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codility.com/sorting/__twice_for.py
Jagrmi-C/jagrmitest
0
12791651
<reponame>Jagrmi-C/jagrmitest ar = [1, 4, 7, 2, 6] for i in range(5): for k in range(i+1, 5): print(i, k) print("array") n = len(ar) for i in range(n): for k in range(i, n): print(ar[i], ar[k])
3.453125
3
server/fire_watch/log/log_configs.py
Aradhya-Tripathi/free-watch
5
12791652
<filename>server/fire_watch/log/log_configs.py import logging import fire_watch from fire_watch.errorfactory import LogsNotEnabled FMT = "%(asctime)s:%(name)s:%(message)s" def get_logger( logger_name: str, filename: str, level: int = 10, ) -> logging.getLogger: """Simple logger configuration implemented to support safe logging. Args: logger_name (str): name given to current logger. level (int): severity level. filename (str): file to throw all logs to. Raises: LogsNotEnabled: Raised if logging is tried with out enabling logger in configurations Returns: logging.getLogger: logger object """ if fire_watch.conf["logs"]: logger = logging.getLogger(logger_name) file_handler = logging.FileHandler(filename, mode="a") file_handler.setFormatter(logging.Formatter(FMT)) file_handler.setLevel(level=level) logger.addHandler(file_handler) return logger raise LogsNotEnabled
2.578125
3
dero/ml/typing.py
whoopnip/dero
0
12791653
from typing import List, Dict, Optional, Union, Any import pandas as pd ModelParam = Optional[Union[str, int, float]] ParamDict = Dict[str, ModelParam] ModelDict = Dict[str, Union[ParamDict, float]] AllModelResultsDict = Dict[str, List[ModelDict]] DfDict = Dict[str, pd.DataFrame] ModelOptionPossibilitiesDict = Dict[str, List[Any]] AllModelOptionPossibilitiesDict = Dict[str, ModelOptionPossibilitiesDict] AllModelKwargs = List[Dict[str, Any]] AllModelKwargsDict = Dict[str, AllModelKwargs]
2.453125
2
agents/Power_Supply/8320/power_supply_monitors.2.0.py
nishanthprakash-hpe/nae-scripts
0
12791654
# -*- coding: utf-8 -*- # # (c) Copyright 2018 Hewlett Packard Enterprise Development LP # # 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. Manifest = { 'Name': 'power_supply_monitor', 'Description': 'System Power Supply monitoring agent', 'Version': '2.0', 'Author': 'Aruba Networks' } class Agent(NAE): def __init__(self): uri1 = '/rest/v1/system/subsystems/chassis/base/power_supplies/*?' \ 'attributes=status' self.m1 = Monitor(uri1, 'PSU status') self.graph_status_transition = Graph([self.m1], title=Title( "PSU Status Transition"), dashboard_display=True) self.r1 = Rule('PSU status transition: OK to Output Fault') self.r1.condition( 'transition {} from "ok" to "fault_output"', [self.m1]) self.r1.action(self.status_ok_to_fault_output) self.r2 = Rule('PSU status transition: OK to Input Fault') self.r2.condition( 'transition {} from "ok" to "fault_input"', [self.m1]) self.r2.action(self.status_ok_to_fault_input) self.r3 = Rule('PSU status transition: OK to Warning') self.r3.condition('transition {} from "ok" to "warning"', [self.m1]) self.r3.action(self.status_ok_to_warning) self.r4 = Rule('PSU status transition: Output Fault to OK') self.r4.condition( 'transition {} from "fault_output" to "ok"', [self.m1]) self.r4.action(self.status_fault_output_to_ok) self.r5 = Rule('PSU status transition: Input Fault to OK') self.r5.condition( 'transition {} from "fault_input" to "ok"', [self.m1]) self.r5.action(self.status_fault_input_to_ok) self.r6 = Rule('PSU status transition: Output Fault to OK') self.r6.condition( 'transition {} from "fault_output" to "ok"', [self.m1]) self.r6.action(self.status_fault_output_to_ok) self.r7 = Rule('PSU status transition: Warning to OK') self.r7.condition('transition {} from "warning" to "ok"', [self.m1]) self.r7.action(self.status_warning_to_ok) self.r8 = Rule('PSU status transition: Unknown to OK') self.r8.condition('transition {} from "unknown" to "ok"', [self.m1]) self.r8.action(self.status_unknown_to_ok) self.r9 = Rule('PSU status transition: OK to Unknown') self.r9.condition('transition {} from "ok" to "unknown"', [self.m1]) self.r9.action(self.status_ok_to_unknown) self.r10 = Rule('PSU status transition: Absent to OK') self.r10.condition( 'transition {} from "fault_absent" to "ok"', [self.m1]) self.r10.action(self.status_fault_absent_to_ok) self.r11 = Rule('PSU status transition: OK to Absent') self.r11.condition( 'transition {} from "ok" to "fault_absent"', [self.m1]) self.r11.action(self.status_ok_to_fault_absent) uri2 = '/rest/v1/system/subsystems/chassis/base/power_supplies/*?' \ 'attributes=characteristics.maximum_power' self.m2 = Monitor(uri2, 'maximum (Power in Watts)') self.graph_max_power = Graph([self.m2], title=Title( "PSU Maximum Power in Watts"), dashboard_display=False) uri3 = '/rest/v1/system/subsystems/chassis/base/power_supplies/*?' \ 'attributes=characteristics.instantaneous_power' self.m3 = Monitor(uri3, 'instantaneous (Power in Watts)') self.graph_instantaneous = Graph([self.m3], title=Title( "PSU Instantaneous Power in Watts"), dashboard_display=False) def status_ok_to_fault_input(self, event): label = event['labels'] self.psu_transition_action(label, 'OK to Input Fault') def status_ok_to_fault_output(self, event): label = event['labels'] self.psu_transition_action(label, 'OK to Output Fault') def status_ok_to_warning(self, event): label = event['labels'] self.psu_transition_action(label, 'OK to Warning') def status_fault_input_to_ok(self, event): label = event['labels'] self.psu_transition_action(label, 'Input Fault to OK') def status_fault_output_to_ok(self, event): label = event['labels'] self.psu_transition_action(label, 'Output Fault to OK') def status_warning_to_ok(self, event): label = event['labels'] self.psu_transition_action(label, 'Warning to OK') def status_unknown_to_ok(self, event): label = event['labels'] self.psu_transition_action(label, 'Unknown to OK') def status_ok_to_unknown(self, event): label = event['labels'] self.psu_transition_action(label, 'OK to Unknown') def status_fault_absent_to_ok(self, event): label = event['labels'] self.psu_transition_action(label, 'Absent to OK') def status_ok_to_fault_absent(self, event): label = event['labels'] self.psu_transition_action(label, 'OK to Absent') def psu_transition_action(self, label, transition): _, psu = label.split(',')[0].split('=') self.logger.debug('PSU(' + psu + ') has changed from ' + transition) ActionSyslog(psu + ' status transition: ' + transition) ActionCLI('show environment power-supply')
2.078125
2
blog/admin/__init__.py
hentt30/education4all
0
12791655
""" Admin access page settings """ from django.contrib import admin from blog.models import get_model_factory from .posts_admin import PostAdmin # Register your models here. admin.site.register(get_model_factory('PostsFactory').create(), PostAdmin)
1.484375
1
Road2Knowledge/inScripter/processALL_OpenAire.py
fbellidopazos/OpenScience-Public
0
12791656
<gh_stars>0 # %% import jsonlines from multiprocessing.pool import ThreadPool import glob import json import os # %% whereData = "../OpenAire/publication/" # where to store and access OpenAire data # MUST end with a slash!!!! def process_one_file(outputName,inputFile): output = open(outputName,"w") output.write("[\n") print(f">> Processing file: {inputFile} ...") with jsonlines.open(inputFile) as reader: for obj in reader: output.write(json.dumps(obj)) output.write(",\n") output.write("{ }]") output.close() print(f">> Done processing file: {inputFile} ...") print os.remove(inputFile) # %% allFiles = sorted(glob.glob(f"{whereData}*.json")) # List of all the files for i in allFiles: process_one_file(f"{i}.2mkgc",i) print(">> Done!")
2.671875
3
setup.py
astariul/pytere
21
12791657
import setuptools with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() # # The following code can be used if you have private dependencies. Basically it requires the user to set an # # environment variable `GH_PAT` to a Github Personal Access Token (with access to the private repository). If the env # # var cannot be found, an error is raised. If it can be found, the private package is installed. # import os # try: # gh_pat = os.environ["GH_PAT"] # except KeyError as e: # raise RuntimeError("You didn't set the environment variable `GH_PAT`. This is necessary because this package " # "relies on private package(s), and you need to be authenticated to install these. Please set " # "`GH_PAT` environment variable to your Personnal Access Token (from Github).") from e # # Example of specifying private dependencies : # reqs = [f"<package_name> @ git+https://{gh_pat}@github.com/<user>/<repo>@<tag>#egg=<package_name>"] reqs = [] extras_require = { "test": ["pytest~=7.0", "pytest-cov~=3.0", "coverage-badge~=1.0"], "hook": ["pre-commit~=2.15"], "lint": ["isort~=5.9", "black~=22.1", "flake518~=1.2", "darglint~=1.8"], "docs": ["mkdocs-material~=8.1", "mkdocstrings[python]~=0.18", "mike~=1.1"], } extras_require["all"] = sum(extras_require.values(), []) extras_require["dev"] = ( extras_require["test"] + extras_require["hook"] + extras_require["lint"] + extras_require["docs"] ) setuptools.setup( name="pytere", version="1.0.0.dev0", author="<NAME>", author_email="<EMAIL>", description="A Python Template Repository", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/astariul/pytere", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3.7", "Operating System :: OS Independent", ], python_requires=">=3.7", install_requires=reqs, extras_require=extras_require, )
1.953125
2
Web/sessionManager.py
cmd2001/Open-TesutoHime
11
12791658
<reponame>cmd2001/Open-TesutoHime from userManager import UserManager from flask import request class SessionManager: def __init__(self): self.mem = {} return def check_user_status(self) -> bool: # to check whether current user has logged in properly lid = request.cookies.get('Login_ID') return lid in self.mem def new_session(self, username: str, login_id: str): self.mem[login_id] = username return def get_username(self) -> str: lid = request.cookies.get('Login_ID') return self.mem[lid] if lid in self.mem else '' def get_friendly_name(self) -> str: lid = request.cookies.get('Login_ID') if not (lid in self.mem): return '' return UserManager().get_friendly_name(self.mem[lid]) def get_privilege(self) -> int: lid = request.cookies.get('Login_ID') if not (lid in self.mem): return -1 # lowest Privilege for Guests return UserManager().get_privilege(self.mem[lid]) Login_Manager = SessionManager()
2.78125
3
miniboss/__init__.py
afroisalreadyinu/miniboss
633
12791659
from .main import cli from .services import Service from .context import Context from .types import set_group_name as group_name
1.125
1
plotsky.py
hagabbar/VItamin
13
12791660
import numpy as np from ligo.skymap import kde import matplotlib matplotlib.use('Agg') from matplotlib.colors import to_rgb from matplotlib import pyplot as plt from mpl_toolkits.basemap import Basemap #matplotlib.rc('text', usetex=True) def greedy(density): i,j = np.shape(density) idx = np.argsort(density.flatten())[::-1] c = np.cumsum(density.flatten()[idx]) c = c/c[-1] np.append(c,1.0) p = np.zeros(i*j) p[idx] = c[:] return p.reshape(i,j) def plot_sky(pts,contour=True,filled=False,ax=None,trueloc=None,cmap='Reds',col='red'): cls = kde.Clustered2DSkyKDE pts[:,0] = pts[:,0] - np.pi skypost = cls(pts, trials=5, jobs=8) # make up some data on a regular lat/lon grid. # nlats = 145; nlons = 291; delta = 2.*np.pi/(nlons-1) nlats = 145; nlons = 291; delta = 2.*np.pi/(nlons-1) lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:]) # lons = (delta*np.indices((nlats,nlons))[1,:,:]) lons = (delta*np.indices((nlats,nlons))[1,:,:]-np.pi) locs = np.column_stack((lons.flatten(),lats.flatten())) prob = skypost(locs).reshape(nlats,nlons) p1 = greedy(prob) # compute mean location of samples nx = np.cos(pts[:,1])*np.cos(pts[:,0]) ny = np.cos(pts[:,1])*np.sin(pts[:,0]) nz = np.sin(pts[:,1]) mean_n = [np.mean(nx),np.mean(ny),np.mean(nz)] # bestloc = [np.remainder(np.arctan2(mean_n[1],mean_n[0]),2.0*np.pi),np.arctan2(mean_n[2],np.sqrt(mean_n[0]**2 + mean_n[1]**2))] bestloc = [trueloc[0],trueloc[1]] if ax is None: # map = Basemap(projection='ortho',lon_0=-bestloc[0]*180/np.pi,lat_0=bestloc[1]*180/np.pi,resolution=None,celestial=True) map = Basemap(projection='moll',lon_0=0,resolution=None,celestial=True) map.drawmapboundary(fill_color='white') # draw lat/lon grid lines every 30 degrees. # map.drawmeridians(np.arange(0,360,30)) meridian = ["-180","-150","-120","-90","-60","-30","0","30","+60","+90","+120","+150"] map.drawmeridians(np.arange(-180,180,30),labels=[1,1,1,1]) for i in np.arange(len(meridian)): plt.annotate(r"$\textrm{%s}$" % meridian[i] + u"\u00b0",xy=map(np.arange(-180,180,30)[i],0),xycoords='data') map.drawparallels(np.arange(-90,90,30),labels=[1,0,0,0]) else: map = ax # compute native map projection coordinates of lat/lon grid. # x, y = map(lons*180./np.pi, lats*180./np.pi) x, y = map(lons*180./np.pi, lats*180./np.pi) # contour data over the map. if filled: base_color = np.array(to_rgb(col)) opp_color = 1.0 - base_color cs1 = map.contourf(x,y,1.0-p1,levels=[0.0,0.1,0.5,1.0],colors=[base_color+opp_color,base_color+0.8*opp_color,base_color+0.6*opp_color,base_color]) cs2 = map.contour(x,y,p1,levels=[0.5,0.9],linewidths=2.0,colors=col) if trueloc is not None: xx, yy = map((trueloc[0]*180./np.pi)-180.0, trueloc[1]*180./np.pi) map.plot(xx,yy,marker='+',markersize=20,linewidth=5,color='black') return map
2.421875
2
TEKDB/TEKDB/apps.py
Ecotrust/TEKDB
4
12791661
# TEKDB/apps.py from django.apps import AppConfig class TEKDBConfig(AppConfig): name = 'TEKDB' verbose_name = 'Records'
1.15625
1
DataStructures/LinkedList/CycleDetection.py
baby5/HackerRank
0
12791662
<reponame>baby5/HackerRank<filename>DataStructures/LinkedList/CycleDetection.py #coding:utf-8 def has_cycle(head): ptr1 = head ptr2 = head while ptr2 and ptr1.next: ptr1 = ptr1.next.next ptr2 = ptr2.next if ptr1 is ptr2: return 1 return 0
3.671875
4
utils/trainer.py
tonouchi510/kfp-project
0
12791663
# DLトレーニングで共通のロジック・モジュール import tensorflow as tf from tensorflow.python.data.ops.readers import TFRecordDatasetV2 from tensorflow.python.keras.callbacks import History from google.cloud import storage from typing import Callable, List import os def get_tfrecord_dataset( dataset_path: str, preprocessing: Callable, global_batch_size: int, split: str, data_augmentation: Callable = lambda x, y: (x, y), ) -> TFRecordDatasetV2: """TFRecordからデータパイプラインを構築する. Args: dataset_path (str): 目的のTFRecordファイルが保存されているパス. preprocessing (Callable): 適用する前処理関数. global_batch_size (int): バッチサイズ(分散処理の場合は合計). split (str): train or valid data_augmentation (Callable, optional): データオーグメンテーション関数. Defaults to lambdax:x. Raises: FileNotFoundError: dataset_pathにファイルが存在しない場合. Returns: TFRecordDatasetV2: 定義済みのデータパイプライン. """ # Build a pipeline file_names = tf.io.gfile.glob( f"{dataset_path}/{split}-*.tfrec" ) dataset = tf.data.TFRecordDataset( file_names, num_parallel_reads=tf.data.AUTOTUNE) if not file_names: raise FileNotFoundError(f"Not found: {dataset}") option = tf.data.Options() if split == "train": option.experimental_deterministic = False dataset = dataset.with_options(option) \ .map(lambda example: preprocessing(example=example), num_parallel_calls=tf.data.AUTOTUNE) \ .map(lambda x, y: data_augmentation(x, y)) \ .shuffle(512, reshuffle_each_iteration=True) \ .batch(global_batch_size, drop_remainder=True) \ .prefetch(tf.data.AUTOTUNE) else: option.experimental_deterministic = True dataset = dataset.with_options(option) \ .map(lambda example: preprocessing(example=example), num_parallel_calls=tf.data.AUTOTUNE) \ .batch(global_batch_size, drop_remainder=False) \ .prefetch(tf.data.AUTOTUNE) return dataset class Training: def __init__( self, build_model_func: Callable, job_dir: str, artifacts_dir: str = "", use_tpu: bool = True, ) -> None: """トレーニングの初期設定を行う. TPUノードの管理、TPUStrategyの設定、モデルのロード、コンパイル、checkpointの復旧などを行う. Arguments: build_model_func (Callable): 実験に使うモデルのbuild関数を渡す. job_dir (str): job管理用のGCSパス. checkpointやlogの保存をする. artifacts_dir (str): 実験結果の保存先GCSパス. use_tpu (bool): トレーニングにTPUを使うかどうか. """ # For job management self.job_dir = job_dir self.artifacts_dir = artifacts_dir self.use_tpu = use_tpu self.last_epoch = self._get_last_epoch() if self.use_tpu: # Tpu cluster setup cluster = tf.distribute.cluster_resolver.TPUClusterResolver() tf.config.experimental_connect_to_cluster(cluster) tf.tpu.experimental.initialize_tpu_system(cluster) self.distribute_strategy = tf.distribute.TPUStrategy(cluster) # Load model in distribute_strategy scope with self.distribute_strategy.scope(): self._setup_model(build_model=build_model_func) else: self._setup_model(build_model=build_model_func) self.callbacks = [ tf.keras.callbacks.TensorBoard(log_dir=f"{self.job_dir}/logs", histogram_freq=1), tf.keras.callbacks.TerminateOnNaN(), tf.keras.callbacks.ModelCheckpoint( filepath=os.path.join(self.job_dir, "checkpoints/{epoch:05d}"), save_weights_only=True, save_freq="epoch" ) ] def _setup_model(self, build_model: Callable) -> None: if self.last_epoch == 0: self.model = build_model() else: checkpoint = f"{self.job_dir}/checkpoints/{self.last_epoch:0>5}" self.model = build_model(checkpoint=checkpoint) def _get_last_epoch(self) -> int: client = storage.Client() bucket_name = self.job_dir.split("/")[2] dest = self.job_dir.replace(f"gs://{bucket_name}/", "") blobs = client.list_blobs(bucket_name, prefix=f"{dest}/checkpoints") checkpoints = [0] for b in blobs: epoch = b.name.replace(f"{dest}/checkpoints/", "").split(".")[0] if epoch: checkpoints.append(int(epoch)) last_epoch = max(checkpoints) return last_epoch def add_callbacks(self, callbacks: List) -> None: self.callbacks.extend(callbacks) def run_train( self, train_ds: TFRecordDatasetV2, valid_ds: TFRecordDatasetV2, epochs: int ) -> History: """トレーニングを実施し、ログや結果を保存する. tf.keras.Model.fitでのトレーニングを行う. 複雑なトレーニングループが必要な場合もtf.keras.Model.train_stepをオーバーライドするなどして使う. Arguments: train_ds (TFRecordDatasetV2): tensorflowのデータセットパイプライン(学習用). valid_ds (TFRecordDatasetV2): tensorflowのデータセットパイプライン(検証用). epochs (int): トレーニングを回す合計エポック数. """ history = self.model.fit( train_ds, validation_data=valid_ds, callbacks=self.callbacks, initial_epoch=self.last_epoch, epochs=epochs ) if self.artifacts_dir: self.model.save(f"{self.artifacts_dir}/saved_model", include_optimizer=False) return history
2.671875
3
cameramodels/align.py
iory/cameramodels
9
12791664
import numpy as np def align_depth_to_rgb( depth, bgr_cameramodel, depth_cameramodel, depth_to_rgb_transform): """Align depth image to color image. Parameters ---------- depth : numpy.ndarray depth image in meter order. bgr_cameramodel : cameramodels.PinholeCameraModel bgr cameramodel depth_cameramodel : cameramodels.PinholeCameraModel depth cameramodel depth_to_rgb_transform : numpy.ndarray 4x4 transformation matrix. Returns ------- aligned_img : numpy.ndarray aligned image. """ if depth.shape[0] != depth_cameramodel.height \ or depth.shape[1] != depth_cameramodel.width: raise ValueError depth = depth.copy() aligned_img = np.zeros((bgr_cameramodel.height, bgr_cameramodel.width), dtype=np.float32) depth[np.isnan(depth)] = 0 v, u = np.array(np.where(depth)) uv = np.array([u, v]).T rotation = depth_to_rgb_transform[:3, :3] translation = depth_to_rgb_transform[:3, 3] xyz_depth_frame = depth_cameramodel.batch_project_pixel_to_3d_ray( uv, depth=depth[depth > 0]) xyz_rgb_frame = (np.matmul( rotation.T, xyz_depth_frame.T) - np.matmul( rotation.T, translation).reshape(3, -1)).T rgb_uv, indices = bgr_cameramodel.batch_project3d_to_pixel( xyz_rgb_frame, project_valid_depth_only=True, return_indices=True) aligned_img.reshape(-1)[bgr_cameramodel.flatten_uv(rgb_uv)] = \ depth[depth > 0][indices] return aligned_img
2.90625
3
tests/fixtures/me.py
akram/ovh-cli
42
12791665
<filename>tests/fixtures/me.py<gh_stars>10-100 # -*- coding: utf-8 -*- def info(): return { 'country': 'FR', 'firstname': 'John', 'legalform': 'individual', 'name': 'Doe', 'currency': { 'code': 'EUR', 'symbol': 'EURO' }, 'ovhSubsidiary': 'FR', 'birthDay': None, 'organisation': '', 'spareEmail': None, 'area': '', 'phone': '+33.123456789', 'nationalIdentificationNumber': None, 'ovhCompany': 'ovh', 'email': '<EMAIL>', 'companyNationalIdentificationNumber': None, 'language': 'fr_FR', 'fax': '', 'zip': '59100', 'nichandle': 'dj12345-ovh', 'corporationType': None, 'sex': None, 'birthCity': None, 'state': 'complete', 'city': 'Roubaix', 'vat': '', 'address': '2 rue Kellermann' } def get_applications(): return [ { 'status': 'active', 'applicationKey': '<KEY>', 'applicationId': 20001, 'name': 'foobar-1', 'description': 'Lorem ipsum 1' }, { 'status': 'active', 'applicationKey': '<KEY>', 'applicationId': 20003, 'name': 'foobar-3', 'description': 'Lorem ipsum 3' }, { 'status': 'active', 'applicationKey': '<KEY>', 'applicationId': 20002, 'name': 'foobar-2', 'description': 'Lorem ipsum 2' } ] def get_credentials(app_id): return [cred for cred in [ { 'ovhSupport': False, 'rules': [ { 'method': 'GET', 'path': '/*' }, { 'method': 'POST', 'path': '/*' }, { 'method': 'PUT', 'path': '/*' }, { 'method': 'DELETE', 'path': '/*' } ], 'expiration': '2016-08-04T17:52:21+02:00', 'status': 'validated', 'credentialId': 50000002, 'applicationId': 20001, 'creation': '2016-08-03T17:52:21+02:00', 'lastUse': '2016-08-03T17:51:12+02:00' }, { 'ovhSupport': True, 'rules': [ { 'method': 'GET', 'path': '/*' }, { 'method': 'POST', 'path': '/*' }, { 'method': 'PUT', 'path': '/*' }, { 'method': 'DELETE', 'path': '/*' } ], 'expiration': '2016-08-04T17:47:33+02:00', 'status': 'validated', 'credentialId': 50000001, 'applicationId': 20001, 'creation': '2016-08-03T17:47:33+02:00', 'lastUse': '2016-08-03T17:50:23+02:00' } ] if cred['applicationId'] == int(app_id)] def get_application(app_id): return next((app for app in get_applications() if app['applicationId'] == int(app_id))) def get_credential(credential_id): return next((app for app in get_credentials('20001') if app['credentialId'] == int(credential_id))) def get_rules(credential_id): return next((app for app in get_credentials('20001') if app['credentialId'] == int(credential_id)))['rules']
1.757813
2
homeassistant/components/hardkernel/const.py
liangleslie/core
30,023
12791666
<filename>homeassistant/components/hardkernel/const.py """Constants for the Hardkernel integration.""" DOMAIN = "hardkernel"
1.039063
1
DataConnector/AppDataPublisher.py
twatteynelinear/dustlink_sierra
4
12791667
#!/usr/bin/python import logging class NullHandler(logging.Handler): def emit(self, record): pass log = logging.getLogger('AppDataPublisher') log.setLevel(logging.ERROR) log.addHandler(NullHandler()) import copy import threading from DustLinkData import DustLinkData from EventBus import EventBusClient class AppDataPublisher(EventBusClient.EventBusClient): ''' \brief Publishes the data into the DustLinkData database. One instance of this class is created for each application. ''' def __init__(self,appName): # store params self._appName = appName # log log.info('creating instance') # initialize parent class EventBusClient.EventBusClient.__init__(self, 'parsedAppData_{0}'.format(self._appName), self._publish, ) self.name = 'DataConnector_AppDataPublisher_{0}'.format(self._appName) # add stats # local variables #======================== public ========================================== #======================== private ========================================= def _publish(self,sender,signal,data): dld = DustLinkData.DustLinkData() if not dld.getFastMode(): # add mote if needed try: dld.addMote(data['mac']) except ValueError: pass # happens when mote already exists # in demo mode, add demo mode apps to mote if dld.getDemoMode(): for appname in dld.DEMO_MODE_APPS.keys(): try: dld.attachAppToMote(data['mac'],appname) except ValueError: pass # happens when app does not exist, or already attached # attach app to mote if needed try: dld.attachAppToMote(data['mac'],self._appName) except ValueError: pass # happens when app not known, or app already attached to mote # publish in DustLinkData dld.indicateData(data['mac'], self._appName, data['fields'], timestamp=data['timestamp'], ) # log if log.isEnabledFor(logging.DEBUG): log.debug('published {0}'.format(data))
2.1875
2
tests/UnitTests/Morphology/Disambiguator/disambiguator_prefix_rule1_test.py
ZenaNugraha/PySastrawi
282
12791668
import unittest from Sastrawi.Morphology.Disambiguator.DisambiguatorPrefixRule1 import DisambiguatorPrefixRule1a, DisambiguatorPrefixRule1b class Test_DisambiguatorPrefixRule1Test(unittest.TestCase): def setUp(self): self.subject1a = DisambiguatorPrefixRule1a() self.subject1b = DisambiguatorPrefixRule1b() return super(Test_DisambiguatorPrefixRule1Test, self).setUp() def test_disambiguate1a(self): self.assertEquals('ia-ia', self.subject1a.disambiguate('beria-ia')) self.assertIsNone(self.subject1a.disambiguate('berlari')) def test_disambiguate1b(self): self.assertEquals('rakit', self.subject1b.disambiguate('berakit')) self.assertIsNone(self.subject1b.disambiguate('bertabur')) if __name__ == '__main__': unittest.main()
2.78125
3
move.py
laybatin/move-to-registry
0
12791669
<filename>move.py<gh_stars>0 import subprocess import requests import json host='registry.host.com' port='16443' url_addr='{}:{}/v2'.format(host,port) print(url_addr) r = requests.get('https://{}/_catalog'.format(url_addr)) js = json.loads(r.content) #print(js) tag_format='https://' + url_addr + '/{IMAGE_NAME}/tags/list' new_port = '' #ex) :5000 if js['repositories'] != None: for v in js['repositories']: tag_request = json.loads(requests.get(tag_format.format(IMAGE_NAME=v)).content) if tag_request['tags']: for tag in tag_request['tags']: image_path = '{HOST}:{PORT}/{IMAGE_NAME}:{TAG}'.format(HOST=host, PORT=port, IMAGE_NAME=v, TAG=tag) change_image_path = '{HOST}{PORT}/{IMAGE_NAME}:{TAG}'.format(HOST=host, PORT=new_port, IMAGE_NAME=v, TAG=tag) print(image_path + "-->" + change_image_path) subprocess.check_output(['docker', 'pull', image_path], universal_newlines=True) subprocess.check_output(['docker', 'tag', image_path, change_image_path], universal_newlines=True) subprocess.check_output(['docker', 'push', change_image_path], universal_newlines=True)
2.375
2
Phase_1/O-18-by-Yuewei.py
yapanliu/ashrae-ob-database
0
12791670
<filename>Phase_1/O-18-by-Yuewei.py import pandas as pd import numpy as np import os # specify the path data_path = 'D:/yapan_office_D/Data/Annex-79-OB-Database/2021-06-03-raw-data/Annex 79 Data Collection/O-18-Nan Gao/CornishCollege_CleanEXPORT (6)/' template_path = 'D:/yapan_office_D/Data/Annex-79-OB-Database/OB Database Consolidation/Templates/' save_path = 'D:/yapan_office_D/Data/Annex-79-OB-Database/2021-06-03-raw-data/Annex 79 Data Collection/O-18-Nan Gao/_yapan_processing/' # read templates into pandas template_occ_num = pd.read_csv(template_path + 'Occupant_Number_Measurement.csv') template_outdoor = pd.read_csv(template_path + 'Outdoor_Measurement.csv') template_hvac = pd.read_csv(template_path + 'HVAC_Measurement.csv') os.chdir(data_path) # pwd df_1 = pd.read_csv('19.csv') df_2 = pd.read_csv('20.csv') df_3 = pd.read_csv('27.csv') df_4 = pd.read_csv('28.csv') df_5 = pd.read_csv('29.csv') df_6 = pd.read_csv('30.csv') df_7 = pd.read_csv('31.csv') df_8 = pd.read_csv('40.csv') df_9 = pd.read_csv('41.csv') df_10 = pd.read_csv('43.csv') df_11 = pd.read_csv('KB1.csv') df_12 = pd.read_csv('KB2.csv') df_13 = pd.read_csv('KB3.csv') df_14 = pd.read_csv('KB4.csv') df_15 = pd.read_csv('KB5.csv') df_16 = pd.read_csv('KB6.csv') df_1['Room_ID'] = 1 df_2['Room_ID'] = 2 df_3['Room_ID'] = 3 df_4['Room_ID'] = 4 df_5['Room_ID'] = 5 df_6['Room_ID'] = 6 df_7['Room_ID'] = 7 df_8['Room_ID'] = 8 df_9['Room_ID'] = 9 df_10['Room_ID'] = 10 df_11['Room_ID'] = 11 df_12['Room_ID'] = 12 df_13['Room_ID'] = 13 df_14['Room_ID'] = 14 df_15['Room_ID'] = 15 df_16['Room_ID'] = 16 df = pd.concat([df_1, df_2, df_3, df_4, df_5, df_6, df_7, df_8, df_9, df_10, df_11, df_12, df_13, df_14, df_15, df_16], ignore_index=True) Date_Time = df['Unnamed: 0'] Occupancy_Measurement = df['Occupied'] Tin = df['IndoorTemperature'] RHin = df['IndoorHumidity'] CO2in = df['IndoorCO2'] Tout = df['OutdoorTemperature'] RHout = df['OutdoorHumidity'] Wind_Direction = df['OutdoorWindDirection'] Wind_Speed = df['OutdoorWindSpeed'] Room_ID = df['Room_ID'] tem_1 = pd.read_csv('Indoor_Measurement.csv') tem_1['Date_Time'] = Date_Time tem_1['Indoor_Temp'] = Tin tem_1['Indoor_RH'] = RHin tem_1['Indoor_CO2'] = CO2in tem_1['Room_ID'] = Room_ID tem_1['Date_Time'].fillna(-999) tem_1['Date_Time'].fillna(-999) tem_1['Indoor_Temp'].fillna(-999) tem_1['Indoor_RH'].fillna(-999) tem_1['Indoor_CO2'].fillna(-999) tem_1['Room_ID'].fillna(-999) tem_1.to_csv('Indoor_Measurement_18.csv', index=False, header=True) tem_2 = pd.read_csv('Occupancy_Measurement.csv') tem_2['Date_Time'] = Date_Time tem_2['Occupancy_Measurement'] = Occupancy_Measurement tem_2['Room_ID'] = Room_ID tem_2['Date_Time'].fillna(-999) tem_2['Room_ID'].fillna(-999) tem_2['Occupancy_Measurement'].fillna(-999) tem_2.to_csv('Occupancy_Measurement_18.csv', index=False, header=True) tem_3 = pd.read_csv('Outdoor_Measurement.csv') tem_3['Date_Time'] = Date_Time tem_3['Outdoor_Temp'] = Tout tem_3['Outdoor_RH'] = RHout tem_3['Wind_Speed'] = Wind_Speed tem_3['Wind_Direction'] = Wind_Direction tem_3['Building_ID'] = 1 tem_3['Date_Time'].fillna(-999) tem_3['Outdoor_Temp'].fillna(-999) tem_3['Outdoor_RH'].fillna(-999) tem_3['Wind_Speed'].fillna(-999) tem_3['Wind_Direction'].fillna(-999) tem_3.to_csv('Outdoor_Measurement_18.csv', index=False, header=True) ''' yapan added ''' df = df.rename(columns={'Unnamed: 0': 'Date_Time'}) # indoor_cols = ['Date_Time', 'IndoorTemperature', 'IndoorHumidity', 'IndoorCO2', 'Room_ID'] outdoor_cols = ['Date_Time', 'OutdoorTemperature', 'OutdoorHumidity', 'OutdoorWindDirection', 'OutdoorWindSpeed', 'Precipitation', 'SolarRadiation', 'Room_ID'] # occ_cols = ['Date_Time', 'Occupied', 'Room_ID'] hvac_cols = ['Date_Time', 'HeatingState', 'CoolingState', 'Room_ID'] outdoor_df = df[outdoor_cols] outdoor_df.columns = ['Date_Time', 'Outdoor_Temp', 'Outdoor_RH', 'Wind_Direction', 'Wind_Speed', 'Precipitation', 'Solar_Radiation', 'Room_ID'] template_outdoor = pd.concat([template_outdoor, outdoor_df], ignore_index=True) template_outdoor['Building_ID'] = 1 template_hvac.columns hvac_df = df[hvac_cols] hvac_df.columns = ['Date_Time', 'Heating_Status', 'Cooling_Status', 'Room_ID'] template_hvac = pd.concat([template_hvac, hvac_df], ignore_index=True) template_hvac['Building_ID'] = 1 template_hvac['HVAC_Zone_ID'] = 1 # check data print(template_outdoor.isnull().sum()) print(template_outdoor.dtypes) print(template_hvac.isnull().sum()) print(template_hvac.dtypes) # change data types template_outdoor['Date_Time'] = pd.to_datetime(template_outdoor['Date_Time'], format="%Y-%m-%d %H:%M:%S") template_hvac['Date_Time'] = pd.to_datetime(template_hvac['Date_Time'], format="%Y-%m-%d %H:%M:%S") template_outdoor['Building_ID'] = template_outdoor['Building_ID'].astype(int) template_outdoor['Room_ID'] = template_outdoor['Room_ID'].astype(int) template_hvac['Heating_Status'] = template_hvac['Heating_Status'].astype(int) template_hvac['Cooling_Status'] = template_hvac['Cooling_Status'].astype(int) template_hvac['Building_ID'] = template_hvac['Building_ID'].astype(int) template_hvac['Room_ID'] = template_hvac['Room_ID'].astype(int) template_hvac['HVAC_Zone_ID'] = template_hvac['HVAC_Zone_ID'].astype(int) # save data template_outdoor.to_csv(save_path + 'Outdoor_Measurement.csv', index=False) template_hvac.to_csv(save_path + 'HVAC_Measurement.csv', index=False)
