk, magtrk, revtruetrk, revmagtrk, length_val, copdist, copnav_id, repatcstar, repatcend, direction, freq_class, status, startpt_id, endpt_id, rtport_id, enrinfo_id, widthright, widthleft, width_uom, mca1_val, mca1_uom, mca1_dir, mca2_val, mca2_uom, mca2_dir, mcapt_id, mcapt_type, tflag_code, remarks, ak_low, ak_high, us_low, us_high, type_code, us_area, pacific, nmagtrk, nrevmagtrk, shape__len
FROM {DATABASE_NAME}.routes{self.dataset_suffix} R
WHERE R.OBJECTID <= 1000
;""")
data_to_insert = convert_decimals_to_ints_in_tuples(data_to_insert)
# Remove OBJECTID from returned tuples
data_to_insert = [f"({50000+idx}, ST_GeomFromText('{t[0]}', {srid}), {tuple_to_str(t[1:])[1:]}"
for idx, t in enumerate(data_to_insert)]
self.new_tuples = ', '.join([t for t in data_to_insert])
def execute(self):
cmd = f"""INSERT INTO {DATABASE_NAME}.routes{self.dataset_suffix} (objectid, SHAPE, global_id, ident, level_, wkhr_code, wkhr_rmk, maa_val, maa_uom, mea_e_val, mea_e_uom, mea_w_val, mea_w_uom, gmea_e_val, gmea_e_uom, gmea_w_val, gmea_w_uom, dmea_val, dmea_uom, moca_val, moca_uom, meagap, truetrk, magtrk, revtruetrk, revmagtrk, length_val, copdist, copnav_id, repatcstar, repatcend, direction, freq_class, status, startpt_id, endpt_id, rtport_id, enrinfo_id, widthright, widthleft, width_uom, mca1_val, mca1_uom, mca1_dir, mca2_val, mca2_uom, mca2_dir, mcapt_id, mcapt_type, tflag_code, remarks, ak_low, ak_high, us_low, us_high, type_code, us_area, pacific, nmagtrk, nrevmagtrk, shape__len)
VALUES {self.new_tuples}
;"""
# InsertNewLines._logger.info(f"Query: {cmd}")
self.adapter.execute(cmd)
self.adapter.commit()
return
def cleanup(self):
cmd = f"""DELETE FROM {DATABASE_NAME}.routes{self.dataset_suffix}
WHERE OBJECTID >= 50000
;"""
InsertNewLines._logger.info(f"Query: {cmd}")
self.adapter.execute(cmd)
self.adapter.commit()
return
class InsertNewPolygons(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Insert New Polygons"
def __init__(self, use_projected_crs=True):
super().__init__(create_mysql_adapter(),
InsertNewPolygons._title, repeat_count=7)
self.dataset_suffix = ""
srid = 4326
if use_projected_crs:
self.dataset_suffix = "_3857"
srid = 3857
data_to_insert = self.adapter.execute(f"""SELECT ST_AsText(SHAPE), global_id, ident, icao_id, name, upper_desc, upper_val, upper_uom, upper_code, lower_desc, lower_val, lower_uom, lower_code, type_code, local_type, class, mil_code, comm_name, level_, sector, onshore, exclusion, wkhr_code, wkhr_rmk, dst, gmtoffset, cont_agent, city, state, country, adhp_id, us_high, ak_high, ak_low, us_low, us_area, pacific, shape__are, shape__len
FROM {DATABASE_NAME}.airspaces{self.dataset_suffix} AS1
WHERE AS1.OBJECTID <= 1000
;""")
data_to_insert = convert_decimals_to_ints_in_tuples(data_to_insert)
# Remove OBJECTID from returned tuples
data_to_insert = [f"({50000+idx}, ST_GeomFromText('{t[0]}', {srid}), {tuple_to_str(t[1:])[1:]}"
for idx, t in enumerate(data_to_insert)]
self.new_tuples = ', '.join([t for t in data_to_insert])
def execute(self):
cmd = f"""INSERT INTO {DATABASE_NAME}.airspaces{self.dataset_suffix} (objectid, SHAPE, global_id, ident, icao_id, name, upper_desc, upper_val, upper_uom, upper_code, lower_desc, lower_val, lower_uom, lower_code, type_code, local_type, class, mil_code, comm_name, level_, sector, onshore, exclusion, wkhr_code, wkhr_rmk, dst, gmtoffset, cont_agent, city, state, country, adhp_id, us_high, ak_high, ak_low, us_low, us_area, pacific, shape__are, shape__len)
VALUES {self.new_tuples}
;"""
# InsertNewPolygons._logger.info(f"Query: {cmd}")
self.adapter.execute(cmd)
self.adapter.commit()
return
def cleanup(self):
cmd = f"""DELETE FROM {DATABASE_NAME}.airspaces{self.dataset_suffix}
WHERE OBJECTID >= 50000
;"""
InsertNewPolygons._logger.info(f"Query: {cmd}")
self.adapter.execute(cmd)
self.adapter.commit()
return
class AirspacesSize(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Airspaces Size"
def __init__(self, use_projected_crs=True):
super().__init__(create_mysql_adapter(),
AirspacesSize._title, repeat_count=1)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
def execute(self):
cmd = f"""SELECT (data_length+index_length)/power(1024,2) tablesize_mb
FROM information_schema.tables
WHERE table_schema='{DATABASE_NAME}' and table_name='airspaces{self.dataset_suffix}'
;"""
AirspacesSize._logger.info(cmd)
return self.adapter.execute(cmd)
class AirportsSize(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Airports Size"
def __init__(self, use_projected_crs=True):
super().__init__(create_mysql_adapter(),
AirportsSize._title, repeat_count=1)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
def execute(self):
cmd = f"""SELECT (data_length+index_length)/power(1024,2) tablesize_mb
FROM information_schema.tables
WHERE table_schema='{DATABASE_NAME}' and table_name='airports{self.dataset_suffix}'
;"""
AirportsSize._logger.info(cmd)
return self.adapter.execute(cmd)
class RoutesSize(MysqlBenchmark):
_logger = logging.getLogger(__name__)
_title = "Routes Size"
def __init__(self, use_projected_crs=True):
super().__init__(create_mysql_adapter(),
RoutesSize._title, repeat_count=1)
self.dataset_suffix = ""
if use_projected_crs:
self.dataset_suffix = "_3857"
def execute(self):
cmd = f"""SELECT (data_length+index_length)/power(1024,2) tablesize_mb
FROM information_schema.tables
WHERE table_schema='{DATABASE_NAME}' and table_name='routes{self.dataset_suffix}'
;"""
RoutesSize._logger.info(cmd)
return self.adapter.execute(cmd)
| 43.735062
| 700
| 0.648519
| 4,427
| 38,793
| 5.3185
| 0.067088
| 0.050924
| 0.0787
| 0.033893
| 0.877256
| 0.874028
| 0.84604
| 0.805436
| 0.778764
| 0.754598
| 0
| 0.029197
| 0.2469
| 38,793
| 886
| 701
| 43.784424
| 0.776724
| 0.007063
| 0
| 0.702332
| 0
| 0.048011
| 0.365138
| 0.124838
| 0
| 0
| 0
| 0
| 0
| 1
| 0.102881
| false
| 0.001372
| 0.015089
| 0.001372
| 0.310014
| 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
|
e61b8543c58f5a36b47c826ab7d4e11699848c9e
| 380
|
py
|
Python
|
src/rl/np/distributions/__init__.py
|
djjh/reinforcement-learning-labs
|
22706dab9e7f16e364ee4ed79c0bd67a343e5b08
|
[
"MIT"
] | 1
|
2019-10-06T11:45:52.000Z
|
2019-10-06T11:45:52.000Z
|
src/rl/np/distributions/__init__.py
|
djjh/reinforcement-learning-labs
|
22706dab9e7f16e364ee4ed79c0bd67a343e5b08
|
[
"MIT"
] | null | null | null |
src/rl/np/distributions/__init__.py
|
djjh/reinforcement-learning-labs
|
22706dab9e7f16e364ee4ed79c0bd67a343e5b08
|
[
"MIT"
] | null | null | null |
from .categorical_probability_distribution import CategoricalProbabilityDistribution
from .categorical_probability_distribution_type import CategoricalProbabilityDistributionType
from .diagonal_gaussian_probability_distribution import DiagonalGaussianProbabilityDistribution
from .diagonal_gaussian_probability_distribution_type import DiagonalGaussianProbabilityDistributionType
| 76
| 104
| 0.947368
| 28
| 380
| 12.428571
| 0.428571
| 0.264368
| 0.149425
| 0.218391
| 0.247126
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.042105
| 380
| 4
| 105
| 95
| 0.956044
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
e629b7ef57f41358d5b719e842f05d05839c8c20
| 8,818
|
py
|
Python
|
discovery/func_test3.py
|
zhengfaning/vnpy_andy
|
403d48768d5915188537aaf376b172b35c8e2999
|
[
"MIT"
] | null | null | null |
discovery/func_test3.py
|
zhengfaning/vnpy_andy
|
403d48768d5915188537aaf376b172b35c8e2999
|
[
"MIT"
] | null | null | null |
discovery/func_test3.py
|
zhengfaning/vnpy_andy
|
403d48768d5915188537aaf376b172b35c8e2999
|
[
"MIT"
] | null | null | null |
#Creating line plots using NumPy arrays
#Import required packages
import numpy as np
import random
# from bokeh.io import output_file, show
# from bokeh.plotting import figure
#Creating an array for the points along the x and y axes
array_x =np.array([1,2,3,4,5,6,8,90,86,84,83,67,70,71,89,91,64,83])
array_y = np.array([5,6,7,8,9,10])
xx = [1,5,6,7,6,8]
def calc(data, window = 0.0003):
v_list = []
p_list = []
if len(data) <= 0:
return
# r = array[::-1]
r = data
l = len(data) - 1
now = r[0]
v_list.append(now)
p_list.append(0)
pos = 1
start_pos = 0
vol = 0
u_tag = None
d_tag = None
end_tag = None
while pos < l:
if now < r[pos]:
u_tag = pos
# if abs(d_vol/r[start_pos]) > window:
if d_tag:
diff = r[start_pos] - r[d_tag]
if abs(diff / r[start_pos]) > window:
end_tag = d_tag
elif now > r[pos]:
d_tag = pos
# if abs(u_vol/r[start_pos]) > window:
if u_tag:
diff = r[start_pos] - r[u_tag]
if abs(diff / r[start_pos]) > window:
end_tag = u_tag
if not end_tag is None:
print("point = {},start = {}, end = {}, vol = {:.2%}".format(
r[end_tag],start_pos, end_tag, vol/r[start_pos]))
start_pos = end_tag
v_list.append(r[end_tag])
p_list.append(end_tag)
end_tag = None
vol += r[pos] - now
now = r[pos]
pos += 1
print(v_list)
print(p_list)
return v_list, p_list
def calc3(array, window = 0.0003):
v_list = []
p_list = []
# r = array[::-1]
r = array
l = len(array) - 1
now = r[0]
pos = 1
start_pos = 0
vol = 0
u_vol = 0
d_vol = 0
u_tag = None
d_tag = None
end_tag = None
while pos < l:
if now < r[pos]:
u_tag = pos
# if abs(d_vol/r[start_pos]) > window:
if d_tag:
diff = r[start_pos] - r[d_tag]
if abs(diff / r[start_pos]) > window:
end_tag = d_tag
elif now > r[pos]:
d_tag = pos
# if abs(u_vol/r[start_pos]) > window:
if u_tag:
diff = r[start_pos] - r[u_tag]
if abs(diff / r[start_pos]) > window:
end_tag = u_tag
if end_tag is None:
n_vol = r[pos] - now
u_vol = u_vol + n_vol
if u_vol < 0:
u_vol = 0
d_vol = d_vol + n_vol
if d_vol > 0:
d_vol = 0
else:
print("point = {},start = {}, end = {}, nvol = {:.2%}, vol = {:.2%}, u_vol = {:.2%}, d_vol = {:.2%},".format(
r[end_tag],start_pos, end_tag, n_vol/now, vol/r[start_pos], u_vol/r[start_pos], d_vol/r[start_pos]))
u_vol = 0
d_vol = 0
start_pos = end_tag
v_list.append(r[end_tag])
p_list.append(end_tag)
end_tag = None
vol += r[pos] - now
# print("now = {}, new = {}, nvol = {:.2%}, vol = {:.2%},u_vol = {:.2%},d_vol = {:.2%},".format(
# now, r[pos], n_vol/now, vol/r[start_pos], u_vol/r[start_pos], d_vol/r[start_pos]))
now = r[pos]
pos += 1
print(v_list)
print(p_list)
return v_list, p_list
def calc_2(array):
v_list = []
p_list = []
# r = array[::-1]
r = array
l = len(array) - 1
param = 2
now = r[0]
pos = 1
x = 0
start_pos = 0
positive_pos = None
negative_pos = None
vol = 0
u_vol = 0
d_vol = 0
u_tag = None
d_tag = None
while pos < l:
n_vol = r[pos] - now
vol += r[pos] - now
u_vol = u_vol + n_vol
if u_vol < 0:
u_vol = 0
d_vol = d_vol + n_vol
if d_vol > 0:
d_vol = 0
if now < r[pos]:
u_tag = pos
if x < param:
# n_vol = r[pos] - now
# vol += n_vol
x = 1 if x < 0 and r[pos] > r[start_pos] else x + 1
print("x+, x = {0}".format(x))
else:
positive_pos = pos
print("positive_pos = {0}".format(positive_pos))
elif now > r[pos]:
d_tag = pos
if x > -param:
x = -1 if x > 0 and r[pos] < r[start_pos] else x - 1
print("x-, x = {0}".format(x))
else:
negative_pos = pos
print("negative_pos = {0}".format(negative_pos))
print("now = {}, new = {}, vol = {:.2%},u_vol = {},d_vol = {},".format(
now, r[pos], vol/r[start_pos], u_vol, d_vol))
if x == 0:
if not positive_pos is None:
u_vol = 0
d_vol = 0
maxval = np.max(r[positive_pos:pos+1])
if maxval > r[positive_pos]:
positive_pos = pos
x += 1
else:
max_val = np.max(r[start_pos:positive_pos + 1])
t_pos = np.argmax(r[start_pos:positive_pos + 1])
t_pos += start_pos
v_list.append(max_val)
p_list.append(t_pos)
start_pos = positive_pos
positive_pos = None
x = -param
negative_pos = pos
elif not negative_pos is None:
u_vol = 0
d_vol = 0
minval = np.min(r[negative_pos:pos+1])
if minval < r[negative_pos]:
negative_pos = pos
x -= 1
else:
min_val = np.min(r[start_pos:negative_pos + 1])
t_pos = np.argmin(r[start_pos:negative_pos + 1])
t_pos += start_pos
v_list.append(min_val)
p_list.append(t_pos)
start_pos = negative_pos
negative_pos = None
x = param
positive_pos = pos
now = r[pos]
pos += 1
print(v_list)
print(p_list)
return v_list, p_list
def calc1(array):
v_list = []
p_list = []
# r = array[::-1]
r = array
l = len(array) - 1
param = 3
now = r[0]
pos = 1
x = 0
start_pos = 0
positive_pos = None
negative_pos = None
while pos < l:
print("now = {}, new = {}".format(now, r[pos]))
if now < r[pos]:
if x < param:
x = 1 if x < 0 and r[pos] > r[start_pos] else x + 1
print("x+, x = {0}".format(x))
else:
positive_pos = pos
print("positive_pos = {0}".format(positive_pos))
elif now > r[pos]:
if x > -param:
x = -1 if x > 0 and r[pos] < r[start_pos] else x - 1
print("x-, x = {0}".format(x))
else:
negative_pos = pos
print("negative_pos = {0}".format(negative_pos))
if x == 0:
if not positive_pos is None:
maxval = np.max(r[positive_pos:pos+1])
if maxval > r[positive_pos]:
positive_pos = pos
x += 1
else:
max_val = np.max(r[start_pos:positive_pos + 1])
t_pos = np.argmax(r[start_pos:positive_pos + 1])
t_pos += start_pos
v_list.append(max_val)
p_list.append(t_pos)
start_pos = positive_pos
positive_pos = None
x = -param
negative_pos = pos
elif not negative_pos is None:
minval = np.min(r[negative_pos:pos+1])
if minval < r[negative_pos]:
negative_pos = pos
x -= 1
else:
min_val = np.min(r[start_pos:negative_pos + 1])
t_pos = np.argmin(r[start_pos:negative_pos + 1])
t_pos += start_pos
v_list.append(min_val)
p_list.append(t_pos)
start_pos = negative_pos
negative_pos = None
x = param
positive_pos = pos
now = r[pos]
pos += 1
print(v_list)
print(p_list)
return v_list, p_list
# pp = np.argmin(array_x)
# print(array_x[pp])
# xp = np.where(array_x==np.max(array_x))
# print(array_x[xp[0]])
# calc1(array_x)
| 29.790541
| 121
| 0.440576
| 1,217
| 8,818
| 2.975349
| 0.085456
| 0.106048
| 0.079536
| 0.039768
| 0.840376
| 0.825463
| 0.811378
| 0.802541
| 0.796465
| 0.768848
| 0
| 0.031765
| 0.443071
| 8,818
| 296
| 122
| 29.790541
| 0.705559
| 0.083466
| 0
| 0.893878
| 0
| 0.004082
| 0.040566
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.016327
| false
| 0
| 0.008163
| 0
| 0.044898
| 0.081633
| 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
|
555e3c4e8a49e32c83564288980cc5276c493492
| 3,045
|
py
|
Python
|
Python/convert_beginning_tabs_to_spaces/tests/convert_beginning_tabs_test.py
|
toddnguyen47/utility-files
|
de7d9839b5265c10d7ec017f8e08dfb8d6caac2a
|
[
"MIT"
] | null | null | null |
Python/convert_beginning_tabs_to_spaces/tests/convert_beginning_tabs_test.py
|
toddnguyen47/utility-files
|
de7d9839b5265c10d7ec017f8e08dfb8d6caac2a
|
[
"MIT"
] | 1
|
2021-04-20T22:01:09.000Z
|
2021-04-20T22:01:09.000Z
|
Python/convert_beginning_tabs_to_spaces/tests/convert_beginning_tabs_test.py
|
toddnguyen47/utility-files
|
de7d9839b5265c10d7ec017f8e08dfb8d6caac2a
|
[
"MIT"
] | 1
|
2021-03-18T03:42:03.000Z
|
2021-03-18T03:42:03.000Z
|
from src.convert_beginning_tabs import ConvertBeginningTabs
import pytest
@pytest.fixture
def convert_beginning_tabs() -> ConvertBeginningTabs:
return ConvertBeginningTabs()
def test_given_beginning_tabs_when_converting_then_return_spaces(
convert_beginning_tabs: ConvertBeginningTabs,
):
test_str = " Hello World"
actual_str = convert_beginning_tabs.convert_tabs_to_spaces(test_str, 4)
assert " Hello World" == actual_str
def test_given_no_tabs_at_beginning_when_converting_then_return_same_input(
convert_beginning_tabs: ConvertBeginningTabs,
):
test_str = "Hello World"
actual_str = convert_beginning_tabs.convert_tabs_to_spaces(test_str, 4)
assert actual_str == test_str
def test_given_tabs_within_tab_when_converting_then_inside_tabs_should_not_be_converted(
convert_beginning_tabs: ConvertBeginningTabs,
):
test_str = " Hello there World"
actual_str = convert_beginning_tabs.convert_tabs_to_spaces(test_str, 4)
assert " Hello there World" == actual_str
def test_given_excess_whitespace_on_right_when_converting_then_remove_excess_whitespace(
convert_beginning_tabs: ConvertBeginningTabs,
):
test_str = " Hello there World "
actual_str = convert_beginning_tabs.convert_tabs_to_spaces(test_str, 4)
assert " Hello there World" == actual_str
@pytest.mark.parametrize(
"input, expected",
[
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
],
)
def test_given_space_tabs_combination_when_converting_with_4_spaces_per_tab_then_convert_correctly(
convert_beginning_tabs: ConvertBeginningTabs, input: str, expected: str
):
actual_str = convert_beginning_tabs.convert_tabs_to_spaces(input, 4)
assert expected == actual_str
@pytest.mark.parametrize(
"input, expected",
[
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
(" Hello there World ", " Hello there World"),
],
)
def test_given_space_tabs_combination_when_converting_with_2_spaces_per_tab_then_convert_correctly(
convert_beginning_tabs: ConvertBeginningTabs, input: str, expected: str
):
actual_str = convert_beginning_tabs.convert_tabs_to_spaces(input, 2)
assert expected == actual_str
| 38.544304
| 99
| 0.671593
| 345
| 3,045
| 5.527536
| 0.144928
| 0.167803
| 0.251704
| 0.27268
| 0.766649
| 0.766649
| 0.746723
| 0.746723
| 0.746723
| 0.746723
| 0
| 0.003498
| 0.248933
| 3,045
| 78
| 100
| 39.038462
| 0.830345
| 0
| 0
| 0.698413
| 0
| 0
| 0.29688
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 1
| 0.111111
| false
| 0
| 0.031746
| 0.015873
| 0.15873
| 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
|
55966011f1a97c25f0f09ae8b1ccf56ff9f7bb32
| 13,610
|
py
|
Python
|
tests/test_type_definition_manager.py
|
toscaware/incubator-ariatosca
|
3c5ce026eacf14e7d57d6678046757263176b7ab
|
[
"Apache-2.0"
] | null | null | null |
tests/test_type_definition_manager.py
|
toscaware/incubator-ariatosca
|
3c5ce026eacf14e7d57d6678046757263176b7ab
|
[
"Apache-2.0"
] | null | null | null |
tests/test_type_definition_manager.py
|
toscaware/incubator-ariatosca
|
3c5ce026eacf14e7d57d6678046757263176b7ab
|
[
"Apache-2.0"
] | 1
|
2020-06-16T15:13:06.000Z
|
2020-06-16T15:13:06.000Z
|
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from tests.fixtures import (# pylint: disable=unused-import
inmemory_model as model,
type_definition_manager,
type_definitions_dir
)
from aria.exceptions import(
TypeDefinitionException,
InvalidTypeDefinitionException,
ParsingError,
TypeDefinitionNotFoundException
)
from tests.helpers import get_type_definition_uri # pylint: disable=ungrouped-imports
from aria.storage.exceptions import NotFoundError # pylint: disable=ungrouped-imports
TYPE_DEFINITION_NAME = 'test10'
TYPE_DEFINITION_VERSION = '1.0'
TYPE_DEFINITION_MAIN_FILE_NAME = 'type_definitions_main.yaml'
class TestTypeDefinitionManager(object):
def test_load_type_definition(self, type_definition_manager, model, type_definitions_dir):
type_definition = type_definition_manager.\
load_type_definition(get_type_definition_uri('valid_type_definition', 'definitions', \
'type_definitions_main.yaml'))
assert type_definition.name == TYPE_DEFINITION_NAME
assert type_definition.version == TYPE_DEFINITION_VERSION
assert type_definition.main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition == model.type_definition.get(type_definition.id)
type_definition_dir = os.path.join(type_definitions_dir, '{0}-{1}'.\
format(TYPE_DEFINITION_NAME, TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir)
assert type_definition_dir == type_definition_manager.\
get_type_definition_dir(type_definition)
def test_invalid_load_type_definition_with_no_file_exist(self, type_definition_manager):
with pytest.raises(TypeDefinitionException) as excinfo:
type_definition_manager.\
load_type_definition(\
get_type_definition_uri('valid_type_definition',\
'definitions', 'invalid'))
assert str(excinfo.value) == 'Could not open/load type definition file'
def test_invalid_load_type_definition_with_topology_template(self, type_definition_manager):
with pytest.raises(InvalidTypeDefinitionException) as excinfo:
type_definition_manager.\
load_type_definition(\
get_type_definition_uri(('invalid_type_definition_with_'
'topology_template'),\
'definitions',\
'type_definitions_main.yaml'))
assert str(excinfo.value) == ("Type definition '{0}' with version '{1}' is invalid."
" It contains topology template in '{2}'.").\
format(TYPE_DEFINITION_MAIN_FILE_NAME,\
TYPE_DEFINITION_VERSION, 'inner_type1.yaml')
def test_invalid_load_type_definition_with_no_metadata(self, type_definition_manager):
with pytest.raises(InvalidTypeDefinitionException) as excinfo:
type_definition_manager.\
load_type_definition(\
get_type_definition_uri('invalid_type_definition_with_no_metadata',
'definitions',\
'type_definitions_main.yaml'))
assert str(excinfo.value) == ('Type definition is invalid.'
' It should have metadata information')
def test_invalid_load_type_definition_with_parsing_error(self, type_definition_manager):
with pytest.raises(ParsingError) as excinfo:
type_definition_manager.\
load_type_definition(\
get_type_definition_uri(('invalid_type_definition_with_'
'parsing_error'),
'definitions',\
'type_definitions_main.yaml'))
assert str(excinfo.value) == 'Failed to parse type definition'
def test_get_type_definition(self, type_definition_manager, model, type_definitions_dir):
type_definition_manager.\
load_type_definition(get_type_definition_uri('valid_type_definition', 'definitions', \
'type_definitions_main.yaml'))
type_definition = type_definition_manager.get_type_definition(TYPE_DEFINITION_NAME, \
TYPE_DEFINITION_VERSION)
assert type_definition.name == TYPE_DEFINITION_NAME
assert type_definition.version == TYPE_DEFINITION_VERSION
assert type_definition.main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition == model.type_definition.get(type_definition.id)
type_definition_dir = os.path.join(type_definitions_dir, '{0}-{1}'.\
format(TYPE_DEFINITION_NAME, TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir)
assert type_definition_dir == type_definition_manager.\
get_type_definition_dir(type_definition)
def test_get_type_definition_not_exist(self, type_definition_manager):
with pytest.raises(NotFoundError):
type_definition_manager.get_type_definition('test', '1.0')
def test_delete_type_definition(self, type_definition_manager, model, type_definitions_dir):
type_definition_manager.\
load_type_definition(get_type_definition_uri('valid_type_definition', 'definitions', \
'type_definitions_main.yaml'))
type_definition = type_definition_manager.\
get_type_definition(TYPE_DEFINITION_NAME, TYPE_DEFINITION_VERSION)
assert type_definition.name == TYPE_DEFINITION_NAME
assert type_definition.version == TYPE_DEFINITION_VERSION
assert type_definition.main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition == model.type_definition.get(type_definition.id)
type_definition_dir = os.path.join(type_definitions_dir, '{0}-{1}'.\
format(TYPE_DEFINITION_NAME, TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir)
assert type_definition_dir == type_definition_manager.\
get_type_definition_dir(type_definition)
type_definition_manager.\
delete_type_definition(TYPE_DEFINITION_NAME, TYPE_DEFINITION_VERSION)
with pytest.raises(NotFoundError):
type_definition_manager.get_type_definition(TYPE_DEFINITION_NAME,\
TYPE_DEFINITION_VERSION)
def test_delete_type_definition_not_exist(self, type_definition_manager):
with pytest.raises(TypeDefinitionNotFoundException) as excinfo:
type_definition_manager.delete_type_definition('test', '1.0')
assert str(excinfo.value) == "Type definition name 'test' version '1.0' does not exist."
def test_list_type_definition(self, type_definition_manager, model, type_definitions_dir):
type_definition_manager.\
load_type_definition(get_type_definition_uri('valid_type_definition', 'definitions', \
'type_definitions_main.yaml'))
type_definition = type_definition_manager.\
get_type_definition(TYPE_DEFINITION_NAME, TYPE_DEFINITION_VERSION)
assert type_definition.name == TYPE_DEFINITION_NAME
assert type_definition.version == TYPE_DEFINITION_VERSION
assert type_definition.main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition == model.type_definition.get(type_definition.id)
type_definition_dir = os.path.join(type_definitions_dir, '{0}-{1}'.\
format(TYPE_DEFINITION_NAME, TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir)
assert type_definition_dir == type_definition_manager.\
get_type_definition_dir(type_definition)
type_definition_list = type_definition_manager.\
list_type_definition(sort_by='uploaded_at', descending=False)
assert type_definition_list[0].name == TYPE_DEFINITION_NAME
assert type_definition_list[0].version == TYPE_DEFINITION_VERSION
assert type_definition_list[0].main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition_list[0] == model.type_definition.get(type_definition.id)
type_definition_dir = os.path.join(type_definitions_dir, '{0}-{1}'.\
format(TYPE_DEFINITION_NAME, \
TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir)
assert type_definition_dir == type_definition_manager.\
get_type_definition_dir(type_definition)
def test_list_type_definition_sort_order(self, type_definition_manager,\
model, type_definitions_dir):
type_definition1 = type_definition_manager.\
load_type_definition(get_type_definition_uri('valid_type_definition1', 'definitions', \
'type_definitions_main.yaml'))
type_definition2 = type_definition_manager.\
load_type_definition(get_type_definition_uri('valid_type_definition2', 'definitions', \
'type_definitions_main.yaml'))
type_definition_list = type_definition_manager.\
list_type_definition(sort_by='name', descending=True)
assert type_definition_list[0].name == 'ball'
assert type_definition_list[0].version == TYPE_DEFINITION_VERSION
assert type_definition_list[0].main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition_list[0] == model.type_definition.get(type_definition2.id)
type_definition_dir1 = os.path.join(type_definitions_dir, '{0}-{1}'.\
format('ball', \
TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir1)
assert type_definition_dir1 == type_definition_manager.\
get_type_definition_dir(type_definition2)
assert type_definition_list[1].name == 'apple'
assert type_definition_list[1].version == TYPE_DEFINITION_VERSION
assert type_definition_list[1].main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition_list[1] == model.type_definition.get(type_definition1.id)
type_definition_dir2 = os.path.join(type_definitions_dir, '{0}-{1}'.\
format('apple', \
TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir2)
assert type_definition_dir2 == type_definition_manager.\
get_type_definition_dir(type_definition1)
type_definition_list = type_definition_manager.\
list_type_definition(sort_by='uploaded_at', descending=False)
assert type_definition_list[0].name == 'apple'
assert type_definition_list[0].version == TYPE_DEFINITION_VERSION
assert type_definition_list[0].main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition_list[0] == model.type_definition.get(type_definition1.id)
type_definition_dir3 = os.path.join(type_definitions_dir, '{0}-{1}'.\
format('apple', \
TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir3)
assert type_definition_dir3 == type_definition_manager.\
get_type_definition_dir(type_definition1)
assert type_definition_list[1].name == 'ball'
assert type_definition_list[1].version == TYPE_DEFINITION_VERSION
assert type_definition_list[1].main_file_name == TYPE_DEFINITION_MAIN_FILE_NAME
assert type_definition_list[1] == model.type_definition.get(type_definition2.id)
type_definition_dir4 = os.path.join(type_definitions_dir, '{0}-{1}'.\
format('ball', \
TYPE_DEFINITION_VERSION))
assert os.path.isdir(type_definition_dir4)
assert type_definition_dir4 == type_definition_manager.\
get_type_definition_dir(type_definition2)
| 61.03139
| 100
| 0.655547
| 1,451
| 13,610
| 5.723639
| 0.10958
| 0.41469
| 0.108368
| 0.068272
| 0.808429
| 0.789043
| 0.757495
| 0.729561
| 0.709332
| 0.656472
| 0
| 0.008105
| 0.274798
| 13,610
| 222
| 101
| 61.306306
| 0.833333
| 0.062454
| 0
| 0.573684
| 0
| 0
| 0.086407
| 0.03979
| 0
| 0
| 0
| 0
| 0.310526
| 1
| 0.057895
| false
| 0
| 0.031579
| 0
| 0.094737
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e95129fa2e013632c3ebb2de652330895c96d44c
| 161
|
py
|
Python
|
environments_utils/is_stdout_enabled.py
|
LucaCappelletti94/environments_utils
|
c6b8cc7a0fa07f770ed361f3bafaf1adee138f77
|
[
"MIT"
] | null | null | null |
environments_utils/is_stdout_enabled.py
|
LucaCappelletti94/environments_utils
|
c6b8cc7a0fa07f770ed361f3bafaf1adee138f77
|
[
"MIT"
] | null | null | null |
environments_utils/is_stdout_enabled.py
|
LucaCappelletti94/environments_utils
|
c6b8cc7a0fa07f770ed361f3bafaf1adee138f77
|
[
"MIT"
] | null | null | null |
import sys
def is_stdout_enabled()->bool:
"""Return a boolean representing if script is in a terminal with stdout enabled."""
return sys.stdout.isatty()
| 32.2
| 87
| 0.732919
| 24
| 161
| 4.833333
| 0.708333
| 0.224138
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.167702
| 161
| 5
| 88
| 32.2
| 0.865672
| 0.478261
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
e9e18a120f0f2d04292a0ff285be5b5783add5e8
| 37,488
|
py
|
Python
|
.venv/lib/python3.8/site-packages/tests/test_signals.py
|
eo1989/VectorBTanalysis
|
bea3deaf2ee3fc114b308146f2af3e4f35f70197
|
[
"MIT"
] | null | null | null |
.venv/lib/python3.8/site-packages/tests/test_signals.py
|
eo1989/VectorBTanalysis
|
bea3deaf2ee3fc114b308146f2af3e4f35f70197
|
[
"MIT"
] | null | null | null |
.venv/lib/python3.8/site-packages/tests/test_signals.py
|
eo1989/VectorBTanalysis
|
bea3deaf2ee3fc114b308146f2af3e4f35f70197
|
[
"MIT"
] | null | null | null |
import vectorbt as vbt
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from vectorbt.signals import nb
seed = 42
day_dt = np.timedelta64(86400000000000)
index = pd.Index([
datetime(2020, 1, 1),
datetime(2020, 1, 2),
datetime(2020, 1, 3),
datetime(2020, 1, 4),
datetime(2020, 1, 5)
])
columns = ['a', 'b', 'c']
sig = pd.DataFrame([
[True, False, False],
[False, True, False],
[False, False, True],
[True, False, False],
[False, True, False]
], index=index, columns=columns)
# ############# accessors.py ############# #
class TestAccessors:
def test_freq(self):
assert sig.vbt.signals.freq == day_dt
assert sig['a'].vbt.signals.freq == day_dt
assert sig.vbt.signals(freq='2D').freq == day_dt * 2
assert sig['a'].vbt.signals(freq='2D').freq == day_dt * 2
assert pd.Series([False, True]).vbt.signals.freq is None
assert pd.Series([False, True]).vbt.signals(freq='3D').freq == day_dt * 3
assert pd.Series([False, True]).vbt.signals(freq=np.timedelta64(4, 'D')).freq == day_dt * 4
def test_shuffle(self):
pd.testing.assert_series_equal(
sig['a'].vbt.signals.shuffle(seed=seed),
pd.Series(
np.array([False, False, False, True, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
np.testing.assert_array_equal(
sig['a'].vbt.signals.shuffle(seed=seed).values,
nb.shuffle_1d_nb(sig['a'].values, seed=seed)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.shuffle(seed=seed),
pd.DataFrame(
np.array([
[False, False, False],
[False, True, False],
[False, False, False],
[True, False, False],
[True, True, True]
]),
index=sig.index,
columns=sig.columns
)
)
@pytest.mark.parametrize(
"test_n",
[1, 2, 3, 4, 5],
)
def test_fshift(self, test_n):
pd.testing.assert_series_equal(sig['a'].vbt.signals.fshift(test_n), sig['a'].shift(test_n, fill_value=False))
np.testing.assert_array_equal(
sig['a'].vbt.signals.fshift(test_n).values,
nb.fshift_1d_nb(sig['a'].values, test_n)
)
pd.testing.assert_frame_equal(sig.vbt.signals.fshift(test_n), sig.shift(test_n, fill_value=False))
def test_empty(self):
pd.testing.assert_series_equal(
pd.Series.vbt.signals.empty(5, index=np.arange(10, 15), name='a'),
pd.Series(np.full(5, False), index=np.arange(10, 15), name='a')
)
pd.testing.assert_frame_equal(
pd.DataFrame.vbt.signals.empty((5, 3), index=np.arange(10, 15), columns=['a', 'b', 'c']),
pd.DataFrame(np.full((5, 3), False), index=np.arange(10, 15), columns=['a', 'b', 'c'])
)
pd.testing.assert_series_equal(
pd.Series.vbt.signals.empty_like(sig['a']),
pd.Series(np.full(sig['a'].shape, False), index=sig['a'].index, name=sig['a'].name)
)
pd.testing.assert_frame_equal(
pd.DataFrame.vbt.signals.empty_like(sig),
pd.DataFrame(np.full(sig.shape, False), index=sig.index, columns=sig.columns)
)
def test_generate(self):
@njit
def choice_func_nb(col, from_i, to_i, n):
if col == 0:
return np.arange(from_i, to_i)
elif col == 1:
return np.full(1, from_i)
else:
return np.full(1, to_i-n)
pd.testing.assert_series_equal(
pd.Series.vbt.signals.generate(5, choice_func_nb, 1, index=sig['a'].index, name=sig['a'].name),
pd.Series(
np.array([True, True, True, True, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_series_equal(
pd.Series.vbt.signals.generate((5,), choice_func_nb, 1, index=sig['a'].index, name=sig['a'].name),
pd.Series(
np.array([True, True, True, True, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
with pytest.raises(Exception) as e_info:
_ = pd.Series.vbt.signals.generate((5, 2), choice_func_nb, 1)
pd.testing.assert_frame_equal(
pd.DataFrame.vbt.signals.generate((5, 3), choice_func_nb, 1, index=sig.index, columns=sig.columns),
pd.DataFrame(
np.array([
[True, True, False],
[True, False, False],
[True, False, False],
[True, False, False],
[True, False, True]
]),
index=sig.index,
columns=sig.columns
)
)
def test_generate_exits(self):
@njit
def choice_func_nb(col, from_i, to_i, n):
if col == 0:
return np.arange(from_i, to_i)
elif col == 1:
return np.full(1, from_i)
else:
return np.full(1, to_i - n)
pd.testing.assert_series_equal(
sig['a'].vbt.signals.generate_exits(choice_func_nb, 1),
pd.Series(
np.array([False, True, True, False, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_exits(choice_func_nb, 1),
pd.DataFrame(
np.array([
[False, False, False],
[True, False, False],
[True, True, False],
[False, False, False],
[True, False, True]
]),
index=sig.index,
columns=sig.columns
)
)
def test_generate_both(self):
@njit
def entry_choice_func_nb(col, from_i, to_i, wait1):
next_pos = col + from_i + wait1
if next_pos < to_i:
return np.array([next_pos])
return np.empty(0, dtype=np.int_)
@njit
def exit_choice_func_nb(col, from_i, to_i, wait2):
next_pos = col + from_i + wait2
if next_pos < to_i:
return np.array([next_pos])
return np.empty(0, dtype=np.int_)
a, b = pd.Series.vbt.signals.generate_both(
5, entry_choice_func_nb, exit_choice_func_nb, (0,), (1,), index=sig['a'].index, name=sig['a'].name)
pd.testing.assert_series_equal(
a,
pd.Series(
np.array([True, False, False, True, False]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_series_equal(
b,
pd.Series(
np.array([False, False, True, False, False]),
index=sig['a'].index,
name=sig['a'].name
)
)
a, b = pd.Series.vbt.signals.generate_both(
(5,), entry_choice_func_nb, exit_choice_func_nb, (0,), (1,), index=sig['a'].index, name=sig['a'].name)
pd.testing.assert_series_equal(
a,
pd.Series(
np.array([True, False, False, True, False]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_series_equal(
b,
pd.Series(
np.array([False, False, True, False, False]),
index=sig['a'].index,
name=sig['a'].name
)
)
a, b = pd.DataFrame.vbt.signals.generate_both(
(5, 3), entry_choice_func_nb, exit_choice_func_nb, (0,), (1,), index=sig.index, columns=sig.columns)
pd.testing.assert_frame_equal(
a,
pd.DataFrame(
np.array([
[True, False, False],
[False, True, False],
[False, False, True],
[True, False, False],
[False, False, False]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
b,
pd.DataFrame(
np.array([
[False, False, False],
[False, False, False],
[True, False, False],
[False, False, False],
[False, True, False]
]),
index=sig.index,
columns=sig.columns
)
)
def test_generate_random(self):
pd.testing.assert_series_equal(
pd.Series.vbt.signals.generate_random(5, n=3, seed=seed, index=sig['a'].index, name=sig['a'].name),
pd.Series(
np.array([False, True, True, False, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_series_equal(
pd.Series.vbt.signals.generate_random((5,), n=3, seed=seed, index=sig['a'].index, name=sig['a'].name),
pd.Series(
np.array([False, True, True, False, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
with pytest.raises(Exception) as e_info:
_ = pd.Series.vbt.signals.generate_random((5, 2), n=3)
pd.testing.assert_frame_equal(
pd.DataFrame.vbt.signals.generate_random((5, 3), n=3, seed=seed, index=sig.index, columns=sig.columns),
pd.DataFrame(
np.array([
[False, False, True],
[True, True, True],
[True, True, False],
[False, True, True],
[True, False, False]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_series_equal(
pd.Series.vbt.signals.generate_random(
5, prob=0.5, seed=seed, index=sig['a'].index, name=sig['a'].name),
pd.Series(
np.array([True, False, False, False, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_series_equal(
pd.Series.vbt.signals.generate_random(
(5,), prob=0.5, seed=seed, index=sig['a'].index, name=sig['a'].name),
pd.Series(
np.array([True, False, False, False, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
with pytest.raises(Exception) as e_info:
_ = pd.Series.vbt.signals.generate_random((5, 2), prob=3)
pd.testing.assert_frame_equal(
pd.DataFrame.vbt.signals.generate_random(
(5, 3), prob=0.5, seed=seed, index=sig.index, columns=sig.columns),
pd.DataFrame(
np.array([
[True, True, True],
[False, True, False],
[False, False, False],
[False, False, True],
[True, False, True]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
pd.DataFrame.vbt.signals.generate_random(
(5, 3), prob=[0., 0.5, 1.], seed=seed, index=sig.index, columns=sig.columns),
pd.DataFrame(
np.array([
[False, True, True],
[False, True, True],
[False, False, True],
[False, False, True],
[False, False, True]
]),
index=sig.index,
columns=sig.columns
)
)
with pytest.raises(Exception) as e_info:
pd.DataFrame.vbt.signals.generate_random((5, 3))
def test_generate_random_exits(self):
pd.testing.assert_series_equal(
sig['a'].vbt.signals.generate_random_exits(seed=seed),
pd.Series(
np.array([False, False, True, False, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_random_exits(seed=seed),
pd.DataFrame(
np.array([
[False, False, False],
[False, False, False],
[True, True, False],
[False, False, False],
[True, False, True]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_series_equal(
sig['a'].vbt.signals.generate_random_exits(prob=0.5, seed=seed),
pd.Series(
np.array([False, True, False, False, False]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_random_exits(prob=0.5, seed=seed),
pd.DataFrame(
np.array([
[False, False, False],
[True, False, False],
[False, False, False],
[False, False, True],
[False, False, False]
]),
index=sig.index,
columns=sig.columns
)
)
def test_generate_random_both(self):
a, b = pd.Series.vbt.signals.generate_random_both(
5, n=1, seed=seed, index=sig['a'].index, name=sig['a'].name)
pd.testing.assert_series_equal(
a,
pd.Series(
np.array([False, True, False, False, False]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_series_equal(
b,
pd.Series(
np.array([False, False, False, False, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
a, b = pd.Series.vbt.signals.generate_random_both(
(5,), n=1, seed=seed, index=sig['a'].index, name=sig['a'].name)
pd.testing.assert_series_equal(
a,
pd.Series(
np.array([False, True, False, False, False]),
index=sig['a'].index,
name=sig['a'].name
)
)
pd.testing.assert_series_equal(
b,
pd.Series(
np.array([False, False, False, False, True]),
index=sig['a'].index,
name=sig['a'].name
)
)
a, b = pd.DataFrame.vbt.signals.generate_random_both(
(5, 3), n=1, seed=seed, index=sig.index, columns=sig.columns)
pd.testing.assert_frame_equal(
a,
pd.DataFrame(
np.array([
[False, False, True],
[True, True, False],
[False, False, False],
[False, False, False],
[False, False, False]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
b,
pd.DataFrame(
np.array([
[False, False, False],
[False, False, True],
[False, False, False],
[False, True, False],
[True, False, False]
]),
index=sig.index,
columns=sig.columns
)
)
a, b = pd.DataFrame.vbt.signals.generate_random_both(
(5, 3), entry_prob=0.5, exit_prob=1., seed=seed, index=sig.index, columns=sig.columns)
pd.testing.assert_frame_equal(
a,
pd.DataFrame(
np.array([
[True, True, True],
[False, False, False],
[False, False, False],
[False, False, True],
[True, False, False]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
b,
pd.DataFrame(
np.array([
[False, False, False],
[True, True, True],
[False, False, False],
[False, False, False],
[False, False, True]
]),
index=sig.index,
columns=sig.columns
)
)
with pytest.raises(Exception) as e_info:
pd.DataFrame.vbt.signals.generate_random((5, 3))
def test_generate_stop_loss_exits(self):
ts = pd.Series([1, 2, 3, 2, 1], index=index, name=sig['a'].name)
pd.testing.assert_series_equal(
sig['a'].vbt.signals.generate_stop_loss_exits(ts, 0.1),
pd.Series(
np.array([False, False, False, False, True]),
index=sig['a'].index,
name=(0.1, sig['a'].name)
)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_stop_loss_exits(ts, 0.1),
pd.DataFrame(
np.array([
[False, False, False],
[False, False, False],
[False, False, False],
[False, False, True],
[True, False, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c')
], names=['stop_loss', None])
)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_stop_loss_exits(ts, 0.1, first=False),
pd.DataFrame(
np.array([
[False, False, False],
[False, False, False],
[False, False, False],
[False, False, True],
[True, False, True]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c')
], names=['stop_loss', None])
)
)
target = pd.DataFrame(
np.array([
[False, False, False, False, False, False],
[False, False, False, False, False, False],
[False, False, False, False, False, False],
[False, False, True, False, False, True],
[True, False, False, True, False, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c'),
(0.2, 'a'),
(0.2, 'b'),
(0.2, 'c')
], names=['stop_loss', None])
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_stop_loss_exits(ts, [0.1, 0.2]),
target
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_stop_loss_exits(
ts, np.concatenate((np.full((1, 5, 3), 0.1), np.full((1, 5, 3), 0.2)))),
target
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_stop_loss_exits(ts, [0.1, 0.2], keys=['sl1', 'sl2']),
pd.DataFrame(
target.values,
index=target.index,
columns=pd.MultiIndex.from_tuples([
('sl1', 'a'),
('sl1', 'b'),
('sl1', 'c'),
('sl2', 'a'),
('sl2', 'b'),
('sl2', 'c')
])
)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_stop_loss_exits(ts, [0.1, 0.2], trailing=True),
pd.DataFrame(
np.array([
[False, False, False, False, False, False],
[False, False, False, False, False, False],
[False, False, False, False, False, False],
[False, True, True, False, True, True],
[True, False, False, True, False, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c'),
(0.2, 'a'),
(0.2, 'b'),
(0.2, 'c')
], names=['trail_stop', None])
)
)
new_entries, new_exits = sig.vbt.signals.generate_stop_loss_exits(ts, 0.1, iteratively=True)
pd.testing.assert_frame_equal(
new_entries,
pd.DataFrame(
np.array([
[True, False, False],
[False, True, False],
[False, False, True],
[False, False, False],
[False, False, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c')
], names=['stop_loss', None])
)
)
pd.testing.assert_frame_equal(
new_exits,
pd.DataFrame(
np.array([
[False, False, False],
[False, False, False],
[False, False, False],
[False, False, True],
[False, True, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c')
], names=['stop_loss', None])
)
)
def test_generate_take_profit_exits(self):
ts = pd.Series([1, 2, 3, 2, 1], index=index, name=sig['a'].name)
pd.testing.assert_series_equal(
sig['a'].vbt.signals.generate_take_profit_exits(ts, 0.1),
pd.Series(
np.array([False, True, False, False, False]),
index=sig['a'].index,
name=(0.1, sig['a'].name)
)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_take_profit_exits(ts, 0.1),
pd.DataFrame(
np.array([
[False, False, False],
[True, False, False],
[False, True, False],
[False, False, False],
[False, False, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c')
], names=['take_profit', None])
)
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_take_profit_exits(ts, 0.1, first=False),
pd.DataFrame(
np.array([
[False, False, False],
[True, False, False],
[True, True, False],
[False, False, False],
[False, False, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c')
], names=['take_profit', None])
)
)
target = pd.DataFrame(
np.array([
[False, False, False, False, False, False],
[True, False, False, True, False, False],
[False, True, False, False, True, False],
[False, False, False, False, False, False],
[False, False, False, False, False, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c'),
(0.2, 'a'),
(0.2, 'b'),
(0.2, 'c')
], names=['take_profit', None])
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_take_profit_exits(ts, [0.1, 0.2]),
target
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_take_profit_exits(
ts, np.concatenate((np.full((1, 5, 3), 0.1), np.full((1, 5, 3), 0.2)))),
target
)
pd.testing.assert_frame_equal(
sig.vbt.signals.generate_take_profit_exits(ts, [0.1, 0.2], keys=['tp1', 'tp2']),
pd.DataFrame(
target.values,
index=target.index,
columns=pd.MultiIndex.from_tuples([
('tp1', 'a'),
('tp1', 'b'),
('tp1', 'c'),
('tp2', 'a'),
('tp2', 'b'),
('tp2', 'c')
])
)
)
new_entries, new_exits = sig.vbt.signals.generate_take_profit_exits(ts, 0.1, iteratively=True)
pd.testing.assert_frame_equal(
new_entries,
pd.DataFrame(
np.array([
[True, False, False],
[False, True, False],
[False, False, True],
[True, False, False],
[False, True, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c')
], names=['take_profit', None])
)
)
pd.testing.assert_frame_equal(
new_exits,
pd.DataFrame(
np.array([
[False, False, False],
[True, False, False],
[False, True, False],
[False, False, False],
[False, False, False]
]),
index=sig.index,
columns=pd.MultiIndex.from_tuples([
(0.1, 'a'),
(0.1, 'b'),
(0.1, 'c')
], names=['take_profit', None])
)
)
def test_map_reduce_between(self):
@njit
def distance_map_nb(col, from_i, to_i):
return to_i - from_i
@njit
def mean_reduce_nb(col, a):
return np.nanmean(a)
other_sig = pd.DataFrame([
[False, False, False],
[False, False, False],
[True, False, False],
[False, True, False],
[True, False, True]
], index=index, columns=columns)
assert sig['a'].vbt.signals.map_reduce_between(
map_func_nb=distance_map_nb,
reduce_func_nb=mean_reduce_nb
) == 3.0
pd.testing.assert_series_equal(
sig.vbt.signals.map_reduce_between(
map_func_nb=distance_map_nb,
reduce_func_nb=mean_reduce_nb
),
pd.Series([3., 3., np.nan], index=sig.columns)
)
assert sig['a'].vbt.signals.map_reduce_between(
other=other_sig['b'],
map_func_nb=distance_map_nb,
reduce_func_nb=mean_reduce_nb
) == 3.0
pd.testing.assert_series_equal(
sig.vbt.signals.map_reduce_between(
other=other_sig,
map_func_nb=distance_map_nb,
reduce_func_nb=mean_reduce_nb
),
pd.Series([1.5, 2., 2.], index=sig.columns)
)
def test_map_reduce_partitions(self):
@njit
def distance_map_nb(col, from_i, to_i):
return to_i - from_i
@njit
def mean_reduce_nb(col, a):
return np.nanmean(a)
assert (~sig['a']).vbt.signals.map_reduce_partitions(
map_func_nb=distance_map_nb,
reduce_func_nb=mean_reduce_nb
) == 1.5
pd.testing.assert_series_equal(
(~sig).vbt.signals.map_reduce_partitions(
map_func_nb=distance_map_nb,
reduce_func_nb=mean_reduce_nb
),
pd.Series([1.5, 1.5, 2.], index=sig.columns)
)
def test_num_signals(self):
assert sig['a'].vbt.signals.num_signals == 2
pd.testing.assert_series_equal(
sig.vbt.signals.num_signals,
pd.Series([2, 2, 1], index=sig.columns)
)
def test_avg_distance(self):
assert sig['a'].vbt.signals.avg_distance == 3.
pd.testing.assert_series_equal(
sig.vbt.signals.avg_distance,
pd.Series([3., 3., np.nan], index=sig.columns)
)
def test_avg_distance_to(self):
other_sig = pd.DataFrame([
[False, False, False],
[False, False, False],
[True, False, False],
[False, True, False],
[True, False, True]
], index=index, columns=columns)
assert sig['a'].vbt.signals.avg_distance_to(other_sig['a']) == 1.5
pd.testing.assert_series_equal(
sig.vbt.signals.avg_distance_to(other_sig),
pd.Series([1.5, 2., 2.], index=sig.columns)
)
def test_rank(self):
pd.testing.assert_series_equal(
(~sig['a']).vbt.signals.rank(),
pd.Series([0, 1, 2, 0, 1], index=sig['a'].index, name=sig['a'].name)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank(),
pd.DataFrame(
np.array([
[0, 1, 1],
[1, 0, 2],
[2, 1, 0],
[0, 2, 1],
[1, 0, 2]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank(after_false=True),
pd.DataFrame(
np.array([
[0, 0, 0],
[1, 0, 0],
[2, 1, 0],
[0, 2, 1],
[1, 0, 2]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank(allow_gaps=True),
pd.DataFrame(
np.array([
[0, 1, 1],
[1, 0, 2],
[2, 2, 0],
[0, 3, 3],
[3, 0, 4]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank(reset_by=sig['a'], allow_gaps=True),
pd.DataFrame(
np.array([
[0, 1, 1],
[1, 0, 2],
[2, 2, 0],
[0, 1, 1],
[1, 0, 2]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank(reset_by=sig, allow_gaps=True),
pd.DataFrame(
np.array([
[0, 1, 1],
[1, 0, 2],
[2, 1, 0],
[0, 2, 1],
[1, 0, 2]
]),
index=sig.index,
columns=sig.columns
)
)
def test_rank_partitions(self):
pd.testing.assert_series_equal(
(~sig['a']).vbt.signals.rank_partitions(),
pd.Series([0, 1, 1, 0, 2], index=sig['a'].index, name=sig['a'].name)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank_partitions(),
pd.DataFrame(
np.array([
[0, 1, 1],
[1, 0, 1],
[1, 2, 0],
[0, 2, 2],
[2, 0, 2]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank_partitions(after_false=True),
pd.DataFrame(
np.array([
[0, 0, 0],
[1, 0, 0],
[1, 1, 0],
[0, 1, 1],
[2, 0, 1]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank_partitions(reset_by=sig['a']),
pd.DataFrame(
np.array([
[0, 1, 1],
[1, 0, 1],
[1, 2, 0],
[0, 1, 1],
[1, 0, 1]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.rank_partitions(reset_by=sig),
pd.DataFrame(
np.array([
[0, 1, 1],
[1, 0, 1],
[1, 1, 0],
[0, 1, 1],
[1, 0, 1]
]),
index=sig.index,
columns=sig.columns
)
)
def test_rank_funs(self):
pd.testing.assert_frame_equal(
(~sig).vbt.signals.first(),
pd.DataFrame(
np.array([
[False, True, True],
[True, False, False],
[False, True, False],
[False, False, True],
[True, False, False]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.nst(2),
pd.DataFrame(
np.array([
[False, False, False],
[False, False, True],
[True, False, False],
[False, True, False],
[False, False, True]
]),
index=sig.index,
columns=sig.columns
)
)
pd.testing.assert_frame_equal(
(~sig).vbt.signals.from_nst(1),
pd.DataFrame(
np.array([
[False, True, True],
[True, False, True],
[True, True, False],
[False, True, True],
[True, False, True]
]),
index=sig.index,
columns=sig.columns
)
)
@pytest.mark.parametrize(
"test_func,test_func_pd",
[
(lambda x, *args, **kwargs: x.AND(*args, **kwargs), lambda x, y: x & y),
(lambda x, *args, **kwargs: x.OR(*args, **kwargs), lambda x, y: x | y),
(lambda x, *args, **kwargs: x.XOR(*args, **kwargs), lambda x, y: x ^ y)
],
)
def test_logical_funcs(self, test_func, test_func_pd):
pd.testing.assert_series_equal(
test_func(sig['a'].vbt.signals, True, [True, False, False, False, False]),
test_func_pd(test_func_pd(sig['a'], True), [True, False, False, False, False])
)
pd.testing.assert_frame_equal(
test_func(sig['a'].vbt.signals, True, [True, False, False, False, False], concat=True),
pd.concat((
test_func_pd(sig['a'], True),
test_func_pd(sig['a'], [True, False, False, False, False])
), axis=1, keys=[0, 1], names=['combine_idx'])
)
pd.testing.assert_frame_equal(
test_func(sig.vbt.signals, True, [[True], [False], [False], [False], [False]]),
test_func_pd(test_func_pd(sig, True), np.broadcast_to([[True], [False], [False], [False], [False]], (5, 3)))
)
pd.testing.assert_frame_equal(
test_func(sig.vbt.signals, True, [[True], [False], [False], [False], [False]], concat=True),
pd.concat((
test_func_pd(sig, True),
test_func_pd(sig, np.broadcast_to([[True], [False], [False], [False], [False]], (5, 3)))
), axis=1, keys=[0, 1], names=['combine_idx'])
)
| 34.970149
| 120
| 0.434886
| 3,975
| 37,488
| 3.962516
| 0.039245
| 0.205701
| 0.208558
| 0.185385
| 0.941147
| 0.923941
| 0.90991
| 0.895816
| 0.857215
| 0.825598
| 0
| 0.024708
| 0.428884
| 37,488
| 1,071
| 121
| 35.002801
| 0.710976
| 0.00032
| 0
| 0.687379
| 0
| 0
| 0.010175
| 0.000588
| 0
| 0
| 0
| 0
| 0.091262
| 1
| 0.028155
| false
| 0
| 0.006796
| 0.003883
| 0.049515
| 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
|
7599b786baae338eb72d881f8450fad409c53f2f
| 2,787
|
py
|
Python
|
runtime/components/Arithmetic/test_cumulative_sum.py
|
ulise/hetida-designer
|
a6be8eb45abf950d5498e3ca756ea1d2e46b5c00
|
[
"MIT"
] | 41
|
2020-11-18T10:12:29.000Z
|
2022-03-28T21:46:41.000Z
|
runtime/components/Arithmetic/test_cumulative_sum.py
|
ulise/hetida-designer
|
a6be8eb45abf950d5498e3ca756ea1d2e46b5c00
|
[
"MIT"
] | 4
|
2020-12-08T15:28:15.000Z
|
2022-02-01T11:40:17.000Z
|
runtime/components/Arithmetic/test_cumulative_sum.py
|
ulise/hetida-designer
|
a6be8eb45abf950d5498e3ca756ea1d2e46b5c00
|
[
"MIT"
] | 14
|
2020-11-18T11:39:17.000Z
|
2022-03-21T15:05:11.000Z
|
import pandas as pd
from .cumulative_sum import main
def test_series():
assert main(
data=pd.Series(
{
"2019-08-01T15:20:10": 0.0,
"2019-08-01T15:20:11": 27.0,
"2019-08-01T15:20:14": 18.0,
"2019-08-01T15:20:16": 22.0,
}
)
)["cum_sum"].equals(
pd.Series(
{
"2019-08-01T15:20:10": 0.0,
"2019-08-01T15:20:11": 27.0,
"2019-08-01T15:20:14": 45.0,
"2019-08-01T15:20:16": 67.0,
}
)
)
def test_date_unsorted():
assert main(
data=pd.Series(
{
"2019-08-01T15:20:14": 18.0,
"2019-08-01T15:20:10": 0.0,
"2019-08-01T15:20:11": 27.0,
"2019-08-01T15:20:16": 22.0,
}
)
)["cum_sum"].equals(
pd.Series(
{
"2019-08-01T15:20:10": 0.0,
"2019-08-01T15:20:11": 27.0,
"2019-08-01T15:20:14": 45.0,
"2019-08-01T15:20:16": 67.0,
}
)
)
def test_numeric_index():
assert main(data=pd.Series({0: 1.0, 1: 4.0, 3: 5.0, 4: 8.0}))["cum_sum"].equals(
pd.Series({0: 1.0, 1: 5.0, 3: 10.0, 4: 18.0})
)
def test_numeric_unsorted():
assert main(data=pd.Series({1: 4.0, 3: 5.0, 0: 1.0, 4: 8.0}))["cum_sum"].equals(
pd.Series({0: 1.0, 1: 5.0, 3: 10.0, 4: 18.0})
)
def test_series_empty():
assert main(data=pd.Series(dtype=float))["cum_sum"].empty
def test_df():
assert main(
data=pd.DataFrame(
{
"a": {
"2019-08-01T15:20:12": -4,
"2019-08-01T15:44:12": 9,
"2019-08-03T16:20:15": 0,
"2019-08-05T12:00:34": -1,
},
"b": {
"2019-08-01T15:20:12": -625,
"2019-08-01T15:44:12": -1,
"2019-08-03T16:20:15": 0,
"2019-08-05T12:00:34": 4,
},
}
)
)["cum_sum"].equals(
pd.DataFrame(
{
"a": {
"2019-08-01T15:20:12": -4,
"2019-08-01T15:44:12": 5,
"2019-08-03T16:20:15": 5,
"2019-08-05T12:00:34": 4,
},
"b": {
"2019-08-01T15:20:12": -625,
"2019-08-01T15:44:12": -626,
"2019-08-03T16:20:15": -626,
"2019-08-05T12:00:34": -622,
},
}
)
)
def test_empty_df():
assert main(data=pd.DataFrame())["cum_sum"].empty
| 26.798077
| 84
| 0.386796
| 363
| 2,787
| 2.914601
| 0.15427
| 0.181474
| 0.249527
| 0.245747
| 0.840265
| 0.763705
| 0.650284
| 0.650284
| 0.650284
| 0.616257
| 0
| 0.362595
| 0.435953
| 2,787
| 103
| 85
| 27.058252
| 0.310433
| 0
| 0
| 0.444444
| 0
| 0
| 0.237173
| 0
| 0
| 0
| 0
| 0
| 0.077778
| 1
| 0.077778
| true
| 0
| 0.022222
| 0
| 0.1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
75b33617e29abc665a92f48a32323457ec25f47d
| 74
|
py
|
Python
|
tests/test_fiyur.py
|
CodeMart10/fiyur
|
78b7dbfcd0bc9389b99307907658703a0ba38563
|
[
"MIT"
] | null | null | null |
tests/test_fiyur.py
|
CodeMart10/fiyur
|
78b7dbfcd0bc9389b99307907658703a0ba38563
|
[
"MIT"
] | null | null | null |
tests/test_fiyur.py
|
CodeMart10/fiyur
|
78b7dbfcd0bc9389b99307907658703a0ba38563
|
[
"MIT"
] | null | null | null |
import budget_calculator
def test_import():
assert budget_calculator
| 14.8
| 28
| 0.810811
| 9
| 74
| 6.333333
| 0.666667
| 0.561404
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148649
| 74
| 4
| 29
| 18.5
| 0.904762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.666667
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
75c797cd8d9ea1914adf288f3ae460c5c357ac9c
| 28,023
|
py
|
Python
|
utils/res_gcn_module.py
|
RyanHangZhou/LG-GAN
|
dfd6e122bf3e824b5e262f24e7a21e3eb7a4161f
|
[
"MIT"
] | 15
|
2020-07-05T06:14:08.000Z
|
2021-04-28T07:43:12.000Z
|
utils/res_gcn_module.py
|
RyanHangZhou/LG-GAN
|
dfd6e122bf3e824b5e262f24e7a21e3eb7a4161f
|
[
"MIT"
] | 3
|
2020-08-25T15:50:37.000Z
|
2021-05-20T12:55:14.000Z
|
utils/res_gcn_module.py
|
RyanHangZhou/LG-GAN
|
dfd6e122bf3e824b5e262f24e7a21e3eb7a4161f
|
[
"MIT"
] | 4
|
2020-08-04T20:45:22.000Z
|
2021-04-01T03:22:26.000Z
|
import tensorflow as tf
import numpy as np
from tf_ops.grouping.tf_grouping import group_point, knn_point
from tf_ops.sampling.tf_sampling import farthest_point_sample, gather_point
from tensorflow.python.training.moving_averages import assign_moving_average
def batch_norm_orig(x, train, beta, gamma, eps=1e-05, decay=0.9, affine=True, name=None):
with tf.variable_scope(name, default_name='BatchNorm2d'):
params_shape = tf.shape(x)[-1:]
moving_mean = tf.get_variable('mean', params_shape,
initializer=tf.zeros_initializer,
trainable=False)
moving_variance = tf.get_variable('variance', params_shape,
initializer=tf.ones_initializer,
trainable=False)
def mean_var_with_update():
mean, variance = tf.nn.moments(x, tf.shape(x)[:-1], name='moments')
with tf.control_dependencies([assign_moving_average(moving_mean, mean, decay),
assign_moving_average(moving_variance, variance, decay)]):
return tf.identity(mean), tf.identity(variance)
mean, variance = tf.cond(train, mean_var_with_update, lambda: (moving_mean, moving_variance))
if affine:
beta = tf.get_variable('beta', params_shape,
initializer=tf.zeros_initializer)
gamma = tf.get_variable('gamma', params_shape,
initializer=tf.ones_initializer)
x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
else:
x = tf.nn.batch_normalization(x, mean, variance, None, None, eps)
return x
# def batch_norm2(x, train, beta, gamma, eps=1e-05, decay=0.9, affine=True, name=None):
# with tf.variable_scope(name, default_name='BatchNorm2d'):
# params_shape = tf.shape(x)[-1:]
# moving_mean = tf.get_variable('mean', params_shape,
# initializer=tf.zeros_initializer,
# trainable=False)
# moving_variance = tf.get_variable('variance', params_shape,
# initializer=tf.ones_initializer,
# trainable=False)
# def mean_var_with_update():
# mean, variance = tf.nn.moments(x, tf.shape(x)[:-1], name='moments')
# with tf.control_dependencies([assign_moving_average(moving_mean, mean, decay),
# assign_moving_average(moving_variance, variance, decay)]):
# return tf.identity(mean), tf.identity(variance)
# mean, variance = tf.cond(train, mean_var_with_update, lambda: (moving_mean, moving_variance))
# x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
# # if affine:
# # beta = tf.get_variable('beta', params_shape,
# # initializer=tf.zeros_initializer)
# # gamma = tf.get_variable('gamma', params_shape,
# # initializer=tf.ones_initializer)
# # x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
# # else:
# # x = tf.nn.batch_normalization(x, mean, variance, None, None, eps)
# return x
# def batch_norm2(x, beta, gamma, train_phase=True, eps=1e-5, decay=0.9, affine=True, name=None, scope_bn='zz'):
# with tf.variable_scope(scope_bn):
# beta = tf.Variable(tf.constant(0.0, shape=[x.shape[-1]]), name='beta', trainable=True)
# gamma = tf.Variable(tf.constant(1.0, shape=[x.shape[-1]]), name='gamma', trainable=True)
# axises = list(np.arange(len(x.shape) - 1))
# batch_mean, batch_var = tf.nn.moments(x, axises, name='moments')
# ema = tf.train.ExponentialMovingAverage(decay)
# def mean_var_with_update():
# ema_apply_op = ema.apply([batch_mean, batch_var])
# with tf.control_dependencies([ema_apply_op]):
# return tf.identity(batch_mean), tf.identity(batch_var)
# mean, var = tf.cond(train_phase, mean_var_with_update,
# lambda: (ema.average(batch_mean), ema.average(batch_var)))
# normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps)
# return normed
def batch_norm2(x, beta, gamma, train, eps=1e-05, decay=0.9, affine=True, name=None):
with tf.variable_scope(name, default_name='BatchNorm2d'):
params_shape = tf.shape(x)[-1:]
print('xxxxxxshapexxxxxxxxxxxxx', np.shape(x))
moving_mean = tf.get_variable('mean', shape=[x.shape[-1]],
initializer=tf.zeros_initializer,
trainable=True)
print('zzzzzzzzzzzzzzzzzzzzzzzzzzzz', np.shape(moving_mean))
moving_variance = tf.get_variable('variance', shape=[x.shape[-1]],
initializer=tf.ones_initializer,
trainable=True)
def mean_var_with_update():
mean, variance = tf.nn.moments(x, list(np.arange(len(x.shape) - 1)), name='moments')
with tf.control_dependencies([assign_moving_average(moving_mean, mean, decay),
assign_moving_average(moving_variance, variance, decay)]):
return tf.identity(mean), tf.identity(variance)
mean, variance = tf.cond(train, mean_var_with_update, lambda: (moving_mean, moving_variance))
print('kkkkkkkkkkkkkkkkkkkkkkkkkk', np.shape(mean))
x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
# if affine:
# beta = tf.get_variable('beta', [x.shape[-1]],
# initializer=tf.zeros_initializer)
# gamma = tf.get_variable('gamma', [x.shape[-1]],
# initializer=tf.ones_initializer)
# x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
# else:
# x = tf.nn.batch_normalization(x, mean, variance, None, None, eps)
return x
# def batch_norm33(x, train_phase=True, eps=1e-5, decay=0.9, affine=True, name=None, scope_bn='zz'):
# with tf.variable_scope(scope_bn):
# beta = tf.Variable(tf.constant(0.0, shape=[x.shape[-1]]), name='beta', trainable=True)
# gamma = tf.Variable(tf.constant(1.0, shape=[x.shape[-1]]), name='gamma', trainable=True)
# axises = list(np.arange(len(x.shape) - 1))
# batch_mean, batch_var = tf.nn.moments(x, axises, name='moments')
# ema = tf.train.ExponentialMovingAverage(decay)
# def mean_var_with_update():
# ema_apply_op = ema.apply([batch_mean, batch_var])
# with tf.control_dependencies([ema_apply_op]):
# return tf.identity(batch_mean), tf.identity(batch_var)
# mean, var = tf.cond(train_phase, mean_var_with_update,
# lambda: (ema.average(batch_mean), ema.average(batch_var)))
# normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps)
# return normed
def batch_norm33(x, train, eps=1e-05, decay=0.9, affine=True, name=None):
with tf.variable_scope(name, default_name='BatchNorm2d'):
params_shape = tf.shape(x)[-1:]
print('xxxxxxshapexxxxxxxxxxxxx', np.shape(x))
moving_mean = tf.get_variable('mean', shape=[x.shape[-1]],
initializer=tf.zeros_initializer,
trainable=True)
print('zzzzzzzzzzzzzzzzzzzzzzzzzzzz', np.shape(moving_mean))
moving_variance = tf.get_variable('variance', shape=[x.shape[-1]],
initializer=tf.ones_initializer,
trainable=True)
def mean_var_with_update():
mean, variance = tf.nn.moments(x, list(np.arange(len(x.shape) - 1)), name='moments')
with tf.control_dependencies([assign_moving_average(moving_mean, mean, decay),
assign_moving_average(moving_variance, variance, decay)]):
return tf.identity(mean), tf.identity(variance)
mean, variance = tf.cond(train, mean_var_with_update, lambda: (moving_mean, moving_variance))
print('kkkkkkkkkkkkkkkkkkkkkkkkkk', np.shape(mean))
# x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
if affine:
beta = tf.get_variable('beta', [x.shape[-1]],
initializer=tf.zeros_initializer)
gamma = tf.get_variable('gamma', [x.shape[-1]],
initializer=tf.ones_initializer)
x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
else:
x = tf.nn.batch_normalization(x, mean, variance, None, None, eps)
return x
def batch_norm4(x, train, eps=1e-05, decay=0.9, affine=True, name=None):
with tf.variable_scope(name, default_name='BatchNorm2dxx'):
params_shape = tf.shape(x)[-1:]
print('xxxxxxshapexxxxxxxxxxxxx', np.shape(x))
moving_mean = tf.get_variable('mean', shape=[x.shape[-1]],
initializer=tf.zeros_initializer,
trainable=True)
print('zzzzzzzzzzzzzzzzzzzzzzzzzzzz', np.shape(moving_mean))
moving_variance = tf.get_variable('variance', shape=[x.shape[-1]],
initializer=tf.ones_initializer,
trainable=True)
def mean_var_with_update():
mean, variance = tf.nn.moments(x, list(np.arange(len(x.shape) - 1)), name='moments')
with tf.control_dependencies([assign_moving_average(moving_mean, mean, decay),
assign_moving_average(moving_variance, variance, decay)]):
return tf.identity(mean), tf.identity(variance)
mean, variance = tf.cond(train, mean_var_with_update, lambda: (moving_mean, moving_variance))
print('kkkkkkkkkkkkkkkkkkkkkkkkkk', np.shape(mean))
# x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
if affine:
beta = tf.get_variable('beta', [x.shape[-1]],
initializer=tf.zeros_initializer)
gamma = tf.get_variable('gamma', [x.shape[-1]],
initializer=tf.ones_initializer)
x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
else:
x = tf.nn.batch_normalization(x, mean, variance, None, None, eps)
return x
def conv2d(input, n_cout, name, kernel_size=(1, 1),
strides=(1, 1), padding='VALID', use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.00001)):
return tf.layers.conv2d(input, n_cout, kernel_size=kernel_size, strides=strides,
padding=padding, name=name, use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
def conv2dbn(input, n_cout, name, is_training, kernel_size=(1, 1),
strides=(1, 1), padding='VALID', use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.00001)):
out1 = tf.layers.conv2d(input, n_cout, kernel_size=kernel_size, strides=strides,
padding=padding, name=name, use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
print('my output', np.shape(out1))
# return tf.layers.batch_normalization(out1, axis=0, training=is_training)
return batch_norm33(out1, is_training, name='bn')
def conv2dbn4(input, n_cout, name, is_training, kernel_size=(1, 1),
strides=(1, 1), padding='VALID', use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.00001)):
out1 = tf.layers.conv2d(input, n_cout, kernel_size=kernel_size, strides=strides,
padding=padding, name=name, use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
print('my output', np.shape(out1))
# return tf.layers.batch_normalization(out1, axis=0, training=is_training)
return batch_norm4(out1, is_training, name='bnxx')
def conv2dbn3(input, n_cout, name, is_training, beta, gamma, kernel_size=(1, 1),
strides=(1, 1), padding='VALID', use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.00001)):
out1 = tf.layers.conv2d(input, n_cout, kernel_size=kernel_size, strides=strides,
padding=padding, name=name, use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
mean, variance = tf.nn.moments(out1, axes=[0, 1, 2], name='moments')
fc = tf.contrib.layers.flatten(beta)
# fc = tf.layers.conv2d(fc, 1, 1, activation=tf.nn.relu)
fc = tf.nn.relu(fc)
beta2 = tf.reshape(fc, beta.get_shape().as_list())
gamma = beta2
# return batch_norm2(out1, beta, gamma)
return batch_norm2(out1, beta, gamma, is_training, name='bn')
# return tf.nn.batch_normalization(out1, mean, variance, beta, gamma, variance_epsilon=1e-5)
# return tf.layers.batch_normalization(out1, axis=0, training=is_training)
def conv2dbn5(input, n_cout, name, is_training, kernel_size=(1, 1),
strides=(1, 1), padding='VALID', use_bias=False,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.00001)):
out1 = tf.layers.conv2d(input, n_cout, kernel_size=kernel_size, strides=strides,
padding=padding, name=name, use_bias=use_bias,
kernel_initializer=kernel_initializer,
kernel_regularizer=kernel_regularizer)
mean, variance = tf.nn.moments(out1, axes=[0, 1, 2], name='moments')
# return batch_norm2(out1, beta, gamma)
return batch_norm5(out1, is_training, name='bnxx')
# return tf.nn.batch_normalization(out1, mean, variance, beta, gamma, variance_epsilon=1e-5)
# return tf.layers.batch_normalization(out1, axis=0, training=is_training)
def batch_norm5(x, train, eps=1e-05, decay=0.9, affine=True, name=None):
with tf.variable_scope(name, default_name='BatchNorm2d'):
params_shape = tf.shape(x)[-1:]
print('xxxxxxshapexxxxxxxxxxxxx', np.shape(x))
moving_mean = tf.get_variable('mean', shape=[x.shape[-1]],
initializer=tf.zeros_initializer,
trainable=True)
print('zzzzzzzzzzzzzzzzzzzzzzzzzzzz', np.shape(moving_mean))
moving_variance = tf.get_variable('variance', shape=[x.shape[-1]],
initializer=tf.ones_initializer,
trainable=True)
def mean_var_with_update():
mean, variance = tf.nn.moments(x, list(np.arange(len(x.shape) - 1)), name='moments')
with tf.control_dependencies([assign_moving_average(moving_mean, mean, decay),
assign_moving_average(moving_variance, variance, decay)]):
return tf.identity(mean), tf.identity(variance)
mean, variance = tf.cond(train, mean_var_with_update, lambda: (moving_mean, moving_variance))
print('kkkkkkkkkkkkkkkkkkkkkkkkkk', np.shape(mean))
# x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
if affine:
beta = tf.get_variable('beta', [x.shape[-1]],
initializer=tf.zeros_initializer)
gamma = tf.get_variable('gamma', [x.shape[-1]],
initializer=tf.ones_initializer)
x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, eps)
else:
x = tf.nn.batch_normalization(x, mean, variance, None, None, eps)
return x
# def conv2dbn3(input, n_cout, name, is_training, beta, gamma, kernel_size=(1, 1),
# strides=(1, 1), padding='VALID', use_bias=False,
# kernel_initializer=tf.contrib.layers.xavier_initializer(),
# kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=0.00001)):
# out1 = tf.layers.conv2d(input, n_cout, kernel_size=kernel_size, strides=strides,
# padding=padding, name=name, use_bias=use_bias,
# kernel_initializer=kernel_initializer,
# kernel_regularizer=kernel_regularizer)
# mean, variance = tf.nn.moments(out1, axes=[0, 1, 2], name='moments')
# fc = tf.contrib.layers.flatten(beta)
# # fc = tf.layers.conv2d(fc, 1, 1, activation=tf.nn.relu)
# fc = tf.nn.relu(fc)
# beta2 = tf.reshape(fc, beta.get_shape().as_list())
# gamma = beta2
# return tf.nn.batch_normalization(out1, mean, variance, beta2, gamma, variance_epsilon=1e-5)
def batch_norm(input, is_training, name, bn_decay=None, use_bn=True, use_ibn=False):
if use_bn:
return tf.layers.batch_normalization(input, name=name, training=is_training, momentum=bn_decay)
if use_ibn:
return tf.contrib.layers.instance_norm(input, scope=name)
return input
def group(xyz, points, k, dilation=1, use_xyz=False):
_, idx = knn_point(k*dilation+1, xyz, xyz)
idx = idx[:, :, 1::dilation]
grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, k, 3)
grouped_xyz -= tf.expand_dims(xyz, 2) # translation normalization
if points is not None:
grouped_points = group_point(points, idx) # (batch_size, npoint, k, channel)
if use_xyz:
grouped_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, k, 3+channel)
else:
grouped_points = grouped_xyz
return grouped_xyz, grouped_points, idx
def pool(xyz, points, k, npoint):
new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz))
_, idx = knn_point(k, xyz, new_xyz)
new_points = tf.reduce_max(group_point(points, idx), axis=2)
return new_xyz, new_points
# 8, 128, 3
def pointcnn(xyz, k, n_cout, n_blocks, is_training, scope, bn_decay=None, use_bn=False, use_ibn=False, activation=tf.nn.relu):
with tf.variable_scope(scope):
# grouped_points: knn points coordinates (normalized: minus centual points)
_, grouped_points, _ = group(xyz, None, k)
# print('n_blocks: ', n_blocks)
# print('is_training: ', is_training)
for idx in range(n_blocks):
with tf.variable_scope('block_{}'.format(idx)):
grouped_points = conv2d(grouped_points, n_cout, name='conv_xyz')
if idx == n_blocks - 1:
return tf.reduce_max(grouped_points, axis=2)
else:
grouped_points = batch_norm(grouped_points, is_training, 'bn_xyz',
bn_decay=bn_decay, use_bn=use_bn, use_ibn=use_ibn)
grouped_points = activation(grouped_points)
def res_gcn_up(xyz, points, k, n_cout, n_blocks, is_training, scope, bn_decay=None, use_bn=False, use_ibn=False, indices=None, up_ratio=2):
with tf.variable_scope(scope):
for idx in range(n_blocks):
with tf.variable_scope('block_{}'.format(idx)):
shortcut = points
# Center Features
points = batch_norm(points, is_training, 'bn_center', bn_decay=bn_decay, use_bn=use_bn, use_ibn=use_ibn)
points = tf.nn.relu(points)
# Neighbor Features
if idx == 0 and indices is None:
_, grouped_points, indices = group(xyz, points, k)
else:
grouped_points = group_point(points, indices)
# Center Conv
center_points = tf.expand_dims(points, axis=2)
points = conv2d(center_points, n_cout, name='conv_center')
# Neighbor Conv
grouped_points_nn = conv2d(grouped_points, n_cout, name='conv_neighbor')
# CNN
points = tf.reduce_mean(tf.concat([points, grouped_points_nn], axis=2), axis=2) + shortcut
if idx == n_blocks - 1:
# Center Conv
points_xyz = conv2d(center_points, 3*up_ratio, name='conv_center_xyz')
# Neighbor Conv
grouped_points_xyz = conv2d(grouped_points, 3*up_ratio, name='conv_neighbor_xyz')
# CNN
new_xyz = tf.reduce_mean(tf.concat([points_xyz, grouped_points_xyz], axis=2), axis=2)
new_xyz = tf.reshape(new_xyz, [-1, new_xyz.get_shape()[1].value, up_ratio, 3])
new_xyz = new_xyz + tf.expand_dims(xyz, axis=2)
new_xyz = tf.reshape(new_xyz, [-1, new_xyz.get_shape()[1].value*up_ratio, 3])
return new_xyz, points
def res_gcn_up2(xyz, points, k, n_cout, n_blocks, is_training, scope, bn_decay=None, use_bn=False, use_ibn=False, indices=None, up_ratio=2):
with tf.variable_scope(scope):
for idx in range(n_blocks):
with tf.variable_scope('block_{}'.format(idx)):
shortcut = points
# Center Features
points = batch_norm(points, is_training, 'bn_center', bn_decay=bn_decay, use_bn=use_bn, use_ibn=use_ibn)
points = tf.nn.relu(points)
# Neighbor Features
if idx == 0 and indices is None:
_, grouped_points, indices = group(xyz, points, k)
else:
grouped_points = group_point(points, indices)
# Center Conv
center_points = tf.expand_dims(points, axis=2)
points = conv2dbn(center_points, n_cout, name='conv_center', is_training=is_training)
# Neighbor Conv
grouped_points_nn = conv2dbn(grouped_points, n_cout, name='conv_neighbor', is_training=is_training)
# CNN
points = tf.reduce_mean(tf.concat([points, grouped_points_nn], axis=2), axis=2) + shortcut
if idx == n_blocks - 1:
# Center Conv
print('qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq', np.shape(center_points))
points_xyz = conv2dbn4(center_points, 3*up_ratio, name='conv_center_xyz', is_training=is_training)
# Neighbor Conv
grouped_points_xyz = conv2dbn4(grouped_points, 3*up_ratio, name='conv_neighbor_xyz', is_training=is_training)
# CNN
new_xyz = tf.reduce_mean(tf.concat([points_xyz, grouped_points_xyz], axis=2), axis=2)
new_xyz = tf.reshape(new_xyz, [-1, new_xyz.get_shape()[1].value, up_ratio, 3])
new_xyz = new_xyz + tf.expand_dims(xyz, axis=2)
new_xyz = tf.reshape(new_xyz, [-1, new_xyz.get_shape()[1].value*up_ratio, 3])
return new_xyz, points
def res_gcn_up3(xyz, points, labels_onehot, k, n_cout, n_blocks, is_training, scope, bn_decay=None, use_bn=False, use_ibn=False, indices=None, up_ratio=2):
with tf.variable_scope(scope):
for idx in range(n_blocks):
with tf.variable_scope('block_{}'.format(idx)):
shortcut = points
# Center Features
points = batch_norm(points, is_training, 'bn_center', bn_decay=bn_decay, use_bn=use_bn, use_ibn=use_ibn)
points = tf.nn.relu(points)
# Neighbor Features
if idx == 0 and indices is None:
_, grouped_points, indices = group(xyz, points, k)
else:
grouped_points = group_point(points, indices)
# Center Conv
center_points = tf.expand_dims(points, axis=2)
# if idx==0 or idx==1 or idx==2:
points = conv2dbn3(center_points, n_cout, name='conv_center', is_training=is_training, beta=labels_onehot, gamma=labels_onehot)
# else:
# points = conv2dbn(center_points, n_cout, name='conv_center', is_training=is_training)
# Neighbor Conv
grouped_points_nn = conv2dbn3(grouped_points, n_cout, name='conv_neighbor', is_training=is_training, beta=labels_onehot, gamma=labels_onehot)
# grouped_points_nn = conv2dbn(grouped_points, n_cout, name='conv_neighbor', is_training=is_training)
# CNN
points = tf.reduce_mean(tf.concat([points, grouped_points_nn], axis=2), axis=2) + shortcut
if idx == n_blocks - 1:
# Center Conv
points_xyz = conv2dbn5(center_points, 3*up_ratio, name='conv_center_xyz', is_training=is_training)
# Neighbor Conv
grouped_points_xyz = conv2dbn5(grouped_points, 3*up_ratio, name='conv_neighbor_xyz', is_training=is_training)
# CNN
new_xyz = tf.reduce_mean(tf.concat([points_xyz, grouped_points_xyz], axis=2), axis=2)
new_xyz = tf.reshape(new_xyz, [-1, new_xyz.get_shape()[1].value, up_ratio, 3])
new_xyz = new_xyz + tf.expand_dims(xyz, axis=2)
new_xyz = tf.reshape(new_xyz, [-1, new_xyz.get_shape()[1].value*up_ratio, 3])
return new_xyz, points
def res_gcn_d(xyz, points, k, n_cout, n_blocks, is_training, scope, bn_decay=None, use_bn=False, use_ibn=False, indices=None):
with tf.variable_scope(scope):
for idx in range(n_blocks):
with tf.variable_scope('block_{}'.format(idx)):
shortcut = points
# Center Features
points = batch_norm(points, is_training, 'bn_center', bn_decay=bn_decay, use_bn=use_bn, use_ibn=use_ibn)
points = tf.nn.leaky_relu(points)
# Neighbor Features
if idx == 0 and indices is None:
_, grouped_points, indices = group(xyz, points, k)
else:
grouped_points = group_point(points, indices)
# Center Conv
center_points = tf.expand_dims(points, axis=2)
points = conv2d(center_points, n_cout, name='conv_center')
# Neighbor Conv
grouped_points_nn = conv2d(grouped_points, n_cout, name='conv_neighbor')
# CNN
points = tf.reduce_mean(tf.concat([points, grouped_points_nn], axis=2), axis=2) + shortcut
return points
def res_gcn_d_last(points, n_cout, is_training, scope, bn_decay=None, use_bn=False, use_ibn=False):
with tf.variable_scope(scope):
points = batch_norm(points, is_training, 'bn_center', bn_decay=bn_decay, use_bn=use_bn, use_ibn=use_ibn)
points = tf.nn.leaky_relu(points)
center_points = tf.expand_dims(points, axis=2)
points = tf.squeeze(conv2d(center_points, n_cout, name='conv_center'), axis=2)
return points
| 54.30814
| 157
| 0.598259
| 3,439
| 28,023
| 4.6467
| 0.054085
| 0.030663
| 0.011389
| 0.030288
| 0.903567
| 0.882478
| 0.881352
| 0.877847
| 0.868523
| 0.861139
| 0
| 0.016938
| 0.287906
| 28,023
| 515
| 158
| 54.413592
| 0.783864
| 0.2342
| 0
| 0.754839
| 0
| 0
| 0.042779
| 0.016558
| 0
| 0
| 0
| 0
| 0
| 1
| 0.077419
| false
| 0
| 0.016129
| 0.003226
| 0.177419
| 0.048387
| 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
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| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
75d23cb9fa4dae105b485a6e72fe21a6c20af7d2
| 15,411
|
py
|
Python
|
sqlathanor/declarative/_json_support.py
|
edupo/sqlathanor
|
a5cfd349d092b25a3ffb3950b996b13878e1db17
|
[
"MIT"
] | 101
|
2018-07-21T00:20:59.000Z
|
2022-02-09T21:33:09.000Z
|
sqlathanor/declarative/_json_support.py
|
edupo/sqlathanor
|
a5cfd349d092b25a3ffb3950b996b13878e1db17
|
[
"MIT"
] | 85
|
2018-06-16T02:15:08.000Z
|
2022-02-24T14:57:24.000Z
|
sqlathanor/declarative/_json_support.py
|
edupo/sqlathanor
|
a5cfd349d092b25a3ffb3950b996b13878e1db17
|
[
"MIT"
