max_stars_repo_path
stringlengths 4
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stringlengths 6
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data.py | Broxibear97/ABSA-Honours | 0 | 100179 | # This class was created to be the goto for any functions that involve data manipulation or reading.
import os
import xml.etree.ElementTree as ET
import praw
# read xml from file.
def get_xml_data(file):
tree = ET.parse(file)
reviews = tree.findall('review')
text, labels = [], []
for review in reviews:
sentences = review.findall('sentence');
for sentence in sentences:
print(sentence.find('text').text)
aspects = sentence.findall('aspectTerms/aspectTerm')
for aspect in aspects:
print(aspect.attrib['term'] + ': ' + aspect.attrib['polarity'])
# displays all comments as per their thread level (from reddit)
def display_all_comments(comments, level):
print((" " * level) + (">" * level) + comments.body)
for commentReply in comments.replies:
display_all_comments(commentReply, level + 1)
print()
# praw setup for retrieving data from reddit.
def setup():
reddit = praw.Reddit(client_id="7vOWHznFDrqBVA", client_secret="<KEY>",
user_agent="windows:UniHonoursApp:v1.0.0 (by /u/Delacarpet)")
submissionLink = input("Enter Reddit URL to perform ABSA\n")
submission = reddit.submission(url=submissionLink)
submission.comments.replace_more(limit=1000)
for comment in submission.comments:
display_all_comments(comment, 0)
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tests/test_stardist2D.py | ianbgroves/stardist | 1 | 15472 | import numpy as np
from stardist import star_dist, relabel_image_stardist
import pytest
from utils import random_image, real_image2d, check_similar, circle_image
@pytest.mark.parametrize('img', (real_image2d()[1], random_image((128, 123))))
@pytest.mark.parametrize('n_rays', (4, 16, 32))
def test_types(img, n_rays):
mode = "cpp"
gt = star_dist(img, n_rays=n_rays, mode=mode)
for dtype in (np.int8, np.int16, np.int32,
np.uint8, np.uint16, np.uint32):
x = star_dist(img.astype(dtype), n_rays=n_rays, mode=mode)
print("test_stardist2D (mode {mode}) for shape {img.shape} and type {dtype}".format(
mode=mode, img=img, dtype=dtype))
check_similar(gt, x)
@pytest.mark.gpu
@pytest.mark.parametrize('img', (real_image2d()[1], random_image((128, 123))))
@pytest.mark.parametrize('n_rays', (4, 16, 32))
def test_types_gpu(img, n_rays):
mode = "opencl"
gt = star_dist(img, n_rays=n_rays, mode=mode)
for dtype in (np.int8, np.int16, np.int32,
np.uint8, np.uint16, np.uint32):
x = star_dist(img.astype(dtype), n_rays=n_rays, mode=mode)
print("test_stardist2D with mode {mode} for shape {img.shape} and type {dtype}".format(
mode=mode, img=img, dtype=dtype))
check_similar(gt, x)
@pytest.mark.gpu
@pytest.mark.parametrize('img', (real_image2d()[1], random_image((128, 123))))
@pytest.mark.parametrize('n_rays', (4, 16, 32))
def test_cpu_gpu(img, n_rays):
s_cpp = star_dist(img, n_rays=n_rays, mode="cpp")
s_ocl = star_dist(img, n_rays=n_rays, mode="opencl")
check_similar(s_cpp, s_ocl)
@pytest.mark.parametrize('n_rays', (32,64))
@pytest.mark.parametrize('eps', ((1,1),(.4,1.3)))
def test_relabel_consistency(n_rays, eps, plot = False):
""" test whether an already star-convex label image gets perfectly relabeld"""
# img = random_image((128, 123))
lbl1 = circle_image(shape=(32,32), radius=8, eps = eps)
lbl1 = relabel_image_stardist(lbl1, n_rays)
lbl2 = relabel_image_stardist(lbl1, n_rays)
rel_error = 1-np.count_nonzero(np.bitwise_and(lbl1>0, lbl2>0))/np.count_nonzero(lbl1>0)
print(rel_error)
assert rel_error<1e-1
if plot:
import matplotlib.pyplot as plt
plt.figure(num=1, figsize=(8,4))
plt.subplot(1,3,1);plt.imshow(lbl1);plt.title("GT")
plt.subplot(1,3,2);plt.imshow(lbl2);plt.title("Reco")
plt.subplot(1,3,3);plt.imshow(1*(lbl1>0)+2*(lbl2>0));plt.title("Overlay")
plt.tight_layout()
plt.show()
return lbl1, lbl2
if __name__ == '__main__':
lbl1, lbl2 = test_relabel_consistency(32,eps = (.7,1), plot = True)
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1678,
1596,
29898,
2674,
29918,
2704,
29897,
13,
1678,
4974,
1104,
29918,
2704,
29966,
29896,
29872,
29899,
29896,
13,
13,
1678,
565,
6492,
29901,
13,
4706,
1053,
22889,
29889,
2272,
5317,
408,
14770,
13,
4706,
14770,
29889,
4532,
29898,
1949,
29922,
29896,
29892,
2537,
2311,
7607,
29947,
29892,
29946,
876,
13,
4706,
14770,
29889,
1491,
5317,
29898,
29896,
29892,
29941,
29892,
29896,
416,
572,
29873,
29889,
326,
4294,
29898,
26648,
29896,
416,
572,
29873,
29889,
3257,
703,
23799,
1159,
13,
4706,
14770,
29889,
1491,
5317,
29898,
29896,
29892,
29941,
29892,
29906,
416,
572,
29873,
29889,
326,
4294,
29898,
26648,
29906,
416,
572,
29873,
29889,
3257,
703,
4789,
29877,
1159,
13,
4706,
14770,
29889,
1491,
5317,
29898,
29896,
29892,
29941,
29892,
29941,
416,
572,
29873,
29889,
326,
4294,
29898,
29896,
16395,
26648,
29896,
29958,
29900,
7240,
29906,
16395,
26648,
29906,
29958,
29900,
2483,
572,
29873,
29889,
3257,
703,
3563,
8387,
1159,
13,
4706,
14770,
29889,
29873,
523,
29918,
2680,
580,
13,
4706,
14770,
29889,
4294,
580,
13,
308,
13,
1678,
736,
301,
2204,
29896,
29892,
301,
2204,
29906,
13,
268,
13,
13,
361,
4770,
978,
1649,
1275,
525,
1649,
3396,
1649,
2396,
13,
1678,
301,
2204,
29896,
29892,
301,
2204,
29906,
353,
1243,
29918,
276,
1643,
29918,
3200,
391,
3819,
29898,
29941,
29906,
29892,
8961,
353,
14544,
29955,
29892,
29896,
511,
6492,
353,
5852,
29897,
13,
2
] |
test/test_python/range_usage.py | liuyuqi123/scenario_runner | 0 | 122218 | <reponame>liuyuqi123/scenario_runner<gh_stars>0
a = range(2, 10, 2)
b = list(a)
c = [a]
print('')
| [
1,
529,
276,
1112,
420,
29958,
492,
8631,
29884,
26461,
29896,
29906,
29941,
29914,
1557,
24893,
29918,
27492,
29966,
12443,
29918,
303,
1503,
29958,
29900,
13,
13,
13,
29874,
353,
3464,
29898,
29906,
29892,
29871,
29896,
29900,
29892,
29871,
29906,
29897,
13,
13,
29890,
353,
1051,
29898,
29874,
29897,
13,
13,
29883,
353,
518,
29874,
29962,
13,
13,
2158,
877,
1495,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
13,
2
] |
models.py | sumukhaithal6/Enduro-Imitation-Learning | 1 | 102228 | <reponame>sumukhaithal6/Enduro-Imitation-Learning
#!/usr/bin/env python3
"""
Model architecture.
Authors:
LICENCE:
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleNet(nn.Module):
"""Test net."""
def __init__(self):
"""Ctor."""
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
# self.fc1 = nn.Linear(29008, 120)
self.fc1 = nn.Linear(21904, 120)
self.fc2 = nn.Linear(120, 64)
self.fc3 = nn.Linear(64, 9)
def forward(self, x):
"""Forward call."""
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
class BigNet(nn.Module):
"""Test net."""
def __init__(self):
"""Ctor."""
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.conv3 = nn.Conv2d(16, 32, 5)
# self.fc1 = nn.Linear(29008, 120)
self.fc1 = nn.Linear(8192, 120)
self.fc2 = nn.Linear(120, 64)
self.fc3 = nn.Linear(64, 9)
def forward(self, x):
"""Forward call."""
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
class ResNet(nn.Module):
"""
ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] <NAME>, <NAME>, <NAME>, <NAME>
Deep Residual Learning for Image Recognition. arXiv:1512.03385
"""
class BasicBlock(nn.Module):
"""Basic block."""
expansion = 1
def __init__(self, in_planes, planes, stride=1):
"""Ctor."""
super().__init__()
self.conv1 = nn.Conv2d(
in_planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False,
)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes, planes, kernel_size=3, stride=1, padding=1, bias=False
)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(
in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(self.expansion * planes),
)
def forward(self, x):
"""Forward call."""
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class Bottleneck(nn.Module):
"""Bottlenect."""
expansion = 4
def __init__(self, in_planes, planes, stride=1):
"""Ctor."""
super().__init__()
self.conv1 = nn.Conv2d(
in_planes,
planes,
kernel_size=1,
bias=False,
)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False,
)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(
planes, self.expansion * planes, kernel_size=1, bias=False
)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(
in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(self.expansion * planes),
)
def forward(self, x):
"""Forward call."""
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
def __init__(self, block, num_blocks, num_classes=9):
"""Ctor."""
super().__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(
3,
64,
kernel_size=3,
stride=1,
padding=1,
bias=False,
)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(12800, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
"""Forward call."""
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
# print(out.size())
out = self.linear(out)
return out
def ResNet18() -> ResNet:
"""Return ResNet 18."""
return ResNet(ResNet.BasicBlock, [2, 2, 2, 2])
def ResNet34() -> ResNet:
"""Return ResNet 34."""
return ResNet(ResNet.BasicBlock, [3, 4, 6, 3])
def ResNet50() -> ResNet:
"""Return ResNet 50."""
return ResNet(ResNet.Bottleneck, [3, 4, 6, 3])
| [
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276,
1112,
420,
29958,
2083,
2679,
2350,
389,
284,
29953,
29914,
5044,
2192,
29899,
1888,
7018,
29899,
29931,
799,
1076,
13,
29937,
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13,
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9995,
3057,
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1213,
15945,
13,
13,
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822,
4770,
2344,
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13,
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9995,
29907,
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13,
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580,
13,
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29889,
10109,
353,
302,
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29889,
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29898,
29906,
29892,
29871,
29906,
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1583,
29889,
20580,
29906,
353,
302,
29876,
29889,
1168,
29894,
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29898,
29953,
29892,
29871,
29896,
29953,
29892,
29871,
29945,
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396,
1583,
29889,
13801,
29896,
353,
302,
29876,
29889,
12697,
29898,
29906,
29929,
29900,
29900,
29947,
29892,
29871,
29896,
29906,
29900,
29897,
13,
4706,
1583,
29889,
13801,
29896,
353,
302,
29876,
29889,
12697,
29898,
29906,
29896,
29929,
29900,
29946,
29892,
29871,
29896,
29906,
29900,
29897,
13,
4706,
1583,
29889,
13801,
29906,
353,
302,
29876,
29889,
12697,
29898,
29896,
29906,
29900,
29892,
29871,
29953,
29946,
29897,
13,
4706,
1583,
29889,
13801,
29941,
353,
302,
29876,
29889,
12697,
29898,
29953,
29946,
29892,
29871,
29929,
29897,
13,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
13,
4706,
9995,
2831,
1328,
1246,
1213,
15945,
13,
4706,
921,
353,
1583,
29889,
10109,
29898,
29943,
29889,
2674,
29884,
29898,
1311,
29889,
20580,
29896,
29898,
29916,
4961,
13,
4706,
921,
353,
1583,
29889,
10109,
29898,
29943,
29889,
2674,
29884,
29898,
1311,
29889,
20580,
29906,
29898,
29916,
4961,
13,
4706,
921,
353,
4842,
305,
29889,
1579,
8606,
29898,
29916,
29892,
29871,
29896,
29897,
29871,
396,
1652,
8606,
599,
13391,
5174,
9853,
13,
4706,
921,
353,
383,
29889,
2674,
29884,
29898,
1311,
29889,
13801,
29896,
29898,
29916,
876,
13,
4706,
921,
353,
383,
29889,
2674,
29884,
29898,
1311,
29889,
13801,
29906,
29898,
29916,
876,
13,
4706,
921,
353,
1583,
29889,
13801,
29941,
29898,
29916,
29897,
13,
4706,
736,
921,
13,
13,
13,
1990,
7997,
6779,
29898,
15755,
29889,
7355,
1125,
13,
1678,
9995,
3057,
7787,
1213,
15945,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
13,
4706,
9995,
29907,
7345,
1213,
15945,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
4706,
1583,
29889,
20580,
29896,
353,
302,
29876,
29889,
1168,
29894,
29906,
29881,
29898,
29941,
29892,
29871,
29953,
29892,
29871,
29945,
29897,
13,
4706,
1583,
29889,
10109,
353,
302,
29876,
29889,
7976,
11426,
29906,
29881,
29898,
29906,
29892,
29871,
29906,
29897,
13,
4706,
1583,
29889,
20580,
29906,
353,
302,
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29889,
1168,
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29906,
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29953,
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29896,
29953,
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13,
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29929,
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29900,
29947,
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29906,
29900,
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302,
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29889,
12697,
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29929,
29906,
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29906,
29900,
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13801,
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302,
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29889,
12697,
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29906,
29900,
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353,
302,
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29953,
29946,
29892,
29871,
29929,
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13,
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822,
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921,
1125,
13,
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9995,
2831,
1328,
1246,
1213,
15945,
13,
4706,
921,
353,
1583,
29889,
10109,
29898,
29943,
29889,
2674,
29884,
29898,
1311,
29889,
20580,
29896,
29898,
29916,
4961,
13,
4706,
921,
353,
1583,
29889,
10109,
29898,
29943,
29889,
2674,
29884,
29898,
1311,
29889,
20580,
29906,
29898,
29916,
4961,
13,
4706,
921,
353,
1583,
29889,
10109,
29898,
29943,
29889,
2674,
29884,
29898,
1311,
29889,
20580,
29941,
29898,
29916,
4961,
13,
4706,
921,
353,
4842,
305,
29889,
1579,
8606,
29898,
29916,
29892,
29871,
29896,
29897,
29871,
396,
1652,
8606,
599,
13391,
5174,
9853,
13,
4706,
921,
353,
383,
29889,
2674,
29884,
29898,
1311,
29889,
13801,
29896,
29898,
29916,
876,
13,
4706,
921,
353,
383,
29889,
2674,
29884,
29898,
1311,
29889,
13801,
29906,
29898,
29916,
876,
13,
4706,
921,
353,
1583,
29889,
13801,
29941,
29898,
29916,
29897,
13,
4706,
736,
921,
13,
13,
13,
1990,
2538,
6779,
29898,
15755,
29889,
7355,
1125,
13,
1678,
9995,
13,
1678,
2538,
6779,
297,
10772,
29911,
25350,
29889,
13,
13,
1678,
1152,
4721,
29899,
11236,
362,
2538,
6779,
29892,
1074,
525,
1457,
627,
29918,
690,
1212,
29889,
2272,
4286,
13,
13,
1678,
12105,
29901,
13,
1678,
518,
29896,
29962,
529,
5813,
10202,
529,
5813,
10202,
529,
5813,
10202,
529,
5813,
29958,
13,
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21784,
2538,
333,
950,
29257,
363,
7084,
3599,
3811,
654,
29889,
564,
29990,
440,
29901,
29896,
29945,
29896,
29906,
29889,
29900,
29941,
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29947,
29945,
13,
1678,
9995,
13,
13,
1678,
770,
19219,
7445,
29898,
15755,
29889,
7355,
1125,
13,
4706,
9995,
16616,
2908,
1213,
15945,
13,
13,
4706,
13184,
353,
29871,
29896,
13,
13,
4706,
822,
4770,
2344,
12035,
1311,
29892,
297,
29918,
9018,
267,
29892,
3814,
267,
29892,
380,
2426,
29922,
29896,
1125,
13,
9651,
9995,
29907,
7345,
1213,
15945,
13,
9651,
2428,
2141,
1649,
2344,
1649,
580,
13,
9651,
1583,
29889,
20580,
29896,
353,
302,
29876,
29889,
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] |
python_code_tips/functions_example/functions-before.py | VLTSankalpa/python_networking | 0 | 40930 | <filename>python_code_tips/functions_example/functions-before.py
#! /usr/bin/env python
import requests
import json
import argparse
# Diable InsecureRequestWarning
requests.packages.urllib3.disable_warnings(
requests.packages.urllib3.exceptions.InsecureRequestWarning
)
# DevNet Always-On Sandbox DNA Center
# https://devnetsandbox.cisco.com/RM/Diagram/Index/471eb739-323e-4805-b2a6-d0ec813dc8fc?diagramType=Topology
dnac = {
"host": "sandboxdnac2.cisco.com",
"username": "devnetuser",
"password": "<PASSWORD>!",
"port": 443,
}
# Setup Arg Parse for Command Line parameters
parser = argparse.ArgumentParser()
# Command Line Parameters for Source and Destination IP
parser.add_argument("source_ip", help="Source IP Address")
parser.add_argument("destination_ip", help="Destination IP Address")
args = parser.parse_args()
# Get Source and Destination IPs from Command Line
source_ip = args.source_ip
destination_ip = args.destination_ip
# Print Starting message
print("Running Troubleshooting Script for ")
print(" Source IP: {} ".format(source_ip))
print(" Destination IP: {}".format(destination_ip))
print("")
# Headers for DNAC
headers = {"content-type": "application/json", "x-auth-token": ""}
# Login to DNAC
url = "https://{}:{}/dna/system/api/v1/auth/token".format(
dnac["host"], dnac["port"]
)
# Make Login request and return the response body
response = requests.request(
"POST",
url,
auth=(dnac["username"], dnac["password"]),
headers=headers,
verify=False,
)
token = response.json()["Token"]
headers["x-auth-token"] = token
# URL for Host Calls
url = "https://{}/api/v1/host".format(dnac["host"])
# Get Host Infomration for Source
source_url = url + "?" + "&hostIp={}".format(source_ip)
# Make API request and return the response body
response = requests.request("GET", source_url, headers=headers, verify=False)
source_host = response.json()["response"][0]
# Print out details about source
print("Source Host Details:")
print("-" * 25)
# If optional host details missing, add as "Unavailable"
if "hostName" not in source_host.keys():
source_host["hostName"] = "Unavailable"
# Print Standard Details
print("Host Name: {}".format(source_host["hostName"]))
print("Network Type: {}".format(source_host["hostType"]))
print(
"Connected Network Device: {}".format(
source_host["connectedNetworkDeviceIpAddress"]
)
) # noqa: E501
# Print Wired/Wireless Details
if source_host["hostType"] == "wired":
print(
"Connected Interface Name: {}".format(
source_host["connectedInterfaceName"]
)
) # noqa: E501
if source_host["hostType"] == "wireless":
print("Connected AP Name: {}".format(source_host["connectedAPName"]))
# Print More Standard Details
print("VLAN: {}".format(source_host["vlanId"]))
print("Host IP: {}".format(source_host["hostIp"]))
print("Host MAC: {}".format(source_host["hostMac"]))
print("Host Sub Type: {}".format(source_host["subType"]))
# Blank line at the end
print("")
# Get Host Infomration for Destination
destination_url = url + "?" + "&hostIp={}".format(destination_ip)
# Make API request and return the response body
response = requests.request(
"GET", destination_url, headers=headers, verify=False
)
destination_host = response.json()["response"][0]
# Print out details about source
print("Detination Host Details:")
print("-" * 25)
# If optional host details missing, add as "Unavailable"
if "hostName" not in destination_host.keys():
destination_host["hostName"] = "Unavailable"
# Print Standard Details
print("Host Name: {}".format(destination_host["hostName"]))
print("Network Type: {}".format(destination_host["hostType"]))
print(
"Connected Network Device: {}".format(
destination_host["connectedNetworkDeviceIpAddress"]
)
) # noqa: E501
# Print Wired/Wireless Details
if destination_host["hostType"] == "wired":
print(
"Connected Interface Name: {}".format(
destination_host["connectedInterfaceName"]
)
) # noqa: E501
if destination_host["hostType"] == "wireless":
print("Connected AP Name: {}".format(destination_host["connectedAPName"]))
# Print More Standard Details
print("VLAN: {}".format(destination_host["vlanId"]))
print("Host IP: {}".format(destination_host["hostIp"]))
print("Host MAC: {}".format(destination_host["hostMac"]))
print("Host Sub Type: {}".format(destination_host["subType"]))
# Blank line at the end
print("")
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] |
towel/incubator/frankenresource.py | enterstudio/towel | 0 | 153437 | from __future__ import absolute_import, unicode_literals
import httplib
from django.contrib.messages.api import get_messages
from towel.api import Resource, APIException
class FrankenResource(Resource):
"""
Really ugly and hacky way of reusing customizations made in a ``ModelView``
subclass for API resources. Reuses the following aspects of the
``ModelView`` instance:
- Basic queryset filtering, i.e. ``get_query_set``
- Form handling and saving, i.e. ``get_form``, ``get_form_instance``,
``save_form``, ``save_model`` and ``post_save``
- Permissions management, i.e. ``adding_allowed``, ``editing_allowed``,
``deletion_allowed``
"""
#: ``ModelView`` instance used for providing permissions and write
#: access to the exposed model
modelview = None
def get_query_set(self):
return self.modelview.get_query_set(self.request)
def post_list(self, request, *args, **kwargs):
"""
POST handler. Only supports full creation of objects by posting to
the listing endpoint currently.
"""
if not self.modelview.adding_allowed(request):
raise APIException(status=httplib.FORBIDDEN)
form_class = self.modelview.get_form(
request,
change=False)
form = self.modelview.get_form_instance(
request,
form_class=form_class,
change=False)
try:
is_valid = form.is_valid()
except TypeError as exc:
# This can happen when POSTing something of type
# application/json with a list instead of a single entry,
# e.g. {"customer_id": ["1"]}
raise APIException('Malformed data', data={
'exception': '%s' % exc})
if not is_valid:
raise APIException(data={
'validation': form.errors,
})
instance = self.modelview.save_form(request, form, change=False)
self.modelview.save_model(request, instance, form, change=False)
self.modelview.post_save(request, instance, form, {}, change=False)
data = self.api.serialize_instance(
instance,
build_absolute_uri=request.build_absolute_uri)
return self.serialize_response(
data,
status=httplib.CREATED,
headers={'Location': data['__uri__']})
def put_detail(self, request, *args, **kwargs):
"""
PUT handler. Only supports update of existing resources. Sets are not
supported.
You are required to provide the full set of fields, otherwise
validation fails. If you are looking for partial updates, have a look
at PATCH.
"""
instance = self.detail_object_or_404()
if not self.modelview.editing_allowed(request, instance):
raise APIException(status=httplib.FORBIDDEN)
# The ModelView code only does the right thing when method is POST
request.method = 'POST'
form_class = self.modelview.get_form(
request,
instance=instance,
change=True)
form = self.modelview.get_form_instance(
request,
form_class=form_class,
instance=instance,
change=True)
if not form.is_valid():
raise APIException(data={
'validation': form.errors,
})
instance = self.modelview.save_form(request, form, change=True)
self.modelview.save_model(request, instance, form, change=True)
self.modelview.post_save(request, instance, form, {}, change=True)
data = self.api.serialize_instance(
instance, build_absolute_uri=request.build_absolute_uri)
return self.serialize_response(data, status=httplib.OK)
def patch_detail(self, request, *args, **kwargs):
"""
PATCH handler. Only supports update of existing resources.
This handler offloads the work to the PUT handler. It starts with the
serialized representation from the database, overwrites values using
the data from the PATCH request and calls PUT afterwards.
"""
instance = self.detail_object_or_404()
if not self.modelview.editing_allowed(request, instance):
raise APIException(status=httplib.FORBIDDEN)
data = self.api.serialize_instance(
instance, build_absolute_uri=request.build_absolute_uri)
for key in request.POST:
if isinstance(data[key], (list, tuple)):
data[key] = request.POST.getlist(key)
else:
data[key] = request.POST[key]
request.POST = data
return self.put_detail(request, *args, **kwargs)
def delete_detail(self, request, *args, **kwargs):
"""
DELETE handler. Only supports deletion of single items at the moment.
"""
instance = self.detail_object_or_404()
if not self.modelview.deletion_allowed(request, instance):
raise APIException(status=httplib.FORBIDDEN, data={
'messages': [{
'message': '%s' % msg,
'tags': msg.tags,
} for msg in get_messages(request)],
})
instance.delete()
return self.serialize_response({}, status=httplib.NO_CONTENT)
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] |
Chap_07/exe_156_sum_collection_numbers.py | aleattene/python-workbook | 0 | 150759 | """
The program SUMS all of the NUMBERS entered by the USER,
while ignoring any input that is not a VALID NUMBER.
"""
# Import MATH module
import math
# Acquisition and Control of the DATA entered by the USER
number = input("Enter the NUMBER to add: ")
numbers = []
while number != "":
try:
# Storing the entered number
if "." not in number:
numbers.append(int(number))
else:
numbers.append(float(number))
# Displaying the CURRENT SUM
current_sum = sum(numbers)
if current_sum - math.trunc(current_sum) == 0:
current_sum = int(current_sum)
print("Current Sum = " + str(current_sum))
number = input("Enter the NUMBER to add: ")
# Exception -> Value Error
except ValueError:
print("Warning, a NON-NUMERIC value has been entered.")
number = input("Enter the NUMBER to add: ")
# Displaying the RESULTS
print("The SUM of the NUMBERS entered is {} ".format(current_sum))
print("NUMBERS entered -> ", end="")
if len(numbers) == 0:
print("NO ONE")
else:
for value in numbers:
print(value, end=" ")
| [
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] |
bindings/pydeck/docs/scripts/embed_examples.py | marsupialmarcos/deck.gl | 2 | 2695 | """Script to embed pydeck examples into .rst pages with code
These populate the files you see once you click into a grid cell
on the pydeck gallery page
"""
from multiprocessing import Pool
import os
import subprocess
import sys
from const import DECKGL_URL_BASE, EXAMPLE_GLOB, GALLERY_DIR, HTML_DIR, HOSTED_STATIC_PATH
from utils import to_presentation_name, to_snake_case_string
from templates import DOC_TEMPLATE
if not os.environ.get("MAPBOX_API_KEY"):
# If running for rtfd.io, set this variable from the Admin panel
raise Exception("MAPBOX_API_KEY not set")
def create_rst(pydeck_example_file_name):
asset_name = to_snake_case_string(file_name=pydeck_example_file_name)
deckgl_docs_layer_name = asset_name.replace("_", "-")
deckgl_doc_url = None
if "layer" in deckgl_docs_layer_name:
# Don't add a deck.gl docs link if we're not referencing a layer
# Obviously very rough, should change this eventually to handle views etc
deckgl_doc_url = DECKGL_URL_BASE + deckgl_docs_layer_name
# Create new .html examples
html_fname = os.path.basename(pydeck_example_file_name).replace(".py", ".html")
# Run the pydeck example and move the .html output
subprocess.call(
"{python} {fname}; mv {html_src} {html_dest}".format(
python=sys.executable, fname=pydeck_example_file_name, html_src=html_fname, html_dest=HTML_DIR
),
shell=True,
)
python_code = open(pydeck_example_file_name, "r").read()
doc_source = DOC_TEMPLATE.render(
page_title=to_presentation_name(asset_name),
snake_name=asset_name,
python_code=python_code,
hosted_html_path=os.path.join(HOSTED_STATIC_PATH, html_fname),
deckgl_doc_url=deckgl_doc_url,
)
rst_path = os.path.join(GALLERY_DIR, asset_name + ".rst")
f = open(rst_path, "w+")
print("* Converted %s to %s" % (pydeck_example_file_name, rst_path))
f.write(doc_source)
f.close()
def main():
pool = Pool(processes=4)
candidate_files = [f for f in EXAMPLE_GLOB]
if not candidate_files:
raise Exception("No files found to convert")
subprocess.call("mkdir -p %s" % HTML_DIR, shell=True)
pool.map(create_rst, candidate_files)
if __name__ == "__main__":
main()
| [
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1275,
376,
1649,
3396,
1649,
1115,
13,
1678,
1667,
580,
13,
2
] |
invest_ml/model_funcs.py | convergenceIM/invest-ML | 6 | 53645 | <reponame>convergenceIM/invest-ML<filename>invest_ml/model_funcs.py
'''
Copyright Convergence Investement Management (2018) All rights reserved.
''' | [
1,
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] |
dnn/get_safe_balanced_split.py | takashi-matsushita/lab | 0 | 135005 | def get_safe_balanced_split(target, train_ratio=0.8, get_test_indices=True, shuffle=False, seed=None):
classes, counts = np.unique(target, return_counts=True)
num_per_class = float(len(target))*float(train_ratio)/float(len(classes))
if num_per_class > np.min(counts):
print("Insufficient data to produce a balanced training data split.")
print("Classes found {}".format(classes))
print("Classes count {}".format(counts))
ts = float(train_ratio*np.min(counts)*len(classes)) / float(len(target))
print("train_ratio is reset from {} to {}".format(train_ratio, ts))
train_ratio = ts
num_per_class = float(len(target))*float(train_ratio)/float(len(classes))
num_per_class = int(num_per_class)
print("Data splitting on {} classes and returning {} per class".format(len(classes), num_per_class ))
# get indices
train_indices = []
for c in classes:
if seed is not None:
np.random.seed(seed)
c_idxs = np.where(target==c)[0]
c_idxs = np.random.choice(c_idxs, num_per_class, replace=False)
train_indices.extend(c_idxs)
# get test indices
test_indices = None
if get_test_indices:
test_indices = list(set(range(len(target))) - set(train_indices))
# shuffle
if shuffle:
train_indices = random.shuffle(train_indices)
if test_indices is not None:
test_indices = random.shuffle(test_indices)
return train_indices, test_indices
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] |
src/book/migrations/0005_alter_book_video.py | benniphilipp/dj_ajax | 0 | 152078 | # Generated by Django 3.2.7 on 2021-09-14 09:00
from django.db import migrations
import embed_video.fields
class Migration(migrations.Migration):
dependencies = [
('book', '0004_alter_book_video'),
]
operations = [
migrations.AlterField(
model_name='book',
name='video',
field=embed_video.fields.EmbedVideoField(blank=True, null=True),
),
]
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cssws/db/dao/userdb.py | jaywoong/learn_django | 0 | 1616560 | # userdb.py Control 역할
import sqlite3
from db.frame.sqlitedao import SqliteDao
from db.sql.sql import Sql
from db.vo.uservo import UserVO
class UserDB(SqliteDao):
def __init__(self, dbName):
super().__init__(dbName);
def insert(self, u):
cc = self.getConn();
cc['cursor'].execute(Sql.insert_userdb,
(u.getId(),u.getPwd(),u.getName())
);
cc['con'].commit();
self.close(cc);
print('%s 등록 되었습니다.' % u);
def delete(self,id):
print('%s 삭제 되었습니다.' % id);
def update(self, u):
print('%s 수정 되었습니다.' % u);
def select(self, id):
result = None;
cc = self.getConn();
cc['cursor'].execute(Sql.select_userdb , (id,));
obj = cc['cursor'].fetchone();
result = UserVO(obj[0],obj[1],obj[2]);
self.close(cc);
return result;
def selectall(self):
results = [];
cc = self.getConn();
cc['cursor'].execute(Sql.selectall_userdb);
all = cc['cursor'].fetchall();
for u in all:
rs = UserVO(u[0],u[1],u[2]);
results.append(rs);
self.close(cc);
return results;
# 기능을 테스트 하기 위한 테스트 영역이다.
if __name__ == '__main__':
print('start test .....');
sqlitedao = SqliteDao('shop');
sqlitedao.makeTable();
udb = UserDB('shop');
# user = udb.select('id99');
# print(user);
# user = UserVO('id99','pwd99','<PASSWORD>');
# udb.insert(user);
# user2 = UserVO('id98','<PASSWORD>','<PASSWORD>');
# udb.insert(user2);
#
users = udb.selectall();
for u in users:
print(u);
print('end test .....');
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] |
ref_task/models/conv_models.py | asappresearch/texrel | 0 | 94512 | <filename>ref_task/models/conv_models.py
from typing import Iterable, Optional, List
from torch import nn
# import abc
import torch
# from ref_task.models import common_models
# from ref_task.models import pre_conv as pre_conv_lib
class CNNModel(nn.Module):
def __init__(
self, grid_planes: int, cnn_sizes: Iterable[int],
batch_norm: bool, max_pooling_size: Optional[int]) -> None:
super().__init__()
self.cnn_sizes = cnn_sizes
last_channels = grid_planes
cnn_blocks = []
for i, cnn_size in enumerate(cnn_sizes):
block: List[nn.Module] = []
# if i != 0:
# block.append(nn.Dropout(dropout))
# block.append(nn.ReLU())
block.append(
nn.Conv2d(in_channels=last_channels, out_channels=cnn_size, kernel_size=3, padding=1))
if batch_norm:
block.append(nn.BatchNorm2d(cnn_size))
block.append(nn.ReLU())
if max_pooling_size is not None:
block.append(nn.MaxPool2d(kernel_size=max_pooling_size))
last_channels = cnn_size
cnn_blocks.append(nn.Sequential(*block))
self.conv = nn.Sequential(*cnn_blocks)
def __call__(self, images: torch.Tensor) -> torch.Tensor:
return super().__call__(images=images)
def forward(self, images: torch.Tensor) -> torch.Tensor:
images = self.conv(images)
return images
# class ConvModelBase(nn.Module, abc.ABC):
# def __call__(self, images: torch.Tensor) -> torch.Tensor:
# return super().__call__(images=images)
# @abc.abstractmethod
# def forward(self, images: torch.Tensor) -> torch.Tensor:
# pass
# class StridedPlusModel(ConvModelBase):
# def __init__(self, params, ds_meta):
# super().__init__()
# self.pre_conv = pre_conv_lib.build_preconv(params=params, ds_meta=ds_meta)
# self.conv = common_models.CNNModel(
# grid_planes=ds_meta.grid_planes,
# cnn_sizes=params.cnn_sizes,
# dropout=params.dropout)
# def forward(self, images):
# images = self.pre_conv(images)
# images = self.conv(images)
# return images
# class Conv4Model(ConvModelBase):
# def __init__(self, params, ds_meta, num_blocks: int = 4, channels: int = 64, kernel_size: int = 3):
# # the model from prototypical paper
# # 4 blocks:
# # - 64 filter 3x3
# # - batch normalization
# # - ReLU
# # - 2x2 max pooling
# super().__init__()
# blocks_l = []
# in_channels = ds_meta.grid_planes
# for b in range(num_blocks):
# block = nn.Sequential(*[
# nn.Conv2d(in_channels=in_channels, out_channels=channels, kernel_size=3, padding=1),
# nn.BatchNorm2d(num_features=channels),
# nn.ReLU(),
# nn.MaxPool2d(kernel_size=2)
# ])
# in_channels = channels
# blocks_l.append(block)
# self.conv = nn.Sequential(*blocks_l)
# def forward(self, images):
# images = self.conv(images)
# return images
# def build_conv_model(conv_model: str, params, ds_meta) -> ConvModelBase:
# ConvModelClass = globals()['f{conv_model}Model']
# conv_model_object = ConvModelClass(params=params, ds_meta=ds_meta)
# return conv_model_object
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] |
utils/camera.py | THU-luvision/SurRF | 3 | 105990 | import pdb
import copy
import numpy as np
import os
import scipy
import math
import torch
import torch
from pytorch3d.transforms import euler_angles_to_matrix, matrix_to_euler_angles
def read_total_poses(cam_file_path):
with open(cam_file_path) as f:
lines = f.readlines()
index_list = np.array(list(range(len(lines))))
index_poses = np.where((index_list % 5) == 0)
index = index_list[index_poses]
total_poses = []
for i in index:
pose = np.empty([4, 4]).astype(np.float32)
pose[0, :] = np.array(lines[i + 1].rstrip().split(' ')[:4], dtype=np.float32)
pose[1, :] = np.array(lines[i + 2].rstrip().split(' ')[:4], dtype=np.float32)
pose[2, :] = np.array(lines[i + 3].rstrip().split(' ')[:4], dtype=np.float32)
pose[3, :] = np.array(lines[i + 4].rstrip().split(' ')[:4], dtype=np.float32)
pose_new = pose[:3, :4]
# pose_new = np.linalg.inv(pose)
# pose_new = np.matmul(trans_mat_inv,pose_new)[:3,:4]
total_poses.append(pose_new)
return total_poses
def readCameraRTK_as_np_tanks(cameraPO_file, datasetName):
with open(cameraPO_file) as f:
lines = f.readlines()
cameraRTO = np.empty((3, 4)).astype(np.float64)
cameraRTO[0, :] = np.array(lines[1].rstrip().split(' ')[:4], dtype=np.float64)
cameraRTO[1, :] = np.array(lines[2].rstrip().split(' ')[:4], dtype=np.float64)
cameraRTO[2, :] = np.array(lines[3].rstrip().split(' ')[:4], dtype=np.float64)
cameraKO = np.empty((3, 3)).astype(np.float64)
cameraKO[0, :] = np.array(lines[7].rstrip().split(' ')[:3], dtype=np.float64)
cameraKO[1, :] = np.array(lines[8].rstrip().split(' ')[:3], dtype=np.float64)
cameraKO[2, :] = np.array(lines[9].rstrip().split(' ')[:3], dtype=np.float64)
if datasetName == 'DTU':
cameraPO = np.dot(cameraKO, cameraRTO)
elif datasetName == 'tanks_COLMAP':
cameraPO = np.dot(cameraKO, cameraRTO)
elif datasetName == 'blendedMVS':
cameraPO = np.dot(cameraKO, cameraRTO)
elif datasetName == 'giga_ours':
cameraPO = np.dot(cameraKO, cameraRTO)
return cameraRTO, cameraKO
def readCameraP0_as_np_tanks(cameraPO_file, datasetName, ):
with open(cameraPO_file) as f:
lines = f.readlines()
cameraRTO = np.empty((3, 4)).astype(np.float64)
cameraRTO[0, :] = np.array(lines[1].rstrip().split(' ')[:4], dtype=np.float64)
cameraRTO[1, :] = np.array(lines[2].rstrip().split(' ')[:4], dtype=np.float64)
cameraRTO[2, :] = np.array(lines[3].rstrip().split(' ')[:4], dtype=np.float64)
cameraKO = np.empty((3, 3)).astype(np.float64)
cameraKO[0, :] = np.array(lines[7].rstrip().split(' ')[:3], dtype=np.float64)
cameraKO[1, :] = np.array(lines[8].rstrip().split(' ')[:3], dtype=np.float64)
cameraKO[2, :] = np.array(lines[9].rstrip().split(' ')[:3], dtype=np.float64)
if datasetName == 'DTU':
cameraPO = np.dot(cameraKO, cameraRTO)
elif datasetName == 'tanks_COLMAP':
cameraPO = np.dot(cameraKO, cameraRTO)
elif datasetName == 'blendedMVS':
cameraPO = np.dot(cameraKO, cameraRTO)
elif datasetName == 'giga_ours':
cameraPO = np.dot(cameraKO, cameraRTO)
return cameraPO
def __readCameraPO_as_np_DTU__(cameraPO_file):
"""
only load a camera PO in the file
------------
inputs:
cameraPO_file: the camera pose file of a specific view
outputs:
cameraPO: np.float64 (3,4)
------------
usage:
>>> p = __readCameraPO_as_np_DTU__(cameraPO_file = './test/cameraPO/pos_060.txt')
>>> p # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
array([[ 1.67373847e+03, -2.15171320e+03, 1.26963515e+03,
...
6.58552305e+02]])
"""
cameraPO = np.loadtxt(cameraPO_file, dtype=np.float64, delimiter=' ')
return cameraPO
def __readCameraPOs_as_np_Middlebury__(cameraPO_file, viewList):
"""
load camera POs of multiple views in one file
------------
inputs:
cameraPO_file: the camera pose file of a specific view
viewList: view list
outputs:
cameraPO: np.float64 (N_views,3,4)
------------
usage:
>>> p = __readCameraPOs_as_np_Middlebury__(cameraPO_file = './test/cameraPO/dinoSR_par.txt', viewList=[3,8])
>>> p # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
array([[[ -1.22933223e+03, 3.08329199e+03, 2.02784015e+02,
...
6.41227584e-01]]])
"""
with open(cameraPO_file) as f:
lines = f.readlines()
cameraPOs = np.empty((len(lines), 3, 4)).astype(np.float64)
for _n, _l in enumerate(lines):
if _n == 0:
continue
_params = np.array(_l.strip().split(' ')[1:], dtype=np.float64)
_K = _params[:9].reshape((3, 3))
_R = _params[9:18].reshape((3, 3))
_t = _params[18:].reshape((3, 1))
cameraPOs[_n] = np.dot(_K, np.c_[_R, _t])
return cameraPOs[viewList]
def readCameraPOs_as_np(
datasetFolder,
datasetName,
poseNamePattern,
# model,
viewList,
model=None,
):
"""
inputs:
datasetFolder: 'x/x/x/middlebury'
datasetName: 'DTU' / 'Middlebury'
#model: 1..128 / 'dinoxx'
viewList: [3,8,21,...]
output:
cameraPOs (N_views,3,4) np.flost64
"""
cameraPOs = np.empty((len(viewList), 3, 4), dtype=np.float64)
cameraRTOs = np.empty((len(viewList), 3, 4), dtype=np.float64)
cameraKOs = np.empty((len(viewList), 3, 3), dtype=np.float64)
if 'Middlebury' in datasetName:
cameraPOs = self.__readCameraPOs_as_np_Middlebury__(
cameraPO_file=os.path.join(datasetFolder, poseNamePattern), viewList=viewList)
elif datasetName == 'tanks':
for _i, _view in enumerate(viewList):
_cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view)).replace(
'@', '{}'.format(_view))))
# _cameraPO = readCameraP0_as_np_tanks(cameraPO_file = datasetFolder+poseNamePattern.replace('#', '{:03}'.format(_view)).replace('@', '{}'.format(_view)))
cameraPOs[_i] = _cameraPO
_cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view)).replace(
'@', '{}'.format(_view))))
cameraRTOs[_i] = _cameraRT
cameraKOs[_i] = _cameraK
elif datasetName == 'tanks_COLMAP': # zhiwei
for _i, _view in enumerate(viewList):
_cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view))),
datasetName=datasetName)
# _cameraPO = readCameraP0_as_np_tanks(cameraPO_file = datasetFolder+poseNamePattern.replace('#', '{:03}'.format(_view)).replace('@', '{}'.format(_view)))
cameraPOs[_i] = _cameraPO
_cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view))),
datasetName=datasetName)
cameraRTOs[_i] = _cameraRT
cameraKOs[_i] = _cameraK
elif datasetName == 'blendedMVS': # zhiwei
for _i, _view in enumerate(viewList):
_cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view))),
datasetName=datasetName)
# _cameraPO = readCameraP0_as_np_tanks(cameraPO_file = datasetFolder+poseNamePattern.replace('#', '{:03}'.format(_view)).replace('@', '{}'.format(_view)))
cameraPOs[_i] = _cameraPO
_cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view))),
datasetName=datasetName)
cameraRTOs[_i] = _cameraRT
cameraKOs[_i] = _cameraK
elif datasetName == 'giga_ours': # zhiwei
for _i, _view in enumerate(viewList):
_cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view))),
datasetName=datasetName)
# _cameraPO = readCameraP0_as_np_tanks(cameraPO_file = datasetFolder+poseNamePattern.replace('#', '{:03}'.format(_view)).replace('@', '{}'.format(_view)))
cameraPOs[_i] = _cameraPO
_cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view))),
datasetName=datasetName)
cameraRTOs[_i] = _cameraRT
cameraKOs[_i] = _cameraK
else: # cameraPOs are stored in different files
# tran_mat_path = os.path.join(datasetFolder,transMatPattern)
for _i, _view in enumerate(viewList):
# if 'DTU' in datasetName:
# _cameraPO = __readCameraPO_as_np_DTU__(cameraPO_file=os.path.join(datasetFolder,
# poseNamePattern.replace('#',
# '{:03}'.format(
# _view)).replace(
# '@', '{}'.format(_view))))
_cameraPO = readCameraP0_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view - 1)).replace(
'@', '{}'.format(_view - 1))),
datasetName=datasetName)
cameraPOs[_i] = _cameraPO
_cameraRT, _cameraK = readCameraRTK_as_np_tanks(cameraPO_file=os.path.join(datasetFolder,
poseNamePattern.replace('#',
'{:03}'.format(
_view - 1)).replace(
'@',
'{}'.format(_view - 1))),
datasetName=datasetName)
cameraRTOs[_i] = _cameraRT
cameraKOs[_i] = _cameraK
# print('cameraPOs', cameraPOs)
return cameraPOs, cameraRTOs, cameraKOs
def readCameraP0s_np_allModel(datasetFolder,
datasetName,
poseNamePatternModels,
modelList,
viewList,
transMatPattern=None
):
cameraPs = []
cameraP4s = []
cameraRTs = []
cameraKs = []
for i in modelList:
if datasetName == 'tanks':
##########TODO###################
cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder,
datasetName,
poseNamePattern,
viewList, )
ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0)
cameraPOs, cameraRTOs, cameraKOs = np.concatenate((cameraPOs, ones), axis=1)
elif datasetName == 'DTU':
cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder,
datasetName,
poseNamePatternModels,
viewList,
)
ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0)
cameraP0s = np.concatenate((cameraPOs, ones), axis=1)
elif datasetName == 'tanks_COLMAP': # zhiwei
cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder,
datasetName,
poseNamePatternModels.replace('$', str(i)),
viewList,
)
ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0)
cameraP0s = np.concatenate((cameraPOs, ones), axis=1)
elif datasetName == 'blendedMVS': # zhiwei
cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder,
datasetName,
poseNamePatternModels.replace('$', str(i)),
viewList,
)
ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0)
cameraP0s = np.concatenate((cameraPOs, ones), axis=1)
elif datasetName == 'giga_ours': # zhiwei
cameraPOs, cameraRTOs, cameraKOs = readCameraPOs_as_np(datasetFolder,
datasetName,
poseNamePatternModels.replace('$', str(i)),
viewList,
)
ones = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=cameraPOs.shape[0], axis=0)
cameraP0s = np.concatenate((cameraPOs, ones), axis=1)
cameraPs.append(cameraPOs)
cameraP4s.append(cameraP0s)
cameraRTs.append(cameraRTOs)
cameraKs.append(cameraKOs)
return (cameraPs, np.array(cameraP4s), np.array(cameraRTs), np.array(cameraKs))
def __cameraP2T__(cameraPO):
"""
cameraPO: (3,4)
return camera center in the world coords: cameraT (3,0)
>>> P = np.array([[798.693916, -2438.153488, 1568.674338, -542599.034996], \
[-44.838945, 1433.912029, 2576.399630, -1176685.647358], \
[-0.840873, -0.344537, 0.417405, 382.793511]])
>>> t = np.array([555.64348632032, 191.10837560939, 360.02470478273])
>>> np.allclose(__cameraP2T__(P), t)
True
"""
homo4D = np.array([np.linalg.det(cameraPO[:, [1, 2, 3]]), -1 * np.linalg.det(cameraPO[:, [0, 2, 3]]),
np.linalg.det(cameraPO[:, [0, 1, 3]]), -1 * np.linalg.det(cameraPO[:, [0, 1, 2]])])
# print('homo4D', homo4D)
cameraT = homo4D[:3] / homo4D[3]
return cameraT
def cameraPs2Ts_all(cameraPOs_all):
"""
"""
model_num = len(cameraPOs_all)
# pdb.set_trace()
cameraT_all = np.zeros((model_num, cameraPOs_all[0].shape[0], 3))
for i in range(model_num):
cameraT_all[i] = cameraPs2Ts(cameraPOs_all[i])
return cameraT_all
def cameraPs2Ts(cameraPOs):
"""
convert multiple POs to Ts.
----------
input:
cameraPOs: list / numpy
output:
cameraTs: list / numpy
"""
if type(cameraPOs) is list:
N = len(cameraPOs)
else:
N = cameraPOs.shape[0]
cameraT_list = []
for _cameraPO in cameraPOs:
cameraT_list.append(__cameraP2T__(_cameraPO))
return cameraT_list if type(cameraPOs) is list else np.stack(cameraT_list)
def inverse_camera_matrix(cameraP0s):
N_Ms = cameraP0s.shape[0]
projection_new = np.zeros((N_Ms, 4, 4))
projection_new[:, 0:3, :] = cameraP0s
projection_new[:, 3, :] = np.array(([[0, 0, 0, 1]]))
projection_new = np.linalg.inv(projection_new)
return projection_new
def calculate_angle_p1_p2_p3(p1, p2, p3, return_angle=True, return_cosine=True):
"""
calculate angle <p1,p2,p3>, which is the angle between the vectors p2p1 and p2p3
Parameters
----------
p1/p2/p3: numpy with shape (3,)
return_angle: return the radian angle
return_cosine: return the cosine value
Returns
-------
angle, cosine
Examples
--------
"""
unit_vector = lambda v: v / np.linalg.norm(v)
angle = lambda v1, v2: np.arccos(np.clip(np.dot(unit_vector(v1), unit_vector(v2)), -1.0, 1.0))
cos_angle = lambda v1, v2: np.clip(np.dot(unit_vector(v1), unit_vector(v2)), -1.0, 1.0)
vect_p2p1 = p1 - p2
vect_p2p3 = p3 - p2
return angle(vect_p2p1, vect_p2p3) if return_angle else None, \
cos_angle(vect_p2p1, vect_p2p3) if return_cosine else None
def k_combination_np(iterable, k=2):
"""
list all the k-combination along the output rows:
input: [2,5,8], list 2-combination to a numpy array
output: np.array([[2,5],[2,8],[5,8]])
----------
usages:
>>> k_combination_np([2,5,8])
array([[2, 5],
[2, 8],
[5, 8]])
>>> k_combination_np([2,5,8]).dtype
dtype('int64')
>>> k_combination_np([2.2,5.5,8.8,9.9], k=3)
array([[ 2.2, 5.5, 8.8],
[ 2.2, 5.5, 9.9],
[ 2.2, 8.8, 9.9],
[ 5.5, 8.8, 9.9]])
"""
combinations = []
for _combination in itertools.combinations(iterable, k):
combinations.append(_combination)
return np.asarray(combinations)
def viewPairAngles_wrt_pts(cameraTs, pts_xyz):
"""
given a set of camera positions and a set of points coordinates, output the angle between camera pairs w.r.t. each 3D point.
-----------
inputs:
cameraTs: (N_views, 3) camera positions
pts_xyz: (N_pts, 3) 3D points' coordinates
-----------
outputs:
viewPairAngle_wrt_pts: (N_pts, N_viewPairs) angle
-----------
usages:
>>> pts_xyz = np.array([[0,0,0],[1,1,1]], dtype=np.float32) # p1 / p2
>>> cameraTs = np.array([[0,0,1], [0,1,1], [1,0,1]], dtype=np.float32) # c1/2/3
>>> viewPairAngles_wrt_pts(cameraTs, pts_xyz) * 180 / math.pi # output[i]: [<c1,pi,c2>, <c1,pi,c3>, <c2,pi,c3>]
array([[ 45., 45., 60.],
[ 45., 45., 90.]], dtype=float32)
"""
unitize_array = lambda array, axis: array / np.linalg.norm(array, axis=axis, ord=2, keepdims=True)
calc_arccos = lambda cos_values: np.arccos(np.clip(cos_values, -1.0, 1.0)) # TODO does it need clip ?
N_views = cameraTs.shape[0]
vector_pts2cameras = pts_xyz[:, None, :] - cameraTs[
None, ...] # (N_pts, 1, 3) - (1, N_views, 3) ==> (N_pts, N_views, 3)
unit_vector_pts2cameras = unitize_array(vector_pts2cameras,
axis=-1) # (N_pts, N_views, 3) unit vector along axis=-1
# do the matrix multiplication for the (N_pats,) tack of (N_views, 3) matrixs
## (N_pts, N_views, 3) * (N_pts, 3, N_views) ==> (N_pts, N_views, N_views)
# viewPairCosine_wrt_pts = np.matmul(unit_vector_pts2cameras, unit_vector_pts2cameras.transpose((0,2,1)))
viewPairs = self.k_combination_np(range(N_views), k=2) # (N_combinations, 2)
viewPairCosine_wrt_pts = np.sum(
np.multiply(unit_vector_pts2cameras[:, viewPairs[:, 0]], unit_vector_pts2cameras[:, viewPairs[:, 1]]),
axis=-1) # (N_pts, N_combinations, 3) elementwise multiplication --> (N_pts, N_combinations) sum over the last axis
viewPairAngle_wrt_pts = calc_arccos(viewPairCosine_wrt_pts) # (N_pts, N_combinations)
return viewPairAngle_wrt_pts
# def viewPairAngles_p0s_pts(self, projection_M, )
def viewPairAngles_wrt_groupView(cameraTs, group_cameraTs, xyz_3D):
'''
:param cameraTs:
shape: (N_views,3)
:param group_cameraTs:
shape:(N_bool_views,3)
:param xyz_3D:
shape:(3)
:return:
angle_total: the angle of group T and camera T
shape: (N_bool_views, N_views)
'''
cameraTs_array = (cameraTs - xyz_3D)[None, :, :, None] # (N_views,3)->(1,N_views, 3,1)
group_cameraTs_array = (group_cameraTs - xyz_3D)[:, None, None, :] # (N_bool_views,3)->(N_bool_views,1,3,1)
dot_two = np.matmul(group_cameraTs_array, cameraTs_array)[:, :, 0, 0] # (N_bool_views, N_views)
len_cameraTs = np.linalg.norm(cameraTs - xyz_3D, axis=1)[None, :] # (1, N_views)
len_group_cameraTs = np.linalg.norm(group_cameraTs - xyz_3D, axis=1)[:, None] # (N_bool_views, 1)
len_total = len_cameraTs * len_group_cameraTs # (N_bool_views, N_views)
cos_total = dot_two / (len_total + 1e-10) # (N_bool_views, N_views)
angle_total = np.arccos(np.clip(cos_total, -1.0, 1.0))
return (angle_total)
def select_group_pairs(projection_M, cameraTs, group_cameraTs, xyz_3D, cube_length, image_shape, angle_thres,
group_pair_num_max, group_pair_num_min, group_pair_index):
'''
given group view number, select groupviews
:param projection_M: the
shape:(N_views, 3,4)
:param cameraTs:
shape:(N_views, 3)
:param group_cameraTs:
shape:(N_boole_views, 3)
:param xyz_3D:
shape:(3)
:param cube_length:
float: the length of the cube
:param image_shape:
(img_h, img_w)
:param angle_thres:
float ses params.in_group_angle
:param group_pair_num_max/min:
int see params.group_pair_num_max/min
:param group_pair_index
list of int pair: see params.group_pair_index
:return:
view_pair_list: list of view_pair index
element in list:
(group_left, group_right, (group_id_left, group_id_right))
group_left/right:
numpy 1d array of view pair number
e.g. [(array([ 6, 16, 4, 2, 6]), array([33, 24, 16, 14, 24]), (0, 2)), (array([ 3, 15, 20, 4, 33]), array([ 7, 36, 5, 19, 4]), (1, 3)), (array([33, 24, 16, 14, 24]), array([24, 15, 22, 34, 15]), (2, 4)), (array([ 7, 36, 5, 19, 4]), array([24, 43, 34, 42, 14]), (3, 5)), (array([24, 15, 22, 34, 15]), array([42, 34, 38, 18, 37]), (4, 6)), (array([24, 43, 34, 42, 14]), array([43, 42, 33, 15, 35]), (5, 7))]
'''
view_in_flag = judge_cubic_center_in_view(projection_M,
xyz_3D,
cube_length,
image_shape,
)
angle_total = viewPairAngles_wrt_groupView(cameraTs, group_cameraTs, xyz_3D)
group_pair_flag = view_in_flag[None, :] * (angle_total < angle_thres)
# print('group_pair_flag', group_pair_flag.shape)
view_list = np.repeat((np.arange(group_pair_flag.shape[1]))[None, :], axis=0, repeats=group_pair_flag.shape[0])
# print(group_pair_flag)
view_num_list = []
for i in range(group_pair_flag.shape[0]):
view_num_i = view_list[i, group_pair_flag[i, :]]
if (view_num_i.shape[0] >= group_pair_num_max):
view_num_i = np.random.choice(view_num_i, group_pair_num_max, replace=False)
view_num_list.append(view_num_i)
view_pair_list = []
for (i, j) in (group_pair_index):
if ((view_num_list[i].shape[0] >= group_pair_num_min) and (view_num_list[j].shape[0] >= group_pair_num_min)):
view_pair_list.append((view_num_list[i], view_num_list[j], (i, j)))
# print('view_pair_list',view_pair_list)
return view_pair_list
def select_group(projection_M, cameraTs, group_cameraTs, xyz_3D, cube_length, image_shape, angle_thres,
group_pair_num_max, group_pair_num_min):
'''
given group view number, select groupviews
:param projection_M: the
shape:(N_views, 3,4)
:param cameraTs:
shape:(N_views, 3)
:param group_cameraTs:
shape:(N_boole_views, 3)
:param xyz_3D:
shape:(3)
:param cube_length:
float: the length of the cube
:param image_shape:
(img_h, img_w)
:param angle_thres:
float ses params.in_group_angle
:param group_pair_num_max/min:
int see params.group_pair_num_max/min
:return:
view_list: list of view index
element in list:
group:
numpy 1d array of view number
'''
# view_in_flag = judge_cubic_center_in_view(projection_M ,
# xyz_3D ,
# cube_length,
# image_shape,
# )
view_in_flag = np.ones((projection_M.shape[0]), dtype=np.bool)
angle_total = viewPairAngles_wrt_groupView(cameraTs, group_cameraTs, xyz_3D)
group_pair_flag = view_in_flag[None, :] * (angle_total < angle_thres)
# print('group_pair_flag', group_pair_flag.shape)
view_list = np.repeat((np.arange(group_pair_flag.shape[1]))[None, :], axis=0, repeats=group_pair_flag.shape[0])
# print(group_pair_flag)
view_num_list = []
for i in range(group_pair_flag.shape[0]):
view_num_i = view_list[i, group_pair_flag[i, :]]
if (view_num_i.shape[0] >= group_pair_num_max):
view_num_i = np.sort(np.random.choice(view_num_i, group_pair_num_max, replace=False), axis=0)
# view_num_i = (np.random.choice(view_num_i, group_pair_num_max, replace = False))
# pdb.set_trace()
if (view_num_i.shape[0] >= group_pair_num_min):
view_num_list.append(view_num_i)
return view_num_list
def perspectiveProj(
projection_M,
xyz_3D,
return_int_hw=True,
return_depth=False):
"""
perform perspective projection from 3D points to 2D points given projection matrix(es)
support multiple projection_matrixes and multiple 3D vectors
notice: [matlabx,matlaby] = [width, height]
----------
inputs:
projection_M: numpy with shape (3,4) / (N_Ms, 3,4), during calculation (3,4) will --> (1,3,4)
xyz_3D: numpy with shape (3,) / (N_pts, 3), during calculation (3,) will --> (1,3)
return_int_hw: bool, round results to integer when True.
----------
outputs:
img_h, img_w: (N_pts,) / (N_Ms, N_pts)
----------
usages:
inputs: (N_Ms, 3,4) & (N_pts, 3), return_int_hw = False/True
>>> np.random.seed(201611)
>>> Ms = np.random.rand(2,3,4)
>>> pts_3D = np.random.rand(2,3)
>>> pts_2Dh, pts_2Dw = perspectiveProj(Ms, pts_3D, return_int_hw = False)
>>> np.allclose(pts_2Dw, np.array([[ 1.35860185, 0.9878389 ],
... [ 0.64522543, 0.76079278 ]]))
True
>>> pts_2Dh_int, pts_2Dw_int = perspectiveProj(Ms, pts_3D, return_int_hw = True)
>>> np.allclose(pts_2Dw_int, np.array([[1, 1], [1, 1]]))
True
inputs: (3,4) & (3,)
>>> np.allclose(
... np.r_[perspectiveProj(Ms[1], pts_3D[0], return_int_hw = False)],
... np.stack((pts_2Dh, pts_2Dw))[:,1,0])
True
"""
if projection_M.shape[-2:] != (3, 4):
raise ValueError(
"perspectiveProj needs projection_M with shape (3,4), however got {}".format(projection_M.shape))
if xyz_3D.ndim == 1:
xyz_3D = xyz_3D[None, :]
if xyz_3D.shape[1] != 3 or xyz_3D.ndim != 2:
raise ValueError(
"perspectiveProj needs xyz_3D with shape (3,) or (N_pts, 3), however got {}".format(xyz_3D.shape))
# perspective projection
N_pts = xyz_3D.shape[0]
xyz1 = np.c_[xyz_3D, np.ones((N_pts, 1))].astype(np.float64) # (N_pts, 3) ==> (N_pts, 4)
pts_3D = np.matmul(projection_M, xyz1.T) # (3, 4)/(N_Ms, 3, 4) * (4, N_pts) ==> (3, N_pts)/(N_Ms,3,N_pts)
# the result is vector: [w,h,1], w is the first dim!!! (matlab's x/y/1')
pts_2D = pts_3D[..., :2, :]
# self.pts_3D = pts_3D
pts_2D /= pts_3D[..., 2:3, :] # (2, N_pts) /= (1, N_pts) | (N_Ms, 2, N_pts) /= (N_Ms, 1, N_pts)
# self.pts_2D = pts_2D
# print(self.pts_2D)
if return_int_hw:
pts_2D = pts_2D.round().astype(np.int64) # (2, N_pts) / (N_Ms, 2, N_pts)
img_w, img_h = pts_2D[..., 0, :], pts_2D[..., 1, :] # (N_pts,) / (N_Ms, N_pts)
if return_depth:
depth = pts_3D[..., 2, :]
return img_h, img_w, depth
return img_h, img_w
def perspectiveProj_cubesCorner(projection_M, cube_xyz_min, cube_D_mm, return_int_hw=True,
return_depth=False):
"""
perform perspective projection from 3D points to 2D points given projection matrix(es)
support multiple projection_matrixes and multiple 3D vectors
notice: [matlabx,matlaby] = [width, height]
----------
inputs:
projection_M: numpy with shape (3,4) / (N_Ms, 3,4), during calculation (3,4) will --> (1,3,4)
cube_xyz_min: numpy with shape (3,) / (N_pts, 3), during calculation (3,) will --> (1,3)
cube_D_mm: cube with shape D^3
return_int_hw: bool, round results to integer when True.
return_depth: bool
----------
outputs:
img_h, img_w: (N_Ms, N_pts, 8)
----------
usages:
inputs: (N_Ms, 3, 4) & (N_pts, 3), return_int_hw = False/True, outputs (N_Ms, N_pts, 8)
>>> np.random.seed(201611)
>>> Ms = np.random.rand(2,3,4)
>>> pts_3D = np.random.rand(2,3)
>>> pts_2Dh, pts_2Dw = perspectiveProj_cubesCorner(Ms, pts_3D, cube_D_mm = 1, return_int_hw = False)
>>> np.allclose(pts_2Dw[:,:,0], np.array([[ 1.35860185, 0.9878389 ],
... [ 0.64522543, 0.76079278 ]]))
True
>>> pts_2Dh_int, pts_2Dw_int = perspectiveProj_cubesCorner(Ms, pts_3D, cube_D_mm = 1, return_int_hw = True)
>>> np.allclose(pts_2Dw_int[:,:,0], np.array([[1, 1], [1, 1]]))
True
inputs: (3,4) & (3,), outputs (1,1,8)
>>> np.allclose(
... perspectiveProj_cubesCorner(Ms[1], pts_3D[0], cube_D_mm = 1, return_int_hw = False)[0],
... pts_2Dh[1,0]) # (1,1,8)
True
"""
if projection_M.shape[-2:] != (3, 4):
raise ValueError(
"perspectiveProj needs projection_M with shape (3,4), however got {}".format(projection_M.shape))
if cube_xyz_min.ndim == 1:
cube_xyz_min = cube_xyz_min[None, :] # (3,) --> (N_pts, 3)
if cube_xyz_min.shape[1] != 3 or cube_xyz_min.ndim != 2:
raise ValueError("perspectiveProj needs cube_xyz_min with shape (3,) or (N_pts, 3), however got {}".format(
cube_xyz_min.shape))
N_pts = cube_xyz_min.shape[0]
cubeCorner_shift = np.indices((2, 2, 2)).reshape((3, -1)).T[None, :, :] * cube_D_mm # (3,2,2,2) --> (1,8,3)
cubeCorner = cube_xyz_min[:, None, :] + cubeCorner_shift # (N_pts, 1, 3) + (1,8,3) --> (N_pts, 8, 3)
img_h, img_w = perspectiveProj(projection_M=projection_M, xyz_3D=cubeCorner.reshape((N_pts * 8, 3)),
return_int_hw=return_int_hw,
return_depth=return_depth) # img_w/h: (N_Ms, N_pts*8)
img_w = img_w.reshape((-1, N_pts, 8))
img_h = img_h.reshape((-1, N_pts, 8))
return img_h, img_w
def image_compress_coef(projection_M,
cube_xyz_min,
cube_D_mm,
_cube_D_,
image_compress_multiple,
compress_ratio=1.0
):
img_h, img_w = perspectiveProj_cubesCorner(projection_M,
cube_xyz_min,
cube_D_mm,
return_int_hw=True,
return_depth=False)
img_h_max = np.max(img_h, axis=2) # (N_Ms, N_pts)
img_w_max = np.max(img_w, axis=2)
img_h_min = np.min(img_h, axis=2)
img_w_min = np.min(img_w, axis=2)
img_h_resol = (img_h_max - img_h_min + 0.0) / _cube_D_
img_w_resol = (img_w_max - img_w_min + 0.0) / _cube_D_
compress_h = compress_ratio * img_h_resol.mean() / image_compress_multiple
compress_w = compress_ratio * img_w_resol.mean() / image_compress_multiple
return ((compress_h), (compress_w))
# def resize_matrix(projection_M, compress_h_new, compress_w_new):
# transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]])
# projection_M_new = np.matmul(transform_matrix, projection_M)
#
# cameraTs = cameraPs2Ts(projection_M)
# cameraTs_new = cameraPs2Ts(projection_M_new)
# trans_vector = (cameraTs - cameraTs_new)[:, :, None]
# identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0)
# bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0)
# transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2)
# transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1)
# projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1)
#
# projection_M_new = np.matmul(transform_matrix2, projection_M_new_f)
# projection_M_new = projection_M_new[:, :3, :]
# return projection_M_new
def resize_image_and_matrix(images,
projection_M,
cube_xyz_min,
cube_D_mm,
_cube_D_,
image_compress_multiple,
return_list=False,
compress_ratio=1.0):
'''
compress image and garantee the camera position is not changing
:param images: all images of one model
type:list or None
if list
list element: image array
shape: (img_h,img_w, 3)
:param projection_M: camera matrix
shape: (N_views, 3, 4)
:param cube_xyz_min: min xyz coordinate
shape: (3,) / (N_pts, 3) usually it is (3,) because we only sample one cubic to judge the resize term
:param cube_D_mm:
cubic length float
:param _cube_D_:
cubic size int
:param image_compress_multiple:
same as param.image_compress_multiple
:param return_list: bool
if False return the numpy array
:param compress_ratio
see self.params.compress_ratio
:return:
if image is not None
images_resized:resized image
shape:(N_view, img_h_new, img_w_new)resize_image_and_matrix
projection_M_new: new cameraP
shape:(N_view,3,4)
(compress_h_new,compress_w_new):(float,float)
elif image is None: only change the matrix
projection_M_new: new cameraP
shape:(N_view,3,4)
(compress_h_new,compress_w_new):(float,float)
'''
(compress_h, compress_w) = image_compress_coef(projection_M,
cube_xyz_min,
cube_D_mm,
_cube_D_,
image_compress_multiple,
compress_ratio=compress_ratio)
resized_h = int(image_compress_multiple * (images[0].shape[0] // (compress_h * image_compress_multiple)))
resized_w = int(image_compress_multiple * (images[0].shape[1] // (compress_w * image_compress_multiple)))
compress_h_new = images[0].shape[0] / (resized_h + 0.0)
compress_w_new = images[0].shape[1] / (resized_w + 0.0)
transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]])
projection_M_new = np.matmul(transform_matrix, projection_M)
cameraTs = cameraPs2Ts(projection_M)
cameraTs_new = cameraPs2Ts(projection_M_new)
trans_vector = (cameraTs - cameraTs_new)[:, :, None]
identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0)
bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0)
transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2)
transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1)
projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1)
projection_M_new = np.matmul(transform_matrix2, projection_M_new_f)
projection_M_new = projection_M_new[:, :3, :]
image_resized_list = []
if (images is not None):
for image in images:
image_resized = scipy.misc.imresize(image, size=(resized_h, resized_w), interp='bicubic')
image_resized = image_resized / 256.0 - 0.5
image_resized_list.append(image_resized)
images_resized = image_resized_list if return_list else np.stack(image_resized_list)
return (images_resized, projection_M_new, (compress_h_new, compress_w_new))
else:
return (None, projection_M_new, (compress_h_new, compress_w_new))
# def resize_multistage_image_and_matrix(images,
# projection_M,
# cube_xyz_min,
# cube_D_mm,
# _cube_D_,
# image_compress_multiple,
# image_compress_stage,
# return_list=False,
# compress_ratio=1.0):
# '''
# compress image and garantee the camera position is not changing
# :param images: all images of one model
# type:list or None
# if list
# list element: image array
# shape: (img_h,img_w, 3)
#
# :param projection_M: camera matrix
# shape: (N_views, 3, 4)
# :param cube_xyz_min: min xyz coordinate
# shape: (3,) / (N_pts, 3) usually it is (3,) because we only sample one cubic to judge the resize term
# :param cube_D_mm:
# cubic length float
# :param _cube_D_:
# cubic size int
# :param image_compress_multiple:
# same as param.image_compress_multiple
# :param image_compress_stage
# same as param.image_compress_stage
# :param return_list: bool
# if False return the numpy array
# :param compress_ratio
# see self.params.compress_ratio
# :return:
# if image is not None
# image_resized_stage_list:multistage of resized image
# length : = image_compress_stage
# ele in each list:
# shape:(N_view, img_h_new//2**iter, img_w_new//2**iter)
# projection_M_new: new cameraP
# shape:(N_view,3,4)
# (compress_h_new,compress_w_new):(float,float)
# elif image is None: only change the matrix
# projection_M_new: new cameraP
# shape:(N_view,3,4)
# (compress_h_new,compress_w_new):(float,float)
# '''
# # (compress_h, compress_w) = image_compress_coef(projection_M,
# # cube_xyz_min,
# # cube_D_mm,
# # _cube_D_,
# # 1,
# # compress_ratio = compress_ratio)
#
# # print('compress_h', compress_h, compress_w)
# compress_h = compress_ratio
# compress_w = compress_ratio
# resized_h = int(image_compress_multiple * (images[0].shape[0] // (compress_h * image_compress_multiple)))
# resized_w = int(image_compress_multiple * (images[0].shape[1] // (compress_w * image_compress_multiple)))
#
# # pdb.set_trace()
# compress_h_new = images[0].shape[0] / (resized_h + 0.0)
# compress_w_new = images[0].shape[1] / (resized_w + 0.0)
# transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]])
# projection_M_new = np.matmul(transform_matrix, projection_M)
#
# cameraTs = cameraPs2Ts(projection_M)
# cameraTs_new = cameraPs2Ts(projection_M_new)
# trans_vector = (cameraTs - cameraTs_new)[:, :, None]
# identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0)
# bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0)
# transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2)
# transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1)
# projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1)
#
# projection_M_new = np.matmul(transform_matrix2, projection_M_new_f)
# projection_M_new = projection_M_new[:, :3, :]
#
# if (images is not None):
# image_resized_stage_list = []
# for iter in range(image_compress_stage):
# image_resized_list = []
# for image in images:
# # print('resized image shape',resized_h, resized_w)
# image_resized = scipy.misc.imresize(image,
# size=(int(resized_h // (2 ** iter)), int(resized_w // (2 ** iter))),
# interp='bicubic')
# image_resized = image_resized / 256.0 - 0.5
# image_resized_list.append(image_resized)
# images_resized = image_resized_list if return_list else np.stack(image_resized_list)
# image_resized_stage_list.append(images_resized)
# return (image_resized_stage_list, projection_M_new, (compress_h_new, compress_w_new))
# else:
# return (None, projection_M_new, (compress_h_new, compress_w_new))
def judge_cubic_center_in_view(projection_M,
xyz_3D,
cube_length,
image_shape,
):
'''
'the bool flag of each view can see the center of cubic:'
:param projection_M:
shape:(N_views, 3, 4)
:param xyz_3D:
shape:(3)
:param cube_length
float
:param image_shape:
(img_h,img_w)
:return:
view_in_flag: bool array
shape: (N_views)
'''
img_h_new, img_w_new = perspectiveProj(
projection_M=projection_M,
xyz_3D=xyz_3D,
)
img_h_100, img_w_100 = perspectiveProj(
projection_M=projection_M,
xyz_3D=xyz_3D + np.array((cube_length, 0, 0)),
)
img_h_010, img_w_010 = perspectiveProj(
projection_M=projection_M,
xyz_3D=xyz_3D + np.array((0, cube_length, 0)),
)
img_h_001, img_w_001 = perspectiveProj(
projection_M=projection_M,
xyz_3D=xyz_3D + np.array((0, 0, cube_length)),
)
img_h_011, img_w_011 = perspectiveProj(
projection_M=projection_M,
xyz_3D=xyz_3D + np.array((0, cube_length, cube_length)),
)
img_h_101, img_w_101 = perspectiveProj(
projection_M=projection_M,
xyz_3D=xyz_3D + np.array((cube_length, 0, cube_length)),
)
img_h_110, img_w_110 = perspectiveProj(
projection_M=projection_M,
xyz_3D=xyz_3D + np.array((cube_length, cube_length, 0)),
)
img_h_111, img_w_111 = perspectiveProj(
projection_M=projection_M,
xyz_3D=xyz_3D + cube_length,
)
img_h_bool = (img_h_new < image_shape[0]) * (img_h_new > 0)
img_w_bool = (img_w_new < image_shape[1]) * (img_w_new > 0)
img_h_bool_001 = (img_h_001 < image_shape[0]) * (img_h_001 > 0)
img_w_bool_001 = (img_w_001 < image_shape[1]) * (img_w_001 > 0)
img_h_bool_010 = (img_h_010 < image_shape[0]) * (img_h_010 > 0)
img_w_bool_010 = (img_w_010 < image_shape[1]) * (img_w_010 > 0)
img_h_bool_100 = (img_h_100 < image_shape[0]) * (img_h_100 > 0)
img_w_bool_100 = (img_w_100 < image_shape[1]) * (img_w_100 > 0)
img_h_bool_011 = (img_h_011 < image_shape[0]) * (img_h_011 > 0)
img_w_bool_011 = (img_w_011 < image_shape[1]) * (img_w_011 > 0)
img_h_bool_110 = (img_h_110 < image_shape[0]) * (img_h_110 > 0)
img_w_bool_110 = (img_w_110 < image_shape[1]) * (img_w_110 > 0)
img_h_bool_101 = (img_h_101 < image_shape[0]) * (img_h_101 > 0)
img_w_bool_101 = (img_w_101 < image_shape[1]) * (img_w_101 > 0)
img_h_bool_111 = (img_h_111 < image_shape[0]) * (img_h_111 > 0)
img_w_bool_111 = (img_w_111 < image_shape[1]) * (img_w_111 > 0)
view_in_flag = img_h_bool * img_w_bool * img_h_bool_001 * img_w_bool_001 * img_h_bool_010 * img_w_bool_010 * img_h_bool_100 * img_w_bool_100 * img_h_bool_110 * img_w_bool_110 * img_h_bool_101 * img_w_bool_101 * img_h_bool_011 * img_w_bool_011 * img_h_bool_111 * img_w_bool_111
print('the bool flag of each view can see the center of cubic:', view_in_flag.sum())
return view_in_flag[:, 0]
def count_gx_gy(projection_M, h_length=1, w_length=1):
projection_M_inverse = inverse_camera_matrix(projection_M)
N_view = projection_M_inverse.shape[0]
vector_101 = np.array(([w_length, 0, 1, 1]))[None, :, None]
vector_011 = np.array(([0, h_length, 1, 1]))[None, :, None]
vector_001 = np.array(([0, 0, 1, 1]))[None, :, None]
global_101 = np.matmul(projection_M_inverse, vector_101)[:, :3, 0] # shape: (N_view, 4,1)->(N_view, 3)
global_011 = np.matmul(projection_M_inverse, vector_011)[:, :3, 0]
global_001 = np.matmul(projection_M_inverse, vector_001)[:, :3, 0]
gx = np.linalg.norm(global_101 - global_001, axis=1) # shape: (N_views)
gy = np.linalg.norm(global_011 - global_001, axis=1)
return (gx, gy)
def generateMetaVector_old(
projection_M,
cube_xyz_min,
cameraTs,
cube_D_resol,
_cube_D_,
):
'''
:param projection_M:
shape:(N_views, 3, 4)
:param cube_xyz_min:
shape:(,3)
:param cameraTs:
shape:(N_views, 3)
:param cube_D_resol: resolution of each voxel
float
:param _cube_D_: length of cube
int
:return:
meta_vector: the array of each vector represent camera position
shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 10)
wrapping_vector: the map from each voxel to image
shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 3)
'''
x = np.arange(0, _cube_D_, 1.0)
y = np.arange(0, _cube_D_, 1.0)
z = np.arange(0, _cube_D_, 1.0)
if not (x.shape[0] == _cube_D_):
print('shape of Meta vector went wrong')
raise TypeError
xx, yy, zz = np.meshgrid(x, y, z)
XYZ = np.array([yy.flatten(), xx.flatten(), zz.flatten()]).reshape(3, _cube_D_, _cube_D_, _cube_D_)
XYZ = np.moveaxis(XYZ, 0, 3)
if not (list(XYZ[0, 1, 3, :]) == [0.0, 1.0, 3.0]):
print('index of Meta vector went wrong')
raise TypeError
cube_xyz = cube_xyz_min[None, None, None, :] + XYZ * cube_D_resol # shape:(_cube_D_, _cube_D_, _cube_D_, 3)
ones = np.ones((_cube_D_, _cube_D_, _cube_D_, 1))
cube_xyz_matmul = np.concatenate((cube_xyz, ones), axis=3)[None, :, :, :, :,
None] # shape:(1, _cube_D_, _cube_D_, _cube_D_, 4, 1)
projection_M_matmul = projection_M[:, None, None, None, :, :] # shape:(N_view, 1, 1, 1, 3, 4)
project_cube_xyz = np.matmul(projection_M_matmul,
cube_xyz_matmul) # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3, 1)
(gx, gy) = count_gx_gy(projection_M)
Z = project_cube_xyz[:, :, :, :, 2,
0] # the depth of each cubic points shape:(N_view, _cube_D_, _cube_D_, _cube_D_)
alpha_x = (Z * gx[:, None, None, None] / cube_D_resol)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1)
alpha_y = (Z * gy[:, None, None, None] / cube_D_resol)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1)
print('the average pixel a cubic can get on x axis', alpha_x.mean())
print('the average pixel a cubic can get on y axis', alpha_y.mean())
tau = project_cube_xyz[:, :, :, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, :, :, 0], axis=4)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3)
vector_xyz = cube_xyz[None, :, :, :, :] - cameraTs[:, None, None, None,
:] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3)
theta = vector_xyz / np.linalg.norm(vector_xyz, axis=4)[:, :, :, :,
None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3)
YX = project_cube_xyz[:, :, :, :, :2, 0] / project_cube_xyz[:, :, :, :, 2, 0][:, :, :, :, None]
H = YX[:, :, :, :, 1][:, :, :, :, None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
W = YX[:, :, :, :, 0][:, :, :, :, None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
D = np.zeros(np.shape(H))
X = H - np.floor(H) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
Y = W - np.floor(W) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis=4)
wrapping_vector = np.concatenate((D, H, W), axis=4)
return (meta_vector, wrapping_vector)
def generateMetaVector(
projection_M,
compress,
cube_xyz_min,
cameraTs,
cube_D_resol,
_cube_D_,
):
'''
:param projection_M:
shape:(N_views, 3, 4)
:param compress
turple: (compress_h, compress_w)
:param cube_xyz_min:
shape:(,3)
:param cameraTs:
shape:(N_views, 3)
:param cube_D_resol: resolution of each voxel
float
:param _cube_D_: length of cube
int
:return:
meta_vector: the array of each vector represent camera position
shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 10)
wrapping_vector: the map from each voxel to image
shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 3)
'''
compress_h, compress_w = compress
x = np.arange(0, _cube_D_, 1.0)
y = np.arange(0, _cube_D_, 1.0)
z = np.arange(0, _cube_D_, 1.0)
if not (x.shape[0] == _cube_D_):
print('shape of Meta vector went wrong')
raise TypeError
xx, yy, zz = np.meshgrid(x, y, z)
XYZ = np.array([yy.flatten(), xx.flatten(), zz.flatten()]).reshape(3, _cube_D_, _cube_D_, _cube_D_)
XYZ = np.moveaxis(XYZ, 0, 3)
if not (list(XYZ[0, 1, 3, :]) == [0.0, 1.0, 3.0]):
print('index of Meta vector went wrong')
raise TypeError
cube_xyz = cube_xyz_min[None, None, None, :] + XYZ * cube_D_resol # shape:(_cube_D_, _cube_D_, _cube_D_, 3)
# print('cube_xyz_min[None, None, None, :]', cube_xyz_min[None, None, None, :])
# print('@(*#@!#!@(*$&!@(*')
# print('cube_xyz[2,3,1,:]', cube_xyz[2,3,1,:])
# print('cube_xyz[2,3,2,:]', cube_xyz[2, 3, 2, :])
# print('cube_xyz[2,4,1,:]', cube_xyz[2, 4, 1, :])
ones = np.ones((_cube_D_, _cube_D_, _cube_D_, 1))
cube_xyz_matmul = np.concatenate((cube_xyz, ones), axis=3)[None, :, :, :, :,
None] # shape:(1, _cube_D_, _cube_D_, _cube_D_, 4, 1)
projection_M_matmul = projection_M[:, None, None, None, :, :] # shape:(N_view, 1, 1, 1, 3, 4)
project_cube_xyz = np.matmul(projection_M_matmul,
cube_xyz_matmul) # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3, 1)
# print('@(*#@!#!@(*$&!@(*')
# print(project_cube_xyz.shape)
# print('project_cube_xyz[2,3,1,:]', project_cube_xyz[44, 2, 3, 1, :])
# print('project_cube_xyz[2,3,2,:]', project_cube_xyz[44, 2, 3, 2, :])
# print('project_cube_xyz[2,4,1,:]', project_cube_xyz[44, 2, 4, 1, :])
(gx, gy) = count_gx_gy(projection_M, h_length=compress_h, w_length=compress_w)
Z = project_cube_xyz[:, :, :, :, 2,
0] # the depth of each cubic points shape:(N_view, _cube_D_, _cube_D_, _cube_D_)
alpha_x = (Z * gx[:, None, None, None] / cube_D_resol)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1)
alpha_y = (Z * gy[:, None, None, None] / cube_D_resol)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1)
print('the average pixel a cubic can get on x axis', alpha_x.mean())
print('the average pixel a cubic can get on y axis', alpha_y.mean())
tau = project_cube_xyz[:, :, :, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, :, :, 0], axis=4)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3)
vector_xyz = cube_xyz[None, :, :, :, :] - cameraTs[:, None, None, None,
:] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3)
theta = vector_xyz / np.linalg.norm(vector_xyz, axis=4)[:, :, :, :,
None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3)
YX = project_cube_xyz[:, :, :, :, :2, 0] / project_cube_xyz[:, :, :, :, 2, 0][:, :, :, :, None]
H = YX[:, :, :, :, 1][:, :, :, :, None] / compress_h # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
W = YX[:, :, :, :, 0][:, :, :, :, None] / compress_w # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
D = np.zeros(np.shape(H))
X = H - np.floor(H) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
Y = W - np.floor(W) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis=4)
wrapping_vector = np.concatenate((W, H, D),
axis=4) # To avoid confusion in notation, let’s note that x corresponds to the width dimension IW, y corresponds to the height dimension IH and z corresponds to the depth dimension ID.