2.421875
2
main.py
pteoh/serverless-telegram-bot
2
12791671
<gh_stars>1-10 import os import telegram import random bot = telegram.Bot(token=os.environ["TELEGRAM_TOKEN"]) # dictionary of trigger words with single 1:1 reply singlereplydict = { "tableflip": "(╯°□°)╯︵ ┻━┻", "bagelflip": "(╯°□°)╯︵ 🥯", "tacoflip": "(╯°□°)╯︵ 🌮", "pizzaflip": "(╯°□°)╯︵ 🍕", "hotdogflip": "(╯°□°)╯︵ 🌭", "kittyparty": "🐈🐱🐆🙌🦁🐅🐯", "puppyparty": "🐕🐩🐕🙌🐩🐕🐩", "ponyparty": "🐎🦄🎠🙌🐎🦄🎠", "piggyparty": "🐖🐽🐷🙌🐷🐽🐖", "bunnyparty": "🥕🐇🥬🐰🙌🐰🥬🐇🥕", "flowerbeam": "( ・◡・)つ━☆🌸🌺🌼", "pastryparty": "🍞🥖🥐🥯🥨🥞🍩🍪🍰🧁", "doubleflip": "┻━┻︵ \\(°□°)/ ︵ ┻━┻", "musicdance": "♪┏(°.°)┛┗(°.°)┓┗(°.°)┛┏(°.°)┓ ♪", "shame": "🔔 🔔 🔔", "shrug": "🤷 ¯\_(ツ)_/¯", "disapprove": "ಠ_ಠ", "octodisco": "🎶🐙🎶", "octodance": "🎶🐙🎶", } # dictionary of trigger words with multiple random responses multireplydict = { "backpack": [ "https://media.giphy.com/media/xUA7aXRRUlmqhoG7q8/giphy.gif", "https://media.giphy.com/media/2DjXJ5UmrqYPm/giphy.gif", "https://media.giphy.com/media/E1MTLQN0vFac8/giphy.gif", "Mmm... yeah... the pack for the back.", "I like turtles.", "I like pie.", "Das ist ein rucksack auf Deutsch!", "Oh, and remember, next Friday is Swedish luggage day, so, you know, if you want to, go ahead and wear a bäckpäck.", ], "dumpsterfire": [ "https://media.giphy.com/media/QLyhWVTvAHbAbAdWcp/giphy.gif", "https://media.giphy.com/media/134vVkHV9wQtaw/giphy.gif", "https://media.giphy.com/media/FqtWrearu5vb2/giphy.gif", ], "chika": [ "https://media1.tenor.com/images/38b0f21d0e76dec2ff58d19e37fcc716/tenor.gif?itemid=4484736", "https://1funny.com/wp-content/uploads/2009/07/diabeetus-cat.jpg", "http://rs367.pbsrc.com/albums/oo112/Aim_fire/sdgfasfdgd.jpg~c200", "https://c1.staticflickr.com/3/2254/2334517660_c5a9522dbd.jpg", "https://media.giphy.com/media/Xbvni0CPHxdRK/giphy.gif", "https://media.giphy.com/media/2oLrxIsfNcMH6/giphy.gif", ], # source https://www.factretriever.com/cat-facts "catfact": [ "(1) Unlike dogs, cats do not have a sweet tooth. Scientists believe this is due to a mutation in a key taste receptor.", "(2) When a cat chases its prey, it keeps its head level. Dogs and humans bob their heads up and down.", "(3) The technical term for a cat’s hairball is a “bezoar”.", "(4) A group of cats is called a “clowder”.", "(5) A cat can’t climb head first down a tree because every claw on a cat’s paw points the same way. To get down from a tree, a cat must back down.", "(6) Cats make about 100 different sounds. Dogs make only about 10.", "(7) Every year, nearly four million cats are eaten in Asia.", "(8) There are more than 500 million domestic cats in the world, with approximately 40 recognized breeds.", "(9) Approximately 24 cat skins can make a coat.", "(10) While it is commonly thought that the ancient Egyptians were the first to domesticate cats, the oldest known pet cat was recently found in a 9,500-year-old grave on the Mediterranean island of Cyprus. This grave predates early Egyptian art depicting cats by 4,000 years or more.", "(11) During the time of the Spanish Inquisition, Pope Innocent VIII condemned cats as evil and thousands of cats were burned. Unfortunately, the widespread killing of cats led to an explosion of the rat population, which exacerbated the effects of the Black Death.", "(12) During the Middle Ages, cats were associated with withcraft, and on St. John’s Day, people all over Europe would stuff them into sacks and toss the cats into bonfires. On holy days, people celebrated by tossing cats from church towers.", "(13) The first cat in space was a French cat named Felicette (a.k.a. “Astrocat”) In 1963, France blasted the cat into outer space. Electrodes implanted in her brains sent neurological signals back to Earth. She survived the trip.", "(14) The group of words associated with cat (catt, cath, chat, katze) stem from the Latin catus, meaning domestic cat, as opposed to feles, or wild cat.", "(15) The term “puss” is the root of the principal word for “cat” in the Romanian term pisica and the root of secondary words in Lithuanian (puz) and Low German puus. Some scholars suggest that “puss” could be imitative of the hissing sound used to get a cat’s attention. As a slang word for the female pudenda, it could be associated with the connotation of a cat being soft, warm, and fuzzy.", "(16) Approximately 40,000 people are bitten by cats in the U.S. annually.", "(17) Cats are North America’s most popular pets: there are 73 million cats compared to 63 million dogs. Over 30% of households in North America own a cat.", "(18) According to Hebrew legend, Noah prayed to God for help protecting all the food he stored on the ark from being eaten by rats. In reply, God made the lion sneeze, and out popped a cat.", "(19) A cat’s hearing is better than a dog’s. And a cat can hear high-frequency sounds up to two octaves higher than a human.", "(20) A cat can travel at a top speed of approximately 31 mph (49 km) over a short distance.", "(21) A cat rubs against people not only to be affectionate but also to mark out its territory with scent glands around its face. The tail area and paws also carry the cat’s scent.", "(22) Researchers are unsure exactly how a cat purrs. Most veterinarians believe that a cat purrs by vibrating vocal folds deep in the throat. To do this, a muscle in the larynx opens and closes the air passage about 25 times per second.", "(23) When a family cat died in ancient Egypt, family members would mourn by shaving off their eyebrows. They also held elaborate funerals during which they drank wine and beat their breasts. The cat was embalmed with a sculpted wooden mask and the tiny mummy was placed in the family tomb or in a pet cemetery with tiny mummies of mice.", "(24) In 1888, more than 300,000 mummified cats were found an Egyptian cemetery. They were stripped of their wrappings and carted off to be used by farmers in England and the U.S. for fertilizer.", "(25) Most cats give birth to a litter of between one and nine kittens. The largest known litter ever produced was 19 kittens, of which 15 survived.", "(26) Smuggling a cat out of ancient Egypt was punishable by death. Phoenician traders eventually succeeded in smuggling felines, which they sold to rich people in Athens and other important cities.", "(27) The earliest ancestor of the modern cat lived about 30 million years ago. Scientists called it the Proailurus, which means “first cat” in Greek. The group of animals that pet cats belong to emerged around 12 million years ago.", "(28) The biggest wildcat today is the Siberian Tiger. It can be more than 12 feet (3.6 m) long (about the size of a small car) and weigh up to 700 pounds (317 kg).", "(29) A cat’s brain is biologically more similar to a human brain than it is to a dog’s. Both humans and cats have identical regions in their brains that are responsible for emotions.", "(30) Many Egyptians worshipped the goddess Bast, who had a woman’s body and a cat’s head.", "(31) Mohammed loved cats and reportedly his favorite cat, Muezza, was a tabby. Legend says that tabby cats have an “M” for Mohammed on top of their heads because Mohammad would often rest his hand on the cat’s head.", "(32) While many parts of Europe and North America consider the black cat a sign of bad luck, in Britain and Australia, black cats are considered lucky.", "(33) The most popular pedigreed cat is the Persian cat, followed by the Maine Coon cat and the Siamese cat.", "(34) The smallest pedigreed cat is a Singapura, which can weigh just 4 lbs (1.8 kg), or about five large cans of cat food. The largest pedigreed cats are Maine Coon cats, which can weigh 25 lbs (11.3 kg), or nearly twice as much as an average cat weighs.", "(35) Some cats have survived falls of over 65 feet (20 meters), due largely to their “righting reflex.” The eyes and balance organs in the inner ear tell it where it is in space so the cat can land on its feet. Even cats without a tail have this ability.", "(36) Some Siamese cats appear cross-eyed because the nerves from the left side of the brain go to mostly the right eye and the nerves from the right side of the brain go mostly to the left eye. This causes some double vision, which the cat tries to correct by “crossing” its eyes.", "(37) Researchers believe the word “tabby” comes from Attabiyah, a neighborhood in Baghdad, Iraq. Tabbies got their name because their striped coats resembled the famous wavy patterns in the silk produced in this city.", "(38) A cat can jump up to five times its own height in a single bound.", "(39) Cats hate the water because their fur does not insulate well when it’s wet. The Turkish Van, however, is one cat that likes swimming. Bred in central Asia, its coat has a unique texture that makes it water resistant.", "(40) The Egyptian Mau is probably the oldest breed of cat. In fact, the breed is so ancient that its name is the Egyptian word for “cat.”", "(41) The first commercially cloned pet was a cat named “<NAME>”. He cost his owner $50,000, making him one of the most expensive cats ever.", "(42) A cat usually has about 12 whiskers on each side of its face.", "(43) A cat’s eyesight is both better and worse than humans. It is better because cats can see in much dimmer light and they have a wider peripheral view. It’s worse because they don’t see color as well as humans do. Scientists believe grass appears red to cats.", "(44) Spanish-Jewish folklore recounts that Adam’s first wife, Lilith, became a black vampire cat, sucking the blood from sleeping babies. This may be the root of the superstition that a cat will smother a sleeping baby or suck out the child’s breath.", "(45) Perhaps the most famous comic cat is the Cheshire Cat in Lewis Carroll’s Alice in Wonderland. With the ability to disappear, this mysterious character embodies the magic and sorcery historically associated with cats.", "(46) The smallest wildcat today is the Black-footed cat. The females are less than 20 inches (50 cm) long and can weigh as little as 2.5 lbs (1.2 kg).", "(47) On average, cats spend 2/3 of every day sleeping. That means a nine-year-old cat has been awake for only three years of its life.", "(48) In the original Italian version of Cinderella, the benevolent fairy godmother figure was a cat.", "(49) The little tufts of hair in a cat’s ear that help keep out dirt direct sounds into the ear, and insulate the ears are called “ear furnishings.”", "(50) The ability of a cat to find its way home is called “psi-traveling.” Experts think cats either use the angle of the sunlight to find their way or that cats have magnetized cells in their brains that act as compasses.", ], } def webhook(request): if request.method == "POST": update = telegram.Update.de_json(request.get_json(force=True), bot) chat_id = update.effective_message.chat.id messagetext = update.effective_message.text # direct 1:1 mapped responses for trigger in singlereplydict: try: if trigger in messagetext.lower(): replytext = singlereplydict[trigger] bot.sendMessage(chat_id=chat_id, text=replytext) except AttributeError: pass # these responses have several options to be selected at random for trigger in multireplydict: try: if trigger in messagetext.lower(): replytext = random.choice(multireplydict[trigger]) bot.sendMessage(chat_id=chat_id, text=replytext) except AttributeError: pass return "ok"
2.46875
2
python/anagram_solver.py
patrickleweryharris/code-snippets
5
12791672
import itertools # This snippet has been turned into a full repo: # github.com/patrickleweryharris/anagram_solver def anagram_solver(lst): """ Return all possible combinations of letters in lst @type lst: [str] @rtype: None """ for i in range(0, len(lst) + 1): for subset in itertools.permutations(lst, i): possible = '' for letter in subset: possible += letter if len(possible) == len(lst): # itertools.permutations returns smaller lists print(possible) if __name__ == '__main__': lst = ['o', 'r', 'y', 'a', 'n'] anagram_solver(lst)
3.84375
4
pwm_motor_control_ros/test/integration/pwm_test_support/xbox_controller_joy_stub.py
GTAeberhard/pwm_motor_control_ros
0
12791673
<filename>pwm_motor_control_ros/test/integration/pwm_test_support/xbox_controller_joy_stub.py from sensor_msgs.msg import Joy class XboxControllerJoyStub: @staticmethod def right_trigger_depressed(): joy_msg = XboxControllerJoyStub.idle_controller() joy_msg.axes[5] = -1.0 return joy_msg @staticmethod def right_trigger_half_depressed(): joy_msg = XboxControllerJoyStub.idle_controller() joy_msg.axes[5] = 0.0 return joy_msg @staticmethod def left_trigger_depressed(): joy_msg = XboxControllerJoyStub.idle_controller() joy_msg.axes[2] = -1.0 return joy_msg @staticmethod def left_trigger_half_depressed(): joy_msg = XboxControllerJoyStub.idle_controller() joy_msg.axes[2] = 0.0 return joy_msg @staticmethod def idle_controller(): joy_msg = Joy() joy_msg.axes = [ 0.0 for i in range(8)] joy_msg.buttons = [ 0 for i in range(15)] joy_msg.axes[2] = 1.0 joy_msg.axes[5] = 1.0 return joy_msg
2.28125
2
BranchFilters/HeadToMasterBranchFilterer.py
Christian-Nunnally/git-chains
0
12791674
from BranchFilters.BranchFilterer import BranchFilterer from Interoperability.ShellCommandExecuter import ShellCommandExecuter from RepositoryWalkers.BranchToCommitWalker import BranchToCommitWalker from Logger import Logger class HeadToMasterBranchFilterer(BranchFilterer): def __init__(self, repository): self.logger = Logger(self) self.repository = repository self.repository_directory = repr(repository).split('\'')[1][:-4] self.head_branch_name = self.repository.head.name[11:] self.generate_log_from_head_to_merge_base() def generate_log_from_head_to_merge_base(self): self.logger.log("Determining commit ids between the current head and master:") self.log_from_head_to_merge_base = [] self.logger.log("v head v") for id in self.walk_log_from_head_to_merge_base(): self.log_from_head_to_merge_base.append(id) self.logger.log(id) self.logger.log("^ master ^") def walk_log_from_head_to_merge_base(self): head_master_merge_base = self.get_merge_base("master", self.head_branch_name) walker = BranchToCommitWalker(self.repository, head_master_merge_base) head_branch = self.repository.branches[self.head_branch_name] for commit in walker.walk(head_branch): yield commit.hex def get_merge_base(self, branch_name, other_branch_name): args = ['git', 'merge-base', branch_name, other_branch_name] executer = ShellCommandExecuter(self.repository_directory, args) return executer.execute_for_output() def should_include_branch(self, branch_name): merge_base = self.get_merge_base(self.head_branch_name, branch_name) return merge_base in self.log_from_head_to_merge_base
2.40625
2
arviz/plots/backends/__init__.py
Ban-zee/arviz
0
12791675
<gh_stars>0 """ArviZ plotting backends."""
1.070313
1
Data Science With Python/14-interactive-data-visualization-with-bokeh/01-basic-plotting-with-bokeh/12-color-mapping.py
aimanahmedmoin1997/DataCamp
3
12791676
''' Colormapping The final glyph customization we'll practice is using the CategoricalColorMapper to color each glyph by a categorical property. Here, you're going to use the automobile dataset to plot miles-per-gallon vs weight and color each circle glyph by the region where the automobile was manufactured. The origin column will be used in the ColorMapper to color automobiles manufactured in the US as blue, Europe as red and Asia as green. The automobile data set is provided to you as a Pandas DataFrame called df. The figure is provided for you as p. INSTRUCTIONS 100XP Import CategoricalColorMapper from bokeh.models. Convert the DataFrame df to a ColumnDataSource called source. This has already been done for you. Make a CategoricalColorMapper object called color_mapper with the CategoricalColorMapper() function. It has two parameters here: factors and palette. Add a circle glyph to the figure p to plot 'mpg' (on the y-axis) vs 'weight' (on the x-axis). Remember to pass in source and 'origin' as arguments to source and legend. For the color parameter, use dict(field='origin', transform=color_mapper). ''' #Import CategoricalColorMapper from bokeh.models from bokeh.models import CategoricalColorMapper # Convert df to a ColumnDataSource: source source = ColumnDataSource(df) # Make a CategoricalColorMapper object: color_mapper color_mapper = CategoricalColorMapper(factors=['Europe', 'Asia', 'US'], palette=['red', 'green', 'blue']) # Add a circle glyph to the figure p p.circle('weight', 'mpg', source=source, color=dict(field='origin', transform=color_mapper), legend='origin') # Specify the name of the output file and show the result output_file('colormap.html') show(p)
3.765625
4
esipysi/esipysi.py
FlyingKiwiBird/EsiPysi
7
12791677
import asyncio import aiohttp import requests import json from .op import EsiOp from .auth import EsiAuth from .cache import EsiCache, DictCache from .esisession import EsiSession import logging logger = logging.getLogger("EsiPysi") class EsiPysi(object): """ The EsiPysi class creates "EsiOp" operations based on a provided swagger spec """ def __init__(self, swagger_url, **kwargs): """ Initialize the class Arguments: swagger_url -- Url to the swagger spec Keyword arguments: user_agent -- user agent to send with ESI calls cache -- EsiCache object to use for caching auth -- EsiAuth to use for authorized calls to ESI retries -- Number of retries when ESI returns a retryable error, 0 disables, -1 is unlimited loop -- Event loop to use for asyncio session -- aiohttp session to use, note: loop will be useless if set with session, set the loop you want in the session instead """ self.args = kwargs cache = kwargs.get("cache", DictCache()) if cache is not None: if not issubclass(type(cache), EsiCache): raise TypeError("cache should be of the type EsiCache") session = self.args.get('session') if session is not None: if not isinstance(type(session), aiohttp.ClientSession): raise TypeError("session must be a aiohttp ClientSession") self.operations = {} self.data = {} r = requests.get(swagger_url) try: data = r.json() except: logger.exception("Parse error, spec written to file") with open('esi-spec-error.json', 'w') as esifile: esifile.write(r.text) return finally: r.close() self.data = data self.__analyze_swagger() def session(self): session = EsiSession(self.base_url, self.operations, **self.args) return session def __analyze_swagger(self): #Get base url self.base_url = "https://" + self.data.get("host","") + self.data.get("basePath", "") #Reformat json paths = self.data.get("paths", {}) #each path for route, verbs in paths.items(): #each http verb in a path for verb, operation in verbs.items(): operation_id = operation.get("operationId") if operation_id is None: continue new_op = operation.copy() new_op["path"] = route new_op["verb"] = verb #Handle parameter refs params = operation.get("parameters") new_op["parameters"] = {} for param in params: path = param.get("$ref") if path is None: param_details = param.copy() else: param_details = self.__get_ref(path) param_name = param_details.get("name") new_op["parameters"][param_name] = param_details self.operations[operation_id] = new_op def __get_ref(self, path): path_split = path.split("/") if path_split[0] != "#": #Unsupported return None ref = self.data for i in range(1, len(path_split)): ref = ref.get(path_split[i], {}) return ref
2.53125
3
language/__init__.py
UoA-ECE-RP/sha
1
12791678
__author__ = 'Avinash'
0.988281
1
Part 1/Chapter 4/example 1.1.py
MineSelf2016/PythonInEconomicManagement
0
12791679
<filename>Part 1/Chapter 4/example 1.1.py<gh_stars>0 score = 92 print("优秀") if score >= 90 else print("及格") a = 1 b = 2 print(type(a)) print(type(b)) print(a/b)
3.328125
3
core/extract_plain_text.py
Gatorix/tranSub
1
12791680
<filename>core/extract_plain_text.py import utils import sys path = r'/Users/caosheng/Downloads/Kota Factory (webm)/(English)(499) Kota Factory - EP 01 - Inventory - YouTube.srt' output_file_name = utils.get_filename(path) def extract_plain_text(path, english_only=False, chinese_only=False): timer = utils.Timer() timer.start() subs = utils.load_sub_file(path) plaintext = utils.get_plaintext(subs) if english_only and chinese_only == True: print('仅保留中文和仅保留英文不能同时勾选\nChinese only and English only cannot be checked at the same time') sys.exit(0) elif chinese_only: chinese_lines=[] for i in range(len(plaintext)): chinese_lines.append(utils.chinese_only(plaintext[i])+'\n') utils.write_lines('%s.txt' % (output_file_name), chinese_lines) elif english_only: english_lines=[] for i in range(len(plaintext)): english_lines.append(utils.english_only(plaintext[i])+'\n') utils.write_lines('%s.txt' % (output_file_name), english_lines) else: utils.write_lines('%s.txt' % (output_file_name), plaintext) timer.stop() print('提取完成,用时%.2f秒' % (timer.elapsed)) extract_plain_text(path)
2.796875
3
python/gpio.py
trojanspike/qbian-server
0
12791681
<gh_stars>0 import RPi.GPIO as GPIO from time import sleep GPIO.setmode(g.BCM) try: GPIO.setup(17, GPIO.OUT) # GPIO17 while True: GPIO.output(17, GPIO.HIGH) sleep(1.5) GPIO.output(17, GPIO.LOW) sleep(1.5) except KeyboardInterrupt: GPIO.cleanup();
2.828125
3
branchdb/git_tools.py
CalgaryMichael/branchdb-python
0
12791682
<reponame>CalgaryMichael/branchdb-python # coding=utf-8 from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import os from git import Repo def get_repo(): """Returns the Repo of the current directory""" call_dir = os.getcwd() return Repo(call_dir, search_parent_directories=True) def get_project_root(repo=None): """Returns the path to the top-level directory of current project""" if repo is None: repo = get_repo() return repo.git.rev_parse(u"--show-toplevel") def get_branch_and_root(): """Returns the active branch name and the current project's root path""" repo = get_repo() root = get_project_root(repo) return repo.active_branch.name, root
2.6875
3
discord/webhook/sync.py
RamzziSudip/nextcord
3
12791683
""" The MIT License (MIT) Copyright (c) 2015-present Rapptz Copyright (c) 2021-present tag-epic 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. -------------- Aliased moodule. See the same file in the nextcord folder for more information Autogenerated by aliasgen.py """ from nextcord.webhook.sync import ( MISSING, TYPE_CHECKING, Any, BaseWebhook, DeferredLock, Dict, DiscordServerError, Forbidden, HTTPException, InvalidArgument, List, Literal, Message, NotFound, Optional, PartialMessageable, Route, SyncWebhook, SyncWebhookMessage, Tuple, Type, TypeVar, Union, WebhookAdapter, _context, _get_webhook_adapter, _log, _WebhookContext, _WebhookState, annotations, handle_message_parameters, json, logging, overload, re, threading, time, urlquote, utils, ) __all__ = ("SyncWebhook", "SyncWebhookMessage")
0.96875
1
player.py
duct-tape/taped-car-stereo
0
12791684
import pifacecad import asyncore from mplayer.async import AsyncPlayer def handle_data(data): if not data.startswith('EOF code'): print('log: %s' % (data, )) else: player.quit() def init_player(): # Don't autospawn because we want to setup the args later player = AsyncPlayer(autospawn=False) # Setup additional args player.args = ['-really-quiet', '-msglevel', 'global=6'] # hook a subscriber to MPlayer's stdout player.stdout.hook(handle_data) # Manually spawn the MPlayer process player.spawn() # play a file player.loadfile('/home/pi/y.mp3') metadata = player.metadata or {} cad = init_cad() cad.lcd.write('P: {name}'.format(name=metadata.get('Title', ''))) listener = pifacecad.SwitchEventListener(chip=cad) def play_next(event): print(str(event.pin_num)) player.loadfile('/home/pi/c.mp3') # for i in range(8): listener.register(0, pifacecad.IODIR_FALLING_EDGE, play_next) listener.activate() # run the asyncore event loop asyncore.loop() def init_cad(): cad = pifacecad.PiFaceCAD() return cad if __name__ == '__main__': init_player()
2.625
3
i_xero2/__init__.py
aracnid/i-xero2
0
12791685
"""A set of functions to retrieve and save data into Xero. """ # import package modules from i_xero2.i_xero import ExpiredCredentialsException from i_xero2.i_xero import XeroInterface from i_xero2.i_xero_ui import XeroInterfaceUI __version__ = '2.4.2'
1.710938
2
lib/utils.py
chawins/entangle-rep
15
12791686
import numpy as np import torch def compute_lid(x, x_train, k, exclude_self=False): """ Calculate LID using the estimation from [1] [1] Ma et al., "Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality," ICLR 2018. """ with torch.no_grad(): x = x.view((x.size(0), -1)) x_train = x_train.view((x_train.size(0), -1)) lid = torch.zeros((x.size(0), )) for i, x_cur in enumerate(x): dist = (x_cur.view(1, -1) - x_train).norm(2, 1) # `largest` should be True when using cosine distance if exclude_self: topk_dist = dist.topk(k + 1, largest=False)[0][1:] else: topk_dist = dist.topk(k, largest=False)[0] mean_log = torch.log(topk_dist / topk_dist[-1]).mean() lid[i] = -1 / mean_log return lid # def cal_class_lid(x, x_train, k, exclude_self=False): # """ # Calculate LID on sample using the estimation from [1] # [1] Ma et al., "Characterizing Adversarial Subspaces Using # Local Intrinsic Dimensionality," ICLR 2018. # """ # x = x.view((x.size(0), -1)) # x_train = x_train.view((x_train.size(0), -1)) # lid = torch.zeros((x.size(0), )) # for i, x_cur in enumerate(x): # dist = (x_cur.view(1, -1) - x_train).norm(2, 1) # # `largest` should be True when using cosine distance # if exclude_self: # topk_dist = dist.topk(k + 1, largest=False)[0][1:] # else: # topk_dist = dist.topk(k, largest=False)[0] # mean_log = torch.log(topk_dist / topk_dist[-1]).mean() # lid[i] = -1 / mean_log # return lid def compute_spnorm(inputs, dknn, layers, batch_size=200): assert inputs.requires_grad num_total = inputs.size(0) norm = np.zeros((num_total, len(layers))) num_batches = int(np.ceil(num_total / batch_size)) for i in range(num_batches): begin, end = i * batch_size, (i + 1) * batch_size x = inputs[begin:end] reps = dknn.get_activations(x) for l, layer in enumerate(layers): y = reps[layer] norm[begin:end, l] = compute_spnorm_batch(x, y) return norm def compute_spnorm_batch(inputs, output): """ :param inputs: (batch_size, input_size) :param output: (batch_size, output_size) :return: jacobian: (batch_size, output_size, input_size) """ batch_size, input_dim = inputs.view(inputs.size(0), -1).size() output = output.view(batch_size, -1) jacobian = torch.zeros((batch_size, output.size(1), input_dim)) for i in range(output.size(1)): grad = torch.autograd.grad( output[:, i].sum(), inputs, retain_graph=True)[0] jacobian[:, i, :] = grad.view(batch_size, input_dim) norm = np.zeros((batch_size, )) for i in range(batch_size): norm[i] = np.linalg.norm(jacobian[i].detach().cpu().numpy(), 2) return norm