] | 6
|
2018-07-25T09:51:02.000Z
|
2022-02-24T14:04:27.000Z
|
# -*- coding: utf-8 -*-
# The lack of a module docstring for this module is **INTENTIONAL**.
# The module is imported into the documentation using Sphinx's autodoc
# extension, and its member function documentation is automatically incorporated
# there as needed.
from validator_collection import checkers
from sqlathanor._compat import json
from sqlathanor.utilities import parse_json
class JSONSupportMixin(object):
"""Mixin that provides JSON serialization/de-serialization support."""
def to_json(self,
max_nesting = 0,
current_nesting = 0,
serialize_function = None,
config_set = None,
**kwargs):
"""Return a JSON representation of the object.
:param max_nesting: The maximum number of levels that the resulting
JSON object can be nested. If set to ``0``, will
not nest other serializable objects. Defaults to ``0``.
:type max_nesting: :class:`int <python:int>`
:param current_nesting: The current nesting level at which the
:class:`dict <python:dict>` representation will reside. Defaults to ``0``.
:type current_nesting: :class:`int <python:int>`
:param serialize_function: Optionally override the default JSON serializer.
Defaults to :obj:`None <python:None>`, which applies the default
:doc:`simplejson <simplejson:index>` JSON serializer.
.. note::
Use the ``serialize_function`` parameter to override the default
JSON serializer.
A valid ``serialize_function`` is expected to accept a single
:class:`dict <python:dict>` and return a :class:`str <python:str>`,
similar to :func:`simplejson.dumps() <simplejson:simplejson.dumps>`.
If you wish to pass additional arguments to your ``serialize_function``
pass them as keyword arguments (in ``kwargs``).
:type serialize_function: callable / :obj:`None <python:None>`
:param config_set: If not :obj:`None <python:None>`, the named configuration set
to use. Defaults to :obj:`None <python:None>`.
:type config_set: :class:`str <python:str>` / :obj:`None <python:None>`
:param kwargs: Optional keyword parameters that are passed to the
JSON serializer function. By default, these are options which are passed
to :func:`simplejson.dumps() <simplejson:simplejson.dumps>`.
:type kwargs: keyword arguments
:returns: A :class:`str <python:str>` with the JSON representation of the
object.
:rtype: :class:`str <python:str>`
:raises SerializableAttributeError: if attributes is empty
:raises MaximumNestingExceededError: if ``current_nesting`` is greater
than ``max_nesting``
:raises MaximumNestingExceededWarning: if an attribute requires nesting
beyond ``max_nesting``
"""
if serialize_function is None:
serialize_function = json.dumps
else:
if checkers.is_callable(serialize_function) is False:
raise ValueError(
'serialize_function (%s) is not callable' % serialize_function
)
as_dict = self._to_dict('json',
max_nesting = max_nesting,
current_nesting = current_nesting,
is_dumping = False,
config_set = config_set)
as_json = serialize_function(as_dict, **kwargs)
return as_json
def dump_to_json(self,
max_nesting = 0,
current_nesting = 0,
serialize_function = None,
config_set = None,
**kwargs):
"""Return a :term:`JSON <JavaScript Object Notation (JSON)>`
representation of the object, *with all attributes* regardless of
configuration.
.. caution::
Nested objects (such as :term:`relationships <relationship>` or
:term:`association proxies <association proxy>`) will **not**
be serialized.
:param max_nesting: The maximum number of levels that the resulting
JSON object can be nested. If set to ``0``, will
not nest other serializable objects. Defaults to ``0``.
:type max_nesting: :class:`int <python:int>`
:param current_nesting: The current nesting level at which the
:class:`dict <python:dict>` representation will reside. Defaults to ``0``.
:type current_nesting: :class:`int <python:int>`
:param serialize_function: Optionally override the default JSON serializer.
Defaults to :obj:`None <python:None>`, which applies the default
:doc:`simplejson <simplejson:index>` JSON serializer.
.. note::
Use the ``serialize_function`` parameter to override the default
JSON serializer.
A valid ``serialize_function`` is expected to accept a single
:class:`dict <python:dict>` and return a :class:`str <python:str>`,
similar to :func:`simplejson.dumps() <simplejson:simplejson.dumps>`.
If you wish to pass additional arguments to your ``serialize_function``
pass them as keyword arguments (in ``kwargs``).
:type serialize_function: callable / :obj:`None <python:None>`
:param config_set: If not :obj:`None <python:None>`, the named configuration set
to use. Defaults to :obj:`None <python:None>`.
:type config_set: :class:`str <python:str>` / :obj:`None <python:None>`
:param kwargs: Optional keyword parameters that are passed to the
JSON serializer function. By default, these are options which are passed
to :func:`simplejson.dumps() <simplejson:simplejson.dumps>`.
:type kwargs: keyword arguments
:returns: A :class:`str <python:str>` with the JSON representation of the
object.
:rtype: :class:`str <python:str>`
:raises SerializableAttributeError: if attributes is empty
:raises MaximumNestingExceededError: if ``current_nesting`` is greater
than ``max_nesting``
:raises MaximumNestingExceededWarning: if an attribute requires nesting
beyond ``max_nesting``
"""
if serialize_function is None:
serialize_function = json.dumps
else:
if checkers.is_callable(serialize_function) is False:
raise ValueError(
'serialize_function (%s) is not callable' % serialize_function
)
as_dict = self._to_dict('json',
max_nesting = max_nesting,
current_nesting = current_nesting,
is_dumping = True,
config_set = config_set)
as_json = serialize_function(as_dict, **kwargs)
return as_json
def update_from_json(self,
input_data,
deserialize_function = None,
error_on_extra_keys = True,
drop_extra_keys = False,
config_set = None,
**kwargs):
"""Update the model instance from data in a JSON string.
:param input_data: The JSON data to de-serialize.
.. note::
If ``input_data`` points to a file, and the file contains a list of
JSON objects, the first JSON object will be considered.
:type input_data: :class:`str <python:str>` or Path-like object
:param deserialize_function: Optionally override the default JSON deserializer.
Defaults to :obj:`None <python:None>`, which calls the default
:func:`simplejson.loads() <simplejson:simplejson.loads>` function from
the :doc:`simplejson <simplejson:index>` library.
.. note::
Use the ``deserialize_function`` parameter to override the default
JSON deserializer.
A valid ``deserialize_function`` is expected to
accept a single :class:`str <python:str>` and return a
:class:`dict <python:dict>`, similar to
:func:`simplejson.loads() <simplejson:simplejson.loads>`.
If you wish to pass additional arguments to your ``deserialize_function``
pass them as keyword arguments (in ``kwargs``).
:type deserialize_function: callable / :obj:`None <python:None>`
:param error_on_extra_keys: If ``True``, will raise an error if an
unrecognized key is found in ``input_data``. If ``False``, will
either drop or include the extra key in the result, as configured in
the ``drop_extra_keys`` parameter. Defaults to ``True``.
.. warning::
Be careful setting ``error_on_extra_keys`` to ``False``.
This method's last step attempts to set an attribute on the model
instance for every top-level key in the parsed/processed input data.
If there is an extra key that cannot be set as an attribute on your
model instance, it *will* raise
:class:`AttributeError <python:AttributeError>`.
:type error_on_extra_keys: :class:`bool <python:bool>`
:param drop_extra_keys: If ``True``, will ignore unrecognized keys in the
input data. If ``False``, will include unrecognized keys or raise an
error based on the configuration of the ``error_on_extra_keys`` parameter.
Defaults to ``False``.
:type drop_extra_keys: :class:`bool <python:bool>`
:param config_set: If not :obj:`None <python:None>`, the named configuration set
to use. Defaults to :obj:`None <python:None>`.
:type config_set: :class:`str <python:str>` / :obj:`None <python:None>`
:param kwargs: Optional keyword parameters that are passed to the
JSON deserializer function.By default, these are options which are passed
to :func:`simplejson.loads() <simplejson:simplejson.loads>`.
:type kwargs: keyword arguments
:raises ExtraKeyError: if ``error_on_extra_keys`` is ``True`` and
``input_data`` contains top-level keys that are not recognized as
attributes for the instance model.
:raises DeserializationError: if ``input_data`` is
not a :class:`str <python:str>` JSON de-serializable object to a
:class:`dict <python:dict>` or if ``input_data`` is empty.
"""
from_json = parse_json(input_data,
deserialize_function = deserialize_function,
**kwargs)
if isinstance(from_json, list):
from_json = from_json[0]
data = self._parse_dict(from_json,
'json',
error_on_extra_keys = error_on_extra_keys,
drop_extra_keys = drop_extra_keys,
config_set = config_set)
for key in data:
setattr(self, key, data[key])
@classmethod
def new_from_json(cls,
input_data,
deserialize_function = None,
error_on_extra_keys = True,
drop_extra_keys = False,
config_set = None,
**kwargs):
"""Create a new model instance from data in JSON.
:param input_data: The JSON data to de-serialize.
.. note::
If ``input_data`` points to a file, and the file contains a list of
JSON objects, the first JSON object will be considered.
:type input_data: :class:`str <python:str>` or Path-like object
:param deserialize_function: Optionally override the default JSON deserializer.
Defaults to :obj:`None <python:None>`, which calls the default
:func:`simplejson.loads() <simplejson:simplejson.loads>`
function from the doc:`simplejson <simplejson:index>` library.
.. note::
Use the ``deserialize_function`` parameter to override the default
JSON deserializer.
A valid ``deserialize_function`` is expected to accept a single
:class:`str <python:str>` and return a :class:`dict <python:dict>`,
similar to :func:`simplejson.loads() <simplejson:simplejson.loads>`.
If you wish to pass additional arguments to your ``deserialize_function``
pass them as keyword arguments (in ``kwargs``).
:type deserialize_function: callable / :obj:`None <python:None>`
:param error_on_extra_keys: If ``True``, will raise an error if an
unrecognized key is found in ``input_data``. If ``False``, will
either drop or include the extra key in the result, as configured in
the ``drop_extra_keys`` parameter. Defaults to ``True``.
.. warning::
Be careful setting ``error_on_extra_keys`` to ``False``.
This method's last step passes the keys/values of the processed input
data to your model's ``__init__()`` method.
If your instance's ``__init__()`` method does not support your extra keys,
it will likely raise a :class:`TypeError <python:TypeError>`.
:type error_on_extra_keys: :class:`bool <python:bool>`
:param drop_extra_keys: If ``True``, will ignore unrecognized top-level
keys in ``input_data``. If ``False``, will include unrecognized keys
or raise an error based on the configuration of
the ``error_on_extra_keys`` parameter. Defaults to ``False``.
:type drop_extra_keys: :class:`bool <python:bool>`
:param config_set: If not :obj:`None <python:None>`, the named configuration set
to use. Defaults to :obj:`None <python:None>`.
:type config_set: :class:`str <python:str>` / :obj:`None <python:None>`
:param kwargs: Optional keyword parameters that are passed to the
JSON deserializer function. By default, these are options which are passed
to :func:`simplejson.loads() <simplejson:simplejson.loads>`.
:type kwargs: keyword arguments
:raises ExtraKeyError: if ``error_on_extra_keys`` is ``True`` and
``input_data`` contains top-level keys that are not recognized as
attributes for the instance model.
:raises DeserializationError: if ``input_data`` is
not a :class:`dict <python:dict>` or JSON object serializable to a
:class:`dict <python:dict>` or if ``input_data`` is empty.
"""
from_json = parse_json(input_data,
deserialize_function = deserialize_function,
**kwargs)
if isinstance(from_json, list):
from_json = from_json[0]
data = cls._parse_dict(from_json,
'json',
error_on_extra_keys = error_on_extra_keys,
drop_extra_keys = drop_extra_keys,
config_set = config_set)
return cls(**data)
| 43.168067
| 88
| 0.605606
| 1,789
| 15,411
| 5.089994
| 0.125769
| 0.028662
| 0.028553
| 0.037338
| 0.872392
| 0.859653
| 0.857676
| 0.857676
| 0.857676
| 0.857676
| 0
| 0.001215
| 0.30595
| 15,411
| 356
| 89
| 43.289326
| 0.850131
| 0.668484
| 0
| 0.780488
| 0
| 0
| 0.02555
| 0
| 0
| 0
| 0
| 0.002809
| 0
| 1
| 0.04878
| false
| 0
| 0.036585
| 0
| 0.134146
| 0
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| 0
| 0
| null | 0
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| 1
| 1
| 1
| 1
| 1
| 0
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| null | 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
f950f99b6a1ea41103aba08cabe0de43442fbb25
| 6,138
|
py
|
Python
|
analysis/analyze_the_data/collect_data/collect_data.py
|
jamorrison/wgms_kit_comparison
|
f0da12f368f91b6ab8b94aaef60574cf5ca09bcf
|
[
"MIT"
] | null | null | null |
analysis/analyze_the_data/collect_data/collect_data.py
|
jamorrison/wgms_kit_comparison
|
f0da12f368f91b6ab8b94aaef60574cf5ca09bcf
|
[
"MIT"
] | null | null | null |
analysis/analyze_the_data/collect_data/collect_data.py
|
jamorrison/wgms_kit_comparison
|
f0da12f368f91b6ab8b94aaef60574cf5ca09bcf
|
[
"MIT"
] | null | null | null |
"""Main script for collecting data."""
import glob
import json
import os
import read_biscuitqc
import samtools_stats
import preseq_reports
import obs_exp_ratio
import cpg_coverage
import trim_reports
import trinuc_meth
import raw_read
TOPDIR='2019_11_07_FallopianTube_WGBS_Kit_Comparison/analysis'
def raw_bams():
"""Function for collecting data for files related to the raw BAMs."""
# Raw read quality data
raw_qual_files = glob.glob(TOPDIR + '/raw_read_quality/pbs/*.log')
raw_quals = {}
for f in raw_qual_files:
samp, data = raw_read.process_file(f)
raw_quals[samp] = data
# CpG distribution tables
cpg_dist_files = glob.glob(TOPDIR + '/cpg_covg/*_cpg_dist_table.txt')
cpg_dists = {}
for f in cpg_dist_files:
samp, data = cpg_coverage.process_dist_table(f)
cpg_dists[samp] = data
# CpG depth tables
cpg_depth_files = glob.glob(TOPDIR + '/cpg_covg/*_cpgs_*_table.txt')
cpg_depths = {}
for f in cpg_depth_files:
samp, data = cpg_coverage.process_depth_table(f)
if samp not in cpg_depths.keys():
cpg_depths[samp] = {}
cpg_depths[samp].update(data)
# Trimming reports
trim_report_files = glob.glob(TOPDIR + '/../trimmed_fastq/*_L000_R1_*report.txt')
trim_results = {}
for f in trim_report_files:
samp, data = trim_reports.process_file(f)
trim_results[samp] = data
# Samtools stats
stats_files = glob.glob(TOPDIR + '/align/*.bam.stat')
stats_results = {}
for f in stats_files:
samp, data = samtools_stats.process_file(f)
stats_results[samp] = data
# BISCUITqc
bisc_qc_dirs = glob.glob(TOPDIR + '/align/*_QC')
bisc_qc_data = {}
for d in bisc_qc_dirs:
samp = os.path.basename(d).replace('_QC', '')
data = read_biscuitqc.process_dir(d, samp)
bisc_qc_data[samp] = data
# Preseq
preseq_files = glob.glob(TOPDIR + '/preseq/*.ccurve.txt')
preseq_results = {}
for f in preseq_files:
samp, data = preseq_reports.process_file(f)
preseq_results[samp] = data
# Obs/Exp ratios
obs_exp_files = glob.glob(TOPDIR + '/analyze_the_data/cpg_questions/exp_vs_obs_coverage/pbs_mappability/*.stdout')
obs_exp_data = {}
for f in obs_exp_files:
samp, data = obs_exp_ratio.process_file(f, prefix='mappy_')
obs_exp_data[samp] = data
# Trinucleotide context (CAH, CAG, CTH, CTG) methylation
trinuc_files = glob.glob(TOPDIR + '/analyze_the_data/cpg_questions/trinuc_methylation/*_raw.tsv')
trinuc_results = {}
for f in trinuc_files:
samp, data = trinuc_meth.process_file(f, '_raw.tsv')
trinuc_results[samp] = data
collected_data = {}
dics = [
raw_quals,
cpg_dists,
cpg_depths,
trim_results,
stats_results,
bisc_qc_data,
preseq_results,
obs_exp_data,
trinuc_results
]
for d in dics:
for key, value in d.items():
if key not in collected_data.keys():
collected_data[key] = {}
collected_data[key].update(value)
with open('kit_comp_collected_data.json', 'w') as write_file:
json.dump(collected_data, write_file, indent=4)
def subsampled_bams():
"""Function for collecting data from the subsampled BAMs."""
# Raw read quality data
raw_qual_files = glob.glob(TOPDIR + '/raw_read_quality/pbs/*.log')
raw_quals = {}
for f in raw_qual_files:
samp, data = raw_read.process_file(f)
raw_quals[samp] = data
# CpG distribution tables
cpg_dist_files = glob.glob(TOPDIR + '/analyze_the_data/subsampling/cpg_covg/*_cpg_dist_table.txt')
cpg_dists = {}
for f in cpg_dist_files:
samp, data = cpg_coverage.process_dist_table(f)
cpg_dists[samp] = data
# CpG depth tables
cpg_depth_files = glob.glob(TOPDIR + '/analyze_the_data/subsampling/cpg_covg/*_cpgs_*_table.txt')
cpg_depths = {}
for f in cpg_depth_files:
samp, data = cpg_coverage.process_depth_table(f)
if samp not in cpg_depths.keys():
cpg_depths[samp] = {}
cpg_depths[samp].update(data)
# Trimming reports
trim_report_files = glob.glob(TOPDIR + '/../trimmed_fastq/*_L000_R1_*report.txt')
trim_results = {}
for f in trim_report_files:
samp, data = trim_reports.process_file(f)
trim_results[samp] = data
# Samtools stats
stats_files = glob.glob(TOPDIR + '/analyze_the_data/subsampling/*.bam.stat')
stats_results = {}
for f in stats_files:
samp, data = samtools_stats.process_file(f)
stats_results[samp] = data
# BISCUITqc
bisc_qc_dirs = glob.glob(TOPDIR + '/analyze_the_data/subsampling/*_QC')
bisc_qc_data = {}
for d in bisc_qc_dirs:
samp = os.path.basename(d).replace('_QC', '')
data = read_biscuitqc.process_dir(d, samp)
bisc_qc_data[samp] = data
# Preseq
preseq_files = glob.glob(TOPDIR + '/analyze_the_data/subsampling/*.ccurve.txt')
preseq_results = {}
for f in preseq_files:
samp, data = preseq_reports.process_file(f)
preseq_results[samp] = data
# Trinucleotide context (CAH, CAG, CTH, CTG) methylation
trinuc_files = glob.glob(TOPDIR + '/analyze_the_data/cpg_questions/trinuc_methylation/*_sub.tsv')
trinuc_results = {}
for f in trinuc_files:
samp, data = trinuc_meth.process_file(f, '_sub.tsv')
trinuc_results[samp] = data
collected_data = {}
dics = [
raw_quals,
cpg_dists,
cpg_depths,
trim_results,
stats_results,
bisc_qc_data,
preseq_results,
trinuc_results
]
for d in dics:
for key, value in d.items():
if key not in collected_data.keys():
collected_data[key] = {}
collected_data[key].update(value)
with open('kit_comp_collected_data_subsampled.json', 'w') as write_file:
json.dump(collected_data, write_file, indent=4)
if __name__ == '__main__':
raw_bams()
subsampled_bams()
| 29.652174
| 118
| 0.651352
| 831
| 6,138
| 4.487365
| 0.141998
| 0.06436
| 0.063824
| 0.076428
| 0.861893
| 0.84634
| 0.84634
| 0.839635
| 0.826227
| 0.814159
| 0
| 0.003878
| 0.243728
| 6,138
| 206
| 119
| 29.796117
| 0.79944
| 0.081623
| 0
| 0.717241
| 0
| 0
| 0.147012
| 0.131668
| 0
| 0
| 0
| 0
| 0
| 1
| 0.013793
| false
| 0
| 0.075862
| 0
| 0.089655
| 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
|
ddb808c241589f006d2886a8b5ec3b3d2292a53d
| 45
|
py
|
Python
|
segment/plots/__init__.py
|
yngtodd/segment
|
fb6fa3d8845cdeef362589182e699c25fdc48bfa
|
[
"MIT"
] | 4
|
2018-11-09T08:16:50.000Z
|
2021-04-16T09:50:07.000Z
|
segment/plots/__init__.py
|
yngtodd/segment
|
fb6fa3d8845cdeef362589182e699c25fdc48bfa
|
[
"MIT"
] | 2
|
2018-09-05T20:00:32.000Z
|
2018-09-10T01:03:47.000Z
|
segment/plots/__init__.py
|
yngtodd/segment
|
fb6fa3d8845cdeef362589182e699c25fdc48bfa
|
[
"MIT"
] | 1
|
2018-11-09T08:16:53.000Z
|
2018-11-09T08:16:53.000Z
|
from .slice_viewer import multi_slice_viewer
| 22.5
| 44
| 0.888889
| 7
| 45
| 5.285714
| 0.714286
| 0.594595
| 0
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| 45
| 1
| 45
| 45
| 0.902439
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| 1
| 0
|
0
| 7
|
fb1134ae5ff32cb47ccfd9838b9b0cb105494e9c
| 15,661
|
py
|
Python
|
chatBotStable/g6.py
|
JKhan01/SM446_TeamXYZ
|
721ba3694bb5d8ecc4cb36f5ad158fabcd8c5f7e
|
[
"Apache-2.0"
] | null | null | null |
chatBotStable/g6.py
|
JKhan01/SM446_TeamXYZ
|
721ba3694bb5d8ecc4cb36f5ad158fabcd8c5f7e
|
[
"Apache-2.0"
] | null | null | null |
chatBotStable/g6.py
|
JKhan01/SM446_TeamXYZ
|
721ba3694bb5d8ecc4cb36f5ad158fabcd8c5f7e
|
[
"Apache-2.0"
] | null | null | null |
# Searching and retrieving emails in inbox
from g4 import Create_Service
import base64
import email
import json
CLIENT_SECRET_FILE = 'credentials2.json'
API_NAME = 'gmail'
API_VERSION = 'v1'
SCOPES = ['https://mail.google.com/']
service = Create_Service(CLIENT_SECRET_FILE, API_NAME, API_VERSION, SCOPES)
user_id = "me"
# print("For retrieving emails from a specific user, give query as 'from: sample@gmail.com'\n" + "For retrieving emails through their labels, give query as 'label_name'")
# query = input(print("Enter your query : "))
#messages = ListMessagesMatchingQuery(service, user_id, query)
# prints every message from the user
#print(messages)
'''
if not messages:
print("No messages found")
else:
print("The latest message from the specified user or label is :")
for message in messages[:1]:
# For retrieving details about the email
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
#print(msg['snippet'])
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
#print(final_msg)
# For getting the entire body of the mail
m = service.users().messages().get(userId = 'me', id = message['id'], format='full').execute()
body = base64.urlsafe_b64decode(m.get("payload").get("body").get("data").encode("ASCII")).decode("utf-8")
print(body)
'''
class Object:
def toJSON(self):
return json.dumps(self, default=lambda o: o.__dict__,
sort_keys=True, indent=4)
def ListMailsMatchingQuery(service, user_id, query):
try:
response = service.users().messages().list(userId=user_id,
q=query).execute()
messages = []
if 'messages' in response:
messages.extend(response['messages'])
while 'nextPageToken' in response:
page_token = response['nextPageToken']
response = service.users().messages().list(userId=user_id, q=query,
pageToken=page_token).execute()
messages.extend(response['messages'])
return messages
except:
print ('An error occurred')
def GetLatestMailFromUser(q, op):
# print("For retrieving emails from a specific user, give query as 'from: sample@gmail.com'")
# query = input(print("Enter your query : "))
messages = ListMailsMatchingQuery(service, user_id, q)
if not messages:
print("No messages found")
else:
#print("The latest message from the specified user or label is :")
for message in messages[:1]:
# print("What information do you need about the latest email : " + "\n 1. Entire Info\n 2. Body snippet\n 3. Subject\n 4. Entire Body")
# op = int(input())
if op == 1 :
# For retrieving details about the email
# Getting the complete body of the email
m = service.users().messages().get(userId = 'me', id = message['id'], format='full').execute()
body = base64.urlsafe_b64decode(m.get("payload").get("body").get("data").encode("ASCII")).decode("utf-8")
# For retrieving details about the email
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
display = Object()
display.From = final_msg["From"]
display.To = final_msg["To"]
display.Subject = final_msg["Subject"]
display.body = body
#print(final_msg) # It prints the entire info about the email
print(display.toJSON())
return (display.toJSON()) # Returns display[] as a JSON file
elif op == 2 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['snippet'])
#return (print(json.dumps(final_msg['snippet']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['snippet'])) # returns the snippet of the mail in JSON format
elif op == 3 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['Subject'])
#return (print(json.dumps(final_msg['Subject']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['Subject'])) # returns the Subject of the mail in JSON format
elif op == 4 :
# For getting the entire body of the mail
m = service.users().messages().get(userId = 'me', id = message['id'], format='full').execute()
body = base64.urlsafe_b64decode(m.get("payload").get("body").get("data").encode("ASCII")).decode("utf-8")
print(body)
#return (print(json.dumps(body))) # this could be used to directly print the JSON file in front end
return (json.dumps(body)) # returns the snippet of the mail in JSON format
else:
print("Invalid Input")
return(json.dumps("Invalid Input"))
def GetLatestMailFromLabel(query, op):
# print("For retrieving emails through their labels, give query as 'label_name'")
# query = input(print("Enter your query : "))
messages = ListMailsMatchingQuery(service, user_id, query)
if not messages:
print("No messages found")
else:
#print("The latest message from the specified user or label is :")
for message in messages[:1]:
# print("What information do you need about the latest email : " + "\n 1. Entire Info\n 2. Body snippet\n 3. Subject\n 4. Entire Body\n 5. Sender\n 6. Receiver")
# op = int(input())
if op == 1 :
# Getting the complete body of the email
m = service.users().messages().get(userId = 'me', id = message['id'], format='full').execute()
body = base64.urlsafe_b64decode(m.get("payload").get("body").get("data").encode("ASCII")).decode("utf-8")
# For retrieving details about the email
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
display = Object()
display.From = final_msg["From"]
display.To = final_msg["To"]
display.Subject = final_msg["Subject"]
display.body = body
#print(final_msg) # It prints the entire info about the email
return (display.toJSON()) # Returns display[] as a JSON file
elif op == 2 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['snippet'])
#return (print(json.dumps(final_msg['snippet']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['snippet'])) # returns the snippet of the mail in JSON format
elif op == 3 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['Subject'])
#return (print(json.dumps(final_msg['Subject']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['Subject'])) # returns the Subject of the mail in JSON format
elif op == 4 :
# For getting the entire body of the mail
m = service.users().messages().get(userId = 'me', id = message['id'], format='full').execute()
body = base64.urlsafe_b64decode(m.get("payload").get("body").get("data").encode("ASCII")).decode("utf-8")
print(body)
#return (print(json.dumps(body))) # this could be used to directly print the JSON file in front end
return (json.dumps(body)) # returns the snippet of the mail in JSON format
elif op == 5 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['From'])
#return (print(json.dumps(final_msg['From']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['From'])) # returns the Sender of the mail in JSON format
elif op == 6 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['To'])
#return (print(json.dumps(final_msg['To']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['To'])) # returns the Receiver of the mail in JSON format
else:
print("Invalid Input")
return(json.dumps("Invalid Input"))
def LatestMailInInbox(op):
#try:
response = service.users().messages().list(userId=user_id,
labelIds = "INBOX").execute()
messages = []
if 'messages' in response:
messages.extend(response['messages'])
while 'nextPageToken' in response:
page_token = response['nextPageToken']
response = service.users().messages().list(userId=user_id, labelIds = "INBOX",
pageToken=page_token).execute()
messages.extend(response['messages'])
# except:
# print ("An error occurred")
if not messages:
print("No messages found")
else:
#print("The latest message in the inbox is :")
for message in messages[:1]:
#print("What information do you need about the latest email : " + "\n 1. Entire Info\n 2. Body snippet\n 3. Subject\n 4. Entire Body\n 5. Sender\n 6. Receiver")
#op = int(input())
if op == 1 :
# Getting the complete body of the email
m = service.users().messages().get(userId = 'me', id = message['id'], format='full').execute()
body = base64.urlsafe_b64decode(m.get("payload").get("body").get("data").encode("ASCII")).decode("utf-8")
# For retrieving details about the email
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
display = Object()
display.From = final_msg["From"]
display.To = final_msg["To"]
display.Subject = final_msg["Subject"]
display.body = body
#print(final_msg) # It prints the entire info about the email
return (display.toJSON()) # Returns display[] as a JSON file
elif op == 2 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['snippet'])
#return (print(json.dumps(final_msg['snippet']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['snippet'])) # returns the snippet of the mail in JSON format
elif op == 3 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['Subject'])
#return (print(json.dumps(final_msg['Subject']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['Subject'])) # returns the Subject of the mail in JSON format
elif op == 4 :
# For getting the entire body of the mail
m = service.users().messages().get(userId = 'me', id = message['id'], format='full').execute()
body = base64.urlsafe_b64decode(m.get("payload").get("body").get("data").encode("ASCII")).decode("utf-8")
print(body)
#return (print(json.dumps(body))) # this could be used to directly print the JSON file in front end
return (json.dumps(body)) # returns the body of the mail in JSON format
elif op == 5 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['From'])
#return (print(json.dumps(final_msg['From']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['From'])) # returns the Sender of the mail in JSON format
elif op == 6 :
msg = service.users().messages().get(userId = 'me', id = message['id'], format='raw').execute()
msg_str = base64.urlsafe_b64decode(msg['raw'].encode('ASCII'))
final_msg = email.message_from_bytes(msg_str)
print(final_msg['To'])
#return (print(json.dumps(final_msg['To']))) # this could be used to directly print the JSON file in front end
return (json.dumps(final_msg['To'])) # returns the Receiver of the mail in JSON format
else:
print("Invalid Input")
return(json.dumps("Invalid Input"))
#GetLatestMailFromUser()
#GetLatestMailFromLabel()
#LatestMailInInbox()
| 46.33432
| 173
| 0.567014
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| 338
| 174
| 46.33432
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| false
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| 0
|
0
| 7
|
3486d8b51490134d613fdb7d4db4e6c87bc2e789
| 184
|
py
|
Python
|
pltools/config/__init__.py
|
PhoenixDL/PytorchLightningTools
|
86185062d4792e6d5eae002a5594bb7b900106a1
|
[
"MIT"
] | 3
|
2020-05-18T06:34:52.000Z
|
2020-07-17T07:11:57.000Z
|
pltools/config/__init__.py
|
PhoenixDL/PytorchLightningTools
|
86185062d4792e6d5eae002a5594bb7b900106a1
|
[
"MIT"
] | 6
|
2021-06-25T18:21:06.000Z
|
2021-06-25T18:21:32.000Z
|
pltools/config/__init__.py
|
PhoenixDL/PytorchLightningTools
|
86185062d4792e6d5eae002a5594bb7b900106a1
|
[
"MIT"
] | 1
|
2020-05-18T06:34:56.000Z
|
2020-05-18T06:34:56.000Z
|
from pltools.config.hydra import nested_get, nested_contains, nested_get_first, patch_dictconf
from pltools.config.config import Config
from pltools.config.decoder import HydraDecoder
| 46
| 94
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|
0
| 7
|
3493676ee4ef2430daff0362c5998e9a4078e57c
| 40,958
|
py
|
Python
|
src/143. Reorder List.py
|
xiaonanln/myleetcode-python
|
95d282f21a257f937cd22ef20c3590a69919e307
|
[
"Apache-2.0"
] | null | null | null |
src/143. Reorder List.py
|
xiaonanln/myleetcode-python
|
95d282f21a257f937cd22ef20c3590a69919e307
|
[
"Apache-2.0"
] | null | null | null |
src/143. Reorder List.py
|
xiaonanln/myleetcode-python
|
95d282f21a257f937cd22ef20c3590a69919e307
|
[
"Apache-2.0"
] | null | null | null |
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution(object):
def reorderList(self, head):
"""
:type head: ListNode
:rtype: void Do not return anything, modify head in-place instead.
"""
nodes = []
p = head
while p is not None:
nodes.append(p)
p = p.next
if len(nodes) <= 2:
return
i, j = 0, len(nodes)-1
while True:
if i+1 < j:
nodes[i].next = nodes[j]
nodes[j].next = nodes[i+1]
i, j = i+1, j-1
elif i + 1 == j:
nodes[i].next = nodes[j]
nodes[j].next = None
break
elif i == j:
nodes[i].next = None
break
return
import utils
head = utils.makelist()
Solution().reorderList(head)
utils.printlist(head)
head = utils.makelist(1)
Solution().reorderList(head)
utils.printlist(head)
head = utils.makelist(1,2,3,4)
Solution().reorderList(head)
utils.printlist(head)
head = utils.makelist(1,2,3,4,5)
Solution().reorderList(head)
utils.printlist(head)
head = utils.makelist(*[3,2,3,3,3,1,3,1,3,3,1,1,3,3,2,1,1,1,1,2,1,1,2,1,2,1,3,2,3,1,1,1,2,2,3,3,1,3,1,2,2,2,3,3,2,3,3,1,1,3,1,3,2,2,1,2,1,1,2,3,2,1,3,2,3,1,2,2,2,2,2,3,1,2,2,3,1,2,3,3,1,1,3,2,2,2,2,2,3,3,2,1,2,2,3,2,2,2,1,3,2,3,2,1,3,2,3,3,3,3,2,3,3,3,1,1,2,2,2,3,3,1,2,3,1,2,2,3,1,2,1,1,2,3,2,1,2,1,3,1,1,3,2,2,1,1,3,2,2,1,2,2,1,2,2,1,1,3,3,1,3,1,2,2,1,1,1,3,2,1,3,3,2,3,1,1,2,2,1,1,3,1,1,1,2,2,1,3,1,3,1,1,3,2,1,1,1,2,2,2,1,3,3,1,3,2,2,2,3,3,2,3,2,3,1,2,2,2,2,3,1,3,2,1,2,2,2,2,3,2,1,2,1,1,3,1,3,1,3,2,3,1,2,1,3,2,3,2,3,1,2,2,2,2,1,2,2,3,2,3,1,1,1,2,1,2,2,1,2,1,1,3,3,1,3,3,3,3,1,3,1,3,3,1,2,3,2,1,1,1,3,3,1,1,1,2,2,3,1,1,2,3,2,3,1,2,1,3,3,3,1,2,2,3,1,2,2,3,3,2,2,1,1,1,3,2,3,3,3,3,3,1,3,1,2,3,1,3,1,1,1,2,3,1,1,1,1,2,1,2,3,2,3,2,1,1,2,3,2,1,3,1,3,2,1,3,1,1,3,3,2,2,2,2,2,2,1,2,3,1,3,2,2,3,3,2,2,3,1,3,3,1,1,3,2,1,2,1,2,2,2,2,1,1,2,3,1,1,2,2,1,3,3,3,2,3,1,2,3,2,3,2,1,2,1,1,3,3,3,2,1,1,1,1,3,3,3,1,2,2,2,2,3,1,1,3,1,3,1,2,2,3,3,1,3,2,3,3,1,1,2,1,2,3,2,2,1,1,2,3,3,2,2,3,1,2,1,2,3,2,3,2,3,2,2,2,2,1,2,1,3,2,3,1,1,3,3,3,1,1,3,3,3,3,1,1,1,2,1,2,3,1,1,2,2,2,2,1,1,3,2,3,2,2,3,1,2,1,3,2,2,1,1,1,3,3,3,1,1,1,1,1,1,3,3,1,3,2,3,1,1,1,3,3,1,2,3,1,2,3,2,2,3,1,1,3,2,2,3,2,3,3,1,2,3,1,2,3,1,2,1,3,1,2,2,3,1,1,1,1,1,2,3,2,3,3,3,3,2,1,1,3,3,1,2,1,3,3,2,1,2,3,1,1,2,2,3,1,3,2,1,3,1,1,2,2,2,3,3,2,2,3,3,1,1,1,3,2,1,1,3,2,2,1,2,1,3,1,2,2,3,2,3,3,3,1,3,3,2,2,1,3,1,3,1,3,3,1,1,1,3,2,2,1,1,3,1,3,3,1,2,2,3,3,2,1,3,1,1,1,2,2,1,2,3,1,3,2,1,3,1,3,1,1,1,1,2,1,1,1,1,1,2,3,2,2,3,3,2,2,3,2,3,1,1,3,3,2,2,2,1,1,2,1,3,2,2,2,1,2,3,3,2,2,2,2,1,1,2,3,3,3,3,2,3,1,2,2,2,1,3,1,1,1,2,1,3,2,2,1,3,1,2,2,1,3,2,3,3,1,1,1,2,2,2,2,3,1,2,1,1,2,3,2,3,2,3,1,3,2,3,3,3,3,1,1,3,1,2,1,1,1,1,2,3,1,3,2,1,3,1,2,3,2,3,2,3,1,2,2,1,2,1,2,3,3,1,3,3,1,3,3,1,2,1,1,2,3,3,1,3,3,3,1,2,2,2,2,3,2,3,1,2,1,1,3,1,3,2,3,1,2,2,1,2,2,2,1,1,1,3,2,3,2,3,1,1,3,1,2,3,1,2,2,2,2,1,3,2,2,1,3,3,1,3,2,2,2,3,2,3,1,1,2,2,2,1,3,1,1,2,1,3,1,2,2,3,1,1,3,3,2,1,2,2,1,2,3,1,3,1,3,2,1,3,1,3,1,1,3,3,3,3,3,1,1,1,2,2,2,2,2,1,1,3,2,3,3,1,3,3,3,1,1,2,3,1,1,3,3,3,1,1,3,1,3,3,3,2,1,1,3,3,3,3,2,3,3,2,2,1,1,1,1,2,2,1,1,1,2,2,2,1,1,3,2,2,1,1,2,3,3,1,3,3,3,3,2,3,2,2,2,1,2,1,3,1,2,2,2,3,1,1,3,3,1,3,1,2,1,2,3,1,1,3,3,2,2,2,2,2,3,1,2,1,3,1,3,2,2,1,2,1,1,3,2,3,2,2,2,1,2,1,2,1,1,1,1,2,3,2,1,1,1,3,3,3,1,2,2,3,2,1,1,1,2,2,2,1,3,2,3,3,1,3,3,1,3,2,3,3,2,3,1,2,3,2,2,3,3,3,1,2,2,1,2,3,1,3,2,2,3,1,1,1,2,2,1,2,2,3,1,3,3,2,1,2,3,3,2,3,3,3,1,1,1,1,2,3,1,1,2,1,1,1,3,1,2,3,1,1,1,3,2,2,1,3,1,1,1,2,3,1,1,1,3,1,1,1,3,2,2,2,3,2,3,2,1,3,3,1,1,2,2,2,1,2,3,2,3,3,1,2,3,3,3,1,3,3,2,1,3,3,3,1,1,3,3,2,1,1,3,1,1,1,3,3,1,2,2,3,3,2,2,1,1,1,2,1,2,1,2,1,1,2,2,3,3,2,1,1,3,1,3,2,1,3,1,2,2,3,3,3,2,1,3,1,1,3,3,3,2,1,2,2,1,1,2,3,3,1,2,3,2,2,2,2,2,1,2,2,3,2,1,1,2,3,2,2,2,1,1,1,1,2,1,2,2,1,1,1,2,2,2,2,3,2,2,1,1,3,3,1,3,3,2,1,2,3,2,1,2,1,3,1,1,3,2,2,1,2,1,1,1,1,1,3,1,2,2,2,1,1,3,3,3,1,2,2,2,2,2,3,2,3,3,2,3,3,1,3,1,2,3,1,2,1,3,1,2,3,3,1,2,1,3,3,2,2,2,2,3,2,3,1,2,3,2,1,2,1,3,3,1,3,2,1,3,1,1,1,3,2,1,3,3,1,3,1,2,1,2,2,3,3,1,2,2,3,2,3,2,1,1,2,3,2,1,3,1,2,2,1,1,2,2,2,1,3,2,1,1,2,3,2,2,1,3,2,1,2,3,2,1,1,1,1,1,1,3,3,1,3,2,1,2,1,3,3,1,1,1,2,3,2,3,3,2,2,2,2,1,1,1,3,3,1,3,3,1,3,3,3,2,1,3,2,2,3,3,2,2,1,2,3,3,2,2,1,2,1,3,3,3,2,2,2,3,3,2,3,2,3,1,2,3,2,3,1,3,1,1,1,1,1,2,1,1,2,1,2,3,1,3,1,3,3,2,3,1,2,1,2,3,2,1,1,2,1,1,1,1,2,3,2,1,2,3,1,3,2,2,3,1,1,3,3,1,2,1,3,2,1,2,1,2,3,2,1,2,2,2,2,2,1,2,1,2,1,2,2,3,3,1,3,2,1,1,1,1,3,1,1,3,2,1,3,1,2,3,1,2,2,3,3,3,2,3,3,1,3,1,1,3,1,1,2,1,1,2,1,2,2,2,3,1,1,3,3,3,2,1,3,2,2,3,3,3,2,2,3,2,3,3,2,2,3,2,2,2,3,2,1,2,3,3,1,3,3,3,1,3,3,1,1,2,3,1,1,3,1,3,1,2,1,2,1,2,2,2,2,1,2,3,2,3,2,2,3,1,3,2,3,2,2,1,1,2,3,1,2,3,2,1,3,1,1,2,2,1,2,1,1,1,1,1,1,3,2,3,3,1,1,1,2,1,2,3,1,3,3,2,1,2,1,2,3,1,2,1,1,2,2,1,1,3,1,1,2,3,3,3,1,3,2,3,1,3,1,1,3,3,1,1,2,1,1,3,1,1,3,2,3,3,2,3,2,2,3,3,3,1,1,1,2,1,1,1,3,2,2,3,2,3,2,3,2,2,3,1,2,1,2,2,3,2,1,1,3,2,2,1,3,1,1,2,2,2,3,3,1,1,3,1,2,2,2,2,1,1,1,1,2,1,3,2,2,2,2,3,2,1,2,2,3,3,1,1,1,3,2,3,1,2,2,2,2,2,2,2,2,1,3,1,2,1,2,2,2,3,3,2,3,2,2,2,1,3,3,1,2,1,3,2,1,1,3,3,2,1,3,1,3,2,1,3,2,3,3,2,3,1,3,3,2,2,1,2,2,3,1,1,3,3,3,2,3,2,1,3,2,3,1,2,2,1,2,2,2,1,2,3,3,2,2,2,1,1,1,1,2,3,3,2,1,2,1,1,2,1,1,1,1,3,1,3,3,2,3,3,2,2,1,3,1,2,2,2,2,3,1,2,3,2,1,2,1,3,3,1,1,3,3,1,3,3,3,3,3,1,2,2,3,1,2,2,3,1,3,2,1,1,1,1,1,3,3,2,2,1,3,3,3,2,2,2,3,3,2,1,3,1,2,1,1,2,1,1,1,3,2,2,3,1,1,1,2,2,3,2,2,2,3,1,2,2,3,3,2,3,2,1,1,1,3,3,2,2,2,1,1,2,3,2,3,2,3,2,1,2,1,1,2,1,1,2,2,3,2,3,1,3,2,1,2,2,2,2,1,1,1,3,2,2,2,3,2,1,2,2,3,1,2,2,3,3,1,1,3,2,2,3,3,1,2,1,1,3,3,1,3,3,3,1,2,2,2,3,1,1,2,1,2,2,1,1,1,3,3,2,1,2,3,1,3,3,1,2,2,3,1,2,1,3,2,1,2,1,1,2,2,3,1,2,3,2,2,2,2,1,2,3,1,3,1,2,1,3,2,3,1,2,1,1,3,1,3,1,1,1,2,3,2,1,3,1,3,2,2,2,1,3,1,2,2,2,1,2,2,3,3,1,1,1,1,1,1,1,2,2,3,1,2,3,1,3,1,2,2,2,1,3,3,1,3,1,3,1,2,2,3,3,2,3,3,2,2,1,3,1,2,3,3,3,3,3,1,1,1,1,1,2,1,2,1,1,1,2,1,1,3,2,1,2,2,1,1,2,2,3,2,1,1,2,2,1,3,1,2,1,1,3,2,1,3,3,1,1,1,1,3,3,2,2,1,2,3,1,1,2,2,1,1,3,2,3,2,2,3,3,1,1,2,2,1,1,3,2,1,3,1,1,2,3,2,1,2,2,3,2,3,1,3,2,2,3,3,1,3,2,3,2,2,1,3,1,3,3,1,1,2,1,2,3,3,2,3,1,1,3,3,3,3,1,2,2,3,3,2,2,1,2,1,1,1,2,2,3,3,3,2,2,2,2,2,3,2,1,3,3,2,3,2,1,1,3,1,2,3,1,2,2,3,1,2,2,3,2,1,2,3,3,3,2,1,1,3,1,3,3,2,2,3,1,3,1,3,2,3,3,2,1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,1,1,3,1,1,1,1,1,1,2,2,3,1,1,1,2,3,1,2,3,1,1,2,3,2,1,2,2,2,2,1,2,3,2,3,1,1,2,2,3,1,3,1,3,3,3,1,2,2,2,3,2,1,1,3,3,1,3,3,3,3,2,3,1,2,1,2,2,1,2,3,3,2,1,2,1,1,3,3,1,3,3,3,3,3,2,1,2,3,3,1,2,2,1,2,2,1,3,2,2,1,1,1,3,2,2,2,1,3,2,3,2,1,1,3,2,3,2,1,3,2,3,3,2,1,2,2,1,1,3,3,2,2,3,1,3,2,2,2,2,3,2,3,3,3,1,1,1,2,2,3,3,3,1,2,1,1,3,1,1,3,3,3,1,3,3,2,1,3,1,1,2,1,3,1,1,3,3,3,3,1,3,2,2,1,3,1,3,2,2,3,1,3,3,2,2,1,2,2,1,1,1,3,3,3,2,3,3,2,2,2,2,1,2,1,2,2,1,3,3,3,2,2,2,1,1,3,3,1,3,3,3,2,3,1,1,2,3,1,1,2,1,3,2,3,2,3,3,2,3,3,1,1,3,3,1,3,1,3,1,2,2,1,1,2,1,1,1,1,3,3,2,1,2,2,3,2,2,1,3,3,2,2,2,3,1,1,1,3,2,2,1,2,3,2,3,3,1,2,2,3,1,3,3,2,3,1,1,1,3,3,2,1,3,3,2,2,2,2,2,3,1,1,3,2,2,1,3,2,3,1,3,1,3,1,3,2,1,1,1,2,3,2,1,2,3,3,3,2,1,1,3,1,1,2,2,2,3,3,2,1,1,2,1,3,3,3,1,1,2,1,1,1,3,2,1,2,3,3,3,3,2,2,3,2,1,1,3,1,2,3,1,3,1,1,3,2,3,2,1,2,1,1,3,2,1,1,3,3,1,2,2,3,2,2,2,3,1,1,1,2,2,2,3,2,2,1,2,3,1,1,1,3,1,3,1,2,3,3,3,3,3,3,1,3,1,3,2,3,1,3,3,3,1,3,3,1,1,2,1,2,1,1,1,2,1,3,2,1,3,3,3,2,1,1,2,1,1,2,1,2,1,2,2,3,3,3,2,2,3,2,2,3,2,3,1,2,2,2,3,3,1,1,1,1,3,3,3,1,3,2,3,3,3,1,3,1,3,1,1,1,1,1,2,3,2,1,2,3,1,3,1,3,2,1,1,2,2,1,3,2,2,1,1,2,3,2,1,1,3,3,2,2,2,1,3,1,1,1,2,3,3,2,1,1,1,1,2,1,1,2,2,1,2,3,1,2,2,3,3,3,1,3,1,1,1,1,3,3,1,3,3,2,3,3,2,3,2,1,3,1,2,3,3,1,3,3,1,2,1,2,3,3,3,2,3,1,1,1,3,2,2,2,2,2,1,1,1,1,3,3,3,1,3,1,3,3,1,2,1,2,3,3,1,2,3,3,1,1,3,2,3,1,3,3,2,2,3,1,3,2,3,2,2,2,3,2,1,2,1,1,3,1,1,3,3,1,2,1,1,3,1,1,2,2,2,3,1,1,3,3,3,1,1,1,1,3,2,2,2,1,3,2,1,2,1,2,3,1,1,2,1,3,3,1,2,2,1,2,2,2,3,2,1,1,1,3,3,2,2,2,3,1,1,2,3,1,2,2,3,2,2,2,1,1,3,1,1,3,2,2,2,1,2,3,2,1,2,1,1,1,2,2,2,3,3,2,2,1,2,3,3,3,1,2,1,3,2,3,2,2,2,1,2,2,1,1,3,3,2,2,2,3,3,2,2,2,3,2,2,1,2,1,1,2,3,1,1,2,1,3,2,3,2,3,1,1,1,3,3,2,1,3,3,3,1,3,3,2,2,3,2,1,3,2,1,1,3,2,2,1,1,1,1,1,2,1,1,1,1,3,2,2,1,1,1,3,1,3,2,2,3,2,2,2,2,3,1,1,2,3,2,2,3,3,2,2,3,2,3,1,3,3,2,1,2,2,1,1,3,1,2,1,2,3,3,1,1,3,1,2,3,3,1,2,1,2,3,2,1,2,2,3,1,3,1,3,3,3,2,1,1,2,2,1,2,2,3,2,1,2,3,3,3,3,1,3,1,2,3,2,3,2,1,3,3,1,3,2,3,3,3,3,1,2,3,2,3,1,2,3,3,1,1,1,3,2,1,2,3,1,1,1,1,1,2,3,2,3,2,1,1,2,3,1,2,2,1,3,3,3,1,1,3,3,2,3,1,1,3,3,3,1,3,1,3,2,2,1,1,2,1,2,1,1,1,3,1,3,3,3,3,2,3,3,3,1,1,3,1,3,1,1,3,2,1,1,3,1,1,3,1,2,2,3,1,3,3,1,1,1,1,2,1,2,2,2,3,1,2,1,2,3,1,2,1,3,2,3,2,3,1,2,1,1,1,3,1,1,3,3,1,3,1,3,3,1,1,3,3,3,2,1,3,1,2,1,3,1,1,2,3,3,1,1,2,2,3,1,3,3,1,2,1,1,2,3,3,2,2,2,3,3,2,3,1,1,2,2,2,1,2,3,2,1,2,1,1,3,3,3,1,2,1,2,3,2,1,3,1,1,3,1,2,2,3,1,1,1,2,3,2,1,1,3,3,2,3,2,2,3,2,3,3,3,2,1,2,1,3,1,2,1,3,2,2,2,3,3,2,1,1,1,2,3,2,2,2,2,3,1,2,3,3,2,3,2,2,2,3,2,3,2,3,1,3,2,3,1,3,1,1,3,2,1,1,3,1,2,2,2,2,1,1,1,3,2,3,3,2,3,2,3,2,1,1,2,3,3,2,1,2,3,3,3,1,1,3,2,2,3,3,2,1,2,1,1,1,2,3,2,2,3,3,1,1,3,3,1,3,2,3,1,1,2,2,1,2,1,1,3,1,3,3,1,3,1,1,2,1,1,2,1,1,3,3,3,3,3,3,1,1,1,3,3,3,2,1,3,2,1,2,2,3,1,1,2,3,3,3,1,3,3,2,2,1,2,2,1,3,3,2,1,2,1,1,2,3,2,3,3,3,3,3,2,1,3,2,3,1,2,3,3,3,1,1,2,2,1,2,2,1,2,3,3,1,3,1,3,1,3,2,2,3,1,3,3,3,2,2,3,1,2,1,2,1,2,2,1,3,3,1,3,1,2,1,2,2,2,2,3,1,2,3,1,1,1,3,1,1,3,2,1,2,3,2,2,1,1,2,2,2,3,1,3,3,1,3,1,2,1,2,2,3,3,3,1,3,3,1,1,1,3,3,3,3,3,3,3,3,3,2,3,3,3,1,2,2,1,2,2,2,3,2,3,3,1,3,2,3,3,2,1,3,2,1,3,3,3,2,2,1,3,1,3,2,1,2,1,3,3,3,3,1,1,1,3,3,3,3,3,2,1,2,2,2,1,1,3,3,1,3,3,3,2,2,3,3,1,1,3,3,1,3,3,2,2,3,2,3,2,1,1,3,2,1,3,3,2,1,2,1,3,3,1,1,1,1,3,3,3,2,2,1,1,2,3,2,1,2,3,2,1,2,3,3,1,2,3,3,3,1,1,1,1,1,1,1,2,3,1,1,2,1,3,1,3,2,3,3,2,3,1,2,3,3,2,2,3,1,3,3,2,1,2,2,1,1,3,1,2,3,2,3,3,2,3,1,3,2,2,1,2,1,2,1,2,1,1,1,2,1,1,2,2,2,3,2,2,3,2,1,2,1,3,2,2,1,3,1,2,2,2,3,3,2,3,2,1,3,1,1,1,2,1,3,3,2,2,1,3,3,1,1,1,3,1,1,1,2,2,3,3,3,3,1,3,3,1,2,1,2,2,2,3,1,2,1,2,2,2,2,1,2,2,3,2,1,1,2,2,1,2,3,1,1,3,3,2,2,1,3,3,1,2,1,2,1,1,1,2,1,1,1,3,3,3,1,3,1,1,2,3,1,1,1,3,3,2,1,1,3,2,2,1,2,1,1,1,2,2,1,2,1,3,2,3,3,1,2,1,3,3,1,2,1,1,1,3,1,3,1,1,1,2,2,3,3,2,2,3,2,1,1,1,2,3,1,1,3,1,1,1,3,3,1,1,1,2,2,1,1,2,3,3,3,2,1,2,3,3,3,2,3,3,1,3,3,2,2,3,1,2,2,2,1,1,1,3,3,1,1,3,2,1,3,2,1,1,2,3,2,2,2,1,2,1,3,2,2,1,3,3,1,3,1,1,3,2,3,2,2,3,3,2,1,2,2,1,3,1,3,2,3,3,3,3,1,3,3,1,1,2,1,2,1,3,3,3,1,1,1,3,2,1,1,3,2,3,1,3,2,3,2,1,2,1,3,3,1,3,1,2,3,3,1,2,3,2,1,2,1,3,2,1,3,1,2,1,3,2,2,3,1,1,2,3,1,1,3,1,2,3,2,3,1,2,1,2,1,3,3,1,1,2,3,3,2,1,2,1,2,3,1,2,2,2,1,3,3,3,3,1,3,1,2,1,1,2,2,2,1,1,2,2,3,3,1,2,1,1,1,3,1,1,3,2,2,2,3,1,2,1,3,3,3,2,3,1,3,1,2,1,3,2,2,2,2,2,1,1,1,3,2,1,3,1,3,3,2,3,2,1,1,3,2,3,3,3,1,1,3,1,3,2,1,2,3,1,3,2,1,1,1,3,3,1,1,2,3,2,3,2,3,1,3,1,1,1,2,2,2,3,2,1,2,3,3,1,2,1,1,3,3,2,3,3,3,3,2,3,2,3,2,3,2,3,1,2,2,1,2,3,3,2,3,1,3,2,1,1,1,1,1,2,1,2,2,2,2,1,1,1,3,1,3,2,2,2,1,3,2,1,2,2,3,1,3,3,2,3,1,3,2,2,3,3,2,1,2,1,3,3,3,1,1,1,3,1,3,1,1,2,1,1,1,3,2,3,1,2,1,2,3,3,3,1,1,1,1,2,1,3,3,3,1,3,3,2,1,2,2,1,1,2,3,2,3,3,2,1,1,1,3,1,1,2,2,3,3,3,3,2,2,1,2,3,3,1,1,3,1,3,2,3,2,3,1,3,3,3,1,2,3,3,2,3,3,2,2,2,2,2,1,2,2,3,1,3,3,3,2,3,2,2,3,3,2,1,1,2,1,3,1,3,1,2,3,2,1,3,3,2,1,1,3,3,2,1,2,2,3,1,2,1,1,1,3,2,3,3,2,1,1,2,2,3,2,1,3,2,3,3,3,3,1,1,3,1,2,3,2,1,1,1,3,3,3,2,3,2,3,1,1,1,3,1,2,3,2,2,1,3,3,2,3,1,3,2,3,2,3,1,2,3,1,2,1,2,1,3,3,2,1,3,3,3,1,1,1,3,1,3,3,1,2,3,3,3,2,1,1,3,2,2,3,3,2,3,3,1,3,2,3,2,3,1,2,3,3,2,2,1,3,1,3,1,2,3,3,2,3,2,2,2,3,2,2,3,3,1,1])
Solution().reorderList(head)
utils.printlist(head)
| 660.612903
| 39,873
| 0.503589
| 20,091
| 40,958
| 1.02643
| 0.002538
| 0.219086
| 0.114926
| 0.056639
| 0.98264
| 0.98264
| 0.98264
| 0.980652
| 0.9791
| 0.890408
| 0
| 0.489723
| 0.005786
| 40,958
| 62
| 39,874
| 660.612903
| 0.016699
| 0.005371
| 0
| 0.410256
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.025641
| false
| 0
| 0.025641
| 0
| 0.128205
| 0.128205
| 0
| 0
| 1
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 12
|
34c61347d915b4d65aeff672b8553475dc2b2717
| 4,431
|
py
|
Python
|
Reference/qpc/examples/qutest/dpp/test_dpp/test_tick.py
|
Harveyhubbell/Paid-RTOS
|
e56a1346cce026428c2bfef05b6a4e6bb2ee7f4e
|
[
"MIT"
] | null | null | null |
Reference/qpc/examples/qutest/dpp/test_dpp/test_tick.py
|
Harveyhubbell/Paid-RTOS
|
e56a1346cce026428c2bfef05b6a4e6bb2ee7f4e
|
[
"MIT"
] | null | null | null |
Reference/qpc/examples/qutest/dpp/test_dpp/test_tick.py
|
Harveyhubbell/Paid-RTOS
|
e56a1346cce026428c2bfef05b6a4e6bb2ee7f4e
|
[
"MIT"
] | null | null | null |
# test script for QUTest unit testing harness
# see https://www.state-machine.com/qtools/qutest.html/qutest.html
# preamble...
def on_reset():
expect_pause()
continue_test()
expect_run()