return (meta_vector, wrapping_vector)
def generate_sparseMetaVector(
projection_M,
compress,
cube_xyz_min,
stage_num,
cameraTs,
cube_D_resol,
_cube_D_,
info_list=None
):
'''
:param projection_M:
shape:(N_views, 3, 4)
:param compress
turple: (compress_h, compress_w)
:param cube_xyz_min:
shape:(,3)
:param stage_num
int
:param cameraTs:
shape:(N_views, 3)
:param cube_D_resol: resolution of each voxel
float
:param _cube_D_: length of cube
int
:param info_list
:return:
meta_list: list of meta\wrapping vector
len: stage_num
ele:
(meta_vector, wrapping_vector)
output_list: list of output vector
len: stage_num
ele:
(q_final, xyz_final, rgb_final, n_final)
'''
meta_list = []
input_list = []
output_list = []
resol_new = cube_D_resol
xyz_3D_new = copy.copy(cube_xyz_min)
cube_D_new = _cube_D_
for i in range(stage_num):
cubes_gt_np = info_list[i]
if (i == (stage_num - 1)):
use_dense = True
else:
use_dense = False
(xyz_global_final, xyz_final, rgb_final, n_final, q_final, sort_index) = generate_sparse(
cube_xyz_min=xyz_3D_new,
cube_D_resol=resol_new,
_cube_D_=cube_D_new,
cubes_gt_np=cubes_gt_np,
use_dense=use_dense
)
(meta_vector, wrapping_vector) = generateMeta_from_xyz(projection_M=projection_M,
compress=compress,
cameraTs=cameraTs,
cube_D_resol=resol_new,
_cube_D_=cube_D_new,
pts_xyz=xyz_global_final
)
meta_list.append((meta_vector, wrapping_vector))
output_list.append((q_final, xyz_final, rgb_final, n_final, xyz_global_final, sort_index))
xyz_3D_new += (resol_new / 2)
resol_new *= 2
cube_D_new /= 2
compress = (compress[0] * 2, compress[1] * 2)
return meta_list, output_list
def generate_sparse(
cube_xyz_min,
cube_D_resol,
_cube_D_,
cubes_gt_np=None,
use_dense=False
):
'''
:param cube_xyz_min:
shape:(,3)
:param cube_D_resol: resolution of each voxel
float
:param _cube_D_: length of cube
int
:param cubes_gt_np
:return:
xyz_global_final : the location of input voxel
shape: (N,3)
xyz_final: the relative location of output voxel
shape: (N,3)
rgb_final: output voxel
shape: (N,3)
n_final: output voxel
shape: (N,3)
q_final: output voxel
shape: bool (N,1)
sort_index: the sort index of the ground truth used for point up convolution
shape: int (N_points,)
'''
# cubes_gt_np_sort = np.sort(cubes_gt_np, order = 'ijk_id')
x = np.arange(0, _cube_D_, 1.0)
y = np.arange(0, _cube_D_, 1.0)
z = np.arange(0, _cube_D_, 1.0)
if not (x.shape[0] == _cube_D_):
print('shape of Meta vector went wrong')
raise TypeError
xx, yy, zz = np.meshgrid(x, y, z)
XYZ = np.array([yy.flatten(), xx.flatten(), zz.flatten()]).T
XYZ_id = 8 * ((_cube_D_ / 2) * (_cube_D_ / 2) * (XYZ[:, 0] // 2) + (_cube_D_ / 2) * (XYZ[:, 1] // 2) + XYZ[:,
2] // 2) + (
4 * (XYZ[:, 0] % 2) + 2 * (XYZ[:, 1] % 2) + XYZ[:, 2] % 2)
XYZ_id_s = (_cube_D_ * _cube_D_ * XYZ[:, 0] + _cube_D_ * XYZ[:, 1] + XYZ[:, 2])
XYZ_np = np.empty((XYZ.shape[0],), dtype=[('ijk', np.uint32, (3,)), ('ijk_id', np.uint32), ('ijk_id_s', np.uint32)])
XYZ_np['ijk'] = XYZ
XYZ_np['ijk_id'] = XYZ_id
XYZ_np['ijk_id_s'] = XYZ_id_s
XYZ_sort_np = np.sort(XYZ_np, order='ijk_id')
XYZ_sort = XYZ_sort_np['ijk']
# xyz_global = np.zeros((XYZ.shape[0], 3))
xyz = np.zeros((XYZ.shape[0], 3))
rgb = np.zeros((XYZ.shape[0], 3))
n = np.zeros((XYZ.shape[0], 3))
q = np.zeros((XYZ.shape[0], 1), dtype=np.bool)
# xyz_global[cubes_gt_np['ijk_id'], :] = cubes_gt_np['xyz_global']
xyz_global = XYZ_sort * cube_D_resol + cube_xyz_min
xyz[cubes_gt_np['ijk_id'], :] = cubes_gt_np['xyz']
rgb[cubes_gt_np['ijk_id'], :] = cubes_gt_np['rgb']
n[cubes_gt_np['ijk_id'], :] = cubes_gt_np['normals']
q[cubes_gt_np['ijk_id'], :] = True
XYZ_big_num = int(XYZ.shape[0] // 8)
xyz_global_new = xyz_global.reshape((XYZ_big_num, 8, 3))
xyz_new = xyz.reshape((XYZ_big_num, 8, 3))
rgb_new = rgb.reshape((XYZ_big_num, 8, 3))
n_new = n.reshape((XYZ_big_num, 8, 3))
q_new = q.reshape((XYZ_big_num, 8, 1))
ijk_id_s_new = XYZ_sort_np['ijk_id_s'].reshape((XYZ_big_num, 8, 1))
if (use_dense):
xyz_global_final = xyz_global_new.reshape((-1, 3))
xyz_final = xyz_new.reshape((-1, 3))
rgb_final = rgb_new.reshape((-1, 3))
n_final = n_new.reshape((-1, 3))
q_final = q_new.reshape((-1, 1))
ijk_id_s_final = ijk_id_s_new.reshape((-1))
else:
cubes_gt_id_big = np.unique(cubes_gt_np['ijk_id'] // 8)
xyz_global_final = xyz_global_new[cubes_gt_id_big, :, :].reshape((-1, 3))
xyz_final = xyz_new[cubes_gt_id_big, :, :].reshape((-1, 3))
rgb_final = rgb_new[cubes_gt_id_big, :, :].reshape((-1, 3))
n_final = n_new[cubes_gt_id_big, :, :].reshape((-1, 3))
q_final = q_new[cubes_gt_id_big, :, :].reshape((-1, 1))
ijk_id_s_final = ijk_id_s_new[cubes_gt_id_big, :, :].reshape((-1))
sort_index = np.argsort(ijk_id_s_final[q_final[:, 0]])
return (xyz_global_final, xyz_final, rgb_final, n_final, q_final, sort_index)
def generateMeta_from_xyz(projection_M,
compress,
cameraTs,
cube_D_resol,
_cube_D_,
pts_xyz
):
'''
:param projection_M:
shape:(N_views, 3, 4)
:param compress
turple: (compress_h, compress_w)
:param cameraTs:
shape:(N_views, 3)
:param cube_D_resol: resolution of each voxel
float
:param _cube_D_: length of cube
int
:param pts_xyz: points of voxel
shape: (N_points, 3)
:return:
meta_vector: the array of each vector represent camera position
shape: (N_views, N_points, 10)
wrapping_vector: the map from each voxel to image
shape: (N_views, N_points, 3)
'''
compress_h, compress_w = compress
N_points = pts_xyz.shape[0]
ones = np.ones((N_points, 1))
cube_xyz_matmul = np.concatenate((pts_xyz, ones), axis=1)[None, :, :, None] # shape:(1, N_points, 4, 1)
projection_M_matmul = projection_M[:, None, :, :] # shape:(N_view, 1, 3, 4)
project_cube_xyz = np.matmul(projection_M_matmul,
cube_xyz_matmul) # shape:(N_view, N_points, 3, 1)
(gx, gy) = count_gx_gy(projection_M, h_length=compress_h, w_length=compress_w)
Z = project_cube_xyz[:, :, 2,
0] # the depth of each cubic points shape:(N_view, N_points,)
alpha_x = (Z * gx[:, None] / cube_D_resol)[:, :, None] # shape:(N_view, N_points, 1)
alpha_y = (Z * gy[:, None] / cube_D_resol)[:, :, None] # shape:(N_view, N_points, 1)
print('the average pixel a cubic can get on x axis', alpha_x.mean())
print('the average pixel a cubic can get on y axis', alpha_y.mean())
tau = project_cube_xyz[:, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, 0], axis=2)[:, :,
None] # shape:(N_view, N_points, 3)
vector_xyz = pts_xyz[None, :, :] - cameraTs[:, None, :] # shape: (N_view, N_points, 3)
theta = vector_xyz / np.linalg.norm(vector_xyz, axis=2)[:, :, None] # shape: (N_view, N_points, 3)
YX = project_cube_xyz[:, :, :2, 0] / project_cube_xyz[:, :, 2, 0][:, :, None] # shape: (N_view, N_points, 2)
H = YX[:, :, 1][:, :, None] / compress_h # shape: (N_view, N_points, 1)
W = YX[:, :, 0][:, :, None] / compress_w # shape: (N_view, N_points, 1)
D = np.zeros(np.shape(H))
X = H - np.floor(H) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
Y = W - np.floor(W) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis=2)
wrapping_vector = np.concatenate((W, H, D),
axis=2) # To avoid confusion in notation, let’s note that x corresponds to the width dimension IW, y corresponds to the height dimension IH and z corresponds to the depth dimension ID.
return (meta_vector, wrapping_vector)
def generateMetaVector(
projection_M,
compress,
cube_xyz_min,
cameraTs,
cube_D_resol,
_cube_D_,
):
'''
:param projection_M:
shape:(N_views, 3, 4)
:param compress
turple: (compress_h, compress_w)
:param cube_xyz_min:
shape:(,3)
:param cameraTs:
shape:(N_views, 3)
:param cube_D_resol: resolution of each voxel
float
:param _cube_D_: length of cube
int
:return:
meta_vector: the array of each vector represent camera position
shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 10)
wrapping_vector: the map from each voxel to image
shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 3)
'''
compress_h, compress_w = compress
x = np.arange(0, _cube_D_, 1.0)
y = np.arange(0, _cube_D_, 1.0)
z = np.arange(0, _cube_D_, 1.0)
if not (x.shape[0] == _cube_D_):
print('shape of Meta vector went wrong')
raise TypeError
xx, yy, zz = np.meshgrid(x, y, z)
XYZ = np.array([yy.flatten(), xx.flatten(), zz.flatten()]).reshape(3, _cube_D_, _cube_D_, _cube_D_)
XYZ = np.moveaxis(XYZ, 0, 3)
if not (list(XYZ[0, 1, 3, :]) == [0.0, 1.0, 3.0]):
print('index of Meta vector went wrong')
raise TypeError
cube_xyz = cube_xyz_min[None, None, None, :] + XYZ * cube_D_resol # shape:(_cube_D_, _cube_D_, _cube_D_, 3)
# print('cube_xyz_min[None, None, None, :]', cube_xyz_min[None, None, None, :])
# print('@(*#@!#!@(*$&!@(*')
# print('cube_xyz[2,3,1,:]', cube_xyz[2,3,1,:])
# print('cube_xyz[2,3,2,:]', cube_xyz[2, 3, 2, :])
# print('cube_xyz[2,4,1,:]', cube_xyz[2, 4, 1, :])
ones = np.ones((_cube_D_, _cube_D_, _cube_D_, 1))
cube_xyz_matmul = np.concatenate((cube_xyz, ones), axis=3)[None, :, :, :, :,
None] # shape:(1, _cube_D_, _cube_D_, _cube_D_, 4, 1)
projection_M_matmul = projection_M[:, None, None, None, :, :] # shape:(N_view, 1, 1, 1, 3, 4)
project_cube_xyz = np.matmul(projection_M_matmul,
cube_xyz_matmul) # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3, 1)
# print('@(*#@!#!@(*$&!@(*')
# print(project_cube_xyz.shape)
# print('project_cube_xyz[2,3,1,:]', project_cube_xyz[44, 2, 3, 1, :])
# print('project_cube_xyz[2,3,2,:]', project_cube_xyz[44, 2, 3, 2, :])
# print('project_cube_xyz[2,4,1,:]', project_cube_xyz[44, 2, 4, 1, :])
(gx, gy) = count_gx_gy(projection_M, h_length=compress_h, w_length=compress_w)
Z = project_cube_xyz[:, :, :, :, 2,
0] # the depth of each cubic points shape:(N_view, _cube_D_, _cube_D_, _cube_D_)
alpha_x = (Z * gx[:, None, None, None] / cube_D_resol)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1)
alpha_y = (Z * gy[:, None, None, None] / cube_D_resol)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1)
print('the average pixel a cubic can get on x axis', alpha_x.mean())
print('the average pixel a cubic can get on y axis', alpha_y.mean())
tau = project_cube_xyz[:, :, :, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, :, :, 0], axis=4)[:, :, :, :,
None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3)
vector_xyz = cube_xyz[None, :, :, :, :] - cameraTs[:, None, None, None,
:] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3)
theta = vector_xyz / np.linalg.norm(vector_xyz, axis=4)[:, :, :, :,
None] # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3)
YX = project_cube_xyz[:, :, :, :, :2, 0] / project_cube_xyz[:, :, :, :, 2, 0][:, :, :, :, None]
H = YX[:, :, :, :, 1][:, :, :, :, None] / compress_h # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
W = YX[:, :, :, :, 0][:, :, :, :, None] / compress_w # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
D = np.zeros(np.shape(H))
X = H - np.floor(H) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
Y = W - np.floor(W) # shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis=4)
wrapping_vector = np.concatenate((W, H, D),
axis=4) # To avoid confusion in notation, let’s note that x corresponds to the width dimension IW, y corresponds to the height dimension IH and z corresponds to the depth dimension ID.
return (meta_vector, wrapping_vector)
def generate_multiImageMetaVector(
projection_M,
compress,
xyz_3D,
stage_num,
cameraTs,
images_resized,
angles,
Ts,
):
'''
:param projection_M:
shape:(N_views, 3, 4)
:param compress
turple: (compress_h, compress_w)
:param stage_num
int
:param cameraTs:
shape:(N_views, 3)
:param images_resized:resized images
list
:return:
meta_list: list of meta\wrapping vector
len: stage_num
ele:
(vector_image, cameraTs)
'''
meta_list = []
for i in range(stage_num):
(vector_image) = generateImageMetaVector(
projection_M,
compress,
cameraTs,
image_size=images_resized[i].shape[1:3]
)
# direction_transfer = generateDirectionMetaVector(vector_image,
# cameraTs,
# xyz_3D,
# angles,
# Ts,
# )
meta_list.append(vector_image)
# meta_list.append(direction_transfer)
compress = (compress[0] * 2, compress[1] * 2)
return meta_list
def generate_matrix(
angles,
ts
):
(alpha, beta, gamma) = angles
ratio = 180 / 3.14159
alpha /= ratio
beta /= ratio
gamma /= ratio
(t_x, t_y, t_z) = ts
R_z = np.array([[np.cos(gamma), -np.sin(gamma), 0],
[np.sin(gamma), np.cos(gamma), 0],
[0, 0, 1]])
R_x = np.array([[1, 0, 0],
[0, np.cos(alpha), -np.sin(alpha)],
[0, np.sin(alpha), np.cos(alpha)]])
R_y = np.array([[np.cos(beta), 0, np.sin(beta)],
[0, 1, 0],
[-np.sin(beta), 0, np.cos(beta)]])
R_rotate = np.matmul(R_x, np.matmul(R_y, R_z))
t_total = np.array([[t_x], [t_y], [t_z]])
RT = np.concatenate((R_rotate, t_total), axis=1)
return R_rotate
def generateDirectionMetaVector(vector_image,
cameraTs,
BB_middle,
angles,
ts
):
R_rotate = generate_matrix(angles, ts)
s = BB_middle[None, :] - cameraTs # shape:(N_views,3)
d_origin = 1
v_length = (s[:, :, None, None] * vector_image).sum(axis=1)[:, None, :, :] # shape:(N_views,1,img_w,img_h)
direction = v_length * vector_image - s[:, :, None, None] # shape:(N_views,3,img_w,img_h)
# R_rotate_inverse = np.linalg.inv(R_rotate) #shape(3,3)
direction_rotate = np.matmul(R_rotate[None, None, None, ...], np.moveaxis(direction, 1, -1)[..., None])
direction_rotate = np.moveaxis(direction_rotate[:, :, :, :, 0], -1, 1) # shape:(N_views,3,img_w,img_h)
td_length = (np.array(ts)[None, :, None, None] * direction_rotate).sum(axis=1)[:, None, :, :]
dd_length = (direction_rotate * direction_rotate).sum(axis=1)[:, None, :, :]
direction_transfer = (1 + td_length / (dd_length + 1e-8)) * direction_rotate
return direction_transfer
# pdb.set_trace()
def generateImageMetaVector(
projection_M,
compress,
cameraTs,
image_size
):
'''
:param projection_M:
shape:(N_views, 3, 4)
:param compress
turple: (compress_h, compress_w)
:param cameraTs:
shape:(N_views, 3)
:param image_size
turple
:return:
meta_vector: the array of each vector represent camera position
shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 10)
wrapping_vector: the map from each voxel to image
shape: (N_views, _cube_D_, _cube_D_, _cube_D_, 3)
'''
compress_h, compress_w = compress
img_h, img_w = image_size
x = np.arange(0, img_w, 1.0) * compress_w
y = np.arange(0, img_h, 1.0) * compress_h
xx, yy = np.meshgrid(x, y)
XY = np.array([yy.flatten(), xx.flatten()]).T.reshape(img_h, img_w, 2)
XY = np.moveaxis(XY, 2, 0)
Z = np.ones((1, img_h, img_w))
XYZ = np.concatenate((XY, Z), axis=0) # shape:(3,img_h, img_w)
image_vector_inverse = inverseImageVector(projection_M, XYZ)
vector_image = image_vector_inverse - cameraTs[..., None, None]
vector_image = vector_image / (1e-8 + np.linalg.norm(vector_image, axis=1)[:, None, :, :])
vector_image = np.repeat(XY[None, ...] / 1600, cameraTs.shape[0], axis=0)
# return (vector_image, cameraTs)
return (vector_image)
# ones = np.ones((_cube_D_, _cube_D_, _cube_D_, 1))
# cube_xyz_matmul = np.concatenate((cube_xyz, ones), axis = 3)[None, :,:,:,:,None] #shape:(1, _cube_D_, _cube_D_, _cube_D_, 4, 1)
# projection_M_matmul = projection_M[:,None,None,None,:,:] #shape:(N_view, 1, 1, 1, 3, 4)
# project_cube_xyz = np.matmul(projection_M_matmul, cube_xyz_matmul) #shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3, 1)
# (gx, gy) = count_gx_gy(projection_M, h_length = compress_h, w_length = compress_w)
# Z = project_cube_xyz[:,:,:,:,2,0] #the depth of each cubic points shape:(N_view, _cube_D_, _cube_D_, _cube_D_)
# alpha_x = (Z * gx[:,None,None,None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1)
# alpha_y = (Z * gy[:,None,None,None] / cube_D_resol)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 1)
# print('the average pixel a cubic can get on x axis', alpha_x.mean())
# print('the average pixel a cubic can get on y axis', alpha_y.mean())
# tau = project_cube_xyz[:, :, :, :, :, 0] / np.linalg.norm(project_cube_xyz[:, :, :, :, :, 0], axis = 4)[:, :, :, :, None] # shape:(N_view, _cube_D_, _cube_D_, _cube_D_, 3)
# vector_xyz = cube_xyz[None, :,:,:,:] - cameraTs[:,None,None,None,:] #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3)
# theta = vector_xyz / np.linalg.norm(vector_xyz, axis = 4)[:, :, :, :, None] #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 3)
# YX = project_cube_xyz[:,:,:,:,:2,0] / project_cube_xyz[:,:,:,:,2,0][:,:,:,:,None]
# H = YX[:,:,:,:,1][:,:,:,:,None] / compress_h #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
# W = YX[:, :, :, :, 0][:,:,:,:,None] / compress_w #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
# D = np.zeros(np.shape(H))
# X = H - np.floor(H) #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
# Y = W - np.floor(W) #shape: (N_view, _cube_D_, _cube_D_, _cube_D_, 1)
# meta_vector = np.concatenate((alpha_x, alpha_y, tau, theta, X, Y), axis = 4)
# wrapping_vector = np.concatenate((W, H, D), axis = 4) #To avoid confusion in notation, let’s note that x corresponds to the width dimension IW, y corresponds to the height dimension IH and z corresponds to the depth dimension ID.
# return (meta_vector, wrapping_vector)
def rotateImageVector(self, pts_3D, image_vector):
'''
pts_3D:
shape:(M_view * N_points * 3)/(N_points * 3)
image_vector:
shape:(M_view * N_points * 3 * img_h * img_w)/(N_points * 3 * img_h * img_w)
----------------------------------------------------
pts_3D = np.random.rand(1,2,3)
image_vector = np.zeros((1,2,3,4,5))
image_vector[...,:,:] = pts_3D[...,None,None]
camera_test = Camera()
q = camera_test.rotateImageVector(pts_3D, image_vector)
print(q.shape)
print(q)
'''
if (len(image_vector.shape) == 4):
image_vector = np.moveaxis(image_vector, 1, -1)
N, img_h, img_w, _ = image_vector.shape
matrix_x = np.zeros((N, img_h, img_w, 3, 3))
matrix_yz = np.zeros((N, img_h, img_w, 3, 3))
elif (len(image_vector.shape) == 5):
image_vector = np.moveaxis(image_vector, 2, -1)
M, N, img_h, img_w, _ = image_vector.shape
matrix_x = np.zeros((M, N, img_h, img_w, 3, 3))
matrix_yz = np.zeros((M, N, img_h, img_w, 3, 3))
else:
raise ValueError('inputs shape is wrong')
a = pts_3D[..., 0]
b = pts_3D[..., 1]
c = pts_3D[..., 2]
# print(pts_3D[...,1:])
norm_bc = np.linalg.norm(pts_3D[..., 1:], axis=-1)
norm_abc = np.linalg.norm(pts_3D[..., :], axis=-1)
matrix_x[..., 0, 0] = 1.0
matrix_x[..., 1, 1] = (c / norm_bc)[..., None, None]
matrix_x[..., 1, 2] = (b / norm_bc)[..., None, None]
matrix_x[..., 2, 1] = (-b / norm_bc)[..., None, None]
matrix_x[..., 2, 2] = (c / norm_bc)[..., None, None]
matrix_yz[..., 1, 1] = 1.0
matrix_yz[..., 0, 0] = (norm_bc / norm_abc)[..., None, None]
matrix_yz[..., 0, 2] = (a / norm_abc)[..., None, None]
matrix_yz[..., 2, 0] = (-a / norm_abc)[..., None, None]
matrix_yz[..., 2, 2] = (norm_bc / norm_abc)[..., None, None]
self.matrix_R = np.matmul(matrix_x, matrix_yz)
image_vector = np.matmul(image_vector[..., None, :], self.matrix_R)
image_vector = image_vector[..., 0, :]
image_vector = np.moveaxis(image_vector, -1, -3)
return (image_vector)
def inverseImageVector(
projection_M,
image_vector):
'''
projection_M:
shape:(N_views, 3, 4)
image_vector:
shape:(3 * img_h * img_w)
:return
image_vector_inverse
shape:(N_views,3,img_h, img_w)
----------------------------------------------------
'''
image_vector = np.moveaxis(image_vector, 0, -1)
N_Ms = projection_M.shape[0]
img_h, img_w, _ = image_vector.shape
image_vector_new = np.ones((1, img_h, img_w, 4))
image_vector_new[..., 0:3] = image_vector
projection_new = np.zeros((N_Ms, 4, 4))
projection_new[:, 0:3, :] = projection_M
projection_new[:, 3, :] = np.array(([[0, 0, 0, 1]]))
projection_new = np.linalg.inv(projection_new)[:, None, None, :, :] # shape:(N_views,img_h, img_w, 4, 4)
image_vector_inverse = np.matmul(projection_new, image_vector_new[..., None]) # shape:(N_views,img_h, img_w, 4, 1)
image_vector_inverse = image_vector_inverse[..., 0:3, 0]
image_vector_inverse = np.moveaxis(image_vector_inverse, -1, -3) # shape:(N_views,3,img_h, img_w)
return (image_vector_inverse)
def K_partition(cameraKO, compress_ratio_h=4.0, compress_ratio_w=4.0, img_size=(1200, 1600)):
cx = cameraKO[0][2]
cy = cameraKO[1][2]
principal_coords_list = []
partition_num = int(compress_ratio_h * compress_ratio_w)
h = int(img_size[0] / compress_ratio_h)
w = int(img_size[1] / compress_ratio_w)
for i in range(compress_ratio_h):
for j in range(compress_ratio_w):
cxx = cx - j * w
cyy = cy - i * h
principal_coord = (cxx, cyy)
principal_coords_list.append(principal_coord)
cameraKOs = np.empty((partition_num, 3, 3), dtype=np.float64)
for k in range(partition_num):
cameraKOs[k] = cameraKO
cameraKOs[k][0][2] = principal_coords_list[k][0]
cameraKOs[k][1][2] = principal_coords_list[k][1]
return cameraKOs
def partition_image_and_matrix(images,
cameraPO4s_model,
cameraRTO4s_model,
cameraKO4s_model,
# image_compress_stage,
# return_list=False,
compress_ratio_h=4.0,
compress_ratio_w=4.0):
'''
Args:
images, list, (N_view, 3, img_h, img_w)
cameraPO4s_model, (N_view, 4, 4)
compress_ratio_h: compress_ratio for the H dimension.
compress_ratio_w: compress_ratio for the W dimension.
Outputs:
parti_imgs, (N_view, N_partition, 3, parti_h, parti_w)
_cameraP04s, (N_view, N_partition, 4, 4).
'''
num_view = len(images)
num_partition = int(compress_ratio_w * compress_ratio_h)
parti_h = int(images[0].shape[1] / compress_ratio_h)
parti_w = int(images[0].shape[2] / compress_ratio_w)
# parti_imgs = []
parti_imgs = np.empty((num_view, num_partition, 3, parti_h, parti_w), dtype=np.float64)
# cameraPO4s = np.empty((num_view, num_partition, 4, 4), dtype=np.float64)
# print('images.shape: ', images[0].shape)
for view_i in range(num_view):
for partition_j in range(num_partition):
start_h_idx = math.floor(partition_j / compress_ratio_w)
start_w_idx = (partition_j % compress_ratio_w)
start_h_pix = start_h_idx * parti_h
start_w_pix = start_w_idx * parti_w
final_h_pix = start_h_pix + parti_h
final_w_pix = start_w_pix + parti_w
# parti_imgs.append(images[view_i][start_h_pix: final_h_pix, start_w_pix: final_w_pix, :])
parti_imgs[view_i, partition_j, :, :, :] = images[view_i][:, start_h_pix: final_h_pix,
start_w_pix: final_w_pix]
# print('^^^^^^^^^^', parti_imgs.shape)
_cameraP04s, _, _ = CameraPOs_as_torch_partitioned(cameraPO4s_model, cameraRTO4s_model, cameraKO4s_model,
compress_ratio_h=compress_ratio_h, compress_ratio_w=compress_ratio_w,
img_size=(images[0].shape[1], images[0].shape[2]))
return parti_imgs, _cameraP04s
def CameraPOs_as_torch_partitioned(cameraPO4s_model, cameraRTO4s_model, cameraKO4s_model,
compress_ratio_h=4.0, compress_ratio_w=4.0, img_size=(1200, 1600)):
'''
Args:
cameraKO4s_models: (N_view, 3, 3)
outputs:
cameraP04s_: (N_view, N_partition, 4, 4).
'''
# num_model = len(cameraKO4s_models)
num_view = cameraPO4s_model.shape[0]
num_partition = int(compress_ratio_w * compress_ratio_h)
# modify the dimension from (3,4) to (4,4)
cameraPO4s = np.empty((num_view, num_partition, 3, 4), dtype=np.float64)
cameraRTO4s = np.empty((num_view, num_partition, 3, 4), dtype=np.float64)
cameraKO4s = np.empty((num_view, num_partition, 3, 3), dtype=np.float64)
# for i in range(num_model):
for j in range(num_view):
cameraK0 = cameraKO4s_model[j]
cameraK0s = K_partition(cameraK0, compress_ratio_h, compress_ratio_w, img_size) # (num_partition, 3, 3)
for k in range(num_partition):
cameraKO4s[j][k] = cameraK0s[k]
cameraRTO4s[j][k] = cameraRTO4s_model[j]
cameraPO4s[j][k] = np.dot(cameraK0s[k], cameraRTO4s_model[j])
# concatenation for PO4, from (..., 3, 4) to (..., 4, 4).
ones1 = np.repeat(np.array([[[0, 0, 0, 1]]]), repeats=num_partition, axis=0)
ones2 = np.repeat(np.expand_dims(ones1, axis=0), repeats=num_view, axis=0)
# ones3 = np.repeat(np.expand_dims(ones2, axis=0), repeats=num_model, axis=0)
cameraP04s = np.concatenate((cameraPO4s, ones2), axis=2)
#print('cameraP04s shape: ', cameraP04s.shape)
# total_num = num_partition * num_view * num_model
# ones = np.repeat(np.array([[[[[0, 0, 0, 1]]]]]), repeats=total_num, axis=0)
# cameraP04s = np.concatenate((cameraPO4s, ones), axis=3)
_cameraP04s = torch.from_numpy(cameraP04s).type(torch.FloatTensor)
_cameraRTO4s = torch.from_numpy(cameraRTO4s).type(torch.FloatTensor)
_cameraKO4s = torch.from_numpy(cameraKO4s).type(torch.FloatTensor)
return _cameraP04s, _cameraRTO4s, _cameraKO4s
def resize_multistage_image_and_matrix(images,
projection_M,
intrinsic_K,
cube_xyz_min,
cube_D_mm,
_cube_D_,
image_compress_multiple,
image_compress_stage,
return_list=False,
compress_ratio=1.0):
# input intrinsic_K (N_views, 3, 3).
'''
compress image and garantee the camera position is not changing
:param images: all images of one model
type:list or None
if list
list element: image array
shape: (img_h,img_w, 3)
:param projection_M: camera matrix
shape: (N_views, 3, 4)
:param intrinsic_K: intrinsic matrix
shape: (N_views, 3, 3)
:param extrinsic_RT: extrinsic matrix
shape: (N_views, 3, 4)
:param cube_xyz_min: min xyz coordinate
shape: (3,) / (N_pts, 3) usually it is (3,) because we only sample one cubic to judge the resize term
:param cube_D_mm:
cubic length float
:param _cube_D_:
cubic size int
:param image_compress_multiple:
same as param.image_compress_multiple
:param image_compress_stage
same as param.image_compress_stage
:param return_list: bool
if False return the numpy array
:param compress_ratio
see self.params.compress_ratio
:return:
if image is not None
image_resized_stage_list:multistage of resized image
length : = image_compress_stage
ele in each list:
shape:(N_view, img_h_new//2**iter, img_w_new//2**iter)
projection_M_new: new cameraP
shape:(N_view,3,4)
(compress_h_new,compress_w_new):(float,float)
elif image is None: only change the matrix
projection_M_new: new cameraP
shape:(N_view,3,4)
(compress_h_new,compress_w_new):(float,float)
'''
compress_h = compress_ratio
compress_w = compress_ratio
resized_h = int(image_compress_multiple * (images[0].shape[0] // (compress_h * image_compress_multiple)))
resized_w = int(image_compress_multiple * (images[0].shape[1] // (compress_w * image_compress_multiple)))
# pdb.set_trace()
compress_h_new = images[0].shape[0] / (resized_h + 0.0)
compress_w_new = images[0].shape[1] / (resized_w + 0.0)
transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]])
projection_M_new = np.matmul(transform_matrix, projection_M)
# calculate the K after resizing.
intrinsic_K_new = np.matmul(transform_matrix, intrinsic_K)
cameraTs = cameraPs2Ts(projection_M)
cameraTs_new = cameraPs2Ts(projection_M_new)
trans_vector = (cameraTs - cameraTs_new)[:, :, None]
identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0)
bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0)
transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2)
transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1)
projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1)
projection_M_new = np.matmul(transform_matrix2, projection_M_new_f)
projection_M_new = projection_M_new[:, :3, :]
if (images is not None):
image_resized_stage_list = []
for iter in range(image_compress_stage):
image_resized_list = []
for image in images:
# print('resized image shape',resized_h, resized_w)
image_resized = scipy.misc.imresize(image,
size=(int(resized_h // (2 ** iter)), int(resized_w // (2 ** iter))),
interp='bicubic')
image_resized = image_resized / 256.0 - 0.5
image_resized_list.append(image_resized)
images_resized = image_resized_list if return_list else np.stack(image_resized_list)
image_resized_stage_list.append(images_resized)
return (image_resized_stage_list, projection_M_new, intrinsic_K_new, (compress_h_new, compress_w_new))
else:
return (None, projection_M_new, intrinsic_K_new, (compress_h_new, compress_w_new))
def resize_matrix(projection_M, intrinsic_K, compress_ratio_total):
"""
input:
projection_M, (N_view, 3, 4).
output:
projection_M_new: (N_view, 3, 4)
intrinsic_K_new: (N_view, 3, 3).
"""
compress_w_new = compress_ratio_total
compress_h_new = compress_ratio_total
transform_matrix = np.array([[[1 / compress_w_new, 0, 0], [0, 1 / compress_h_new, 0], [0, 0, 1]]])
projection_M_new = np.matmul(transform_matrix, projection_M)
# calculate the K after resizing.
intrinsic_K_new = np.matmul(transform_matrix, intrinsic_K)
cameraTs = cameraPs2Ts(projection_M)
cameraTs_new = cameraPs2Ts(projection_M_new)
trans_vector = (cameraTs - cameraTs_new)[:, :, None]
identical_matrix = np.repeat(np.array([[[1, 0, 0], [0, 1, 0], [0, 0, 1]]]), cameraTs.shape[0], axis=0)
bottom_matrix = np.repeat(np.array([[[0, 0, 0, 1]]]), cameraTs.shape[0], axis=0)
transform_matrix2 = np.concatenate((identical_matrix, trans_vector), axis=2)
transform_matrix2 = np.concatenate((transform_matrix2, bottom_matrix), axis=1)
projection_M_new_f = np.concatenate((projection_M_new, bottom_matrix), axis=1)
projection_M_new = np.matmul(transform_matrix2, projection_M_new_f)
projection_M_new = projection_M_new[:, :3, :]
return projection_M_new, intrinsic_K_new
def interpolate_cameras(cameraRTO4s_models, cameraKO4s_models, chooose_list, rate_list, interpolate_num=4, direction=1,
zoomin_flag=False):
if zoomin_flag:
cameraRT_ch1 = torch.Tensor(
np.array([[0.7404845135614985, 0.2325005573497136, 0.6305753991446678, -357.5776160575932],
[-0.6113867548639025, 0.6226553427405186, 0.48837052366865125, -224.67534057994519],
[-0.2790849063168464, -0.747156625483257, 0.6032149168942565, -314.37583021393465]]))
cameraRT_ch2 = torch.Tensor(
np.array([[0.7818430657776811, -0.5105525697800951, 0.3578509652459924, -236.34755864003523],
[0.011400854998416882, 0.5855732302942881, 0.8105398357749803, -432.3902637782938],
[-0.6233711434370777, -0.6296345988675118, 0.46364696910912734, -86.72248020694681]]))
for i in range(len(chooose_list)):
if chooose_list[i] == 1:
cameraRT_new = zoomin_camera(cameraRTO4s_models[0, i], cameraKO4s_models[0, i], rate_list[i])
cameraRTO4s_models[0, i] = cameraRT_new
cameraRTO4s_models[0, 2] = cameraRT_ch1
cameraRTO4s_models[0, 3] = cameraRT_ch2
# cameraK_new = expand_camera(cameraRTO4s_models[0,1], cameraKO4s_models[0,1], 0.5)
# cameraKO4s_models[0,1] = cameraK_new
camera_angles = matrix_to_euler_angles(cameraRTO4s_models[:, :, :, :3],
convention="XYZ") # shape:(N_models, N_view, 3)
camera_ts = cameraRTO4s_models[:, :, :, 3:4] # shape:(N_models, N_view, 3, 1)
camera_angles_begin_expand = camera_angles[:, 0:-1, :][:, :, None, :].expand(-1, -1, interpolate_num,
-1) # shape:(N_models, N_view - 1, interpolate_num, 3)
camera_angles_end_expand = camera_angles[:, 1:, :][:, :, None, :].expand(-1, -1, interpolate_num,
-1) # shape:(N_models, N_view - 1, interpolate_num, 3)
camera_ts_begin_expand = camera_ts[:, 0:-1, ...][:, :, None, ...].expand(-1, -1, interpolate_num, -1,
-1) # shape:(N_models, N_view - 1, interpolate_num, 3, 1)
camera_ts_end_expand = camera_ts[:, 1:, ...][:, :, None, ...].expand(-1, -1, interpolate_num, -1,
-1) # shape:(N_models, N_view - 1, interpolate_num, 3, 1)
if (direction == 1):
interpolate_alpha = torch.arange(0, 1, 1.0 / interpolate_num) # shape : (interpolate_num)
else:
interpolate_alpha = 1 - 1.0 / interpolate_num - torch.arange(0, 1,
1.0 / interpolate_num) # shape : (interpolate_num)
camera_angles_new_expand = interpolate_alpha[None, None, :, None] * camera_angles_begin_expand + (
1 - interpolate_alpha[None, None, :,
None]) * camera_angles_end_expand # shape:(N_models, N_view - 1, interpolate_num, 3)
# camera_angles_new_expand = camera_angles_begin_expand
camera_ts_new_expand = interpolate_alpha[None, None, :, None, None] * camera_ts_begin_expand + (
1 - interpolate_alpha[None, None, :, None,
None]) * camera_ts_end_expand # shape:(N_models, N_view - 1, interpolate_num, 3, 1)
# camera_ts_new_expand = camera_ts_begin_expand
camera_rs_new_expand = euler_angles_to_matrix(camera_angles_new_expand,
convention="XYZ") # shape:(N_models, N_view - 1, interpolate_num, 3, 3)
camera_rt_new_expand = torch.cat((camera_rs_new_expand, camera_ts_new_expand), dim=4)
camera_rt_new = camera_rt_new_expand.reshape(camera_rt_new_expand.shape[0], -1, 3,
4) # shape:(N_models, N_view_new, 3, 4)
camera_ks_expand = cameraKO4s_models[:, 0:-1, None, :, :].expand(-1, -1, interpolate_num, -1,
-1) # shape:(N_models, N_view - 1, interpolate_num, 3, 3)
camera_ks_new = camera_ks_expand.reshape(camera_rt_new_expand.shape[0], -1, 3,
3) # shape:(N_models, N_view_new, 3, 3)
# pdb.set_trace()
camera_p0s_new = torch.matmul(camera_ks_new, camera_rt_new) # shape:(N_models, N_view_new, 3, 4)
camera_ts_new = torch.from_numpy(cameraPs2Ts_all(camera_p0s_new.numpy())).type(camera_p0s_new.dtype)
ones = torch.tensor([0, 0, 0, 1]).type(camera_rt_new.dtype)[None, None, None, :].expand(camera_rt_new.shape[0],
camera_rt_new.shape[1], 1,
-1) # shape:(N_models, N_view_new, 1, 4)
camera_p04s_new = torch.cat((camera_p0s_new, ones), dim=2) # shape:(N_models, N_view_new, 4, 4)
# pdb.set_trace()
# return more camera parameters.
return camera_p04s_new, camera_p0s_new, camera_ks_new, camera_rt_new, camera_ts_new | [
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tests/test_clients.py | rodrigoapereira/python-hydra-sdk | 0 | 9690 | # Copyright (C) 2017 O.S. Systems Software LTDA.
# This software is released under the MIT License
import unittest
from hydra import Hydra, Client
class ClientsTestCase(unittest.TestCase):
def setUp(self):
self.hydra = Hydra('http://localhost:4444', 'client', 'secret')
self.client = Client(
name='new-client',
secret='client-secret',
scopes=['devices', 'products'],
redirect_uris=['http://localhost/callback'],
)
def test_can_create_client(self):
client = self.hydra.clients.create(self.client)
self.addCleanup(self.hydra.clients.delete, client_id=client.id)
self.assertEqual(client.name, 'new-client')
self.assertEqual(client.secret, 'client-secret')
self.assertEqual(client.scopes, ['devices', 'products'])
self.assertEqual(client.redirect_uris, ['http://localhost/callback'])
def test_can_get_client(self):
client_id = self.hydra.clients.create(self.client).id
self.addCleanup(self.hydra.clients.delete, client_id=client_id)
client = self.hydra.clients.get(client_id)
self.assertEqual(client.id, client_id)
def test_can_update_client(self):
client = self.hydra.clients.create(self.client)
self.addCleanup(self.hydra.clients.delete, client_id=client.id)
self.assertEqual(client.name, 'new-client')
client.name = 'new-client-name'
self.hydra.clients.update(client)
self.assertEqual(client.name, 'new-client-name')
def test_can_delete_client(self):
client = self.hydra.clients.create(self.client)
self.addCleanup(self.hydra.clients.delete, client_id=client.id)
self.assertIsNotNone(self.hydra.clients.get(client.id))
self.hydra.clients.delete(client.id)
self.assertIsNone(self.hydra.clients.get(client.id))
def test_can_list_all_clients(self):
client1 = self.hydra.clients.create(self.client)
self.addCleanup(self.hydra.clients.delete, client_id=client1.id)
client2 = self.hydra.clients.create(self.client)
self.addCleanup(self.hydra.clients.delete, client_id=client2.id)
clients = [c.id for c in self.hydra.clients.all()]
self.assertIn(client1.id, clients)
self.assertIn(client2.id, clients)
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] |
tests/test_binaryop_add.py | ioancornea/niveristand-python | 6 | 139222 | import sys
from niveristand import nivs_rt_sequence
from niveristand import realtimesequencetools
from niveristand.clientapi import ChannelReference, DoubleValue, I32Value
from niveristand.clientapi import RealTimeSequence
from niveristand.errors import TranslateError, VeristandError
from niveristand.library.primitives import localhost_wait
import pytest
from testutilities import rtseqrunner, validation
a = 1
b = 2
@nivs_rt_sequence
def _return_constant():
a = DoubleValue(5)
return a.value
@nivs_rt_sequence
def add_simple_numbers():
a = DoubleValue(0)
a.value = 1 + 2
return a.value
@nivs_rt_sequence
def add_num_nivsdatatype():
a = DoubleValue(0)
a.value = 1 + DoubleValue(2)
return a.value
@nivs_rt_sequence
def add_nivsdatatype_nivsdatatype():
a = DoubleValue(0)
a.value = DoubleValue(1) + DoubleValue(2)
return a.value
@nivs_rt_sequence
def add_nivsdatatype_nivsdatatype1():
a = DoubleValue(0)
a.value = DoubleValue(1) + I32Value(2)
return a.value
@nivs_rt_sequence
def add_nivsdatatype_nivsdatatype2():
a = DoubleValue(0)
a.value = I32Value(1) + DoubleValue(2)
return a.value
@nivs_rt_sequence
def add_nivsdatatype_nivsdatatype3():
a = DoubleValue(0)
a.value = I32Value(1) + I32Value(2)
return a.value
@nivs_rt_sequence
def add_multiple_types():
a = DoubleValue(0)
a.value = 1 + DoubleValue(2) + 3.0
return a.value
@nivs_rt_sequence
def add_multiple_types1():
a = I32Value(0)
a.value = 1 + I32Value(2) + 3.0 + DoubleValue(4)
return a.value
@nivs_rt_sequence
def add_use_rtseq():
a = DoubleValue(0)
a.value = 1 + _return_constant()
return a.value
@nivs_rt_sequence
def add_use_rtseq1():
a = DoubleValue(0)
a.value = _return_constant() + 1
return a.value
@nivs_rt_sequence
def add_use_rtseq2():
a = DoubleValue(0)
a.value = DoubleValue(1) + _return_constant()
return a.value
@nivs_rt_sequence
def add_use_rtseq3():
a = DoubleValue(0)
a.value = _return_constant() + DoubleValue(1)
return a.value
@nivs_rt_sequence
def add_use_rtseq4():
a = DoubleValue(0)
a.value = I32Value(1) + _return_constant()
return a.value
@nivs_rt_sequence
def add_use_rtseq5():
a = DoubleValue(0)
a.value = _return_constant() + I32Value(1)
return a.value
@nivs_rt_sequence
def add_with_parentheses():
a = DoubleValue(0)
a.value = 1 + (2 + 3)
return a.value
@nivs_rt_sequence
def add_with_parentheses1():
a = DoubleValue(0)
a.value = 1 + (DoubleValue(2) + I32Value(5))
return a.value
@nivs_rt_sequence
def add_with_parentheses2():
a = DoubleValue(0)
a.value = DoubleValue(1) + (I32Value(2) + 3.0) + DoubleValue(4)
return a.value
@nivs_rt_sequence
def add_variables():
a = DoubleValue(5)
b = DoubleValue(0)
b.value = 1 + a
return b.value
@nivs_rt_sequence
def add_variables1():
a = DoubleValue(5)
b = DoubleValue(0)
b.value = 1 + a.value
return b.value
@nivs_rt_sequence
def add_variable_variable():
a = DoubleValue(1)
b = DoubleValue(2)
c = DoubleValue(0)
c.value = a.value + b.value
return c.value
@nivs_rt_sequence
def add_variable_variable1():
a = DoubleValue(1)
b = DoubleValue(2)
c = DoubleValue(0)
c.value = a.value + b.value
return c.value
@nivs_rt_sequence
def add_variable_rtseq():
a = DoubleValue(1)
b = DoubleValue(0)
b.value = a.value + _return_constant()
return b.value
@nivs_rt_sequence
def add_variable_rtseq1():
a = DoubleValue(1)
b = DoubleValue(0)
b.value = _return_constant() + a.value
return b.value
@nivs_rt_sequence
def add_to_channel_ref():
a = DoubleValue(0)
b = ChannelReference("Aliases/DesiredRPM")
b.value = 5.0
localhost_wait()
a.value = 1 + b.value
return a.value
@nivs_rt_sequence
def add_binary_unary():
a = DoubleValue(0)
a.value = 2 + - 1
return a.value
@nivs_rt_sequence
def add_with_multiple_plus():
a = DoubleValue(0)
a.value = 1 ++ 2 # noqa: E225 it's ok to test this
return a.value
@nivs_rt_sequence
def add_with_multiple_plus1():
a = DoubleValue(0)
a.value = 1 +++ 2 # noqa: E225 it's ok to test this
return a.value
@nivs_rt_sequence
def add_binary_unary_sequence():
a = DoubleValue(0)
a.value = 1+ - - - - -2 # noqa: E225 it's ok to test this
return a.value
@nivs_rt_sequence
def add_complex_expr():
a = DoubleValue(0)
a.value = 1 + (2 if 2 < 3 else 4)
return a.value
# <editor-fold desc=Augassign tests>
@nivs_rt_sequence
def aug_add_simple_numbers():
a = DoubleValue(1)
a.value += 2
return a.value
@nivs_rt_sequence
def aug_add_num_nivsdatatype():
a = DoubleValue(1)
a.value += DoubleValue(2)
return a.value
@nivs_rt_sequence
def aug_add_use_rtseq():
a = DoubleValue(1)
a.value += _return_constant()
return a.value
@nivs_rt_sequence
def aug_add_with_parentheses():
a = DoubleValue(1)
a.value += (I32Value(2) + 3.0) + DoubleValue(4)
return a.value
@nivs_rt_sequence
def aug_add_variables():
a = DoubleValue(5)
b = DoubleValue(1)
b.value += a.value
return b.value
@nivs_rt_sequence
def aug_add_to_channel_ref():
a = DoubleValue(1)
b = ChannelReference("Aliases/DesiredRPM")
b.value = 5.0
localhost_wait()
a.value += b.value
return a.value
@nivs_rt_sequence
def aug_add_unary():
a = DoubleValue(1)
a.value += -1
return a.value
# </editor-fold>
# <editor-fold desc=Invalid tests>
@nivs_rt_sequence
def add_invalid_variables():
return a.value + b
@nivs_rt_sequence
def add_invalid_variables1():
return a.value + b.value
@nivs_rt_sequence
def add_to_None():
a = DoubleValue(0)
a.value = None + 1
return a.value
@nivs_rt_sequence
def add_invalid_rtseq_call():
a = DoubleValue(0)
a.value = _return_constant + 1
return a.value
# </editor-fold>
run_tests = [
(add_simple_numbers, (), 3),
(add_num_nivsdatatype, (), 3),
(add_nivsdatatype_nivsdatatype, (), 3),
(add_nivsdatatype_nivsdatatype1, (), 3),
(add_nivsdatatype_nivsdatatype2, (), 3),
(add_nivsdatatype_nivsdatatype3, (), 3),
(add_multiple_types, (), 6),
(add_multiple_types1, (), 10),
(add_with_parentheses, (), 6),
(add_with_parentheses1, (), 8),
(add_with_parentheses2, (), 10),
(add_variables, (), 6),
(add_variables1, (), 6),
(add_variable_variable, (), 3),
(add_variable_variable1, (), 3),
(add_binary_unary, (), 1),
(aug_add_simple_numbers, (), 3),
(aug_add_variables, (), 6),
(aug_add_num_nivsdatatype, (), 3),
(aug_add_with_parentheses, (), 10),
(aug_add_unary, (), 0),
(add_complex_expr, (), 3),
(add_use_rtseq, (), 6),
(add_use_rtseq1, (), 6),
(add_use_rtseq2, (), 6),
(add_use_rtseq3, (), 6),
(add_use_rtseq4, (), 6),
(add_use_rtseq5, (), 6),
(add_variable_rtseq, (), 6),
(add_variable_rtseq1, (), 6),
(aug_add_use_rtseq, (), 6),
(add_to_channel_ref, (), 6),
(aug_add_to_channel_ref, (), 6),
(add_binary_unary_sequence, (), -1),
]
fail_transform_tests = [
(add_invalid_variables, (), TranslateError),
(add_invalid_variables1, (), TranslateError),
(add_with_multiple_plus, (), VeristandError), # "UnaryAdd not supported by SPE"
(add_with_multiple_plus1, (), VeristandError), # "UnaryAdd not supported by SPE"
(add_to_None, (), TranslateError),
(add_invalid_rtseq_call, (), VeristandError),
]
def idfunc(val):
try:
return val.__name__
except AttributeError:
return str(val)
@pytest.mark.parametrize("func_name, params, expected_result", run_tests, ids=idfunc)
def test_transform(func_name, params, expected_result):
RealTimeSequence(func_name)
@pytest.mark.parametrize("func_name, params, expected_result", run_tests, ids=idfunc)
def test_runpy(func_name, params, expected_result):
actual = func_name(*params)
assert actual == expected_result
@pytest.mark.parametrize("func_name, params, expected_result", run_tests, ids=idfunc)
def test_run_py_as_rts(func_name, params, expected_result):
actual = realtimesequencetools.run_py_as_rtseq(func_name)
assert actual == expected_result
@pytest.mark.parametrize("func_name, params, expected_result", run_tests, ids=idfunc)
def test_run_in_VM(func_name, params, expected_result):
actual = rtseqrunner.run_rtseq_in_VM(func_name)
assert actual == expected_result
@pytest.mark.parametrize("func_name, params, expected_result", fail_transform_tests, ids=idfunc)
def test_failures(func_name, params, expected_result):
with pytest.raises(expected_result):
RealTimeSequence(func_name)
with pytest.raises(expected_result):
func_name(*params)
def test_check_all_tested():
validation.test_validate(sys.modules[__name__])
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pypy/translator/cli/rte.py | benoitc/pypy | 1 | 149322 | <gh_stars>1-10
"""
Support for an automatically compiled Run Time Environment.
The source of the RTE is in the src/ directory.
"""
import os
import os.path
import shutil
import py
import subprocess
from pypy.translator.cli.sdk import SDK
from pypy.tool.ansi_print import ansi_log
log = py.log.Producer("cli")
py.log.setconsumer("cli", ansi_log)
class Target:
SOURCES = []
OUTPUT = None
ALIAS = None
FLAGS = []
DEPENDENCIES = []
SRC_DIR = os.path.join(os.path.dirname(__file__), 'src/')
def _filename(cls, name, path=None):
rel_path = os.path.join(cls.SRC_DIR, name)
return os.path.abspath(rel_path)
_filename = classmethod(_filename)
def get_COMPILER(cls):
return SDK.csc()
get_COMPILER = classmethod(get_COMPILER)
def get(cls):
for dep in cls.DEPENDENCIES:
dep.get()
sources = [cls._filename(src) for src in cls.SOURCES]
out = cls._filename(cls.OUTPUT)
alias = cls._filename(cls.ALIAS or cls.OUTPUT)
recompile = True
try:
src_mtime = max([os.stat(src).st_mtime for src in sources])
alias_mtime = os.stat(alias).st_mtime
if src_mtime <= alias_mtime:
recompile = False
except OSError:
pass
if recompile:
cls.compile(sources, out)
return out
get = classmethod(get)
def compile(cls, sources, out):
log.red("Compiling %s" % (cls.ALIAS or cls.OUTPUT))
oldcwd = os.getcwd()
os.chdir(cls.SRC_DIR)
compiler = subprocess.Popen([cls.get_COMPILER()] + cls.FLAGS + ['/out:%s' % out] + sources,
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = compiler.communicate()
retval = compiler.wait()
assert retval == 0, 'Failed to compile %s: the compiler said:\n %s' % (
cls.OUTPUT, stdout + stderr)
if cls.ALIAS is not None:
alias = cls._filename(cls.ALIAS)
shutil.copy(out, alias)
os.chdir(oldcwd)
compile = classmethod(compile)
class MainStub(Target):
SOURCES = ['stub/main.il']
OUTPUT = 'main.exe'
def get_COMPILER(cls):
return SDK.ilasm()
get_COMPILER = classmethod(get_COMPILER)
class PyPyLibDLL(Target):
SOURCES = ['pypylib.cs', 'll_os.cs', 'll_os_path.cs', 'errno.cs', 'll_math.cs',
'debug.cs']
OUTPUT = 'pypylib.dll'
FLAGS = ['/t:library', '/unsafe', '/r:main.exe']
DEPENDENCIES = [MainStub]
def compile(cls, sources, out):
from pypy.translator.cli.query import pypylib
remove_cache_for_assembly(pypylib)
Target.compile.im_func(cls, sources, out)
compile = classmethod(compile)
class RPythonNetModule(Target):
SOURCES = []
OUTPUT = 'rpython.netmodule'
def compile(cls, sources, out):
pass
class Query(Target):
SOURCES = ['query.cs']
OUTPUT = 'query.exe'
def compile(cls, sources, out):
# assume that if query.exe need to be recompiled the descriptions cache is invalid
from pypy.translator.cli.query import mscorlib, pypylib
remove_cache_for_assembly(mscorlib)
remove_cache_for_assembly(pypylib)
Target.compile.im_func(cls, sources, out)
compile = classmethod(compile)
class Support(Target):
SOURCES = ['support.cs']
OUTPUT = 'support.dll'
FLAGS = ['/t:library']
def get_pypy_dll():
return PyPyLibDLL.get()
def remove_cache_for_assembly(ass):
from pypy.translator.cli.query import get_cachedir
cache = get_cachedir().join(ass + '.pickle')
if cache.check():
cache.remove()
if __name__ == '__main__':
import shutil
pypylib = get_pypy_dll()
shutil.copy(pypylib, '.')
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] |
migcon/attachment_info.py | rappdw/migcon | 0 | 139016 | <gh_stars>0
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List
from markdown_it.token import Token
@dataclass
class Attachment:
"""
A class to hold information about an attachment. With Page attachments in confluence, multiple files
can share the same name, also with potentially different types, e.g. application/json and application/octet-stream.
This is modeled by having a dictionary that maps attachment type to a list of file names.
"""
meaningful_name: str
files: Dict[str, List[str]] # key: attachment type, value: list of file names
destination_file: Path = None
multiple_copies_warning: bool = False
@dataclass
class AttachmentInfo:
"""
This class is used to store the information about the attachments, specifically we derive the page id from the
attachment url string (Path(url).parent.name). As multiple versions of the same file can be attached to a
confluence page, we keep a dictionary of the "meaningful name" that maps to an attachment
which holds a list of all file names that are associated to that "attachment".
"""
page_id: str
page_name: str
attachments: Dict[str, Attachment] # key: meaningful attachment name, value: attachment
def process_tokens(tokens: List[Token], page_name: str) -> AttachmentInfo:
"""
Process the list of tokens that were generated from the "attachments" section of the input file.
:param tokens: the list of tokens that holds attachment information
:param page_name:
:return: a dictionary of attachment information
"""
attachments = {}
page_id = 'unset'
in_heading = False
in_attachment_section = False
for token in tokens:
if token.type == 'heading_open':
if in_attachment_section:
break
in_heading = True
elif token.type == 'heading_close':
in_heading = False
elif token.type == 'inline':
if in_heading:
if token.content == 'Attachments:':
in_attachment_section = True
elif in_attachment_section:
att_type = att_name = att_url = None
in_link = False
soft_break = False
for child in token.children:
if child.type == 'link_open':
att_url = child.attrs['href']
in_link = True
soft_break = False
elif child.type == 'link_close':
in_link = False
soft_break = False
elif child.type == 'text':
if in_link:
if soft_break:
att_name += f" {child.content.strip()}"
else:
att_name = child.content.strip()
else:
att_type = child.content.strip()
elif child.type == 'softbreak':
soft_break = True
if att_url and att_name and att_type:
page_id = Path(att_url).parent.name
att_name = att_name.replace(' ', '_')
if att_name in attachments:
if att_type in attachments[att_name].files:
attachments[att_name].files[att_type].append(att_url)
else:
attachments[att_name].files[att_type] = [att_url]
else:
attachments[att_name] = Attachment(att_name, {att_type: [att_url]})
att_type = att_name = att_url = None
return AttachmentInfo(page_id, page_name, attachments)
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] |
cyberfloodClient/cyberfloodClient.py | acastaner/cf-py-cfclient | 0 | 99705 | <gh_stars>0
from requests.packages.urllib3.exceptions import InsecureRequestWarning
import requests
import json
import pickle
import logging
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
class CfClient:
def __init__(self, userName, userPassword, controllerAddress):
self.userName = userName
self.userPassword = <PASSWORD>
self.controllerAddress = "https://" + controllerAddress + "/api/v2"
self.__bearerToken = ""
self.__isLogged = False
self.__session = requests.session()
self.__session.verify = False
# logging.basicConfig()
# logging.getLogger().setLevel(logging.DEBUG)
## requests_log = logging.getLogger("requests.packages.urllib3")
# requests_log.setLevel(logging.DEBUG)
## requests_log.propagate = True
def generateToken(self):
response = self.__session.post(self.controllerAddress + '/token',
data={'email': self.userName,
'password': <PASSWORD>})
if response.status_code == 201:
self.__bearerToken = json.loads(response.text)['token']
self.__session.headers.update(
{'Authorization': 'Bearer {0}'.format(self.__bearerToken)})
self.__isLogged = True
return self.__bearerToken
def invalidateToken(self):
return self.__session.delete(
self.controllerAddress + '/token'
)
def isLogged(self):
return self.__isLogged
def getFile(self, fileId):
return self.__session.get(
self.controllerAddress + '/files/' + fileId
)
def downloadFile(self, fileId):
return self.__session.get(
self.controllerAddress + '/files/' + fileId + '/download'
)
def getFiles(self):
return self.__session.get(
self.controllerAddress + '/files'
)
def uploadFileMultipart(self, filePath):
files = {'file': open(filePath, "rb")}
response = self.__session.post(
self.controllerAddress + '/files?type=multipart',
files=files
)
return response
def deleteFile(self, fileId):
return self.__session.delete(
self.controllerAddress + '/files' + fileId
)
def listAttackProfiles(self):
return self.__session.get(
self.controllerAddress + '/profiles/attacks'
)
def createAttackProfile(self, name, description, scenarioIds):
response = self.__session.post(
self.controllerAddress + '/profiles/attacks',
json={
'name': name,
'description': description,
'scenarios': scenarioIds,
'scenarioTypes': ['attack']
}
)
return response
def createApplicationProfile(self, name, description, scenarioIds):
response = self.__session.post(
self.controllerAddress + '/profiles/apps',
json={
'name': name,
'description': description,
'category': 'Miscellaneous',
'scenarios': scenarioIds,
'scenarioTypes': ['app']
}
)
return response
def createMalwareProfile(self, name, description, scenarioIds):
response = self.__session.post(
self.controllerAddress + '/profiles/malware',
json={
'name': name,
'description': description,
'scenarios': scenarioIds,
'scenarioTypes': ['malware'],
'contentType': 'malware'
}
)
return response
def listMalwareProfiles(self):
return self.__session.get(
self.controllerAddress + '/profiles/malware'
)
def listApplicationProfiles(self):
return self.__session.get(
self.controllerAddress + '/profiles/apps'
)
def createAttackScenario(self, fileId, name, description):
response = self.__session.post(
self.controllerAddress + '/scenarios/attacks',
json={
'fileId': fileId,
'name': name,
'description': description
}
)
return response
def createApplicationScenario(self, fileId, name, description):
response = self.__session.post(
self.controllerAddress + '/scenarios/apps',
json={'fileId': fileId,
'name': name,
'description': description,
'category': 'Miscellaneous'
}
)
return response
def createMalwareScenario(self, fileId, name, description):
response = self.__session.post(
self.controllerAddress + '/scenarios/malware',
json={'fileId': fileId,
'name': name,
'description': description,
'category': 'Miscellaneous'
}
)
return response
def getAttackProfile(self, profileId):
return self.__session.get(
self.controllerAddress + "/profiles/attacks/" + profileId
)
def getCyberSecurityAssessmentTest(self, csaTestId):
return self.__session.get(
self.controllerAddress + "/tests/cyber_security_assessment/" + csaTestId
)
def getHttpThroughputTest(self, testId):
return self.__session.get(
self.controllerAddress + "//tests/http_throughput/" + testId
)
def updateCyberSecurityAssessmentTest(self, csaTest, csaTestId):
return self.__session.put(
self.controllerAddress +
"/tests/cyber_security_assessment/" + csaTestId,
json=csaTest
)
def startTest(self, testId):
return self.__session.put(
self.controllerAddress + "/tests/" + testId + "/start"
)
def getTestRun(self, testRunId):
return self.__session.get(
self.controllerAddress + "/test_runs/" + testRunId
)
def fetchTestRunTimelineStatistics(self, testRunId):
return self.__session.get(
self.controllerAddress + "/test_runs/" + testRunId + "/timeline"
)
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] |
djangosige/apps/cadastro/models/empresa.py | MateusMolina/lunoERP | 0 | 6525 | # -*- coding: utf-8 -*-
import os
from django.db import models
from django.db.models.signals import post_delete
from django.dispatch import receiver
from .base import Pessoa
from djangosige.apps.login.models import Usuario
from djangosige.configs.settings import MEDIA_ROOT
def logo_directory_path(instance, filename):
extension = os.path.splitext(filename)[1]
return 'imagens/empresas/logo_{0}_{1}{2}'.format(instance.nome_razao_social, instance.id, extension)
class Empresa(Pessoa):
logo_file = models.ImageField(
upload_to=logo_directory_path, default='imagens/logo.png', blank=True, null=True)
cnae = models.CharField(max_length=10, blank=True, null=True)
iest = models.CharField(max_length=32, null=True, blank=True)
class Meta:
verbose_name = "Empresa"
@property
def caminho_completo_logo(self):
if self.logo_file.name != 'imagens/logo.png':
return os.path.join(MEDIA_ROOT, self.logo_file.name)
else:
return ''
def save(self, *args, **kwargs):
# Deletar logo se ja existir um
try:
obj = Empresa.objects.get(id=self.id)
if obj.logo_file != self.logo_file and obj.logo_file != 'imagens/logo.png':
obj.logo_file.delete(save=False)
except:
pass
super(Empresa, self).save(*args, **kwargs)
def __unicode__(self):
return u'%s' % self.nome_razao_social
def __str__(self):
return u'%s' % self.nome_razao_social
# Deletar logo quando empresa for deletada
@receiver(post_delete, sender=Empresa)
def logo_post_delete_handler(sender, instance, **kwargs):
# Nao deletar a imagem default 'logo.png'
if instance.logo_file != 'imagens/logo.png':
instance.logo_file.delete(False)
class MinhaEmpresa(models.Model):
m_empresa = models.ForeignKey(
Empresa, on_delete=models.CASCADE, related_name='minha_empresa', blank=True, null=True)
m_usuario = models.ForeignKey(
Usuario, on_delete=models.CASCADE, related_name='empresa_usuario')
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] |
anyconfig/backend/json.py | masatake/python-anyconfig | 0 | 64670 | #
# Copyright (C) 2011 - 2018 <NAME> <<EMAIL>>
# License: MIT
#
# Ref. python -c "import json; help(json)"
#
# pylint: disable=import-error
r"""JSON backend:
- Format to support: JSON, http://www.json.org
- Requirements: json in python standard library (>= python 2.6) or simplejson
- Development Status :: 5 - Production/Stable
- Limitations: None obvious
- Special options:
- All options of json.load{s,} and json.dump{s,} except object_hook
should work.
- See also: https://docs.python.org/3/library/json.html or
https://docs.python.org/2/library/json.html dependent on the python version
to use.
Changelog:
.. versionchanged:: 0.9.6
- Add support of loading primitives other than mapping objects.
.. versionadded:: 0.0.1
"""
from __future__ import absolute_import
try:
import json
_CID = "std"
except ImportError:
import simplejson as json
_CID = "simplejson"
import anyconfig.backend.base
import anyconfig.compat
_LOAD_OPTS = ["cls", "object_hook", "parse_float", "parse_int",
"parse_constant"]
_DUMP_OPTS = ["skipkeys", "ensure_ascii", "check_circular", "allow_nan",
"cls", "indent", "separators", "default", "sort_keys"]
_DICT_OPTS = ["object_hook"]
# It seems that 'encoding' argument is not allowed in json.load[s] and
# json.dump[s] in JSON module in python 3.x.
if not anyconfig.compat.IS_PYTHON_3:
_LOAD_OPTS.append("encoding")
_DUMP_OPTS.append("encoding")
if not anyconfig.compat.IS_PYTHON_2_6:
_LOAD_OPTS.append("object_pairs_hook")
_DICT_OPTS.insert(0, "object_pairs_hook") # Higher prio. than object_hook
class Parser(anyconfig.backend.base.StringStreamFnParser):
"""
Parser for JSON files.
"""
_cid = _CID
_type = "json"
_extensions = ["json", "jsn", "js"]
_load_opts = _LOAD_OPTS
_dump_opts = _DUMP_OPTS
_ordered = not anyconfig.compat.IS_PYTHON_2_6
_allow_primitives = True
_dict_opts = _DICT_OPTS
_load_from_string_fn = anyconfig.backend.base.to_method(json.loads)
_load_from_stream_fn = anyconfig.backend.base.to_method(json.load)
_dump_to_string_fn = anyconfig.backend.base.to_method(json.dumps)
_dump_to_stream_fn = anyconfig.backend.base.to_method(json.dump)
# vim:sw=4:ts=4:et:
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messages/GetActiveHostsResponseMessage.py | zadjii/nebula | 2 | 198562 | # last generated 2016-04-10 21:56:22.756000
from messages import BaseMessage
from msg_codes import GET_ACTIVE_HOSTS_RESPONSE as GET_ACTIVE_HOSTS_RESPONSE
__author__ = 'Mike'
class GetActiveHostsResponseMessage(BaseMessage):
def __init__(self, cloud=None):
# type: (Cloud) -> None
super(GetActiveHostsResponseMessage, self).__init__()
self.type = GET_ACTIVE_HOSTS_RESPONSE
self.cname = ''
self.cloud_uname = ''
self.hosts = []
if cloud is not None:
self.cname = cloud.name
self.cloud_uname = cloud.uname()
self.hosts = cloud.get_get_hosts_dict(active_only=True)
@staticmethod
def deserialize(json_dict):
msg = GetActiveHostsResponseMessage()
msg.cname = json_dict['cname']
msg.cloud_uname = json_dict['cloud_uname']
msg.hosts = json_dict['hosts']
return msg
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src/features/videos/speech/extract_speech.py | ClaasM/VideoArticleRetrieval | 0 | 41282 | import os
from pocketsphinx import AudioFile
from pocketsphinx import Pocketsphinx
from src import util
test_video = os.environ['DATA_PATH'] + "/other/sphinx_test_video/beachball.mp4"
test_audio = os.environ['DATA_PATH'] + "/other/sphinx_test_audio/interview.wav"
fps = 100 # default
audio_file = AudioFile(audio_file=test_audio, frate=100)
for phrase in audio_file: # frate (default=100)
print(" ".join([s.word for s in phrase.seg()]))
#print(Pocketsphinx().decode(audio_file=test_audio))
# i'm home i'm a i'm won't home all cool i'm wall long and lulu move up at bay back when when i'm i'm home
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tests/test_cases.py | dcramer/exam | 1 | 1602414 | from mock import sentinel, MagicMock
from unittest2 import TestCase
import itertools
from exam.decorators import fixture, before, after, around, patcher
from exam.cases import Exam
from describe import expect
from dummy import get_thing, get_it
class FakeTest(object):
def __init__(self):
self.cleanups = []
self.setups = 0
self.teardowns = 0
def setUp(self):
self.setups += 1
def tearDown(self):
self.teardowns += 1
def run(self, *args, **kwargs):
self.state_when_run = list(self.calls)
def addCleanup(self, func):
self.cleanups.append(func)
class DummyTest(Exam, FakeTest):
@patcher('tests.dummy.thing')
def dummy_thing(self):
return sentinel.mock
dummy_it = patcher('tests.dummy.it', return_value=12)
def __init__(self):
self.calls = []
super(DummyTest, self).__init__()
@before
def append_one(self):
self.calls.append(1)
@after
def append_two(self):
self.calls.append(2)
@around
def append_5_then_6(self):
self.calls.append(5)
yield
self.calls.append(6)
class ExtendedDummy(DummyTest):
@before
def append_3(self):
self.calls.append(3)
@after
def append_4(self):
self.calls.append(4)
@around
def append_7_then_8(self):
self.calls.append(7)
yield
self.calls.append(8)
# TODO: Make the subclass checking just be a subclass of the test case
class TestExam(Exam, TestCase):
@fixture
def case(self):
return DummyTest()
@fixture
def subclass_case(self):
return ExtendedDummy()
@after
def stop_patchers(self):
cleanups = (self.case.cleanups, self.subclass_case.cleanups)
for cleanup in itertools.chain(*cleanups):
if hasattr(cleanup.im_self, 'is_local'): # Is the mock started?
cleanup()
@property
def other_thing(self):
return get_thing()
@property
def other_it(self):
return get_it()
def test_before_adds_each_method_to_set_up(self):
expect(self.case.calls).to == []
self.case.setUp()
expect(self.case.calls).to == [1]
def test_after_adds_each_method_to_tear_down(self):
expect(self.case.calls).to == []
self.case.tearDown()
expect(self.case.calls).to == [2]
def test_around_calls_methods_before_and_after_run(self):
expect(self.case.calls).to == []
self.case.run()
expect(self.case.state_when_run).to == [5]
expect(self.case.calls).to == [5, 6]
def test_before_works_on_subclasses(self):
expect(self.subclass_case.calls).to == []
self.subclass_case.setUp()
expect(self.subclass_case.calls).to == [3, 1]
def test_after_works_on_subclasses(self):
expect(self.subclass_case.calls).to == []
self.subclass_case.tearDown()
expect(self.subclass_case.calls).to == [4, 2]
def test_around_works_with_subclasses(self):
expect(self.subclass_case.calls).to == []
self.subclass_case.run()
expect(self.subclass_case.state_when_run).to == [7, 5]
expect(self.subclass_case.calls).to == [7, 5, 8, 6]
def test_patcher_start_value_is_added_to_case_dict_on_setup(self):
self.case.setUp()
expect(self.case.dummy_thing).to == sentinel.mock
def test_patcher_patches_object_on_setup_and_adds_patcher_to_cleanup(self):
expect(self.other_thing).to != sentinel.mock
self.case.setUp()
expect(self.other_thing).to == sentinel.mock
[cleanup() for cleanup in self.case.cleanups]
expect(self.other_thing).to != sentinel.mock
def test_patcher_lifecycle_works_on_subclasses(self):
expect(self.other_thing).to != sentinel.mock
self.subclass_case.setUp()
expect(self.other_thing).to == sentinel.mock
[cleanup() for cleanup in self.subclass_case.cleanups]
expect(self.other_thing).to != sentinel.mock
def test_patcher_patches_with_a_magic_mock_if_no_function_decorated(self):
expect(self.other_it()).to != 12
self.case.setUp()
expect(self.other_it()).to == 12
self.case.cleanups[0]()
expect(self.other_thing).to != 12
def test_calls_super_setup(self):
expect(self.case.setups).to == 0
self.case.setUp()
expect(self.case.setups).to == 1
def test_calls_super_teardown(self):
expect(self.case.teardowns).to == 0
self.case.tearDown()
expect(self.case.teardowns).to == 1
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quffka/gq.py | maremun/quffka | 15 | 144781 | <reponame>maremun/quffka<gh_stars>10-100
# enconding: utf-8
# gaussquad.py
# TODO fix Documentation
import numpy as np
import numpy.polynomial.hermite as herm
from .basics import get_D
def generate_gq_weights(d, n, deg=2):
'''
Subsample points and weights for Gauss-Hermite quadrature.