2.4375
2
plico/utils/loop.py
lbusoni/plico
0
12791687
import abc from six import with_metaclass class Loop(with_metaclass(abc.ABCMeta, object)): @abc.abstractmethod def name(self): assert False @abc.abstractmethod def close(self): assert False @abc.abstractmethod def open(self): assert False @abc.abstractmethod def isClosed(self): assert False @abc.abstractmethod def performOnePass(self): assert False @abc.abstractmethod def getConvergenceStepCount(self): assert False @abc.abstractmethod def hasConverged(self): assert False class LoopException(Exception): def __init__(self, message): Exception.__init__(self, message)
2.9375
3
neuralmt/utils.py
anoopsarkar/nlp-class-hw
7
12791688
<gh_stars>1-10 import io import numpy as np import seaborn as sns import matplotlib.pyplot as plt from PIL import Image def alphaPlot(alpha, output, source): plt.figure(figsize=(14, 6)) sns.heatmap(alpha, xticklabels=output.split(), yticklabels=source.split(), linewidths=.05, cmap="Blues") plt.ylabel('Source') plt.xlabel('Target') plt.xticks(rotation=60) plt.yticks(rotation=0) plt.tight_layout() buff = io.BytesIO() plt.savefig(buff, format='jpg') buff.seek(0) return np.array(Image.open(buff))
2.9375
3
bookorbooks/school/models/class_model.py
talhakoylu/SummerInternshipBackend
1
12791689
<reponame>talhakoylu/SummerInternshipBackend from django.core.exceptions import ValidationError from constants.school_strings import SchoolStrings from django.db import models from school.models.abstract_base_model import AbstractSchoolBaseModel class Class(AbstractSchoolBaseModel): school = models.ForeignKey( "school.School", on_delete=models.CASCADE, related_name="classes_school", verbose_name=SchoolStrings.ClassStrings.school_verbose_name) instructor = models.ForeignKey( "account.InstructorProfile", on_delete=models.CASCADE, related_name="instructors_school", verbose_name=SchoolStrings.ClassStrings.instructor_verbose_name) name = models.CharField( max_length=50, verbose_name=SchoolStrings.ClassStrings.name_verbose_name) grade = models.IntegerField( verbose_name=SchoolStrings.ClassStrings.grade_verbose_name) class Meta: verbose_name = SchoolStrings.ClassStrings.meta_verbose_name verbose_name_plural = SchoolStrings.ClassStrings.meta_verbose_name_plural ordering = ["name", "grade"] def __str__(self): return f"{self.school.name} - {self.name} - Grade: {self.grade}" def clean(self) -> None: """ This method checks whether the teacher trying to be assigned to the class is working in that school. """ if self.instructor.school != self.school: raise ValidationError(SchoolStrings.ClassStrings.instructor_not_working_at_this_school_error)
2.375
2
app/admin/__init__.py
sunshineinwater/flask-Purchase_and_sale
122
12791690
<filename>app/admin/__init__.py #-*- coding:utf-8 -*- # author:Agam # datetime:2018-11-05 from flask import Blueprint admin=Blueprint('admin',__name__) import app.admin.views
1.46875
1
cstorage/tests/listen-gcs.py
sebgoa/triggers
4
12791691
<filename>cstorage/tests/listen-gcs.py #!/usr/bin/env python from google.cloud import pubsub_v1 sub = pubsub_v1.SubscriberClient() #topic = 'project/skippbox/topics/barfoo' sub_name = 'projects/skippbox/subscriptions/carogoasub' subscription = sub.subscribe(sub_name) def callback(message): print message message.ack() future = subscription.open(callback) future.result()
2.078125
2
scripts/irgen.py
srijan-paul/meep
6
12791692
<gh_stars>1-10 # This is a helper script to automatically generate code # since Javascript doesn't have enums and using objects as \ # enums is painful. import re IR = """ pop_ push_ inc dec add sub equals set_var get_var inc_n false_ true_ load_byte print start_if close_if_body end_if start_else end_else start_loop end_loop popn cmp_less cmp_greater load_string make_bus index_var not make_sized_bus set_at_index input len """ opcodes = re.findall(r"\w+", IR) irFile = open('../src/ir.js', 'w') out = "const IR = Object.freeze({\n" k = 0 for op in opcodes: out += f"\t{op}: {k},\n" k += 1 out += "});\n\n" out += """ function irToString(op) { \tswitch(op) { """ for op in opcodes: out += f"\tcase IR.{op}: return '{op.upper()}';\n" out += "\t}\n}" out += """ module.exports = {IR, irToString}; """ irFile.write(out)
2.609375
3
janitor/functions/groupby_agg.py
thatlittleboy/pyjanitor
225
12791693
from typing import Callable, List, Union import pandas_flavor as pf import pandas as pd from janitor.utils import deprecated_alias @pf.register_dataframe_method @deprecated_alias(new_column="new_column_name", agg_column="agg_column_name") def groupby_agg( df: pd.DataFrame, by: Union[List, Callable, str], new_column_name: str, agg_column_name: str, agg: Union[Callable, str], dropna: bool = True, ) -> pd.DataFrame: """Shortcut for assigning a groupby-transform to a new column. This method does not mutate the original DataFrame. Intended to be the method-chaining equivalent of: ```python df = df.assign(...=df.groupby(...)[...].transform(...)) ``` Example: Basic usage. >>> import pandas as pd >>> import janitor >>> df = pd.DataFrame({ ... "item": ["shoe", "shoe", "bag", "shoe", "bag"], ... "quantity": [100, 120, 75, 200, 25], ... }) >>> df.groupby_agg( ... by="item", ... agg="mean", ... agg_column_name="quantity", ... new_column_name="avg_quantity", ... ) item quantity avg_quantity 0 shoe 100 140.0 1 shoe 120 140.0 2 bag 75 50.0 3 shoe 200 140.0 4 bag 25 50.0 Example: Set `dropna=False` to compute the aggregation, treating the null values in the `by` column as an isolated "group". >>> import pandas as pd >>> import janitor >>> df = pd.DataFrame({ ... "x": ["a", "a", None, "b"], "y": [9, 9, 9, 9], ... }) >>> df.groupby_agg( ... by="x", ... agg="count", ... agg_column_name="y", ... new_column_name="y_count", ... dropna=False, ... ) x y y_count 0 a 9 2 1 a 9 2 2 None 9 1 3 b 9 1 :param df: A pandas DataFrame. :param by: Column(s) to groupby on, will be passed into `DataFrame.groupby`. :param new_column_name: Name of the aggregation output column. :param agg_column_name: Name of the column to aggregate over. :param agg: How to aggregate. :param dropna: Whether or not to include null values, if present in the `by` column(s). Default is True (null values in `by` are assigned NaN in the new column). :returns: A pandas DataFrame. """ # noqa: E501 return df.assign( **{ new_column_name: df.groupby(by, dropna=dropna)[ agg_column_name ].transform(agg), } )
3.578125
4
lambda/build/lambda_start_pipeline.py
acere/amazon-sagemaker-drift-detection
27
12791694
<gh_stars>10-100 import boto3 from botocore.config import Config from botocore.exceptions import ClientError import json import os import logging LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO").upper() logger = logging.getLogger() logger.setLevel(LOG_LEVEL) config = Config(retries={"max_attempts": 10, "mode": "standard"}) codepipeline = boto3.client("codepipeline", config=config) sm_client = boto3.client("sagemaker") def check_pipeline(job_id, pipeline_name, pipeline_execution_arn=None): try: if pipeline_execution_arn is None: logger.info( f"Starting SageMaker Pipeline: {pipeline_name} for job: {job_id}" ) response = sm_client.start_pipeline_execution( PipelineName=pipeline_name, PipelineExecutionDisplayName=f"codepipeline-{job_id}", PipelineParameters=[ {"Name": "InputSource", "Value": "CodePipeline"}, ], PipelineExecutionDescription="SageMaker Drift Detection Pipeline", ClientRequestToken=job_id, ) logger.debug(response) pipeline_execution_arn = response["PipelineExecutionArn"] logger.info(f"SageMaker Pipeline arn: {pipeline_execution_arn} started") else: logger.info( f"Checking SageMaker Pipeline: {pipeline_execution_arn} for job: {job_id}" ) response = sm_client.describe_pipeline_execution( PipelineExecutionArn=pipeline_execution_arn ) logger.debug(response) pipeline_execution_status = response["PipelineExecutionStatus"] logger.info( f"SageMaker Pipeline arn: {pipeline_execution_arn} {pipeline_execution_status}" ) if pipeline_execution_status in ["Failed", "Stopped"]: result = { "type": "JobFailed", "message": f"Pipeline Status is {pipeline_execution_status}", "externalExecutionId": pipeline_execution_arn, } codepipeline.put_job_failure_result(jobId=job_id, failureDetails=result) return 400, result elif pipeline_execution_status in ["Executing", "Succeeded"]: result = { "Status": pipeline_execution_status, "PipelineExecutionArn": pipeline_execution_arn, } codepipeline.put_job_success_result( jobId=job_id, outputVariables=result ) return 200, result logger.info(f"Continuing code pipeline job: {job_id}") codepipeline.put_job_success_result( jobId=job_id, continuationToken=pipeline_execution_arn, ) return 202, {"PipelineExecutionArn": pipeline_execution_arn} except ClientError as e: error_code = e.response["Error"]["Code"] error_message = e.response["Error"]["Message"] result = { "type": "JobFailed", "message": error_message, } logger.error(error_message) if error_code != "InvalidJobStateException": codepipeline.put_job_failure_result(jobId=job_id, failureDetails=result) return 500, result except Exception as e: logger.error(e) raise e def lambda_handler(event, context): logger.debug(json.dumps(event)) job_id = event["CodePipeline.job"]["id"] job_data = event["CodePipeline.job"]["data"] user_parameters = job_data["actionConfiguration"]["configuration"]["UserParameters"] pipeline_name = json.loads(user_parameters)["PipelineName"] pipeline_execution_arn = None if "continuationToken" in job_data: pipeline_execution_arn = job_data["continuationToken"] status_code, result = check_pipeline(job_id, pipeline_name, pipeline_execution_arn) logger.debug(json.dumps(result)) return {"statusCode": status_code, "body": json.dumps(result)}
1.992188
2
archive/srcKelly/optimizationMooring.py
mattEhall/FloatingSE
1
12791695
<gh_stars>1-10 from openmdao.main.api import Component, Assembly, convert_units from openmdao.main.datatypes.api import Float, Array, Enum, Str, Int, Bool from openmdao.lib.drivers.api import COBYLAdriver, SLSQPdriver from mooring import Mooring import time import numpy as np class optimizationMooring(Assembly): # variables def configure(self): self.add('driver',COBYLAdriver()) self.add('mooring',Mooring()) self.driver.workflow.add('mooring') self.driver.add_objective('mooring.mooring_total_cost') self.driver.add_parameter('mooring.scope_ratio',low=15.,high=50.,scaler=0.1) self.driver.add_parameter('mooring.pretension_percent',low=2.5,high=20.) self.driver.add_parameter('mooring.mooring_diameter',low=3.,high=10.,scaler=0.01) self.driver.add_constraint('mooring.heel_angle <= 6.') self.driver.add_constraint('mooring.min_offset_unity < 1.0') self.driver.add_constraint('mooring.max_offset_unity < 1.0') def sys_print(example): print 'scope ratio: ',example.scope_ratio print 'pretension percent: ',example.pretension_percent print 'mooring diameter: ',example.mooring_diameter print 'heel angle: ',example.heel_angle print 'min offset unity: ',example.min_offset_unity print 'max offset unity: ',example.max_offset_unity print 'total mooring cost: ',example.mooring_total_cost def example_218WD_3MW(): tt = time.time() example = optimizationMooring() # Mooring,settings example.mooring.fairlead_depth = 13. example.mooring.scope_ratio = 1.5 example.mooring.pretension_percent = 5.0 example.mooring.mooring_diameter = 0.090 example.mooring.number_of_mooring_lines = 3 example.mooring.permanent_ballast_height = 3. example.mooring.fixed_ballast_height = 5. example.mooring.permanent_ballast_density = 4492. example.mooring.fixed_ballast_density = 4000. example.mooring.water_depth = 218. example.mooring.mooring_type = 'CHAIN' example.mooring.anchor_type = 'PILE' example.mooring.fairlead_offset_from_shell = 0.5 # from,spar.py example.mooring.shell_buoyancy = [0.000,144905.961,688303.315,3064761.078] example.mooring.shell_mass = [40321.556,88041.563,137796.144,518693.048] example.mooring.bulkhead_mass = [0.000,10730.836,0.000,24417.970] example.mooring.ring_mass = [1245.878,5444.950,6829.259,28747.490] example.mooring.spar_start_elevation = [13., 7., -5., -20.] example.mooring.spar_end_elevation = [7., -5., -20., -67.] example.mooring.spar_keel_to_CG = 35.861 example.mooring.spar_keel_to_CB = 30.324 example.mooring.spar_outer_diameter = [5.000,6.000,6.000,9.000] example.mooring.spar_wind_force = [1842.442,1861.334,0.000,0.000] example.mooring.spar_wind_moment = [100965.564,85586.296,0.000,0.000] example.mooring.spar_current_force = [0.000,449016.587,896445.823,49077.906] example.mooring.spar_current_moment = [0.000,19074749.640,28232958.052,72692.688] example.mooring.wall_thickness = [0.05,0.05,0.05,0.05] example.mooring.load_condition = 'N' # from,tower_RNA.py example.mooring.RNA_mass = 125000.000 example.mooring.tower_mass = 127877.000 example.mooring.tower_center_of_gravity = 23.948 example.mooring.RNA_keel_to_CG = 142.000 example.mooring.tower_wind_force = 19950.529 example.mooring.tower_wind_moment = 1634522.835 example.mooring.RNA_wind_force = 391966.178 example.mooring.RNA_wind_moment = 47028560.389 example.mooring.RNA_center_of_gravity_x = 4.1 example.run() print '--------------example_218WD_3MW------------------' print "Elapsed time: ", time.time()-tt, " seconds" sys_print(example.mooring) def example_218WD_6MW(): tt = time.time() example = optimizationMooring() example.mooring.fairlead_depth = 13. example.mooring.scope_ratio = 1.5 example.mooring.pretension_percent = 5.0 example.mooring.mooring_diameter = 0.090 example.mooring.number_of_mooring_lines = 3 example.mooring.permanent_ballast_height = 3. example.mooring.fixed_ballast_height = 7. example.mooring.permanent_ballast_density = 4492. example.mooring.fixed_ballast_density = 4000. example.mooring.water_depth = 218. example.mooring.mooring_type = 'CHAIN' example.mooring.anchor_type = 'PILE' example.mooring.fairlead_offset_from_shell = 0.5 example.mooring.shell_buoyancy = [0.000,257610.598,1356480.803,7074631.036] example.mooring.shell_mass = [55118.458,117635.366,193284.525,830352.783] example.mooring.bulkhead_mass = [0.000,19239.055,0.000,51299.008] example.mooring.ring_mass = [3838.515,16391.495,21578.677,127137.831] example.mooring.spar_start_elevation = [13., 7., -5., -20.] example.mooring.spar_end_elevation = [7., -5., -20., -72.] example.mooring.spar_keel_to_CG = 37.177 example.mooring.spar_keel_to_CB = 32.337 example.mooring.spar_outer_diameter = [7.,8.,8.,13.] example.mooring.spar_wind_force = [2374.194,2345.237,0.000,0.000] example.mooring.spar_wind_moment = [137246.585,114777.740,0.000,0.0000] example.mooring.spar_current_force = [0.000,824040.566,1968613.701,182335.850] example.mooring.spar_current_moment = [0.000,37445057.967,67469109.912,353876.402] example.mooring.wall_thickness = [0.05,0.05,0.05,0.05] example.mooring.load_condition = 'N' example.mooring.RNA_mass = 365500.000 example.mooring.tower_mass = 366952.000 example.mooring.tower_center_of_gravity = 33.381 example.mooring.RNA_keel_to_CG = 169.000 example.mooring.tower_wind_force = 33125.492 example.mooring.tower_wind_moment = 3124462.452 example.mooring.RNA_wind_force = 820818.422 example.mooring.RNA_wind_moment = 118970074.187 example.mooring.RNA_center_of_gravity_x = 5.750 example.run() print '--------------example_218WD_6MW------------------' print "Elapsed time: ", time.time()-tt, " seconds" sys_print(example.mooring) def example_218WD_10MW(): tt = time.time() example = optimizationMooring() example.mooring.fairlead_depth = 13. example.mooring.scope_ratio = 1.5 example.mooring.pretension_percent = 5.0 example.mooring.mooring_diameter = 0.090 example.mooring.number_of_mooring_lines = 3 example.mooring.permanent_ballast_height = 4. example.mooring.fixed_ballast_height = 9. example.mooring.permanent_ballast_density = 4492. example.mooring.fixed_ballast_density = 4000. example.mooring.water_depth = 218. example.mooring.mooring_type = 'CHAIN' example.mooring.anchor_type = 'PILE' example.mooring.fairlead_offset_from_shell = 0.5 example.mooring.shell_buoyancy = [0.000,326038.413,1775098.024,13041536.503] example.mooring.shell_mass = [62516.908,132432.268,221028.715,1335368.667] example.mooring.bulkhead_mass = [0.000,24417.970,0.000,68438.752] example.mooring.ring_mass = [6963.553,29512.202,39460.135,617575.510] example.mooring.spar_start_elevation = [13., 7., -5., -20.] example.mooring.spar_end_elevation = [7., -5., -20., -92.] example.mooring.spar_keel_to_CG = 45. example.mooring.spar_keel_to_CB = 42.108 example.mooring.spar_outer_diameter = [8.,9.,9.,15.] example.mooring.spar_wind_force = [2572.428,2522.369,0.000,0.000] example.mooring.spar_wind_moment = [183034.454,157067.701,0.000,0.000] example.mooring.spar_current_force = [0.000,1125719.734,3051908.296,425853.543] example.mooring.spar_current_moment = [0.000,66158450.987,145104271.963,2244211.189] example.mooring.wall_thickness = [0.050,0.050,0.050,0.050] example.mooring.load_condition = 'N' example.mooring.RNA_mass = 677000.000 example.mooring.tower_mass = 698235.000 example.mooring.tower_center_of_gravity = 40.983 example.mooring.RNA_keel_to_CG = 211.000 example.mooring.tower_wind_force = 53037.111 example.mooring.tower_wind_moment = 6112673.024 example.mooring.RNA_wind_force = 1743933.574 example.mooring.RNA_wind_moment = 314378753.986 example.mooring.RNA_center_of_gravity_x = 7.070 example.run() print '--------------example_218WD_10MW------------------' print "Elapsed time: ", time.time()-tt, " seconds" sys_print(example.mooring) if __name__ == "__main__": #example_218WD_3MW() #example_218WD_6MW() example_218WD_10MW()
2.0625
2
OCR.py
developerVictorNkuna/frontend-tools
0
12791696
import pytesseract import requests from PIL import Image from PIL import ImageFilter import io def process_image(url): image= _get_image(url) image.filter(ImageFilter.SHARPEN) return pytesseract.image_to_string(image) def _get_image(url): pattern_string = requests.get(url).content() return Image.open(io.StringIO(pattern_string))
2.875
3
Jasper/Light.py
Granyy/maison_intelligente
0
12791697
#******************************************************************************# #* @TITRE : Light.py *# #* @VERSION : 1.0 *# #* @CREATION : 05 01, 2017 *# #* @MODIFICATION : 05 21, 2017 *# #* @AUTEURS : <NAME> *# #* @COPYRIGHT : Copyright (c) 2017 *# #* <NAME> *# #* <NAME> *# #* <NAME> *# #* <NAME> *# #* <NAME> *# #* @LICENSE : MIT License (MIT) *# #******************************************************************************# import re import datetime import struct import urllib import feedparser import requests import bs4 from client.app_utils import getTimezone from semantic.dates import DateService WORDS = ["LIGHT", "DOWN", "ON"] def handle(text, mic, profile): targetUrl = profile['target']["IP_PORT"] targetMn = profile['target']["ID_MN"] targetAE = 'LED1' url = 'http://' + targetUrl + '/~/' + targetMn + '/mn-name/' + targetAE if re.search(r'\bDOWN\b',text,re.IGNORECASE): querystring = {"op":"ALLfalse"} sentence = "I have turned down the light, sir" else: querystring = {"op":"ALLtrue"} sentence = "I have turned on the light, sir" headers = { 'x-m2m-origin': "admin:admin", 'cache-control': "no-cache", 'postman-token': "<PASSWORD>" } response = requests.request("POST", url, headers=headers, params=querystring) print(response.text) print sentence mic.say(sentence) def isValid(text): return bool(re.search(r'\blight\b', text, re.IGNORECASE))
2.21875
2
plots/model_explorer/app_hooks.py
ZviBaratz/pylabber
3
12791698
<filename>plots/model_explorer/app_hooks.py from .setup import load_django def on_server_loaded(server_context): load_django()
1.367188
1
routely/__init__.py
jhags/routely
1
12791699
<filename>routely/__init__.py from .routely import Route
1.304688
1
nc/migrations/0035_portfolio_rawportfoliodata.py
kfarrelly/nucleo
1
12791700
<gh_stars>1-10 # -*- coding: utf-8 -*- # Generated by Django 1.11.13 on 2018-07-16 15:20 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('nc', '0034_auto_20180710_2209'), ] operations = [ migrations.CreateModel( name='Portfolio', fields=[ ('profile', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, primary_key=True, related_name='portfolio', serialize=False, to='nc.Profile')), ], ), migrations.CreateModel( name='RawPortfolioData', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True, db_index=True)), ('value', models.FloatField(default=-1.0)), ('portfolio', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='rawdata', to='nc.Portfolio')), ], options={ 'ordering': ('-created',), 'abstract': False, 'get_latest_by': 'created', }, ), ]
1.6875
2
src/scs_core/sys/modem.py
south-coast-science/scs_core
3
12791701
""" Created on 24 Mar 2021 @author: <NAME> (<EMAIL>) Modem ----- modem.generic.device-identifier : 3f07553c31ce11715037ac16c24ceddcfb6f7a0b modem.generic.manufacturer : QUALCOMM INCORPORATED modem.generic.model : QUECTEL Mobile Broadband Module modem.generic.revision : EC21EFAR06A01M4G ... modem.3gpp.imei : 867962041294151 example JSON: {"id": "3f07553c31ce11715037ac16c24ceddcfb6f7a0b", "imei": "867962041294151", "mfr": "QUALCOMM INCORPORATED", "model": "QUECTEL Mobile Broadband Module", "rev": "EC21EFAR06A01M4G"} ModemConnection --------------- modem.generic.state : connected modem.generic.state-failed-reason : -- modem.generic.signal-quality.value : 67 modem.generic.signal-quality.recent : yes example JSON: {"state": "connected", "signal": {"quality": 67, "recent": true}} SIM (Subscriber Identity Module) -------------------------------- sim.dbus-path : /org/freedesktop/ModemManager1/SIM/0 sim.properties.imsi : 234104886708667 sim.properties.iccid : 8944110068256270054 sim.properties.operator-code : 23410 sim.properties.operator-name : giffgaff sim.properties.emergency-numbers.length : 2 sim.properties.emergency-numbers.value[1] : 999 sim.properties.emergency-numbers.value[2] : 00112 example JSON: {"imsi": "123", "iccid": "456", "operator-code": "789 012", "operator-name": "<NAME>"} """ import re from collections import OrderedDict from scs_core.data.datum import Datum from scs_core.data.json import JSONable # -------------------------------------------------------------------------------------------------------------------- class ModemList(object): """ modem-list.value[1] : /org/freedesktop/ModemManager1/Modem/0 """ # ---------------------------------------------------------------------------------------------------------------- @classmethod def construct_from_mmcli(cls, lines): modems = [] for line in lines: match = re.match(r'modem-list.value\[[\d]+]\s+:\s+([\S]+)', line) if match: modems.append(match.groups()[0]) return cls(modems) # ---------------------------------------------------------------------------------------------------------------- def __init__(self, modems): """ Constructor """ self.__modems = modems # array of string def __len__(self): return len(self.__modems) # ---------------------------------------------------------------------------------------------------------------- def modem(self, index): return self.__modems[index] def number(self, index): pieces = self.modem(index).split('/') return pieces[-1] # ---------------------------------------------------------------------------------------------------------------- def __str__(self, *args, **kwargs): return "ModemList:{modems:%s}" % self.__modems # -------------------------------------------------------------------------------------------------------------------- class Modem(JSONable): """ modem.generic.device-identifier : 3f07553c31ce11715037ac16c24ceddcfb6f7a0b modem.generic.manufacturer : QUALCOMM INCORPORATED modem.generic.model : QUECTEL Mobile Broadband Module modem.generic.revision : EC21EFAR06A01M4G ... modem.3gpp.imei : 867962041294151 """ # ---------------------------------------------------------------------------------------------------------------- @classmethod def construct_from_jdict(cls, jdict): if not jdict: return None id = jdict.get('id') imei = jdict.get('imei') mfr = jdict.get('mfr') model = jdict.get('model') rev = jdict.get('rev') return cls(id, imei, mfr, model, rev) @classmethod def construct_from_mmcli(cls, lines): id = None imei = None mfr = None model = None rev = None for line in lines: match = re.match(r'modem\.generic\.device-identifier\s+:\s+(\S+)', line) if match: id = match.groups()[0] continue match = re.match(r'.*\.imei\s+:\s+(\d+)', line) if match: imei = match.groups()[0] continue match = re.match(r'modem\.generic\.manufacturer\s+:\s+(\S.*\S)', line) if match: mfr = match.groups()[0] continue match = re.match(r'modem\.generic\.model\s+:\s+(\S.*\S)', line) if match: model = match.groups()[0] continue match = re.match(r'modem\.generic\.revision\s+:\s+(\S+)', line) if match: rev = match.groups()[0] continue return cls(id, imei, mfr, model, rev) # ---------------------------------------------------------------------------------------------------------------- def __init__(self, id, imei, mfr, model, rev): """ Constructor """ self.__id = id # string self.__imei = imei # string self.__mfr = mfr # string self.__model = model # string self.__rev = rev # string def __eq__(self, other): try: return self.id == other.id and self.imei == other.imei and self.mfr == other.mfr and \ self.model == other.model and self.rev == other.rev except (TypeError, AttributeError): return False # ---------------------------------------------------------------------------------------------------------------- @property def id(self): return self.__id @property def imei(self): return self.__imei @property def mfr(self): return self.__mfr @property def model(self): return self.__model @property def rev(self): return self.__rev # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict() jdict['id'] = self.id jdict['imei'] = self.imei jdict['mfr'] = self.mfr jdict['model'] = self.model jdict['rev'] = self.rev return jdict # ---------------------------------------------------------------------------------------------------------------- def __str__(self, *args, **kwargs): return "Modem:{id:%s, imei:%s, mfr:%s, model:%s, rev:%s}" % \ (self.id, self.imei, self.mfr, self.model, self.rev) # -------------------------------------------------------------------------------------------------------------------- class ModemConnection(JSONable): """ modem.generic.state : connected modem.generic.state-failed-reason : -- modem.generic.signal-quality.value : 67 modem.generic.signal-quality.recent : yes """ UNAVAILABLE_STATE = "unavailable" # ---------------------------------------------------------------------------------------------------------------- @classmethod def construct_from_jdict(cls, jdict): if not jdict: return None state = jdict.get('state') failure = jdict.get('failure') signal = Signal.construct_from_jdict(jdict.get('signal')) return cls(state, failure, signal) @classmethod def construct_from_mmcli(cls, lines): state = None failure = None quality = None recent = None for line in lines: match = re.match(r'modem\.generic\.state\s+:\s+([a-z]+)', line) if match: state = match.groups()[0] continue match = re.match(r'modem\.generic\.state-failed-reason\s+:\s+(\S.*\S)', line) if match: reported_failure = match.groups()[0] failure = None if reported_failure == '--' else reported_failure continue match = re.match(r'modem\.generic\.signal-quality\.value\s+:\s+([\d]+)', line) if match: quality = match.groups()[0] continue match = re.match(r'modem\.generic\.signal-quality\.recent\s+:\s+([a-z]+)', line) if match: recent = match.groups()[0] == 'yes' continue return cls(state, failure, Signal(quality, recent)) @classmethod def null_datum(cls): return cls(cls.UNAVAILABLE_STATE, None, Signal.null_datum()) # ---------------------------------------------------------------------------------------------------------------- def __init__(self, state, failure, signal): """ Constructor """ self.__state = state # string self.__failure = failure # string self.__signal = signal # Signal # ---------------------------------------------------------------------------------------------------------------- @property def state(self): return self.__state @property def failure(self): return self.__failure @property def signal(self): return self.__signal # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict() jdict['state'] = self.state if self.failure is not None: jdict['failure'] = self.failure jdict['signal'] = self.signal return jdict # ---------------------------------------------------------------------------------------------------------------- def __str__(self, *args, **kwargs): return "ModemConnection:{state:%s, failure:%s, signal:%s}" % (self.state, self.failure, self.signal) # -------------------------------------------------------------------------------------------------------------------- class Signal(JSONable): """ modem.generic.signal-quality.value : 67 modem.generic.signal-quality.recent : yes """ __SIGNIFICANT_QUALITY_DIFFERENCE = 10 # ---------------------------------------------------------------------------------------------------------------- @classmethod def construct_from_jdict(cls, jdict): if not jdict: return None quality = jdict.get('quality') recent = jdict.get('recent') return cls(quality, recent) @classmethod def null_datum(cls): return cls(None, None) # ---------------------------------------------------------------------------------------------------------------- def __init__(self, quality, recent): """ Constructor """ self.