# tests...
test("tick")
glb_filter(GRP_ALL)
current_obj(OBJ_TE, "l_philo[2].timeEvt")
tick()
expect(" Tick<0> Ctr=*")
expect(" TE0-ADis Obj=l_philo[2].timeEvt,AO=AO_Philo[2]")
expect("@timestamp TE0-Post Obj=l_philo[2].timeEvt,Sig=TIMEOUT_SIG,AO=AO_Philo[2]")
expect("@timestamp AO-Post Sdr=QS_RX,Obj=AO_Philo[2],Evt<Sig=TIMEOUT_SIG,Pool=0,Ref=0>,*")
expect("@timestamp AO-GetL Obj=AO_Philo[2],Evt<Sig=TIMEOUT_SIG,Pool=0,Ref=0>")
expect("@timestamp Disp===> Obj=AO_Philo[2],Sig=TIMEOUT_SIG,State=Philo_thinking")
expect("@timestamp TE0-DisA Obj=l_philo[2].timeEvt,AO=AO_Philo[2]")
expect("===RTC===> St-Exit Obj=AO_Philo[2],State=Philo_thinking")
expect("@timestamp MP-Get Obj=EvtPool1,*")
expect("@timestamp QF-New Sig=HUNGRY_SIG,*")
expect("@timestamp AO-Post Sdr=AO_Philo[2],Obj=AO_Table,Evt<Sig=HUNGRY_SIG,Pool=1,Ref=1>,*")
expect("@timestamp QUTEST_ON_POST HUNGRY_SIG,Obj=AO_Table 2")
expect("===RTC===> St-Entry Obj=AO_Philo[2],State=Philo_hungry")
expect("@timestamp ===>Tran Obj=AO_Philo[2],Sig=TIMEOUT_SIG,State=Philo_thinking->Philo_hungry")
expect("@timestamp AO-GetL Obj=AO_Table,Evt<Sig=HUNGRY_SIG,Pool=1,Ref=1>")
expect("@timestamp Disp===> Obj=AO_Table,Sig=HUNGRY_SIG,State=Table_serving")
expect("@timestamp BSP_CALL BSP_displayPhilStat 2 hungry ")
expect("@timestamp MP-Get Obj=EvtPool1,*")
expect("@timestamp QF-New Sig=EAT_SIG,*")
expect("@timestamp QF-Pub Sdr=AO_Table,Evt<Sig=EAT_SIG,Pool=1,Ref=0>")
expect("@timestamp AO-Post Sdr=AO_Table,Obj=AO_Philo[4],Evt<Sig=EAT_SIG,Pool=1,Ref=2>,*")
expect("@timestamp QUTEST_ON_POST EAT_SIG,Obj=AO_Philo[4] 2")
expect("@timestamp AO-Post Sdr=AO_Table,Obj=AO_Philo[3],Evt<Sig=EAT_SIG,Pool=1,Ref=3>,*")
expect("@timestamp QUTEST_ON_POST EAT_SIG,Obj=AO_Philo[3] 2")
expect("@timestamp AO-Post Sdr=AO_Table,Obj=AO_Philo[2],Evt<Sig=EAT_SIG,Pool=1,Ref=4>,*")
expect("@timestamp QUTEST_ON_POST EAT_SIG,Obj=AO_Philo[2] 2")
expect("@timestamp AO-Post Sdr=AO_Table,Obj=AO_Philo[1],Evt<Sig=EAT_SIG,Pool=1,Ref=5>,*")
expect("@timestamp QUTEST_ON_POST EAT_SIG,Obj=AO_Philo[1] 2")
expect("@timestamp AO-Post Sdr=AO_Table,Obj=AO_Philo[0],Evt<Sig=EAT_SIG,Pool=1,Ref=6>,*")
expect("@timestamp QUTEST_ON_POST EAT_SIG,Obj=AO_Philo[0] 2")
expect("@timestamp QF-gcA Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp BSP_CALL BSP_displayPhilStat 2 eating ")
expect("@timestamp =>Intern Obj=AO_Table,Sig=HUNGRY_SIG,State=Table_serving")
expect("@timestamp QF-gc Evt<Sig=HUNGRY_SIG,Pool=1,Ref=1>")
expect("@timestamp MP-Put Obj=EvtPool1,*")
expect("@timestamp AO-GetL Obj=AO_Philo[4],Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp Disp===> Obj=AO_Philo[4],Sig=EAT_SIG,State=Philo_thinking")
expect("@timestamp =>Intern Obj=AO_Philo[4],Sig=EAT_SIG,State=Philo_thinking")
expect("@timestamp QF-gcA Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp AO-GetL Obj=AO_Philo[3],Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp Disp===> Obj=AO_Philo[3],Sig=EAT_SIG,State=Philo_thinking")
expect("@timestamp =>Intern Obj=AO_Philo[3],Sig=EAT_SIG,State=Philo_thinking")
expect("@timestamp QF-gcA Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp AO-GetL Obj=AO_Philo[2],Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp Disp===> Obj=AO_Philo[2],Sig=EAT_SIG,State=Philo_hungry")
expect("@timestamp BSP_CALL BSP_random *")
expect("@timestamp TE0-Arm Obj=l_philo[2].timeEvt,AO=AO_Philo[2],Tim=*,Int=0")
expect("===RTC===> St-Entry Obj=AO_Philo[2],State=Philo_eating")
expect("@timestamp ===>Tran Obj=AO_Philo[2],Sig=EAT_SIG,State=Philo_hungry->Philo_eating")
expect("@timestamp QF-gcA Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp AO-GetL Obj=AO_Philo[1],Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp Disp===> Obj=AO_Philo[1],Sig=EAT_SIG,State=Philo_thinking")
expect("@timestamp =>Intern Obj=AO_Philo[1],Sig=EAT_SIG,State=Philo_thinking")
expect("@timestamp QF-gcA Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp AO-GetL Obj=AO_Philo[0],Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp Disp===> Obj=AO_Philo[0],Sig=EAT_SIG,State=Philo_thinking")
expect("@timestamp =>Intern Obj=AO_Philo[0],Sig=EAT_SIG,State=Philo_thinking")
expect("@timestamp QF-gc Evt<Sig=EAT_SIG,Pool=1,*")
expect("@timestamp MP-Put Obj=EvtPool1,*")
expect("@timestamp Trg-Done QS_RX_TICK")
| 58.302632
| 96
| 0.735951
| 781
| 4,431
| 3.988476
| 0.113956
| 0.264848
| 0.102729
| 0.06549
| 0.85939
| 0.830498
| 0.786838
| 0.756982
| 0.738363
| 0.704013
| 0
| 0.022524
| 0.068156
| 4,431
| 75
| 97
| 59.08
| 0.731896
| 0.029113
| 0
| 0.132353
| 0
| 0.25
| 0.814246
| 0.478585
| 0
| 0
| 0
| 0
| 0
| 1
| 0.014706
| true
| 0
| 0
| 0
| 0.014706
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
34d21bd7171568ba25bf3b2c96e32aea7a9e13e4
| 37,063
|
py
|
Python
|
EDA/Line_plots.py
|
Charmii/Covid-19-total-cases-prediction
|
d3d2347c3e63812f952ba53fc86271e62e314619
|
[
"MIT"
] | null | null | null |
EDA/Line_plots.py
|
Charmii/Covid-19-total-cases-prediction
|
d3d2347c3e63812f952ba53fc86271e62e314619
|
[
"MIT"
] | null | null | null |
EDA/Line_plots.py
|
Charmii/Covid-19-total-cases-prediction
|
d3d2347c3e63812f952ba53fc86271e62e314619
|
[
"MIT"
] | null | null | null |
#LINEPLOTS
import seaborn as sns
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(6,5,1)
sns.lineplot(df['total_cases'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('total cases')
plt.subplot(6,5,2)
sns.lineplot(df['total_cases'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('total cases')
plt.subplot(6,5,3)
sns.lineplot(df['total_cases'],df['population'])
plt.xlabel('population')
plt.ylabel('total cases')
plt.subplot(6,5,4)
sns.lineplot(df['total_cases'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('total cases')
plt.subplot(6,5,5)
sns.lineplot(df['total_cases'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('total cases')
plt.subplot(6,5,6)
sns.lineplot(df['total_cases'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('total cases')
plt.subplot(6,5,7)
sns.lineplot(df['total_cases'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('total cases')
plt.subplot(6,5,8)
sns.lineplot(df['total_cases'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('total cases')
plt.subplot(6,5,9)
sns.lineplot(df['total_cases'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('total cases')
plt.subplot(6,5,10)
sns.lineplot(df['total_cases'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('total cases')
plt.subplot(6,5,11)
sns.lineplot(df['total_cases'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('total cases')
plt.subplot(6,5,12)
sns.lineplot(df['total_cases'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('total cases')
plt.subplot(6,5,13)
sns.lineplot(df['total_cases'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('total cases')
plt.subplot(6,5,14)
sns.lineplot(df['total_cases'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('total cases')
plt.subplot(6,5,15)
sns.lineplot(df['total_cases'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('total cases')
plt.subplot(6,5,16)
sns.lineplot(df['total_cases'],df['population_density'])
plt.xlabel('population_density')
plt.ylabel('total cases')
plt.subplot(6,5,17)
sns.lineplot(df['total_cases'],df['median_age'])
plt.xlabel('median_age')
plt.ylabel('total cases')
plt.subplot(6,5,18)
sns.lineplot(df['total_cases'],df['aged_65_older'])
plt.xlabel('aged_65_older')
plt.ylabel('total cases')
plt.subplot(6,5,19)
sns.lineplot(df['total_cases'],df['aged_70_older'])
plt.xlabel('aged_70_older')
plt.ylabel('total cases')
plt.subplot(6,5,20)
sns.lineplot(df['total_cases'],df['gdp_per_capita'])
plt.xlabel('gdp_per_capita')
plt.ylabel('total cases')
plt.subplot(6,5,21)
sns.lineplot(df['total_cases'],df['extreme_poverty'])
plt.xlabel('extreme_poverty')
plt.ylabel('total cases')
plt.subplot(6,5,22)
sns.lineplot(df['total_cases'],df['cvd_death_rate'])
plt.xlabel('cvd_death_rate')
plt.ylabel('total cases')
plt.subplot(6,5,23)
sns.lineplot(df['total_cases'],df['diabetes_prevalence'])
plt.xlabel('diabetes_prevalence')
plt.ylabel('total cases')
plt.subplot(6,5,24)
sns.lineplot(df['total_cases'],df['female_smokers'])
plt.xlabel('female_smokers')
plt.ylabel('total cases')
plt.subplot(6,5,25)
sns.lineplot(df['total_cases'],df['male_smokers'])
plt.xlabel('male_smokers')
plt.ylabel('total cases')
plt.subplot(6,5,26)
sns.lineplot(df['total_cases'],df['handwashing_facilities'])
plt.xlabel('handwashing_facilities')
plt.ylabel('total cases')
plt.subplot(6,5,27)
sns.lineplot(df['total_cases'],df['hospital_beds_per_thousand'])
plt.xlabel('hospital_beds_per_thousand')
plt.ylabel('total cases')
plt.subplot(6,5,28)
sns.lineplot(df['total_cases'],df['life_expectancy'])
plt.xlabel('life_expectancy')
plt.ylabel('total cases')
#Lineplot for new_cases
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['new_cases'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('new_cases')
plt.subplot(4,4,2)
sns.lineplot(df['new_cases'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('new_cases')
plt.subplot(4,4,3)
sns.lineplot(df['new_cases'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('new_cases')
plt.subplot(4,4,4)
sns.lineplot(df['new_cases'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('new_cases')
plt.subplot(4,4,5)
sns.lineplot(df['new_cases'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('new_cases')
plt.subplot(4,4,6)
sns.lineplot(df['new_cases'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('new_cases')
plt.subplot(4,4,7)
sns.lineplot(df['new_cases'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('new_cases')
plt.subplot(4,4,8)
sns.lineplot(df['new_cases'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('new_cases')
plt.subplot(4,4,9)
sns.lineplot(df['new_cases'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('new_cases')
plt.subplot(4,4,10)
sns.lineplot(df['new_cases'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('new_cases')
plt.subplot(4,4,11)
sns.lineplot(df['new_cases'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('new_cases')
plt.subplot(4,4,12)
sns.lineplot(df['new_cases'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('new_cases')
plt.subplot(4,4,13)
sns.lineplot(df['new_cases'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('new_cases')
plt.subplot(4,4,14)
sns.lineplot(df['new_cases'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('new_cases')
plt.subplot(4,4,15)
sns.lineplot(df['new_cases'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('new_cases')
#Lineplot for total_deaths
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['total_deaths'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('total_deaths')
plt.subplot(4,4,2)
sns.lineplot(df['total_deaths'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('total_deaths')
plt.subplot(4,4,3)
sns.lineplot(df['total_deaths'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('total_deaths')
plt.subplot(4,4,4)
sns.lineplot(df['total_deaths'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('total_deaths')
plt.subplot(4,4,5)
sns.lineplot(df['total_deaths'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('total_deaths')
plt.subplot(4,4,6)
sns.lineplot(df['total_deaths'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('total_deaths')
plt.subplot(4,4,7)
sns.lineplot(df['total_deaths'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('total_deaths')
plt.subplot(4,4,8)
sns.lineplot(df['total_deaths'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('total_deaths')
plt.subplot(4,4,9)
sns.lineplot(df['total_deaths'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('total_deaths')
plt.subplot(4,4,10)
sns.lineplot(df['total_deaths'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('total_deaths')
plt.subplot(4,4,11)
sns.lineplot(df['total_deaths'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('total_deaths')
plt.subplot(4,4,12)
sns.lineplot(df['total_deaths'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('total_deaths')
plt.subplot(4,4,13)
sns.lineplot(df['total_deaths'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('total_deathstotal_deaths')
plt.subplot(4,4,14)
sns.lineplot(df['total_deaths'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('total_deaths')
plt.subplot(4,4,15)
sns.lineplot(df['total_deaths'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('total_deaths')
#Lineplot for new_deaths
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['new_deaths'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('new_deaths')
plt.subplot(4,4,2)
sns.lineplot(df['new_deaths'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('new_deaths')
plt.subplot(4,4,3)
sns.lineplot(df['new_deaths'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('new_deaths')
plt.subplot(4,4,4)
sns.lineplot(df['new_deaths'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('new_deaths')
plt.subplot(4,4,5)
sns.lineplot(df['new_deaths'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('new_deaths')
plt.subplot(4,4,6)
sns.lineplot(df['new_deaths'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('new_deaths')
plt.subplot(4,4,7)
sns.lineplot(df['new_deaths'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('new_deaths')
plt.subplot(4,4,8)
sns.lineplot(df['new_deaths'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('new_deaths')
plt.subplot(4,4,9)
sns.lineplot(df['new_deaths'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('new_deaths')
plt.subplot(4,4,10)
sns.lineplot(df['new_deaths'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('new_deaths')
plt.subplot(4,4,11)
sns.lineplot(df['new_deaths'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('new_deaths')
plt.subplot(4,4,12)
sns.lineplot(df['new_deaths'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('new_deaths')
plt.subplot(4,4,13)
sns.lineplot(df['new_deaths'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('new_deaths')
plt.subplot(4,4,14)
sns.lineplot(df['new_deaths'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('new_deaths')
plt.subplot(4,4,15)
sns.lineplot(df['new_deaths'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('new_deaths')
#Lineplot for total_cases_per_million
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['total_cases_per_million'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,2)
sns.lineplot(df['total_cases_per_million'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,3)
sns.lineplot(df['total_cases_per_million'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,4)
sns.lineplot(df['total_cases_per_million'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,5)
sns.lineplot(df['total_cases_per_million'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,6)
sns.lineplot(df['total_cases_per_million'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,7)
sns.lineplot(df['total_cases_per_million'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,8)
sns.lineplot(df['total_cases_per_million'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,9)
sns.lineplot(df['total_cases_per_million'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,10)
sns.lineplot(df['total_cases_per_million'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,11)
sns.lineplot(df['total_cases_per_million'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,12)
sns.lineplot(df['total_cases_per_million'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,13)
sns.lineplot(df['total_cases_per_million'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,14)
sns.lineplot(df['total_cases_per_million'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('total_cases_per_million')
plt.subplot(4,4,15)
sns.lineplot(df['total_cases_per_million'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('total_cases_per_million')
#Lineplot for new_cases_per_million
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['new_cases_per_million'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,2)
sns.lineplot(df['new_cases_per_million'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,3)
sns.lineplot(df['new_cases_per_million'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,4)
sns.lineplot(df['new_cases_per_million'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,5)
sns.lineplot(df['new_cases_per_million'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,6)
sns.lineplot(df['new_cases_per_million'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,7)
sns.lineplot(df['new_cases_per_million'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,8)
sns.lineplot(df['new_cases_per_million'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,9)
sns.lineplot(df['new_cases_per_million'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,10)
sns.lineplot(df['new_cases_per_million'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,11)
sns.lineplot(df['new_cases_per_million'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,12)
sns.lineplot(df['new_cases_per_million'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,13)
sns.lineplot(df['new_cases_per_million'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,14)
sns.lineplot(df['new_cases_per_million'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('new_cases_per_million')
plt.subplot(4,4,15)
sns.lineplot(df['new_cases_per_million'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('new_cases_per_million')
#Lineplot for total_deaths_per_million
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['total_deaths_per_million'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,2)
sns.lineplot(df['total_deaths_per_million'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,3)
sns.lineplot(df['total_deaths_per_million'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,4)
sns.lineplot(df['total_deaths_per_million'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,5)
sns.lineplot(df['total_deaths_per_million'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,6)
sns.lineplot(df['total_deaths_per_million'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,7)
sns.lineplot(df['total_deaths_per_million'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,8)
sns.lineplot(df['total_deaths_per_million'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,9)
sns.lineplot(df['total_deaths_per_million'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,10)
sns.lineplot(df['total_deaths_per_million'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,11)
sns.lineplot(df['total_deaths_per_million'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,12)
sns.lineplot(df['total_deaths_per_million'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,13)
sns.lineplot(df['total_deaths_per_million'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,14)
sns.lineplot(df['total_deaths_per_million'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('total_deaths_per_million')
plt.subplot(4,4,15)
sns.lineplot(df['total_deaths_per_million'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('total_deaths_per_million')
#Lineplot for new_deaths_per_million
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['new_deaths_per_million'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,2)
sns.lineplot(df['new_deaths_per_million'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,3)
sns.lineplot(df['new_deaths_per_million'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,4)
sns.lineplot(df['new_deaths_per_million'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,5)
sns.lineplot(df['new_deaths_per_million'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,6)
sns.lineplot(df['new_deaths_per_million'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,7)
sns.lineplot(df['new_deaths_per_million'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,8)
sns.lineplot(df['new_deaths_per_million'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,9)
sns.lineplot(df['new_deaths_per_million'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,10)
sns.lineplot(df['new_deaths_per_million'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,11)
sns.lineplot(df['new_deaths_per_million'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,12)
sns.lineplot(df['new_deaths_per_million'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,13)
sns.lineplot(df['new_deaths_per_million'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,14)
sns.lineplot(df['new_deaths_per_million'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('new_deaths_per_million')
plt.subplot(4,4,15)
sns.lineplot(df['new_deaths_per_million'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('new_deaths_per_million')
#Lineplot for total_tests
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['total_tests'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('total_tests')
plt.subplot(4,4,2)
sns.lineplot(df['total_tests'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('total_tests')
plt.subplot(4,4,3)
sns.lineplot(df['total_tests'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('total_tests')
plt.subplot(4,4,4)
sns.lineplot(df['total_tests'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('total_tests')
plt.subplot(4,4,5)
sns.lineplot(df['total_tests'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('total_tests')
plt.subplot(4,4,6)
sns.lineplot(df['total_tests'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('total_tests')
plt.subplot(4,4,7)
sns.lineplot(df['total_tests'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('total_tests')
plt.subplot(4,4,8)
sns.lineplot(df['total_tests'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('total_tests')
plt.subplot(4,4,9)
sns.lineplot(df['total_tests'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('total_tests')
plt.subplot(4,4,10)
sns.lineplot(df['total_tests'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('total_tests')
plt.subplot(4,4,11)
sns.lineplot(df['total_tests'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('total_tests')
plt.subplot(4,4,12)
sns.lineplot(df['total_tests'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('total_tests')
plt.subplot(4,4,13)
sns.lineplot(df['total_tests'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('total_tests')
plt.subplot(4,4,14)
sns.lineplot(df['total_tests'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('total_tests')
plt.subplot(4,4,15)
sns.lineplot(df['total_tests'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('total_tests')
#Lineplot for new_tests
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['new_tests'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('new_tests')
plt.subplot(4,4,2)
sns.lineplot(df['new_tests'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('new_tests')
plt.subplot(4,4,3)
sns.lineplot(df['new_tests'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('new_tests')
plt.subplot(4,4,4)
sns.lineplot(df['new_tests'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('new_tests')
plt.subplot(4,4,5)
sns.lineplot(df['new_tests'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('new_tests')
plt.subplot(4,4,6)
sns.lineplot(df['new_tests'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('new_tests')
plt.subplot(4,4,7)
sns.lineplot(df['new_tests'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('new_tests')
plt.subplot(4,4,8)
sns.lineplot(df['new_tests'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('new_tests')
plt.subplot(4,4,9)
sns.lineplot(df['new_tests'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('new_tests')
plt.subplot(4,4,10)
sns.lineplot(df['new_tests'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('new_tests')
plt.subplot(4,4,11)
sns.lineplot(df['new_tests'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('new_tests')
plt.subplot(4,4,12)
sns.lineplot(df['new_tests'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('new_tests')
plt.subplot(4,4,13)
sns.lineplot(df['new_tests'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('new_tests')
plt.subplot(4,4,14)
sns.lineplot(df['new_tests'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('new_tests')
plt.subplot(4,4,15)
sns.lineplot(df['new_tests'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('new_tests')
#Lineplot for total_tests_per_thousand
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['total_tests_per_thousand'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,2)
sns.lineplot(df['total_tests_per_thousand'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,3)
sns.lineplot(df['total_tests_per_thousand'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,4)
sns.lineplot(df['total_tests_per_thousand'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,5)
sns.lineplot(df['total_tests_per_thousand'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,6)
sns.lineplot(df['total_tests_per_thousand'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,7)
sns.lineplot(df['total_tests_per_thousand'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,8)
sns.lineplot(df['total_tests_per_thousand'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,9)
sns.lineplot(df['total_tests_per_thousand'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,10)
sns.lineplot(df['total_tests_per_thousand'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,11)
sns.lineplot(df['total_tests_per_thousand'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,12)
sns.lineplot(df['total_tests_per_thousand'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,13)
sns.lineplot(df['total_tests_per_thousand'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,14)
sns.lineplot(df['total_tests_per_thousand'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('total_tests_per_thousand')
plt.subplot(4,4,15)
sns.lineplot(df['total_tests_per_thousand'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('total_tests_per_thousand')
#Lineplot for new_tests_per_thousand
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['new_tests_per_thousand'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,2)
sns.lineplot(df['new_tests_per_thousand'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,3)
sns.lineplot(df['new_tests_per_thousand'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,4)
sns.lineplot(df['new_tests_per_thousand'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,5)
sns.lineplot(df['new_tests_per_thousand'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,6)
sns.lineplot(df['new_tests_per_thousand'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,7)
sns.lineplot(df['new_tests_per_thousand'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,8)
sns.lineplot(df['new_tests_per_thousand'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,9)
sns.lineplot(df['new_tests_per_thousand'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,10)
sns.lineplot(df['new_tests_per_thousand'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,11)
sns.lineplot(df['new_tests_per_thousand'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,12)
sns.lineplot(df['new_tests_per_thousand'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,13)
sns.lineplot(df['new_tests_per_thousand'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,14)
sns.lineplot(df['new_tests_per_thousand'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('new_tests_per_thousand')
plt.subplot(4,4,15)
sns.lineplot(df['new_tests_per_thousand'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('new_tests_per_thousand')
#Lineplot for new_tests_smoothed
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['new_tests_smoothed'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,2)
sns.lineplot(df['new_tests_smoothed'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,3)
sns.lineplot(df['new_tests_smoothed'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,4)
sns.lineplot(df['new_tests_smoothed'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,5)
sns.lineplot(df['new_tests_smoothed'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,6)
sns.lineplot(df['new_tests_smoothed'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,7)
sns.lineplot(df['new_tests_smoothed'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,8)
sns.lineplot(df['new_tests_smoothed'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,9)
sns.lineplot(df['new_tests_smoothed'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,10)
sns.lineplot(df['new_tests_smoothed'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,11)
sns.lineplot(df['new_tests_smoothed'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,12)
sns.lineplot(df['new_tests_smoothed'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,13)
sns.lineplot(df['new_tests_smoothed'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,14)
sns.lineplot(df['new_tests_smoothed'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('new_tests_smoothed')
plt.subplot(4,4,15)
sns.lineplot(df['new_tests_smoothed'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('new_tests_smoothed')
#Lineplot for new_tests_smoothed_per_thousand
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,2)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,3)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,4)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,5)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,6)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,7)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,8)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,9)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,10)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,11)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,12)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,13)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,14)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('new_tests_smoothed_per_thousand')
plt.subplot(4,4,15)
sns.lineplot(df['new_tests_smoothed_per_thousand'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('new_tests_smoothed_per_thousand')
#Lineplot for stringency_index
fig = plt.figure(figsize = (19,10))
plt.subplots_adjust(top = 0.99, bottom=0.01, hspace=1.0, wspace=0.4)
plt.subplot(4,4,1)
sns.lineplot(df['stringency_index'],df['new_cases'])
plt.xlabel('new cases')
plt.ylabel('stringency_index')
plt.subplot(4,4,2)
sns.lineplot(df['stringency_index'],df['total_deaths'])
plt.xlabel('total_deaths')
plt.ylabel('stringency_index')
plt.subplot(4,4,3)
sns.lineplot(df['stringency_index'],df['new_deaths'])
plt.xlabel('new_deaths')
plt.ylabel('stringency_index')
plt.subplot(4,4,4)
sns.lineplot(df['stringency_index'],df['total_cases_per_million'])
plt.xlabel('total_cases_per_million')
plt.ylabel('stringency_index')
plt.subplot(4,4,5)
sns.lineplot(df['stringency_index'],df['new_cases_per_million'])
plt.xlabel('new_cases_per_million')
plt.ylabel('stringency_index')
plt.subplot(4,4,6)
sns.lineplot(df['stringency_index'],df['total_deaths_per_million'])
plt.xlabel('total_deaths_per_million')
plt.ylabel('stringency_index')
plt.subplot(4,4,7)
sns.lineplot(df['stringency_index'],df['new_deaths_per_million'])
plt.xlabel('new_deaths_per_million')
plt.ylabel('stringency_index')
plt.subplot(4,4,8)
sns.lineplot(df['stringency_index'],df['total_tests'])
plt.xlabel('total_tests')
plt.ylabel('stringency_index')
plt.subplot(4,4,9)
sns.lineplot(df['stringency_index'],df['new_tests'])
plt.xlabel('new_tests')
plt.ylabel('stringency_index')
plt.subplot(4,4,10)
sns.lineplot(df['stringency_index'],df['total_tests_per_thousand'])
plt.xlabel('total_tests_per_thousand')
plt.ylabel('stringency_index')
plt.subplot(4,4,11)
sns.lineplot(df['stringency_index'],df['new_tests_per_thousand'])
plt.xlabel('new_tests_per_thousand')
plt.ylabel('stringency_index')
plt.subplot(4,4,12)
sns.lineplot(df['stringency_index'],df['new_tests_smoothed'])
plt.xlabel('new_tests_smoothed')
plt.ylabel('stringency_index')
plt.subplot(4,4,13)
sns.lineplot(df['stringency_index'],df['new_tests_smoothed_per_thousand'])
plt.xlabel('new_tests_smoothed_per_thousand')
plt.ylabel('stringency_index')
plt.subplot(4,4,14)
sns.lineplot(df['stringency_index'],df['stringency_index'])
plt.xlabel('stringency_index')
plt.ylabel('stringency_index')
plt.subplot(4,4,15)
sns.lineplot(df['stringency_index'],df['total_cases'])
plt.xlabel('total_cases')
plt.ylabel('stringency_index')
| 34.477209
| 89
| 0.78264
| 6,361
| 37,063
| 4.257507
| 0.014149
| 0.090097
| 0.114246
| 0.093051
| 0.976996
| 0.971531
| 0.960306
| 0.951222
| 0.917547
| 0.89229
| 0
| 0.028928
| 0.034671
| 37,063
| 1,074
| 90
| 34.509311
| 0.728017
| 0.01198
| 0
| 0.713123
| 0
| 0
| 0.450437
| 0.277049
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.001017
| 0
| 0.001017
| 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
|
34fdb9e3b63947e10c3a4574b7d03e1e2f285c34
| 76
|
py
|
Python
|
__init__.py
|
vitorpavinato/abcranger
|
71f950817bedeebed12d13610d8747c6dcc75a72
|
[
"MIT"
] | 1
|
2020-07-29T13:31:43.000Z
|
2020-07-29T13:31:43.000Z
|
__init__.py
|
vitorpavinato/abcranger
|
71f950817bedeebed12d13610d8747c6dcc75a72
|
[
"MIT"
] | null | null | null |
__init__.py
|
vitorpavinato/abcranger
|
71f950817bedeebed12d13610d8747c6dcc75a72
|
[
"MIT"
] | null | null | null |
import sys
sys.path.insert(0,'./build/pytest')
sys.path.insert(0,'./build')
| 19
| 35
| 0.697368
| 13
| 76
| 4.076923
| 0.538462
| 0.264151
| 0.490566
| 0.528302
| 0.716981
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027778
| 0.052632
| 76
| 4
| 36
| 19
| 0.708333
| 0
| 0
| 0
| 0
| 0
| 0.272727
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 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
|
550470644c86772bd80f3e5cce49d6a3d81dd99b
| 125
|
py
|
Python
|
proteus/util.py
|
robertsawko/proteus
|
6f1e4c2ca1af85a906b35a5162430006f0343861
|
[
"NASA-1.3"
] | null | null | null |
proteus/util.py
|
robertsawko/proteus
|
6f1e4c2ca1af85a906b35a5162430006f0343861
|
[
"NASA-1.3"
] | null | null | null |
proteus/util.py
|
robertsawko/proteus
|
6f1e4c2ca1af85a906b35a5162430006f0343861
|
[
"NASA-1.3"
] | null | null | null |
""" basic utilities in Proteus
"""
import os
def get_include_dir():
return os.path.dirname(os.path.realpath(__file__))
| 15.625
| 54
| 0.72
| 18
| 125
| 4.666667
| 0.833333
| 0.142857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.144
| 125
| 7
| 55
| 17.857143
| 0.785047
| 0.208
| 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
|
55145058590684f1b07cad9f0646622bd222bade
| 1,975
|
py
|
Python
|
deucevalere/tests/test_api_system_counter_manager.py
|
BenjamenMeyer/deuce-valere
|
e9ba7d96825e81e51ddd5f99f1464990969a8f07
|
[
"Apache-2.0"
] | null | null | null |
deucevalere/tests/test_api_system_counter_manager.py
|
BenjamenMeyer/deuce-valere
|
e9ba7d96825e81e51ddd5f99f1464990969a8f07
|
[
"Apache-2.0"
] | null | null | null |
deucevalere/tests/test_api_system_counter_manager.py
|
BenjamenMeyer/deuce-valere
|
e9ba7d96825e81e51ddd5f99f1464990969a8f07
|
[
"Apache-2.0"
] | null | null | null |
"""
Deuce Valere - Tests - API - System - Counter Manager
"""
import unittest
from deucevalere.api.system import CounterManager
class DeuceValereApiSystemCounterManagerTest(unittest.TestCase):
def setUp(self):
super().setUp()
def tearDown(self):
super().tearDown()
def test_create_counter_manager(self):
name = 'testing'
countermanager = CounterManager(name)
self.assertEqual(countermanager.name, name)
self.assertEqual(countermanager.count, 0)
self.assertEqual(countermanager.size, 0)
def test_counter_manager_add(self):
name = 'testing'
countermanager = CounterManager(name)
self.assertEqual(countermanager.name, name)
self.assertEqual(countermanager.count, 0)
self.assertEqual(countermanager.size, 0)
running_counter = 0
running_size = 0
for c, b in [(x, x * 1024) for x in range(100)]:
countermanager.add(c, b)
running_counter = running_counter + c
running_size = running_size + b
self.assertEqual(countermanager.count, running_counter)
self.assertEqual(countermanager.size, running_size)
def test_counter_manager_reset(self):
name = 'testing'
countermanager = CounterManager(name)
self.assertEqual(countermanager.name, name)
self.assertEqual(countermanager.count, 0)
self.assertEqual(countermanager.size, 0)
running_counter = 0
running_size = 0
for c, b in [(x, x * 1024) for x in range(100)]:
countermanager.add(c, b)
running_counter = running_counter + c
running_size = running_size + b
self.assertEqual(countermanager.count, running_counter)
self.assertEqual(countermanager.size, running_size)
countermanager.reset()
self.assertEqual(countermanager.count, 0)
self.assertEqual(countermanager.size, 0)
| 29.477612
| 67
| 0.652658
| 207
| 1,975
| 6.10628
| 0.188406
| 0.178006
| 0.344146
| 0.156646
| 0.761076
| 0.761076
| 0.761076
| 0.761076
| 0.761076
| 0.761076
| 0
| 0.017735
| 0.257722
| 1,975
| 66
| 68
| 29.924242
| 0.844475
| 0.026835
| 0
| 0.75
| 0
| 0
| 0.010972
| 0
| 0
| 0
| 0
| 0
| 0.340909
| 1
| 0.113636
| false
| 0
| 0.045455
| 0
| 0.181818
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
9b833e0e634841c51ef148b5b9c4d3512319109b
| 8,892
|
py
|
Python
|
tests/AOJ/test_ITP1_11_D.py
|
nabetama-training/CompetitionProgrammingPractice
|
0801173df3992c2e78b02b383f2df9ba792cbf2f
|
[
"BSD-2-Clause"
] | null | null | null |
tests/AOJ/test_ITP1_11_D.py
|
nabetama-training/CompetitionProgrammingPractice
|
0801173df3992c2e78b02b383f2df9ba792cbf2f
|
[
"BSD-2-Clause"
] | 2
|
2020-07-04T04:19:28.000Z
|
2020-07-26T06:16:07.000Z
|
tests/AOJ/test_ITP1_11_D.py
|
nabetama-training/CompetitionProgrammingPractice
|
0801173df3992c2e78b02b383f2df9ba792cbf2f
|
[
"BSD-2-Clause"
] | null | null | null |
import sys
from io import StringIO
import unittest
from src.AOJ.ITP1_11_D import resolve
class TestClass(unittest.TestCase):
def assertIO(self, input, output):
stdout, stdin = sys.stdout, sys.stdin
sys.stdout, sys.stdin = StringIO(), StringIO(input)
resolve()
sys.stdout.seek(0)
out = sys.stdout.read()[:]
sys.stdout, sys.stdin = stdout, stdin
self.assertEqual(out, output)
def test_1(self):
input = """3
1 2 3 4 5 6
6 2 4 3 5 1
6 5 4 3 2 1
"""
output = """No
"""
self.assertIO(input, output)
def test_2(self):
input = """3
1 2 3 4 5 6
6 5 4 3 2 1
5 4 3 2 1 6
"""
output = """Yes
"""
self.assertIO(input, output)
def test_3(self):
input = """2
1 2 3 4 5 6
1 2 3 4 5 6
1 2 3 4 5 6
"""
output = """No
"""
self.assertIO(input, output)
def test_4(self):
input = """2
1 2 2 2 2 2
2 2 2 1 2 2
"""
output = """No
"""
self.assertIO(input, output)
def test_5(self):
input = """2
1 2 3 4 5 6
1 5 2 6 3 4
"""
output = """Yes
"""
self.assertIO(input, output)
def test_6(self):
input = """2
1 2 3 4 5 6
2 3 5 6 4 1
"""
output = """Yes
"""
self.assertIO(input, output)
def test_7(self):
input = """2
1 2 3 4 5 6
3 4 1 6 2 5
"""
output = """Yes
"""
self.assertIO(input, output)
def test_8(self):
input = """2
1 2 3 4 5 6
4 3 6 5 1 2
"""
output = """Yes
"""
self.assertIO(input, output)
def test_9(self):
input = """2
1 2 3 4 5 6
5 6 4 3 2 1
"""
output = """Yes
"""
self.assertIO(input, output)
def test_10(self):
input = """2
1 2 3 4 5 6
6 2 4 5 3 1
"""
output = """Yes
"""
self.assertIO(input, output)
def test_11(self):
input = """30
1 2 3 4 5 6
1 2 3 4 6 5
1 2 3 5 4 6
1 2 3 5 6 4
1 2 3 6 4 5
1 2 3 6 5 4
1 2 4 3 5 6
1 2 4 3 6 5
1 2 4 5 3 6
1 2 4 5 6 3
1 2 4 6 3 5
1 2 4 6 5 3
1 2 5 3 4 6
1 2 5 3 6 4
1 2 5 4 3 6
1 2 5 4 6 3
1 2 5 6 3 4
1 2 5 6 4 3
1 2 6 3 4 5
1 2 6 3 5 4
1 2 6 4 3 5
1 2 6 4 5 3
1 2 6 5 3 4
1 2 6 5 4 3
1 3 4 5 6 2
1 3 4 6 5 2
1 3 5 4 6 2
1 3 5 6 4 2
1 3 6 4 5 2
1 3 6 5 4 2
"""
output = """Yes
"""
self.assertIO(input, output)
def test_12(self):
input = """100
1 2 3 4 5 6
1 2 3 4 6 5
1 2 3 5 4 6
1 2 3 5 6 4
1 2 3 6 4 5
1 2 3 6 5 4
1 2 4 3 5 6
1 2 4 3 6 5
1 2 4 5 3 6
1 2 4 5 6 3
1 2 4 6 3 5
1 2 4 6 5 3
1 2 5 3 4 6
1 2 5 3 6 4
1 2 5 4 3 6
1 2 5 4 6 3
1 2 5 6 3 4
1 2 5 6 4 3
1 2 6 3 4 5
1 2 6 3 5 4
1 2 6 4 3 5
1 2 6 4 5 3
1 2 6 5 3 4
1 2 6 5 4 3
1 3 4 5 6 2
1 3 4 6 5 2
1 3 5 4 6 2
1 3 5 6 4 2
1 3 6 4 5 2
1 3 6 5 4 2
7 2 3 4 5 6
7 2 3 4 6 5
7 2 3 5 4 6
7 2 3 5 6 4
7 2 3 6 4 5
7 2 3 6 5 4
7 2 4 3 5 6
7 2 4 3 6 5
7 2 4 5 3 6
7 2 4 5 6 3
7 2 4 6 3 5
7 2 4 6 5 3
7 2 5 3 4 6
7 2 5 3 6 4
7 2 5 4 3 6
7 2 5 4 6 3
7 2 5 6 3 4
7 2 5 6 4 3
7 2 6 3 4 5
7 2 6 3 5 4
7 2 6 4 3 5
7 2 6 4 5 3
7 2 6 5 3 4
7 2 6 5 4 3
7 3 4 5 6 2
7 3 4 6 5 2
7 3 5 4 6 2
7 3 5 6 4 2
7 3 6 4 5 2
7 3 6 5 4 2
7 2 3 8 5 6
7 2 3 8 6 5
7 2 3 5 8 6
7 2 3 5 6 8
7 2 3 6 8 5
7 2 3 6 5 8
7 2 8 3 5 6
7 2 8 3 6 5
7 2 8 5 3 6
7 2 8 5 6 3
7 2 8 6 3 5
7 2 8 6 5 3
7 2 5 3 8 6
7 2 5 3 6 8
7 2 5 8 3 6
7 2 5 8 6 3
7 2 5 6 3 8
7 2 5 6 8 3
7 2 6 3 8 5
7 2 6 3 5 8
7 2 6 8 3 5
7 2 6 8 5 3
7 2 6 5 3 8
7 2 6 5 8 3
7 3 8 5 6 2
7 3 8 6 5 2
7 3 5 8 6 2
7 3 5 6 8 2
7 3 6 8 5 2
7 3 6 5 8 2
7 9 6 8 3 5
7 9 6 8 5 3
7 9 6 5 3 8
7 9 6 5 8 3
7 3 8 5 6 9
7 3 8 6 5 9
7 3 5 8 6 9
7 3 5 6 8 9
7 3 6 8 5 9
7 3 6 5 8 9
"""
output = """Yes
"""
self.assertIO(input, output)
def test_13(self):
input = """100
1 2 3 4 5 6
1 2 3 4 6 5
1 2 3 5 4 6
1 2 3 5 6 4
1 2 3 6 4 5
1 2 3 6 5 4
1 2 4 3 5 6
1 2 4 3 6 5
2 3 1 6 4 5
1 2 4 5 6 3
1 2 4 6 3 5
1 2 4 6 5 3
1 2 5 3 4 6
1 2 5 3 6 4
1 2 5 4 3 6
1 2 5 4 6 3
1 2 5 6 3 4
1 2 5 6 4 3
1 2 6 3 4 5
1 2 6 3 5 4
1 2 6 4 3 5
1 2 6 4 5 3
1 2 6 5 3 4
1 2 6 5 4 3
1 3 4 5 6 2
1 3 4 6 5 2
1 3 5 4 6 2
1 3 5 6 4 2
1 3 6 4 5 2
1 3 6 5 4 2
7 2 3 4 5 6
7 2 3 4 6 5
7 2 3 5 4 6
7 2 3 5 6 4
7 2 3 6 4 5
7 2 3 6 5 4
7 2 4 3 5 6
7 2 4 3 6 5
7 2 4 5 3 6
7 2 4 5 6 3
7 2 4 6 3 5
7 2 4 6 5 3
7 2 5 3 4 6
7 2 5 3 6 4
7 2 5 4 3 6
7 2 5 4 6 3
7 2 5 6 3 4
7 2 5 6 4 3
7 2 6 3 4 5
7 2 6 3 5 4
7 2 6 4 3 5
7 2 6 4 5 3
7 2 6 5 3 4
7 2 6 5 4 3
7 3 4 5 6 2
7 3 4 6 5 2
7 3 5 4 6 2
7 3 5 6 4 2
7 3 6 4 5 2
7 3 6 5 4 2
7 2 3 8 5 6
7 2 3 8 6 5
7 2 3 5 8 6
7 2 3 5 6 8
7 2 3 6 8 5
7 2 3 6 5 8
7 2 8 3 5 6
7 2 8 3 6 5
7 2 8 5 3 6
7 2 8 5 6 3
7 2 8 6 3 5
7 2 8 6 5 3
7 2 5 3 8 6
7 2 5 3 6 8
7 2 5 8 3 6
7 2 5 8 6 3
7 2 5 6 3 8
7 2 5 6 8 3
7 2 6 3 8 5
7 2 6 3 5 8
7 2 6 8 3 5
7 2 6 8 5 3
7 2 6 5 3 8
7 2 6 5 8 3
7 3 8 5 6 2
7 3 8 6 5 2
7 3 5 8 6 2
7 3 5 6 8 2
7 3 6 8 5 2
7 3 6 5 8 2
7 9 6 8 3 5
7 9 6 8 5 3
7 9 6 5 3 8
7 9 6 5 8 3
7 3 8 5 6 9
7 3 8 6 5 9
7 3 5 8 6 9
7 3 5 6 8 9
7 3 6 8 5 9
6 3 2 5 4 1
"""
output = """No
"""
self.assertIO(input, output)
def test_14(self):
input = """100
1 2 3 4 5 6
1 2 3 4 6 5
1 2 3 5 4 6
1 2 3 5 6 4
1 2 3 6 4 5
1 2 3 6 5 4
1 2 4 3 5 6
1 2 4 3 6 5
1 2 4 5 3 6
1 2 4 5 6 3
1 2 4 6 3 5
1 2 4 6 5 3
1 2 5 3 4 6
1 2 5 3 6 4
1 2 5 4 3 6
1 2 5 4 6 3
1 2 5 6 3 4
1 2 5 6 4 3
1 2 6 3 4 5
1 2 6 3 5 4
1 2 6 4 3 5
1 2 6 4 5 3
1 2 6 5 3 4
1 4 2 5 3 6
1 3 4 5 6 2
1 3 4 6 5 2
1 3 5 4 6 2
1 3 5 6 4 2
1 3 6 4 5 2
1 3 6 5 4 2
7 2 3 4 5 6
7 2 3 4 6 5
7 2 3 5 4 6
7 2 3 5 6 4
7 2 3 6 4 5
7 2 3 6 5 4
7 2 4 3 5 6
7 2 4 3 6 5
7 2 4 5 3 6
7 2 4 5 6 3
7 2 4 6 3 5
7 2 4 6 5 3
7 2 5 3 4 6
7 2 5 3 6 4
7 2 5 4 3 6
7 2 5 4 6 3
7 2 5 6 3 4
7 2 5 6 4 3
7 2 6 3 4 5
7 2 6 3 5 4
7 2 6 4 3 5
7 2 6 4 5 3
7 2 6 5 3 4
7 2 6 5 4 3
7 3 4 5 6 2
7 3 4 6 5 2
7 3 5 4 6 2
7 3 5 6 4 2
7 3 6 4 5 2
7 3 6 5 4 2
7 2 3 8 5 6
7 2 3 8 6 5
7 2 3 5 8 6
7 2 3 5 6 8
7 2 3 6 8 5
7 2 3 6 5 8
7 2 8 3 5 6
7 2 8 3 6 5
7 2 8 5 3 6
7 2 8 5 6 3
7 2 8 6 3 5
7 2 8 6 5 3
7 2 5 3 8 6
7 2 5 3 6 8
7 2 5 8 3 6
7 2 5 8 6 3
7 2 5 6 3 8
7 2 5 6 8 3
7 2 6 3 8 5
7 2 6 3 5 8
7 2 6 8 3 5
7 2 6 8 5 3
7 2 6 5 3 8
7 2 6 5 8 3
7 3 8 5 6 2
7 3 8 6 5 2
7 3 5 8 6 2
7 3 5 6 8 2
7 3 6 8 5 2
7 3 6 5 8 2
7 9 6 8 3 5
7 9 6 8 5 3
7 9 6 5 3 8
7 9 6 5 8 3
7 3 8 5 6 9
7 3 8 6 5 9
7 3 5 8 6 9
7 3 5 6 8 9
7 3 6 8 5 9
5 3 6 1 4 2
"""
output = """No
"""
self.assertIO(input, output)
def test_15(self):
input = """100
1 2 3 4 5 6
1 2 3 4 6 5
1 2 3 5 4 6
1 2 3 5 6 4
1 2 3 6 4 5
1 2 3 6 5 4
1 2 4 3 5 6
1 2 4 3 6 5
1 2 4 5 3 6
1 2 4 5 6 3
1 2 4 6 3 5
1 2 4 6 5 3
1 2 5 3 4 6
1 2 5 3 6 4
1 2 5 4 3 6
1 2 5 4 6 3
1 2 5 6 3 4
1 2 5 6 4 3
1 2 6 3 4 5
1 2 6 3 5 4
1 2 6 4 3 5
1 2 6 4 5 3
1 2 6 5 3 4
1 2 6 5 4 3
1 3 4 5 6 2
1 3 4 6 5 2
1 3 5 4 6 2
1 3 5 6 4 2
1 3 6 4 5 2
3 5 1 6 2 4
6 2 4 3 5 1
7 2 3 4 6 5
7 2 3 5 4 6
7 2 3 5 6 4
7 2 3 6 4 5
7 2 3 6 5 4
7 2 4 3 5 6
7 2 4 3 6 5
7 2 4 5 3 6
7 2 4 5 6 3
7 2 4 6 3 5
7 2 4 6 5 3
7 2 5 3 4 6
7 2 5 3 6 4
7 2 5 4 3 6
7 2 5 4 6 3
7 2 5 6 3 4
7 2 5 6 4 3
7 2 6 3 4 5
7 2 6 3 5 4
7 2 6 4 3 5
7 2 6 4 5 3
7 2 6 5 3 4
7 2 6 5 4 3
7 3 4 5 6 2
7 3 4 6 5 2
7 3 5 4 6 2
7 3 5 6 4 2
7 3 6 4 5 2
7 3 6 5 4 2
7 2 3 8 5 6
7 2 3 8 6 5
7 2 3 5 8 6
7 2 3 5 6 8
7 2 3 6 8 5
7 2 3 6 5 8
7 2 8 3 5 6
7 2 8 3 6 5
7 2 8 5 3 6
7 2 8 5 6 3
7 2 8 6 3 5
7 2 8 6 5 3
7 2 5 3 8 6
7 2 5 3 6 8
7 2 5 8 3 6
7 2 5 8 6 3
7 2 5 6 3 8
7 2 5 6 8 3
7 2 6 3 8 5
7 2 6 3 5 8
7 2 6 8 3 5
7 2 6 8 5 3
7 2 6 5 3 8
7 2 6 5 8 3
7 3 8 5 6 2
7 3 8 6 5 2
7 3 5 8 6 2
7 3 5 6 8 2
7 3 6 8 5 2
7 3 6 5 8 2
7 9 6 8 3 5
7 9 6 8 5 3
7 9 6 5 3 8
7 9 6 5 8 3
7 3 8 5 6 9
7 3 8 6 5 9
7 3 5 8 6 9
7 3 5 6 8 9
7 3 6 8 5 9
7 3 6 5 8 9
"""
output = """No
"""
self.assertIO(input, output)
def test_16(self):
input = """100
1 2 3 4 5 6
1 2 3 4 6 5
1 2 3 5 4 6
1 2 3 5 6 4
1 2 3 6 4 5
1 2 3 6 5 4
1 2 4 3 5 6
1 2 4 3 6 5
1 2 4 5 3 6
1 2 4 5 6 3
1 2 4 6 3 5
1 2 4 6 5 3
1 2 5 3 4 6
1 2 5 3 6 4
1 2 5 4 3 6
1 2 5 4 6 3
1 2 5 6 3 4
1 2 5 6 4 3
1 2 6 3 4 5
1 2 6 3 5 4
1 2 6 4 3 5
1 2 6 4 5 3
1 2 6 5 3 4
1 2 6 5 4 3
1 3 4 5 6 2
1 3 4 6 5 2
1 3 5 4 6 2
1 3 5 6 4 2
1 3 6 4 5 2
1 3 6 5 4 2
4 6 2 5 1 3
7 2 3 4 6 5
7 2 3 5 4 6
7 2 3 5 6 4
7 2 3 6 4 5
7 2 3 6 5 4
7 2 4 3 5 6
7 2 4 3 6 5
7 2 4 5 3 6
7 2 4 5 6 3
7 2 4 6 3 5
7 2 4 6 5 3
7 2 5 3 4 6
7 2 5 3 6 4
7 2 5 4 3 6
7 2 5 4 6 3
7 2 5 6 3 4
7 2 5 6 4 3
7 2 6 3 4 5
7 2 6 3 5 4
7 2 6 4 3 5
7 2 6 4 5 3
7 2 6 5 3 4
7 2 6 5 4 3
7 3 4 5 6 2
7 3 4 6 5 2
7 3 5 4 6 2
7 3 5 6 4 2
7 3 6 4 5 2
7 3 6 5 4 2
7 2 3 8 5 6
7 2 3 8 6 5
7 2 3 5 8 6
7 2 3 5 6 8
7 2 3 6 8 5
7 2 3 6 5 8
7 2 8 3 5 6
7 2 8 3 6 5
7 2 8 5 3 6
7 2 8 5 6 3
7 2 8 6 3 5
7 2 8 6 5 3
7 2 5 3 8 6
7 2 5 3 6 8
7 2 5 8 3 6
7 2 5 8 6 3
7 2 5 6 3 8
7 2 5 6 8 3
7 2 6 3 8 5
7 2 6 3 5 8
7 2 6 8 3 5
7 2 6 8 5 3
7 2 6 5 3 8
7 2 6 5 8 3
7 3 8 5 6 2
7 3 8 6 5 2
7 3 5 8 6 2
7 3 5 6 8 2
7 3 6 8 5 2
7 3 6 5 8 2
7 9 6 8 3 5
7 9 6 8 5 3
7 9 6 5 3 8
7 9 6 5 8 3
7 3 8 5 6 9
7 3 8 6 5 9
7 3 5 8 6 9
7 3 5 6 8 9
7 3 6 8 5 9
7 3 6 5 8 9
"""
output = """No
"""
self.assertIO(input, output)
| 13.038123
| 59
| 0.498313
| 3,567
| 8,892
| 1.237174
| 0.013176
| 0.107863
| 0.040789
| 0.028099
| 0.924994
| 0.910492
| 0.90392
| 0.902107
| 0.85792
| 0.758214
| 0
| 0.700312
| 0.458502
| 8,892
| 681
| 60
| 13.057269
| 0.216199
| 0
| 0
| 0.932024
| 0
| 0
| 0.757535
| 0
| 0
| 0
| 0
| 0
| 0.02719
| 1
| 0.02568
| false
| 0
| 0.006042
| 0
| 0.033233
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
32d0401f7ce918cde3334bf0a362a8a5fb79c1b8
| 87
|
py
|
Python
|
pyplanner/__init__.py
|
fjsanzano/pyplanner
|
69a48f22ea19cab676323077b5adc98e3f21129d
|
[
"MIT"
] | 1
|
2021-08-22T02:12:59.000Z
|
2021-08-22T02:12:59.000Z
|
pyplanner/__init__.py
|
fjsanzano/pyplanner
|
69a48f22ea19cab676323077b5adc98e3f21129d
|
[
"MIT"
] | null | null | null |
pyplanner/__init__.py
|
fjsanzano/pyplanner
|
69a48f22ea19cab676323077b5adc98e3f21129d
|
[
"MIT"
] | null | null | null |
from pyplanner import pyplanner
def version():
return (u'pyplanner Version 0.0.1')
| 21.75
| 39
| 0.735632
| 13
| 87
| 4.923077
| 0.692308
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.041096
| 0.16092
| 87
| 4
| 39
| 21.75
| 0.835616
| 0
| 0
| 0
| 0
| 0
| 0.261364
| 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
|
bd0c07700db062e23033d9f44b0d902448222257
| 106
|
py
|
Python
|
vitalfilepy/__init__.py
|
hulab-ucsf/vitalfilepy
|
d40b39681e0808409ae274f3ea227ae1f6db1dc0
|
[
"MIT"
] | null | null | null |
vitalfilepy/__init__.py
|
hulab-ucsf/vitalfilepy
|
d40b39681e0808409ae274f3ea227ae1f6db1dc0
|
[
"MIT"
] | null | null | null |
vitalfilepy/__init__.py
|
hulab-ucsf/vitalfilepy
|
d40b39681e0808409ae274f3ea227ae1f6db1dc0
|
[
"MIT"
] | null | null | null |
from .vitalfile import VITALBINARY
from .vitalfile import VitalFileError
from .vitalfile import VitalFile
| 26.5
| 37
| 0.858491
| 12
| 106
| 7.583333
| 0.416667
| 0.428571
| 0.626374
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113208
| 106
| 3
| 38
| 35.333333
| 0.968085
| 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
|
bd35ac088003a95d91b7d6238e804ba29e929809
| 2,313
|
py
|
Python
|
pepdb/core/migrations/0037_auto_20151025_2137.py
|
dchaplinsky/pep.org.ua
|
8633a65fb657d7f04dbdb12eb8ae705fa6be67e3
|
[
"MIT"
] | 7
|
2015-12-21T03:52:46.000Z
|
2020-07-24T19:17:23.000Z
|
pepdb/core/migrations/0037_auto_20151025_2137.py
|
dchaplinsky/pep.org.ua
|
8633a65fb657d7f04dbdb12eb8ae705fa6be67e3
|
[
"MIT"
] | 12
|
2016-03-05T18:11:05.000Z
|
2021-06-17T20:20:03.000Z
|
pepdb/core/migrations/0037_auto_20151025_2137.py
|
dchaplinsky/pep.org.ua
|
8633a65fb657d7f04dbdb12eb8ae705fa6be67e3
|
[
"MIT"
] | 4
|
2016-07-17T20:19:38.000Z
|
2021-03-23T12:47:20.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0036_auto_20151025_2058'),
]
operations = [
migrations.AlterField(
model_name='person',
name='first_name',
field=models.CharField(max_length=40, verbose_name="\u0406\u043c'\u044f"),
),
migrations.AlterField(
model_name='person',
name='first_name_en',
field=models.CharField(max_length=40, null=True, verbose_name="\u0406\u043c'\u044f"),
),
migrations.AlterField(
model_name='person',
name='first_name_ua',
field=models.CharField(max_length=40, null=True, verbose_name="\u0406\u043c'\u044f"),
),
migrations.AlterField(
model_name='person',
name='last_name',
field=models.CharField(max_length=40, verbose_name='\u041f\u0440\u0456\u0437\u0432\u0438\u0449\u0435'),
),
migrations.AlterField(
model_name='person',
name='last_name_en',
field=models.CharField(max_length=40, null=True, verbose_name='\u041f\u0440\u0456\u0437\u0432\u0438\u0449\u0435'),
),
migrations.AlterField(
model_name='person',
name='last_name_ua',
field=models.CharField(max_length=40, null=True, verbose_name='\u041f\u0440\u0456\u0437\u0432\u0438\u0449\u0435'),
),
migrations.AlterField(
model_name='person',
name='patronymic',
field=models.CharField(max_length=40, verbose_name='\u041f\u043e-\u0431\u0430\u0442\u044c\u043a\u043e\u0432\u0456', blank=True),
),
migrations.AlterField(
model_name='person',
name='patronymic_en',
field=models.CharField(max_length=40, null=True, verbose_name='\u041f\u043e-\u0431\u0430\u0442\u044c\u043a\u043e\u0432\u0456', blank=True),
),
migrations.AlterField(
model_name='person',
name='patronymic_ua',
field=models.CharField(max_length=40, null=True, verbose_name='\u041f\u043e-\u0431\u0430\u0442\u044c\u043a\u043e\u0432\u0456', blank=True),
),
]
| 38.55
| 151
| 0.610895
| 253
| 2,313
| 5.399209
| 0.221344
| 0.131772
| 0.164714
| 0.191069
| 0.887262
| 0.887262
| 0.887262
| 0.879941
| 0.844802
| 0.770864
| 0
| 0.152287
| 0.253351
| 2,313
| 59
| 152
| 39.20339
| 0.63868
| 0.009079
| 0
| 0.622642
| 0
| 0
| 0.248908
| 0.152838
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.037736
| 0
| 0.09434
| 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
|
951ee4a2692ec71f3bb9ab7f5075058f05aadc02
| 21,525
|
py
|
Python
|
scripts/run_inference.py
|
quanc1989/SV-ONT-Tibetan
|
77202d337b1f088ba274c6f71863d0aeb3e0a374
|
[
"MIT"
] | 7
|
2020-12-03T07:57:34.000Z
|
2021-06-01T14:03:05.000Z
|
scripts/run_inference.py
|
quanc1989/SV-ONT-Tibetan
|
77202d337b1f088ba274c6f71863d0aeb3e0a374
|
[
"MIT"
] | 2
|
2021-11-13T16:55:13.000Z
|
2022-01-18T01:42:33.000Z
|
scripts/run_inference.py
|
quanc1989/SV-ONT-Tibetan
|
77202d337b1f088ba274c6f71863d0aeb3e0a374
|
[
"MIT"
] | 5
|
2021-06-01T14:05:46.000Z
|
2022-01-22T09:06:18.000Z
|
import configargparse
import numpy
import Optimize_Functions
import dadi
def config_opts():
parser = configargparse.ArgumentParser(
description='func_vcf_filter.py',
config_file_parser_class=configargparse.YAMLConfigFileParser,
formatter_class=configargparse.ArgumentDefaultsHelpFormatter)
parser.add('-s', '--sfs', required=True,
help='sfs')
parser.add('-p', '--prefix', required=True,
help='save prefix')
parser.add('-m', '--model', required=True,
help='model name')
parser.add('--pts', required=False, default='80,90,100',
help='init pts')
parser.add('--project', required=False,
help='project to a small size')
parser.add('--param', required=False,
help='param')
parser.add('--rounds', required=False, default=9, type=int,
help='round number')
parser.add('--unfolded', default=False, action='store_true',
help='if unfolded')
return parser
if __name__ == '__main__':
parser = config_opts()
opt = parser.parse_args()
path_sfs = opt.sfs
fs = dadi.Spectrum.from_file(path_sfs)
if opt.project:
fs = fs.project([int(x.strip()) for x in opt.project.split(',')])
opt.folded = not opt.unfolded
ns = fs.sample_sizes
popid = fs.pop_ids
print(fs.pop_ids)
print("\n\n============================================================================")
print("\nData for site frequency spectrum:\n")
print("Projection: {}".format(ns))
print("Sample sizes: {}".format(fs.sample_sizes))
print("Sum of SFS: {}".format(numpy.around(fs.S(), 2)))
print("\n============================================================================\n")
from Three_Population_Pipeline.Models_3D import split_nomig
from Three_Population_Pipeline.Models_3D import split_symmig_all
from Three_Population_Pipeline.Models_3D import split_symmig_all_init
from Three_Population_Pipeline.Models_3D import split_symmig_all_grow
from Three_Population_Pipeline.Models_3D import split_symmig_all_grow_init
from Three_Population_Pipeline.Models_3D import split_asymmig_all
from Three_Population_Pipeline.Models_3D import split_nomig_size
from Three_Population_Pipeline.Models_3D import split_sym_mig_size
from Three_Population_Pipeline.Models_3D import split_asym_mig_size
from Three_Population_Pipeline.Models_3D import sim_split_no_mig
from Three_Population_Pipeline.Models_3D import sim_split_no_mig_size
from Three_Population_Pipeline.Models_3D import sim_split_sym_mig_all
from Three_Population_Pipeline.Models_3D import sim_split_asym_mig_all
from Three_Population_Pipeline.Models_3D import sim_split_refugia_sym_mig_all
# create a prefix to label the output files
prefix = opt.prefix
# Remember the order for mandatory arguments as below
# Optimize_Routine(fs, pts, outfile, model_name, func, rounds, param_number, fs_folded)
pts_l = [int(x.strip()) for x in opt.pts.split(',')]
# reps = [10, 10, 20]
# maxiters = [5, 5, 5]
# folds = [3, 2, 1]
if opt.param:
inparam = [float(x) for x in opt.param.split(',')]
else:
inparam=None
if opt.model == 'sim_split_no_mig':
p_labels = 'nu1, nu2, nu3, T1'
in_upper = [5, 5, 5, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="sim_split_no_mig",
func=sim_split_no_mig,
rounds=opt.rounds,
param_number=4,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_nomig':
p_labels = 'nu1, nuA, nu2, nu3, T1, T2'
in_upper = [5, 5, 5, 5, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_nomig",
func=split_nomig,
rounds=opt.rounds,
param_number=6,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_nomig_size':
p_labels = 'nu1a, nuA, nu2a, nu3a, nu1b, nu2b, nu3b, T1, T2, T3'
in_upper = [5, 5, 5, 5, 5, 5, 5, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_nomig_size",
func=split_nomig_size,
rounds=opt.rounds,
param_number=10,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_sym_mig_size':
p_labels = 'nu1a, nuA, nu2a, nu3a, nu1b, nu2b, nu3b, mA12, mB12, mB23, mB13, mC12, mC23, mC13, T1, T2, T3'
in_upper = [5, 5, 5, 5, 5, 5, 5, 0.1, 0.1, 0.1,0.1, 0.1, 0.1,0.1, 0.1, 0.1,0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001,0.001, 0.001, 0.001,0.001, 0.001, 0.001,0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_sym_mig_size",
func=split_sym_mig_size,
rounds=opt.rounds,
param_number=17,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_asym_mig_size':
p_labels = 'nu1a, nuA, nu2a, nu3a, nu1b, nu2b, nu3b, mA12, mA21, mB12, mB21, mB23, mB32, mB13, mB31, mC12, mC21, mC23, mC32, mC13, mC31, T1, T2, T3'
in_upper = [5, 5, 5, 5, 5, 5, 5, 0.1, 0.1, 0.1,0.1, 0.1, 0.1,0.1, 0.1, 0.1,0.1, 0.1, 0.1,0.1, 0.1, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,
0.001, 0.001, 0.001,0.001, 0.001, 0.001,0.001,
0.001, 0.001,0.001, 0.001, 0.001,0.001, 0.001, 0.001,0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_asym_mig_size",
func=split_asym_mig_size,
rounds=opt.rounds,
param_number=24,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_symmig_all':
p_labels = 'nu1, nuA, nu2, nu3, mA, m1, m2, m3, T1, T2'
in_upper = [5, 5, 5, 5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_symmig_all",
func=split_symmig_all,
rounds=opt.rounds,
param_number=10,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_symmig_all_init':
p_labels = 'nu0, nu1, nuA, nu2, nu3, mA, m1, m2, m3, T0, T1, T2'
in_upper = [5, 5, 5, 5, 5,
0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.01,
0.001, 0.001, 0.001, 0.001, 0.0001,
0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_symmig_all_init",
func=split_symmig_all_init,
rounds=opt.rounds,
param_number=12,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_symmig_all_grow':
p_labels = 'nu1, nuA, nu2, nu3, nu1B, nu2B, nu3B, mA, m1, m2, m3, T1, T2'
in_upper = [5, 5, 5, 5, 5, 5, 5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_symmig_all_grow",
func=split_symmig_all_grow,
rounds=opt.rounds,
param_number=13,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_symmig_all_grow_init':
p_labels = 'nu0, nu1, nuA, nu2, nu3, nu2B, nu3B, mA, m1, m2, m3, T0, T1, T2'
in_upper = [5, 5, 5, 5, 5,
5, 5, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.01,
0.01, 0.01, 0.001, 0.001, 0.001,
0.001, 0.0001, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_symmig_all_grow_init",
func=split_symmig_all_grow_init,
rounds=opt.rounds,
param_number=14,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'split_asymmig_all':
p_labels = 'nu1, nuA, nu2, nu3, mA1, mA2, m12, m21, m23, m32, m13, m31, T1, T2'
in_upper = [5, 5, 5, 5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="split_asymmig_all",
func=split_asymmig_all,
rounds=opt.rounds,
param_number=14,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'sim_split_sym_mig_all':
p_labels = 'nu1, nu2, nu3, m1, m2, m3, T1'
in_upper = [5, 5, 5, 0.1, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="sim_split_sym_mig_all",
func=sim_split_sym_mig_all,
rounds=opt.rounds,
param_number=7,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
elif opt.model == 'sim_split_refugia_sym_mig_all':
p_labels = 'nu1, nu2, nu3, m1, m2, m3, T1, T2'
in_upper = [5, 5, 5, 0.1, 0.1, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="sim_split_refugia_sym_mig_all",
func=sim_split_refugia_sym_mig_all,
rounds=opt.rounds,
param_number=8,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper
)
elif opt.model == 'sim_split_no_mig_size':
p_labels = 'nu1a, nu2a, nu3a, nu1b, nu2b, nu3b, T1, T2'
in_upper = [5, 5, 5, 5, 5, 5, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="sim_split_no_mig_size",
func=sim_split_no_mig_size,
rounds=opt.rounds,
param_number=8,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper
)
elif opt.model == 'sim_split_asym_mig_all':
p_labels = 'nu1, nu2, nu3, m12, m23, m31, m21, m32, m13, T1'
in_upper = [5, 5, 5, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
in_lower = [0.01, 0.01, 0.01, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001]
Optimize_Functions.Optimize_Routine(fs=fs,
pts=pts_l,
outfile=prefix,
model_name="sim_split_asym_mig_all",
func=sim_split_asym_mig_all,
rounds=opt.rounds,
param_number=10,
fs_folded=opt.folded,
param_labels=p_labels,
optimizer="log",
# reps=reps,
# maxiters=maxiters,
# folds=folds,
in_params=inparam,
in_lower=in_lower,
in_upper=in_upper)
| 55.051151
| 156
| 0.373333
| 2,093
| 21,525
| 3.615385
| 0.091734
| 0.022995
| 0.028941
| 0.038589
| 0.802696
| 0.769526
| 0.743095
| 0.718911
| 0.703053
| 0.668429
| 0
| 0.103869
| 0.535285
| 21,525
| 390
| 157
| 55.192308
| 0.652604
| 0.039024
| 0
| 0.482428
| 0
| 0.019169
| 0.086354
| 0.023357
| 0
| 0
| 0
| 0
| 0
| 1
| 0.003195
| false
| 0
| 0.057508
| 0
| 0.063898
| 0.022364
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 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
|
1f6d127732cdcd22d37384fcd5471364c5593592
| 2,257
|
py
|
Python
|
src/genie/libs/parser/iosxe/tests/ShowIsisFlexAlgo/cli/equal/golden_output_5_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | null | null | null |
src/genie/libs/parser/iosxe/tests/ShowIsisFlexAlgo/cli/equal/golden_output_5_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | null | null | null |
src/genie/libs/parser/iosxe/tests/ShowIsisFlexAlgo/cli/equal/golden_output_5_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | null | null | null |
expected_output = {
"tag": {
"2": {
"flex_algo": {
"global": {
"level": {
"1": {
"delay_metric": True
}
}
},
"128": {
"level": {
"1": {
"def_priority": 128,
"def_source": "R1-1.00",
"def_equal_to_local": True,
"def_metric_type": "IGP",
"def_prefix_metric": False,
"disabled": False,
"microloop_avoidance_timer_running": False
}
},
"local_priority": 128,
"frr_disabled": False,
"microloop_avoidance_disabled": False
}
},
"flex_algo_count": 4,
"use_delay_metric_advertisement": [
"Application",
"Legacy"
]
},
"1": {
"flex_algo": {
"global": {
"level": {
"1": {
"delay_metric": True
}
}
},
"128": {
"level": {
"1": {
"def_priority": 128,
"def_source": "R5.00",
"def_equal_to_local": True,
"def_metric_type": "IGP",
"def_prefix_metric": False,
"disabled": False,
"microloop_avoidance_timer_running": False
}
},
"local_priority": 128,
"frr_disabled": False,
"microloop_avoidance_disabled": False
}
},
"flex_algo_count": 3,
"use_delay_metric_advertisement": [
"Application",
"Legacy"
]
}
}
}
| 33.191176
| 70
| 0.291094
| 122
| 2,257
| 5
| 0.319672
| 0.127869
| 0.144262
| 0.203279
| 0.957377
| 0.957377
| 0.813115
| 0.813115
| 0.813115
| 0.813115
| 0
| 0.037801
| 0.613203
| 2,257
| 68
| 71
| 33.191176
| 0.660939
| 0
| 0
| 0.602941
| 0
| 0
| 0.250221
| 0.080602
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 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
|
2f19fb1aaefdf8c5f3de1dfd6e979a8efdedaa53
| 169
|
py
|
Python
|
backend/naki/naki/schemas/login.py
|
iimcz/emod
|
432094c020247597a94e95f76cc524c20b68b685
|
[
"MIT"
] | null | null | null |
backend/naki/naki/schemas/login.py
|
iimcz/emod
|
432094c020247597a94e95f76cc524c20b68b685
|
[
"MIT"
] | 6
|
2021-03-08T23:32:15.000Z
|
2022-02-26T08:11:38.000Z
|
backend/naki/naki/schemas/login.py
|
iimcz/emod
|
432094c020247597a94e95f76cc524c20b68b685
|
[
"MIT"
] | null | null | null |
import colander
class LoginSchema(colander.MappingSchema):
username = colander.SchemaNode(colander.String())
password = colander.SchemaNode(colander.String())
| 24.142857
| 53
| 0.775148
| 16
| 169
| 8.1875
| 0.5625
| 0.274809
| 0.396947
| 0.48855
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.118343
| 169
| 6
| 54
| 28.166667
| 0.879195
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.25
| 0.25
| 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
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 7
|
c80189469b5c97ba1309876d2dff7fe2e756058f
| 26,919
|
py
|
Python
|
tests/python-opcua/opcua/server/standard_address_space/standard_address_space_part13.py
|
iit-danieli-joint-lab/opcua-modeling-tool
|
f8c3d940a61334b0e6deda9099844a6b429d7c08
|
[
"MIT"
] | 32
|
2018-03-27T12:25:24.000Z
|
2022-01-11T21:20:06.000Z
|
tests/python-opcua/opcua/server/standard_address_space/standard_address_space_part13.py
|
iit-danieli-joint-lab/opcua-modeling-tool
|
f8c3d940a61334b0e6deda9099844a6b429d7c08
|
[
"MIT"
] | 5
|
2018-04-09T07:35:54.000Z
|
2020-03-10T11:04:45.000Z
|
tests/python-opcua/opcua/server/standard_address_space/standard_address_space_part13.py
|
iit-danieli-joint-lab/opcua-modeling-tool
|
f8c3d940a61334b0e6deda9099844a6b429d7c08
|
[
"MIT"
] | 12
|
2018-06-04T20:06:06.000Z
|
2021-07-02T22:09:53.000Z
|
"""
DO NOT EDIT THIS FILE!
It is automatically generated from opcfoundation.org schemas.