Args
d: the number of dimensions of the integral.
n: number of SR rules to be equivalent to in terms of output feature
dimensionality D.
deg: the degree of the polynomial that is approximated accurately by
this quadrule is 2-1.
Returns
W_subsampled: the points, matrix of size d x D.
A_subsampled: the weights, vector of size D.
'''
W, A = herm.hermgauss(deg)
# Use ortho for higher dimensions
# W, A = orth.line.schemes.hermite(d, 30)
# make sure we are not working with object datatype
# W = np.array(W, dtype='float64')
# A = np.array(A, dtype='float64')
# normalize weights
# this is where the 1/sqrt(pi) constant is hidden
# since sum of the weights is exactly the constant!
A = A / A.sum()
D = get_D(d, n)
c = np.empty((d, D))
I = np.arange(W.shape[0])
for i in range(d):
c[i] = np.random.choice(I, D, True, A)
c = c.astype('int')
W_subsampled = W[c]
A_subsampled = np.sum(np.log(A[c]), axis=0)
A_subsampled -= A_subsampled.max()
A_subsampled = np.exp(A_subsampled)
# I = np.arange(W.shape[0])
# c = np.random.choice(I, d*L, True, A)
# W_subsampled = np.reshape(W[c], (d,L))
# A_subsampled = np.prod(np.reshape(A[c], (d,L)), axis=0)
## W_subsampled = np.ascontiguousarray(W_subsampled.T).T # for faster tensordot
# normalize weights of subsampled points
A_subsampled /= A_subsampled.sum()
W_subsampled *= np.sqrt(2)
return W_subsampled.T, np.sqrt(A_subsampled)
def explicit_map(Z, W):
nsamples = W.shape[0]
ZW = Z @ W
fZ = np.hstack((np.cos(ZW), np.sin(ZW)))
return fZ
def rbf_kernel_via_explicit_map(X, Y, A):
A = np.sqrt(A)
A = np.concatenate((A, A))
X = np.einsum('j,ij->ij', A, X)
Y = np.einsum('j,ij->ij', A, Y)
K = X @ Y.T
return K
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deps/lib/python3.5/site-packages/netdisco/discoverables/samsung_tv.py | jfarmer08/hassio | 78 | 6537 | """Discover Samsung Smart TV services."""
from . import SSDPDiscoverable
from ..const import ATTR_NAME
# For some models, Samsung forces a [TV] prefix to the user-specified name.
FORCED_NAME_PREFIX = '[TV]'
class Discoverable(SSDPDiscoverable):
"""Add support for discovering Samsung Smart TV services."""
def get_entries(self):
"""Get all the Samsung RemoteControlReceiver entries."""
return self.find_by_st(
"urn:samsung.com:device:RemoteControlReceiver:1")
def info_from_entry(self, entry):
"""Get most important info, by default the description location."""
info = super().info_from_entry(entry)
# Strip the forced prefix, if present
if info[ATTR_NAME].startswith(FORCED_NAME_PREFIX):
info[ATTR_NAME] = info[ATTR_NAME][len(FORCED_NAME_PREFIX):].strip()
return info
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nuscenes/eval/detection/evaluate.py | WJ-Lai/NightFusion | 0 | 6332 | # nuScenes dev-kit.
# Code written by <NAME> & <NAME>, 2018.
# Licensed under the Creative Commons [see licence.txt]
import argparse
import json
import os
import random
import time
from typing import Tuple, Dict, Any
import numpy as np
from nuscenes import NuScenes
from nuscenes.eval.detection.algo import accumulate, calc_ap, calc_tp
from nuscenes.eval.detection.config import config_factory
from nuscenes.eval.detection.constants import TP_METRICS
from nuscenes.eval.detection.data_classes import DetectionConfig, MetricDataList, DetectionMetrics, EvalBoxes
from nuscenes.eval.detection.loaders import load_prediction, load_gt, add_center_dist, filter_eval_boxes
from nuscenes.eval.detection.render import summary_plot, class_pr_curve, class_tp_curve, dist_pr_curve, visualize_sample
class NuScenesEval:
"""
This is the official nuScenes detection evaluation code.
Results are written to the provided output_dir.
nuScenes uses the following metrics:
- Mean Average Precision (mAP): Uses center-distance as matching criterion; averaged over distance thresholds.
- True Positive (TP) metrics: Average of translation, velocity, scale, orientation and attribute errors.
- nuScenes Detection Score (NDS): The weighted sum of the above.
Here is an overview of the functions in this method:
- init: Loads GT annotations an predictions stored in JSON format and filters the boxes.
- run: Performs evaluation and dumps the metric data to disk.
- render: Renders various plots and dumps to disk.
We assume that:
- Every sample_token is given in the results, although there may be not predictions for that sample.
Please see https://github.com/nutonomy/nuscenes-devkit for more details.
"""
def __init__(self,
nusc: NuScenes,
config: DetectionConfig,
result_path: str,
eval_set: str,
output_dir: str = None,
verbose: bool = True):
"""
Initialize a NuScenesEval object.
:param nusc: A NuScenes object.
:param config: A DetectionConfig object.
:param result_path: Path of the nuScenes JSON result file.
:param eval_set: The dataset split to evaluate on, e.g. train or val.
:param output_dir: Folder to save plots and results to.
:param verbose: Whether to print to stdout.
"""
self.nusc = nusc
self.result_path = result_path
self.eval_set = eval_set
self.output_dir = output_dir
self.verbose = verbose
self.cfg = config
# Make dirs.
self.plot_dir = os.path.join(self.output_dir, 'plots')
if not os.path.isdir(self.output_dir):
os.makedirs(self.output_dir)
if not os.path.isdir(self.plot_dir):
os.makedirs(self.plot_dir)
# Load data.
self.pred_boxes, self.meta = load_prediction(self.result_path, self.cfg.max_boxes_per_sample, verbose=verbose)
self.gt_boxes = load_gt(self.nusc, self.eval_set, verbose=verbose)
assert set(self.pred_boxes.sample_tokens) == set(self.gt_boxes.sample_tokens), \
"Samples in split doesn't match samples in predictions."
# Add center distances.
self.pred_boxes = add_center_dist(nusc, self.pred_boxes)
self.gt_boxes = add_center_dist(nusc, self.gt_boxes)
# Filter boxes (distance, points per box, etc.).
if verbose:
print('Filtering predictions')
self.pred_boxes = filter_eval_boxes(nusc, self.pred_boxes, self.cfg.class_range, verbose=verbose)
if verbose:
print('Filtering ground truth annotations')
self.gt_boxes = filter_eval_boxes(nusc, self.gt_boxes, self.cfg.class_range, verbose=verbose)
self.sample_tokens = self.gt_boxes.sample_tokens
def evaluate(self) -> Tuple[DetectionMetrics, MetricDataList]:
"""
Performs the actual evaluation.
:return: A tuple of high-level and the raw metric data.
"""
start_time = time.time()
# -----------------------------------
# Step 1: Accumulate metric data for all classes and distance thresholds.
# -----------------------------------
if self.verbose:
print('Accumulating metric data')
metric_data_list = MetricDataList()
for class_name in self.cfg.class_names:
for dist_th in self.cfg.dist_ths:
md = accumulate(self.gt_boxes, self.pred_boxes, class_name, self.cfg.dist_fcn, dist_th)
metric_data_list.set(class_name, dist_th, md)
# -----------------------------------
# Step 2: Calculate metrics from the data.
# -----------------------------------
if self.verbose:
print('Calculating metrics')
metrics = DetectionMetrics(self.cfg)
for class_name in self.cfg.class_names:
for dist_th in self.cfg.dist_ths:
metric_data = metric_data_list[(class_name, dist_th)]
ap = calc_ap(metric_data, self.cfg.min_recall, self.cfg.min_precision)
metrics.add_label_ap(class_name, dist_th, ap)
for metric_name in TP_METRICS:
metric_data = metric_data_list[(class_name, self.cfg.dist_th_tp)]
if class_name in ['traffic_cone'] and metric_name in ['attr_err', 'vel_err', 'orient_err']:
tp = np.nan
elif class_name in ['barrier'] and metric_name in ['attr_err', 'vel_err']:
tp = np.nan
else:
tp = calc_tp(metric_data, self.cfg.min_recall, metric_name)
metrics.add_label_tp(class_name, metric_name, tp)
metrics.add_runtime(time.time() - start_time)
return metrics, metric_data_list
def render(self, metrics: DetectionMetrics, md_list: MetricDataList) -> None:
"""
Renders various PR and TP curves.
:param metrics: DetectionMetrics instance.
:param md_list: MetricDataList instance.
"""
def savepath(name):
return os.path.join(self.plot_dir, name + '.pdf')
summary_plot(md_list, metrics, min_precision=self.cfg.min_precision, min_recall=self.cfg.min_recall,
dist_th_tp=self.cfg.dist_th_tp, savepath=savepath('summary'))
for detection_name in self.cfg.class_names:
class_pr_curve(md_list, metrics, detection_name, self.cfg.min_precision, self.cfg.min_recall,
savepath=savepath(detection_name + '_pr'))
class_tp_curve(md_list, metrics, detection_name, self.cfg.min_recall, self.cfg.dist_th_tp,
savepath=savepath(detection_name + '_tp'))
for dist_th in self.cfg.dist_ths:
dist_pr_curve(md_list, metrics, dist_th, self.cfg.min_precision, self.cfg.min_recall,
savepath=savepath('dist_pr_' + str(dist_th)))
def main(self,
plot_examples: int = 0,
render_curves: bool = True) -> Dict[str, Any]:
"""
Main function that loads the evaluation code, visualizes samples, runs the evaluation and renders stat plots.
:param plot_examples: How many example visualizations to write to disk.
:param render_curves: Whether to render PR and TP curves to disk.
:return: A dict that stores the high-level metrics and meta data.
"""
if plot_examples > 0:
# Select a random but fixed subset to plot.
random.seed(43)
sample_tokens = list(self.sample_tokens)
random.shuffle(sample_tokens)
sample_tokens = sample_tokens[:plot_examples]
# Visualize samples.
example_dir = os.path.join(self.output_dir, 'examples')
if not os.path.isdir(example_dir):
os.mkdir(example_dir)
for sample_token in sample_tokens:
visualize_sample(self.nusc,
sample_token,
self.gt_boxes if self.eval_set != 'test' else EvalBoxes(),
# Don't render test GT.
self.pred_boxes,
eval_range=max(self.cfg.class_range.values()),
savepath=os.path.join(example_dir, '{}.png'.format(sample_token)))
# Run evaluation.
metrics, metric_data_list = self.evaluate()
# Render PR and TP curves.
if render_curves:
self.render(metrics, metric_data_list)
# Dump the metric data, meta and metrics to disk.
if self.verbose:
print('Saving metrics to: %s' % self.output_dir)
metrics_summary = metrics.serialize()
metrics_summary['meta'] = self.meta.copy()
with open(os.path.join(self.output_dir, 'metrics_summary.json'), 'w') as f:
json.dump(metrics_summary, f, indent=2)
with open(os.path.join(self.output_dir, 'metrics_details.json'), 'w') as f:
json.dump(metric_data_list.serialize(), f, indent=2)
# Print high-level metrics.
print('mAP: %.4f' % (metrics_summary['mean_ap']))
err_name_mapping = {
'trans_err': 'mATE',
'scale_err': 'mASE',
'orient_err': 'mAOE',
'vel_err': 'mAVE',
'attr_err': 'mAAE'
}
for tp_name, tp_val in metrics_summary['tp_errors'].items():
print('%s: %.4f' % (err_name_mapping[tp_name], tp_val))
print('NDS: %.4f' % (metrics_summary['nd_score']))
print('Eval time: %.1fs' % metrics_summary['eval_time'])
return metrics_summary
if __name__ == "__main__":
# Settings.
parser = argparse.ArgumentParser(description='Evaluate nuScenes result submission.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('result_path', type=str, help='The submission as a JSON file.')
parser.add_argument('--output_dir', type=str, default='~/nuscenes-metrics',
help='Folder to store result metrics, graphs and example visualizations.')
parser.add_argument('--eval_set', type=str, default='val',
help='Which dataset split to evaluate on, train, val or test.')
parser.add_argument('--dataroot', type=str, default='/data/sets/nuscenes',
help='Default nuScenes data directory.')
parser.add_argument('--version', type=str, default='v1.0-trainval',
help='Which version of the nuScenes dataset to evaluate on, e.g. v1.0-trainval.')
parser.add_argument('--config_name', type=str, default='cvpr_2019',
help='Name of the configuration to use for evaluation, e.g. cvpr_2019.')
parser.add_argument('--plot_examples', type=int, default=10,
help='How many example visualizations to write to disk.')
parser.add_argument('--render_curves', type=int, default=1,
help='Whether to render PR and TP curves to disk.')
parser.add_argument('--verbose', type=int, default=1,
help='Whether to print to stdout.')
args = parser.parse_args()
result_path_ = os.path.expanduser(args.result_path)
output_dir_ = os.path.expanduser(args.output_dir)
eval_set_ = args.eval_set
dataroot_ = args.dataroot
version_ = args.version
config_name_ = args.config_name
plot_examples_ = args.plot_examples
render_curves_ = bool(args.render_curves)
verbose_ = bool(args.verbose)
cfg_ = config_factory(config_name_)
nusc_ = NuScenes(version=version_, verbose=verbose_, dataroot=dataroot_)
nusc_eval = NuScenesEval(nusc_, config=cfg_, result_path=result_path_, eval_set=eval_set_,
output_dir=output_dir_, verbose=verbose_)
nusc_eval.main(plot_examples=plot_examples_, render_curves=render_curves_)
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] |
sp_api/auth/access_token_client.py | onesdata/python-amazon-sp-api | 0 | 1609563 | <reponame>onesdata/python-amazon-sp-api
import json
import os
import requests
from botocore.exceptions import ClientError
import hashlib
import logging
from cachetools import TTLCache
import boto3
from sp_api.base import BaseClient
from .credentials import Credentials
from .access_token_response import AccessTokenResponse
from .exceptions import AuthorizationError
cache = TTLCache(maxsize=10, ttl=3600)
grantless_cache = TTLCache(maxsize=10, ttl=3600)
logger = logging.getLogger(__name__)
class AccessTokenClient(BaseClient):
host = 'api.amazon.com'
grant_type = 'refresh_token'
path = '/auth/o2/token'
def __init__(self, refresh_token=None, account='default', credentials=None):
super().__init__(account, credentials)
self.cred = Credentials(refresh_token, self.credentials)
def _request(self, url, data, headers):
response = requests.post(url, data=data, headers=headers)
response_data = response.json()
if response.status_code != 200:
error_message = response_data.get('error_description')
error_code = response_data.get('error')
raise AuthorizationError(error_code, error_message, response.status_code)
return response_data
def get_auth(self) -> AccessTokenResponse:
"""
Get's the access token
:return:AccessTokenResponse
"""
global cache
cache_key = self._get_cache_key()
try:
access_token = cache[cache_key]
except KeyError:
cache_ttl = 3600
access_token = None
if self.use_secrets():
access_token = self.get_secret()
if not access_token:
request_url = self.scheme + self.host + self.path
access_token = self._request(request_url, self.data, self.headers)
if self.use_secrets():
self.put_access_token(access_token)
else:
cache_ttl = access_token.get('expires_in')
cache = TTLCache(maxsize=10, ttl=cache_ttl - 15)
cache[cache_key] = access_token
return AccessTokenResponse(**access_token)
def use_secrets(self):
return os.environ.get('SP_API_AWS_SECRET_ID') and os.environ.get('SP_API_USE_SECRET_ACCESS_TOKEN_ROTATION')
def get_grantless_auth(self):
"""
POST /auth/o2/token HTTP/l.l
Host: api.amazon.com
Content-Type: application/x-www-form-urlencoded;charset=UTF-8
grant_type=client_credentials
&scope=sellingpartnerapi::notifications
&client_id=foodev
&client_secret=Y76SDl2F
:return:
"""
global grantless_cache
cache_key = self._get_cache_key()
try:
access_token = grantless_cache[cache_key]
logger.debug('from_cache')
except KeyError:
request_url = self.scheme + self.host + self.path
access_token = self._request(request_url, data=self.grantless_data, headers=self.headers)
logger.debug('token_refreshed')
grantless_cache = TTLCache(maxsize=10, ttl=3600)
grantless_cache[cache_key] = access_token
return AccessTokenResponse(**access_token)
def authorize_auth_code(self, auth_code):
request_url = self.scheme + self.host + self.path
res = self._request(request_url, data=self._auth_code_request_body(auth_code), headers=self.headers)
return res
def _auth_code_request_body(self, auth_code):
return {
'grant_type': 'authorization_code',
'code': auth_code,
'client_id': self.cred.client_id,
'client_secret': self.cred.client_secret
}
@property
def grantless_data(self):
return {
'grant_type': 'client_credentials',
'client_id': self.cred.client_id,
'scope': 'sellingpartnerapi::notifications',
'client_secret': self.cred.client_secret
}
@property
def data(self):
return {
'grant_type': self.grant_type,
'client_id': self.cred.client_id,
'refresh_token': self.cred.refresh_token,
'client_secret': self.cred.client_secret
}
@property
def headers(self):
return {
'User-Agent': self.user_agent,
'content-type': self.content_type
}
def _get_cache_key(self):
return 'access_token_' + hashlib.md5(self.cred.refresh_token.encode('utf-8')).hexdigest()
def get_secret(self):
try:
client = boto3.client('secretsmanager')
response = client.get_secret_value(
SecretId=os.environ.get('SP_API_AWS_SECRET_ID')
)
secret = json.loads(response.get('SecretString'))
except ClientError:
pass
else:
try:
return json.loads(secret.get('SP_API_ACCESS_TOKEN__{}'.format(self._get_cache_key())))
except TypeError:
return
def put_access_token(self, access_token):
try:
client = boto3.client('secretsmanager')
response = client.get_secret_value(
SecretId=os.environ.get('SP_API_AWS_SECRET_ID')
)
secret = json.loads(response.get('SecretString'))
secret.update({'SP_API_ACCESS_TOKEN__{}'.format(self._get_cache_key()): json.dumps(access_token)})
client.put_secret_value(SecretId=os.environ.get('SP_API_AWS_SECRET_ID'), SecretString=json.dumps(secret))
except ClientError:
pass
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] |
pandas/io/parsers/readers.py | KneeShard/pandas | 0 | 64542 | """
Module contains tools for processing files into DataFrames or other objects
"""
from __future__ import annotations
from collections import abc
import csv
import sys
from textwrap import fill
from typing import Any
import warnings
import numpy as np
import pandas._libs.lib as lib
from pandas._libs.parsers import STR_NA_VALUES
from pandas._typing import (
ArrayLike,
DtypeArg,
FilePathOrBuffer,
StorageOptions,
)
from pandas.errors import (
AbstractMethodError,
ParserWarning,
)
from pandas.util._decorators import (
Appender,
deprecate_nonkeyword_arguments,
)
from pandas.util._validators import validate_bool_kwarg
from pandas.core.dtypes.common import (
is_file_like,
is_float,
is_integer,
is_list_like,
)
from pandas.core import generic
from pandas.core.frame import DataFrame
from pandas.core.indexes.api import RangeIndex
from pandas.io.common import validate_header_arg
from pandas.io.parsers.base_parser import (
ParserBase,
is_index_col,
parser_defaults,
)
from pandas.io.parsers.c_parser_wrapper import CParserWrapper
from pandas.io.parsers.python_parser import (
FixedWidthFieldParser,
PythonParser,
)
_doc_read_csv_and_table = (
r"""
{summary}
Also supports optionally iterating or breaking of the file
into chunks.
Additional help can be found in the online docs for
`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
Parameters
----------
filepath_or_buffer : str, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
expected. A local file could be: file://localhost/path/to/table.csv.
If you want to pass in a path object, pandas accepts any ``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method, such as
a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
sep : str, default {_default_sep}
Delimiter to use. If sep is None, the C engine cannot automatically detect
the separator, but the Python parsing engine can, meaning the latter will
be used and automatically detect the separator by Python's builtin sniffer
tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
different from ``'\s+'`` will be interpreted as regular expressions and
will also force the use of the Python parsing engine. Note that regex
delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, default ``None``
Alias for sep.
header : int, list of int, default 'infer'
Row number(s) to use as the column names, and the start of the
data. Default behavior is to infer the column names: if no names
are passed the behavior is identical to ``header=0`` and column
names are inferred from the first line of the file, if column
names are passed explicitly then the behavior is identical to
``header=None``. Explicitly pass ``header=0`` to be able to
replace existing names. The header can be a list of integers that
specify row locations for a multi-index on the columns
e.g. [0,1,3]. Intervening rows that are not specified will be
skipped (e.g. 2 in this example is skipped). Note that this
parameter ignores commented lines and empty lines if
``skip_blank_lines=True``, so ``header=0`` denotes the first line of
data rather than the first line of the file.
names : array-like, optional
List of column names to use. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, default ``None``
Column(s) to use as the row labels of the ``DataFrame``, either given as
string name or column index. If a sequence of int / str is given, a
MultiIndex is used.
Note: ``index_col=False`` can be used to force pandas to *not* use the first
column as the index, e.g. when you have a malformed file with delimiters at
the end of each line.
usecols : list-like or callable, optional
Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or strings
that correspond to column names provided either by the user in `names` or
inferred from the document header row(s). For example, a valid list-like
`usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
To instantiate a DataFrame from ``data`` with element order preserved use
``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
in ``['foo', 'bar']`` order or
``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
for ``['bar', 'foo']`` order.
If callable, the callable function will be evaluated against the column
names, returning names where the callable function evaluates to True. An
example of a valid callable argument would be ``lambda x: x.upper() in
['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
parsing time and lower memory usage.
squeeze : bool, default False
If the parsed data only contains one column then return a Series.
prefix : str, optional
Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
mangle_dupe_cols : bool, default True
Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
'X'...'X'. Passing in False will cause data to be overwritten if there
are duplicate names in the columns.
dtype : Type name or dict of column -> type, optional
Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32,
'c': 'Int64'}}
Use `str` or `object` together with suitable `na_values` settings
to preserve and not interpret dtype.
If converters are specified, they will be applied INSTEAD
of dtype conversion.
engine : {{'c', 'python'}}, optional
Parser engine to use. The C engine is faster while the python engine is
currently more feature-complete.
converters : dict, optional
Dict of functions for converting values in certain columns. Keys can either
be integers or column labels.
true_values : list, optional
Values to consider as True.
false_values : list, optional
Values to consider as False.
skipinitialspace : bool, default False
Skip spaces after delimiter.
skiprows : list-like, int or callable, optional
Line numbers to skip (0-indexed) or number of lines to skip (int)
at the start of the file.
If callable, the callable function will be evaluated against the row
indices, returning True if the row should be skipped and False otherwise.
An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
skipfooter : int, default 0
Number of lines at bottom of file to skip (Unsupported with engine='c').
nrows : int, optional
Number of rows of file to read. Useful for reading pieces of large files.
na_values : scalar, str, list-like, or dict, optional
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values. By default the following values are interpreted as
NaN: '"""
+ fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ")
+ """'.
keep_default_na : bool, default True
Whether or not to include the default NaN values when parsing the data.
Depending on whether `na_values` is passed in, the behavior is as follows:
* If `keep_default_na` is True, and `na_values` are specified, `na_values`
is appended to the default NaN values used for parsing.
* If `keep_default_na` is True, and `na_values` are not specified, only
the default NaN values are used for parsing.
* If `keep_default_na` is False, and `na_values` are specified, only
the NaN values specified `na_values` are used for parsing.
* If `keep_default_na` is False, and `na_values` are not specified, no
strings will be parsed as NaN.
Note that if `na_filter` is passed in as False, the `keep_default_na` and
`na_values` parameters will be ignored.
na_filter : bool, default True
Detect missing value markers (empty strings and the value of na_values). In
data without any NAs, passing na_filter=False can improve the performance
of reading a large file.
verbose : bool, default False
Indicate number of NA values placed in non-numeric columns.
skip_blank_lines : bool, default True
If True, skip over blank lines rather than interpreting as NaN values.
parse_dates : bool or list of int or names or list of lists or dict, \
default False
The behavior is as follows:
* boolean. If True -> try parsing the index.
* list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
each as a separate date column.
* list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as
a single date column.
* dict, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
result 'foo'
If a column or index cannot be represented as an array of datetimes,
say because of an unparsable value or a mixture of timezones, the column
or index will be returned unaltered as an object data type. For
non-standard datetime parsing, use ``pd.to_datetime`` after
``pd.read_csv``. To parse an index or column with a mixture of timezones,
specify ``date_parser`` to be a partially-applied
:func:`pandas.to_datetime` with ``utc=True``. See
:ref:`io.csv.mixed_timezones` for more.
Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, default False
If True and `parse_dates` is enabled, pandas will attempt to infer the
format of the datetime strings in the columns, and if it can be inferred,
switch to a faster method of parsing them. In some cases this can increase
the parsing speed by 5-10x.
keep_date_col : bool, default False
If True and `parse_dates` specifies combining multiple columns then
keep the original columns.
date_parser : function, optional
Function to use for converting a sequence of string columns to an array of
datetime instances. The default uses ``dateutil.parser.parser`` to do the
conversion. Pandas will try to call `date_parser` in three different ways,
advancing to the next if an exception occurs: 1) Pass one or more arrays
(as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
string values from the columns defined by `parse_dates` into a single array
and pass that; and 3) call `date_parser` once for each row using one or
more strings (corresponding to the columns defined by `parse_dates`) as
arguments.
dayfirst : bool, default False
DD/MM format dates, international and European format.
cache_dates : bool, default True
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce significant speed-up when parsing duplicate
date strings, especially ones with timezone offsets.
.. versionadded:: 0.25.0
iterator : bool, default False
Return TextFileReader object for iteration or getting chunks with
``get_chunk()``.
.. versionchanged:: 1.2
``TextFileReader`` is a context manager.
chunksize : int, optional
Return TextFileReader object for iteration.
See the `IO Tools docs
<https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
for more information on ``iterator`` and ``chunksize``.
.. versionchanged:: 1.2
``TextFileReader`` is a context manager.
compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer'
For on-the-fly decompression of on-disk data. If 'infer' and
`filepath_or_buffer` is path-like, then detect compression from the
following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
decompression). If using 'zip', the ZIP file must contain only one data
file to be read in. Set to None for no decompression.
thousands : str, optional
Thousands separator.
decimal : str, default '.'
Character to recognize as decimal point (e.g. use ',' for European data).
lineterminator : str (length 1), optional
Character to break file into lines. Only valid with C parser.
quotechar : str (length 1), optional
The character used to denote the start and end of a quoted item. Quoted
items can include the delimiter and it will be ignored.
quoting : int or csv.QUOTE_* instance, default 0
Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
doublequote : bool, default ``True``
When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
whether or not to interpret two consecutive quotechar elements INSIDE a
field as a single ``quotechar`` element.
escapechar : str (length 1), optional
One-character string used to escape other characters.
comment : str, optional
Indicates remainder of line should not be parsed. If found at the beginning
of a line, the line will be ignored altogether. This parameter must be a
single character. Like empty lines (as long as ``skip_blank_lines=True``),
fully commented lines are ignored by the parameter `header` but not by
`skiprows`. For example, if ``comment='#'``, parsing
``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
treated as the header.
encoding : str, optional
Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
standard encodings
<https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .
.. versionchanged:: 1.2
When ``encoding`` is ``None``, ``errors="replace"`` is passed to
``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``.
This behavior was previously only the case for ``engine="python"``.
.. versionchanged:: 1.3.0
``encoding_errors`` is a new argument. ``encoding`` has no longer an
influence on how encoding errors are handled.
encoding_errors : str, optional, default "strict"
How encoding errors are treated. `List of possible values
<https://docs.python.org/3/library/codecs.html#error-handlers>`_ .
.. versionadded:: 1.3.0
dialect : str or csv.Dialect, optional
If provided, this parameter will override values (default or not) for the
following parameters: `delimiter`, `doublequote`, `escapechar`,
`skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
override values, a ParserWarning will be issued. See csv.Dialect
documentation for more details.
error_bad_lines : bool, default ``None``
Lines with too many fields (e.g. a csv line with too many commas) will by
default cause an exception to be raised, and no DataFrame will be returned.
If False, then these "bad lines" will be dropped from the DataFrame that is
returned.
.. deprecated:: 1.3.0
The ``on_bad_lines`` parameter should be used instead to specify behavior upon
encountering a bad line instead.
warn_bad_lines : bool, default ``None``
If error_bad_lines is False, and warn_bad_lines is True, a warning for each
"bad line" will be output.
.. deprecated:: 1.3.0
The ``on_bad_lines`` parameter should be used instead to specify behavior upon
encountering a bad line instead.
on_bad_lines : {{'error', 'warn', 'skip'}}, default 'error'
Specifies what to do upon encountering a bad line (a line with too many fields).
Allowed values are :
- 'error', raise an Exception when a bad line is encountered.
- 'warn', raise a warning when a bad line is encountered and skip that line.
- 'skip', skip bad lines without raising or warning when they are encountered.
.. versionadded:: 1.3.0
delim_whitespace : bool, default False
Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
is set to True, nothing should be passed in for the ``delimiter``
parameter.
low_memory : bool, default True
Internally process the file in chunks, resulting in lower memory use
while parsing, but possibly mixed type inference. To ensure no mixed
types either set False, or specify the type with the `dtype` parameter.
Note that the entire file is read into a single DataFrame regardless,
use the `chunksize` or `iterator` parameter to return the data in chunks.
(Only valid with C parser).
memory_map : bool, default False
If a filepath is provided for `filepath_or_buffer`, map the file object
directly onto memory and access the data directly from there. Using this
option can improve performance because there is no longer any I/O overhead.
float_precision : str, optional
Specifies which converter the C engine should use for floating-point
values. The options are ``None`` or 'high' for the ordinary converter,
'legacy' for the original lower precision pandas converter, and
'round_trip' for the round-trip converter.
.. versionchanged:: 1.2
{storage_options}
.. versionadded:: 1.2
Returns
-------
DataFrame or TextParser
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.
See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_fwf : Read a table of fixed-width formatted lines into DataFrame.
Examples
--------
>>> pd.{func_name}('data.csv') # doctest: +SKIP
"""
)
_c_parser_defaults = {
"delim_whitespace": False,
"na_filter": True,
"low_memory": True,
"memory_map": False,
"error_bad_lines": None,
"warn_bad_lines": None,
"float_precision": None,
}
_fwf_defaults = {"colspecs": "infer", "infer_nrows": 100, "widths": None}
_c_unsupported = {"skipfooter"}
_python_unsupported = {"low_memory", "float_precision"}
_deprecated_defaults: dict[str, Any] = {"error_bad_lines": None, "warn_bad_lines": None}
_deprecated_args: set[str] = {"error_bad_lines", "warn_bad_lines"}
def validate_integer(name, val, min_val=0):
"""
Checks whether the 'name' parameter for parsing is either
an integer OR float that can SAFELY be cast to an integer
without losing accuracy. Raises a ValueError if that is
not the case.
Parameters
----------
name : str
Parameter name (used for error reporting)
val : int or float
The value to check
min_val : int
Minimum allowed value (val < min_val will result in a ValueError)
"""
msg = f"'{name:s}' must be an integer >={min_val:d}"
if val is not None:
if is_float(val):
if int(val) != val:
raise ValueError(msg)
val = int(val)
elif not (is_integer(val) and val >= min_val):
raise ValueError(msg)
return val
def _validate_names(names):
"""
Raise ValueError if the `names` parameter contains duplicates or has an
invalid data type.
Parameters
----------
names : array-like or None
An array containing a list of the names used for the output DataFrame.
Raises
------
ValueError
If names are not unique or are not ordered (e.g. set).
"""
if names is not None:
if len(names) != len(set(names)):
raise ValueError("Duplicate names are not allowed.")
if not (
is_list_like(names, allow_sets=False) or isinstance(names, abc.KeysView)
):
raise ValueError("Names should be an ordered collection.")
def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
"""Generic reader of line files."""
if kwds.get("date_parser", None) is not None:
if isinstance(kwds["parse_dates"], bool):
kwds["parse_dates"] = True
# Extract some of the arguments (pass chunksize on).
iterator = kwds.get("iterator", False)
chunksize = validate_integer("chunksize", kwds.get("chunksize", None), 1)
nrows = kwds.get("nrows", None)
# Check for duplicates in names.
_validate_names(kwds.get("names", None))
# Create the parser.
parser = TextFileReader(filepath_or_buffer, **kwds)
if chunksize or iterator:
return parser
with parser:
return parser.read(nrows)
@deprecate_nonkeyword_arguments(
version=None, allowed_args=["filepath_or_buffer"], stacklevel=3
)
@Appender(
_doc_read_csv_and_table.format(
func_name="read_csv",
summary="Read a comma-separated values (csv) file into DataFrame.",
_default_sep="','",
storage_options=generic._shared_docs["storage_options"],
)
)
def read_csv(
filepath_or_buffer: FilePathOrBuffer,
sep=lib.no_default,
delimiter=None,
# Column and Index Locations and Names
header="infer",
names=lib.no_default,
index_col=None,
usecols=None,
squeeze=False,
prefix=lib.no_default,
mangle_dupe_cols=True,
# General Parsing Configuration
dtype: DtypeArg | None = None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
# NA and Missing Data Handling
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
# Datetime Handling
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
# Iteration
iterator=False,
chunksize=None,
# Quoting, Compression, and File Format
compression="infer",
thousands=None,
decimal: str = ".",
lineterminator=None,
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
encoding_errors: str | None = "strict",
dialect=None,
# Error Handling
error_bad_lines=None,
warn_bad_lines=None,
# TODO (2.0): set on_bad_lines to "error".
# See _refine_defaults_read comment for why we do this.
on_bad_lines=None,
# Internal
delim_whitespace=False,
low_memory=_c_parser_defaults["low_memory"],
memory_map=False,
float_precision=None,
storage_options: StorageOptions = None,
):
# locals() should never be modified
kwds = locals().copy()
del kwds["filepath_or_buffer"]
del kwds["sep"]
kwds_defaults = _refine_defaults_read(
dialect,
delimiter,
delim_whitespace,
engine,
sep,
error_bad_lines,
warn_bad_lines,
on_bad_lines,
names,
prefix,
defaults={"delimiter": ","},
)
kwds.update(kwds_defaults)
return _read(filepath_or_buffer, kwds)
@deprecate_nonkeyword_arguments(
version=None, allowed_args=["filepath_or_buffer"], stacklevel=3
)
@Appender(
_doc_read_csv_and_table.format(
func_name="read_table",
summary="Read general delimited file into DataFrame.",
_default_sep=r"'\\t' (tab-stop)",
storage_options=generic._shared_docs["storage_options"],
)
)
def read_table(
filepath_or_buffer: FilePathOrBuffer,
sep=lib.no_default,
delimiter=None,
# Column and Index Locations and Names
header="infer",
names=lib.no_default,
index_col=None,
usecols=None,
squeeze=False,
prefix=lib.no_default,
mangle_dupe_cols=True,
# General Parsing Configuration
dtype: DtypeArg | None = None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
# NA and Missing Data Handling
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
# Datetime Handling
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
cache_dates=True,
# Iteration
iterator=False,
chunksize=None,
# Quoting, Compression, and File Format
compression="infer",
thousands=None,
decimal: str = ".",
lineterminator=None,
quotechar='"',
quoting=csv.QUOTE_MINIMAL,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
# Error Handling
error_bad_lines=None,
warn_bad_lines=None,
# TODO (2.0): set on_bad_lines to "error".
# See _refine_defaults_read comment for why we do this.
on_bad_lines=None,
encoding_errors: str | None = "strict",
# Internal
delim_whitespace=False,
low_memory=_c_parser_defaults["low_memory"],
memory_map=False,
float_precision=None,
):
# locals() should never be modified
kwds = locals().copy()
del kwds["filepath_or_buffer"]
del kwds["sep"]
kwds_defaults = _refine_defaults_read(
dialect,
delimiter,
delim_whitespace,
engine,
sep,
error_bad_lines,
warn_bad_lines,
on_bad_lines,
names,
prefix,
defaults={"delimiter": "\t"},
)
kwds.update(kwds_defaults)
return _read(filepath_or_buffer, kwds)
def read_fwf(
filepath_or_buffer: FilePathOrBuffer,
colspecs="infer",
widths=None,
infer_nrows=100,
**kwds,
):
r"""
Read a table of fixed-width formatted lines into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
Additional help can be found in the `online docs for IO Tools
<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
Parameters
----------
filepath_or_buffer : str, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be:
``file://localhost/path/to/table.csv``.
If you want to pass in a path object, pandas accepts any
``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method,
such as a file handle (e.g. via builtin ``open`` function)
or ``StringIO``.
colspecs : list of tuple (int, int) or 'infer'. optional
A list of tuples giving the extents of the fixed-width
fields of each line as half-open intervals (i.e., [from, to[ ).
String value 'infer' can be used to instruct the parser to try
detecting the column specifications from the first 100 rows of
the data which are not being skipped via skiprows (default='infer').
widths : list of int, optional
A list of field widths which can be used instead of 'colspecs' if
the intervals are contiguous.
infer_nrows : int, default 100
The number of rows to consider when letting the parser determine the
`colspecs`.
**kwds : optional
Optional keyword arguments can be passed to ``TextFileReader``.
Returns
-------
DataFrame or TextParser
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.
See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
Examples
--------
>>> pd.read_fwf('data.csv') # doctest: +SKIP
"""
# Check input arguments.
if colspecs is None and widths is None:
raise ValueError("Must specify either colspecs or widths")
elif colspecs not in (None, "infer") and widths is not None:
raise ValueError("You must specify only one of 'widths' and 'colspecs'")
# Compute 'colspecs' from 'widths', if specified.
if widths is not None:
colspecs, col = [], 0
for w in widths:
colspecs.append((col, col + w))
col += w
kwds["colspecs"] = colspecs
kwds["infer_nrows"] = infer_nrows
kwds["engine"] = "python-fwf"
return _read(filepath_or_buffer, kwds)
class TextFileReader(abc.Iterator):
"""
Passed dialect overrides any of the related parser options
"""
def __init__(self, f, engine=None, **kwds):
self.f = f
if engine is not None:
engine_specified = True
else:
engine = "python"
engine_specified = False
self.engine = engine
self._engine_specified = kwds.get("engine_specified", engine_specified)
_validate_skipfooter(kwds)
dialect = _extract_dialect(kwds)
if dialect is not None:
kwds = _merge_with_dialect_properties(dialect, kwds)
if kwds.get("header", "infer") == "infer":
kwds["header"] = 0 if kwds.get("names") is None else None
self.orig_options = kwds
# miscellanea
self._currow = 0
options = self._get_options_with_defaults(engine)
options["storage_options"] = kwds.get("storage_options", None)
self.chunksize = options.pop("chunksize", None)
self.nrows = options.pop("nrows", None)
self.squeeze = options.pop("squeeze", False)
self._check_file_or_buffer(f, engine)
self.options, self.engine = self._clean_options(options, engine)
if "has_index_names" in kwds:
self.options["has_index_names"] = kwds["has_index_names"]
self._engine = self._make_engine(self.engine)
def close(self):
self._engine.close()
def _get_options_with_defaults(self, engine):
kwds = self.orig_options
options = {}
default: object | None
for argname, default in parser_defaults.items():
value = kwds.get(argname, default)
# see gh-12935
if argname == "mangle_dupe_cols" and not value:
raise ValueError("Setting mangle_dupe_cols=False is not supported yet")
else:
options[argname] = value
for argname, default in _c_parser_defaults.items():
if argname in kwds:
value = kwds[argname]
if engine != "c" and value != default:
if "python" in engine and argname not in _python_unsupported:
pass
elif value == _deprecated_defaults.get(argname, default):
pass
else:
raise ValueError(
f"The {repr(argname)} option is not supported with the "
f"{repr(engine)} engine"
)
else:
value = _deprecated_defaults.get(argname, default)
options[argname] = value
if engine == "python-fwf":
for argname, default in _fwf_defaults.items():
options[argname] = kwds.get(argname, default)
return options
def _check_file_or_buffer(self, f, engine):
# see gh-16530
if is_file_like(f) and engine != "c" and not hasattr(f, "__next__"):
# The C engine doesn't need the file-like to have the "__next__"
# attribute. However, the Python engine explicitly calls
# "__next__(...)" when iterating through such an object, meaning it
# needs to have that attribute
raise ValueError(
"The 'python' engine cannot iterate through this file buffer."
)
def _clean_options(self, options, engine):
result = options.copy()
fallback_reason = None
# C engine not supported yet
if engine == "c":
if options["skipfooter"] > 0:
fallback_reason = "the 'c' engine does not support skipfooter"
engine = "python"
sep = options["delimiter"]
delim_whitespace = options["delim_whitespace"]
if sep is None and not delim_whitespace:
if engine == "c":
fallback_reason = (
"the 'c' engine does not support "
"sep=None with delim_whitespace=False"
)
engine = "python"
elif sep is not None and len(sep) > 1:
if engine == "c" and sep == r"\s+":
result["delim_whitespace"] = True
del result["delimiter"]
elif engine not in ("python", "python-fwf"):
# wait until regex engine integrated
fallback_reason = (
"the 'c' engine does not support "
"regex separators (separators > 1 char and "
r"different from '\s+' are interpreted as regex)"
)
engine = "python"
elif delim_whitespace:
if "python" in engine:
result["delimiter"] = r"\s+"
elif sep is not None:
encodeable = True
encoding = sys.getfilesystemencoding() or "utf-8"
try:
if len(sep.encode(encoding)) > 1:
encodeable = False
except UnicodeDecodeError:
encodeable = False
if not encodeable and engine not in ("python", "python-fwf"):
fallback_reason = (
f"the separator encoded in {encoding} "
"is > 1 char long, and the 'c' engine "
"does not support such separators"
)
engine = "python"
quotechar = options["quotechar"]
if quotechar is not None and isinstance(quotechar, (str, bytes)):
if (
len(quotechar) == 1
and ord(quotechar) > 127
and engine not in ("python", "python-fwf")
):
fallback_reason = (
"ord(quotechar) > 127, meaning the "
"quotechar is larger than one byte, "
"and the 'c' engine does not support such quotechars"
)
engine = "python"
if fallback_reason and self._engine_specified:
raise ValueError(fallback_reason)
if engine == "c":
for arg in _c_unsupported:
del result[arg]
if "python" in engine:
for arg in _python_unsupported:
if fallback_reason and result[arg] != _c_parser_defaults[arg]:
raise ValueError(
"Falling back to the 'python' engine because "
f"{fallback_reason}, but this causes {repr(arg)} to be "
"ignored as it is not supported by the 'python' engine."
)
del result[arg]
if fallback_reason:
warnings.warn(
(
"Falling back to the 'python' engine because "
f"{fallback_reason}; you can avoid this warning by specifying "
"engine='python'."
),
ParserWarning,
stacklevel=5,
)
index_col = options["index_col"]
names = options["names"]
converters = options["converters"]
na_values = options["na_values"]
skiprows = options["skiprows"]
validate_header_arg(options["header"])
for arg in _deprecated_args:
parser_default = _c_parser_defaults[arg]
depr_default = _deprecated_defaults[arg]
if result.get(arg, depr_default) != depr_default:
msg = (
f"The {arg} argument has been deprecated and will be "
"removed in a future version.\n\n"
)
warnings.warn(msg, FutureWarning, stacklevel=7)
else:
result[arg] = parser_default
if index_col is True:
raise ValueError("The value of index_col couldn't be 'True'")
if is_index_col(index_col):
if not isinstance(index_col, (list, tuple, np.ndarray)):
index_col = [index_col]
result["index_col"] = index_col
names = list(names) if names is not None else names
# type conversion-related
if converters is not None:
if not isinstance(converters, dict):
raise TypeError(
"Type converters must be a dict or subclass, "
f"input was a {type(converters).__name__}"
)
else:
converters = {}
# Converting values to NA
keep_default_na = options["keep_default_na"]
na_values, na_fvalues = _clean_na_values(na_values, keep_default_na)
# handle skiprows; this is internally handled by the
# c-engine, so only need for python parsers
if engine != "c":
if is_integer(skiprows):
skiprows = list(range(skiprows))
if skiprows is None:
skiprows = set()
elif not callable(skiprows):
skiprows = set(skiprows)
# put stuff back
result["names"] = names
result["converters"] = converters
result["na_values"] = na_values
result["na_fvalues"] = na_fvalues
result["skiprows"] = skiprows
return result, engine
def __next__(self):
try:
return self.get_chunk()
except StopIteration:
self.close()
raise
def _make_engine(self, engine="c"):
mapping: dict[str, type[ParserBase]] = {
"c": CParserWrapper,
"python": PythonParser,
"python-fwf": FixedWidthFieldParser,
}
if engine not in mapping:
raise ValueError(
f"Unknown engine: {engine} (valid options are {mapping.keys()})"
)
# error: Too many arguments for "ParserBase"
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
def _failover_to_python(self):
raise AbstractMethodError(self)
def read(self, nrows=None):
nrows = validate_integer("nrows", nrows)
index, columns, col_dict = self._engine.read(nrows)
if index is None:
if col_dict:
# Any column is actually fine:
new_rows = len(next(iter(col_dict.values())))
index = RangeIndex(self._currow, self._currow + new_rows)
else:
new_rows = 0
else:
new_rows = len(index)
df = DataFrame(col_dict, columns=columns, index=index)
self._currow += new_rows
if self.squeeze and len(df.columns) == 1:
return df[df.columns[0]].copy()
return df
def get_chunk(self, size=None):
if size is None:
size = self.chunksize
if self.nrows is not None:
if self._currow >= self.nrows:
raise StopIteration
size = min(size, self.nrows - self._currow)
return self.read(nrows=size)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def TextParser(*args, **kwds):
"""
Converts lists of lists/tuples into DataFrames with proper type inference
and optional (e.g. string to datetime) conversion. Also enables iterating
lazily over chunks of large files
Parameters
----------
data : file-like object or list
delimiter : separator character to use
dialect : str or csv.Dialect instance, optional
Ignored if delimiter is longer than 1 character
names : sequence, default
header : int, default 0
Row to use to parse column labels. Defaults to the first row. Prior
rows will be discarded
index_col : int or list, optional
Column or columns to use as the (possibly hierarchical) index
has_index_names: bool, default False
True if the cols defined in index_col have an index name and are
not in the header.
na_values : scalar, str, list-like, or dict, optional
Additional strings to recognize as NA/NaN.
keep_default_na : bool, default True
thousands : str, optional
Thousands separator
comment : str, optional
Comment out remainder of line
parse_dates : bool, default False
keep_date_col : bool, default False
date_parser : function, optional
skiprows : list of integers
Row numbers to skip
skipfooter : int
Number of line at bottom of file to skip
converters : dict, optional
Dict of functions for converting values in certain columns. Keys can
either be integers or column labels, values are functions that take one
input argument, the cell (not column) content, and return the
transformed content.
encoding : str, optional
Encoding to use for UTF when reading/writing (ex. 'utf-8')
squeeze : bool, default False
returns Series if only one column.
infer_datetime_format: bool, default False
If True and `parse_dates` is True for a column, try to infer the
datetime format based on the first datetime string. If the format
can be inferred, there often will be a large parsing speed-up.
float_precision : str, optional
Specifies which converter the C engine should use for floating-point
values. The options are `None` or `high` for the ordinary converter,
`legacy` for the original lower precision pandas converter, and
`round_trip` for the round-trip converter.
.. versionchanged:: 1.2
"""
kwds["engine"] = "python"
return TextFileReader(*args, **kwds)
def _clean_na_values(na_values, keep_default_na=True):
na_fvalues: set | dict
if na_values is None:
if keep_default_na:
na_values = STR_NA_VALUES
else:
na_values = set()
na_fvalues = set()
elif isinstance(na_values, dict):
old_na_values = na_values.copy()
na_values = {} # Prevent aliasing.
# Convert the values in the na_values dictionary
# into array-likes for further use. This is also
# where we append the default NaN values, provided
# that `keep_default_na=True`.
for k, v in old_na_values.items():
if not is_list_like(v):
v = [v]
if keep_default_na:
v = set(v) | STR_NA_VALUES
na_values[k] = v
na_fvalues = {k: _floatify_na_values(v) for k, v in na_values.items()}
else:
if not is_list_like(na_values):
na_values = [na_values]
na_values = _stringify_na_values(na_values)
if keep_default_na:
na_values = na_values | STR_NA_VALUES
na_fvalues = _floatify_na_values(na_values)
return na_values, na_fvalues
def _floatify_na_values(na_values):
# create float versions of the na_values
result = set()
for v in na_values:
try:
v = float(v)
if not np.isnan(v):
result.add(v)
except (TypeError, ValueError, OverflowError):
pass
return result
def _stringify_na_values(na_values):
""" return a stringified and numeric for these values """
result: list[int | str | float] = []
for x in na_values:
result.append(str(x))
result.append(x)
try:
v = float(x)
# we are like 999 here
if v == int(v):
v = int(v)
result.append(f"{v}.0")
result.append(str(v))
result.append(v)
except (TypeError, ValueError, OverflowError):
pass
try:
result.append(int(x))
except (TypeError, ValueError, OverflowError):
pass
return set(result)
def _refine_defaults_read(
dialect: str | csv.Dialect,
delimiter: str | object,
delim_whitespace: bool,
engine: str,
sep: str | object,
error_bad_lines: bool | None,
warn_bad_lines: bool | None,
on_bad_lines: str | None,
names: ArrayLike | None | object,
prefix: str | None | object,
defaults: dict[str, Any],
):
"""Validate/refine default values of input parameters of read_csv, read_table.
Parameters
----------
dialect : str or csv.Dialect
If provided, this parameter will override values (default or not) for the
following parameters: `delimiter`, `doublequote`, `escapechar`,
`skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
override values, a ParserWarning will be issued. See csv.Dialect
documentation for more details.
delimiter : str or object
Alias for sep.
delim_whitespace : bool
Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
used as the sep. Equivalent to setting ``sep='\\s+'``. If this option
is set to True, nothing should be passed in for the ``delimiter``
parameter.
engine : {{'c', 'python'}}
Parser engine to use. The C engine is faster while the python engine is
currently more feature-complete.
sep : str or object
A delimiter provided by the user (str) or a sentinel value, i.e.
pandas._libs.lib.no_default.
error_bad_lines : str or None
Whether to error on a bad line or not.
warn_bad_lines : str or None
Whether to warn on a bad line or not.
on_bad_lines : str or None
An option for handling bad lines or a sentinel value(None).
names : array-like, optional
List of column names to use. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
prefix : str, optional
Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
defaults: dict
Default values of input parameters.
Returns
-------
kwds : dict
Input parameters with correct values.
Raises
------
ValueError :
If a delimiter was specified with ``sep`` (or ``delimiter``) and
``delim_whitespace=True``.
If on_bad_lines is specified(not ``None``) and ``error_bad_lines``/
``warn_bad_lines`` is True.
"""
# fix types for sep, delimiter to Union(str, Any)
delim_default = defaults["delimiter"]
kwds: dict[str, Any] = {}
# gh-23761
#
# When a dialect is passed, it overrides any of the overlapping
# parameters passed in directly. We don't want to warn if the
# default parameters were passed in (since it probably means
# that the user didn't pass them in explicitly in the first place).
#
# "delimiter" is the annoying corner case because we alias it to
# "sep" before doing comparison to the dialect values later on.
# Thus, we need a flag to indicate that we need to "override"
# the comparison to dialect values by checking if default values
# for BOTH "delimiter" and "sep" were provided.
if dialect is not None:
kwds["sep_override"] = delimiter is None and (
sep is lib.no_default or sep == delim_default
)
if delimiter and (sep is not lib.no_default):
raise ValueError("Specified a sep and a delimiter; you can only specify one.")
if names is not lib.no_default and prefix is not lib.no_default:
raise ValueError("Specified named and prefix; you can only specify one.")
kwds["names"] = None if names is lib.no_default else names
kwds["prefix"] = None if prefix is lib.no_default else prefix
# Alias sep -> delimiter.
if delimiter is None:
delimiter = sep
if delim_whitespace and (delimiter is not lib.no_default):
raise ValueError(
"Specified a delimiter with both sep and "
"delim_whitespace=True; you can only specify one."
)
if delimiter is lib.no_default:
# assign default separator value
kwds["delimiter"] = delim_default
else:
kwds["delimiter"] = delimiter
if engine is not None:
kwds["engine_specified"] = True
else:
kwds["engine"] = "c"
kwds["engine_specified"] = False
# Ensure that on_bad_lines and error_bad_lines/warn_bad_lines
# aren't specified at the same time. If so, raise. Otherwise,
# alias on_bad_lines to "error" if error/warn_bad_lines not set
# and on_bad_lines is not set. on_bad_lines is defaulted to None
# so we can tell if it is set (this is why this hack exists).
if on_bad_lines is not None:
if error_bad_lines is not None or warn_bad_lines is not None:
raise ValueError(
"Both on_bad_lines and error_bad_lines/warn_bad_lines are set. "
"Please only set on_bad_lines."
)
if on_bad_lines == "error":
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
elif on_bad_lines == "warn":
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
elif on_bad_lines == "skip":
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.SKIP
else:
raise ValueError(f"Argument {on_bad_lines} is invalid for on_bad_lines")
else:
if error_bad_lines is not None:
# Must check is_bool, because other stuff(e.g. non-empty lists) eval to true
validate_bool_kwarg(error_bad_lines, "error_bad_lines")
if error_bad_lines:
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
else:
if warn_bad_lines is not None:
# This is the case where error_bad_lines is False
# We can only warn/skip if error_bad_lines is False
# None doesn't work because backwards-compatibility reasons
validate_bool_kwarg(warn_bad_lines, "warn_bad_lines")
if warn_bad_lines:
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
else:
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.SKIP
else:
# Backwards compat, when only error_bad_lines = false, we warn
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.WARN
else:
# Everything None -> Error
kwds["on_bad_lines"] = ParserBase.BadLineHandleMethod.ERROR
return kwds
def _extract_dialect(kwds: dict[str, Any]) -> csv.Dialect | None:
"""
Extract concrete csv dialect instance.
Returns
-------
csv.Dialect or None
"""
if kwds.get("dialect") is None:
return None
dialect = kwds["dialect"]
if dialect in csv.list_dialects():
dialect = csv.get_dialect(dialect)
_validate_dialect(dialect)
return dialect
MANDATORY_DIALECT_ATTRS = (
"delimiter",
"doublequote",
"escapechar",
"skipinitialspace",
"quotechar",
"quoting",
)
def _validate_dialect(dialect: csv.Dialect) -> None:
"""
Validate csv dialect instance.
Raises
------
ValueError
If incorrect dialect is provided.
"""
for param in MANDATORY_DIALECT_ATTRS:
if not hasattr(dialect, param):
raise ValueError(f"Invalid dialect {dialect} provided")
def _merge_with_dialect_properties(
dialect: csv.Dialect,
defaults: dict[str, Any],
) -> dict[str, Any]:
"""
Merge default kwargs in TextFileReader with dialect parameters.
Parameters
----------
dialect : csv.Dialect
Concrete csv dialect. See csv.Dialect documentation for more details.
defaults : dict
Keyword arguments passed to TextFileReader.
Returns
-------
kwds : dict
Updated keyword arguments, merged with dialect parameters.
"""
kwds = defaults.copy()
for param in MANDATORY_DIALECT_ATTRS:
dialect_val = getattr(dialect, param)
parser_default = parser_defaults[param]
provided = kwds.get(param, parser_default)
# Messages for conflicting values between the dialect
# instance and the actual parameters provided.
conflict_msgs = []
# Don't warn if the default parameter was passed in,
# even if it conflicts with the dialect (gh-23761).
if provided != parser_default and provided != dialect_val:
msg = (
f"Conflicting values for '{param}': '{provided}' was "
f"provided, but the dialect specifies '{dialect_val}'. "
"Using the dialect-specified value."
)
# Annoying corner case for not warning about
# conflicts between dialect and delimiter parameter.
# Refer to the outer "_read_" function for more info.
if not (param == "delimiter" and kwds.pop("sep_override", False)):
conflict_msgs.append(msg)
if conflict_msgs:
warnings.warn("\n\n".join(conflict_msgs), ParserWarning, stacklevel=2)
kwds[param] = dialect_val
return kwds
def _validate_skipfooter(kwds: dict[str, Any]) -> None:
"""
Check whether skipfooter is compatible with other kwargs in TextFileReader.
Parameters
----------
kwds : dict
Keyword arguments passed to TextFileReader.
Raises
------
ValueError
If skipfooter is not compatible with other parameters.
"""
if kwds.get("skipfooter"):
if kwds.get("iterator") or kwds.get("chunksize"):
raise ValueError("'skipfooter' not supported for iteration")
if kwds.get("nrows"):
raise ValueError("'skipfooter' not supported with 'nrows'")
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] |
example/controller/tests/testing/foo.py | donghak-shin/dp-tornado | 18 | 39575 | <filename>example/controller/tests/testing/foo.py
# -*- coding: utf-8 -*-
from dp_tornado.engine.controller import Controller
class FooController(Controller):
def post(self):
"""
.. test::
expect(text='foo==bar', params={'foo': 'bar'})
expect(code=400, params={'foo': 'foo'})
expect(
code=200,
text='done')
!expect(code=400, text='foo==bar', params={'foo': 'baz'})
"""
if self.get_argument('foo') == 'bar':
return self.finish('foo==bar')
elif self.get_argument('foo') == 'foo':
return self.finish_with_error(400)
self.finish('done')
def get(self, a, b):
"""
.. test::
expect(text='30', args=(10, 20), params={'foo': 'bar'})
"""
a = int(a)
b = int(b)
self.finish(str(a + b))
def put(self, a, b):
"""
.. test::
expect(code=200, args=(10, 20))
expect(json={'a': 10, 'b': 20}, args=(10, 20))
"""
a = int(a)
b = int(b)
self.finish({
'a': a,
'b': b
})
| [
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] |
arc/arc115/arc115b.py | c-yan/atcoder | 1 | 87658 | <reponame>c-yan/atcoder
N = int(input())
C = [list(map(int, input().split())) for _ in range(N)]
INF = 10 ** 18
a = INF
i, j = 0, 0
for y in range(N):
for x in range(N):
if C[y][x] >= a:
continue
i, j = y, x
a = C[y][x]
def f(z):
A = [INF] * N
B = [INF] * N
A[i] = z
for x in range(N):
B[x] = C[i][x] - z
for y in range(N):
A[y] = C[y][0] - B[0]
for y in range(N):
for x in range(N):
if C[y][x] != A[y] + B[x]:
return None
return A, B
for z in range(max(0, a // 2 - 10), min(a // 2 + 10, a + 1)):
result = f(z)
if result is None:
continue
print('Yes')
print(*result[0])
print(*result[1])
break
else:
print('No')
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] |
tools/diff-db-disk.py | machao19902/st2 | 1 | 64781 | <reponame>machao19902/st2
#!/usr/bin/env python
# Licensed to the StackStorm, Inc ('StackStorm') 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.
"""
Tags: Ops tool.
A utility script that diffs models registered in st2 db versus what's on disk.
"""
from __future__ import absolute_import
import difflib
import json
import os
from oslo_config import cfg
from st2common import config
from st2common.util.monkey_patch import monkey_patch
from st2common.constants.pack import DEFAULT_PACK_NAME
from st2common.content.loader import ContentPackLoader
from st2common.content.loader import MetaLoader
from st2common.bootstrap.base import ResourceRegistrar
import st2common.content.utils as content_utils
from st2common.models.api.action import ActionAPI
from st2common.models.api.sensor import SensorTypeAPI
from st2common.models.api.rule import RuleAPI
from st2common.service_setup import db_setup
from st2common.service_setup import db_teardown
from st2common.models.system.common import ResourceReference
from st2common.persistence.rule import Rule
from st2common.persistence.sensor import SensorType
from st2common.persistence.action import Action
registrar = ResourceRegistrar()
registrar.ALLOWED_EXTENSIONS = ['.yaml', '.yml', '.json']
meta_loader = MetaLoader()
API_MODELS_ARTIFACT_TYPES = {
'actions': ActionAPI,
'sensors': SensorTypeAPI,
'rules': RuleAPI
}
API_MODELS_PERSISTENT_MODELS = {
Action: ActionAPI,
SensorType: SensorTypeAPI,
Rule: RuleAPI
}
def do_register_cli_opts(opts, ignore_errors=False):
for opt in opts:
try:
cfg.CONF.register_cli_opt(opt)
except:
if not ignore_errors:
raise
def _get_api_models_from_db(persistence_model, pack_dir=None):
filters = {}
if pack_dir:
pack_name = os.path.basename(os.path.normpath(pack_dir))
filters = {'pack': pack_name}
models = persistence_model.query(**filters)
models_dict = {}
for model in models:
model_pack = getattr(model, 'pack', None) or DEFAULT_PACK_NAME
model_ref = ResourceReference.to_string_reference(name=model.name, pack=model_pack)
if getattr(model, 'id', None):
del model.id
API_MODEL = API_MODELS_PERSISTENT_MODELS[persistence_model]
models_dict[model_ref] = API_MODEL.from_model(model)
return models_dict
def _get_api_models_from_disk(artifact_type, pack_dir=None):
loader = ContentPackLoader()
artifacts = None
if pack_dir:
artifacts_dir = loader.get_content_from_pack(pack_dir, artifact_type)
pack_name = os.path.basename(os.path.normpath(pack_dir))
artifacts = {pack_name: artifacts_dir}
else:
packs_dirs = content_utils.get_packs_base_paths()
artifacts = loader.get_content(packs_dirs, artifact_type)
artifacts_dict = {}
for pack_name, pack_path in artifacts.items():
artifacts_paths = registrar.get_resources_from_pack(pack_path)
for artifact_path in artifacts_paths:
artifact = meta_loader.load(artifact_path)
if artifact_type == 'sensors':
sensors_dir = os.path.dirname(artifact_path)
sensor_file_path = os.path.join(sensors_dir, artifact['entry_point'])
artifact['artifact_uri'] = 'file://' + sensor_file_path
name = artifact.get('name', None) or artifact.get('class_name', None)
if not artifact.get('pack', None):
artifact['pack'] = pack_name
ref = ResourceReference.to_string_reference(name=name,
pack=pack_name)
API_MODEL = API_MODELS_ARTIFACT_TYPES[artifact_type]
# Following conversions are required because we add some fields with
# default values in db model. If we don't do these conversions,
# we'll see a unnecessary diff for those fields.
artifact_api = API_MODEL(**artifact)
artifact_db = API_MODEL.to_model(artifact_api)
artifact_api = API_MODEL.from_model(artifact_db)
artifacts_dict[ref] = artifact_api
return artifacts_dict
def _content_diff(artifact_type=None, artifact_in_disk=None, artifact_in_db=None,
verbose=False):
artifact_in_disk_str = json.dumps(
artifact_in_disk.__json__(), sort_keys=True,
indent=4, separators=(',', ': ')
)
artifact_in_db_str = json.dumps(
artifact_in_db.__json__(), sort_keys=True,
indent=4, separators=(',', ': ')
)
diffs = difflib.context_diff(artifact_in_db_str.splitlines(),
artifact_in_disk_str.splitlines(),
fromfile='DB contents', tofile='Disk contents')
printed = False
for diff in diffs:
if not printed:
identifier = getattr(artifact_in_db, 'ref', getattr(artifact_in_db, 'name'))
print('%s %s in db differs from what is in disk.' % (artifact_type.upper(),
identifier))
printed = True
print(diff)
if verbose:
print('\n\nOriginal contents:')
print('===================\n')
print('Artifact in db:\n\n%s\n\n' % artifact_in_db_str)
print('Artifact in disk:\n\n%s\n\n' % artifact_in_disk_str)
def _diff(persistence_model, artifact_type, pack_dir=None, verbose=True,
content_diff=True):
artifacts_in_db_dict = _get_api_models_from_db(persistence_model, pack_dir=pack_dir)
artifacts_in_disk_dict = _get_api_models_from_disk(artifact_type, pack_dir=pack_dir)
# print(artifacts_in_disk_dict)
all_artifacts = set(list(artifacts_in_db_dict.keys()) + list(artifacts_in_disk_dict.keys()))
for artifact in all_artifacts:
artifact_in_db = artifacts_in_db_dict.get(artifact, None)
artifact_in_disk = artifacts_in_disk_dict.get(artifact, None)
artifact_in_disk_pretty_json = None
artifact_in_db_pretty_json = None
if verbose:
print('******************************************************************************')
print('Checking if artifact %s is present in both disk and db.' % artifact)
if not artifact_in_db:
print('##############################################################################')
print('%s %s in disk not available in db.' % (artifact_type.upper(), artifact))
artifact_in_disk_pretty_json = json.dumps(
artifact_in_disk.__json__(), sort_keys=True,
indent=4, separators=(',', ': ')
)
if verbose:
print('File contents: \n')
print(artifact_in_disk_pretty_json)
continue
if not artifact_in_disk:
print('##############################################################################')
print('%s %s in db not available in disk.' % (artifact_type.upper(), artifact))
artifact_in_db_pretty_json = json.dumps(
artifact_in_db.__json__(), sort_keys=True,
indent=4, separators=(',', ': ')
)
if verbose:
print('DB contents: \n')
print(artifact_in_db_pretty_json)
continue
if verbose:
print('Artifact %s exists in both disk and db.' % artifact)
if content_diff:
if verbose:
print('Performing content diff for artifact %s.' % artifact)
_content_diff(artifact_type=artifact_type,
artifact_in_disk=artifact_in_disk,
artifact_in_db=artifact_in_db,
verbose=verbose)
def _diff_actions(pack_dir=None, verbose=False, content_diff=True):
_diff(Action, 'actions', pack_dir=pack_dir,
verbose=verbose, content_diff=content_diff)
def _diff_sensors(pack_dir=None, verbose=False, content_diff=True):
_diff(SensorType, 'sensors', pack_dir=pack_dir,
verbose=verbose, content_diff=content_diff)
def _diff_rules(pack_dir=None, verbose=True, content_diff=True):
_diff(Rule, 'rules', pack_dir=pack_dir,
verbose=verbose, content_diff=content_diff)
def main():
monkey_patch()
cli_opts = [
cfg.BoolOpt('sensors', default=False,
help='diff sensor alone.'),
cfg.BoolOpt('actions', default=False,
help='diff actions alone.'),
cfg.BoolOpt('rules', default=False,
help='diff rules alone.'),
cfg.BoolOpt('all', default=False,
help='diff sensors, actions and rules.'),
cfg.BoolOpt('verbose', default=False),
cfg.BoolOpt('simple', default=False,
help='In simple mode, tool only tells you if content is missing.' +
'It doesn\'t show you content diff between disk and db.'),
cfg.StrOpt('pack-dir', default=None, help='Path to specific pack to diff.')
]
do_register_cli_opts(cli_opts)
config.parse_args()
# Connect to db.
db_setup()
# Diff content
pack_dir = cfg.CONF.pack_dir or None
content_diff = not cfg.CONF.simple
if cfg.CONF.all:
_diff_sensors(pack_dir=pack_dir, verbose=cfg.CONF.verbose, content_diff=content_diff)
_diff_actions(pack_dir=pack_dir, verbose=cfg.CONF.verbose, content_diff=content_diff)
_diff_rules(pack_dir=pack_dir, verbose=cfg.CONF.verbose, content_diff=content_diff)
return
if cfg.CONF.sensors:
_diff_sensors(pack_dir=pack_dir, verbose=cfg.CONF.verbose, content_diff=content_diff)
if cfg.CONF.actions:
_diff_actions(pack_dir=pack_dir, verbose=cfg.CONF.verbose, content_diff=content_diff)
if cfg.CONF.rules:
_diff_rules(pack_dir=pack_dir, verbose=cfg.CONF.verbose, content_diff=content_diff)
# Disconnect from db.
db_teardown()
if __name__ == '__main__':
main()
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] |
asreader/text_comprehension/eval/fusion.py | rkadlec/asreader | 113 | 73694 | <gh_stars>100-1000
from __future__ import division
import argparse
import glob
import numpy
import scipy.optimize as opt
import os
import pickle
import re
"""
This script computes ensemble predictions based on multiple ASReader models.
In an optional first step it loads a dumped model and generates predictions from a validation and test dataset.
Subsequently it combines the predictions of several models using one of three methods
* AverageAll - the ensemble prediction is a mean of all the supplied single-model predictions
* pBest - sorts the candidate models by validation accuracy and selects the best proportion p of models to form the ensemble
* AddImprover - sorts the candidate models by validation accuracy and then tries adding them to the ensemble in that order
keeping each model in the ensemble only if it improves its val. accuracy
Validation and test accuracies are printed for the ensemble and the best single model.
typical usage:
python fusion.py -pr "out_dir/best_predictions/*.y_hat_valid" -o out_dir/best_predictions/fusion.y_hat -t foo --fusion_method AverageAll
where the best_predictions directory should contain predictions selected by the copyBestPredictions.py script
"""
def accuracy_k_best(probas, mute=False, k_vals=[1,2,5]):
"""
Gives the percentage of predictions that have the correct answer among the k most likely suggested answers for k=[1,2,5]
:param probas: a list of numpy arrays, each containing a distribution of probabilities over candidates answers for one example
:param mute: if True, stops the function from printing the accuracies into std out
:param k_vals: values
:return: an array of recall@k for k=1,2,5
"""
recall_k = {}
for k in k_vals:
recall_k[k] = 0
line = 0
for row in probas:
line += 1
indices = numpy.argpartition(row, -k)[-k:] # Gives indices of k highest probabilities
recall_k[k] += (0 in indices) # Uses the fact that correct answer is at index 0.
recall_k[k] /= len(probas)
if not mute:
print 'recall@%d' % k, recall_k[k]
return recall_k
def accuracy(probas):
"""
Returns the proportion of predictions that assign the highest probability to the correct answer
:param probas: a list of numpy arrays, each containing a distribution of probabilities over candidates answers for one example
:return: accuracy
"""
ncorrect = 0
for row in probas:
# We use the convention of having the ground truth answer at index 0
ncorrect += (numpy.argmax(row) == 0)
return ncorrect / len(probas)
def dump_model_predictions(model_filenames, input_data, suffix="y_hat", regenerate=False):
"""
Loops through model files and uses the cbt_memory_pointer script to generate the y_hat predictions from each model
:param model_filenames: list of filenames of saved models
:param input_data: the dataset to which the models will be applied to generate predictions
:param suffix: suffix of the generated prediction files (the rest of the filename is the same as the model filename
:param regenerate: if true the model rewrites the prediction files even if they're already present
:return: list of filenames of the generated predictions
"""
prediction_filenames = []
for model_file in model_filenames:
y_hat_file = model_file + "." + suffix
if not os.path.isfile(y_hat_file) or regenerate:
load_model_command = 'python ' + args.blocks_nlp_path + 'as_reader.py --load_model ' + model_file + ' --y_hat_out_file ' + y_hat_file + ' --files_to_visualize ' + input_data + ' --no_html'
if args.cnn:
load_model_command += ' --dataset_type cnn'
os.system(load_model_command)
prediction_filenames.append(y_hat_file)
return prediction_filenames
def adjust_length(pred_line, lineN, max_length):
"""
Messy function that handles problems that arise if predictions for the same example have different lengths
which may happen due to using a different batch size for each model. Normally it shouldn't be needed.
:param pred_line:
:param lineN:
:param max_length:
:return:
"""
pred_line = numpy.trim_zeros(pred_line, trim='b')
# The following takes care of lines that are shorter than the ones for previous files due to 0-trimming
if lineN > len(max_length):
maxLen = numpy.append(max_length, len(pred_line))
while len(pred_line) < maxLen[lineN - 1]:
pred_line = numpy.append(pred_line, 0)
# print "Tail zero added to line "+str(lineN)+" of "+pred_file
if len(pred_line) > maxLen[lineN - 1]:
print '!!! Warning: Line ' + str(lineN) + ' is longer than the corresponding lines of previous files.'
maxLen[lineN - 1] = len(pred_line)
return pred_line, max_length
def predictions_from_csv(fh, max_length):
"""
Loads single model predictions from a csv file where lines may differ in length
:param fh: file handle to the csv file
:return: list of numpy arrays representing the predictions of individual examples
"""
preds = list()
lineN = 0
for line in fh:
lineN += 1
pred_line = numpy.fromstring(line, sep=', ')
if (args.trim_zeros):
# If different batch sizes are used for the fused models, the prediction vectors need to be adjusted
pred_line, max_length = adjust_length(pred_line, lineN, max_length)
preds.append(pred_line)
return preds, max_length
def load_all_predictions(prediction_filenames):
# list of model predictions
all_preds = []
# list of corresponding accuracies
model_accuracies=[]
# the length of the longest prediction vector for each training example
max_lengths = numpy.array([0])
for pred_file in prediction_filenames:
pred_fh = open(pred_file, 'r')
# Predictions can be saved either in a csv or a pickle file
if args.prediction_format == 'csv':
preds, max_lengths =predictions_from_csv(pred_fh, max_lengths)
else:
preds = pickle.load(pred_fh)
pred_fh.close()
print "Results for " + pred_file
acc = accuracy(preds)
model_accuracies.append(acc)
print 'Accuracy: ' + str(acc)
all_preds.append(preds)
return all_preds, model_accuracies
def fuse_predictions(prediction_filenames, weights=None):
"""
reads the y_hat files and averages all the predictions
:param prediction_filenames:
:param weights: a list of weights given to the individual predictions within the fusion (defaults to equel weights)
:return:
"""
all_preds, model_accuracies = load_all_predictions(prediction_filenames)
print
print "Ensemble (equal weights): "
ensemble_accuracy = accuracy(numpy.mean(all_preds, 0))
print "Accuracy:\t"+str(ensemble_accuracy)
# If weights were provided, calculate the prediction of a weighted ensemble
averaged = numpy.average(all_preds, axis=0, weights=weights)
if weights is not None:
print "Weighted ensemble: "
print "Accuracy:\t"+accuracy(averaged)
return {'averaged': averaged, 'model_preds': all_preds, 'ensemble_acc': ensemble_accuracy,
'model_accuracies': model_accuracies}
def greedy_add(prediction_filenames, greedy_iterations=1):
"""
Builds up an ensemble by starting with the best validation model and then adding each model only if it improves
the ensemble performance
:param prediction_filenames: List of files containing candidate models' validation predictions
:param greedy_iterations: int how many times each candidate model is considered for adding into the ensemble
:return:
"""
all_preds, model_accuracies = load_all_predictions(prediction_filenames)
# Sort models by validation accuracy
sorted_indices = numpy.argsort(model_accuracies)[::-1]
# Indices of models included in the ensemble
ensemble = numpy.array([], dtype='i')
ensemble_accuracy = 0
# List of predictions of models included in the ensemble
member_predictions = []
for _ in range(greedy_iterations):
for i in sorted_indices:
# Create a candidate ensemble and test whether it's better than the current ensemble
if len(member_predictions) == 0:
candidate_prediction = all_preds[i]
else:
candidate_member_predictions = member_predictions + [all_preds[i]]
candidate_prediction = numpy.mean(candidate_member_predictions, 0)
candidate_accuracy = accuracy(candidate_prediction)
if candidate_accuracy > ensemble_accuracy:
ensemble = numpy.hstack([ensemble, [i]])
ensemble_accuracy = candidate_accuracy
member_predictions.append(all_preds[i])
print
print 'Predictions included in the ensemble and their validation accuracies:'
for i in ensemble:
print str(model_accuracies[i]) + "\t" + prediction_filenames[i]
best_single_valid_acc = model_accuracies[ensemble[0]]
print
print 'Ensemble accuracy: ' + str(ensemble_accuracy)
ensemble_pred = numpy.mean(member_predictions, 0)
return {'ensemble_prediction': ensemble_pred, 'ensemble_indices': ensemble,
'ens_member_predictions': member_predictions, 'best_single_valid_acc': best_single_valid_acc,
'ensemble_acc': ensemble_accuracy}
def p_best_models(prediction_filenames, p=0.7):
"""
Sorts models by validation accuracy and forms the ensemble from the best ones. A proportion p of models is included.