__quality = Datum.int(quality) # int self.__recent = recent # bool # ---------------------------------------------------------------------------------------------------------------- @property def quality(self): return self.__quality @property def recent(self): return self.__recent # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict() jdict['quality'] = self.quality jdict['recent'] = self.recent return jdict # ---------------------------------------------------------------------------------------------------------------- def __str__(self, *args, **kwargs): return "Signal:{quality:%s, recent:%s}" % (self.quality, self.recent) # -------------------------------------------------------------------------------------------------------------------- class SIMList(object): """ modem.generic.sim : /org/freedesktop/ModemManager1/SIM/0 """ # ---------------------------------------------------------------------------------------------------------------- @classmethod def construct_from_mmcli(cls, lines): sims = [] for line in lines: match = re.match(r'modem\.generic\.sim\s+:\s+([\S]+)', line) if match: sims.append(match.groups()[0]) return cls(sims) # ---------------------------------------------------------------------------------------------------------------- def __init__(self, sims): """ Constructor """ self.__sims = sims # array of string def __len__(self): return len(self.__sims) # ---------------------------------------------------------------------------------------------------------------- def sim(self, index): return self.__sims[index] def number(self, index): pieces = self.sim(index).split('/') return pieces[-1] # ---------------------------------------------------------------------------------------------------------------- def __str__(self, *args, **kwargs): return "SIMList:{sims:%s}" % self.__sims # -------------------------------------------------------------------------------------------------------------------- class SIM(JSONable): """ classdocs """ # ---------------------------------------------------------------------------------------------------------------- @classmethod def construct_from_jdict(cls, jdict): if not jdict: return None imsi = jdict.get('imsi') iccid = jdict.get('iccid') operator_code = jdict.get('operator-code') operator_name = jdict.get('operator-name') return cls(imsi, iccid, operator_code, operator_name) @classmethod def construct_from_mmcli(cls, lines): imsi = None iccid = None operator_code = None operator_name = None for line in lines: match = re.match(r'sim\.properties\.imsi\s+:\s+([\d]+)', line) if match: imsi = match.groups()[0] continue match = re.match(r'sim\.properties\.iccid\s+:\s+([\d]+)', line) if match: iccid = match.groups()[0] continue match = re.match(r'sim\.properties\.operator-code\s+:\s+([\d]+)', line) if match: operator_code = match.groups()[0] continue match = re.match(r'sim\.properties\.operator-name\s+:\s+(\S.*)', line) if match: reported_name = match.groups()[0].strip() operator_name = None if reported_name == '--' else reported_name return cls(imsi, iccid, operator_code, operator_name) # ---------------------------------------------------------------------------------------------------------------- def __init__(self, imsi, iccid, operator_code, operator_name): """ Constructor """ self.__imsi = imsi # numeric string self.__iccid = iccid # numeric string self.__operator_code = operator_code # string self.__operator_name = operator_name # string def __eq__(self, other): try: return self.imsi == other.imsi and self.iccid == other.iccid and \ self.operator_code == other.operator_code and self.operator_name == other.operator_name except (TypeError, AttributeError): return False # ---------------------------------------------------------------------------------------------------------------- @property def imsi(self): return self.__imsi @property def iccid(self): return self.__iccid @property def operator_code(self): return self.__operator_code @property def operator_name(self): return self.__operator_name # ---------------------------------------------------------------------------------------------------------------- def as_json(self): jdict = OrderedDict() jdict['imsi'] = str(self.imsi) jdict['iccid'] = str(self.iccid) jdict['operator-code'] = self.operator_code jdict['operator-name'] = self.operator_name return jdict # ---------------------------------------------------------------------------------------------------------------- def __str__(self, *args, **kwargs): return "SIM:{imsi:%s, iccid:%s, operator_code:%s, operator_name:%s}" % \ (self.imsi, self.iccid, self.operator_code, self.operator_name)
1.1875
1
build/lib.linux-armv7l-2.7/bibliopixel/drivers/WS2801.py
sethshill/final
6
12791702
<reponame>sethshill/final<gh_stars>1-10 from spi_driver_base import DriverSPIBase, ChannelOrder import os from .. import gamma class DriverWS2801(DriverSPIBase): """Main driver for WS2801 based LED strips on devices like the Raspberry Pi and BeagleBone""" def __init__(self, num, c_order=ChannelOrder.RGB, use_py_spi=True, dev="/dev/spidev0.0", SPISpeed=1): if SPISpeed > 1 or SPISpeed <= 0: raise ValueError( "WS2801 requires an SPI speed no greater than 1MHz or SPI speed was set <= 0") super(DriverWS2801, self).__init__(num, c_order=c_order, use_py_spi=use_py_spi, dev=dev, SPISpeed=SPISpeed) self.gamma = gamma.WS2801 # WS2801 requires gamma correction so we run it through gamma as the # channels are ordered def _fixData(self, data): for a, b in enumerate(self.c_order): self._buf[a:self.numLEDs * 3:3] = [self.gamma[v] for v in data[b::3]] MANIFEST = [ { "id": "WS2801", "class": DriverWS2801, "type": "driver", "display": "WS2801 (SPI Native)", "desc": "Interface with WS2801 strips over a native SPI port (Pi, BeagleBone, etc.)", "params": [{ "id": "num", "label": "# Pixels", "type": "int", "default": 1, "min": 1, "help": "Total pixels in display." }, { "id": "c_order", "label": "Channel Order", "type": "combo", "options": { 0: "RGB", 1: "RBG", 2: "GRB", 3: "GBR", 4: "BRG", 5: "BGR" }, "options_map": [ [0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0, 1], [2, 1, 0] ], "default": 0 }, { "id": "dev", "label": "SPI Device Path", "type": "str", "default": "/dev/spidev0.0", }, { "id": "use_py_spi", "label": "Use PySPI", "type": "bool", "default": True, "group": "Advanced" }] } ]
2.640625
3
migrations/versions/004_Create_User_table.py
LCBRU/batch_demographics
0
12791703
<gh_stars>0 from sqlalchemy import ( MetaData, Table, Column, Integer, NVARCHAR, DateTime, Boolean, DateTime, ) def upgrade(migrate_engine): meta = MetaData() meta.bind = migrate_engine user = Table( "user", meta, Column("id", Integer, primary_key=True), Column("email", NVARCHAR(255), nullable=False, unique=True), Column("password", NVARCHAR(255), nullable=False), Column("first_name", NVARCHAR(255)), Column("last_name", NVARCHAR(255)), Column("active", Boolean()), Column("confirmed_at", DateTime()), Column("last_login_at", DateTime()), Column("current_login_at", DateTime()), Column("last_name", NVARCHAR(255)), Column("last_login_ip", NVARCHAR(255)), Column("current_login_ip", NVARCHAR(255)), Column("login_count", Integer()), Column("created_date", DateTime(), nullable=False), ) user.create() def downgrade(migrate_engine): meta = MetaData() meta.bind = migrate_engine user = Table("user", meta, autoload=True) user.drop()
2.359375
2
marsDemonstrator/__init__.py
tum-fml/marsDemonstrator
1
12791704
<reponame>tum-fml/marsDemonstrator from .designMethods import MARSInput, ENComputation, LoadCollectivePrediction, load_all_gps, InputFileError # noqa: F401 from .main_app import MainApplication, ResultWriter # noqa: F401 __all__ = ["MARSInput", "Computation", "LoadCollectivePrediction", "MainApplication", "ResultWriter"]
1.398438
1
pydnameth/model/tree.py
AaronBlare/pydnameth
2
12791705
<filename>pydnameth/model/tree.py from anytree import PostOrderIter from pydnameth.infrastucture.save.info import save_info from pydnameth.model.context import Context import hashlib from anytree.exporter import JsonExporter def calc_tree(root): for node in PostOrderIter(root): config = node.config configs_child = [node_child.config for node_child in node.children] context = Context(config) context.pipeline(config, configs_child) def build_tree(root): for node in PostOrderIter(root): node_status = node.config.is_root node.config.is_root = True node.name = str(node.config) exporter = JsonExporter(sort_keys=True) node_json = exporter.export(node).encode('utf-8') hash = hashlib.md5(node_json).hexdigest() node.config.set_hash(hash) if node.config.is_run: save_info(node) node.config.is_root = node_status node.name = str(node.config)
2.375
2
B3Analyzer/utilities.py
mauromatsudo/brazilian-stocks-analyzer
0
12791706
<filename>B3Analyzer/utilities.py from requests import get from bs4 import BeautifulSoup import pandas as pd class Stock: def __init__(self, ticker): self._ticker = ticker self._url = f'https://statusinvest.com.br/acoes/{self._ticker}' def get_all_indicators(self): html = get(self._url).text soup = BeautifulSoup(html, 'html.parser') html_table = soup.select("div.width-auto:nth-child(2)")[0] # select the exactly table with the fundamental indicators indicators = [element.text for element in html_table.find_all('h3')] valuations = [element.text for element in html_table.find_all('strong')] if len(indicators) != len(valuations): # the h3 tags show the indicador name on site, however that are some duplicates h3 tags that are not showm in the front end, but exists and # refers to the same indicador, to avoid errors, the shown = ('P/VP', 'P/L', 'P/Ebitda', 'P/Ebit', 'P/Ativo', 'EV/Ebitda', 'EV/EBIT', 'PSR', 'P/Cap.Giro', 'P/Ativo Circ Liq', 'Margem Bruta', 'Margem Ebitda', 'Margem Ebit', 'Margem Líquida', 'Giro Ativos', 'ROE', 'ROA', 'ROIC', 'LPA', 'VPA', 'Dívida Líquida / Patrimônio', 'Dívida Líquida / EBITDA', 'Dívida Líquida / EBIT', 'Patrimônio / Ativos', 'Passivos / Ativos', 'Liquidez Corrente', 'CAGR Receitas 5 Anos', 'CAGR Lucros 5 Anos') indicators = [element for element in indicators if (element in shown) == True] for index in range(len(valuations)): value = valuations[index] if '%' in value: value = value[:-1] try: value = value.replace(',', '.') value = float(value) except ValueError: value = 'Not avaiable' valuations[index] = value fundamental_indicators = dict(zip(indicators, valuations)) return fundamental_indicators class Firm(Stock): def __repr__(self): return f'BrazilianStock object <{self._ticker.upper()}>' @property def profit_indicators(super): indicators = ('ROE', 'ROIC', 'Margem Ebitda', 'Margem Líquida') return {key: values for (key, values) in super.get_all_indicators().items() if key in indicators} @property def price_indicators(self): indicators = ('P/VP', 'P/L', 'P/Ativo') return {key:values for (key, values) in self.get_all_indicators().items() if key in indicators} @property def debt_indicators(self): indicators = ('Dívida Líquida / Patrimônio', 'Dívida Líquida / EBITDA', 'Passivos / Ativos') # there is a pŕoblem with this indicators, because the html source get more h3 tags here, and some # indicadors are not the same return {key: values for (key, values) in self.get_all_indicators().items() if key in indicators} class B3: def __repr__(self): return 'Brazilian Trader object' def __init__(self): self._firms = pd.read_excel('data/B3_list.xlsx') @property def overall_report(self): return self._firms @property def companies_list(self): return tuple(self._firms['Ticker']) def get_by_industry(self, industry): return tuple(self._firms[self._firms['Industry'] == industry]['Ticker']) class Analyzer: def __repr__(self): return f'FundamentalAnalyzer object' def __init__(self, ticker): self._ticker = ticker self._points = 0 self._basic_fundamentals = { 'price_indicators': { 'P/VP': 3, 'P/L': 20, 'P/Ativo': 2}, 'profit_indicators': { 'Margem Ebtida': 15, 'Margem Líquida': 8, 'ROE': 10, 'ROIC': 5}, 'debt_indicadors': { 'Dívida Líq/Patrim': 1, 'Dívida Líq/EBITDA': 3, 'Passivos / Ativos': 1}} def analyze_metrics(self, indicators): chosen_metrics = ('ROE', 'ROIC', 'Margem Ebitda', 'Margem Líquida', 'P/VP', 'P/L', 'P/Ativo', 'Dívida Líquida / Patrimônio', 'Dívida Líquida / EBITDA', 'Passivos / Ativos') indicators = {key: indicators[key] for key in chosen_metrics} metrics_df = pd.DataFrame.from_dict(indicators, orient='index', columns=["Current Value"]) for column in ("Min", "Max", "Weigh", " +Points", "-Points"): metrics_df[column] = pd.Series() if __name__ == "__main__": # Testing area '''request = get('https://statusinvest.com.br/acoes/cvcb3').text soup = BeautifulSoup(request, 'html.parser') print(soup.select("div.width-auto:nth-child(2)")) petr = Data(ticker='petr4') cvc = Data('cvcb3') print(cvc.get_all_indicators()) print(cvc.get_all_indicators()) print(petr.profit_indicators) clas = Stock('cvcb3') print(clas.get_all_indicators()) print(clas.price_indicators, '\n', clas.profit_indicators, '\n', clas.debt_indicators)''' print(B3().get_by_industry('Materiais Básicos'))
3.078125
3
virtual/bin/django-admin.py
vinnyotach7/insta-photo
0
12791707
#!/home/moringaschool/Documents/django projects/insta-moringa/virtual/bin/python3.6 from django.core import management if __name__ == "__main__": management.execute_from_command_line()
1.070313
1
pelayanan/apps.py
diaksizz/Adisatya
0
12791708
from django.apps import AppConfig class PelayananConfig(AppConfig): name = 'pelayanan'
1.171875
1
dqn_cartpole.py
subinlab/dqn
1
12791709
import gym import math import random import numpy as np import matplotlib import matplotlib.pyplot as plt from collections import namedtuple from itertools import count from PIL import Image import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import torchvision.transforms as T env = gym.make('CartPole-v0').unwrapped # set up matplotlib is_ipython = 'inline' in matplotlib.get_backend() if is_ipython: from IPython import display plt.ion() # if gpu is to be used device = torch.device("cuda" if torch.cuda.is_available() else "cpu") Transition = namedtuple('Transition',('state', 'action', 'next_state', 'reward')) class ReplayMemory(object): def __init__(self, capacity): self.capacity = capacity self.memory = [] self.position = 0 def push(self, *args): """Saves a transition.""" if len(self.memory) < self.capacity: self.memory.append(None) self.memory[self.position] = Transition(*args) self.position = (self.position+1)%self.capacity def sample(self, batch_size): """Select a random batch of transitions for training""" return random.sample(self.memory, batch_size) def __len__(self): """Return length of replay memory""" return len(self.memory) class Network(object): def __init__(self, h, w, outputs): self.convNet = self.convNet(h, w, outputs) def convNet(h, w, outputs): self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2) self.bn1 = nn.BatchNorm2d(16) self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2) self.bn2 = nn.BatchNorm2d(32) self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2) self.bn3 = nn.BatchNorm2d(32) # Number of Linear input connections depends on output of conv2d layers # and therefore the input image size, so compute it. def conv2d_size_out(size, kernel_size = 5, stride = 2): return (size - (kernel_size - 1) - 1) // stride + 1 convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w))) convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h))) linear_input_size = convw * convh * 32 self.head = nn.Linear(linear_input_size, outputs) def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.relu(self.bn3(self.conv3(x))) return self.head(x.view(x.size(0), -1)) class DQN(object): def __init__(self, batch_size=64, discount_rate=0.99, max_episodes=300): self.env = gym.make('CartPole-v0') self.obs = self.env.observation_space.shape[0] self.n_actions = self.env.action_space.n self.BATCH_SIZE = batch_size self.DISCOUNT_RATE = discount_rate self.MAX_EPISODES = max_episodes self.TARGET_UPDAT_FREQUENCY = 5 self.EPS_START = 0.9 self.EPS_END = 0.05 self.EPS_DECAY = 200 self.main_Qnet = self.Network(obs, n_actions) self.target_Qnet = self.Network(obs, n_actions) def plot_durations(): plt.figure(2) plt.clf() durations_t = torch.tensor(episode_durations, dtype=torch.float) plt.title('Training...') plt.xlabel('Episode') plt.ylabel('Duration') plt.plot(durations_t.numpy()) # Take 100 episode averages and plot them too if len(durations_t) >= 100: means = durations_t.unfold(0, 100, 1).mean(1).view(-1) means = torch.cat((torch.zeros(99), means)) plt.plot(means.numpy()) plt.pause(0.001) # pause a bit so that plots are updated if is_ipython: display.clear_output(wait=True) display.display(plt.gcf()) def train(self): buffer = ReplayMemory() for episode in range(self.MAX_EPISODES): e = 1/.((episode/10)+1) done = False step_count = 0 state = self.env.reset() while not done: state = np.reshape(state, (1,self.obs)) if np.random.rand() < e: action = self.env.action_space.sample() else: action = np.argmax(self.main_Qnet.predict(state)) next_state, reward, done, info = self.env.step(action) if done: reward = -1 buffer.push((state, action, reward, next_state)) if len(buffer) > self.BATCH_SIZE: minibatch = random.sample(buffer, self.BATCH_SIZE) states = np.vstack([x[0] for x in minibatch]) actions = np.array([x[1] for x in minibatch]) rewards = np.array([x[2] for x in minibatch]) next_states = np.vstack([x[3] for x in minibatch]) Q_target = rewards + self.DISCOUNT_RATE+np.max(self.target_Qnet.forward(next_states), axis=1) y = self.main_Qnet(states) y[np.arrange(len(states)), actions] = Q_target # self.main_Qnet.train_on if step_count % self.TARGET_UPDAT_FREQUENCY == 0: # self.target_Qnet state = next_state step_count +=1 print("Episode: {} steps: {}".format(episode, step_count)) from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Activation from keras import regularizers from keras.models import Model from keras.optimizers import Adam import numpy as np # from collections import deque import random import gym # from typing import List import argparse class DQN(): def __init__(self, discount=0.99, batch_size = 64, max_episodes = 300): self.env = gym.make('CartPole-v0') # self.env.wrappers.Monitor(env, directory="results/", force=True) self.input_size= self.env.observation_space.shape[0] self.output_size= self.env.action_space.n self.DISCOUNT_RATE=discount self.BATCH_SIZE = batch_size self.TARGET_UPDATE_FREQUENCY = 5 self.MAX_EPISODES = max_episodes self.main_dqn = self.build() self.target_dqn = self.build() self.main_dqn.compile(optimizer = Adam(), loss ="mean_squared_error") self.target_dqn.set_weights(self.main_dqn.get_weights()) def build(self, h_size = 16, lr = 0.001): state = Input(shape=(self.input_size,)) dense1 = Dense(h_size, activation = "relu")(state) action = Dense(self.output_size, kernel_regularizer=regularizers.l2(0.01))(dense1) model = Model(state, action) return model def train(self): if len(memory) < BATCH_SIZE: return transitions = memory.sample(BATCH_SIZE) # Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for # detailed explanation). This converts batch-array of Transitions # to Transition of batch-arrays. batch = Transition(*zip(*transitions)) # Compute a mask of non-final states and concatenate the batch elements # (a final state would've been the one after which simulation ended) non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, batch.next_state)), device=device, dtype=torch.uint8) non_final_next_states = torch.cat([s for s in batch.next_state if s is not None]) state_batch = torch.cat(batch.state) action_batch = torch.cat(batch.action) reward_batch = torch.cat(batch.reward) Q_value = main_Qnet(state_batch).gather(1, action_batch) next_state_values = torch.zeros(BATCH_SIZE, device=device) next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach() # Compute the expected Q values next_Q_values = (next_state_values * DISCOUNT_RATE) + reward_batch # Compute Huber loss loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1)) # Optimize the model optimizer.zero_grad() loss.backward() for param in policy_net.parameters(): param.grad.data.clamp_(-1, 1) optimizer.step() num_episodes = 50 for i_episode in range(num_episodes): # Initialize the environment and state env.reset() last_screen = get_screen() current_screen = get_screen() state = current_screen - last_screen for t in count(): # Select and perform an action action = select_action(state) _, reward, done, _ = env.step(action.item()) reward = torch.tensor([reward], device=device) # Observe new state last_screen = current_screen current_screen = get_screen() if not done: next_state = current_screen - last_screen else: next_state = None memory.push(state, action, next_state, reward) # Move to the next state state = next_state optimize_model() if done: episode_durations.append(t + 1) plot_durations() break if i_episode % TARGET_UPDATE == 0: target_net.load_state_dict(policy_net.state_dict()) if __name__ == "__main__": ap = argparse.ArgumentParser() ap.add_argument("-b", "--batch", required=False) ap.add_argument("-d", "--discount", required=False) ap.add_argument("-ep", "--max", required=False) args = vars(ap.parse_args()) dqn = DQN(int(args["batch"]), float(args["discount"]), int(args["max"])) dqn.train() print('Complete') env.render() env.close() plt.ioff() plt.show()
2.640625
3
src/transposer.py
polifonia-project/harmonic-similarity
0
12791710
import sys import os import re, getopt key_list = [('A',), ('A#', 'Bb'), ('B', 'Cb'), ('C',), ('C#', 'Db'), ('D',), ('D#', 'Eb'), ('E',), ('F',), ('F#', 'Gb'), ('G',), ('G#', 'Ab')] sharp_flat = ['#', 'b'] sharp_flat_preferences = { 'A': '#', 'A#': 'b', 'Bb': 'b', 'B': '#', 'C': 'b', 'C#': 'b', 'Db': 'b', 'D': '#', 'D#': 'b', 'Eb': 'b', 'E': '#', 'F': 'b', 'F#': '#', 'Gb': '#', 'G': '#', 'G#': 'b', 'Ab': 'b', } key_regex = re.compile(r"[ABCDEFG][#b]?") def get_index_from_key(source_key): """Gets the internal index of a key >>> get_index_from_key('Bb') 1 """ for key_names in key_list: if source_key in key_names: return key_list.index(key_names) raise Exception("Invalid key: %s" % source_key) def get_key_from_index(index, to_key): """Gets the key at the given internal index. Sharp or flat depends on the target key. >>> get_key_from_index(1, 'Eb') 'Bb' """ key_names = key_list[index % len(key_list)] if len(key_names) > 1: sharp_or_flat = sharp_flat.index(sharp_flat_preferences[to_key]) return key_names[sharp_or_flat] return key_names[0] def get_transponation_steps(source_key, target_key): """Gets the number of half tones to transpose >>> get_transponation_steps('D', 'C') -2 """ source_index = get_index_from_key(source_key) target_index = get_index_from_key(target_key) return target_index - source_index def transpose_file(file_name, from_key, to_key): """Transposes a file from a key to another. >>> transpose_file('example.txt', 'D', 'E') 'Rocking start, jazzy ending\\n| E | A B | Cm7#11/D# |\\n' """ direction = get_transponation_steps(from_key, to_key) result = '' try: for line in open(file_name): result += transpose_line(line, direction, to_key) return result except IOError: print("Invalid filename!") usage() def transpose_line(source_line, direction, to_key): """Transposes a line a number of keys if it starts with a pipe. Examples: >>> transpose_line('| A | A# | Bb | C#m7/F# |', -2, 'C') '| G | Ab | Ab | Bm7/E |' Different keys will be sharp or flat depending on target key. >>> transpose_line('| A | A# | Bb | C#m7/F# |', -2, 'D') '| G | G# | G# | Bm7/E |' It will use the more common key if sharp/flat, for example F# instead of Gb. >>> transpose_line('| Gb |', 0, 'Gb') '| F# |' Lines not starting with pipe will not be transposed >>> transpose_line('A | Bb |', -2, 'C') 'A | Bb |' """ if source_line[0] != '|': return source_line source_chords = key_regex.findall(source_line) return recursive_line_transpose(source_line, source_chords, direction, to_key) def recursive_line_transpose(source_line, source_chords, direction, to_key): if not source_chords or not source_line: return source_line source_chord = source_chords.pop(0) chord_index = source_line.find(source_chord) after_chord_index = chord_index + len(source_chord) return source_line[:chord_index] + \ transpose(source_chord, direction, to_key) + \ recursive_line_transpose(source_line[after_chord_index:], source_chords, direction, to_key) def transpose(source_chord, direction, to_key): """Transposes a chord a number of half tones. Sharp or flat depends on target key. >>> transpose('C', 3, 'Bb') 'Eb' """ source_index = get_index_from_key(source_chord) return get_key_from_index(source_index + direction, to_key) def usage(): print() 'Usage:' print() '%s --from=Eb --to=F# input_filename' % os.path.basename(__file__) sys.exit(2) def main(): from_key = 'C' to_key = 'C' file_name = None try: options, arguments = getopt.getopt(sys.argv[1:], 'f:t:', ['from=', 'to=', 'doctest']) except getopt.GetoptError as err: print(str(err), usage(), sys.exit(2)) for option, value in options: if option in ('-f', '--from'): from_key = value elif option in ('-t', '--to'): to_key = value elif option == '--doctest': import doctest doctest.testmod() exit() else: usage() if arguments: file_name = arguments[0] else: usage() result = transpose_file(file_name, from_key, to_key) print("Result (%s -> %s):" % (from_key, to_key)) print(result) if __name__ == '__main__': print(transpose_line('|Eb', 2, 'C'))
2.828125
3
web/views/blog.py
aHugues/blog
0
12791711
<reponame>aHugues/blog from flask import Blueprint from flask import render_template from flask import request from flask import jsonify from ..services import ArticlesService blog_views = Blueprint('blog_views', __name__) articles_service = ArticlesService() @blog_views.route('/blog') def home_page(): articles = articles_service.listArticles() return render_template('blog.html', articles=articles, current_page="blog", ) @blog_views.route('/api/articles', methods=["GET", "POST"]) def display_articles_list(): if request.method == 'GET': articles = articles_service.listArticles() return jsonify(articles) elif request.method == 'POST': article_title = request.json['title'] article_content = request.json['content'] articles_service.addArticle(article_title, article_content) return "ok", 200
2.859375
3
worker/worker.py
GeorgianBadita/remote-code-execution-engine
0
12791712
import logging import subprocess from typing import Optional from celery import Celery from celery.utils.log import get_logger from code_execution.code_execution import CodeExcution from utils import generate_random_file tmp_dir_path = '/worker/tmp' compiled_dir_path = '/worker/tmp/compiled_files' # Create the celery app and get the logger celery_app = Celery('code-executions-tasks', broker='pyamqp://guest@rabbit//', backend='amqp://guest@rabbit//') # Add CELERY_ACKS_LATE in order to wait for infinite loop code executions # celery_app.conf.update( # CELERY_ACKS_LATE=True # ) logger = get_logger(__name__) @celery_app.task def execute_code(language: str, code: str, submission: bool = False, timeout: Optional[float] = 10) -> dict: """ Task for code execution @param language: code programming language @param code: code to be executed @param submission: flag which tells if the code to be executed is a submission or a normal execution @param timeout: maximum time the code is allowed to run @return: dict containgin execution results """ logger.info("Starting code execution") in_file_path = (f"{tmp_dir_path}/in_files/{generate_random_file()}." f"{CodeExcution.get_lang_extension(language)}") compiled_file = f'{compiled_dir_path}/{generate_random_file()}.out' command_to_execute_code = CodeExcution.provide_code_execution_command( in_file_path, language, compiled_file, submission) default_dict = { "has_error": False, "out_of_resources": False, "exit_code": 0, "out_of_time": False, "raw_output": "" } try: code_output = CodeExcution.execute_code( command_to_execute_code, in_file_path, compiled_file, code, timeout) logging.info(f"Code Returned, result: {code_output}") default_dict["raw_output"] = code_output except subprocess.CalledProcessError as cpe: logging.debug(f"Code execution was errored: {cpe}") default_dict["has_error"] = True default_dict["exit_code"] = cpe.returncode default_dict["raw_output"] = cpe.output except subprocess.TimeoutExpired as te: logger.debug(f"Code timeout after: {te.timeout}") default_dict["has_error"] = True default_dict["exit_code"] = 124 default_dict["out_of_time"] = True default_dict["raw_output"] = "Time Limit Exceeded" return default_dict
2.484375
2
databroker/databroker.py
EliasKal/ai4eu_pipeline_visualization
0
12791713