"""
from opcua import ua
def create_standard_address_space_Part13(server):
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11187")
node.BrowseName = ua.QualifiedName.from_string("AggregateConfigurationType")
node.NodeClass = ua.NodeClass.ObjectType
node.ParentNodeId = ua.NodeId.from_string("i=58")
node.ReferenceTypeId = ua.NodeId.from_string("i=45")
attrs = ua.ObjectTypeAttributes()
attrs.DisplayName = ua.LocalizedText("AggregateConfigurationType")
attrs.IsAbstract = False
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = ua.NodeId.from_string("i=46")
ref.SourceNodeId = ua.NodeId.from_string("i=11187")
ref.TargetNodeClass = ua.NodeClass.DataType
ref.TargetNodeId = ua.NodeId.from_string("i=11188")
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = ua.NodeId.from_string("i=46")
ref.SourceNodeId = ua.NodeId.from_string("i=11187")
ref.TargetNodeClass = ua.NodeClass.DataType
ref.TargetNodeId = ua.NodeId.from_string("i=11189")
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = ua.NodeId.from_string("i=46")
ref.SourceNodeId = ua.NodeId.from_string("i=11187")
ref.TargetNodeClass = ua.NodeClass.DataType
ref.TargetNodeId = ua.NodeId.from_string("i=11190")
refs.append(ref)
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = ua.NodeId.from_string("i=46")
ref.SourceNodeId = ua.NodeId.from_string("i=11187")
ref.TargetNodeClass = ua.NodeClass.DataType
ref.TargetNodeId = ua.NodeId.from_string("i=11191")
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11188")
node.BrowseName = ua.QualifiedName.from_string("TreatUncertainAsBad")
node.NodeClass = ua.NodeClass.Variable
node.ParentNodeId = ua.NodeId.from_string("i=11187")
node.ReferenceTypeId = ua.NodeId.from_string("i=46")
node.TypeDefinition = ua.NodeId.from_string("i=68")
attrs = ua.VariableAttributes()
attrs.DisplayName = ua.LocalizedText("TreatUncertainAsBad")
attrs.DataType = ua.NodeId(ua.ObjectIds.Boolean)
attrs.ValueRank = -1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = ua.NodeId.from_string("i=37")
ref.SourceNodeId = ua.NodeId.from_string("i=11188")
ref.TargetNodeClass = ua.NodeClass.DataType
ref.TargetNodeId = ua.NodeId.from_string("i=78")
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11189")
node.BrowseName = ua.QualifiedName.from_string("PercentDataBad")
node.NodeClass = ua.NodeClass.Variable
node.ParentNodeId = ua.NodeId.from_string("i=11187")
node.ReferenceTypeId = ua.NodeId.from_string("i=46")
node.TypeDefinition = ua.NodeId.from_string("i=68")
attrs = ua.VariableAttributes()
attrs.DisplayName = ua.LocalizedText("PercentDataBad")
attrs.DataType = ua.NodeId(ua.ObjectIds.Byte)
attrs.ValueRank = -1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = ua.NodeId.from_string("i=37")
ref.SourceNodeId = ua.NodeId.from_string("i=11189")
ref.TargetNodeClass = ua.NodeClass.DataType
ref.TargetNodeId = ua.NodeId.from_string("i=78")
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11190")
node.BrowseName = ua.QualifiedName.from_string("PercentDataGood")
node.NodeClass = ua.NodeClass.Variable
node.ParentNodeId = ua.NodeId.from_string("i=11187")
node.ReferenceTypeId = ua.NodeId.from_string("i=46")
node.TypeDefinition = ua.NodeId.from_string("i=68")
attrs = ua.VariableAttributes()
attrs.DisplayName = ua.LocalizedText("PercentDataGood")
attrs.DataType = ua.NodeId(ua.ObjectIds.Byte)
attrs.ValueRank = -1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = ua.NodeId.from_string("i=37")
ref.SourceNodeId = ua.NodeId.from_string("i=11190")
ref.TargetNodeClass = ua.NodeClass.DataType
ref.TargetNodeId = ua.NodeId.from_string("i=78")
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11191")
node.BrowseName = ua.QualifiedName.from_string("UseSlopedExtrapolation")
node.NodeClass = ua.NodeClass.Variable
node.ParentNodeId = ua.NodeId.from_string("i=11187")
node.ReferenceTypeId = ua.NodeId.from_string("i=46")
node.TypeDefinition = ua.NodeId.from_string("i=68")
attrs = ua.VariableAttributes()
attrs.DisplayName = ua.LocalizedText("UseSlopedExtrapolation")
attrs.DataType = ua.NodeId(ua.ObjectIds.Boolean)
attrs.ValueRank = -1
node.NodeAttributes = attrs
server.add_nodes([node])
refs = []
ref = ua.AddReferencesItem()
ref.IsForward = True
ref.ReferenceTypeId = ua.NodeId.from_string("i=37")
ref.SourceNodeId = ua.NodeId.from_string("i=11191")
ref.TargetNodeClass = ua.NodeClass.DataType
ref.TargetNodeId = ua.NodeId.from_string("i=78")
refs.append(ref)
server.add_references(refs)
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2341")
node.BrowseName = ua.QualifiedName.from_string("Interpolative")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("At the beginning of each interval, retrieve the calculated value from the data points on either side of the requested timestamp.")
attrs.DisplayName = ua.LocalizedText("Interpolative")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2342")
node.BrowseName = ua.QualifiedName.from_string("Average")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the average value of the data over the interval.")
attrs.DisplayName = ua.LocalizedText("Average")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2343")
node.BrowseName = ua.QualifiedName.from_string("TimeAverage")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the time weighted average data over the interval using Interpolated Bounding Values.")
attrs.DisplayName = ua.LocalizedText("TimeAverage")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11285")
node.BrowseName = ua.QualifiedName.from_string("TimeAverage2")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the time weighted average data over the interval using Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("TimeAverage2")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2344")
node.BrowseName = ua.QualifiedName.from_string("Total")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the total (time integral) of the data over the interval using Interpolated Bounding Values.")
attrs.DisplayName = ua.LocalizedText("Total")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11304")
node.BrowseName = ua.QualifiedName.from_string("Total2")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the total (time integral) of the data over the interval using Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("Total2")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2346")
node.BrowseName = ua.QualifiedName.from_string("Minimum")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the minimum raw value in the interval with the timestamp of the start of the interval.")
attrs.DisplayName = ua.LocalizedText("Minimum")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2347")
node.BrowseName = ua.QualifiedName.from_string("Maximum")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the maximum raw value in the interval with the timestamp of the start of the interval.")
attrs.DisplayName = ua.LocalizedText("Maximum")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2348")
node.BrowseName = ua.QualifiedName.from_string("MinimumActualTime")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the minimum value in the interval and the Timestamp of the minimum value.")
attrs.DisplayName = ua.LocalizedText("MinimumActualTime")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2349")
node.BrowseName = ua.QualifiedName.from_string("MaximumActualTime")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the maximum value in the interval and the Timestamp of the maximum value.")
attrs.DisplayName = ua.LocalizedText("MaximumActualTime")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2350")
node.BrowseName = ua.QualifiedName.from_string("Range")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the difference between the minimum and maximum Value over the interval.")
attrs.DisplayName = ua.LocalizedText("Range")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11286")
node.BrowseName = ua.QualifiedName.from_string("Minimum2")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the minimum value in the interval including the Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("Minimum2")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11287")
node.BrowseName = ua.QualifiedName.from_string("Maximum2")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the maximum value in the interval including the Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("Maximum2")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11305")
node.BrowseName = ua.QualifiedName.from_string("MinimumActualTime2")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the minimum value with the actual timestamp including the Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("MinimumActualTime2")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11306")
node.BrowseName = ua.QualifiedName.from_string("MaximumActualTime2")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the maximum value with the actual timestamp including the Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("MaximumActualTime2")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11288")
node.BrowseName = ua.QualifiedName.from_string("Range2")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the difference between the Minimum2 and Maximum2 value over the interval.")
attrs.DisplayName = ua.LocalizedText("Range2")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2351")
node.BrowseName = ua.QualifiedName.from_string("AnnotationCount")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the number of Annotations in the interval.")
attrs.DisplayName = ua.LocalizedText("AnnotationCount")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2352")
node.BrowseName = ua.QualifiedName.from_string("Count")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the number of raw values over the interval.")
attrs.DisplayName = ua.LocalizedText("Count")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11307")
node.BrowseName = ua.QualifiedName.from_string("DurationInStateZero")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the time a Boolean or numeric was in a zero state using Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("DurationInStateZero")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11308")
node.BrowseName = ua.QualifiedName.from_string("DurationInStateNonZero")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the time a Boolean or numeric was in a non-zero state using Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("DurationInStateNonZero")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2355")
node.BrowseName = ua.QualifiedName.from_string("NumberOfTransitions")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the number of changes between zero and non-zero that a Boolean or Numeric value experienced in the interval.")
attrs.DisplayName = ua.LocalizedText("NumberOfTransitions")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2357")
node.BrowseName = ua.QualifiedName.from_string("Start")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the value at the beginning of the interval using Interpolated Bounding Values.")
attrs.DisplayName = ua.LocalizedText("Start")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2358")
node.BrowseName = ua.QualifiedName.from_string("End")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the value at the end of the interval using Interpolated Bounding Values.")
attrs.DisplayName = ua.LocalizedText("End")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2359")
node.BrowseName = ua.QualifiedName.from_string("Delta")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the difference between the Start and End value in the interval.")
attrs.DisplayName = ua.LocalizedText("Delta")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11505")
node.BrowseName = ua.QualifiedName.from_string("StartBound")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the value at the beginning of the interval using Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("StartBound")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11506")
node.BrowseName = ua.QualifiedName.from_string("EndBound")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the value at the end of the interval using Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("EndBound")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11507")
node.BrowseName = ua.QualifiedName.from_string("DeltaBounds")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the difference between the StartBound and EndBound value in the interval.")
attrs.DisplayName = ua.LocalizedText("DeltaBounds")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2360")
node.BrowseName = ua.QualifiedName.from_string("DurationGood")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the total duration of time in the interval during which the data is good.")
attrs.DisplayName = ua.LocalizedText("DurationGood")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2361")
node.BrowseName = ua.QualifiedName.from_string("DurationBad")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the total duration of time in the interval during which the data is bad.")
attrs.DisplayName = ua.LocalizedText("DurationBad")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2362")
node.BrowseName = ua.QualifiedName.from_string("PercentGood")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the percent of data (0 to 100) in the interval which has a good StatusCode.")
attrs.DisplayName = ua.LocalizedText("PercentGood")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2363")
node.BrowseName = ua.QualifiedName.from_string("PercentBad")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the percent of data (0 to 100) in the interval which has a bad StatusCode.")
attrs.DisplayName = ua.LocalizedText("PercentBad")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=2364")
node.BrowseName = ua.QualifiedName.from_string("WorstQuality")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the worst StatusCode of data in the interval.")
attrs.DisplayName = ua.LocalizedText("WorstQuality")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11292")
node.BrowseName = ua.QualifiedName.from_string("WorstQuality2")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the worst StatusCode of data in the interval including the Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("WorstQuality2")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11426")
node.BrowseName = ua.QualifiedName.from_string("StandardDeviationSample")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the standard deviation for the interval for a sample of the population (n-1).")
attrs.DisplayName = ua.LocalizedText("StandardDeviationSample")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11427")
node.BrowseName = ua.QualifiedName.from_string("StandardDeviationPopulation")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the standard deviation for the interval for a complete population (n) which includes Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("StandardDeviationPopulation")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11428")
node.BrowseName = ua.QualifiedName.from_string("VarianceSample")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the variance for the interval as calculated by the StandardDeviationSample.")
attrs.DisplayName = ua.LocalizedText("VarianceSample")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
node = ua.AddNodesItem()
node.RequestedNewNodeId = ua.NodeId.from_string("i=11429")
node.BrowseName = ua.QualifiedName.from_string("VariancePopulation")
node.NodeClass = ua.NodeClass.Object
node.TypeDefinition = ua.NodeId.from_string("i=2340")
attrs = ua.ObjectAttributes()
attrs.Description = ua.LocalizedText("Retrieve the variance for the interval as calculated by the StandardDeviationPopulation which includes Simple Bounding Values.")
attrs.DisplayName = ua.LocalizedText("VariancePopulation")
attrs.EventNotifier = 0
node.NodeAttributes = attrs
server.add_nodes([node])
| 45.625424
| 172
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| 0.082196
| 0.072581
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| 0.895265
| 0.887304
| 0.799266
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| 0.77378
| 0.765095
| 0
| 0.02353
| 0.16011
| 26,919
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| 173
| 45.702886
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| 1
| 0.003717
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| false
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| 0.001859
| 0
| 0.003717
| 0
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| 0
| null | 0
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0
| 7
|
c83e118fa8ecf24f2e5a8d3ea61aff975a7b5668
| 17,458
|
py
|
Python
|
agent.py
|
ironmanciti/rltrader
|
7f585ee3214094e62a20b11cf983d2bd83a7eb5e
|
[
"MIT"
] | null | null | null |
agent.py
|
ironmanciti/rltrader
|
7f585ee3214094e62a20b11cf983d2bd83a7eb5e
|
[
"MIT"
] | null | null | null |
agent.py
|
ironmanciti/rltrader
|
7f585ee3214094e62a20b11cf983d2bd83a7eb5e
|
[
"MIT"
] | null | null | null |
import numpy as np
from settings import *
import random
from keras.models import Sequential
from keras.layers import Activation, Dense, LSTM, BatchNormalization
from keras.optimizers import Adam
class DQNAgent:
<<<<<<< HEAD
class Agent: # 매수, 매도, Hold 를 수행하는 주체 (policy_learner.init 에서 생성)
# 에이전트 상태가 구성하는 값 개수
STATE_DIM = 2 # 주식 보유 비율, 포트폴리오 가치 비율, policy_learner 에서 입력 data feature 로 추가
# 매매 수수료 및 세금
TRADING_CHARGE = 0.00015 # 거래 수수료(일반적으로 0.015%)
TRADING_TAX = 0.003 # 거래세 0.3%
# 행동
ACTION_BUY = 0 # 매수
ACTION_SELL = 1 # 매도
ACTION_HOLD = 2 # 홀딩 (관망)
ACTIONS = [ACTION_BUY, ACTION_SELL, ACTION_HOLD] # 인공 신경망에서 softmax 로 구할 행동들
NUM_ACTIONS = len(ACTIONS) # 인공 신경망에서 고려할 출력값의 개수 (3)
def __init__(
self, environment, min_trading_unit=0, max_trading_unit=0,
delayed_reward_threshold=0):
# Environment 객체
self.environment = environment # 현재 주식 가격을 가져오기 위해 환경 참조
# 최소 매매 단위, 최대 매매 단위, 지연보상 임계치
self.min_trading_unit = min_trading_unit # 최소 단일 거래 단위
self.max_trading_unit = max_trading_unit # 최대 단일 거래 단위
self.delayed_reward_threshold = delayed_reward_threshold # 지연보상 임계치 (손익률이 threshold 를 넘으면 지연보상 발생)
# Agent 클래스의 속성
self.initial_balance = 0 # 초기 자본금 (투자 시작 시점의 보유 현금)
self.balance = 0 # 현재 현금 잔고
self.num_stocks = 0 # 현재 보유 주식 수
self.portfolio_value = 0 # balance + num_stocks * {현재 주식 가격} --> 포트폴리오 가치
self.base_portfolio_value = 0 # 직전 학습 시점의 PV (기준 포트폴리오)
self.base_stock_price = 0 # 직전 학습 시점의 주가
self.num_buy = 0 # 매수 횟수
self.num_sell = 0 # 매도 횟수
self.num_hold = 0 # 홀딩 횟수
self.immediate_reward = 0 # 즉시 보상 (행동을 수행한 시점에 수익발행 +1, 아니면 -1)
self.buy_charge = 0 # 매수 수수료
self.sell_charge = 0 # 매도 수수료
self.sell_tax = 0 # 거래세
# Agent 클래스의 상태
self.ratio_hold = 0 # 주식 보유 비율 (현재보유하고있는 주식수/최대로 보유할수 있는 주식수)
self.ratio_portfolio_value = 0 # 포트폴리오 가치 비율 (현재 PV/직전지연보상시점의 PV)
def reset(self): # class 속성 초기화 (매 epoch 마다)
self.balance = self.initial_balance
self.num_stocks = 0
self.portfolio_value = self.initial_balance
self.base_portfolio_value = self.initial_balance
self.num_buy = 0
self.num_sell = 0
self.num_hold = 0
self.immediate_reward = 0
self.buy_charge = 0
self.sell_charge = 0
self.sell_tax = 0
self.ratio_hold = 0
self.ratio_portfolio_value = 0
def set_balance(self, balance):
self.initial_balance = balance # 초기 자본금 설정
def get_states(self): # agent 의 상태 반환
self.ratio_hold = self.num_stocks / int(
self.portfolio_value / self.environment.get_price()) # 보유주식수/(PV/현재주가)
self.ratio_portfolio_value = self.portfolio_value / self.base_portfolio_value # PV/기준PV
return (
self.ratio_hold, # 주식보유비율 - 0: 주식 없음, 1: 최대보유
self.ratio_portfolio_value # 포트폴리오 가치 비율 - < 1 : 손실, 1 > : 수익발생
)
def decide_action(self, policy_network, sample, epsilon):
# confidence = 0.
# 탐험 결정
if np.random.rand() < epsilon:
exploration = True
action = np.random.randint(self.NUM_ACTIONS) # 무작위로 행동 결정
else:
exploration = False
probs = policy_network.predict(sample) # 각 행동에 대한 확률, 지금까지 훈련시킨 청책망으로 다음 action 결정
action = np.argmax(probs)
# confidence = probs[action]
# return action, confidence, exploration
return action, exploration # 0:매수, 1:매도, 2:HOLD / False/True(탐험여부)
def validate_action(self, action):
validity = True
if action == Agent.ACTION_BUY: # 매수의 경우
# 적어도 1주를 살 수 있는지 확인
if self.balance < self.environment.get_price() * (
1 + self.TRADING_CHARGE) * self.min_trading_unit:
validity = False
elif action == Agent.ACTION_SELL: # 매도의 경우
# 주식 잔고가 있는지 확인하여 없으면 False return
if self.num_stocks <= 0:
validity = False
return validity
def decide_trading_unit(self, action, curr_price): # 매수 단위 결정
if action == Agent.ACTION_BUY: # 매수의 경우
trading_unit = int(self.balance / (curr_price * (1 + self.TRADING_CHARGE))) # 가능한 최대 주식 매수
elif action == Agent.ACTION_SELL: # 매도의 경우
trading_unit = self.num_stocks # 보유수량 전부 매도
else:
trading_unit = 0 # Hold
return trading_unit
=======
def __init__(self, env, state_size):
# Environment 객체
self.env = env # 현재 주식 가격을 가져오기 위해 환경 참조
self.gamma = GAMMA
self.epsilon = EPSILON
self.epsilon_decay = EPSILON_DECAY
self.learning_rate = LEARNING_RATE
self.state_size = state_size
self.model = self._build_model()
self.target_model = self._build_model()
# # 최소 매매 단위, 최대 매매 단위, 지연보상 임계치
# self.min_trading_unit = min_trading_unit # 최소 단일 거래 단위
# self.max_trading_unit = max_trading_unit # 최대 단일 거래 단위
# self.delayed_reward_threshold = delayed_reward_threshold # 지연보상 임계치 (손익률이 threshold 를 넘으면 지연보상 발생)
#
# # Agent 클래스의 속성
# self.initial_balance = 0 # 초기 자본금 (투자 시작 시점의 보유 현금)
# self.balance = 0 # 현재 현금 잔고
# self.num_stocks = 0 # 현재 보유 주식 수
# self.portfolio_value = 0 # balance + num_stocks * {현재 주식 가격} --> 포트폴리오 가치
# self.base_portfolio_value = 0 # 직전 학습 시점의 PV (기준 포트폴리오)
# self.base_stock_price = 0 # 직전 학습 시점의 주가
self.num_buy = 0 # 매수 횟수
self.num_sell = 0 # 매도 횟수
self.num_hold = 0 # 홀딩 횟수
self.profitloss = 0 # 누적 손익
# self.immediate_reward = 0 # 즉시 보상 (행동을 수행한 시점에 수익발행 +1, 아니면 -1)
# self.buy_charge = 0 # 매수 수수료
# self.sell_charge = 0 # 매도 수수료
# self.sell_tax = 0 # 거래세
#
# # Agent 클래스의 상태
# self.ratio_hold = 0 # 주식 보유 비율 (현재보유하고있는 주식수/최대로 보유할수 있는 주식수)
# self.ratio_portfolio_value = 0 # 포트폴리오 가치 비율 (현재 PV/직전지연보상시점의 PV)
#
def _build_model(self):
model = Sequential()
model.add(LSTM(128, input_shape=(None, self.state_size),
return_sequences=False, dropout=0.2))
# model.add(BatchNormalization())
# model.add(LSTM(64, return_sequences=False, dropout=0.2))
model.add(BatchNormalization())
model.add(Dense(32))
model.add(Dense(ACTION_SIZE, activation='linear'))
model.compile(optimizer=Adam(lr=LEARNING_RATE), loss='mse')
return model
def replay(self, replay_buffer, batch_size):
if len(replay_buffer) < batch_size:
return
minibatch = random.sample(replay_buffer, batch_size)
for episode_rewarded in minibatch:
y = np.zeros((len(episode_rewarded), ACTION_SIZE))
for (state, action, reward, next_state, done) in episode_rewarded:
target_f = self.target_model.predict(state)
if done:
target_f[:, action] = reward
if target_f.shape[0] != 1:
print("Inavlid target_f shape : ", target_f.shape)
else:
target_f[:, action] = (reward + self.gamma * \
np.amax(self.target_model.predict(next_state)[:, action]))
if target_f.shape[0] != 1:
print("Inavlid target_f shape : ", target_f.shape)
self.model.fit(state, target_f, epochs=1, verbose=0)
# def reset(self): # class 속성 초기화 (매 epoch 마다)
# self.balance = self.initial_balance
# self.num_stocks = 0
# self.portfolio_value = self.initial_balance
# self.base_portfolio_value = self.initial_balance
# self.num_buy = 0
# self.num_sell = 0
# self.num_hold = 0
# self.immediate_reward = 0
# self.buy_charge = 0
# self.sell_charge = 0
# self.sell_tax = 0
# self.ratio_hold = 0
# self.ratio_portfolio_value = 0
#
# def set_balance(self, balance):
# self.initial_balance = balance # 초기 자본금 설정
#
# def get_states(self): # agent 의 상태 반환
# self.ratio_hold = self.num_stocks / int(
# self.portfolio_value / self.environment.get_price()) # 보유주식수/(PV/현재주가)
# self.ratio_portfolio_value = self.portfolio_value / self.base_portfolio_value # PV/기준PV
# return (
# self.ratio_hold, # 주식보유비율 - 0: 주식 없음, 1: 최대보유
# self.ratio_portfolio_value # 포트폴리오 가치 비율 - < 1 : 손실, 1 > : 수익발생
# )
# def decide_action(self, policy_network, sample, epsilon):
# # confidence = 0.
# # 탐험 결정
# if np.random.rand() < epsilon:
# exploration = True
# action = np.random.randint(self.NUM_ACTIONS) # 무작위로 행동 결정
# else:
# exploration = False
# probs = policy_network.predict(sample) # 각 행동에 대한 확률, 지금까지 훈련시킨 청책망으로 다음 action 결정
# action = np.argmax(probs)
# # confidence = probs[action]
#
# # return action, confidence, exploration
# return action, exploration # 0:매수, 1:매도, 2:HOLD / False/True(탐험여부)
#
# def validate_action(self, action):
# validity = True
# if action == Agent.ACTION_BUY: # 매수의 경우
# # 적어도 1주를 살 수 있는지 확인
# if self.balance < self.environment.get_price() * (
# 1 + self.TRADING_CHARGE) * self.min_trading_unit:
# validity = False
# elif action == Agent.ACTION_SELL: # 매도의 경우
# # 주식 잔고가 있는지 확인하여 없으면 False return
# if self.num_stocks <= 0:
# validity = False
# return validity
#
# def decide_trading_unit(self, action, curr_price): # 매수 단위 결정
# if action == Agent.ACTION_BUY: # 매수의 경우
# trading_unit = int(self.balance / (curr_price * (1 + self.TRADING_CHARGE))) # 가능한 최대 주식 매수
# elif action == Agent.ACTION_SELL: # 매도의 경우
# trading_unit = self.num_stocks # 보유수량 전부 매도
# else:
# trading_unit = 0 # Hold
# return trading_unit
>>>>>>> 6ad1fe99b5f6f32b80563112803f402ef76f9fa6
# def decide_trading_unit(self, confidence): # 확률이 높을수록 더 많은 주식 거래 --> 차라리 주식 변동 예상 폭이 클때로 변경이 좋을 것임(다음 version 고려)
# if np.isnan(confidence):
# return self.min_trading_unit
# added_traiding = max(min(
# int(confidence * (self.max_trading_unit - self.min_trading_unit)),
# self.max_trading_unit-self.min_trading_unit
# ), 0)
# return self.min_trading_unit + added_traiding
#def act(self, action, confidence):
<<<<<<< HEAD
def act(self, action): # Agent 가 결정한 행동을 수행
if not self.validate_action(action): # action 을 할 수 없는 경우 관망
action = Agent.ACTION_HOLD
# 환경에서 현재 가격 얻기 (매일의 종가로 매매를 가정함)
curr_price = self.environment.get_price()
# 즉시 보상 초기화
self.immediate_reward = 0
# 매수/매도시 거래할 주식 수 결정
trading_unit = self.decide_trading_unit(action, curr_price)
# 매수
if action == Agent.ACTION_BUY:
balance = self.balance - curr_price * (1 + self.TRADING_CHARGE) * trading_unit
# 보유 현금이 모자라거나 최대 trading_unit 을 초과한 경우 가능한 만큼 최대한 매수
# if balance < 0:
# trading_unit = max(min(
# int(self.balance / (
# curr_price * (1 + self.TRADING_CHARGE))), self.max_trading_unit),
# self.min_trading_unit
# )
# 수수료를 적용하여 총 매수 금액 산정
buy_amount = curr_price * (1 + self.TRADING_CHARGE) * trading_unit
self.balance -= buy_amount # 보유 현금을 갱신
self.num_stocks += trading_unit # 보유 주식 수를 갱신
self.num_buy += 1 # 매수 횟수 증가
self.buy_charge += curr_price * self.TRADING_CHARGE * trading_unit # 매수 수수료 누적
# 매도
elif action == Agent.ACTION_SELL:
# 보유 주식이 모자랄 경우 가능한 만큼 최대한 매도
#trading_unit = min(trading_unit, self.num_stocks)
sell_amount = curr_price * (
1 - (self.TRADING_TAX + self.TRADING_CHARGE)) * trading_unit
self.num_stocks -= trading_unit # 보유 주식 수를 갱신
self.balance += sell_amount # 보유 현금을 갱신
self.num_sell += 1 # 매도 횟수 증가
self.sell_charge += curr_price * self.TRADING_CHARGE * trading_unit # 매도 수수료 누적
self.sell_tax += curr_price * self.TRADING_TAX * trading_unit # 거래세 누적
# 홀딩
elif action == Agent.ACTION_HOLD:
self.num_hold += 1 # 홀딩 횟수 증가
else:
print("invalid action", action)
quit()
# 포트폴리오 가치 갱신
self.portfolio_value = self.balance + curr_price * self.num_stocks
# 주식 보유 비율 계산
num_stocks_ratio = self.num_stocks / (self.balance / curr_price + self.num_stocks)
#-------- 초기 포트폴리오 가치와 현재 포트폴리오 가치 비교하여 손익 비율 계산
profitloss = (self.portfolio_value - self.base_portfolio_value) / self.base_portfolio_value
# 즉시 보상 판단
self.immediate_reward = 1 if profitloss >= 0 else -1
# 지연 보상 판단
if profitloss > self.delayed_reward_threshold:
delayed_reward = 1
# 목표 수익률 달성하여 기준 포트폴리오 가치 갱신
self.base_portfolio_value = self.portfolio_value
elif profitloss < -self.delayed_reward_threshold:
delayed_reward = -1
# 손실 기준치를 초과하여 기준 포트폴리오 가치 갱신
self.base_portfolio_value = self.portfolio_value
else:
delayed_reward = 0
self.base_stock_price = curr_price # 기준 포트폴리오 가치 갱신 시점의 주가 save
return self.immediate_reward, delayed_reward
=======
# def act(self, action): # Agent 가 결정한 행동을 수행
# if not self.validate_action(action): # action 을 할 수 없는 경우 관망
# action = Agent.ACTION_HOLD
#
# # 환경에서 현재 가격 얻기 (매일의 종가로 매매를 가정함)
# curr_price = self.environment.get_price()
#
# # 즉시 보상 초기화
# self.immediate_reward = 0
#
# # 매수/매도시 거래할 주식 수 결정
# trading_unit = self.decide_trading_unit(action, curr_price)
#
# # 매수
# if action == Agent.ACTION_BUY:
# balance = self.balance - curr_price * (1 + self.TRADING_CHARGE) * trading_unit
# # 보유 현금이 모자라거나 최대 trading_unit 을 초과한 경우 가능한 만큼 최대한 매수
# # if balance < 0:
# # trading_unit = max(min(
# # int(self.balance / (
# # curr_price * (1 + self.TRADING_CHARGE))), self.max_trading_unit),
# # self.min_trading_unit
# # )
# # 수수료를 적용하여 총 매수 금액 산정
# buy_amount = curr_price * (1 + self.TRADING_CHARGE) * trading_unit
# self.balance -= buy_amount # 보유 현금을 갱신
# self.num_stocks += trading_unit # 보유 주식 수를 갱신
# self.num_buy += 1 # 매수 횟수 증가
# self.buy_charge += curr_price * self.TRADING_CHARGE * trading_unit # 매수 수수료 누적
# # 매도
# elif action == Agent.ACTION_SELL:
# # 보유 주식이 모자랄 경우 가능한 만큼 최대한 매도
# #trading_unit = min(trading_unit, self.num_stocks)
# sell_amount = curr_price * (
# 1 - (self.TRADING_TAX + self.TRADING_CHARGE)) * trading_unit
# self.num_stocks -= trading_unit # 보유 주식 수를 갱신
# self.balance += sell_amount # 보유 현금을 갱신
# self.num_sell += 1 # 매도 횟수 증가
# self.sell_charge += curr_price * self.TRADING_CHARGE * trading_unit # 매도 수수료 누적
# self.sell_tax += curr_price * self.TRADING_TAX * trading_unit # 거래세 누적
# # 홀딩
# elif action == Agent.ACTION_HOLD:
# self.num_hold += 1 # 홀딩 횟수 증가
# else:
# print("invalid action", action)
# quit()
#
# # 포트폴리오 가치 갱신
# self.portfolio_value = self.balance + curr_price * self.num_stocks
# # 주식 보유 비율 계산
# num_stocks_ratio = self.num_stocks / (self.balance / curr_price + self.num_stocks)
# #-------- 초기 포트폴리오 가치와 현재 포트폴리오 가치 비교하여 손익 비율 계산
# profitloss = (self.portfolio_value - self.base_portfolio_value) / self.base_portfolio_value
#
# # 즉시 보상 판단
# self.immediate_reward = 1 if profitloss >= 0 else -1
#
# # 지연 보상 판단
# if profitloss > self.delayed_reward_threshold:
# delayed_reward = 1
# # 목표 수익률 달성하여 기준 포트폴리오 가치 갱신
# self.base_portfolio_value = self.portfolio_value
# elif profitloss < -self.delayed_reward_threshold:
# delayed_reward = -1
# # 손실 기준치를 초과하여 기준 포트폴리오 가치 갱신
# self.base_portfolio_value = self.portfolio_value
# else:
# delayed_reward = 0
#
# self.base_stock_price = curr_price # 기준 포트폴리오 가치 갱신 시점의 주가 save
#
# return self.immediate_reward, delayed_reward
>>>>>>> 6ad1fe99b5f6f32b80563112803f402ef76f9fa6
| 41.665871
| 122
| 0.568507
| 2,271
| 17,458
| 4.179216
| 0.142668
| 0.070699
| 0.030134
| 0.032452
| 0.830576
| 0.828048
| 0.828048
| 0.816668
| 0.809082
| 0.809082
| 0
| 0.017832
| 0.341505
| 17,458
| 418
| 123
| 41.76555
| 0.807759
| 0.479895
| 0
| 0.28
| 0
| 0
| 0.008359
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.034286
| null | null | 0.017143
| 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
| 1
| 0
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| 0
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| 0
| 0
|
0
| 7
|
c84fb487c09f3bf9241b84d78e25f84ded5ca908
| 157,296
|
py
|
Python
|
jsrloader.py
|
sks316/JSRLoader
|
a16790f46e8ffbb1f36d9441d58db7791f588adb
|
[
"MIT"
] | 1
|
2022-03-22T02:25:33.000Z
|
2022-03-22T02:25:33.000Z
|
jsrloader.py
|
sks316/JSRLoader
|
a16790f46e8ffbb1f36d9441d58db7791f588adb
|
[
"MIT"
] | 3
|
2020-03-02T01:33:57.000Z
|
2021-08-19T02:28:52.000Z
|
jsrloader.py
|
sks316/JSRLoader
|
a16790f46e8ffbb1f36d9441d58db7791f588adb
|
[
"MIT"
] | null | null | null |
import urllib.request
import requests
import re
import xml.etree.ElementTree as ET
import json
import os
from os import system, name
#--Set app version--#
appver = "JSRLoader v1.3.0 by PrincessLillie"
appverTV = "JSRLoader v1.3.0 by PrincessLillie - JSRLTV downloader"
#--Function for clearing the screen--#
def clearScreen():
#--Check if OS is Windows--#
if name == 'nt':
_ = system('cls')
else:
_ = system('clear')
def classicDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/classic/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Classic")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "classic";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Classic/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/classic/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Classic"):
os.makedirs("Downloaded music/Classic")
#--Write the downloaded song to a file--#
with open("Downloaded music/Classic/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Classic")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def futureDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/future/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Future")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "future";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Future/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/future/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Future"):
os.makedirs("Downloaded music/Future")
#--Write the downloaded song to a file--#
with open("Downloaded music/Future/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Future")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def ggsDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/ggs/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: GG's")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "ggs";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/GG's/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/ggs/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/GG's"):
os.makedirs("Downloaded music/GG's")
#--Write the downloaded song to a file--#
with open("Downloaded music/GG's/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: GG's")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def bumpsDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/bumps/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Bumps")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("bumpsArray[bumpsArray.length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Bumps/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/bumps/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Bumps"):
os.makedirs("Downloaded music/Bumps")
#--Write the downloaded song to a file--#
with open("Downloaded music/Bumps/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Bumps")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def poisonJamDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/poisonjam/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Poison Jam")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "poisonjam";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Poison Jam/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/poisonjam/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Poison Jam"):
os.makedirs("Downloaded music/Poison Jam")
#--Write the downloaded song to a file--#
with open("Downloaded music/Poison Jam/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Poison Jam")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def noiseTanksDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/noisetanks/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Noise Tanks")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "noisetanks";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Noise Tanks/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/noisetanks/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Noise Tanks"):
os.makedirs("Downloaded music/Noise Tanks")
#--Write the downloaded song to a file--#
with open("Downloaded music/Noise Tanks/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Noise Tanks")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def loveShockersDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/loveshockers/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Love Shockers")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "loveshockers";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Love Shockers/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/loveshockers/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Love Shockers"):
os.makedirs("Downloaded music/Love Shockers")
#--Write the downloaded song to a file--#
with open("Downloaded music/Love Shockers/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Love Shockers")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def rapid99DL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/rapid99/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Rapid 99")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "rapid99";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Rapid 99/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/rapid99/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Rapid 99"):
os.makedirs("Downloaded music/Rapid 99")
#--Write the downloaded song to a file--#
with open("Downloaded music/Rapid 99/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Rapid 99")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def immortalsDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/immortals/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: The Immortals")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "immortals";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/The Immortals/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/immortals/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/The Immortals"):
os.makedirs("Downloaded music/The Immortals")
#--Write the downloaded song to a file--#
with open("Downloaded music/The Immortals/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: The Immortals")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def doomRidersDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/doomriders/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Doom Riders")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "doomriders";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Doom Riders/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/doomriders/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Doom Riders"):
os.makedirs("Downloaded music/Doom Riders")
#--Write the downloaded song to a file--#
with open("Downloaded music/Doom Riders/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Doom Riders")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def goldenRhinosDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/goldenrhinos/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Golden Rhinos")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "goldenrhinos";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Golden Rhinos/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/goldenrhinos/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Golden Rhinos"):
os.makedirs("Downloaded music/Golden Rhinos")
#--Write the downloaded song to a file--#
with open("Downloaded music/Golden Rhinos/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Golden Rhinos")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def summerDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/summer/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Summer")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace('if(systemMonth == "June" || systemMonth == "July" || systemMonth == "August"){', '')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "summer";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace("}", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Summer/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/summer/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Summer"):
os.makedirs("Downloaded music/Summer")
#--Write the downloaded song to a file--#
with open("Downloaded music/Summer/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Summer")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def christmasDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/christmas/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Christmas")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace('if(systemMonth == "December" && systemDay < 27){', '')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "christmas";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace("}", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Christmas/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/christmas/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Christmas"):
os.makedirs("Downloaded music/Christmas")
#--Write the downloaded song to a file--#
with open("Downloaded music/Christmas/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Christmas")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def halloweenDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/halloween/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Halloween")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace('if(systemMonth == "October"){', '')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "halloween";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace("}", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Halloween/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/halloween/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Halloween"):
os.makedirs("Downloaded music/Halloween")
#--Write the downloaded song to a file--#
with open("Downloaded music/Halloween/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Halloween")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def memoriesOfTokyoToDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradiofuture.live/radio/stations/memoriesoftokyoto/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Memories of Tokyo-To")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station were made by 2 Mello. Please support their music! https://2mellomakes.bandcamp.com/")
print("This station was removed from JSRL, and is instead being downloaded from a clone server at jetsetradiofuture.live.")
print("-------------------------------------")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "memoriesoftokyoto";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Memories of Tokyo-To/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradiofuture.live/radio/stations/memoriesoftokyoto/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Memories of Tokyo-To"):
os.makedirs("Downloaded music/Memories of Tokyo-To")
#--Write the downloaded song to a file--#
with open("Downloaded music/Memories of Tokyo-To/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Memories of Tokyo-To")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def ultraRemixesDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/ultraremixes/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Ultra Remixes")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "ultraremixes";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Ultra Remixes/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/ultraremixes/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Ultra Remixes"):
os.makedirs("Downloaded music/Ultra Remixes")
#--Write the downloaded song to a file--#
with open("Downloaded music/Ultra Remixes/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Ultra Remixes")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def kfadDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradiofuture.live/radio/stations/siivagunner/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: King for Another Day")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station were made by contributors to SiIvaGunner. Please support their music! https://gilvasunner.bandcamp.com/")
print("This station was removed from JSRL, and is instead being downloaded from a clone server at jetsetradiofuture.live.")
print("-------------------------------------")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "siivagunner";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/King for Another Day/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradiofuture.live/radio/stations/siivagunner/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/King for Another Day"):
os.makedirs("Downloaded music/King for Another Day")
#--Write the downloaded song to a file--#
with open("Downloaded music/King for Another Day/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: King for Another Day")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def lofiDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/lofi/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Lo-Fi")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "lofi";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Lo-Fi/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/lofi/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Lo-Fi"):
os.makedirs("Downloaded music/Lo-Fi")
#--Write the downloaded song to a file--#
with open("Downloaded music/Lo-Fi/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Lo-Fi")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def elaquentDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradiofuture.live/radio/stations/elaquent/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Forever Is A Pretty Long Time")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station were made by Elaquent. Please support their music! https://elaquent-mello-catalog.bandcamp.com/")
print("This station was removed from JSRL, and is instead being downloaded from a clone server at jetsetradiofuture.live.")
print("-------------------------------------")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "elaquent";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Forever Is A Pretty Long Time/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradiofuture.live/radio/stations/elaquent/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Forever Is A Pretty Long Time"):
os.makedirs("Downloaded music/Forever Is A Pretty Long Time")
#--Write the downloaded song to a file--#
with open("Downloaded music/Forever Is A Pretty Long Time/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Forever Is A Pretty Long Time")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def olliekingDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/ollieking/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Ollie King")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "ollieking";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Ollie King/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/ollieking/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Ollie King"):
os.makedirs("Downloaded music/Ollie King")
#--Write the downloaded song to a file--#
with open("Downloaded music/Ollie King/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Ollie King")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def toejamDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/toejamandearl/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Toe Jam & Earl")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station are from the game ToeJam & Earl: Back in the Groove! Please support the original product!")
print("-------------------------------------")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "toejamandearl";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Toe Jam & Earl/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/toejamandearl/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Toe Jam & Earl"):
os.makedirs("Downloaded music/Toe Jam & Earl")
#--Write the downloaded song to a file--#
with open("Downloaded music/Toe Jam & Earl/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Toe Jam & Earl")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def crazyTaxiDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/crazytaxi/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Crazy Taxi")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station are from the game Crazy Taxi. Please support the original product!")
print("-------------------------------------")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "crazytaxi";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Crazy Taxi/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/crazytaxi/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Crazy Taxi"):
os.makedirs("Downloaded music/Crazy Taxi")
#--Write the downloaded song to a file--#
with open("Downloaded music/Crazy Taxi/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Crazy Taxi")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def hoverDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/hover/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Hover")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station are from the game Hover. Please support the original product!")
print("-------------------------------------")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "hover";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Hover/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/hover/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Hover"):
os.makedirs("Downloaded music/Hover")
#--Write the downloaded song to a file--#
with open("Downloaded music/Hover/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Hover")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def butterfliesDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/butterflies/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Butterflies")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station are from the fangame Butterflies. Please support the original product! http://bit.ly/2TbMM6e")
print("-------------------------------------")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "butterflies";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Butterflies/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/butterflies/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Butterflies"):
os.makedirs("Downloaded music/Butterflies")
#--Write the downloaded song to a file--#
with open("Downloaded music/Butterflies/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Butterflies")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def revolutionDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/revolution/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Revolution")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "revolution";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Revolution/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/revolution/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Revolution"):
os.makedirs("Downloaded music/Revolution")
#--Write the downloaded song to a file--#
with open("Downloaded music/Revolution/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Revolution")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def endOfDaysDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/endofdays/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: End of Days")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "endofdays";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/End of Days/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/endofdays/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/End of Days"):
os.makedirs("Downloaded music/End of Days")
#--Write the downloaded song to a file--#
with open("Downloaded music/End of Days/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: End of Days")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def garageDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/garage/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: The Garage")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "garage";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/The Garage/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/garage/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/The Garage"):
os.makedirs("Downloaded music/The Garage")
#--Write the downloaded song to a file--#
with open("Downloaded music/The Garage/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: The Garage")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def spaceDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/outerspace/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Outer Space")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "outerspace";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Outer Space/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/outerspace/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Outer Space"):
os.makedirs("Downloaded music/Outer Space")
#--Write the downloaded song to a file--#
with open("Downloaded music/Outer Space/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Outer Space")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def ganjahDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/ganjah/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Ganjah")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "ganjah";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Ganjah/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/ganjah/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Ganjah"):
os.makedirs("Downloaded music/Ganjah")
#--Write the downloaded song to a file--#
with open("Downloaded music/Ganjah/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Ganjah")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def chiptunesDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/chiptunes/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Chiptunes")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "chiptunes";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Chiptunes/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/chiptunes/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Chiptunes"):
os.makedirs("Downloaded music/Chiptunes")
#--Write the downloaded song to a file--#
with open("Downloaded music/Chiptunes/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Chiptunes")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def retroRemixDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/retroremix/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Retro Remix")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "retroremix";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Retro Remix/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/retroremix/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Retro Remix"):
os.makedirs("Downloaded music/Retro Remix")
#--Write the downloaded song to a file--#
with open("Downloaded music/Retro Remix/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Retro Remix")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def classicalRemixDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/classical/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Classical Remix")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "classical";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Classical Remix/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/classical/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Classical Remix"):
os.makedirs("Downloaded music/Classical Remix")
#--Write the downloaded song to a file--#
with open("Downloaded music/Classical Remix/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Classical Remix")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def bonafideBloomDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/bonafidebloom/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: BonafideBloom")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station were made by Bonafide Bloom. Please support their music! http://www.bonafidebloom.com/")
print("-------------------------------------")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "bonafidebloom";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/BonafideBloom/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/bonafidebloom/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/BonafideBloom"):
os.makedirs("Downloaded music/BonafideBloom")
#--Write the downloaded song to a file--#
with open("Downloaded music/BonafideBloom/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: BonafideBloom")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def veraFXDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/verafx/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: VeraFX")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station were made by VeraFX. Please support their music! https://verafx.bandcamp.com/")
print("-------------------------------------")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "verafx";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/VeraFX/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/verafx/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/VeraFX"):
os.makedirs("Downloaded music/VeraFX")
#--Write the downloaded song to a file--#
with open("Downloaded music/VeraFX/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: VeraFX")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def snowfiDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/snowfi/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: Snow-fi")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace('if(systemMonth == "December" && systemDay < 27){', '')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "snowfi";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/Snow-fi/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/snowfi/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/Snow-fi"):
os.makedirs("Downloaded music/Snow-fi")
#--Write the downloaded song to a file--#
with open("Downloaded music/Snow-fi/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: Snow-fi")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def mashradioDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradiofuture.live/radio/stations/jetmashradio/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: JET MASH RADIO")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("Getting list of songs to download...")
print()
print("-------------------------------------")
print("/!\ NOTICE: This station was removed from JSRL, and is instead being downloaded from a clone server at jetsetradiofuture.live.")
print("-------------------------------------")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "jetmashradio";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/JET MASH RADIO/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradiofuture.live/radio/stations/jetmashradio/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/JET MASH RADIO"):
os.makedirs("Downloaded music/JET MASH RADIO")
#--Write the downloaded song to a file--#
with open("Downloaded music/JET MASH RADIO/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: JET MASH RADIO")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def djchidowDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradio.live/radio/stations/djchidow/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: DJ Chidow")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station were made by DJ Chidow. Please support their music! https://chidowhouse-shop.bandcamp.com/")
print("-------------------------------------")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "djchidow";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/DJ Chidow/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradio.live/radio/stations/djchidow/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/DJ Chidow"):
os.makedirs("Downloaded music/DJ Chidow")
#--Write the downloaded song to a file--#
with open("Downloaded music/DJ Chidow/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: DJ Chidow")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def futuregenDL():
#--Get list of songs from JSRL--#
url = "https://jetsetradiofuture.live/radio/stations/futuregeneration/~list.js"
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You wanted to download songs from station: FUTURE GENERATION")
print("This process will download all the songs from the station. This will take some time. Go grab a snack or read a book or something.")
print("If a download takes longer than a few minutes, please force-close the application and check your connection.")
print()
print("-------------------------------------")
print("/!\ NOTICE: All songs on this station were made by SASHKO NAGANUMA. Please support their music! https://sashkonaganuma.bandcamp.com/")
print("This station was removed from JSRL, and is instead being downloaded from a clone server at jetsetradiofuture.live.")
print("-------------------------------------")
print()
print("Getting list of songs to download...")
print()
with urllib.request.urlopen(url) as file:
#--Make the file actually usable by JSRLoader--#
data = file.read().decode('utf-8')
data = data.replace("//Choose a name for the station", '')
data = data.replace('stationName = "futuregeneration";', '')
data = data.replace("//Add it to the array of stations", '')
data = data.replace("stationsArray[stationsArray.length] = stationName;", '')
data = data.replace("//Define an array for tracks", '')
data = data.replace('this[stationName+"_tracks"] = new Array();', '')
data = data.replace("//TRACKS", '')
data = data.replace("this[stationName+'_tracks'][this[stationName+'_tracks'].length] = ", '')
data = data.replace(";", '')
data = data.replace('"', '')
#--Save the file as data.txt--#
with open('data.txt', 'w') as f:
f.write(data)
#--Open data.txt--#
with open('data.txt', 'r') as data:
for x in list(data):
#--Make the file usable (again)--#
song = x.replace("\n", '')
#--Check if song name is blank--#
if not song == '':
#--Check if already downloaded--#
if not os.path.exists("Downloaded music/FUTURE GENERATION/" + song + ".mp3"):
#--Download the song--#
print("Downloading " + song + "... Please be patient.")
print()
song = "https://jetsetradiofuture.live/radio/stations/futuregeneration/" + song + ".mp3"
with requests.get(song) as result:
song = x.replace("\n", '')
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded music/"):
os.makedirs("Downloaded music/")
if not os.path.exists("Downloaded music/FUTURE GENERATION"):
os.makedirs("Downloaded music/FUTURE GENERATION")
#--Write the downloaded song to a file--#
with open("Downloaded music/FUTURE GENERATION/" + song + '.mp3', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
#--Code to execute if song exists--#
else:
print("Skipped " + song + ": Already downloaded!")
print()
else:
pass
if os.path.exists("data.txt"):
os.remove("data.txt")
#--Print final success message--#
print("Successfully downloaded all songs from station: FUTURE GENERATION")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
gotoStart()
def about():
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("Welcome to JSRLoader! This is a project by PrincessLillie to allow downloading of songs from the online radio station JetSetRadio.live.")
print("Most online radio stations would play songs server-side and stream that to you via an M3U playlist file. With JetSetRadio.live, that isn't the case. JSRL serves songs as MP3s, which are then streamed to your browser. It keeps track of these songs by listing them in a JS array. While this does make things simpler on the server side, it also means that we can use the URLs of the MP3 files to download the songs directly from JSRL for free!")
print()
print("JSRLoader was made possible by:")
print("PrincessLillie - Creating the application itself")
print("JetSetRadio.live - They do provide the music...")
print("All artists on JSRL - Well, they made the music!")
print("All GitHub contributors - Adding their own flair to JSRLoader by making it better or fixing bugs")
print("You, the user - Using this application, it puts a smile on my face :)")
print()
print("JSRLoader is open-source at https://github.com/sks316/JSRLoader")
print("For support, open an issue on the GitHub tracker or contact PrincessLillie#2523 on Discord.")
print()
input("Please press ENTER to continue. ")
gotoStart()
#--Start menu--#
def gotoStart():
#--Print introduction--#
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("You can download from the following stations:")
print("1: Classic")
print("2: Future")
print("3: GG's")
print("4: Poison Jam")
print("5: Noise Tanks")
print("6: Love Shockers")
print("7: Rapid 99")
print("8: The Immortals")
print("9: Doom Riders")
print("10: Golden Rhinos")
print("11: Summer")
print("12: Christmas")
print("13: Halloween")
print("14: Bumps")
print("15: Memories of Tokyo-To")
print("M: More stations")
print()
print("T: JSRLTV Downloader")
print("A: About JSRLoader")
print("E: Exit JSRLoader")
print()
print()
#--Ask the user to select a station to download--#
selection = input("Please enter the number that corresponds with your selection and press ENTER: ")
if selection == '1':
classicDL()
elif selection == '2':
futureDL()
elif selection == '3':
ggsDL()
elif selection == '4':
poisonJamDL()
elif selection == '5':
noiseTanksDL()
elif selection == '6':
loveShockersDL()
elif selection == '7':
rapid99DL()
elif selection == '8':
immortalsDL()
elif selection == '9':
doomRidersDL()
elif selection == '10':
goldenRhinosDL()
elif selection == '11':
summerDL()
elif selection == '12':
christmasDL()
elif selection == '13':
halloweenDL()
elif selection == '14':
bumpsDL()
elif selection == '15':
memoriesOfTokyoToDL()
elif selection == 'm':
moreStations()
elif selection == 'M':
moreStations()
elif selection == 't':
JSRLTV()
elif selection == 'T':
JSRLTV()
elif selection == 'a':
about()
elif selection == 'A':
about()
elif selection == 'e':
exit()
elif selection == 'E':
exit()
else:
gotoStart()
def moreStations():
#--Print introduction--#
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("More stations:")
print("1: Ultra Remixes")
print("2: King for Another Day")
print("3: Lo-Fi")
print("4: Forever Is A Pretty Long Time")
print("5: Ollie King")
print("6: Toe Jam & Earl")
print("7: Crazy Taxi")
print("8: Hover")
print("9: Butterflies")
print("10: Revolution")
print("11: End of Days")
print("12: JET MASH RADIO")
print("13: DJ Chidow")
print("14: FUTURE GENERATION")
print("15: Outer Space")
print("M: Even more stations")
print("B: Go back")
print()
print("A: About JSRLoader")
print("E: Exit JSRLoader")
print()
print()
#--Ask the user to select a station to download--#
selection = input("Please enter the number that corresponds with your selection and press ENTER: ")
if selection == '1':
ultraRemixesDL()
elif selection == '2':
kfadDL()
elif selection == '3':
lofiDL()
elif selection == '4':
elaquentDL()
elif selection == '5':
olliekingDL()
elif selection == '6':
toejamDL()
elif selection == '7':
crazyTaxiDL()
elif selection == '8':
hoverDL()
elif selection == '9':
butterfliesDL()
elif selection == '10':
revolutionDL()
elif selection == '11':
endOfDaysDL()
elif selection == '12':
mashradioDL()
elif selection == '13':
djchidowDL()
elif selection == '14':
futuregenDL()
elif selection == '15':
spaceDL()
elif selection == 'm':
evenMoreStations()
elif selection == 'M':
evenMoreStations()
elif selection == 'b':
gotoStart()
elif selection == 'B':
gotoStart()
elif selection == 'a':
about()
elif selection == 'A':
about()
elif selection == 'e':
exit()
elif selection == 'E':
exit()
else:
moreStations()
def evenMoreStations():
#--Print introduction--#
clearScreen()
print()
print(appver)
print("-------------------------------------")
print()
print("Even more stations:")
print("1: The Garage")
print("2: Ganjah")
print("3: Chiptunes")
print("4: Retro Remix")
print("5: Classical Remix")
print("6: BonafideBloom")
print("7: VeraFX")
print("8: Snow-fi")
print("B: Go back")
print()
print("A: About JSRLoader")
print("E: Exit JSRLoader")
print()
print()
#--Ask the user to select a station to download--#
selection = input("Please enter the number that corresponds with your selection and press ENTER: ")
if selection == '1':
garageDL()
elif selection == '2':
ganjahDL()
elif selection == '3':
chiptunesDL()
elif selection == '4':
retroRemixDL()
elif selection == '5':
classicalRemixDL()
elif selection == '6':
bonafideBloomDL()
elif selection == '7':
veraFXDL()
elif selection == '8':
snowfiDL()
elif selection == 'b':
moreStations()
elif selection == 'B':
moreStations()
elif selection == 'a':
about()
elif selection == 'A':
about()
elif selection == 'e':
exit()
elif selection == 'E':
exit()
else:
moreStations()
def TVpinned():
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download pinned")
print("This process will download the pinned video, the one that plays first every time you open JSRLTV. This will take only a few moments.")
print("If the download takes longer than half an hour, please force-close the application and check your connection.")
print()
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Pinned/pinned.mp4"):
#--download video--#
print("Downloading pinned... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/pinned/pinned.mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Pinned"):
os.makedirs("Downloaded videos/Pinned")
#--write downloaded video to file--#
with open("Downloaded videos/Pinned/pinned.mp4", 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded pinned")
print()
#--Code to execute if song exists--#
else:
print("Skipped pinned: Already downloaded!")
print()
#--Print final success message--#
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch1():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 1")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray1'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 1/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch1/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 1"):
os.makedirs("Downloaded videos/Channel 1")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 1/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 1")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch2():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 2")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray2'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 2/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch2/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 2"):
os.makedirs("Downloaded videos/Channel 2")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 2/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 2")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch3():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 3")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray3'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 3/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch3/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 3"):
os.makedirs("Downloaded videos/Channel 3")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 3/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 3")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch4():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 4")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray4'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 4/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch4/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 4"):
os.makedirs("Downloaded videos/Channel 4")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 4/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 4")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch5():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 5")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray5'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 5/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch5/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 5"):
os.makedirs("Downloaded videos/Channel 5")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 5/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 5")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch6():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 6")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray6'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 6/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch6/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 6"):
os.makedirs("Downloaded videos/Channel 6")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 6/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 6")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch7():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 7")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray7'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 7/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch7/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 7"):
os.makedirs("Downloaded videos/Channel 7")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 7/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 7")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch8():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 8")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray8'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 8/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch8/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 8"):
os.makedirs("Downloaded videos/Channel 8")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 8/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 8")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def TVch9():
#--Get list of videos from JSRLTV--#
url = "https://jetsetradio.live/tv/APP/preloader/retrieveTotalFilesAndFilesList.php"
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("You chose to download videos from channel 9")
print("This process will download all the videos from the channel. This will take a while. Go grab a snack or read a book or something.")
print("If a download takes longer than half an hour, please force-close the application and check your connection.")
print()
print("Getting list of videos to download...")
print()
#--download list of videos--#
with urllib.request.urlopen(url) as file:
#--make the XML usable--#
data = file.read().decode('utf-8')
#--open videos.xml on disk and write downloaded data--#
with open('videos.xml', 'w') as f:
f.write(data)
#--parse XML and convert to JSON--#
tree = ET.parse('videos.xml')
root = tree.getroot()
for item in root.findall('./fileListArray9'):
#--open file.json on disk--#
with open('videos.json', 'w') as f:
#--remove extra characters and backslashes--#
string = str(item.text.encode('utf8'))[2:-1]
string = string.replace("/", '')
string = string.replace("\\", '')
#--write JSON to videos.json--#
f.write(string)
#--parse JSON--#
with open('videos.json', 'r') as f:
data = f.read()
data = json.loads(data)
#--get names of videos from JSON--#
for x in data:
#--Check if already downloaded--#
if not os.path.exists("Downloaded videos/Channel 9/" + x + ".mp4"):
#--download video--#
print("Downloading " + x + "... Please be patient.")
print()
video = "https://jetsetradio.live/tv/APP/videoplayer/ch9/" + x + ".mp4"
#--save video to disk--#
with requests.get(video) as result:
#--Check if our download directory exists, and if it doesn't, create it--#
if not os.path.exists("Downloaded videos/"):
os.makedirs("Downloaded videos/")
if not os.path.exists("Downloaded videos/Channel 9"):
os.makedirs("Downloaded videos/Channel 9")
#--write downloaded video to file--#
with open("Downloaded videos/Channel 9/" + x + '.mp4', 'wb') as end:
end.write(result.content)
#--Print success message--#
print("Successfully downloaded " + x)
print()
#--Code to execute if song exists--#
else:
print("Skipped " + x + ": Already downloaded!")
print()
#--once done, remove the JSON and XML file--#
if os.path.exists("videos.json"):
os.remove("videos.json")
if os.path.exists("videos.xml"):
os.remove("videos.xml")
#--Print final success message--#
print("Successfully downloaded all videos from channel 9")
print()
input("Please press ENTER to continue. ")
#--Go back to start--#
JSRLTV()
def JSRLTV():
#--Print JSRLTV downloader menu--#
clearScreen()
print()
print(appverTV)
print("-------------------------------------")
print()
print("Welcome to the JSRLTV downloader!")
print("With this, you can download videos from the JSRLTV player at jetsetradio.live.")
print("Just so you know, this can take a lot of drive space. Please make sure you have at least 70GB if you intend to download all channels.")
print("Select a channel to download:")
print()
print("P: pinned")
print("1: Channel 1")
print("2: Channel 2")
print("3: Channel 3")
print("4: Channel 4")
print("5: Channel 5")
print("6: Channel 6")
print("7: Channel 7")
print("8: Channel 8")
print("9: Channel 9")
print()
print("B: Go back")
print("E: Exit JSRLoader")
print()
print()
#--Ask the user to select a channel to download--#
selection = input("Please enter the number that corresponds with your selection and press ENTER: ")
if selection == 'P':
TVpinned()
elif selection == 'p':
TVpinned()
elif selection == '1':
TVch1()
elif selection == '2':
TVch2()
elif selection == '3':
TVch3()
elif selection == '4':
TVch4()
elif selection == '5':
TVch5()
elif selection == '6':
TVch6()
elif selection == '7':
TVch7()
elif selection == '8':
TVch8()
elif selection == '9':
TVch9()
elif selection == 'b':
gotoStart()
elif selection == 'B':
gotoStart()
elif selection == 'e':
exit()
elif selection == 'E':
exit()
else:
JSRLTV()
gotoStart()
| 42.001602
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| 452
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0
| 7
|
c85a4be45b5ba54c4c4491e2bb0dd464903a54d2
| 12,558
|
py
|
Python
|
TEKDB/Relationships/migrations/0008_auto_20211021_1607.py
|
Ecotrust/TEKDB
|
c92500aa9c8271398721bf069b93d63d11510529
|
[
"MIT"
] | 4
|
2017-12-26T05:43:52.000Z
|
2022-01-07T02:54:41.000Z
|
TEKDB/Relationships/migrations/0008_auto_20211021_1607.py
|
Ecotrust/TEKDB
|
c92500aa9c8271398721bf069b93d63d11510529
|
[
"MIT"
] | 134
|
2017-02-13T23:26:26.000Z
|
2020-09-24T23:13:02.000Z
|
TEKDB/Relationships/migrations/0008_auto_20211021_1607.py
|
Ecotrust/TEKDB
|
c92500aa9c8271398721bf069b93d63d11510529
|
[
"MIT"
] | 2
|
2018-03-02T04:01:16.000Z
|
2019-11-06T01:51:49.000Z
|
# Generated by Django 3.2.8 on 2021-10-21 23:07
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('Lookup', '0005_alter_lookupuserinfo_id'),
('TEKDB', '0004_auto_20200107_1604'),
('Relationships', '0007_auto_20200107_1707'),
]
operations = [
migrations.AlterField(
model_name='localitygisselections',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='localitygisselections',
name='localityid',
field=models.ForeignKey(blank=True, db_column='localityid', default=None, null=True, on_delete=django.db.models.deletion.SET_DEFAULT, to='TEKDB.locality', verbose_name='locality'),
),
migrations.AlterField(
model_name='localityplaceresourceevent',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='localityplaceresourceevent',
name='localityid',
field=models.ForeignKey(db_column='localityid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.locality', verbose_name='locality'),
),
migrations.AlterField(
model_name='localityplaceresourceevent',
name='placeresourceid',
field=models.ForeignKey(db_column='placeresourceid', on_delete=django.db.models.deletion.PROTECT, to='Relationships.placesresourceevents', verbose_name='place resource'),
),
migrations.AlterField(
model_name='mediacitationevents',
name='citationid',
field=models.ForeignKey(db_column='citationid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.citations', verbose_name='citation'),
),
migrations.AlterField(
model_name='mediacitationevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='mediacitationevents',
name='mediaid',
field=models.ForeignKey(db_column='mediaid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.media', verbose_name='media'),
),
migrations.AlterField(
model_name='placealtindigenousname',
name='placeid',
field=models.ForeignKey(blank=True, db_column='placeid', default=None, null=True, on_delete=django.db.models.deletion.SET_DEFAULT, to='TEKDB.places', verbose_name='place'),
),
migrations.AlterField(
model_name='placegisselections',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='placegisselections',
name='placeid',
field=models.ForeignKey(blank=True, db_column='placeid', default=None, null=True, on_delete=django.db.models.deletion.SET_DEFAULT, to='TEKDB.places', verbose_name='place'),
),
migrations.AlterField(
model_name='placescitationevents',
name='citationid',
field=models.ForeignKey(db_column='citationid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.citations', verbose_name='citation'),
),
migrations.AlterField(
model_name='placescitationevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='placescitationevents',
name='placeid',
field=models.ForeignKey(db_column='placeid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.places', verbose_name='place'),
),
migrations.AlterField(
model_name='placesmediaevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='placesmediaevents',
name='mediaid',
field=models.ForeignKey(db_column='mediaid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.media', verbose_name='media'),
),
migrations.AlterField(
model_name='placesmediaevents',
name='placeid',
field=models.ForeignKey(db_column='placeid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.places', verbose_name='place'),
),
migrations.AlterField(
model_name='placesresourcecitationevents',
name='citationid',
field=models.ForeignKey(db_column='citationid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.citations', verbose_name='citation'),
),
migrations.AlterField(
model_name='placesresourcecitationevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='placesresourcecitationevents',
name='placeresourceid',
field=models.ForeignKey(db_column='placeresourceid', on_delete=django.db.models.deletion.PROTECT, to='Relationships.placesresourceevents', verbose_name='place resource'),
),
migrations.AlterField(
model_name='placesresourceevents',
name='customaryuse',
field=models.ForeignKey(blank=True, db_column='customaryuse', default=None, max_length=255, null=True, on_delete=django.db.models.deletion.SET_DEFAULT, to='Lookup.lookupcustomaryuse', verbose_name='customary use'),
),
migrations.AlterField(
model_name='placesresourceevents',
name='partused',
field=models.ForeignKey(blank=True, db_column='partused', default=None, max_length=255, null=True, on_delete=django.db.models.deletion.SET_DEFAULT, to='Lookup.lookuppartused', verbose_name='part used'),
),
migrations.AlterField(
model_name='placesresourceevents',
name='placeid',
field=models.ForeignKey(db_column='placeid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.places', verbose_name='place'),
),
migrations.AlterField(
model_name='placesresourceevents',
name='resourceid',
field=models.ForeignKey(db_column='resourceid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.resources', verbose_name='resource'),
),
migrations.AlterField(
model_name='placesresourceevents',
name='season',
field=models.ForeignKey(blank=True, db_column='season', default=None, max_length=255, null=True, on_delete=django.db.models.deletion.SET_DEFAULT, to='Lookup.lookupseason'),
),
migrations.AlterField(
model_name='placesresourceevents',
name='timing',
field=models.ForeignKey(blank=True, db_column='timing', default=None, max_length=255, null=True, on_delete=django.db.models.deletion.SET_DEFAULT, to='Lookup.lookuptiming'),
),
migrations.AlterField(
model_name='placesresourcemediaevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='placesresourcemediaevents',
name='mediaid',
field=models.ForeignKey(db_column='mediaid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.media', verbose_name='media'),
),
migrations.AlterField(
model_name='placesresourcemediaevents',
name='placeresourceid',
field=models.ForeignKey(db_column='placeresourceid', on_delete=django.db.models.deletion.PROTECT, to='Relationships.placesresourceevents', verbose_name='place - resource'),
),
migrations.AlterField(
model_name='resourceactivitycitationevents',
name='citationid',
field=models.ForeignKey(db_column='citationid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.citations', verbose_name='citation'),
),
migrations.AlterField(
model_name='resourceactivitycitationevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='resourceactivitycitationevents',
name='resourceactivityid',
field=models.ForeignKey(db_column='resourceactivityid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.resourcesactivityevents', verbose_name='resource activity'),
),
migrations.AlterField(
model_name='resourceactivitymediaevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='resourceactivitymediaevents',
name='mediaid',
field=models.ForeignKey(db_column='mediaid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.media', verbose_name='media'),
),
migrations.AlterField(
model_name='resourceactivitymediaevents',
name='resourceactivityid',
field=models.ForeignKey(db_column='resourceactivityid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.resourcesactivityevents', verbose_name='resource activity'),
),
migrations.AlterField(
model_name='resourcealtindigenousname',
name='resourceid',
field=models.ForeignKey(blank=True, db_column='resourceid', default=None, null=True, on_delete=django.db.models.deletion.SET_DEFAULT, to='TEKDB.resources', verbose_name='resource'),
),
migrations.AlterField(
model_name='resourceresourceevents',
name='altresourceid',
field=models.ForeignKey(db_column='altresourceid', on_delete=django.db.models.deletion.PROTECT, related_name='resource_b', to='TEKDB.resources'),
),
migrations.AlterField(
model_name='resourceresourceevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='resourceresourceevents',
name='resourceid',
field=models.ForeignKey(db_column='resourceid', on_delete=django.db.models.deletion.PROTECT, related_name='resource_a', to='TEKDB.resources'),
),
migrations.AlterField(
model_name='resourcescitationevents',
name='citationid',
field=models.ForeignKey(db_column='citationid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.citations', verbose_name='citation'),
),
migrations.AlterField(
model_name='resourcescitationevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='resourcescitationevents',
name='resourceid',
field=models.ForeignKey(db_column='resourceid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.resources', verbose_name='resource'),
),
migrations.AlterField(
model_name='resourcesmediaevents',
name='id',
field=models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='resourcesmediaevents',
name='mediaid',
field=models.ForeignKey(db_column='mediaid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.media', verbose_name='media'),
),
migrations.AlterField(
model_name='resourcesmediaevents',
name='resourceid',
field=models.ForeignKey(db_column='resourceid', on_delete=django.db.models.deletion.PROTECT, to='TEKDB.resources', verbose_name='resource'),
),
]
| 51.892562
| 226
| 0.652652
| 1,220
| 12,558
| 6.553279
| 0.089344
| 0.11257
| 0.140713
| 0.163227
| 0.936585
| 0.925078
| 0.794747
| 0.748968
| 0.748968
| 0.74434
| 0
| 0.006463
| 0.223762
| 12,558
| 241
| 227
| 52.107884
| 0.813705
| 0.003583
| 0
| 0.885106
| 1
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| 0.208457
| 0.072896
| 0
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| false
| 0
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| null | 0
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| 0
| 0
|
0
| 9
|
c0743b48ed7511e91e002300ed4d4cada594d416
| 8,539
|
py
|
Python
|
tests/gilded_rose_test.py
|
Thavarshan/python-code-katas
|
496d9224bbef3ee83a0e94f3a27b8e03159f84c5
|
[
"MIT"
] | 14
|
2020-08-03T05:29:13.000Z
|
2021-08-07T09:53:18.000Z
|
tests/gilded_rose_test.py
|
Thavarshan/python-code-katas
|
496d9224bbef3ee83a0e94f3a27b8e03159f84c5
|
[
"MIT"
] | null | null | null |
tests/gilded_rose_test.py
|
Thavarshan/python-code-katas
|
496d9224bbef3ee83a0e94f3a27b8e03159f84c5
|
[
"MIT"
] | null | null | null |
from src.katas.gilded_rose import gilded_rose
import unittest
class GildedRoseTest(unittest.TestCase):
def test_it_updates_normal_items_before_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('normal', 10, 5)
item.tick()
self.assertEqual(9, item.quality)
self.assertEqual(4, item.sell_in)
def test_it_updates_normal_items_on_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('normal', 10, 0)
item.tick()
self.assertEqual(8, item.quality)
self.assertEqual(-1, item.sell_in)
def test_it_updates_normal_items_after_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('normal', 10, -5)
item.tick()
self.assertEqual(8, item.quality)
self.assertEqual(-6, item.sell_in)
def test_it_updates_normal_items_with_a_quality_of_zero(self):
shop = gilded_rose.GildedRose()
item = shop.of('normal', 0, 5)
item.tick()
self.assertEqual(0, item.quality)
self.assertEqual(4, item.sell_in)
def test_it_updates_brie_items_before_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Aged Brie', 10, 5)
item.tick()
self.assertEqual(11, item.quality)
self.assertEqual(4, item.sell_in)
def test_it_updates_brie_items_before_the_sell_date_with_maximum_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of('Aged Brie', 50, 5)
item.tick()
self.assertEqual(50, item.quality)
self.assertEqual(4, item.sell_in)
def test_it_updates_brie_items_on_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Aged Brie', 10, 0)
item.tick()
self.assertEqual(12, item.quality)
self.assertEqual(-1, item.sell_in)
def test_it_updates_brie_items_on_the_sell_date_near_maximum_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of('Aged Brie', 49, 0)
item.tick()
self.assertEqual(50, item.quality)
self.assertEqual(-1, item.sell_in)
def test_it_updates_brie_items_on_the_sell_date_with_maximum_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of('Aged Brie', 50, 0)
item.tick()
self.assertEqual(50, item.quality)
self.assertEqual(-1, item.sell_in)
def test_it_updates_brie_items_after_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Aged Brie', 10, -10)
item.tick()
self.assertEqual(12, item.quality)
self.assertEqual(-11, item.sell_in)
def test_it_updates_brie_items_after_the_sell_date_with_maximum_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of('Aged Brie', 50, -10)
item.tick()
self.assertEqual(50, item.quality)
self.assertEqual(-11, item.sell_in)
def test_it_updates_sulfuras_items_before_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Sulfuras, Hand of Ragnaros', 10, 5)
item.tick()
self.assertEqual(10, item.quality)
self.assertEqual(5, item.sell_in)
def test_it_updates_sulfuras_items_on_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Sulfuras, Hand of Ragnaros', 10, 5)
item.tick()
self.assertEqual(10, item.quality)
self.assertEqual(5, item.sell_in)
def test_it_updates_sulfuras_items_after_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Sulfuras, Hand of Ragnaros', 10, -1)
item.tick()
self.assertEqual(10, item.quality)
self.assertEqual(-1, item.sell_in)
def test_it_updates_backstage_pass_items_long_before_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
10,
11
)
item.tick()
self.assertEqual(11, item.quality)
self.assertEqual(10, item.sell_in)
def test_it_updates_backstage_pass_items_close_to_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
10,
10
)
item.tick()
self.assertEqual(12, item.quality)
self.assertEqual(9, item.sell_in)
def test_it_updates_backstage_pass_items_close_to_the_sell_data_at_max_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
50,
10
)
item.tick()
self.assertEqual(50, item.quality)
self.assertEqual(9, item.sell_in)
def test_it_updates_backstage_pass_items_very_close_to_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
10,
5
)
item.tick()
self.assertEqual(13, item.quality)
self.assertEqual(4, item.sell_in)
def test_it_updates_backstage_pass_items_very_close_to_the_sell_date_at_max_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
50,
5
)
item.tick()
self.assertEqual(50, item.quality)
self.assertEqual(4, item.sell_in)
def test_it_updates_backstage_pass_items_with_one_day_left_to_sell(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
10,
1
)
item.tick()
self.assertEqual(13, item.quality)
self.assertEqual(0, item.sell_in)
def test_it_updates_backstage_pass_items_with_one_day_left_to_sell_at_max_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
50,
1
)
item.tick()
self.assertEqual(50, item.quality)
self.assertEqual(0, item.sell_in)
def test_it_updates_backstage_pass_items_on_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
10,
0
)
item.tick()
self.assertEqual(0, item.quality)
self.assertEqual(-1, item.sell_in)
def test_it_updates_backstage_pass_items_after_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of(
'Backstage passes to a TAFKAL80ETC concert',
10,
-1
)
item.tick()
self.assertEqual(0, item.quality)
self.assertEqual(-2, item.sell_in)
def test_it_updates_conjured_items_before_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Conjured Juna Cake', 10, 10)
item.tick()
self.assertEqual(8, item.quality)
self.assertEqual(9, item.sell_in)
def test_it_updates_conjured_items_at_zero_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of('Conjured Juna Cake', 0, 10)
item.tick()
self.assertEqual(0, item.quality)
self.assertEqual(9, item.sell_in)
def test_it_updates_conjured_items_on_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Conjured Juna Cake', 10, 0)
item.tick()
self.assertEqual(6, item.quality)
self.assertEqual(-1, item.sell_in)
def test_it_updates_conjured_items_on_the_sell_date_at_0_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of('Conjured Juna Cake', 0, 0)
item.tick()
self.assertEqual(0, item.quality)
self.assertEqual(-1, item.sell_in)
def test_it_updates_conjured_items_after_the_sell_date(self):
shop = gilded_rose.GildedRose()
item = shop.of('Conjured Juna Cake', 10, -10)
item.tick()
self.assertEqual(6, item.quality)
self.assertEqual(-11, item.sell_in)
def test_it_updates_conjured_items_after_the_sell_date_at_zero_quality(self):
shop = gilded_rose.GildedRose()
item = shop.of('Conjured Juna Cake', 0, -10)
item.tick()
self.assertEqual(0, item.quality)
self.assertEqual(-11, item.sell_in)
| 31.164234
| 94
| 0.636023
| 1,120
| 8,539
| 4.546429
| 0.065179
| 0.170856
| 0.051257
| 0.091123
| 0.97447
| 0.974077
| 0.968578
| 0.968578
| 0.966222
| 0.879615
| 0
| 0.030448
| 0.265371
| 8,539
| 273
| 95
| 31.278388
| 0.781285
| 0
| 0
| 0.671362
| 0
| 0
| 0.075184
| 0
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| 0
| 0.2723
| 1
| 0.13615
| false
| 0.084507
| 0.00939
| 0
| 0.150235
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 1
| 1
| 1
| 1
| 1
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| null | 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
c07506fc3ed2fd2dcaf48c7ce556d7b0f9ca59e6
| 215
|
py
|
Python
|
news/backends/__init__.py
|
kuc2477/news
|
215f87e6ce1a7fc99175596e6fd5b4b50a3179c6
|
[
"MIT"
] | 2
|
2016-01-21T04:16:57.000Z
|
2016-04-27T04:46:13.000Z
|
news/backends/__init__.py
|
kuc2477/news
|
215f87e6ce1a7fc99175596e6fd5b4b50a3179c6
|
[
"MIT"
] | null | null | null |
news/backends/__init__.py
|
kuc2477/news
|
215f87e6ce1a7fc99175596e6fd5b4b50a3179c6
|
[
"MIT"
] | null | null | null |
""":mod:`news.backends` --- News backends
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
News backends for schedule and news persistence.