:param prediction_filenames:
:param p: proportion of the provided models that is included in the ensemble
:return:
"""
all_preds, model_accuracies = load_all_predictions(prediction_filenames)
# Sort models by validation accuracy
sorted_indices = numpy.argsort(model_accuracies)[::-1]
ensemble_size = int(p * len(sorted_indices))
ensemble = sorted_indices[0:ensemble_size]
# List of predictions of models included in the ensemble
member_predictions = []
for i in ensemble:
member_predictions.append(all_preds[i])
ensemble_pred = numpy.mean(member_predictions, 0) # the ensemble prediction
print
print 'Predictions included in the ensemble and their validation accuracies:'
for i in ensemble:
print str(model_accuracies[i]) + "\t" + prediction_filenames[i]
best_single_valid_acc = model_accuracies[ensemble[0]]
print
ensemble_accuracy = accuracy(ensemble_pred)
print 'Ensemble accuracy: ' + str(ensemble_accuracy)
ensemble_pred = numpy.mean(member_predictions, 0)
# print 'Worse case: ' + str(accuracy_k_best(ensemble_pred)[1])
return {'ensemble_prediction': ensemble_pred, 'ensemble_indices': ensemble,
'ens_member_predictions': member_predictions, 'best_single_valid_acc': best_single_valid_acc,
'ensemble_acc': ensemble_accuracy}
def optimize_weights(ensemble_indices, ens_member_predictions):
"""
Optimizes the weights of models in the ensemble using the Constrained Optimization by Linear Approximation
(COBYLA) method to maximize the validation accuracy
:param ensemble_indices: list of indices of individual models that should be included in the optimized ensemble
:param ens_member_predictions: list of prediction lists of the individual models
:return: optimal weights, predictions of the optimal ensemble
"""
def weight_accuracy(weights):
# Objective function (negative accuracy) to be minimized
averaged_pred = numpy.average(ens_member_predictions, axis=0, weights=weights)
return -accuracy(averaged_pred)
opt_result = opt.minimize(weight_accuracy, numpy.ones(len(ensemble_indices)) / len(ensemble_indices),
method='COBYLA',
constraints=({'type': 'ineq', 'fun': lambda x: 1 - sum(x)}))
averaged_pred = numpy.average(ens_member_predictions, axis=0, weights=opt_result['x'])
print 'Optimized ensemble accuracy: '
print accuracy(averaged_pred)
print 'Optimal weights: ' + str(opt_result['x'])
return opt_result['x'], averaged_pred
def predictions2csv(predictions, fh):
"""
Dump predictions in a csv format using filehandle fh
"""
fh.write("\n".join(",".join(numpy.char.mod('%f', row)) for row in predictions))
"""
Parse command line arguments
"""
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Utility for fusing multiple classifier results.")
parser.add_argument('-m', '--models', nargs="+", default=None,
help='files containing models to fuse')
parser.add_argument('-mr', '--models_regexp', default=None,
help='regexp to match model files to fuse')
parser.add_argument('-d', '--input_data', default=None,
help='Input data to which we apply the models')
parser.add_argument('-t', '--test_data', default=None,
help='Test data for the ensemble')
parser.add_argument('-p', '--predictions', nargs="+", default=None,
help='files containing previously generated predictions')
parser.add_argument('-pr', '--prediction_regexp', default=None,
help='regexp to match prediction files to fuse')
parser.add_argument('-o', '--output', default=None,
help='file where fused predictions will be saved')
parser.add_argument('--blocks_nlp_path', default='~/dev/blocks-nlp/text_comprehension/',
help='absolute path of the directory containing as_reader.py ending with "(...)/blocks-nlp/text_comprehension/"')
parser.add_argument('--fusion_method', default='AddImprover', choices=['AddImprover', 'AverageAll', 'pBest'],
help='Choose the method of fusing models')
parser.add_argument('--greedy_iterations', type=int, default=1,
help='How many times the greedy algorithm iterates over the candidate models.')
parser.add_argument('--regenerate', action='store_true',
help='Force models to regenerate predictions even if they are already present in the directory')
parser.add_argument('--pred_file_suffix', default="",
help="Appends an additional suffix to prediction files - useful for regenerating predictions while keeping the old ones.")
parser.add_argument('--cnn', action='store_true', help='Indicates that datasets are in the CNN format.')
parser.add_argument('--optimize_weights', action='store_true',
help='Optimize weights of ensemble models to maximize validation accuracy.')
parser.add_argument('-f', '--prediction_format', default='csv', choices=['csv', 'pickle'],
help='format of the saved predictions (at the moment cannot generate csv from models)')
parser.add_argument('-es', '--ensemble_size', type=float, default=0.7,
help='proportion of models to be included in the ensemble (if relevant to the fusion method used)')
parser.add_argument('--trim_zeros', action='store_true',
help='Trims tail zeros of the predictions. Don\'t use for CBT.')
args = parser.parse_args()
# Filenames of dumped models to be used
to_fuse = []
# Add the model files from both arguments to a common array:
if args.models_regexp:
to_fuse += glob.glob(args.models_regexp)
if args.models:
to_fuse += args.models
print "Models to be fused:"
for model in enumerate(to_fuse):
print model
print
# Save model predictions to disk and retain their paths
prediction_files = dump_model_predictions(to_fuse, args.input_data, 'y_hat_valid' + args.pred_file_suffix)
# Add previously generated prediction files if specified
if args.prediction_regexp:
prediction_files += glob.glob(args.prediction_regexp)
if args.predictions:
prediction_files += args.predictions
# Build the ensemble and generate the fused prediction:
if args.fusion_method == 'AddImprover':
result = greedy_add(prediction_files, greedy_iterations=args.greedy_iterations)
ensemble_indices = result['ensemble_indices']
ens_member_predictions = result['ens_member_predictions']
best_single_valid = result['best_single_valid_acc']
if (args.optimize_weights):
ens_weights, fused = optimize_weights(ensemble_indices, ens_member_predictions)
else:
fused = result['ensemble_prediction']
ens_weights = None
elif args.fusion_method == 'pBest':
result = p_best_models(prediction_files, args.ensemble_size)
ens_member_predictions = result['ens_member_predictions']
ensemble_indices = result['ensemble_indices']
best_single_valid = result['best_single_valid_acc']
if args.optimize_weights:
ens_weights, fused = optimize_weights(ensemble_indices, ens_member_predictions)
else:
fused = result['ensemble_prediction']
ens_weights = None
elif args.fusion_method == 'AverageAll':
result = fuse_predictions(prediction_files)
ens_member_predictions = result['model_preds']
ensemble_indices = numpy.argsort(result['model_accuracies'])[::-1]
best_single_valid = max(result['model_accuracies'])
if args.optimize_weights:
ens_weights, fused = optimize_weights(ensemble_indices, ens_member_predictions)
else:
fused = result['averaged']
ens_weights = None
ensemble_valid = result['ensemble_acc']
print "Ensemble size: " + str(len(ensemble_indices))
# Optionally, save the fusion predictions
if args.output:
output_fh = open(args.output, 'w')
if args.prediction_format == 'csv':
predictions2csv(fused,output_fh)
else:
pickle.dump(fused, output_fh)
output_fh.close()
print "Fused validation predictions saved to " + args.output
# Generate prediction files for ensemble models from test data
if args.test_data:
print
print '___________ Applying ensemble models to test data __________'
# Create a list of filenames of dumped models included in the ensemble
ensemble_models = []
for i in ensemble_indices:
ensemble_models += [re.sub('\.y_hat(_valid)?' + args.pred_file_suffix + '$', '', prediction_files[i])]
# Use these models to generate test predictions
prediction_files = dump_model_predictions(ensemble_models, args.test_data, 'y_hat_test' + args.pred_file_suffix)
# Fuse these predictions
result = fuse_predictions(prediction_files, ens_weights)
ensemble_test_prediction = result['averaged'].squeeze()
best_single_test = result['model_accuracies'][0]
ensemble_test = result['ensemble_acc']
# If required, dump the ensemble test prediction
if args.output:
output_fh = open(args.output + '_test', 'w')
if args.prediction_format == 'csv':
predictions2csv(ensemble_test_prediction,output_fh)
else:
pickle.dump(ensemble_test_prediction, output_fh)
output_fh.close()
print
print "Fused test predictions saved to " + args.output + '_test'
print
print "Summary of results (model - valid. acc. - test acc.):"
print "Best single model:\t" + str(best_single_valid) + "\t" + str(best_single_test)
print args.fusion_method + " Ensemble:\t" + str(ensemble_valid) + "\t" + str(ensemble_test)
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] |
accounts/admin.py | GuilhemN/site-interludes | 0 | 3749 | from django.contrib import admin
from django.contrib.auth.models import Group
from accounts.models import EmailUser
from shared.admin import ExportCsvMixin
# no need for groups - we only have regular users and superusers
admin.site.unregister(Group)
@admin.register(EmailUser)
class EmailUserAdmin(ExportCsvMixin, admin.ModelAdmin):
"""option d'affichage des activités dans la vue django admin"""
filename = "export_utilisateurs.csv"
list_display = ("email", "last_name", "first_name", "is_superuser", "is_active", "email_confirmed",)
list_filter = ("is_superuser","is_active", "email_confirmed",)
fields = ("email", "last_name", "first_name", "is_superuser", "is_staff", "is_active", "email_confirmed",
("date_joined", "last_login",),
)
ordering = ("last_name", "first_name")
readonly_fields = ("date_joined", "last_login",)
list_per_page = 200
csv_export_exclude = ["password"]
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reviews/layout/components.py | adam-tokarski/reviews | 134 | 1617937 | <reponame>adam-tokarski/reviews
from datetime import datetime
from rich.panel import Panel
from rich.table import Table
class Header:
"""Display header with clock."""
def __rich__(self) -> Panel:
grid = Table.grid(expand=True)
grid.add_column(justify="center", ratio=1)
grid.add_column(justify="right")
grid.add_row(
"[b]Code Review[/b] Manager",
datetime.now().ctime().replace(":", "[blink]:[/]"),
)
return Panel(grid, style="white on blue")
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demo.py | Ph1lippK/anonymizer | 0 | 37300 | import streamlit as st
st.image(
"https://datascientest.com/wp-content/uploads/2020/10/logo-text-right.png.webp"
)
st.header("Développer et déployer une application de Machine learning en **Streamlit**")
st.info("Webinar du 04/05/2021")
st.markdown("---")
st.markdown(
"""
**Objectifs 🎯**
* Se familiariser avec Streamlit
* Découvrir les différents types de widgets
* Créér une démo d'application de Machine Learning
* Déployer cette application 🚀
"""
)
first_name = st.sidebar.text_input("Prénom")
last_name = st.sidebar.text_input("Nom")
job = st.sidebar.selectbox(
"Profession",
options=("Data Scientist", "Data Engineer", "Développeur", "Autre"),
)
experience = st.sidebar.slider(
"Années d'expériences", min_value=0, max_value=10, value=2, step=1
)
interests = st.sidebar.multiselect(
"Intérêts",
options=["technologie", "IA", "développement", "python", "statistiques", "R"],
default=["python", "IA"],
)
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src/headtracking_network/live_training.py | NaviRice/HeadTracking | 1 | 35153 | <reponame>NaviRice/HeadTracking<filename>src/headtracking_network/live_training.py
import numpy as np
import tensorflow as tf
import os
import navirice_image_pb2
import cv2
import random
import sys
from navirice_generate_data import generate_bitmap_label
from navirice_helpers import navirice_image_to_np
from navirice_helpers import navirice_ir_to_np
from navirice_helpers import map_depth_and_rgb
from navirice_head_detect import get_head_from_img
tf.logging.set_verbosity(tf.logging.INFO)
def cnn_model_fn(features):
# unkown amount, higrt and width, channel
input_layer = tf.reshape(features, [-1, 424, 512, 1])
mp0 = input_layer
mp1 = max_pool_2x2(mp0)
mp2 = max_pool_2x2(mp1)
mp3 = max_pool_2x2(mp2)
encoder1 = coder(mp1, [10,10,1,2], True)
encoder2 = coder(mp2, [10,10,1,4], True)
encoder3 = coder(mp3, [10,10,1,4], True)
encoder4 = coder(encoder1, [10,10,2,4], True)
encoder5 = coder(encoder2, [10,10,4,8], True)
encoder6 = coder(encoder3, [10,10,4,8], True)
W_fc1 = weight_variable([256*212*4, 1024])
encoder4_last_flat = tf.reshape(encoder4, [-1, 256*212*4])
h_fc1 = tf.matmul(encoder4_last_flat, W_fc1)
W_fc2 = weight_variable([128*106*8, 1024])
encoder5_last_flat = tf.reshape(encoder5, [-1, 128*106*8])
h_fc2 = tf.matmul(encoder5_last_flat, W_fc2)
W_fc3 = weight_variable([64*53*8, 1024])
encoder6_last_flat = tf.reshape(encoder6, [-1, 64*53*8])
h_fc3 = tf.matmul(encoder6_last_flat, W_fc3)
merge_layer = tf.nn.sigmoid(h_fc3 + h_fc2 + h_fc1)
W_fc2 = weight_variable([1024, 3])
h_fc2 = tf.nn.sigmoid(tf.matmul(merge_layer, W_fc2))
return h_fc2
def coder(input_layer, shape, do_relu):
W_conv = weight_variable(shape)
if do_relu:
h_conv = tf.nn.leaky_relu(conv2d(input_layer, W_conv))
return h_conv
else:
h_conv = conv2d(input_layer, W_conv)
return h_conv
def conv2d(x, W):
"""conv2d returns a 2d convolution layer with full stride."""
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
"""max_pool_2x2 downsamples a feature map by 2X."""
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def weight_variable(shape):
"""weight_variable generates a weight variable of a given shape."""
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
"""bias_variable generates a bias variable of a given shape."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def main():
scale_val = 1.0/8.0
x = tf.placeholder(tf.float32, [None, 424, 512, 1])
y_ = tf.placeholder(tf.float32, [None, 3])
y_conv = cnn_model_fn(x)
#cost = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)
cost = tf.square(y_ - y_conv)
train_step = tf.train.AdamOptimizer(1e-4).minimize(cost)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
print("------------------OUT SHAPES-------------------")
print(y_.get_shape())
print(y_conv.get_shape())
print("-----------------------------------------------")
cnt = 0
from navirice_get_image import KinectClient
kc = KinectClient('127.0.0.1', 29000)
kc.navirice_capture_settings(False, True, True)
s_train = False
r_train = False
train_set_input = []
train_set_expected =[]
train_set_size = 100000
saver = tf.train.Saver()
while(True):
img_set, last_count = kc.navirice_get_image()
if(s_train):
s_train = False
if(img_set != None and img_set.IR.width > 0 and img_set.Depth.width > 0):
ir_image = navirice_ir_to_np(img_set.IR)
depth_image = navirice_image_to_np(img_set.Depth)
inverted_depth = np.ones(depth_image.shape)
inverted_depth = inverted_depth - depth_image
cv_result = get_head_from_img(ir_image)
if cv_result is not None:
arr = [cv_result[0], cv_result[1], cv_result[2]]
if len(train_set_input) < train_set_size:
train_set_input.append(inverted_depth)
train_set_expected.append(arr)
else:
if(random.randint(0, 10000) > -1):
i = random.randint(0, train_set_size-1)
train_set_input[i] = inverted_depth
train_set_expected[i] = arr
#train_step.run(session=sess, feed_dict={x: train_set_input, y_: train_set_expected})
dp = inverted_depth.copy()
cv2.circle(dp, (int(cv_result[0]*512), int(cv_result[1]*424)), int(cv_result[2]*400), (255, 0, 0), thickness=3, lineType=8, shift=0)
cv2.imshow("idl", dp)
print("db count: ", len(train_set_input))
if(img_set != None and img_set.IR.width > 0 and img_set.Depth.width > 0):
depth_image = navirice_image_to_np(img_set.Depth)
ir_image = navirice_ir_to_np(img_set.IR)
inverted_depth = np.ones(depth_image.shape)
inverted_depth = inverted_depth - depth_image
tests = []
tests.append(inverted_depth)
outs = sess.run(y_conv, feed_dict={x: tests})
xf = outs[0][0]
yf = outs[0][1]
radiusf = outs[0][2]
print("nnoutput x:", xf, "y: ", yf," r:", radiusf)
if radiusf < 0:
radiusf = 0
cv2.circle(tests[0], (int(xf*512), int(yf*424)), int(radiusf*400), (255, 0, 0), thickness=3, lineType=8, shift=0)
cv2.imshow("id",tests[0])
if(r_train):
tsi=[]
tse=[]
for i in range(100):
random_index = random.randint(0, len(train_set_input)-1)
tsi.append(train_set_input[random_index])
tse.append(train_set_expected[random_index])
print("TRAINING")
train_step.run(session=sess, feed_dict={x: tsi, y_: tse})
key = cv2.waitKey(10) & 0xFF
#print("key: ", key)
# train
if(key == ord('t')):
r_train = True
# rest
if(key == ord('r')):
r_train = False
# (space) capture
if(key == 32):
s_train = True
# save model
if(key == ord('s')):
loc = input("Enter file destination to save: ")
if(len(loc) > 0):
try:
saver.save(sess, loc)
except ValueError:
print("Error: Did not enter a path..")
# load model
if(key == ord('l')):
loc = input("Enter file destination to load: ")
if(len(loc) > 0):
try:
saver.restore(sess, loc)
except ValueError:
print("Error: no file with that destination")
if __name__ == "__main__":
main()
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] |
fbpcs/private_computation/service/input_data_validation_stage_service.py | emmanuelsalawu/fbpcs | 0 | 143645 | #!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
import json
import logging
from typing import List, Optional
from fbpcp.service.onedocker import OneDockerService
from fbpcs.common.entity.stage_state_instance import StageStateInstance
from fbpcs.onedocker_binary_names import OneDockerBinaryNames
from fbpcs.private_computation.entity.pc_validator_config import (
PCValidatorConfig,
)
from fbpcs.private_computation.entity.private_computation_instance import (
PrivateComputationInstance,
PrivateComputationRole,
)
from fbpcs.private_computation.entity.private_computation_status import (
PrivateComputationInstanceStatus,
)
from fbpcs.private_computation.service.private_computation_stage_service import (
PrivateComputationStageService,
)
from fbpcs.private_computation.service.utils import (
get_pc_status_from_stage_state,
)
# 20 minutes
PRE_VALIDATION_CHECKS_TIMEOUT: int = 1200
class InputDataValidationStageService(PrivateComputationStageService):
"""
This InputDataValidation stage service validates input data files.
Validation fails if the issues detected in the data file
do not pass the input_data_validation configuration minimum
valid thresholds. A failing validation stage will prevent the next
stage from running.
It is implemented in a Cloud agnostic way.
"""
def __init__(
self, pc_validator_config: PCValidatorConfig, onedocker_svc: OneDockerService
) -> None:
self._logger: logging.Logger = logging.getLogger(__name__)
self._failed_status: PrivateComputationInstanceStatus = (
PrivateComputationInstanceStatus.INPUT_DATA_VALIDATION_FAILED
)
self._pc_validator_config: PCValidatorConfig = pc_validator_config
self._onedocker_svc = onedocker_svc
async def run_async(
self,
pc_instance: PrivateComputationInstance,
server_ips: Optional[List[str]] = None,
) -> PrivateComputationInstance:
"""
Updates the status to COMPLETED and returns the pc_instance
"""
self._logger.info("[PCPreValidation] - Starting stage")
if self._should_run_pre_validation(pc_instance):
self._logger.info(
"[PCPreValidation] - starting a pc_pre_validation_cli run"
)
await self.run_pc_pre_validation_cli(pc_instance)
else:
self._logger.info("[PCPreValidation] - skipped run validations")
self._logger.info("[PCPreValidation] - finished run_async")
return pc_instance
async def run_pc_pre_validation_cli(
self, pc_instance: PrivateComputationInstance
) -> None:
region = self._pc_validator_config.region
cmd_args = [
f"--input-file-path={pc_instance.input_path}",
"--cloud-provider=AWS",
f"--region={region}",
]
threshold_overrides = (
self._pc_validator_config.data_validation_threshold_overrides
)
if threshold_overrides:
threshold_overrides_str = json.dumps(threshold_overrides)
cmd_args.append(f"--valid-threshold-override='{threshold_overrides_str}'")
cmd_args_str = " ".join(cmd_args)
container_instance = self._onedocker_svc.start_container(
package_name=OneDockerBinaryNames.PC_PRE_VALIDATION.value,
timeout=PRE_VALIDATION_CHECKS_TIMEOUT,
cmd_args=cmd_args_str,
)
stage_state = StageStateInstance(
pc_instance.instance_id,
pc_instance.current_stage.name,
containers=[container_instance],
)
pc_instance.instances.append(stage_state)
self._logger.info(
f"[PCPreValidation] - Started container instance_id: {container_instance.instance_id} status: {container_instance.status}"
)
def get_status(
self,
pc_instance: PrivateComputationInstance,
) -> PrivateComputationInstanceStatus:
"""
Returns the pc_instance's current status
"""
# When this stage is enabled, it should return the status based on the container status
if self._should_run_pre_validation(pc_instance):
instance_status = get_pc_status_from_stage_state(
pc_instance, self._onedocker_svc
)
task_id = ""
if pc_instance.instances:
last_instance = pc_instance.instances[-1]
if isinstance(last_instance, StageStateInstance):
last_container = last_instance.containers[-1]
task_id = (
last_container.instance_id.split("/")[-1]
if last_container
else ""
)
if instance_status == self._failed_status and task_id:
region = self._pc_validator_config.region
cluster = self._onedocker_svc.container_svc.get_cluster()
failed_task_link = f"https://{region}.console.aws.amazon.com/ecs/home?region={region}#/clusters/{cluster}/tasks/{task_id}/details"
error_message = (
f"[PCPreValidation] - stage failed because of some failed validations. Please check the logs in ECS for task id '{task_id}' to see the validation issues:\n"
+ f"Failed task link: {failed_task_link}"
)
self._logger.error(error_message)
elif instance_status == self._failed_status:
self._logger.error(
"[PCPreValidation] - stage failed because of some failed validations. Please check the logs in ECS"
)
return instance_status
return PrivateComputationInstanceStatus.INPUT_DATA_VALIDATION_COMPLETED
def _should_run_pre_validation(
self, pc_instance: PrivateComputationInstance
) -> bool:
return (
self._pc_validator_config.pc_pre_validator_enabled
and pc_instance.role == PrivateComputationRole.PARTNER
)
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app.py | gnmerritt/slack-translation-strings | 0 | 1602284 | <filename>app.py<gh_stars>0
import random
import os
from threading import Thread
import requests
from flask import Flask, jsonify, request
app = Flask(__name__)
VERIFICATION_TOKEN = os.environ.get('V_TOKEN')
APP_TOKEN = os.environ.get('A_TOKEN')
CHANNEL = os.environ.get('A_IN_CHANNEL')
@app.route('/slack', methods=['POST'])
def pick_char():
data = request.get_json()
token = data.get('token', None)
if token != VERIFICATION_TOKEN:
Exception(f"IllegalRequest, got t='{token}'")
challenge = data.get('challenge', None)
if challenge is not None:
return challenge
event = data.get('event', {})
type = event.get('type', None)
channel = event.get('channel')
if type == 'message' and channel == CHANNEL:
text = event.get('text', '')
user = event.get('user', None)
if text and user is not None:
t = Thread(target=mangle_post, args=(user, text))
t.start()
return "Ok"
def mangle_post(user, text):
data = {'text': make_translation(user, text), 'channel': "#translations"}
print(f"sending {data}")
headers = {'Authorization': f"Bearer {APP_TOKEN}"}
res = requests.post('https://slack.com/api/chat.postMessage', json=data, headers=headers)
print(f"got res={res}, json={res.json()}")
def make_translation(user, text):
words = text.split(" ")
some = [w for w in words if random.uniform(0, 1) > 0.25]
return f"<@{user}>: {'_'.join(some)}"
if __name__ == "__main__":
app.run(host='0.0.0.0', port=5002)
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gryphon/lib/exchange/itbit_btc_usd.py | scooke11/gryphon | 1,109 | 66717 | <filename>gryphon/lib/exchange/itbit_btc_usd.py<gh_stars>1000+
"""
Exchange documentation: https://api.itbit.com/docs
"""
# -*- coding: utf-8 -*-
import base64
from collections import OrderedDict, defaultdict
import hashlib
import hmac
import json
import time
import urllib
import cdecimal
from cdecimal import Decimal
from gryphon.lib.exchange import exceptions
from gryphon.lib.exchange import order_types
from gryphon.lib.exchange.exchange_api_wrapper import ExchangeAPIWrapper
from gryphon.lib.logger import get_logger
from gryphon.lib.models.exchange import Balance
from gryphon.lib.money import Money
from gryphon.lib.time_parsing import parse
logger = get_logger(__name__)
class ItbitBTCUSDExchange(ExchangeAPIWrapper):
def __init__(self, session=None, configuration=None):
super(ItbitBTCUSDExchange, self).__init__(session)
self.name = u'ITBIT_BTC_USD'
self.friendly_name = u'Itbit BTC-USD'
self.base_url = 'https://api.itbit.com/v1'
self.currency = 'USD'
self.bid_string = 'buy'
self.ask_string = 'sell'
self.nonce = 1
# Configurables with defaults.
self.market_order_fee = Decimal('0.002')
self.limit_order_fee = Decimal('0')
self.fee = self.market_order_fee
self.fiat_balance_tolerance = Money('0.0001', 'USD')
self.volume_balance_tolerance = Money('0.00000001', 'BTC')
self.max_tick_speed = 1
self.min_order_size = Money('0', 'BTC')
self.use_cached_orderbook = False
if configuration:
self.configure(configuration)
@property
def wallet_id(self):
try:
self._wallet_id
except AttributeError:
self._wallet_id = self._load_env('ITBIT_BTC_USD_WALLET_ID')
return self._wallet_id
def req(self, req_method, url, **kwargs):
# Our auth_request method expects the params in the url.
assert '?' not in url
if 'params' in kwargs:
if kwargs['params']: # Check that it's not empty.
url += '?' + urllib.urlencode(kwargs['params'])
del kwargs['params']
req = super(ItbitBTCUSDExchange, self).req(req_method, url, **kwargs)
return req
def resp(self, req):
response = super(ItbitBTCUSDExchange, self).resp(req)
if 'error' in response and response['error']:
raise exceptions.ExchangeAPIErrorException(self, response['error'])
if 'code' in response:
errors_string = str(response['description'])
error_code = int(response['code'])
if error_code == 81001:
raise exceptions.InsufficientFundsError()
elif error_code == 10002:
raise exceptions.NonceError()
elif error_code == 81002:
raise exceptions.CancelOrderNotFoundError()
else:
raise exceptions.ExchangeAPIErrorException(
self,
'Code %s: %s' % (
error_code, errors_string,
))
return response
def all_trades(self, page=1):
req = self.all_trades_req(page)
return self.all_trades_resp(req)
def all_trades_req(self, page=1):
params = {}
if page:
params['page'] = page
return self.req(
'get',
'/wallets/%s/trades' % self.wallet_id,
params=params,
)
def all_trades_resp(self, req):
response = self.resp(req)
return response['tradingHistory']
def trades_for_orders(self, order_ids):
req = self.trades_for_orders_req()
return self.trades_for_orders_resp(req, order_ids)
def trades_for_orders_req(self):
return self.all_trades_req()
def trades_for_orders_resp(self, req, order_ids):
order_ids = [str(o) for o in order_ids]
trades = self.all_trades_resp(req)
matching_trades = defaultdict(list)
for trade in trades:
oid = str(trade['orderId'])
if oid in order_ids:
matching_trades[oid].append(trade)
return matching_trades
def all_orders(self, status=None, page=1):
req = self.all_orders_req(status, page)
return self.all_orders_resp(req)
def all_orders_req(self, status=None, page=1):
params = {}
if status:
params['status'] = status
if page:
params['page'] = page
return self.req(
'get',
'/wallets/%s/orders' % self.wallet_id,
params=params,
)
def all_orders_resp(self, req):
raw_orders = self.resp(req)
orders = []
for raw_order in raw_orders:
mode = self._order_mode_to_const(raw_order['side'])
volume = Money(raw_order['amount'], 'BTC')
volume_filled = Money(raw_order['amountFilled'], 'BTC')
volume_remaining = volume - volume_filled
order = {
'mode': mode,
'id': str(raw_order['id']),
'price': Money(raw_order['price'], 'USD'),
'volume': volume,
'volume_remaining': volume_remaining,
'status': raw_order['status']
}
orders.append(order)
return orders
# Common Exchange Methods
def auth_request(self, req_method, url, request_args):
"""
This modifies request_args.
"""
try:
self.api_key
self.secret
except AttributeError:
self.api_key = self._load_env('ITBIT_BTC_USD_API_KEY')
self.secret = self._load_env('ITBIT_BTC_USD_API_SECRET').encode('utf-8')
timestamp = int(round(time.time() * 1000))
nonce = self.nonce
body = ''
if 'data' in request_args:
body = json.dumps(request_args['data'])
request_args['data'] = body
message = self._auth_create_message(req_method, url, body, nonce, timestamp)
sig = self._auth_sign_message(message, nonce, url, self.secret)
if 'headers' not in request_args:
request_args['headers'] = {}
headers = request_args['headers']
headers['Authorization'] = self.api_key + ':' + sig
headers['X-Auth-Timestamp'] = str(timestamp)
headers['X-Auth-Nonce'] = str(nonce)
headers['Content-Type'] = 'application/json'
def _auth_create_message(self, verb, url, body, nonce, timestamp):
return json.dumps(
[verb.upper(), url, body, str(nonce), str(timestamp)],
separators=(',', ':'),
)
def _auth_sign_message(self, message, nonce, url, api_secret):
sha256_hash = hashlib.sha256()
nonced_message = str(nonce) + message
sha256_hash.update(nonced_message)
hash_digest = sha256_hash.digest()
msg_to_hmac = url.encode('utf8') + hash_digest
hmac_digest = hmac.new(api_secret, msg_to_hmac, hashlib.sha512).digest()
sig = base64.b64encode(hmac_digest)
return sig
def get_balance_req(self):
try:
self.user_id
except AttributeError:
self.user_id = self._load_env('ITBIT_BTC_USD_USER_ID')
return self.req('get', '/wallets/%s' % self.wallet_id)
def get_balance_resp(self, req):
response = self.resp(req)
raw_balances = response['balances']
btc_available = None
usd_available = None
for raw_balance in raw_balances:
if raw_balance['currency'] == 'XBT':
btc_available = Money(raw_balance['availableBalance'], 'BTC')
elif raw_balance['currency'] == 'USD':
usd_available = Money(raw_balance['availableBalance'], 'USD')
if btc_available is None or usd_available is None:
raise exceptions.ExchangeAPIErrorException(
self,
'missing expected balances',
)
balance = Balance()
balance['BTC'] = btc_available
balance['USD'] = usd_available
return balance
def get_ticker_req(self, verify=True):
return self.req(
'get',
'/markets/XBTUSD/ticker',
no_auth=True,
verify=verify,
)
def get_ticker_resp(self, req):
response = self.resp(req)
return {
'high': Money(response['high24h'], 'USD'),
'low': Money(response['low24h'], 'USD'),
'last': Money(response['lastPrice'], 'USD'),
'volume': Money(response['volume24h'], 'BTC')
}
def _get_orderbook_from_api_req(self, verify=True):
return self.req(
'get',
'/markets/XBTUSD/order_book',
no_auth=True,
verify=verify,
)
def place_order_req(self, mode, volume, price=None, order_type=order_types.LIMIT_ORDER):
side = self._order_mode_from_const(mode)
if price.currency != 'USD':
raise ValueError('price must be in USD')
if volume.currency != 'BTC':
raise ValueError('volume must be in BTC')
# Truncate the volume instead of rounding it because it's better# to trade too
# little than too much.
volume = volume.round_to_decimal_places(8, rounding=cdecimal.ROUND_DOWN)
volume_str = '%.8f' % volume.amount
price_str = '%.2f' % price.amount
payload = {
'type': 'limit',
'currency': 'XBT',
'side': side,
'amount': volume_str,
'price': price_str,
'instrument': 'XBTUSD'
}
return self.req(
'post',
'/wallets/%s/orders/' % self.wallet_id,
data=payload,
)
def place_order_resp(self, req):
response = self.resp(req)
try:
order_id = str(response['id'])
return {'success': True, 'order_id': order_id}
except KeyError:
raise exceptions.ExchangeAPIErrorException(
self,
'response does not contain an order id',
)
def get_open_orders_req(self):
return self.all_orders_req(status='open')
def get_open_orders_resp(self, req):
open_orders = self.all_orders_resp(req)
for o in open_orders:
del o['status']
return open_orders
def get_order_details(self, order_id):
req = self.get_order_details_req()
return self.get_order_details_resp(req, order_id)
def get_order_details_req(self):
return self.get_multi_order_details_req()
def get_order_details_resp(self, req, order_id):
return self.get_multi_order_details_resp(req, [order_id])[order_id]
def get_multi_order_details(self, order_ids):
req = self.get_multi_order_details_req()
return self.get_multi_order_details_resp(req, order_ids)
def get_multi_order_details_req(self):
return self.trades_for_orders_req()
def get_multi_order_details_resp(self, req, order_ids):
# This is modeled after Bitstamp, where we get the order details from the
# trades endpoint directly. The caveat is that order_details will only work
# for the most recent 50 trades. Since we are always accounting trades right
# after they happen, this should be ok (and also affects Bitstamp).
order_ids = [str(o) for o in order_ids]
multi_trades = self.trades_for_orders_resp(req, order_ids)
data = {}
for order_id in order_ids:
total_usd = Money('0', 'USD')
total_btc = Money('0', 'BTC')
our_trades = []
our_type = None
if order_id in multi_trades:
trades = multi_trades[order_id]
for t in trades:
assert(t['currency1'] == 'XBT')
btc_amount = Money(t['currency1Amount'], 'BTC')
assert(t['currency2'] == 'USD')
usd_amount = Money(t['currency2Amount'], 'USD')
# This might also come back as XBT, but since ItBit has 0-fee
# trading right now, I can't tell.
assert(t['commissionCurrency'] == 'USD')
fee = Money(t['commissionPaid'], 'USD')
total_usd += usd_amount
total_btc += btc_amount
our_type = self._order_mode_to_const(t['direction'])
our_trades.append({
'time': parse(t['timestamp']).epoch,
'trade_id': None,
'fee': fee,
'btc': btc_amount,
'fiat': usd_amount,
})
time_created = None
if our_trades:
time_created = min([t['time'] for t in our_trades])
data[order_id] = {
'time_created': time_created,
'type': our_type,
'btc_total': total_btc,
'fiat_total': total_usd,
'trades': our_trades
}
return data
def cancel_order_req(self, order_id):
return self.req(
'delete',
'/wallets/%s/orders/%s' % (self.wallet_id, order_id),
)
def cancel_order_resp(self, req):
# In the success case, no response is given but we need to call resp() so it
# can catch any error cases.
response = self.resp(req) # noqa
return {'success': True}
def withdraw_crypto_req(self, address, volume):
if not isinstance(address, basestring):
raise TypeError('Withdrawal address must be a string')
if not isinstance(volume, Money) or volume.currency != self.volume_currency:
raise TypeError('Withdrawal volume must be in %s' % self.volume_currency)
volume_str = '%.8f' % volume.amount
payload = {
'currency': 'XBT',
'amount': volume_str,
'address': address,
}
return self.req(
'post',
'/wallets/%s/cryptocurrency_withdrawals' % self.wallet_id,
data=payload,
)
def withdraw_crypto_resp(self, req):
response = self.resp(req)
return {'success': True, 'exchange_withdrawal_id': response['withdrawalId']}
def get_order_audit_data(self, skip_recent=0, page=1):
"""
Returns an OrderedDict of order ids mapped to their filled volume (only include
orders that have some trades).
Dropped the skip_recent flag because we don't seem to be using it anywhere.
"""
if skip_recent != 0:
raise ValueEror('skip_recent is deprecated')
orders = OrderedDict()
trades_to_audit = self.all_trades(page=page)
for trade in trades_to_audit:
order_id = str(trade['orderId'])
assert(trade['currency1'] == 'XBT')
trade_amount = abs(Money(trade['currency1Amount'], 'BTC'))
try:
orders[order_id] += trade_amount
except KeyError:
orders[order_id] = trade_amount
# Remove the oldest 2 orders, because its trades might be wrapped around a
# page gap and this would give us an innacurate volume_filled number.
# We need to remove 2 because there could be an ask and a bid.
try:
orders.popitem()
orders.popitem()
except KeyError:
pass
return orders
def fiat_deposit_fee(self, deposit_amount):
return Money('5', 'USD')
def fiat_withdrawal_fee(self, withdrawal_amount):
"""
Itbit fee is from their documentation, and an extra $15 is being charged to us
before it shows up in our bank account (as of the September 2016), so I assume
that's an intermediary fee.
The fee should be a flat $50 on withdrawals > $10k, but we'll see.
"""
fee = Money('0', 'USD')
if withdrawal_amount < Money('10,000', 'USD'):
itbit_fee = Money('15', 'USD')
intermediary_fee = Money('15', 'USD')
fee = itbit_fee + intermediary_fee
else:
fee = Money('50', 'USD')
return fee
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backend/users/serializers.py | alessondelmiro/github_monitor | 0 | 170967 | <reponame>alessondelmiro/github_monitor
from rest_framework.serializers import ModelSerializer
from .models import User
from django.core.paginator import Paginator
class UserSerializer(ModelSerializer):
class Meta:
model = User
fields = (
'id', 'email', 'username', 'avatar',
)
read_only_fields = ('id', 'email', 'username', 'avatar',)
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tests/smiley.py | zoso95/xArm | 0 | 105354 | from xarm.arm import *
import math
import time
grip_close()
def reset():
movej((500, 500, 500, 500, 500), 2000)
power_off()
#jDepart = (497, 426, 738, 55, 500)
jDepart = (497, 426, 700, 55, 500)
movej(jDepart, 2000)
eye_x = 10
z_up = 0
z_down = -18
#(495, 420, 700, 64, 502)
servo_coord = get_position(False)
#(135, -3, -21)
center_origin = get_position(True)
print(servo_coord)
print(center_origin)
# y(+), x(inv), up(+)
#movel(appro(pA, (30, 0, 0)), 1000)
movel(appro(center_origin, (50, eye_x, z_up)), 1000)
movel(appro(center_origin, (50, eye_x, z_down)), 100)
movel(appro(center_origin, (30, eye_x, z_down)), 3000)
movel(appro(center_origin, (30, eye_x, z_up)), 100)
movel(appro(center_origin, (50, -eye_x, z_up)), 1000)
movel(appro(center_origin, (50, -eye_x, z_down)), 100)
movel(appro(center_origin, (30, -eye_x, z_down)), 3000)
movel(appro(center_origin, (30, -eye_x, z_up)), 100)
cx, cy = 0, 30
r = 30
for theta in range(0, -180, -15):
angle = (theta/360)*2*math.pi
x,y = cx+r*math.cos(angle), cy+r*math.sin(angle)
movel(appro(center_origin, (y, x, z_down)), 1000)
reset()
"""
x_start = 40
movel(appro(center_origin, (50, -x_start, 5)), 1000)
movel(appro(center_origin, (50, -x_start, 0)), 100)
"""
"""
pA = get_position(True)
movel(appro(pA, (30, 0, 5)), 1000)
grip_close(850)
pAppr = appro(pA, (30, 0, 25))
movel(pAppr, 1000)
movel(appro(pA, (30, 0, 5)), 1000)
grip_open()
time.sleep(2)
movej((500, 500, 500, 500, 500), 2000)
power_off()
"""
#import code
#code.interact(local=locals())
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Dark_Scripts/calc_IPOIndex_v2.py | zmlabe/predictGMSTrate | 2 | 142606 | """
Script for calculating the IPO index in each ensemble member
Author : <NAME>
Date : 17 September 2021
Version : 12
"""
### Import packages
import sys
import math
import time
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
import palettable.cubehelix as cm
import palettable.cartocolors.qualitative as cc
import palettable.scientific.sequential as sss
import cmocean as cmocean
import calc_Utilities as UT
import calc_dataFunctions as df
import calc_Stats as dSS
import scipy.stats as sts
import scipy.sparse as sparse
import scipy.sparse.linalg as linalg
import matplotlib
import cmasher as cmr
from eofs.standard import Eof
### Plotting defaults
plt.rc('text',usetex=True)
plt.rc('font',**{'family':'sans-serif','sans-serif':['Avant Garde']})
###############################################################################
###############################################################################
###############################################################################
### Data preliminaries
modelGCMs = ['CESM2-LE']
dataset_obs = 'ERA5'
allDataLabels = modelGCMs
letters = ["a","b","c","d","e","f","g","h","i","j","k","l","m"]
datasetsingle = ['CESM2le']
monthlychoiceq = ['annual']
variables = ['SST']
reg_name = 'SMILEGlobe'
level = 'surface'
###############################################################################
###############################################################################
randomalso = False
timeper = 'hiatus'
shuffletype = 'GAUSS'
###############################################################################
###############################################################################
land_only = False
ocean_only = False
###############################################################################
###############################################################################
baseline = np.arange(1951,1980+1,1)
###############################################################################
###############################################################################
window = 0
if window == 0:
rm_standard_dev = False
ravel_modelens = False
ravelmodeltime = False
else:
rm_standard_dev = True
ravelmodeltime = False
ravel_modelens = True
yearsall = np.arange(1979+window,2099+1,1)
yearsobs = np.arange(1979+window,2020+1,1)
###############################################################################
###############################################################################
numOfEns = 40
lentime = len(yearsall)
###############################################################################
###############################################################################
dataset = datasetsingle[0]
lat_bounds,lon_bounds = UT.regions(reg_name)
###############################################################################
###############################################################################
ravelyearsbinary = False
ravelbinary = False
lensalso = True
###############################################################################
###############################################################################
### Processing data steps
rm_ensemble_mean = True
###############################################################################
###############################################################################
###############################################################################
###############################################################################
### Read in model and observational/reanalysis data
def read_primary_dataset(variq,dataset,monthlychoice,numOfEns,lensalso,randomalso,ravelyearsbinary,ravelbinary,shuffletype,timeper,lat_bounds=lat_bounds,lon_bounds=lon_bounds):
data,lats,lons = df.readFiles(variq,dataset,monthlychoice,numOfEns,lensalso,randomalso,ravelyearsbinary,ravelbinary,shuffletype,timeper)
datar,lats,lons = df.getRegion(data,lats,lons,lat_bounds,lon_bounds)
print('\nOur dataset: ',dataset,' is shaped',data.shape)
return datar,lats,lons
def read_obs_dataset(variq,dataset_obs,numOfEns,lensalso,randomalso,ravelyearsbinary,ravelbinary,shuffletype,lat_bounds=lat_bounds,lon_bounds=lon_bounds):
data_obs,lats_obs,lons_obs = df.readFiles(variq,dataset_obs,monthlychoice,numOfEns,lensalso,randomalso,ravelyearsbinary,ravelbinary,shuffletype,timeper)
data_obs,lats_obs,lons_obs = df.getRegion(data_obs,lats_obs,lons_obs,lat_bounds,lon_bounds)
print('our OBS dataset: ',dataset_obs,' is shaped',data_obs.shape)
return data_obs,lats_obs,lons_obs
### Call functions
vv = 0
mo = 0
variq = variables[vv]
monthlychoice = monthlychoiceq[mo]
directoryfigure = '/Users/zlabe/Desktop/GmstTrendPrediction/ANN_v2/PDO/'
saveData = monthlychoice + '_' + variq + '_' + reg_name + '_' + dataset_obs
print('*Filename == < %s >' % saveData)
### Read data
models,lats,lons = read_primary_dataset(variq,dataset,monthlychoice,numOfEns,
lensalso,randomalso,ravelyearsbinary,
ravelbinary,shuffletype,timeper,
lat_bounds,lon_bounds)
obs,lats_obs,lons_obs = read_obs_dataset(variq,dataset_obs,numOfEns,lensalso,randomalso,ravelyearsbinary,ravelbinary,shuffletype,lat_bounds=lat_bounds,lon_bounds=lon_bounds)
### Slice for number of years to begin hiatus
AGWstart = 1990
yearsq_m = np.where((yearsall >= AGWstart))[0]
yearsq_o = np.where((yearsobs >= AGWstart))[0]
models_slice = models[:,yearsq_m,:,:]
obs_slice = obs[yearsq_o,:,:]
### Calculate global mean temperature
lon2,lat2 = np.meshgrid(lons,lats)
modelsm = UT.calc_weightedAve(models_slice,lat2)
obsm = UT.calc_weightedAve(obs_slice,lat2)
###############################################################################
### Calculate ensemble spread statistics
meaens = np.nanmean(modelsm[:,:],axis=0)
maxens = np.nanmax(modelsm[:,:],axis=0)
minens = np.nanmin(modelsm[:,:],axis=0)
spread = maxens - minens
###############################################################################
### Remove ensemble mean
if rm_ensemble_mean == True:
models_var = dSS.remove_ensemble_mean(models_slice,ravel_modelens,
ravelmodeltime,rm_standard_dev,
numOfEns)
###############################################################################
###############################################################################
###############################################################################
### Calculate SST regions
region1 = models_var.copy() # 25°N–45°N, 140°E–145°W
latr1 = np.where((lats >= 25) & (lats <= 45))[0]
lonr1 = np.where((lons >= 140) & (lons <= (180+(180-145))))[0]
latRegion1 = lats[latr1]
lonRegion1 = lons[lonr1]
lon2r1,lat2r1 = np.meshgrid(lonRegion1,latRegion1)
sstregion1lat = region1[:,:,latr1,:]
sstregion1 = sstregion1lat[:,:,:,lonr1]
mean_r1 = UT.calc_weightedAve(sstregion1,lat2r1)
region2 = models_var.copy() # 10°S–10°N, 170°E–90°W
latr2 = np.where((lats >= -10) & (lats <= 10))[0]
lonr2 = np.where((lons >= 170) & (lons <= (180+(180-90))))[0]
latRegion2 = lats[latr2]
lonRegion2 = lons[lonr2]
lon2r2,lat2r2 = np.meshgrid(lonRegion2,latRegion2)
sstregion2lat = region2[:,:,latr2,:]
sstregion2 = sstregion2lat[:,:,:,lonr2]
mean_r2 = UT.calc_weightedAve(sstregion2,lat2r2)
region3 = models_var.copy() # 50°S–15°S, 150°E–160°W
latr3 = np.where((lats >= -50) & (lats <= -15))[0]
lonr3 = np.where((lons >= 150) & (lons <= (180+(180-160))))[0]
latRegion3 = lats[latr3]
lonRegion3 = lons[lonr3]
lon2r3,lat2r3 = np.meshgrid(lonRegion3,latRegion3)
sstregion3lat = region3[:,:,latr3,:]
sstregion3 = sstregion3lat[:,:,:,lonr3]
mean_r3 = UT.calc_weightedAve(sstregion3,lat2r3)
###############################################################################
### Calculate IPO
IPOindex = mean_r2 - ((mean_r1 + mean_r3)/2)
IPOindexz = (IPOindex - np.mean(IPOindex))/np.std(IPOindex)
### Save IPO index
directoryoutput = '/Users/zlabe/Documents/Research/GmstTrendPrediction/Data/IPO/'
np.savetxt(directoryoutput + 'IPO_CESM2LE_1990-2099.txt',IPOindexz )
###############################################################################
###############################################################################
###############################################################################
###############################################################################
### Read in PDO index
directorydata = '/Users/zlabe/Documents/Research/GmstTrendPrediction/Data/'
PDO = np.genfromtxt(directorydata + '/PDO/PDO_CESM2LE_1990-2099.txt',unpack=True).transpose()
###############################################################################
### Compare PDO and IPO correlations
corr = np.empty((models_var.shape[0]))
p = np.empty((models_var.shape[0]))
for i in range(models_var.shape[0]):
corr[i],p[i] = sts.pearsonr(IPOindexz[i],PDO[i])
###############################################################################
###############################################################################
###############################################################################
### Create plot of correlations for the ensembles
fig = plt.figure()
ax = plt.subplot(111)
def adjust_spines(ax, spines):
for loc, spine in ax.spines.items():
if loc in spines:
spine.set_position(('outward', 5))
else:
spine.set_color('none')
if 'left' in spines:
ax.yaxis.set_ticks_position('left')
else:
ax.yaxis.set_ticks([])
if 'bottom' in spines:
ax.xaxis.set_ticks_position('bottom')
else:
ax.xaxis.set_ticks([])
adjust_spines(ax, ['left', 'bottom'])
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')
ax.spines['left'].set_color('dimgrey')
ax.spines['bottom'].set_color('dimgrey')
ax.spines['left'].set_linewidth(2)
ax.spines['bottom'].set_linewidth(2)
ax.tick_params('both',length=4,width=2,which='major',color='dimgrey')
ax.tick_params(axis='x',labelsize=7,pad=4)
ax.tick_params(axis='y',labelsize=7,pad=4)
ax.yaxis.grid(zorder=1,color='dimgrey',alpha=0.35,clip_on=False)
plt.plot(corr,color='maroon',linewidth=3,clip_on=False,alpha=1,
marker='o',markersize=7,zorder=10)
plt.xlabel(r'\textbf{CESM2-LE Ensemble Member \#}',fontsize=8,color='dimgrey')
plt.ylabel(r'\textbf{IPO \& PDO: Correlation Coefficient}',fontsize=8,color='dimgrey')
plt.yticks(np.arange(0,1.1,0.1),map(str,np.round(np.arange(0,1.1,0.1),2)))
plt.xticks(np.arange(0,39+1,3),map(str,np.arange(1,40+1,3)))
plt.xlim([0,39])
plt.ylim([0,1])
plt.subplots_adjust(bottom=0.15)
plt.savefig(directoryfigure + 'IPO-PDO_CorrelationCESM2le.png',
dpi=600)
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src/repair_mesh.py | vg-lab/MeshReconstruct | 0 | 43326 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import optparse
import os
import re
import sys
import vtk
from multiprocessing import Process
import parse_imx
RADIUS = 3 # For Open and Gauss
SCALE = 50.0 # For Rasterization
class RepairMeshParser(optparse.OptionParser):
def __init__(self):
optparse.OptionParser.__init__(self)
self.add_option("-a", "--areas", dest="areas_file",
help="The output areas file in csv", metavar="FILE")
self.add_option("-i", "--input-vrml", dest="input_vrml",
help="The mesh to de repaired in vrml file format", metavar="FILE")
self.add_option("-d", "--auto-dir", dest="vrmls_dir",
help="A directory with a bunch of vrmls", metavar="FILE")
self.add_option("-o", "--output-dir", dest="output_dir",
help="The output dir ussed when provides a dir as input", metavar="FILE")
self.add_option("-s", "--scale-factor", dest="scale", default=50.0,
help="Tehe scale factor used in the rasterization")
self.add_option("-c", "--combine", action="store_true", dest="combine",
help="Combine all polydatas in one object")
def write_image(image, filename):
"""Write vtk image data to file."""
aWriter = vtk.vtkMetaImageWriter()
aWriter.SetInputData(image)
aWriter.SetFileName(filename)
aWriter.SetFileDimensionality(3)
aWriter.SetCompression(False)
aWriter.Write()
def voxelizer(polydata, scale=SCALE, radius=RADIUS):
""" volume voxelization not anti-aliased """
# Get selection boundaries.
(minX, maxX, minY, maxY, minZ, maxZ) = [int(x * scale) for x in
polydata.GetBounds()] # convert tuple of floats to ints
# print(" Selection bounds are %s" % str((minX, maxX, minY, maxY, minZ, maxZ))) # dimensions of the resulting image
# print(" Dimensions: %s" % str((maxX - minX, maxY - minY, maxZ - minZ)))
padd = radius + 6
(minX, maxX, minY, maxY, minZ, maxZ) = (
minX - padd, maxX + padd, minY - padd, maxY + padd, minZ - padd, maxZ + padd)
ps1 = 1.0 / float(scale) # pixel size for the stencil, make sure it's a float division!
ps2 = 1.0 # pixel size for the image
## Convert a surface mesh into an image stencil that can be used to mask an image with vtkImageStencil.
polyToStencilFilter = vtk.vtkPolyDataToImageStencil()
polyToStencilFilter.SetInputData(polydata)
polyToStencilFilter.SetOutputWholeExtent(minX, maxX, minY, maxY, minZ, maxZ)
polyToStencilFilter.SetOutputSpacing(ps1, ps1, ps1)
polyToStencilFilter.SetOutputOrigin(0.0, 0.0, 0.0)
polyToStencilFilter.Update()
# Create an empty (3D) image of appropriate size.
image = vtk.vtkImageData()
image.SetSpacing(ps2, ps2, ps2)
image.SetOrigin(0.0, 0.0, 0.0)
image.SetExtent(minX, maxX, minY, maxY, minZ, maxZ)
image.AllocateScalars(vtk.VTK_UNSIGNED_CHAR, 1)
# Mask the empty image with the image stencil.
# First All the background to 0
# Needed otherwise introduces noise
stencil = vtk.vtkImageStencil()
stencil.SetInputData(image)
stencil.SetStencilData(polyToStencilFilter.GetOutput())
stencil.ReverseStencilOff()
stencil.SetBackgroundValue(0)
stencil.Update()
# Foreground to 255
stencil2 = vtk.vtkImageStencil()
stencil2.SetInputData(stencil.GetOutput())
stencil2.SetStencilData(polyToStencilFilter.GetOutput())
stencil2.ReverseStencilOn()
stencil2.SetBackgroundValue(255)
stencil2.Update()
finishImage = stencil2.GetOutput()
print(finishImage.GetNumberOfCells())
return stencil2.GetOutput()
def axisAligment(actor):
polyData = actor.GetMapper().GetInput()
centerCalculer = vtk.vtkCenterOfMass()
centerCalculer.SetInputData(polyData)
centerCalculer.SetUseScalarsAsWeights(False)
centerCalculer.Update()
center = centerCalculer.GetCenter()
print(center)
centerTransform = vtk.vtkTransform()
centerTransform.Translate(-center[0], -center[1], -center[2])
transformFilter = vtk.vtkTransformFilter()
transformFilter.SetInputData(polyData)
transformFilter.SetTransform(centerTransform)
transformFilter.Update()
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputData(transformFilter.GetOutput())
mapper.Update()
actor.SetMapper(mapper)
polyData = actor.GetMapper().GetInput()
centerCalculer = vtk.vtkCenterOfMass()
centerCalculer.SetInputData(polyData)
centerCalculer.SetUseScalarsAsWeights(False)
centerCalculer.Update()
centerAux = centerCalculer.GetCenter()
print(centerAux)
pointsMatrixAux = []
for i in range(0, polyData.GetNumberOfPoints()):
point = polyData.GetPoint(i)
pointsMatrixAux.append(point)
pointMatrix = np.matrix(pointsMatrixAux)
pointMatrixT = pointMatrix.transpose()
covarianzeMatrix = pointMatrixT * pointMatrix
u, s, vh = np.linalg.svd(covarianzeMatrix, full_matrices=True)
rotationMatrix = vtk.vtkMatrix4x4()
for i in range(3):
for j in range(3):
rotationMatrix.SetElement(i, j, u[i, j])
rotationMatrix.SetElement(i, 3, 0)
for i in range(3):
rotationMatrix.SetElement(3, i, 0)
rotationMatrix.SetElement(3, 3, 1)
rotationTransform = vtk.vtkTransform()
rotationTransform.SetMatrix(rotationMatrix)
transformFilter = vtk.vtkTransformFilter()
transformFilter.SetInputData(actor.GetMapper().GetInput())
transformFilter.SetTransform(rotationTransform)
transformFilter.Update()
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputData(transformFilter.GetOutput())
actor.SetMapper(mapper)
return center, rotationTransform
def open_image(image, radius):
openFilter = vtk.vtkImageDilateErode3D()
openFilter.SetDilateValue(255)
openFilter.SetErodeValue(0)
openFilter.SetKernelSize(radius, radius, radius)
openFilter.SetInputData(image)
openFilter.Update()
return openFilter.GetOutput()
def dump_voxels(actor, filename):
poly = actor.GetMapper().GetInput()
pre_image = voxelizer(poly, 50)
image = open_image(pre_image, RADIUS)
write_image(image, filename)
def open_actor(actor, actor_index=0, scale=SCALE, radius=RADIUS):
poly = actor.GetMapper().GetInput()
pre_image = voxelizer(poly, scale)
opened_image = open_image(pre_image, radius)
gauss = vtk.vtkImageGaussianSmooth()
gauss.SetDimensionality(3)
gauss.SetStandardDeviation(radius, radius, radius)
gauss.SetInputData(opened_image)
gauss.Update()
image_to_contour = gauss.GetOutput()
contour = vtk.vtkMarchingCubes()
contour.SetInputData(image_to_contour)
contour.SetValue(0, 127.5)
contour.ComputeScalarsOff()
contour.Update()
repared_poly = contour.GetOutput()
if repared_poly.GetNumberOfCells() == 0:
print("ERROR: number_of_cells = 0", end=' ')
# write_image(image_to_contour, "/tmp/%d.mhd"%actor_index)
raise ValueError("ERROR: number_of_cells = 0")
# (minX, maxX, minY, maxY, minZ, maxZ) = [int(x) for x in repared_poly.GetBounds()] #convert tuple of floats to ints
# print " Repared bounds are %s"%str((minX, maxX, minY, maxY, minZ, maxZ)) #dimensions of the resulting image
# print " Dimensions: %s"%str((maxX - minX, maxY - minY, maxZ - minZ))
actor.GetMapper().SetInputData(repared_poly)
def compute_area(actor):
polydata = actor.GetMapper().GetInput()
number_of_cells = polydata.GetNumberOfCells()
area = 0
for i in range(number_of_cells):
area += vtk.vtkMeshQuality.TriangleArea(polydata.GetCell(i))
return area
def combine_actors(actors_list):
appender = vtk.vtkAppendPolyData()
for actor in actors_list:
poly = actor.GetMapper().GetInput()
appender.AddInput(poly)
appender.Update()
combined_poly = appender.GetOutput()
combined_actor = vtk.vtkActor()
combined_actor.SetMapper(vtk.vtkPolyDataMapper())
combined_actor.GetMapper().SetInputData(combined_poly)
return combined_actor
def show_actor(actor, ren, rw):
ren.RemoveAllViewProps()
ren.AddActor(actor)
ren.ResetCamera()
rw.Render()
def compute_all_areas(actors_list, scale=SCALE):
areas = []
for i, actor in enumerate(actors_list):
# scale = SCALE
sys.stdout.write("%d " % i)
area_pre = compute_area(actor)
try:
open_actor(actor, i, scale)
except ValueError as e:
# [KNOWN BUG] The sizes are corrected, but not the position
scale = scale * 2
open_actor(actor, i, scale)
area_post = compute_area(actor) / scale ** 2
areas.append((i, area_pre, area_post, area_post / area_pre))
sys.stdout.flush()
print("\n")
return areas
def compute_centroids(actors_list):
return [a.GetCenter() for a in actors_list]
def csv_areas(actors_list, filename, scale=SCALE, names=None):
centroids = compute_centroids(actors_list) # Centroids of original actors
print(
"-------- Repairing original mesh and Calculating areas (This process might take a long time, please wait) -----------")
areas = compute_all_areas(actors_list, scale)
print("-------- Saving CSV file -----------")
if names is not None:
csv = "Object,Pre_Area,Post_Area,Post/Pre,X,Y,Z,Name\n"
for i in range(len(areas)):
data = []
data.extend(areas[i])
data.extend(centroids[i])
data.append(names[i])
csv += "%d,%f,%f,%f,%f,%f,%f,%s\n" % tuple(data)
else:
csv = "Object,Pre_Area,Post_Area,Post/Pre,X,Y,Z\n"
for i in range(len(areas)):
data = []
data.extend(areas[i])
data.extend(centroids[i])
csv += "%d,%f,%f,%f,%f,%f,%f\n" % tuple(data)
with open(filename, 'w') as f:
f.write(csv)
def underScale(actor, scale):
transform = vtk.vtkTransform()
relation = float(1) / float(scale)
transform.Scale(relation, relation, relation)
transformFilter = vtk.vtkTransformFilter()
transformFilter.SetInputData(actor.GetMapper().GetInput())
transformFilter.SetTransform(transform)
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputConnection(transformFilter.GetOutputPort())
mapper.Update()
actor.SetMapper(mapper)
return actor
def reduceMesh(actor, reduction):
decimate = vtk.vtkDecimatePro()
decimate.SetInputData(actor.GetMapper().GetInput())
decimate.SetTargetReduction(reduction / 100)
decimate.Update()
decimateMapper = vtk.vtkPolyDataMapper()
decimateMapper.SetInputConnection(decimate.GetOutputPort())
decimateMapper.Update()
actor.SetMapper(decimateMapper)
return actor
# Only for future versions of VTK, at the moment is a beta feature
def save_obj(rw, dir, name):
exporter = vtk.vtkOBJExporter()
if not os.path.isdir(dir):
os.makedirs(dir)
path = "%s/%s" % (dir, name)
exporter.SetFilePrefix(path)
exporter.SetRenderWindow(rw)
exporter.Write()
def save_stl(polydata, dir, name):
exporter = vtk.vtkSTLWriter()
if not os.path.isdir(dir):
os.makedirs(dir)
path = '%s/%s.stl' % (dir, name)
exporter.SetFileName(path)
exporter.SetInputData(polydata)
exporter.Write()
def save_vrml(name, dir, rw):
if not os.path.isdir(dir):
os.makedirs(dir)
path = '%s/%s.vrml' % (dir, name)
rw.Render()
exporter = vtk.vtkVRMLExporter()
exporter.SetFileName(path)
exporter.SetRenderWindow(rw)
rw.Render()
exporter.Write()
def initActorForExport(actor, rw, scale, reduction):
ren = rw.GetRenderers().GetFirstRenderer()
actor = underScale(actor, scale)
actor = reduceMesh(actor, reduction)
ren.AddActor(actor)
def toOriginalPos(actor, center, rotationTransform):
rotMat = vtk.vtkMatrix4x4()
rotationTransform.GetTranspose(rotMat)
rotTrans = vtk.vtkTransform()
rotTrans.SetMatrix(rotMat)
transformFilter = vtk.vtkTransformFilter()
transformFilter.SetInputData(actor.GetMapper().GetInput())
transformFilter.SetTransform(rotTrans)
transformFilter.Update()
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputConnection(transformFilter.GetOutputPort())
mapper.Update()
actor.SetMapper(mapper)
centerTransform = vtk.vtkTransform()
centerTransform.Translate(center[0], center[1], center[2])
transformFilter = vtk.vtkTransformFilter()
transformFilter.SetInputData(actor.GetMapper().GetInput())
transformFilter.SetTransform(centerTransform)
transformFilter.Update()
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputConnection(transformFilter.GetOutputPort())
mapper.Update()
actor.SetMapper(mapper)
centerCalculer = vtk.vtkCenterOfMass()
centerCalculer.SetInputData(actor.GetMapper().GetInput())
centerCalculer.SetUseScalarsAsWeights(False)
centerCalculer.Update()
center = centerCalculer.GetCenter()
print(center)
def main(input_filename, areas_filename, scale, is_imx, exportPath=None, exportType=False, reduction=70, radius=RADIUS,
combine=False):
# TODO: The following doesn't hide the RenderWindow :/
# factGraphics = vtk.vtkGraphicsFactory()
# factGraphics.SetUseMesaClasses(1)
# factImage = vtk.vtkImagingFactory()
# factImage.SetUseMesaClasses(1)
if exportPath is None:
pos = areas_filename.rfind("/")
filename = os.path.splitext(input_filename)[0]
posFilename = filename.rfind("/")
exportPath = areas_filename[:pos] + "/Meshes" + filename[posFilename:]
else:
filename = os.path.splitext(input_filename)[0]
pos = filename.rfind("/")
if pos == -1:
exportPath += "/"
exportPath += filename[pos:]
print(exportPath)
if is_imx:
vrml_filename = os.path.splitext(input_filename)[0] + ".vrml"
names_filename = os.path.splitext(input_filename)[0] + ".names"
args = ["{}".format(input_filename), "{}".format(vrml_filename), "{}".format(names_filename)]
p = Process(target=parse_imx.main, args=[args])
p.start()
p.join()
names_list = []
with open(names_filename) as f:
for line in f:
line = re.sub(r'\n', '', line)
names_list.append(line)
else:
vrml_filename = input_filename
names_list = None
rw = vtk.vtkRenderWindow()
rwi = vtk.vtkRenderWindowInteractor()
rwi.SetRenderWindow(rw)
# rw.OffScreenRenderingOn()
importer = vtk.vtkVRMLImporter()
importer.SetFileName(vrml_filename)
# importer = vtk.vtk3DSImporter()
# importer.SetFileName("cube.3ds")
importer.Read()
importer.SetRenderWindow(rw)
importer.Update()
rw.Render()
ren = importer.GetRenderer()
actors = ren.GetActors()
actors.InitTraversal()
rwExport = vtk.vtkRenderWindow()
# rwExport.OffScreenRenderingOn()
renExport = vtk.vtkRenderer()
rwExport.AddRenderer(renExport)
rwExport.Render()
if is_imx:
csv = "Object,Pre_Area,Post_Area,Post/Pre,X,Y,Z,Name\n"
else:
csv = "Object,Pre_Area,Post_Area,Post/Pre,X,Y,Z\n"
print(
"-------- Repairing original mesh and Calculating areas (This process might take a long time, please wait) -----------")
for i in range(ren.GetNumberOfPropsRendered()):
sys.stdout.write("%d _" % i)
actor = actors.GetNextActor()
polydata = actor.GetMapper().GetInput()
polydataCopy = vtk.vtkPolyData()
polydataCopy.DeepCopy(polydata)
area_pre = compute_area(actor)
centroid = actor.GetCenter()
rescaled = False
try:
rw.Render()
(center, rotation) = axisAligment(actor)
rw.Render()
open_actor(actor, i, scale, radius)
except ValueError as e:
# [KNOWN BUG] The sizes are corrected, but not the position
scale = scale * 2
open_actor(actor, i, scale, radius)
rescaled = True
area_post = compute_area(actor) / scale ** 2
if is_imx:
data = []
data.extend([i, area_pre, area_post, area_post / area_pre])
data.extend(centroid)
data.append(names_list[i])
csv += "%d,%f,%f,%f,%f,%f,%f,%s\n" % tuple(data)
else:
data = []
data.extend([i, area_pre, area_post, area_post / area_pre])
data.extend(centroid)
csv += "%d,%f,%f,%f,%f,%f,%f\n" % tuple(data)
if exportType != "None":
initActorForExport(actor, rwExport, scale, reduction)
toOriginalPos(actor, center, rotation)
if names_list is not None:
name = names_list[i]
else:
name = i
if exportType == "Stl":
save_stl(actor.GetMapper().GetInput(), exportPath, str(name) + "_R")
save_stl(polydataCopy, exportPath, str(name) + "_O")
renExport.RemoveActor(actor)
elif exportType == "Vrml":
save_vrml(str(name) + "_R", exportPath, rwExport)
renExport.RemoveActor(actor)
actorOld = vtk.vtkActor()
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputData(polydataCopy)
actorOld.SetMapper(mapper)
renExport.AddActor(actorOld)
save_vrml(str(name) + "_O", exportPath, rwExport)
renExport.RemoveActor(actorOld)
elif exportType == "Obj":
save_obj(rwExport, exportPath, str(name) + "_R")
renExport.RemoveActor(actor)
actorOld = vtk.vtkActor()
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputData(polydataCopy)
actorOld.SetMapper(mapper)
renExport.AddActor(actorOld)
save_obj(rwExport, exportPath, str(name) + "_O")
renExport.RemoveActor(actorOld)
ren.RemoveActor(actor)
if rescaled:
scale /= 2
with open(areas_filename, 'w') as f:
f.write(csv)
if is_imx:
os.remove(vrml_filename)
os.remove(names_filename)
rw.Finalize()
print("")
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extract-airbnb-properties.py | townofchapelhill/hospitality-python | 1 | 115674 | import airbnb
import airbnb_secrets
import csv
items=1
output_filename="airbnb_chapelhill.csv"
# Set CSV Header & line format
csv_header = ['City','Latitude','Longitude','Type','Bathrooms','Bedrooms','Public Address','Localized City','Source']
api = airbnb.Api()
# api = airbnb.Api(airbnb_secrets.login, airbnb_secrets.password)
api = airbnb.Api(access_token=airbnb_secrets.access_token)
try:
output_file = open(output_filename, 'w')
csvwriter = csv.writer(output_file, dialect='excel')
except IOError:
print("Output file creation failed")
exit(1)
csvwriter.writerow(csv_header)
while True:
try:
response = api.get_homes("Chapel Hill, NC, USA",items_per_grid=10, offset=items)
except Exception:
print("Terminating on error")
raise Exception
break
print("Starting item: "+ str(items) + " responses: " + str(len(response['explore_tabs'][0]['sections'][0]['listings'])))
# items += 50
items += len(response['explore_tabs'][0]['sections'][0]['listings'])
if len(response['explore_tabs'][0]['sections'][0]['listings']) == 0:
break
# ETL processing result set
for x in range(0, len(response['explore_tabs'][0]['sections'][0]['listings'])):
# build the output values in key order
csv_output=['null']*9
csv_output[0]=response['explore_tabs'][0]['sections'][0]['listings'][x]['listing']['city']
csv_output[1]=response['explore_tabs'][0]['sections'][0]['listings'][x]['listing']['lat']
csv_output[2]=response['explore_tabs'][0]['sections'][0]['listings'][x]['listing']['lng']
csv_output[3]=response['explore_tabs'][0]['sections'][0]['listings'][x]['listing']['room_and_property_type']
csv_output[4]=response['explore_tabs'][0]['sections'][0]['listings'][x]['listing']['bathrooms']
csv_output[5]=response['explore_tabs'][0]['sections'][0]['listings'][x]['listing']['bedrooms']
csv_output[6]=response['explore_tabs'][0]['sections'][0]['listings'][x]['listing']['public_address']
csv_output[7]=response['explore_tabs'][0]['sections'][0]['listings'][x]['listing']['localized_city']
csv_output[8]="AirBnB"
csvwriter.writerow(csv_output)
# cleanup and exit
output_file.close()
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bogglesolver.py | gammazero/pybogglesolver | 0 | 2331 | <reponame>gammazero/pybogglesolver
"""
Module to generate solutions for Boggle grids.
<NAME> 22 Dec. 2009
"""
from __future__ import print_function
import os
import sys
import collections
import trie
if sys.version < '3':
range = xrange
class BoggleSolver(object):
"""
This class uses an external words file as a dictionary of acceptable boggle
words. When an instance of this class is created, it sets up an internal
dictionary to look up valid boggle answers. The class' solve method can be
used repeatedly to generate solutions for different boggle grids.
"""
def __init__(self, words_file, xlen=4, ylen=4, pre_compute_adj=False):
"""Create and initialize BoggleSolver instance.
This creates the internal trie for fast word lookup letter-by-letter.
Words that begin with capital letters and words that are not within the
specified length limits are filtered out.
Arguments:
xlen -- X dimension (width) of board.
ylen -- Y dimension (height) of board.
pre_compute_adj -- Pre-compute adjacency matrix.
"""
assert(xlen > 1)
assert(ylen > 1)
self.xlen = xlen
self.ylen = ylen
self.board_size = xlen * ylen
if pre_compute_adj:
self.adjacency = BoggleSolver._create_adjacency_matrix(xlen, ylen)
else:
self.adjacency = None
self.trie = BoggleSolver._load_dictionary(
words_file, self.board_size, 3)
def solve(self, grid):
"""Generate all solutions for the given boggle grid.
Arguments:
grid -- A string of 16 characters representing the letters in a boggle
grid, from top left to bottom right.
Returns:
A list of words found in the boggle grid.
None if given invalid grid.
"""
if self.trie is None:
raise RuntimeError('words file not loaded')
if len(grid) != self.board_size:
raise RuntimeError('invalid board')
board = list(grid)
trie = self.trie
words = set()
q = collections.deque()
adjs = self.adjacency
for init_sq in range(self.board_size):
c = board[init_sq]
q.append((init_sq, c, trie.get_child(c), [init_sq]))
while q:
parent_sq, prefix, pnode, seen = q.popleft()
pnode_get_child = pnode.get_child
if adjs:
adj = adjs[parent_sq]
else:
adj = self._calc_adjacency(self.xlen, self.ylen, parent_sq)
for cur_sq in adj:
if cur_sq in seen:
continue
c = board[cur_sq]
cur_node = pnode_get_child(c)
if cur_node is None:
continue
s = prefix + c
q.append((cur_sq, s, cur_node, seen + [cur_sq]))
if cur_node._is_word:
if s[0] == 'q':
# Rehydrate q-words with 'u'.
words.add('qu' + s[1:])
else:
words.add(s)
return words
def show_grid(self, grid):
"""Utility method to print a 4x4 boggle grid.
Arguments:
grid -- A string of X*Y characters representing the letters in a boggle
grid, from top left to bottom right.
"""
for y in range(self.ylen):
print('+' + '---+' * self.xlen)
yi = y * self.xlen
line = ['| ']
for x in range(self.xlen):
cell = grid[yi+x].upper()
if cell == 'Q':
line.append('Qu')
line.append('| ')
else:
line.append(cell)
line.append(' | ')
print(''.join(line))
print('+' + '---+' * self.xlen)
def find_substrings(self, string):
"""Find all valid substrings in the given string.
This method is not necessary for the boggle solver, but is a utility
for testing that all substrings of a word are correctly found.
Arguments:
string -- The string in which to search for valid substrings.
Returns:
List of substrings that are valid words.
"""
found = set()
for start in range(len(string)):
cur = self.trie
letters = [None] * self.board_size
count = 0
for l in string[start:]:
letters[count] = l
count += 1
cur = cur.get_child(l)
if cur is None:
break
if cur._is_word:
found.add(''.join(letters[:count]))
if not cur.has_children():
break
return found
@staticmethod
def _load_dictionary(words_file, max_len, min_len):
"""Private method to create the trie for finding words.
Arguments:
words_file -- Path of file containing words for reference.
Return:
Count of words inserted into trie.