#imports import haversine as hs import pandas as pd import numpy as np import random import time from concurrent import futures import grpc import databroker_pb2_grpc import databroker_pb2 port = 8061 class Databroker(databroker_pb2_grpc.DatabrokerServicer): def __init__(self): self.current_row = 0 #load required datasets self.no2_data = pd.read_csv('./data/no2_testset.csv') self.pm10_data = pd.read_csv('./data/pm10_testset.csv') self.pm25_data = pd.read_csv('./data/pm25_testset.csv') self.gps_data = pd.read_csv('./data/sensor_gps.csv') self.sensor_gps = pd.read_csv('./data/low_cost_sensors.csv') def get_next(self, request, context): response = databroker_pb2.Features() if self.current_row >= self.no2_data.shape[0]: context.set_code(grpc.StatusCode.NOT_FOUND) context.set_details("all data has been processed") else: #load 1 row from each dataset and convert to numpy # create response format dataframe no2 = pd.DataFrame(data=None, columns=self.no2_data.columns) pm10 = pd.DataFrame(data=None, columns=self.pm10_data.columns) pm25 = pd.DataFrame(data=None, columns=self.pm25_data.columns) for sensor in range(self.sensor_gps.shape[0]): id = self.sensor_gps.deviceID[sensor] counter=1 for i in range(23,0,-1): lat1 = np.rad2deg(self.sensor_gps.iloc[sensor,4]) lon1 = np.rad2deg(self.sensor_gps.iloc[sensor,5]) lat2 = self.gps_data.iloc[0,i*2+1] lon2 = self.gps_data.iloc[0,i*2] distance = hs.haversine((lat2, lon2), (lat1, lon1)) self.no2_data.iloc[self.current_row,counter] = distance self.pm10_data.iloc[self.current_row,counter] = distance self.pm25_data.iloc[self.current_row,counter] = distance counter +=1 no2 = no2.append(self.no2_data.iloc[self.current_row,:]) pm10 = pm10.append(self.pm10_data.iloc[self.current_row,:]) pm25 = pm25.append(self.pm25_data.iloc[self.current_row,:]) no2_input= no2.iloc[:,1:].to_numpy() pm10_input= pm10.iloc[:,1:].to_numpy() pm25_input= pm25.iloc[:,1:].to_numpy() no2_input = np.ndarray.tobytes(no2_input) pm10_input = np.ndarray.tobytes(pm10_input) pm25_input = np.ndarray.tobytes(pm25_input) #add output to response response.no2_data = no2_input response.pm10_data = pm10_input response.pm25_data = pm25_input #add 1 to row counter(maybe we could make it cyclical with mod later) self.current_row += 1 return response #host server server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) databroker_pb2_grpc.add_DatabrokerServicer_to_server(Databroker(), server) print("Starting server. Listening on port : " + str(port)) server.add_insecure_port("[::]:{}".format(port)) server.start() try: while True: time.sleep(86400) except KeyboardInterrupt: server.stop(0)
2.546875
3
pyrallis/wrappers/dataclass_wrapper.py
eladrich/pyrallis
22
12791714
<reponame>eladrich/pyrallis<filename>pyrallis/wrappers/dataclass_wrapper.py<gh_stars>10-100 import argparse import dataclasses from dataclasses import _MISSING_TYPE from logging import getLogger from typing import Dict, List, Optional, Type, Union, cast from pyrallis.utils import Dataclass from . import docstring from .field_wrapper import FieldWrapper from .wrapper import Wrapper from .. import utils logger = getLogger(__name__) class DataclassWrapper(Wrapper[Dataclass]): def __init__( self, dataclass: Type[Dataclass], name: Optional[str] = None, default: Union[Dataclass, Dict] = None, prefix: str = "", parent: "DataclassWrapper" = None, _field: dataclasses.Field = None, field_wrapper_class: Type[FieldWrapper] = FieldWrapper ): # super().__init__(dataclass, name) self.dataclass = dataclass self._name = name self.default = default self.prefix = prefix self.fields: List[FieldWrapper] = [] self._required: bool = False self._explicit: bool = False self._dest: str = "" self._children: List[DataclassWrapper] = [] self._parent = parent # the field of the parent, which contains this child dataclass. self._field = _field # the default values self._defaults: List[Dataclass] = [] if default: self.defaults = [default] self.optional: bool = False for field in dataclasses.fields(self.dataclass): if not field.init: continue elif utils.is_tuple_or_list_of_dataclasses(field.type): raise NotImplementedError( f"Field {field.name} is of type {field.type}, which isn't " f"supported yet. (container of a dataclass type)" ) elif dataclasses.is_dataclass(field.type): # handle a nested dataclass attribute dataclass, name = field.type, field.name child_wrapper = DataclassWrapper( dataclass, name, parent=self, _field=field ) self._children.append(child_wrapper) elif utils.contains_dataclass_type_arg(field.type): dataclass = utils.get_dataclass_type_arg(field.type) name = field.name child_wrapper = DataclassWrapper( dataclass, name, parent=self, _field=field, default=None ) child_wrapper.required = False child_wrapper.optional = True self._children.append(child_wrapper) else: # a normal attribute field_wrapper = field_wrapper_class(field, parent=self, prefix=self.prefix) logger.debug( f"wrapped field at {field_wrapper.dest} has a default value of {field_wrapper.default}" ) self.fields.append(field_wrapper) logger.debug( f"The dataclass at attribute {self.dest} has default values: {self.defaults}" ) def add_arguments(self, parser: argparse.ArgumentParser): from pyrallis.argparsing import ArgumentParser parser = cast(ArgumentParser, parser) option_fields = [field for field in self.fields if field.arg_options] if len(option_fields) > 0: # Only show groups with parameters group = parser.add_argument_group( title=self.title, description=self.description ) for wrapped_field in option_fields: logger.debug( f"Arg options for field '{wrapped_field.name}': {wrapped_field.arg_options}" ) group.add_argument( *wrapped_field.option_strings, **wrapped_field.arg_options ) @property def name(self) -> str: return self._name @property def parent(self) -> Optional["DataclassWrapper"]: return self._parent @property def defaults(self) -> List[Dataclass]: if self._defaults: return self._defaults if self._field is None: return [] assert self.parent is not None if self.parent.defaults: self._defaults = [] for default in self.parent.defaults: if default is None: default = None else: default = getattr(default, self.name) self._defaults.append(default) else: try: default_field_value = utils.default_value(self._field) except TypeError as e: # utils.default_value tries to construct the field to get default value and might fail # if the field has some required arguments logger.debug( f"Could not get default value for field '{self._field.name}'\n\tUnderlying Error: {e}") default_field_value = dataclasses.MISSING if isinstance(default_field_value, _MISSING_TYPE): self._defaults = [] else: self._defaults = [default_field_value] return self._defaults @defaults.setter def defaults(self, value: List[Dataclass]): self._defaults = value @property def title(self) -> str: title = self.dataclass.__qualname__ if self.dest is not None: # Show name if exists title += f" ['{self.dest}']" return title @property def description(self) -> str: if self.parent and self._field: doc = docstring.get_attribute_docstring( self.parent.dataclass, self._field.name ) if doc is not None: if doc.docstring_below: return doc.docstring_below elif doc.comment_above: return doc.comment_above elif doc.comment_inline: return doc.comment_inline class_doc = self.dataclass.__doc__ or "" if class_doc.startswith(f'{self.dataclass.__name__}('): return "" # The base dataclass doc looks confusing, remove it return class_doc @property def required(self) -> bool: return self._required @required.setter def required(self, value: bool): self._required = value for field in self.fields: field.required = value for child_wrapper in self._children: child_wrapper.required = value @property def descendants(self): for child in self._children: yield child yield from child.descendants
2.25
2
MCMC_plotting.py
jlindsey1/MappingExoplanets
0
12791715
<reponame>jlindsey1/MappingExoplanets<filename>MCMC_plotting.py #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Wed Nov 15 21:12:41 2017 @author: Jordan """ # The following plots several figures from the MCMC. Not all are relevant, but the code has been kept in this single file # for simplicity. print "Start..." # Import modules import numpy as np import matplotlib.pyplot as plt from lmfit.models import SkewedGaussianModel import matplotlib.ticker as mticker from mpl_toolkits.axes_grid1.inset_locator import inset_axes # Import MCMC results with open('hist_values1.txt') as f: hist_values1 = f.read().splitlines() with open('hist_values2.txt') as f: hist_values2 = f.read().splitlines() with open('hist_values3.txt') as f: hist_values3 = f.read().splitlines() with open('hist_values4.txt') as f: hist_values4 = f.read().splitlines() with open('hist_values5.txt') as f: hist_values5 = f.read().splitlines() hist_values1=[float(i) for i in hist_values1] hist_values2=[float(i) for i in hist_values2] hist_values3=[float(i) for i in hist_values3] hist_values4=[float(i) for i in hist_values4] hist_values5=[float(i) for i in hist_values5] # Double Ttot and Tfull as only half values were used in the MCMC (to simplify maths) hist_values2=np.array(hist_values2)*2 hist_values5=np.array(hist_values5)*2 include_middle=True if include_middle==True: inputfile='generated_data1' if include_middle==False: inputfile='generated_data_nomid' chi2file=np.genfromtxt(str(inputfile)+'.txt', names=True, delimiter=';',dtype=None) modeldata1=np.genfromtxt('uniformingress1.txt', names=True, delimiter=';',dtype=None) #Uniform model modeldata2=np.genfromtxt('uniformegress1.txt', names=True, delimiter=';',dtype=None) #modeldata1=np.genfromtxt('nolimbingress1.txt', names=True, delimiter=';',dtype=None) #No-limb model #modeldata2=np.genfromtxt('nolimbgress1.txt', names=True, delimiter=';',dtype=None) # Import graph specifications graphspecs=np.genfromtxt('graph_specs.txt', names=True, delimiter=';',dtype=None) P_total,P_full,P,flux_star,t_occultation,Initial,Length,Nslices=graphspecs['P_total'],graphspecs['P_full'],graphspecs['P'],graphspecs['flux_star'],graphspecs['t_occultation'],graphspecs['Initial'],graphspecs['Length'],graphspecs['Nslices'] print P_total,P_full,P,flux_star,t_occultation,Initial,Length,Nslices P_total_initial=P_total*2 P_full_initial=P_full*2 Initial_initial=Initial savefigures=False sigma_value=35*1e-6 #SD per point mean=np.mean(hist_values1) median=np.median(hist_values1) standard_dev=np.std(hist_values1) mean2=np.mean(hist_values2) median2=np.median(hist_values2) standard_dev2=np.std(hist_values2) mean3=np.mean(hist_values5) median3=np.median(hist_values5) standard_dev3=np.std(hist_values5) print "mean: ", mean, "SD: ", standard_dev, "Median: ", median print "mean2: ", mean2, "SD2: ", standard_dev2, "Median2: ", median2 print "mean3: ", mean3, "SD3: ", standard_dev3, "Median3: ", median3 # Defines the model generation function def generate_model(full,tot,mid,verbose): Initial=mid P_full=full P_total=tot if verbose==True: print "Details: ", Initial, P_full, P_total, Length plotrange=np.linspace(-P_total+Initial,-P_full+Initial, num=Nslices) plotrange2=np.linspace(P_full+Initial,P_total+Initial, num=Nslices) stepdifference=np.abs(plotrange[0]-plotrange[1]) rangedifference=np.abs(plotrange2[0]-plotrange[-1]) Nsteps_needed=int(round(rangedifference/stepdifference)) plotrange3=np.linspace(plotrange[-1]+stepdifference,plotrange2[0]-stepdifference,num=Nsteps_needed) uniform_curve_x,uniform_curve_y=[],[] total_amount = np.sum(modeldata1['bin_values']) for i in range(Nslices): total_amount = total_amount - modeldata1['bin_values'][i] fractional_flux = (total_amount+flux_star)/(flux_star) uniform_curve_x.append(plotrange[i]) uniform_curve_y.append(fractional_flux) if include_middle==True: for i in range(len(plotrange3)): uniform_curve_x.append(plotrange3[i]) uniform_curve_y.append(1.) total_amount = 0 for i in range(Nslices): total_amount = total_amount + modeldata2['bin_values'][Nslices-i-1] fractional_flux = (total_amount+flux_star)/(flux_star) uniform_curve_x.append(plotrange2[i]) uniform_curve_y.append(fractional_flux) maxvalue=np.max(uniform_curve_y) uniform_curve_x.append(1) uniform_curve_y.append(maxvalue) uniform_curve_x.insert(0,0) uniform_curve_y.insert(0,maxvalue) return uniform_curve_x,uniform_curve_y interpolation_datax,interpolation_dataf=generate_model(0.00730,0.0080,0.50035,verbose=False) plt.plot(interpolation_datax,interpolation_dataf) plt.scatter(chi2file['x_values'],chi2file['flux_values'],c='b',s=8,lw=0)#,zorder=2) if sigma_value!=0: plt.errorbar(chi2file['x_values'],chi2file['flux_values'],yerr=sigma_value,c='#696969',lw=1,ls='none') plt.xlim(0.47,0.53) plt.ylim(np.min(chi2file['flux_values']),np.max(chi2file['flux_values'])) plt.xlabel('Phase') plt.ylabel('$F(t)/F$') if savefigures==True: plt.savefig('final-mcmc-lightcurve1.pdf') plt.show() heatmap, xedges, yedges = np.histogram2d(hist_values1, hist_values3, bins=(100,100),normed=True) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row') contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys') ax2.axis('off') ax1.hist(hist_values1,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step") ax4.hist(hist_values3,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal") ax3.axis('tight') ax3.ticklabel_format(useOffset=False) #myLocator = mticker.MultipleLocator(0.00) #ax3.xaxis.set_major_locator(myLocator) ax3.set_xlabel('Midpoint Phase Position') ax3.set_ylabel('Chi-Squared Value') ax1.set_ylabel('PDF') ax4.set_xlabel('PDF') ax3.set_xlim(np.min(hist_values1),np.max(hist_values1)) ax3.set_ylim(np.min(hist_values3)*0.95,np.max(hist_values3)) if savefigures==True: plt.savefig('chisquared-corner1.pdf') plt.show() plt.hist2d(hist_values1,hist_values3, bins=100) plt.xlabel('Midpoint Phase Position') plt.ylabel('Chi-Squared') if savefigures==True: plt.savefig('chisquared-hist1.pdf') plt.show() plt.hist2d(hist_values2,hist_values3, bins=100) plt.xlabel('Total Duration Phase') plt.ylabel('Chi-Squared') if savefigures==True: plt.savefig('chisquared-hist2.pdf') plt.show() plt.hist2d(hist_values1,hist_values3, bins=200) plt.xlabel('Midpoint Phase Position') plt.ylabel('Chi-Squared') if savefigures==True: plt.savefig('chisquared-hist3.pdf') plt.show() plt.hist2d(hist_values2,hist_values3, bins=200) plt.xlabel('Total Duration Phase') plt.ylabel('Chi-Squared') if savefigures==True: plt.savefig('chisquared-hist4.pdf') plt.show() plt.hist2d(hist_values5,hist_values3, bins=200) plt.xlabel('Full Duration Phase') plt.ylabel('Chi-Squared') if savefigures==True: plt.savefig('chisquared-hist5.pdf') plt.show() heatmap, xedges, yedges = np.histogram2d(hist_values2, hist_values3, bins=(100,100),normed=True) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row') contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys') ax2.axis('off') ax1.hist(hist_values2,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step") ax4.hist(hist_values3,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal") ax3.axis('tight') ax3.ticklabel_format(useOffset=False) #myLocator = mticker.MultipleLocator(0.00) #ax3.xaxis.set_major_locator(myLocator) ax3.set_xlabel('Total Duration Phase') ax3.set_ylabel('Chi-Squared Value') ax1.set_ylabel('Marginalised Chi-Squared PDF') ax4.set_xlabel('Marginalised Chi-Squared PDF') ax3.set_xlim(np.min(hist_values2),np.max(hist_values2)) ax3.set_ylim(np.min(hist_values3)*0.95,np.max(hist_values3)) if savefigures==True: plt.savefig('chisquared-corner2.pdf') plt.show() y,x,_=plt.hist(hist_values1,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF") plt.axvline(x=Initial_initial,c='k',lw=2,label='Origin') plt.xlabel('Midpoint Phase Position') plt.ylabel('Marginalised Chi-Squared PDF') plt.ylim(0,y.max()*(1.05)) plt.vlines(x=(mean), ymin=0, ymax=y.max()*(1.05), color='g', label='Mean') plt.vlines(x=(mean-standard_dev), ymin=0, ymax=y.max()*(1.05), color='r', label='$\sigma_-$') plt.vlines(x=(mean-standard_dev*2), ymin=0, ymax=y.max()*(1.05), color='m', label='$2\sigma_-$') plt.vlines(x=(mean+standard_dev), ymin=0, ymax=y.max()*(1.05), color='b', label='$\sigma_+$') plt.vlines(x=(mean+standard_dev*2), ymin=0, ymax=y.max()*(1.05), color='c', label='$2\sigma_+$') plt.legend() if savefigures==True: plt.savefig('PDF1-modified.pdf') plt.show() n_hist, b_hist, patches_hist = plt.hist(hist_values1,bins=200,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF") plt.hist(hist_values1,bins=200,normed=1,facecolor="black",edgecolor='None',alpha=0.1,label="PDF") plt.xlabel('Midpoint Phase Position') plt.ylabel('Normalised PDF') if savefigures == True: plt.savefig('plottemp.pdf') bin_max = np.where(n_hist == n_hist.max()) print "Mode:", b_hist[bin_max][0] ### CONFIDENCE INTERVAL SELECTOR: ######################################## bin_heights, bin_borders, _ = n_hist, b_hist, patches_hist bin_center = bin_borders[:-1] + np.diff(bin_borders) / 2 xvals, yvals = bin_center, bin_heights model = SkewedGaussianModel() params = model.guess(yvals, x=xvals) result = model.fit(yvals, params, x=xvals) print result.fit_report() plt.plot(xvals, result.best_fit,c='c',lw=2) #Mode Finder: maxval=0 maxvalx=0 for i in range(len(xvals)): if result.best_fit[i]>maxval: maxval=result.best_fit[i] maxvalx=xvals[i] print "Curve Mode:", maxvalx area = np.trapz(result.best_fit, x=xvals)#, dx=5) print "area =", area summation1=0 summation2=0 prev_highest=[0] prev_highest_position=[1e9] i=0 newx1=[] newy1=[] newx2=[] newy2=[] while i < len(xvals): position1=result.best_fit[i] newx1.append(xvals[i]) newy1.append(position1) summation1=np.trapz(newy1,x=newx1) found = False for j in range(len(xvals)): loc=len(xvals)-1-j if loc==-1: raise Exception("Array error.") position2=result.best_fit[loc] if (position2>=position1) and (found==False) and (xvals[loc]<=prev_highest_position[-1]) and (position2 >= prev_highest[-1]): if (position2>1e3*position1) and (position1!=0): raise Exception("Corresponding position for probability=({}) not correctly found. E1".format(position1)) found = True prev_highest.append(position2) prev_highest_position.append(xvals[loc]) #plt.axvline(xvals[loc],c='m') if j>=len(n_hist) and found==False: raise Exception("Corresponding position for probability=({}) not found. E2".format(position1)) if found == True: newx2.append(xvals[loc]) newy2.append(position2) break summation2=np.abs(np.trapz(newy2,x=newx2)) testcondition=1-(summation1+summation2) if testcondition<0.69: plt.axvline(Initial_initial,c='r') plt.axvline(maxvalx,c='k') plt.axvline(newx1[-1],c='#505050') plt.axvline(newx2[-1],c='#505050') print "Lower: ", np.abs(maxvalx-newx1[-1]) print "Upper: ", np.abs(maxvalx-newx2[-1]) break else: i+=1 #plt.axvline(xvals[i],c='b') print testcondition if savefigures == True: plt.savefig('asymmetric1.pdf') plt.show() ### y,x,_=plt.hist(hist_values2,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF") plt.axvline(x=P_total_initial,c='k',lw=2,label='Origin') plt.xlabel('Total Duration Phase') plt.ylabel('Marginalised Chi-Squared PDF') plt.ylim(0,y.max()*(1.05)) plt.vlines(x=(mean2), ymin=0, ymax=y.max()*(1.05), color='g', label='Mean') plt.vlines(x=(mean2-standard_dev2), ymin=0, ymax=y.max()*(1.05), color='r', label='$\sigma_-$') plt.vlines(x=(mean2-standard_dev2*2), ymin=0, ymax=y.max()*(1.05), color='m', label='$2\sigma_-$') plt.vlines(x=(mean2+standard_dev2), ymin=0, ymax=y.max()*(1.05), color='b', label='$\sigma_+$') plt.vlines(x=(mean2+standard_dev2*2), ymin=0, ymax=y.max()*(1.05), color='c', label='$2\sigma_+$') plt.legend() if savefigures==True: plt.savefig('PDF2-modified.pdf') plt.show() n_hist, b_hist, patches_hist = plt.hist(hist_values2,bins=200,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF") plt.hist(hist_values2,bins=200,normed=1,facecolor="black",edgecolor='None',alpha=0.1,label="PDF") plt.xlabel('Total Occultation Duration') plt.ylabel('Normalised PDF') if savefigures == True: plt.savefig('plottemp2.pdf') bin_max = np.where(n_hist == n_hist.max()) print "Mode:", b_hist[bin_max][0] ### CONFIDENCE INTERVAL SELECTOR: ######################################## bin_heights, bin_borders, _ = n_hist, b_hist, patches_hist bin_center = bin_borders[:-1] + np.diff(bin_borders) / 2 xvals, yvals = bin_center, bin_heights model = SkewedGaussianModel() params = model.guess(yvals, x=xvals) result = model.fit(yvals, params, x=xvals) print result.fit_report() plt.plot(xvals, result.best_fit,c='c',lw=2) #Mode Finder: maxval=0 maxvalx=0 for i in range(len(xvals)): if result.best_fit[i]>maxval: maxval=result.best_fit[i] maxvalx=xvals[i] print "Curve Mode:", maxvalx area = np.trapz(result.best_fit, x=xvals)#, dx=5) print "area =", area summation1=0 summation2=0 prev_highest=[0] prev_highest_position=[1e9] i=0 newx1=[] newy1=[] newx2=[] newy2=[] while i < len(xvals): position1=result.best_fit[i] newx1.append(xvals[i]) newy1.append(position1) summation1=np.trapz(newy1,x=newx1) found = False for j in range(len(xvals)): loc=len(xvals)-1-j if loc==-1: raise Exception("Array error.") position2=result.best_fit[loc] if (position2>=position1) and (found==False) and (xvals[loc]<=prev_highest_position[-1]) and (position2 >= prev_highest[-1]): if (position2>1e3*position1) and (position1!=0): raise Exception("Corresponding position for probability=({}) not correctly found. E1".format(position1)) found = True prev_highest.append(position2) prev_highest_position.append(xvals[loc]) #plt.axvline(xvals[loc],c='m') if j>=len(n_hist) and found==False: raise Exception("Corresponding position for probability=({}) not found. E2".format(position1)) if found == True: newx2.append(xvals[loc]) newy2.append(position2) break summation2=np.abs(np.trapz(newy2,x=newx2)) testcondition=1-(summation1+summation2) if testcondition<0.69: plt.axvline(maxvalx,c='k') plt.axvline(P_total_initial,c='r') plt.axvline(newx1[-1],c='#505050') plt.axvline(newx2[-1],c='#505050') print "Lower: ", np.abs(maxvalx-newx1[-1]) print "Upper: ", np.abs(maxvalx-newx2[-1]) break else: i+=1 print testcondition if savefigures == True: plt.savefig('asymmetric2.pdf') plt.show() ### y,x,_=plt.hist(hist_values5,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF") plt.axvline(x=P_full_initial,c='k',lw=2,label='Origin') plt.xlabel('Full Duration Phase') plt.ylabel('Marginalised Chi-Squared PDF') plt.ylim(0,y.max()*(1.05)) plt.vlines(x=(mean3), ymin=0, ymax=y.max()*(1.05), color='g', label='Mean') plt.vlines(x=(mean3-standard_dev3), ymin=0, ymax=y.max()*(1.05), color='r', label='$\sigma_-$') plt.vlines(x=(mean3-standard_dev3*2), ymin=0, ymax=y.max()*(1.05), color='m', label='$2\sigma_-$') plt.vlines(x=(mean3+standard_dev3), ymin=0, ymax=y.max()*(1.05), color='b', label='$\sigma_+$') plt.vlines(x=(mean3+standard_dev3*2), ymin=0, ymax=y.max()*(1.05), color='c', label='$2\sigma_+$') plt.legend() if savefigures==True: plt.savefig('PDF3-modified.pdf') plt.show() n_hist, b_hist, patches_hist = plt.hist(hist_values5,bins=200,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF") plt.hist(hist_values5,bins=200,normed=1,facecolor="black",edgecolor='None',alpha=0.1,label="PDF") plt.xlabel('Full Occultation Duration') plt.ylabel('Normalised PDF') if savefigures == True: plt.savefig('plottemp3.pdf') bin_max = np.where(n_hist == n_hist.max()) print "Mode:", b_hist[bin_max][0] ### CONFIDENCE INTERVAL SELECTOR: ######################################## bin_heights, bin_borders, _ = n_hist, b_hist, patches_hist bin_center = bin_borders[:-1] + np.diff(bin_borders) / 2 xvals, yvals = bin_center, bin_heights model = SkewedGaussianModel() params = model.guess(yvals, x=xvals) result = model.fit(yvals, params, x=xvals) print result.fit_report() plt.plot(xvals, result.best_fit,c='c',lw=2) #Mode Finder: maxval=0 maxvalx=0 for i in range(len(xvals)): if result.best_fit[i]>maxval: maxval=result.best_fit[i] maxvalx=xvals[i] print "Curve Mode:", maxvalx area = np.trapz(result.best_fit, x=xvals)#, dx=5) print "area =", area summation1=0 summation2=0 prev_highest=[0] prev_highest_position=[1e9] i=0 newx1=[] newy1=[] newx2=[] newy2=[] while i < len(xvals): position1=result.best_fit[i] newx1.append(xvals[i]) newy1.append(position1) summation1=np.trapz(newy1,x=newx1) found = False for j in range(len(xvals)): loc=len(xvals)-1-j if loc==-1: raise Exception("Array error.") position2=result.best_fit[loc] if (position2>=position1) and (found==False) and (xvals[loc]<=prev_highest_position[-1]) and (position2 >= prev_highest[-1]): if (position2>1e3*position1) and (position1!=0): raise Exception("Corresponding position for probability=({}) not correctly found. E1".format(position1)) found = True prev_highest.append(position2) prev_highest_position.append(xvals[loc]) #plt.axvline(xvals[loc],c='m') if j>=len(n_hist) and found==False: raise Exception("Corresponding position for probability=({}) not found. E2".format(position1)) if found == True: newx2.append(xvals[loc]) newy2.append(position2) break summation2=np.abs(np.trapz(newy2,x=newx2)) testcondition=1-(summation1+summation2) if testcondition<0.69: plt.axvline(maxvalx,c='k') plt.axvline(P_full_initial,c='r') plt.axvline(newx1[-1],c='#505050') plt.axvline(newx2[-1],c='#505050') print "Lower: ", np.abs(maxvalx-newx1[-1]) print "Upper: ", np.abs(maxvalx-newx2[-1]) break else: i+=1 print testcondition if savefigures == True: plt.savefig('asymmetric3.pdf') plt.show() ### xpoints1=np.linspace(0,len(hist_values1),num=len(hist_values1)) xpoints2=np.linspace(0,len(hist_values2),num=len(hist_values2)) plt.scatter(xpoints1,hist_values1,c='r',s=3) plt.xlabel('Number of Samples') plt.ylabel('Midpoint Phase Position') if savefigures==True: plt.savefig('parameter-variation1.pdf') plt.show() plt.scatter(xpoints2,hist_values2,c='b',s=3) plt.xlabel('Number of Samples') plt.ylabel('Total Duration Phase') if savefigures==True: plt.savefig('parameter-variation2.pdf') plt.show() plt.scatter(xpoints2,hist_values5,c='b',s=3) plt.xlabel('Number of Samples') plt.ylabel('Full Duration Phase') if savefigures==True: plt.savefig('parameter-variation3.pdf') plt.show() plt.scatter(xpoints2,hist_values4,c='m',s=3) plt.xlabel('Number of Samples') plt.ylabel('Reduced Chi Squared') if savefigures==True: plt.savefig('parameter-variation3.pdf') plt.show() heatmap, xedges, yedges = np.histogram2d(hist_values1, hist_values2, bins=(100,100),normed=True) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row') contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys') axins1 = inset_axes(ax3, width="5%", height="92.5%", loc=1) plt.colorbar(contourplot, cax=axins1, orientation="vertical") ax2.axis('off') ax1.hist(hist_values1,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step") ax4.hist(hist_values2,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal") ax3.axis('tight') ax3.ticklabel_format(useOffset=False) myLocator = mticker.MultipleLocator(0.0003) ax3.xaxis.set_major_locator(myLocator) ax3.set_xlabel('Midpoint Position') ax3.set_ylabel('Total Duration') ax1.set_ylabel('Marginalised PDF') ax4.set_xlabel('Marginalised PDF') ax3.set_xlim(np.min(hist_values1),np.max(hist_values1)) ax3.set_ylim(np.min(hist_values2),np.max(hist_values2)) if savefigures==True: plt.savefig('corner-modified.pdf') plt.show() heatmap, xedges, yedges = np.histogram2d(hist_values1, hist_values5, bins=(100,100),normed=True) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row') contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys') axins1 = inset_axes(ax3, width="5%", height="92.5%", loc=1) plt.colorbar(contourplot, cax=axins1, orientation="vertical")#, ticks=[1, 2, 3]) #plt.colorbar(contourplot,ax=ax3) ax2.axis('off') ax1.hist(hist_values1,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step") ax4.hist(hist_values5,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal") ax3.axis('tight') ax3.ticklabel_format(useOffset=False) myLocator = mticker.MultipleLocator(0.0003) ax3.xaxis.set_major_locator(myLocator) ax3.set_xlabel('Midpoint Position') ax3.set_ylabel('Full Duration') ax1.set_ylabel('Marginalised PDF') ax4.set_xlabel('Marginalised PDF') ax3.set_xlim(np.min(hist_values1),np.max(hist_values1)) ax3.set_ylim(np.min(hist_values5),np.max(hist_values5)) if savefigures==True: plt.savefig('corner-modified2.pdf') plt.show() heatmap, xedges, yedges = np.histogram2d(hist_values2, hist_values5, bins=(100,100),normed=True) extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]] fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row') contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys') axins1 = inset_axes(ax3, width="5%", height="92.5%", loc=1) plt.colorbar(contourplot, cax=axins1, orientation="vertical") ax2.axis('off') ax1.hist(hist_values2,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step") ax4.hist(hist_values5,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal") ax3.axis('tight') ax3.ticklabel_format(useOffset=False) #myLocator = mticker.MultipleLocator(0.00) #ax3.xaxis.set_major_locator(myLocator) ax3.set_xlabel('Total Duration') ax3.set_ylabel('Full Duration') ax1.set_ylabel('Marginalised PDF') ax4.set_xlabel('Marginalised PDF') ax3.set_xlim(np.min(hist_values2),np.max(hist_values2)) ax3.set_ylim(np.min(hist_values5),np.max(hist_values5)) if savefigures==True: plt.savefig('corner-modified3.pdf') plt.show() ######################################## print "Done."
