max_stars_repo_path
stringlengths
4
237
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stringlengths
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int64
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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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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('')
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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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]
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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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. '''
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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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11855, 29897, 13, 13, 4706, 5174, 7865, 2392, 408, 321, 29901, 13, 9651, 396, 518, 29968, 6632, 16048, 350, 23338, 29962, 450, 15786, 526, 24114, 29892, 541, 451, 278, 2602, 13, 9651, 6287, 353, 6287, 334, 29871, 29906, 13, 9651, 1722, 29918, 7168, 29898, 7168, 29892, 474, 29892, 6287, 29892, 11855, 29897, 13, 9651, 620, 29883, 7943, 353, 5852, 13, 4706, 4038, 29918, 2490, 353, 10272, 29918, 6203, 29898, 7168, 29897, 847, 6287, 3579, 29871, 29906, 13, 13, 4706, 565, 338, 29918, 326, 29916, 29901, 13, 9651, 848, 353, 5159, 13, 9651, 848, 29889, 21843, 4197, 29875, 29892, 4038, 29918, 1457, 29892, 4038, 29918, 2490, 29892, 4038, 29918, 2490, 847, 4038, 29918, 1457, 2314, 13, 9651, 848, 29889, 21843, 29898, 1760, 1007, 29897, 13, 9651, 848, 29889, 4397, 29898, 7039, 29918, 1761, 29961, 29875, 2314, 13, 9651, 11799, 4619, 11860, 29881, 24163, 29888, 24163, 29888, 24163, 29888, 24163, 29888, 24163, 29888, 24163, 29888, 24163, 29879, 29905, 29876, 29908, 1273, 18761, 29898, 1272, 29897, 13, 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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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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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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))
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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 """
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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')
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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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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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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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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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1159, 13, 4706, 1596, 29898, 15003, 29889, 657, 29918, 5416, 3101, 13, 4706, 9637, 29889, 842, 29918, 5416, 29898, 25118, 29889, 25118, 29946, 3101, 13, 4706, 9637, 29889, 842, 29918, 5416, 29898, 710, 29898, 25118, 29889, 25118, 29946, 22130, 29871, 396, 1033, 367, 263, 1347, 13, 4706, 9637, 29889, 21976, 703, 4951, 635, 1159, 13, 13, 1678, 822, 1243, 29918, 2523, 356, 29898, 1311, 1125, 13, 4706, 9637, 353, 4522, 11591, 29898, 21707, 29897, 13, 4706, 269, 353, 376, 201, 131, 29871, 201, 132, 29871, 201, 133, 29871, 201, 134, 29871, 201, 135, 29871, 201, 136, 29871, 201, 137, 29871, 201, 138, 29871, 201, 139, 29871, 201, 140, 29871, 201, 141, 29871, 201, 142, 29871, 201, 143, 29871, 201, 144, 29871, 201, 145, 29871, 31127, 29871, 201, 147, 29871, 201, 148, 29871, 30703, 29871, 201, 150, 29871, 201, 151, 29871, 201, 152, 29871, 201, 153, 29871, 201, 154, 29871, 201, 155, 29871, 201, 156, 29871, 201, 157, 29871, 201, 158, 29871, 201, 159, 29871, 201, 160, 29871, 201, 161, 29871, 201, 162, 29871, 201, 163, 29871, 30556, 29871, 201, 165, 29871, 201, 166, 29871, 201, 167, 29871, 201, 168, 29871, 201, 169, 29871, 201, 170, 29871, 201, 171, 29871, 201, 172, 29871, 201, 173, 29871, 201, 174, 29871, 201, 175, 29871, 201, 176, 29871, 201, 177, 29871, 201, 178, 29871, 30416, 29871, 201, 180, 29871, 201, 181, 29871, 201, 182, 29871, 201, 183, 29871, 201, 184, 29871, 201, 185, 29871, 201, 186, 29871, 201, 187, 29871, 201, 188, 29871, 201, 189, 29871, 201, 190, 29871, 201, 191, 29871, 201, 192, 29871, 201, 193, 29871, 201, 194, 29871, 202, 131, 29871, 202, 132, 29871, 202, 133, 29871, 202, 134, 29871, 202, 135, 29871, 202, 136, 29871, 202, 137, 29871, 202, 138, 29871, 202, 139, 29871, 202, 140, 29871, 202, 141, 29871, 202, 142, 29871, 202, 143, 29871, 202, 144, 29871, 30359, 29871, 202, 146, 29871, 30638, 29871, 202, 148, 29871, 30971, 29871, 202, 150, 29871, 30730, 29871, 202, 152, 29871, 202, 153, 29871, 202, 154, 29871, 202, 155, 29871, 202, 156, 29871, 202, 157, 29871, 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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;">&nbsp;</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="{}">{}&nbsp;</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('&nbsp;&nbsp;' * (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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30004, 13, 1678, 298, 29920, 29889, 4012, 26471, 13, 2 ]
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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1609, 13, 2 ]
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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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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