"""
if not os.path.isfile(words_file):
raise RuntimeError('words file not found: ' + words_file)
print('creating dictionary...')
root = trie.Trie()
word_count = 0
if words_file.endswith('gz'):
import gzip
f = gzip.open(words_file)
elif words_file.endswith('bz2'):
import bz2
f = bz2.BZ2File(words_file)
else:
f = open(words_file)
try:
for word in f:
if sys.version < '3':
word = word.strip()
else:
word = word.strip().decode("utf-8")
# Skip words that are too long or too short.
word_len = len(word)
if word_len > max_len or word_len < min_len:
continue
# Skip words that start with capital letter.
if word[0].isupper():
continue
if word[0] == 'q':
# Skip words starting with q not followed by u.
if word[1] != 'u':
continue
# Remove "u" from q-words so that only the q is matched.
word = 'q' + word[2:]
root.insert(word)
word_count += 1
finally:
f.close()
print('Loaded', word_count, 'words from file.')
return root
@staticmethod
def _create_adjacency_matrix(xlim, ylim):
adj_list = [[]] * (ylim * xlim)
for i in range(ylim * xlim):
# Current cell index = y * xlim + x
adj = BoggleSolver._calc_adjacency(xlim, ylim, i)
adj_list[i] = adj
return adj_list
@staticmethod
def _calc_adjacency(xlim, ylim, sq):
adj = []
y = int(sq / xlim)
x = sq - (y * xlim)
# Look at row above current cell.
if y-1 >= 0:
above = sq - xlim
# Look to upper left.
if x-1 >= 0:
adj.append(above - 1)
# Look above.
adj.append(above)
# Look upper right.
if x+1 < xlim:
adj.append(above + 1)
# Look at same row that current cell is on.
# Look to left of current cell.
if x-1 >= 0:
adj.append(sq - 1)
# Look to right of current cell.
if x+1 < xlim:
adj.append(sq + 1)
# Look at row below current cell.
if y+1 < ylim:
below = sq + xlim
# Look to lower left.
if x-1 >= 0:
adj.append(below - 1)
# Look below.
adj.append(below)
# Look to lower rigth.
if x+1 < xlim:
adj.append(below + 1)
return adj
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cyberpunk_theme/widget/option_menu.py | pyrustic/tk-cyberpunk-theme | 5 | 123846 | <gh_stars>1-10
import tkstyle
from cyberpunk_theme import constant
def get_style(font_family=constant.FONT_FAMILY,
font_size=constant.FONT_SIZE):
style = tkstyle.OptionMenu()
style.font = (font_family, font_size, "normal")
style.relief = "flat"
style.selectBackground = "#B4C7EF"
style.highlightThickness = 0
style.borderWidth = 0
style.foreground = "#B4C7EF"
style.relief = "flat"
style.inactiveSelectBackground = "#B4C7EF"
style.insertBackground = "#B4C7EF"
style.highlightColor = "gray"
return style
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link_to_file.py | HoChiak/DownloadPDFs | 1 | 106886 | # -*- coding: utf-8 -*-
"""
Created Nov 2018
@author: henss
"""
# import built in libarys
import os
from urllib.request import urlretrieve, urlopen
# import 3rd party libarys
from bs4 import BeautifulSoup
# import local libarys
# define classes and functions
class Weblink():
"""
Class for weblinks containing Filetypes to be downloaded.
"""
def __init__(self, weblink, LinkList=list()):
"""
Class constructor
"""
self.Weblink = weblink
self.LinkList = LinkList
def tweak_links(self):
"""
Instance method to tweak relative links to absolute links
"""
for tp in enumerate(self.LinkList):
if not(tp[1].startswith('http:') or
tp[1].startswith('https:') or
tp[1].startswith('www.')):
if self.LinkList[tp[0]][0] == '/':
first_part = self.Weblink.partition('www.')[0]
sec_part = self.Weblink.partition('www.')[2]
sec_part = sec_part.partition('/')[0]+tp[1]
self.LinkList[tp[0]] = str(first_part + sec_part)
else:
first_part = self.Weblink.partition('www.')[0]
sec_part = self.Weblink.partition('www.')[2]
sec_part = sec_part.partition('/')[0]+'/'+tp[1]
self.LinkList[tp[0]] = str(first_part + sec_part)
return(self.LinkList)
def fetch_links(self, filetype='pdf'):
"""
Instance method to fetch only links of the given filetype from weblink
"""
html = urlopen(self.Weblink).read()
pagesoup = BeautifulSoup(html, 'html.parser')
tp_link = ''
for tp in pagesoup.find_all('a'):
tp_link = tp.get('href')
try:
if (tp_link.endswith(filetype) and tp_link is not None):
self.LinkList.append(tp_link)
except AttributeError:
pass
# html.close()
self.tweak_links()
return(self.LinkList)
def download_links(self, localpath):
"""
Instance method to download Links
"""
os.chdir(localpath)
for tp in enumerate(self.LinkList):
link_html = self.LinkList[tp[0]]
link_name = self.LinkList[tp[0]].rpartition('/')[2]
urlretrieve(link_html, link_name)
print("Links are downloaed to: " + str(localpath))
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] |
utils/combine_csv_lists.py | DAA233/futurelab-ai-2018-image | 1 | 185684 | import csv
def load_csv_file(filepath):
list_file = []
with open(filepath, 'r') as csv_file:
all_lines = csv.reader(csv_file)
for line in all_lines:
list_file.append(line)
list_file.remove(list_file[0])
return list_file
def write_list_to_csv(filepath, data_list):
with open(filepath, 'w') as csv_file:
csv_file.write('FILE_ID,CATEGORY_ID\n')
for item in data_list:
csv_file.write(str(item[0]) + ',' + str(item[1]) + '\n')
csv_file_a = '/media/ouc/4T_B/DuAngAng/datasets/futurelab/train_plus/5-fold-1-train.csv'
csv_file_b = '/media/ouc/4T_B/DuAngAng/datasets/futurelab/test/a/checked-test-data.csv'
data_list_a = load_csv_file(csv_file_a)
data_list_b = load_csv_file(csv_file_b)
data_list_combined = data_list_a
for item in data_list_b:
data_list_combined.append(item)
write_list_to_csv('/media/ouc/4T_B/DuAngAng/datasets/futurelab/train_plus/train_plus.csv',
data_list_combined) | [
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14538,
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29914,
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29914,
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29918,
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29899,
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13,
7638,
29918,
1445,
29918,
29890,
353,
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9799,
29914,
29049,
29914,
29946,
29911,
29918,
29933,
29914,
29928,
29884,
9928,
9928,
29914,
14538,
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13,
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1761,
29918,
17743,
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353,
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29918,
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29918,
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13,
1454,
2944,
297,
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29918,
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29918,
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29918,
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29918,
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13,
3539,
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1761,
29918,
517,
29918,
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11219,
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29946,
29911,
29918,
29933,
29914,
29928,
29884,
9928,
9928,
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1691,
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29888,
9130,
8205,
29914,
14968,
29918,
11242,
29914,
14968,
29918,
11242,
29889,
7638,
742,
13,
462,
29871,
848,
29918,
1761,
29918,
17743,
1312,
29897,
2
] |
src/pycolocstats/tools/jobparamsdict.py | hyperbrowser/conglomerate | 2 | 175608 | <gh_stars>1-10
from __future__ import absolute_import, division, print_function, unicode_literals
from pycolocstats.core.types import PathStr, PathStrList
from past.builtins import basestring
from collections import MutableMapping
__metaclass__ = type
class JobParamsDict(MutableMapping):
def __init__(self, paramDefDict):
self._paramDefDict = paramDefDict
self._params = dict()
def __getitem__(self, key):
return self._params[key]
def __setitem__(self, key, val):
assert key in self.getAllowedKeys(), \
'"{}" not in allowed keys: {}'.format(key, ', '.join(self.getAllowedKeys()))
allowedType = self.getType(key)
if allowedType == PathStr:
assert isinstance(val, basestring), '"{}" not of correct type: {}'.format(val, str)
#assert os.path.exists(val), 'File "{}" does not exist'.format(val) #TODO: Had to temporarily disable due to generic copying from dat to bed..
val = PathStr(val)
elif allowedType == PathStrList:
assert isinstance(val, list), '"{}" not of correct type: {}'.format(val, list)
assert all(isinstance(f, basestring) for f in val), \
'Some of the entries of "{}" are not of correct type: {}'.format(val, str)
# assert all(os.path.exists(f) for f in val), \
# 'Some of the entries of "{}" do not exist'.format(val) #TODO: disabled due to dat to bed..
val = PathStrList(val)
else:
assert isinstance(val, allowedType), '"{}" (type:{}) not of correct type: {}'.format(val, type(val), allowedType)
self._params[key] = val
def __delitem__(self, key):
del self._params[key]
def __iter__(self):
return iter(self._params)
def __len__(self):
return len(self._params)
def getAllowedKeys(self):
return self._paramDefDict.keys()
def getType(self, key):
return self._paramDefDict[key]['type']
def isMandatory(self, key):
return self._paramDefDict[key]['mandatory']
def getAbsentMandatoryParameters(self):
absentMandatoryParameters = []
for key in self.getAllowedKeys():
if self.isMandatory(key) and key not in self:
absentMandatoryParameters.append(key)
return absentMandatoryParameters
def __repr__(self):
retStr = repr(self._params)
retStr += '\nAllowed params:\n'
for key in self.getAllowedKeys():
retStr += '\t%s: %s %s\n' % (key, self.getType(key), '[x]' if self.isMandatory(key) else '[ ]')
retStr += '[ ] for optional parameter, [x] for mandatory parameter'
return retStr
| [
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511,
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13,
13,
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3032,
7529,
29961,
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29962,
353,
659,
13,
13,
1678,
822,
4770,
6144,
667,
12035,
1311,
29892,
1820,
1125,
13,
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628,
1583,
3032,
7529,
29961,
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29962,
13,
13,
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822,
4770,
1524,
12035,
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1125,
13,
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736,
4256,
29898,
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3032,
7529,
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13,
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822,
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1125,
13,
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736,
7431,
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3032,
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13,
13,
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822,
679,
15930,
287,
15506,
29898,
1311,
1125,
13,
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736,
1583,
3032,
3207,
3206,
21533,
29889,
8149,
580,
13,
13,
1678,
822,
679,
1542,
29898,
1311,
29892,
1820,
1125,
13,
4706,
736,
1583,
3032,
3207,
3206,
21533,
29961,
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2033,
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13,
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338,
29924,
392,
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13,
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679,
4920,
18616,
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13,
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29924,
392,
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353,
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13,
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363,
1820,
297,
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29889,
657,
15930,
287,
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7295,
13,
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565,
1583,
29889,
275,
29924,
392,
7606,
29898,
1989,
29897,
322,
1820,
451,
297,
1583,
29901,
13,
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29207,
29924,
392,
7606,
11507,
29889,
4397,
29898,
1989,
29897,
13,
4706,
736,
29207,
29924,
392,
7606,
11507,
13,
13,
1678,
822,
4770,
276,
558,
12035,
1311,
1125,
13,
4706,
3240,
5015,
353,
2062,
29898,
1311,
3032,
7529,
29897,
13,
4706,
3240,
5015,
4619,
11297,
29876,
15930,
287,
8636,
3583,
29876,
29915,
13,
4706,
363,
1820,
297,
1583,
29889,
657,
15930,
287,
15506,
7295,
13,
9651,
3240,
5015,
4619,
11297,
29873,
29995,
29879,
29901,
1273,
29879,
1273,
29879,
29905,
29876,
29915,
1273,
313,
1989,
29892,
1583,
29889,
657,
1542,
29898,
1989,
511,
525,
29961,
29916,
29962,
29915,
565,
1583,
29889,
275,
29924,
392,
7606,
29898,
1989,
29897,
1683,
525,
29961,
4514,
1495,
13,
4706,
3240,
5015,
4619,
525,
29961,
4514,
363,
13136,
3443,
29892,
518,
29916,
29962,
363,
9619,
7606,
3443,
29915,
13,
4706,
736,
3240,
5015,
13,
2
] |
PyCharm/Aulas/Aula10.py | gabrieldtc/CursoEmVideoPython | 0 | 159961 | <filename>PyCharm/Aulas/Aula10.py
nome = str(input('Qual é o seu nome? ')).strip().upper()
if nome == 'GABRIEL':
print('Que lindo nome você tem!')
else:
print('Seu nome é tão normal!')
print('Bom dia, {}'.format(nome)) | [
1,
529,
9507,
29958,
19737,
1451,
2817,
29914,
29909,
15173,
29914,
29909,
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29901,
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877,
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13,
2158,
877,
29933,
290,
9766,
29892,
6571,
4286,
4830,
29898,
25155,
876,
2
] |
Configuration/DataProcessing/python/Impl/cosmicsEra_Run2_50ns.py | ckamtsikis/cmssw | 6 | 153057 | <gh_stars>1-10
#!/usr/bin/env python
"""
_cosmicsEra_Run2_50ns_
Scenario supporting cosmic data taking
"""
import os
import sys
from Configuration.Eras.Era_Run2_50ns_cff import Run2_50ns
from Configuration.DataProcessing.Impl.cosmics import cosmics
class cosmicsEra_Run2_50ns(cosmics):
def __init__(self):
cosmics.__init__(self)
self.eras = Run2_50ns
"""
_cosmicsEra_Run2_50ns_
Implement configuration building for data processing for cosmic
data taking in Run2
"""
| [
1,
529,
12443,
29918,
303,
1503,
29958,
29896,
29899,
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13,
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29914,
2109,
29914,
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13,
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13,
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29918,
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29918,
29945,
29900,
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29906,
29918,
29945,
29900,
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29889,
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29889,
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13,
13,
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27973,
1199,
29923,
336,
29918,
6558,
29906,
29918,
29945,
29900,
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29898,
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13,
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822,
4770,
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13,
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353,
7525,
29906,
29918,
29945,
29900,
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13,
1678,
9995,
13,
1678,
903,
3944,
29885,
1199,
29923,
336,
29918,
6558,
29906,
29918,
29945,
29900,
1983,
29918,
13,
13,
1678,
1954,
2037,
5285,
5214,
363,
848,
9068,
363,
6776,
13076,
13,
1678,
848,
5622,
297,
7525,
29906,
13,
13,
1678,
9995,
13,
2
] |
api/analyses/analyses.py | capdragon/cannlytics | 0 | 96618 | """
Analyses Views | Cannlytics API
Created: 4/21/2021
API to interface with cannabis regulation information.
"""
from rest_framework import status
from rest_framework.decorators import api_view
from rest_framework.response import Response
@api_view(['GET', 'POST', 'DELETE'])
def analyses(request, format=None):
"""Get, create, or update information about cannabis analyses."""
if request.method == 'GET':
# TODO: Implement filters!
# data = get_collection(f"labs/{org_id}/analyses")
return Response({'error': 'not_implemented'}, content_type='application/json')
elif request.method == 'POST':
return Response({'error': 'not_implemented'}, content_type='application/json')
elif request.method == 'DELETE':
return Response({'error': 'not_implemented'}, content_type='application/json')
@api_view(['GET', 'POST', 'DELETE'])
def analytes(request, format=None):
"""Get, create, or update information about cannabis analysis analytes."""
if request.method == 'GET':
return Response({'error': 'not_implemented'}, content_type='application/json')
elif request.method == 'POST':
return Response({'error': 'not_implemented'}, content_type='application/json')
elif request.method == 'DELETE':
return Response({'error': 'not_implemented'}, content_type='application/json')
| [
1,
9995,
13,
21067,
952,
267,
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1853,
2433,
6214,
29914,
3126,
1495,
13,
2
] |
sample/html2txt.py | rodsenra/koondor | 2 | 108275 | import codecs
from glob import glob
import os
import sys
from bs4 import BeautifulSoup
dir_with_news_html = sys.argv[1]
dir_with_news_txt = sys.argv[2]
dirpath = os.path.join(dir_with_news_html, "*.html")
for filename in glob(dirpath):
html_file = codecs.open(filename, "r", encoding="utf-8")
soup = BeautifulSoup(html_file.read())
try:
print "Converting " + filename
txt_content = soup.find_all('div', class_='materia-conteudo')[0].text
txt_content = txt_content.replace('\n', ' ')
txt_file = os.path.join(dir_with_news_txt, os.path.basename(filename))
txt_file = txt_file.replace('.html', '.txt')
codecs.open(txt_file, "wb", encoding="utf-8").write(txt_content)
except IndexError:
pass
html_file.close()
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2
] |
pytests/cbas/cbas_backup_restore.py | ashwin2002/TAF | 0 | 177705 | import random
from cbas.cbas_base import CBASBaseTest
from Jython_tasks.task import CreateDatasetsTask, DropDatasetsTask, \
CreateSynonymsTask, DropSynonymsTask, DropDataversesTask, \
CreateCBASIndexesTask, DropCBASIndexesTask, CreateUDFTask, DropUDFTask
from cbas_utils.cbas_utils_v2 import BackupUtils
from remote.remote_util import RemoteMachineShellConnection
from TestInput import TestInputSingleton
import urllib
class BackupRestoreTest(CBASBaseTest):
def setUp(self):
self.input = TestInputSingleton.input
self.num_dataverses = int(self.input.param("no_of_dv", 1))
self.ds_per_dv = int(self.input.param("ds_per_dv", 1))
self.kv_name_cardinality = self.input.param("kv_name_cardinality", 3)
self.cbas_name_cardinality = self.input.param(
"cbas_name_cardinality", 2)
self.ds_per_collection = self.input.param("ds_per_collection", 1)
if self.input.param('setup_infra', True):
if "bucket_spec" not in self.input.test_params:
self.input.test_params.update(
{"bucket_spec": "analytics.default"})
else:
if "default_bucket" not in self.input.test_params:
self.input.test_params.update({"default_bucket": False})
super(BackupRestoreTest, self).setUp()
self.log_setup_status(self.__class__.__name__, "Started",
stage=self.setUp.__name__)
self.synonyms_per_ds = int(self.input.param("synonyms_per_ds", 1))
self.overlap_path = self.input.param("overlap_path", False)
self.drop_datasets = self.input.param("drop_datasets", True)
self.drop_synonyms = self.input.param("drop_synonyms", True)
self.drop_indexes = self.input.param("drop_synonyms", True)
self.drop_dataverses = self.input.param("drop_dataverses", True)
self.drop_udfs = self.input.param("drop_udfs", True)
self.remap_bucket = self.input.param("remap_bucket", False)
self.ds_fields = ['DatasetName', 'DataverseName', 'BucketName', 'ScopeName',
'CollectionName']
self.backup_util = BackupUtils(self.cluster.servers[0], self.cbas_node)
self.log_setup_status(self.__class__.__name__, "Finished",
stage=self.setUp.__name__)
def tearDown(self):
self.log_setup_status(self.__class__.__name__, "Started",
stage=self.tearDown.__name__)
self.backup_util.shell.disconnect()
super(BackupRestoreTest, self).tearDown()
self.log_setup_status(self.__class__.__name__, "Finished",
stage=self.tearDown.__name__)
def validate_restore(self, dv_before_backup, ds_before_backup,
syn_before_backup, idx_before_backup, include, exclude,
original_bucket, original_scope, original_collection,
remap_bucket, remap_scope, remap_collection,
level="cluster"):
dv_after_restore = self.cbas_util_v2.get_dataverses(retries=1)
ds_after_restore = self.cbas_util_v2.get_datasets(retries=1,
fields=self.ds_fields)
syn_after_restore = self.cbas_util_v2.get_synonyms(retries=1)
idx_after_restore = self.cbas_util_v2.get_indexes(retries=1)
if include:
if not isinstance(include, list):
include = urllib.quote(include).split(",")
self.assertEquals(len(ds_after_restore), len(include))
self.assertEquals(len(idx_after_restore), len(include) * 2)
elif exclude:
if not isinstance(exclude, list):
exclude = urllib.quote(exclude).split(",")
self.assertEquals(len(ds_after_restore),
len(ds_before_backup) - len(exclude))
self.assertEquals(len(idx_after_restore),
len(idx_before_backup) - (len(exclude) * 2))
else:
self.assertEquals(len(ds_after_restore), len(ds_before_backup))
self.assertEquals(len(idx_after_restore), len(idx_before_backup))
if level == "cluster":
self.assertEquals(len(dv_after_restore), len(dv_before_backup))
self.assertEquals(len(syn_after_restore), len(syn_before_backup))
if remap_bucket or remap_scope or remap_collection:
original_ds = list(
filter(
lambda ds:
ds['BucketName'] == original_bucket.name
and
ds['ScopeName'] == original_scope.name
and
ds['CollectionName'] == original_collection.name,
ds_before_backup))
for ds_restored in ds_after_restore:
backed_up_ds = list(
filter(
lambda ds: ds['DatasetName'] == \
ds_restored['DatasetName'] and \
ds['DataverseName'] == \
ds_restored['DataverseName'], original_ds))
if backed_up_ds:
self.assertEquals(ds_restored['BucketName'],
remap_bucket.name)
self.assertEquals(ds_restored['ScopeName'],
remap_scope.name)
self.assertEquals(ds_restored['CollectionName'],
remap_collection.name)
def create_datasets(self,
creation_methods=["cbas_collection", "cbas_dataset"]):
create_datasets_task = CreateDatasetsTask(
bucket_util=self.bucket_util,
cbas_name_cardinality=self.cbas_name_cardinality,
cbas_util=self.cbas_util_v2,
kv_name_cardinality=self.kv_name_cardinality,
ds_per_dv=self.ds_per_dv, ds_per_collection=self.ds_per_collection,
creation_methods=creation_methods)
self.task_manager.add_new_task(create_datasets_task)
self.task_manager.get_task_result(create_datasets_task)
return create_datasets_task.result
def create_synonyms(self, cbas_entities=[], synonym_on_synonym=False):
if not cbas_entities:
cbas_entities = self.cbas_util_v2.list_all_dataset_objs()
results = []
for ds in cbas_entities:
synonyms_task = CreateSynonymsTask(
cbas_util=self.cbas_util_v2, cbas_entity=ds,
dataverse=self.cbas_util_v2.dataverses[ds.dataverse_name],
synonyms_per_entity=self.synonyms_per_ds,
synonym_on_synonym=synonym_on_synonym)
self.task_manager.add_new_task(synonyms_task)
self.task_manager.get_task_result(synonyms_task)
results.append(synonyms_task.result)
return all(results)
def create_indexes(self, datasets=[], prefix=""):
results = []
if not datasets:
datasets = self.cbas_util_v2.list_all_dataset_objs()
for ds in datasets:
create_index_task = CreateCBASIndexesTask(self.cbas_util_v2, ds)
create_index_task.call()
results.append(create_index_task.result)
return all(results)
def create_udfs(self):
if self.input.param("udfs_on_datasets", True):
for num, ds in enumerate(self.cbas_util_v2.list_all_dataset_objs()):
name = "func_ds_" + str(num)
dv = self.cbas_util_v2.dataverses[ds.dataverse_name]
ds_name = ds.full_name
body = "select count(*) from {0}".format(ds_name)
create_udf_task = CreateUDFTask(
self.cbas_util_v2, name, dv, body,
parameters=[], referenced_entities=[dv.datasets[ds_name]])
self.task_manager.add_new_task(create_udf_task)
self.task_manager.get_task_result(create_udf_task)
if self.input.param("udfs_on_synonyms", False):
for num, syn in enumerate(self.cbas_util_v2.get_synonyms()):
name = "func_syn_" + str(num)
dv, syn_name = syn.split(".")
body = "SELECT COUNT(*) FROM {0}".format(syn)
create_udf_task = CreateUDFTask(self.cbas_util_v2, name,
self.cbas_util_v2.dataverses[dv],
body, parameters=[],
referenced_entities=[syn])
self.task_manager.add_new_task(create_udf_task)
self.task_manager.get_task_result(create_udf_task)
def drop_all_udfs(self):
for dv in self.cbas_util_v2.dataverses.values():
drop_udf_task = DropUDFTask(self.cbas_util_v2, dv)
self.task_manager.add_new_task(drop_udf_task)
self.task_manager.get_task_result(drop_udf_task)
def drop_all_indexes(self, datasets=[]):
results = []
if not datasets:
datasets = self.cbas_util_v2.list_all_dataset_objs()
for ds in datasets:
drop_indexes_task = DropCBASIndexesTask(self.cbas_util_v2, ds)
drop_indexes_task.call()
results.append(drop_indexes_task.result)
return all(results)
def drop_all_datasets(self):
drop_datasets_task = DropDatasetsTask(self.cbas_util_v2,
self.kv_name_cardinality)
self.task_manager.add_new_task(drop_datasets_task)
self.task_manager.get_task_result(drop_datasets_task)
return drop_datasets_task.result
def drop_all_synonyms(self):
drop_synonyms_task = DropSynonymsTask(self.cbas_util_v2)
self.task_manager.add_new_task(drop_synonyms_task)
self.task_manager.get_task_result(drop_synonyms_task)
return drop_synonyms_task.result
def drop_all_dataverses(self):
drop_dataverses_task = DropDataversesTask(self.cbas_util_v2)
self.task_manager.add_new_task(drop_dataverses_task)
self.task_manager.get_task_result(drop_dataverses_task)
return drop_dataverses_task.result
def test_cluster_level_backup_restore(self):
"""
cluster level api is used to backup entities which are not related to \
buckets
1. Create KV infra
2. Create CBAS infra with synonyms
3. Take backup using cluster level API "/api/v1/backup"
4. Validate backed up metadata
5. Drop CBAS infra
6. Restore using backed up metadata
7. Validate CBAS infra.(Dataverses, Synonyms, Datasets and \
Ingestion)
"""
self.cbas_logger("test_cluster_level_backup started", "DEBUG")
self.create_datasets()
self.create_synonyms()
self.create_indexes()
self.create_udfs()
syn_before_backup = self.cbas_util_v2.get_synonyms()
dv_before_backup = self.cbas_util_v2.get_dataverses()
status, backup, response = self.backup_util.rest_backup_cbas(
level="cluster")
self.assertTrue(status)
if self.drop_synonyms:
self.drop_all_synonyms()
if self.drop_udfs:
self.drop_all_udfs()
if self.drop_datasets:
self.drop_all_datasets()
if self.drop_dataverses:
self.drop_all_dataverses()
status, restore, response = self.backup_util.rest_restore_cbas(
level="cluster", backup=backup)
self.assertTrue(status)
syn_after_restore = self.cbas_util_v2.get_synonyms(retries=1)
dv_after_restore = self.cbas_util_v2.get_dataverses(retries=1)
ds_after_restore = self.cbas_util_v2.get_datasets(retries=1)
idx_after_restore = self.cbas_util_v2.get_indexes(retries=1)
self.assertEquals(len(syn_before_backup), len(syn_after_restore))
self.assertEquals(len(dv_before_backup), len(dv_after_restore))
if self.drop_datasets:
self.assertEquals(len(ds_after_restore), 0)
if self.drop_indexes:
self.assertEquals(len(idx_after_restore), 0)
self.cbas_logger("test_cluster_level_backup finished", "DEBUG")
def test_bucket_level_backup(self):
"""
1. Create KV infra
2. Create CBAS infra
3. Take backup using bucket level API "/api/v1/bucket/[BUCKET]/backup"\
with args include or exclude
4. Validate backed up metadata
5. Drop CBAS infra
6. Restore using backed up metadata
7. Validate CBAS infra.(Dataverses, Links, Datasets and Ingestion)
"""
self.cbas_logger("test_cluster_level_backup started", "DEBUG")
include = self.input.param("include", True)
exclude = self.input.param("exclude", False)
self.create_datasets()
self.create_synonyms()
self.create_indexes()
self.create_udfs()
dv_before_backup = self.cbas_util_v2.get_dataverses()
ds_before_backup = self.cbas_util_v2.get_datasets(fields=self.ds_fields)
syn_before_backup = self.cbas_util_v2.get_synonyms()
idx_before_backup = self.cbas_util_v2.get_indexes()
bucket = random.choice(self.bucket_util.buckets)
scope = random.choice(self.bucket_util.get_active_scopes(bucket))
collection = random.choice(self.bucket_util.get_active_collections(
bucket, scope.name))
path = scope.name + "." + collection.name
if self.overlap_path:
path = scope.name + "," + path
path = urllib.quote(path)
if include:
include = path
else:
include = ""
if exclude:
exclude = path
else:
exclude = ""
status, backup, response = self.backup_util.rest_backup_cbas(
level="bucket", bucket=bucket.name, include=include,
exclude=exclude)
if (include and exclude) or self.overlap_path:
self.assertFalse(status)
else:
self.assertTrue(status)
if self.drop_indexes:
self.drop_all_indexes()
if self.drop_synonyms:
self.drop_all_synonyms()
if self.drop_udfs:
self.drop_all_udfs()
if self.drop_datasets:
self.drop_all_datasets()
if self.drop_dataverses:
self.drop_all_dataverses()
status, restore, response = self.backup_util.rest_restore_cbas(
level="bucket", bucket=bucket.name, backup=backup)
self.assertTrue(status)
self.validate_restore(dv_before_backup, ds_before_backup,
syn_before_backup, idx_before_backup,
include, exclude,
bucket, scope, collection, None,
None, None, level="bucket")
# validate metadata
self.cbas_logger("test_cluster_level_backup finished", "DEBUG")
def test_bucket_level_restore(self):
"""
1. Create KV infra
2. Create CBAS infra
3. Take backup using cluster level API "/api/v1/bucket/[BUCKET]/backup"\
with args include or exclude
4. Validate backed up metadata
5. Drop CBAS infra
6. Restore using backed up metadata along with remap arg
7. Validate CBAS infra.(Dataverses, Links, Datasets and Ingestion)
"""
include = self.input.param("include", True)
exclude = self.input.param("exclude", False)
self.cbas_logger("test_cluster_level_backup started", "DEBUG")
self.create_datasets()
self.create_synonyms()
self.create_indexes()
self.create_udfs()
dv_before_backup = self.cbas_util_v2.get_dataverses()
ds_before_backup = self.cbas_util_v2.get_datasets(fields=self.ds_fields)
syn_before_backup = self.cbas_util_v2.get_synonyms()
idx_before_backup = self.cbas_util_v2.get_indexes()
original_bucket = random.choice(self.bucket_util.buckets)
# remap collections
original_scope = random.choice(
self.bucket_util.get_active_scopes(original_bucket))
original_collection = random.choice(
self.bucket_util.get_active_collections(
original_bucket, original_scope.name))
if self.remap_bucket:
remap_bucket = random.choice(
list(
filter(
lambda b:
b.name != original_bucket.name,
self.bucket_util.buckets)))
else:
remap_bucket = original_bucket
remap_scope = random.choice(
list(filter(lambda scope: scope.name != original_scope.name,
self.bucket_util.get_active_scopes(
remap_bucket))))
remap_collection = random.choice(
self.bucket_util.get_active_collections(remap_bucket,
remap_scope.name))
remap = urllib.quote("{0}.{1}:{2}.{3}".format(original_scope.name,
original_collection.name,
remap_scope.name,
remap_collection.name))
if self.overlap_path:
remap += urllib.quote(",{0}:{1}".format(
original_scope.name, remap_scope.name))
if include:
include = urllib.quote("{0}.{1}".format(
original_scope.name, original_collection.name))
else:
include = ""
if exclude:
exclude = urllib.quote("{0}.{1}".format(
original_scope.name, original_collection.name))
else:
exclude = ""
status, backup, response = self.backup_util.rest_backup_cbas(
level="bucket", bucket=original_bucket.name)
self.assertTrue(status)
if self.drop_indexes:
self.drop_all_indexes()
if self.drop_synonyms:
self.drop_all_synonyms()
if self.drop_udfs:
self.drop_all_udfs()
if self.drop_datasets:
self.drop_all_datasets()
if self.drop_dataverses:
self.drop_all_dataverses()
status, restore, response = self.backup_util.rest_restore_cbas(
level="bucket", bucket=remap_bucket.name, backup=backup,
include=include, exclude=exclude, remap=remap)
if (include and exclude) or self.overlap_path:
self.assertFalse(status)
else:
self.assertTrue(status)
self.validate_restore(dv_before_backup, ds_before_backup,
syn_before_backup, idx_before_backup,
include, exclude,
original_bucket, original_scope,
original_collection, remap_bucket,
remap_scope, remap_collection, level="bucket")
def test_backup_with_cbbackupmgr(self):
self.cbas_logger("test_cluster_level_backup started", "DEBUG")
include = self.input.param("include", True)
exclude = self.input.param("exclude", False)
self.create_datasets()
self.create_synonyms()
self.create_indexes()
self.create_udfs()
dv_before_backup = self.cbas_util_v2.get_dataverses()
ds_before_backup = self.cbas_util_v2.get_datasets()
syn_before_backup = self.cbas_util_v2.get_synonyms()
idx_before_backup = self.cbas_util_v2.get_indexes()
bucket = random.choice(self.bucket_util.buckets)
scope = random.choice(self.bucket_util.get_active_scopes(bucket))
collection = random.choice(self.bucket_util.get_active_collections(
bucket, scope.name))
paths = [(bucket.name + "." + scope.name + "." + collection.name)]
if self.overlap_path:
paths.append(bucket.name + "." + scope.name)
if include:
include = paths
else:
include = []
if exclude:
exclude = paths
else:
exclude = []
o = self.backup_util.cbbackupmgr_backup_cbas(self.cbas_node,
include=include,
exclude=exclude)
if (include and exclude) or self.overlap_path:
self.assertFalse('Backup completed successfully' in ''.join(o),
msg='Backup was successful')
else:
self.assertTrue('Backup completed successfully' in ''.join(o),
msg='Backup was unsuccessful')
if self.drop_indexes:
self.drop_all_indexes()
if self.drop_synonyms:
self.drop_all_synonyms()
if self.drop_udfs:
self.drop_all_udfs()
if self.drop_datasets:
self.drop_all_datasets()
if self.drop_dataverses:
self.drop_all_dataverses()
o = self.backup_util.cbbackupmgr_restore_cbas(self.cbas_node)
self.assertTrue('Restore completed successfully' in ''.join(o),
msg='Restore was unsuccessful')
self.validate_restore(dv_before_backup, ds_before_backup,
syn_before_backup, idx_before_backup,
include, exclude,
bucket, scope, collection, None,
None, None)
# validate metadata
self.cbas_logger("test_cluster_level_backup finished", "DEBUG")
def test_restore_with_cbbackupmgr(self):
"""
1. Create KV infra
2. Create CBAS infra
3. Take backup using cluster level API "/api/v1/bucket/[BUCKET]/backup"\
with args include or exclude
4. Validate backed up metadata
5. Drop CBAS infra
6. Restore using backed up metadata along with remap arg
7. Validate CBAS infra.(Dataverses, Links, Datasets and Ingestion)
"""
self.cbas_logger("test_cluster_level_backup started", "DEBUG")
include = self.input.param("include", True)
exclude = self.input.param("exclude", False)
self.create_datasets()
self.create_synonyms()
self.create_indexes()
self.create_udfs()
dv_before_backup = self.cbas_util_v2.get_dataverses()
ds_before_backup = self.cbas_util_v2.get_datasets(fields=self.ds_fields)
syn_before_backup = self.cbas_util_v2.get_synonyms()
idx_before_backup = self.cbas_util_v2.get_indexes()
original_bucket = random.choice(self.bucket_util.buckets)
# remap collections
original_scope = random.choice(
self.bucket_util.get_active_scopes(original_bucket))
original_collection = random.choice(
self.bucket_util.get_active_collections(
original_bucket, original_scope.name))
if self.remap_bucket:
remap_bucket = random.choice(
list(
filter(
lambda b:
b.name != original_bucket.name,
self.bucket_util.buckets)))
else:
remap_bucket = original_bucket
remap_scope = random.choice(
list(filter(lambda scope: scope.name != original_scope.name,
self.bucket_util.get_active_scopes(remap_bucket))))
remap_collection = random.choice(
self.bucket_util.get_active_collections(remap_bucket, remap_scope.name))
mappings = ["{0}.{1}.{2}={3}.{4}.{5}".format(original_bucket.name,
original_scope.name,
original_collection.name,
remap_bucket.name,
remap_scope.name,
remap_collection.name)]
if self.overlap_path:
mappings.append("{0}.{1}={2}.{3}".format(original_bucket.name,
original_scope.name,
remap_bucket.name,
remap_scope.name))
if include:
include = ["{0}.{1}.{2}".format(original_bucket.name,
original_scope.name,
original_collection.name)]
else:
include = []
if exclude:
exclude = ["{0}.{1}.{2}".format(original_bucket.name,
original_scope.name,
original_collection.name)]
else:
exclude = []
o = self.backup_util.cbbackupmgr_backup_cbas(self.cbas_node)
self.assertTrue('Backup completed successfully' in ''.join(o),
msg='Backup was unsuccessful')
if self.drop_indexes:
self.drop_all_indexes()
if self.drop_synonyms:
self.drop_all_synonyms()
if self.drop_udfs:
self.drop_all_udfs()
if self.drop_datasets:
self.drop_all_datasets()
if self.drop_dataverses:
self.drop_all_dataverses()
o = self.backup_util.cbbackupmgr_restore_cbas(self.cbas_node,
include=include,
exclude=exclude,
mappings=mappings)
if (include and exclude) or self.overlap_path:
self.assertFalse('Restore completed successfully' in ''.join(o),
msg='Restore was successful')
else:
self.assertTrue('Restore completed successfully' in ''.join(o),
msg='Restore was unsuccessful')
self.validate_restore(dv_before_backup, ds_before_backup,
syn_before_backup, idx_before_backup,include,
exclude, original_bucket, original_scope,
original_collection, remap_bucket,
remap_scope, remap_collection)
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] |
apps/accounts/views.py | Chamane/django-starter-project | 1 | 91093 | <reponame>Chamane/django-starter-project
from django.shortcuts import render, redirect
from django.contrib.auth import login as auth_login, authenticate, get_user_model
from django.urls import reverse
from .forms import LoginForm, RegisterForm
def login(request):
# if request is a HTTP POST try to pull out the relevant information
if request.method == 'POST':
login_form = LoginForm(request.POST)
if login_form.is_valid():
email = login_form.cleaned_data['email']
password = login_form.cleaned_data['password']
user = authenticate(request, username=email, password=password)
#if user is not None:
#auth_login(request, user)
#if user.is_activate:
#return redirect('doctor_dashboard', user_slug=user.slug)
#else:
#return redirect(reverse('home'))
else:
login_form = LoginForm()
return render(request, 'accounts/login.html', {'login_form': login_form,})
def register(request):
if request.method == 'POST':
register_form = RegisterForm(request.POST)
if register_form.is_valid(): # TODO: add recaptcha validation
user = register_form.save()
auth_login(request, user)
return redirect(reverse('home'))
else:
register_form = RegisterForm()
return render(
request, 'accounts/register.html',
{'register_form': register_form}
)
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statsmodels/examples/ex_pareto_plot.py | madhushree14/statsmodels | 6,931 | 1611452 | # -*- coding: utf-8 -*-
"""
Created on Sun Aug 01 19:20:16 2010
Author: josef-pktd
"""
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
nobs = 1000
r = stats.pareto.rvs(1, size=nobs)
#rhisto = np.histogram(r, bins=20)
rhisto, e = np.histogram(np.clip(r, 0 , 1000), bins=50)
plt.figure()
plt.loglog(e[:-1]+np.diff(e)/2, rhisto, '-o')
plt.figure()
plt.loglog(e[:-1]+np.diff(e)/2, nobs-rhisto.cumsum(), '-o')
##plt.figure()
##plt.plot(e[:-1]+np.diff(e)/2, rhisto.cumsum(), '-o')
##plt.figure()
##plt.semilogx(e[:-1]+np.diff(e)/2, nobs-rhisto.cumsum(), '-o')
rsind = np.argsort(r)
rs = r[rsind]
rsf = nobs-rsind.argsort()
plt.figure()
plt.loglog(rs, nobs-np.arange(nobs), '-o')
print(stats.linregress(np.log(rs), np.log(nobs-np.arange(nobs))))
plt.show()
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notification.py | jsalazardev/webhookslack | 0 | 193480 | <reponame>jsalazardev/webhookslack<gh_stars>0
#!/usr/bin/env python2.7
# Import Libraries
import fire
from module.webHook import send_message_to_slack
class Notification(object):
def __init__(self, text, footer='', title='', status='good', username='', field=''):
self._text = text
self._footer = footer
self._title = title
self._status = status
self._username = username
self._field = field
def deploy(self):
params = {
'text': self._text,
'title': 'Notification Deploy' if self._title == '' else self._title,
'footer': 'Instance {footer} '.format(footer='dev' if self._footer == '' else self._footer),
'status': self._status,
'username': 'Amazon EC2' if self._username == '' else self._username,
'field':'' if self._field == '' else self._field
}
send_message_to_slack(**params)
def pull_request(self):
params = {
'text': self._text,
'title': 'Notification Pull-Request' if self._title == '' else self._title,
'footer': 'Merge {footer} '.format(footer='dev' if self._footer == '' else self._footer),
'status': self._status,
'username': 'Amazon CodeCommit' if self._username == '' else self._username,
'field':'' if self._field == '' else self._field
}
send_message_to_slack(**params)
def war(self):
params = {
'text': self._text,
'title': 'Build WAR ' if self._title == '' else self._title,
'footer': 'Version {footer} '.format(footer='' if self._footer == '' else self._footer),
'status': self._status,
'username': 'S3 build war' if self._username == '' else self._username,
'field':'' if self._field == '' else [{'title':'Url', 'value':'<'+self._field+'>'}]
}
send_message_to_slack(**params)
def notification(self):
params = {
'text': self._text,
'title': 'Notification ' if self._title == '' else self._title,
'footer': '{footer} '.format(footer='' if self._footer == '' else self._footer),
'status': self._status,
'username': 'Application' if self._username == '' else self._username,
'field':'' if self._field == '' else self._field
}
send_message_to_slack(**params)
if __name__ == '__main__':
fire.Fire(Notification)
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] |
bluebottle/utils/migrations/0006_auto_20210825_1018.py | terrameijar/bluebottle | 10 | 1611342 | <filename>bluebottle/utils/migrations/0006_auto_20210825_1018.py
# Generated by Django 2.2.20 on 2021-08-25 08:18
from django.db import migrations, models
import django.db.models.deletion
import parler.fields
class Migration(migrations.Migration):
dependencies = [
('utils', '0005_auto_20200626_1145'),
]
operations = [
migrations.AddField(
model_name='language',
name='default',
field=models.BooleanField(default=False),
),
migrations.AddField(
model_name='language',
name='sub_code',
field=models.CharField(blank=True, max_length=2),
),
]
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2
] |
pupil/sampling/uncertainty.py | hadi-gharibi/pupil | 2 | 52966 | <gh_stars>1-10
from __future__ import annotations
import math
from typing import Callable
import numpy as np
from pupil.types import NDArray2D
def least_confidence(prob_dist: NDArray2D) -> np.ndarray:
"""
Returns the uncertainty score of an array using
least confidence sampling in a 0-1 range where 1 is most uncertain.
Example:
Assumes probability distribution is a numpy array, like ``np.array ([[0.0321, 0.6439, 0.0871, 0.2369]])``
The restults will be ``(1 – 0.6439) × (4 / 3) = 0.4748``
Args:
prob_dist (NDArray2D): a 2D numpy array of real numbers between 0 and 1
each row is a data point, and each column shows the probability of that class
Returns:
np.ndarray: shape(n_rows)
"""
simple_least_conf = np.max(prob_dist, axis=1)
num_labels = prob_dist.shape[1]
normalized_least_conf = (1 - simple_least_conf) * (num_labels / (num_labels - 1))
return normalized_least_conf
def margin_confidence(prob_dist: NDArray2D) -> np.ndarray:
"""
Returns the uncertainty score of an array using
least confidence sampling in a 0-1 range where 1 is most uncertain.
Example:
Assumes probability distribution is a numpy array, like:
``np.array([[0.0321, 0.6439, 0.0871, 0.2369]])``
The results would will be ``1.0 - (0.6439 - 0.2369) = 0.5930``
Args:
prob_dist (NDArray2D): a 2D numpy array of real numbers between 0 and 1
each row is a data point, and each column shows the probability of that class.
Returns:
np.ndarray: shape(n_rows)
"""
prob_dist = np.sort(prob_dist)
difference = prob_dist[:, -1] - prob_dist[:, -2] # type: ignore
margin_conf = 1 - difference
return margin_conf
def ratio_confidence(prob_dist: NDArray2D) -> np.ndarray:
"""
Returns the uncertainty score of an array using
least confidence sampling in a 0-1 range where 1 is most uncertain.
Example:
Assumes probability distribution is a numpy array, like
``np.array ***([[0.0321, 0.6439, 0.0871, 0.2369]])``
The results will be
``0.6439 / 0.2369 = 2.71828``
Args:
prob_dist (NDArray2D): a 2D numpy array of real numbers between 0 and 1
each row is a data point, and each column shows the probability of that class
Returns:
np.ndarray: shape(n_rows)
"""
prob_dist = np.sort(prob_dist)
difference = prob_dist[:, -1] / prob_dist[:, -2]
return difference
def entropy_based(prob_dist: NDArray2D) -> np.ndarray:
"""
Returns the uncertainty score of an array using
least confidence sampling in a 0-1 range where 1 is most uncertain.
Example:
Assumes probability distribution is a numpy array, like:
``np.array([[0.0321, 0.6439, 0.0871, 0.2369]])``
The results will be
``P(y|x) log2(P(y|x)) = 0 – SUM(–0.159, –0.409, –0.307, –0.492) = 1.367``
``1.367 / log2(n_classes = 4) = 0.684``
Args:
prob_dist (NDArray2D): a 2D numpy array of real numbers between 0 and 1
each row is a data point, and each column shows the probability of that class
Returns:
np.ndarray: shape(n_rows)
"""
log_probs = prob_dist * np.log2(prob_dist)
raw_entropy = 0 - np.sum(log_probs, axis=1)
normalized_entropy = raw_entropy / math.log2(prob_dist.shape[1])
return normalized_entropy
class UncertaintySampler:
"""
Uncertainty sampling is a set of techniques for identifying
unlabeled items that are near a decision boundary in your
current machine learning model.
"""
def __init__(self, sampling_strategy: Callable[[np.ndarray], np.ndarray]):
self.sampling_strategy = sampling_strategy
self.indices_ = None
@classmethod
def from_strategy(cls, strategy: str) -> UncertaintySampler:
"""classmethod to help picking the sampling strategy
Args:
strategy (str): Should be one of:
``['least_confidence', 'margin_confidence', 'ratio_confidence', 'entropy_based']``
Raises:
ValueError: If strategy is not in the valid list
Returns:
UncertaintySampler:
"""
strategies = [
"least_confidence",
"margin_confidence",
"ratio_confidence",
"entropy_based",
]
if strategy not in strategies:
raise ValueError(f"{strategy} must be one of {strategies}")
return cls(eval(strategy))
def __call__(self, prob_dist: np.ndarray) -> UncertaintySampler:
self.fit(prob_dist)
return self.indices_ # type: ignore
def fit(self, prob_dist: NDArray2D) -> None:
"""Get the 2D numpy array of model predictions and retun an array on indecies
with the order of highest to lowst uncertainty.
Args:
prob_dist (NDArray2D):
"""
ranks = self.sampling_strategy(prob_dist)
self.indices_ = np.flip(ranks.argsort())
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] |
asposecadcloud/models/requests/http_request.py | aspose-cad-cloud/aspose-cad-cloud-python | 2 | 77287 | -8
# ----------------------------------------------------------------------------
# <copyright company="Aspose" file="HttpRequest.py">
# Copyright (c) 2018-2019 Aspose Pty Ltd. All rights reserved.
# </copyright>
# <summary>
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
# </summary>
# ----------------------------------------------------------------------------
class HttpRequest(object):
def __init__(self, resource_path, path_params, query_params, header_params,
form_params, body_params, files, collection_formats,
auth_settings, return_http_data_only=None,
preload_content=None, request_timeout=None):
"""Makes the HTTP request (synchronous) and returns deserialized data.
To make an async request, set the async parameter.
:param resource_path: Path to method endpoint.
:param path_params: Path parameters in the url.
:param query_params: Query parameters in the url.
:param header_params: Header parameters to be placed in the request
header.
:param form_params: Request post form parameters,
for `application/x-www-form-urlencoded`, `multipart/form-data`.
:param body_params: Request body.
:param files: dict key -> filename, value -> filepath,
for `multipart/form-data`.
:param collection_formats: dict of collection formats for path, query,
header, and post parameters.
:param auth_settings: Auth Settings names for the request.
:param return_http_data_only: response data without head status code
and headers
:param preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Request thread.
"""
self.resource_path = resource_path
self.path_params = path_params
self.query_params = query_params
self.header_params = header_params
self.form_params = form_params
self.body_params = body_params
self.files = files
self.collection_formats = collection_formats
self.auth_settings = auth_settings
self.return_http_data_only = return_http_data_only or True
self.preload_content = preload_content or True
self.request_timeout = request_timeout or ''
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django_orm/sports_orm/leagues/migrations/0002_auto_20161031_1620.py | gfhuertac/coding_dojo_python | 0 | 5989 | <filename>django_orm/sports_orm/leagues/migrations/0002_auto_20161031_1620.py
# -*- coding: utf-8 -*-
# Generated by Django 1.10.2 on 2016-10-31 23:20
from __future__ import unicode_literals
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('leagues', '0001_initial'),
]
operations = [
migrations.RenameField(
model_name='team',
old_name='city',
new_name='location',
),
]
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spark_fhir_schemas/stu3/complex_types/messageheader_source.py | icanbwell/SparkFhirSchemas | 2 | 114958 | <filename>spark_fhir_schemas/stu3/complex_types/messageheader_source.py
from typing import Union, List, Optional
from pyspark.sql.types import StructType, StructField, StringType, ArrayType, DataType
# This file is auto-generated by generate_schema so do not edit manually
# noinspection PyPep8Naming
class MessageHeader_SourceSchema:
"""
The header for a message exchange that is either requesting or responding to
an action. The reference(s) that are the subject of the action as well as
other information related to the action are typically transmitted in a bundle
in which the MessageHeader resource instance is the first resource in the
bundle.
"""
# noinspection PyDefaultArgument
@staticmethod
def get_schema(
max_nesting_depth: Optional[int] = 6,
nesting_depth: int = 0,
nesting_list: List[str] = [],
max_recursion_limit: Optional[int] = 2,
include_extension: Optional[bool] = False,
extension_fields: Optional[List[str]] = [
"valueBoolean",
"valueCode",
"valueDate",
"valueDateTime",
"valueDecimal",
"valueId",
"valueInteger",
"valuePositiveInt",
"valueString",
"valueTime",
"valueUnsignedInt",
"valueUri",
"valueQuantity",
],
extension_depth: int = 0,
max_extension_depth: Optional[int] = 2,
) -> Union[StructType, DataType]:
"""
The header for a message exchange that is either requesting or responding to
an action. The reference(s) that are the subject of the action as well as
other information related to the action are typically transmitted in a bundle
in which the MessageHeader resource instance is the first resource in the
bundle.
id: unique id for the element within a resource (for internal references). This
may be any string value that does not contain spaces.
extension: May be used to represent additional information that is not part of the basic
definition of the element. In order to make the use of extensions safe and
manageable, there is a strict set of governance applied to the definition and
use of extensions. Though any implementer is allowed to define an extension,
there is a set of requirements that SHALL be met as part of the definition of
the extension.
name: Human-readable name for the source system.
software: May include configuration or other information useful in debugging.
version: Can convey versions of multiple systems in situations where a message passes
through multiple hands.
contact: An e-mail, phone, website or other contact point to use to resolve issues with
message communications.
endpoint: Identifies the routing target to send acknowledgements to.
"""
from spark_fhir_schemas.stu3.complex_types.extension import ExtensionSchema
from spark_fhir_schemas.stu3.complex_types.contactpoint import (
ContactPointSchema,
)
if (
max_recursion_limit
and nesting_list.count("MessageHeader_Source") >= max_recursion_limit
) or (max_nesting_depth and nesting_depth >= max_nesting_depth):
return StructType([StructField("id", StringType(), True)])
# add my name to recursion list for later
my_nesting_list: List[str] = nesting_list + ["MessageHeader_Source"]
schema = StructType(
[
# unique id for the element within a resource (for internal references). This
# may be any string value that does not contain spaces.
StructField("id", StringType(), True),
# May be used to represent additional information that is not part of the basic
# definition of the element. In order to make the use of extensions safe and
# manageable, there is a strict set of governance applied to the definition and
# use of extensions. Though any implementer is allowed to define an extension,
# there is a set of requirements that SHALL be met as part of the definition of
# the extension.
StructField(
"extension",
ArrayType(
ExtensionSchema.get_schema(
max_nesting_depth=max_nesting_depth,
nesting_depth=nesting_depth + 1,
nesting_list=my_nesting_list,
max_recursion_limit=max_recursion_limit,
include_extension=include_extension,
extension_fields=extension_fields,
extension_depth=extension_depth,
max_extension_depth=max_extension_depth,
)
),
True,
),
# Human-readable name for the source system.
StructField("name", StringType(), True),
# May include configuration or other information useful in debugging.
StructField("software", StringType(), True),
# Can convey versions of multiple systems in situations where a message passes
# through multiple hands.
StructField("version", StringType(), True),
# An e-mail, phone, website or other contact point to use to resolve issues with
# message communications.
StructField(
"contact",
ContactPointSchema.get_schema(
max_nesting_depth=max_nesting_depth,
nesting_depth=nesting_depth + 1,
nesting_list=my_nesting_list,
max_recursion_limit=max_recursion_limit,
include_extension=include_extension,
extension_fields=extension_fields,
extension_depth=extension_depth + 1,
max_extension_depth=max_extension_depth,
),
True,
),
# Identifies the routing target to send acknowledgements to.
StructField("endpoint", StringType(), True),
]
)
if not include_extension:
schema.fields = [
c
if c.name != "extension"
else StructField("extension", StringType(), True)
for c in schema.fields
]
return schema
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scraping/spider/spiders/links_spider.py | Shakk17/WebsiteReader | 0 | 150094 | <gh_stars>0
import random
import scrapy
from scrapy import signals
from helpers.printer import green, magenta
from helpers.scraper import scrape_links
from helpers.utility import add_scheme, get_domain, get_time
class LinksSpider(scrapy.Spider):
name = "links"
visited_links = []
@classmethod
def from_crawler(cls, crawler, *args, **kwargs):
spider = super(LinksSpider, cls).from_crawler(crawler, *args, **kwargs)
crawler.signals.connect(spider.spider_closed, signal=signals.spider_closed)
# Get values passed as parameters.
settings = crawler.settings
url = settings.get('url')
# Get the domain from the url.
domain = get_domain(url)
spider.allowed_domains = [domain]
spider.start_urls = [url]
# Instantiate the class.
return spider
def __init__(self, **kwargs):
super().__init__(**kwargs)
def parse(self, response):
url = response.url
self.visited_links.append(url)
print(magenta(f"{get_time()} ({len(self.visited_links)}) Scraping {response.url}"))
page_links_items = scrape_links(html=response.body, url=url)
# Shuffle the links to improve variance.
random.shuffle(page_links_items)
# Save all the links in the database.
for page_link_item in page_links_items:
yield page_link_item
print(f"({get_domain(url)} - {len(self.visited_links)}) {len(page_links_items)} links saved.")
# Analyze each link found in the page.
for (i, page_link_item) in enumerate(page_links_items):
link_url = add_scheme(page_link_item["link_url"])
# If the link has not been visited yet, visit it.
if link_url not in self.visited_links and self.allowed_domains[0] in link_url:
yield response.follow(link_url, callback=self.parse)
def spider_closed(self, spider):
print(green(f"[CRAWLER] Crawling of {self.start_urls[0]} finished."))
spider.logger.info('Spider closed: %s', spider.name)
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src/examples/g_lists_and_tuples/main.py | acc-cosc-1336-spring-2022/acc-cosc-1336-spring-2022-rObErT-a93 | 0 | 49743 | <gh_stars>0
import lists
nums = [99,100,101,102]
lists.loop_list_w_for(nums)
print('----------')
for n in nums:
print(n)
print('*************')
num = [99,100,101,102]
lists.loop_list_w_while(num)
print('----------')
for n in num:
print(n)
print('**************')
lists.collect_home_values()
print('-----------------3/24 Lecture------------------------')
print()
list1 = ["C++", "C#", "Python", "Java"]
item1 = input('Enter item to find: ')
result = lists.find_items_in_lists(item1, list1)
if(result):
print(item1, 'in the list')
else:
print(item1, 'not in the list')
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] |
Lib/dos_8x3/cgihttps.py | AtjonTV/Python-1.4 | 0 | 143617 | """CGI-savvy HTTP Server.
This module builds on SimpleHTTPServer by implementing GET and POST
requests to cgi-bin scripts.
"""
__version__ = "0.3"
import os
import sys
import time
import socket
import string
import urllib
import BaseHTTPServer
import SimpleHTTPServer
class CGIHTTPRequestHandler(SimpleHTTPServer.SimpleHTTPRequestHandler):
"""Complete HTTP server with GET, HEAD and POST commands.
GET and HEAD also support running CGI scripts.
The POST command is *only* implemented for CGI scripts.
"""
def do_POST(self):
"""Serve a POST request.
This is only implemented for CGI scripts.
"""
if self.is_cgi():
self.run_cgi()
else:
self.send_error(501, "Can only POST to CGI scripts")
def send_head(self):
"""Version of send_head that support CGI scripts"""
if self.is_cgi():
return self.run_cgi()
else:
return SimpleHTTPServer.SimpleHTTPRequestHandler.send_head(self)
def is_cgi(self):
"""test whether PATH corresponds to a CGI script.
Return a tuple (dir, rest) if PATH requires running a
CGI script, None if not. Note that rest begins with a
slash if it is not empty.
The default implementation tests whether the path
begins with one of the strings in the list
self.cgi_directories (and the next character is a '/'
or the end of the string).
"""
path = self.path
for x in self.cgi_directories:
i = len(x)
if path[:i] == x and (not path[i:] or path[i] == '/'):
self.cgi_info = path[:i], path[i+1:]
return 1
return 0
cgi_directories = ['/cgi-bin', '/htbin']
def run_cgi(self):
"""Execute a CGI script."""
dir, rest = self.cgi_info
i = string.rfind(rest, '?')
if i >= 0:
rest, query = rest[:i], rest[i+1:]
else:
query = ''
i = string.find(rest, '/')
if i >= 0:
script, rest = rest[:i], rest[i:]
else:
script, rest = rest, ''
scriptname = dir + '/' + script
scriptfile = self.translate_path(scriptname)
if not os.path.exists(scriptfile):
self.send_error(404, "No such CGI script (%s)" % `scriptname`)
return
if not os.path.isfile(scriptfile):
self.send_error(403, "CGI script is not a plain file (%s)" %
`scriptname`)
return
if not executable(scriptfile):
self.send_error(403, "CGI script is not executable (%s)" %
`scriptname`)
return
nobody = nobody_uid()
self.send_response(200, "Script output follows")
self.wfile.flush() # Always flush before forking
pid = os.fork()
if pid != 0:
# Parent
pid, sts = os.waitpid(pid, 0)
if sts:
self.log_error("CGI script exit status x%x" % sts)
return
# Child
try:
# Reference: http://hoohoo.ncsa.uiuc.edu/cgi/env.html
# XXX Much of the following could be prepared ahead of time!
env = {}
env['SERVER_SOFTWARE'] = self.version_string()
env['SERVER_NAME'] = self.server.server_name
env['GATEWAY_INTERFACE'] = 'CGI/1.1'
env['SERVER_PROTOCOL'] = self.protocol_version
env['SERVER_PORT'] = str(self.server.server_port)
env['REQUEST_METHOD'] = self.command
uqrest = urllib.unquote(rest)
env['PATH_INFO'] = uqrest
env['PATH_TRANSLATED'] = self.translate_path(uqrest)
env['SCRIPT_NAME'] = scriptname
if query:
env['QUERY_STRING'] = query
host = self.address_string()
if host != self.client_address[0]:
env['REMOTE_HOST'] = host
env['REMOTE_ADDR'] = self.client_address[0]
# AUTH_TYPE
# REMOTE_USER
# REMOTE_IDENT
env['CONTENT_TYPE'] = self.headers.type
length = self.headers.getheader('content-length')
if length:
env['CONTENT_LENGTH'] = length
accept = []
for line in self.headers.getallmatchingheaders('accept'):
if line[:1] in string.whitespace:
accept.append(string.strip(line))
else:
accept = accept + string.split(line[7:])
env['HTTP_ACCEPT'] = string.joinfields(accept, ',')
ua = self.headers.getheader('user-agent')
if ua:
env['HTTP_USER_AGENT'] = ua
# XXX Other HTTP_* headers
import regsub
decoded_query = regsub.gsub('+', ' ', query)
try:
os.setuid(nobody)
except os.error:
pass
os.dup2(self.rfile.fileno(), 0)
os.dup2(self.wfile.fileno(), 1)
print scriptfile, script, decoded_query
os.execve(scriptfile,
[script, decoded_query],
env)
except:
self.server.handle_error(self.request, self.client_address)
os._exit(127)
nobody = None
def nobody_uid():
"""Internal routine to get nobody's uid"""
global nobody
if nobody:
return nobody
import pwd
try:
nobody = pwd.getpwnam('nobody')[2]
except pwd.error:
nobody = 1 + max(map(lambda x: x[2], pwd.getpwall()))
return nobody
def executable(path):
"""Test for executable file."""
try:
st = os.stat(path)
except os.error:
return 0
return st[0] & 0111 != 0
def test(HandlerClass = CGIHTTPRequestHandler,
ServerClass = BaseHTTPServer.HTTPServer):
import sys
if sys.argv[1:2] == ['-r']:
db = MyArchive()
db.regenindices()
return
SimpleHTTPServer.test(HandlerClass, ServerClass)
if __name__ == '__main__':
test()
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580,
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] |
step4e/aufrecht_transcode.py | funderburkjim/boesp-prep | 0 | 99323 | <filename>step4e/aufrecht_transcode.py
# coding=utf-8
""" aufrecht_transcode.py
"""
import sys, re,codecs
data="""
a1 ā
n2 ṇ
r2 ṛ
u7 ϋ
o7 ӧ
a7 ӓ
h2 ḥ
m2 ṃ
s2 ṣ
S4 Ś
s4 ś
u1 ū
n5 ñ
n3 ṅ
t2 ṭ
â ā
î ī
û ū
ç ś
Ç Ś
"""
class Rec(object):
def __init__(self,auf,uni):
self.auf = auf
self.uni = uni
def init_recs():
lines = data.splitlines()
recs = []
for line in lines:
line = line.strip()
parts = line.split(' ')
if len(parts) != 2:
continue
auf,uni = parts
recs.append(Rec(auf,uni))
return recs
def rec_to_entrylines(rec):
lines = []
lines.append('<entry>')
info = '<info L="%s" page="6.%s" gtypes="S"/>' %(rec.L2,rec.page)
lines.append(info)
lines.append('<S n="%s">' % rec.L2)
lines.append('<s></s>')
lines.append('<s></s>')
lines.append('<s></s>')
lines.append('<s></s>')
lines.append('</S>')
lines.append('<D n="%s">' % rec.L2)
lines.append('')
lines.append('</D>')
lines.append('<F n="%s">' % rec.L2)
lines.append('')
lines.append('</F>')
lines.append('</entry>')
return lines
def write(fileout,linesout):
with codecs.open(fileout,"w","utf-8") as f:
for line in linesout:
f.write(line+'\n')
print(len(linesout),"lines written to",fileout)
def transcode(line,recs):
for rec in recs:
line = line.replace(rec.auf,rec.uni)
return line
if __name__=="__main__":
filein = sys.argv[1]
fileout = sys.argv[2]
with codecs.open(filein,"r","utf-8") as f:
lines = [x.rstrip('\r\n') for x in f]
print(len(lines),"from",filein)
recs = init_recs()
print(len(recs),"transcode records")
newlines = []
for line in lines:
newline = transcode(line,recs)
newlines.append(newline)
write(fileout,newlines)
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] |
tests/sync_test_megasync.py | lfcontato/meganz | 0 | 85542 | """
Application for testing syncing algorithm
(c) 2013-2014 by Mega Limited, Wellsford, New Zealand
This file is part of the MEGA SDK - Client Access Engine.
Applications using the MEGA API must present a valid application key
and comply with the the rules set forth in the Terms of Service.
The MEGA SDK is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
@copyright Simplified (2-clause) BSD License.
You should have received a copy of the license along with this
program.
"""
import sys
import os
import time
import subprocess
from sync_test_base import get_random_str
from sync_test import SyncTest
from sync_test_app import SyncTestApp
import unittest
import xmlrunner
import logging
import argparse
import platform
import math
class SyncTestMegaSyncApp(SyncTestApp):
"""
operates with megasync application
"""
def __init__(self, work_dir, remote_folder, delete_tmp_files=True, use_large_files=True):
"""
work_dir: a temporary folder to place generated files
remote_folder: a remote folder to sync
"""
self.megasync_ch_in = None
self.megasync_ch_out = None
self.local_mount_in = os.path.join(work_dir, "sync_in")
self.local_mount_out = os.path.join(work_dir, "sync_out")
self.work_dir = os.path.join(work_dir, "tmp")
self.remote_folder = remote_folder
# init base class
super(SyncTestMegaSyncApp, self).__init__(self.local_mount_in, self.local_mount_out, self.work_dir, delete_tmp_files, use_large_files)
try:
os.makedirs(self.local_mount_in)
except OSError:
pass
try:
os.makedirs(self.local_mount_out)
except OSError:
pass
try:
os.makedirs(self.work_dir)
except OSError:
pass
if not os.access(self.local_mount_in, os.W_OK | os.X_OK):
raise Exception("Not enough permissions to create / write to directory")
if not os.access(self.local_mount_out, os.W_OK | os.X_OK):
raise Exception("Not enough permissions to create / write to directory")
if not os.access(self.work_dir, os.W_OK | os.X_OK):
raise Exception("Not enough permissions to create / write to directory")
def start_megasync(self, local_folder, type_str):
"""
fork and launch "megasimplesync" application
local_folder: local folder to sync
"""
# launch megasimplesync
base_path = os.path.join(os.path.dirname(__file__), '..')
# the app is either in examples/ or in the project's root
if platform.system() == "Windows":
app_name = "megasimplesync.exe"
else:
app_name = "megasimplesync"
bin_path = os.path.join(base_path, "examples")
tmp = os.path.join(bin_path, app_name)
if not os.path.isfile(tmp) or not os.access(tmp, os.X_OK):
bin_path = os.path.join(base_path, "")
tmp = os.path.join(bin_path, app_name)
if not os.path.isfile(tmp) or not os.access(tmp, os.X_OK):
raise Exception("megasimplesync application is not found!")
pargs = [os.path.join(bin_path, app_name), local_folder, self.remote_folder]
output_fname = os.path.join(self.work_dir, "megasimplesync" + "_" + type_str + "_" + get_random_str() + ".log")
output_log = open(output_fname, "w")
logging.info("Launching cmd: \"%s\", log: \"%s\"" % (" ".join(pargs), output_fname))
try:
ch = subprocess.Popen(pargs, universal_newlines=True, stdout=output_log, stderr=subprocess.STDOUT, bufsize=1, shell=False, env=os.environ)
except OSError, e:
logging.error("Failed to launch megasimplesync process: %s" % e)
return None
return ch
def sync(self):
"""
TODO: wait for full synchronization
"""
if not hasattr(self,'attempt'):
self.attempt=self.nr_retries
if (self.attempt==self.nr_retries):
time.sleep(5)
else:
time.sleep(math.pow(1.0181780986123312,self.attempt+1)*0.5) #~200*5 secs
def start(self):
"""
prepare and run tests
"""
if os.environ.get('MEGA_EMAIL') is None or os.environ.get('MEGA_PWD') is None:
logging.error("Environment variables MEGA_EMAIL and MEGA_PWD are not set !")
return False
# start "in" instance
self.megasync_ch_in = self.start_megasync(self.local_mount_in, "in")
# pause
time.sleep(5)
# start "out" instance
self.megasync_ch_out = self.start_megasync(self.local_mount_out, "out")
# check both instances
if self.megasync_ch_in is None or self.megasync_ch_out is None:
return False
return True
def finish(self):
"""
kill megasync instances, remove temp folders
"""
self.sync()
# kill instances
for _ in range(0, 5):
if self.megasync_ch_in:
self.megasync_ch_in.terminate()
self.megasync_ch_in.poll()
if (self.megasync_ch_in.poll() is not None):
if self.megasync_ch_in.returncode is not None: logging.debug("IN process terminated due to signal "+ str(self.megasync_ch_in.returncode))
break
logging.debug("waiting for IN process to end")
time.sleep(5)
else:
break
for _ in range(0, 5):
if self.megasync_ch_out:
self.megasync_ch_out.terminate()
if (self.megasync_ch_out.poll() is not None):
if self.megasync_ch_out.returncode is not None: logging.debug("OUT process terminated due to signal "+ str(self.megasync_ch_out.returncode))
break
logging.debug("waiting for OUT process to end")
time.sleep(5)
else:
break
def is_alive(self):
"""
return True if application instance is running
"""
if not self.megasync_ch_in or not self.megasync_ch_out:
return False
return self.megasync_ch_in.poll() is None and self.megasync_ch_out.poll() is None
def pause(self):
"""
pause application
"""
def unpause(self):
"""
unpause application
"""
if __name__ == "__main__":
parser = argparse.ArgumentParser(epilog="Please set MEGA_EMAIL and MEGA_PWD environment variables.")
parser.add_argument("--test1", help="test_create_delete_files", action="store_true")
parser.add_argument("--test2", help="test_create_rename_delete_files", action="store_true")
parser.add_argument("--test3", help="test_create_delete_dirs", action="store_true")
parser.add_argument("--test4", help="test_create_rename_delete_dirs", action="store_true")
parser.add_argument("--test5", help="test_sync_files_write", action="store_true")
parser.add_argument("--test6", help="test_local_operations", action="store_true")
parser.add_argument("--test7", help="test_update_mtime", action="store_true")
parser.add_argument("--test8", help="test_create_rename_delete_unicode_files_dirs", action="store_true")
parser.add_argument("-a", "--all", help="run all tests", action="store_true")
parser.add_argument("-b", "--basic", help="run basic, stable tests", action="store_true")
parser.add_argument("-d", "--debug", help="use debug output", action="store_true")
parser.add_argument("-l", "--large", help="use large files for testing", action="store_true")
parser.add_argument("-n", "--nodelete", help="Do not delete work files", action="store_false")
parser.add_argument("-f", "--files", type=int,help="Number of files")
parser.add_argument("-c", "--folders", type=int,help="Number of folders")
parser.add_argument("-g", "--changes", type=int,help="Number of changes")
parser.add_argument("-r", "--retries", type=int,help="Number of retries")
parser.add_argument("work_dir", help="local work directory")
parser.add_argument("sync_dir", help="remote directory for synchronization")
parser.add_argument("--only-empty-files", help="use only empty files", action="store_true")
args = parser.parse_args()
if args.debug:
lvl = logging.DEBUG
# megasync will use verbose output
os.environ["MEGA_DEBUG"] = "2"
else:
lvl = logging.INFO
if args.all:
args.test1 = args.test2 = args.test3 = args.test4 = args.test5 = args.test6 = args.test7 = args.test8 = True
if args.basic:
args.test1 = args.test2 = args.test3 = args.test4 = True
# logging stuff, output to stdout
logging.StreamHandler(sys.stdout)
logging.basicConfig(format='[%(asctime)s] %(message)s', datefmt='%Y-%m-%d %H:%M:%S', level=lvl)
with SyncTestMegaSyncApp(args.work_dir, args.sync_dir, args.nodelete, args.large) as app:
suite = unittest.TestSuite()
app.only_empty_files=args.only_empty_files
app.only_empty_folders=False
if args.retries is not None:
app.nr_retries=args.retries
synctests=[]
if args.test1:
synctests.append(SyncTest("test_create_delete_files", app))
if args.test2:
synctests.append(SyncTest("test_create_rename_delete_files", app))
if args.test3:
synctests.append(SyncTest("test_create_delete_dirs", app))
if args.test4:
synctests.append(SyncTest("test_create_rename_delete_dirs", app))
if args.test5:
synctests.append(SyncTest("test_sync_files_write", app))
if args.test6:
synctests.append(SyncTest("test_local_operations", app))
if args.test7:
synctests.append(SyncTest("test_update_mtime", app))
if args.test8:
synctests.append(SyncTest("test_create_rename_delete_unicode_files_dirs", app))
for st in synctests:
if args.files is not None:
st.nr_files=args.files
st.local_obj_nr=args.files
if args.folders is not None: st.nr_dirs=args.folders
if args.changes is not None:
st.nr_changes=args.changes
st.nr_time_changes=args.changes
if args.retries is not None:
st.nr_retries=args.retries
suite.addTest(st)
testRunner = xmlrunner.XMLTestRunner(output='test-reports')
testRunner.run(suite)
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] |
FM/deepFM.py | sameul-yuan/markdowns | 0 | 34396 | import os
import sys
import numpy as np
import pandas as pd
import tensorflow as tf
from losses import focal_loss,weighted_binary_crossentropy
from utils import Dataset
class DeepFM(object):
def __init__(self, params):
self.feature_size = params['feature_size']
self.field_size = params['field_size']
self.embedding_size = params['embedding_size']
self.deep_layers = params['deep_layers']
self.l2_reg_coef = params['l2_reg']
self.learning_rate = params['learning_rate']
self.pos_ratio = params['pos_ratio']
self.keep_prob_v = params['keep_prob']
self.activate = tf.nn.relu
self.weight = {}
self.saver=None
self.checkpoint_dir = params['checkpoint_dir']
self.build()
def build(self):
"""
feature_size: N
field_size: F
embedding_size: K
batch_size: None
"""
self.feat_index = tf.placeholder(tf.int32, shape=[None, None], name='feature_index')
self.feat_value = tf.placeholder(tf.float32, shape=[None, None], name='feature_value')
self.label = tf.placeholder(tf.float32, shape=[None,1], name='label')
self.keep_prob = tf.placeholder(tf.float32, shape=[], name='keep_prob') # scaler
self.is_training= tf.placeholder(tf.bool, shape=[],name='is_training')
#1、-------------------------定义权值-----------------------------------------
# FM部分中一次项的权值定义
self.weight['first_order'] = tf.Variable(tf.random_normal([self.feature_size, 1], 0.0, 0.05), # N * 1
name='first_order')
# One-hot编码后的输入层与Dense embeddings层的权值定义,即DNN的输入embedding。
self.weight['embedding_weight'] = tf.Variable(tf.random_normal([self.feature_size, self.embedding_size], 0.0, 0.05), # N*K
name='embedding_weight')
# deep网络部分的weight和bias, deep网络初始输入维度:input_size = F*K
num_layer = len(self.deep_layers)
input_size = self.field_size * self.embedding_size
# glorot_normal = np.sqrt(2.0 / (input_size + self.deep_layers[0])) # for sigmoid
he_normal = np.sqrt(2.0 /input_size) # for relu
self.weight['layer_0'] = tf.Variable(np.random.normal(loc=0, scale=he_normal, size=(input_size, self.deep_layers[0])), dtype=np.float32)
self.weight['bias_0'] = tf.Variable(np.random.normal(loc=0, scale=he_normal, size=(1, self.deep_layers[0])), dtype=np.float32)
# 生成deep network里面每层的weight 和 bias
for i in range(1, num_layer):
he_normal = np.sqrt(2.0 / (self.deep_layers[i - 1]))
self.weight['layer_' + str(i)] = tf.Variable(np.random.normal(loc=0, scale=he_normal, size=(self.deep_layers[i - 1], self.deep_layers[i])),
dtype=np.float32)
self.weight['bias_' + str(i)] = tf.Variable(np.random.normal(loc=0, scale=he_normal, size=(1, self.deep_layers[i])),dtype=np.float32)
# deep部分output_size + 一次项output_size + 二次项output_size
last_layer_size = self.deep_layers[-1] + self.field_size + self.embedding_size
glorot_normal = np.sqrt(2.0 / (last_layer_size + 1))
# 生成最后一层的weight和bias
self.weight['last_layer'] = tf.Variable(np.random.normal(loc=0, scale=glorot_normal, size=(last_layer_size, 1)), dtype=np.float32)
self.weight['last_bias'] = tf.Variable(tf.constant(0.0), dtype=np.float32)
#2、----------------------前向传播------------------------------------
# None*F*K
self.embedding_index = tf.nn.embedding_lookup(self.weight['embedding_weight'],self.feat_index)
# [None*F*K] .*[None*F*1] = None*F*K
self.embedding_part = tf.multiply(self.embedding_index, tf.reshape(self.feat_value, [-1, self.field_size, 1]))
# FM部分一阶特征
# None * F*1
self.embedding_first = tf.nn.embedding_lookup(self.weight['first_order'],
self.feat_index)
#[None*F*1].*[None*F*1] = None*F*1
self.embedding_first = tf.multiply(self.embedding_first, tf.reshape(self.feat_value, [-1, self.field_size, 1]))
# None*F
self.first_order = tf.reduce_sum(self.embedding_first, 2)
# 二阶特征 None*K
self.sum_second_order = tf.reduce_sum(self.embedding_part, 1)
self.sum_second_order_square = tf.square(self.sum_second_order)
self.square_second_order = tf.square(self.embedding_part)
self.square_second_order_sum = tf.reduce_sum(self.square_second_order, 1)
# 1/2*((a+b)^2 - a^2 - b^2)=ab
# None*K
self.second_order = 0.5 * tf.subtract(self.sum_second_order_square, self.square_second_order_sum)
# FM部分的输出 None*(F+K)
self.fm_part = tf.concat([self.first_order, self.second_order], axis=1)
# DNN部分
# None*(F*K)
self.deep_embedding = tf.reshape(self.embedding_part, [-1, self.field_size * self.embedding_size])
# 全连接部分
for i in range(0, len(self.deep_layers)):
self.deep_embedding = tf.add(tf.matmul(self.deep_embedding, self.weight["layer_%d" % i]),
self.weight["bias_%d" % i])
# self.deep_embedding =tf.matmul(self.deep_embedding, self.weight["layer_%d" % i])
self.bn_out = tf.layers.batch_normalization(self.deep_embedding, training=self.is_training)
# self.bn_out = tf.layers.dropout(self.deep_embedding, rate=self.keep_prob,training=self.is_training)
self.deep_embedding = self.activate(self.bn_out)
self.deep_embedding = tf.layers.dropout(self.deep_embedding, rate =1.0-self.keep_prob, training= self.is_training)
# FM输出与DNN输出拼接 None*(F+K+layer[-1]])
din_all = tf.concat([self.fm_part, self.deep_embedding], axis=1)
#None*1
self.out = tf.add(tf.matmul(din_all, self.weight['last_layer']), self.weight['last_bias'])
#3. ------------------确定损失---------------------------------------
# loss部分 None*1
self.prob = tf.nn.sigmoid(self.out)
# self.entropy_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels= self.label, logits= self.out))
# self.entropy_loss = -tf.reduce_mean(
# self.label * tf.log(tf.clip_by_value(self.prob, 1e-10, 1.0))+ (1 - self.label)* tf.log(tf.clip_by_value(1-self.prob,1e-10,1.0)))
self.entropy_loss = focal_loss(self.prob, self.label, alpha=0.5, gamma=2)
# self.entropy_loss = weighted_binary_crossentropy(self.prob, self.label, pos_ratio=self.pos_ratio)
# 正则:sum(w^2)/2*l2_reg_coef
self.reg_loss = tf.contrib.layers.l2_regularizer(self.l2_reg_coef)(self.weight["last_layer"])
for i in range(len(self.deep_layers)):
self.reg_loss += tf.contrib.layers.l2_regularizer(self.l2_reg_coef)(self.weight["layer_%d" % i])
# tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(self.l2_reg_coef)(self.weight['layer_1']))
# print(self.entropy_loss.shape.as_list(), self.reg_loss.shape.as_list())
self.loss = self.entropy_loss + self.reg_loss
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.learning_rate = tf.train.exponential_decay(self.learning_rate, self.global_step,3000, 0.99,staircase=False)
opt = tf.train.AdamOptimizer(self.learning_rate)
# opt = tf.train.GradientDescentOptimizer(self.learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
trainable_params = tf.trainable_variables()
gradients = tf.gradients(self.loss, trainable_params)
clip_gradients, _ = tf.clip_by_global_norm(gradients, 5)
with tf.control_dependencies(update_ops):
# self.train_op = opt.minimize(self.loss, global_step = self.global_step)
self.train_op = opt.apply_gradients(zip(clip_gradients, trainable_params), global_step=self.global_step)
self.saver = tf.train.Saver(max_to_keep=3)
def train(self, sess, feat_index, feat_value, label):
_, step = sess.run([self.train_op, self.global_step], feed_dict={
self.feat_index: feat_index,
self.feat_value: feat_value,
self.label: label,
self.keep_prob: self.keep_prob_v,
self.is_training:True})
return step
def predict(self, sess, feat_index, feat_value, batch_size=None):
if batch_size is None:
prob = sess.run([self.prob], feed_dict={
self.feat_index: feat_index,
self.feat_value: feat_value,
self.keep_prob: 1,
self.is_training:False})[0]
else:
data =Dataset(feat_value, feat_index, [None]*len(feat_index), batch_size, shuffle=False)
probs =[]
for feat_index, feat_value, _ in data:
prob = sess.run([self.prob], feed_dict={
self.feat_index: feat_index,
self.feat_value: feat_value,
self.keep_prob: 1,
self.is_training:False})[0]
probs.append(prob.ravel())
prob = np.concatenate(probs)
return prob.ravel()
def evaluate(self, sess, feat_index, feat_value, label, batch_size=None):
tloss, entloss,regloss = 0,0,0
if batch_size is None:
tloss, entloss,regloss = sess.run([self.loss, self.entropy_loss, self.reg_loss],feed_dict={
self.feat_index: feat_index,
self.feat_value: feat_value,
self.label: label,
self.keep_prob: 1,
self.is_training:False})
else:
data = Dataset(feat_value,feat_index,label, batch_size, shuffle=False)
for i, (feat_index, feat_value, label) in enumerate(data,1):
_tloss, _entloss, _regloss = sess.run([self.loss, self.entropy_loss, self.reg_loss],feed_dict={
self.feat_index: feat_index,
self.feat_value: feat_value,
self.label: label,
self.keep_prob: 1,
self.is_training:False})
tloss = tloss+ (_tloss-tloss)/i
entloss = entloss + (_entloss-entloss)/i
regloss = regloss + (_regloss-regloss)/i
return tloss, entloss, regloss
def save(self, sess, path, global_step):
if self.saver is not None:
self.saver.save(sess, save_path=path, global_step= global_step)
def restore(self, sess, path):
model_file = tf.train.latest_checkpoint(path)
if model_file is not None:
print('restore model:', model_file)
self.saver.restore(sess, save_path=model_file)
if __name__ == '__main__':
BASE_PATH = os.path.dirname(os.path.abspath(__file__))
params ={'feature_size':None,
'field_size':None,
'embedding_size':4,
'deep_layers':[32,32,32],
'epoch':200,
'batch_size':128,
'learning_rate':0.001,
'l2_reg': 0.001,
'keep_prob':0.7,
'checkpoint_dir':os.path.join(BASE_PATH,'data/deepfm'),
'training_model':True}
with tf.Session() as sess:
model = DeepFM(params)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer()) # global_step counter etc.