"""
from .django import DjangoBackend
from .sqlalchemy import SQLAlchemyBackend
| 23.888889
| 48
| 0.623256
| 20
| 215
| 6.7
| 0.65
| 0.268657
| 0.238806
| 0.358209
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| 0
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| 0
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| 0.106977
| 215
| 8
| 49
| 26.875
| 0.697917
| 0.604651
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| 0
| true
| 0
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| 0
| 0
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| 1
| 1
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| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
c0dfb9db57693b4560f7ccab7655fb29e11ca6be
| 673
|
py
|
Python
|
CourierManagement/DispatchAPI/models.py
|
GSri30/CourierManagementProject
|
023fd2d72772d7045ea91ec6bc1a0d3ff212c93c
|
[
"MIT"
] | 2
|
2020-12-25T06:47:29.000Z
|
2022-01-15T14:34:26.000Z
|
CourierManagement/DispatchAPI/models.py
|
GSri30/CourierManagementProject
|
023fd2d72772d7045ea91ec6bc1a0d3ff212c93c
|
[
"MIT"
] | null | null | null |
CourierManagement/DispatchAPI/models.py
|
GSri30/CourierManagementProject
|
023fd2d72772d7045ea91ec6bc1a0d3ff212c93c
|
[
"MIT"
] | 1
|
2022-01-15T14:42:31.000Z
|
2022-01-15T14:42:31.000Z
|
from django.db import models
class EmailMapping(models.Model):
ParentMail=models.CharField(max_length=50,blank=False)
ChildMail=models.CharField(max_length=50,blank=False)
HashedPassword=models.CharField(max_length=70,blank=False)
Verified=models.BooleanField(default=False)
def __str__(self):
return self.ParentMail
class MobileNumberMapping(models.Model):
ParentMail=models.CharField(max_length=50,blank=False)
ChildNumber=models.CharField(max_length=50,blank=False)
HashedPassword=models.CharField(max_length=70,blank=False)
Verified=models.BooleanField(default=False)
def __str__(self):
return self.ParentMail
| 35.421053
| 62
| 0.774146
| 83
| 673
| 6.108434
| 0.325301
| 0.177515
| 0.213018
| 0.284024
| 0.832347
| 0.832347
| 0.832347
| 0.832347
| 0.832347
| 0.832347
| 0
| 0.020339
| 0.123328
| 673
| 19
| 63
| 35.421053
| 0.838983
| 0
| 0
| 0.666667
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0.133333
| false
| 0.133333
| 0.066667
| 0.133333
| 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
| 1
| 1
| 0
|
0
| 11
|
8d074f5faa0dcf3f6c2cc3a6f0321b90abbbbf3b
| 13,319
|
py
|
Python
|
lookup_dict.py
|
SeeTheGround/Decode_QA
|
9ab85dd37ac82750303b19657f38b292aa482e47
|
[
"Unlicense"
] | null | null | null |
lookup_dict.py
|
SeeTheGround/Decode_QA
|
9ab85dd37ac82750303b19657f38b292aa482e47
|
[
"Unlicense"
] | null | null | null |
lookup_dict.py
|
SeeTheGround/Decode_QA
|
9ab85dd37ac82750303b19657f38b292aa482e47
|
[
"Unlicense"
] | null | null | null |
bit_flags = {
"Collection 1": {
"pixel_qa": {
"L45": {
"Fill": [0],
"Clear": [1],
"Water": [2],
"Cloud Shadow": [3],
"Snow": [4],
"Cloud": [5],
"Low Cloud Confidence": [6],
"Medium Cloud Confidence": [7],
"High Cloud Confidence": [6, 7]
},
"L7": {
"Fill": [0],
"Clear": [1],
"Water": [2],
"Cloud Shadow": [3],
"Snow": [4],
"Cloud": [5],
"Low Cloud Confidence": [6],
"Medium Cloud Confidence": [7],
"High Cloud Confidence": [6, 7]
},
"L8": {
"Fill": [0],
"Clear": [1],
"Water": [2],
"Cloud Shadow": [3],
"Snow": [4],
"Cloud": [5],
"Low Cloud Confidence": [6],
"Medium Cloud Confidence": [7],
"High Cloud Confidence": [6, 7],
"Low Cirrus Confidence": [8],
"Medium Cirrus Confidence": [9],
"High Cirrus Confidence": [8, 9],
"Terrain Occlusion": [10]
}
},
"sr_cloud_qa": {
"L45": {
"DDV": [0],
"Cloud": [1],
"Cloud Shadow": [2],
"Adjacent to Cloud": [3],
"Snow": [4],
"Water": [5]
},
"L7": {
"DDV": [0],
"Cloud": [1],
"Cloud Shadow": [2],
"Adjacent to Cloud": [3],
"Snow": [4],
"Water": [5]
}
},
"radsat_qa": {
"L45": {
"Fill": [0],
"Band 1 Data Saturation": [1],
"Band 2 Data Saturation": [2],
"Band 3 Data Saturation": [3],
"Band 4 Data Saturation": [4],
"Band 5 Data Saturation": [5],
"Band 6 Data Saturation": [6],
"Band 7 Data Saturation": [7]
},
"L7": {
"Fill": [0],
"Band 1 Data Saturation": [1],
"Band 2 Data Saturation": [2],
"Band 3 Data Saturation": [3],
"Band 4 Data Saturation": [4],
"Band 5 Data Saturation": [5],
"Band 6 Data Saturation": [6],
"Band 7 Data Saturation": [7]
},
"L8": {
"Fill": [0],
"Band 1 Data Saturation": [1],
"Band 2 Data Saturation": [2],
"Band 3 Data Saturation": [3],
"Band 4 Data Saturation": [4],
"Band 5 Data Saturation": [5],
"Band 6 Data Saturation": [6],
"Band 7 Data Saturation": [7],
"Band 9 Data Saturation": [9],
"Band 10 Data Saturation": [10],
"Band 11 Data Saturation": [11]
}
},
"BQA": {
"L45": {
"Fill": [0],
"Dropped Pixel": [1],
"Low Radiometric Saturation": [2],
"Medium Radiometric Saturation": [3],
"High Radiometric Saturation": [2, 3],
"Cloud": [4],
"Low Cloud Confidence": [5],
"Medium Cloud Confidence": [6],
"High Cloud Confidence": [5, 6],
"Low Cloud Shadow Confidence": [7],
"High Cloud Shadow Confidence": [7, 8],
"Low Snow/Ice Confidence": [9],
"High Snow/Ice Confidence": [9, 10]
},
"L7": {
"Fill": [0],
"Dropped Pixel": [1],
"Low Radiometric Saturation": [2],
"Medium Radiometric Saturation": [3],
"High Radiometric Saturation": [2, 3],
"Cloud": [4],
"Low Cloud Confidence": [5],
"Medium Cloud Confidence": [6],
"High Cloud Confidence": [5, 6],
"Low Cloud Shadow Confidence": [7],
"High Cloud Shadow Confidence": [7, 8],
"Low Snow/Ice Confidence": [9],
"High Snow/Ice Confidence": [9, 10]
},
"L8": {
"Fill": [0],
"Terrain Occlusion": [1],
"Low Radiometric Saturation": [2],
"Medium Radiometric Saturation": [3],
"High Radiometric Saturation": [2, 3],
"Cloud": [4],
"Low Cloud Confidence": [5],
"Medium Cloud Confidence": [6],
"High Cloud Confidence": [5, 6],
"Low Cloud Shadow Confidence": [7],
"High Cloud Shadow Confidence": [7, 8],
"Low Snow/Ice Confidence": [9],
"High Snow/Ice Confidence": [9, 10],
"Low Cirrus Confidence": [11],
"High Cirrus Confidence": [11, 12]
}
},
"sr_aerosol": {
"L8": {
"Fill": [0],
"Valid Aerosol Retrieval": [1],
"Water": [2],
"Cloud or Cirrus": [3],
"Cloud Shadow": [4],
"Interpolated Aerosol Retrieval": [5],
"Low Aerosol": [6],
"Medium Aerosol": [7],
"High Aerosol": [6, 7]
}
},
"l2_flags": {
"L8": {
"Atmospheric Correction Failed": [0],
"Land": [1],
"Sun Glint": [3],
"High Viewing Zenith Angle": [5],
"Shallow Water": [6],
"SeaDAS Cloud": [7],
"Cloud Shadow": [8],
"Cloud": [9],
"Coccolithophores": [10],
"Turbid Water": [11],
"High Solar Zenith Angle": [12],
"Low Water-Leaving Radiance": [14],
"Chlorophyll-a Calculation Failed": [15],
"Navigation quality is suspect": [16],
"Invalid Rrs Value": [18],
"Max Iterations reached for Aerosol Correction": [19],
"Moderate sun glint contamination": [20],
"Invalid Chlorophyll-a Value": [21],
"Atmospheric Correction Warning": [22],
"Possible Sea Ice Contamination": [24],
"Navigation failure": [25],
"Insufficient Valid Pixels in the Filter": [26],
"Unused": [2, 4, 13, 17, 23, 27, 28, 29, 30, 31]
}
}
},
"Collection 2": {
"pixel_qa": {
"L45": {
"Fill": [0],
"Dilated Cloud": [1],
"Cloud": [3],
"Cloud Shadow": [4],
"Snow": [5],
"Clear": [6],
"Water": [7],
"Low Cloud Confidence": [8],
"Medium Cloud Confidence": [9],
"High Cloud Confidence": [8, 9],
"Low Cloud Shadow Confidence": [10],
"High Cloud Shadow Confidence": [10, 11],
"Low Snow/Ice Confidence": [12],
"High Snow/Ice Confidence": [12, 13]
},
"L7": {
"Fill": [0],
"Dilated Cloud": [1],
"Cloud": [3],
"Cloud Shadow": [4],
"Snow": [5],
"Clear": [6],
"Water": [7],
"Low Cloud Confidence": [8],
"Medium Cloud Confidence": [9],
"High Cloud Confidence": [8, 9],
"Low Cloud Shadow Confidence": [10],
"High Cloud Shadow Confidence": [10, 11],
"Low Snow/Ice Confidence": [12],
"High Snow/Ice Confidence": [12, 13]
},
"L8": {
"Fill": [0],
"Dilated Cloud": [1],
"Cloud": [3],
"Cloud Shadow": [4],
"Snow": [5],
"Clear": [6],
"Water": [7],
"Low Cloud Confidence": [8],
"Medium Cloud Confidence": [9],
"High Cloud Confidence": [8, 9],
"Low Cloud Shadow Confidence": [10],
"High Cloud Shadow Confidence": [10, 11],
"Low Snow/Ice Confidence": [12],
"High Snow/Ice Confidence": [12, 13]
}
},
"sr_cloud_qa": {
"L45": {
"DDV": [0],
"Cloud": [1],
"Cloud Shadow": [2],
"Adjacent to Cloud": [3],
"Snow": [4],
"Water": [5]
},
"L7": {
"DDV": [0],
"Cloud": [1],
"Cloud Shadow": [2],
"Adjacent to Cloud": [3],
"Snow": [4],
"Water": [5]
}
},
"radsat_qa": {
"L45": {
"Band 1 Data Saturation": [0],
"Band 2 Data Saturation": [1],
"Band 3 Data Saturation": [2],
"Band 4 Data Saturation": [3],
"Band 5 Data Saturation": [4],
"Band 6 Data Saturation": [5],
"Band 7 Data Saturation": [6],
"Dropped Pixel": [9]
},
"L7": {
"Band 1 Data Saturation": [0],
"Band 2 Data Saturation": [1],
"Band 3 Data Saturation": [2],
"Band 4 Data Saturation": [3],
"Band 5 Data Saturation": [4],
"Band 6L Data Saturation": [5],
"Band 7 Data Saturation": [6],
"Band 6H Data Saturation": [8],
"Dropped Pixel": [9]
},
"L8": {
"Band 1 Data Saturation": [0],
"Band 2 Data Saturation": [1],
"Band 3 Data Saturation": [2],
"Band 4 Data Saturation": [3],
"Band 5 Data Saturation": [4],
"Band 6 Data Saturation": [5],
"Band 7 Data Saturation": [6],
"Band 9 Data Saturation": [8],
"Terrain Occlusion": [11]
}
},
"BQA": {
"L45": {
"Fill": [0],
"Dilated Cloud": [1],
"Cloud": [3],
"Cloud Shadow": [4],
"Snow": [5],
"Clear": [6],
"Water": [7],
"Low Cloud Confidence": [8],
"Medium Cloud Confidence": [9],
"High Cloud Confidence": [8, 9],
"Low Cloud Shadow Confidence": [10],
"Medium Cloud Shadow Confidence": [11],
"High Cloud Shadow Confidence": [10, 11],
"Low Snow/Ice Confidence": [12],
"Medium Snow/Ice Confidence": [13],
"High Snow/Ice Confidence": [12, 13]
},
"L7": {
"Fill": [0],
"Dilated Cloud": [1],
"Cloud": [3],
"Cloud Shadow": [4],
"Snow": [5],
"Clear": [6],
"Water": [7],
"Low Cloud Confidence": [8],
"Medium Cloud Confidence": [9],
"High Cloud Confidence": [8, 9],
"Low Cloud Shadow Confidence": [10],
"Medium Cloud Shadow Confidence": [11],
"High Cloud Shadow Confidence": [10, 11],
"Low Snow/Ice Confidence": [12],
"Medium Snow/Ice Confidence": [13],
"High Snow/Ice Confidence": [12, 13]
},
"L8": {
"Fill": [0],
"Dilated Cloud": [1],
"Cirrus": [2],
"Cloud": [3],
"Cloud Shadow": [4],
"Snow": [5],
"Clear": [6],
"Water": [7],
"Low Cloud Confidence": [8],
"Medium Cloud Confidence": [9],
"High Cloud Confidence": [8, 9],
"Low Cloud Shadow Confidence": [10],
"Medium Cloud Shadow Confidence": [11],
"High Cloud Shadow Confidence": [10, 11],
"Low Snow/Ice Confidence": [12],
"Medium Snow/Ice Confidence": [13],
"High Snow/Ice Confidence": [12, 13],
"Low Cirrus Confidence": [14],
"Medium Cirrus Confidence": [15],
"High Cirrus Confidence": [14, 15]
}
},
"sr_aerosol": {
"L8": {
"Fill": [0],
"Valid Aerosol Retrieval": [1],
"Water": [2],
"Interpolated Aerosol Retrieval": [5],
"Low Aerosol": [6],
"Medium Aerosol": [7],
"High Aerosol": [6, 7]
}
}
}
}
| 35.803763
| 70
| 0.365793
| 1,134
| 13,319
| 4.285714
| 0.102293
| 0.135391
| 0.090741
| 0.05679
| 0.805761
| 0.799588
| 0.799588
| 0.799588
| 0.799588
| 0.790741
| 0
| 0.072067
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| 13,319
| 371
| 71
| 35.90027
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0
| 7
|
23dbd46297a9c4dfa7f6872761f558e0b63be69f
| 3,287
|
py
|
Python
|
game/systems.py
|
taojy123/PokemonCard
|
b92a8c1f19a9b388d5de8c030bd2ca372d959d48
|
[
"MIT"
] | null | null | null |
game/systems.py
|
taojy123/PokemonCard
|
b92a8c1f19a9b388d5de8c030bd2ca372d959d48
|
[
"MIT"
] | null | null | null |
game/systems.py
|
taojy123/PokemonCard
|
b92a8c1f19a9b388d5de8c030bd2ca372d959d48
|
[
"MIT"
] | null | null | null |
"""
普 格 飞 毒 地 岩 虫 鬼 火 水 草 电 超 冰 龙
普 1.0 1.0 1.0 1.0 1.0 0.5 1.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
格 2.0 1.0 0.5 0.5 1.0 2.0 0.5 0.0 1.0 1.0 1.0 1.0 0.5 2.0 1.0
飞 1.0 2.0 1.0 1.0 1.0 0.5 2.0 1.0 1.0 1.0 2.0 0.5 1.0 1.0 1.0
毒 1.0 1.0 1.0 0.5 0.5 0.5 2.0 0.5 1.0 1.0 2.0 1.0 1.0 1.0 1.0
地 1.0 1.0 0.0 2.0 1.0 2.0 0.5 1.0 2.0 1.0 0.5 2.0 1.0 1.0 1.0
岩 1.0 0.5 2.0 1.0 0.5 1.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 2.0 1.0
虫 1.0 0.5 0.5 2.0 1.0 1.0 1.0 0.5 0.5 1.0 2.0 1.0 2.0 1.0 1.0
鬼 0.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0
火 1.0 1.0 1.0 1.0 1.0 0.5 2.0 1.0 0.5 0.5 2.0 1.0 1.0 2.0 0.5
水 1.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 2.0 0.5 0.5 1.0 1.0 1.0 0.5
草 1.0 1.0 0.5 0.5 2.0 2.0 0.5 1.0 0.5 2.0 0.5 1.0 1.0 1.0 0.5
电 1.0 1.0 2.0 1.0 0.0 1.0 1.0 1.0 1.0 2.0 0.5 0.5 1.0 1.0 0.5
超 1.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.5 1.0 1.0
冰 1.0 1.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 0.5 2.0 1.0 1.0 0.5 2.0
龙 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0
"""
SYSTEM_MAP = {
'普': {
'普': 1, '格': 1, '飞': 1, '毒': 1, '地': 1, '岩': 0.5, '虫': 1, '鬼': 0, '火': 1, '水': 1, '草': 1, '电': 1, '超': 1, '冰': 1, '龙': 1,
},
'格': {
'普': 2, '格': 1, '飞': 0.5, '毒': 0.5, '地': 1, '岩': 2, '虫': 0.5, '鬼': 0, '火': 1, '水': 1, '草': 1, '电': 1, '超': 0.5, '冰': 2, '龙': 1,
},
'飞': {
'普': 1, '格': 2, '飞': 1, '毒': 1, '地': 1, '岩': 0.5, '虫': 2, '鬼': 1, '火': 1, '水': 1, '草': 2, '电': 0.5, '超': 1, '冰': 1, '龙': 1,
},
'毒': {
'普': 1, '格': 1, '飞': 1, '毒': 0.5, '地': 0.5, '岩': 0.5, '虫': 2, '鬼': 0.5, '火': 1, '水': 1, '草': 2, '电': 1, '超': 1, '冰': 1, '龙': 1,
},
'地': {
'普': 1, '格': 1, '飞': 0, '毒': 2, '地': 1, '岩': 2, '虫': 0.5, '鬼': 1, '火': 2, '水': 1, '草': 0.5, '电': 2, '超': 1, '冰': 1, '龙': 1,
},
'岩': {
'普': 1, '格': 0.5, '飞': 2, '毒': 1, '地': 0.5, '岩': 1, '虫': 2, '鬼': 1, '火': 2, '水': 1, '草': 1, '电': 1, '超': 1, '冰': 2, '龙': 1,
},
'虫': {
'普': 1, '格': 0.5, '飞': 0.5, '毒': 2, '地': 1, '岩': 1, '虫': 1, '鬼': 0.5, '火': 0.5, '水': 1, '草': 2, '电': 1, '超': 2, '冰': 1, '龙': 1,
},
'鬼': {
'普': 0, '格': 1, '飞': 1, '毒': 1, '地': 1, '岩': 1, '虫': 1, '鬼': 2, '火': 1, '水': 1, '草': 1, '电': 1, '超': 0, '冰': 1, '龙': 1,
},
'火': {
'普': 1, '格': 1, '飞': 1, '毒': 1, '地': 1, '岩': 0.5, '虫': 2, '鬼': 1, '火': 0.5, '水': 0.5, '草': 2, '电': 1, '超': 1, '冰': 2, '龙': 0.5,
},
'水': {
'普': 1, '格': 1, '飞': 1, '毒': 1, '地': 2, '岩': 2, '虫': 1, '鬼': 1, '火': 2, '水': 0.5, '草': 0.5, '电': 1, '超': 1, '冰': 1, '龙': 0.5,
},
'草': {
'普': 1, '格': 1, '飞': 0.5, '毒': 0.5, '地': 2, '岩': 2, '虫': 0.5, '鬼': 1, '火': 0.5, '水': 2, '草': 0.5, '电': 1, '超': 1, '冰': 1, '龙': 0.5,
},
'电': {
'普': 1, '格': 1, '飞': 2, '毒': 1, '地': 0, '岩': 1, '虫': 1, '鬼': 1, '火': 1, '水': 2, '草': 0.5, '电': 0.5, '超': 1, '冰': 1, '龙': 0.5,
},
'超': {
'普': 1, '格': 2, '飞': 1, '毒': 2, '地': 1, '岩': 1, '虫': 1, '鬼': 1, '火': 1, '水': 1, '草': 1, '电': 1, '超': 0.5, '冰': 1, '龙': 1,
},
'冰': {
'普': 1, '格': 1, '飞': 2, '毒': 1, '地': 2, '岩': 1, '虫': 1, '鬼': 1, '火': 1, '水': 0.5, '草': 2, '电': 1, '超': 1, '冰': 0.5, '龙': 2,
},
'龙': {
'普': 1, '格': 1, '飞': 1, '毒': 1, '地': 1, '岩': 1, '虫': 1, '鬼': 1, '火': 1, '水': 1, '草': 1, '电': 1, '超': 1, '冰': 1, '龙': 2,
},
}
| 48.338235
| 139
| 0.302099
| 985
| 3,287
| 1.007107
| 0.02132
| 0.288306
| 0.356855
| 0.354839
| 0.893145
| 0.877016
| 0.820565
| 0.759073
| 0.700605
| 0.58871
| 0
| 0.316186
| 0.313964
| 3,287
| 67
| 140
| 49.059701
| 0.123725
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| 0.105033
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| 0
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| null | 1
| 1
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| 1
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| 1
| 1
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| 0
|
0
| 11
|
23f25fef0c2d953d0c13cb2b8be0016bb13bde21
| 101,919
|
py
|
Python
|
gym/envs/mujoco/arm_shaping.py
|
leopauly/Observation-Learning-Simulations
|
462c04a87c45aae51537b8ea5b44646afa31d3a5
|
[
"MIT"
] | 49
|
2017-12-11T11:00:02.000Z
|
2022-03-30T05:19:31.000Z
|
gym/envs/mujoco/arm_shaping.py
|
leopauly/Observation-Learning-Simulations
|
462c04a87c45aae51537b8ea5b44646afa31d3a5
|
[
"MIT"
] | 2
|
2018-01-01T17:39:56.000Z
|
2019-07-24T04:49:08.000Z
|
gym/envs/mujoco/arm_shaping.py
|
leopauly/Observation-Learning-Simulations
|
462c04a87c45aae51537b8ea5b44646afa31d3a5
|
[
"MIT"
] | 12
|
2017-12-13T11:52:17.000Z
|
2020-12-03T00:53:29.000Z
|
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import gfile
import pickle
import scipy.misc
from nets import inception_v3
import scipy.misc
slim = tf.contrib.slim
def transform(image, resize_height=36, resize_width=64):
cropped_image = scipy.misc.imresize(image, [resize_height, resize_width])
return np.array(cropped_image)/127.5 - 1.
def inverse_transform(images):
return (images+1.)/2.
def lrelu(x, leak=0.2, name="lrelu"):
return tf.maximum(x, leak*x)
def conv2d(input_, output_dim,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="conv2d"):
with tf.variable_scope(name):
w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding='SAME')
biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
return conv
class batch_norm(object):
def __init__(self, epsilon=1e-5, momentum = 0.9, name="batch_norm"):
with tf.variable_scope(name):
self.epsilon = epsilon
self.momentum = momentum
self.name = name
def __call__(self, x, train=False):
return tf.contrib.layers.batch_norm(x,
decay=self.momentum,
updates_collections=None,
epsilon=self.epsilon,
scale=True,
is_training=train,
scope=self.name)
def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
shape = input_.get_shape().as_list()
with tf.variable_scope(scope or "Linear"):
matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
tf.random_normal_initializer(stddev=stddev))
bias = tf.get_variable("bias", [output_size],
initializer=tf.constant_initializer(bias_start))
if with_w:
return tf.matmul(input_, matrix) + bias, matrix, bias
else:
return tf.matmul(input_, matrix) + bias
def deconv2d(input_, output_shape,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="deconv2d", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
try:
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
# Support for verisons of TensorFlow before 0.7.0
except AttributeError:
deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
if with_w:
return deconv, w, biases
else:
return deconv
class AutoDC:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
self.d_bn3 = batch_norm(name='d_bn3')
self.g_bn0 = batch_norm(name='g_bn0')
self.g_bn1 = batch_norm(name='g_bn1')
self.g_bn2 = batch_norm(name='g_bn2')
self.g_bn3 = batch_norm(name='g_bn3')
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
len_video = imgshape[1]
featsize = 1024
self.batch_size = len_video
image = tf.reshape(image, [self.batch_size] + imgshape[-3:])
with tf.variable_scope("conv") as scope:
h0 = conv2d(image, self.df_dim, name='d_h0_conv')
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
self.h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
h4 = linear(tf.reshape(self.h3, [self.batch_size, -1]), featsize, 'd_h3_lin')
# self.sim = h4[:, :featsize]
# self.diff = h4[:, featsize:]
# self.simreshape = tf.reshape(self.sim, (-1, len_video, featsize))
# m = self.sim.get_shape().as_list()[0]
# self.simloss = tf.nn.l2_loss(self.simreshape[0] - self.simreshape[1]) / m
# self.diffloss = tf.nn.l2_loss(tf.matmul(self.sim, tf.transpose(self.diff))) / m / m
# print(m)
self.z = h4#self.sim + self.diff
print(self.z.get_shape())
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(
self.z, self.gf_dim*8*s_h16*s_w16, 'g_h0_lin', with_w=True)
self.h0 = tf.reshape(
self.z_, [-1, s_h16, s_w16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(self.h0))
self.h1, self.h1_w, self.h1_b = deconv2d(
h0, [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='g_h1', with_w=True)
h1 = tf.nn.relu(self.g_bn1(self.h1))
h2, self.h2_w, self.h2_b = deconv2d(
h1, [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='g_h2', with_w=True)
h2 = tf.nn.relu(self.g_bn2(h2))
h3, self.h3_w, self.h3_b = deconv2d(
h2, [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='g_h3', with_w=True)
h3 = tf.nn.relu(self.g_bn3(h3))
h4, self.h4_w, self.h4_b = deconv2d(
h3, [self.batch_size, s_h, s_w, self.c_dim], name='g_h4', with_w=True)
self.out = h4#tf.nn.tanh(h4)
self.reconloss = tf.nn.l2_loss(self.out - image)
self.loss = self.reconloss# + self.diffloss + self.simloss
class TimeDC:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
self.d_bn3 = batch_norm(name='d_bn3')
def build(self, image, times):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[0]
featsize = 1024
with tf.variable_scope("conv") as scope:
h0 = conv2d(image, self.df_dim, name='d_h0_conv')
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
self.h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
h4 = lrelu(linear(tf.reshape(self.h3, [self.batch_size, -1]), featsize, 'd_h3_lin'))
h5 = lrelu(linear(h4, featsize, 'd_h4_lin'))
out = linear(h4, 1, 'd_h5_lin')
# self.sim = h4[:, :featsize]
# self.diff = h4[:, featsize:]
# self.simreshape = tf.reshape(self.sim, (-1, len_video, featsize))
# m = self.sim.get_shape().as_list()[0]
# self.simloss = tf.nn.l2_loss(self.simreshape[0] - self.simreshape[1]) / m
# self.diffloss = tf.nn.l2_loss(tf.matmul(self.sim, tf.transpose(self.diff))) / m / m
# print(m)
self.z = out#self.sim + self.diff
print(self.z.get_shape())
# self.out = h4#tf.nn.tanh(h4)
# self.reconloss = tf.nn.l2_loss(self.out - image)
self.loss = tf.nn.l2_loss(out - times) # + self.diffloss + self.simloss
self.diffs = out - times
class SubspaceAE:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
self.d_bn3 = batch_norm(name='d_bn3')
self.d_bn4 = batch_norm(name='d_bn4')
self.d_bn5 = batch_norm(name='d_bn5')
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
len_video = imgshape[1]
featsize = 1024
self.batch_size = len_video * 2
imageorig = image
image = tf.reshape(image, [self.batch_size] + imgshape[-3:])
with tf.variable_scope("conv") as scope:
h0 = conv2d(image, self.df_dim, name='d_h0_conv')
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
h4 = lrelu(self.d_bn4(linear(tf.reshape(h3, [self.batch_size, -1]), featsize, 'd_h4_lin')))
h5 = lrelu(self.d_bn5(linear(h4, featsize, 'd_h5_lin')))
z = linear(h5, featsize, 'd_hz_lin')
# self.sim = h4[:, :featsize]
# self.diff = h4[:, featsize:]
self.simreshape = tf.reshape(z, (-1, len_video, featsize))
# m = self.sim.get_shape().as_list()[0]
self.simloss = tf.nn.l2_loss(self.simreshape[0] - self.simreshape[1])# / len_video
# self.diffloss = tf.nn.l2_loss(tf.matmul(self.sim, tf.transpose(self.diff))) / m / m
# print(m)
self.z = z#self.sim + self.diff
print(self.z.get_shape())
outs = []
for j in range(2):
with tf.variable_scope("deconv" + str(j)) as scope:
self.g_bn0 = batch_norm(name='g_bn0')
self.g_bn1 = batch_norm(name='g_bn1')
self.g_bn2 = batch_norm(name='g_bn2')
self.g_bn3 = batch_norm(name='g_bn3')
self.g_bn4 = batch_norm(name='g_bn4')
self.g_bn5 = batch_norm(name='g_bn5')
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
z = self.simreshape[j]
h5 = lrelu(self.g_bn5(linear(z, featsize, 'g_h5_lin')))
h4 = lrelu(self.g_bn4(linear(h5, featsize, 'g_h4_lin')))
print (h4.get_shape())
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(
h4, self.gf_dim*8*s_h16*s_w16, 'g_h0_lin', with_w=True)
self.h0 = tf.reshape(
self.z_, [-1, s_h16, s_w16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(self.h0))
self.h1, self.h1_w, self.h1_b = deconv2d(
h0, [len_video, s_h8, s_w8, self.gf_dim*4], name='g_h1', with_w=True)
h1 = tf.nn.relu(self.g_bn1(self.h1))
h2, self.h2_w, self.h2_b = deconv2d(
h1, [len_video, s_h4, s_w4, self.gf_dim*2], name='g_h2', with_w=True)
h2 = tf.nn.relu(self.g_bn2(h2))
h3, self.h3_w, self.h3_b = deconv2d(
h2, [len_video, s_h2, s_w2, self.gf_dim*1], name='g_h3', with_w=True)
h3 = tf.nn.relu(self.g_bn3(h3))
h4, self.h4_w, self.h4_b = deconv2d(
h3, [len_video, s_h, s_w, self.c_dim], name='g_h4', with_w=True)
outs.append(h4)
self.outs = outs#tf.nn.tanh(h4)
self.reconloss = tf.nn.l2_loss(imageorig - self.outs)
self.loss = self.reconloss + self.simloss
nclass = 50
class TimeSoftmax:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
self.d_bn3 = batch_norm(name='d_bn3')
def build(self, image, times):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[0]
featsize = 1024
with tf.variable_scope("conv") as scope:
h0 = conv2d(image, self.df_dim, name='d_h0_conv')
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
self.h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
h4 = lrelu(linear(tf.reshape(self.h3, [self.batch_size, -1]), featsize, 'd_h3_lin'))
h5 = lrelu(linear(h4, featsize//2, 'd_h4_lin'))
logits = linear(h5, nclass, 'd_h5_lin')
self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, times))#self.sim + self.diff
self.z = tf.nn.softmax(logits)
print(self.z.get_shape())
# self.out = h4#tf.nn.tanh(h4)
# self.reconloss = tf.nn.l2_loss(self.out - image)
# self.loss = tf.nn.l2_loss(out - times) / batch_size# + self.diffloss + self.simloss
class TimePred:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
self.d_bn3 = batch_norm(name='d_bn3')
def build(self, image, times):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[0]
featsize = 1024
with tf.variable_scope("conv") as scope:
h0 = conv2d(image, self.df_dim, name='d_h0_conv')
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
self.h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
h4 = lrelu(linear(tf.reshape(self.h3, [self.batch_size, -1]), featsize, 'd_h3_lin'))
h5 = lrelu(linear(h4, featsize//2, 'd_h4_lin'))
self.z = linear(h5, 1, 'd_h5_lin')
self.loss = tf.reduce_mean((self.z - times) ** 2)
print(self.z.get_shape())
class ReachAE:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
self.d_bn3 = batch_norm(name='d_bn3')
self.d_bn4 = batch_norm(name='d_bn4')
self.d_bn5 = batch_norm(name='d_bn5')
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[0]
featsize = 1024
with tf.variable_scope("conv") as scope:
h0 = conv2d(image, self.df_dim, name='d_h0_conv')
h1 = lrelu(self.d_bn1(conv2d(h0, self.df_dim*2, name='d_h1_conv')))
h2 = lrelu(self.d_bn2(conv2d(h1, self.df_dim*4, name='d_h2_conv')))
h3 = lrelu(self.d_bn3(conv2d(h2, self.df_dim*8, name='d_h3_conv')))
h4 = lrelu(self.d_bn4(linear(tf.reshape(h3, [self.batch_size, -1]), featsize, 'd_h4_lin')))
#h5 = lrelu(self.d_bn5(linear(h4, featsize, 'd_h5_lin')))
z = linear(h4, featsize, 'd_hz_lin')
# self.sim = h4[:, :featsize]
# self.diff = h4[:, featsize:]
# self.simreshape = tf.reshape(z, (-1, len_video, featsize))
# m = self.sim.get_shape().as_list()[0]
# self.simloss = tf.nn.l2_loss(self.simreshape[0] - self.simreshape[1])# / len_video
# self.diffloss = tf.nn.l2_loss(tf.matmul(self.sim, tf.transpose(self.diff))) / m / m
# print(m)
# self.z = self.simreshape#self.sim + self.diff
self.z = z
print(self.z.get_shape())
with tf.variable_scope("deconv") as scope:
self.g_bn0 = batch_norm(name='g_bn0')
self.g_bn1 = batch_norm(name='g_bn1')
self.g_bn2 = batch_norm(name='g_bn2')
self.g_bn3 = batch_norm(name='g_bn3')
self.g_bn4 = batch_norm(name='g_bn4')
self.g_bn5 = batch_norm(name='g_bn5')
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
print(z.get_shape())
#h5 = lrelu(self.g_bn5(linear(z, featsize, 'g_h5_lin')))
h4 = lrelu(self.g_bn4(linear(z, featsize, 'g_h4_lin')))
print(h4.get_shape())
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(
h4, self.gf_dim*8*s_h16*s_w16, 'g_h0_lin', with_w=True)
self.h0 = tf.reshape(
self.z_, [-1, s_h16, s_w16, self.gf_dim * 8])
h0 = tf.nn.relu(self.g_bn0(self.h0))
self.h1, self.h1_w, self.h1_b = deconv2d(
h0, [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='g_h1', with_w=True)
h1 = tf.nn.relu(self.g_bn1(self.h1))
self.h2, self.h2_w, self.h2_b = deconv2d(
h1, [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='g_h2', with_w=True)
h2 = tf.nn.relu(self.g_bn2(self.h2))
h3, self.h3_w, self.h3_b = deconv2d(
h2, [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='g_h3', with_w=True)
h3 = tf.nn.relu(self.g_bn3(h3))
h4, self.h4_w, self.h4_b = deconv2d(
h3, [self.batch_size, s_h, s_w, self.c_dim], name='g_h4', with_w=True)
self.out = h4#tf.nn.tanh(h4)
self.reconloss = tf.nn.l2_loss(image - self.out)
self.loss = self.reconloss# + self.simloss
class ContextAE:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
with tf.variable_scope("conv_context") as scope:
h0 = conv2d(contextimg, self.df_dim, name='h0_conv')
c_bn1 = batch_norm(name='c_bn1')
c_bn2 = batch_norm(name='c_bn2')
c_bn3 = batch_norm(name='c_bn3')
c_bn4 = batch_norm(name='c_bn4')
h1 = lrelu(c_bn1(conv2d(h0, self.df_dim*2, name='h1_conv')))
h2 = lrelu(c_bn2(conv2d(h1, self.df_dim*4, name='h2_conv')))
h3 = lrelu(c_bn3(conv2d(h2, self.df_dim*8, name='h3_conv')))
h4 = lrelu(c_bn4(linear(tf.reshape(h3, [self.batch_size, -1]), featsize, 'h4_lin')))
#h5 = lrelu(self.d_bn5(linear(h4, featsize, 'd_h5_lin')))
z_ctx = linear(h4, featsize, 'hz_lin')
with tf.variable_scope("conv") as scope:
h0 = conv2d(inputimg, self.df_dim, name='h0_conv')
c_bn1 = batch_norm(name='c_bn1')
c_bn2 = batch_norm(name='c_bn2')
c_bn3 = batch_norm(name='c_bn3')
c_bn4 = batch_norm(name='c_bn4')
h1 = lrelu(c_bn1(conv2d(h0, self.df_dim*2, name='h1_conv')))
h2 = lrelu(c_bn2(conv2d(h1, self.df_dim*4, name='h2_conv')))
h3 = lrelu(c_bn3(conv2d(h2, self.df_dim*8, name='h3_conv')))
h4 = lrelu(c_bn4(linear(tf.reshape(h3, [self.batch_size, -1]), featsize, 'h4_lin')))
#h5 = lrelu(self.d_bn5(linear(h4, featsize, 'd_h5_lin')))
z = linear(h4, featsize, 'hz_lin')
# self.sim = h4[:, :featsize]
# self.diff = h4[:, featsize:]
# self.simreshape = tf.reshape(z, (-1, len_video, featsize))
# m = self.sim.get_shape().as_list()[0]
# self.simloss = tf.nn.l2_loss(self.simreshape[0] - self.simreshape[1])# / len_video
# self.diffloss = tf.nn.l2_loss(tf.matmul(self.sim, tf.transpose(self.diff))) / m / m
# print(m)
# self.z = self.simreshape#self.sim + self.diff
self.z = z
print(self.z.get_shape())
with tf.variable_scope("deconv") as scope:
d_bn0 = batch_norm(name='d_bn0')
d_bn1 = batch_norm(name='d_bn1')
d_bn2 = batch_norm(name='d_bn2')
d_bn3 = batch_norm(name='d_bn3')
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
#h5 = lrelu(self.g_bn5(linear(z, featsize, 'g_h5_lin')))
# h4 = lrelu(d_bn4(linear(z, featsize, 'd_h4_lin')))
# print(h4.get_shape())
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(
tf.concat([z, z_ctx], 1), self.gf_dim*8*s_h16*s_w16, 'd_h0_lin', with_w=True)
self.h0 = tf.reshape(
self.z_, [-1, s_h16, s_w16, self.gf_dim * 8])
h0 = lrelu(d_bn0(self.h0))
self.h1, self.h1_w, self.h1_b = deconv2d(
h0, [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1', with_w=True)
h1 = lrelu(d_bn1(self.h1))
h2, self.h2_w, self.h2_b = deconv2d(
h1, [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2', with_w=True)
h2 = lrelu(d_bn2(h2))
h3, self.h3_w, self.h3_b = deconv2d(
h2, [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3', with_w=True)
h3 = lrelu(d_bn3(h3))
h4, self.h4_w, self.h4_b = deconv2d(
h3, [self.batch_size, s_h, s_w, self.c_dim], name='d_h4', with_w=True)
self.out = h4#tf.nn.tanh(h4)
self.loss = tf.nn.l2_loss(outputimg - self.out)
# self.loss = self.reconloss# + self.simloss
class ContextSkipAE:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
with tf.variable_scope("conv_context") as scope:
c_bn0 = batch_norm(name='c_bn0')
c_bn1 = batch_norm(name='c_bn1')
c_bn2 = batch_norm(name='c_bn2')
c_bn3 = batch_norm(name='c_bn3')
c_bn4 = batch_norm(name='c_bn4')
ctx_h0 = lrelu(c_bn0(conv2d(contextimg, self.df_dim, name='h0_conv')))
ctx_h1 = lrelu(c_bn1(conv2d(ctx_h0, self.df_dim*2, name='h1_conv')))
ctx_h2 = lrelu(c_bn2(conv2d(ctx_h1, self.df_dim*4, name='h2_conv')))
ctx_h3 = lrelu(c_bn3(conv2d(ctx_h2, self.df_dim*8, name='h3_conv')))
ctx_h4 = lrelu(c_bn4(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin')))
#h5 = lrelu(self.d_bn5(linear(h4, featsize, 'd_h5_lin')))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv") as scope:
c_bn0 = batch_norm(name='c_bn0')
c_bn1 = batch_norm(name='c_bn1')
c_bn2 = batch_norm(name='c_bn2')
c_bn3 = batch_norm(name='c_bn3')
c_bn4 = batch_norm(name='c_bn4')
h0 = lrelu(c_bn0(conv2d(inputimg, self.df_dim, name='h0_conv')))
h1 = lrelu(c_bn1(conv2d(h0, self.df_dim*2, name='h1_conv')))
h2 = lrelu(c_bn2(conv2d(h1, self.df_dim*4, name='h2_conv')))
h3 = lrelu(c_bn3(conv2d(h2, self.df_dim*8, name='h3_conv')))
print(h3.get_shape())
h4 = lrelu(c_bn4(linear(tf.reshape(h3, [self.batch_size, -1]), featsize, 'h4_lin')))
#h5 = lrelu(self.d_bn5(linear(h4, featsize, 'd_h5_lin')))
z = linear(h4, featsize, 'hz_lin')
# self.sim = h4[:, :featsize]
# self.diff = h4[:, featsize:]
# self.simreshape = tf.reshape(z, (-1, len_video, featsize))
# m = self.sim.get_shape().as_list()[0]
# self.simloss = tf.nn.l2_loss(self.simreshape[0] - self.simreshape[1])# / len_video
# self.diffloss = tf.nn.l2_loss(tf.matmul(self.sim, tf.transpose(self.diff))) / m / m
# print(m)
# self.z = self.simreshape#self.sim + self.diff
self.z = z
print(self.z.get_shape())
with tf.variable_scope("deconv") as scope:
d_bn0 = batch_norm(name='d_bn0')
d_bn1 = batch_norm(name='d_bn1')
d_bn2 = batch_norm(name='d_bn2')
d_bn3 = batch_norm(name='d_bn3')
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
#h5 = lrelu(self.g_bn5(linear(z, featsize, 'g_h5_lin')))
# h4 = lrelu(d_bn4(linear(z, featsize, 'd_h4_lin')))
# print(h4.get_shape())
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(
tf.concat([z, ctx_z], 1), self.gf_dim*8*s_h16*s_w16, 'd_h0_lin', with_w=True)
h0 = tf.reshape(
self.z_, [-1, s_h16, s_w16, self.gf_dim * 8])
h0 = lrelu(d_bn0(h0))
h1, self.h1_w, self.h1_b = deconv2d(
tf.concat([h0, ctx_h3], 3), [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1', with_w=True)
h1 = lrelu(d_bn1(h1))
h2, self.h2_w, self.h2_b = deconv2d(
tf.concat([h1, ctx_h2], 3), [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2', with_w=True)
h2 = lrelu(d_bn2(h2))
h3, self.h3_w, self.h3_b = deconv2d(
tf.concat([h2, ctx_h1], 3), [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3', with_w=True)
h3 = lrelu(d_bn3(h3))
h4, self.h4_w, self.h4_b = deconv2d(
tf.concat([h3, ctx_h0], 3), [self.batch_size, s_h, s_w, self.c_dim], name='d_h4', with_w=True)
self.out = h4#tf.nn.tanh(h4)
self.loss = tf.nn.l2_loss(outputimg - self.out)
# self.loss = self.reconloss# + self.simloss
class ContextNoBNAE:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
with tf.variable_scope("conv_context") as scope:
# c_bn0 = batch_norm(name='c_bn0')
# c_bn1 = batch_norm(name='c_bn1')
# c_bn2 = batch_norm(name='c_bn2')
# c_bn3 = batch_norm(name='c_bn3')
# c_bn4 = batch_norm(name='c_bn4')
ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
#h5 = lrelu(self.d_bn5(linear(h4, featsize, 'd_h5_lin')))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv") as scope:
# c_bn0 = batch_norm(name='c_bn0')
# c_bn1 = batch_norm(name='c_bn1')
# c_bn2 = batch_norm(name='c_bn2')
# c_bn3 = batch_norm(name='c_bn3')
# c_bn4 = batch_norm(name='c_bn4')
h0 = lrelu(conv2d(inputimg, self.df_dim, name='h0_conv'))
h1 = lrelu(conv2d(h0, self.df_dim*2, name='h1_conv'))
h2 = lrelu(conv2d(h1, self.df_dim*4, name='h2_conv'))
h3 = lrelu(conv2d(h2, self.df_dim*8, name='h3_conv'))
print(h3.get_shape())
h4 = lrelu(linear(tf.reshape(h3, [self.batch_size, -1]), featsize, 'h4_lin'))
#h5 = lrelu(self.d_bn5(linear(h4, featsize, 'd_h5_lin')))
z = linear(h4, featsize, 'hz_lin')
# self.sim = h4[:, :featsize]
# self.diff = h4[:, featsize:]
# self.simreshape = tf.reshape(z, (-1, len_video, featsize))
# m = self.sim.get_shape().as_list()[0]
# self.simloss = tf.nn.l2_loss(self.simreshape[0] - self.simreshape[1])# / len_video
# self.diffloss = tf.nn.l2_loss(tf.matmul(self.sim, tf.transpose(self.diff))) / m / m
# print(m)
# self.z = self.simreshape#self.sim + self.diff
self.z = z
self.simloss = 0
for j in range(3):
self.asdf = z[j*25:(j+1) * 25]- z[(j+1) * 25 : (j+2) * 25]
self.simloss += tf.reduce_mean((z[j*25:(j+1) * 25]- z[(j+1) * 25 : (j+2) * 25]) ** 2)/3
mean, var = tf.nn.moments(self.z,axes=[0])
print(var.get_shape())
self.simloss /= tf.reduce_mean(var)
print(self.z.get_shape())
with tf.variable_scope("deconv") as scope:
# d_bn0 = batch_norm(name='d_bn0')
# d_bn1 = batch_norm(name='d_bn1')
# d_bn2 = batch_norm(name='d_bn2')
# d_bn3 = batch_norm(name='d_bn3')
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
#h5 = lrelu(self.g_bn5(linear(z, featsize, 'g_h5_lin')))
# h4 = lrelu(d_bn4(linear(z, featsize, 'd_h4_lin')))
# print(h4.get_shape())
# project `z` and reshape
self.z_, self.h0_w, self.h0_b = linear(
tf.concat([z, ctx_z], 1), self.gf_dim*8*s_h16*s_w16, 'd_h0_lin', with_w=True)
h0 = tf.reshape(
self.z_, [-1, s_h16, s_w16, self.gf_dim * 8])
h0 = lrelu(h0)
h1, self.h1_w, self.h1_b = deconv2d(
tf.concat([h0, ctx_h3], 3), [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1', with_w=True)
h1 = lrelu(h1)
h2, self.h2_w, self.h2_b = deconv2d(
tf.concat([h1, ctx_h2], 3), [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2', with_w=True)
h2 = lrelu(h2)
h3, self.h3_w, self.h3_b = deconv2d(
tf.concat([h2, ctx_h1], 3), [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3', with_w=True)
h3 = lrelu(h3)
h4, self.h4_w, self.h4_b = deconv2d(
tf.concat([h3, ctx_h0], 3), [self.batch_size, s_h, s_w, self.c_dim], name='d_h4', with_w=True)
self.out = h4#tf.nn.tanh(h4)
self.loss = tf.nn.l2_loss(outputimg - self.out) + 1e3 * self.simloss
# self.loss = self.reconloss# + self.simloss
class ContextAEDeconv:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
with tf.variable_scope("conv_context") as scope:
ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv_input") as scope:
input_h0 = lrelu(conv2d(inputimg, self.df_dim, name='h0_conv'))
input_h1 = lrelu(conv2d(input_h0, self.df_dim*2, name='h1_conv'))
input_h2 = lrelu(conv2d(input_h1, self.df_dim*4, name='h2_conv'))
input_h3 = lrelu(conv2d(input_h2, self.df_dim*8, name='h3_conv'))
print(input_h3.get_shape())
input_h4 = lrelu(linear(tf.reshape(input_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
input_z = linear(input_h4, featsize, 'hz_lin')
self.input_z = input_z
self.simloss = tf.zeros(())
print(self.input_z.get_shape())
scope.reuse_variables()
truth_h0 = lrelu(conv2d(outputimg, self.df_dim, name='h0_conv'))
truth_h1 = lrelu(conv2d(truth_h0, self.df_dim*2, name='h1_conv'))
truth_h2 = lrelu(conv2d(truth_h1, self.df_dim*4, name='h2_conv'))
truth_h3 = lrelu(conv2d(truth_h2, self.df_dim*8, name='h3_conv'))
print(truth_h3.get_shape())
truth_h4 = lrelu(linear(tf.reshape(truth_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
truth_z = linear(truth_h4, featsize, 'hz_lin')
self.truth_z = truth_z
self.simloss = tf.zeros(())
print(self.truth_z.get_shape())
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
self.output_z_ = lrelu(linear(
tf.concat([input_z, ctx_z], 1), self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(self.output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
self.output_h1 = lrelu(deconv2d(tf.concat([output_h0, ctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
self.output_h2 = lrelu(deconv2d(tf.concat([self.output_h1, ctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
self.output_h3 = lrelu(deconv2d(tf.concat([self.output_h2, ctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(tf.concat([self.output_h3, ctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(
tf.concat([truth_z, ctx_z], 1), self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(tf.concat([truthoutput_h0, ctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(tf.concat([truthoutput_h1, ctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
print(truthoutput_h2.get_shape())
self.simloss = tf.nn.l2_loss(truthoutput_h2 - self.output_h2)
self.out = output_h4#tf.nn.tanh(h4)
self.loss = tf.nn.l2_loss(outputimg - self.out) + 1e2 * self.simloss
# self.loss = self.reconloss# + self.simloss
class ContextRes:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
with tf.variable_scope("conv_context") as scope:
ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv_input") as scope:
input_h0 = lrelu(conv2d(inputimg, self.df_dim, name='h0_conv'))
input_h1 = lrelu(conv2d(input_h0, self.df_dim*2, name='h1_conv'))
input_h2 = lrelu(conv2d(input_h1, self.df_dim*4, name='h2_conv'))
input_h3 = lrelu(conv2d(input_h2, self.df_dim*8, name='h3_conv'))
print(input_h3.get_shape())
input_h4 = lrelu(linear(tf.reshape(input_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
input_z = linear(input_h4, featsize, 'hz_lin')
self.input_z = input_z
# self.simloss = tf.zeros(())
print(self.input_z.get_shape())
self.simloss = 0
for j in range(3):
# self.asdf = z[j*25:(j+1) * 25]- z[(j+1) * 25 : (j+2) * 25]
self.simloss += tf.reduce_mean((input_z[j*25:(j+1) * 25]-
input_z[(j+1) * 25 : (j+2) * 25]) ** 2)
mean, var = tf.nn.moments(input_z,axes=[0])
print(var.get_shape())
self.simloss /= tf.reduce_mean(var)
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
self.output_z_ = lrelu(linear(
tf.concat([input_z, ctx_z], 1), self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(self.output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(output_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(output_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(output_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(output_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
# self.simloss = tf.nn.l2_loss(truthoutput_h2 - self.output_h2)
self.out = output_h4 + contextimg#tf.nn.tanh(h4)
self.loss = tf.nn.l2_loss(outputimg - self.out) + 1e3 * self.simloss
# self.loss = self.reconloss# + self.simloss
class ContextResTranslate:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
with tf.variable_scope("conv_context") as scope:
ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv_input") as scope:
input_h0 = lrelu(conv2d(inputimg, self.df_dim, name='h0_conv'))
input_h1 = lrelu(conv2d(input_h0, self.df_dim*2, name='h1_conv'))
input_h2 = lrelu(conv2d(input_h1, self.df_dim*4, name='h2_conv'))
input_h3 = lrelu(conv2d(input_h2, self.df_dim*8, name='h3_conv'))
print(input_h3.get_shape())
input_h4 = lrelu(linear(tf.reshape(input_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
input_z = lrelu(linear(input_h4, featsize, 'hz_lin'))
self.input_z = input_z
input_zh0 = lrelu(linear(tf.concat([input_z, ctx_z], 1), featsize, 'zh0'))
self.translated_z = linear(input_zh0, featsize, 'translate_z')
# self.simloss = tf.zeros(())
# print(self.input_z.get_shape())
# self.simloss = 0
# for j in range(3):
# # self.asdf = z[j*25:(j+1) * 25]- z[(j+1) * 25 : (j+2) * 25]
# self.simloss += tf.reduce_mean((input_z[j*25:(j+1) * 25]-
# input_z[(j+1) * 25 : (j+2) * 25]) ** 2)
# mean, var = tf.nn.moments(input_z,axes=[0])
# print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
scope.reuse_variables()
truth_h0 = lrelu(conv2d(outputimg, self.df_dim, name='h0_conv'))
truth_h1 = lrelu(conv2d(truth_h0, self.df_dim*2, name='h1_conv'))
truth_h2 = lrelu(conv2d(truth_h1, self.df_dim*4, name='h2_conv'))
truth_h3 = lrelu(conv2d(truth_h2, self.df_dim*8, name='h3_conv'))
print(truth_h3.get_shape())
truth_h4 = lrelu(linear(tf.reshape(truth_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
truth_z = lrelu(linear(truth_h4, featsize, 'hz_lin'))
self.truth_z = truth_z
self.simloss = tf.zeros(())
print(self.truth_z.get_shape())
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
self.output_z_ = lrelu(linear(self.translated_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(self.output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(output_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(output_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(output_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(output_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(truth_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(truthoutput_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(truthoutput_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(truthoutput_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(truthoutput_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
# self.simloss = tf.nn.l2_loss(truthoutput_h2 - self.output_h2)
self.simloss = tf.nn.l2_loss(self.translated_z - self.truth_z)
self.out = output_h4 + contextimg#tf.nn.tanh(h4)
self.recon1 = tf.nn.l2_loss(outputimg - self.out)
self.recon2 = tf.nn.l2_loss(outputimg - (contextimg + truthoutput_h4))
self.loss = self.recon1 + self.recon2 + 1e3 * self.simloss
# self.loss = self.reconloss# + self.simloss
class ContextVanilla:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
with tf.variable_scope("conv_context") as scope:
ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv_input") as scope:
input_h0 = lrelu(conv2d(inputimg, self.df_dim, name='h0_conv'))
input_h1 = lrelu(conv2d(input_h0, self.df_dim*2, name='h1_conv'))
input_h2 = lrelu(conv2d(input_h1, self.df_dim*4, name='h2_conv'))
input_h3 = lrelu(conv2d(input_h2, self.df_dim*8, name='h3_conv'))
print(input_h3.get_shape())
input_h4 = lrelu(linear(tf.reshape(input_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
input_z = lrelu(linear(input_h4, featsize, 'hz_lin'))
self.input_z = input_z
input_zh0 = lrelu(linear(tf.concat([input_z, ctx_z], 1), featsize, 'zh0'))
self.translated_z = linear(input_zh0, featsize, 'translate_z')
# self.simloss = tf.zeros(())
# print(self.input_z.get_shape())
# self.simloss = 0
# for j in range(3):
# # self.asdf = z[j*25:(j+1) * 25]- z[(j+1) * 25 : (j+2) * 25]
# self.simloss += tf.reduce_mean((input_z[j*25:(j+1) * 25]-
# input_z[(j+1) * 25 : (j+2) * 25]) ** 2)
# mean, var = tf.nn.moments(input_z,axes=[0])
# print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
scope.reuse_variables()
truth_h0 = lrelu(conv2d(outputimg, self.df_dim, name='h0_conv'))
truth_h1 = lrelu(conv2d(truth_h0, self.df_dim*2, name='h1_conv'))
truth_h2 = lrelu(conv2d(truth_h1, self.df_dim*4, name='h2_conv'))
truth_h3 = lrelu(conv2d(truth_h2, self.df_dim*8, name='h3_conv'))
print(truth_h3.get_shape())
truth_h4 = lrelu(linear(tf.reshape(truth_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
truth_z = lrelu(linear(truth_h4, featsize, 'hz_lin'))
self.truth_z = truth_z
self.simloss = tf.zeros(())
print(self.truth_z.get_shape())
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
self.output_z_ = lrelu(linear(self.translated_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(self.output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(output_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(output_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(output_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(output_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(truth_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(truthoutput_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(truthoutput_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(truthoutput_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(truthoutput_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
# self.simloss = tf.nn.l2_loss(truthoutput_h2 - self.output_h2)
self.simloss = tf.reduce_mean((self.translated_z - self.truth_z) ** 2) * 1e3
mean, var = tf.nn.moments(self.truth_z,axes=[0])
print(var.get_shape())
self.simloss /= tf.reduce_mean(var)
print(self.truth_z.get_shape())
self.out = output_h4# + contextimg#tf.nn.tanh(h4)
self.out2 = truthoutput_h4
self.recon1 = tf.nn.l2_loss(outputimg - self.out)
self.recon2 = tf.nn.l2_loss(outputimg - self.out2)
self.loss = self.recon1 + self.recon2 + self.simloss
# self.loss = self.reconloss# + self.simloss
class ContextAEFixed:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
with tf.variable_scope("conv_input") as scope:
input_h0 = lrelu(conv2d(inputimg, self.df_dim, name='h0_conv'))
input_h1 = lrelu(conv2d(input_h0, self.df_dim*2, name='h1_conv'))
input_h2 = lrelu(conv2d(input_h1, self.df_dim*4, name='h2_conv'))
input_h3 = lrelu(conv2d(input_h2, self.df_dim*8, name='h3_conv'))
print(input_h3.get_shape())
input_h4 = lrelu(linear(tf.reshape(input_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
input_z = lrelu(linear(input_h4, featsize, 'hz_lin'))
self.input_z = input_z
with tf.variable_scope("trans") as scope2:
ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
input_zh0 = lrelu(linear(tf.concat([input_z, ctx_z], 1), featsize*2, 'zh0'))
input_zh1 = lrelu(linear(input_zh0, featsize*2, 'zh1'))
input_zh2 = lrelu(linear(input_zh1, featsize*2, 'zh2'))
self.translated_z = linear(input_zh2, featsize, 'translate_z')
# self.simloss = tf.zeros(())
# print(self.input_z.get_shape())
# self.simloss = 0
# for j in range(3):
# # self.asdf = z[j*25:(j+1) * 25]- z[(j+1) * 25 : (j+2) * 25]
# self.simloss += tf.reduce_mean((input_z[j*25:(j+1) * 25]-
# input_z[(j+1) * 25 : (j+2) * 25]) ** 2)
# mean, var = tf.nn.moments(input_z,axes=[0])
# print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
scope.reuse_variables()
truth_h0 = lrelu(conv2d(outputimg, self.df_dim, name='h0_conv'))
truth_h1 = lrelu(conv2d(truth_h0, self.df_dim*2, name='h1_conv'))
truth_h2 = lrelu(conv2d(truth_h1, self.df_dim*4, name='h2_conv'))
truth_h3 = lrelu(conv2d(truth_h2, self.df_dim*8, name='h3_conv'))
print(truth_h3.get_shape())
truth_h4 = lrelu(linear(tf.reshape(truth_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
truth_z = lrelu(linear(truth_h4, featsize, 'hz_lin'))
self.truth_z = truth_z
self.simloss = tf.zeros(())
print(self.truth_z.get_shape())
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
self.output_z_ = lrelu(linear(self.translated_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(self.output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(output_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(output_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(output_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(output_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(truth_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(truthoutput_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(truthoutput_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(truthoutput_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(truthoutput_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
# self.simloss = tf.nn.l2_loss(truthoutput_h2 - self.output_h2)
self.simloss = tf.nn.l2_loss(self.translated_z - self.truth_z)
# mean, var = tf.nn.moments(self.truth_z,axes=[0])
# print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
# print(self.truth_z.get_shape())
self.out = output_h4 #tf.nn.tanh(h4)
self.out2 = truthoutput_h4
self.recon1 = tf.nn.l2_loss(outputimg - self.out)
self.recon2 = tf.nn.l2_loss(outputimg - self.out2)
self.loss = self.recon1 + self.recon2# + self.simloss
# self.loss = self.reconloss# + self.simloss
class ContextSkipNew:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
srcimg = image[0]
tgtimg = image[2]
tgtctx = image[1]
with tf.variable_scope("conv_context") as scope:
tgtctx_h0 = lrelu(conv2d(tgtctx, self.df_dim, name='h0_conv'))
tgtctx_h1 = lrelu(conv2d(tgtctx_h0, self.df_dim*2, name='h1_conv'))
tgtctx_h2 = lrelu(conv2d(tgtctx_h1, self.df_dim*4, name='h2_conv'))
tgtctx_h3 = lrelu(conv2d(tgtctx_h2, self.df_dim*8, name='h3_conv'))
tgtctx_h4 = lrelu(linear(tf.reshape(tgtctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
tgtctx_z = linear(tgtctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv") as scope:
srcimg_h0 = lrelu(conv2d(srcimg, self.df_dim, name='h0_conv'))
srcimg_h1 = lrelu(conv2d(srcimg_h0, self.df_dim*2, name='h1_conv'))
srcimg_h2 = lrelu(conv2d(srcimg_h1, self.df_dim*4, name='h2_conv'))
srcimg_h3 = lrelu(conv2d(srcimg_h2, self.df_dim*8, name='h3_conv'))
print(srcimg_h3.get_shape())
srcimg_h4 = lrelu(linear(tf.reshape(srcimg_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
srcimg_z = lrelu(linear(srcimg_h4, featsize, 'hz_lin'))
self.input_z = srcimg_z
scope.reuse_variables()
tgtimg_h0 = lrelu(conv2d(tgtimg, self.df_dim, name='h0_conv'))
tgtimg_h1 = lrelu(conv2d(tgtimg_h0, self.df_dim*2, name='h1_conv'))
tgtimg_h2 = lrelu(conv2d(tgtimg_h1, self.df_dim*4, name='h2_conv'))
tgtimg_h3 = lrelu(conv2d(tgtimg_h2, self.df_dim*8, name='h3_conv'))
tgtimg_h4 = lrelu(linear(tf.reshape(tgtimg_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
tgtimg_z = lrelu(linear(tgtimg_h4, featsize, 'hz_lin'))
with tf.variable_scope("translate") as scope:
trans_h0 = lrelu(linear(tf.concat([srcimg_z, tgtctx_z], 1), featsize, 'trans_h0'))
trans_z = linear(trans_h0, featsize, 'trans_z')
self.translated_z = trans_z
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
output_z_ = lrelu(linear(trans_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(tf.concat([output_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(tf.concat([output_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(tf.concat([output_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(tf.concat([output_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(tgtimg_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(tf.concat([truthoutput_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(tf.concat([truthoutput_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(tf.concat([truthoutput_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(tf.concat([truthoutput_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
self.simloss = tf.reduce_mean((trans_z - tgtimg_z) ** 2) * 1e3
mean, var = tf.nn.moments(tgtimg_z, axes=[0])
print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
print(tgtimg_z.get_shape())
self.out = output_h4# + contextimg#tf.nn.tanh(h4)
self.out2 = truthoutput_h4
self.recon1 = tf.nn.l2_loss(tgtimg - self.out)
self.recon2 = tf.nn.l2_loss(tgtimg - self.out2)
self.loss = self.recon1 + self.recon2 + self.simloss
class ContextAETied:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
# with tf.variable_scope("conv_context") as scope:
# ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
# ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
# ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
# ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
# ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
# ctx_z = linear(ctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv_input") as scope:
input_h0 = lrelu(conv2d(inputimg, self.df_dim, name='h0_conv'))
input_h1 = lrelu(conv2d(input_h0, self.df_dim*2, name='h1_conv'))
input_h2 = lrelu(conv2d(input_h1, self.df_dim*4, name='h2_conv'))
input_h3 = lrelu(conv2d(input_h2, self.df_dim*8, name='h3_conv'))
print(input_h3.get_shape())
input_h4 = lrelu(linear(tf.reshape(input_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
input_z = lrelu(linear(input_h4, featsize, 'hz_lin'))
self.input_z = input_z
scope.reuse_variables()
ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
# self.simloss = tf.zeros(())
# print(self.input_z.get_shape())
# self.simloss = 0
# for j in range(3):
# # self.asdf = z[j*25:(j+1) * 25]- z[(j+1) * 25 : (j+2) * 25]
# self.simloss += tf.reduce_mean((input_z[j*25:(j+1) * 25]-
# input_z[(j+1) * 25 : (j+2) * 25]) ** 2)
# mean, var = tf.nn.moments(input_z,axes=[0])
# print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
scope.reuse_variables()
truth_h0 = lrelu(conv2d(outputimg, self.df_dim, name='h0_conv'))
truth_h1 = lrelu(conv2d(truth_h0, self.df_dim*2, name='h1_conv'))
truth_h2 = lrelu(conv2d(truth_h1, self.df_dim*4, name='h2_conv'))
truth_h3 = lrelu(conv2d(truth_h2, self.df_dim*8, name='h3_conv'))
print(truth_h3.get_shape())
truth_h4 = lrelu(linear(tf.reshape(truth_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
truth_z = lrelu(linear(truth_h4, featsize, 'hz_lin'))
self.truth_z = truth_z
self.simloss = tf.zeros(())
print(self.truth_z.get_shape())
with tf.variable_scope("translate") as scope:
# input_zh0 = lrelu(linear(tf.concat([input_z, ctx_z], 1), featsize, 'zh0'))
self.translated_z = linear(tf.concat([input_z, ctx_z], 1), featsize, 'translate_z')
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
self.output_z_ = lrelu(linear(self.translated_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(self.output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(output_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(output_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(output_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(output_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(truth_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(truthoutput_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(truthoutput_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(truthoutput_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(truthoutput_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
# self.simloss = tf.nn.l2_loss(truthoutput_h2 - self.output_h2)
self.simloss = tf.reduce_mean((self.translated_z - self.truth_z) ** 2) * 1e3
mean, var = tf.nn.moments(self.truth_z,axes=[0])
print(var.get_shape())
self.simloss /= tf.reduce_mean(var)
print(self.truth_z.get_shape())
self.out = output_h4# + contextimg#tf.nn.tanh(h4)
self.out2 = truthoutput_h4
self.recon1 = tf.nn.l2_loss(outputimg - self.out)
self.recon2 = tf.nn.l2_loss(outputimg - self.out2)
self.loss = self.recon1 + self.recon2 + self.simloss
# self.loss = self.reconloss# + self.simloss
keep_prob = 1.0
class ContextVanillaDrop:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 1024
inputimg = image[0]
contextimg = image[1]
outputimg = image[2]
# with tf.variable_scope("conv_context") as scope:
# ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
# ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
# ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
# ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
# ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
# ctx_z = linear(ctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv_input") as scope:
input_h0 = lrelu(conv2d(inputimg, self.df_dim, name='h0_conv'))
input_h1 = lrelu(conv2d(input_h0, self.df_dim*2, name='h1_conv'))
input_h2 = lrelu(conv2d(input_h1, self.df_dim*4, name='h2_conv'))
input_h3 = lrelu(conv2d(input_h2, self.df_dim*8, name='h3_conv'))
print(input_h3.get_shape())
input_h4 = lrelu(linear(tf.reshape(input_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
input_z = lrelu(linear(input_h4, featsize, 'hz_lin'))
self.input_z = input_z
scope.reuse_variables()
ctx_h0 = lrelu(conv2d(contextimg, self.df_dim, name='h0_conv'))
ctx_h1 = lrelu(conv2d(ctx_h0, self.df_dim*2, name='h1_conv'))
ctx_h2 = lrelu(conv2d(ctx_h1, self.df_dim*4, name='h2_conv'))
ctx_h3 = lrelu(conv2d(ctx_h2, self.df_dim*8, name='h3_conv'))
ctx_h4 = lrelu(linear(tf.reshape(ctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
ctx_z = linear(ctx_h4, featsize, 'hz_lin')
# self.simloss = tf.zeros(())
# print(self.input_z.get_shape())
# self.simloss = 0
# for j in range(3):
# # self.asdf = z[j*25:(j+1) * 25]- z[(j+1) * 25 : (j+2) * 25]
# self.simloss += tf.reduce_mean((input_z[j*25:(j+1) * 25]-
# input_z[(j+1) * 25 : (j+2) * 25]) ** 2)
# mean, var = tf.nn.moments(input_z,axes=[0])
# print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
scope.reuse_variables()
truth_h0 = lrelu(conv2d(outputimg, self.df_dim, name='h0_conv'))
truth_h1 = lrelu(conv2d(truth_h0, self.df_dim*2, name='h1_conv'))
truth_h2 = lrelu(conv2d(truth_h1, self.df_dim*4, name='h2_conv'))
truth_h3 = lrelu(conv2d(truth_h2, self.df_dim*8, name='h3_conv'))
print(truth_h3.get_shape())
truth_h4 = lrelu(linear(tf.reshape(truth_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
truth_z = lrelu(linear(truth_h4, featsize, 'hz_lin'))
self.truth_z = truth_z
self.simloss = tf.zeros(())
print(self.truth_z.get_shape())
with tf.variable_scope("translate") as scope:
# input_zh0 = lrelu(linear(tf.concat([input_z, ctx_z], 1), featsize, 'zh0'))
self.translated_z = linear(tf.nn.dropout(tf.concat([input_z, ctx_z], 1), keep_prob), featsize, 'translate_z')
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4, s_h8, s_h16 = \
int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
s_w2, s_w4, s_w8, s_w16 = \
int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
self.output_z_ = lrelu(linear(self.translated_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(self.output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(output_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(output_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(output_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(output_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(truth_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(truthoutput_h0,
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(truthoutput_h1,
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(truthoutput_h2,
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(truthoutput_h3,
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
# self.simloss = tf.nn.l2_loss(truthoutput_h2 - self.output_h2)
self.simloss = tf.reduce_mean((self.translated_z - self.truth_z) ** 2) * 1e3
mean, var = tf.nn.moments(self.truth_z,axes=[0])
self.var = var
print(var.get_shape())
print((var - np.ones(featsize,)).get_shape())
self.regloss = tf.zeros(())#tf.reduce_mean((var - np.ones(featsize,)) ** 2) * 1e3
print(self.truth_z.get_shape())
self.out = output_h4# + contextimg#tf.nn.tanh(h4)
self.out2 = truthoutput_h4
self.recon1 = tf.nn.l2_loss(outputimg - self.out)
self.recon2 = tf.nn.l2_loss(outputimg - self.out2)
self.loss = self.recon1 + self.recon2 + self.simloss + self.regloss
# self.loss = self.reconloss# + self.simloss
class ContextAEReal:
def __init__(self, gf_dim=64, df_dim=64,
gfc_dim=1024, dfc_dim=1024,
c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width = imgshape[-3:-1]
self.batch_size = imgshape[1]
featsize = 100
srcimg = image[0]
tgtimg = image[2]
tgtctx = image[1]
nf0 = 32
nf1 = 16
nf2 = 16
nf3 = 8
ns0 = 1
ns1 = 2
ns2 = 1
ns3 = 2
# with tf.variable_scope("conv_context") as scope:
def encode(img):
img_h0 = lrelu(conv2d(img, nf0, d_h=ns0, d_w=ns0, name='h0_conv'))
img_h1 = lrelu(conv2d(img_h0, nf1, d_h=ns1, d_w=ns1, name='h1_conv'))
img_h2 = lrelu(conv2d(img_h1, nf2, d_h=ns2, d_w=ns2, name='h2_conv'))
img_h3 = lrelu(conv2d(img_h2, nf3, d_h=ns3, d_w=ns3, name='h3_conv'))
print(img_h3.get_shape())
img_h4 = lrelu(linear(tf.nn.dropout(tf.reshape(img_h3, [self.batch_size, -1]), keep_prob), featsize, 'h4_lin'))
img_z = lrelu(linear(tf.nn.dropout(img_h4, keep_prob), featsize, 'hz_lin'))
return img_h0, img_h1, img_h2, img_h3, img_h4, img_z
with tf.variable_scope("conv") as scope:
srcimg_h0, srcimg_h1, srcimg_h2, srcimg_h3, srcimg_h4, srcimg_z = encode(srcimg)
self.input_z = srcimg_z
scope.reuse_variables()
tgtimg_h0, tgtimg_h1, tgtimg_h2, tgtimg_h3, tgtimg_h4, tgtimg_z = encode(tgtimg)
tgtctx_h0, tgtctx_h1, tgtctx_h2, tgtctx_h3, tgtctx_h4, tgtctx_z = encode(tgtctx)
with tf.variable_scope("translate") as scope:
trans_h0 = lrelu(linear(tf.nn.dropout(tf.concat([srcimg_z, tgtctx_z], 1), keep_prob), featsize, 'trans_h0'))
trans_z = linear(tf.nn.dropout(trans_h0, keep_prob), featsize, 'trans_z')
self.translated_z = trans_z
s_h, s_w = self.output_height, self.output_width
s_h0, s_h1, s_h2, s_h3 = \
int(s_h/ns0), int(s_h/ns0/ns1), int(s_h/ns0/ns1/ns2), int(s_h/ns0/ns1/ns2/ns3)
s_w0, s_w1, s_w2, s_w3 = \
int(s_w/ns0), int(s_w/ns0/ns1), int(s_w/ns0/ns1/ns2), int(s_w/ns0/ns1/ns2/ns3)
def decode(z, skip_h3, skip_h2, skip_h1, skip_h0):
z_ = lrelu(linear(tf.nn.dropout(z, keep_prob), nf3*s_h3*s_w3, 'd_h0_lin'))
h0 = tf.nn.dropout(tf.reshape(z_, [-1, s_h3, s_w3, nf3]), keep_prob)
h1 = lrelu(deconv2d(tf.concat([h0, skip_h3], 3),
[self.batch_size, s_h2, s_w2, nf2], name='d_h1', d_h=ns3, d_w=ns3))
h2 = lrelu(deconv2d(tf.concat([h1, skip_h2], 3),
[self.batch_size, s_h1, s_w1, nf1], name='d_h2', d_h=ns2, d_w=ns2))
h3 = lrelu(deconv2d(tf.concat([h2, skip_h1], 3),
[self.batch_size, s_h0, s_w0, nf0], name='d_h3', d_h=ns1, d_w=ns1))
print(h3.get_shape())
h4 = deconv2d(tf.concat([h3, skip_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4', d_h=ns0, d_w=ns0)
return h4
with tf.variable_scope("deconv") as scope:
output_h4 = decode(trans_z, tgtctx_h3, tgtctx_h2, tgtctx_h1, tgtctx_h0)
scope.reuse_variables()
truthoutput_h4 = decode(tgtimg_z, tgtctx_h3, tgtctx_h2, tgtctx_h1, tgtctx_h0)
self.simloss = tf.reduce_mean((trans_z - tgtimg_z) ** 2) * 1e3
print(tgtimg_z.get_shape())
self.out = output_h4
self.out2 = truthoutput_h4
print(self.out.get_shape())
self.recon1 = tf.nn.l2_loss(tgtimg - self.out)
self.recon2 = tf.nn.l2_loss(tgtimg - self.out2)
self.loss = self.recon1 + self.recon2 + self.simloss
class ContextAEInception:
def __init__(self, gf_dim=256, df_dim=256,
gfc_dim=1024, dfc_dim=1024):
self.gf_dim = gf_dim
self.df_dim = df_dim
# self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width, self.c_dim = imgshape[-3:]
self.batch_size = imgshape[1]
featsize = 1024
srcimg = image[0]
tgtimg = image[2]
tgtctx = image[1]
with tf.variable_scope("conv_context") as scope:
tgtctx_h0 = lrelu(conv2d(tgtctx, self.df_dim, name='h0_conv'))
tgtctx_h1 = lrelu(conv2d(tgtctx_h0, self.df_dim*2, name='h1_conv'))
tgtctx_h2 = lrelu(conv2d(tgtctx_h1, self.df_dim*4, name='h2_conv'))
tgtctx_h3 = lrelu(conv2d(tgtctx_h2, self.df_dim*8, name='h3_conv'))
tgtctx_h4 = lrelu(linear(tf.reshape(tgtctx_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
tgtctx_z = linear(tgtctx_h4, featsize, 'hz_lin')
with tf.variable_scope("conv") as scope:
srcimg_h0 = lrelu(conv2d(srcimg, self.df_dim, name='h0_conv'))
srcimg_h1 = lrelu(conv2d(srcimg_h0, self.df_dim*2, name='h1_conv'))
srcimg_h2 = lrelu(conv2d(srcimg_h1, self.df_dim*4, name='h2_conv'))
srcimg_h3 = lrelu(conv2d(srcimg_h2, self.df_dim*8, name='h3_conv'))
print(srcimg_h3.get_shape())
srcimg_h4 = lrelu(linear(tf.reshape(srcimg_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
srcimg_z = lrelu(linear(srcimg_h4, featsize, 'hz_lin'))
self.input_z = srcimg_z
scope.reuse_variables()
tgtimg_h0 = lrelu(conv2d(tgtimg, self.df_dim, name='h0_conv'))
tgtimg_h1 = lrelu(conv2d(tgtimg_h0, self.df_dim*2, name='h1_conv'))
tgtimg_h2 = lrelu(conv2d(tgtimg_h1, self.df_dim*4, name='h2_conv'))
tgtimg_h3 = lrelu(conv2d(tgtimg_h2, self.df_dim*8, name='h3_conv'))
tgtimg_h4 = lrelu(linear(tf.reshape(tgtimg_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
tgtimg_z = lrelu(linear(tgtimg_h4, featsize, 'hz_lin'))
with tf.variable_scope("translate") as scope:
trans_h0 = lrelu(linear(tf.concat([srcimg_z, tgtctx_z], 1), featsize, 'trans_h0'))
trans_z = linear(trans_h0, featsize, 'trans_z')
self.translated_z = trans_z
with tf.variable_scope("deconv") as scope:
s_h, s_w = self.output_height, self.output_width
# s_h2, s_h4, s_h8, s_h16 = \
# int(s_h/2), int(s_h/4), int(s_h/8), int(s_h/16)
# s_w2, s_w4, s_w8, s_w16 = \
# int(s_w/2), int(s_w/4), int(s_w/8), int(s_w/16)
_, s_h2, s_w2, _ = tgtctx_h0.get_shape().as_list()
_, s_h4, s_w4, _ = tgtctx_h1.get_shape().as_list()
_, s_h8, s_w8, _ = tgtctx_h2.get_shape().as_list()
_, s_h16, s_w16, _ = tgtctx_h3.get_shape().as_list()
output_z_ = lrelu(linear(trans_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
output_h0 = tf.reshape(output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
output_h1 = lrelu(deconv2d(tf.concat([output_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
output_h2 = lrelu(deconv2d(tf.concat([output_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
output_h3 = lrelu(deconv2d(tf.concat([output_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
output_h4 = deconv2d(tf.concat([output_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
scope.reuse_variables()
truthoutput_z_ = lrelu(linear(tgtimg_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
truthoutput_h1 = lrelu(deconv2d(tf.concat([truthoutput_h0, tgtctx_h3], 3),
[self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
truthoutput_h2 = lrelu(deconv2d(tf.concat([truthoutput_h1, tgtctx_h2], 3),
[self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
truthoutput_h3 = lrelu(deconv2d(tf.concat([truthoutput_h2, tgtctx_h1], 3),
[self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
truthoutput_h4 = deconv2d(tf.concat([truthoutput_h3, tgtctx_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
self.simloss = tf.reduce_mean((trans_z - tgtimg_z) ** 2) * 1e3
mean, var = tf.nn.moments(tgtimg_z, axes=[0])
print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
print(tgtimg_z.get_shape())
self.out = output_h4 + tgtctx#tf.nn.tanh(h4)
self.out2 = truthoutput_h4 + tgtctx
self.recon1 = tf.nn.l2_loss(tgtimg - self.out)
self.recon2 = tf.nn.l2_loss(tgtimg - self.out2)
self.loss = self.recon1 + self.recon2 + self.simloss
class ContextAEInception2:
def __init__(self, strides, kernels, filters):
self.strides = strides
self.kernels = kernels
self.filters = filters
def build(self, image):
imgshape = image.get_shape().as_list()
print(imgshape)
self.output_height, self.output_width, self.c_dim = imgshape[-3:]
self.batch_size = imgshape[1]
featsize = 1024
srcimg = image[0]
tgtimg = image[2]
tgtctx = image[1]
s1, s2, s3, s4 = self.strides
k1, k2, k3, k4 = self.kernels
f1, f2, f3, f4 = self.filters
def encode(img):
img_h0 = lrelu(conv2d(img, f1, k1, k1, s1, s1, name='h0_conv'))
img_h1 = lrelu(conv2d(img_h0, f2, k2, k2, s2, s2, name='h1_conv'))
img_h2 = lrelu(conv2d(img_h1, f3, k3, k3, s3, s3, name='h2_conv'))
img_h3 = lrelu(conv2d(img_h2, f4, k4, k4, s4, s4, name='h3_conv'))
print(img_h3.get_shape())
img_h4 = lrelu(linear(tf.reshape(img_h3, [self.batch_size, -1]), featsize, 'h4_lin'))
img_z = lrelu(linear(img_h4, featsize, 'hz_lin'))
return img_h0, img_h1, img_h2, img_h3, img_h4, img_z
with tf.variable_scope("conv_context") as scope:
tgtctx_h0, tgtctx_h1, tgtctx_h2, tgtctx_h3, tgtctx_h4, tgtctx_z = encode(tgtctx)
with tf.variable_scope("conv") as scope:
srcimg_h0, srcimg_h1, srcimg_h2, srcimg_h3, srcimg_h4, srcimg_z = encode(srcimg)
self.input_z = srcimg_z
scope.reuse_variables()
tgtimg_h0, tgtimg_h1, tgtimg_h2, tgtimg_h3, tgtimg_h4, tgtimg_z = encode(tgtimg)
with tf.variable_scope("translate") as scope:
trans_h0 = lrelu(linear(tf.concat([srcimg_z, tgtctx_z], 1), featsize, 'trans_h0'))
trans_z = linear(trans_h0, featsize, 'trans_z')
self.translated_z = trans_z
s_h, s_w = self.output_height, self.output_width
_, s_h2, s_w2, _ = tgtctx_h0.get_shape().as_list()
_, s_h4, s_w4, _ = tgtctx_h1.get_shape().as_list()
_, s_h8, s_w8, _ = tgtctx_h2.get_shape().as_list()
_, s_h16, s_w16, _ = tgtctx_h3.get_shape().as_list()
def decode(z, skip_h3, skip_h2, skip_h1, skip_h0):
z_ = lrelu(linear(z, f4*s_h16*s_w16, 'd_h0_lin'))
h0 = tf.reshape(z_, [-1, s_h16, s_w16, f4])
h1 = lrelu(deconv2d(tf.concat([h0, skip_h3], 3),
[self.batch_size, s_h8, s_w8, f3], name='d_h1',
d_h=s4, d_w=s4, k_h=k4, k_w=k4))
h2 = lrelu(deconv2d(tf.concat([h1, skip_h2], 3),
[self.batch_size, s_h4, s_w4, f2], name='d_h2',
d_h=s3, d_w=s3, k_h=k3, k_w=k3))
h3 = lrelu(deconv2d(tf.concat([h2, skip_h1], 3),
[self.batch_size, s_h2, s_w2, f1], name='d_h3',
d_h=s2, d_w=s2, k_h=k2, k_w=k2))
print(h3.get_shape())
h4 = deconv2d(tf.concat([h3, skip_h0], 3),
[self.batch_size, s_h, s_w, self.c_dim], name='d_h4',
d_h=s1, d_w=s1, k_h=k1, k_w=k1)
return h4
with tf.variable_scope("deconv") as scope:
output_h4 = decode(trans_z, tgtctx_h3, tgtctx_h2, tgtctx_h1, tgtctx_h0)
scope.reuse_variables()
truthoutput_h4 = decode(tgtimg_z, tgtctx_h3, tgtctx_h2, tgtctx_h1, tgtctx_h0)
# with tf.variable_scope("deconv") as scope:
# output_z_ = lrelu(linear(trans_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
# output_h0 = tf.reshape(output_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
# output_h1 = lrelu(deconv2d(tf.concat([output_h0, tgtctx_h3], 3),
# [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
# output_h2 = lrelu(deconv2d(tf.concat([output_h1, tgtctx_h2], 3),
# [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
# output_h3 = lrelu(deconv2d(tf.concat([output_h2, tgtctx_h1], 3),
# [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
# output_h4 = deconv2d(tf.concat([output_h3, tgtctx_h0], 3),
# [self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
# scope.reuse_variables()
# truthoutput_z_ = lrelu(linear(tgtimg_z, self.gf_dim*8*s_h16*s_w16, 'd_h0_lin'))
# truthoutput_h0 = tf.reshape(truthoutput_z_, [-1, s_h16, s_w16, self.gf_dim * 8])
# truthoutput_h1 = lrelu(deconv2d(tf.concat([truthoutput_h0, tgtctx_h3], 3),
# [self.batch_size, s_h8, s_w8, self.gf_dim*4], name='d_h1'))
# truthoutput_h2 = lrelu(deconv2d(tf.concat([truthoutput_h1, tgtctx_h2], 3),
# [self.batch_size, s_h4, s_w4, self.gf_dim*2], name='d_h2'))
# truthoutput_h3 = lrelu(deconv2d(tf.concat([truthoutput_h2, tgtctx_h1], 3),
# [self.batch_size, s_h2, s_w2, self.gf_dim*1], name='d_h3'))
# truthoutput_h4 = deconv2d(tf.concat([truthoutput_h3, tgtctx_h0], 3),
# [self.batch_size, s_h, s_w, self.c_dim], name='d_h4')
self.simloss = tf.reduce_mean((trans_z - tgtimg_z) ** 2) * 1e3