2.34375
2
src/the_tale/the_tale/finances/shop/relations.py
al-arz/the-tale
85
12791716
import smart_imports smart_imports.all() INFINIT_PREMIUM_DESCRIPTION = 'Вечная подписка даёт вам все бонусы подписчика на всё время игры.' class PERMANENT_PURCHASE_TYPE(rels_django.DjangoEnum): description = rels.Column(unique=False) might_required = rels.Column(unique=False, single_type=False) level_required = rels.Column(unique=False, single_type=False) full_name = rels.Column() records = (('INFINIT_SUBSCRIPTION', 12, 'Вечная подписка', INFINIT_PREMIUM_DESCRIPTION, None, None, 'Вечная подписка'),) class GOODS_GROUP(rels_django.DjangoEnum): uid = rels.Column() uid_prefix = rels.Column(unique=False) records = (('PREMIUM', 0, 'подписка', 'subscription', 'subscription-'), ('ENERGY', 1, 'энергия', 'energy', 'energy-'), ('CHEST', 2, 'сундук', 'random-premium-chest', 'random-premium-chest'), ('PREFERENCES', 3, 'предпочтения', 'preference', 'preference-'), ('PREFERENCES_RESET', 4, 'сброс предпочтений', 'preference-reset', 'hero-preference-reset-'), ('HABITS', 5, 'черты', 'habits', 'hero-habits-'), ('ABILITIES', 6, 'способности', 'abilities', 'hero-abilities-'), ('CLANS', 7, 'гильдии', 'clans', 'clan-'), ('CARDS', 8, 'Карты судьбы', 'cards', 'cards-')) CARDS_MIN_PRICES = {cards_relations.RARITY.COMMON: 2, cards_relations.RARITY.UNCOMMON: 10, cards_relations.RARITY.RARE: 25, cards_relations.RARITY.EPIC: 50, cards_relations.RARITY.LEGENDARY: 100}
1.882813
2
latextools_utils/is_tex_file.py
MPvHarmelen/MarkdownCiteCompletions
0
12791717
<filename>latextools_utils/is_tex_file.py from .settings import get_setting strbase = str def get_tex_extensions(): tex_file_exts = get_setting('tex_file_exts', ['.tex']) return [s.lower() for s in set(tex_file_exts)] def is_tex_file(file_name): if not isinstance(file_name, strbase): raise TypeError('file_name must be a string') tex_file_exts = get_tex_extensions() for ext in tex_file_exts: if file_name.lower().endswith(ext): return True return False
2.6875
3
spasco/main.py
NiklasTiede/spasco
2
12791718
"""spasco - spaces to underscores ============================== Command line tool for replacing/removing whitespaces or other patterns of file- and directory names. """ # Copyright (c) 2021, <NAME>. # All rights reserved. Distributed under the MIT License. import argparse import configparser import fnmatch import logging import os import sys from argparse import _SubParsersAction from argparse import HelpFormatter from typing import List from typing import Tuple from spasco import __src_url__ from spasco import __title__ from spasco import __version__ from spasco.term_color import fmt from spasco.term_color import Txt base, file = os.path.split(__file__) settings_file = os.path.join(base, 'settings.ini') # set up a settings file and then a logger: config = configparser.ConfigParser() config.read(settings_file) # default values for log record are created: if not config.read(settings_file): config['VALUE-SETTINGS'] = { 'search_value': "' '", 'new_value': '_', } config['LOG-SETTINGS'] = { 'Logging_turned_on': "False", 'logger_filename': f'{__title__}.log', 'logger_location': os.environ['HOME'], } with open(settings_file, 'w') as f: config.write(f) def get_logger_path() -> str: logger_location = config.get('LOG-SETTINGS', 'logger_location') logger_filename = config.get('LOG-SETTINGS', 'logger_filename') return f"{logger_location}/{logger_filename}" logger_path = get_logger_path() logging.basicConfig( filename=logger_path, level=logging.INFO, format='%(levelname)s | %(asctime)s | %(message)s', ) if (sys.platform != 'linux' and sys.platform != 'darwin'): print(f"{__title__!r} is currently not optimized for platforms other than OS X / linux") def main(argv: List[str]) -> int: """ Main program. :argument argv: command-line arguments, such as sys.argv (including the program name in argv[0]). :return Zero on successful program termination, non-zero otherwise. """ main_parser, config_subparser = __build_parser() argv = argv[1:] args = main_parser.parse_args(args=argv) # triggering config subparser if vars(args).get('command', None) == 'config': execute_config(config_subparser, argv) return 0 ########################### # 1 select and sort paths # ########################### files_dirs = [] if isinstance(args.file_or_dir, str): args.file_or_dir = [args.file_or_dir] if args.file_or_dir and not args.recursive: files_dirs.extend(args.file_or_dir) if args.recursive: files_dirs = recurse_dirs_and_files() # sort paths (longest paths first) so that renaming starts with the deepest nested file/directory: files_dirs = [x.split('/') for x in files_dirs] sorted_paths = sorted(files_dirs, key=len, reverse=True) files_dirs = ['/'.join(path_as_lst) for path_as_lst in sorted_paths] ######################## # 2: path filtration # ######################## SEARCH_VALUE = args.search_value if args.search_value else config.get( 'VALUE-SETTINGS', 'search_value', ) if SEARCH_VALUE == "' '": SEARCH_VALUE = ' ' filtered_paths = [] all_selected_files_dirs = files_dirs.copy() # ------ no file/dir existent ---- if not files_dirs: print('No directory or file present!') return 1 # ------ search-value filter ------ # [files_dirs.remove(x) for x in all_selected_files_dirs if SEARCH_VALUE not in x.split('/')[-1]] for x in all_selected_files_dirs: if SEARCH_VALUE not in x.split('/')[-1]: files_dirs.remove(x) if not files_dirs: searchval_msg = f"None of the {len(all_selected_files_dirs)} present files/directories contain the search value '{SEARCH_VALUE}'!" print(searchval_msg) return 1 # ------ pattern-only filter ------ # [files_dirs.remove(x) for x in files_dirs.copy() if args.pattern_only and not fnmatch.fnmatch(os.path.split(x)[1], args.pattern_only)] for x in files_dirs.copy(): if args.pattern_only and not fnmatch.fnmatch(os.path.split(x)[1], args.pattern_only): files_dirs.remove(x) if not files_dirs: print(f'None of the {len(all_selected_files_dirs)} present files/directories contain the pattern {args.pattern_only!r}!') return 1 # ------ except-pattern filter ----- # [files_dirs.remove(x) for x in files_dirs.copy() if args.except_pattern and fnmatch.fnmatch(os.path.split(x)[-1], args.except_pattern)] for x in files_dirs.copy(): if args.except_pattern and fnmatch.fnmatch(os.path.split(x)[-1], args.except_pattern): files_dirs.remove(x) if not files_dirs: print(f'None of the exception-pattern matching files/directories contain the search-value {SEARCH_VALUE!r}.',) return 1 # ------ dirs-only filter ----- # [files_dirs.remove(x) for x in files_dirs.copy() if args.dirs_only and not os.path.isdir(x)] for x in files_dirs.copy(): if args.dirs_only and not os.path.isdir(x): files_dirs.remove(x) if not files_dirs: print('No directory present for renaming.') return 1 # ------ files-only filter ----- # [files_dirs.remove(x) for x in files_dirs.copy() if args.files_only and not os.path.isfile(x)] for x in files_dirs.copy(): if args.files_only and not os.path.isfile(x): files_dirs.remove(x) if not files_dirs: print('No file present for renaming.') return 1 filtered_paths = files_dirs ################ # 3 renaming # ################ if args.new_value == '': NEW_VALUE = '' if args.new_value: NEW_VALUE = args.new_value if args.new_value is None: NEW_VALUE = config.get('VALUE-SETTINGS', 'new_value') if NEW_VALUE == "''" or NEW_VALUE == '""': NEW_VALUE = '' filecount, dircount, renamed_paths = path_renaming( path_lst=filtered_paths, search_value=SEARCH_VALUE, new_value=NEW_VALUE, ) if args.immediately: is_proceeding = 'y' else: msg = f'You can rename {len(filtered_paths)} files and/or directories.' # 🔨 colored_msg = fmt(msg) # , Txt.greenblue print(colored_msg) print() before_heading = fmt('Before', Txt.pink, bolded=True) after_heading = fmt('After', Txt.blue, bolded=True) sep_line = fmt('──', Txt.greenblue) print(f"{before_heading} {' ' * (max([len(x) for x in filtered_paths]) - len('before') + 6)} {after_heading}",) print(f"{sep_line * (max([len(x) for x in filtered_paths]) + 4)}") for before, after in list(zip(filtered_paths, renamed_paths)): before_renaming = fmt(before, Txt.pink) after_renaming = fmt(after, Txt.blue) print(f"'{before_renaming}'{' ' * (max([len(x) for x in filtered_paths]) - len(before))} {fmt('🡆', Txt.greenblue)} '{after_renaming}'",) print(f"{sep_line * (max([len(x) for x in filtered_paths]) + 4)}") print() q = fmt(' [y/n] ', Txt.pink) proceed_msg = fmt('OK to proceed with renaming?') # , Txt.greenblue is_proceeding = input(proceed_msg + q) if is_proceeding.lower() == 'y': filecount, dircount, new_pathnames = path_renaming( path_lst=filtered_paths, search_value=SEARCH_VALUE, new_value=NEW_VALUE, renaming=True, ) success_msg = fmt(f'All done! {filecount} files and {dircount} directories were renamed! ✨💄✨', Txt.greenblue) print(success_msg) return 0 else: print(fmt("Command aborted.", textcolor=Txt.pink)) return 1 settings_msg = f"""{fmt("value settings:", Txt.greenblue)} search_value: {config.get('VALUE-SETTINGS', 'search_value')} new_value: {config.get('VALUE-SETTINGS', 'new_value')} {fmt("log settings:", Txt.greenblue)} logging_turned_on: {config.getboolean('LOG-SETTINGS', 'logging_turned_on')} logger_filename: {config.get('LOG-SETTINGS', 'logger_filename')} logger_location: {config.get('LOG-SETTINGS', 'logger_location')}""" def execute_config(config_subparser: argparse.ArgumentParser, argv: List[str]) -> int: """ Boolean logic of config subparser triggering. """ args = config_subparser.parse_args(argv[1:]) if args.show_settings: print(settings_msg) return 0 if args.turn_log_on: config['LOG-SETTINGS']['logging_turned_on'] = args.turn_log_on.capitalize() with open(settings_file, 'w') as fp: config.write(fp) log_state = config.getboolean('LOG-SETTINGS', 'logging_turned_on') if log_state: print('Logging is activated.') else: print('Logging is deactivated.') return 0 if args.log_name: old_logger_path = get_logger_path() config['LOG-SETTINGS']['logger_filename'] = args.log_name with open(settings_file, 'w') as fp: config.write(fp) new_logger_path = get_logger_path() os.rename(old_logger_path, new_logger_path) print(f"The new log filename is {config.get('LOG-SETTINGS', 'logger_filename')!r}.",) return 0 if args.log_location: old_logger_path = get_logger_path() log_location = args.log_location if '~' in args.log_location: log_location = os.path.expanduser(args.log_location) if not os.path.isdir(log_location): print(f'The given path {args.log_location!r} is not a valid directory!') return 1 config['LOG-SETTINGS']['logger_location'] = log_location with open(settings_file, 'w') as fp: config.write(fp) new_logger_path = get_logger_path() os.rename(old_logger_path, new_logger_path) print(f"The new log location is {config.get('LOG-SETTINGS', 'logger_location')!r}.",) return 0 if args.set_search_value: if args.set_search_value == ' ': config['VALUE-SETTINGS']['search_value'] = "' '" with open(settings_file, 'w') as fp: config.write(fp) print(f"The new search-value is {config.get('VALUE-SETTINGS', 'search_value')}.",) else: config['VALUE-SETTINGS']['search_value'] = args.set_search_value with open(settings_file, 'w') as fp: config.write(fp) print(f"The new search-value is {config.get('VALUE-SETTINGS', 'search_value')!r}.",) return 0 if args.set_new_value == '': config['VALUE-SETTINGS']['new_value'] = "''" with open(settings_file, 'w') as fp: config.write(fp) print(f"The new 'new-value' is {config.get('VALUE-SETTINGS', 'new_value')}.") return 0 if args.set_new_value: config['VALUE-SETTINGS']['new_value'] = args.set_new_value with open(settings_file, 'w') as fp: config.write(fp) print(f"The new 'new-value' is {config.get('VALUE-SETTINGS', 'new_value')!r}.") return 0 config_subparser.print_help() return 1 def path_renaming(path_lst: List[str], search_value: str, new_value: str, renaming: bool = False) -> Tuple[int, int, List[str]]: """ List of filtered files and directories are renamed and their names returned. Furthermore, the number fo directories/files which were renamed are also returned. :returns Tuples containing the number of directories, files and the names of them after renaming """ renamed_paths = [] dircount, filecount = 0, 0 for old_path_name in path_lst: path_base, file = os.path.split(old_path_name) new_name = file.replace(search_value, new_value) full_new = os.path.join(path_base, new_name) renamed_paths.append(full_new) if renaming: os.rename(old_path_name, full_new) if os.path.isdir(full_new): dircount += 1 elif os.path.isfile(full_new): filecount += 1 logging.info(f" working dir: {os.getcwd()!r} | naming: {old_path_name!r} --> {full_new!r}",) return (filecount, dircount, renamed_paths) def recurse_dirs_and_files() -> List[str]: """ All files/directories within the current working directory are mapped into a list. :returns List of all file/directory paths, recursively and sorted """ all_files_dirs = [] base_path = os.getcwd() # collect all rel. paths in a list (rel to cwd): for dirpath, dirnames, filenames in os.walk(base_path): for filename in filenames: full_filepath = dirpath + '/' + filename rel_filepath = os.path.relpath(full_filepath, base_path) all_files_dirs.append(rel_filepath) for dirname in dirnames: full_dirpath = dirpath + '/' + dirname rel_dirpath = os.path.relpath(full_dirpath, base_path) all_files_dirs.append(rel_dirpath) return all_files_dirs # hack for removing the metavar below the subparsers (config) title class NoSubparsersMetavarFormatter(HelpFormatter): def _format_action_invocation(self, action): # type: ignore if isinstance(action, _SubParsersAction): return "" return super()._format_action_invocation(action) class MyOwnFormatter(NoSubparsersMetavarFormatter, argparse.RawDescriptionHelpFormatter): """ Removes metavar of config subparser and adds RawDescription """ pass def __build_parser() -> Tuple[argparse.ArgumentParser, argparse.ArgumentParser]: """ Constructs the main_parser for the command line arguments. :returns An ArgumentParser instance for the CLI. """ main_parser = argparse.ArgumentParser( prog=__title__, add_help=False, description=f'Spasco is a glorified replace function. By default it replaces whitespaces\n' f'of all file- and directory names within your current working directory by \n' f'underscores.\n\nsrc: {__src_url__}', epilog='Make your files more computer-friendly 😄', formatter_class=lambda prog: MyOwnFormatter( prog, max_help_position=80, ), ) # optional arguments: main_parser.add_argument( "-t", dest='file_or_dir', metavar='file_or_dir', action='store', nargs='?', default=os.listdir(), help='Select a single file or directory for renaming.', ) main_parser.add_argument( '-s', dest='search_value', nargs='?', action='store', metavar='search_value', help="Define custom search-value (default: ' ').", ) main_parser.add_argument( '-n', dest='new_value', nargs='?', action='store', metavar='new_value', help="Define custom new-value (default: '_')." ) main_parser.add_argument( '-p', dest='pattern_only', nargs='?', action='store', metavar='pattern_only', help='Only files/dirs containing the pattern are renamed.', ) main_parser.add_argument( '-e', metavar='except_pattern', dest='except_pattern', nargs='?', action='store', help='Only files/dirs not containing the pattern are renamed.', ) main_parser.add_argument( '-d', '--dirs-only', action='store_true', help='Only directories are renamed.', ) main_parser.add_argument( '-f', '--files-only', action='store_true', help='Only files are renamed.', ) main_parser.add_argument( '-r', '--recursive', action='store_true', help='Recurse into directories.', ) main_parser.add_argument( '-i', '--immediately', action='store_true', help='Skip security question, renaming preview and execute immediately.', ) main_parser.add_argument( '-v', '--version', action='version', help='Show version number and exit.', version=f'%(prog)s {__version__}', ) add_parser_help(main_parser) # ---- configuration structured as subparser ----- config_subparsers = main_parser.add_subparsers( title='log and renaming configuration', ) config_subparser = add_config_subparser(config_subparsers) return main_parser, config_subparser def add_config_subparser(sub_parsers: argparse._SubParsersAction) -> argparse.ArgumentParser: """ Parser for configuring spasco. """ config_subparser = sub_parsers.add_parser( name='config', description='search-value and new-value can be changed. Logging to record all ' 'renaming actions as log file can be activated.', usage=f'{__title__} config [--show-setting] [-o true/false] [-n [filename]] [-l [pathname]] [-h, --help ]', add_help=False, formatter_class=lambda prog: argparse.RawDescriptionHelpFormatter( prog, max_help_position=33, ), help=f"Sub-command to interact with {__title__}'s logging and rename settings.", ) config_subparser.add_argument( '--show-settings', action='store_true', help='Returns your current settings for logging and renaming.', ) add_parser_help(config_subparser) config_subparser_logging = config_subparser.add_argument_group( 'log settings', ) config_subparser_logging.add_argument( '-o', nargs='?', metavar='true/false', dest='turn_log_on', choices=['true', 'false'], help="Logging is turned on/off (default: off).", ) config_subparser_logging.add_argument( '-f', nargs='?', metavar='filename', dest='log_name', help='Set a new filename for the logger.', ) config_subparser_logging.add_argument( '-l', nargs='?', metavar='pathname', dest='log_location', help='Set a new file location for the logger.', ) config_subparser_renaming = config_subparser.add_argument_group( 'renaming settings', ) config_subparser_renaming.add_argument( '-s', nargs='?', metavar='search_value', dest='set_search_value', help="Set a new 'search-value' permanently.", ) config_subparser_renaming.add_argument( '-n', nargs='?', metavar='new_value', dest='set_new_value', help="Set a new 'new-value' permanently.", ) config_subparser.set_defaults(command='config') return config_subparser def add_parser_help(parser: argparse.ArgumentParser) -> None: """ Custom help-argument to have consistent style. add_help=False to enable this. """ parser.add_argument( '-h', '--help', action='help', help="Show this help message and exit.", ) def run_main() -> None: try: sys.exit(main(sys.argv)) except Exception as e: sys.stderr.write(__title__ + ': ' + str(e) + '\n') sys.exit(1) if __name__ == '__main__': run_main()
2.703125
3
pyc_compat.py
jplevyak/pyc
3
12791719
<reponame>jplevyak/pyc __pyc_declare__ = None
1.054688
1
flask-proj/manage.py
uninstallHahaha/flask-project
0
12791720
<filename>flask-proj/manage.py<gh_stars>0 from App import create_app # 初始化模块 manager = create_app() if __name__ == '__main__': manager.run()
1.40625
1
melodyrnn/dataset.py
bfw930/uv-eurovision-ai
0
12791721
''' imports ''' # filesystem management import os # tensors and nn modules import torch # array handling import numpy as np # midi file import and parse from mido import MidiFile class MelodyDataset(torch.utils.data.Dataset): ''' dataset class for midi files ''' def __init__(self, dir_path: str, cache = False, ds: int = 20): ''' init dataset, import midi files ''' super().__init__() # store downsampling factor self.ds = ds # get and store list midi files in directory self.file_names = [ name for name in os.listdir(dir_path) if 'mid' in name[-4:] ] # import and store midi files self.midi_files = [ MidiFile(os.path.join(dir_path, file_name)) for file_name in self.file_names ] # case filter by key if False: # get index for only midi with meta plus [melody, chords, bass] tracks j = [ i for i in range(len(self.file_names)) if len(self.midi_files[i].tracks) > 3 and "key='{}'".format(key) in str(self.midi_files[i].tracks[0][2]) ] if False: # get index for only midi with meta plus [melody, chords, bass] tracks j = [ i for i in range(len(self.file_names)) if len(self.midi_files[i].tracks) > 3 ] # filter midi file and file name lists self.midi_files = [ self.midi_files[i] for i in j ] self.file_names = [ self.file_names[i] for i in j ] # init store of import state self.import_list = [ None for _ in range(len(self.midi_files)) ] # pre-cache all data if cache: # iterate through midi files for index in range(len(self.file_names)): # import data to memory self.import_data(index) def import_data(self, index): ''' import midi data to memory ''' # get midi by index midi = self.midi_files[index] # get midi tracks tracks = self.midi2tracks(midi) # get note tracks matrix matrix = self.tracks2matrix(tracks) # get melody format from matrix melody = self.matrix2melody(matrix) # downsample over time melody = melody[::self.ds] # store matrix in import list self.import_list[index] = melody def midi2tracks(self, midi): ''' extract tracks from mido.MidiFile ''' # initialise tracks list tracks = [] if len(midi.tracks) == 1: ts = [0] else: ts = range(len(midi.tracks))[1:4] # iterate over tracks in midi (excl. meta track, extra), [melody, chords, bass] #for i in range(len(midi.tracks))[1:4]: for i in ts: # store track data as dict for processing track = [] # iterate messages in track for msg in midi.tracks[i][:]: # ensure note data only if msg.type in ['note_on', 'note_off']: # init note data dict note = {} # store each note data #note['type'] = msg.type #note['channel'] = msg.channel note['note'] = msg.note note['time'] = msg.time #note['velocity'] = msg.velocity note['velocity'] = 0 if msg.type == 'note_off' else 1 # store note data track.append(note) # store track notes tracks.append(track) # return extracted midi tracks return tracks def tracks2matrix(self, tracks: list): ''' convert tracks to matrix ''' # initialise track matricies list m = [] # iterate tracks for track in tracks: # initialise note state vector, 7-bit note depth N = np.zeros(128, dtype = np.int16) # initialise track note matrix (zero init column) M = np.zeros((128, 1), dtype = np.int16) # iterate messages in track for msg in track: # if time step changes, store intermediate notes if int(msg['time']) != 0: # extend note state vector over range time step n = np.stack([ N for _ in range( int(msg['time']) ) ]).T # append note state vector to track note matrix M = np.concatenate( [M, n], axis = 1 ) # update value of note vector by index N[int(msg['note'])] = int(msg['velocity']) # store track note matrix m.append(M) # get max length track s = max([ track.shape[1] for track in m ]) # pad tracks to max length of time axis, stack on new axis M = np.stack([ np.pad(track, ((0, 0), (0, s - track.shape[1]))) for track in m ], axis = 2) # return stacked tracks note matrix return M def matrix2melody(self, matrix): ''' extract melody from note matrix ''' # get track note matrix for melody only M = matrix[:,:,0] # init zero melody, default negative one #melody = np.ones(M.shape[1])*-1 melody = np.zeros(M.shape[1]) # get index (note, time) where nonzero j = np.where( M != 0 ) # set melody note at time by index melody[j[1]] = j[0] # return extracted melody return melody def __getitem__(self, index): ''' return tracks note matrix ''' # check for import state if self.import_list[index] is None: # import data to memory self.import_data(index) # return data if already imported return self.import_list[index] ''' def linear_quantize(samples, q_levels): samples = samples.clone() samples -= samples.min(dim=-1)[0].expand_as(samples) samples /= samples.max(dim=-1)[0].expand_as(samples) samples *= q_levels - EPSILON samples += EPSILON / 2 return samples.long() def linear_dequantize(samples, q_levels): return samples.float() / (q_levels / 2) - 1 def q_zero(q_levels): return q_levels // 2 ''' def __len__(self): ''' return total midi files ''' # return number of midi files return len(self.file_names) class MelodyDataLoader(torch.utils.data.DataLoader): def __init__(self, dataset, batch_size, seq_len, overlap_len, *args, **kwargs): super().__init__(dataset, batch_size, *args, **kwargs) self.seq_len = seq_len self.overlap_len = overlap_len def __iter__(self): for batch in super().__iter__(): (batch_size, n_samples) = batch.size() reset = True #print(self.overlap_len, n_samples, self.seq_len) for seq_begin in range(self.overlap_len, n_samples, self.seq_len)[:-1]: from_index = seq_begin - self.overlap_len to_index = seq_begin + self.seq_len sequences = batch[:, from_index : to_index] input_sequences = sequences[:, : -1] #print(input_sequences.shape) target_sequences = sequences[:, self.overlap_len :].contiguous() yield (input_sequences, reset, target_sequences) reset = False def __len__(self): raise NotImplementedError()
2.71875
3
tests/test_pickle_funcs.py
Mishne-Lab/cidan
2
12791722
from cidan.LSSC.functions.pickle_funcs import * def test_pickle_funcs(): test_dir = "test_pickle" pickle_set_dir(test_dir) if not os.path.isdir(test_dir): os.mkdir(test_dir) pickle_clear(trial_num=0) assert not pickle_exist("test", trial_num=0) obj = "pickle save" pickle_save(obj, "test",trial_num=0) assert len([f for f in os.listdir("{0}/0/".format(test_dir))]) == 1 assert pickle_load("test", trial_num=0)==obj assert pickle_exist("test", trial_num=0) pickle_clear(trial_num=0) assert not pickle_exist("test", trial_num=0) assert len([f for f in os.listdir("{0}/0/".format(test_dir))]) == 0 os.rmdir("{0}/0/".format(test_dir)) os.rmdir(test_dir)
2.5625
3
libs/redis.py
fightingfish008/tornado-extensions
5
12791723
<reponame>fightingfish008/tornado-extensions # -*- coding:utf-8 -*- import traceback import logging import aioredis from tornado.options import options class AsyncRedisClient(object): def __init__(self,loop=None): self.loop = loop async def init_pool(self, db=None): if db is None: _db = options.redis_db4 else: _db = db uri = 'redis://{}:{}/{}'.format( options.redis_host, options.redis_port, _db ) self.pool = await aioredis.create_pool( uri, password=options.redis_password, # encoding="utf-8", minsize=5, maxsize=10, loop = self.loop, ) super(AsyncRedisClient, self).__init__() async def execute(self, command, *args, **kwargs): try: async with self.pool.get() as conn: retsult = await conn.execute(command, *args, **kwargs) return retsult except Exception as e: logging.error(traceback.print_exc()) logging.error("redis execute error: %s", e) async def get(self, key): return await self.execute('get', key) async def set(self, key, value): return await self.execute('set', key, value) async def setex(self, key, seconds, value): return await self.execute('setex', key, seconds, value) async def keys(self, key): return await self.execute('keys', key) async def hgetall(self, key): return await self.execute('hgetall', key) async def scan(self, key): return await self.execute('scan', key) async def connect(loop, db=None): client = AsyncRedisClient(loop) await client.init_pool(db) return client
2.234375
2
errorhandler.py
BenjaminHalko/WiiMusicEditorPlus
7
12791724
from PyQt5.QtCore import Qt from PyQt5.QtWidgets import QDialog from errorhandler_ui import Ui_Error class ShowError(QDialog,Ui_Error): def __init__(self,error,message,parent=None,geckocode=False): super().__init__(parent) self.setWindowFlag(Qt.WindowContextHelpButtonHint,False) self.setupUi(self) if(not geckocode): self.ErrorTitle.setText(error) self.ErrorMessage.setText(message) self.ErrorClose.clicked.connect(self.close) else: self.clicked = False self.ErrorTitle_GC.setText(error) self.ErrorMessage_GC.setText(message) self.ErrorClose_GC.clicked.connect(self.close) self.ErrorCreate_GC.clicked.connect(self.GeckoCodeCreate) self.MainWidget.setCurrentIndex(1) self.show() self.exec() def GeckoCodeCreate(self): self.clicked = True self.close()
2.46875
2
PyFlow/Packages/PyFlowBase/Nodes/forLoopBegin.py
luzpaz/PyFlow
1,463
12791725