sys.stdout.flush()
if params['training_model']:
#---------------training---------------------------------
for i in range(params['epoch']):
print('epoch ={}'.format(i).center(50,'-'))
for j, (xi, xv, y) in enumerate(train_data):
loss,_, step = model.train(sess, xi, xv, y)
if j %1000 ==0:
train_loss,train_entropy,train_reg = model.evaluate(sess, Xi,Xv, Y)
val_loss,val_entropy, val_reg = model.evaluate(sess, val_Xi, val_Xv, val_y)
print('---batch= %d--- \n train_loss=%f,\t train_entropy=%f,\t train_reg=%f \n val_loss=%f,\t val_entropy=%f,\t val_reg=%f' % (
j,train_loss,train_entropy,train_reg, val_loss,val_entropy,val_reg))
if i%10 ==0 or i == params['epoch']-1:
model.save(sess, model.checkpoint_dir, i)
prob = model.predict(sess, Xi, Xv)
hit_rate, top_k = top_ratio_hit_rate(np.array(Y).ravel(), np.array(prob[0]).ravel(), top_ratio=0.001) # ravel return view, flatten return copy
print('top-k={}, train-hit-rate={}'.format(top_k ,hit_rate))
#-----------------test-----------------------------------
probs =[]
test_y=[]
for xi, xv, y in test_data:
prob = model.predict(sess, xi, xv) # list of np.ndarry
probs.extend(prob[0].ravel().tolist())
test_y.extend(y.tolist())
hit_rate, top_k = top_ratio_hit_rate(np.array(test_y).ravel(), np.array(probs).ravel(), top_ratio=0.001)
print('top-k={}, test-hit-rate={}'.format(top_k ,hit_rate))
calc_threshold_vs_depth(np.asarray(test_y).ravel(), np.asarray(probs).ravel())
else:
model.restore(sess, os.path.split(model.checkpoint_dir)[0])
probs=[]
Y =[]
for xi, xv, y in train_data:
prob = model.predict(sess, xi, xv) # np.ndarry
probs.extend(prob[0].ravel().tolist())
Y.extend(y.tolist())
hit_rate, top_k = top_ratio_hit_rate(np.array(Y).ravel(), np.array(probs).ravel(), top_ratio=0.001)
print('train-top-k={}, train-hit-rate={}'.format(top_k ,hit_rate))
probs=[]
test_y=[]
for xi, xv, y in test_data:
prob = model.predict(sess, xi, xv) # np.ndarry
probs.extend(prob[0].ravel().tolist())
test_y.extend(y.tolist())
hit_rate, top_k = top_ratio_hit_rate(np.array(test_y).ravel(), np.array(probs).ravel(), top_ratio=0.001)
print('test-top-k={}, test-hit-rate={}'.format(top_k ,hit_rate))
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frappe-bench/apps/erpnext/erpnext/shopping_cart/test_shopping_cart.py | Semicheche/foa_frappe_docker | 0 | 105988 | # Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors
# License: GNU General Public License v3. See license.txt
from __future__ import unicode_literals
import unittest
import frappe
from frappe.utils import nowdate, add_months
from erpnext.shopping_cart.cart import _get_cart_quotation, update_cart, get_party
from erpnext.tests.utils import create_test_contact_and_address
# test_dependencies = ['Payment Terms Template']
class TestShoppingCart(unittest.TestCase):
"""
Note:
Shopping Cart == Quotation
"""
def setUp(self):
frappe.set_user("Administrator")
create_test_contact_and_address()
self.enable_shopping_cart()
def tearDown(self):
frappe.set_user("Administrator")
self.disable_shopping_cart()
def test_get_cart_new_user(self):
self.login_as_new_user()
# test if lead is created and quotation with new lead is fetched
quotation = _get_cart_quotation()
self.assertEqual(quotation.quotation_to, "Customer")
self.assertEqual(quotation.contact_person,
frappe.db.get_value("Contact", dict(email_id="<EMAIL>")))
self.assertEqual(quotation.lead, None)
self.assertEqual(quotation.contact_email, frappe.session.user)
return quotation
def test_get_cart_customer(self):
self.login_as_customer()
# test if quotation with customer is fetched
quotation = _get_cart_quotation()
self.assertEqual(quotation.quotation_to, "Customer")
self.assertEqual(quotation.customer, "_Test Customer")
self.assertEqual(quotation.lead, None)
self.assertEqual(quotation.contact_email, frappe.session.user)
return quotation
def test_add_to_cart(self):
self.login_as_customer()
# remove from cart
self.remove_all_items_from_cart()
# add first item
update_cart("_Test Item", 1)
quotation = self.test_get_cart_customer()
self.assertEqual(quotation.get("items")[0].item_code, "_Test Item")
self.assertEqual(quotation.get("items")[0].qty, 1)
self.assertEqual(quotation.get("items")[0].amount, 10)
# add second item
update_cart("_Test Item 2", 1)
quotation = self.test_get_cart_customer()
self.assertEqual(quotation.get("items")[1].item_code, "_Test Item 2")
self.assertEqual(quotation.get("items")[1].qty, 1)
self.assertEqual(quotation.get("items")[1].amount, 20)
self.assertEqual(len(quotation.get("items")), 2)
def test_update_cart(self):
# first, add to cart
self.test_add_to_cart()
# update first item
update_cart("_Test Item", 5)
quotation = self.test_get_cart_customer()
self.assertEqual(quotation.get("items")[0].item_code, "_Test Item")
self.assertEqual(quotation.get("items")[0].qty, 5)
self.assertEqual(quotation.get("items")[0].amount, 50)
self.assertEqual(quotation.net_total, 70)
self.assertEqual(len(quotation.get("items")), 2)
def test_remove_from_cart(self):
# first, add to cart
self.test_add_to_cart()
# remove first item
update_cart("_Test Item", 0)
quotation = self.test_get_cart_customer()
self.assertEqual(quotation.get("items")[0].item_code, "_Test Item 2")
self.assertEqual(quotation.get("items")[0].qty, 1)
self.assertEqual(quotation.get("items")[0].amount, 20)
self.assertEqual(quotation.net_total, 20)
self.assertEqual(len(quotation.get("items")), 1)
def test_tax_rule(self):
self.login_as_customer()
quotation = self.create_quotation()
from erpnext.accounts.party import set_taxes
tax_rule_master = set_taxes(quotation.customer, "Customer", \
quotation.transaction_date, quotation.company, None, None, \
quotation.customer_address, quotation.shipping_address_name, 1)
self.assertEqual(quotation.taxes_and_charges, tax_rule_master)
self.assertEqual(quotation.total_taxes_and_charges, 1000.0)
self.remove_test_quotation(quotation)
def create_quotation(self):
quotation = frappe.new_doc("Quotation")
values = {
"doctype": "Quotation",
"quotation_to": "Customer",
"order_type": "Shopping Cart",
"customer": get_party(frappe.session.user).name,
"docstatus": 0,
"contact_email": frappe.session.user,
"selling_price_list": "_Test Price List Rest of the World",
"currency": "USD",
"taxes_and_charges" : "_Test Tax 1 - _TC",
"conversion_rate":1,
"transaction_date" : nowdate(),
"valid_till" : add_months(nowdate(), 1),
"items": [{
"item_code": "_Test Item",
"qty": 1
}],
"taxes": frappe.get_doc("Sales Taxes and Charges Template", "_Test Tax 1 - _TC").taxes,
"company": "_Test Company"
}
quotation.update(values)
quotation.insert(ignore_permissions=True)
return quotation
def remove_test_quotation(self, quotation):
frappe.set_user("Administrator")
quotation.delete()
# helper functions
def enable_shopping_cart(self):
settings = frappe.get_doc("Shopping Cart Settings", "Shopping Cart Settings")
settings.update({
"enabled": 1,
"company": "_Test Company",
"default_customer_group": "_Test Customer Group",
"quotation_series": "_T-Quotation-",
"price_list": "_Test Price List India"
})
# insert item price
if not frappe.db.get_value("Item Price", {"price_list": "_Test Price List India",
"item_code": "_Test Item"}):
frappe.get_doc({
"doctype": "Item Price",
"price_list": "_Test Price List India",
"item_code": "_Test Item",
"price_list_rate": 10
}).insert()
frappe.get_doc({
"doctype": "Item Price",
"price_list": "_Test Price List India",
"item_code": "_Test Item 2",
"price_list_rate": 20
}).insert()
settings.save()
frappe.local.shopping_cart_settings = None
def disable_shopping_cart(self):
settings = frappe.get_doc("Shopping Cart Settings", "Shopping Cart Settings")
settings.enabled = 0
settings.save()
frappe.local.shopping_cart_settings = None
def login_as_new_user(self):
self.create_user_if_not_exists("<EMAIL>")
frappe.set_user("<EMAIL>")
def login_as_customer(self):
self.create_user_if_not_exists("<EMAIL>",
"_Test Contact For _Test Customer")
frappe.set_user("<EMAIL>")
def remove_all_items_from_cart(self):
quotation = _get_cart_quotation()
quotation.flags.ignore_permissions=True
quotation.delete()
def create_user_if_not_exists(self, email, first_name = None):
if frappe.db.exists("User", email):
return
frappe.get_doc({
"doctype": "User",
"user_type": "Website User",
"email": email,
"send_welcome_email": 0,
"first_name": first_name or email.split("@")[0]
}).insert(ignore_permissions=True)
test_dependencies = ["Sales Taxes and Charges Template", "Price List", "Item Price", "Shipping Rule", "Currency Exchange",
"Customer Group", "Lead", "Customer", "Contact", "Address", "Item", "Tax Rule"]
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shellish/command/contrib/ini.py | personnelink/shellish | 4 | 29127 | <gh_stars>1-10
"""
Show the INI config(s) used by a command tree.
"""
from .. import command
class Show(command.Command):
""" Show current INI configuration.
Programs may make use of a configuration file which is usually located in
your $HOME directory as .<prog>_config. The file is a standard INI
style config file where each `[section]` is the full path of a command
including spaces. """
name = 'show'
def setup_args(self, parser):
self.add_argument('section', nargs='?', help='Only show config for '
'this section.')
self.add_argument('--all', '-a', action='store_true', help='Show '
'all sections')
super().setup_args(parser)
def run(self, args):
if args.section:
try:
config = {args.section: self.session.config[args.section]}
except KeyError:
raise SystemExit("Invalid section: %s" % args.section)
else:
config = self.session.config
for section, values in config.items():
if values or args.all:
print("[%s]" % section)
for k, v in values.items():
print(" %s = %s" % (k, v))
print()
class INI(command.Command):
""" INI style configuration.
Commands support user configuration in an INI style config file. """
name = 'ini'
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.add_subcommand(Show, default=True)
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usaspending_api/awards/migrations/0074_auto_20170320_1607.py | toolness/usaspending-api | 1 | 12855 | # -*- coding: utf-8 -*-
# Generated by Django 1.10.1 on 2017-03-20 16:07
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('awards', '0073_auto_20170320_1455'),
]
operations = [
migrations.AlterField(
model_name='award',
name='fain',
field=models.CharField(blank=True, db_index=True, help_text='An identification code assigned to each financial assistance award tracking purposes. The FAIN is tied to that award (and all future modifications to that award) throughout the award’s life. Each FAIN is assigned by an agency. Within an agency, FAIN are unique: each new award must be issued a new FAIN. FAIN stands for Federal Award Identification Number, though the digits are letters, not numbers.', max_length=30, null=True),
),
migrations.AlterField(
model_name='award',
name='period_of_performance_current_end_date',
field=models.DateField(db_index=True, help_text='The current, not original, period of performance end date', null=True, verbose_name='End Date'),
),
migrations.AlterField(
model_name='award',
name='period_of_performance_start_date',
field=models.DateField(db_index=True, help_text='The start date for the period of performance', null=True, verbose_name='Start Date'),
),
migrations.AlterField(
model_name='award',
name='piid',
field=models.CharField(blank=True, db_index=True, help_text='Procurement Instrument Identifier - A unique identifier assigned to a federal contract, purchase order, basic ordering agreement, basic agreement, and blanket purchase agreement. It is used to track the contract, and any modifications or transactions related to it. After October 2017, it is between 13 and 17 digits, both letters and numbers.', max_length=50, null=True),
),
migrations.AlterField(
model_name='award',
name='potential_total_value_of_award',
field=models.DecimalField(blank=True, db_index=True, decimal_places=2, help_text='The sum of the potential_value_of_award from associated transactions', max_digits=20, null=True, verbose_name='Potential Total Value of Award'),
),
migrations.AlterField(
model_name='award',
name='total_obligation',
field=models.DecimalField(db_index=True, decimal_places=2, help_text='The amount of money the government is obligated to pay for the award', max_digits=15, null=True, verbose_name='Total Obligated'),
),
migrations.AlterField(
model_name='award',
name='total_outlay',
field=models.DecimalField(db_index=True, decimal_places=2, help_text='The total amount of money paid out for this award', max_digits=15, null=True),
),
migrations.AlterField(
model_name='award',
name='type',
field=models.CharField(choices=[('U', 'Unknown Type'), ('02', 'Block Grant'), ('03', 'Formula Grant'), ('04', 'Project Grant'), ('05', 'Cooperative Agreement'), ('06', 'Direct Payment for Specified Use'), ('07', 'Direct Loan'), ('08', 'Guaranteed/Insured Loan'), ('09', 'Insurance'), ('10', 'Direct Payment unrestricted'), ('11', 'Other'), ('A', 'BPA Call'), ('B', 'Purchase Order'), ('C', 'Delivery Order'), ('D', 'Definitive Contract')], db_index=True, default='U', help_text='\tThe mechanism used to distribute funding. The federal government can distribute funding in several forms. These award types include contracts, grants, loans, and direct payments.', max_length=5, null=True, verbose_name='Award Type'),
),
migrations.AlterField(
model_name='award',
name='uri',
field=models.CharField(blank=True, db_index=True, help_text='The uri of the award', max_length=70, null=True),
),
migrations.AlterField(
model_name='transaction',
name='federal_action_obligation',
field=models.DecimalField(blank=True, db_index=True, decimal_places=2, help_text='The obligation of the federal government for this transaction', max_digits=20, null=True),
),
]
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] |
logtrace/test.py | metazet/logtrace | 9 | 59152 | # -*- coding: utf-8 -*-
import uuid
import unittest
import logging
from __init__ import LogTrace
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
s = [98, 127, 133, 147, 170, 197, 201, 211, 255]
class TestLogTrace(unittest.TestCase):
def test_logtrace_simple(self):
trace = LogTrace(logger, unique_id=True, verbosity='v')
trace.add("first message")
trace.add("second message")
print(trace.emit_string())
trace.emit("finally")
def test_logtrace_unique_id(self):
trace = LogTrace(logger=logger, unique_id=True, level=logging.INFO, verbosity='vvv')
trace.add("first message")
trace.add("second message")
print(trace.emit_string())
def test_function(self):
trace = LogTrace(logger=logger, level=logging.INFO)
standard_deviation(s, trace=trace)
print(trace.emit_string())
def test_function_tag(self):
trace = LogTrace(logger=logger, tag='STDDEV', level=logging.INFO, verbosity='vv')
standard_deviation(s, trace=trace)
print(trace.emit_string())
def test_logtrace_uuid(self):
trace = LogTrace(logger, unique_id=True, level=logging.INFO)
trace.add("first message")
trace.add("second message")
print(trace.get_uid())
trace.set_uid(uuid.uuid4())
trace.set_uid(str(uuid.uuid4())) # could be a string
trace.emit("finally")
def test_unicode(self):
trace = LogTrace(logger)
s = "ƀ Ɓ Ƃ ƃ Ƅ ƅ Ɔ Ƈ ƈ Ɖ Ɗ Ƌ ƌ ƍ Ǝ Ə Ɛ Ƒ ƒ Ɠ Ɣ ƕ Ɩ Ɨ Ƙ ƙ ƚ ƛ Ɯ Ɲ ƞ Ɵ Ơ ơ Ƣ ƣ Ƥ ƥ Ʀ Ƨ ƨ Ʃ ƪ ƫ Ƭ ƭ Ʈ Ư ư Ʊ Ʋ Ƴ ƴ Ƶ ƶ Ʒ Ƹ ƹ ƺ ƻ Ƽ ƽ ƾ ƿ ǀ ǁ ǂ ǃ DŽ Dž dž LJ Lj lj NJ Nj nj Ǎ ǎ Ǐ ǐ Ǒ ǒ Ǔ ǔ Ǖ ǖ Ǘ ǘ Ǚ ǚ Ǜ ǜ ǝ Ǟ ǟ Ǡ ǡ Ǣ ǣ Ǥ ǥ Ǧ ǧ Ǩ ǩ Ǫ ǫ Ǭ ǭ Ǯ ǯ ǰ DZ Dz dz Ǵ ǵ Ǻ ǻ Ǽ ǽ Ǿ ǿ Ȁ ȁ Ȃ ȃ"
trace.add(s)
print(trace.emit_string())
def test_extra(self):
trace = LogTrace(logger)
trace.emit('finish', extra={'key': 'value'})
def standard_deviation(lst, population=True, trace=None):
"""Calculates the standard deviation for a list of numbers.
Just for testing LogTrace().
"""
num_items = len(lst)
trace.add('num_items={}'.format(num_items))
mean = sum(lst) / num_items
trace.add('mean={}'.format(mean))
differences = [x - mean for x in lst]
sq_differences = [d ** 2 for d in differences]
ssd = sum(sq_differences)
trace.add('ssd={}'.format(ssd))
trace.add('population={}'.format(population))
if population:
variance = ssd / num_items
trace.add('variance={}'.format(variance))
return variance
else:
variance = ssd / (num_items - 1)
sd = sqrt(variance)
trace.add('sd={}'.format(sd))
return sd
if __name__ == '__main__':
unittest.main()
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] |
tests/common/test_run/triangle_run.py | KnowingNothing/akg-test | 0 | 11359 | # Copyright 2019 Huawei Technologies Co., Ltd
#
# 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 numpy as np
from tests.common.tensorio import compare_tensor
from tests.common.test_op import triangle
from akg.utils import kernel_exec as utils
from tests.common.gen_random import random_gaussian
def triangle_execute(shape, const_value, lower, dtype, attrs):
support_type = ['float16', 'float32']
assert dtype in support_type
assert len(shape) <= 2
if attrs is None:
attrs = {'enable_pre_poly_loop_partition': False}
attrs['enable_pre_poly_loop_partition'] = False
attrs['enable_post_poly_loop_partition'] = False
attrs['enable_convert_if'] = True
attrs['enable_double_buffer'] = False
output_shape = shape
if len(shape) == 1:
output_shape = [shape[0], shape[0]]
input, bench_mark = gen_data(shape, output_shape, const_value, lower, dtype)
op_attrs = [const_value, lower]
mod = triangle_compile(shape, dtype, op_attrs, attrs)
source_code = mod.imported_modules[0].get_source()
output = np.full(output_shape, np.nan, dtype)
output = utils.mod_launch(mod, (input, output), expect=bench_mark)
# compare result
compare_result = compare_tensor(output, bench_mark, rtol=5e-3, equal_nan=True)
return input, output, bench_mark, compare_result
def triangle_compile(shape, dtype, op_attrs, attrs):
return utils.op_build_test(triangle.triangle, [shape], [dtype], op_attrs, kernel_name='triangle', attrs=attrs)
def gen_data(shape, output_shape, const_value, lower, dtype):
input = random_gaussian(shape, miu=1, sigma=0.3).astype(dtype)
if len(shape) == 2:
bench_mark = input
else:
bench_mark = np.zeros(output_shape).astype(dtype)
for i in range(output_shape[0]):
bench_mark[i] = input
if lower:
for i in range(output_shape[0]):
bench_mark[i][i + 1:] = const_value
else:
for i in range(output_shape[0]):
bench_mark[i][:i] = const_value
return input, bench_mark
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] |
saucelab_api_client/base_classes/insights_api.py | Slamnlc/saucelab-api-client | 0 | 134578 | <filename>saucelab_api_client/base_classes/insights_api.py
from datetime import datetime
from saucelab_api_client.category import Base
from saucelab_api_client.models.insights import Insight
from saucelab_api_client.models.service import get_dict_from_locals, get_datetime_for_insights
class Insights(Base):
__sub_host = '/v1/analytics'
def test_results(self, start: datetime, end: datetime, scope=None, owner=None, status=None, build=None, from_=None,
max_results=None, missing_build=None, query=None, desc=None, error=None):
"""
https://docs.saucelabs.com/dev/api/insights/#get-test-results
Returns run data for all tests that match the request criteria
:param start: The starting date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param end: The ending date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param scope: Specifies the scope of the owner parameter
:param owner: The name of one or more users in the requestor's organization who executed the requested tests.
This parameter is required if the scope parameter is set to single.
:param status: Limit results to only those with a specified status
:param build: Limit results to those grouped by this build name
:param from_: Begin results list from this record number
:param max_results: The maximum number of results to return
:param missing_build: Requires no value. If this parameter is included in the query string,
results will only include tests with no assigned build
:param query: Limit results to only those with this test name
:param desc: Set to true to sort results in descending order by creation time. Default value is false
:param error: Limit results to only those that threw this error message
:return:
"""
start, end = get_datetime_for_insights(start, end)
params = get_dict_from_locals(locals())
return self._valid(self._session.request('get', f'{self.__sub_host}/tests', params=params), Insight, 'items')
def get_summary_of_test_metric(self, start: datetime, end: datetime, scope=None, owner=None, status=None,
query=None, os=None, browser=None):
"""
https://docs.saucelabs.com/dev/api/insights/#get-summary-of-test-metrics
Returns an aggregate of metric values for runs of a specified test during the specified time period
:param start: The starting date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param end: The ending date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param scope: Specifies the scope of the owner parameter
:param owner: The name of one or more users in the requestor's organization who executed the requested tests.
This parameter is required if the scope parameter is set to single.
:param status: Limit results to only those with a specified status
:param query: The name of the test for which results are requested
:param os: Limit results to only those run on the specified operating systems
:param browser: Limit results to only those run on the specified browsers
:return:
"""
start, end = get_datetime_for_insights(start, end)
params = get_dict_from_locals(locals())
return self._session.request('get', f'{self.__sub_host}/insights/test-metrics', params=params)
def get_test_trends(self, start: datetime, end: datetime, interval: str, scope=None, owner=None, status=None,
os=None, browser=None):
"""
https://docs.saucelabs.com/dev/api/insights/#get-test-trends
Returns a set of data "buckets" representing tests that were run in each time interval defined
by the request parameters
:param start: The starting date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param end: The ending date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param interval: The amount of time representing the boundary of each data bucket
:param scope: Specifies the scope of the owner parameter
:param owner: The name of one or more users in the requestor's organization who executed the requested tests.
This parameter is required if the scope parameter is set to single.
:param status: Limit results to only those with a specified status
:param os: Limit results to only those run on the specified operating systems
:param browser: Limit results to only those run on the specified browsers
:return:
"""
start, end = get_datetime_for_insights(start, end)
params = get_dict_from_locals(locals())
return self._session.request('get', f'{self.__sub_host}/trends/tests', params=params)
def get_builds_and_tests(self, start: datetime, end: datetime, scope=None, owner=None, status=None, os=None,
browser=None):
"""
https://docs.saucelabs.com/dev/api/insights/#get-builds-and-tests
Returns the set of all tests run within the specified time period, grouped by whether
each test was part of a build or not
:param start: The starting date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param end: The ending date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param scope: Specifies the scope of the owner parameter
:param owner: The name of one or more users in the requestor's organization who executed the requested tests.
This parameter is required if the scope parameter is set to single.
:param status: Limit results to only those with a specified status
:param os: Limit results to only those run on the specified operating systems
:param browser: Limit results to only those run on the specified browsers
:return:
"""
start, end = get_datetime_for_insights(start, end)
params = get_dict_from_locals(locals())
return self._session.request('get', f'{self.__sub_host}/trends/builds_tests', params=params)
def get_error_trends(self, start: datetime, end: datetime, scope=None, owner=None, status=None, os=None,
browser=None):
"""
https://docs.saucelabs.com/dev/api/insights/#get-error-trends
Returns an array of errors that occurred on all tests run within the specified time period.
:param start: The starting date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param end: The ending date of the period during which the test runs executed, in YYY-MM-DDTHH:MM:SSZ
or Unix time format.
:param scope: Specifies the scope of the owner parameter
:param owner: The name of one or more users in the requestor's organization who executed the requested tests.
This parameter is required if the scope parameter is set to single.
:param status: Limit results to only those with a specified status
:param os: Limit results to only those run on the specified operating systems
:param browser: Limit results to only those run on the specified browsers
:return:
"""
start, end = get_datetime_for_insights(start, end)
params = get_dict_from_locals(locals())
return self._session.request('get', f'{self.__sub_host}/trends/errors', params=params)
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] |
pyansys/rst.py | JackGuyver/pyansys | 0 | 78645 | <reponame>JackGuyver/pyansys
"""Read ANSYS binary result files (*.rst)
Used:
/usr/ansys_inc/v150/ansys/customize/include/fdresu.inc
"""
from collections.abc import Iterable
import time
import warnings
from threading import Thread
from functools import wraps
import vtk
import numpy as np
import pyvista as pv
from tqdm import tqdm
from pyansys import _binary_reader, _reader
from pyansys.mesh import Mesh
from pyansys._binary_reader import (cells_with_any_nodes, cells_with_all_nodes,
populate_surface_element_result)
from pyansys._rst_keys import (geometry_header_keys, element_index_table_info,
solution_data_header_keys, solution_header_keys_dp,
result_header_keys, boundary_condition_index_table,
DOF_REF)
from pyansys._mp_keys import mp_keys
from pyansys.common import (read_table, parse_header, AnsysBinary,
read_standard_header, rotate_to_global,
PRINCIPAL_STRESS_TYPES, STRESS_TYPES,
STRAIN_TYPES, THERMAL_STRAIN_TYPES)
from pyansys.misc import vtk_cell_info, break_apart_surface
from pyansys.rst_avail import AvailableResults
VTK9 = vtk.vtkVersion().GetVTKMajorVersion() >= 9
# # Create logger
# LOG = logging.getLogger(__name__)
# LOG.setLevel('DEBUG')
EMAIL_ME = """Please raise an issue at:
https://github.com/akaszynski/pyansys/issues
Or email the developer at <EMAIL>
"""
np.seterr(divide='ignore', invalid='ignore')
# Pointer information from ansys interface manual
# =============================================================================
# Individual element index table
ELEMENT_INDEX_TABLE_KEYS = ['EMS', 'ENF', 'ENS', 'ENG', 'EGR',
'EEL', 'EPL', 'ECR', 'ETH', 'EUL',
'EFX', 'ELF', 'EMN', 'ECD', 'ENL',
'EHC', 'EPT', 'ESF', 'EDI', 'ETB',
'ECT', 'EXY', 'EBA', 'ESV', 'MNL']
ELEMENT_RESULT_NCOMP = {'ENS': 6,
'EEL': 7,
'EPL': 7,
'ECR': 7,
'ETH': 8,
'ENL': 10,
'EDI': 7}
class Result(AnsysBinary):
"""Reads a binary ANSYS result file.
Parameters
----------
filename : str, optional
Filename of the ANSYS binary result file.
ignore_cyclic : bool, optional
Ignores any cyclic properties.
read_mesh : bool, optional
Debug parameter. Set to False to disable reading in the
mesh from the result file.
Examples
--------
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
"""
def __init__(self, filename, read_mesh=True, **kwargs):
"""Loads basic result information from result file and
initializes result object.
"""
self.filename = filename
self._resultheader = self._read_result_header()
self._animating = False
# Get the total number of results and log it
self.nsets = len(self._resultheader['rpointers'])
# LOG.debug('There are %d result(s) in this file', self.nsets)
# Get indices to resort nodal and element results
self._sidx = np.argsort(self._resultheader['neqv'])
self._sidx_elem = np.argsort(self._resultheader['eeqv'])
# Store node numbering in ANSYS
self._neqv = self._resultheader['neqv']
self._eeqv = self._resultheader['eeqv'] # unsorted
# cache geometry header
table = self.read_record(self._resultheader['ptrGEO'])
self._geometry_header = parse_header(table, geometry_header_keys)
self._element_table = self._load_element_table()
self._materials = None
self._section_data = None
self._available_results = AvailableResults(self._resultheader['AvailData'],
self._is_thermal)
# store mesh for later retrival
self._mesh = None
if read_mesh:
self._store_mesh()
@property
def mesh(self):
"""Mesh from result file
Examples
--------
>>> rst.mesh
ANSYS Mesh
Number of Nodes: 1448
Number of Elements: 226
Number of Element Types: 1
Number of Node Components: 0
Number of Element Components: 0
"""
if self._mesh is None:
raise ValueError('Pass ``read_mesh=True`` to store the mesh'
' when initializing the result')
return self._mesh
@property
def n_sector(self):
"""Number of sectors"""
return self._resultheader['nSector']
@property
def n_results(self):
"""Number of results"""
return len(self.time_values)
def _read_result_header(self):
"""Returns pointers used to access results from an ANSYS result file.
Parameters
----------
filename : string
Filename of result file.
Returns
-------
resultheader : dictionary
Result header
"""
# consider moving this to the main class
standard_header = read_standard_header(self.filename)
# Read .RST FILE HEADER
header = parse_header(self.read_record(103), result_header_keys)
resultheader = merge_two_dicts(header, standard_header)
# Read nodal equivalence table
resultheader['neqv'] = self.read_record(resultheader['ptrNOD'])
# Read nodal equivalence table
resultheader['eeqv'] = self.read_record(resultheader['ptrELM'])
# Read table of pointers to locations of results
nsets = resultheader['nsets']
# Data sets index table. This record contains the record pointers
# for the beginning of each data set. The first resmax records are
# the first 32 bits of the index, the second resmax records are
# the second 32 bits f.seek((ptrDSIl + 0) * 4)
record = self.read_record(resultheader['ptrDSI'])
raw0 = record[:resultheader['resmax']].tobytes()
raw1 = record[resultheader['resmax']:].tobytes()
# this combines the two ints, not that efficient
subraw0 = [raw0[i*4:(i+1)*4] for i in range(nsets)]
subraw1 = [raw1[i*4:(i+1)*4] for i in range(nsets)]
longraw = [subraw0[i] + subraw1[i] for i in range(nsets)]
longraw = b''.join(longraw)
rpointers = np.frombuffer(longraw, 'i8')
assert (rpointers >= 0).all(), 'Data set index table has negative pointers'
resultheader['rpointers'] = rpointers
# read in time values
record = self.read_record(resultheader['ptrTIM'])
resultheader['time_values'] = record[:resultheader['nsets']]
# load harmonic index of each result
if resultheader['ptrCYC']:
record = self.read_record(resultheader['ptrCYC'])
hindex = record[:resultheader['nsets']]
# ansys 15 doesn't track negative harmonic indices
if not np.any(hindex < -1):
# check if duplicate frequencies
tvalues = resultheader['time_values']
for i in range(tvalues.size - 1):
# adjust tolarance(?)
if np.isclose(tvalues[i], tvalues[i + 1]):
hindex[i + 1] *= -1
resultheader['hindex'] = hindex
# load step table with columns:
# [loadstep, substep, and cumulative]
record = self.read_record(resultheader['ptrLSP'])
resultheader['ls_table'] = record[:resultheader['nsets']*3].reshape(-1, 3)
return resultheader
def parse_coordinate_system(self):
"""Reads in coordinate system information from a binary result
file.
Returns
-------
c_systems : dict
Dictionary containing one entry for each defined coordinate
system. If no non-standard coordinate systems have been
defined, there will be only one None. First coordinate system
is assumed to be global cartesian.
Notes
-----
euler angles : [THXY, THYZ, THZX]
- First rotation about local Z (positive X toward Y).
- Second rotation about local X (positive Y toward Z).
- Third rotation about local Y (positive Z toward X).
PAR1
Used for elliptical, spheroidal, or toroidal systems. If KCS = 1
or 2, PAR1 is the ratio of the ellipse Y-axis radius to X-axis
radius (defaults to 1.0 (circle)). If KCS = 3, PAR1 is the major
radius of the torus.
PAR2
Used for spheroidal systems. If KCS = 2, PAR2 = ratio of ellipse
Z-axis radius to X-axis radius (defaults to 1.0 (circle)).
Coordinate system type:
- 0: Cartesian
- 1: Cylindrical (circular or elliptical)
- 2: Spherical (or spheroidal)
- 3: Toroidal
"""
# number of coordinate systems
maxcsy = self._geometry_header['maxcsy']
# load coordinate system index table
ptr_csy = self._geometry_header['ptrCSY']
if ptr_csy:
csy = self.read_record(ptr_csy)
# parse each coordinate system
# The items stored in each record:
# * Items 1-9 are the transformation matrix.
# * Items 10-12 are the coordinate system origin (XC,YC,ZC).
# * Items 13-14 are the coordinate system parameters (PAR1, PAR2).
# * Items 16-18 are the angles used to define the coordinate system.
# * Items 19-20 are theta and phi singularity keys.
# * Item 21 is the coordinate system type (0, 1, 2, or 3).
# * Item 22 is the coordinate system reference number.
c_systems = [None]
for i in range(maxcsy):
if not csy[i]:
c_system = None
else:
data = self.read_record(ptr_csy + csy[i])
c_system = {'transformation matrix': np.array(data[:9].reshape(-1, 3)),
'origin': np.array(data[9:12]),
'PAR1': data[12],
'PAR2': data[13],
'euler angles': data[15:18], # may not be euler
'theta singularity': data[18],
'phi singularity': data[19],
'type': int(data[20]),
'reference num': int(data[21],)
}
c_systems.append(c_system)
return c_systems
def _load_materials(self):
"""Reads the material properties from the result file.
Materials property table is stored at ptrMAT, which contains
pointers to each material property array for each material.
Each array contains a material property (e.g. EX, or elastic
modulus in the X direction) and there may be one or many
values for each material (up to 101).
"""
def read_mat_data(ptr):
"""reads double precision material data given an offset from ptrMAT"""
arr = self.read_record(self._geometry_header['ptrMAT'] + ptr)
arr = arr[arr != 0]
if arr.size == 1:
return arr[0]
mat_table = self.read_record(self._geometry_header['ptrMAT'])
self._materials = {}
for i in range(self._geometry_header['nummat']):
# pointers to the material data for each material
mat_data_ptr = mat_table[3 + 176*i:3 + 176*i + 159]
material = {}
for j, key in enumerate(mp_keys):
ptr = mat_data_ptr[j + 1]
if ptr:
material[key] = read_mat_data(ptr)
# store by material number
self._materials[mat_data_ptr[0]] = material
@property
def materials(self):
"""Result file material properties.
Returns
-------
materials : dict
Dictionary of Materials. Keys are the material numbers,
and each material is a dictionary of the material
properrties of that material with only the valid entries filled.
NOTES
-----
Material properties:
- EX : Elastic modulus, element x direction (Force/Area)
- EY : Elastic modulus, element y direction (Force/Area)
- EZ : Elastic modulus, element z direction (Force/Area)
- ALPX : Coefficient of thermal expansion, element x direction (Strain/Temp)
- ALPY : Coefficient of thermal expansion, element y direction (Strain/Temp)
- ALPZ : Coefficient of thermal expansion, element z direction (Strain/Temp)
- REFT : Reference temperature (as a property) [TREF]
- PRXY : Major Poisson's ratio, x-y plane
- PRYZ : Major Poisson's ratio, y-z plane
- PRX Z : Major Poisson's ratio, x-z plane
- NUXY : Minor Poisson's ratio, x-y plane
- NUYZ : Minor Poisson's ratio, y-z plane
- NUXZ : Minor Poisson's ratio, x-z plane
- GXY : Shear modulus, x-y plane (Force/Area)
- GYZ : Shear modulus, y-z plane (Force/Area)
- GXZ : Shear modulus, x-z plane (Force/Area)
- DAMP : K matrix multiplier for damping [BETAD] (Time)
- MU : Coefficient of friction (or, for FLUID29 and FLUID30
elements, boundary admittance)
- DENS : Mass density (Mass/Vol)
- C : Specific heat (Heat/Mass*Temp)
- ENTH : Enthalpy (e DENS*C d(Temp)) (Heat/Vol)
- KXX : Thermal conductivity, element x direction
(Heat*Length / (Time*Area*Temp))
- KYY : Thermal conductivity, element y direction
(Heat*Length / (Time*Area*Temp))
- KZZ : Thermal conductivity, element z direction
(Heat*Length / (Time*Area*Temp))
- HF : Convection (or film) coefficient (Heat / (Time*Area*Temp))
- EMIS : Emissivity
- QRATE : Heat generation rate (MASS71 element only) (Heat/Time)
- VISC : Viscosity (Force*Time / Length2)
- SONC : Sonic velocity (FLUID29 and FLUID30 elements only) (Length/Time)
- RSVX : Electrical resistivity, element x direction (Resistance*Area / Length)
- RSVY : Electrical resistivity, element y direction (Resistance*Area / Length)
- RSVZ : Electrical resistivity, element z direction (Resistance*Area / Length)
- PERX : Electric permittivity, element x direction (Charge2 / (Force*Length))
- PERY : Electric permittivity, element y direction (Charge2 / (Force*Length))
- PERZ : Electric permittivity, element z direction (Charge2 / (Force*Length))
- MURX : Magnetic relative permeability, element x direction
- MURY : Magnetic relative permeability, element y direction
- MURZ : Magnetic relative permeability, element z direction
- MGXX : Magnetic coercive force, element x direction (Charge / (Length*Time))
- MGYY : Magnetic coercive force, element y direction (Charge / (Length*Time))
- MGZZ : Magnetic coercive force, element z direction (Charge / (Length*Time))
"""
if self._materials is None:
self._load_materials()
return self._materials
def _load_section_data(self):
"""Loads the section data from the result file"""
ptr_sec = self._geometry_header['ptrSEC']
sec_table = self.read_record(ptr_sec)
self._section_data = {}
for offset in sec_table:
if offset:
table = self.read_record(ptr_sec + offset)
self._section_data[int(table[0])] = table[1:]
# it might be possible to interpert the section data...
# sec[3] # total thickness
# sec[4] # number of layers (?)
# sec[5] # total integration points (?)
# sec[29] # start of a layer (thickness)
# - MatID
# - Ori. Angle
# - Num Intg.
# sec[33] # start of another layer
# sec[37] # start of another layer
@property
def section_data(self):
"""The section data from the result file
Returns
-------
section_data : dict
Dictionary of the section data with the section numbers as
keys.
Notes
-----
There is limited documentation on how ANSYS stores the
sections within a result file, and as such it may be difficult
to interpret the section data for a given model.
"""
if self._section_data is None:
self._load_section_data()
return self._section_data
def plot(self, node_components=None, element_components=None,
sel_type_all=True, **kwargs):
"""Plot result geometry
Parameters
----------
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
**kwargs : keyword arguments
Optional keyword arguments. See help(pyvista.plot)
Returns
-------
cpos : list
List of camera position, focal point, and view up.
Examples
--------
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> rst.plot()
Plot just the element component 'ROTOR_SHAFT'
>>> rst.plot(element_components='ROTOR_SHAFT')
Plot two node components
>>> rst.plot(node_components=['MY_COMPONENT', 'MY_OTHER_COMPONENT'])
"""
kwargs.setdefault('show_edges', True)
if node_components:
grid, _ = self._extract_node_components(node_components, sel_type_all)
elif element_components:
grid, _ = self._extract_element_components(element_components)
else:
grid = self.grid
return self._plot_point_scalars(None, grid=grid, **kwargs)
def plot_nodal_solution(self, rnum, comp=None,
show_displacement=False,
displacement_factor=1.0,
node_components=None,
element_components=None,
sel_type_all=True,
**kwargs):
"""Plots the nodal solution.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
comp : str, optional
Display component to display. Options are ``'X'``,
``'Y'``, ``'Z'``, ``'NORM'``, or an available degree of
freedom. Result may also include other degrees of
freedom, check ``rst.result_dof`` for available degrees of
freedoms for a given result. Defaults to ``"NORM"`` for a
structural displacement result, and ``"TEMP"`` for a
thermal result.
show_displacement : bool, optional
Deforms mesh according to the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
Returns
-------
cpos : list
Camera position from vtk render window.
Examples
--------
Plot the nodal solution result 0 of verification manual
example
>>> import pyansys
>>> result = pyansys.download_verification_result(33)
>>> result.plot_nodal_solution(0)
Plot with a white background and showing edges
>>> result.plot_nodal_solution(0, background='w', show_edges=True)
"""
rnum = self.parse_step_substep(rnum)
nnum, result = self.nodal_solution(rnum)
dof = self.result_dof(rnum)
if self._is_thermal:
label = 'Temperature'
else:
label = 'Displacement'
if comp is None:
if self._is_thermal:
comp = 'TEMP'
else:
comp = 'NORM'
if not isinstance(comp, str):
raise TypeError('``comp`` must be a string')
comp = comp.upper()
# check component is in available degrees of freedom
if comp in ('X', 'Y', 'Z'):
comp = 'U' + comp
if comp == 'NORM':
if dof[:3] != ['UX', 'UY', 'UZ'] and dof[:2] != ['UX', 'UY']:
raise AttributeError('Unable to compute norm given the DOF(s) %s'
% str(dof))
# Normalize displacement
scalars = np.linalg.norm(result[:, :3], axis=1)
comp = 'Normalized'
else:
if comp not in dof:
raise ValueError('Component %s not in the available ' % comp +
'degrees of freedom:\n``%s``' % dof)
scalars = result[:, dof.index(comp)]
# sometimes there are less nodes in the result than in the geometry
npoints = self.grid.number_of_points
if nnum.size != npoints:
new_scalars = np.zeros(npoints)
nnum_grid = self.grid.point_arrays['ansys_node_num']
new_scalars[np.in1d(nnum_grid, nnum, assume_unique=True)] = scalars
scalars = new_scalars
ind = None
if node_components:
grid, ind = self._extract_node_components(node_components, sel_type_all)
scalars = scalars[ind]
elif element_components:
grid, ind = self._extract_element_components(element_components)
scalars = scalars[ind]
else:
grid = self.grid
kwargs.setdefault('stitle', '%s\n%s\n' % (comp, label))
return self._plot_point_scalars(scalars, rnum=rnum, grid=grid,
show_displacement=show_displacement,
displacement_factor=displacement_factor,
node_components=node_components,
element_components=element_components,
sel_type_all=sel_type_all,
**kwargs)
@wraps(plot_nodal_solution)
def plot_nodal_displacement(self, *args, **kwargs):
"""wraps plot_nodal_solution"""
if self._is_thermal:
raise AttributeError('Thermal solution does not contain nodal '
'displacement results')
return self.plot_nodal_solution(*args, **kwargs)
@property
def node_components(self):
"""Dictionary of ansys node components from the result file.
Examples
--------
>>> import pyansys
>>> from pyansys import examples
>>> rst = pyansys.read_binary(examples.rstfile)
>>> rst.node_components.keys()
dict_keys(['ECOMP1', 'ECOMP2', 'ELEM_COMP'])
>>> rst.node_components['NODE_COMP']
array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20], dtype=int32)
"""
return self._mesh.node_components
@property
def element_components(self):
"""Dictionary of ansys element components from the result file.
Examples
--------
>>> import pyansys
>>> from pyansys import examples
>>> rst = pyansys.read_binary(examples.rstfile)
>>> rst.element_components
{'ECOMP1': array([17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40], dtype=int32),
'ECOMP2': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 23, 24], dtype=int32),
'ELEM_COMP': array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20], dtype=int32)}
"""
return self._mesh.element_components
def _extract_node_components(self, node_components,
sel_type_all=True, grid=None):
"""Return the part of the grid matching node components """
if grid is None:
grid = self.grid
if not self._mesh.node_components: # pragma: no cover
raise Exception('Missing component information.\n' +
'Either no components have been stored, or ' +
'the version of this result file is <18.2')
if isinstance(node_components, str):
node_components = [node_components]
mask = np.zeros(grid.n_points, np.bool)
for component in node_components:
component = component.upper()
if component not in grid.point_arrays:
raise KeyError('Result file does not contain node ' +
'component "%s"' % component)
mask += grid.point_arrays[component].view(np.bool)
# need to extract the mesh
cells, offset = vtk_cell_info(grid)
if sel_type_all:
cell_mask = cells_with_all_nodes(offset, cells, grid.celltypes,
mask.view(np.uint8))
else:
cell_mask = cells_with_any_nodes(offset, cells, grid.celltypes,
mask.view(np.uint8))
if not cell_mask.any():
raise RuntimeError('Empty component')
reduced_grid = grid.extract_cells(cell_mask)
if not reduced_grid.n_cells:
raise RuntimeError('Empty mesh due to component selection\n' +
'Try "sel_type_all=False"')
ind = reduced_grid.point_arrays['vtkOriginalPointIds']
if not ind.any():
raise RuntimeError('Invalid selection.\n' +
'Try ``sel_type_all=False``')
return reduced_grid, ind
def _extract_element_components(self, element_components, grid=None):
"""Return the part of the grid matching element components"""
if grid is None:
grid = self.grid
if not self._mesh.element_components: # pragma: no cover
raise Exception('Missing component information.\n' +
'Either no components have been stored, or ' +
'the version of this result file is <18.2')
if isinstance(element_components, str):
element_components = [element_components]
cell_mask = np.zeros(grid.n_cells, np.bool)
for component in element_components:
component = component.upper()
if component not in grid.cell_arrays:
raise KeyError('Result file does not contain element ' +
'component "%s"' % component)
cell_mask += grid.cell_arrays[component].view(np.bool)
if not cell_mask.any():
raise RuntimeError('Empty component')
reduced_grid = grid.extract_cells(cell_mask)
ind = reduced_grid.point_arrays['vtkOriginalPointIds']
if not ind.any():
raise RuntimeError('Invalid component')
return reduced_grid, ind
@property
def time_values(self):
return self._resultheader['time_values']
def animate_nodal_solution(self, rnum, comp='norm',
node_components=None,
element_components=None,
sel_type_all=True, add_text=True,
displacement_factor=0.1, nangles=100,
loop=True, movie_filename=None,
**kwargs):
"""Animate nodal solution. Assumes nodal solution is a
displacement array from a modal solution.
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
comp : str, optional
Scalar component to display. Options are ``'x'``,
``'y'``, ``'z'``, and ``'norm'``, and ``None``.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
add_text : bool, optional
Adds information about the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
nangles : int, optional
Number of "frames" between each full cycle.
loop : bool, optional
Loop the animation. Default ``True``. Disable this to
animate once and close.
movie_filename : str, optional
Filename of the movie to open. Filename should end in
``'mp4'``, but other filetypes may be supported. See
``imagio.get_writer``. A single loop of the mode will be
recorded.
kwargs : optional keyword arguments, optional
See help(pyvista.Plot) for additional keyword arguments.
Examples
--------
Animate first result
>>> rst.animate_nodal_solution(0)
Animate second result while displaying the x scalars
without looping
>>> rst.animate_nodal_solution(1, comp='x', loop=False)
Animate second result and save as a movie
>>> rst.animate_nodal_solution(0, movie_filename='disp.mp4')
"""
scalars = None
if comp:
_, disp = self.nodal_solution(rnum)
disp = disp[:, :3]
if comp == 'x':
axis = 0
elif comp == 'y':
axis = 1
elif comp == 'z':
axis = 2
else:
axis = None
if axis is not None:
scalars = disp[:, axis]
else:
scalars = (disp*disp).sum(1)**0.5
if node_components:
grid, ind = self._extract_node_components(node_components,
sel_type_all)
if comp:
scalars = scalars[ind]
elif element_components:
grid, ind = self._extract_element_components(element_components)
if comp:
scalars = scalars[ind]
else:
grid = self.grid
return self._plot_point_scalars(scalars, rnum=rnum, grid=grid,
add_text=add_text,
animate=True,
node_components=node_components,
element_components=element_components,
sel_type_all=sel_type_all,
nangles=nangles,
displacement_factor=displacement_factor,
movie_filename=movie_filename,
loop=loop, **kwargs)
@wraps(animate_nodal_solution)
def animate_nodal_displacement(self, *args, **kwargs):
"""wraps animate_nodal_solution"""
return self.animate_nodal_solution(*args, **kwargs)
def animate_nodal_solution_set(self, rnums=None, comp='norm',
node_components=None,
element_components=None,
sel_type_all=True,
loop=True, movie_filename=None,
add_text=True,
fps=20,
**kwargs):
"""Animate a set of nodal solutions.
Animates the scalars of all the result sets. Best when used
with a series of static analyses.
rnums : collection.Iterable
Range or list containing the zero based indexed cumulative
result numbers to animate.
comp : str, optional
Scalar component to display. Options are ``'x'``,
``'y'``, ``'z'``, and ``'norm'``, and ``None``. Not
applicable for a thermal analysis.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
loop : bool, optional
Loop the animation. Default ``True``. Disable this to
animate once and close.
movie_filename : str, optional
Filename of the movie to open. Filename should end in
``'mp4'``, but other filetypes may be supported. See
``imagio.get_writer``. A single loop of the mode will be
recorded.
add_text : bool, optional
Adds information about the result to the animation.
fps : int, optional
Frames per second. Defaults to 20 and limited to hardware
capabilities and model density.
kwargs : optional keyword arguments, optional
See help(pyvista.Plot) for additional keyword arguments.
Examples
--------
Animate all results
>>> rst.animate_nodal_solution_set()
Animate every 50th result in a set of results and save to a
gif. Use the "zx" camera position to view the ZX plane from
the top down.
>>> rsets = range(0, rst.nsets, 50)
>>> rst.animate_nodal_solution_set(rsets, stitle='Temp', lighting=False,
cpos='zx', movie_filename='example.gif')
"""
if self.nsets == 1:
raise RuntimeError('Unable to animate. Solution contains only one '
'result set')
if rnums is None:
rnums = range(self.nsets)
elif not isinstance(rnums, Iterable):
raise TypeError('``rnums`` must be a range or list of result numbers')
def get_scalars(rnum):
_, data = self.nodal_solution(rnum)
if not self._is_thermal:
data = data[:, :3]
axis = None
if comp == 'x':
axis = 0
elif comp == 'y':
axis = 1
elif comp == 'z':
axis = 2
if axis is not None:
data = data[:, axis]
else:
data = (data*data).sum(1)**0.5
else:
data = data.ravel()
return data
ind = None
if node_components:
grid, ind = self._extract_node_components(node_components,
sel_type_all)
elif element_components:
grid, ind = self._extract_element_components(element_components)
else:
grid = self.grid
scalars = []
for rnum in tqdm(rnums, desc='Caching scalars'):
if ind is None:
scalars.append(get_scalars(rnum))
else:
scalars.append(get_scalars(rnum)[ind])
text = None
if add_text:
time_values = self.time_values
text = ['Time Value: %12.4f' % time_values[i] for i in rnums]
return self._animate_point_scalars(scalars, grid=grid,
text=text,
movie_filename=movie_filename,
loop=loop,
fps=fps,
**kwargs)
def nodal_time_history(self, solution_type='NSL', in_nodal_coord_sys=False):
"""The DOF solution for each node for all result sets.
The nodal results are returned returned in the global
cartesian coordinate system or nodal coordinate system.
Parameters
----------
solution_type: str, optional
The solution type. Must be either nodal displacements
(``'NSL'``), nodal velocities (``'VEL'``) or nodal
accelerations (``'ACC'``).
in_nodal_coord_sys : bool, optional
When ``True``, returns results in the nodal coordinate system.
Default ``False``.
Returns
-------
nnum : int np.ndarray
Node numbers associated with the results.
result : float np.ndarray
Nodal solution for all result sets. Array is sized
``rst.nsets x nnod x Sumdof``, which is the number of
time steps by number of nodes by degrees of freedom.
"""
if not isinstance(solution_type, str):
raise TypeError('Solution type must be a string')
if solution_type == 'NSL':
func = self.nodal_solution
elif solution_type == 'VEL':
func = self.nodal_velocity
elif solution_type == 'ACC':
func = self.nodal_acceleration
else:
raise ValueError("Argument 'solution type' must be either 'NSL', "
"'VEL', or 'ACC'")
# size based on the first result
nnum, sol = func(0)
data = np.empty((self.nsets, sol.shape[0], sol.shape[1]), np.float64)
for i in range(self.nsets):
data[i] = func(i)[1]
return nnum, data
def nodal_solution(self, rnum, in_nodal_coord_sys=False):
"""Returns the DOF solution for each node in the global
cartesian coordinate system or nodal coordinate system.
Solution may be nodal temperatures or nodal displacements
depending on the type of the solution.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
in_nodal_coord_sys : bool, optional
When ``True``, returns results in the nodal coordinate system.
Default ``False``.
Returns
-------
nnum : int np.ndarray
Node numbers associated with the results.
result : float np.ndarray
Array of nodal displacements or nodal temperatures. Array
is (``nnod`` x ``sumdof``), the number of nodes by the
number of degrees of freedom which includes ``numdof`` and
``nfldof``
Examples
--------
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, data = rst.nodal_solution(0)
Notes
-----
Some solution results may not include results for each node.
These results are removed by and the node numbers of the
solution results are reflected in ``nnum``.
"""
return self._nodal_solution_result(rnum, 'NSL', in_nodal_coord_sys)
def nodal_velocity(self, rnum, in_nodal_coord_sys=False):
"""Nodal velocities for a given result set.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
in_nodal_coord_sys : bool, optional
When ``True``, returns results in the nodal coordinate
system. Default False.
Returns
-------
nnum : int np.ndarray
Node numbers associated with the results.
result : float np.ndarray
Array of nodal velocities. Array is (``nnod`` x
``sumdof``), the number of nodes by the number of degrees
of freedom which includes ``numdof`` and ``nfldof``
Examples
--------
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, data = rst.nodal_velocity(0)
Notes
-----
Some solution results may not include results for each node.
These results are removed by and the node numbers of the
solution results are reflected in ``nnum``.
"""
return self._nodal_solution_result(rnum, 'VSL', in_nodal_coord_sys)
def nodal_acceleration(self, rnum, in_nodal_coord_sys=False):
"""Nodal velocities for a given result set.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
in_nodal_coord_sys : bool, optional
When ``True``, returns results in the nodal coordinate
system. Default False.
Returns
-------
nnum : int np.ndarray
Node numbers associated with the results.
result : float np.ndarray
Array of nodal accelerations. Array is (``nnod`` x
``sumdof``), the number of nodes by the number of degrees
of freedom which includes ``numdof`` and ``nfldof``
Examples
--------
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, data = rst.nodal_acceleration(0)
Notes
-----
Some solution results may not include results for each node.
These results are removed by and the node numbers of the
solution results are reflected in ``nnum``.
"""
return self._nodal_solution_result(rnum, 'ASL', in_nodal_coord_sys)
def _nodal_solution_result(self, rnum, solution_type, in_nodal_coord_sys=False):
"""Returns the DOF solution for each node in the global
cartesian coordinate system or nodal coordinate system.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
solution_type: str, optional
Specify, whether nodal displacements (``'NSL'``), nodal
velocities (``'VEL'``) or nodal accelerations (``'ACC'``)
will be read.
in_nodal_coord_sys : bool, optional
When True, returns results in the nodal coordinate system.
Default False.
Returns
-------
nnum : int np.ndarray
Node numbers associated with the results.
result : float np.ndarray
Result is (``nnod`` x ``sumdof``), or number of nodes by degrees
of freedom which includes ``numdof`` and ``nfldof``.
Notes
-----
Some solution results may not include results for each node.
These results are removed by and the node numbers of the
solution results are reflected in ``nnum``.
"""
if solution_type not in ('NSL', 'VSL', 'ASL'): # pragma: no cover
raise ValueError("Argument ``solution type`` must be either "
"'NSL', 'VSL', or 'ASL'")
# check if nodal solution exists
if not self.available_results[solution_type]:
raise AttributeError('Result file is missing "%s"' %
self.available_results.description[solution_type])
# result pointer
rnum = self.parse_step_substep(rnum) # convert to cumulative index
ptr_rst = self._resultheader['rpointers'][rnum]
result_solution_header = self._result_solution_header(rnum)
nnod = result_solution_header['nnod']
numdof = result_solution_header['numdof']
nfldof = result_solution_header['nfldof']
sumdof = numdof + nfldof
ptr = result_solution_header['ptr' + solution_type]
if ptr == 0: # shouldn't get here...
raise AttributeError('Result file is missing "%s"' %
self.available_results.description[solution_type])
# Read the nodal solution
result, bufsz = self.read_record(ptr + ptr_rst, True)
result = result.reshape(-1, sumdof)
# additional entries are sometimes written for no discernible
# reason
if result.shape[0] > nnod:
result = result[:nnod]
# it's possible that not all results are written
if result.shape[0] < nnod:
# read second buffer containing the node indices of the
# results and convert from fortran to zero indexing
sidx = self.read_record(ptr + ptr_rst + bufsz) - 1
unsort_nnum = self._resultheader['neqv'][sidx]
# now, sort using the new sorted node numbers indices
new_sidx = np.argsort(unsort_nnum)
nnum = unsort_nnum[new_sidx]
result = result[new_sidx]
else:
nnum = self._neqv[self._sidx]
result = result.take(self._sidx, 0)
# Convert result to the global coordinate system
if not in_nodal_coord_sys:
euler_angles = self._mesh.node_angles[self._insolution].T
rotate_to_global(result, euler_angles)
# check for invalid values (mapdl writes invalid values as 2*100)
result[result == 2**100] = 0
return nnum, result
@wraps(nodal_solution)
def nodal_displacement(self, *args, **kwargs):
"""wraps plot_nodal_solution"""
if self._is_thermal:
raise AttributeError('Thermal solution does not contain nodal '
'displacement results')
return self.nodal_solution(*args, **kwargs)
def _read_components(self):
"""Read components from an ANSYS result file
Returns
-------
components : dict
Dictionary of components
Notes
-----
Only available as of ~v194 and newer.
From fdresu.inc:
Component/assembly data. The first word will describe the
type of component or assembly (1=node component, 2=elem
component, 11->15 assembly) For components, values 2->9 are
the component name converted to integers. The remaining
values are the list of nodes/elems in the component. For
assemblies, the remaining values (in groups of 8) are the
component names converted to integers.
"""
node_comp = {}
elem_comp = {}
ncomp = self._geometry_header['maxcomp']
# Read through components
file_ptr = self._geometry_header['ptrCOMP']
for _ in range(ncomp):
table, sz = self.read_record(file_ptr, True)
file_ptr += sz # increment file_pointer
name = table[1:9].tobytes().split(b'\x00')[0].decode('latin')
name = name[:4][::-1] + name[4:8][::-1] + name[8:12][::-1] +\
name[12:16][::-1] + name[16:20][::-1] + name[20:24][::-1] +\
name[24:28][::-1] + name[28:32][::-1]
name = name.strip()
data = table[9:]
if data.any():
if table[0] == 1: # node component
node_comp[name] = _reader.component_interperter(data)
elif table[0] == 2:
elem_comp[name] = _reader.component_interperter(data)
return node_comp, elem_comp
def _load_element_table(self):
"""Store element type information"""
maxety = self._geometry_header['maxety'] # number of elemet types
# pointer to the element type index table
e_type_table = self.read_record(self._geometry_header['ptrETY'])
# store information for each element type
nodelm = np.empty(10000, np.int32) # n nodes for this element type
nodfor = np.empty(10000, np.int32) # n nodes per element having nodal forces
nodstr = np.empty(10000, np.int32) # n nodes per element having nodal stresses
ekey = []
keyopts = np.zeros((10000, 11), np.int16)
for i in range(maxety):
if not e_type_table[i]:
continue
ptr = self._geometry_header['ptrETY'] + e_type_table[i]
einfo = self.read_record(ptr)
etype_ref = einfo[0]
ekey.append(einfo[:2])
# Items 3-14 - element type option keys (keyopts)
keyopts[etype_ref] = einfo[2:13]
# Item 61 - number of nodes for this element type (nodelm)
nodelm[etype_ref] = einfo[60]
# Item 63 - number of nodes per element having nodal
# forces, etc. (nodfor)
nodfor[etype_ref] = einfo[62]
# Item 94 - number of nodes per element having nodal
# stresses, etc. (nodstr). This number is the number
# of corner nodes for higher-ordered elements.
nodstr[etype_ref] = einfo[93]
# with KEYOPT(8)=0, the record contains stresses at
# each corner node (first at the bottom shell surface,
# then the top surface)
#
# Only valid for SHELL181 or SHELL281 elements.
if einfo[1] == 181 or einfo[1] == 281:
if keyopts[etype_ref, 7] == 0:
nodstr[etype_ref] *= 2
# store element table data
return {'nodelm': nodelm,
'nodfor': nodfor,
'nodstr': nodstr,
'keyopts': keyopts,
'ekey': np.array(ekey)}
def _store_mesh(self):
"""Store the mesh from the result file"""
# Node information
nnod = self._geometry_header['nnod']
nnum = np.empty(nnod, np.int32)
nodes = np.empty((nnod, 6), np.float)
_binary_reader.load_nodes(self.filename, self._geometry_header['ptrLOC'],
nnod, nodes, nnum)
# the element description table
# must view this record as int64, even though ansys reads
# it in as a 32 bit int
ptr_eid = self._geometry_header['ptrEID']
e_disp_table = self.read_record(ptr_eid).view(np.int64)
# get pointer to start of element table and adjust element pointers
ptr_elem = self._geometry_header['ptrEID'] + e_disp_table[0]
e_disp_table -= e_disp_table[0]
# read in coordinate systems, material properties, and sections
self._c_systems = self.parse_coordinate_system()
# load elements
nelm = self._geometry_header['nelm']
elem, elem_off = _binary_reader.load_elements(self.filename, ptr_elem,
nelm, e_disp_table)
# Store geometry and parse to VTK quadradic and null unallowed
ncomp, ecomp = self._read_components()
self._mesh = Mesh(nnum, nodes, elem, elem_off,
self._element_table['ekey'],
node_comps=ncomp, elem_comps=ecomp)
self.quadgrid = self._mesh._parse_vtk(null_unallowed=True,
fix_midside=False)
self.grid = self.quadgrid.linear_copy()
# identify nodes that are actually in the solution
self._insolution = np.in1d(self._mesh.nnum, self._resultheader['neqv'],
assume_unique=True)
def solution_info(self, rnum):
"""Return an informative dictionary of solution data for a
result.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
header : dict
Double precision solution header data.
Notes
-----
The keys of the solution header are described below:
- timfrq : Time value (or frequency value, for a modal or
harmonic analysis)
- lfacto : the "old" load factor (used in ramping a load
between old and new values)
- lfactn : The "new" load factor
- cptime : Elapsed CPU time (in seconds)
- tref : The reference temperature
- tunif : The uniform temperature
- tbulk : Bulk temp for FLOTRAN film coefs.
- VolBase : Initial total volume for VOF
- tstep : Time Step size for FLOTRAN analysis
- 0.0 : Position not used
- accel : Linear acceleration terms
- omega : Angular velocity (first 3 terms) and angular acceleration
(second 3 terms)
- omegacg : Angular velocity (first 3 terms) and angular
acceleration (second 3 terms) these
velocity/acceleration terms are computed about the
center of gravity
- cgcent : (X,y,z) location of center of gravity
- fatjack : Fatjack ocean wave data (wave height and period)
- dval1 : If pmeth=0: FATJACK ocean wave direction
if pmeth=1: p-method convergence values
- pCnvVal : P-method convergence values
"""
# convert to cumulative index
rnum = self.parse_step_substep(rnum)
# skip pointers table
ptr = self._resultheader['rpointers'][rnum]
_, sz = self.read_record(ptr, True)
table = self.read_record(ptr + sz)
return parse_header(table, solution_header_keys_dp)
def _element_solution_header(self, rnum):
"""Return the element solution header information.
Parameters
----------
rnum : int
Result number.
Returns
-------
ele_ind_table :
A table of pointers relative to the element result table
offset containing element result pointers.
nodstr : np.ndarray
Array containing the number of nodes with values for each element.
etype : np.ndarray
Element type array
element_rst_ptr : int
Offset to the start of the element result table.
"""
# Get the header information from the header dictionary
rpointers = self._resultheader['rpointers']
nodstr = self._element_table['nodstr']
# read result solution header
solution_header = self._result_solution_header(rnum)
# key to extrapolate integration
# = 0 - move
# = 1 - extrapolate unless active
# non-linear
# = 2 - extrapolate always
if solution_header['rxtrap'] == 0:
warnings.warn('Strains and stresses are being evaluated at ' +
'gauss points and not extrapolated')
# 64-bit pointer to element solution
if not solution_header['ptrESL']:
raise ValueError('No element solution in result set %d\n'
% (rnum + 1) + 'Try running with "MXPAND,,,,YES"')
# Seek to element result header
element_rst_ptr = rpointers[rnum] + solution_header['ptrESL']
ele_ind_table = self.read_record(element_rst_ptr).view(np.int64)
# ele_ind_table += element_rst_ptr
return ele_ind_table, nodstr, self._mesh._ans_etype, element_rst_ptr
def result_dof(self, rnum):
"""Return a list of degrees of freedom for a given result number.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
dof : list
List of degrees of freedom.
Examples
--------
>>> rst.result_dof(0)
['UX', 'UY', 'UZ']
"""
rnum = self.parse_step_substep(rnum)
return [DOF_REF[dof_idx] for dof_idx in self._solution_header(rnum)['DOFS']]
def nodal_boundary_conditions(self, rnum):
"""Nodal boundary conditions for a given result number.
These nodal boundary conditions are generally set with the
APDL command ``D``. For example, ``D, 25, UX, 0.001``
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : np.ndarray
Node numbers of the nodes with boundary conditions.
dof : np.ndarray
Array of indices of the degrees of freedom of the nodes
with boundary conditions. See ``rst.result_dof`` for the
degrees of freedom associated with each index.
bc : np.ndarray
Boundary conditions.
Examples
--------
Print the boundary conditions where:
- Node 3 is fixed
- Node 25 has UX=0.001
- Node 26 has UY=0.0011
- Node 27 has UZ=0.0012
>>> rst.nodal_boundary_conditions(0)
(array([ 3, 3, 3, 25, 26, 27], dtype=int32),
array([1, 2, 3, 1, 2, 3], dtype=int32),
array([0. , 0. , 0. , 0.001 , 0.0011, 0.0012]))
"""
bc_ptr, bc_header = self._bc_header(rnum)
if not bc_header['ptrDIX']:
raise IndexError('This result contains no nodal input forces')
# nodes with boundary conditions
ptr_dix = bc_ptr + bc_header['ptrDIX']
bc_nnum, sz = self.read_record(ptr_dix, return_bufsize=True)
if bc_header['format']:
# there is a record immediately following containing the DOF data
dof = self.read_record(ptr_dix + sz)
else:
raise NotImplementedError('Format "0" is unhandled.\n%s' % EMAIL_ME)
# RECORD : DIS dp 1 4*numdis
# Nodal constraints. This record contains present and
# previous values (real and imaginary) of the nodal
# constraints at each DOF.
#
# Repeated 4 times for real/imag for present/prior
# numdis : number of nodal constraints
bc_record = self.read_record(bc_ptr + bc_header['ptrDIS'])
bc = bc_record.reshape(bc_nnum.size, -1)[:, 0] # use only present values
return bc_nnum, dof, bc
def nodal_input_force(self, rnum):
"""Nodal input force for a given result number.
Nodal input force is generally set with the APDL command
``F``. For example, ``F, 25, FX, 0.001``
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : np.ndarray
Node numbers of the nodes with nodal forces.
dof : np.ndarray
Array of indices of the degrees of freedom of the nodes
with input force. See ``rst.result_dof`` for the degrees
of freedom associated with each index.
force : np.ndarray
Nodal input force.
Examples
--------
Print the nodal input force where:
- Node 25 has FX=20
- Node 26 has FY=30
- Node 27 has FZ=40
>>> rst.nodal_input_force(0)
(array([ 71, 52, 127], dtype=int32),
array([2, 1, 3], dtype=int32),
array([30., 20., 40.]))
"""
bc_ptr, bc_header = self._bc_header(rnum)
if not bc_header['ptrFIX']:
raise IndexError('This result contains no nodal input forces')
# nodes with input force
ptr_fix = bc_ptr + bc_header['ptrFIX']
f_nnum, sz = self.read_record(ptr_fix, return_bufsize=True)
if bc_header['format']:
# there is a record immediately following containing the DOF data
dof = self.read_record(ptr_fix + sz)
else:
raise NotImplementedError('Format "0" is unhandled.\n%s' % EMAIL_ME)
# Repeated 4 times for real/imag for present/prior
# numdis : number of nodal constraints
f_record = self.read_record(bc_ptr + bc_header['ptrFOR'])
force = f_record.reshape(f_nnum.size, -1)[:, 0] # use only present values
return f_nnum, dof, force
def _bc_header(self, rnum):
"""Return the boundary conditions ptr and header for a given result number"""
rnum = self.parse_step_substep(rnum)
rptr = self._result_pointers[rnum]
# read bc_header
bc_ptr = rptr + self._solution_header(rnum)['ptrBC']
bc_header = parse_header(self.read_record(bc_ptr),
boundary_condition_index_table)
return bc_ptr, bc_header
def _solution_header(self, rnum):
"""The solution header for a given cumulative result"""
record = self.read_record(self._result_pointers[rnum])
return parse_header(record, solution_data_header_keys)
@property
def _result_pointers(self):
"""Array of result pointers (position within file)"""
return self._resultheader['rpointers']
@property
def version(self):
"""The version of MAPDL used to generate this result file.
Examples
--------
>>> rst.version
20.1
"""
return float(self._resultheader['verstring'])
def element_stress(self, rnum, principal=False, in_element_coord_sys=False,
**kwargs):
"""Retrives the element component stresses.
Equivalent ANSYS command: PRESOL, S
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
principal : bool, optional
Returns principal stresses instead of component stresses.
Default False.
in_element_coord_sys : bool, optional
Returns the results in the element coordinate system.
Default False and will return the results in the global
coordinate system.
**kwargs : optional keyword arguments
Hidden options for distributed result files.
Returns
-------
enum : np.ndarray
ANSYS element numbers corresponding to each element.
element_stress : list
Stresses at each element for each node for Sx Sy Sz Sxy
Syz Sxz or SIGMA1, SIGMA2, SIGMA3, SINT, SEQV when
principal is True.
enode : list
Node numbers corresponding to each element's stress
results. One list entry for each element.
Examples
--------
Element component stress for the first result set.
>>> rst.element_stress(0)
Element principal stress for the first result set.
>>> enum, element_stress, enode = result.element_stress(0, principal=True)
Notes
-----
Shell stresses for element 181 are returned for top and bottom
layers. Results are ordered such that the top layer and then
the bottom layer is reported.
"""
rnum = self.parse_step_substep(rnum)
ele_ind_table, nodstr, etype, ptr_off = self._element_solution_header(rnum)
# certain element types do not output stress
elemtype = self._mesh.etype
# load in raw results
nnode = nodstr[etype]
nelemnode = nnode.sum()
# bitmask (might use this at some point)
# bitmask = bin(int(hex(self._resultheader['rstsprs']), base=16)).lstrip('0b')
# description maybe in resucm.inc
if self.version >= 14.5:
if self._resultheader['rstsprs'] != 0:
nitem = 6
else:
nitem = 11
# add extra elements to data array. Sometimes there are
# more items than listed in the result header (or there's
# a mistake here)
ele_data_arr = np.empty((nelemnode + 50, nitem), np.float64)
ele_data_arr[:] = np.nan # necessary? should do this in read stress
_binary_reader.read_element_stress(self.filename,
ele_ind_table,
nodstr.astype(np.int64),
etype, ele_data_arr,
nitem, elemtype,
ptr_off,
as_global=not in_element_coord_sys)
if nitem != 6:
ele_data_arr = ele_data_arr[:, :6]
else:
raise NotImplementedError('Not implemented for ANSYS older than v14.5')
# trim off extra data
ele_data_arr = ele_data_arr[:nelemnode]
if principal:
ele_data_arr, isnan = _binary_reader.compute_principal_stress(ele_data_arr)
ele_data_arr[isnan] = np.nan
splitind = np.cumsum(nnode)
element_stress = np.split(ele_data_arr, splitind[:-1])
# just return the distributed result if requested
if kwargs.get('is_dist_rst', False):
return element_stress
# reorder list using sorted indices
enum = self._eeqv
sidx = np.argsort(enum)
element_stress = [element_stress[i] for i in sidx]
enode = []
for i in sidx:
enode.append(self._mesh.elem[i][10:10+nnode[i]])
# Get element numbers
elemnum = self._eeqv[self._sidx_elem]
return elemnum, element_stress, enode
def element_solution_data(self, rnum, datatype, sort=True, **kwargs):
"""Retrives element solution data. Similar to ETABLE.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
datatype : str
Element data type to retreive.
- EMS: misc. data
- ENF: nodal forces
- ENS: nodal stresses
- ENG: volume and energies
- EGR: nodal gradients
- EEL: elastic strains
- EPL: plastic strains
- ECR: creep strains
- ETH: thermal strains
- EUL: euler angles
- EFX: nodal fluxes
- ELF: local forces
- EMN: misc. non-sum values
- ECD: element current densities
- ENL: nodal nonlinear data
- EHC: calculated heat generations
- EPT: element temperatures
- ESF: element surface stresses
- EDI: diffusion strains
- ETB: ETABLE items
- ECT: contact data
- EXY: integration point locations
- EBA: back stresses
- ESV: state variables
- MNL: material nonlinear record
sort : bool
Sort results by element number. Default ``True``.