mean, var = tf.nn.moments(tgtimg_z, axes=[0])
print(var.get_shape())
# self.simloss /= tf.reduce_mean(var)
print(tgtimg_z.get_shape())
self.out = output_h4 + tgtctx#tf.nn.tanh(h4)
self.out2 = truthoutput_h4 + tgtctx
self.recon1 = tf.nn.l2_loss(tgtimg - self.out)
self.recon2 = tf.nn.l2_loss(tgtimg - self.out2)
self.loss = self.recon1 + self.recon2 + self.simloss
autodc = None
tfinput = None
sess = None
goodmean = None
pcaW = None
tftime = None
def initialize():
global autodc, tfinput, sess, goodmean, pcaW
if autodc is not None:
return
batch_size = 25
idims = (299, 299)
image = tf.placeholder(tf.uint8, (batch_size, ) + idims + (3, ), name='image')
image_trans = tf.image.convert_image_dtype(image, dtype=tf.float32)
image_trans = tf.subtract(image_trans, 0.5)
image_trans = tf.multiply(image_trans, 2.0)
with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
model = inception_v3.inception_v3(image_trans, num_classes=1001, is_training=False, dropout_keep_prob=1.0)
variables_to_restore = slim.get_variables_to_restore()
restorer = tf.train.Saver(variables_to_restore)
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
# sess.run(tf.global_variables_initializer())
restorer.restore(sess, "model/inception_v3.ckpt")
bird = scipy.misc.imread('model/bird.jpg')
bird = scipy.misc.imresize(bird, idims)
logits = sess.run(model[0], {image:[bird]*batch_size})
print(np.argsort(logits[0])[-20:])
autodc = model
tfinput = image
# def initialize():
# global autodc, tfinput, sess, goodmean, pcaW
# if autodc is not None:
# return
# idim = (36, 64)
# batch_size=1
# tfinput = tf.placeholder(tf.float32, (3, batch_size) + idim + (3, ), name='x')
# autodc = ContextAEReal()
# # with tf.variable_scope("valid") as scope:
# autodc.build(tfinput)
# config = tf.ConfigProto(
# # device_count = {'GPU': 0}
# )
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# learning_rate = tf.placeholder(tf.float32, shape=[])
# # optimizer = tf.train.AdamOptimizer(learning_rate).minimize(autodc.loss)
# sess.run(tf.global_variables_initializer())
# allloss = []
# validloss = []
# itr = 0
# saver = tf.train.Saver()
# saver.restore(sess, 'model/ctxskiprealnew62575')
# saver.restore(sess, 'model/ctxgoal-1024-vanilla-vp-distract13397')
# saver.restore(sess, 'model/ctxgoal-1024-vanilla-vp-distract62598')
# saver.restore(sess, 'model/ctxgoal-1024-vanilla-vp52430')
# saver.restore(sess, '/home/andrewliu/research/viewpoint/ctxgoal-1024-aefix33728')
# saver.restore(sess, '/home/andrewliu/research/viewpoint/ctxstarrtgoal-1024-aefix109433')
# goodmean = np.load('/home/andrewliu/research/viewpoint/ctxfeatsresgoal.npy')
# def initialize():
# global autodc, tfinput, sess, goodmean, pcaW
# if autodc is not None:
# return
# idim = (48, 48)
# batch_size=1
# tfinput = tf.placeholder(tf.float32, (3, batch_size) + idim + (3, ), name='x')
# autodc = ContextAE()
# # with tf.variable_scope("valid") as scope:
# autodc.build(tfinput)
# config = tf.ConfigProto(
# device_count = {'GPU': 0}
# )
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# learning_rate = tf.placeholder(tf.float32, shape=[])
# # optimizer = tf.train.AdamOptimizer(learning_rate).minimize(autodc.loss)
# sess.run(tf.global_variables_initializer())
# allloss = []
# validloss = []
# itr = 0
# saver = tf.train.Saver()
# saver.restore(sess, '/home/andrewliu/research/viewpoint/ctx1024-AE18833')
# goodmean = np.load('/home/andrewliu/research/viewpoint/ctxfeatsmult.npy')
# def initialize():
# global autodc, tfinput, sess, goodmean, pcaW
# if autodc is not None:
# return
# idim = (64, 64)
# batch_size=1
# tfinput = tf.placeholder(tf.float32, (batch_size,) + idim + (3, ), name='x')
# autodc = ReachAE()
# # with tf.variable_scope("valid") as scope:
# autodc.build(tfinput)
# config = tf.ConfigProto(
# device_count = {'GPU': 0}
# )
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# learning_rate = tf.placeholder(tf.float32, shape=[])
# # optimizer = tf.train.AdamOptimizer(learning_rate).minimize(autodc.loss)
# sess.run(tf.global_variables_initializer())
# allloss = []
# validloss = []
# itr = 0
# saver = tf.train.Saver()
# saver.restore(sess, '/home/andrewliu/research/viewpoint/singleviewpoint1024-AE3412')
# goodmean = np.load('/home/andrewliu/research/viewpoint/aefeatsconvsingle.npy')
# def initialize():
# global autodc, tfinput, sess, goodmean, pcaW
# if autodc is not None:
# return
# idim = (64, 64)
# batch_size=1
# tfinput = tf.placeholder(tf.float32, (batch_size,) + idim + (3, ), name='x')
# autodc = AE()
# autodc.build(tfinput)
# config = tf.ConfigProto(
# device_count = {'GPU': 0}
# )
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# learning_rate = tf.placeholder(tf.float32, shape=[])
# # optimizer = tf.train.AdamOptimizer(learning_rate).minimize(autodc.loss)
# sess.run(tf.global_variables_initializer())
# allloss = []
# validloss = []
# itr = 0
# saver = tf.train.Saver()
# saver.restore(sess, '/home/andrewliu/research/viewpoint/singleviewpoint1024-AE9409')
# goodmean = np.load('/home/andrewliu/research/viewpoint/aefeatssingle.npz')['arr_0']
# def initialize():
# global autodc, tfinput, sess, goodmean, pcaW, tftime
# if autodc is not None:
# return
# idim = (64, 64)
# batch_size=1
# tfinput = tf.placeholder(tf.float32, (batch_size, ) + idim + (3, ), name='x')
# tftime = tf.placeholder(tf.float32, (batch_size, nclass), name='y')
# autodc = TimeSoftmax()
# autodc.build(tfinput, tftime)
# config = tf.ConfigProto(
# device_count = {'GPU': 0}
# )
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# learning_rate = tf.placeholder(tf.float32, shape=[])
# optimizer = tf.train.AdamOptimizer(learning_rate).minimize(autodc.loss)
# sess.run(tf.global_variables_initializer())
# allloss = []
# validloss = []
# itr = 0
# saver = tf.train.Saver()
# saver.restore(sess, '/home/andrewliu/research/viewpoint/singleviewpointtimesoftmax8084')
# def initialize():
# global autodc, tfinput, sess, goodmean, pcaW, tftime
# if autodc is not None:
# return
# idim = (64, 64)
# batch_size=1
# tfinput = tf.placeholder(tf.float32, (batch_size, ) + idim + (3, ), name='x')
# tftime = tf.placeholder(tf.float32, (batch_size, 1), name='y')
# autodc = TimePred()
# # with tf.variable_scope("valid") as scope:
# autodc.build(tfinput, tftime)
# config = tf.ConfigProto(
# device_count = {'GPU': 0}
# )
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# learning_rate = tf.placeholder(tf.float32, shape=[])
# # optimizer = tf.train.AdamOptimizer(learning_rate).minimize(autodc.loss)
# sess.run(tf.global_variables_initializer())
# allloss = []
# validloss = []
# itr = 0
# saver = tf.train.Saver()
# saver.restore(sess, '/home/andrewliu/research/viewpoint/singleviewpointtime2328')
# def initialize():
# global autodc, tfinput, sess, goodmean, pcaW
# if autodc is not None:
# return
# idim = (64, 64)
# batch_size=1
# tfinput = tf.placeholder(tf.float32, (batch_size, ) + idim + (3, ), name='x')
# tftime = tf.placeholder(tf.float32, (batch_size, 1), name='y')
# autodc = TimeDC()
# autodc.build(tfinput, tftime)
# config = tf.ConfigProto(
# device_count = {'GPU': 0}
# )
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# learning_rate = tf.placeholder(tf.float32, shape=[])
# optimizer = tf.train.AdamOptimizer(learning_rate).minimize(autodc.loss)
# sess.run(tf.global_variables_initializer())
# allloss = []
# validloss = []
# itr = 0
# saver = tf.train.Saver()
# saver.restore(sess, '/home/andrewliu/research/viewpoint/noviewpointtime7058')
# # goodmean = np.load('./shaping/goodmean.npz')['arr_0']
# # pcaW = np.load('./shaping/goodw.npz')['arr_0']
# def initialize():
# global autodc, tfinput, sess, goodmean, pcaW
# if autodc is not None:
# return
# idim = (64, 64)
# batch_size=1
# tfinput = tf.placeholder(tf.float32, (2, batch_size) + idim + (3, ), name='x')
# autodc = SubspaceAE()
# autodc.build(tfinput)
# config = tf.ConfigProto(
# device_count = {'GPU': 0}
# )
# config.gpu_options.allow_growth=True
# sess = tf.Session(config=config)
# learning_rate = tf.placeholder(tf.float32, shape=[])
# # optimizer = tf.train.AdamOptimizer(learning_rate).minimize(autodc.loss)
# sess.run(tf.global_variables_initializer())
# allloss = []
# validloss = []
# itr = 0
# saver = tf.train.Saver()
# saver.restore(sess, '/home/andrewliu/research/viewpoint/twoviewpoint1024-AE3456')
# goodmean = np.load('/home/andrewliu/research/viewpoint/subspacemean.npz')['arr_0']
# # pcaW = np.load('./shaping/goodw.npz')['arr_0']
| 47.514685
| 124
| 0.567215
| 15,068
| 101,919
| 3.567892
| 0.024356
| 0.020182
| 0.029966
| 0.026562
| 0.928498
| 0.919942
| 0.905861
| 0.896021
| 0.889325
| 0.879448
| 0
| 0.058449
| 0.290417
| 101,919
| 2,144
| 125
| 47.536847
| 0.684928
| 0.196323
| 0
| 0.819876
| 0
| 0
| 0.041244
| 0.00029
| 0
| 0
| 0
| 0
| 0
| 1
| 0.036577
| false
| 0
| 0.005521
| 0.00207
| 0.066253
| 0.05521
| 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
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| null | 0
| 0
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| 0
| 0
|
0
| 7
|
f1bc7a6807333ac02243475f2fff9d55b88cef54
| 9,390
|
py
|
Python
|
COSMIC/erc-training/dataloader.py
|
tae898/conv-emotion
|
6ada88d3334d8f70f56036e2454dcd371888e7a3
|
[
"MIT"
] | 1
|
2021-12-09T18:36:35.000Z
|
2021-12-09T18:36:35.000Z
|
COSMIC/erc-training/dataloader.py
|
tae898/conv-emotion
|
6ada88d3334d8f70f56036e2454dcd371888e7a3
|
[
"MIT"
] | null | null | null |
COSMIC/erc-training/dataloader.py
|
tae898/conv-emotion
|
6ada88d3334d8f70f56036e2454dcd371888e7a3
|
[
"MIT"
] | null | null | null |
import torch
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
import pickle
import pandas as pd
class IEMOCAPRobertaCometDataset(Dataset):
def __init__(self, split):
'''
label index mapping = {'hap':0, 'sad':1, 'neu':2, 'ang':3, 'exc':4, 'fru':5}
'''
self.speakers, self.labels, \
self.roberta1, self.roberta2, self.roberta3, self.roberta4,\
self.sentences, self.trainIds, self.testIds, self.validIds \
= pickle.load(open('iemocap/iemocap_features_roberta.pkl', 'rb'), encoding='latin1')
self.xIntent, self.xAttr, self.xNeed, self.xWant, self.xEffect, self.xReact, self.oWant, self.oEffect, self.oReact \
= pickle.load(open('iemocap/iemocap_features_comet.pkl', 'rb'), encoding='latin1')
if split == 'train':
self.keys = [x for x in self.trainIds]
elif split == 'test':
self.keys = [x for x in self.testIds]
elif split == 'valid':
self.keys = [x for x in self.validIds]
self.len = len(self.keys)
def __getitem__(self, index):
vid = self.keys[index]
return torch.FloatTensor(self.roberta1[vid]),\
torch.FloatTensor(self.roberta2[vid]),\
torch.FloatTensor(self.roberta3[vid]),\
torch.FloatTensor(self.roberta4[vid]),\
torch.FloatTensor(self.xIntent[vid]),\
torch.FloatTensor(self.xAttr[vid]),\
torch.FloatTensor(self.xNeed[vid]),\
torch.FloatTensor(self.xWant[vid]),\
torch.FloatTensor(self.xEffect[vid]),\
torch.FloatTensor(self.xReact[vid]),\
torch.FloatTensor(self.oWant[vid]),\
torch.FloatTensor(self.oEffect[vid]),\
torch.FloatTensor(self.oReact[vid]),\
torch.FloatTensor([[1, 0] if x == 'M' else [0, 1] for x in self.speakers[vid]]),\
torch.FloatTensor([1]*len(self.labels[vid])),\
torch.LongTensor(self.labels[vid]),\
vid
def __len__(self):
return self.len
def collate_fn(self, data):
dat = pd.DataFrame(data)
return [pad_sequence(dat[i]) if i < 14 else pad_sequence(dat[i], True) if i < 16 else dat[i].tolist() for i in dat]
class MELDRobertaCometDataset(Dataset):
def __init__(self, split, classify='emotion'):
'''
label index mapping =
'''
self.speakers, self.emotion_labels, self.sentiment_labels, \
self.roberta1, self.roberta2, self.roberta3, self.roberta4, \
self.sentences, self.trainIds, self.testIds, self.validIds \
= pickle.load(open('meld/meld_features_roberta.pkl', 'rb'), encoding='latin1')
self.xIntent, self.xAttr, self.xNeed, self.xWant, self.xEffect, self.xReact, self.oWant, self.oEffect, self.oReact \
= pickle.load(open('meld/meld_features_comet.pkl', 'rb'), encoding='latin1')
if split == 'train':
self.keys = [x for x in self.trainIds]
elif split == 'test':
self.keys = [x for x in self.testIds]
elif split == 'valid':
self.keys = [x for x in self.validIds]
if classify == 'emotion':
self.labels = self.emotion_labels
else:
self.labels = self.sentiment_labels
self.len = len(self.keys)
def __getitem__(self, index):
vid = self.keys[index]
return torch.FloatTensor(self.roberta1[vid]),\
torch.FloatTensor(self.roberta2[vid]),\
torch.FloatTensor(self.roberta3[vid]),\
torch.FloatTensor(self.roberta4[vid]),\
torch.FloatTensor(self.xIntent[vid]),\
torch.FloatTensor(self.xAttr[vid]),\
torch.FloatTensor(self.xNeed[vid]),\
torch.FloatTensor(self.xWant[vid]),\
torch.FloatTensor(self.xEffect[vid]),\
torch.FloatTensor(self.xReact[vid]),\
torch.FloatTensor(self.oWant[vid]),\
torch.FloatTensor(self.oEffect[vid]),\
torch.FloatTensor(self.oReact[vid]),\
torch.FloatTensor(self.speakers[vid]),\
torch.FloatTensor([1]*len(self.labels[vid])),\
torch.LongTensor(self.labels[vid]),\
vid
def __len__(self):
return self.len
def collate_fn(self, data):
dat = pd.DataFrame(data)
return [pad_sequence(dat[i]) if i < 14 else pad_sequence(dat[i], True) if i < 16 else dat[i].tolist() for i in dat]
class DailyDialogueRobertaCometDataset(Dataset):
def __init__(self, split):
self.speakers, self.labels, \
self.roberta1, self.roberta2, self.roberta3, self.roberta4, \
self.sentences, self.trainIds, self.testIds, self.validIds \
= pickle.load(open('dailydialog/dailydialog_features_roberta.pkl', 'rb'), encoding='latin1')
self.xIntent, self.xAttr, self.xNeed, self.xWant, self.xEffect, self.xReact, self.oWant, self.oEffect, self.oReact \
= pickle.load(open('dailydialog/dailydialog_features_comet.pkl', 'rb'), encoding='latin1')
if split == 'train':
self.keys = [x for x in self.trainIds]
elif split == 'test':
self.keys = [x for x in self.testIds]
elif split == 'valid':
self.keys = [x for x in self.validIds]
self.len = len(self.keys)
def __getitem__(self, index):
vid = self.keys[index]
return torch.FloatTensor(self.roberta1[vid]),\
torch.FloatTensor(self.roberta2[vid]),\
torch.FloatTensor(self.roberta3[vid]),\
torch.FloatTensor(self.roberta4[vid]),\
torch.FloatTensor(self.xIntent[vid]),\
torch.FloatTensor(self.xAttr[vid]),\
torch.FloatTensor(self.xNeed[vid]),\
torch.FloatTensor(self.xWant[vid]),\
torch.FloatTensor(self.xEffect[vid]),\
torch.FloatTensor(self.xReact[vid]),\
torch.FloatTensor(self.oWant[vid]),\
torch.FloatTensor(self.oEffect[vid]),\
torch.FloatTensor(self.oReact[vid]),\
torch.FloatTensor([[1, 0] if x == '0' else [0, 1] for x in self.speakers[vid]]),\
torch.FloatTensor([1]*len(self.labels[vid])),\
torch.LongTensor(self.labels[vid]),\
vid
def __len__(self):
return self.len
def collate_fn(self, data):
dat = pd.DataFrame(data)
return [pad_sequence(dat[i]) if i < 14 else pad_sequence(dat[i], True) if i < 16 else dat[i].tolist() for i in dat]
class EmoryNLPRobertaCometDataset(Dataset):
def __init__(self, split, classify='emotion'):
'''
label index mapping = {'Joyful': 0, 'Mad': 1, 'Peaceful': 2, 'Neutral': 3, 'Sad': 4, 'Powerful': 5, 'Scared': 6}
'''
self.speakers, self.emotion_labels, \
self.roberta1, self.roberta2, self.roberta3, self.roberta4, \
self.sentences, self.trainId, self.testId, self.validId \
= pickle.load(open('emorynlp/emorynlp_features_roberta.pkl', 'rb'), encoding='latin1')
sentiment_labels = {}
for item in self.emotion_labels:
array = []
# 0 negative, 1 neutral, 2 positive
for e in self.emotion_labels[item]:
if e in [1, 4, 6]:
array.append(0)
elif e == 3:
array.append(1)
elif e in [0, 2, 5]:
array.append(2)
sentiment_labels[item] = array
self.xIntent, self.xAttr, self.xNeed, self.xWant, self.xEffect, self.xReact, self.oWant, self.oEffect, self.oReact \
= pickle.load(open('emorynlp/emorynlp_features_comet.pkl', 'rb'), encoding='latin1')
if split == 'train':
self.keys = [x for x in self.trainId]
elif split == 'test':
self.keys = [x for x in self.testId]
elif split == 'valid':
self.keys = [x for x in self.validId]
if classify == 'emotion':
self.labels = self.emotion_labels
elif classify == 'sentiment':
self.labels = sentiment_labels
self.len = len(self.keys)
def __getitem__(self, index):
vid = self.keys[index]
return torch.FloatTensor(self.roberta1[vid]),\
torch.FloatTensor(self.roberta2[vid]),\
torch.FloatTensor(self.roberta3[vid]),\
torch.FloatTensor(self.roberta4[vid]),\
torch.FloatTensor(self.xIntent[vid]),\
torch.FloatTensor(self.xAttr[vid]),\
torch.FloatTensor(self.xNeed[vid]),\
torch.FloatTensor(self.xWant[vid]),\
torch.FloatTensor(self.xEffect[vid]),\
torch.FloatTensor(self.xReact[vid]),\
torch.FloatTensor(self.oWant[vid]),\
torch.FloatTensor(self.oEffect[vid]),\
torch.FloatTensor(self.oReact[vid]),\
torch.FloatTensor([[1, 0] if x == '0' else [0, 1] for x in self.speakers[vid]]),\
torch.FloatTensor([1]*len(self.labels[vid])),\
torch.LongTensor(self.labels[vid]),\
vid
def __len__(self):
return self.len
def collate_fn(self, data):
dat = pd.DataFrame(data)
return [pad_sequence(dat[i]) if i < 14 else pad_sequence(dat[i], True) if i < 16 else dat[i].tolist() for i in dat]
| 40.474138
| 124
| 0.585942
| 1,126
| 9,390
| 4.808171
| 0.102131
| 0.177318
| 0.196528
| 0.208164
| 0.893055
| 0.881049
| 0.839675
| 0.839675
| 0.823421
| 0.823421
| 0
| 0.014613
| 0.271246
| 9,390
| 231
| 125
| 40.649351
| 0.77656
| 0.026411
| 0
| 0.777778
| 0
| 0
| 0.049383
| 0.031746
| 0
| 0
| 0
| 0
| 0
| 1
| 0.088889
| false
| 0
| 0.027778
| 0.022222
| 0.205556
| 0
| 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
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
f1d776057835c0eb11306bfbe3bec382b0cc4ffd
| 219
|
py
|
Python
|
resttools/tests.py
|
mattjmuw/iam-resttools
|
9062656b0341c5d13999bdf812ae3b1962e1d4dc
|
[
"Apache-2.0"
] | 3
|
2016-06-14T19:23:03.000Z
|
2018-11-08T02:05:39.000Z
|
resttools/tests.py
|
mattjmuw/iam-resttools
|
9062656b0341c5d13999bdf812ae3b1962e1d4dc
|
[
"Apache-2.0"
] | 7
|
2016-03-25T03:46:16.000Z
|
2017-07-17T17:54:05.000Z
|
resttools/tests.py
|
mattjmuw/iam-resttools
|
9062656b0341c5d13999bdf812ae3b1962e1d4dc
|
[
"Apache-2.0"
] | 3
|
2015-09-10T23:56:14.000Z
|
2016-06-20T21:28:16.000Z
|
# various nose tests
from nose.tools import *
import resttools.test.test_settings as settings
from resttools.test.irws import IRWS_Test
from resttools.test.nws import NWS_Test
from resttools.test.gws import GWS_Test
| 21.9
| 47
| 0.826484
| 35
| 219
| 5.057143
| 0.371429
| 0.293785
| 0.288136
| 0.237288
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.123288
| 219
| 9
| 48
| 24.333333
| 0.921875
| 0.082192
| 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
|
f1faa62e726a20e414e987aca5ec00d63568f460
| 5,465
|
py
|
Python
|
test/unit/plugin/data/browser_azure_data.py
|
daltonconley/amazon-redshift-python-driver
|
d862cccc75a521b68a4ba3f1cb5de3b83f9c072b
|
[
"Apache-2.0",
"BSD-3-Clause"
] | 125
|
2020-11-04T19:04:22.000Z
|
2022-03-26T15:16:34.000Z
|
test/unit/plugin/data/browser_azure_data.py
|
daltonconley/amazon-redshift-python-driver
|
d862cccc75a521b68a4ba3f1cb5de3b83f9c072b
|
[
"Apache-2.0",
"BSD-3-Clause"
] | 65
|
2020-11-06T20:07:33.000Z
|
2022-03-28T23:38:14.000Z
|
test/unit/plugin/data/browser_azure_data.py
|
daltonconley/amazon-redshift-python-driver
|
d862cccc75a521b68a4ba3f1cb5de3b83f9c072b
|
[
"Apache-2.0",
"BSD-3-Clause"
] | 33
|
2020-11-08T17:32:43.000Z
|
2022-03-17T19:57:00.000Z
|
code: str = "helloworld"
state: str = "abcdefghij"
valid_response: bytes = (
b"POST /redshift/ HTTP/1.1\r\nHost: localhost:51187\r\nConnection: keep-alive\r\nContent-Length: 695\r\n"
b"Cache-Control: max-age=0\r\nUpgrade-Insecure-Requests: 1\r\nOrigin: null\r\n"
b"Content-Type: application/x-www-form-urlencoded\r\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
b"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36\r\nAccept: text/html,"
b"application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,"
b"application/signed-exchange;v=b3;q=0.9\r\nSec-Fetch-Site: cross-site\r\nSec-Fetch-Mode: navigate\r\n"
b"Sec-Fetch-Dest: document\r\nAccept-Encoding: gzip, deflate, br\r\nAccept-Language: en-US,en;q=0.9\r\n"
b"\r\ncode=" + code.encode("utf-8") + b"&state=" + state.encode("utf-8") + b"&session_state=hooplah"
)
missing_state_response: bytes = (
b"POST /redshift/ HTTP/1.1\r\nHost: localhost:51187\r\nConnection: keep-alive\r\nContent-Length: 695\r\n"
b"Cache-Control: max-age=0\r\nUpgrade-Insecure-Requests: 1\r\nOrigin: null\r\n"
b"Content-Type: application/x-www-form-urlencoded\r\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
b"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36\r\nAccept: text/html,"
b"application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,"
b"application/signed-exchange;v=b3;q=0.9\r\nSec-Fetch-Site: cross-site\r\nSec-Fetch-Mode: navigate\r\n"
b"Sec-Fetch-Dest: document\r\nAccept-Encoding: gzip, deflate, br\r\nAccept-Language: en-US,en;q=0.9\r\n"
b"\r\ncode=" + code.encode("utf-8")
)
mismatched_state_response: bytes = (
b"POST /redshift/ HTTP/1.1\r\nHost: localhost:51187\r\nConnection: keep-alive\r\nContent-Length: 695\r\n"
b"Cache-Control: max-age=0\r\nUpgrade-Insecure-Requests: 1\r\nOrigin: null\r\n"
b"Content-Type: application/x-www-form-urlencoded\r\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
b"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36\r\nAccept: text/html,"
b"application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,"
b"application/signed-exchange;v=b3;q=0.9\r\nSec-Fetch-Site: cross-site\r\nSec-Fetch-Mode: navigate\r\n"
b"Sec-Fetch-Dest: document\r\nAccept-Encoding: gzip, deflate, br\r\nAccept-Language: en-US,en;q=0.9\r\n"
b"\r\ncode=" + code.encode("utf-8") + b"&state=" + state[::-1].encode("utf-8") + b"&session_state=hooplah"
)
missing_code_response: bytes = (
b"POST /redshift/ HTTP/1.1\r\nHost: localhost:51187\r\nConnection: keep-alive\r\nContent-Length: 695\r\n"
b"Cache-Control: max-age=0\r\nUpgrade-Insecure-Requests: 1\r\nOrigin: null\r\n"
b"Content-Type: application/x-www-form-urlencoded\r\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
b"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36\r\nAccept: text/html,"
b"application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,"
b"application/signed-exchange;v=b3;q=0.9\r\nSec-Fetch-Site: cross-site\r\nSec-Fetch-Mode: navigate\r\n"
b"Sec-Fetch-Dest: document\r\nAccept-Encoding: gzip, deflate, br\r\nAccept-Language: en-US,en;q=0.9\r\n"
b"&state=" + state.encode("utf-8") + b"&session_state=hooplah"
)
empty_code_response: bytes = (
b"POST /redshift/ HTTP/1.1\r\nHost: localhost:51187\r\nConnection: keep-alive\r\nContent-Length: 695\r\n"
b"Cache-Control: max-age=0\r\nUpgrade-Insecure-Requests: 1\r\nOrigin: null\r\n"
b"Content-Type: application/x-www-form-urlencoded\r\nUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
b"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36\r\nAccept: text/html,"
b"application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,"
b"application/signed-exchange;v=b3;q=0.9\r\nSec-Fetch-Site: cross-site\r\nSec-Fetch-Mode: navigate\r\n"
b"Sec-Fetch-Dest: document\r\nAccept-Encoding: gzip, deflate, br\r\nAccept-Language: en-US,en;q=0.9\r\n"
b"\r\ncode=" + b"&state=" + state.encode("utf-8") + b"&session_state=hooplah"
)
saml_response = b"my_access_token"
valid_json_response: dict = {
"token_type": "Bearer",
"expires_in": "3599",
"ext_expires_in": "3599",
"expires_on": "1602782647",
"resource": "spn:1234567891011121314151617181920",
"access_token": "bXlfYWNjZXNzX3Rva2Vu", # base64.urlsafe_64encode(saml_response)
"issued_token_type": "urn:ietf:params:oauth:token-type:saml2",
"refresh_token": "my_refresh_token",
"id_token": "my_id_token",
}
json_response_no_access_token: dict = {
"token_type": "Bearer",
"expires_in": "3599",
"ext_expires_in": "3599",
"expires_on": "1602782647",
"resource": "spn:1234567891011121314151617181920",
"issued_token_type": "urn:ietf:params:oauth:token-type:saml2",
"refresh_token": "my_refresh_token",
"id_token": "my_id_token",
}
json_response_empty_access_token: dict = {
"token_type": "Bearer",
"expires_in": "3599",
"ext_expires_in": "3599",
"expires_on": "1602782647",
"resource": "spn:1234567891011121314151617181920",
"access_token": "",
"issued_token_type": "urn:ietf:params:oauth:token-type:saml2",
"refresh_token": "my_refresh_token",
"id_token": "my_id_token",
}
| 57.526316
| 118
| 0.702836
| 925
| 5,465
| 4.067027
| 0.142703
| 0.010633
| 0.015949
| 0.010633
| 0.957735
| 0.957735
| 0.957735
| 0.957735
| 0.946039
| 0.946039
| 0
| 0.081964
| 0.109241
| 5,465
| 94
| 119
| 58.138298
| 0.690838
| 0.006953
| 0
| 0.694118
| 0
| 0.411765
| 0.785806
| 0.418802
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
9e7269b9467e60abcfc7103bdad2438c4ebd4462
| 662
|
py
|
Python
|
minitorch/__init__.py
|
chris-tng/minitorch
|
f5facd366aac6a4d3a437796c43ac02a9b7069ff
|
[
"MIT"
] | null | null | null |
minitorch/__init__.py
|
chris-tng/minitorch
|
f5facd366aac6a4d3a437796c43ac02a9b7069ff
|
[
"MIT"
] | null | null | null |
minitorch/__init__.py
|
chris-tng/minitorch
|
f5facd366aac6a4d3a437796c43ac02a9b7069ff
|
[
"MIT"
] | null | null | null |
try:
from .tensor_data import * # noqa: F401,F403
from .tensor import * # noqa: F401,F403
from .tensor_ops import * # noqa: F401,F403
from .operators import * # noqa: F401,F403
# from .util import * # noqa: F401,F403
# from .scalar import *
from .module import * # noqa: F401,F403
from .autodiff import * # noqa: F401,F403
except:
from tensor_data import * # noqa: F401,F403
from tensor import * # noqa: F401,F403
from tensor_ops import * # noqa: F401,F403
from operators import *
# from util import *
# from scalar import *
from module import *
from autodiff import * # noqa: F401,F403
| 34.842105
| 49
| 0.635952
| 87
| 662
| 4.793103
| 0.172414
| 0.263789
| 0.369305
| 0.47482
| 0.935252
| 0.82494
| 0.527578
| 0.527578
| 0.527578
| 0.527578
| 0
| 0.136364
| 0.268882
| 662
| 18
| 50
| 36.777778
| 0.725207
| 0.391239
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.857143
| 0
| 0.857143
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
9eae16dbd2c4a83aa006ebbbb319f4013cd9ddab
| 334
|
py
|
Python
|
tests/test_fdf_identifier.py
|
tobigrimm/fdfgen
|
3061181a6109e6cf89a415d8eb51969139d3fab7
|
[
"BSD-3-Clause"
] | 130
|
2015-01-20T04:45:13.000Z
|
2022-03-06T12:19:27.000Z
|
tests/test_fdf_identifier.py
|
tobigrimm/fdfgen
|
3061181a6109e6cf89a415d8eb51969139d3fab7
|
[
"BSD-3-Clause"
] | 37
|
2015-02-04T15:29:39.000Z
|
2021-12-29T10:12:45.000Z
|
tests/test_fdf_identifier.py
|
tobigrimm/fdfgen
|
3061181a6109e6cf89a415d8eb51969139d3fab7
|
[
"BSD-3-Clause"
] | 30
|
2015-01-13T23:56:00.000Z
|
2021-12-17T20:03:50.000Z
|
import fdfgen
def test_identifier_with_slash():
expected_identifier = b'/Off'
result = fdfgen.FDFIdentifier('/Off').value
assert result == expected_identifier
def test_identifier_without_slash():
expected_identifier = b'/Off'
result = fdfgen.FDFIdentifier('Off').value
assert result == expected_identifier
| 23.857143
| 47
| 0.739521
| 38
| 334
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0
| 8
|
9eba5713a6461d3d5744cf94be23dcd56986d703
| 38,294
|
py
|
Python
|
pynetdicom3/tests/test_presentation.py
|
mcgregorandrew/pynetdicom3
|
1c798f9b0ad086cf0a8d0619e57f2bc2fbbf13f1
|
[
"MIT"
] | null | null | null |
pynetdicom3/tests/test_presentation.py
|
mcgregorandrew/pynetdicom3
|
1c798f9b0ad086cf0a8d0619e57f2bc2fbbf13f1
|
[
"MIT"
] | null | null | null |
pynetdicom3/tests/test_presentation.py
|
mcgregorandrew/pynetdicom3
|
1c798f9b0ad086cf0a8d0619e57f2bc2fbbf13f1
|
[
"MIT"
] | 2
|
2020-09-27T06:41:41.000Z
|
2021-02-07T06:53:02.000Z
|
"""Tests for the presentation module."""
import logging
import sys
import pytest
from pydicom._uid_dict import UID_dictionary
from pydicom.uid import UID
from pynetdicom3 import StoragePresentationContexts
from pynetdicom3.presentation import (
PresentationContext,
PresentationService,
DEFAULT_TRANSFER_SYNTAXES,
VerificationPresentationContexts,
StoragePresentationContexts,
QueryRetrievePresentationContexts,
BasicWorklistManagementPresentationContexts,
_build_context,
)
@pytest.fixture(params=[
(1, '1.1.1', ['1.2.840.10008.1.2']),
(1, '1.1.1', ['1.2.840.10008.1.2', '1.2.840.10008.1.2.1']),
(1, '1.1.1', ['1.2.840.10008.1.2', '1.2.840.10008.1.2.1', '1.2.3']),
(1, '1.1.1', []),
(1, '1.2.840.10008.1.2.1.1.3', ['1.2.3']),
(1, '1.2.840.10008.1.2.1.1.3', ['1.2.3', '1.2.840.10008.1.2'])
])
def good_init(request):
"""Good init values."""
return request.param
class TestPresentationContext(object):
"""Test the PresentationContext class"""
def test_setter(self, good_init):
"""Test the presentation context class init"""
(context_id, abs_syn, tran_syn) = good_init
pc = PresentationContext()
pc.context_id = context_id
pc.abstract_syntax = abs_syn
pc.transfer_syntax = tran_syn
assert pc.context_id == context_id
assert pc.abstract_syntax == abs_syn
assert pc.transfer_syntax == tran_syn
assert pc._scu_role is None
assert pc._scp_role is None
assert pc.result is None
def test_add_transfer_syntax(self):
"""Test adding transfer syntaxes"""
pc = PresentationContext()
pc.context_id = 1
pc.add_transfer_syntax('1.2.840.10008.1.2')
pc.add_transfer_syntax(b'1.2.840.10008.1.2.1')
pc.add_transfer_syntax(UID('1.2.840.10008.1.2.2'))
pc.add_transfer_syntax(UID(''))
# Test adding an invalid value
pc.add_transfer_syntax(1234)
assert 1234 not in pc.transfer_syntax
@pytest.mark.skipif(sys.version_info[:2] == (3, 4), reason='pytest missing caplog')
def test_add_transfer_syntax_nonconformant(self, caplog):
"""Test adding non-conformant transfer syntaxes"""
caplog.set_level(logging.DEBUG, logger='pynetdicom3.presentation')
pc = PresentationContext()
pc.context_id = 1
pc.add_transfer_syntax('1.2.3.')
assert ("A non-conformant UID has been added to 'transfer_syntax'"
in caplog.text)
pc.add_transfer_syntax('1.2.840.10008.1.1')
assert ("A UID has been added to 'transfer_syntax' that is not a "
"transfer syntax" in caplog.text)
def test_add_private_transfer_syntax(self):
"""Test adding private transfer syntaxes"""
pc = PresentationContext()
pc.context_id = 1
pc.add_transfer_syntax('2.16.840.1.113709.1.2.2')
assert '2.16.840.1.113709.1.2.2' in pc._transfer_syntax
pc.transfer_syntax = ['2.16.840.1.113709.1.2.1']
assert '2.16.840.1.113709.1.2.1' in pc._transfer_syntax
def test_add_transfer_syntax_duplicate(self):
"""Test add_transfer_syntax with a duplicate UID"""
pc = PresentationContext()
pc.add_transfer_syntax('1.3')
pc.add_transfer_syntax('1.3')
assert pc.transfer_syntax == ['1.3']
def test_equality(self):
"""Test presentation context equality"""
pc_a = PresentationContext()
pc_a.context_id = 1
pc_a.abstract_syntax = '1.1.1'
pc_a.transfer_syntax = ['1.2.840.10008.1.2']
pc_b = PresentationContext()
pc_b.context_id = 1
pc_b.abstract_syntax = '1.1.1'
pc_b.transfer_syntax = ['1.2.840.10008.1.2']
assert pc_a == pc_a
assert pc_a == pc_b
assert not pc_a != pc_b
assert not pc_a != pc_a
pc_a._scp_role = True
assert not pc_a == pc_b
pc_b._scp_role = True
assert pc_a == pc_b
pc_a._scu_role = True
assert not pc_a == pc_b
pc_b._scu_role = True
assert pc_a == pc_b
assert not 'a' == pc_b
def test_string_output(self):
"""Test string output"""
pc = PresentationContext()
pc.context_id = 1
pc.abstract_syntax = '1.1.1'
pc.transfer_syntax = ['1.2.840.10008.1.2', '1.2.3']
pc._scp_role = True
pc._scu_role = False
pc.result = 0x02
assert '1.1.1' in pc.__str__()
assert 'Implicit' in pc.__str__()
assert 'Provider Rejected' in pc.__str__()
def test_context_id(self):
"""Test setting context_id."""
pc = PresentationContext()
pc.context_id = 1
assert pc.context_id == 1
pc.context_id = 255
assert pc.context_id == 255
with pytest.raises(ValueError):
pc.context_id = 0
with pytest.raises(ValueError):
pc.context_id = 256
with pytest.raises(ValueError):
pc.context_id = 12
def test_abstract_syntax(self):
"""Test abstract syntax setter"""
pc = PresentationContext()
pc.context_id = 1
pc.abstract_syntax = '1.1.1'
assert pc.abstract_syntax == UID('1.1.1')
assert isinstance(pc.abstract_syntax, UID)
pc.abstract_syntax = b'1.2.1'
assert pc.abstract_syntax == UID('1.2.1')
assert isinstance(pc.abstract_syntax, UID)
pc.abstract_syntax = UID('1.3.1')
assert pc.abstract_syntax == UID('1.3.1')
assert isinstance(pc.abstract_syntax, UID)
def test_abstract_syntax_raises(self):
"""Test exception raised if invalid abstract syntax"""
pc = PresentationContext()
with pytest.raises(TypeError):
pc.abstract_syntax = 1234
@pytest.mark.skipif(sys.version_info[:2] == (3, 4), reason='pytest missing caplog')
def test_abstract_syntax_nonconformant(self, caplog):
"""Test adding non-conformant abstract syntaxes"""
caplog.set_level(logging.DEBUG, logger='pynetdicom3.presentation')
pc = PresentationContext()
pc.context_id = 1
pc.abstract_syntax = UID('1.4.1.')
assert pc.abstract_syntax == UID('1.4.1.')
assert isinstance(pc.abstract_syntax, UID)
assert ("'abstract_syntax' set to a non-conformant UID" in caplog.text)
def test_transfer_syntax(self):
"""Test transfer syntax setter"""
pc = PresentationContext()
pc.context_id = 1
pc.transfer_syntax = ['1.2.840.10008.1.2']
assert pc.transfer_syntax[0] == UID('1.2.840.10008.1.2')
assert isinstance(pc.transfer_syntax[0], UID)
pc.transfer_syntax = [b'1.2.840.10008.1.2.1']
assert pc.transfer_syntax[0] == UID('1.2.840.10008.1.2.1')
assert isinstance(pc.transfer_syntax[0], UID)
pc.transfer_syntax = [UID('1.2.840.10008.1.2.2')]
assert pc.transfer_syntax[0] == UID('1.2.840.10008.1.2.2')
assert isinstance(pc.transfer_syntax[0], UID)
with pytest.raises(TypeError):
pc.transfer_syntax = UID('1.4.1')
pc.transfer_syntax = [1234, UID('1.4.1')]
assert pc.transfer_syntax == [UID('1.4.1')]
def test_transfer_syntax_duplicate(self):
"""Test the transfer_syntax setter with duplicate UIDs."""
pc = PresentationContext()
pc.transfer_syntax = ['1.3', '1.3']
assert pc.transfer_syntax == ['1.3']
@pytest.mark.skipif(sys.version_info[:2] == (3, 4), reason='pytest missing caplog')
def test_transfer_syntax_nonconformant(self, caplog):
"""Test setting non-conformant transfer syntaxes"""
caplog.set_level(logging.DEBUG, logger='pynetdicom3.presentation')
pc = PresentationContext()
pc.context_id = 1
pc.transfer_syntax = ['1.4.1.', '1.2.840.10008.1.2']
assert pc.transfer_syntax == ['1.4.1.', '1.2.840.10008.1.2']
assert ("A non-conformant UID has been added to 'transfer_syntax'" in
caplog.text)
def test_status(self):
"""Test presentation context status"""
pc = PresentationContext()
pc.context_id = 1
statuses = [None, 0x00, 0x01, 0x02, 0x03, 0x04, 0x05]
results = ['Pending', 'Accepted', 'User Rejected', 'Provider Rejected',
'Abstract Syntax Not Supported',
'Transfer Syntax(es) Not Supported', 'Unknown']
for status, result in zip(statuses, results):
pc.result = status
assert pc.status == result
def test_tuple(self):
"""Test the .as_tuple"""
context = PresentationContext()
context.context_id = 3
context.abstract_syntax = '1.2.840.10008.1.1'
context.transfer_syntax = ['1.2.840.10008.1.2']
out = context.as_tuple
assert out.context_id == 3
assert out.abstract_syntax == '1.2.840.10008.1.1'
assert out.transfer_syntax == '1.2.840.10008.1.2'
class TestPresentationServiceAcceptor(object):
"""Tests for the PresentationService class when running as acceptor."""
def setup(self):
ps = PresentationService()
self.test_func = ps.negotiate_as_acceptor
def test_no_req_no_acc(self):
"""Test negotiation with no contexts."""
result = self.test_func([], [])
assert result == []
def test_one_req_no_acc(self):
"""Test negotiation with one requestor, no acceptor contexts."""
context = PresentationContext()
context.context_id = 1
context.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context.transfer_syntax = ['1.2.840.10008.1.2']
result = self.test_func([context], [])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.transfer_syntax[0] == '1.2.840.10008.1.2'
assert len(context.transfer_syntax) == 1
assert context.result == 0x03
def test_no_req_one_acc(self):
"""Test negotiation with no requestor, one acceptor contexts."""
context = PresentationContext()
context.context_id = 1
context.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context.transfer_syntax = ['1.2.840.10008.1.2']
result = self.test_func([], [context])
assert result == []
def test_dupe_abs_req_no_acc(self):
"""Test negotiation with duplicate requestor, no acceptor contexts."""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2']
context_b = PresentationContext()
context_b.context_id = 3
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1']
context_c = PresentationContext()
context_c.context_id = 5
context_c.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_c.transfer_syntax = ['1.2.840.10008.1.2.2']
context_list = [context_a, context_b, context_c]
result = self.test_func(context_list, [])
assert len(result) == 3
for context in result:
assert context.context_id in [1, 3, 5]
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x03
def test_dupe_abs_req(self):
"""Test negotiation with duplicate requestor, no acceptor contexts."""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2']
context_b = PresentationContext()
context_b.context_id = 3
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1']
context_c = PresentationContext()
context_c.context_id = 5
context_c.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_c.transfer_syntax = ['1.2.840.10008.1.2.2']
t_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
context_d = PresentationContext()
context_d.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_d.transfer_syntax = t_syntax
context_list = [context_a, context_b, context_c]
result = self.test_func(context_list, [context_d])
assert len(result) == 3
for ii, context in enumerate(result):
assert context.context_id in [1, 3, 5]
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == [t_syntax[ii]]
def test_one_req_one_acc_match(self):
"""Test negotiation one req/acc, matching accepted."""
context = PresentationContext()
context.context_id = 1
context.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context.transfer_syntax = ['1.2.840.10008.1.2']
result = self.test_func([context], [context])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.840.10008.1.2']
def test_one_req_one_acc_accept_trans(self):
"""Test negotiation one req/acc, matching accepted, multi trans."""
context = PresentationContext()
context.context_id = 1
context.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
result = self.test_func([context], [context])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.840.10008.1.2']
def test_one_req_one_acc_accept_trans_diff(self):
"""Test negotiation one req/acc, matching accepted, multi trans."""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.2']
result = self.test_func([context_a], [context_b])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.840.10008.1.2.2']
def test_one_req_one_acc_reject_trans(self):
"""Test negotiation one req/acc, matching rejected trans"""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.2']
result = self.test_func([context_a], [context_b])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x04
def test_one_req_one_acc_mismatch(self):
"""Test negotiation one req/acc, mismatched rejected"""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.4'
context_b.transfer_syntax = ['1.2.840.10008.1.2.2']
result = self.test_func([context_a], [context_b])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x03
def test_private_transfer_syntax(self):
"""Test negotiation with private transfer syntax"""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.3.4']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1',
'1.2.3.4']
result = self.test_func([context_a], [context_b])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.3.4']
def test_private_abstract_syntax(self):
"""Test negotiation with private abstract syntax"""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.3.4'
context_a.transfer_syntax = ['1.2.840.10008.1.2.1']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.3.4'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1']
result = self.test_func([context_a], [context_b])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.3.4'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.840.10008.1.2.1']
def test_typical(self):
"""Test a typical set of presentation context negotiations."""
req_contexts = []
for ii, context in enumerate(StoragePresentationContexts):
pc = PresentationContext()
pc.context_id = ii * 2 + 1
pc.abstract_syntax = context.abstract_syntax
pc.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
req_contexts.append(pc)
acc_contexts = []
for uid in UID_dictionary:
pc = PresentationContext()
pc.abstract_syntax = uid
pc.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
acc_contexts.append(pc)
results = self.test_func(req_contexts, acc_contexts)
assert len(results) == len(req_contexts)
for ii, context in enumerate(req_contexts):
assert results[ii].context_id == context.context_id
assert results[ii].abstract_syntax == context.abstract_syntax
if results[ii].abstract_syntax == '1.2.840.10008.5.1.4.1.1.1.1.1.1':
assert results[ii].result == 0x03
elif results[ii].abstract_syntax == '1.2.840.10008.5.1.1.4.1.1.3.1':
assert results[ii].result == 0x03
else:
assert results[ii].result == 0x00
assert results[ii].transfer_syntax == ['1.2.840.10008.1.2']
@pytest.mark.skip()
class TestPresentationServiceAcceptorWithRoleSelection(object):
"""Tests for the PresentationService as acceptor with role selection."""
@pytest.mark.parametrize("req, acc, out", [
((None, None), (None, None), (False, True)),
((None, None), (True, True), (False, True)),
((None, None), (True, False), (False, True)),
((None, None), (False, False), (False, True)),
((None, None), (False, True), (False, True)),
((True, True), (None, None), (False, True)),
((True, True), (True, True), (True, True)),
((True, True), (True, False), (True, True)),
((True, True), (False, False), (False, True)),
((True, True), (False, True), (False, True)),
((False, True), (None, None), (False, True)),
((False, True), (True, True), (False, True)),
((False, True), (True, False), (False, True)),
((False, True), (False, False), (False, True)),
((False, True), (False, True), (False, True)),
((False, False), (None, None), (False, True)),
((False, False), (True, True), (False, True)),
((False, False), (True, False), (False, False)),
((False, False), (False, False), (False, False)),
((False, False), (False, True), (False, True)),
((True, False), (None, None), (False, True)),
((True, False), (True, True), (True, True)),
((True, False), (True, False), (True, False)),
((True, False), (False, False), (False, False)),
((True, False), (False, True), (False, True)),
])
def test_scp_scu_role_negotiation(self, req, acc, out):
"""Test presentation service negotiation with role selection."""
# Rules!
# - If the requestor doesn't ask, then there is no reply
# - If the requestor is False then the reply is False
# - If the requestor is True then the reply is either True or False
# - If the acceptor is True then the reply is True
# - If the acceptor is False then the reply is False
# Note: if the requestor and acceptor can't agree then the default
# roles should be used, which as we are acceptor is SCP True, SCU False
rq = PresentationContext(1,
'1.2.3.4',
['1.2.840.10008.1.2'])
rq.SCP = req[0]
rq.SCU = req[1]
ac = PresentationContext(1,
'1.2.3.4',
['1.2.840.10008.1.2'])
ac.SCP = acc[0]
ac.SCU = acc[0]
result = self.test_func([rq], [ac])
assert result[0].abstract_syntax == '1.2.3.4'
assert result[0].transfer_syntax[0] == '1.2.840.10008.1.2'
assert result[0].SCP == out[0]
assert result[0].SCU == out[1]
assert result[0].result == 0x00
class TestPresentationServiceRequestor(object):
"""Tests for the PresentationService class when running as requestor."""
def setup(self):
ps = PresentationService()
self.test_acc = ps.negotiate_as_acceptor
self.test_func = ps.negotiate_as_requestor
def test_no_req_no_acc_raise(self):
"""Test negotiation with no contexts."""
with pytest.raises(ValueError):
result = self.test_func([], [])
def test_one_req_no_acc(self):
"""Test negotiation with one requestor, no acceptor contexts."""
context = PresentationContext()
context.context_id = 1
context.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context.transfer_syntax = ['1.2.840.10008.1.2']
result = self.test_func([context], [])
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.transfer_syntax[0] == '1.2.840.10008.1.2'
assert len(context.transfer_syntax) == 1
assert context.result == 0x02
def test_no_req_one_acc_raise(self):
"""Test negotiation with no requestor, one acceptor contexts."""
context = PresentationContext()
context.context_id = 1
context.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context.transfer_syntax = ['1.2.840.10008.1.2']
with pytest.raises(ValueError):
result = self.test_func([], [context])
def test_dupe_abs_req_no_acc(self):
"""Test negotiation with duplicate requestor, no acceptor contexts."""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2']
context_b = PresentationContext()
context_b.context_id = 3
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1']
context_c = PresentationContext()
context_c.context_id = 5
context_c.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_c.transfer_syntax = ['1.2.840.10008.1.2.2']
rq_contexts = [context_a, context_b, context_c]
acc_contexts = self.test_acc(rq_contexts, [])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func(rq_contexts, acc_contexts)
assert len(result) == 3
for context in result:
assert context.context_id in [1, 3, 5]
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x03
def test_dupe_abs_req(self):
"""Test negotiation with duplicate requestor, no acceptor contexts."""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2']
context_b = PresentationContext()
context_b.context_id = 3
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1']
context_c = PresentationContext()
context_c.context_id = 5
context_c.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_c.transfer_syntax = ['1.2.840.10008.1.2.2']
t_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
context_d = PresentationContext()
context_d.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_d.transfer_syntax = t_syntax
rq_contexts = [context_a, context_b, context_c]
acc_contexts = self.test_acc(rq_contexts, [context_d])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func(rq_contexts, acc_contexts)
assert len(result) == 3
for ii, context in enumerate(result):
assert context.context_id in [1, 3, 5]
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == [t_syntax[ii]]
def test_one_req_one_acc_match(self):
"""Test negotiation one req/acc, matching accepted."""
context = PresentationContext()
context.context_id = 1
context.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context.transfer_syntax = ['1.2.840.10008.1.2']
rq_contexts = [context]
acc_contexts = self.test_acc([context], [context])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func(rq_contexts, acc_contexts)
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.840.10008.1.2']
def test_one_req_one_acc_accept_trans(self):
"""Test negotiation one req/acc, matching accepted, multi trans."""
context = PresentationContext()
context.context_id = 1
context.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
rq_contexts = [context]
acc_contexts = self.test_acc([context], [context])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func(rq_contexts, acc_contexts)
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.840.10008.1.2']
def test_one_req_one_acc_accept_trans_diff(self):
"""Test negotiation one req/acc, matching accepted, multi trans."""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.2']
rq_contexts = [context_a]
acc_contexts = self.test_acc(rq_contexts, [context_b])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func(rq_contexts, acc_contexts)
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.840.10008.1.2.2']
def test_one_req_one_acc_reject_trans(self):
"""Test negotiation one req/acc, matching rejected trans"""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.2']
rq_contexts = [context_a]
acc_contexts = self.test_acc(rq_contexts, [context_b])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func(rq_contexts, acc_contexts)
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x04
def test_one_req_one_acc_mismatch(self):
"""Test negotiation one req/acc, mismatched rejected"""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.4'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1']
rq_contexts = [context_a]
acc_contexts = self.test_acc(rq_contexts, [context_b])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func(rq_contexts, acc_contexts)
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x03
def test_private_transfer_syntax(self):
"""Test negotiation with private transfer syntax"""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_a.transfer_syntax = ['1.2.3.4']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.840.10008.5.1.4.1.1.2'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1',
'1.2.3.4']
acc_contexts = self.test_acc([context_a], [context_b])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func([context_a], acc_contexts)
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.840.10008.5.1.4.1.1.2'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.3.4']
def test_private_abstract_syntax(self):
"""Test negotiation with private abstract syntax"""
context_a = PresentationContext()
context_a.context_id = 1
context_a.abstract_syntax = '1.2.3.4'
context_a.transfer_syntax = ['1.2.840.10008.1.2.1']
context_b = PresentationContext()
context_b.abstract_syntax = '1.2.3.4'
context_b.transfer_syntax = ['1.2.840.10008.1.2.1']
rq_contexts = [context_a]
acc_contexts = self.test_acc(rq_contexts, [context_b])
for context in acc_contexts:
context._abstract_syntax = None
result = self.test_func(rq_contexts, acc_contexts)
assert len(result) == 1
context = result[0]
assert context.context_id == 1
assert context.abstract_syntax == '1.2.3.4'
assert context.result == 0x00
assert context.transfer_syntax == ['1.2.840.10008.1.2.1']
def test_typical(self):
"""Test a typical set of presentation context negotiations."""
req_contexts = []
for ii, context in enumerate(StoragePresentationContexts):
pc = PresentationContext()
pc.context_id = ii * 2 + 1
pc.abstract_syntax = context.abstract_syntax
pc.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
req_contexts.append(pc)
acc_contexts = []
for uid in UID_dictionary:
pc = PresentationContext()
pc.abstract_syntax = uid
pc.transfer_syntax = ['1.2.840.10008.1.2',
'1.2.840.10008.1.2.1',
'1.2.840.10008.1.2.2']
acc_contexts.append(pc)
acc_contexts = self.test_acc(req_contexts, acc_contexts)
for context in acc_contexts:
context._abstract_syntax = None
results = self.test_func(req_contexts, acc_contexts)
assert len(results) == len(req_contexts)
for ii, context in enumerate(req_contexts):
assert results[ii].context_id == context.context_id
assert results[ii].abstract_syntax == context.abstract_syntax
if results[ii].abstract_syntax == '1.2.840.10008.5.1.4.1.1.1.1.1.1':
assert results[ii].result == 0x03
elif results[ii].abstract_syntax == '1.2.840.10008.5.1.1.4.1.1.3.1':
assert results[ii].result == 0x03
else:
assert results[ii].result == 0x00
assert results[ii].transfer_syntax == ['1.2.840.10008.1.2']
def test_default_transfer_syntaxes():
"""Test that the default transfer syntaxes are correct."""
assert len(DEFAULT_TRANSFER_SYNTAXES) == 3
assert '1.2.840.10008.1.2' in DEFAULT_TRANSFER_SYNTAXES
assert '1.2.840.10008.1.2.1' in DEFAULT_TRANSFER_SYNTAXES
assert '1.2.840.10008.1.2.2' in DEFAULT_TRANSFER_SYNTAXES
def test_build_context():
"""Test _build_context()"""
context = _build_context('1.2.840.10008.1.1')
assert context.abstract_syntax == '1.2.840.10008.1.1'
assert context.transfer_syntax == DEFAULT_TRANSFER_SYNTAXES
assert context.context_id is None
context = _build_context('1.2.840.10008.1.1', ['1.2.3', '4.5.6'])
assert context.abstract_syntax == '1.2.840.10008.1.1'
assert context.transfer_syntax == ['1.2.3', '4.5.6']
assert context.context_id is None
class TestServiceContexts(object):
def test_verification(self):
"""Test the verification service presentation contexts."""
contexts = VerificationPresentationContexts
assert len(contexts) == 1
assert contexts[0].abstract_syntax == '1.2.840.10008.1.1'
assert contexts[0].transfer_syntax == DEFAULT_TRANSFER_SYNTAXES
assert contexts[0].context_id is None
def test_storage(self):
"""Test the storage service presentation contexts"""
contexts = StoragePresentationContexts
assert len(contexts) == 117
for context in contexts:
assert context.transfer_syntax == DEFAULT_TRANSFER_SYNTAXES
assert context.context_id is None
assert contexts[0].abstract_syntax == '1.2.840.10008.5.1.4.1.1.1'
assert contexts[80].abstract_syntax == '1.2.840.10008.5.1.4.1.1.78.8'
assert contexts[-1].abstract_syntax == '1.2.840.10008.5.1.4.45.1'
def test_qr(self):
"""Test the query/retrieve service presentation contexts."""
contexts = QueryRetrievePresentationContexts
assert len(contexts) == 9
for context in contexts:
assert context.transfer_syntax == DEFAULT_TRANSFER_SYNTAXES
assert context.context_id is None
assert contexts[0].abstract_syntax == '1.2.840.10008.5.1.4.1.2.1.1'
assert contexts[4].abstract_syntax == '1.2.840.10008.5.1.4.1.2.2.2'
assert contexts[-1].abstract_syntax == '1.2.840.10008.5.1.4.1.2.3.3'
def test_worklist(self):
"""Test the basic worklist service presentation contexts."""
contexts = BasicWorklistManagementPresentationContexts
assert len(contexts) == 1
assert contexts[0].transfer_syntax == DEFAULT_TRANSFER_SYNTAXES
assert contexts[0].context_id is None
assert contexts[0].abstract_syntax == '1.2.840.10008.5.1.4.31'
| 41.132116
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0
| 7
|
7b5e355f925abd3a5bf40cc8764623675931c837
| 8,740
|
py
|
Python
|
tool/decbash.py
|
080HACKER/FB-COVID-19
|
2381419791315dead44c15895e65c4c1389c1ac7
|
[
"Apache-2.0"
] | 3
|
2020-04-08T10:06:22.000Z
|
2021-01-01T14:03:01.000Z
|
tool/decbash.py
|
080HACKER/FB-COVID-19
|
2381419791315dead44c15895e65c4c1389c1ac7
|
[
"Apache-2.0"
] | null | null | null |
tool/decbash.py
|
080HACKER/FB-COVID-19
|
2381419791315dead44c15895e65c4c1389c1ac7
|
[
"Apache-2.0"
] | 2
|
2021-02-19T17:57:23.000Z
|
2021-03-24T07:00:03.000Z
|
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**| * *s,\x00\x00\x00\x1b[91m| /_ | | \\||| \\_ | /| || __/ | |s*\x00\x00\x00\x1b[91m\\____\\_/ \\|\\____/\\_/\\_\\_/\\_/ \\_/s6\x00\x00\x00 \x1b[91m*\x1b[95mScipt Bashs5\x00\x00\x00\x1b[94m===================== \x1b[91m=====================s=\x00\x00\x00\x1b[91m[1]\x1b[97mEncript #\x1b[92mAuthor :\x1b[36;1mAsecC|~|eror404s3\x00\x00\x00\x1b[91m[0]\x1b[97mKeluar #\x1b[92mTeam :\x1b[36;1mHaxIDsE\x00\x00\x00 #\x1b[92mgithub :\x1b[36;1mhttps://github.com/muhammadfathulsG\x00\x00\x00 #\x1b[92mFacebook :\x1b[36;1mhttps://www.facebook.com/komaro.baes5\x00\x00\x00\x1b[92m===================== 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| 4,370
| 8,725
| 0.732952
| 1,877
| 8,740
| 3.384124
| 0.118274
| 0.315491
| 0.167191
| 0.088791
| 0.527078
| 0.465523
| 0.329503
| 0.286052
| 0.23961
| 0.204345
| 0
| 0.391854
| 0.022426
| 8,740
| 2
| 8,725
| 4,370
| 0.351592
| 0
| 0
| 0
| 0
| 0.5
| 0.995081
| 0.921176
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 11
|
7b737db8d554a267935656f842d6feff76b53fce
| 98
|
py
|
Python
|
tohu/v7/custom_generator/__init__.py
|
maxalbert/tohu
|
3adf0c58b13ef1e1d716d7d613484d2adc58fb60
|
[
"MIT"
] | 1
|
2019-03-07T19:58:45.000Z
|
2019-03-07T19:58:45.000Z
|
tohu/v7/custom_generator/__init__.py
|
maxalbert/tohu
|
3adf0c58b13ef1e1d716d7d613484d2adc58fb60
|
[
"MIT"
] | 9
|
2017-10-04T15:08:53.000Z
|
2021-02-02T21:51:41.000Z
|
tohu/v7/custom_generator/__init__.py
|
maxalbert/tohu
|
3adf0c58b13ef1e1d716d7d613484d2adc58fb60
|
[
"MIT"
] | null | null | null |
from .tohu_items_class import make_tohu_items_class
from .custom_generator import CustomGenerator
| 32.666667
| 51
| 0.897959
| 14
| 98
| 5.857143
| 0.642857
| 0.219512
| 0.341463
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081633
| 98
| 2
| 52
| 49
| 0.911111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
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| 0
| 0
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| 1
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| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| 1
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| 0
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
b519b0c8743d0b886b7ab72aa78ba0ad8ed635fa
| 79
|
py
|
Python
|
Buybuybuy/celery_tasks/config.py
|
zlyuzhi/C2B
|
eef74a55c1d654911c29e829e0a4dba43d43ec94
|
[
"MIT"
] | 2
|
2018-10-21T13:13:39.000Z
|
2018-10-23T04:19:00.000Z
|
Buybuybuy/celery_tasks/config.py
|
zlyuzhi/C2B
|
eef74a55c1d654911c29e829e0a4dba43d43ec94
|
[
"MIT"
] | null | null | null |
Buybuybuy/celery_tasks/config.py
|
zlyuzhi/C2B
|
eef74a55c1d654911c29e829e0a4dba43d43ec94
|
[
"MIT"
] | null | null | null |
# booker_url='redis://127.0.0.1:6379/15'
broker_url='redis://127.0.0.1:6379/15'
| 39.5
| 40
| 0.683544
| 18
| 79
| 2.888889
| 0.5
| 0.307692
| 0.423077
| 0.461538
| 0.769231
| 0.769231
| 0.769231
| 0.769231
| 0
| 0
| 0
| 0.311688
| 0.025316
| 79
| 2
| 41
| 39.5
| 0.363636
| 0.481013
| 0
| 0
| 0
| 0
| 0.625
| 0.625
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 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
| 0
| 0
| 0
| 0
| 0
|
0
| 12
|
b51c82e7243aaeefcb5d48f4bd346f8c5cfb6a59
| 3,683
|
py
|
Python
|
plugins/IngestAPI/DairycompDataIngest/dc_file_fix.py
|
OscarTHZhang/docker-airflow
|
d1e70b3084b1d80cefac6c42111a9a69df42c66a
|
[
"Apache-2.0"
] | null | null | null |
plugins/IngestAPI/DairycompDataIngest/dc_file_fix.py
|
OscarTHZhang/docker-airflow
|
d1e70b3084b1d80cefac6c42111a9a69df42c66a
|
[
"Apache-2.0"
] | null | null | null |
plugins/IngestAPI/DairycompDataIngest/dc_file_fix.py
|
OscarTHZhang/docker-airflow
|
d1e70b3084b1d80cefac6c42111a9a69df42c66a
|
[
"Apache-2.0"
] | null | null | null |
import csv
import logging
import os.path
import ntpath
import uuid
__author__ = "Steven Wangen"
__version__ = "1.0.1"
__email__ = "srwangen@wisc.edu"
__status__ = "Development"
logger = logging.getLogger(__name__)
# def fix_animal_file(filename):
# subprocess.call(["sed", "$d", filename], stdout=open(filename + ".fixed", "w"))
# return filename + ".fixed"
def fix_animal_file(in_filename):
num_columns = 0
filename, file_extension = os.path.splitext(in_filename)
out_filename = "/tmp/" + ntpath.basename(filename) + str(uuid.uuid4())
with open(out_filename, "w+") as out_csv:
csv_writer = csv.writer(out_csv, delimiter=',')
with open(in_filename) as in_csv:
csv_reader = csv.reader(in_csv, delimiter=',')
num_columns = 0
for row in csv_reader:
row.pop()
if row[0][0:5] != 'Total':
if num_columns == 0:
num_columns = len(row)
logger.debug("Set row count to: " + str(num_columns))
elif len(row) > num_columns:
logger.debug("unshrunk row: " + str(row))
shrunk_row = shrink_animal_row(row, num_columns)
logger.debug("shrunk row: " + str(shrunk_row))
elif len(row) < num_columns:
logger.error("Row has too few columns!")
logger.error("row = " + str(row))
exit(1)
for pos in range(len(row)):
row[pos] = row[pos].strip()
logger.debug("writing row: " + str(row))
csv_writer.writerow(row)
return out_filename
def shrink_animal_row(row, num_columns):
# how many extra rows?
extra_row_count = len(row) - num_columns
if extra_row_count > 0:
remark = row[15] + " "
for i in range(extra_row_count):
remark += row.pop(16)
row[8] = remark
return row
def fix_event_file(in_filename):
num_columns = 0
filename, file_extension = os.path.splitext(in_filename)
# out_filename = filename + file_extension + ".fixed"
out_filename = "/tmp/" + ntpath.basename(filename) + "-" + str(uuid.uuid4())
with open(out_filename, "w+") as out_csv:
csv_writer = csv.writer(out_csv, delimiter=',')
with open(in_filename) as in_csv:
csv_reader = csv.reader(in_csv, delimiter=',')
num_columns = 0
for row in csv_reader:
row.pop()
if num_columns == 0:
num_columns = len(row)
logger.debug("Set row count to: " + str(num_columns))
elif len(row) > num_columns:
logger.debug("unshrunk row: " + str(row))
shrunk_row = shrink_row(row, num_columns)
logger.debug("shrunk row: " + str(shrunk_row))
elif len(row) < num_columns:
logger.error("Row has too few columns!")
logger.error("row = " + str(row))
exit(1)
for pos in range(len(row)):
row[pos] = row[pos].strip()
logger.debug("writing row: " + str(row))
csv_writer.writerow(row)
return out_filename
def shrink_row(row, num_columns):
# how many extra rows?
extra_row_count = len(row) - num_columns
if extra_row_count > 0:
remark = row[8] + " "
for i in range(extra_row_count):
remark += row.pop(9)
row[8] = remark
return row
| 36.465347
| 85
| 0.536248
| 442
| 3,683
| 4.242081
| 0.196833
| 0.106667
| 0.069333
| 0.0512
| 0.8368
| 0.816533
| 0.810133
| 0.810133
| 0.810133
| 0.810133
| 0
| 0.010842
| 0.3489
| 3,683
| 101
| 86
| 36.465347
| 0.771059
| 0.064893
| 0
| 0.716049
| 0
| 0
| 0.071574
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.049383
| false
| 0
| 0.061728
| 0
| 0.160494
| 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
|
b578b55e540463c2453c8fc544a7697c64e6fdd0
| 154
|
py
|
Python
|
Python/Tests/GlassTests/PythonTests/Python2x/StepPythonToNative_Compare/py_mod.py
|
techkey/PTVS
|
8355e67eedd8e915ca49bd38a2f36172696fd903
|
[
"Apache-2.0"
] | 404
|
2019-05-07T02:21:57.000Z
|
2022-03-31T17:03:04.000Z
|
Python/Tests/GlassTests/PythonTests/Python2x/StepPythonToNative_Compare/py_mod.py
|
techkey/PTVS
|
8355e67eedd8e915ca49bd38a2f36172696fd903
|
[
"Apache-2.0"
] | 1,672
|
2019-05-06T21:09:38.000Z
|
2022-03-31T23:16:04.000Z
|
Python/Tests/GlassTests/PythonTests/Python2x/StepPythonToNative_Compare/py_mod.py
|
techkey/PTVS
|
8355e67eedd8e915ca49bd38a2f36172696fd903
|
[
"Apache-2.0"
] | 186
|
2019-05-13T03:17:37.000Z
|
2022-03-31T16:24:05.000Z
|
import cpp_mod
cpp_obj = cpp_mod.CppObj()
cpp_obj == cpp_obj
cpp_obj != cpp_obj
cpp_obj < cpp_obj
cpp_obj <= cpp_obj
cpp_obj > cpp_obj
cpp_obj >= cpp_obj
| 17.111111
| 26
| 0.75974
| 32
| 154
| 3.1875
| 0.15625
| 0.764706
| 1.058824
| 1.294118
| 0.705882
| 0.705882
| 0.705882
| 0.705882
| 0.705882
| 0.705882
| 0
| 0
| 0.149351
| 154
| 8
| 27
| 19.25
| 0.778626
| 0
| 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 | 1
| 1
| 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
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
b59aebfadbb73dc098e0352bb0edef3ce220b6b8
| 21,147
|
py
|
Python
|
proganomaly_modules/training_module/trainer/subclassed_models.py
|
ryangillard/P-CEAD
|
d4e95fa17112af07eb99cd581470bd3146d1c8e5
|
[
"Apache-2.0"
] | 6
|
2021-07-01T23:37:10.000Z
|
2022-02-19T03:12:41.000Z
|
proganomaly_modules/training_module/trainer/subclassed_models.py
|
ryangillard/P-CEAD
|
d4e95fa17112af07eb99cd581470bd3146d1c8e5
|
[
"Apache-2.0"
] | null | null | null |
proganomaly_modules/training_module/trainer/subclassed_models.py
|
ryangillard/P-CEAD
|
d4e95fa17112af07eb99cd581470bd3146d1c8e5
|
[
"Apache-2.0"
] | 2
|
2021-07-01T23:37:30.000Z
|
2021-12-21T18:19:21.000Z
|
# Copyright 2020 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow as tf
from . import image_to_vector_networks
from . import vector_to_image_networks
class Decoder(tf.keras.Model, vector_to_image_networks.VectorToImageNetwork):
"""Decoder that decodes vector into an image through adversarial training.
Attributes:
vector_to_image_name: str, name of decoder.
kernel_regularizer: `l1_l2_regularizer` object, regularizer for
kernel variables.
bias_regularizer: `l1_l2_regularizer` object, regularizer for bias
variables.
params: dict, user passed parameters.
alpha_var: variable, alpha for weighted sum of fade-in of layers.
growth_idx: int, the current resolution growth index.
block_idx: int, the current resolution block index.
vector_to_image_input_layer: `Input` layer of every
VectorToImageNetwork model.
projection_dense_layer: `WeightScaledDense` layer used for projecting
noise latent vectors into an image.
vector_to_image_conv_layers: list, `Conv2D` layers.
vector_to_image_leaky_relu_layers: list, leaky relu layers that follow
`Conv2D` layers.
vector_to_image_weighted_sum_layer: `WeightedSum` layer used for
combining growing and shrinking network paths during transition
phases.
to_rgb_conv_layers: list, `Conv2D` toRGB layers.
vector_to_image_outputs_fn: unbound function, function that returns
the vector to image outputs.
"""
def __init__(
self,
kernel_regularizer,
bias_regularizer,
name,
params,
alpha_var,
layers_dict,
growth_idx
):
"""Instantiates and builds decoder network.
Args:
kernel_regularizer: `l1_l2_regularizer` object, regularizer for
kernel variables.
bias_regularizer: `l1_l2_regularizer` object, regularizer for bias
variables.
name: str, name of `Decoder`.
params: dict, user passed parameters.
alpha_var: variable, alpha for weighted sum of fade-in of layers.
layers_dict: dict, dictionary of Keras layers.
growth_idx: int, the current resolution growth index.
"""
tf.keras.Model.__init__(self)
# Set names.
self.vector_to_image_name = "{}_{}".format(name, "decoder")
# Store regularizers.
self.kernel_regularizer = kernel_regularizer
self.bias_regularizer = bias_regularizer
# Store parameters.
self.params = params
# Store reference to alpha variable.
self.alpha_var = alpha_var
self.growth_idx = growth_idx
self.block_idx = (self.growth_idx + 1) // 2
self.vector_to_image_input_layer = (
layers_dict["vector_to_image_input_layer"]
)
self.projection_dense_layer = layers_dict["projection_dense_layer"]
self.vector_to_image_conv_layers = (
layers_dict["vector_to_image_conv_layers"]
)
self.vector_to_image_leaky_relu_layers = (
layers_dict["vector_to_image_leaky_relu_layers"]
)
self.vector_to_image_weighted_sum_layer = (
layers_dict["vector_to_image_weighted_sum_layer"]
)
self.to_rgb_conv_layers = layers_dict["to_rgb_conv_layers"]
if self.growth_idx == 0:
self.vector_to_image_outputs_fn = (
self._get_vector_to_image_base_model_outputs
)
elif self.growth_idx % 2 == 1:
self.vector_to_image_outputs_fn = (
self._get_vector_to_image_growth_transition_model_outputs
)
else:
self.vector_to_image_outputs_fn = (
self._get_vector_to_image_growth_stable_model_outputs
)
self.build(
input_shape=(None, self.params["generator"]["latent_size"])
)
def call(self, inputs, training=False):
"""Overrides call method of tf.keras.Model for forward pass.
Args:
inputs: tensor, rank 2 tensor of shape (batch_size, latent_size).
training: bool, whether model is in training mode.
Returns:
vector_to_image_outputs: tensor, rank 4 tensor of shape
(batch_size, 4 * 2 ** block_idx, 4 * 2 ** block_idx, depth).
"""
vector_to_image_outputs = self.vector_to_image_outputs_fn(
inputs, training, self.block_idx
)
return vector_to_image_outputs
class Encoder(tf.keras.Model, image_to_vector_networks.ImageToVectorNetwork):
"""Encoder that encodes an image into vector through adversarial training.
Attributes:
image_to_vector_name: str, name of encoder.
kernel_regularizer: `l1_l2_regularizer` object, regularizer for
kernel variables.
bias_regularizer: `l1_l2_regularizer` object, regularizer for bias
variables.
params: dict, user passed parameters.
alpha_var: variable, alpha for weighted sum of fade-in of layers.
growth_idx: int, the current resolution growth index.
block_idx: int, the current resolution block index.
image_to_vector_input_layers: list, `Input` layers for each resolution
of image.
from_rgb_conv_layers: list, `Conv2D` fromRGB layers.
from_rgb_leaky_relu_layers: list, leaky relu layers that follow
`Conv2D` fromRGB layers.
image_to_vector_conv_layers: list, `Conv2D` layers.
image_to_vector_leaky_relu_layers: list, leaky relu layers that follow
`Conv2D` layers.
growing_downsample_layers: list, `AveragePooling2D` layers for growing
branch.
shrinking_downsample_layers: list, `AveragePooling2D` layers for
shrinking branch.
image_to_vector_weighted_sum_layer: `WeightedSum` layer used for
combining growing and shrinking network paths during transition
phases.
minibatch_stddev_layer: `MiniBatchStdDev` layer, applies minibatch
stddev to image to add an additional feature channel based on the
sample.
flatten_layer: `Flatten` layer, flattens image for logits layer.
logits_layer: `Dense` layer, used for calculating logits.
unet_encoder_activations: list, activations after each conv block of
ImageToVector network.
image_to_vector_outputs_fn: unbound function, function that returns
the image to vector outputs.
"""
def __init__(
self,
kernel_regularizer,
bias_regularizer,
name,
params,
alpha_var,
layers_dict,
growth_idx,
network_type
):
"""Instantiates and builds encoder generator network.
Args:
kernel_regularizer: `l1_l2_regularizer` object, regularizer for
kernel variables.
bias_regularizer: `l1_l2_regularizer` object, regularizer for bias
variables.
name: str, name of `Encoder`.
params: dict, user passed parameters.
alpha_var: variable, alpha for weighted sum of fade-in of layers.
layers_dict: dict, dictionary of Keras layers.
growth_idx: int, the current resolution growth index.
network_type: str, the network type: discriminator or encoder.
"""
tf.keras.Model.__init__(self)
# Set names.
self.image_to_vector_name = "{}_{}".format(name, "encoder")
# Store regularizers.
self.kernel_regularizer = kernel_regularizer
self.bias_regularizer = bias_regularizer
# Store parameters.
self.params = params
# Store reference to alpha variable.
self.alpha_var = alpha_var
self.growth_idx = growth_idx
self.block_idx = (self.growth_idx + 1) // 2
self.image_to_vector_input_layers = (
layers_dict["image_to_vector_input_layers"]
)
self.image_to_vector_input_layers = (
layers_dict["image_to_vector_input_layers"]
)
self.from_rgb_conv_layers = layers_dict["from_rgb_conv_layers"]
self.from_rgb_leaky_relu_layers = (
layers_dict["from_rgb_leaky_relu_layers"]
)
self.image_to_vector_conv_layers = (
layers_dict["image_to_vector_conv_layers"]
)
self.image_to_vector_leaky_relu_layers = (
layers_dict["image_to_vector_leaky_relu_layers"]
)
self.growing_downsample_layers = (
layers_dict["growing_downsample_layers"]
)
self.shrinking_downsample_layers = (
layers_dict["shrinking_downsample_layers"]
)
self.image_to_vector_weighted_sum_layer = (
layers_dict["image_to_vector_weighted_sum_layer"]
)
self.minibatch_stddev_layer = layers_dict["minibatch_stddev_layer"]
self.flatten_layer = layers_dict["flatten_layer"]
self.logits_layer = layers_dict["logits_layer"]
self.unet_encoder_activations = [None] * 9
if self.growth_idx == 0:
self.image_to_vector_outputs_fn = (
self._get_image_to_vector_base_model_outputs
)
elif self.growth_idx % 2 == 1:
self.image_to_vector_outputs_fn = (
self._get_image_to_vector_growth_transition_model_outputs
)
else:
self.image_to_vector_outputs_fn = (
self._get_image_to_vector_growth_stable_model_outputs
)
height, width = self.params["generator"]["projection_dims"][0:2]
self.build(
input_shape=(
None,
height * 2 ** self.block_idx,
width * 2 ** self.block_idx,
self.params["training"]["reconstruction"]["image_depth"]
)
)
def call(self, inputs, training=False):
"""Overrides call method of tf.keras.Model for forward pass.
Args:
inputs: tensor, rank 4 tensor of shape
(batch_size, 4 * 2 ** block_idx, 4 * 2 ** block_idx, depth).
training: bool, whether model is in training mode.