## Copyright 2015-2019 <NAME>, <NAME> ## Licensed under the Apache License, Version 2.0 (the "License"); ## you may not use this file except in compliance with the License. ## You may obtain a copy of the License at ## http://www.apache.org/licenses/LICENSE-2.0 ## Unless required by applicable law or agreed to in writing, software ## distributed under the License is distributed on an "AS IS" BASIS, ## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ## See the License for the specific language governing permissions and ## limitations under the License. from PyFlow.Core import NodeBase from PyFlow.Core.PathsRegistry import PathsRegistry from PyFlow.Core.NodeBase import NodePinsSuggestionsHelper from PyFlow.Core.Common import * from PyFlow.Packages.PyFlowBase.Nodes import FLOW_CONTROL_ORANGE import threading class forLoopBegin(NodeBase): def __init__(self, name): super(forLoopBegin, self).__init__(name) self._working = False self.currentIndex = 0 self.prevIndex = -1 self.inExec = self.createInputPin('inExec', 'ExecPin', None, self.compute) self.firstIndex = self.createInputPin('Start', 'IntPin') self.lastIndex = self.createInputPin('Stop', 'IntPin') self.loopEndNode = self.createInputPin('Paired block', 'StringPin') self.loopEndNode.setInputWidgetVariant("ObjectPathWIdget") self.loopBody = self.createOutputPin('LoopBody', 'ExecPin') self.index = self.createOutputPin('Index', 'IntPin') self.headerColor = FLOW_CONTROL_ORANGE self.setExperimental() @staticmethod def pinTypeHints(): helper = NodePinsSuggestionsHelper() helper.addInputDataType('ExecPin') helper.addInputDataType('IntPin') helper.addOutputDataType('ExecPin') helper.addOutputDataType('IntPin') helper.addInputStruct(StructureType.Single) helper.addOutputStruct(StructureType.Single) return helper @staticmethod def category(): return 'FlowControl' @staticmethod def keywords(): return ['iter'] @staticmethod def description(): return 'For loop begin block' def reset(self): self.currentIndex = 0 self.prevIndex = -1 #self._working = False def isDone(self): indexTo = self.lastIndex.getData() if self.currentIndex >= indexTo: self.reset() #loopEndNode = PathsRegistry().getEntity(self.loopEndNode.getData()) #loopEndNode.completed.call() self._working = False return True return False def onNext(self, *args, **kwargs): while not self.isDone(): if self.currentIndex > self.prevIndex: self.index.setData(self.currentIndex) self.prevIndex = self.currentIndex self.loopBody.call() def compute(self, *args, **kwargs): self.reset() endNodePath = self.loopEndNode.getData() loopEndNode = PathsRegistry().getEntity(endNodePath) if loopEndNode is not None: if loopEndNode.loopBeginNode.getData() != self.path(): self.setError("Invalid pair") return if self.graph() is not loopEndNode.graph(): err = "block ends in different graphs" self.setError(err) loopEndNode.setError(err) return else: self.setError("{} not found".format(endNodePath)) if not self._working: self.thread = threading.Thread(target=self.onNext,args=(self, args, kwargs)) self.thread.start() self._working = True #self.onNext(*args, **kwargs)
2.09375
2
day23/script1.py
Moremar/advent_of_code_2015
0
12791726
import re class Command: def __init__(self, name, register, jump_addr=None): self.name = name self.register = register self.jump_addr = jump_addr class Program: def __init__(self, commands, registers): self.commands = commands self.registers = registers self.instr_ptr = 0 def exec_next_command(self): cmd = self.commands[self.instr_ptr] if cmd.name == "hlf": self.registers[cmd.register] //= 2 self.instr_ptr += 1 elif cmd.name == "tpl": self.registers[cmd.register] *= 3 self.instr_ptr += 1 elif cmd.name == "inc": self.registers[cmd.register] += 1 self.instr_ptr += 1 elif cmd.name == "jmp": self.instr_ptr += cmd.jump_addr elif cmd.name == "jie": self.instr_ptr += cmd.jump_addr if self.registers[cmd.register] % 2 == 0 else 1 elif cmd.name == "jio": self.instr_ptr += cmd.jump_addr if self.registers[cmd.register] == 1 else 1 else: raise ValueError("Unsupported command: ", cmd.name) def run(self): while self.instr_ptr < len(self.commands): self.exec_next_command() def solve(commands): pgm = Program(commands, {"a": 0, "b": 0}) pgm.run() return pgm.registers["b"] def parse(file_name): with open(file_name, "r") as f: commands = [] for line in f.readlines(): if any([cmd in line for cmd in ["inc", "tpl", "hlf"]]): _, cmd, r, _ = re.split(r"([a-z]+) ([a|b])", line) commands.append(Command(cmd, r)) elif "jmp" in line: _, cmd, jmp_addr, _ = re.split(r"([a-z]+) ([+|-][0-9]+)", line) commands.append(Command(cmd, None, int(jmp_addr))) if any([cmd in line for cmd in ["jie", "jio"]]): _, cmd, r, jmp_addr, _ = re.split(r"([a-z]+) ([a|b]), ([+\-0-9]+)", line) commands.append(Command(cmd, r, int(jmp_addr))) return commands if __name__ == '__main__': print(solve(parse("data.txt")))
3.3125
3
example/paywall/migrations/0002_auto_20200417_2107.py
wuuuduu/django-getpaid
6
12791727
# Generated by Django 3.0.5 on 2020-04-17 21:07 import uuid import django_fsm from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("paywall", "0001_initial"), ] operations = [ migrations.RemoveField(model_name="paymententry", name="payment",), migrations.AddField( model_name="paymententry", name="ext_id", field=models.CharField(db_index=True, default=uuid.uuid4, max_length=100), ), migrations.AddField( model_name="paymententry", name="fraud_status", field=django_fsm.FSMField( choices=[ ("unknown", "unknown"), ("accepted", "accepted"), ("rejected", "rejected"), ("check", "needs manual verification"), ], default="unknown", max_length=50, protected=True, ), ), migrations.AddField( model_name="paymententry", name="payment_status", field=django_fsm.FSMField( choices=[ ("new", "new"), ("prepared", "in progress"), ("pre-auth", "pre-authed"), ("charge_started", "charge process started"), ("partially_paid", "partially paid"), ("paid", "paid"), ("failed", "failed"), ("refund_started", "refund started"), ("refunded", "refunded"), ], default="prepared", max_length=50, protected=True, ), ), ]
1.898438
2
chemvae/vae_examples.py
amirnikooie/chemical_vae
0
12791728
from vae_model import * #====================== """ Creating the VAE object and initializing the instance """ model_DIR = "./aux_files/" vae = VAE_Model(directory=model_DIR) print("The VAE object created successfully!") ''' #============ """ Working with a sample smiles string to reconstruct it and predict its properties """ sample_smiles = 'OC1=CC=C(C2=C(C3=CC=C(O)C=C3S2)N2C3=CC=C(C=C3C=C2)OCCN2CCCCC2)C=C1' z_rep = vae.smiles_to_z(sample_smiles, standardized=True) X_hat = vae.z_to_smiles(z_rep, standardized=True, verified=False) # decoding # to molecular space without verifying its validity. predicted_props = vae.predict_prop_z(z_rep, standardized=True) print("### {:20s} : {}".format('Input', sample_smiles)) print("### {:20s} : {}".format('Reconstruction', X_hat[0])) print("### {:20s} : {} with norm {:.3f}".format('Z representation:', z_rep.shape, np.linalg.norm(z_rep))) print("### {:20s} : {}".format('Number of properties', vae.n_props)) print("### {:20s} : {}\n\n".format('Predicted properties', predicted_props)) #====================== """ Property prediction for 20 samples from multivariate standard normal distribution """ z_mat = np.random.normal(0, 1, size=(20,z_rep.shape[1])) pred_prop = vae.predict_prop_z(z_mat, standardized=True) #====================== """ Converting those random representations to valid molecules """ x_hat_list = vae.z_to_smiles(z_mat, standardized=True, verified=True) # decoding # to valid molecules verified_x_hat = [item for item in x_hat_list if item!='None'] print("\n### {} out of 20 compounds are verified!".format(len(verified_x_hat))) print("### {:20s} : {}".format('Predicted properties:', pred_prop)) #====================== """ Iteratively sampling from vicinity of a point in the latent space """ df = vae.iter_sampling_from_ls(z_rep, decode_attempts=500, num_iter=10, noise_norm=0.5, constant_norm=False, verbose=False) #====================== """ Saving generated molecules to a pdf file as well as CSV using data frame above """ vae.save_gen_mols(df, cols_of_interest=['comp1','comp2','comp3'], out_file="gen_mols.pdf", out_dir="./test_out/") #====================== ''' """ prediction performance analysis for a component of interest with option of drawing parity plot for that component """ #input_data = string showing the location and name of the dataset to for # prediction perfomance analysis. Could be the test set. filename = 'validation_set.csv' #'validation_set.csv' nsamples = 800 #600000 rmses = vae.component_parity_check(model_DIR+filename, ssize=nsamples, seed=235, histplot=True, parplot=True, hexbinp=False) #xlims=[0,1], ylims=[0,1]) print(rmses)
2.765625
3
python/search_in_binary_search_tree.py
anishLearnsToCode/leetcode-algorithms
17
12791729
# Definition for a binary tree node. from typing import Optional class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def searchBST(self, root: Optional[TreeNode], val: int) -> Optional[TreeNode]: if root is None or root.val == val: return root return self.searchBST(root.right, val) if root.val < val else self.searchBST(root.left, val)
3.828125
4
sdk/python/feast/infra/offline_stores/contrib/postgres_repo_configuration.py
ibnummuhammad/feast
0
12791730
from feast.infra.offline_stores.contrib.postgres_offline_store.tests.data_source import ( PostgreSQLDataSourceCreator, ) from tests.integration.feature_repos.integration_test_repo_config import ( IntegrationTestRepoConfig, ) FULL_REPO_CONFIGS = [ IntegrationTestRepoConfig( provider="local", offline_store_creator=PostgreSQLDataSourceCreator, online_store_creator=PostgreSQLDataSourceCreator, ), ]
0.921875
1
mod_arrow_func.py
pfalcon/python-imphook
22
12791731
<reponame>pfalcon/python-imphook<gh_stars>10-100 # This imphook module implements "arrow functions", similar to JavaScript. # (a, b) => a + b ---> lambda a, b: a + b import tokenize import imphook class TokBuf: def __init__(self): self.tokens = [] def append(self, t): self.tokens.append(t) def clear(self): self.tokens.clear() def empty(self): return not self.tokens def spool(self): yield from self.tokens self.clear() def xform(token_stream): tokbuf = TokBuf() for t in token_stream: if t[1] == "(": # We're interested only in the deepest parens. if not tokbuf.empty(): yield from tokbuf.spool() tokbuf.append(t) elif t[1] == ")": nt1 = next(token_stream) nt2 = next(token_stream) if nt1[1] == "=" and nt2[1] == ">": yield (tokenize.NAME, "lambda") yield from tokbuf.tokens[1:] tokbuf.clear() yield (tokenize.OP, ":") else: yield from tokbuf.spool() yield t yield nt1 yield nt2 elif not tokbuf.empty(): tokbuf.append(t) else: yield t def hook(modname, filename): with open(filename, "r") as f: # Fairly speaking, tokenizing just to convert back to string form # isn't too efficient, but CPython doesn't offer us a way to parse # token stream so far, so we have no choice. source = tokenize.untokenize(xform(tokenize.generate_tokens(f.readline))) mod = type(imphook)(modname) exec(source, vars(mod)) return mod imphook.add_import_hook(hook, (".py",))
2.53125
3
apps/terreno/admin.py
Ajerhy/proyectosigetebr
1
12791732
<filename>apps/terreno/admin.py from django.contrib import admin from .models import Ubicacion from .models import Lote from .models import Manzano from .models import Medida from .models import Distrito """ class lotesInlines(admin.TabularInline): model = Manzano.lotes.through class LoteAdmin(admin.ModelAdmin): inlines = [ lotesInlines, ] class ManzanoAdmin(admin.ModelAdmin): inlines = [ lotesInlines, ] exclude = ('lotes',) """ admin.site.register(Ubicacion) admin.site.register(Lote) #admin.site.register(Manzano,ManzanoAdmin) admin.site.register(Manzano) admin.site.register(Medida) admin.site.register(Distrito)
2.015625
2
preprocessor/constants.py
AhsanAliLodhi/statistical_data_preprocessing
0
12791733
# TODO: Substitue all strings with constants # Column types (in context of data science) TYPE = { 'numerical', 'categorical', 'datetime' } # List of date time features available in pandas date time columns DATE_TIME_FEATURES = { 'NUMERICS':['year', 'month', 'day', 'hour', 'dayofyear', 'weekofyear', 'week', 'dayofweek', 'quarter'], 'BOOLEANS':['is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', 'is_year_end'] } # Possible methods to fill nans in numerical columns FILL_NAN_METHODS = { 'MEAN':'mean','MEDIAN':'median' } # Possible methods to fill infs in numerical columns FILL_INF_METHODS = { 'MAXMIN':'maxmin','NAN':'nan' }
3.3125
3
sim/python/plot_logsim.py
wpisailbot/boat
4
12791734
<filename>sim/python/plot_logsim.py #!/usr/bin/python3 import numpy as np import sys from matplotlib import pyplot as plt def norm_theta(theta): while (theta > np.pi): theta -= 2 * np.pi while (theta < -np.pi): theta += 2 * np.pi return theta def plot_vec(d, starti, name, ax, maxy=50): t = data[:, 0] x = data[:, starti+0] y = data[:, starti+1] z = data[:, starti+2] plt.figure() plt.subplot(111, sharex=ax) plt.plot(t, x, label=name+" x") plt.plot(t, y, label=name+" y") plt.plot(t, z, label=name+" z") # plt.ylim(-maxy, maxy) plt.legend() data = np.genfromtxt("sep11logsim.csv", delimiter=',')[:, :] x = [] y = [] vx = [] vy = [] speed = [] t = [] yaw = [] heel = [] pitch = [] heading = [] leeway = [] sail = [] rudder = [] alphaw = [] pitchvar = [] wind_speed = [] true_alphaw = [] true_wind_speed = [] heading_cmd = [] rudder_mode = [] orig_yaw = [] orig_heel = [] orig_speed = [] for row in data: if row[0] < 4000: continue for i in range(len(row)): if abs(row[i]) > 1e5: row[i] = float("nan") # if row[0] > 4485: # break t.append(row[0]) sail.append(row[3] * 180. / np.pi) rudder.append(row[4] * 180. / np.pi) yaw.append(norm_theta(row[5]) * 180. / np.pi) orig_yaw.append(norm_theta(row[20]) * 180. / np.pi) heel.append(norm_theta(row[6]) * 180. / np.pi) orig_heel.append(norm_theta(row[21]) * 180. / np.pi) pitch.append(norm_theta(row[7]) * 180. / np.pi) pitchvarstart = max(-100, -len(pitch)) pitchvar.append(np.std(pitch[pitchvarstart:])) x.append(row[8]) y.append(row[9]) vx.append(row[10]) vy.append(row[11]) speed.append(np.hypot(vx[-1], vy[-1])) orig_speed.append(np.hypot(row[25], row[26])) heading.append(np.arctan2(vy[-1], vx[-1]) * 180. / np.pi) leeway.append(norm_theta((heading[-1] - yaw[-1]) * np.pi / 180.) * 180. / np.pi) alphaw.append(np.arctan2(row[2], row[1]) * 180. / np.pi) wind_speed.append(np.sqrt(row[1] ** 2 + row[2] ** 2)) true_alphaw.append(norm_theta(np.arctan2(row[13], row[12]))* 180. / np.pi) true_wind_speed.append(np.sqrt(row[12] ** 2 + row[13] ** 2)) heading_cmd.append(row[15] * 180. / np.pi) rudder_mode.append(row[16] * 10) plt.plot(x, y, label="Boat Path") #plt.plot([-76.477516, -76.475533, -76.474373, -76.477615, -76.479126], [38.98278, 38.98209, 38.98365, 38.985771, 38.983952], '*-', label="waypoints") if False: plt.quiver(x, y, vx, vy, np.hypot(vx, vy)) plt.colorbar(label="Speed (m/s)") plt.title("Boat Position (Wind is blowing bottom-right-to-top-left on screen)--Arrows and colors represent velocities") plt.xlabel("X position (deg longitude)") plt.ylabel("Y position (deg latitude)") plt.legend() plt.figure() ax = plt.subplot(111) ax.plot(t, x - x[0], label='x less bias') ax.plot(t, y - y[0], label='y less bias') ax2 = ax.twinx() ax2.plot(t, vx, 'c*', label='vx') ax2.plot(t, vy, 'r*', label='vy') ax2.plot(t, speed, 'g*', label='speed') ax2.plot(t, wind_speed, label='Wind Speed (m/s)') ax2.plot(t, true_wind_speed, label='True Wind Speed (m/s)') ax.legend(loc='upper left') ax2.legend(loc='upper right') plt.figure() axyh = plt.subplot(111, sharex=ax) axyh.plot(t, yaw, label='Yaw') axyh.plot(t, orig_yaw, 'b--', label='Original Yaw') axyh.plot(t, heel, 'g', label='Heel') axyh.plot(t, orig_heel, 'g--', label='Original Heel') axyh.plot(t, pitch, label='Pitch') axyh.plot(t, [n * 100 for n in pitchvar], label='Pitch Stddev * 100') axyh.legend() plt.figure() axyaw = plt.subplot(111, sharex=ax) axyaw.plot(np.matrix(t).T, np.matrix(yaw).T + 0, 'b', label='Heading') axyaw.plot(t, orig_yaw, 'b--', label='Orig Yaw') axyaw.plot(t, alphaw, 'g', label='Apparent Wind Angle') axyaw.plot(t, heading_cmd, 'b-.', label='Heading Cmd') axyaw.plot(t, rudder_mode, 'r*', label='Rudder Mode') #axyaw.plot(t, true_alphaw, 'm', label='True Wind Angle') axrudder = axyaw.twinx() axrudder.plot(t, rudder, 'r', label='Rudder') axrudder.plot(t, sail, 'm', label='Sail') axrudder.plot(t, heel, 'c', label='Heel'); axrudder.plot(t, orig_heel, 'c--', label='Orig Heel'); axrudder.plot(t, leeway, 'y', label='Leeway Angle') axrudder.plot(t, np.hypot(vx, vy) * 10, 'k', label='Boat Speed') axrudder.plot(t, np.array(orig_speed) * 10, 'k--', label='Orig Boat Speed') axrudder.set_ylim([-45, 45]) axyaw.legend(loc='upper left') axrudder.legend(loc='upper right') plt.title('Boat data while beam reaching and close hauled') axyaw.set_ylabel('Heading and Apparent Wind (upwind = 0) (deg)') axrudder.set_ylabel('Rudder, Heel, and Leeway (deg)\n Boat Speed (tenths of a meter / sec)') axyaw.set_xlabel('Time (sec)') plt.grid() plt.figure() axwind = plt.subplot(111, sharex=ax) axwind.plot(t, true_wind_speed, 'r', label="True Wind Speed (m/s)") axwind.plot(t, wind_speed, 'b', label="Apparent Wind Speed (m/s)") axwinddir = axwind.twinx(); axwinddir.plot(t, true_alphaw, 'c', label="True Wind Dir (deg)") axwind.legend(loc='upper left') axwinddir.legend(loc='upper right') plot_vec(data, 27, "Sail Force", ax) plot_vec(data, 30, "Rudder Force", ax) plot_vec(data, 33, "Keel Force", ax) plot_vec(data, 36, "Hull Force", ax) plot_vec(data, 39, "Net Force", ax) plot_vec(data, 42, "Sail Torque", ax) plot_vec(data, 45, "Rudder Torque", ax) plot_vec(data, 48, "Keel Torque", ax) plot_vec(data, 51, "Hull Torque", ax) plot_vec(data, 54, "Righting Torque", ax) plot_vec(data, 57, "Net Torque", ax) ax.set_xlim([4000, 4500]) plt.show()
3.0625
3
models/spec_analyse.py
zaqwes8811/voicegen
0
12791735
<reponame>zaqwes8811/voicegen #!/usr/bin/python #-*- coding: utf-8 -*- import wave as wv import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 5, 0.1); y = np.sin(x) plt.plot(x, y)
2.125
2
mg/pyguitools/easy_settings.py
mgotz/PyGUITools
0
12791736
<reponame>mgotz/PyGUITools #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ easily editable settings: wrapper around formlayout """ from formlayout import fedit class EasyEditSettings(): """a class around formlayout to give easy to use settings initalized with a list of tuples that specifiy the settings it can return a dictionary with the settings to easily use in the application """ def __init__(self, settings): """ initialize the advanced settings Parameters ---------- setting : list of tuples each entry in the list is a setting with its name as the first element and current value as second like for formlayout from fedit """ self.settingsDict = {} self.settingsList = settings self.update_dict() def update_dict(self): for element in self.settingsList: if type(element[1]) == list: self.settingsDict[element[0]] = element[1][element[1][0]+1] else: self.settingsDict[element[0]] = element[1] def get_settings(self): return self.settingsDict def change_settings(self, title="Edit advanced settings" ): newSettings = fedit(self.settingsList, title=title) if newSettings != None: for i, newSetting in enumerate(newSettings): if type(self.settingsList[i][1]) == list: tempList = self.settingsList[i][1] tempList[0] = newSetting self.settingsList[i] = (self.settingsList[i][0],tempList) else: self.settingsList[i] = (self.settingsList[i][0],newSetting) self.update_dict()
2.953125
3
Chapter 6/Code/servo_minimum.py
professor-li/book-dow-iot-projects
17
12791737
<gh_stars>10-100 from gpiozero import Servo servoPin=17 servoCorrection=0.5 maxPW=(2.0+servoCorrection)/1000 minPW=(1.0-servoCorrection)/1000 servo=Servo(servoPin, min_pulse_width=minPW, max_pulse_width=maxPW) servo.min()
2.4375
2
lang/py/cookbook/v2/source/cb2_20_6_sol_1.py
ch1huizong/learning
0
12791738
<reponame>ch1huizong/learning import inspect def wrapfunc(obj, name, processor, avoid_doublewrap=True): """ patch obj.<name> so that calling it actually calls, instead, processor(original_callable, *args, **kwargs) """ # get the callable at obj.<name> call = getattr(obj, name) # optionally avoid multiple identical wrappings if avoid_doublewrap and getattr(call, 'processor', None) is processor: return # get underlying function (if any), and anyway def the wrapper closure original_callable = getattr(call, 'im_func', call) def wrappedfunc(*args, **kwargs): return processor(original_callable, *args, **kwargs) # set attributes, for future unwrapping and to avoid double-wrapping wrappedfunc.original = call wrappedfunc.processor = processor # 2.4 only: wrappedfunc.__name__ = getattr(call, '__name__', name) # rewrap staticmethod and classmethod specifically (iff obj is a class) if inspect.isclass(obj): if hasattr(call, 'im_self'): if call.im_self: wrappedfunc = classmethod(wrappedfunc) else: wrappedfunc = staticmethod(wrappedfunc) # finally, install the wrapper closure as requested setattr(obj, name, wrappedfunc) def unwrapfunc(obj, name): ''' undo the effects of wrapfunc(obj, name, processor) ''' setattr(obj, name, getattr(obj, name).original)
3.265625
3
utils/utils.py
sarrouti/multi-class-text-classification-pytorch
3
12791739
<filename>utils/utils.py #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Apr 9 18:56:39 2020 @author: sarroutim2 """ import torch import torchtext import json class Vocabulary (object): SYM_PAD = '<pad>' # padding. SYM_UNK = '<unk>' # Unknown word. def __init__(self): self.word2idx={} self.idx2word={} self.idx=0 self.add_word(self.SYM_PAD) self.add_word(self.SYM_UNK) def add_word (self, word): if word not in self.word2idx: self.word2idx [word] = self.idx self.idx2word [self.idx] = word self.idx += 1 def remove_word(self, word): """Removes a specified word and updates the total number of unique words. Args: word: String representation of the word. """ if word in self.word2idx: self.word2idx.pop(word) self.idx2word.pop(self.idx) self.idx -= 1 def __call__(self, word): if word not in self.word2idx: return self.word2idx[self.SYM_UNK] return self.word2idx[word] def __len__(self): return len(self.word2idx) def save(self, location): with open(location, 'w') as f: json.dump({'word2idx': self.word2idx, 'idx2word': self.idx2word, 'idx': self.idx}, f) def load(self, location): with open(location, 'rb') as f: data = json.load(f) self.word2idx = data['word2idx'] self.idx2word = data['idx2word'] self.idx = data['idx'] def get_glove_embedding(name, embed_size, vocab): """Construct embedding tensor. Args: name (str): Which GloVe embedding to use. embed_size (int): Dimensionality of embeddings. vocab: Vocabulary to generate embeddings. Returns: embedding (vocab_size, embed_size): Tensor of GloVe word embeddings. """ glove = torchtext.vocab.GloVe(name=name, dim=str(embed_size)) vocab_size = len(vocab) embedding = torch.zeros(vocab_size, embed_size) for i in range(vocab_size): embedding[i] = glove[vocab.idx2word[str(i)]] return embedding # =========================================================== # Helpers. # =========================================================== def process_lengths(inputs, pad=0): """Calculates the lenght of all the sequences in inputs. Args: inputs: A batch of tensors containing the question or response sequences. Returns: A list of their lengths. """ max_length = inputs.size(1) if inputs.size(0) == 1: lengths = list(max_length - inputs.data.eq(pad).sum(1)) else: lengths = list(max_length - inputs.data.eq(pad).sum(1).squeeze()) return lengths
3.09375
3
manage.py
larryTheGeek/ride_my_way_v2
0
12791740
<gh_stars>0 import os from app.app import create_app from app.models.db import Db environment = os.getenv('config_name') app = create_app(environment) #creates database tables if they don't exist Db.create_tables() if __name__ == '__main__': app.run()
2.171875
2
albackup/__main__.py
campenberger/albackup
0
12791741
<gh_stars>0 import argparse import logging import json import sqlalchemy as sa from .dump import Dump from .restore import Restore from . import Password if __name__ == '__main__': parser=argparse.ArgumentParser("python -m albackup") parser.add_argument('mode',metavar='MODE',choices=('dump','restore','chg-password'), help="mode of operation (dump or restore,chg-password)") parser.add_argument('--cfg','-c',dest='cfg_file',default='albackup.json', help="Configuration for dump or restore operation") parser.add_argument('--meta-cache',default=None, help="Allow caching of database meta data") parser.add_argument('--backup-dir',default='backup',help="Target directory for backups") parser.add_argument('--debug','-d',action="store_true",default=False,help="Run in debug mode") args=parser.parse_args() logging.basicConfig( level=logging.DEBUG if args.debug else logging.INFO, format="%(asctime)s:%(name)-20s:%(levelname)-7s:%(message)s" if args.debug else "%(asctime)s: %(message)s" ) logging.getLogger('sqlalchemy.engine').setLevel( logging.INFO if args.debug else logging.ERROR ) logger=logging.getLogger() cfg=None with open(args.cfg_file,'r') as fh: cfg=json.load(fh) logger.info('Read configuration from %s',args.cfg_file) if args.mode!='chg-password': p=Password(args.cfg_file,cfg) pw=p.password logger.info('Database configuration:') logger.info(' user : %s',cfg['db_user']) logger.info(' password: %s','*'*len(pw)) logger.info(' server : %s',cfg['db_server']) logger.info(' port : %d',cfg['db_port']) logger.info(' db : %s',cfg['db_name']) engine=sa.create_engine('mssql+pyodbc://{}:{}@{}:{}/{}?driver=FreeTDS&odbc_options="TDS_Version=8.0"'.format( cfg['db_user'], pw, cfg['db_server'], cfg['db_port'], cfg['db_name'] ),deprecate_large_types=True) logger.info('SQLAlchemy engine created.') if args.mode=='dump': dump=Dump(args.backup_dir, args.meta_cache, engine, cfg['db_name'], cfg['db_server']) dump.run() logger.info('Dump finished') elif args.mode=='restore': if not cfg['allow_restore']: raise Exception('Configuration file prohibits restore') enable_ri_check=cfg['enable_ri_check'] restore=Restore(args.backup_dir,engine) restore.run() if enable_ri_check: restore.changeRIChecks(off=False) else: logger.info('RI checks where left off') logger.info('Restore finished') elif args.mode=='chg-password': pw=Password(args.cfg_file, cfg) pw.change() else: argparse.error("Invalid program mode")
2.3125
2
train/utils.py
mcclow12/chatbot
0
12791742