**kwargs : optional keyword arguments
Hidden options for distributed result files.
Returns
-------
enum : np.ndarray
Element numbers.
element_data : list
List with one data item for each element.
enode : list
Node numbers corresponding to each element.
results. One list entry for each element.
Notes
-----
See ANSYS element documentation for available items for each
element type. See:
https://www.mm.bme.hu/~gyebro/files/ans_help_v182/ans_elem/
Examples
--------
Retreive "LS" solution results from an PIPE59 element for result set 1
>>> enum, edata, enode = result.element_solution_data(0, datatype='ENS')
>>> enum[0] # first element number
>>> enode[0] # nodes belonging to element 1
>>> edata[0] # data belonging to element 1
array([ -4266.19 , -376.18857, -8161.785 , -64706.766 ,
-4266.19 , -376.18857, -8161.785 , -45754.594 ,
-4266.19 , -376.18857, -8161.785 , 0. ,
-4266.19 , -376.18857, -8161.785 , 45754.594 ,
-4266.19 , -376.18857, -8161.785 , 64706.766 ,
-4266.19 , -376.18857, -8161.785 , 45754.594 ,
-4266.19 , -376.18857, -8161.785 , 0. ,
-4266.19 , -376.18857, -8161.785 , -45754.594 ,
-4274.038 , -376.62527, -8171.2603 , 2202.7085 ,
-29566.24 , -376.62527, -8171.2603 , 1557.55 ,
-40042.613 , -376.62527, -8171.2603 , 0. ,
-29566.24 , -376.62527, -8171.2603 , -1557.55 ,
-4274.038 , -376.62527, -8171.2603 , -2202.7085 ,
21018.164 , -376.62527, -8171.2603 , -1557.55 ,
31494.537 , -376.62527, -8171.2603 , 0. ,
21018.164 , -376.62527, -8171.2603 , 1557.55 ],
dtype=float32)
This data corresponds to the results you would obtain directly
from MAPDL with ESOL commands:
>>> ansys.esol(nvar='2', elem=enum[0], node=enode[0][0], item='LS', comp=1)
>>> ansys.vget(par='SD_LOC1', ir='2', tstrt='1') # store in a variable
>>> ansys.read_float_parameter('SD_LOC1(1)')
-4266.19
"""
table_ptr = datatype.upper()
if table_ptr not in ELEMENT_INDEX_TABLE_KEYS:
err_str = 'Data type %s is invalid\n' % str(datatype)
err_str += '\nAvailable types:\n'
for key in ELEMENT_INDEX_TABLE_KEYS:
err_str += '\t%s: %s\n' % (key, element_index_table_info[key])
raise ValueError(err_str)
if table_ptr in self.available_results._parsed_bits:
if table_ptr not in self.available_results:
raise ValueError('Result %s is not available in this result file'
% table_ptr)
# location of data pointer within each element result table
table_index = ELEMENT_INDEX_TABLE_KEYS.index(table_ptr)
rnum = self.parse_step_substep(rnum)
ele_ind_table, nodstr, etype, ptr_off = self._element_solution_header(rnum)
# read element data
element_data = []
for ind in ele_ind_table:
if ind == 0:
element_data.append(None)
else:
# read element table index pointer to data
ptr = self.read_record(ind + ptr_off)[table_index]
if ptr > 0:
record = self.read_record(ptr_off + ind + ptr)
element_data.append(record)
else:
element_data.append(None)
# just return the distributed result if requested
if kwargs.get('is_dist_rst', False):
return element_data
enum = self._eeqv
if sort:
sidx = np.argsort(enum)
enum = enum[sidx]
element_data = [element_data[i] for i in sidx]
enode = []
nnode = nodstr[etype]
if sort:
for i in sidx:
enode.append(self._mesh.elem[i][10:10+nnode[i]])
else:
for i in range(enum.size):
enode.append(self._mesh.elem[i][10:10+nnode[i]])
return enum, element_data, enode
def principal_nodal_stress(self, rnum):
"""Computes the principal component stresses for each node in
the solution.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nodenum : numpy.ndarray
Node numbers of the result.
pstress : numpy.ndarray
Principal stresses, stress intensity, and equivalent stress.
[sigma1, sigma2, sigma3, sint, seqv]
Examples
--------
Load the principal nodal stress for the first solution.
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, stress = rst.principal_nodal_stress(0)
Notes
-----
ANSYS equivalent of:
PRNSOL, S, PRIN
which returns:
S1, S2, S3 principal stresses, SINT stress intensity, and SEQV
equivalent stress.
Internal averaging algorthim averages the component values
from the elements at a common node and then calculates the
principal using the averaged value.
See the MAPDL ``AVPRIN`` command for more details.
``pyansys`` uses the default ``AVPRIN, 0`` option.
"""
# get component stress
nodenum, stress = self.nodal_stress(rnum)
pstress, isnan = _binary_reader.compute_principal_stress(stress)
pstress[isnan] = np.nan
return nodenum, pstress
def plot_principal_nodal_stress(self, rnum, comp=None,
show_displacement=False,
displacement_factor=1.0,
node_components=None,
element_components=None,
sel_type_all=True, **kwargs):
"""Plot the principal stress.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
comp : string
Stress component to plot. S1, S2, S3 principal stresses, SINT
stress intensity, and SEQV equivalent stress.
Stress type must be a string from the following list:
``['S1', 'S2', 'S3', 'SINT', 'SEQV']``
show_displacement : bool, optional
Deforms mesh according to the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
kwargs : keyword arguments
Additional keyword arguments. See ``help(pyvista.plot)``
Returns
-------
cpos : list
VTK camera position.
Examples
--------
Plot the equivalent von mises stress
>>> rst.plot_principal_nodal_stress(0, comp='SEQV')
"""
# get the correct component of the principal stress
comp = comp.upper()
idx = check_comp(PRINCIPAL_STRESS_TYPES, comp)
stress = self.principal_nodal_stress(rnum)[1][:, idx]
if node_components:
grid, ind = self._extract_node_components(node_components, sel_type_all)
stress = stress[ind]
elif element_components:
grid, ind = self._extract_element_components(element_components)
stress = stress[ind]
else:
grid = self.grid
stype_stitle_map = {'S1': 'Principal Stress 1',
'S2': 'Principal Stress 2',
'S3': 'Principal Stress 3',
'SINT': 'Stress Intensity',
'SEQV': 'von Mises Stress'}
kwargs.setdefault('stitle', stype_stitle_map[comp])
return self._plot_point_scalars(stress, grid=grid, rnum=rnum,
show_displacement=show_displacement,
displacement_factor=displacement_factor,
**kwargs)
def cs_4x4(self, cs_cord, as_vtk_matrix=False):
""" return a 4x4 transformation array for a given coordinate system """
# assemble 4 x 4 matrix
csys = self._c_systems[cs_cord]
trans = np.hstack((csys['transformation matrix'],
csys['origin'].reshape(-1, 1)))
matrix = trans_to_matrix(trans)
if as_vtk_matrix:
return matrix
return pv.trans_from_matrix(matrix)
def _plot_point_scalars(self, scalars, rnum=None, grid=None,
show_displacement=False, displacement_factor=1,
add_text=True, animate=False, nangles=100,
overlay_wireframe=False, node_components=None,
element_components=None,
sel_type_all=True, movie_filename=None,
**kwargs):
"""Plot point scalars on active mesh.
Parameters
----------
scalars : np.ndarray
Node scalars to plot.
rnum : int, optional
Cumulative result number. Used for adding informative
text.
grid : pyvista.PolyData or pyvista.UnstructuredGrid, optional
Uses self.grid by default. When specified, uses this grid
instead.
show_displacement : bool, optional
Deforms mesh according to the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
add_text : bool, optional
Adds information about the result when rnum is given.
overlay_wireframe : bool, optional
Overlay a wireframe of the original undeformed mesh.
kwargs : keyword arguments
Additional keyword arguments. See ``help(pyvista.plot)``
Returns
-------
cpos : list
Camera position.
"""
if rnum:
rnum = self.parse_step_substep(rnum)
loop = kwargs.pop('loop', False)
if grid is None:
grid = self.grid
disp = None
if show_displacement and not animate:
disp = self.nodal_solution(rnum)[1][:, :3]*displacement_factor
if node_components:
_, ind = self._extract_node_components(node_components, sel_type_all)
disp = disp[ind]
elif element_components:
_, ind = self._extract_element_components(element_components)
disp = disp[ind]
if disp.shape[1] == 2: # ignore Z
new_points = grid.points.copy()
new_points[:, :-1] += disp
else:
new_points = disp + grid.points
grid = grid.copy()
grid.points = new_points
elif animate:
disp = self.nodal_solution(rnum)[1][:, :3]*displacement_factor
# extract mesh surface
mapped_indices = None
if 'vtkOriginalPointIds' in grid.point_arrays:
mapped_indices = grid.point_arrays['vtkOriginalPointIds']
# weird bug in some edge cases, have to roll our own indices here
if '_temp_id' not in grid.point_arrays:
grid.point_arrays['_temp_id'] = np.arange(grid.n_points)
mesh = grid.extract_surface()
ind = mesh.point_arrays['_temp_id']
if disp is not None:
if mapped_indices is not None:
disp = disp[mapped_indices][ind]
else:
disp = disp[ind]
if scalars is not None:
if scalars.ndim == 2:
scalars = scalars[:, ind]
else:
scalars = scalars[ind]
if animate:
smax = np.abs(scalars).max()
rng = kwargs.pop('rng', [-smax, smax])
else:
rng = kwargs.pop('rng', [scalars.min(), scalars.max()])
else:
rng = kwargs.pop('rng', None)
cmap = kwargs.pop('cmap', 'jet')
window_size = kwargs.pop('window_size', pv.rcParams['window_size'])
full_screen = kwargs.pop('full_screen', pv.rcParams.get('full_screen', None))
notebook = kwargs.pop('notebook', pv.rcParams.get('notebook', None))
off_screen = kwargs.pop('off_screen', pv.OFF_SCREEN)
cpos = kwargs.pop('cpos', None)
screenshot = kwargs.pop('screenshot', None)
interactive = kwargs.pop('interactive', True)
text_color = kwargs.pop('text_color', None)
use_ipyvtk = kwargs.pop('use_ipyvtk', pv.rcParams['use_ipyvtk'])
kwargs.setdefault('smooth_shading', pv.rcParams.get('smooth_shading', True))
kwargs.setdefault('color', 'w')
kwargs.setdefault('interpolate_before_map', True)
plotter = pv.Plotter(off_screen=off_screen, notebook=notebook)
# set axes
if kwargs.pop('show_axes', True):
plotter.add_axes()
# set background
plotter.background_color = kwargs.pop('background', None)
# remove extra keyword args
kwargs.pop('node_components', None)
kwargs.pop('sel_type_all', None)
if overlay_wireframe:
plotter.add_mesh(self.grid, style='wireframe', color='w',
opacity=0.5)
copied_mesh = mesh.copy()
plotter.add_mesh(copied_mesh,
scalars=scalars,
rng=rng,
cmap=cmap,
**kwargs)
# set scalar bar text colors
if text_color:
from pyvista.plotting.theme import parse_color
text_color = parse_color(text_color)
plotter.scalar_bar.GetLabelTextProperty().SetColor(text_color)
plotter.scalar_bar.GetAnnotationTextProperty().SetColor(text_color)
plotter.scalar_bar.GetTitleTextProperty().SetColor(text_color)
# NAN/missing data are white
# plotter.renderers[0].SetUseDepthPeeling(1) # <-- for transparency issues
plotter.mapper.GetLookupTable().SetNanColor(1, 1, 1, 1)
if cpos:
plotter.camera_position = cpos
if movie_filename:
if movie_filename.strip()[-3:] == 'gif':
plotter.open_gif(movie_filename)
else:
plotter.open_movie(movie_filename)
# add table
if add_text and rnum is not None:
result_text = self.text_result_table(rnum)
plotter.add_text(result_text, font_size=20, color=text_color)
if animate:
orig_pts = copied_mesh.points.copy()
plotter.show(interactive=False, auto_close=False,
window_size=window_size,
full_screen=full_screen,
interactive_update=not off_screen,
use_ipyvtk=use_ipyvtk)
self._animating = True
def q_callback():
"""exit when user wants to leave"""
self._animating = False
plotter.add_key_event("q", q_callback)
first_loop = True
cached_normals = [None for _ in range(nangles)]
while self._animating:
for j, angle in enumerate(np.linspace(0, np.pi*2, nangles + 1)[:-1]):
mag_adj = np.sin(angle)
if scalars is not None:
copied_mesh.active_scalars[:] = scalars*mag_adj
copied_mesh.points[:] = orig_pts + disp*mag_adj
# normals have to be updated on the fly
if kwargs['smooth_shading']:
if cached_normals[j] is None:
copied_mesh.compute_normals(cell_normals=False,
inplace=True)
cached_normals[j] = copied_mesh.point_arrays['Normals']
else:
copied_mesh.point_arrays['Normals'][:] = cached_normals[j]
if add_text:
# 2 maps to vtk.vtkCornerAnnotation.UpperLeft
plotter.textActor.SetText(2, '%s\nPhase %.1f Degrees' %
(result_text, (angle*180/np.pi)))
# at max supported framerate
plotter.update(1, force_redraw=True)
if not self._animating:
break
if movie_filename and first_loop:
plotter.write_frame()
first_loop = False
if not loop:
break
plotter.close()
cpos = plotter.camera_position
elif screenshot:
cpos = plotter.show(auto_close=False, interactive=interactive,
window_size=window_size,
full_screen=full_screen)
if screenshot is True:
img = plotter.screenshot()
else:
plotter.screenshot(screenshot)
plotter.close()
else:
cpos = plotter.show(interactive=interactive,
window_size=window_size,
full_screen=full_screen)
if screenshot is True:
return cpos, img
return cpos
def _animate_point_scalars(self, scalars, grid=None,
text=None,
movie_filename=None,
loop=True,
fps=20,
**kwargs):
"""Animate a set of point scalars on active mesh.
Parameters
----------
scalars : List
List of node scalars to plot. One set of values for each frame.
grid : pyvista.PolyData or pyvista.UnstructuredGrid, optional
Uses self.grid by default. When specified, uses this grid
instead.
text : List, optional
One string for each value in ``scalars``.
kwargs : keyword arguments
Additional keyword arguments. See ``help(pyvista.plot)``
Returns
-------
cpos : list
Camera position.
"""
# if rnum:
# rnum = self.parse_step_substep(rnum)
if grid is None:
grid = self.grid
# disp = None
# if show_displacement and not animate:
# disp = self.nodal_solution(rnum)[1][:, :3]*displacement_factor
# if node_components:
# _, ind = self._extract_node_components(node_components, sel_type_all)
# disp = disp[ind]
# elif element_components:
# _, ind = self._extract_element_components(element_components)
# disp = disp[ind]
# new_points = disp + grid.points
# grid = grid.copy()
# grid.points = new_points
# elif animate:
# disp = self.nodal_solution(rnum)[1][:, :3]*displacement_factor
# extract mesh surface
# mapped_indices = None
# if 'vtkOriginalPointIds' in grid.point_arrays:
# mapped_indices = grid.point_arrays['vtkOriginalPointIds']
# weird bug in some edge cases, have to roll our own indices here
if '_temp_id' not in grid.point_arrays:
grid['_temp_id'] = np.arange(grid.n_points)
mesh = grid.extract_surface()
ind = mesh.point_arrays['_temp_id']
# if disp is not None:
# if mapped_indices is not None:
# disp = disp[mapped_indices][ind]
# else:
# disp = disp[ind]
# remap scalars
for i in range(len(scalars)):
scalars[i] = scalars[i][ind]
rng = kwargs.pop('rng', [np.min(scalars), np.max(scalars)])
cmap = kwargs.pop('cmap', 'jet')
window_size = kwargs.pop('window_size', [1024, 768])
full_screen = kwargs.pop('full_screen', False)
notebook = kwargs.pop('notebook', False)
off_screen = kwargs.pop('off_screen', None)
cpos = kwargs.pop('cpos', None)
# screenshot = kwargs.pop('screenshot', None)
# interactive = kwargs.pop('interactive', True)
text_color = kwargs.pop('text_color', None)
kwargs.setdefault('smooth_shading', True)
kwargs.setdefault('color', 'w')
kwargs.setdefault('interpolate_before_map', True)
plotter = pv.Plotter(off_screen=off_screen, notebook=notebook)
# set axes
if kwargs.pop('show_axes', True):
plotter.add_axes()
# set background
plotter.background_color = kwargs.pop('background', None)
# remove extra keyword args
kwargs.pop('node_components', None)
kwargs.pop('sel_type_all', None)
# if overlay_wireframe:
# plotter.add_mesh(self.grid, style='wireframe', color='w',
# opacity=0.5)
copied_mesh = mesh.copy()
plotter.add_mesh(copied_mesh,
scalars=scalars[0],
rng=rng,
cmap=cmap,
**kwargs)
# set scalar bar text colors
if text_color:
from pyvista.plotting.theme import parse_color
text_color = parse_color(text_color)
plotter.scalar_bar.GetLabelTextProperty().SetColor(text_color)
plotter.scalar_bar.GetAnnotationTextProperty().SetColor(text_color)
plotter.scalar_bar.GetTitleTextProperty().SetColor(text_color)
# NAN/missing data are white
# plotter.renderers[0].SetUseDepthPeeling(1) # <-- for transparency issues
plotter.mapper.GetLookupTable().SetNanColor(1, 1, 1, 1)
if cpos:
plotter.camera_position = cpos
if movie_filename:
if movie_filename.strip()[-3:] == 'gif':
plotter.open_gif(movie_filename)
else:
plotter.open_movie(movie_filename)
# add table
if text is not None:
if len(text) != len(scalars):
raise ValueError('Length of ``text`` must be the same as '
'``scalars``')
plotter.add_text(text[0], font_size=20, color=text_color)
# orig_pts = copied_mesh.points.copy()
plotter.show(interactive=False, auto_close=False,
window_size=window_size,
full_screen=full_screen,
interactive_update=not off_screen)
self._animating = True
def q_callback():
"""exit when user wants to leave"""
self._animating = False
plotter.add_key_event("q", q_callback)
if off_screen:
twait = 0
else:
twait = 1/fps
first_loop = True
while self._animating:
for i, data in enumerate(scalars):
tstart_render = time.time()
copied_mesh.active_scalars[:] = data
if text is not None:
# 2 maps to vtk.vtkCornerAnnotation.UpperLeft
plotter.textActor.SetText(2, text[i])
# at max supported framerate
plotter.update(1, force_redraw=True)
if not self._animating:
break
if movie_filename and first_loop:
plotter.write_frame()
# wait to maintain desired frame rate
telap = time.time() - tstart_render
if telap < twait:
time.sleep(twait - telap)
first_loop = False
if not loop:
break
plotter.close()
return plotter.camera_position
def text_result_table(self, rnum):
""" Returns a text result table for plotting """
ls_table = self._resultheader['ls_table']
timevalue = self.time_values[rnum]
text = 'Cumulative Index: {:3d}\n'.format(ls_table[rnum, 2])
if self._resultheader['nSector'] > 1:
hindex = self._resultheader['hindex'][rnum]
text += 'Harmonic Index {:3d}\n'.format(hindex)
text += 'Loadstep: {:3d}\n'.format(ls_table[rnum, 0])
text += 'Substep: {:3d}\n'.format(ls_table[rnum, 1])
text += 'Time Value: {:10.4f}'.format(timevalue)
return text
def plot_nodal_stress(self, rnum, comp=None,
show_displacement=False,
displacement_factor=1,
node_components=None,
element_components=None,
sel_type_all=True, **kwargs):
"""Plots the stresses at each node in the solution.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
comp : str, optional
Stress component to display. Available options:
- ``"X"``
- ``"Y"``
- ``"Z"``
- ``"XY"``
- ``"YZ"``
- ``"XZ"``
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
kwargs : keyword arguments
Additional keyword arguments. See help(pyvista.plot)
Returns
-------
cpos : list
3 x 3 vtk camera position.
Examples
--------
Plot the X component nodal stress while showing displacement.
>>> rst.plot_nodal_stress(0, comp='x', show_displacement=True)
"""
kwargs['stitle'] = '%s Component Nodal Stress' % comp
return self._plot_nodal_result(rnum, 'ENS', comp, STRESS_TYPES,
show_displacement,
displacement_factor, node_components,
element_components,
sel_type_all, **kwargs)
def save_as_vtk(self, filename, rsets=None, result_types=['ENS']):
"""Writes results to a vtk readable file.
The file extension will select the type of writer to use.
``'.vtk'`` will use the legacy writer, while ``'.vtu'`` will
select the VTK XML writer.
Parameters
----------
filename : str
Filename of grid to be written. The file extension will
select the type of writer to use. ``'.vtk'`` will use the
legacy writer, while ``'.vtu'`` will select the VTK XML
writer.
rsets : collections.Iterable
List of result sets to write. For example ``range(3)``
result_types : list
Result type to write. For example ``['ENF', 'ENS']``
List of some or all of the following:
- EMS: misc. data
- ENF: nodal forces
- ENS: nodal stresses
- ENG: volume and energies
- EGR: nodal gradients
- EEL: elastic strains
- EPL: plastic strains
- ECR: creep strains
- ETH: thermal strains
- EUL: euler angles
- EFX: nodal fluxes
- ELF: local forces
- EMN: misc. non-sum values
- ECD: element current densities
- ENL: nodal nonlinear data
- EHC: calculated heat generations
- EPT: element temperatures
- ESF: element surface stresses
- EDI: diffusion strains
- ETB: ETABLE items
- ECT: contact data
- EXY: integration point locations
- EBA: back stresses
- ESV: state variables
- MNL: material nonlinear record
Notes
-----
Binary files write much faster than ASCII, but binary files
written on one system may not be readable on other systems.
Binary can only be selected for the legacy writer.
Examples
--------
Write in binary
>>> rst.save_as_vtk('results.vtk')
Write using the xml writer
>>> rst.save_as_vtk('results.vtu')
Write only nodal and elastic strain for the first result
>>> rst.save_as_vtk('results.vtk', [0], ['EEL', 'EPL']
"""
# Copy grid as to not write results to original object
grid = self.grid.copy()
if rsets is None:
rsets = range(self.nsets)
elif isinstance(rsets, int):
rsets = [rsets]
elif not isinstance(rsets, Iterable):
raise TypeError('rsets must be an iterable like [0, 1, 2] or range(3)')
if result_types is None:
result_types = ELEMENT_INDEX_TABLE_KEYS
elif not isinstance(result_types, list):
raise TypeError('result_types must be a list of solution types')
else:
for item in result_types:
if item not in ELEMENT_INDEX_TABLE_KEYS:
raise ValueError('Invalid result type "%s" % item')
try:
from tqdm import tqdm
pbar = tqdm(total=len(rsets), desc='Saving to file')
except ImportError:
pbar = None
for i in rsets:
# Nodal results
_, val = self.nodal_solution(i)
grid.point_arrays['Nodal Solution {:d}'.format(i)] = val
# Nodal results
for rtype in self.available_results:
if rtype in result_types:
_, values = self._nodal_result(i, rtype)
desc = element_index_table_info[rtype]
grid.point_arrays['{:s} {:d}'.format(desc, i)] = values
if pbar is not None:
pbar.update(1)
grid.save(filename)
if pbar is not None:
pbar.close()
def write_tables(self, filename):
"""Write binary tables to ASCII. Assumes int32
Parameters
----------
filename : str
Filename to write the tables to.
Examples
--------
>>> rst.write_tables('tables.txt')
"""
rawresult = open(self.filename, 'rb')
with open(filename, 'w') as f:
while True:
try:
table = read_table(rawresult)
f.write('*** %d ***\n' % len(table))
for item in table:
f.write(str(item) + '\n')
f.write('\n\n')
except:
break
rawresult.close()
def parse_step_substep(self, user_input):
""" Converts (step, substep) to a cumulative index """
if is_int(user_input):
# check if result exists
if user_input > self.nsets - 1:
raise ValueError('There are only %d result(s) in the result file.'
% self.nsets)
return user_input
elif isinstance(user_input, (list, tuple)):
if len(user_input) != 2:
raise ValueError('Input must contain (step, loadstep) using ' +
'1 based indexing (e.g. (1, 1)).')
ls_table = self._resultheader['ls_table']
step, substep = user_input
mask = np.logical_and(ls_table[:, 0] == step,
ls_table[:, 1] == substep)
if not np.any(mask):
raise ValueError('Load step table does not contain ' +
'step %d and substep %d' % tuple(user_input))
index = mask.nonzero()[0]
assert index.size == 1, 'Multiple cumulative index matches'
return index[0]
else:
raise TypeError('Input must be either an int or a list')
def __repr__(self):
if self._is_distributed:
rst_info = ['PyANSYS MAPDL Distributed Result']
else:
rst_info = ['PyANSYS MAPDL Result']
keys = ['title', 'subtitle', 'units']
for key in keys:
value = self._resultheader[key]
if value:
rst_info.append('{:<12s}: {:s}'.format(key.capitalize(), value))
value = self._resultheader['verstring']
rst_info.append('{:<12s}: {:s}'.format('Version', value))
value = str(self._resultheader['nSector'] > 1)
rst_info.append('{:<12s}: {:s}'.format('Cyclic', value))
value = self._resultheader['nsets']
rst_info.append('{:<12s}: {:d}'.format('Result Sets', value))
value = self._resultheader['nnod']
rst_info.append('{:<12s}: {:d}'.format('Nodes', value))
value = self._resultheader['nelm']
rst_info.append('{:<12s}: {:d}'.format('Elements', value))
rst_info.append('\n')
rst_info.append(str(self.available_results))
return '\n'.join(rst_info)
def _nodal_result(self, rnum, result_type):
"""Generic load nodal result
Parameters
----------
rnum : int
Result number.
result_type : int
EMS: misc. data
ENF: nodal forces
ENS: nodal stresses
ENG: volume and energies
EGR: nodal gradients
EEL: elastic strains
EPL: plastic strains
ECR: creep strains
ETH: thermal strains
EUL: euler angles
EFX: nodal fluxes
ELF: local forces
EMN: misc. non-sum values
ECD: element current densities
ENL: nodal nonlinear data
EHC: calculated heat
EPT: element temperatures
ESF: element surface stresses
EDI: diffusion strains
ETB: ETABLE items(post1 only
ECT: contact data
EXY: integration point locations
EBA: back stresses
ESV: state variables
MNL: material nonlinear record
sort : bool, optional
Unused by base class.
Returns
-------
nnum : np.ndarray
ANSYS node numbers
result : np.ndarray
Array of result data
"""
# check result exists
if not self.available_results[result_type]:
raise ValueError('Result %s is not available in this result file'
% result_type)
# element header
rnum = self.parse_step_substep(rnum)
ele_ind_table, nodstr, etype, ptr_off = self._element_solution_header(rnum)
result_type = result_type.upper()
nitem = self._result_nitem(rnum, result_type)
result_index = ELEMENT_INDEX_TABLE_KEYS.index(result_type)
# Element types for nodal averaging
cells, offset = vtk_cell_info(self.grid)
data, ncount = _binary_reader.read_nodal_values(self.filename,
self.grid.celltypes,
ele_ind_table,
offset,
cells,
nitem,
self.grid.n_points,
nodstr,
etype,
self._mesh.etype,
result_index,
ptr_off)
if result_type == 'ENS' and nitem != 6:
data = data[:, :6]
nnum = self.grid.point_arrays['ansys_node_num']
if not np.any(ncount):
raise ValueError('Result file contains no %s records for result %d' %
(element_index_table_info[result_type.upper()], rnum))
# average across nodes
ncount = ncount.reshape(-1, 1)
return nnum, data/ncount
def _result_nitem(self, rnum, result_type):
"""Return the number of items for a given result type"""
if self._resultheader['rstsprs'] == 0 and result_type == 'ENS':
nitem = 11
elif result_type == 'ENF':
# number of items is nodfor*NDOF*M where:
# NDOF is the number of DOFs/node for this element, nodfor
# is the number of nodes per element having nodal forces
# (defined in element type description record), and M may
# be 1, 2, or 3. For a static analysis, M=1 only. For a
# transient analysis, M can be 1, 2, or 3.
solution_header = self._result_solution_header(rnum)
numdof = solution_header['numdof']
nitem = numdof*1 # m = 1 for static, 1, 2 or 3 for transient
else:
nitem = ELEMENT_RESULT_NCOMP.get(result_type, 1)
return nitem
def nodal_reaction_forces(self, rnum, sort=True):
"""Nodal reaction forces.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
sort : bool
Sort results by node number. Default ``True``.
Returns
-------
rforces : np.ndarray
Reaction Forces
nnum : np.ndarray
Node numbers corresponding to the reaction forces. Does
not necessarily corresponds to ``rst.mesh.nnum`` as each
node may not have a reaction force in each degree of
freedom.
dof : np.ndarray
Degree of freedom corresponding to each node using the
MAPDL degree of freedom reference table. See
``rst.result_dof`` for the corresponding degrees of
freedom for a given solution.
Examples
--------
Get the nodal reaction forces for the first result and print
the reaction forces of a single node.
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> rforces, nnum, dof = rst.nodal_reaction_forces(0)
>>> dof_ref = rst.result_dof(0)
>>> rforces[:3], nnum[:3], dof[:3], dof_ref
(array([ 24102.21376091, -109357.01854005, 22899.5303263 ]),
array([4142, 4142, 4142]),
array([1, 2, 3], dtype=int32),
['UX', 'UY', 'UZ'])
"""
# PROGRAMMERS NOTE:
# RF LONG 1 nrf Reaction force DOFs. This
# index is calculated as
# (N-1)*numdof+DOF, where N
# is the position number of
# the node in the nodal
# equivalence table, and DOF
# is the DOF reference
# number.
#
# --- dp 1 nrf Reaction forces. The force
# values are ordered
# according to the DOF order
# shown above in the DOF
# number reference table.
# pointer to result reaction forces
rnum = self.parse_step_substep(rnum)
rpointers = self._resultheader['rpointers']
ptr = rpointers[rnum] + self._solution_header(rnum)['ptrRF']
# table is always ANSYS LONG (INT64)
table, bufsz = self.read_record(ptr, True)
table = table.view(np.int64)
solution_header = self._result_solution_header(rnum)
numdof = solution_header['numdof']
rforces = self.read_record(ptr + bufsz)[:table.size]
shifted_table = (table - 1) / numdof
index = np.array(shifted_table, np.int64)
dof = np.round(1 + numdof*(shifted_table - index)).astype(np.int32)
if sort:
sidx = np.argsort(shifted_table)
index = index[sidx]
dof = dof[sidx]
rforces = rforces[sidx]
return rforces, index, dof
def plot_element_result(self, rnum, result_type, item_index,
in_element_coord_sys=False, **kwargs):
"""Plot an element result.
Parameters
----------
rnum : int
Result number.
result_type : str
Element data type to retreive.
- EMS: misc. data
- ENF: nodal forces
- ENS: nodal stresses
- ENG: volume and energies
- EGR: nodal gradients
- EEL: elastic strains
- EPL: plastic strains
- ECR: creep strains
- ETH: thermal strains
- EUL: euler angles
- EFX: nodal fluxes
- ELF: local forces
- EMN: misc. non-sum values
- ECD: element current densities
- ENL: nodal nonlinear data
- EHC: calculated heat generations
- EPT: element temperatures
- ESF: element surface stresses
- EDI: diffusion strains
- ETB: ETABLE items
- ECT: contact data
- EXY: integration point locations
- EBA: back stresses
- ESV: state variables
- MNL: material nonlinear record
item_index : int
Index of the data item for each node within the element.
in_element_coord_sys : bool, optional
Returns the results in the element coordinate system.
Default False and will return the results in the global
coordinate system.
Returns
-------
nnum : np.ndarray
ANSYS node numbers
result : np.ndarray
Array of result data
"""
# check result exists
result_type = result_type.upper()
if not self.available_results[result_type]:
raise ValueError('Result %s is not available in this result file'
% result_type)
if result_type not in ELEMENT_INDEX_TABLE_KEYS:
raise ValueError('Result "%s" is not an element result' % result_type)
bsurf = self._extract_surface_element_result(rnum,
result_type,
item_index,
in_element_coord_sys)
desc = self.available_results.description[result_type].capitalize()
kwargs.setdefault('stitle', desc)
return bsurf.plot(scalars='_scalars', **kwargs)
def _extract_surface_element_result(self, rnum, result_type, item_index,
in_element_coord_sys):
"""Return the surface of the grid with the active scalars
being the element result"""
# element header
rnum = self.parse_step_substep(rnum)
ele_ind_table, nodstr, etype, ptr_off = self._element_solution_header(rnum)
# the number of items per node
nitem = self._result_nitem(rnum, result_type)
if item_index > nitem - 1:
raise ValueError('Item index greater than the number of items in '
'this result type %s' % result_type)
# extract the surface and separate the surface faces
# TODO: add element/node components
surf = self.grid.extract_surface()
bsurf = break_apart_surface(surf, force_linear=True)
nnum_surf = surf.point_arrays['ansys_node_num'][bsurf['orig_ind']]
faces = bsurf.faces
if faces.dtype != np.int64:
faces = faces.astype(np.int64)
elem_ind = surf.cell_arrays['vtkOriginalCellIds']
# index within the element table pointing to the data of interest
result_index = ELEMENT_INDEX_TABLE_KEYS.index(result_type)
data = populate_surface_element_result(self.filename,
ele_ind_table,
nodstr,
etype,
nitem,
ptr_off, # start of result data
result_index,
bsurf.n_points,
faces,
bsurf.n_faces,
nnum_surf,
elem_ind,
self._mesh._elem,
self._mesh._elem_off,
item_index,
as_global=not in_element_coord_sys)
bsurf['_scalars'] = data
return bsurf
def _result_solution_header(self, rnum):
"""Return the solution header for a given cumulative result index"""
ptr = self._resultheader['rpointers'][rnum]
return parse_header(self.read_record(ptr), solution_data_header_keys)
def nodal_stress(self, rnum):
"""Retrieves the component stresses for each node in the
solution.
The order of the results corresponds to the sorted node
numbering.
Computes the nodal stress by averaging the stress for each
element at each node. Due to the discontinuities across
elements, stresses will vary based on the element they are
evaluated from.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : numpy.ndarray
Node numbers of the result.
stress : numpy.ndarray
Stresses at X, Y, Z, XY, YZ, and XZ averaged at each corner
node.
Examples
--------
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, stress = rst.nodal_stress(0)
Notes
-----
Nodes without a stress value will be NAN.
Equivalent ANSYS command: PRNSOL, S
"""
return self._nodal_result(rnum, 'ENS')
def cylindrical_nodal_stress(self, rnum):
"""Retrieves the stresses for each node in the solution in the
cylindrical coordinate system as the following values:
``R``, ``THETA``, ``Z``, ``RTHETA``, ``THETAZ``, and ``RZ``
The order of the results corresponds to the sorted node
numbering.
Computes the nodal stress by averaging the stress for each
element at each node. Due to the discontinuities across
elements, stresses will vary based on the element they are
evaluated from.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : numpy.ndarray
Node numbers of the result.
stress : numpy.ndarray
Stresses at R, THETA, Z, RTHETA, THETAZ, and RZ averaged
at each corner node where R is radial.
Examples
--------
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, stress = rst.cylindrical_nodal_stress(0)
Notes
-----
Nodes without a stress value will be NAN.
Equivalent ANSYS commands:
RSYS, 1
PRNSOL, S
"""
nnum, stress = self._nodal_result(rnum, 'ENS')
# angles relative to the XZ plane
if nnum.size != self._mesh.nodes.shape[0]:
mask = np.in1d(nnum, self._mesh.nnum)
angle = np.arctan2(self._mesh.nodes[mask, 1],
self._mesh.nodes[mask, 0])
else:
angle = np.arctan2(self._mesh.nodes[:, 1],
self._mesh.nodes[:, 0])
_binary_reader.euler_cart_to_cyl(stress, angle) # mod stress inplace
return nnum, stress
def nodal_temperature(self, rnum, **kwargs):
"""Retrieves the temperature for each node in the
solution.
The order of the results corresponds to the sorted node
numbering.
Equivalent MAPDL command: PRNSOL, TEMP
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : numpy.ndarray
Node numbers of the result.
temperature : numpy.ndarray
Temperature at each node.
Examples
--------
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, stress = rst.nodal_temperature(0)
"""
if self._is_thermal:
nnum, temp = self.nodal_solution(rnum)
else:
nnum, temp = self._nodal_result(rnum, 'EPT')
temp = temp.ravel()
return nnum, temp
def plot_cylindrical_nodal_stress(self, rnum, comp=None, show_displacement=False,
displacement_factor=1, node_components=None,
element_components=None, sel_type_all=True,
**kwargs):
"""Plot nodal_stress in the cylindrical coordinate system.
Parameters
----------
rnum : int
Result number
comp : str, optional
Stress component to display. Available options:
- ``"R"``
- ``"THETA"``
- ``"Z"``
- ``"RTHETA"``
- ``"THETAZ"``
- ``"RZ"``
show_displacement : bool, optional
Deforms mesh according to the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
**kwargs : keyword arguments
Optional keyword arguments. See help(pyvista.plot)
Examples
--------
Plot nodal stress in the radial direction
>>> import pyansys
>>> result = pyansys.read_binary('file.rst')
>>> result.plot_cylindrical_nodal_stress(0, 'R')
"""
available_comps = ['R', 'THETA', 'Z', 'RTHETA', 'THETAZ', 'RZ']
idx = check_comp(available_comps, comp)
_, scalars = self.cylindrical_nodal_stress(rnum)
scalars = scalars[:, idx]
grid = self.grid
if node_components:
grid, ind = self._extract_node_components(node_components, sel_type_all)
scalars = scalars[ind]
elif element_components:
grid, ind = self._extract_element_components(element_components)
scalars = scalars[ind]
kwargs.setdefault('stitle', '%s Cylindrical\nNodal Stress' % comp)
return self._plot_point_scalars(scalars, grid=grid, rnum=rnum,
show_displacement=show_displacement,
displacement_factor=displacement_factor,
**kwargs)
def plot_nodal_temperature(self, rnum, show_displacement=False,
displacement_factor=1, node_components=None,
element_components=None, sel_type_all=True,
**kwargs):
"""Plot nodal temperature
Parameters
----------
rnum : int
Result number
show_displacement : bool, optional
Deforms mesh according to the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
**kwargs : keyword arguments
Optional keyword arguments. See help(pyvista.plot)
Examples
--------
Plot temperature of a result
>>> import pyansys
>>> result = pyansys.read_binary('file.rst')
>>> result.plot_nodal_temperature(0)
Plot while showing edges and disabling lighting
>>> result.plot_nodal_temperature(0, show_edges=True, lighting=False)
"""
_, scalars = self.nodal_temperature(rnum)
grid = self.grid
if node_components:
grid, ind = self._extract_node_components(node_components, sel_type_all)
scalars = scalars[ind]
elif element_components:
grid, ind = self._extract_element_components(element_components)
scalars = scalars[ind]
return self._plot_point_scalars(scalars, grid=grid, rnum=rnum,
show_displacement=show_displacement,
displacement_factor=displacement_factor,
stitle='Nodal Tempature',
**kwargs)
def nodal_thermal_strain(self, rnum):
"""Nodal component thermal strain.
This record contains strains in the order X, Y, Z, XY, YZ, XZ,
EQV, and eswell (element swelling strain). Thermal strains
are always values at the integration points moved to the
nodes.
Equivalent MAPDL command: PRNSOL, EPTH, COMP
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : np.ndarray
ANSYS node numbers.
thermal_strain : np.ndarray
Nodal component plastic strains. Array is in the order
X, Y, Z, XY, YZ, XZ, EQV, ESWELL
Examples
--------
Load the nodal thermal strain for the first solution
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, thermal_strain = rst.nodal_thermal_strain(0)
"""
return self._nodal_result(rnum, 'ETH')
def plot_nodal_thermal_strain(self, rnum,
comp=None,
stitle='Nodal Thermal Strain',
show_displacement=False,
displacement_factor=1,
node_components=None,
element_components=None,
sel_type_all=True, **kwargs):
"""Plot nodal component thermal strains.
Equivalent MAPDL command: PLNSOL, EPTH, COMP
Parameters
----------
rnum : int
Result number
comp : str, optional
Thermal strain component to display. Available options:
- ``"X"``
- ``"Y"``
- ``"Z"``
- ``"XY"``
- ``"YZ"``
- ``"XZ"``
- ``"EQV"``
- ``"ESWELL"``
show_displacement : bool, optional
Deforms mesh according to the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
**kwargs : keyword arguments
Optional keyword arguments. See ``help(pyvista.plot)``
Examples
--------
Plot thermal strain for result 0 of verification manual example 33
>>> import pyansys
>>> result = pyansys.download_verification_result(33)
>>> result.plot_nodal_thermal_strain(0)
"""
return self._plot_nodal_result(rnum, 'ETH', comp, THERMAL_STRAIN_TYPES,
show_displacement=show_displacement,
displacement_factor=displacement_factor,
node_components=node_components,
element_components=element_components,
sel_type_all=sel_type_all,
stitle=stitle,
**kwargs)
def nodal_elastic_strain(self, rnum):
"""Nodal component elastic strains. This record contains
strains in the order X, Y, Z, XY, YZ, XZ, EQV.
Elastic strains can be can be nodal values extrapolated from
the integration points or values at the integration points
moved to the nodes.
Equivalent MAPDL command: ``PRNSOL, EPEL``
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : np.ndarray
ANSYS node numbers.
elastic_strain : np.ndarray
Nodal component elastic strains. Array is in the order
X, Y, Z, XY, YZ, XZ, EQV.
Examples
--------
Load the nodal elastic strain for the first result.
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, elastic_strain = rst.nodal_elastic_strain(0)
Notes
-----
Nodes without a strain will be NAN.
"""
return self._nodal_result(rnum, 'EEL')
def plot_nodal_elastic_strain(self, rnum, comp,
stitle='Nodal Elastic Strain',
show_displacement=False,
displacement_factor=1,
node_components=None,
element_components=None,
sel_type_all=True, **kwargs):
"""Plot nodal elastic strain.
Parameters
----------
rnum : int
Result number
comp : str, optional
Elastic strain component to display. Available options:
- ``"X"``
- ``"Y"``
- ``"Z"``
- ``"XY"``
- ``"YZ"``
- ``"XZ"``
- ``"EQV"``
show_displacement : bool, optional
Deforms mesh according to the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
**kwargs : keyword arguments
Optional keyword arguments. See help(pyvista.plot)
Examples
--------
Plot nodal elastic strain for a static pontoon model
>>> import pyansys
>>> result = pyansys.download_pontoon()
>>> result.plot_nodal_elastic_strain(0)
"""
stitle = ' '.join([comp.upper(), stitle])
return self._plot_nodal_result(rnum, 'EEL',
comp,
STRAIN_TYPES,
show_displacement=show_displacement,
displacement_factor=displacement_factor,
node_components=node_components,
element_components=element_components,
sel_type_all=sel_type_all,
stitle=stitle,
**kwargs)
def nodal_plastic_strain(self, rnum):
"""Nodal component plastic strains. This record contains
strains in the order X, Y, Z, XY, YZ, XZ, EQV.
Plastic strains are always values at the integration points
moved to the nodes.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : np.ndarray
ANSYS node numbers.
plastic_strain : np.ndarray
Nodal component plastic strains. Array is in the order
X, Y, Z, XY, YZ, XZ, EQV.
Examples
--------
Load the nodal plastic strain for the first solution.
>>> import pyansys
>>> rst = pyansys.read_binary('file.rst')
>>> nnum, plastic_strain = rst.nodal_plastic_strain(0)
"""
return self._nodal_result(rnum, 'EPL')
def plot_nodal_plastic_strain(self, rnum, comp,
stitle='Nodal Plastic Strain',
show_displacement=False,
displacement_factor=1,
node_components=None,
element_components=None,
sel_type_all=True, **kwargs):
"""Plot nodal component plastic strain.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
comp : str, optional
Plastic strain component to display. Available options:
- ``"X"``
- ``"Y"``
- ``"Z"``
- ``"XY"``
- ``"YZ"``
- ``"XZ"``
- ``"EQV"``
show_displacement : bool, optional
Deforms mesh according to the result.
displacement_factor : float, optional
Increases or decreases displacement by a factor.
node_components : list, optional
Accepts either a string or a list strings of node
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
element_components : list, optional
Accepts either a string or a list strings of element
components to plot. For example:
``['MY_COMPONENT', 'MY_OTHER_COMPONENT]``
sel_type_all : bool, optional
If node_components is specified, plots those elements
containing all nodes of the component. Default True.
**kwargs : keyword arguments
Optional keyword arguments. See help(pyvista.plot)
Examples
--------
Plot plastic strain for a static pontoon model
>>> import pyansys
>>> result = pyansys.download_pontoon()
>>> result.plot_nodal_plastic_strain(0)
"""
stitle = ' '.join([comp.upper(), stitle])
return self._plot_nodal_result(rnum, 'EPL',
comp,
STRAIN_TYPES,
show_displacement=show_displacement,
displacement_factor=displacement_factor,
node_components=node_components,
element_components=element_components,
sel_type_all=sel_type_all,
stitle=stitle,
**kwargs)
def _plot_nodal_result(self, rnum, result_type, comp, available_comps,
show_displacement=False, displacement_factor=1,
node_components=None, element_components=None,
sel_type_all=True, **kwargs):
"""Plot nodal result"""
component_index = check_comp(available_comps, comp)
_, result = self._nodal_result(rnum, result_type)
scalars = result[:, component_index]
if node_components:
grid, ind = self._extract_node_components(node_components, sel_type_all)
scalars = scalars[ind]
elif element_components:
grid, ind = self._extract_element_components(element_components)
scalars = scalars[ind]
else:
grid = self.grid
return self._plot_point_scalars(scalars, grid=grid, rnum=rnum,
show_displacement=show_displacement,
displacement_factor=displacement_factor,
**kwargs)
def _animate_time_solution(self, result_type, index=0, frame_rate=10,
show_displacement=True, displacement_factor=1,
off_screen=None):
"""Animate time solution result"""
# load all results
results = []
for i in range(self.nsets):
results.append(self._nodal_result(i, result_type)[1][:, index])
if show_displacement:
disp = []
for i in range(self.nsets):
disp.append(self.nodal_solution(i)[1][:, :3]*displacement_factor)
mesh = self.grid.copy()
results = np.array(results)
if np.all(np.isnan(results)):
raise ValueError('Result file contains no %s records' %
element_index_table_info[result_type.upper()])
# prepopulate mesh with data
mesh['data'] = results[0]
# set default range
rng = [results.min(), results.max()]
t_wait = 1/frame_rate
def q_callback():
"""exit when user wants to leave"""
self._animating = False
self._animating = True
def plot_thread():
plotter = pv.Plotter(off_screen=off_screen)
plotter.add_key_event("q", q_callback)
plotter.add_mesh(mesh, scalars='data', rng=rng)
plotter.show(auto_close=False, interactive_update=True, interactive=False)
text_actor = plotter.add_text('Result 1')
while self._animating:
for i in range(self.nsets):
mesh['data'] = results[i]
if show_displacement:
mesh.points = self.grid.points + disp[i]
# if interactive:
plotter.update(30, force_redraw=True)
if hasattr(text_actor, 'SetInput'):
text_actor.SetInput('Result %d' % (i + 1))
else:
text_actor.SetText(0, 'Result %d' % (i + 1))
time.sleep(t_wait)
if off_screen:
break
plotter.close()
thread = Thread(target=plot_thread)
thread.start()
@property
def available_results(self):
"""Available result types.
Examples
--------
>>> rst.available_results
Available Results:
ENS : Nodal stresses
ENG : Element energies and volume
EEL : Nodal elastic strains
EPL : Nodal plastic strains
ETH : Nodal thermal strains (includes swelling strains)
EUL : Element euler angles
ENL : Nodal nonlinear items, e.g. equivalent plastic strains
EPT : Nodal temperatures
NSL : Nodal displacements
RF : Nodal reaction forces
"""
return self._available_results
def nodal_static_forces(self, rnum):
"""Return the nodal forces averaged at the nodes
Nodal forces are computed on an element by element basis, and
this method averages the nodal forces for each element for
each node.
Parameters
----------
rnum : int or list
Cumulative result number with zero based indexing, or a
list containing (step, substep) of the requested result.
Returns
-------
nnum : np.ndarray
ANSYS node numbers.
forces : np.ndarray
Averaged nodal forces. Array is sized ``[nnod x numdof]``
where ``nnod`` is the number of nodes and ``numdof`` is the
number of degrees of freedom for this solution.
Examples
--------
Load the nodal static forces for the first result using the
example hexahedral result file.
>>> import pyansys
>>> from pyansys import examples
>>> rst = pyansys.read_binary(examples.rstfile)
>>> nnum, forces = rst.nodal_static_forces(0)
Notes
-----
Nodes without a a nodal will be NAN. These are generally
midside (quadratic) nodes.
"""
return self._nodal_result(rnum, 'ENF')
@property
def _is_distributed(self):
"""True when this result file is part of a distributed result
Only True when Global number of nodes does not equal the
number of nodes in this file.
Notes
-----
Not a reliabile indicator if a cyclic result.
"""
return self._resultheader['Glbnnod'] != self._resultheader['nnod']
def _is_main(self):
"""Result file written from the main process"""
return bool(self._resultheader['ptrGNOD'])
@property
def _is_thermal(self):
"""True when result file is a rth file"""
return self.filename[-3:] == 'rth'
@property
def _is_cyclic(self):
"""True when the result file is from a cyclic analysis"""
return self.n_sector > 1
def pol2cart(rho, phi):
""" Convert cylindrical to cartesian """
x = rho * np.cos(phi)
y = rho * np.sin(phi)
return x, y
def is_int(value):
""" Return true if can be parsed as an int """
try:
int(value)
return True
except:
return False
def trans_to_matrix(trans):
""" Convert a numpy.ndarray to a vtk.vtkMatrix4x4 """
matrix = vtk.vtkMatrix4x4()
for i in range(trans.shape[0]):
for j in range(trans.shape[1]):
matrix.SetElement(i, j, trans[i, j])
return matrix
def transform(points, trans):
"""In-place 3d transformation of a points array given a 4x4
transformation matrix.
Parameters
----------
points : np.ndarray or vtk.vtkTransform
Points to transform.
transform : np.ndarray or vtk.vtkTransform
4x4 transformation matrix.
"""
if isinstance(trans, vtk.vtkMatrix4x4):
trans = pv.trans_from_matrix(trans)
_binary_reader.affline_transform(points, trans)
def merge_two_dicts(x, y):
merged = x.copy() # start with x's keys and values
merged.update(y) # modifies z with y's keys and values & returns None
return merged
def check_comp(available_comps, comp):
"""Check if a component is in available components and return a
helpful error message
Parameters
----------
available_comps : list
List of available components. For example:
``['R', 'THETA', 'Z', 'RTHETA', 'THETAZ', 'RZ']``
Case should be all caps
comp : str
Component to check. Any case.
Returns
-------
idx : int
Index in ``available_comps``.
"""
if comp is None:
raise ValueError('Missing "comp" parameter. Please select'
' from the following:\n%s' % available_comps)
comp = comp.upper()
if comp not in available_comps:
raise ValueError('Invalid "comp" parameter %s. Please select' % comp +
' from the following:\n%s' % available_comps)
return available_comps.index(comp)
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] |
stock_quantity_history_location/tests/test_stock_quantity_history_location.py | NextERP-Romania/addons_extern | 0 | 14826 | <reponame>NextERP-Romania/addons_extern
# Copyright 2019 ForgeFlow S.L.
# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl).
from odoo.tests.common import SavepointCase
class TestStockQuantityHistoryLocation(SavepointCase):
@classmethod
def setUpClass(cls):
super(TestStockQuantityHistoryLocation, cls).setUpClass()
cls.supplier_location = cls.env.ref("stock.stock_location_suppliers")
cls.main_company = cls.env.ref("base.main_company")
cls.product = cls.env.ref("product.product_product_3")
cls.test_stock_loc = cls.env["stock.location"].create(
{
"usage": "internal",
"name": "Test Stock Location",
"company_id": cls.main_company.id,
}
)
cls.child_test_stock_loc = cls.env["stock.location"].create(
{
"usage": "internal",
"name": "Child Test Stock Location",
"location_id": cls.test_stock_loc.id,
"company_id": cls.main_company.id,
}
)
# Create a move for the past
move = cls.env["stock.move"].create(
{
"name": "Stock move in",
"location_id": cls.supplier_location.id,
"location_dest_id": cls.child_test_stock_loc.id,
"product_id": cls.product.id,
"product_uom": cls.product.uom_id.id,
"product_uom_qty": 100.0,
}
)
move._action_confirm()
move._action_assign()
move_line = move.move_line_ids[0]
move_line.qty_done = 100.0
move._action_done()
move.date = "2019-08-11"
def test_wizard_past_date(self):
wizard = self.env["stock.quantity.history"].create(
{
"location_id": self.test_stock_loc.id,
"include_child_locations": True,
"inventory_datetime": "2019-08-12 00:00:00",
}
)
action = wizard.with_context(company_owned=True).open_at_date()
self.assertEquals(
self.product.with_context(action["context"]).qty_available, 100.0
)
self.assertEquals(
self.product.with_context(
location=self.child_test_stock_loc.id, to_date="2019-08-10"
).qty_available,
0.0,
)
def test_wizard_current(self):
wizard = self.env["stock.quantity.history"].create(
{"location_id": self.test_stock_loc.id, "include_child_locations": False}
)
action = wizard.with_context().open_at_date()
self.assertEquals(action["context"]["compute_child"], False)
self.assertEquals(action["context"]["location"], self.test_stock_loc.id)
wizard = self.env["stock.quantity.history"].create(
{"location_id": self.test_stock_loc.id, "include_child_locations": True}
)
action = wizard.with_context().open_at_date()
self.assertEquals(action["context"]["compute_child"], True)
self.assertEquals(action["context"]["location"], self.test_stock_loc.id)
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advisor/api/urls.py | Sachin-c/api-test | 0 | 23044 | <filename>advisor/api/urls.py<gh_stars>0
from django.urls import path
from advisor.api.views import (
# api_advisor_view,
api_advisor_view_post,
)
app_name = 'advisor'
urlpatterns = [
path('admin/advisor/', api_advisor_view_post, name="post"),
# path('user/<int:id>/advisor/', api_advisor_view, name="detail"),
]
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Homework/2019/Task4/1/code/SVM/DGA.py | ohhuola/Data-Mining-for-Cybersecurity | 0 | 1603431 | <gh_stars>0
import csv
import math
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score,recall_score
import joblib
from sklearn.preprocessing import StandardScaler
goodlist = []
badlist = []
def length(domain_name):
return len(domain_name)
def infoentropy(domain_name):
charlist = []
for char in domain_name:
if char not in charlist:
charlist.append(char)
countlist = []
for char in charlist:
countlist.append(domain_name.count(char))
result = 0
for count in countlist:
result += (-1 * count/length(domain_name) * math.log2(count/length(domain_name)))
return result
def vowel(domain_name):
vcount = 0
domain_name = domain_name.split('.')
maxlength, domain_name = max([(len(x),x) for x in domain_name])
for char in domain_name:
if char in ['a', 'e', 'i', 'o', 'u']:
vcount += 1
return vcount/length(domain_name)
def consonant(domain_name):
ccount = 0
for i in range(len(domain_name)):
if domain_name[i] not in ['a', 'e', 'i', 'o', 'u'] and domain_name[i].isalpha() and i + 1 < len(domain_name):
if domain_name[i + 1] not in ['a', 'e', 'i', 'o', 'u'] and domain_name[i].isalpha():
ccount += 1
i += 1
return ccount
def num(domain_name):
result = 0
for char in domain_name:
if char.isalnum():
result += 1
return result
with open("top-1m.csv") as f:
f_csv = csv.reader(f)
for row in f_csv:
good = row[1]
goodlist.append([length(good), infoentropy(good), vowel(good), consonant(good), num(good)])
if len(goodlist) == 10000:
break
with open("dga.txt") as f:
data = f.readlines()
for row in data:
bad = row.split()[1]
badlist.append([length(bad), infoentropy(bad), vowel(bad), consonant(bad), num(bad)])
if __name__ == '__main__':
list = badlist + goodlist[:10000]
matrix = np.array(list)
# bad = 0, good = 1
label = []
for i in range(len(badlist)):
label.append(0)
for i in range(10000):
label.append(1)
sc = StandardScaler()
X_train, X_test, y_train, y_test = train_test_split(matrix, label, test_size=0.1)
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
print("start training:")
svm = SVC(kernel="rbf", random_state=1, C=0.9,gamma="auto", max_iter=100000)
svm.fit(X_train_std, y_train)
joblib.dump(svm, "svm_model")
y_pred = svm.predict(X_test_std)
print("acc:{}".format(accuracy_score(y_test, y_pred)))
print("recall:{}".format(recall_score(y_test, y_pred)))
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inventory/suppliers/api/views.py | cnobile2012/inventory | 10 | 179912 | #
# inventory/suppliers/api/views.py
#
"""
Supplier Views.
"""
__docformat__ = "restructuredtext en"
import logging
from decimal import Decimal
from rest_framework.generics import (
ListCreateAPIView, RetrieveUpdateDestroyAPIView)
from rest_framework.exceptions import PermissionDenied, NotAuthenticated
from rest_framework.permissions import IsAuthenticated
from rest_framework.settings import api_settings
from rest_condition import ConditionalPermission, C, And, Or, Not
from inventory.common.api.permissions import (
IsAdminSuperUser, IsAdministrator, IsProjectOwner, IsProjectManager,
IsProjectDefaultUser, IsUserActive, IsReadOnly)
from inventory.common.api.pagination import SmallResultsSetPagination
from inventory.common.api.parsers import parser_factory
from inventory.common.api.renderers import renderer_factory
from inventory.common.api.view_mixins import (
TrapDjangoValidationErrorCreateMixin, TrapDjangoValidationErrorUpdateMixin)
from inventory.suppliers.models import Supplier
from .serializers import SupplierSerializerVer01
log = logging.getLogger('api.suppliers.views')
#
# Supplier Views
#
class SupplierMixin:
parser_classes = (parser_factory('suppliers')
+ api_settings.DEFAULT_PARSER_CLASSES)
renderer_classes = (renderer_factory('suppliers')
+ api_settings.DEFAULT_RENDERER_CLASSES)
def get_serializer_class(self):
serializer = None
if self.request.version == Decimal("1"):
serializer = SupplierSerializerVer01
# elif self.request.version == Decimal("2"):
# serializer = SupplierSerializerVer02
return serializer
class SupplierList(TrapDjangoValidationErrorCreateMixin,
SupplierMixin,
ListCreateAPIView):
"""
Supplier list endpoint.
"""
queryset = Supplier.objects.all()
permission_classes = (
And(IsUserActive, IsAuthenticated,
Or(IsAdminSuperUser,
IsAdministrator,
IsProjectOwner,
IsProjectManager,
And(IsProjectDefaultUser, IsReadOnly)
),
),
)
pagination_class = SmallResultsSetPagination
lookup_field = 'public_id'
supplier_list = SupplierList.as_view()
class SupplierDetail(TrapDjangoValidationErrorUpdateMixin,
SupplierMixin,
RetrieveUpdateDestroyAPIView):
"""
Supplier detail endpoint.
"""
queryset = Supplier.objects.all()
permission_classes = (
And(IsUserActive, IsAuthenticated,
Or(IsAdminSuperUser,
IsAdministrator,
IsProjectOwner,
IsProjectManager,
And(IsProjectDefaultUser, IsReadOnly)
),
),
)
lookup_field = 'public_id'
supplier_detail = SupplierDetail.as_view()
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tests/text/test_chrf.py | stancld/metrics | 769 | 80306 | from functools import partial
from typing import Sequence
import pytest
from torch import Tensor, tensor
from tests.text.helpers import TextTester
from tests.text.inputs import _inputs_multiple_references, _inputs_single_sentence_multiple_references
from torchmetrics.functional.text.chrf import chrf_score
from torchmetrics.text.chrf import CHRFScore
from torchmetrics.utilities.imports import _SACREBLEU_AVAILABLE
if _SACREBLEU_AVAILABLE:
from sacrebleu.metrics import CHRF
def sacrebleu_chrf_fn(
preds: Sequence[str],
targets: Sequence[Sequence[str]],
char_order: int,
word_order: int,
lowercase: bool,
whitespace: bool,
) -> Tensor:
sacrebleu_chrf = CHRF(
char_order=char_order, word_order=word_order, lowercase=lowercase, whitespace=whitespace, eps_smoothing=True
)
# Sacrebleu CHRF expects different format of input
targets = [[target[i] for target in targets] for i in range(len(targets[0]))]
sacrebleu_chrf = sacrebleu_chrf.corpus_score(preds, targets).score / 100
return tensor(sacrebleu_chrf)
@pytest.mark.parametrize(
["char_order", "word_order", "lowercase", "whitespace"],
[
(6, 2, False, False),
(6, 2, False, True),
(4, 2, True, False),
(6, 0, True, False),
(6, 0, True, True),
(4, 0, False, True),
],
)
@pytest.mark.parametrize(
["preds", "targets"],
[(_inputs_multiple_references.preds, _inputs_multiple_references.targets)],
)
@pytest.mark.skipif(not _SACREBLEU_AVAILABLE, reason="test requires sacrebleu")
class TestCHRFScore(TextTester):
@pytest.mark.parametrize("ddp", [False, True])
@pytest.mark.parametrize("dist_sync_on_step", [False, True])
def test_chrf_score_class(
self, ddp, dist_sync_on_step, preds, targets, char_order, word_order, lowercase, whitespace
):
metric_args = {
"n_char_order": char_order,
"n_word_order": word_order,
"lowercase": lowercase,
"whitespace": whitespace,
}
nltk_metric = partial(
sacrebleu_chrf_fn, char_order=char_order, word_order=word_order, lowercase=lowercase, whitespace=whitespace
)
self.run_class_metric_test(
ddp=ddp,
preds=preds,
targets=targets,
metric_class=CHRFScore,
sk_metric=nltk_metric,
dist_sync_on_step=dist_sync_on_step,
metric_args=metric_args,
)
def test_chrf_score_functional(self, preds, targets, char_order, word_order, lowercase, whitespace):
metric_args = {
"n_char_order": char_order,
"n_word_order": word_order,
"lowercase": lowercase,
"whitespace": whitespace,
}
nltk_metric = partial(
sacrebleu_chrf_fn, char_order=char_order, word_order=word_order, lowercase=lowercase, whitespace=whitespace
)
self.run_functional_metric_test(
preds,
targets,
metric_functional=chrf_score,
sk_metric=nltk_metric,
metric_args=metric_args,
)
def test_chrf_score_differentiability(self, preds, targets, char_order, word_order, lowercase, whitespace):
metric_args = {
"n_char_order": char_order,
"n_word_order": word_order,
"lowercase": lowercase,
"whitespace": whitespace,
}
self.run_differentiability_test(
preds=preds,
targets=targets,
metric_module=CHRFScore,
metric_functional=chrf_score,
metric_args=metric_args,
)
def test_chrf_empty_functional():
hyp = []
ref = [[]]
assert chrf_score(hyp, ref) == tensor(0.0)
def test_chrf_empty_class():
chrf = CHRFScore()
hyp = []
ref = [[]]
assert chrf(hyp, ref) == tensor(0.0)
def test_chrf_return_sentence_level_score_functional():
hyp = _inputs_single_sentence_multiple_references.preds
ref = _inputs_single_sentence_multiple_references.targets
_, chrf_sentence_score = chrf_score(hyp, ref, return_sentence_level_score=True)
isinstance(chrf_sentence_score, Tensor)
def test_chrf_return_sentence_level_class():
chrf = CHRFScore(return_sentence_level_score=True)
hyp = _inputs_single_sentence_multiple_references.preds
ref = _inputs_single_sentence_multiple_references.targets
_, chrf_sentence_score = chrf(hyp, ref)
isinstance(chrf_sentence_score, Tensor)
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] |
day08/python/day8.py | mattvperry/AoC_2016 | 0 | 163355 | from re import compile
from functools import reduce
rectRe = compile(r'(rect) (\d+)x(\d+)')
rotaRe = compile(r'(rotate [rc])\w* \w=(\d+) by (\d+)')
def makeScreen(x, y):
return [[' '] * x for _ in range(y)]
def transpose(l):
return list(map(list, zip(*l)))
def parseInstruction(inst):
rect = rectRe.search(inst)
rota = rotaRe.search(inst)
a, b, c = rect.groups() if rect else rota.groups()
return a, int(b), int(c)
def fill(x, y, screen):
return [['#'] * x + r[x:] for r in screen[:y]] + screen[y:]
def rotate(a, b, screen):
newRow = screen[a][-b:] + screen[a][:-b]
return screen[:a] + [newRow] + screen[a + 1:]
def light(input):
instMap = {
'rect': fill,
'rotate r': rotate,
'rotate c': lambda a, b, s: transpose(rotate(a, b, transpose(s)))
}
screen = makeScreen(50, 6)
for cmd, a, b in map(parseInstruction, input):
screen = instMap[cmd](a, b, screen)
return screen
def day8(input):
screen = light(input)
print(sum(r.count('#') for r in screen))
return "\n".join("".join(x) for x in screen)
input = open("../input.txt").read()
input = [x.strip() for x in input.split("\n")]
print(day8(input)) | [
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] |
public/python/GlobalData.py | IzzatHalabi/newpix_prototype | 0 | 157200 | students = []
references = []
benefits = []
MODE_SPECIFIC = '1. SPECIFIC'
MODE_HOSTEL = '2. HOSTEL'
MODE_MCDM = '3. MCDM'
MODE_REMAIN = '4. REMAIN'
| [
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outlier_detector.py | Sean-Ker/data_homework | 0 | 9459 | <gh_stars>0
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
'''
A function that detects outliers, where k is a tandard deviation threshold hyperparameter preferablly (2, 2.5, 3).
The algo could handle multivariable data frames with any number of features d.
For that manner, it first reduces the dimensionality to 2 using PCA, makes sure that the matrix is positive definite and calculates the Mahalanobis Distance with a threshold value.
Returns a series of n rows back.
'''
def outlier_detector(data, k=2.5):
# Calculate Principal Component Analysis
pca = PCA(n_components=data.shape[1], svd_solver='full')
df = pd.DataFrame(pca.fit_transform(
data), index=data.index, columns=data.columns)
# Calculate covariance and its inverse matrices
cov_matrix = np.cov(df.values, rowvar=False)
inv_cov = np.linalg.inv(cov_matrix)
mean = df.values.mean(axis=0)
# Check matrices are positive definite: https://en.wikipedia.org/wiki/Definiteness_of_a_matrix
assert is_pos_def(cov_matrix) and is_pos_def(inv_cov)
# Calculate Mahalanobis Distance https://en.wikipedia.org/wiki/Mahalanobis_distance
md = mahalanobis_dist(inv_cov, mean, df.values, verbose=False)
threshold = np.mean(md) * k
# res = pd.DataFrame(index=data.index,columns=data.columns)
return data[md > threshold]
# https://www.youtube.com/watch?v=spNpfmWZBmg&t=0s
def mahalanobis_dist(inv_cov_matrix, mean_distr, data, verbose=False):
diff = data - mean_distr
md = []
for i in range(len(diff)):
md.append(np.sqrt(diff[i].dot(inv_cov_matrix).dot(diff[i])))
return np.array(md)
# Check that matrix is positive definite
def is_pos_def(A):
if np.allclose(A, A.T):
try:
np.linalg.cholesky(A)
return True
except np.linalg.LinAlgError:
return False
else:
return False
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comments/admin.py | lesspointless/Shakal-NG | 10 | 62555 | # -*- coding: utf-8 -*-
from django.contrib import admin
from django.contrib.contenttypes.admin import GenericTabularInline
from django.utils.html import format_html, escape, mark_safe
from mptt.admin import DraggableMPTTAdmin
from .models import Comment, RootHeader
from attachment.admin import AttachmentInline, AttachmentAdminMixin
class CommentAdmin(AttachmentAdminMixin, DraggableMPTTAdmin):
fieldsets = (
(
'Komentár',
{'fields': ('subject', 'user', 'user_name', 'original_comment')}
),
(
'Metainformácie',
{'fields': ('ip_address', 'is_public', 'is_removed', 'is_locked')}
),
)
list_display = ('tree_actions', 'get_subject', 'name', 'ip_address', 'created', 'is_public', 'is_removed', 'is_locked')
list_display_links = ('get_subject',)
list_filter = ('created', 'is_public', 'is_removed',)
raw_id_fields = ('user',)
search_fields = ('filtered_comment', 'user__username', 'user_name', 'ip_address')
inlines = [AttachmentInline]
def get_subject(self, obj):
return mark_safe(('<span style="display: inline-block; border-left: 1px solid #ddd; width: 16px; padding-top: 4px; padding-bottom: 8px; margin-top: -4px; margin-bottom: -8px;"> </span>' * (obj._mpttfield('level')-1)) + escape(obj.subject))
get_subject.short_description = 'Predmet'
get_subject.admin_order_field = 'subject'
def get_actions(self, request):
actions = super().get_actions(request)
if not request.user.is_superuser:
actions.pop('delete_selected', None)
return actions
def get_queryset(self, request):
qs = super().get_queryset(request).exclude(level=0)
if 'content_type_id__exact' in request.GET and 'object_id__exact' in request.GET:
try:
content_type_id = int(request.GET['content_type_id__exact'])
object_id = int(request.GET['object_id__exact'])
return qs.filter(content_type_id=content_type_id, object_id=object_id)
except ValueError:
return qs.none()
if request.resolver_match.view_name in ('admin:comments_comment_change', 'admin:comments_comment_delete', 'admin:comments_comment_history', 'admin:comments_comment_add'):
return qs
return qs.none()
def get_model_perms(self, request):
perms = super().get_model_perms(request)
if request.resolver_match.view_name not in ('admin:comments_comment_changelist', 'admin:comments_comment_change', 'admin:comments_comment_delete', 'admin:comments_comment_history', 'admin:comments_comment_add'):
perms['delete'] = False
perms['add'] = False
perms['change'] = False
return perms
class RootHeaderAdmin(admin.ModelAdmin):
date_hierarchy = 'pub_date'
list_display = ('get_name', 'get_link')
list_display_links = None
def get_queryset(self, request):
return super().get_queryset(request).select_related('content_type')
def has_add_permission(self, request):
return False
def get_name(self, obj):
return format_html('<a href="{}">{} </a>', obj.get_admin_url(), obj.content_object)
get_name.short_description = "Názov"
def get_link(self, obj):
return format_html('<a href="{}">Zobraziť</a>', obj.get_absolute_url())
get_link.short_description = "Zobraziť"
class CommentInline(GenericTabularInline):
model = Comment
fields = ('get_subject',)
readonly_fields = ('get_subject',)
template = 'admin/edit_inline/comments.html'
verbose_name = 'komentár'
verbose_name_plural = 'komentáre'
ct_field = 'content_type'
ct_fk_field = 'object_id'
extra = 0
def get_queryset(self, request):
return super().get_queryset(request).order_by('lft')
def get_subject(self, obj):
indent = ''
if obj.level:
indent = mark_safe(' ' * (obj.level-1))
return format_html("{}{}", indent, obj.subject)
get_subject.short_description = "Predmet"
admin.site.register(Comment, CommentAdmin)
admin.site.register(RootHeader, RootHeaderAdmin)
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hitomezashi_grid.py | DavidLitwin/hitomizashi | 0 | 178021 | # -*- coding: utf-8 -*-
"""
Created on Mon Jan 3 10:45:39 2022
@author: dgbli
"""
import numpy as np
import matplotlib.pyplot as plt
def return_true(n):
x = []
for i in range(n):
if i%2==0:
x.append(i)
x.append(i+1)
else:
x.append(None)
return x
def return_false(n):
x = []
for i in range(n):
if i%2==1:
x.append(i)
x.append(i+1)
else:
x.append(None)
return x
class Hitomizashi:
def __init__(self, rows, columns, color='k', linewidth=1.0, capstyle='projecting'):
self.a = columns
self.b = rows
self.Na = len(self.a)
self.Nb = len(self.b)
self.color = color
self.lw = linewidth
self.cs = capstyle
def draw(self):
fig, ax = plt.subplots()
#horizontals:
for i in range(self.Na):
if b[i] == True:
x = np.array(return_true(self.Na))
ax.plot(x,i*np.ones(len(x)), color=self.color, linewidth=self.lw, solid_capstyle=self.cs)
elif b[i] == False:
x = np.array(return_false(self.Na))
ax.plot(x,i*np.ones(len(x)), color=self.color, linewidth=self.lw, solid_capstyle=self.cs)
else:
raise TypeError('Input should be boolean')
#verticals:
for i in range(len(a)):
if a[i] == True:
y = np.array(return_true(self.Nb))
ax.plot(i*np.ones(len(y)), y, color=self.color, linewidth=self.lw, solid_capstyle=self.cs)
elif a[i] == False:
y = np.array(return_false(self.Nb))
ax.plot(i*np.ones(len(y)), y, color=self.color, linewidth=self.lw, solid_capstyle=self.cs)
else:
raise TypeError('Input should be boolean')
ax.set_axis_off()
if __name__ == "__main__":
a = np.random.rand(25) > 0.5
b = np.random.rand(25) > 0.5
hz = Hitomizashi(a,b, linewidth=4, color='dodgerblue', capstyle='round')
hz.draw()
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] |
ntcore/entry_notifier.py | amorygalili/pynetworktables | 0 | 118202 | # validated: 2017-10-01 DS e4a8bff70e77 cpp/EntryNotifier.cpp cpp/EntryNotifier.h cpp/IEntryNotifier.h
"""----------------------------------------------------------------------------"""
""" Copyright (c) FIRST 2017. All Rights Reserved. """
""" Open Source Software - may be modified and shared by FRC teams. The code """
""" must be accompanied by the FIRST BSD license file in the root directory of """
""" the project. """
"""----------------------------------------------------------------------------"""
from collections import namedtuple
from .callback_manager import CallbackManager, CallbackThread
from .constants import (
NT_NOTIFY_IMMEDIATE,
NT_NOTIFY_LOCAL,
NT_NOTIFY_UPDATE,
NT_NOTIFY_FLAGS,
)
_EntryListenerData = namedtuple(
"EntryListenerData",
[
"prefix",
"local_id", # we don't have entry handles like ntcore has
"flags",
"callback",
"poller_uid",
],
)
#
_EntryNotification = namedtuple(
"EntryNotification", ["name", "value", "flags", "local_id"]
)
_assign_both = NT_NOTIFY_UPDATE | NT_NOTIFY_FLAGS
_immediate_local = NT_NOTIFY_IMMEDIATE | NT_NOTIFY_LOCAL
class EntryNotifierThread(CallbackThread):
def __init__(self):
CallbackThread.__init__(self, "entry-notifier")
def matches(self, listener, data):
if not data.value:
return False
# Flags must be within requested flag set for this listener.
# Because assign messages can result in both a value and flags update,
# we handle that case specially.
listen_flags = listener.flags & ~_immediate_local
flags = data.flags & ~_immediate_local
if (flags & _assign_both) == _assign_both:
if (listen_flags & _assign_both) == 0:
return False
listen_flags &= ~_assign_both
flags &= ~_assign_both
if (flags & ~listen_flags) != 0:
return False
# must match local id or prefix
if listener.local_id is not None:
if listener.local_id != data.local_id:
return False
else:
if not data.name.startswith(listener.prefix):
return False
return True
def setListener(self, data, listener_uid):
pass
def doCallback(self, callback, data):
callback(data)
class EntryNotifier(CallbackManager):
THREAD_CLASS = EntryNotifierThread
def __init__(self, verbose):
CallbackManager.__init__(self, verbose)
self.m_local_notifiers = False
def add(self, callback, prefix, flags):
if (flags & NT_NOTIFY_LOCAL) != 0:
self.m_local_notifiers = True
return self.doAdd(_EntryListenerData(prefix, None, flags, callback, None))
def addById(self, callback, local_id, flags):
if (flags & NT_NOTIFY_LOCAL) != 0:
self.m_local_notifiers = True
return self.doAdd(_EntryListenerData(None, local_id, flags, callback, None))
def addPolled(self, poller_uid, prefix, flags):
if (flags & NT_NOTIFY_LOCAL) != 0:
self.m_local_notifiers = True
return self.doAdd(_EntryListenerData(prefix, None, flags, None, poller_uid))
def addPolledById(self, poller_uid, local_id, flags):
if (flags & NT_NOTIFY_LOCAL) != 0:
self.m_local_notifiers = True
return self.doAdd(_EntryListenerData(None, local_id, flags, None, poller_uid))
def notifyEntry(self, local_id, name, value, flags, only_listener=None):
# optimization: don't generate needless local queue entries if we have
# no local listeners (as this is a common case on the server side)
if not self.m_local_notifiers and (flags & NT_NOTIFY_LOCAL) != 0:
return
self.send(only_listener, _EntryNotification(name, value, flags, local_id))
def start(self):
CallbackManager.start(self)
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] |
image_storage/apps.py | Addono/Django-Kubernetes-DevOps | 2 | 115424 | from django.apps import AppConfig
class ImageStorageConfig(AppConfig):
name = 'image_storage'
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test_package/conanfile.py | karasusan/conan-gtest-example | 0 | 76601 | <gh_stars>0
from conans.model.conan_file import ConanFile
from conans import CMake
class TestConanGTestExample(ConanFile):
name = "TestConanGTestExample"
version = "0.1.0"
author = "lasote"
url = "https://github.com/karasusan/conan-gtest-example"
license = "MIT"
settings = "os", "compiler", "arch", "build_type"
generators = "cmake"
cmake = None
requires = "gtest/1.7.0@bincrafters/stable"
default_options = "gtest:shared=True"
def build(self):
self.cmake = CMake(self)
self.cmake.configure()
self.cmake.build()
def imports(self):
self.copy("*.so", "bin", "lib")
self.copy("*.dll", "bin", "bin")
self.copy("*.dylib", "bin", "lib")
def test(self):
target_test = "RUN_TESTS" if self.settings.os == "Windows" else "test"
self.cmake.build(target=target_test)
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src/soap/__init__.py | thelabnyc/instrumented-soap | 1 | 117570 | from suds.client import Client
from suds.plugin import MessagePlugin
from suds.cache import FileCache
from .http import HttpTransport
from . import settings
import logging
logger = logging.getLogger(__name__)
#: Cache of :class:`suds.client.Client <suds.client.Client>` objects
#: When unit-testing SOAP APIs it's probably wise to reset this to an empty
#: dictionary in-between tests.
clients = {}
# Set cache deletion preference
FileCache.remove_default_location_on_exit = settings.REMOVE_CACHE_ON_EXIT
class LogPlugin(MessagePlugin):
"""Suds plugin used in DEBUG mode. Logs all incoming and outgoing XML data at the DEBUG level."""
def __init__(self, prefix):
self.prefix = prefix
def sending(self, context):
"""Called when sending a SOAP request"""
logger.debug("%s Request: %s" % (self.prefix, context.envelope))
def received(self, context):
"""Called when receiving a SOAP response"""
logger.debug("%s Response: %s" % (self.prefix, context.reply))
def get_transport():
"""
Build a new :class:`soap.http.HttpTransport <soap.http.HttpTransport>` object. Unit tests can patch this
function to return a custom transport object for the client to use. This can be useful when trying to mock
an API rather than actually call it during a test.