Returns:
image_to_vector_outputs: tensor, rank 2 tensor of shape
(batch_size, latent_size).
"""
image_to_vector_outputs = self.image_to_vector_outputs_fn(
inputs, training, self.block_idx
)
return image_to_vector_outputs
class UNetGenerator(
tf.keras.Model,
image_to_vector_networks.ImageToVectorNetwork,
vector_to_image_networks.VectorToImageNetwork
):
"""U-net generator that creates an image through adversarial training.
Attributes:
image_to_vector_name: str, name of encoder.
vector_to_image_name: str, name of decoder.
kernel_regularizer: `l1_l2_regularizer` object, regularizer for
kernel variables.
bias_regularizer: `l1_l2_regularizer` object, regularizer for bias
variables.
params: dict, user passed parameters.
alpha_var: variable, alpha for weighted sum of fade-in of layers.
growth_idx: int, the current resolution growth index.
block_idx: int, the current resolution block index.
image_to_vector_input_layers: list, `Input` layers for each resolution
of image.
from_rgb_conv_layers: list, `Conv2D` fromRGB layers.
from_rgb_leaky_relu_layers: list, leaky relu layers that follow
`Conv2D` fromRGB layers.
image_to_vector_conv_layers: list, `Conv2D` layers.
image_to_vector_leaky_relu_layers: list, leaky relu layers that follow
`Conv2D` layers.
growing_downsample_layers: list, `AveragePooling2D` layers for growing
branch.
shrinking_downsample_layers: list, `AveragePooling2D` layers for
shrinking branch.
image_to_vector_weighted_sum_layer: `WeightedSum` layer used for
combining growing and shrinking network paths during transition
phases.
minibatch_stddev_layer: `MiniBatchStdDev` layer, applies minibatch
stddev to image to add an additional feature channel based on the
sample.
flatten_layer: `Flatten` layer, flattens image for logits layer.
logits_layer: `Dense` layer, used for calculating logits.
vector_to_image_input_layer: `Input` layer of every
VectorToImageNetwork model.
projection_dense_layer: `WeightScaledDense` layer used for projecting
noise latent vectors into an image.
vector_to_image_conv_layers: list, `Conv2D` layers.
vector_to_image_leaky_relu_layers: list, leaky relu layers that follow
`Conv2D` layers.
vector_to_image_weighted_sum_layer: `WeightedSum` layer used for
combining growing and shrinking network paths during transition
phases.
to_rgb_conv_layers: list, `Conv2D` toRGB layers.
unet_encoder_activations: list, activations after each conv block of
ImageToVector network.
image_to_vector_outputs_fn: unbound function, function that returns
the image to vector outputs.
vector_to_image_outputs_fn: unbound function, function that returns
the vector to image outputs.
"""
def __init__(
self,
kernel_regularizer,
bias_regularizer,
name,
params,
alpha_var,
layers_dict,
growth_idx
):
"""Instantiates and builds U-net generator network.
Args:
kernel_regularizer: `l1_l2_regularizer` object, regularizer for
kernel variables.
bias_regularizer: `l1_l2_regularizer` object, regularizer for bias
variables.
name: str, name of `UNetGenerator`.
params: dict, user passed parameters.
alpha_var: variable, alpha for weighted sum of fade-in of layers.
layers_dict: dict, dictionary of Keras layers.
growth_idx: int, the current resolution growth index.
"""
tf.keras.Model.__init__(self)
# Set names.
self.image_to_vector_name = "{}_{}".format(name, "encoder")
self.vector_to_image_name = "{}_{}".format(name, "decoder")
# Store regularizers.
self.kernel_regularizer = kernel_regularizer
self.bias_regularizer = bias_regularizer
# Store parameters.
self.params = params
# Store reference to alpha variable.
self.alpha_var = alpha_var
self.growth_idx = growth_idx
self.block_idx = (self.growth_idx + 1) // 2
self.image_to_vector_input_layers = (
layers_dict["image_to_vector_input_layers"]
)
self.image_to_vector_input_layers = (
layers_dict["image_to_vector_input_layers"]
)
self.from_rgb_conv_layers = layers_dict["from_rgb_conv_layers"]
self.from_rgb_leaky_relu_layers = (
layers_dict["from_rgb_leaky_relu_layers"]
)
self.image_to_vector_conv_layers = (
layers_dict["image_to_vector_conv_layers"]
)
self.image_to_vector_leaky_relu_layers = (
layers_dict["image_to_vector_leaky_relu_layers"]
)
self.growing_downsample_layers = (
layers_dict["growing_downsample_layers"]
)
self.shrinking_downsample_layers = (
layers_dict["shrinking_downsample_layers"]
)
self.image_to_vector_weighted_sum_layer = (
layers_dict["image_to_vector_weighted_sum_layer"]
)
self.minibatch_stddev_layer = layers_dict["minibatch_stddev_layer"]
self.flatten_layer = layers_dict["flatten_layer"]
self.logits_layer = layers_dict["logits_layer"]
self.vector_to_image_input_layer = (
layers_dict["vector_to_image_input_layer"]
)
self.projection_dense_layer = layers_dict["projection_dense_layer"]
self.vector_to_image_conv_layers = (
layers_dict["vector_to_image_conv_layers"]
)
self.vector_to_image_leaky_relu_layers = (
layers_dict["vector_to_image_leaky_relu_layers"]
)
self.vector_to_image_weighted_sum_layer = (
layers_dict["vector_to_image_weighted_sum_layer"]
)
self.to_rgb_conv_layers = layers_dict["to_rgb_conv_layers"]
self.unet_encoder_activations = [None] * 9
if self.growth_idx == 0:
self.image_to_vector_outputs_fn = (
self._get_image_to_vector_base_model_outputs
)
self.vector_to_image_outputs_fn = (
self._get_vector_to_image_base_model_outputs
)
elif self.growth_idx % 2 == 1:
self.image_to_vector_outputs_fn = (
self._get_image_to_vector_growth_transition_model_outputs
)
self.vector_to_image_outputs_fn = (
self._get_vector_to_image_growth_transition_model_outputs
)
else:
self.image_to_vector_outputs_fn = (
self._get_image_to_vector_growth_stable_model_outputs
)
self.vector_to_image_outputs_fn = (
self._get_vector_to_image_growth_stable_model_outputs
)
height, width = self.params["generator"]["projection_dims"][0:2]
self.build(
input_shape=(
None,
height * 2 ** self.block_idx,
width * 2 ** self.block_idx,
self.params["training"]["reconstruction"]["image_depth"]
)
)
def call_encoder_model(self, inputs, training):
"""Calls encoder model for forward pass.
Args:
inputs: tensor, rank 4 tensor of shape
(batch_size, 4 * 2 ** block_idx, 4 * 2 ** block_idx, depth).
training: bool, whether model is in training mode.
Returns:
image_to_vector_outputs: tensor, rank 2 tensor of shape
(batch_size, latent_size).
"""
image_to_vector_outputs = self.image_to_vector_outputs_fn(
inputs, training, self.block_idx
)
return image_to_vector_outputs
def call_decoder_model(self, inputs, training):
"""Calls decoder model for forward pass.
Args:
inputs: tensor, rank 2 tensor of shape (batch_size, latent_size).
training: bool, whether model is in training mode.
Returns:
vector_to_image_outputs: tensor, rank 4 tensor of shape
(batch_size, 4 * 2 ** block_idx, 4 * 2 ** block_idx, depth).
"""
vector_to_image_outputs = self.vector_to_image_outputs_fn(
inputs, training, self.block_idx
)
return vector_to_image_outputs
def call(self, inputs, training=False):
"""Overrides call method of tf.keras.Model for forward pass.
Args:
inputs: tensor, rank 4 tensor of shape
(batch_size, 4 * 2 ** block_idx, 4 * 2 ** block_idx, depth).
training: bool, whether model is in training mode.
Returns:
image_to_vector_outputs: tensor, rank 2 tensor of shape
(batch_size, latent_size).
vector_to_image_outputs: tensor, rank 4 tensor of shape
(batch_size, 4 * 2 ** block_idx, 4 * 2 ** block_idx, depth).
"""
image_to_vector_outputs = self.call_encoder_model(
inputs=inputs, training=training
)
if (
self.params["generator"]["GANomaly"]["add_uniform_noise_to_z"] and
training
):
# shape = (batch_size, latent_size)
image_to_vector_outputs = tf.math.add(
x=image_to_vector_outputs,
y=tf.random.uniform(
shape=tf.shape(input=image_to_vector_outputs),
dtype=tf.float32
)
)
vector_to_image_outputs = self.call_decoder_model(
inputs=image_to_vector_outputs, training=training
)
return image_to_vector_outputs, vector_to_image_outputs
| 37.362191
| 80
| 0.637017
| 2,462
| 21,147
| 5.118603
| 0.095045
| 0.037772
| 0.068084
| 0.039676
| 0.911998
| 0.893588
| 0.888351
| 0.888351
| 0.876766
| 0.873274
| 0
| 0.00749
| 0.292902
| 21,147
| 565
| 81
| 37.428319
| 0.835284
| 0.463328
| 0
| 0.70428
| 0
| 0
| 0.110731
| 0.078968
| 0
| 0
| 0
| 0
| 0
| 1
| 0.031128
| false
| 0
| 0.011673
| 0
| 0.07393
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| 0
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| 0
| 0
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| 0
| 1
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| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
a90e2c3ab772cbf3f46fe9fff99f4260312850d3
| 3,237
|
py
|
Python
|
users/migrations/0049_auto_20210113_1518.py
|
SDOS2020/Team_2_Mentoring_Platform
|
fc016bf24025c237bbd5648391ce7d3d104c8cdb
|
[
"Apache-2.0"
] | null | null | null |
users/migrations/0049_auto_20210113_1518.py
|
SDOS2020/Team_2_Mentoring_Platform
|
fc016bf24025c237bbd5648391ce7d3d104c8cdb
|
[
"Apache-2.0"
] | 9
|
2021-01-17T14:46:18.000Z
|
2021-09-22T19:41:12.000Z
|
users/migrations/0049_auto_20210113_1518.py
|
SDOS2020/Team_2_Mentoring_Platform
|
fc016bf24025c237bbd5648391ce7d3d104c8cdb
|
[
"Apache-2.0"
] | null | null | null |
# Generated by Django 3.1.3 on 2021-01-13 09:48
import django.core.validators
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('users', '0048_auto_20210107_1154'),
]
operations = [
migrations.AlterField(
model_name='account',
name='age',
field=models.IntegerField(default=20, null=True, validators=[django.core.validators.MinValueValidator(16), django.core.validators.MaxValueValidator(100)]),
),
migrations.AlterField(
model_name='menteeexpectedrolefield',
name='field',
field=models.IntegerField(choices=[(1, 'Computer Science and Engineering'), (2, 'Electronics and Communication Engineering'), (3, 'Computer Science and Design'), (4, 'Computer Science and Mathematics'), (5, 'Computer Science and Social Sciences'), (6, 'Computer Science and Artificial Intelligence')], null=True),
),
migrations.AlterField(
model_name='menteeexpectedrolefield',
name='role',
field=models.IntegerField(choices=[(1, 'Faculty'), (2, 'Industry Researcher'), (3, 'BTech'), (4, 'MTech'), (5, 'PhD')], null=True),
),
migrations.AlterField(
model_name='menteerolefield',
name='field',
field=models.IntegerField(choices=[(1, 'Computer Science and Engineering'), (2, 'Electronics and Communication Engineering'), (3, 'Computer Science and Design'), (4, 'Computer Science and Mathematics'), (5, 'Computer Science and Social Sciences'), (6, 'Computer Science and Artificial Intelligence')], null=True),
),
migrations.AlterField(
model_name='menteerolefield',
name='role',
field=models.IntegerField(choices=[(1, 'Faculty'), (2, 'Industry Researcher'), (3, 'BTech'), (4, 'MTech'), (5, 'PhD')], null=True),
),
migrations.AlterField(
model_name='mentorexpectedrolefield',
name='field',
field=models.IntegerField(choices=[(1, 'Computer Science and Engineering'), (2, 'Electronics and Communication Engineering'), (3, 'Computer Science and Design'), (4, 'Computer Science and Mathematics'), (5, 'Computer Science and Social Sciences'), (6, 'Computer Science and Artificial Intelligence')], null=True),
),
migrations.AlterField(
model_name='mentorexpectedrolefield',
name='role',
field=models.IntegerField(choices=[(1, 'Faculty'), (2, 'Industry Researcher')], null=True),
),
migrations.AlterField(
model_name='mentorrolefield',
name='field',
field=models.IntegerField(choices=[(1, 'Computer Science and Engineering'), (2, 'Electronics and Communication Engineering'), (3, 'Computer Science and Design'), (4, 'Computer Science and Mathematics'), (5, 'Computer Science and Social Sciences'), (6, 'Computer Science and Artificial Intelligence')], null=True),
),
migrations.AlterField(
model_name='mentorrolefield',
name='role',
field=models.IntegerField(choices=[(1, 'Faculty'), (2, 'Industry Researcher')], null=True),
),
]
| 53.95
| 325
| 0.632067
| 322
| 3,237
| 6.31677
| 0.217391
| 0.147493
| 0.176991
| 0.128319
| 0.829892
| 0.829892
| 0.793019
| 0.793019
| 0.726647
| 0.726647
| 0
| 0.030339
| 0.226135
| 3,237
| 59
| 326
| 54.864407
| 0.781637
| 0.013902
| 0
| 0.792453
| 1
| 0
| 0.377429
| 0.03605
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.037736
| 0
| 0.09434
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
| 0
| 0
| 0
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| 1
| 1
| 0
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| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
a92cbb685d5566b06eb877393f80ad57ff5f1660
| 6,746
|
py
|
Python
|
djangocms_wow/migrations/0001_initial.py
|
compoundpartners/djangocms-wow
|
2dece45476170e24e14903f19f9bf400c10ebf42
|
[
"BSD-3-Clause"
] | 2
|
2015-11-22T16:13:09.000Z
|
2017-02-26T09:39:30.000Z
|
djangocms_wow/migrations/0001_initial.py
|
compoundpartners/djangocms-wow
|
2dece45476170e24e14903f19f9bf400c10ebf42
|
[
"BSD-3-Clause"
] | 1
|
2015-05-02T22:27:25.000Z
|
2015-07-07T18:27:14.000Z
|
djangocms_wow/migrations/0001_initial.py
|
compoundpartners/djangocms-wow
|
2dece45476170e24e14903f19f9bf400c10ebf42
|
[
"BSD-3-Clause"
] | 2
|
2020-04-01T13:10:59.000Z
|
2020-06-08T10:34:47.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('cms', '__first__'),
]
operations = [
migrations.CreateModel(
name='Animation',
fields=[
('cmsplugin_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='cms.CMSPlugin')),
('animation_class', models.CharField(default=b'bounce', max_length=25, choices=[(b'bounce', 'Bounce'), (b'flash', 'Flash'), (b'pulse', 'Pulse'), (b'rubberBand', 'Rubber Band'), (b'shake', 'Shake'), (b'swing', 'Swing'), (b'tada', 'Ta-Da'), (b'wobble', 'Wobble'), (b'bounceIn', 'Bounce In'), (b'bounceInDown', 'Bounce In Down'), (b'bounceInLeft', 'Bounce In Left'), (b'bounceInRight', 'Bounce In Right'), (b'bounceInUp', 'Bounce In Up'), (b'bounceOut', 'Bounce Out'), (b'bounceOutDown', 'Bounce Out Down'), (b'bounceOutLeft', 'Bounce Out Left'), (b'bounceOutRight', 'Bounce Out Right'), (b'bounceOutUp', 'Bounce Out Up'), (b'fadeIn', 'Fade In'), (b'fadeInDown', 'Fade In Down'), (b'fadeInDownBig', 'Fade In Down Big'), (b'fadeInLeft', 'Fade In Left'), (b'fadeInLeftBig', 'Fade In Left Big'), (b'fadeInRight', 'Fade In Right'), (b'fadeInRightBig', 'Fade In Right Big'), (b'fadeInUp', 'Fade In Up'), (b'fadeInUpBig', 'Fade In Up Big'), (b'fadeOut', 'Fade Out'), (b'fadeOutDown', 'Fade Out Down'), (b'fadeOutDownBig', 'Fade Out Down Big'), (b'fadeOutLeft', 'Fade Out Left'), (b'fadeOutLeftBig', 'Fade Out Left Big'), (b'fadeOutRight', 'Fade Out Right'), (b'fadeOutRightBig', 'Fade Out Right Big'), (b'fadeOutUp', 'Fade Out Up'), (b'fadeOutUpBig', 'Fade Out Up Big'), (b'flipInX', 'Flip In X'), (b'flipInY', 'Flip In Y'), (b'flipOutX', 'Flip Out X'), (b'flipOutY', 'Flip Out Y'), (b'lightSpeedIn', 'Light Speed In'), (b'lightSpeedOut', 'Light Speed Out'), (b'rotateIn', 'Rotate In'), (b'rotateInDownLeft', 'Rotate In Down Left'), (b'rotateInDownRight', 'Rotate In Down Right'), (b'rotateInUpLeft', 'Rotate In Up Left'), (b'rotateInUpRight', 'Rotate In Up Right'), (b'rotateOut', 'Rotate Out'), (b'rotateOutDownLeft', 'Rotate Out Down Left'), (b'rotateOutDownRight', 'Rotate Out Down Right'), (b'rotateOutUpLeft', 'Rotate Out Up Left'), (b'rotateOutUpRight', 'Rotate Out Up Right'), (b'hinge', 'Hinge'), (b'rollIn', 'Roll In'), (b'rollOut', 'Roll Out'), (b'zoomIn', 'Zoom In'), (b'zoomInDown', 'Zoom Out'), (b'zoomInLeft', 'Zoom In Left'), (b'zoomInRight', 'Zoom In Right'), (b'zoomInUp', 'Zoom In Up'), (b'zoomOut', 'Zoom Out'), (b'zoomOutDown', 'Zoom Out Down'), (b'zoomOutLeft', 'Zoom Out Left'), (b'zoomOutRight', 'Zoom Out Right'), (b'zoomOutUp', 'Zoom Out Up'), (b'slideInDown', 'Slide In Down'), (b'slideInLeft', 'Slide In Left'), (b'slideInRight', 'Slide In Right'), (b'slideInUp', 'Slide In Up'), (b'slideOutDown', 'Slide Out Down'), (b'slideOutLeft', 'Slide Out Left'), (b'slideOutRight', 'Slide Out Right'), (b'slideOutUp', 'Slide Out Up')])),
('infinite', models.NullBooleanField(help_text='Infinite Loop')),
],
options={
'abstract': False,
},
bases=('cms.cmsplugin',),
),
migrations.CreateModel(
name='WOWAnimation',
fields=[
('cmsplugin_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='cms.CMSPlugin')),
('animation_class', models.CharField(default=b'bounce', max_length=25, choices=[(b'bounce', 'Bounce'), (b'flash', 'Flash'), (b'pulse', 'Pulse'), (b'rubberBand', 'Rubber Band'), (b'shake', 'Shake'), (b'swing', 'Swing'), (b'tada', 'Ta-Da'), (b'wobble', 'Wobble'), (b'bounceIn', 'Bounce In'), (b'bounceInDown', 'Bounce In Down'), (b'bounceInLeft', 'Bounce In Left'), (b'bounceInRight', 'Bounce In Right'), (b'bounceInUp', 'Bounce In Up'), (b'bounceOut', 'Bounce Out'), (b'bounceOutDown', 'Bounce Out Down'), (b'bounceOutLeft', 'Bounce Out Left'), (b'bounceOutRight', 'Bounce Out Right'), (b'bounceOutUp', 'Bounce Out Up'), (b'fadeIn', 'Fade In'), (b'fadeInDown', 'Fade In Down'), (b'fadeInDownBig', 'Fade In Down Big'), (b'fadeInLeft', 'Fade In Left'), (b'fadeInLeftBig', 'Fade In Left Big'), (b'fadeInRight', 'Fade In Right'), (b'fadeInRightBig', 'Fade In Right Big'), (b'fadeInUp', 'Fade In Up'), (b'fadeInUpBig', 'Fade In Up Big'), (b'fadeOut', 'Fade Out'), (b'fadeOutDown', 'Fade Out Down'), (b'fadeOutDownBig', 'Fade Out Down Big'), (b'fadeOutLeft', 'Fade Out Left'), (b'fadeOutLeftBig', 'Fade Out Left Big'), (b'fadeOutRight', 'Fade Out Right'), (b'fadeOutRightBig', 'Fade Out Right Big'), (b'fadeOutUp', 'Fade Out Up'), (b'fadeOutUpBig', 'Fade Out Up Big'), (b'flipInX', 'Flip In X'), (b'flipInY', 'Flip In Y'), (b'flipOutX', 'Flip Out X'), (b'flipOutY', 'Flip Out Y'), (b'lightSpeedIn', 'Light Speed In'), (b'lightSpeedOut', 'Light Speed Out'), (b'rotateIn', 'Rotate In'), (b'rotateInDownLeft', 'Rotate In Down Left'), (b'rotateInDownRight', 'Rotate In Down Right'), (b'rotateInUpLeft', 'Rotate In Up Left'), (b'rotateInUpRight', 'Rotate In Up Right'), (b'rotateOut', 'Rotate Out'), (b'rotateOutDownLeft', 'Rotate Out Down Left'), (b'rotateOutDownRight', 'Rotate Out Down Right'), (b'rotateOutUpLeft', 'Rotate Out Up Left'), (b'rotateOutUpRight', 'Rotate Out Up Right'), (b'hinge', 'Hinge'), (b'rollIn', 'Roll In'), (b'rollOut', 'Roll Out'), (b'zoomIn', 'Zoom In'), (b'zoomInDown', 'Zoom Out'), (b'zoomInLeft', 'Zoom In Left'), (b'zoomInRight', 'Zoom In Right'), (b'zoomInUp', 'Zoom In Up'), (b'zoomOut', 'Zoom Out'), (b'zoomOutDown', 'Zoom Out Down'), (b'zoomOutLeft', 'Zoom Out Left'), (b'zoomOutRight', 'Zoom Out Right'), (b'zoomOutUp', 'Zoom Out Up'), (b'slideInDown', 'Slide In Down'), (b'slideInLeft', 'Slide In Left'), (b'slideInRight', 'Slide In Right'), (b'slideInUp', 'Slide In Up'), (b'slideOutDown', 'Slide Out Down'), (b'slideOutLeft', 'Slide Out Left'), (b'slideOutRight', 'Slide Out Right'), (b'slideOutUp', 'Slide Out Up')])),
('duration', models.PositiveSmallIntegerField(help_text='Change the animation duration', null=True, blank=True)),
('delay', models.PositiveSmallIntegerField(help_text='Delay before the animation starts', null=True, blank=True)),
('offset', models.PositiveSmallIntegerField(help_text='Distance to start the animation (related to the browser bottom)', null=True, blank=True)),
('iteration', models.PositiveSmallIntegerField(help_text='Number of times the animation is repeated', null=True, blank=True)),
],
options={
'abstract': False,
},
bases=('cms.cmsplugin',),
),
]
| 160.619048
| 2,563
| 0.636081
| 879
| 6,746
| 4.852105
| 0.188851
| 0.028136
| 0.01313
| 0.036577
| 0.846893
| 0.846893
| 0.829543
| 0.829543
| 0.829543
| 0.829543
| 0
| 0.000877
| 0.155055
| 6,746
| 41
| 2,564
| 164.536585
| 0.747368
| 0.003113
| 0
| 0.514286
| 0
| 0
| 0.551539
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.057143
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
a9668fadcb10e4276939e070bfecfc55f9a3ae37
| 118
|
py
|
Python
|
src/onqg/models/decoders/__init__.py
|
YuxiXie/RL-for-Question-Generation
|
188cd7b04528e4f192023a596a072b3245c62838
|
[
"MIT"
] | 24
|
2020-10-30T04:34:04.000Z
|
2022-03-13T02:55:31.000Z
|
src/onqg/models/decoders/__init__.py
|
teacherpeterpan/RL-for-Question-Generation
|
188cd7b04528e4f192023a596a072b3245c62838
|
[
"MIT"
] | 3
|
2020-12-10T06:24:57.000Z
|
2021-11-29T08:06:00.000Z
|
src/onqg/models/decoders/__init__.py
|
YuxiXie/RL-for-Question-Generation
|
188cd7b04528e4f192023a596a072b3245c62838
|
[
"MIT"
] | 7
|
2020-11-03T04:37:27.000Z
|
2021-11-25T09:16:33.000Z
|
from onqg.models.decoders.RNNDecoder import RNNDecoder
from onqg.models.decoders.TransfDecoder import TransfDecoder
| 39.333333
| 61
| 0.864407
| 14
| 118
| 7.285714
| 0.5
| 0.156863
| 0.27451
| 0.431373
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.084746
| 118
| 2
| 62
| 59
| 0.944444
| 0
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| 1
| 0
| true
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| null | 0
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| null | 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
8d126070f03e986bf6654ca55808551cb9fedd25
| 66,275
|
py
|
Python
|
wedc/node.py
|
ZwEin27/wedc-one-class-classification
|
e5fbae3caeca7d0da31902399614692bd61f5561
|
[
"Apache-2.0"
] | null | null | null |
wedc/node.py
|
ZwEin27/wedc-one-class-classification
|
e5fbae3caeca7d0da31902399614692bd61f5561
|
[
"Apache-2.0"
] | null | null | null |
wedc/node.py
|
ZwEin27/wedc-one-class-classification
|
e5fbae3caeca7d0da31902399614692bd61f5561
|
[
"Apache-2.0"
] | 3
|
2020-12-13T21:58:41.000Z
|
2020-12-13T21:58:43.000Z
|
# -*- coding: utf-8 -*-
# @Author: ZwEin
# @Date: 2016-08-08 11:46:11
# @Last Modified by: ZwEin
# @Last Modified time: 2016-09-09 14:19:39
from vendor.crf_tokenizer import CrfTokenizer
from common import cleaner
from seed import seeds
DC_NODE_EXT_FEATURE_NAME_CONTENT = 'extracted_text' # extracted_text
DC_NODE_EXT_FEATURE_NAME_POSTTIME = 'posttime'
DC_NODE_EXT_FEATURE_NAME_CITY = 'city'
DC_NODE_EXT_FEATURE_NAME_TEXT = 'text'
DC_NODE_EXT_FEATURE_NAME_REGION = 'region'
DC_NODE_EXT_FEATURE_NAME_TITLE = 'title'
DC_NODE_EXT_FEATURE_NAME_USERLOCATION = 'userlocation'
DC_NODE_EXT_FEATURE_NAME_PHONENUMBER = 'phonenumber'
DC_NODE_EXT_FEATURE_NAME_SID = 'sid'
DC_NODE_EXT_FEATURE_NAME_OTHERADS = 'otherads'
DC_NODE_EXT_FEATURE_NAME_AGE = 'age'
DC_NODE_EXT_FEATURE_NAME_DOCID = 'doc_id'
DC_NODE_EXT_FEATURE_NAME_EMAIL = 'email'
DC_NODE_EXT_FEATURE_NAME_DRUG_USE = 'drug_use'
DC_NODE_EXT_FEATURE_NAME_PRICE = 'price'
DC_NODE_EXT_FEATURE_NAME_LOCATION = 'location'
DC_NODE_EXT_FEATURE_NAME_PRICE_PER_HOUR = 'price_per_hour'
DC_NODE_EXT_FEATURE_NAME_BUSINESS_TYPE = 'business_type'
DC_NODE_EXT_FEATURE_NAME_URL = 'url'
DC_NODE_EXT_FEATURE_NAME_SERVICES = 'services'
DC_NODE_EXT_FEATURE_NAME_GENDER = 'gender'
DC_NODE_EXT_FEATURE_NAME_PHONE = 'phone'
DC_NODE_EXT_FEATURES = [
DC_NODE_EXT_FEATURE_NAME_LOCATION,
DC_NODE_EXT_FEATURE_NAME_URL,
DC_NODE_EXT_FEATURE_NAME_GENDER,
DC_NODE_EXT_FEATURE_NAME_PHONE,
DC_NODE_EXT_FEATURE_NAME_AGE
]
DC_NODE_FEATURES = DC_NODE_EXT_FEATURES + sorted(seeds)
# DC_NODE_FEATURES = sorted(seeds)
# print DC_NODE_FEATURES
class Node(object):
def __init__(self, content, sid=None, label=None, **attrs):
self._content = content.encode('utf-8') if content else None
self._sid = sid
self._label = label
self._seeds = seeds
self._attrs = attrs
self._features = self.load_features(content)
self._vector = self.generate_vector()
# print self._features
# for attr in sorted(self.attrs.keys()):
# print attr, ':', attrs[attr]
#################################################
# Clean Content
#################################################
def clean(self, text):
if not text:
return ''
# text = text.encode('ascii', 'ignore')
text = cleaner.clean_text(text)
text = text.encode('utf-8')
# text = unicode(text, 'utf-8')
# try:
# text = text.encode('ascii', 'ignore')
# except:
# text = text.decode('utf-8', 'ignore')
t = CrfTokenizer()
t.setRecognizeHtmlEntities(True)
t.setRecognizeHtmlTags(True)
t.setSkipHtmlTags(True)
tokens = t.tokenize(text)
tokens = [cleaner.clean_token(token) for token in tokens]
tokens = [_ for _ in set(tokens) if _]
try:
str(' '.join(tokens))
except:
print text.encode('utf-8')
print tokens
return str(' '.join(tokens)).decode('utf-8', 'ignore').encode('ascii', 'ignore')
#################################################
# Load Feature
#################################################
def load_seed_features(self, content):
if not content:
return {}
ans = {}
content = self.clean(content)
seed_words = seeds.keys()
seeds_size = len(seed_words)
# seed_words.sort()
tokens = content.split(' ')
for i in range(seeds_size):
if seed_words[i] in tokens:
ans.setdefault(seed_words[i], str(1.0 * float(seeds[seed_words[i]])))
return ans
def load_ext_features(self, content=None):
def raw2json(raw):
import ast
if not raw:
return []
try:
return ast.literal_eval(raw)
except:
return raw
def __ac_func_location(raw):
if raw:
return 1.
return 0.
def __ac_func_email(raw):
if raw:
return float(len(raw))
return 0.
def __ac_func_drug_use(raw):
if raw:
return 1.
return 0.
def __ac_func_price(raw):
if raw:
return float(len(raw))
return 0.
def __ac_func_price_per_hour(raw):
if raw:
# return raw
# print raw
return float(len(raw))
return 0.
def __ac_func_business_type(raw):
if raw:
return float(len(raw))
return 0.
def __ac_func_url(raw):
if raw:
return 1.
return 0.
def __ac_func_services(raw):
if raw:
return float(len(raw))
return 0.
def __ac_func_gender(raw):
if raw:
for val in raw:
if val == 'female':
return 1.
return 0.
def __ac_func_phone(raw):
if raw:
return float(len(raw))
return 0.
def __ac_func_age(raw):
if raw:
return float(len(raw))
return 0.
attr_content_funcs = {
'location': __ac_func_location,
'email': __ac_func_email,
'drug_use': __ac_func_drug_use,
'price': __ac_func_price,
'price_per_hour': __ac_func_price_per_hour,
'business_type': __ac_func_business_type,
'url': __ac_func_url,
'services': __ac_func_services,
'gender': __ac_func_gender,
'phone': __ac_func_phone,
'age': __ac_func_age,
'special_symbol': lambda x: x
}
if self._attrs:
# print self._attrs
ans = {}
for (attr_name, attr_value) in self._attrs.iteritems():
# print attr_value, attr_value
if attr_value and attr_value != '':
ans.setdefault(attr_name, attr_content_funcs[attr_name](raw2json(attr_value)))
# ans.setdefault('unicode_symbol', load_unicode_symbol(content))
return ans
return {}
def load_features(self, content):
return dict(self.load_seed_features(content).items() + self.load_ext_features(content).items())
#################################################
# Transformat
#################################################
def generate_vector(self):
features = DC_NODE_FEATURES
feature_size = len(features)
if not self._features:
return ' '.join(['0'] * feature_size)
vector = ['0'] * feature_size
for i in range(feature_size):
if features[i].lower() in self._features:
vector[i] = str(1.0 * float(self._features[features[i]]))
return ' '.join(vector)
if __name__ == '__main__':
contents = [
" Publicado: domingo, 3 de julio de 2016, 1:38 PM Has encontrado el lugar donde una masajista te dar el tiempo de realizarte un masaje relajante sensual sensitivo y ertico, estimulando tus sentidos al final cerrando con un orgasmo, ven y explora nuevos sentidos, de aqu el tantrico 1 hora 15000 Edad de la persona que public el anuncio: 21 Ubicacin: Heredia Taco Bell, San Francisco Identificacin de la publicacin: 2899225 costarica Otros anuncios de este usuario: DOMINGO SENSUAL Atrevida Es KIRA 71857376 - Costa Rica anuncios clasificados erticos y para adultos - erticos - backpage.com Has encontrado el lugar donde una masajista te dar el tiempo de realizarte un masaje relajante sensual sensitivo y ertico, estimulando tus sentidos al final cerrando con un orgasmo, ven y explora nuevos sentidos, de aqu el tantrico 1 hora 15000 DOMINGO SENSUAL Atrevida Es KIRA 71857376 - 21",
"whitehouse disfruta en un lugar seguro y discreto full privacidad 70791261 / 70501012 - 23 whitehouse disfruta en un lugar seguro y discreto full privacidad 70791261 / 70501012 - Costa Rica anuncios clasificados erticos y para adultos - erticos - backpage.com Somos varias chicas dispuestas a complacer todas tus fantasas Publicado: sbado, 9 de julio de 2016, 12:17 PM Somos varias chicas dispuestas a complacer todas tus fantasas Edad de la persona que public el anuncio: 23 Ubicacin: alajuela montecillos Identificacin de la publicacin: 3204126 costarica Otros anuncios de este usuario: ",
"Visiting: Treat Yourself to the Luxury of Two Experienced Goddesses Sharing Their Gifts - 37 Visiting: Treat Yourself to the Luxury of Two Experienced Goddesses Sharing Their Gifts - Boston body rubs - backpage.com Ajna & Maya embody tantric intuition & deep integrity. With over 20 yrs. of experience, we bring healing, relaxation & bliss to every seeker. We invite you to experience the power & pleasure of 4 skilled hands & 2 generous hearts for unparalleled sensual touch! Visiting 6/27 - 7/2. Read Ajna's ad below, and then call her if you have questions or are ready to schedule this uniquely luxurious experience... http://seattle.backpage.com/BodyRubs/exceptional-touch-and-tantric-training/790465 Location: Boston, Historic Downtown Boston, Fanueil Hall Ajna & Maya embody tantric intuition & deep integrity. With over 20 yrs. of experience, we bring healing, relaxation & bliss to every seeker. We invite you to experience the power & pleasure of 4 skilled hands & 2 generous hearts for unparalleled sensual touch! Visiting 6/27 - 7/2. Read Ajna's ad below, and then call her if you have questions or are ready to schedule this uniquely luxurious experience... http://seattle.backpage.com/BodyRubs/exceptional-touch-and-tantric-training/790465",
"100% Real Pictures Open military and veteran 15% off parking in white stalls only 808-206-4393 . Call or text808-206-4393 $60/1h house fee. HANABI SPAReal pictures SweetOpen military and veteran 15% off - 24 Posted: Saturday, July 2, 2016 12:49 PM 100% Real Pictures Open military and veteran 15% off parking in white stalls only 808-206-4393 . Call or text808-206-4393 $60/1h house fee. Poster's age: 24 Location: 23 S vineyard Blvd. #201, Honolulu (Oahu) Post ID: 6048613 hawaii Other ads by this user: HANABI SPAReal pictures SweetOpen military and veteran 15% off - Hawaii body rubs - backpage.com",
" Posted: Monday, July 11, 2016 8:33 PM Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 Poster's age: 21 Location: Babylon, Hempstead, Islip, Long Island, Sunrise Hwy / Route 27 Post ID: 82768326 longisland Other ads by this user: Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 $40 Monday Madness Special !!Grand Opening! Brazilian & Latina Bombshells Await!! Call Now $40 - Long Island body rubs - backpage.com $40 Monday Madness Special !!Grand Opening! Brazilian & Latina Bombshells Await!! Call Now $40 - 21",
"Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 Posted: Tuesday, June 28, 2016 10:36 PM Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 Poster's age: 21 Location: Babylon, Brookhaven, Glen Cove, Hempstead, Huntington, Islip, Long Island, North Hempstead, Oyster Bay, Smithtown, Sunrise Hwy / Route 27 Post ID: 81995194 longisland Other ads by this user: $40 Tasty Tuesday Special !!Grand Opening! Brazilian & Latina Bombshells Await!! Call Now $40 - 21 $40 Tasty Tuesday Special !!Grand Opening! Brazilian & Latina Bombshells Await!! Call Now $40 - Long Island body rubs - backpage.com",
"The Professional's Choice - 43 Posted: Sunday, July 3, 2016 2:50 PM Ever wonder what true relaxation and a pleasnt experience is like? Don't wait a second longer! Clear your mind, leave refreshed, focused and relieved of everyday stress/this is the hour of the true gent who wants nothing but the best. Please be over age 40 and very respectful, slight screening required for all. Poster's age: 43 Location: Beautiful North Office, Loveland, Boulder, Denver, Ft Collins Post ID: 24282181 denver The Professional's Choice - Denver body rubs - backpage.com Ever wonder what true relaxation and a pleasnt experience is like? Don't wait a second longer! Clear your mind, leave refreshed, focused and relieved of everyday stress/this is the hour of the true gent who wants nothing but the best. Please be over age 40 and very respectful, slight screening required for all.",
"10$OFF TODAY Pretty And young Asia girls! - 35 Try our NEW GIRL we will not let you down Deep Tissue Light Touch Hot stone essential oil WE ACCEPT CASH &CREDIT CARD TABLE MASSAGE 30min:$40 60min:$60 90min:$90 120min:$120 FUTON MASSAGE 30min:$60 60min:$70 90min:$110 120min$130 Four Hands---$130 Open 7days a week 10:00-10:00 Monday--Saturday 10:00-9:30 Saturday Call952-934-3671 15762 Venture Lane Eden Prairie MN,55344 10$OFF TODAY Pretty And young Asia girls! - Minneapolis / St. Paul body rubs - backpage.com Posted: Friday, July 1, 2016 6:47 PM Try our NEW GIRL we will not let you down Deep Tissue Light Touch Hot stone essential oil WE ACCEPT CASH &CREDIT CARD TABLE MASSAGE 30min:$40 60min:$60 90min:$90 120min:$120 FUTON MASSAGE 30min:$60 60min:$70 90min:$110 120min$130 Four Hands---$130 Open 7days a week 10:00-10:00 Monday--Saturday 10:00-9:30 Saturday Call952-934-3671 15762 Venture Lane Eden Prairie MN,55344 Poster's age: 35 Location: 15762 venture lane Eden prairie MN 55344, Minneapolis / St. paul Post ID: 17921365 minneapolis ",
"Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 Posted: Wednesday, June 29, 2016 7:47 PM Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 Poster's age: 21 Location: Babylon, Brookhaven, Glen Cove, Hempstead, Huntington, Islip, Long Island, North Hempstead, Oyster Bay, Smithtown, Sunrise Hwy / Route 27 Post ID: 82055071 longisland Other ads by this user: $40 Wacky Wednesday Special !Grand Opening! Brazilian & Latina Bombshells Await!! Call Now $40 - Long Island body rubs - backpage.com $40 Wacky Wednesday Special !Grand Opening! Brazilian & Latina Bombshells Await!! Call Now $40 - 21",
" Posted: Saturday, July 9, 2016 12:50 PM 24HrS Body to Body Couples Fetishes Unrushed Sessions 2-GIRL-Sessions Private Parking Directions;South on Powerline right at Ihop then 1st left into shopping plaza suite 1415 next to Dunkin Donuts behind Checkers!!! Poster's age: 21 Location: 1415 South Powerline Road,Pompano, West Palm Beach Post ID: 32553828 westpalmbeach 24HrS Body to Body Couples Fetishes Unrushed Sessions 2-GIRL-Sessions Private Parking Directions;South on Powerline right at Ihop then 1st left into shopping plaza suite 1415 next to Dunkin Donuts behind Checkers!!! #1 Voted BEST BoDyrUbS \" SPA MAGIC \"JENNY\" & \"CANDY\" &\" SARAH\" (954)793-7155 - West Palm Beach body rubs - backpage.com #1 Voted BEST BoDyrUbS \" SPA MAGIC \"JENNY\" & \"CANDY\" &\" SARAH\" (954)793-7155 - 21",
" Posted: Sunday, July 3, 2016 12:36 PM Ever wonder what true relaxation and a pleasnt experience is like? Don't wait a second longer! Clear your mind, leave refreshed, focused and relieved of everyday stress/this is the hour of the true gent who wants nothing but the best. Please be over age 40 and very respectful, slight screening required for all. Poster's age: 43 Location: Beautiful North Office, Loveland, Boulder, Denver, Ft Collins Post ID: 24282181 fortcollins The Professional's Choice - 43 Ever wonder what true relaxation and a pleasnt experience is like? Don't wait a second longer! Clear your mind, leave refreshed, focused and relieved of everyday stress/this is the hour of the true gent who wants nothing but the best. Please be over age 40 and very respectful, slight screening required for all. The Professional's Choice - Fort Collins body rubs - backpage.com",
" Posted: Saturday, June 25, 2016 10:57 PM Looking for beautiful woman 18-35 interested in the adult industry. Location: Austin, East Bay, Monterey, North Bay, San Francisco, San Jose / South Bay Post ID: 36475547 sanjose Beautiful girls wanted free trip to Austin - San Jose model, actor, auditionsand casting calls - backpage.com Beautiful girls wanted free trip to Austin",
"bye guys! ve back next week! - 28 Posted: Thursday, June 16, 2016 2:52 AM be back next week! Poster's age: 28 Location: Rockville, Gaithersburg, Bethesda, Southern Maryland Post ID: 20832226 washingtondc Other ads by this user: be back next week! bye guys! ve back next week! - Washington body rubs - backpage.com",
"24hr 718-559-7060HOT BODY2BODY SLIDING NEW LINEUP 4 U - Queens body rubs - backpage.com 24hr 718-559-7060HOT BODY2BODY SLIDING NEW LINEUP 4 U - 23 Posted: Sunday, July 10, 2016 4:16 PM The best bodyru and nuru massage from our hottest Chinese 718-559-7060) 24 hours on Poster's age: 23 Location: 35th Ave & Prince St, Flushing, Flushing, Queens Post ID: 61713056 queens The best bodyru and nuru massage from our hottest Chinese 718-559-7060) 24 hours on",
"Tues 1-8pm - Louisville body rubs - backpage.com What is Reiki? Reiki is a Japanese technique for stress reduction and relaxation that also promotes healing. It is administered by \"laying on hands\" and is based on the idea that an unseen \"life force energy\" flows through us and is what causes us to be alive. If one's \"life force energy\" is low, then we are more likely to get sick or feel stress, and if it is high, we are more capable of being happy and healthy. Tues 1-8pm - 99 Posted: Saturday, July 2, 2016 8:42 AM What is Reiki? Reiki is a Japanese technique for stress reduction and relaxation that also promotes healing. It is administered by \"laying on hands\" and is based on the idea that an unseen \"life force energy\" flows through us and is what causes us to be alive. If one's \"life force energy\" is low, then we are more likely to get sick or feel stress, and if it is high, we are more capable of being happy and healthy. Poster's age: 99 Location: Bardstown/Taylorsville Rd, Louisville Post ID: 9231358 louisville ",
"Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 $40 Thirsty Thursday Special !!Grand Opening! Brazilian & Latina Bombshells Await!! Call $40 - 21 Posted: Thursday, June 30, 2016 3:11 PM Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 Poster's age: 21 Location: Babylon, Brookhaven, Glen Cove, Hempstead, Huntington, Islip, Long Island, North Hempstead, Oyster Bay, Smithtown, Sunrise Hwy / Route 27 Post ID: 82107117 longisland Other ads by this user: $40 Thirsty Thursday Special !!Grand Opening! Brazilian & Latina Bombshells Await!! Call $40 - Long Island body rubs - backpage.com",
"Sensual Soothing Bodywork - 55 Sensual Soothing Bodywork - Ocala body rubs - backpage.com Posted: Wednesday, June 22, 2016 9:42 AM It's Hot! Cool Down With Sensual Soothing Touch! Email: sendinfo2zen@gmail.com Poster's age: 55 Location: Ocala, Wildwood S Ocala Lady Lake Villages Area Post ID: 9455516 ocala It's Hot!Cool Down With Sensual Soothing Touch!Email: sendinfo2zen@gmail.com",
" Posted: Tuesday, July 12, 2016 3:28 PM Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 Poster's age: 21 Location: Babylon, Hempstead, Islip, Long Island, Sunrise Hwy / Route 27 Post ID: 82809306 longisland Other ads by this user: Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 $40 Tasty Tuesday Special !!Grand Opening! Brazilian & Latina Bombshells Await!! Call Now $40 - 21 $40 Tasty Tuesday Special !!Grand Opening! Brazilian & Latina Bombshells Await!! Call Now $40 - Long Island body rubs - backpage.com",
" Posted: Friday, July 8, 2016 1:27 PM =======Li Beauty & Spa===== Dear New Yorkers: you have to know where your pointless. If you want really Massage with romantic touch,There is your best choice. *Table shower*Swedish * Shiatsu * Deep tissue *Body scrub**Table shower? II.(( Downtown =BEAVER ST))646_490_6850((CLOSE BROADWAY)) ((OPEN 10:00AM-8:30pM)) SAT & SUN =OPEN 11:00AM-7:00pm . Poster's age: 28 Location: beaver st, Downtown, Manhattan Post ID: 42844768 manhattan Other ads by this user: =======Li Beauty & Spa===== Dear New Yorkers: you have to know where your pointless. If you want really Massage with romantic touch,There is your best choice. *Table shower*Swedish * Shiatsu * Deep tissue *Body scrub**Table shower? II.(( Downtown =BEAVER ST))646_490_6850((CLOSE BROADWAY)) ((OPEN 10:00AM-8:30pM)) SAT & SUN =OPEN 11:00AM-7:00pm . (Downtown)(what's important on your bodysoft touch &best deep tissues in NY NO.1You will be back - Manhattan body rubs - backpage.com (Downtown)(what's important on your bodysoft touch &best deep tissues in NY NO.1You will be back - 28",
"Has encontrado el lugar donde una masajista te dar el tiempo de realizarte un masaje relajante sensual sensitivo y ertico, estimulando tus sentidos al final cerrando con un orgasmo, ven y explora nuevos sentidos, de aqu el tantrico 1 hora 15000 Antojado de una Nuevita Esta ANGELA 71857376 - Costa Rica anuncios clasificados erticos y para adultos - erticos - backpage.com Publicado: martes, 21 de junio de 2016, 11:24 AM Has encontrado el lugar donde una masajista te dar el tiempo de realizarte un masaje relajante sensual sensitivo y ertico, estimulando tus sentidos al final cerrando con un orgasmo, ven y explora nuevos sentidos, de aqu el tantrico 1 hora 15000 Edad de la persona que public el anuncio: 24 Ubicacin: FOTOS NUEVAS **. Identificacin de la publicacin: 2900421 costarica Otros anuncios de este usuario: Antojado de una Nuevita Esta ANGELA 71857376 - 24",
"El Masaje Ertico Descubre Para Ti Nuevas Formas De Estimular Tu Cuerpo, Desde Perspectivas Distintas Y Novedosas. Te Ofrecemos Una Exclusiva Carta De Masajes Cuyo Fin Primordial Es Llegar A Niveles Mucho Ms Profundos Y Ayudar A Fluir De Manera Ms Positiva Para Un ptimo Rendimiento En Tu Vida. Vistanos a la alt cdra. 18 de arenales c 2 canevaro y Llmanos al 967822424 Publicado: sbado, 9 de julio de 2016, 6:21 PM El Masaje Ertico Descubre Para Ti Nuevas Formas De Estimular Tu Cuerpo, Desde Perspectivas Distintas Y Novedosas. Te Ofrecemos Una Exclusiva Carta De Masajes Cuyo Fin Primordial Es Llegar A Niveles Mucho Ms Profundos Y Ayudar A Fluir De Manera Ms Positiva Para Un ptimo Rendimiento En Tu Vida. Vistanos a la alt cdra. 18 de arenales c 2 canevaro y Llmanos al 967822424 Edad de la persona que public el anuncio: 22 Ubicacin: 18 de arenales, Lima Identificacin de la publicacin: 4064102 lima Otros anuncios de este usuario: MASAJES suaves full relajantes en lince - 22 MASAJES suaves full relajantes en lince - Lima anuncios clasificados erticos y para adultos - erticos - backpage.com",
"Soy SHARLIN 15MIL asaje % TANTRICO * 71857376 - Costa Rica anuncios clasificados erticos y para adultos - erticos - backpage.com Has encontrado el lugar donde una masajista te dar el tiempo de realizarte un masaje relajante sensual sensitivo y ertico, estimulando tus sentidos al final cerrando con un orgasmo, ven y explora nuevos sentidos, de aqu el tantrico 1 hora 15000 Publicado: mircoles, 29 de junio de 2016, 7:41 PM Has encontrado el lugar donde una masajista te dar el tiempo de realizarte un masaje relajante sensual sensitivo y ertico, estimulando tus sentidos al final cerrando con un orgasmo, ven y explora nuevos sentidos, de aqu el tantrico 1 hora 15000 Edad de la persona que public el anuncio: 26 Ubicacin: Heredia Taco Bell, San Francisco0T! Identificacin de la publicacin: 3048001 costarica Otros anuncios de este usuario: Soy SHARLIN 15MIL asaje % TANTRICO * 71857376 - 26",
"Saturday 12-4 still avbl booking fast! - 99 What is Reiki? Reiki is a Japanese technique for stress reduction and relaxation that also promotes healing. It is administered by \"laying on hands\" and is based on the idea that an unseen \"life force energy\" flows through us and is what causes us to be alive. If one's \"life force energy\" is low, then we are more likely to get sick or feel stress, and if it is high, we are more capable of being happy and healthy. Saturday 12-4 still avbl booking fast! - Kentucky body rubs - backpage.com Posted: Friday, July 8, 2016 12:03 PM What is Reiki? Reiki is a Japanese technique for stress reduction and relaxation that also promotes healing. It is administered by \"laying on hands\" and is based on the idea that an unseen \"life force energy\" flows through us and is what causes us to be alive. If one's \"life force energy\" is low, then we are more likely to get sick or feel stress, and if it is high, we are more capable of being happy and healthy. Poster's age: 99 Location: Bardstown/Taylorsville Rd, Louisville Post ID: 9231358 kentucky ",
" Posted: Wednesday, 13 July 2016, 19:16 Description . , , , , . . : 2000 . Tel: 8-981-765-29-65 Location: Sankt-Peterburg Post ID: 2960882 sankt-peterburg - Sankt-peterburg body rubs - backpage.com",
" Posted: Thursday, June 23, 2016 5:27 PM Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 Poster's age: 21 Location: Babylon, Brookhaven, Glen Cove, Hempstead, Huntington, Islip, Long Island, North Hempstead, Oyster Bay, Smithtown, Sunrise Hwy / Route 27 Post ID: 81655759 newyork Other ads by this user: Swedish Deep Tissue Oil MassageBody Slides Nuru Quite, Clean, and Comfortable Private Room Available for one Parking Lot Available Full body massage Every staff is friendly and kindly. Provide a happy day for you. Real pic or you're free to walk away OPEN 7 DAYS /11AM - 11PM Foot Friendly Role Play Toys Golden 631-526-9939 $40 Thirsty Thursday Special !!Grand Opening! NEW Brazilian & Latina Dolls Await!! Call $40 - New York body rubs - backpage.com $40 Thirsty Thursday Special !!Grand Opening! NEW Brazilian & Latina Dolls Await!! Call $40 - 21",
"Ariel Capri, Elite and Independent Courtesan for the Discerning Gentleman! This site contains adult material. If you are under 21 years of age or it is illegal to view adult material in your community, you must leave now. By continuing further into this site you release the owner and creator of this site of all liability. If you decide to continue, you do so at your own risk and accept full responsibility for your actions. 2016 Copyright DivaDivine.net, All Rights Reserved - Site Design by 4Front Web Design ",
"Wed 12:30-5:30pm - 99 Posted: Wednesday, July 6, 2016 8:37 AM What is Reiki? Reiki is a Japanese technique for stress reduction and relaxation that also promotes healing. It is administered by \"laying on hands\" and is based on the idea that an unseen \"life force energy\" flows through us and is what causes us to be alive. If one's \"life force energy\" is low, then we are more likely to get sick or feel stress, and if it is high, we are more capable of being happy and healthy. Poster's age: 99 Location: Bardstown/Taylorsville Rd, Louisville Post ID: 9231358 kentucky What is Reiki? Reiki is a Japanese technique for stress reduction and relaxation that also promotes healing. It is administered by \"laying on hands\" and is based on the idea that an unseen \"life force energy\" flows through us and is what causes us to be alive. If one's \"life force energy\" is low, then we are more likely to get sick or feel stress, and if it is high, we are more capable of being happy and healthy. Wed 12:30-5:30pm - Kentucky body rubs - backpage.com",
"https://twitter.com/vip_masseuse1 I offer the best in the art of sensual and therapeutic massage (Shower Available) Professional deep tissue massage 1hour session $80.00 Bikini massage $150.00hr Mutual touch bodyslide massage $250.00hr Vip massage $400.00hr Posted: Thursday, June 23, 2016 3:53 AM I offer the best in the art of sensual and therapeutic massage (Shower Available) Professional deep tissue massage 1hour session $80.00 Bikini massage $150.00hr Mutual touch bodyslide massage $250.00hr Vip massage $400.00hr Poster's age: 36 Location: Toledo, Visiting private Hotel suite Post ID: 26700688 ohio Visiting Vip Sensual Masseuse... 917-600-5591 - Ohio body rubs - backpage.com Visiting Vip Sensual Masseuse... 917-600-5591 - 36",
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"se habla espanolConvenient Locations Fort Lauderdale: 3925 N Andrews Avenue, 33309. Diagonal to Seven Eleven. Pompano Hard Rock Casino Area: 5100 S State Road 7, Davie, Fl. Suites 5102, 5104, 5106 & 5124 Latinas SPARelaxing therapies, body scrub.. Posted: Sunday, July 3, 2016 8:20 AM se habla espanolConvenient Locations Fort Lauderdale: 3925 N Andrews Avenue, 33309. Diagonal to Seven Eleven. Pompano Hard Rock Casino Area: 5100 S State Road 7, Davie, Fl. Suites 5102, 5104, 5106 & 5124 Latinas SPARelaxing therapies, body scrub.. Poster's age: 22 Location: City of Ft. Lauderdale, Fort Lauderdale, Pompano Beach, Hollywoo, Ft Lauderdale Post ID: 34080274 ftlauderdale Other ads by this user: Beautiful LatinasNew TherapistsWalk insEarly bird special Body Rub session 80 754 281 1279 - 22 Beautiful LatinasNew TherapistsWalk insEarly bird special Body Rub session 80 754 281 1279 - Fort Lauderdale body rubs - backpage.com",
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"What is Reiki? Reiki is a Japanese technique for stress reduction and relaxation that also promotes healing. It is administered by \"laying on hands\" and is based on the idea that an unseen \"life force energy\" flows through us and is what causes us to be alive. If one's \"life force energy\" is low, then we are more likely to get sick or feel stress, and if it is high, we are more capable of being happy and healthy. Friday 65hh spcl - 99 Friday 65hh spcl - Louisville body rubs - backpage.com Posted: Friday, July 8, 2016 12:03 PM What is Reiki? Reiki is a Japanese technique for stress reduction and relaxation that also promotes healing. It is administered by \"laying on hands\" and is based on the idea that an unseen \"life force energy\" flows through us and is what causes us to be alive. If one's \"life force energy\" is low, then we are more likely to get sick or feel stress, and if it is high, we are more capable of being happy and healthy. Poster's age: 99 Location: Bardstown/Taylorsville Rd, Louisville Post ID: 9231358 louisville ",
"Indulge Yourself in Pure Body Bliss With Michelle Relax your Body and your Mind Great Special Massage ShowerJacuzzi 732 2439987 2595 Woodbrige Avenue Edison 08817 Exotic Sensual Brazilian Michelle 732 243 9987 - South Jersey body rubs - backpage.com Posted: Saturday, July 9, 2016 4:10 PM Indulge Yourself in Pure Body Bliss With Michelle Relax your Body and your Mind Great Special Massage ShowerJacuzzi 732 2439987 2595 Woodbrige Avenue Edison 08817 Poster's age: 23 Location: Central Jersey, Edison 2595 Woodbrige Avenue, Jersey Shore, North Jersey, South Jersey Post ID: 29379593 southjersey Other ads by this user: Exotic Sensual Brazilian Michelle 732 243 9987 - 23",
"The best online for massage schooling for free. Learn how to do body rubs, post body rub ads, do markeiting for your online business. The number one body rub and massage resource online avaliable. Rate providers and leave reviews Error! Only registered users are allowed to see this page Massage and Bodyrub Independent Providers",
" Posted: Saturday, July 2, 2016 1:22 PM ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Poster's age: 24 Location: 21150 W Capitol Dr,#3,Brookfield, WI 530, La Crosse, Madison, Milwaukee, Sheboygan, Wausau Post ID: 16205832 sheboygan ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - Sheboygan body rubs - backpage.com Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - 24",
"ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - Wausau body rubs - backpage.com Posted: Monday, June 20, 2016 1:16 PM ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Poster's age: 24 Location: 21150 W Capitol Dr,#3,Brookfield, WI 530, La Crosse, Madison, Milwaukee, Sheboygan, Wausau Post ID: 16092852 wausau Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - 24",
" Posted: Saturday, July 2, 2016 1:22 PM ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Poster's age: 24 Location: 21150 W Capitol Dr,#3,Brookfield, WI 530, La Crosse, Madison, Milwaukee, Sheboygan, Wausau Post ID: 16205832 madison Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - Madison body rubs - backpage.com ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - 24",
" Posted: Sunday, June 26, 2016 1:23 PM ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Poster's age: 24 Location: 21150 W Capitol Dr,#3,Brookfield, WI 530, La Crosse, Madison, Milwaukee, Sheboygan, Wausau Post ID: 16149778 wisconsin Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - Wisconsin body rubs - backpage.com ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - 24",
" Posted: Wednesday, June 22, 2016 1:08 PM ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Poster's age: 24 Location: 21150 W Capitol Dr,#3,Brookfield, WI 530, La Crosse, Madison, Milwaukee, Sheboygan, Wausau Post ID: 16110433 wausau ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - Wausau body rubs - backpage.com Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - 24",
" Posted: Friday, June 24, 2016 1:41 PM ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Poster's age: 24 Location: 21150 W Capitol Dr,#3,Brookfield, WI 530, La Crosse, Madison, Milwaukee, Sheboygan, Wausau Post ID: 16130118 wisconsin Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - Wisconsin body rubs - backpage.com ASIAN MASSAGE Nice smoothing music in a calm enviroment will help you feel relaxed. Give us a call & come experience one of the best massages of your life. Masseuse has license Address: 21150 W Capitol Dr,#3,Brookfield, WI 53072 733 Tel: *262-765-9858 Open 7days a week, 9:00am to 10:00 pm Beautiful professional Asian masseuse ,Will help you feel relaxed Walk-ins, OK Free table shower *SOOTHING RELAXIN Nurture Massage Spa 262-765-9858--- - 24"
]
import json
for content in contents:
# content = ''
node = Node(content)
print json.dumps(node._features, indent=4)
# print node._vector
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| 0.077759
| 0.300083
| 66,275
| 290
| 1,407
| 228.534483
| 0.832647
| 0.010426
| 0
| 0.189427
| 0
| 0.312775
| 0.885826
| 0.003923
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.026432
| null | null | 0.013216
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
8d3136717c29103910b6e3d557701f01c567aca1
| 30
|
py
|
Python
|
z01.py
|
lenmon7/cocacola
|
6e97035c93b9d465e1e88d65c405afc2c9a3d77d
|
[
"MIT"
] | null | null | null |
z01.py
|
lenmon7/cocacola
|
6e97035c93b9d465e1e88d65c405afc2c9a3d77d
|
[
"MIT"
] | null | null | null |
z01.py
|
lenmon7/cocacola
|
6e97035c93b9d465e1e88d65c405afc2c9a3d77d
|
[
"MIT"
] | null | null | null |
#@Time :2019/2/11 14:50
| 6
| 26
| 0.5
| 6
| 30
| 2.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.52381
| 0.3
| 30
| 4
| 27
| 7.5
| 0.190476
| 0.833333
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
8d3be6ed2cb38706067d9abc6ca8eee140310bc9
| 9,524
|
py
|
Python
|
commonroad/visualization/renderer.py
|
CommonRoad/commonroad-io
|
93824961da9c41eb7768b5cf1acbed9a07446dc2
|
[
"BSD-3-Clause"
] | 3
|
2022-01-05T09:10:18.000Z
|
2022-03-22T15:09:43.000Z
|
commonroad/visualization/renderer.py
|
CommonRoad/commonroad-io
|
93824961da9c41eb7768b5cf1acbed9a07446dc2
|
[
"BSD-3-Clause"
] | null | null | null |
commonroad/visualization/renderer.py
|
CommonRoad/commonroad-io
|
93824961da9c41eb7768b5cf1acbed9a07446dc2
|
[
"BSD-3-Clause"
] | null | null | null |
from abc import ABCMeta, abstractmethod
class IRenderer(metaclass=ABCMeta):
@abstractmethod
def draw_scenario(self, obj, draw_params, call_stack):
"""
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_static_obstacle(self, obj, draw_params, call_stack):
"""
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_dynamic_obstacle(self, obj, draw_params, call_stack):
"""
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_phantom_obstacle(self, obj, draw_params, call_stack):
"""
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_environment_obstacle(self, obj, draw_params, call_stack):
"""
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_trajectory(self, obj, draw_params, call_stack):
"""
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_trajectories(self, obj, draw_params, call_stack):
pass
@abstractmethod
def draw_polygon(self, vertices, draw_params, call_stack):
"""
Draws a polygon shape
:param vertices: vertices of the polygon
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_rectangle(self, vertices, draw_params, call_stack):
"""
Draws a rectangle shape
:param vertices: vertices of the rectangle
:param draw_params: parameters for plotting given by a nested dict that
recreates the structure of an object,
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_ellipse(self, center, radius_x, radius_yt, draw_params,
call_stack):
"""
Draws a circle shape
:param ellipse: center position of the ellipse
:param radius_x: radius of the ellipse along the x-axis
:param radius_y: radius of the ellipse along the y-axis
:param draw_params: parameters for plotting given by a nested dict that
recreates the structure of an object,
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_state(self, state, draw_params, call_stack):
"""
Draws a state as an arrow of its velocity vector
:param state: state to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_lanelet_network(self, obj, draw_params, call_stack):
"""
Draws a lanelet network
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict that
recreates the structure of an object,
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_goal_region(self, obj, draw_params, call_stack):
"""
Draw goal states from goal region
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_planning_problem(self, obj, draw_params, call_stack):
"""
Draw initial state and goal region of the planning problem
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_planning_problem_set(self, obj, draw_params, call_stack):
"""
Draws all or selected planning problems from the planning problem
set. Planning problems can be selected by providing IDs in
`drawing_params[planning_problem_set][draw_ids]`
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_initital_state(self, obj, draw_params, call_stack):
"""
Draw initial state with label
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_goal_state(self, obj, draw_params, call_stack):
"""
Draw goal states
:param obj: object to be plotted
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
@abstractmethod
def draw_traffic_light_sign(self, obj, draw_params, call_stack):
"""
Draw traffic sings and lights
:param obj: object to be drawn
:param draw_params: parameters for plotting given by a nested dict
that recreates the structure of an object or a ParamServer object
:param call_stack: tuple of string containing the call stack,
which allows for differentiation of plotting styles
depending on the call stack
:return: None
"""
pass
| 38.873469
| 79
| 0.653822
| 1,201
| 9,524
| 5.094088
| 0.086595
| 0.101504
| 0.066688
| 0.055901
| 0.889506
| 0.886564
| 0.855345
| 0.840307
| 0.826577
| 0.799771
| 0
| 0
| 0.303759
| 9,524
| 244
| 80
| 39.032787
| 0.922636
| 0.645737
| 0
| 0.631579
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.315789
| false
| 0.315789
| 0.017544
| 0
| 0.350877
| 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
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
a5c64ed9d158f7331e549f430bafd5627dbb347f
| 10,130
|
py
|
Python
|
tests/sdk/odometer/test_odometer.py
|
ObliviousAI/smartnoise-sdk
|
6c5b9bdd16852a08ee01299193a1fac93def99cd
|
[
"MIT"
] | null | null | null |
tests/sdk/odometer/test_odometer.py
|
ObliviousAI/smartnoise-sdk
|
6c5b9bdd16852a08ee01299193a1fac93def99cd
|
[
"MIT"
] | null | null | null |
tests/sdk/odometer/test_odometer.py
|
ObliviousAI/smartnoise-sdk
|
6c5b9bdd16852a08ee01299193a1fac93def99cd
|
[
"MIT"
] | null | null | null |
import os
import subprocess
from opendp.smartnoise.sql.odometer import Odometer, OdometerHeterogeneous
from opendp.smartnoise.sql.privacy import Privacy
from opendp.smartnoise.sql.private_reader import PrivateReader
import pandas as pd
import numpy as np
from opendp.smartnoise.metadata.collection import CollectionMetadata
git_root_dir = subprocess.check_output("git rev-parse --show-toplevel".split(" ")).decode("utf-8").strip()
meta_path = os.path.join(git_root_dir, os.path.join("datasets", "PUMS.yaml"))
csv_path = os.path.join(git_root_dir, os.path.join("datasets", "PUMS.csv"))
pums = pd.read_csv(csv_path)
privacy = Privacy(epsilon=1.0)
meta_obj = CollectionMetadata.from_(meta_path)
class TestOdometer:
def test_count_pid_query(self):
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_path)
assert(priv.odometer.spent == (0.0, 0.0))
assert(priv.odometer.k == 0)
res = priv.execute("SELECT COUNT(DISTINCT pid) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 1)
def test_count_query(self):
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_path)
assert(priv.odometer.spent == (0.0, 0.0))
assert(priv.odometer.k == 0)
res = priv.execute("SELECT COUNT(age) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 2)
def test_count_row_privacy(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
assert(priv.odometer.spent == (0.0, 0.0))
assert(priv.odometer.k == 0)
res = priv.execute("SELECT COUNT(*) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 1)
def test_count_row_privacy_col(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
assert(priv.odometer.spent == (0.0, 0.0))
assert(priv.odometer.k == 0)
res = priv.execute("SELECT COUNT(age) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 1)
def test_count_row_privacy_col_quantifier(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
assert(priv.odometer.spent == (0.0, 0.0))
assert(priv.odometer.k == 0)
res = priv.execute("SELECT COUNT(DISTINCT age) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 2)
def test_count_row_privacy_no_censor(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = False
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
assert(priv.odometer.spent == (0.0, 0.0))
assert(priv.odometer.k == 0)
res = priv.execute("SELECT COUNT(DISTINCT age) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 1)
def test_variance(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.execute("SELECT VAR(age) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 3)
def test_std(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.execute("SELECT STD(age) FROM PUMS.PUMS GROUP BY sex")
assert(priv.odometer.k == 3)
def test_avg(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.execute("SELECT AVG(age) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 2)
def test_sum(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.execute("SELECT SUM(age) FROM PUMS.PUMS GROUP BY educ")
assert(priv.odometer.k == 2)
def test_three_var(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.execute("SELECT VAR(age), VAR(educ), VAR(income) FROM PUMS.PUMS GROUP BY sex")
assert(priv.odometer.k == 5)
def test_odo_hom(self):
privacy = Privacy(epsilon=0.1, delta = 1/(1000))
odo = Odometer(privacy)
for _ in range(300):
odo.spend()
eps, delt = odo.spent
assert(np.isclose(eps, 8.4917))
assert(np.isclose(delt, 0.19256))
def test_odo_het(self):
privacy = Privacy(epsilon=0.1, delta = 1/(1000))
odo = OdometerHeterogeneous(privacy)
for _ in range(300):
odo.spend()
eps, delt = odo.spent
assert(np.isclose(eps, 8.2519))
assert(np.isclose(delt, 0.2596633))
def test_odo_het_alternate(self):
privacy = Privacy(epsilon=0.1, delta = 1/(1000))
odo = OdometerHeterogeneous()
for _ in range(300):
odo.spend(privacy)
eps, delt = odo.spent
assert(np.isclose(eps, 8.2519))
assert(np.isclose(delt, 0.2596633))
class TestMultiplier:
def test_count_pid_query(self):
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_path)
res = priv.get_budget_multiplier("SELECT COUNT(DISTINCT pid) FROM PUMS.PUMS GROUP BY educ")
assert(res == 1)
def test_count_query(self):
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_path)
res = priv.get_budget_multiplier("SELECT COUNT(age) FROM PUMS.PUMS GROUP BY educ")
assert(res == 2)
def test_count_row_privacy(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.get_budget_multiplier("SELECT COUNT(*) FROM PUMS.PUMS GROUP BY educ")
assert(res == 1)
def test_count_row_privacy_col(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.get_budget_multiplier("SELECT COUNT(age) FROM PUMS.PUMS GROUP BY educ")
assert(res == 1)
def test_count_row_privacy_col_quantifier(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.get_budget_multiplier("SELECT COUNT(DISTINCT age) FROM PUMS.PUMS GROUP BY educ")
assert(res == 2)
def test_count_row_privacy_no_censor(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = False
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.get_budget_multiplier("SELECT COUNT(DISTINCT age) FROM PUMS.PUMS GROUP BY educ")
assert(res == 1)
def test_variance(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.get_budget_multiplier("SELECT VAR(age) FROM PUMS.PUMS GROUP BY educ")
assert(res == 3)
def test_std(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.get_budget_multiplier("SELECT STD(age) FROM PUMS.PUMS GROUP BY sex")
assert(res == 3)
def test_avg(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.get_budget_multiplier("SELECT AVG(age) FROM PUMS.PUMS GROUP BY educ")
assert(res == 2)
def test_sum(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
res = priv.get_budget_multiplier("SELECT SUM(age) FROM PUMS.PUMS GROUP BY educ")
assert(res == 2)
def test_three_var(self):
meta_obj['PUMS.PUMS'].row_privacy = True
meta_obj['PUMS.PUMS']['pid'].is_key = False
meta_obj['PUMS.PUMS'].censor_dims = True
priv = PrivateReader.from_connection(pums, privacy=privacy, metadata=meta_obj)
query = "SELECT VAR(age), VAR(educ), VAR(income) FROM PUMS.PUMS GROUP BY sex"
res = priv.get_budget_multiplier(query)
assert(res == 5)
eps, _ = priv.get_privacy_cost(query)
assert(eps <= 5.0)
| 49.901478
| 106
| 0.661303
| 1,422
| 10,130
| 4.532349
| 0.078762
| 0.091854
| 0.088751
| 0.121024
| 0.904577
| 0.896974
| 0.893716
| 0.893716
| 0.893716
| 0.893251
| 0
| 0.015475
| 0.208983
| 10,130
| 202
| 107
| 50.148515
| 0.788843
| 0
| 0
| 0.774359
| 0
| 0
| 0.165449
| 0
| 0
| 0
| 0
| 0
| 0.210256
| 1
| 0.128205
| false
| 0
| 0.041026
| 0
| 0.179487
| 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
|
573cfe0a9ece093c134daa65101ef4c830c0b1eb
| 551,763
|
py
|
Python
|
View/PY/file_RC.py
|
GeovaniTech/DownTo-Videos-Musics
|
0c34a104ab349b8491c8d3e474bf165e562cd858
|
[
"MIT"
] | null | null | null |
View/PY/file_RC.py
|
GeovaniTech/DownTo-Videos-Musics
|
0c34a104ab349b8491c8d3e474bf165e562cd858
|
[
"MIT"
] | null | null | null |
View/PY/file_RC.py
|
GeovaniTech/DownTo-Videos-Musics
|
0c34a104ab349b8491c8d3e474bf165e562cd858
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qt_version = [int(v) for v in QtCore.qVersion().split('.')]
if qt_version < [5, 8, 0]:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
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0
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9e1711c1db10e21249b0fa8e405b5f3ab002aec4
| 6,407
|
py
|
Python
|
loldib/getratings/models/NA/na_quinn/na_quinn_bot.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_quinn/na_quinn_bot.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_quinn/na_quinn_bot.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
from getratings.models.ratings import Ratings
class NA_Quinn_Bot_Aatrox(Ratings):
pass
class NA_Quinn_Bot_Ahri(Ratings):
pass
class NA_Quinn_Bot_Akali(Ratings):
pass
class NA_Quinn_Bot_Alistar(Ratings):
pass
class NA_Quinn_Bot_Amumu(Ratings):
pass
class NA_Quinn_Bot_Anivia(Ratings):
pass
class NA_Quinn_Bot_Annie(Ratings):
pass
class NA_Quinn_Bot_Ashe(Ratings):
pass
class NA_Quinn_Bot_AurelionSol(Ratings):
pass
class NA_Quinn_Bot_Azir(Ratings):
pass
class NA_Quinn_Bot_Bard(Ratings):
pass
class NA_Quinn_Bot_Blitzcrank(Ratings):
pass
class NA_Quinn_Bot_Brand(Ratings):
pass
class NA_Quinn_Bot_Braum(Ratings):
pass
class NA_Quinn_Bot_Caitlyn(Ratings):
pass
class NA_Quinn_Bot_Camille(Ratings):
pass
class NA_Quinn_Bot_Cassiopeia(Ratings):
pass
class NA_Quinn_Bot_Chogath(Ratings):
pass
class NA_Quinn_Bot_Corki(Ratings):
pass
class NA_Quinn_Bot_Darius(Ratings):
pass
class NA_Quinn_Bot_Diana(Ratings):
pass
class NA_Quinn_Bot_Draven(Ratings):
pass
class NA_Quinn_Bot_DrMundo(Ratings):
pass
class NA_Quinn_Bot_Ekko(Ratings):
pass
class NA_Quinn_Bot_Elise(Ratings):
pass
class NA_Quinn_Bot_Evelynn(Ratings):
pass
class NA_Quinn_Bot_Ezreal(Ratings):
pass
class NA_Quinn_Bot_Fiddlesticks(Ratings):
pass
class NA_Quinn_Bot_Fiora(Ratings):
pass
class NA_Quinn_Bot_Fizz(Ratings):
pass
class NA_Quinn_Bot_Galio(Ratings):
pass
class NA_Quinn_Bot_Gangplank(Ratings):
pass
class NA_Quinn_Bot_Garen(Ratings):
pass
class NA_Quinn_Bot_Gnar(Ratings):
pass
class NA_Quinn_Bot_Gragas(Ratings):
pass
class NA_Quinn_Bot_Graves(Ratings):
pass
class NA_Quinn_Bot_Hecarim(Ratings):
pass
class NA_Quinn_Bot_Heimerdinger(Ratings):
pass
class NA_Quinn_Bot_Illaoi(Ratings):
pass
class NA_Quinn_Bot_Irelia(Ratings):
pass
class NA_Quinn_Bot_Ivern(Ratings):
pass
class NA_Quinn_Bot_Janna(Ratings):
pass
class NA_Quinn_Bot_JarvanIV(Ratings):
pass
class NA_Quinn_Bot_Jax(Ratings):
pass
class NA_Quinn_Bot_Jayce(Ratings):
pass
class NA_Quinn_Bot_Jhin(Ratings):
pass
class NA_Quinn_Bot_Jinx(Ratings):
pass
class NA_Quinn_Bot_Kalista(Ratings):
pass
class NA_Quinn_Bot_Karma(Ratings):
pass
class NA_Quinn_Bot_Karthus(Ratings):
pass
class NA_Quinn_Bot_Kassadin(Ratings):
pass
class NA_Quinn_Bot_Katarina(Ratings):
pass
class NA_Quinn_Bot_Kayle(Ratings):
pass
class NA_Quinn_Bot_Kayn(Ratings):
pass
class NA_Quinn_Bot_Kennen(Ratings):
pass
class NA_Quinn_Bot_Khazix(Ratings):
pass
class NA_Quinn_Bot_Kindred(Ratings):
pass
class NA_Quinn_Bot_Kled(Ratings):
pass
class NA_Quinn_Bot_KogMaw(Ratings):
pass
class NA_Quinn_Bot_Leblanc(Ratings):
pass
class NA_Quinn_Bot_LeeSin(Ratings):
pass
class NA_Quinn_Bot_Leona(Ratings):
pass
class NA_Quinn_Bot_Lissandra(Ratings):
pass
class NA_Quinn_Bot_Lucian(Ratings):
pass
class NA_Quinn_Bot_Lulu(Ratings):
pass
class NA_Quinn_Bot_Lux(Ratings):
pass
class NA_Quinn_Bot_Malphite(Ratings):
pass
class NA_Quinn_Bot_Malzahar(Ratings):
pass
class NA_Quinn_Bot_Maokai(Ratings):
pass
class NA_Quinn_Bot_MasterYi(Ratings):
pass
class NA_Quinn_Bot_MissFortune(Ratings):
pass
class NA_Quinn_Bot_MonkeyKing(Ratings):
pass
class NA_Quinn_Bot_Mordekaiser(Ratings):
pass
class NA_Quinn_Bot_Morgana(Ratings):
pass
class NA_Quinn_Bot_Nami(Ratings):
pass
class NA_Quinn_Bot_Nasus(Ratings):
pass
class NA_Quinn_Bot_Nautilus(Ratings):
pass
class NA_Quinn_Bot_Nidalee(Ratings):
pass
class NA_Quinn_Bot_Nocturne(Ratings):
pass
class NA_Quinn_Bot_Nunu(Ratings):
pass
class NA_Quinn_Bot_Olaf(Ratings):
pass
class NA_Quinn_Bot_Orianna(Ratings):
pass
class NA_Quinn_Bot_Ornn(Ratings):
pass
class NA_Quinn_Bot_Pantheon(Ratings):
pass
class NA_Quinn_Bot_Poppy(Ratings):
pass
class NA_Quinn_Bot_Quinn(Ratings):
pass
class NA_Quinn_Bot_Rakan(Ratings):
pass
class NA_Quinn_Bot_Rammus(Ratings):
pass
class NA_Quinn_Bot_RekSai(Ratings):
pass
class NA_Quinn_Bot_Renekton(Ratings):
pass
class NA_Quinn_Bot_Rengar(Ratings):
pass
class NA_Quinn_Bot_Riven(Ratings):
pass
class NA_Quinn_Bot_Rumble(Ratings):
pass
class NA_Quinn_Bot_Ryze(Ratings):
pass
class NA_Quinn_Bot_Sejuani(Ratings):
pass
class NA_Quinn_Bot_Shaco(Ratings):
pass
class NA_Quinn_Bot_Shen(Ratings):
pass
class NA_Quinn_Bot_Shyvana(Ratings):
pass
class NA_Quinn_Bot_Singed(Ratings):
pass
class NA_Quinn_Bot_Sion(Ratings):
pass
class NA_Quinn_Bot_Sivir(Ratings):
pass
class NA_Quinn_Bot_Skarner(Ratings):
pass
class NA_Quinn_Bot_Sona(Ratings):
pass
class NA_Quinn_Bot_Soraka(Ratings):
pass
class NA_Quinn_Bot_Swain(Ratings):
pass
class NA_Quinn_Bot_Syndra(Ratings):
pass
class NA_Quinn_Bot_TahmKench(Ratings):
pass
class NA_Quinn_Bot_Taliyah(Ratings):
pass
class NA_Quinn_Bot_Talon(Ratings):
pass
class NA_Quinn_Bot_Taric(Ratings):
pass
class NA_Quinn_Bot_Teemo(Ratings):
pass
class NA_Quinn_Bot_Thresh(Ratings):
pass
class NA_Quinn_Bot_Tristana(Ratings):
pass
class NA_Quinn_Bot_Trundle(Ratings):
pass
class NA_Quinn_Bot_Tryndamere(Ratings):
pass
class NA_Quinn_Bot_TwistedFate(Ratings):
pass
class NA_Quinn_Bot_Twitch(Ratings):
pass
class NA_Quinn_Bot_Udyr(Ratings):
pass
class NA_Quinn_Bot_Urgot(Ratings):
pass
class NA_Quinn_Bot_Varus(Ratings):
pass
class NA_Quinn_Bot_Vayne(Ratings):
pass
class NA_Quinn_Bot_Veigar(Ratings):
pass
class NA_Quinn_Bot_Velkoz(Ratings):
pass
class NA_Quinn_Bot_Vi(Ratings):
pass
class NA_Quinn_Bot_Viktor(Ratings):
pass
class NA_Quinn_Bot_Vladimir(Ratings):
pass
class NA_Quinn_Bot_Volibear(Ratings):
pass
class NA_Quinn_Bot_Warwick(Ratings):
pass
class NA_Quinn_Bot_Xayah(Ratings):
pass
class NA_Quinn_Bot_Xerath(Ratings):
pass
class NA_Quinn_Bot_XinZhao(Ratings):
pass
class NA_Quinn_Bot_Yasuo(Ratings):
pass
class NA_Quinn_Bot_Yorick(Ratings):
pass
class NA_Quinn_Bot_Zac(Ratings):
pass
class NA_Quinn_Bot_Zed(Ratings):
pass
class NA_Quinn_Bot_Ziggs(Ratings):
pass
class NA_Quinn_Bot_Zilean(Ratings):
pass
class NA_Quinn_Bot_Zyra(Ratings):
pass
| 15.364508
| 46
| 0.761667
| 972
| 6,407
| 4.59465
| 0.151235
| 0.216301
| 0.370802
| 0.463502
| 0.797582
| 0.797582
| 0
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| 0.173404
| 6,407
| 416
| 47
| 15.401442
| 0.843278
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| true
| 0.498195
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|
0
| 7
|
9e2604a3faf16542ca6784bb553c8a83ee1446ab
| 142
|
py
|
Python
|
wos_api_wrapper/wos/__init__.py
|
FEFU-Analytics-Sector/wos-api-wrapper
|
03f2ee792e0f66224f79ad469673fc6b52de452d
|
[
"MIT"
] | 4
|
2022-03-04T07:17:09.000Z
|
2022-03-29T05:24:06.000Z
|
wos_api_wrapper/wos/__init__.py
|
FEFU-Analytics-Sector/wos-api-wrapper
|
03f2ee792e0f66224f79ad469673fc6b52de452d
|
[
"MIT"
] | null | null | null |
wos_api_wrapper/wos/__init__.py
|
FEFU-Analytics-Sector/wos-api-wrapper
|
03f2ee792e0f66224f79ad469673fc6b52de452d
|
[
"MIT"
] | null | null | null |
from wos_api_wrapper.wos.utils import *
from wos_api_wrapper.wos.user_query_search import *
from wos_api_wrapper.wos.article_detail import *
| 28.4
| 51
| 0.84507
| 24
| 142
| 4.625
| 0.458333
| 0.189189
| 0.27027
| 0.459459
| 0.648649
| 0.468468
| 0
| 0
| 0
| 0
| 0
| 0
| 0.091549
| 142
| 4
| 52
| 35.5
| 0.860465
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| 0
| true
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| null | 0
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| null | 0
| 0
| 0
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| 1
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| 1
| 0
| 1
| 0
|
0
| 7
|
f51fd73e71c68f817d645974bab5ee42e3ef6c58
| 1,688
|
py
|
Python
|
pylel/evaluate/evaluate_expr/language_core/standard_language_functions/lel_math.py
|
osaatcioglu/pylel
|
f994e58bc886cd4b3d9a903db917fbaf084e5992
|
[
"MIT"
] | 1
|
2018-11-29T09:19:36.000Z
|
2018-11-29T09:19:36.000Z
|
pylel/evaluate/evaluate_expr/language_core/standard_language_functions/lel_math.py
|
osaatcioglu/pylel
|
f994e58bc886cd4b3d9a903db917fbaf084e5992
|
[
"MIT"
] | null | null | null |
pylel/evaluate/evaluate_expr/language_core/standard_language_functions/lel_math.py
|
osaatcioglu/pylel
|
f994e58bc886cd4b3d9a903db917fbaf084e5992
|
[
"MIT"
] | null | null | null |
import functools
from pylel.token import Symbols, Token
def _addition(*numbers):
if any([number.type != Symbols.NUMBER for number in numbers]):
raise Exception("+ only operates on NUMBER type")
return functools.reduce(lambda acc, cur: Token(Symbols.NUMBER, acc.value + cur.value), numbers)
def _subtraction(x_number, y_number):
if x_number.type != Symbols.NUMBER or y_number.type != Symbols.NUMBER:
raise Exception("- only operates on NUMBER type")
return Token(Symbols.NUMBER, x_number.value - y_number.value)
def _division(x_number, y_number):
if x_number.type != Symbols.NUMBER or y_number.type != Symbols.NUMBER:
raise Exception("/ only operates on NUMBER type")
if y_number.value == 0:
raise Exception("Divison by zero error")
return Token(Symbols.NUMBER, x_number.value / y_number.value)
def _multiplication(*numbers):
if any([number.type != Symbols.NUMBER for number in numbers]):
raise Exception("* only operates on NUMBER type")
return functools.reduce(lambda acc, cur: Token(Symbols.NUMBER, acc.value * cur.value), numbers)
def _power(x_number, y_number):
if x_number.type != Symbols.NUMBER or y_number.type != Symbols.NUMBER:
raise Exception("** only operates on NUMBER type")
return Token(Symbols.NUMBER, pow(x_number.value, y_number.value))
def _mod(x_number, y_number):
if x_number.type != Symbols.NUMBER or y_number.type != Symbols.NUMBER:
raise Exception("% only operates on NUMBER type")
if y_number.value == 0:
raise Exception("Divison by zero error")
return Token(Symbols.NUMBER, x_number.value % y_number.value)
LEL_MATH = {
"+": _addition,
"-": _subtraction,
"*": _multiplication,
"/": _division,
"**": _power,
"%": _mod,
}
| 36.695652
| 96
| 0.736967
| 247
| 1,688
| 4.878543
| 0.17004
| 0.13278
| 0.141079
| 0.190871
| 0.86888
| 0.86888
| 0.86888
| 0.846473
| 0.846473
| 0.846473
| 0
| 0.001369
| 0.134479
| 1,688
| 45
| 97
| 37.511111
| 0.823409
| 0
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| 0.263158
| 0
| 0
| 0.136256
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| 1
| 0.157895
| false
| 0
| 0.052632
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| 0.368421
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| null | 0
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| 1
| 1
| 1
| 1
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| null | 0
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| 0
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| 0
| 0
| 0
|
0
| 7
|
191997256306328cbf5d1d4b671b5305a9c2330a
| 4,506
|
py
|
Python
|
plugins/polio/migrations/0002_auto_20210505_0952.py
|
ekhalilbsq/iaso
|
e6400c52aeb4f67ce1ca83b03efa3cb11ef235ee
|
[
"MIT"
] | 29
|
2020-12-26T07:22:19.000Z
|
2022-03-07T13:40:09.000Z
|
plugins/polio/migrations/0002_auto_20210505_0952.py
|
ekhalilbsq/iaso
|
e6400c52aeb4f67ce1ca83b03efa3cb11ef235ee
|
[
"MIT"
] | 150
|
2020-11-09T15:03:27.000Z
|
2022-03-07T15:36:07.000Z
|
plugins/polio/migrations/0002_auto_20210505_0952.py
|
ekhalilbsq/iaso
|
e6400c52aeb4f67ce1ca83b03efa3cb11ef235ee
|
[
"MIT"
] | 4
|
2020-11-09T10:38:13.000Z
|
2021-10-04T09:42:47.000Z
|
# Generated by Django 3.0.14 on 2021-05-05 09:52
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("polio", "0001_initial"),
]
operations = [
migrations.AlterField(
model_name="campaign",
name="ag_nopv_group_met_at",
field=models.DateField(blank=True, null=True, verbose_name="AG/nOPV Group"),
),
migrations.AlterField(
model_name="campaign",
name="cvdpv2_notified_at",
field=models.DateField(blank=True, null=True, verbose_name="cVDPV2 Notication"),
),
migrations.AlterField(
model_name="campaign",
name="cvdpv_notified_at",
field=models.DateField(blank=True, null=True, verbose_name="cVDPV Notication"),
),
migrations.AlterField(
model_name="campaign",
name="detection_first_draft_submitted_at",
field=models.DateField(blank=True, null=True, verbose_name="1st Draft Submission"),
),
migrations.AlterField(
model_name="campaign",
name="detection_rrt_oprtt_approval_at",
field=models.DateField(blank=True, null=True, verbose_name="RRT/OPRTT Approval"),
),
migrations.AlterField(
model_name="campaign",
name="dg_authorized_at",
field=models.DateField(blank=True, null=True, verbose_name="DG Authorization"),
),
migrations.AlterField(
model_name="campaign",
name="eomg",
field=models.DateField(blank=True, null=True, verbose_name="EOMG"),
),
migrations.AlterField(
model_name="campaign",
name="investigation_at",
field=models.DateField(blank=True, null=True, verbose_name="Field Investigation Date"),
),
migrations.AlterField(
model_name="campaign",
name="onset_at",
field=models.DateField(blank=True, help_text="When the campaign starts", null=True),
),
migrations.AlterField(
model_name="campaign",
name="pv2_notified_at",
field=models.DateField(blank=True, null=True, verbose_name="PV2 Notication"),
),
migrations.AlterField(
model_name="campaign",
name="pv_notified_at",
field=models.DateField(blank=True, null=True, verbose_name="PV Notication"),
),
migrations.AlterField(
model_name="campaign",
name="risk_assessment_first_draft_submitted_at",
field=models.DateField(blank=True, null=True, verbose_name="1st Draft Submission"),
),
migrations.AlterField(
model_name="campaign",
name="risk_assessment_rrt_oprtt_approval_at",
field=models.DateField(blank=True, null=True, verbose_name="RRT/OPRTT Approval"),
),
migrations.AlterField(
model_name="campaign",
name="three_level_call_at",
field=models.DateField(blank=True, null=True, verbose_name="3 level call"),
),
migrations.AlterField(
model_name="round",
name="ended_at",
field=models.DateField(blank=True, null=True),
),
migrations.AlterField(
model_name="round",
name="im_ended_at",
field=models.DateField(blank=True, null=True),
),
migrations.AlterField(
model_name="round",
name="im_started_at",
field=models.DateField(blank=True, null=True),
),
migrations.AlterField(
model_name="round",
name="lqas_ended_at",
field=models.DateField(blank=True, null=True),
),
migrations.AlterField(
model_name="round",
name="lqas_started_at",
field=models.DateField(blank=True, null=True),
),
migrations.AlterField(
model_name="round",
name="mop_up_ended_at",
field=models.DateField(blank=True, null=True),
),
migrations.AlterField(
model_name="round",
name="mop_up_started_at",
field=models.DateField(blank=True, null=True),
),
migrations.AlterField(
model_name="round",
name="started_at",
field=models.DateField(blank=True, null=True),
),
]
| 36.33871
| 99
| 0.581003
| 452
| 4,506
| 5.59292
| 0.170354
| 0.174051
| 0.217563
| 0.252373
| 0.87144
| 0.87144
| 0.743671
| 0.672073
| 0.644778
| 0.607595
| 0
| 0.008591
| 0.302486
| 4,506
| 123
| 100
| 36.634146
| 0.795737
| 0.010209
| 0
| 0.666667
| 1
| 0
| 0.176985
| 0.031853
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.008547
| 0
| 0.034188
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
| 0
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| 0
| 0
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| 0
| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
194d145e1f39412aa314ed8cb2c1e9202e67e5bf
| 15,819
|
py
|
Python
|
arkdbtools/tests/test_core.py
|
Nijmegen-Consultancy-Group/ark-db-tools
|
7b46e5539352b9982a0999b96b57c96e374060e2
|
[
"MIT"
] | null | null | null |
arkdbtools/tests/test_core.py
|
Nijmegen-Consultancy-Group/ark-db-tools
|
7b46e5539352b9982a0999b96b57c96e374060e2
|
[
"MIT"
] | null | null | null |
arkdbtools/tests/test_core.py
|
Nijmegen-Consultancy-Group/ark-db-tools
|
7b46e5539352b9982a0999b96b57c96e374060e2
|
[
"MIT"
] | null | null | null |
from unittest import TestCase
class TestCore(TestCase):
def setUp(self):
from arkdbtools.dbtools import set_connection, set_sender
set_connection(
host='localhost',
database='ark_mainnet',
user='ark'
)
set_sender()
def tearDown(self):
from arkdbtools.dbtools import set_sender
set_sender()
def test_payoutsender_default_share(self):
from arkdbtools.dbtools import set_sender, Core
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {'address': {
'share': 100 * ARK,
'vote_timestamp': 10,
'last_payout': 100,
'status': True
}}
data = 'address', test_dict['address']
set_sender(
default_share= 0.7,
cover_fees=False,
share_percentage_exceptions=None,
timestamp_brackets=None,
min_payout_daily=0,
min_payout_weekly=0,
min_payout_monthly=0,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=14,
payoutsender_test=True,
sender_exception=None
)
result, delegate_share, amount = Core.payoutsender(data)
self.assertTrue(result)
self.assertEqual(amount, 70*ARK)
self.assertEqual(delegate_share, 30*ARK)
def test_payoutsender_settings_cover_fees(self):
from arkdbtools.dbtools import set_sender, Core
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {'address': {
'share': 100 * ARK,
'vote_timestamp': 10,
'last_payout': 100,
'status': True
}}
data = 'address', test_dict['address']
set_sender(
default_share=0.7,
cover_fees=True,
share_percentage_exceptions=None,
timestamp_brackets=None,
min_payout_daily=0,
min_payout_weekly=0,
min_payout_monthly=0,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=14,
payoutsender_test=True,
sender_exception=None
)
test_result_amount = 70*ARK + TX_FEE
test_result_delegate_share = 30*ARK - TX_FEE
result, delegate_share, amount = Core.payoutsender(data)
self.assertTrue(result)
self.assertEqual(amount, test_result_amount)
self.assertEqual(delegate_share, test_result_delegate_share)
def test_payoutsender_settings_share_percentage_exceptions_no_cover_fees(self):
from arkdbtools.dbtools import set_sender, Core
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {'address': {
'share': 100 * ARK,
'vote_timestamp': 10,
'last_payout': 100,
'status': True
}}
exceptions = {'address': 0.8}
data = 'address', test_dict['address']
set_sender(
default_share=0.7,
cover_fees=False,
share_percentage_exceptions=exceptions,
timestamp_brackets=None,
min_payout_daily=0,
min_payout_weekly=0,
min_payout_monthly=0,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=14,
payoutsender_test=True,
sender_exception=None
)
test_result_amount = 80*ARK
test_result_delegate_share = 20*ARK
result, delegate_share, amount = Core.payoutsender(data)
self.assertTrue(result)
self.assertEqual(amount, test_result_amount)
self.assertEqual(delegate_share, test_result_delegate_share)
def test_payoutsender_settings_share_percentage_exceptions_cover_fees(self):
from arkdbtools.dbtools import set_sender, Core
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {'address': {
'share': 100 * ARK,
'vote_timestamp': 10,
'last_payout': 100,
'status': True
}}
exceptions = {'address': 0.8}
data = 'address', test_dict['address']
set_sender(
default_share=0.7,
cover_fees=True,
share_percentage_exceptions=exceptions,
timestamp_brackets=None,
min_payout_daily=0,
min_payout_weekly=0,
min_payout_monthly=0,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=14,
payoutsender_test=True,
sender_exception=None
)
test_result_amount = 80*ARK + TX_FEE
test_result_delegate_share = 20*ARK - TX_FEE
result, delegate_share, amount = Core.payoutsender(data)
self.assertTrue(result)
self.assertEqual(amount, test_result_amount)
self.assertEqual(delegate_share, test_result_delegate_share)
def test_payoutsender_settings_timestamp_brackets_cover_fees(self):
from arkdbtools.dbtools import set_sender, Core
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {
'address1': {
'share': 100 * ARK,
'vote_timestamp': 10,
'last_payout': 100,
'status': True},
'address2': {
'share': 100 * ARK,
'vote_timestamp': 90,
'last_payout': 100,
'status': True}
}
timestamp_brackets = {
float('inf'): 0.8,
20: 0.9
}
data1 = 'address1', test_dict['address1']
data2 = 'address2', test_dict['address2']
set_sender(
default_share=0.7,
cover_fees=True,
share_percentage_exceptions=None,
timestamp_brackets=timestamp_brackets,
min_payout_daily=0,
min_payout_weekly=0,
min_payout_monthly=0,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=14,
payoutsender_test=True,
sender_exception=None
)
test_result_amount1 = 90*ARK + TX_FEE
test_result_delegate_share1 = 10*ARK - TX_FEE
test_result_amount2 = 80 * ARK + TX_FEE
test_result_delegate_share2 = 20 * ARK - TX_FEE
result1, delegate_share1, amount1 = Core.payoutsender(data1)
result2, delegate_share2, amount2 = Core.payoutsender(data2)
self.assertTrue(result1)
self.assertTrue(result2)
self.assertEqual(amount1, test_result_amount1)
self.assertEqual(delegate_share1, test_result_delegate_share1)
self.assertEqual(amount2, test_result_amount2)
self.assertEqual(delegate_share2, test_result_delegate_share2)
def test_payoutsender_settings_timestamp_brackets_no_cover_fees(self):
from arkdbtools.dbtools import set_sender, Core
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {
'address1': {
'share': 100 * ARK,
'vote_timestamp': 10,
'last_payout': 100,
'status': True},
'address2': {
'share': 100 * ARK,
'vote_timestamp': 90,
'last_payout': 100,
'status': True}
}
timestamp_brackets = {
float('inf'): 0.8,
20: 0.9
}
data1 = 'address1', test_dict['address1']
data2 = 'address2', test_dict['address2']
set_sender(
default_share=0.7,
cover_fees=False,
share_percentage_exceptions=None,
timestamp_brackets=timestamp_brackets,
min_payout_daily=0,
min_payout_weekly=0,
min_payout_monthly=0,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=14,
payoutsender_test=True,
sender_exception=None
)
test_result_amount1 = 90 * ARK
test_result_delegate_share1 = 10 * ARK
test_result_amount2 = 80 * ARK
test_result_delegate_share2 = 20 * ARK
result1, delegate_share1, amount1 = Core.payoutsender(data1)
result2, delegate_share2, amount2 = Core.payoutsender(data2)
self.assertTrue(result1)
self.assertTrue(result2)
self.assertEqual(amount1, test_result_amount1)
self.assertEqual(delegate_share1, test_result_delegate_share1)
self.assertEqual(amount2, test_result_amount2)
self.assertEqual(delegate_share2, test_result_delegate_share2)
def test_payoutsender_settings_min_payout_weekly(self):
from arkdbtools.dbtools import set_sender, Core, TxParameterError
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {
# has min amount, so should pass
'address1': {
'share': 10000 * ARK,
'vote_timestamp': 10,
'last_payout': 10,
'status': True},
# does not have the min amount, but last_payout is early enough
'address2': {
'share': 50 * ARK,
'vote_timestamp': 90,
'last_payout': 100,
'status': True},
# share > than min, but last payout not recent enough
'address3': {
'share': 10000 * ARK,
'vote_timestamp': 90,
'last_payout': float('inf'),
'status': True}
}
frq_dict = {
'address1': 2,
'address2': 2,
'address3': 2,
}
data1 = 'address1', test_dict['address1']
data2 = 'address2', test_dict['address2']
data3 = 'address3', test_dict['address3']
set_sender(
default_share=0.7,
cover_fees=False,
share_percentage_exceptions=None,
timestamp_brackets=None,
min_payout_daily=0,
min_payout_weekly=90*ARK,
min_payout_monthly=0,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=datetime.today().month,
payoutsender_test=True,
sender_exception=None
)
test_result_amount1 = 7000 * ARK
test_result_delegate_share1 = 3000 * ARK
result1, delegate_share1, amount1 = Core.payoutsender(data1, frq_dict=frq_dict)
self.assertTrue(result1)
self.assertTrue(test_result_amount1 == amount1)
self.assertTrue(test_result_delegate_share1, delegate_share1)
self.assertRaises(TxParameterError, Core.payoutsender, data=data2, frq_dict=frq_dict)
self.assertRaises(TxParameterError, Core.payoutsender, data=data3, frq_dict=frq_dict)
def test_payoutsender_settings_min_payout_monthly(self):
from arkdbtools.dbtools import set_sender, Core, TxParameterError
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {
# has min amount, so should pass
'address1': {
'share': 10000 * ARK,
'vote_timestamp': 10,
'last_payout': 10,
'status': True},
# does not have the min amount, but last_payout is early enough
'address2': {
'share': 50 * ARK,
'vote_timestamp': 90,
'last_payout': 100,
'status': True},
# share > than min, but last payout not recent enough
'address3': {
'share': 10000 * ARK,
'vote_timestamp': 90,
'last_payout': float('inf'),
'status': True}
}
frq_dict = {
'address1': 3,
'address2': 3,
'address3': 3,
}
data1 = 'address1', test_dict['address1']
data2 = 'address2', test_dict['address2']
data3 = 'address3', test_dict['address3']
set_sender(
default_share=0.7,
cover_fees=False,
share_percentage_exceptions=None,
timestamp_brackets=None,
min_payout_daily=0,
min_payout_weekly=0,
min_payout_monthly=90*ARK,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=datetime.today().month,
payoutsender_test=True,
sender_exception=None
)
test_result_amount1 = 7000 * ARK
test_result_delegate_share1 = 3000 * ARK
result1, delegate_share1, amount1 = Core.payoutsender(data1, frq_dict=frq_dict)
self.assertTrue(result1)
self.assertTrue(test_result_amount1 == amount1)
self.assertTrue(test_result_delegate_share1, delegate_share1)
self.assertRaises(TxParameterError, Core.payoutsender, data=data2, frq_dict=frq_dict)
self.assertRaises(TxParameterError, Core.payoutsender, data=data3, frq_dict=frq_dict)
def test_payoutsender_settings_sender_exceptions(self):
from arkdbtools.dbtools import set_sender, Core, AllocationError
from arkdbtools.config import TX_FEE, ARK
from datetime import datetime
set_sender()
test_dict = {
# has min amount, so should pass
'address1': {
'share': 10000 * ARK,
'vote_timestamp': 10,
'last_payout': 10,
'status': True},
'address2': {
'share': 50 * ARK,
'vote_timestamp': 90,
'last_payout': 100,
'status': True},
'address3': {
'share': 50 * ARK,
'vote_timestamp': 90,
'last_payout': float('inf'),
'status': True}
}
sender_exceptions = {
#has an illegal frequency, so should raise AllocationError
'address1': {
'AMOUNT': 45 * ARK,
'FREQUENCY': 4
},
# amount < share, so should pass
'address2' : {
'AMOUNT': 45 * ARK,
'FREQUENCY': 1
},
# amount > share, so should raise AllocationError
'address3': {
'AMOUNT': 4500 * ARK,
'FREQUENCY': 1}
}
data1 = 'address1', test_dict['address1']
data2 = 'address2', test_dict['address2']
data3 = 'address3', test_dict['address3']
set_sender(
default_share=0.7,
cover_fees=False,
share_percentage_exceptions=None,
timestamp_brackets=None,
min_payout_daily=0,
min_payout_weekly=0,
min_payout_monthly=0,
day_weekly_payout=datetime.today().weekday(),
day_monthly_payout=datetime.today().month,
payoutsender_test=True,
sender_exception=sender_exceptions
)
self.assertRaises(AllocationError, Core.payoutsender, data1)
self.assertRaises(AllocationError, Core.payoutsender, data3)
test_delegate_share = 5*ARK
test_amount = 45 * ARK
result, delageteshare, amount = Core.payoutsender(data2)
self.assertTrue(result)
self.assertEquals(delageteshare, test_delegate_share)
self.assertEquals(amount, test_amount)
| 34.690789
| 93
| 0.577407
| 1,602
| 15,819
| 5.4201
| 0.076779
| 0.04146
| 0.037314
| 0.031671
| 0.914085
| 0.894161
| 0.869285
| 0.854659
| 0.848439
| 0.848439
| 0
| 0.038769
| 0.336368
| 15,819
| 455
| 94
| 34.767033
| 0.788341
| 0.028826
| 0
| 0.799492
| 0
| 0
| 0.076668
| 0
| 0
| 0
| 0
| 0
| 0.098985
| 1
| 0.027919
| false
| 0
| 0.076142
| 0
| 0.106599
| 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
|
5fd582405778b4d22493a9d4b1e919fe691de663
| 64,247
|
py
|
Python
|
data/XIdata_loader.py
|
yankunxi/JointFontGAN
|
54e7333335b17f66d3060fd0cb6feb4bc9181a25
|
[
"MIT"
] | 7
|
2020-11-27T14:50:12.000Z
|
2021-07-09T05:26:31.000Z
|
data/XIdata_loader.py
|
yankunxi/JointFontGAN
|
54e7333335b17f66d3060fd0cb6feb4bc9181a25
|
[
"MIT"
] | 2
|
2021-08-30T16:39:50.000Z
|
2021-09-13T01:54:36.000Z
|
data/XIdata_loader.py
|
yankunxi/JointFontGAN
|
54e7333335b17f66d3060fd0cb6feb4bc9181a25
|
[
"MIT"
] | null | null | null |
#=============================
# JointFontGAN
# Modified from https://github.com/azadis/MC-GAN
# By Yankun Xi
#=============================
from JointFontGAN.data.XIimage_folder import ImageFolder
from JointFontGAN.data.XIbase_data_loader import BaseDataLoader
import random
import torch.utils.data
import torchvision.transforms as transforms
from builtins import object
import os
import numpy as np
from torch import LongTensor
import warnings
import pickle
from skimage.morphology import skeletonize
from skimage.util import invert
def normalize_stack(input, val=0.5):
# normalize an tensor with arbitrary number of channels:
# each channel with mean=std=val
val=0.5
len_ = input.size(0)
mean = (val,)*len_
std = (val,)*len_
t_normal_stack = transforms.Compose([
transforms.Normalize(mean, std)])
return t_normal_stack(input)
def CreateDataLoader(opt):
data_loader = None
# if opt.stack:
# data_loader = StackDataLoader()
# elif opt.partial:
# data_loader = PartialDataLoader()
if opt.data_loader == 'base':
data_loader = DataLoader()
elif opt.data_loader == 'stack':
data_loader = StackDataLoader()
elif opt.data_loader == 'Estack':
data_loader = EStackDataLoader()
elif opt.data_loader == 'partial':
data_loader = PartialDataLoader()
elif opt.data_loader == 'extended_half':
data_loader = ExHalfDataLoader()
elif opt.data_loader == 'extended_half_t':
data_loader = ExHalfTDataLoader()
elif opt.data_loader == 'extended_half_t_sk':
data_loader = ExHalfTskDataLoader()
elif opt.data_loader == 'skeleton':
data_loader = skDataLoader()
elif opt.data_loader == 'EHskeleton':
data_loader = EHskDataLoader()
data_loader.initialize(opt)
print(data_loader.name())
return data_loader
class FlatData(object):
def __init__(self, data_loader, data_loader_base, fineSize, max_dataset_size, rgb, dict_test={},base_font=False, blanks=0.7):
self.data_loader = data_loader
self.data_loader_base = data_loader_base
self.fineSize = fineSize
self.max_dataset_size = max_dataset_size
self.rgb = rgb
self.blanks = blanks
self.data_loader_base_iter = iter(self.data_loader_base)
self.A_base,self.A_base_paths = next(self.data_loader_base_iter)
self.base_font = base_font
self.random_dict=dict_test
self.dict = False
if len(self.random_dict.keys()):
self.dict = True
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
AB, AB_paths = next(self.data_loader_iter)
w_total = AB.size(3)
w = int(w_total/2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = torch.mean(AB, dim=1) # only one of the RGB channels
A = A[:,None,:,:] # (m,1,64,64*26)
B = torch.mean(AB, dim=1) # only one of the RGB channels
B = B[:,None,:,:] # (m,1,64,64*26)
n_rgb = 3 if self.rgb else 1
target_size = A.size(2)
AA = A.clone()
if self.blanks != 0:
# randomly remove some of the glyphs
if not self.dict:
blank_ind = np.random.permutation(A.size(3)/target_size)[0:int(self.blanks*A.size(3)/target_size)]
else:
file_name = map(lambda x:x.split("/")[-1],AB_paths)
blank_ind = self.random_dict[file_name][0:int(self.blanks*A.size(3)/target_size)]
blank_ind = np.tile(range(target_size), len(blank_ind)) + np.repeat(blank_ind*target_size,target_size)
AA.index_fill_(3,LongTensor(list(blank_ind)),1)
# t_topil = transforms.Compose([
# transforms.ToPILImage()])
# AA_ = t_topil(AA[0,0,:,:].unsqueeze_(0))
# misc.imsave('./AA_0.png',AA_)
return {'A': AA, 'A_paths': AB_paths, 'B':B, 'B_paths':AB_paths}
# generate alphabet
class Data(object):
def __init__(self, str_indices, data_loader, fineSize,
max_dataset_size, rgb, dict_test={}, blanks=0.7):
self.str_indices = str_indices
self.data_loader = data_loader
self.fineSize = fineSize
self.max_dataset_size = max_dataset_size
self.rgb = rgb
self.n_rgb = 3 if self.rgb else 1
self.blanks = blanks
self.random_dict = dict_test
self.dict = False
if len(self.random_dict.keys()):
self.dict = True
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
AB, AB_paths = next(self.data_loader_iter)
w_total = AB.size(3)
w = int(w_total / 2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
B = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset: w_offset + self.fineSize]
self.ab_size = int(A.size(1) / self.n_rgb)
remain_ind = list(set(self.str_indices) - {-1})
if remain_ind:
self.remainnum = len(remain_ind)
self.blanknum = self.ab_size - self.remainnum
blank_ind = list(set(range(self.ab_size)) - set(remain_ind))
else:
self.blanknum = int(self.blanks * self.ab_size)
self.remainnum = self.ab_size - self.blanknum
# randomly remove some of the glyphs in input
if not self.dict:
blank_ind = np.random.permutation(
range(self.ab_size))[0: self.blanknum]
# print(blank_ind)
else:
file_name = list(map(lambda x: x.split("/")[-1],
AB_paths))
if len(file_name) > 1:
raise Exception('batch size should be 1')
file_name = file_name[0]
blank_ind = self.random_dict[file_name][0:self.blanknum]
rgb_inds = np.tile(range(self.n_rgb), self.blanknum)
blank_ind = np.repeat(blank_ind, self.n_rgb)
blank_ind = blank_ind * self.n_rgb + rgb_inds
AA = A.clone()
AA.index_fill_(1, LongTensor(list(blank_ind)), 1)
# A_ab = A.clone()
#
# A_ab.index_fill_(1, 1, LongTensor(list(blank_ind)),
# transforms.ToTensor(Image.fromarray()))
return {'A': AA, 'A_paths': AB_paths, 'B': B,
'B_paths': AB_paths,
# 'RB': A_ab,
'indices': remain_ind}
# generate alphabet with skeletons
class skData(object):
def __init__(self, str_indices, data_loader, fineSize,
max_dataset_size, rgb, dict_test={}, blanks=0.7,
skmode=1):
self.skmode = skmode
self.str_indices = str_indices
self.data_loader = data_loader
self.fineSize = fineSize
self.max_dataset_size = max_dataset_size
self.rgb = rgb
self.n_rgb = 3 if self.rgb else 1
self.blanks = blanks
self.random_dict = dict_test
self.dict = False
if len(self.random_dict.keys()):
self.dict = True
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
AB, AB_paths = next(self.data_loader_iter)
w_total = AB.size(3)
w = int(w_total / 2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
self.ab_size = int(A.size(1) / self.n_rgb)
remain_ind = list(set(self.str_indices) - {-1})
if remain_ind:
self.remainnum = len(remain_ind)
self.blanknum = self.ab_size - self.remainnum
blank_ind = list(set(range(self.ab_size)) - set(remain_ind))
else:
self.blanknum = int(self.blanks * self.ab_size)
self.remainnum = self.ab_size - self.blanknum
# randomly remove some of the glyphs in input
if not self.dict:
blank_ind = np.random.permutation(
range(self.ab_size))[0: self.blanknum]
# print(blank_ind)
else:
file_name = list(map(lambda x: x.split("/")[-1],
AB_paths))
if len(file_name) > 1:
raise Exception('batch size should be 1')
file_name = file_name[0]
blank_ind = self.random_dict[file_name][0:self.blanknum]
rgb_inds = np.tile(range(self.n_rgb), self.blanknum)
blank_ind = np.repeat(blank_ind, self.n_rgb)
blank_ind = blank_ind * self.n_rgb + rgb_inds
AA = A.clone()
B = A.clone()
A.cpu()
if not self.skmode == 1:
Bsk0 = A.numpy()
for i in range(A.size(0)):
for j in range(A.size(1)):
Bsk0[i, j, :, :] = skeletonize(Bsk0[i, j, :,
:] > 0)
Bsk0[i, j, :, :] = invert(Bsk0[i, j, :, :])
Bsk0 = LongTensor(Bsk0)
Ask0 = Bsk0.clone()
Ask0.index_fill_(1, LongTensor(list(blank_ind)), 1)
if not self.skmode == 0:
Bsk1 = A.numpy()
for i in range(A.size(0)):
for j in range(A.size(1)):
Bsk1[i, j, :, :] = invert(Bsk1[i, j, :, :])
Bsk1[i, j, :, :] = skeletonize(Bsk1[i, j,:,:] > 0)
Bsk1[i, j, :, :] = invert(Bsk1[i, j, :, :])
Bsk1 = LongTensor(Bsk1)
Ask1 = Bsk1.clone()
Ask1.index_fill_(1, LongTensor(list(blank_ind)), 1)
AA.index_fill_(1, LongTensor(list(blank_ind)), 1)
if self.skmode == 0:
return {'A': AA, 'A_paths': AB_paths, 'B': B,
'B_paths': AB_paths, 'Ask0': Ask0, 'Bsk0': Bsk0, 'indices': remain_ind}
if self.skmode == 1:
return {'A': AA, 'A_paths': AB_paths, 'B': B,
'B_paths': AB_paths, 'Ask1': Ask1, 'Bsk1': Bsk1, 'indices': remain_ind}
if self.skmode == 2:
return {'A': AA, 'A_paths': AB_paths, 'B': B,
'B_paths': AB_paths, 'Ask0': Ask0, 'Bsk0': Bsk0,
'Ask1': Ask1, 'Bsk1': Bsk1, 'indices': remain_ind}
# generate randomly extended alphabet with skeletons
class EHskData(object):
def __init__(self, str_indices, data_loader, fineSize,
max_dataset_size, rgb, dict_test={}, blanks=0.7,
skmode=1):
self.skmode = skmode
self.str_indices = str_indices
self.data_loader = data_loader
self.fineSize = fineSize
self.max_dataset_size = max_dataset_size
self.rgb = rgb
self.n_rgb = 3 if self.rgb else 1
self.blanks = blanks
self.random_dict = dict_test
self.dict = False
if len(self.random_dict.keys()):
self.dict = True
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
AB, AB_paths = next(self.data_loader_iter)
w_total = AB.size(3)
w = int(w_total / 2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
B = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
self.ab_size = int(A.size(1) / self.n_rgb)
remain_inds = list(set(self.str_indices) - {-1})
if remain_inds:
self.remainnum = len(remain_inds)
self.blanknum = self.ab_size - self.remainnum
blank_inds = list(set(range(self.ab_size)) - set(
remain_inds))
else:
self.blanknum = int(self.blanks * self.ab_size)
self.remainnum = self.ab_size - self.blanknum
# randomly remove some of the glyphs in input
if not self.dict:
blank_inds = np.random.permutation(
range(self.ab_size))[0: self.blanknum]
# print(blank_ind)
else:
file_name = list(map(lambda x: x.split("/")[-1],
AB_paths))
if len(file_name) > 1:
raise Exception('batch size should be 1')
file_name = file_name[0]
blank_inds = self.random_dict[file_name][
0:self.blanknum]
remain_inds = list(set(range(self.ab_size)) - set(
blank_inds))
rgb_inds = np.tile(range(self.n_rgb), self.blanknum)
blank_inds = np.repeat(blank_inds, self.n_rgb)
blank_inds = blank_inds * self.n_rgb + rgb_inds
# if not self.dict:
L = A[:, [remain_inds[random.randint(0, self.remainnum -
1)] for _ in range(A.size(1))], :, :]
R = A[:, [remain_inds[random.randint(0, self.remainnum -
1)] for _ in range(A.size(1))], :, :]
# else:
# L = A[:, [remain_inds[((self.random_dict[file_name][
# (i * 9 + 7) % A.size(1)]
# * 9 + 7) % self.remainnum)]
# for i in range(A.size(1))], :, :]
# R = A[:, [remain_inds[((self.random_dict[file_name][
# (i * 9 - 7) % A.size(1)]
# * 9 + 7) % self.remainnum)]
# for i in range(A.size(1))], :, :]
AA = torch.cat((L, A, R), 3)
AAA = AA.clone()
BB = torch.cat((L, B, R), 3)
AA.cpu()
if not self.skmode == 1:
Bsk0 = AA.numpy()
for i in range(AA.size(0)):
for j in range(AA.size(1)):
Bsk0[i, j, :, :] = skeletonize(Bsk0[i, j, :,
:] > 0)
Bsk0[i, j, :, :] = invert(Bsk0[i, j, :, :])
Bsk0 = LongTensor(Bsk0)
Ask0 = Bsk0.clone()
Ask0.index_fill_(1, LongTensor(list(blank_inds)), 1)
if not self.skmode == 0:
Bsk1 = AA.numpy()
for i in range(AA.size(0)):
for j in range(AA.size(1)):
Bsk1[i, j, :, :] = invert(Bsk1[i, j, :, :])
Bsk1[i, j, :, :] = skeletonize(Bsk1[i, j,:,:] > 0)
Bsk1[i, j, :, :] = invert(Bsk1[i, j, :, :])
Bsk1 = LongTensor(Bsk1)
Ask1 = Bsk1.clone()
Ask1.index_fill_(1, LongTensor(list(blank_inds)), 1)
AAA.index_fill_(1, LongTensor(list(blank_inds)), 1)
if self.skmode == 0:
return {'A': AAA, 'A_paths': AB_paths, 'B': BB,
'B_paths': AB_paths, 'Ask0': Ask0, 'Bsk0':
Bsk0, 'indices': remain_inds}
if self.skmode == 1:
return {'A': AAA, 'A_paths': AB_paths, 'B': BB,
'B_paths': AB_paths, 'Ask1': Ask1, 'Bsk1':
Bsk1, 'indices': remain_inds}
if self.skmode == 2:
return {'A': AAA, 'A_paths': AB_paths, 'B': BB,
'B_paths': AB_paths, 'Ask0': Ask0, 'Bsk0': Bsk0,
'Ask1': Ask1, 'Bsk1': Bsk1, 'indices':
remain_inds}
# generate randomly extended alphabet
class ExHalfData(object):
def __init__(self, data_loader, fineSize, max_dataset_size, rgb,
dict_test={}, blanks=0.7):
self.data_loader = data_loader
self.fineSize = fineSize
self.max_dataset_size = max_dataset_size
self.rgb = rgb
self.n_rgb = 3 if self.rgb else 1
self.blanks = blanks
self.random_dict = dict_test
self.dict = False
if len(self.random_dict.keys()):
self.dict = True
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
AB, AB_paths = next(self.data_loader_iter)
w_total = AB.size(3)
w = int(w_total / 2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
B = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset: w_offset + self.fineSize]
self.ab_size = int(A.size(1) / self.n_rgb)
self.blanknum = int(self.blanks * A.size(1) / self.n_rgb)
self.remainnum = int(A.size(1) / self.n_rgb) - self.blanknum
if self.blanks == 0:
AA = A.clone()
else:
# randomly remove some of the glyphs in input
if not self.dict:
blank_inds = np.random.permutation(
range(self.ab_size))[0:self.blanknum]
else:
file_name = list(map(lambda x: x.split("/")[-1],
AB_paths))
if len(file_name) > 1:
raise Exception('batch size should be 1')
file_name = file_name[0]
blank_inds = self.random_dict[file_name][
0:self.blanknum]
remain_inds = list(set(range(self.ab_size)) - set(
blank_inds))
# print(remain_inds)
blank_inds = np.repeat(blank_inds, self.n_rgb)
rgb_inds = np.tile(range(self.n_rgb), self.blanknum)
blank_inds = blank_inds * self.n_rgb + rgb_inds
if not self.dict:
L = A[:, [remain_inds[random.randint(
0, self.remainnum - 1)] for _ in
range(A.size(1))], :, :]
R = A[:, [remain_inds[random.randint(
0, self.remainnum - 1)] for _ in
range(A.size(1))], :, :]
else:
L = A[:, [remain_inds[((self.random_dict[file_name][
(i * 9 + 7) % A.size(1)]
* 9 + 7) % self.remainnum)]
for i in range(A.size(1))], :, :]
R = A[:, [remain_inds[((self.random_dict[file_name][
(i * 9 - 7) % A.size(1)]
* 9 + 7) % self.remainnum)]
for i in range(A.size(1))], :, :]
AA = torch.cat((L, A, R), 3)
BB = torch.cat((L, B, R), 3)
AA.index_fill_(1, LongTensor(list(blank_inds)), 1)
return {'A': AA, 'A_paths': AB_paths, 'B': BB,
'B_paths': AB_paths, 'indices': remain_inds}
# generate randomly extended alphabet along timestep
class ExHalfTData(object):
def __init__(self, str_indices, data_loader, fineSize,
max_dataset_size, rgb,
dict_test={}, blanks=0.7):
self.str_indices = str_indices
self.data_loader = data_loader
self.fineSize = fineSize
self.max_dataset_size = max_dataset_size
self.rgb = rgb
self.n_rgb = 3 if self.rgb else 1
self.blanks = blanks
self.random_dict = dict_test
self.dict = False
if len(self.random_dict.keys()):
self.dict = True
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
AB, AB_paths = next(self.data_loader_iter)
w_total = AB.size(3)
w = int(w_total / 2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
B = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset: w_offset + self.fineSize]
self.ab_size = int(A.size(1) / self.n_rgb)
remain_inds = list(set(self.str_indices) - {-1})
if remain_inds:
self.str_num = len(self.str_indices)
self.remainnum = len(remain_inds)
self.blanknum = self.ab_size - self.remainnum
blank_inds = list(
set(range(self.ab_size)) - set(remain_inds))
else:
self.blanknum = int(self.blanks * A.size(1) / self.n_rgb)
self.remainnum = int(
A.size(1) / self.n_rgb) - self.blanknum
self.str_num = self.remainnum
# randomly remove some of the glyphs in input
if not self.dict:
blank_inds = np.random.permutation(
range(self.ab_size))[0:self.blanknum]
else:
file_name = list(map(lambda x: x.split("/")[-1],
AB_paths))
if len(file_name) > 1:
raise Exception('batch size should be 1')
file_name = file_name[0]
blank_inds = self.random_dict[file_name][
0:self.blanknum]
remain_inds = list(set(range(self.ab_size))
- set(blank_inds))
self.str_indices = remain_inds
blank_ind = np.repeat(blank_inds, self.n_rgb)
rgb_inds = np.tile(range(self.n_rgb), self.blanknum)
blank_inds = blank_inds * self.n_rgb + rgb_inds
AAA = A.unsqueeze(0)
AAA = AAA.repeat([self.str_num, 1, 1, 1, 3])
BBB = B.unsqueeze(0)
BBB = BBB.repeat([self.str_num, 1, 1, 1, 3])
for i in range(self.str_num):
L = A[:, [self.str_indices[i] for _ in range(A.size(1))], :, :]
R = A[:, [self.str_indices[(i + 1) % self.str_num] for
_ in range(A.size(1))], :, :]
# A.index_fill_(1, LongTensor(list(blank_inds)), 1)
AA = torch.cat((L, A, R), 3)
AA.index_fill_(1, LongTensor(list(blank_inds)), 1)
BB = torch.cat((L, B, R), 3)
AAA[i, :, :, :, :] = AA
BBB[i, :, :, :, :] = BB
return {'A': AAA, 'A_paths': AB_paths, 'B': BBB,
'B_paths': AB_paths, 'indices': remain_inds}
# generate randomly extended alphabet with skeletons along timestep
class ExHalfTskData(object):
def __init__(self, str_indices, data_loader, fineSize,
max_dataset_size, rgb,
dict_test={}, blanks=0.7, skmode=1):
self.skmode = skmode
self.str_indices = str_indices
self.data_loader = data_loader
self.fineSize = fineSize
self.max_dataset_size = max_dataset_size
self.rgb = rgb
self.n_rgb = 3 if self.rgb else 1
self.blanks = blanks
self.random_dict = dict_test
self.dict = False
if len(self.random_dict.keys()):
self.dict = True
def __iter__(self):
self.data_loader_iter = iter(self.data_loader)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
AB, AB_paths = next(self.data_loader_iter)
w_total = AB.size(3)
w = int(w_total / 2)
h = AB.size(2)
w_offset = random.randint(0, max(0, w - self.fineSize - 1))
h_offset = random.randint(0, max(0, h - self.fineSize - 1))
A = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset:w_offset + self.fineSize]
B = AB[:, :, h_offset:h_offset + self.fineSize,
w_offset: w_offset + self.fineSize]
self.ab_size = int(A.size(1) / self.n_rgb)
remain_inds = list(set(self.str_indices) - {-1})
if remain_inds:
self.str_num = len(self.str_indices)
self.remainnum = len(remain_inds)
self.blanknum = self.ab_size - self.remainnum
blank_inds = list(
set(range(self.ab_size)) - set(remain_inds))
else:
self.blanknum = int(self.blanks * A.size(1) / self.n_rgb)
self.remainnum = int(
A.size(1) / self.n_rgb) - self.blanknum
self.str_num = self.remainnum
# randomly remove some of the glyphs in input
if not self.dict:
blank_inds = np.random.permutation(
range(self.ab_size))[0:self.blanknum]
else:
file_name = list(map(lambda x: x.split("/")[-1],
AB_paths))
if len(file_name) > 1:
raise Exception('batch size should be 1')
file_name = file_name[0]
blank_inds = self.random_dict[file_name][
0:self.blanknum]
remain_inds = list(set(range(self.ab_size))
- set(blank_inds))
self.str_indices = remain_inds
blank_inds = np.repeat(blank_inds, self.n_rgb)
rgb_inds = np.tile(range(self.n_rgb), self.blanknum)
blank_inds = blank_inds * self.n_rgb + rgb_inds
AAA = A.unsqueeze(0)
AAA = AAA.repeat([self.str_num, 1, 1, 1, 3])
AAsk0 = AAA.clone()
AAsk0.cpu()
AAsk1 = AAsk0.clone()
BBB = B.unsqueeze(0)
BBB = BBB.repeat([self.str_num, 1, 1, 1, 3])
BBsk0 = AAsk0.clone()
BBsk1 = AAsk0.clone()
for k in range(self.str_num):
if not self.dict:
L = A[:, [remain_inds[random.randint(
0, self.remainnum - 1)] for _ in
range(A.size(1))], :, :]
R = A[:, [remain_inds[random.randint(
0, self.remainnum - 1)] for _ in
range(A.size(1))], :, :]
else:
L = A[:, [remain_inds[((self.random_dict[file_name][
(i * 9 + 7) % A.size(1)]
* 9 + 7) % self.remainnum)]
for i in range(A.size(1))], :, :]
R = A[:, [remain_inds[((self.random_dict[file_name][
(i * 9 - 7) % A.size(1)]
* 9 + 7) % self.remainnum)]
for i in range(A.size(1))], :, :]
# A.index_fill_(1, LongTensor(list(blank_inds)), 1)
AA = torch.cat((L, A, R), 3)
BB = AA.clone()
AA.index_fill_(1, LongTensor(list(blank_inds)), 1)
AAA[k, :, :, :, :] = AA
BBB[k, :, :, :, :] = BB
BB.cpu()
if not self.skmode == 1:
Bsk0 = BB.numpy()
for i in range(A.size(0)):
for j in range(A.size(1)):
Bsk0[i, j, :, :] = skeletonize(Bsk0[i, j, :,
:] > 0)
Bsk0[i, j, :, :] = invert(Bsk0[i, j, :, :])
Bsk0 = LongTensor(Bsk0)
BBsk0[k, :, :, :, :] = Bsk0
Ask0 = Bsk0.clone()
Ask0.index_fill_(1, LongTensor(list(blank_inds)), 1)
AAsk0[k, :, :, :, :] = Ask0
if not self.skmode == 0:
Bsk1 = BB.numpy()
for i in range(A.size(0)):
for j in range(A.size(1)):
Bsk1[i, j, :, :] = invert(Bsk1[i, j, :, :])
Bsk1[i, j, :, :] = skeletonize(
Bsk1[i, j, :, :] > 0)
Bsk1[i, j, :, :] = invert(Bsk1[i, j, :, :])
Bsk1 = LongTensor(Bsk1)
BBsk1[k, :, :, :, :] = Bsk1
Ask1 = Bsk1.clone()
Ask1.index_fill_(1, LongTensor(list(blank_inds)), 1)
AAsk1[k, :, :, :, :] = Ask1
if self.skmode == 0:
return {'A': AAA, 'A_paths': AB_paths, 'B': BBB,
'B_paths': AB_paths, 'Ask0': AAsk0, 'Bsk0':
BBsk0, 'indices': remain_inds}
if self.skmode == 1:
return {'A': AAA, 'A_paths': AB_paths, 'B': BBB,
'B_paths': AB_paths, 'Ask1': AAsk1, 'Bsk1':
BBsk1, 'indices': remain_inds}
if self.skmode == 2:
return {'A': AAA, 'A_paths': AB_paths, 'B': BBB,
'B_paths': AB_paths, 'Ask0': AAsk0, 'Bsk0': BBsk0,
'Ask1': AAsk1, 'Bsk1': BBsk1, 'indices':
remain_inds}
# generate alphabet for selected font
class PartialData(object):
def __init__(self, data_loader_A, data_loader_B, data_loader_base, fineSize, loadSize, max_dataset_size, phase, base_font=False, blanks=0):
self.data_loader_A = data_loader_A
self.data_loader_B = data_loader_B
self.data_loader_base = data_loader_base
self.fineSize = fineSize
self.loadSize = loadSize
self.max_dataset_size = max_dataset_size
self.blanks = blanks
self.base_font = base_font
if base_font:
self.data_loader_base_iter = iter(self.data_loader_base)
self.A_base,self.A_base_paths = next(self.data_loader_base_iter)
print(self.A_base.size(3))
self.A_base[0,:,:,:]=normalize_stack(self.A_base[0,:,:,:])
else:
self.A_base = []
self.phase =phase
if self.phase=='train':
t_tensor = transforms.Compose([
transforms.ToTensor(),])
t_topil = transforms.Compose([
transforms.ToPILImage()])
if self.base_font:
for ind in range(self.A_base.size(1)):
A_base = t_topil(self.A_base[0,ind,:,:].unsqueeze(0))
def __iter__(self):
self.data_loader_iter_A = iter(self.data_loader_A)
self.data_loader_iter_B = iter(self.data_loader_B)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
A, A_paths = next(self.data_loader_iter_A)
B, B_paths = next(self.data_loader_iter_B)
t_topil = transforms.Compose([
transforms.ToPILImage()])
t_scale = transforms.Compose([
transforms.Resize(self.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
t_normal = transforms.Compose([
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
t_tensor = transforms.Compose([
transforms.ToTensor(),])
# observed_glyph = list(set(np.nonzero(1 - A[0, :, :, :])[:,
# 0]))
# print(observed_glyph)
for index in range(A.size(0)):
A[index,:,:,:]=normalize_stack(A[index,:,:,:])
B[index,:,:,:]=normalize_stack(B[index,:,:,:])
BB=t_topil(B[index,:,:,:])
# remove more of the glyphs to make prediction harder
if self.blanks!=0:
gt_glyph = [int(A_paths[index].split('/')[-1].split('.png')[0].split('_')[-1])]
observed_glyph = list(set(np.nonzero(1-A[index,:,:,:])[:,0]) - set(gt_glyph))
observed_glyph = np.random.permutation(observed_glyph)
blank_nums = 1
for i in range(blank_nums):
A[index,observed_glyph[i],:,:] = 1
return {'A': A, 'A_paths': A_paths, 'B':B, 'B_paths':B_paths, 'A_base':self.A_base, 'indices': []}
# generate randomly extended alphabet for selected font
class EPartialData(object):
def __init__(self, data_loader_A, data_loader_B, data_loader_base, fineSize, loadSize, max_dataset_size, phase, base_font=False, blanks=0):
self.data_loader_A = data_loader_A
self.data_loader_B = data_loader_B
self.data_loader_base = data_loader_base
self.fineSize = fineSize
self.loadSize = loadSize
self.max_dataset_size = max_dataset_size
self.blanks = blanks
self.base_font = base_font
if base_font:
self.data_loader_base_iter = iter(self.data_loader_base)
self.A_base,self.A_base_paths = next(self.data_loader_base_iter)
print(self.A_base.size(3))
self.A_base[0,:,:,:]=normalize_stack(self.A_base[0,:,:,:])
else:
self.A_base = []
self.phase =phase
if self.phase=='train':
t_tensor = transforms.Compose([
transforms.ToTensor(),])
t_topil = transforms.Compose([
transforms.ToPILImage()])
if self.base_font:
for ind in range(self.A_base.size(1)):
A_base = t_topil(self.A_base[0,ind,:,:].unsqueeze(0))
def __iter__(self):
self.data_loader_iter_A = iter(self.data_loader_A)
self.data_loader_iter_B = iter(self.data_loader_B)
self.iter = 0
return self
def __next__(self):
self.iter += 1
if self.iter > self.max_dataset_size:
raise StopIteration
A, A_paths = next(self.data_loader_iter_A)
B, B_paths = next(self.data_loader_iter_B)
t_topil = transforms.Compose([
transforms.ToPILImage()])
t_scale = transforms.Compose([
transforms.Resize(self.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
t_normal = transforms.Compose([
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
t_tensor = transforms.Compose([
transforms.ToTensor(),])
observed_glyph = list(set(np.nonzero(1 - A[0, :, :, :])[:,
0]))
observed_num = len(observed_glyph)
# print(observed_glyph)
for index in range(A.size(0)):
A[index,:,:,:]=normalize_stack(A[index,:,:,:])
B[index,:,:,:]=normalize_stack(B[index,:,:,:])
BB=t_topil(B[index,:,:,:])
# remove more of the glyphs to make prediction harder
if self.blanks!=0:
gt_glyph = [int(A_paths[index].split('/')[-1].split('.png')[0].split('_')[-1])]
observed_glyph_ = list(set(np.nonzero(1 - A[index,
:, :, :])[:,0]) - set(gt_glyph))
observed_glyph_ = np.random.permutation(
observed_glyph_)
blank_nums = 1
for i in range(blank_nums):
A[index,observed_glyph_[i],:,:] = 1
# observed_glyph_ = np.random.permutation(observed_glyph)
added_glyph = [
observed_glyph[random.randint(0, observed_num-1)]
for _ in range(A.size(1))]
added_glyph_ = [added_glyph[i] for i in observed_glyph]
L = A
L[:, observed_glyph, :, :] = A[:, added_glyph_, :, :]
# L_ = B
# L_[:, :, :, :] = B[:, added_glyph, :, :]
added_glyph = [
observed_glyph[random.randint(0, observed_num - 1)]
for _ in range(A.size(1))]
added_glyph_ = [added_glyph[i] for i in observed_glyph]
R = A
R[:, observed_glyph, :, :] = A[:, added_glyph_, :, :]
# R_ = B
# R_[:, :, :, :] = B[:, added_glyph, :, :]
AA = torch.cat((L, A, R), 3)
# BB = torch.cat((L_, B, R_), 3)
return {'A': AA, 'A_paths': A_paths, 'B': B,
'B_paths': B_paths, 'A_base': self.A_base,
'indices': observed_glyph}
# data loader for second stage with PartialData
class StackDataLoader(BaseDataLoader):
""" a subset of the glyphs are observed and being used for transferring style
train a pix2pix model conditioned on b/w glyphs
to generate colored glyphs.
"""
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
])
dic_phase = {'train':'Train', 'test':'Test'}
# Dataset A
dataset_A = ImageFolder(root=self.dataroot +'A/'+ opt.phase, transform=transform, return_paths=True, rgb=opt.rgb_in, fineSize=opt.fineSize, loadSize=opt.loadSize, font_trans=True, no_permutation=opt.no_permutation)
len_A = len(dataset_A.imgs)
shuffle_inds = np.random.permutation(len_A)
dataset_B = ImageFolder(root=self.dataroot + 'B/'+ opt.phase,
transform=transform, return_paths=True, rgb=opt.rgb_out,
fineSize=opt.fineSize, loadSize=opt.loadSize,
font_trans=False, no_permutation=opt.no_permutation)
if len(dataset_A.imgs)!=len(dataset_B.imgs):
raise Exception("number of images in source folder and target folder does not match")
if (opt.partial and (not self.opt.serial_batches)):
dataset_A.imgs = [dataset_A.imgs[i] for i in shuffle_inds]
dataset_B.imgs = [dataset_B.imgs[i] for i in shuffle_inds]
dataset_A.img_crop = [dataset_A.img_crop[i] for i in shuffle_inds]
dataset_B.img_crop = [dataset_B.img_crop[i] for i in shuffle_inds]
shuffle = False
else:
shuffle = not self.opt.serial_batches
data_loader_A = torch.utils.data.DataLoader(
dataset_A,
batch_size=self.opt.batchSize,
shuffle=shuffle,
num_workers=int(self.opt.nThreads), drop_last=True)
data_loader_B = torch.utils.data.DataLoader(
dataset_B,
batch_size=self.opt.batchSize,
shuffle=shuffle,
num_workers=int(self.opt.nThreads), drop_last=True)
if opt.base_font:
# Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=self.baseroot,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base,
batch_size=1,
shuffle=False,
num_workers=int(self.opt.nThreads))
else:
data_loader_base=None
self.dataset_A = dataset_A
self._data = PartialData(data_loader_A, data_loader_B,
data_loader_base, opt.fineSize,
opt.loadSize, opt.max_dataset_size,
opt.phase, opt.base_font, opt.blanks)
def name(self):
return 'StackDataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset_A), self.opt.max_dataset_size)
# data loader for second stage with EPartialData
class EStackDataLoader(BaseDataLoader):
""" a subset of the glyphs are observed and being used for transferring style
train a pix2pix model conditioned on b/w glyphs
to generate colored glyphs.
"""
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
])
dic_phase = {'train':'Train', 'test':'Test'}
# Dataset A
dataset_A = ImageFolder(root=self.dataroot +'A/'+ opt.phase, transform=transform, return_paths=True, rgb=opt.rgb_in, fineSize=opt.fineSize, loadSize=opt.loadSize, font_trans=True, no_permutation=opt.no_permutation)
len_A = len(dataset_A.imgs)
shuffle_inds = np.random.permutation(len_A)
dataset_B = ImageFolder(root=self.dataroot + 'B/'+ opt.phase,
transform=transform, return_paths=True, rgb=opt.rgb_out,
fineSize=opt.fineSize, loadSize=opt.loadSize,
font_trans=False, no_permutation=opt.no_permutation)
if len(dataset_A.imgs)!=len(dataset_B.imgs):
raise Exception("number of images in source folder and target folder does not match")
if (opt.partial and (not self.opt.serial_batches)):
dataset_A.imgs = [dataset_A.imgs[i] for i in shuffle_inds]
dataset_B.imgs = [dataset_B.imgs[i] for i in shuffle_inds]
dataset_A.img_crop = [dataset_A.img_crop[i] for i in shuffle_inds]
dataset_B.img_crop = [dataset_B.img_crop[i] for i in shuffle_inds]
shuffle = False
else:
shuffle = not self.opt.serial_batches
data_loader_A = torch.utils.data.DataLoader(
dataset_A,
batch_size=self.opt.batchSize,
shuffle=shuffle,
num_workers=int(self.opt.nThreads), drop_last=True)
data_loader_B = torch.utils.data.DataLoader(
dataset_B,
batch_size=self.opt.batchSize,
shuffle=shuffle,
num_workers=int(self.opt.nThreads), drop_last=True)
if opt.base_font:
# Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=self.baseroot,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base,
batch_size=1,
shuffle=False,
num_workers=int(self.opt.nThreads))
else:
data_loader_base=None
self.dataset_A = dataset_A
self._data = EPartialData(data_loader_A, data_loader_B,
data_loader_base, opt.fineSize,
opt.loadSize, opt.max_dataset_size,
opt.phase, opt.base_font, opt.blanks)
def name(self):
return 'StackDataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset_A), self.opt.max_dataset_size)
class PartialDataLoader(BaseDataLoader):
""" a subset of the glyphs are observed and being used for training stlye
train a pix2pix model conditioned on b/w glyphs
to generate colored glyphs.
In the pix2pix model it is simmilar to the unaligned data class.
"""
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dic_phase = {'train':'Train', 'test':'Test'}
# Dataset A
dataset_A = ImageFolder(root=self.dataroot + 'A/' + opt.phase,
transform=transform,
return_paths=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize,
font_trans=False,
no_permutation=opt.no_permutation)
len_A = len(dataset_A.imgs)
if not opt.no_permutation:
shuffle_inds = np.random.permutation(len_A)
else:
shuffle_inds = range(len_A)
dataset_B = ImageFolder(root=self.dataroot + 'B/' + opt.phase,
transform=transform,
return_paths=True,
rgb=opt.rgb, fineSize=opt.fineSize,
loadSize=opt.loadSize,
font_trans=False,
no_permutation=opt.no_permutation)
if len(dataset_A.imgs)!=len(dataset_B.imgs):
raise Exception("number of images in source folder "
"and target folder does not match")
if (opt.partial and (not self.opt.serial_batches)):
dataset_A.imgs = [dataset_A.imgs[i] for i in shuffle_inds]
dataset_B.imgs = [dataset_B.imgs[i] for i in shuffle_inds]
dataset_A.img_crop = [dataset_A.img_crop[i] for
i in shuffle_inds]
dataset_B.img_crop = [dataset_B.img_crop[i] for
i in shuffle_inds]
shuffle = False
else:
shuffle = not self.opt.serial_batches
data_loader_A = torch.utils.data.DataLoader(
dataset_A,
batch_size=self.opt.batchSize,
shuffle=shuffle,
num_workers=int(self.opt.nThreads), drop_last=True)
data_loader_B = torch.utils.data.DataLoader(
dataset_B,
batch_size=self.opt.batchSize,
shuffle=shuffle,
num_workers=int(self.opt.nThreads), drop_last=True)
if opt.base_font:
#Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=opt.base_root,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base, batch_size=1, shuffle=False,
num_workers=int(self.opt.nThreads))
else:
data_loader_base = None
self.dataset_A = dataset_A
self._data = PartialData(data_loader_A,data_loader_B,
data_loader_base, opt.fineSize,
opt.loadSize, opt.max_dataset_size,
opt.phase, opt.base_font)
def name(self):
return 'PartialDataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset_A), self.opt.max_dataset_size)
# data loaders
class DataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Dataset A
dataset = ImageFolder(root=os.path.join(self.dataroot, opt.phase),
transform=transform, return_paths=True, font_trans=(not opt.flat), rgb=opt.rgb,
fineSize=opt.fineSize, loadSize=opt.loadSize)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=self.opt.batchSize,
shuffle=not self.opt.serial_batches,
num_workers=int(self.opt.nThreads), drop_last=True)
self.dataset = dataset
dict_inds = {}
test_dict = self.dataroot+'/test_dict/dict.pkl'
# if opt.phase == 'train':
if opt.phase.startswith('test'):
if os.path.isfile(test_dict):
dict_inds = pickle.load(open(test_dict, 'rb'))
else:
warnings.warn('Blanks in test data are random. create a pkl file in ~/data_path/test_dict/dict.pkl including predifined random indices')
str_indices = self.opt.str_input_indices
if opt.base_font:
# Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=self.baseroot,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base,
batch_size=1,
shuffle=False,
num_workers=int(self.opt.nThreads))
if opt.flat:
self._data = FlatData(data_loader, data_loader_base, opt.fineSize, opt.max_dataset_size, opt.rgb, dict_inds, opt.base_font, opt.blanks)
else:
self._data = Data(str_indices, data_loader,
opt.fineSize, opt.max_dataset_size, opt.rgb, dict_inds, opt.blanks)
def name(self):
return 'DataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset), self.opt.max_dataset_size)
class skDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Dataset A
dataset = ImageFolder(root=os.path.join(self.dataroot, opt.phase),
transform=transform, return_paths=True, font_trans=(not opt.flat), rgb=opt.rgb,
fineSize=opt.fineSize, loadSize=opt.loadSize)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=self.opt.batchSize,
shuffle=not self.opt.serial_batches,
num_workers=int(self.opt.nThreads), drop_last=True)
self.dataset = dataset
dict_inds = {}
test_dict = self.dataroot+'/test_dict/dict.pkl'
# if opt.phase == 'train':
if opt.phase.startswith('test'):
if os.path.isfile(test_dict):
dict_inds = pickle.load(open(test_dict, 'rb', ))
else:
warnings.warn('Blanks in test data are random. create a pkl file in ~/data_path/test_dict/dict.pkl including predifined random indices')
str_indices = self.opt.str_input_indices
if opt.base_font:
# Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=self.baseroot,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base,
batch_size=1,
shuffle=False,
num_workers=int(self.opt.nThreads))
if opt.flat:
self._data = FlatData(data_loader, data_loader_base, opt.fineSize, opt.max_dataset_size, opt.rgb, dict_inds, opt.base_font, opt.blanks)
else:
self._data = skData(str_indices, data_loader,
opt.fineSize, opt.max_dataset_size,
opt.rgb, dict_inds, opt.blanks,
skmode=opt.skmode)
def name(self):
return 'skDataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset), self.opt.max_dataset_size)
class EHskDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Dataset A
dataset = ImageFolder(root=os.path.join(self.dataroot, opt.phase),
transform=transform, return_paths=True, font_trans=(not opt.flat), rgb=opt.rgb,
fineSize=opt.fineSize, loadSize=opt.loadSize)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=self.opt.batchSize,
shuffle=not self.opt.serial_batches,
num_workers=int(self.opt.nThreads), drop_last=True)
self.dataset = dataset
dict_inds = {}
test_dict = self.dataroot+'/test_dict/dict.pkl'
# if opt.phase == 'train':
if opt.phase.startswith('test'):
if os.path.isfile(test_dict):
dict_inds = pickle.load(open(test_dict, 'rb', ))
else:
warnings.warn('Blanks in test data are random. create a pkl file in ~/data_path/test_dict/dict.pkl including predifined random indices')
str_indices = self.opt.str_input_indices
if opt.base_font:
# Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=self.baseroot,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base,
batch_size=1,
shuffle=False,
num_workers=int(self.opt.nThreads))
if opt.flat:
self._data = FlatData(data_loader, data_loader_base, opt.fineSize, opt.max_dataset_size, opt.rgb, dict_inds, opt.base_font, opt.blanks)
else:
self._data = EHskData(str_indices, data_loader,
opt.fineSize, opt.max_dataset_size,
opt.rgb, dict_inds, opt.blanks,
skmode=opt.skmode)
def name(self):
return 'EHskDataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset), self.opt.max_dataset_size)
class ExHalfDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Dataset A
dataset = ImageFolder(root=os.path.join(self.dataroot, opt.phase),
transform=transform, return_paths=True,
font_trans=(not opt.flat), rgb=opt.rgb,
fineSize=opt.fineSize, loadSize=opt.loadSize)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=self.opt.batchSize,
shuffle=(not self.opt.serial_batches),
num_workers=int(self.opt.nThreads), drop_last=True)
self.dataset = dataset
if opt.base_font:
# Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=self.baseroot,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base,
batch_size=1,
shuffle=False,
num_workers=int(self.opt.nThreads))
dict_inds = {}
test_dict = self.dataroot + '/test_dict/dict.pkl'
if opt.phase == 'test':
if os.path.isfile(test_dict):
dict_inds = pickle.load(open(test_dict, 'rb', ))
else:
warnings.warn(
'Blanks in test data are random. create a pkl file in ~/data_path/test_dict/dict.pkl including predifined random indices')
if opt.flat:
self._data = FlatData(data_loader, data_loader_base, opt.fineSize,
opt.max_dataset_size, opt.rgb, dict_inds,
opt.base_font, opt.blanks)
else:
self._data = ExHalfData(data_loader, opt.fineSize,
opt.max_dataset_size,
opt.rgb, dict_inds, opt.blanks)
def name(self):
return 'ExHalfDataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset), self.opt.max_dataset_size)
class ExHalfTDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Dataset A
dataset = ImageFolder(root=os.path.join(self.dataroot, opt.phase),
transform=transform, return_paths=True,
font_trans=(not opt.flat), rgb=opt.rgb,
fineSize=opt.fineSize, loadSize=opt.loadSize)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=self.opt.batchSize,
shuffle=(not self.opt.serial_batches),
num_workers=int(self.opt.nThreads), drop_last=True)
self.dataset = dataset
if opt.base_font:
# Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=self.baseroot,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base,
batch_size=1,
shuffle=False,
num_workers=int(self.opt.nThreads))
dict_inds = {}
test_dict = self.dataroot + '/test_dict/dict.pkl'
if opt.phase == 'test':
if os.path.isfile(test_dict):
dict_inds = pickle.load(open(test_dict, 'rb',))
else:
warnings.warn(
'Blanks in test data are random. create a pkl file in ~/data_path/test_dict/dict.pkl including predifined random indices')
str_indices = self.opt.str_input_indices
if opt.flat:
self._data = FlatData(data_loader, data_loader_base, opt.fineSize,
opt.max_dataset_size, opt.rgb, dict_inds,
opt.base_font, opt.blanks)
else:
self._data = ExHalfTData(str_indices, data_loader,
opt.fineSize,
opt.max_dataset_size, opt.rgb,
dict_inds, opt.blanks)
def name(self):
return 'ExHalf_tDataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset), self.opt.max_dataset_size)
class ExHalfTskDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transform = transforms.Compose([
# TODO: Scale
transforms.Resize(opt.loadSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))])
# Dataset A
dataset = ImageFolder(root=os.path.join(self.dataroot, opt.phase),
transform=transform, return_paths=True,
font_trans=(not opt.flat), rgb=opt.rgb,
fineSize=opt.fineSize, loadSize=opt.loadSize)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=self.opt.batchSize,
shuffle=(not self.opt.serial_batches),
num_workers=int(self.opt.nThreads), drop_last=True)
self.dataset = dataset
dict_inds = {}
test_dict = self.dataroot + '/test_dict/dict.pkl'
if opt.phase == 'test':
if os.path.isfile(test_dict):
dict_inds = pickle.load(open(test_dict, 'rb',))
else:
warnings.warn(
'Blanks in test data are random. create a pkl file in ~/data_path/test_dict/dict.pkl including predifined random indices')
str_indices = self.opt.str_input_indices
if opt.base_font:
# Read and apply transformation on the BASE font
dataset_base = ImageFolder(root=self.baseroot,
transform=transform,
return_paths=True,
font_trans=True, rgb=opt.rgb,
fineSize=opt.fineSize,
loadSize=opt.loadSize)
data_loader_base = torch.utils.data.DataLoader(
dataset_base,
batch_size=1,
shuffle=False,
num_workers=int(self.opt.nThreads))
if opt.flat:
self._data = FlatData(data_loader, data_loader_base, opt.fineSize,
opt.max_dataset_size, opt.rgb, dict_inds,
opt.base_font, opt.blanks)
else:
self._data = ExHalfTskData(str_indices, data_loader,
opt.fineSize,
opt.max_dataset_size, opt.rgb,
dict_inds, opt.blanks,
skmode=opt.skmode)
def name(self):
return 'ExHalfTskDataLoader'
def load_data(self):
return self._data
def __len__(self):
return min(len(self.dataset), self.opt.max_dataset_size)
| 41.827474
| 222
| 0.528647
| 7,646
| 64,247
| 4.238033
| 0.037667
| 0.049994
| 0.025923
| 0.006789
| 0.929947
| 0.920257
| 0.906894
| 0.894797
| 0.891217
| 0.881188
| 0
| 0.016252
| 0.359301
| 64,247
| 1,535
| 223
| 41.854723
| 0.770959
| 0.05826
| 0
| 0.85103
| 0
| 0.004754
| 0.032448
| 0.002985
| 0
| 0
| 0
| 0.001954
| 0
| 1
| 0.051506
| false
| 0
| 0.010301
| 0.021395
| 0.118067
| 0.002377
| 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
|
5fe2cf86c7587b0c4e7a0935c7e791f0bfb3b243
| 291
|
py
|
Python
|
generator/exceptions.py
|
forskolan-natet/natet-sos-generator
|
0062aa49ed134af7de8bf04a1bb4b058b445e4d3
|
[
"MIT"
] | null | null | null |
generator/exceptions.py
|
forskolan-natet/natet-sos-generator
|
0062aa49ed134af7de8bf04a1bb4b058b445e4d3
|
[
"MIT"
] | 1
|
2021-04-05T20:36:28.000Z
|
2021-04-05T20:36:28.000Z
|
generator/exceptions.py
|
forskolan-natet/natet-sos-generator
|
0062aa49ed134af7de8bf04a1bb4b058b445e4d3
|
[
"MIT"
] | null | null | null |
class DeadlockInGenerationError(Exception):
pass
class NotPossibleToGenerateSosError(Exception):
pass
class TooManyMembersOnDayError(Exception):
pass
class BadDistributionBetweenDepartmentsError(Exception):
pass
class DepartmentNotAvailableError(Exception):
pass
| 15.315789
| 56
| 0.800687
| 20
| 291
| 11.65
| 0.4
| 0.27897
| 0.309013
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147766
| 291
| 18
| 57
| 16.166667
| 0.939516
| 0
| 0
| 0.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 1
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
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