:return: :class:`soap.http.HttpTransport <soap.http.HttpTransport>` object
:rtype: soap.http.HttpTransport
"""
return HttpTransport()
def get_client(wsdl, log_prefix, plugins=[], **kwargs):
"""
Get a SOAP Client object for the given WSDL. Client objects are cached in :attr:`soap.clients <soap.clients>`
and keyed by the WSDL URL.
:param wsdl: String URL of a SOAP WSDL
:param log_prefix: String prefix to prepend to log lines (when logging XML traffic in DEBUG mode)
:param plugins: List of additional plugins :class:`suds.plugin.Plugin <suds.plugin.Plugin>` to pass on to the
:class:`suds.client.Client <suds.client.Client>` object.
:param kwargs: Optional keyword arguments to pass on to the :class:`suds.client.Client <suds.client.Client>` object
:return: :class:`suds.client.Client <suds.client.Client>` object
:rtype: suds.client.Client
"""
if wsdl in settings.WSDL_INTERCEPTS:
wsdl = settings.WSDL_INTERCEPTS[wsdl]
if wsdl not in clients:
if settings.DEBUG:
plugins.append(LogPlugin(log_prefix))
try:
clients[wsdl] = Client(
wsdl, plugins=plugins, transport=get_transport(), **kwargs
)
except Exception as e:
logger.fatal("Failed to create SOAP client with WSDL at %s" % wsdl)
raise e
return clients[wsdl]
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app/api/v2/handlers/agent_api.py | resteex0/caldera | 1 | 149925 | import aiohttp_apispec
from aiohttp import web
from app.api.v2.handlers.base_object_api import BaseObjectApi
from app.api.v2.managers.agent_api_manager import AgentApiManager
from app.api.v2.schemas.deploy_command_schemas import DeployCommandsSchema
from app.api.v2.schemas.base_schemas import BaseGetAllQuerySchema, BaseGetOneQuerySchema
from app.objects.c_agent import Agent, AgentSchema
class AgentApi(BaseObjectApi):
def __init__(self, services):
super().__init__(description='agent', obj_class=Agent, schema=AgentSchema, ram_key='agents',
id_property='paw', auth_svc=services['auth_svc'])
self._api_manager = AgentApiManager(data_svc=services['data_svc'], file_svc=services['file_svc'])
def add_routes(self, app: web.Application):
router = app.router
router.add_get('/agents', self.get_agents)
router.add_get('/agents/{paw}', self.get_agent_by_id)
router.add_post('/agents', self.create_agent)
router.add_patch('/agents/{paw}', self.update_agent)
router.add_put('/agents/{paw}', self.create_or_update_agent)
router.add_delete('/agents/{paw}', self.delete_agent)
router.add_get('/deploy_commands', self.get_deploy_commands)
router.add_get('/deploy_commands/{ability_id}', self.get_deploy_commands_for_ability)
@aiohttp_apispec.docs(tags=['agents'])
@aiohttp_apispec.querystring_schema(BaseGetAllQuerySchema)
@aiohttp_apispec.response_schema(AgentSchema(many=True, partial=True))
async def get_agents(self, request: web.Request):
agents = await self.get_all_objects(request)
return web.json_response(agents)
@aiohttp_apispec.docs(tags=['agents'])
@aiohttp_apispec.querystring_schema(BaseGetOneQuerySchema)
@aiohttp_apispec.response_schema(AgentSchema(partial=True))
async def get_agent_by_id(self, request: web.Request):
agent = await self.get_object(request)
return web.json_response(agent)
@aiohttp_apispec.docs(tags=['agents'])
@aiohttp_apispec.request_schema(AgentSchema)
@aiohttp_apispec.response_schema(AgentSchema)
async def create_agent(self, request: web.Request):
agent = await self.create_object(request)
return web.json_response(agent.display)
@aiohttp_apispec.docs(tags=['agents'])
@aiohttp_apispec.request_schema(AgentSchema(partial=True, only=['group',
'trusted',
'sleep_min',
'sleep_max',
'watchdog',
'pending_contact']))
@aiohttp_apispec.response_schema(AgentSchema)
async def update_agent(self, request: web.Request):
agent = await self.update_object(request)
return web.json_response(agent.display)
@aiohttp_apispec.docs(tags=['agents'],
summary="Create or Update an Agent",
description="Update an agent, or if a existing agent match cannot be found, "
"create one. Use the paw field in the URL to specify matching "
"criteria and the fields from the AgentSchema in the request body"
" to specify new field values.",
parameters=[{
'in': 'path',
'name': 'paw',
'schema': {'type': 'string'},
'required': 'true',
'description': 'paw of the Agent to be retrieved'
}])
@aiohttp_apispec.request_schema(AgentSchema(partial=True))
@aiohttp_apispec.response_schema(AgentSchema, description="Json dictionary representation of the created or "
"updated Agent")
async def create_or_update_agent(self, request: web.Request):
agent = await self.create_or_update_object(request)
return web.json_response(agent.display)
@aiohttp_apispec.docs(tags=['agents'],
summary="Delete an Agent",
description="Delete an agent. Use the paw field in the URL to specify matching "
"criteria for the agent(s) to delete.",
parameters=[{
'in': 'path',
'name': 'paw',
'schema': {'type': 'string'},
'required': 'true',
'description': 'paw of the Agent to be deleted'
}])
@aiohttp_apispec.response_schema(AgentSchema(only=[]), description="Returns HTTP 200")
async def delete_agent(self, request: web.Request):
await self.delete_object(request)
return web.HTTPNoContent()
@aiohttp_apispec.docs(tags=['agents'],
summary="Retrieve deploy commands",
description="Retrieve the deploy commands currently configured within Caldera.")
@aiohttp_apispec.response_schema(DeployCommandsSchema, description="Json dictionary representation of deploy "
"commands, sorted by Ability ID")
async def get_deploy_commands(self, request: web.Request):
deploy_commands = await self._api_manager.get_deploy_commands()
return web.json_response(deploy_commands)
@aiohttp_apispec.docs(tags=['agents'],
summary="Retrieve deploy commands for an Ability",
description="Retrieve the deploy commands associated with a given ability ID. "
"Use the 'ability_id' field in the URL specify which ability to "
"retrieve deploy commands for.",
parameters=[{
'in': 'path',
'name': 'ability_id',
'schema': {'type': 'string'},
'required': 'true',
'description': 'ID of the ability to retrieve deploy commands for'
}])
@aiohttp_apispec.response_schema(DeployCommandsSchema, description="Json dictionary representation of deploy "
"commands for the specified Ability ID")
async def get_deploy_commands_for_ability(self, request: web.Request):
ability_id = request.match_info.get('ability_id')
deploy_commands = await self._api_manager.get_deploy_commands(ability_id)
return web.json_response(deploy_commands)
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junky/dataset/__init__.py | fostroll/junky | 9 | 1610831 | # -*- coding: utf-8 -*-
# junky lib: dataset
#
# Copyright (C) 2020-present by <NAME>, <NAME>
# License: BSD, see LICENSE for details
"""
Provides implementagions of torch.utils.data.Dataset for different purposes.
"""
from junky.dataset.dummy_dataset import *
from junky.dataset.len_dataset import *
from junky.dataset.label_dataset import *
from junky.dataset.word_dataset import *
from junky.dataset.bert_dataset import *
from junky.dataset.bert_tokenized_dataset import *
from junky.dataset.char_dataset import *
from junky.dataset.token_dataset import *
from junky.dataset.frame_dataset import *
from junky.dataset.word_cat_dataset import *
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cpc/criterion/clustering/clustering_quantization.py | anuragkumar95/CPC_audio | 6 | 24183 | import os
import sys
import json
import argparse
import progressbar
from pathlib import Path
from random import shuffle
from time import time
import torch
from cpc.dataset import findAllSeqs
from cpc.feature_loader import buildFeature, FeatureModule, loadModel, buildFeature_batch
from cpc.criterion.clustering import kMeanCluster
#from cpc.criterion.research.clustering import kMeanCluster
def readArgs(pathArgs):
print(f"Loading args from {pathArgs}")
with open(pathArgs, 'r') as file:
args = argparse.Namespace(**json.load(file))
return args
def loadClusterModule(pathCheckpoint, norm_vec_len=False):
print(f"Loading ClusterModule at {pathCheckpoint}")
state_dict = torch.load(pathCheckpoint)
if "state_dict" in state_dict: #kmeans
clusterModule = kMeanCluster(torch.zeros(1, state_dict["n_clusters"], state_dict["dim"]), norm_vec_len)
clusterModule.load_state_dict(state_dict["state_dict"])
else: #dpmeans
clusterModule = kMeanCluster(state_dict["mu"])
clusterModule = clusterModule.cuda()
return clusterModule
def parseArgs(argv):
# Run parameters
parser = argparse.ArgumentParser(description='Quantize audio files using CPC Clustering Module.')
parser.add_argument('pathCheckpoint', type=str,
help='Path to the clustering checkpoint.')
parser.add_argument('pathDB', type=str,
help='Path to the dataset that we want to quantize.')
parser.add_argument('pathOutput', type=str,
help='Path to the output directory.')
parser.add_argument('--pathSeq', type=str,
help='Path to the sequences (file names) to be included used.')
parser.add_argument('--split', type=str, default=None,
help="If you want to divide the dataset in small splits, specify it "
"with idxSplit-numSplits (idxSplit > 0), eg. --split 1-20.")
parser.add_argument('--file_extension', type=str, default=".flac",
help="Extension of the audio files in the dataset (default: .flac).")
parser.add_argument('--max_size_seq', type=int, default=10240,
help='Maximal number of frames to consider '
'when computing a batch of features (defaut: 10240).')
parser.add_argument('--batch_size', type=int, default=8,
help='Batch size used to compute features '
'when computing each file (defaut: 8).')
parser.add_argument('--strict', type=bool, default=True,
help='If activated, each batch of feature '
'will contain exactly max_size_seq frames (defaut: True).')
parser.add_argument('--debug', action='store_true',
help="Load only a very small amount of files for "
"debugging purposes.")
parser.add_argument('--nobatch', action='store_true',
help="Don't use batch implementation of when building features."
"NOTE: This can have better quantized units as we can set "
"model.gAR.keepHidden = True (line 162), but the quantization"
"will be a bit longer.")
parser.add_argument('--recursionLevel', type=int, default=1,
help='Speaker level in pathDB (defaut: 1). This is only helpful'
'when --separate-speaker is activated.')
parser.add_argument('--separate-speaker', action='store_true',
help="Separate each speaker with a different output file.")
parser.add_argument('--norm_vec_len', action='store_true',
help="Normalize vector lengths.")
return parser.parse_args(argv)
def main(argv):
# Args parser
args = parseArgs(argv)
print("=============================================================")
print(f"Quantizing data from {args.pathDB}")
print("=============================================================")
# Check if directory exists
if not os.path.exists(args.pathOutput):
print("")
print(f"Creating the output directory at {args.pathOutput}")
Path(args.pathOutput).mkdir(parents=True, exist_ok=True)
# Get splits
if args.split:
assert len(args.split.split("-"))==2 and int(args.split.split("-")[1]) >= int(args.split.split("-")[0]) >= 1, \
"SPLIT must be under the form idxSplit-numSplits (numSplits >= idxSplit >= 1), eg. --split 1-20"
idx_split, num_splits = args.split.split("-")
idx_split = int(idx_split)
num_splits = int(num_splits)
# Find all sequences
print("")
print(f"Looking for all {args.file_extension} files in {args.pathDB} with speakerLevel {args.recursionLevel}")
seqNames, speakers = findAllSeqs(args.pathDB,
speaker_level=args.recursionLevel,
extension=args.file_extension,
loadCache=True)
if args.pathSeq:
with open(args.pathSeq, 'r') as f:
seqs = set([x.strip() for x in f])
filtered = []
for s in seqNames:
if s[1].split('/')[-1].split('.')[0] in seqs:
filtered.append(s)
seqNames = filtered
print(f"Done! Found {len(seqNames)} files and {len(speakers)} speakers!")
if args.separate_speaker:
seqNames_by_speaker = {}
for seq in seqNames:
speaker = seq[1].split("/")[args.recursionLevel-1]
if speaker not in seqNames_by_speaker:
seqNames_by_speaker[speaker] = []
seqNames_by_speaker[speaker].append(seq)
# Check if output file exists
if not args.split:
nameOutput = "quantized_outputs.txt"
else:
nameOutput = f"quantized_outputs_split_{idx_split}-{num_splits}.txt"
if args.separate_speaker is False:
outputFile = os.path.join(args.pathOutput, nameOutput)
assert not os.path.exists(outputFile), \
f"Output file {outputFile} already exists !!!"
# Get splits
if args.split:
startIdx = len(seqNames) // num_splits * (idx_split-1)
if idx_split == num_splits:
endIdx = len(seqNames)
else:
endIdx = min(len(seqNames) // num_splits * idx_split, len(seqNames))
seqNames = seqNames[startIdx:endIdx]
print("")
print(f"Quantizing split {idx_split} out of {num_splits} splits, with {len(seqNames)} files (idx in range({startIdx}, {endIdx})).")
# Debug mode
if args.debug:
nsamples=20
print("")
print(f"Debug mode activated, only load {nsamples} samples!")
# shuffle(seqNames)
seqNames = seqNames[:nsamples]
# Load Clustering args
assert args.pathCheckpoint[-3:] == ".pt"
if os.path.exists(args.pathCheckpoint[:-3] + "_args.json"):
pathConfig = args.pathCheckpoint[:-3] + "_args.json"
elif os.path.exists(os.path.join(os.path.dirname(args.pathCheckpoint), "checkpoint_args.json")):
pathConfig = os.path.join(os.path.dirname(args.pathCheckpoint), "checkpoint_args.json")
else:
assert False, \
f"Args file not found in the directory {os.path.dirname(args.pathCheckpoint)}"
clustering_args = readArgs(pathConfig)
print("")
print(f"Clutering args:\n{json.dumps(vars(clustering_args), indent=4, sort_keys=True)}")
print('-' * 50)
# Load CluterModule
clusterModule = loadClusterModule(args.pathCheckpoint, norm_vec_len=args.norm_vec_len)
clusterModule.cuda()
# Load FeatureMaker
print("")
print("Loading CPC FeatureMaker")
if 'level_gru' in vars(clustering_args) and clustering_args.level_gru is not None:
updateConfig = argparse.Namespace(nLevelsGRU=clustering_args.level_gru)
else:
updateConfig = None
model = loadModel([clustering_args.pathCheckpoint], updateConfig=updateConfig)[0]
## If we don't apply batch implementation, we can set LSTM model to keep hidden units
## making the quality of the quantized units better
if args.nobatch:
model.gAR.keepHidden = True
featureMaker = FeatureModule(model, clustering_args.encoder_layer)
if clustering_args.dimReduction is not None:
dimRed = loadDimReduction(clustering_args.dimReduction, clustering_args.centroidLimits)
featureMaker = torch.nn.Sequential(featureMaker, dimRed)
if not clustering_args.train_mode:
featureMaker.eval()
featureMaker.cuda()
def feature_function(x):
if args.nobatch is False:
res0 = buildFeature_batch(featureMaker, x,
seqNorm=False,
strict=args.strict,
maxSizeSeq=args.max_size_seq,
batch_size=args.batch_size)
if args.norm_vec_len:
# [!] we actually used CPC_audio/scripts/quantize_audio.py for that in the end
res0Lengths = torch.sqrt((res0*res0).sum(2))
res0 = res0 / res0Lengths.view(*(res0Lengths.shape), 1)
return res0
else:
res0 = buildFeature(featureMaker, x,
seqNorm=False,
strict=args.strict)
if args.norm_vec_len:
# [!] we actually used CPC_audio/scripts/quantize_audio.py for that in the end
res0Lengths = torch.sqrt((res0*res0).sum(2))
res0 = res0 / res0Lengths.view(*(res0Lengths.shape), 1)
return res0
print("CPC FeatureMaker loaded!")
# Quantization of files
print("")
print(f"Quantizing audio files...")
seqQuantLines = []
bar = progressbar.ProgressBar(maxval=len(seqNames))
bar.start()
start_time = time()
for index, vals in enumerate(seqNames):
bar.update(index)
file_path = vals[1]
file_path = os.path.join(args.pathDB, file_path)
# Get features & quantizing
cFeatures = feature_function(file_path).cuda()
nGroups = cFeatures.size(-1)//clusterModule.Ck.size(-1)
cFeatures = cFeatures.view(1, -1, clusterModule.Ck.size(-1))
if len(vals) > 2 and int(vals[-1]) > 9400000: # Librilight, to avoid OOM
clusterModule = clusterModule.cpu()
cFeatures = cFeatures.cpu()
qFeatures = torch.argmin(clusterModule(cFeatures), dim=-1)
clusterModule = clusterModule.cuda()
else:
qFeatures = torch.argmin(clusterModule(cFeatures), dim=-1)
qFeatures = qFeatures[0].detach().cpu().numpy()
# Transform to quantized line
quantLine = ",".join(["-".join([str(i) for i in item]) for item in qFeatures.reshape(-1, nGroups)])
seqQuantLines.append(quantLine)
bar.finish()
print(f"...done {len(seqQuantLines)} files in {time()-start_time} seconds.")
# Saving outputs
print("")
print(f"Saving outputs to {outputFile}")
outLines = []
for vals, quantln in zip(seqNames, seqQuantLines):
file_path = vals[1]
file_name = os.path.splitext(os.path.basename(file_path))[0]
outLines.append("\t".join([file_name, quantln]))
with open(outputFile, "w") as f:
f.write("\n".join(outLines))
if __name__ == "__main__":
args = sys.argv[1:]
main(args)
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src/python/tags.py | alexjgriffith/alpha-score | 0 | 44721 | #!/usr/bin/env python2.7
#
# This file is part of peakAnalysis, http://github.com/alexjgriffith/peaks/,
# and is Copyright (C) University of Ottawa, 2014. It is Licensed under
# the three-clause BSD License; see doc/LICENSE.txt.
# Contact: <EMAIL>
#
# Created : AUG262014
# File : buildPeaksClass
# Author : <NAME>
# Lab : Dr. Brand and Dr. Perkins
import sys
import numpy
import argparse
from peak_functions import *
from buildPeaksClass import buildPeaks
from peakClass import peak,peakCap,peakCapHandler
from defineClass import define
from subsetClass import subsetWraper
def fromStdin():
parser=argparse.ArgumentParser(prog="TAG",description="Combine and tag input files.",epilog="File Format: <bed file location><catagory><value>...")
parser.add_argument('-i','--in-file',dest='fileLocation')
parser.add_argument('-c','--chromosome',dest='chromosomeLoc')
parser.add_argument('-m','--macs',dest='MACS',action='store_true')
parser.add_argument('-t','--tags',dest='TAGS',action='store_true')
parser.add_argument('-s','--summits',dest='SUMMITS',action='store_true')
return parser.parse_args(sys.argv[1:])
def zeroTest(double,value):
lower=double-value
upper=double+value-1
if lower<0:
double=0
upper=upper-lower
return str(int(lower)),str(int(upper))
def main():
args=fromStdin()
rawpeaks=(buildPeaks(args.fileLocation,trip=False,chromosomeLoc=args.chromosomeLoc))()
peaks=peakCapHandler()
peaks.add(rawpeaks)
peaks.overlap(350)
a=lambda i :i.data[0].chro
b=lambda i :i.define.data["name"]
c=lambda i ,o :zeroTest(i.summit,o)
d=lambda i : [str(j.score) for j in i.data]
e=lambda i : [str(j.summit) for j in i.data]
x=[[a(i),c(i,150),d(i),b(i),e(i)] for i in peaks.data]
for i in range(len(peaks.data)):
sys.stdout.write( x[i][0]+"\t"+ str(x[i][1][0])+"\t"+ str(x[i][1][1])+"\t")
if(args.MACS):
sys.stdout.write("{")
for j in range(len(x[i][2])-1):
sys.stdout.write(str(x[i][3][j])+":"+str(x[i][2][j])+",")
sys.stdout.write(str(x[i][3][-1])+":"+x[i][2][-1]+"}\t")
if(args.SUMMITS):
sys.stdout.write("{")
for j in range(len(x[i][4])-1):
sys.stdout.write(str(x[i][3][j])+":"+str(x[i][4][j])+",")
sys.stdout.write(str(x[i][3][-1])+":"+x[i][4][-1]+"}\t")
if(args.TAGS):
for j in range(len(x[i][3])-1):
sys.stdout.write(str(x[i][3][j])+"-")
sys.stdout.write(x[i][3][-1])
sys.stdout.write("\n")
if __name__=='__main__':
main()
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2_prediction/regression.py | scale-lab/EDAonCloud | 7 | 127637 | import matplotlib.pyplot as plt
from dgl.data.utils import load_graphs
import numpy as np
from sklearn import linear_model
plt.rcParams.update({'font.size': 14})
def design_size(design_file):
g, _ = load_graphs('data/dgl/' + design_file + '.def.dgl')
return g[0].num_nodes(), g[0].num_edges()
def analyze():
train_file = 'data/train.csv'
test_file = 'data/test.csv'
num_nodes = []
num_edges = []
runtimes = []
with open(train_file, 'r') as f:
f.readline() # to skip header
for line in f:
design_file, runtime = line.strip().split(',')
nodes, edges = design_size(design_file)
num_nodes.append(nodes)
num_edges.append(edges)
runtimes.append(float(runtime))
with open(test_file, 'r') as f:
f.readline() # to skip header
for line in f:
design_file, runtime = line.strip().split(',')
nodes, edges = design_size(design_file)
num_nodes.append(nodes)
num_edges.append(edges)
runtimes.append(float(runtime))
s = [x for x in zip(num_nodes, runtimes) if x[0] >= 5000 and x[0] <= 20000]
x, y = list(zip(*s))
plt.scatter(x, y)
plt.xlabel('Design Size (# cells)')
plt.ylabel('Runtime (seconds)')
plt.tight_layout()
plt.show()
def train():
train_file = 'data/train.csv'
test_file = 'data/test.csv'
num_nodes = []
num_edges = []
runtimes = []
with open(train_file, 'r') as f:
f.readline() # to skip header
for line in f:
design_file, runtime = line.strip().split(',')
nodes, edges = design_size(design_file)
num_nodes.append(nodes)
num_edges.append(edges)
runtimes.append(float(runtime))
regr = linear_model.LinearRegression()
regr.fit(np.array(num_nodes).reshape(-1, 1), np.array(runtimes).reshape(-1, 1))
num_nodes = []
num_edges = []
runtimes = []
with open(test_file, 'r') as f:
f.readline() # to skip header
for line in f:
design_file, runtime = line.strip().split(',')
nodes, edges = design_size(design_file)
num_nodes.append(nodes)
num_edges.append(edges)
runtimes.append(float(runtime))
pred = regr.predict(np.array(num_nodes).reshape(-1, 1))
# calculate avg error
errors = []
for i in range(len(pred)):
e = abs(pred[i].item() - runtimes[i]) / min(pred[i].item(), runtimes[i])
errors.append(e)
print(sum(errors) / len(errors))
if __name__ == '__main__':
analyze()
train()
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sdk/communication/azure-communication-phonenumbers/azure/communication/phonenumbers/_generated/models/_models_py3.py | mohamedshabanofficial/azure-sdk-for-python | 0 | 194372 | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import datetime
from typing import List, Optional, Union
from azure.core.exceptions import HttpResponseError
import msrest.serialization
from ._phone_numbers_client_enums import *
class AcquiredPhoneNumber(msrest.serialization.Model):
"""Represents an acquired phone number.
All required parameters must be populated in order to send to Azure.
:param id: Required. The id of the phone number, e.g. 11234567890.
:type id: str
:param phone_number: Required. String of the E.164 format of the phone number, e.g.
+11234567890.
:type phone_number: str
:param country_code: Required. The ISO 3166-2 code of the phone number's country, e.g. US.
:type country_code: str
:param phone_number_type: Required. The phone number's type, e.g. Geographic, TollFree.
Possible values include: "geographic", "tollFree".
:type phone_number_type: str or ~azure.communication.phonenumbers.models.PhoneNumberType
:param capabilities: Required. Capabilities of a phone number.
:type capabilities: ~azure.communication.phonenumbers.models.PhoneNumberCapabilities
:param assignment_type: Required. The assignment type of the phone number. A phone number can
be assigned to a person, or to an application. Possible values include: "person",
"application".
:type assignment_type: str or
~azure.communication.phonenumbers.models.PhoneNumberAssignmentType
:param purchase_date: Required. The date and time that the phone number was purchased.
:type purchase_date: ~datetime.datetime
:param cost: Required. The incurred cost for a single phone number.
:type cost: ~azure.communication.phonenumbers.models.PhoneNumberCost
"""
_validation = {
'id': {'required': True},
'phone_number': {'required': True},
'country_code': {'required': True},
'phone_number_type': {'required': True},
'capabilities': {'required': True},
'assignment_type': {'required': True},
'purchase_date': {'required': True},
'cost': {'required': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'phone_number': {'key': 'phoneNumber', 'type': 'str'},
'country_code': {'key': 'countryCode', 'type': 'str'},
'phone_number_type': {'key': 'phoneNumberType', 'type': 'str'},
'capabilities': {'key': 'capabilities', 'type': 'PhoneNumberCapabilities'},
'assignment_type': {'key': 'assignmentType', 'type': 'str'},
'purchase_date': {'key': 'purchaseDate', 'type': 'iso-8601'},
'cost': {'key': 'cost', 'type': 'PhoneNumberCost'},
}
def __init__(
self,
*,
id: str,
phone_number: str,
country_code: str,
phone_number_type: Union[str, "PhoneNumberType"],
capabilities: "PhoneNumberCapabilities",
assignment_type: Union[str, "PhoneNumberAssignmentType"],
purchase_date: datetime.datetime,
cost: "PhoneNumberCost",
**kwargs
):
super(AcquiredPhoneNumber, self).__init__(**kwargs)
self.id = id
self.phone_number = phone_number
self.country_code = country_code
self.phone_number_type = phone_number_type
self.capabilities = capabilities
self.assignment_type = assignment_type
self.purchase_date = purchase_date
self.cost = cost
class AcquiredPhoneNumbers(msrest.serialization.Model):
"""The list of acquired phone numbers.
All required parameters must be populated in order to send to Azure.
:param phone_numbers: Required. Represents a list of phone numbers.
:type phone_numbers: list[~azure.communication.phonenumbers.models.AcquiredPhoneNumber]
:param next_link: Represents the URL link to the next page of phone number results.
:type next_link: str
"""
_validation = {
'phone_numbers': {'required': True},
}
_attribute_map = {
'phone_numbers': {'key': 'phoneNumbers', 'type': '[AcquiredPhoneNumber]'},
'next_link': {'key': 'nextLink', 'type': 'str'},
}
def __init__(
self,
*,
phone_numbers: List["AcquiredPhoneNumber"],
next_link: Optional[str] = None,
**kwargs
):
super(AcquiredPhoneNumbers, self).__init__(**kwargs)
self.phone_numbers = phone_numbers
self.next_link = next_link
class CommunicationError(msrest.serialization.Model):
"""The Communication Services error.
Variables are only populated by the server, and will be ignored when sending a request.
All required parameters must be populated in order to send to Azure.
:param code: Required. The error code.
:type code: str
:param message: Required. The error message.
:type message: str
:ivar target: The error target.
:vartype target: str
:ivar details: Further details about specific errors that led to this error.
:vartype details: list[~azure.communication.phonenumbers.models.CommunicationError]
:ivar inner_error: The inner error if any.
:vartype inner_error: ~azure.communication.phonenumbers.models.CommunicationError
"""
_validation = {
'code': {'required': True},
'message': {'required': True},
'target': {'readonly': True},
'details': {'readonly': True},
'inner_error': {'readonly': True},
}
_attribute_map = {
'code': {'key': 'code', 'type': 'str'},
'message': {'key': 'message', 'type': 'str'},
'target': {'key': 'target', 'type': 'str'},
'details': {'key': 'details', 'type': '[CommunicationError]'},
'inner_error': {'key': 'innererror', 'type': 'CommunicationError'},
}
def __init__(
self,
*,
code: str,
message: str,
**kwargs
):
super(CommunicationError, self).__init__(**kwargs)
self.code = code
self.message = message
self.target = None
self.details = None
self.inner_error = None
class CommunicationErrorResponse(msrest.serialization.Model):
"""The Communication Services error.
All required parameters must be populated in order to send to Azure.
:param error: Required. The Communication Services error.
:type error: ~azure.communication.phonenumbers.models.CommunicationError
"""
_validation = {
'error': {'required': True},
}
_attribute_map = {
'error': {'key': 'error', 'type': 'CommunicationError'},
}
def __init__(
self,
*,
error: "CommunicationError",
**kwargs
):
super(CommunicationErrorResponse, self).__init__(**kwargs)
self.error = error
class PhoneNumberCapabilities(msrest.serialization.Model):
"""Capabilities of a phone number.
All required parameters must be populated in order to send to Azure.
:param calling: Required. Capability value for calling. Possible values include: "none",
"inbound", "outbound", "inbound+outbound".
:type calling: str or ~azure.communication.phonenumbers.models.PhoneNumberCapabilityType
:param sms: Required. Capability value for SMS. Possible values include: "none", "inbound",
"outbound", "inbound+outbound".
:type sms: str or ~azure.communication.phonenumbers.models.PhoneNumberCapabilityType
"""
_validation = {
'calling': {'required': True},
'sms': {'required': True},
}
_attribute_map = {
'calling': {'key': 'calling', 'type': 'str'},
'sms': {'key': 'sms', 'type': 'str'},
}
def __init__(
self,
*,
calling: Union[str, "PhoneNumberCapabilityType"],
sms: Union[str, "PhoneNumberCapabilityType"],
**kwargs
):
super(PhoneNumberCapabilities, self).__init__(**kwargs)
self.calling = calling
self.sms = sms
class PhoneNumberCapabilitiesRequest(msrest.serialization.Model):
"""Capabilities of a phone number.
:param calling: Capability value for calling. Possible values include: "none", "inbound",
"outbound", "inbound+outbound".
:type calling: str or ~azure.communication.phonenumbers.models.PhoneNumberCapabilityType
:param sms: Capability value for SMS. Possible values include: "none", "inbound", "outbound",
"inbound+outbound".
:type sms: str or ~azure.communication.phonenumbers.models.PhoneNumberCapabilityType
"""
_attribute_map = {
'calling': {'key': 'calling', 'type': 'str'},
'sms': {'key': 'sms', 'type': 'str'},
}
def __init__(
self,
*,
calling: Optional[Union[str, "PhoneNumberCapabilityType"]] = None,
sms: Optional[Union[str, "PhoneNumberCapabilityType"]] = None,
**kwargs
):
super(PhoneNumberCapabilitiesRequest, self).__init__(**kwargs)
self.calling = calling
self.sms = sms
class PhoneNumberCost(msrest.serialization.Model):
"""The incurred cost for a single phone number.
All required parameters must be populated in order to send to Azure.
:param amount: Required. The cost amount.
:type amount: float
:param currency_code: Required. The ISO 4217 currency code for the cost amount, e.g. USD.
:type currency_code: str
:param billing_frequency: Required. The frequency with which the cost gets billed. Possible
values include: "monthly".
:type billing_frequency: str or ~azure.communication.phonenumbers.models.BillingFrequency
"""
_validation = {
'amount': {'required': True},
'currency_code': {'required': True},
'billing_frequency': {'required': True},
}
_attribute_map = {
'amount': {'key': 'amount', 'type': 'float'},
'currency_code': {'key': 'currencyCode', 'type': 'str'},
'billing_frequency': {'key': 'billingFrequency', 'type': 'str'},
}
def __init__(
self,
*,
amount: float,
currency_code: str,
billing_frequency: Union[str, "BillingFrequency"],
**kwargs
):
super(PhoneNumberCost, self).__init__(**kwargs)
self.amount = amount
self.currency_code = currency_code
self.billing_frequency = billing_frequency
class PhoneNumberOperation(msrest.serialization.Model):
"""Long running operation.
Variables are only populated by the server, and will be ignored when sending a request.
All required parameters must be populated in order to send to Azure.
:param status: Required. Status of operation. Possible values include: "notStarted", "running",
"succeeded", "failed".
:type status: str or ~azure.communication.phonenumbers.models.PhoneNumberOperationStatus
:param resource_location: URL for retrieving the result of the operation, if any.
:type resource_location: str
:param created_date_time: Required. The date that the operation was created.
:type created_date_time: ~datetime.datetime
:param error: The Communication Services error.
:type error: ~azure.communication.phonenumbers.models.CommunicationError
:param id: Required. Id of operation.
:type id: str
:param operation_type: Required. The type of operation, e.g. Search. Possible values include:
"purchase", "releasePhoneNumber", "search", "updatePhoneNumberCapabilities".
:type operation_type: str or ~azure.communication.phonenumbers.models.PhoneNumberOperationType
:ivar last_action_date_time: The most recent date that the operation was changed.
:vartype last_action_date_time: ~datetime.datetime
"""
_validation = {
'status': {'required': True},
'created_date_time': {'required': True},
'id': {'required': True},
'operation_type': {'required': True},
'last_action_date_time': {'readonly': True},
}
_attribute_map = {
'status': {'key': 'status', 'type': 'str'},
'resource_location': {'key': 'resourceLocation', 'type': 'str'},
'created_date_time': {'key': 'createdDateTime', 'type': 'iso-8601'},
'error': {'key': 'error', 'type': 'CommunicationError'},
'id': {'key': 'id', 'type': 'str'},
'operation_type': {'key': 'operationType', 'type': 'str'},
'last_action_date_time': {'key': 'lastActionDateTime', 'type': 'iso-8601'},
}
def __init__(
self,
*,
status: Union[str, "PhoneNumberOperationStatus"],
created_date_time: datetime.datetime,
id: str,
operation_type: Union[str, "PhoneNumberOperationType"],
resource_location: Optional[str] = None,
error: Optional["CommunicationError"] = None,
**kwargs
):
super(PhoneNumberOperation, self).__init__(**kwargs)
self.status = status
self.resource_location = resource_location
self.created_date_time = created_date_time
self.error = error
self.id = id
self.operation_type = operation_type
self.last_action_date_time = None
class PhoneNumberPurchaseRequest(msrest.serialization.Model):
"""The phone number search purchase request.
:param search_id: The search id.
:type search_id: str
"""
_attribute_map = {
'search_id': {'key': 'searchId', 'type': 'str'},
}
def __init__(
self,
*,
search_id: Optional[str] = None,
**kwargs
):
super(PhoneNumberPurchaseRequest, self).__init__(**kwargs)
self.search_id = search_id
class PhoneNumberSearchRequest(msrest.serialization.Model):
"""Represents a phone number search request to find phone numbers. Found phone numbers are temporarily held for a following purchase.
All required parameters must be populated in order to send to Azure.
:param phone_number_type: Required. The type of phone numbers to search for, e.g. geographic,
or tollFree. Possible values include: "geographic", "tollFree".
:type phone_number_type: str or ~azure.communication.phonenumbers.models.PhoneNumberType
:param assignment_type: Required. The assignment type of the phone numbers to search for. A
phone number can be assigned to a person, or to an application. Possible values include:
"person", "application".
:type assignment_type: str or
~azure.communication.phonenumbers.models.PhoneNumberAssignmentType
:param capabilities: Required. Capabilities of a phone number.
:type capabilities: ~azure.communication.phonenumbers.models.PhoneNumberCapabilities
:param area_code: The area code of the desired phone number, e.g. 425.
:type area_code: str
:param quantity: The quantity of desired phone numbers. The default value is 1.
:type quantity: int
"""
_validation = {
'phone_number_type': {'required': True},
'assignment_type': {'required': True},
'capabilities': {'required': True},
'quantity': {'maximum': 2147483647, 'minimum': 1},
}
_attribute_map = {
'phone_number_type': {'key': 'phoneNumberType', 'type': 'str'},
'assignment_type': {'key': 'assignmentType', 'type': 'str'},
'capabilities': {'key': 'capabilities', 'type': 'PhoneNumberCapabilities'},
'area_code': {'key': 'areaCode', 'type': 'str'},
'quantity': {'key': 'quantity', 'type': 'int'},
}
def __init__(
self,
*,
phone_number_type: Union[str, "PhoneNumberType"],
assignment_type: Union[str, "PhoneNumberAssignmentType"],
capabilities: "PhoneNumberCapabilities",
area_code: Optional[str] = None,
quantity: Optional[int] = 1,
**kwargs
):
super(PhoneNumberSearchRequest, self).__init__(**kwargs)
self.phone_number_type = phone_number_type
self.assignment_type = assignment_type
self.capabilities = capabilities
self.area_code = area_code
self.quantity = quantity
class PhoneNumberSearchResult(msrest.serialization.Model):
"""The result of a phone number search operation.
All required parameters must be populated in order to send to Azure.
:param search_id: Required. The search id.
:type search_id: str
:param phone_numbers: Required. The phone numbers that are available. Can be fewer than the
desired search quantity.
:type phone_numbers: list[str]
:param phone_number_type: Required. The phone number's type, e.g. geographic, or tollFree.
Possible values include: "geographic", "tollFree".
:type phone_number_type: str or ~azure.communication.phonenumbers.models.PhoneNumberType
:param assignment_type: Required. Phone number's assignment type. Possible values include:
"person", "application".
:type assignment_type: str or
~azure.communication.phonenumbers.models.PhoneNumberAssignmentType
:param capabilities: Required. Capabilities of a phone number.
:type capabilities: ~azure.communication.phonenumbers.models.PhoneNumberCapabilities
:param cost: Required. The incurred cost for a single phone number.
:type cost: ~azure.communication.phonenumbers.models.PhoneNumberCost
:param search_expires_by: Required. The date that this search result expires and phone numbers
are no longer on hold. A search result expires in less than 15min, e.g.
2020-11-19T16:31:49.048Z.
:type search_expires_by: ~datetime.datetime
"""
_validation = {
'search_id': {'required': True},
'phone_numbers': {'required': True},
'phone_number_type': {'required': True},
'assignment_type': {'required': True},
'capabilities': {'required': True},
'cost': {'required': True},
'search_expires_by': {'required': True},
}
_attribute_map = {
'search_id': {'key': 'searchId', 'type': 'str'},
'phone_numbers': {'key': 'phoneNumbers', 'type': '[str]'},
'phone_number_type': {'key': 'phoneNumberType', 'type': 'str'},
'assignment_type': {'key': 'assignmentType', 'type': 'str'},
'capabilities': {'key': 'capabilities', 'type': 'PhoneNumberCapabilities'},
'cost': {'key': 'cost', 'type': 'PhoneNumberCost'},
'search_expires_by': {'key': 'searchExpiresBy', 'type': 'iso-8601'},
}
def __init__(
self,
*,
search_id: str,
phone_numbers: List[str],
phone_number_type: Union[str, "PhoneNumberType"],
assignment_type: Union[str, "PhoneNumberAssignmentType"],
capabilities: "PhoneNumberCapabilities",
cost: "PhoneNumberCost",
search_expires_by: datetime.datetime,
**kwargs
):
super(PhoneNumberSearchResult, self).__init__(**kwargs)
self.search_id = search_id
self.phone_numbers = phone_numbers
self.phone_number_type = phone_number_type
self.assignment_type = assignment_type
self.capabilities = capabilities
self.cost = cost
self.search_expires_by = search_expires_by
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12415,
11614,
613,
13,
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1853,
29901,
7761,
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4557,
7900,
10194,
1542,
12436,
13,
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20590,
29918,
1256,
29901,
12865,
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13,
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3438,
29901,
376,
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4557,
25733,
613,
13,
4706,
3579,
19290,
13,
268,
1125,
13,
4706,
2428,
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10644,
5958,
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4557,
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1583,
467,
1649,
2344,
12035,
1068,
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13,
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333,
353,
1178,
13,
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6710,
29918,
4537,
353,
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29918,
1853,
13,
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5030,
11614,
353,
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13,
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465,
10194,
29918,
1853,
353,
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1853,
13,
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27574,
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1256,
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20590,
29918,
1256,
13,
4706,
1583,
29889,
18253,
353,
3438,
13,
13,
13,
1990,
7255,
5958,
9861,
29478,
29898,
1516,
5060,
29889,
15550,
2133,
29889,
3195,
1125,
13,
1678,
9995,
1576,
1051,
310,
16692,
9008,
3694,
29889,
13,
13,
1678,
2178,
3734,
4128,
1818,
367,
24146,
297,
1797,
304,
3638,
304,
12634,
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13,
13,
1678,
584,
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29901,
830,
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830,
4569,
1237,
263,
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310,
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27820,
362,
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17607,
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2596,
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9794,
29889,
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4557,
29962,
13,
1678,
584,
3207,
2446,
29918,
2324,
29901,
830,
4569,
1237,
278,
3988,
1544,
304,
278,
2446,
1813,
310,
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1353,
2582,
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13,
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584,
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2446,
29918,
2324,
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851,
13,
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13,
13,
1678,
903,
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353,
426,
13,
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6710,
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2396,
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12403,
2396,
5852,
1118,
13,
1678,
500,
13,
13,
1678,
903,
12715,
29918,
1958,
353,
426,
13,
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525,
6710,
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20326,
2396,
11117,
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6710,
29478,
742,
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1853,
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29961,
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4557,
29962,
16675,
13,
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4622,
29918,
2324,
2396,
11117,
1989,
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525,
4622,
6595,
742,
525,
1853,
2396,
525,
710,
16675,
13,
1678,
500,
13,
13,
1678,
822,
4770,
2344,
12035,
13,
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1583,
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13,
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334,
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13,
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3366,
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4557,
12436,
13,
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2446,
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2324,
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13,
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268,
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6710,
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13,
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1583,
29889,
4622,
29918,
2324,
353,
2446,
29918,
2324,
13,
13,
13,
1990,
22365,
362,
2392,
29898,
1516,
5060,
29889,
15550,
2133,
29889,
3195,
1125,
13,
1678,
9995,
1576,
22365,
362,
15538,
1059,
29889,
13,
13,
1678,
9586,
1849,
526,
871,
24146,
491,
278,
1923,
29892,
322,
674,
367,
17262,
746,
9348,
263,
2009,
29889,
13,
13,
1678,
2178,
3734,
4128,
1818,
367,
24146,
297,
1797,
304,
3638,
304,
12634,
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13,
13,
1678,
584,
3207,
775,
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830,
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450,
1059,
775,
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13,
1678,
584,
1853,
775,
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13,
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2643,
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830,
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450,
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13,
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13,
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440,
279,
3646,
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450,
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13,
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442,
668,
3646,
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13,
1678,
584,
440,
279,
4902,
29901,
8725,
4902,
1048,
2702,
4436,
393,
5331,
304,
445,
1059,
29889,
13,
1678,
584,
29894,
442,
668,
4902,
29901,
1051,
29961,
30022,
17688,
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27820,
362,
29889,
17607,
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2596,
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9794,
29889,
5261,
2523,
362,
2392,
29962,
13,
1678,
584,
440,
279,
6426,
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2704,
29901,
450,
6426,
1059,
565,
738,
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13,
1678,
584,
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442,
668,
6426,
29918,
2704,
29901,
3695,
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27820,
362,
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2596,
29889,
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5261,
2523,
362,
2392,
13,
1678,
9995,
13,
13,
1678,
903,
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353,
426,
13,
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401,
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2396,
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13,
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12403,
2396,
5852,
1118,
13,
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11117,
949,
6194,
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5852,
1118,
13,
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525,
14144,
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949,
6194,
2396,
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1118,
13,
4706,
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3993,
29918,
2704,
2396,
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949,
6194,
2396,
5852,
1118,
13,
1678,
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13,
13,
1678,
903,
12715,
29918,
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353,
426,
13,
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525,
401,
2396,
11117,
1989,
2396,
525,
401,
742,
525,
1853,
2396,
525,
710,
16675,
13,
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525,
4906,
2396,
11117,
1989,
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525,
4906,
742,
525,
1853,
2396,
525,
710,
16675,
13,
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5182,
2396,
11117,
1989,
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525,
5182,
742,
525,
1853,
2396,
525,
710,
16675,
13,
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525,
14144,
2396,
11117,
1989,
2396,
525,
14144,
742,
525,
1853,
2396,
525,
29961,
5261,
2523,
362,
2392,
29962,
16675,
13,
4706,
525,
3993,
29918,
2704,
2396,
11117,
1989,
2396,
525,
3993,
2704,
742,
525,
1853,
2396,
525,
5261,
2523,
362,
2392,
16675,
13,
1678,
500,
13,
13,
1678,
822,
4770,
2344,
12035,
13,
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1583,
29892,
13,
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334,
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13,
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775,
29901,
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13,
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2643,
29901,
851,
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13,
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3579,
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13,
268,
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13,
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2428,
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5261,
2523,
362,
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467,
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12035,
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13,
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2643,
13,
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353,
6213,
13,
4706,
1583,
29889,
14144,
353,
6213,
13,
4706,
1583,
29889,
3993,
29918,
2704,
353,
6213,
13,
13,
13,
1990,
22365,
362,
2392,
5103,
29898,
1516,
5060,
29889,
15550,
2133,
29889,
3195,
1125,
13,
1678,
9995,
1576,
22365,
362,
15538,
1059,
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13,
13,
1678,
2178,
3734,
4128,
1818,
367,
24146,
297,
1797,
304,
3638,
304,
12634,
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13,
13,
1678,
584,
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1059,
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830,
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450,
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362,
15538,
1059,
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13,
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584,
1853,
1059,
29901,
3695,
17688,
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27820,
362,
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17607,
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2596,
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9794,
29889,
5261,
2523,
362,
2392,
13,
1678,
9995,
13,
13,
1678,
903,
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353,
426,
13,
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525,
2704,
2396,
11117,
12403,
2396,
5852,
1118,
13,
1678,
500,
13,
13,
1678,
903,
12715,
29918,
1958,
353,
426,
13,
4706,
525,
2704,
2396,
11117,
1989,
2396,
525,
2704,
742,
525,
1853,
2396,
525,
5261,
2523,
362,
2392,
16675,
13,
1678,
500,
13,
13,
1678,
822,
4770,
2344,
12035,
13,
4706,
1583,
29892,
13,
4706,
334,
29892,
13,
4706,
1059,
29901,
376,
5261,
2523,
362,
2392,
613,
13,
4706,
3579,
19290,
13,
268,
1125,
13,
4706,
2428,
29898,
5261,
2523,
362,
2392,
5103,
29892,
1583,
467,
1649,
2344,
12035,
1068,
19290,
29897,
13,
4706,
1583,
29889,
2704,
353,
1059,
13,
13,
13,
1990,
24323,
4557,
12415,
11614,
29898,
1516,
5060,
29889,
15550,
2133,
29889,
3195,
1125,
13,
1678,
9995,
12415,
11614,
310,
263,
9008,
1353,
29889,
13,
13,
1678,
2178,
3734,
4128,
1818,
367,
24146,
297,
1797,
304,
3638,
304,
12634,
29889,
13,
13,
1678,
584,
3207,
5432,
29901,
830,
5958,
29889,
5915,
3097,
995,
363,
5432,
29889,
20049,
1819,
3160,
29901,
376,
9290,
613,
13,
268,
376,
262,
9917,
613,
376,
449,
9917,
613,
376,
262,
9917,
29974,
449,
9917,
1642,
13,
1678,
584,
1853,
5432,
29901,
851,
470,
3695,
17688,
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27820,
362,
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17607,
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2596,
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9794,
29889,
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4557,
12415,
3097,
1542,
13,
1678,
584,
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269,
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5915,
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995,
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317,
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376,
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262,
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613,
13,
268,
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449,
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613,
376,
262,
9917,
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449,
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13,
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1853,
269,
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470,
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17688,
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27820,
362,
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2596,
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12415,
3097,
1542,
13,
1678,
9995,
13,
13,
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903,
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353,
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13,
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4804,
292,
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13,
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12403,
2396,
5852,
1118,
13,
1678,
500,
13,
13,
1678,
903,
12715,
29918,
1958,
353,
426,
13,
4706,
525,
4804,
292,
2396,
11117,
1989,
2396,
525,
4804,
292,
742,
525,
1853,
2396,
525,
710,
16675,
13,
4706,
525,
29879,
1516,
2396,
11117,
1989,
2396,
525,
29879,
1516,
742,
525,
1853,
2396,
525,
710,
16675,
13,
1678,
500,
13,
13,
1678,
822,
4770,
2344,
12035,
13,
4706,
1583,
29892,
13,
4706,
334,
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13,
4706,
5432,
29901,
7761,
29961,
710,
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376,
9861,
4557,
12415,
3097,
1542,
12436,
13,
4706,
269,
1516,
29901,
7761,
29961,
710,
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376,
9861,
4557,
12415,
3097,
1542,
12436,
13,
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3579,
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13,
268,
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13,
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2428,
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4557,
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1068,
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292,
353,
5432,
13,
4706,
1583,
29889,
29879,
1516,
353,
269,
1516,
13,
13,
13,
1990,
24323,
4557,
12415,
11614,
3089,
29898,
1516,
5060,
29889,
15550,
2133,
29889,
3195,
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13,
1678,
9995,
12415,
11614,
310,
263,
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1353,
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13,
13,
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5432,
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5915,
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13,
268,
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449,
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613,
376,
262,
9917,
29974,
449,
9917,
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13,
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584,
1853,
5432,
29901,
851,
470,
3695,
17688,
29889,
27820,
362,
29889,
17607,
18605,
2596,
29889,
9794,
29889,
9861,
4557,
12415,
3097,
1542,
13,
1678,
584,
3207,
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1516,
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13,
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13,
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1853,
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10718,
29899,
29947,
29953,
29900,
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13,
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2704,
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1989,
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2704,
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5261,
2523,
362,
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13,
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4230,
4276,
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10718,
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13,
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13,
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13,
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334,
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13,
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4660,
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7761,
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4557,
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2230,
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13,
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7761,
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12436,
13,
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268,
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12035,
1068,
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13,
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4882,
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4660,
13,
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10314,
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1256,
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2230,
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1256,
29918,
2230,
13,
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2704,
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1059,
13,
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333,
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13,
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16453,
29918,
1853,
353,
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29918,
1853,
13,
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29889,
4230,
29918,
2467,
29918,
1256,
29918,
2230,
353,
6213,
13,
13,
13,
1990,
24323,
4557,
29925,
27574,
3089,
29898,
1516,
5060,
29889,
15550,
2133,
29889,
3195,
1125,
13,
1678,
9995,
1576,
9008,
1353,
2740,
20590,
2009,
29889,
13,
13,
1678,
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2740,
29918,
333,
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450,
2740,
1178,
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13,
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29918,
333,
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13,
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13,
13,
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903,
12715,
29918,
1958,
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13,
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525,
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333,
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1989,
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1204,
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13,
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13,
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822,
4770,
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13,
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13,
4706,
1583,
29889,
4478,
29918,
333,
353,
2740,
29918,
333,
13,
13,
13,
1990,
24323,
4557,
7974,
3089,
29898,
1516,
5060,
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15550,
2133,
29889,
3195,
1125,
13,
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263,
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2740,
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304,
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7460,
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3694,
526,
5382,
6275,
4934,
363,
263,
1494,
20590,
29889,
13,
13,
1678,
2178,
3734,
4128,
1818,
367,
24146,
297,
1797,
304,
3638,
304,
12634,
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13,
13,
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9794,
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12827,
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319,
13,
268,
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508,
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304,
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2022,
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304,
385,
2280,
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29901,
13,
268,
376,
10532,
613,
376,
6214,
1642,
13,
1678,
584,
1853,
12827,
29918,
1853,
29901,
851,
470,
13,
268,
3695,
17688,
29889,
27820,
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] |
management/views.py | EsraelDawit-a/Drims-Fse-Project | 0 | 30255 | from django.shortcuts import render,redirect,get_object_or_404
from .forms import *
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from .models import *
from django.views.generic import DetailView,DeleteView
from django.urls import reverse_lazy
from test2.notification import send_message
from django.core.paginator import Paginator
from real_price.models import *
from django.core.mail import send_mail
# Create your views here.
@login_required
def add_product(request):
md = ProductCatagory.objects.all()
catagory = AddCatagory()
if request.method == 'POST':
form= AddProducts(request.POST, request.FILES)
context ={'form':form,'cat':md}
if form.is_valid():
form.save()
context ={'form':form,'cat':md,'catagory':catagory}
messages.success(request, 'You Have Sucessfully Added new Product ')
return render(request,'product_add.html',context)
else:
print(form.errors)
context ={'form':form,'cat':md,'catagory':catagory}
print(request.user.username)
print(request.user.id)
return render(request,'product_add.html',context)
else:
form = AddProducts()
context ={'form':form,'cat':md,'catagory':catagory}
if request.user.role == 'Producer':
return render(request,'product_add.html',context)
else:
return redirect('homepage')
return render(request,'product_add.html',context)
@login_required
def delete_product(request,pk):
item = Products.objects.get(pk=pk)
item.delete()
messages.success(request, 'You Have Sucessfully Deleted A Product ')
return redirect ('user_products')
@login_required
def update_product(request,pk):
cat = ProductCatagory.objects.all()
pro = Products.objects.get(pk=pk)
if request.method == 'POST':
form1 = AddProducts(request.POST,request.FILES or None,instance = pro)
if form1.is_valid():
form1.save()
print('Product Updated')
return redirect('user_products')
else:
print(form1.errors)
form1 = AddProducts(instance = pro)
context = {'form1':form1,'product':pro,'cat':cat,'pk':pk}
return render(request,'product_update.html',context)
print(form1.errors)
else:
pro = Products.objects.get(pk=pk)
form1 = AddProducts(instance = pro)
context = {'form1':form1,'product':pro,'cat':cat,'pk':pk}
return render(request,'product_update.html',context)
@login_required
def update_transport(request,pk):
pro = Transports.objects.get(pk=pk)
if request.method == 'POST':
form1 = AddTransports(request.POST,request.FILES or None,instance = pro)
if form1.is_valid():
form1.save()
print('Transport Updated')
messages.success(request, 'Transport Updated Sucessfully !')
return redirect('user_products')
else:
print(form1.errors)
form1 = AddTransports(instance = pro)
context = {'form1':form1,'product':pro,'pk':pk}
return render(request,'transport_update.html',context)
print(form1.errors)
else:
pro = Transports.objects.get(pk=pk)
form1 = AddTransports(instance = pro)
context = {'form1':form1,'product':pro,'pk':pk}
return render(request,'transport_update.html',context)
def contactus(request):
if request.method == "POST":
form = MessageSend(request.POST)
if form.is_valid():
form.save()
print('saved')
messages.success(request, 'Your Message Have Been Sucssusfuly Sent ')
print(messages)
return redirect('contactus')
else:
print('notsaved')
print(form.errors)
messages.error(request, 'Your Message Have Not Been Sent ')
print(messages)
form = MessageSend()
context = {'form':form}
return render(request,'contact.html',context)
else:
form = MessageSend()
context = {'form':form}
return render(request,'contact.html',context)
@login_required
def delete_orders(request,pk):
order = ProductOrders.objects.get(pk=pk)
order.delete()
messages.success(request, 'Order Deleted Successfully ')
return redirect('user_products')
@login_required
def delete_transports(request,pk):
order = Transports.objects.get(pk=pk)
order.delete()
messages.success(request, 'Transport Deleted Successfully')
return redirect('user_products')
@login_required
def user_products(request):
my_orders = ProductOrders.objects.filter(ordered_by = request.user)
my_order = my_orders.count()
print(request.user.role)
if request.user.role == 'Producer':
orders = ProductOrders.objects.filter(prouct_owner = request.user)
order = orders.count()
print(orders)
print(request.user.role)
print(request.user.role)
man = request.user
v = Products.objects.filter(user=man)
context = {}
amount = v.count()
context ={'form':v,'amount':amount,'my_orders':my_orders,'my_order':my_order,'orders':orders,'order':order}
return render(request,'userproducts.html',context)
elif request.user.role == 'Transport-Provider':
t_orders = TransportOrders.objects.filter(transport_owner = request.user)
t_order = t_orders.count()
man = request.user
v = Transports.objects.filter(user=man)
amount = v.count()
context ={'transport':v,'amount':amount,'t_orders':t_orders,'t_order':t_order,'my_orders':my_orders,'my_order':my_order,}
return render(request,'userproducts.html',context)
elif request.user.role == 'Price_teller':
orders = ProductOrders.objects.filter(ordered_by = request.user)
price_teller = ProductPrice.objects.filter(user = request.user)
price_count = price_teller.count()
order = orders.count()
context = {'orders':orders,'order':order,'price_teller':price_teller,'price_count':price_count}
return render(request,'price_teller_orders.html',context)
elif request.user.role =='Buyer':
orders = ProductOrders.objects.filter(ordered_by = request.user)
order = orders.count()
context = {'orders':orders,'order':order}
return render(request,'buyer_orders.html',context)
else:
return redirect('homepage')
@login_required
def add_catagory(request):
if request.method == 'POST':
catagory = AddCatagory(request.POST)
if catagory.is_valid():
catagory.save()
messages.success(request, 'New Catagory Added ! ')
return redirect('create_product')
else:
messages.success(request, 'Invalid Catagory ! ')
return redirect('create_product')
else:
return redirect('create_product')
@login_required
def product_detail(request,pk):
form1 = CommentRev()
com = CustomUser.objects.all()
item = Products.objects.get(pk=pk)
print(pk)
if item.user.Adress:
m =item.user.Adress.split(',')
la = m[0]
lo = m[-2]
else:
la = '89.002'
lo = '42.442'
commentes = CommentReview.objects.filter(product_id=pk)
print(commentes)
if request.method =="POST":
form = CommentRev(request.POST)
if form.is_valid():
print('valid')
form.save()
print('saved')
messages.success(request, 'You Have Sucessfully Commented ')
return render(request,'product_detail_view.html',context={'item':item,'comment':commentes,'com ':com,'form':form1,'la':la,'lo':lo})
else:
messages.success(request, 'Unable To Comment! ')
return render(request,'product_detail_view.html',context={'item':item,'com ':com,'comment':commentes,'form':form1,'la':la,'lo':lo})
return render(request,'product_detail_view.html',context={'item':item,'com ':com,'comment':commentes,'form':form1,'la':la,'lo':lo})
@login_required
def change_status(request,pk):
item = ProductOrders.objects.get(pk=pk)
if item.accecpted:
item.accecpted = False
man = request.user.username
body1 = f'\nDear Customer Sorry Your Product Orderd From {man} is Declined Try another Options \n'\
f'Product_owener:{item.prouct_owner.username}\n' \
f'orderd_by :{item.ordered_by.username}\n' \
f'Ordered Date:{item.order_date}\n'\
f'Ordere Id:{item.pk}\n'\
f'Price:{item.ordered_item.price} birr {item.ordered_item.amount} \n'\
f'Delivery Adress:{request.user.optional_adress}\n'\
f'ordered_item :{item.ordered_item}\n\n' \
f'FROM DRIMS TEAM. \nTank You For Using Our Service'
if item.ordered_by.Email_Adress:
email = item.ordered_by.Email_Adress
send_mail("Hello From Drims Team",
body1,
'With Regards Drims Team',
[email],
fail_silently = False)
print('owener email')
item.save()
messages.success(request, 'Request Declined')
return redirect('user_products')
else:
item.accecpted = True
phone = item.ordered_by.phone
owners_phone = item. prouct_owner.phone
body1 = f'\nDear Customer You Have Sucsessfully Orderd A Product \n'\
f'Your Request is Accepted \n'\
f'Product_owener:{item.prouct_owner.username}\n' \
f'orderd_by :{item.ordered_by.username}\n' \
f'Ordered Date:{item.order_date}\n'\
f'Ordere Id:{item.pk}\n'\
f'Price:{item.ordered_item.price} birr {item.ordered_item.amount} \n'\
f'Delivery Adress:{request.user.optional_adress}\n'\
f'ordered_item :{item.ordered_item}\n\n' \
f'Product_owners Phone Number :{owners_phone}\n\n' \
f'FROM DRIMS TEAM. \nTank You For Using Our Service'
man = item.ordered_by.username
body2 = f'\nDear Customer You Have Sucsessfully Accepted Order from {man} \n'\
f'Your Accepted Order Request \n'\
f'Product_owener:{item.prouct_owner.username}\n' \
f'orderd_by :{item.ordered_by.username}\n' \
f'Ordered Date:{item.order_date}\n'\
f'Ordere Id:{item.pk}\n'\
f'Price:{item.ordered_item.price} birr {item.ordered_item.amount} \n'\
f'Delivery Adress:{request.user.optional_adress}\n'\
f'ordered_item :{item.ordered_item}\n\n' \
f'Orderers Phone Number :{phone}\n\n' \
f'FROM DRIMS TEAM. \nTank You For Using Our Service'
if item.ordered_by.Email_Adress:
email = item.ordered_by.Email_Adress
print('have orderer email')
send_mail("Hello From Drims Team",
body1,
'With Regards Drims Team',
[email],
fail_silently = False)
if item. prouct_owner.Email_Adress:
email = item. prouct_owner.Email_Adress
send_mail("Hello From Drims Team",
body2,
'With Regards Drims Team',
[email],
fail_silently = False)
print('owener email')
print(body1)
print(body2)
item.save()
messages.success(request, 'You Have Sucessfully Accepted A Product Request You will Get Message Notification Soon ')
return redirect('user_products')
return redirect('user_products')
@login_required
def change_status_transport(request,pk):
item = TransportOrders.objects.get(pk=pk)
if item.status:
item.status = False
man =request.user.username
body1 = f'\nDear {item.ordered_by} Sorry Your Request To Get Transport acess From {man} is Declined \n'\
f'Transport owener:{item.transport_owner.username}\n' \
f'orderd_by :{item.ordered_by.username}\n' \
f'Ordered Date:{item.order_date}\n'\
f'Ordere Id:{item.pk}\n'\
f'Try another Options\n'\
f'FROM DRIMS TEAM. \nTank You For Using Our Service'
if item.ordered_by.Email_Adress:
email = item.ordered_by.Email_Adress
send_mail("Hello From Drims Team",
body1,
'With Regards Drims Team',
[email],
fail_silently = False)
print('owener email')
item.save()
messages.success(request, 'Request Declined')
return redirect('user_products')
else:
item.status = True
owners_phone = item.transport_owner.phone
man = request.user.username
body1 = f'\nDear Your Request To Get Transport access From {man} is Accepted \n'\
f'Your Request is Accepted \n'\
f'Transport owener:{item.transport_owner.username}\n' \
f'orderd_by :{item.ordered_by.username}\n' \
f'Ordered Date:{item.order_date}\n'\
f'Ordere Id:{item.pk}\n'\
f'Transport_owners Phone Number :{owners_phone}\n\n' \
f'FROM DRIMS TEAM. \nTank You For Using Our Service'
if item.ordered_by.Email_Adress:
email = item.ordered_by.Email_Adress
send_mail("Hello From Drims Team",
body1,
'With Regards Drims Team',
[email],
)
print('owener email')
send_to = item.ordered_by.phone
print(body1)
item.save()
messages.success(request, 'You Have Sucessfully Accepted A Transport Access Request ')
return redirect('user_products')
@login_required
def order_product(request,pk):
pro = Products.objects.get(pk=pk)
obj = ProductOrders.objects.create(
ordered_by = request.user,
order_source_adress = Products.objects.get(pk=pk).user.Adress,
ordered_item=pro,
prouct_owner = Products.objects.get(pk=pk).user,
order_destination = request.user.Adress,
orderer_optional_adress = request.user.optional_adress
)
phone = request.user.phone
body = f'\nDear Customer You Have Sucsessfully Orderd A Product \n'\
f'Product_owener:{Products.objects.get(pk=pk).user}\n' \
f'orderd_by :{request.user}\n' \
f'Ordered Date:{obj.order_date}\n'\
f'Price:{pro.price}$ {pro.amount}\n'\
f'Delivery Adress:{request.user.optional_adress}\n'\
f'ordered_item :{pro.product_name}\n\n' \
f'Product_owners Phone Number :{pro.user.phone}\n\n' \
f'FROM DRIMS TEAM. \nTank You For Using Our Service'
messages.success(request, 'You Have Sucessfully Sent Order Request For Product Owner\n You will Get Message Notification Soon Wen Request Approved !\n Tank You ! ')
return redirect('product_detail', pk=pk)
@login_required
def search_transport(request):
query = request.GET['query']
filter_by = request.GET['filter_by']
if filter_by == 'description':
product = Transports.objects.filter(description__icontains = query)
paginator = Paginator(product,4)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
messages.success(request, 'Your Search Result Here .. ')
return render(request,'transport_search.html',context={'page_obj':page_obj})
if filter_by == 'specific_adress':
product = Transports.objects.filter(specific_adress__icontains = query)
paginator = Paginator(product,4)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
messages.success(request, 'Your Search Result Here .. ')
return render(request,'transport_search.html',context={'page_obj':page_obj})
if filter_by == 'transport_name':
product = Transports.objects.filter(transport_name__icontains = query)
paginator = Paginator(product,4)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
messages.success(request, 'Your Search Result Here .. ')
return render(request,'transport_search.html',context={'page_obj':page_obj})
if filter_by == 'price':
product = Transports.objects.filter(price__icontains = query)
paginator = Paginator(product,4)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
return render(request,'transport_search.html',context={'page_obj':page_obj})
@login_required
def wantedproductlist(request):
ob = WantedProducts.objects.all()
paginator = Paginator(ob,4)
print(paginator)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
return render(request,'wanted_products_list.html',context={'page_obj':page_obj})
@login_required
def wanted_product_detail(request,pk):
item = WantedProducts.objects.get(pk=pk)
if request.method == 'POST':
phone = item.user.phone
man =request.user.username
body = f'\nDear Customer Your Product Have Been Found By {man} \n'\
f'request id :{item.pk}\n' \
f'founded By :{request.user}\n' \
f'request Date:{item.post_date}\n'\
f'Specific Adress:{item.specific_adress}\n'\
f'requested_item :{item.product_name}\n\n' \
f'Founder Phone Number :{request.user.phone}\n\n' \
f'FROM DRIMS TEAM. \nTank You For Using Our Service'
messages.success(request, 'Your Request is Sent We Will Get To You Later ! Tankyou')
print(body)
return redirect ('wanted_product_detail' ,pk=pk)
else:
context ={'item':item}
return render(request,'wanted_list.html',context)
@login_required
def add_wanted_product(request):
if request.method == 'POST':
form= AddWantedProducts(request.POST, request.FILES)
context ={'form':form}
if form.is_valid():
form.save()
context ={'form':form}
messages.success(request, 'You Have Sucessfully Added Product To Wanted List ')
return render(request,'wanted_product.html',context)
else:
print(form.errors)
context ={'form':form}
print(request.user.username)
print(request.user.id)
return render(request,'wanted_product.html',context)
else:
form = AddWantedProducts()
context ={'form':form}
return render(request,'wanted_product.html',context)
@login_required
def transport_add(request):
form = AddTransports()
context = {'form':form}
if request.method == 'POST':
form = AddTransports(request.POST,request.FILES)
if form.is_valid():
form.save()
messages.success(request, 'You Have Sucessfully Added Transport Options')
return redirect('transport_add')
else:
messages.success(request, 'You Please Provide Valid Information !!!')
return redirect('transport_add')
else:
return render(request,'add_transports.html',context)
@login_required
def display_transports(request):
ob = Transports.objects.all()
paginator = Paginator(ob,4)
print(paginator)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
return render(request,'transport_list.html',context={'page_obj':page_obj})
@login_required
def transport_product_detail(request,pk):
item = Transports.objects.get(pk=pk)
if request.method == 'POST':
print("Request Sent")
messages.success(request, 'Your Request is Sent We Will Get To You Later ! Tankyou')
return redirect ('transport_product_detail' ,pk=pk)
else:
context ={'item':item}
return render(request,'transport_details.html',context)
@login_required
def order_Transport(request,pk):
pro = Transports.objects.get(pk=pk)
obj = TransportOrders.objects.create(
ordered_by = request.user,
order_source_adress = Transports.objects.get(pk=pk).user.Adress,
transport_owner = Transports.objects.get(pk=pk).user,
orderer_optional_adress = request.user.optional_adress
)
messages.success(request, 'You Have Sucessfully Requested A Transport Acsess We Will Get \nTo You When Request Is Accpted Tankyou ! ')
return redirect('transport_product_detail', pk=pk)
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333,
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267,
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565,
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543,
5438,
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13,
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13,
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16432,
29918,
1493,
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267,
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308,
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29889,
562,
346,
29883,
415,
287,
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7700,
13,
4706,
767,
353,
2009,
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1792,
29889,
6786,
13,
4706,
3573,
29896,
353,
285,
12764,
29876,
29928,
799,
21886,
8221,
3575,
10969,
8170,
29881,
3645,
426,
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29913,
338,
3826,
21354,
3967,
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29918,
20348,
29889,
6786,
1012,
29876,
29915,
320,
13,
9651,
285,
29915,
536,
2018,
29918,
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attention_module.py | mourga/variational-lstm | 13 | 102866 | import torch
from torch import nn
from torch.nn import functional as F
def masked_normalization(logits, mask):
scores = F.softmax(logits, dim=-1)
# apply the mask - zero out masked timesteps
masked_scores = scores * mask.float()
# re-normalize the masked scores
normed_scores = masked_scores.div(masked_scores.sum(-1, keepdim=True))
return normed_scores
def sequence_mask(lengths, max_len=None):
"""
Creates a boolean mask from sequence lengths.
"""
batch_size = lengths.numel()
max_len = max_len or lengths.max()
return (torch.arange(0, max_len, device=lengths.device)
.type_as(lengths)
.unsqueeze(0).expand(batch_size, max_len)
.lt(lengths.unsqueeze(1)))
class SelfAttention(nn.Module):
def __init__(self, attention_size,
baseline=False,
batch_first=True,
layers=1,
dropout=.0,
non_linearity="tanh"):
super(SelfAttention, self).__init__()
self.batch_first = batch_first
if non_linearity == "relu":
activation = nn.ReLU()
else:
activation = nn.Tanh()
if baseline:
layers = 2
modules = []
for i in range(layers - 1):
modules.append(nn.Linear(attention_size, attention_size))
modules.append(activation)
modules.append(nn.Dropout(dropout))
# last attention layer must output 1
modules.append(nn.Linear(attention_size, 1))
# modules.append(activation)
# modules.append(nn.Dropout(dropout))
self.attention = nn.Sequential(*modules)
# self.softmax = nn.Softmax(dim=-1)
def forward(self, sequence, lengths):
"""
:param sequence: shape: (batch_size, seq_length, hidden_size)
:param lengths:
:return:
"""
energies = self.attention(sequence).squeeze(-1)
# construct a mask, based on sentence lengths
mask = sequence_mask(lengths, energies.size(1))
# scores = masked_normalization_inf(energies, mask)
scores = masked_normalization(energies, mask)
# scores are of shape: (batch_size, seq_length)
contexts = (sequence * scores.unsqueeze(-1)).sum(1)
return contexts, scores
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Scripts/HLS_Stream_Dowloader/hls_dowloader.py | WilliamMokoena/portfolio | 0 | 39490 | import sys, os, asyncio, shutil
import wget
from ffmpeg import FFmpeg
# Func calls wget to download the file given in url arg
def webget(url):
wget.download(url)
# Fuc calls ffmpeg to transcode .m3u8 to .mp4
def transcode(ffmpeg):
@ffmpeg.on('stderr')
def on_stderr(line):
print(line)
@ffmpeg.on('progress')
def on_progress(progress):
print(progress)
@ffmpeg.on('completed')
def on_completed():
print('\nCompleted')
@ffmpeg.on('error')
def on_error(code):
print('Error:', code)
loop = asyncio.get_event_loop()
loop.run_until_complete(ffmpeg.execute())
loop.close()
def parse_m3u8_url(input_url):
parse_url = input_url.split('/')
input_m3u8 = parse_url[len(parse_url) - 1]
base_url = input_url[:-len(input_m3u8)]
if '?' in input_m3u8:
input_m3u8 = input_m3u8.split('?')[0]
return base_url, input_m3u8
def create_manifest(input_m3u8):
with open(f'./{input_m3u8}', 'r') as f:
file = f.readlines()
manifest = []
for el in file:
el = el[:-1]
if 'http' in el and '?' in el and '=' in el:
manifest.append(el)
elif 'https' in el:
el = el.split('/')
el = el[len(el)-1]
manifest.append(el)
else:
manifest.append(el)
with open('./manifest.m3u8', 'a') as f:
for elm in manifest:
f.write(elm+'\n')
def cleanup_working_dir(input_m3u8, storage_folder):
try:
# Create folder given in arg
os.mkdir(storage_folder)
except FileExistsError:
print('\nWARNING: Output folder exists')
cwd = os.getcwd()
files = os.listdir()
print(f'\nMESSAGE: Cleaning up and Packaging things nicely')
os.mkdir(f'{storage_folder}/{storage_folder}')
for f in files:
# Logic for moving the output file
if f[-3:] == 'mp4':
original = f'{cwd}/{f}'
target = f'{cwd}/{storage_folder}'
# Moving the output file
print(f'\nMESSAGE: Moving {input_m3u8} to {storage_folder}')
shutil.move(original,target)
if f[-4:] == 'm3u8':
original = f'{os.getcwd()}/{f}'
target = f'{os.getcwd()}/{storage_folder}/{storage_folder}'
shutil.move(original,target)
if f[-2:] == 'ts':
original = f'{os.getcwd()}/{f}'
target = f'{os.getcwd()}/{storage_folder}/{storage_folder}'
shutil.move(original,target)
# Read cli args : 'hls-downloader.py ["m3u8_url"] ["mp4_output_name"] ["storage_folder"]'
input_url = sys.argv[1]
output_filename = sys.argv[2]
storage_folder = "./site/media"
base_url, input_m3u8 = parse_m3u8_url(input_url)
# Call wget to download files
if input_m3u8 in os.listdir():
print(f'WARNING: {input_m3u8} already exists')
else:
print(f'MESSAGE: Downloading m3u8 file')
webget(input_url)
print(f'\nMESSAGE: Creating manifest.m3u8')
create_manifest(input_m3u8)
print(f'\nMESSAGE: Reading {input_m3u8}')
data = None
if 'movcloud' in input_url:
with open('playlist.m3u8', 'r') as f:
data = f.read()
elif 'manifest.m3u8' in os.listdir():
with open('manifest.m3u8', 'r') as f:
data = f.read()
if data != None:
contents = data.split('\n')
print(f'\nMESSAGE: Attempting to download items from {input_m3u8}')
for item in contents:
if item in os.listdir():
continue
if 'http' in item and '?' in item and '=' in item:
webget(item)
if 'movcloud' in item:
item_sp = item.split('/')
if item_sp[len(item_sp)-1] in os.listdir():
continue
else:
webget(item)
else:
stxt = item[0:5]
entxt = item[-2:]
if stxt == 'https':
l = item.split('/')
name = item[len(l)-1]
webget(item)
elif entxt == 'ts':
cut = slice(0,-len(input_m3u8))
webget(input_url[cut] + item)
# Configuring ffmpeg
## ffmpeg -i "./folder/file.m3u8" -c copy file.mp4
_ffmpeg = FFmpeg().option('n').input('./manifest.m3u8').output(output_filename,{'c': 'copy'})
print(f'\n\nMESSAGE: Running command: ffmpeg -i ./manifest.m3u8 -c copy {output_filename}')
transcode(_ffmpeg)
cleanup_working_dir(input_m3u8, storage_folder)
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] |
src/inmediag/apps.py | TechFitU/MDSOM | 0 | 197598 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.apps import AppConfig
class MDSOMConfig(AppConfig):
name = 'inmediag'
label = 'inmediag'
verbose_name = u"<NAME>" | [
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Subsets and Splits