import random def utils_min_required(): responses = [ "Sorry, I need your opinion on a movie "\ "before I can give you quality recommendations.", "Sorry, I don't have enough information yet "\ "to make a good recommendation.", "I can't give a good recommendation yet. Please "\ "tell me about some movies you've watched first.", "It's gonna be hard for me to give you some good "\ "recommendations if I don't know anything about your tastes.", "I don't think I'm ready to give a recommendation yet. "\ "How about you tell me about some movies you've watched?", "Please tell me about some movies you watched first. "\ "Then I'll be able to give you some great recommendations" ] return random.choice(responses) def utils_quotations(): responses = [ "Hmm seems like you messed up your quotation marks. " \ "Try again.", "Uh oh, I don't think your quotation marks are correct. ", "It's hard for me to understand which movie you're talking about.", "To help me understand, please put quotation marks around the " \ "movie like this \"The Wizard of Oz\"", "It's hard for me to understand with your quotation marks.", "Oops, seems like your quotation marks aren't quite right.", "Please re-check your quotation marks. There should be two "\ "in your response surrounding the movie title.", "I'm having trouble reading your sentence because of the "\ "quotation marks. Can you please try again? ", ] return random.choice(responses) def utils_new_movie(): responses = [ "Interesting, I haven't heard of that movie.", "Hmm I haven't heard of that movie.", "Wow that movie is new to me. I don't know much about it.", "I've actually never heard of that movie before! Unfortunately "\ "that means \nI can't give you some good recommendations based "\ "on that one.", "That movie is actually unfamiliar to me.", "To be honest, I haven't seen that movie before, so it'll "\ "be hard to recommend you a movie based on that one." ] return random.choice(responses) def utils_liked(): responses1 = [ "Great, glad you liked that one.", "Okay got it that was a good movie.", "Nice, sounds like that movie was right up your alley." "Wow so you like those kinds of movies. "\ "I think you'll like my recommendations.", "Glad you liked the movie.", "Sounds like you enjoyed that one.", "Good, glad you enjoyed it.", "Okay, got it, I think I have some other ones that you'll like as well.", "Awesome, glad you liked it." ] responses2 = [ " Now feel free to tell me about some more movies or say "\ "'Recommendations please!' to hear my recommendations. ", " Any more movies you've seen? ", " You're giving me some great feedback.", " What other movies have you seen? ", " Any other movies you've seen? ", " Any more movie opinions I should know?", " Anything else you want to tell me before I give my recommendations?" ] response1 = random.choice(responses1) response2 = '' if random.uniform(0, 1) < 0.3: response2 = random.choice(responses2) return response1 + response2 def utils_disliked(): responses1 = [ "Okay got it you didn't like that one.", "Gotcha so that wasn't the movie for you.", "Okay you didn't like that one.", "Yeah I've heard other people didn't like that one as well.", "So you didn't like that one got it.", "That really wasn't your movie huh.", "That movie wasn't for you then. I'll keep that in mind.", "Okay so you did not like that one.", ] responses2 = [ " Now feel free to tell me about some more movies or say "\ "'Recommendations please!' to hear my recommendations. ", " Any more movies you've seen? ", " You're giving me some great feedback.", " What other movies have you seen?", " Any other movies you've seen?", " Got any more hot takes?", " Any more movie opinions I should know?", " Anything else you want to tell me before I give my recommendations?" ] response1 = random.choice(responses1) response2 = '' if random.uniform(0, 1) < 0.3: response2 = random.choice(responses2) return response1 + response2 def utils_more_opinions(): responses = [ " Now feel free to tell me about some more movies or say "\ "'Recommendations please!' to hear my recommendations.", " Any more movies you've seen? ", " You're giving me some great feedback.", " What other movies have you seen?", " Any other movies you've seen?", " Got any more opinions on movies you've seen?", " Any more movie opinions I should know?", " Anything else you want to tell me before I give my recommendations?" ] return random.choice(responses) def utils_liked_match(match): responses = [ f"Got it! So you liked {match}.", f"Okay so {match} was your type of movie.", f"Gotcha so {match} was a good fit for you.", f"Okay got it you liked {match}.", f"Sounds like {match} was right up your alley.", f"Okay so your tastes align with {match}, got it." ] return random.choice(responses) def utils_disliked_match(match): responses = [ f"Okay sounds like {match} wasn't the " \ "movie for you.", f"Okay got it {match} wasn't your cup of tea.", f"So you did not like {match}. Got it.", f"Gotcha so you didn't like {match}.", f"Okay so {match} was the movie you didn't like.", f"{match} wasn't the movie for you then.", f"Got it you didn't like {match}." ] return random.choice(responses) def utils_low_confidence(): responses = [ "Sorry, I couldn't tell if you liked that " \ "movie or not.", "Sorry I'm not sure if you liked that one.", "I can't quite tell what you think about that movie.", "I'm not quite sure if you liked that movie or not.", "Wait.. did you like or dislike that movie?", "I think I need some more information to tell whether you "\ "liked that movie or not.", "Hang on, I couldn't tell if you liked that movie or not." ] return random.choice(responses)
3.328125
3
Phenotyping/Phenotyping.py
lsymuyu/Digital-Plant-Phenotyping-Platform
10
12791743
''' The main function to conduct phenotyping experiments 11/09/2017 <NAME> ''' import os from adel import AdelR from adel.geometric_elements import Leaves from adel.AdelR import R_xydb, R_srdb, genGeoLeaf import pandas as pd from adel.plantgen import plantgen_interface import numpy as np from adel.astk_interface import AdelWheat from adel.stand.Generate_canopy import get_exposed_areas from adel.postprocessing import axis_statistics_simple, plot_statistics_simple, plot_statistics_simple_filter, axis_statistics_simple_filter from adel.povray.povray_ind import povray_Green from pyDOE import * from scipy.stats import uniform from openalea.core.path import path from adel.ADEL_OPT.Adel_OPT_Ind import Adel_Leaves, Adel_development import prosail from adel.ADEL_OPT.Adel_OPT_Ind import plot_LAI from adel.macro.povray_pixels_several_colors import set_color_metamers_organs from adel.povray.FAPAR import Sampling_diagnal, Hemispherical_IM, Sampling_GF, Hemispherical_IM_Sun from adel.povray.GF_RGB import Green_Fract, Pov_Scene from adel.povray.Canray import duplicate_scene, Optical_canopy, Optical_soil, povray_RF def Phenotyping_Wheat(Param, Ind, thermals, Canopy, Adel_output, LAI = False,save_scene = False, GF = False, GF_camera = [], FAPAR = False, Sunlit_TT = [], Sunlit_Ang = [], Multi_spectral = False, Ray_camera = [], Ray_light = []): try: # Adel parameters development_parameters = Adel_development(N_phytomer_potential = float(Param['N_leaf']), a_cohort = float(Param['a_cohort']), TT_hs_0 = float(Param['T_cohort']), TT_flag_ligulation = float(Param['TT_flag_ligulation']), n0 = float(Param['n0']), n1 = float(Param['n1']), n2 = float(Param['n2']), number_tillers = float(Param['number_tillers']), Lamina_L1 = float(Param['Lamina_L1']), N2 = float(Param['N2']), incl1 = float(Param['incl1']), incl2 = float(Param['incl2']), N_elg = float(Param['N_elg']), density = float(Param['Density'])) wheat_leaves = Adel_Leaves(incline = float(Param['incl_leaf']), dev_Az_Leaf = float(Param['dev_Az_Leaf'])) # canopy configuration sim_width = float(Canopy['width']) # m, generate three rows dup_length = float(Canopy['length']) Row_spacing = float(Param['Row_spacing']) run_adel_pars = {'senescence_leaf_shrink': 0.01, 'leafDuration': 2, 'fracLeaf': 0.2, 'stemDuration': 2. / 1.2, 'dHS_col': 0.2, 'dHS_en': 0, 'epsillon': 1e-6, 'HSstart_inclination_tiller': 1, 'rate_inclination_tiller': float(Param['rate_Tiller']), 'drop_empty': True} # build the distribution pattern table to interpolate the density Wheat_Adel = AdelWheat(density = float(Param['Density']), duplicate = 40, devT = development_parameters, leaves = wheat_leaves, pattern='regular', run_adel_pars = run_adel_pars, incT = float(Param['Deta_Incl_Tiller']), ibmM = float(Param['incl_main']), depMin = float(Param['min_Tiller']), dep = float(Param['max_Tiller']), inter_row = Row_spacing, width = sim_width, length = dup_length) del development_parameters, wheat_leaves domain = Wheat_Adel.domain domain_area = Wheat_Adel.domain_area nplants = Wheat_Adel.nplants for TT in thermals: Canopy_Adel = Wheat_Adel.setup_canopy(age=TT) plantgl_scene = set_color_metamers_organs(Canopy_Adel)[0] # Summary LAI if LAI: new_plot_df = plot_LAI(Canopy_Adel, TT, domain_area, nplants, Adel_output, Ind) if 'plot_df' in locals(): plot_df = pd.concat([plot_df,new_plot_df]) else: plot_df = new_plot_df del Canopy_Adel # Save geometry file name_canopy = '%s%s%s%s.bgeom'%('Ind_',Ind,'_TT_',TT) if save_scene: plantgl_scene.save(Adel_output + '/' + name_canopy, 'BGEOM') # Green fraction if GF: sampling_times = GF_camera['Times_sampling'] cameras = Sampling_GF(domain, sampling_times, Azimuth = GF_camera['azimuth'], Zenith = GF_camera['zenith'], Row_spacing = Row_spacing, fov = GF_camera['fov'])[0] povfile_mesh, povfile_box, z_top = Pov_Scene(plantgl_scene, domain, output_directory = Adel_output, thermal = TT, Ind = Ind) povfile_scene, new_df = Green_Fract(povfile_mesh, povfile_box, thermal = TT, Ind = Ind, cameras = cameras, image_height = GF_camera['image_height'], image_width = GF_camera['image_width'], relative_height = GF_camera['distance'], z_top = 0, output_directory = Adel_output) if 'result_df' in locals(): result_df = pd.concat([result_df,new_df]) else: result_df = new_df # Fisheye for FAPAR if FAPAR: Azimuth_fisheye = [0] Zenith_fisheye = [0] sampling_times = 7 dup_width = 8.0 New_canopy, New_nplants, New_domain, New_area = duplicate_scene(plantgl_scene, nplants, canopy_width = dup_width, canopy_length = dup_length, sim_width = sim_width, Row_spacing = Row_spacing) domain = New_domain del plantgl_scene cameras_fisheye = Sampling_diagnal(New_domain, sampling_times, Azimuth_fisheye, Zenith_fisheye, Row_spacing, fov_fisheye)[0] povfile_mesh_new, povfile_box_new, z_top_new = Pov_Scene(New_canopy, New_domain, output_directory = Adel_output, thermal = TT, Ind = Ind) del New_canopy povray_image_fisheye = Hemispherical_IM(povfile_mesh = povfile_mesh_new, z_top = z_top_new, cameras = cameras_fisheye, image_height = 2000, image_width = 2000, relative_height = relative_height, output_directory = Adel_output) if TT in Sunlit_TT: for A_sun in Sunlit_Ang: povray_image_fisheye = Hemispherical_IM_Sun(povfile_mesh = povfile_mesh_new, z_top = z_top_new, cameras = cameras_fisheye, A_sun = A_sun, image_height = 2000, image_width = 2000, relative_height = relative_height, output_directory = Adel_output) # Simulate BRDF (need large scene) if Multi_spectral: # Setting of prosail RT = prosail.prospect_5b(n = Param['N'], cab = Param['Cab'], car = Param['Car'], cbrown = Param['Cbrown'], cw = Param['Cw'], cm = Param['Cm']) Full_wave = range(400, 2501) R = RT[:,0] T = RT[:,1] for wave in Ray_camera['Waves']: Plant_optical = Optical_canopy(wave=wave, Full_wave=Full_wave, R=R, T=T) soil_ref = Optical_soil(wave, brightness=Param['brightness']) Output_file = povray_RF(Ray_light=Ray_light, Ray_camera=Ray_camera, Plant_optical=Plant_optical, soil_ref=soil_ref, domain=domain, povfile_scene=povfile_mesh, wave=wave, soil_type = Param['soil_type'], dict=Adel_output) if not os.path.exists(Output_file): Output_file = povray_RF(Ray_light=Ray_light, Ray_camera=Ray_camera, Plant_optical=Plant_optical, soil_ref=soil_ref, domain=domain, povfile_scene=povfile_mesh, wave=wave,soil_type = Param['soil_type'], dict=Adel_output) if 'plot_df' in locals(): result_plot_path = path(os.path.join(Adel_output, '%s%s%s'%('plot_LAI_',Ind,'.csv'))) plot_df.to_csv(result_plot_path, index=False) if 'result_df' in locals(): result_df_path = path(os.path.join(Adel_output, '%s%s%s'%('Fraction_',Ind,'.csv'))) result_df.to_csv(result_df_path, index=False) except TypeError: print 'Pass it and move forward!!!***' result_df_path = [] pass return Adel_output def Phenotyping_Wheat_TT(Param, Ind, TT, Adel_output, Ray_light = [], Ray_camera = [], Zenith_GF = [], FAPAR = True, GF = True, Multi_spectral = False, save_scene = False): try: # Adel parameters Row_spacing = float(Param['Row_spacing']) sim_width = 1.0 dup_length = 12.0 development_parameters = Adel_development(N_phytomer_potential = float(Param['N_leaf']), a_cohort = float(Param['a_cohort']), TT_hs_0 = float(Param['T_cohort']), TT_flag_ligulation = float(Param['TT_flag_ligulation']), n0 = float(Param['n0']), n1 = float(Param['n1']), n2 = float(Param['n2']), number_tillers = float(Param['number_tillers']), Lamina_L1 = float(Param['Lamina_L1']), N2 = float(Param['N2']), incl1 = float(Param['incl1']), incl2 = float(Param['incl2']), N_elg = float(Param['N_elg']), density = float(Param['Density'])) wheat_leaves = Adel_Leaves(incline = float(Param['incl_leaf']), dev_Az_Leaf = float(Param['dev_Az_Leaf'])) run_adel_pars = {'senescence_leaf_shrink': 0.01, 'leafDuration': 2, 'fracLeaf': 0.2, 'stemDuration': 2. / 1.2, 'dHS_col': 0.2, 'dHS_en': 0, 'epsillon': 1e-6, 'HSstart_inclination_tiller': 1, 'rate_inclination_tiller': float(Param['rate_Tiller']), 'drop_empty': True} # build the distribution pattern table to interpolate the density Wheat_Adel = AdelWheat(density = float(Param['Density']), duplicate = 20, devT = development_parameters, leaves = wheat_leaves, pattern='regular', run_adel_pars = run_adel_pars, incT = float(Param['Deta_Incl_Tiller']), ibmM = float(Param['incl_main']), depMin = float(Param['min_Tiller']), dep = float(Param['max_Tiller']), inter_row = Row_spacing, width = sim_width, length = dup_length) del Param, development_parameters, wheat_leaves domain = Wheat_Adel.domain domain_area = Wheat_Adel.domain_area nplants = Wheat_Adel.nplants Canopy_Adel = Wheat_Adel.setup_canopy(age=TT) del Wheat_Adel plantgl_scene = set_color_metamers_organs(Canopy_Adel)[0] # Summary LAI plot_df = plot_LAI(Canopy_Adel, TT, domain_area, nplants, Adel_output, Ind) result_plot_path = path(os.path.join(Adel_output, '%s%s%s%s%s'%('plot_LAI_',Ind,'_TT_',TT,'.csv'))) plot_df.to_csv(result_plot_path, index=False) del plot_df, Canopy_Adel # Save geometry file name_canopy = '%s%s%s%s.bgeom'%('Ind_',Ind,'_TT_',TT) if save_scene: plantgl_scene.save(Adel_output + '/' + name_canopy, 'BGEOM') # Common setting relative_height = 200 # camera above the canopy # Green fraction if GF: Azimuth = [0] fov = [10] sampling_times = 4 cameras = Sampling_GF(domain, sampling_times, Azimuth, Zenith_GF, Row_spacing, fov)[0] povfile_mesh, povfile_box, z_top = Pov_Scene(plantgl_scene, domain, output_directory = Adel_output, thermal = TT, Ind = Ind) povfile_scene, result_df = Green_Fract(povfile_mesh, povfile_box, thermal = TT, Ind = Ind, cameras = cameras, image_height = 1000, image_width = 1000, relative_height = relative_height, z_top = z_top, output_directory = Adel_output) result_df_path = path(os.path.join(Adel_output, '%s%s%s%s%s'%('Fraction_',Ind,'_TT_',TT,'.csv'))) result_df.to_csv(result_df_path, index=False) # Fisheye for FAPAR if FAPAR: Azimuth_fisheye = [0] Zenith_fisheye = [0] fov_fisheye = [120] dup_width = 12.0 sampling_times = 7 New_canopy, New_nplants, New_domain, New_area = duplicate_scene(plantgl_scene, nplants, canopy_width = dup_width, canopy_length = dup_length, sim_width = sim_width, Row_spacing = Row_spacing) del plantgl_scene cameras_fisheye = Sampling_diagnal(New_domain, sampling_times, Azimuth_fisheye, Zenith_fisheye, Row_spacing, fov_fisheye)[0] povfile_mesh_new, povfile_box_new, z_top_new = Pov_Scene(New_canopy, New_domain, output_directory = Adel_output, thermal = TT, Ind = Ind) del New_canopy povray_image_fisheye = Hemispherical_IM(povfile_mesh = povfile_mesh_new, z_top = z_top_new, cameras = cameras_fisheye, image_height = 2000, image_width = 2000, relative_height = relative_height, output_directory = Adel_output) # Simulate BRDF (need large scene) if Multi_spectral: # Setting of prosail RT = prosail.prospect_5b(n = Param['N'], cab = Param['Cab'], car = Param['Car'], cbrown = Param['Cbrown'], cw = Param['Cw'], cm = Param['Cm']) Full_wave = range(400, 2501) R = RT[:,0] T = RT[:,1] for wave in Waves_camera: Plant_optical = Optical_canopy(wave=wave, Full_wave=Full_wave, R=R, T=T) soil_ref = Optical_soil(wave, brightness=Param['brightness']) Output_file = povray_RF(Ray_light=Ray_light, Ray_camera=Ray_camera, Plant_optical=Plant_optical, soil_ref=soil_ref, domain=New_domain, povfile_scene=povfile_scene, wave=wave, dict=Adel_output) if not os.path.exists(Output_file): Output_file = povray_RF(Ray_light=Ray_light, Ray_camera=Ray_camera, Plant_optical=Plant_optical, soil_ref=soil_ref, domain=New_domain, povfile_scene=povfile_scene, wave=wave, dict=Adel_output) except TypeError: print 'Pass it and move forward!!!***' result_df_path = [] pass return Adel_output
2.078125
2
cmds/moderations.py
james10949/sijingprogram
0
12791744
<reponame>james10949/sijingprogram<gh_stars>0 import discord from discord.ext import commands from core.classes import Cog_Extension class Moderations(Cog_Extension): @commands.command() async def clean(self, ctx, num : int): await ctx.channel.purge(limit = num+1) def setup(bot): bot.add_cog(Moderations(bot))
2.25
2
padinfo/view_state/otherinfo.py
chasehult/padbot-cogs
0
12791745
<reponame>chasehult/padbot-cogs<gh_stars>0 from padinfo.pane_names import IdMenuPaneNames from padinfo.view_state.base_id import ViewStateBaseId class OtherInfoViewState(ViewStateBaseId): def serialize(self): ret = super().serialize() ret.update({ 'pane_type': IdMenuPaneNames.otherinfo, }) return ret
2.046875
2
tests/test_draft.py
edsn60/tensorbay-python-sdk
0
12791746
<reponame>edsn60/tensorbay-python-sdk #!/usr/bin/env python3 # # Copyright 2021 Graviti. Licensed under MIT License. # import pytest from tensorbay.client import GAS from tensorbay.client.gas import DEFAULT_BRANCH from tensorbay.client.struct import Draft from tensorbay.exception import ResourceNotExistError, ResponseError, StatusError from .utility import get_dataset_name, get_draft_number_by_title class TestDraft: def test_create_draft(self, accesskey, url): gas_client = GAS(access_key=accesskey, url=url) dataset_name = get_dataset_name() dataset_client = gas_client.create_dataset(dataset_name) draft_number_1 = dataset_client.create_draft("draft-1", "description") assert draft_number_1 == 1 assert dataset_client.status.is_draft assert dataset_client.status.draft_number == draft_number_1 assert dataset_client.status.commit_id is None with pytest.raises(StatusError): dataset_client.create_draft("draft-2") draft_number = get_draft_number_by_title(dataset_client.list_drafts(), "draft-1") assert draft_number_1 == draft_number gas_client.delete_dataset(dataset_name) def test_list_drafts(self, accesskey, url): gas_client = GAS(access_key=accesskey, url=url) dataset_name = get_dataset_name() dataset_client = gas_client.create_dataset(dataset_name) dataset_client.create_draft("draft-1", "description for draft 1") dataset_client.commit("commit-draft-1") draft_number_2 = dataset_client.create_draft("draft-2", "description for draft 2") # After committing, the draft will be deleted with pytest.raises(TypeError): get_draft_number_by_title(dataset_client.list_drafts(), "draft-1") drafts = dataset_client.list_drafts() assert len(drafts) == 1 assert drafts[0] == Draft( draft_number_2, "draft-2", DEFAULT_BRANCH, "OPEN", "description for draft 2" ) with pytest.raises(TypeError): get_draft_number_by_title(dataset_client.list_drafts(), "draft-3") gas_client.delete_dataset(dataset_name) def test_commit_draft(self, accesskey, url): gas_client = GAS(access_key=accesskey, url=url) dataset_name = get_dataset_name() dataset_client = gas_client.create_dataset(dataset_name) dataset_client.create_draft("draft-1") dataset_client.commit("commit-1") dataset_client.create_draft("draft-2") dataset_client.commit("commit-2", tag="V1") dataset_client.create_draft("draft-3") with pytest.raises(ResponseError): dataset_client.commit("commit-3", tag="V1") dataset_client.commit("commit-3", tag="V2") assert not dataset_client.status.is_draft assert dataset_client.status.draft_number is None assert dataset_client.status.commit_id is not None # After committing, the draft will be deleted with pytest.raises(TypeError): get_draft_number_by_title(dataset_client.list_drafts(), "draft-3") gas_client.delete_dataset(dataset_name) def test_update_draft(self, accesskey, url): gas_client = GAS(access_key=accesskey, url=url) dataset_name = get_dataset_name() dataset_client = gas_client.create_dataset(dataset_name) dataset_client.create_draft("draft-1") dataset_client.commit("commit-1", "test", tag="V1") dataset_client.create_draft("draft-2") dataset_client.checkout("V1") dataset_client.create_branch("T123") dataset_client.create_draft("draft-3", "description00") dataset_client.update_draft(title="draft-4", description="description01") draft = dataset_client.get_draft(3) assert draft.title == "draft-4" assert draft.description == "description01" dataset_client.update_draft(2, title="draft-4", description="description02") draft = dataset_client.get_draft(2) assert draft.title == "draft-4" assert draft.description == "description02" gas_client.delete_dataset(dataset_name) def test_close_draft(self, accesskey, url): gas_client = GAS(access_key=accesskey, url=url) dataset_name = get_dataset_name() dataset_client = gas_client.create_dataset(dataset_name) dataset_client.create_draft("draft-1") dataset_client.commit("commit-1", "test", tag="V1") dataset_client.create_draft("draft-2") dataset_client.checkout("V1") dataset_client.create_branch("T123") dataset_client.create_draft("draft-3") dataset_client.close_draft() with pytest.raises(ResourceNotExistError): dataset_client.get_draft(3) dataset_client.close_draft(2) with pytest.raises(ResourceNotExistError): dataset_client.get_draft(2) gas_client.delete_dataset(dataset_name)
1.875
2
ObjDetector_CV.py
JunHong-1998/OpenCV-Scikit-ObjectSizeDetector-
3
12791747
import cv2 import math import imutils import numpy as np import warnings from sklearn.cluster import KMeans from skimage.morphology import * from skimage.util import * class OD_CV: def loadImage(self, filepath): return cv2.imread(filepath) def resizeImage(self, image, kar, width, height): if kar: return imutils.resize(image, width=width) else: return cv2.resize(image, (width, height)) def maskIMG(self, image, pts): mask = np.zeros(image.shape[:2], np.uint8) mask = cv2.drawContours(mask, [pts], -1, (255,255,255), -1) image = cv2.bitwise_and(image.copy(), image.copy(), mask=mask) return image def cropIMG(self, image, coords): return image[coords[1]:coords[1]+coords[3], coords[0]:coords[0]+coords[2]] def dmntCOLOR(self, image): image = cv2.resize(image, (0, 0), None, 0.5, 0.5) with warnings.catch_warnings(): warnings.simplefilter("ignore") clt = KMeans(n_clusters=5, random_state=0).fit(image.reshape(-1, 3)) numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1) hist, _ = np.histogram(clt.labels_, bins=numLabels) # normalize the histogram, such that it sums to one hist = hist.astype("float") hist /= hist.sum() palette = np.zeros((40, 200, 3), dtype="uint8") startX = 0 # loop over the percentage of each cluster and the color of # each cluster for percent, color in zip(hist, clt.cluster_centers_): # plot the relative percentage of each cluster endX = startX + (percent * 200) cv2.rectangle(palette, (int(startX), 0), (int(endX), 40), color.astype("uint8").tolist(), -1) startX = endX return palette def thinning(self, image, flag): image = img_as_float(image) if flag: #live streaming, faster computation skeleton = skeletonize(image > 0) else: # upload image mode skeleton = skeletonize(image > 0, method='lee') return img_as_ubyte(skeleton) def thresholding(self, image, auto, lower, max): if auto: _, image = cv2.threshold(image.copy(), 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU) else: _, image = cv2.threshold(image.copy(), lower, max, cv2.THRESH_BINARY) return image def color_CVT(self, image, flag): if flag==1: return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) elif flag==2: return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) def compareIMG(self, image): h,w = image[0].shape[:2] bg = np.zeros((h*2+3, w*2+3, 3), np.uint8) bg[0:h, 0:w] = image[0] bg[0:h, w+3:w*2+3] = image[1] bg[h+3:h*2+3, 0:w] = image[2] bg[h+3:h*2+3, w+3:w*2+3] = image[3] bg[0:h*2+3, w:w+3] = (255,255,255) bg[0:h * 2 + 3, w+1:w + 2] = (0,0,0) bg[h:h+3, 0:w*2+3] = (255,255,255) bg[h+1:h + 2, 0:w * 2 + 3] = (0,0,0) return bg def Color_picker(self, color, size, wid=(10,20)): image = np.zeros((size[0], size[1], 3), np.uint8) image[:] = color if wid[0]>0: cv2.rectangle(image, (int(size[0]*.01), int(size[1]*.01)), (int(size[0]*.99), int(size[1]*.99)), (0,0,0), wid[0], cv2.LINE_AA) if wid[1]>0: cv2.rectangle(image, (int(size[0]*.1), int(size[1]*.1)), (int(size[0]*.9), int(size[1]*.9)), (255,255,255), wid[1], cv2.LINE_AA) return image def drawPrimitives(self, image, flag, points, color, thick, width=None, height=None): if flag==1: cv2.polylines(image, points, True, color, thick) elif flag==2: cv2.rectangle(image, (points[0]-10, points[1]-10), (points[0]+points[2]+10, points[1]+points[3]+10), color, thick) elif flag==3: x, y, w, h = points width_Total = x+int(w*0.05)+width if width_Total>x+w+10: width_Total = x+w+10 cv2.rectangle(image, (x+int(w*0.05),y-10-height), (width_Total, y-10-2), color, thick) elif flag == 4: x, y, w, h = points if width!=0: w = width cv2.rectangle(image, (x-10,y+10+h), (x+10+w, y+10+h+height), color, thick) def drawText(self, flag, image, text, coords, fontstyle, color, thick, height=None): font = None if fontstyle == 0: font = cv2.FONT_HERSHEY_COMPLEX elif fontstyle == 1: font = cv2.FONT_HERSHEY_COMPLEX_SMALL elif fontstyle == 2: font = cv2.FONT_HERSHEY_DUPLEX elif fontstyle == 3: font = cv2.FONT_HERSHEY_PLAIN elif fontstyle == 4: font = cv2.FONT_HERSHEY_SCRIPT_COMPLEX elif fontstyle == 5: font = cv2.FONT_HERSHEY_SCRIPT_SIMPLEX elif fontstyle == 6: font = cv2.FONT_HERSHEY_TRIPLEX elif fontstyle == 7: font = cv2.FONT_ITALIC x, y, w, h = coords if flag==1: cv2.putText(image, text, (x+int(w*0.07),y-19), font, thick, color, 1) elif flag==2: cv2.putText(image, text, (x-10,y+10+h+height-5), font, thick, color, 1) def canny(self, image, GK_size, GSigma, DK_size, D_i, EK_size, E_i, cAuto, cThres_L, cThres_H, isDIL, isERO, isThin=None): imgGray = self.color_CVT(image.copy(), 1) image = cv2.GaussianBlur(imgGray, (GK_size, GK_size), GSigma) if cAuto: sigma = 0.33 v = np.median(image.copy()) # apply automatic Canny edge detection using the computed median lower = int(max(0, (1.0 - sigma) * v)) upper = int(min(255, (1.0 + sigma) * v)) else: lower, upper = cThres_L, cThres_H image = cv2.Canny(image, lower, upper) if isThin: image = self.thinning(image) edge = image.copy() if isDIL: Dial_K = np.ones((DK_size, DK_size)) image = cv2.dilate(image, Dial_K, iterations=D_i) if isERO: Ero_K = np.ones((EK_size, EK_size)) image = cv2.erode(image, Ero_K, iterations=E_i) return image, edge def sobel(self, image, GK_size, GSigma, DK_size, D_i, EK_size, E_i, Ksize, isDIL, isERO, isThin, Thres_auto, Thres_L, Thres_H, isThres, live_flag): imgGray = self.color_CVT(image.copy(), 1) imgBlur = cv2.GaussianBlur(imgGray, (GK_size, GK_size), GSigma) Sobel_X = cv2.Sobel(imgBlur.copy(), cv2.CV_64F, 1, 0, ksize=Ksize) Sobel_Y = cv2.Sobel(imgBlur.copy(), cv2.CV_64F, 0, 1, ksize=Ksize) sobel_img = cv2.bitwise_or(cv2.convertScaleAbs(Sobel_X), cv2.convertScaleAbs(Sobel_Y)) if isThres: sobel_img = self.thresholding(sobel_img.copy(), Thres_auto, Thres_L, Thres_H) if isThin: sobel_img = self.thinning(sobel_img, live_flag) image = sobel_img edge = image.copy() if isDIL: Dial_K = np.ones((DK_size, DK_size)) image = cv2.dilate(image, Dial_K, iterations=D_i) if isERO: Ero_K = np.ones((EK_size, EK_size)) image = cv2.erode(image, Ero_K, iterations=E_i) return image, edge def prewitt(self, image, GK_size, GSigma, DK_size, D_i, EK_size, E_i, isDIL, isERO, isThin, Thres_auto, Thres_L, Thres_H, isThres, live_flag): imgGray = self.color_CVT(image.copy(), 1) imgBlur = cv2.GaussianBlur(imgGray, (GK_size, GK_size), GSigma) kernelx = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]]) kernelx2 = np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]) kernely = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]) kernely2 = np.array([[1, 0, -1], [1, 0, -1], [1, 0, -1]]) kernels = [kernelx, kernelx2, kernely, kernely2] prewitt_img = np.zeros_like(imgGray) for k in kernels: prewitt_img = cv2.bitwise_or(prewitt_img, cv2.filter2D(imgBlur.copy(), -1, k)) if isThres: prewitt_img = self.thresholding(prewitt_img.copy(), Thres_auto, Thres_L, Thres_H) if isThin: prewitt_img = self.thinning(prewitt_img, live_flag) image = prewitt_img edge = image.copy() if isDIL: Dial_K = np.ones((DK_size, DK_size)) image = cv2.dilate(image, Dial_K, iterations=D_i) if isERO: Ero_K = np.ones((EK_size, EK_size)) image = cv2.erode(image, Ero_K, iterations=E_i) return image, edge def getTarget_Contour(self, image, image_edg, minArea, shapes, circular, color, thick): contours, _ = cv2.findContours(image_edg.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) finalCountours = [] for c in contours: for i, shape in enumerate(shapes): if not shape: continue area = cv2.contourArea(c) if area > minArea[i]: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.02 * peri, True) bbox = cv2.boundingRect(approx) rect = cv2.minAreaRect(c) box = cv2.boxPoints(rect) rbox = np.int0(box) if i==0 and len(approx) == 3: #Shape >>> vertices finalCountours.append((approx, bbox, c, i, rbox)) elif i==1 and len(approx) == 4: finalCountours.append((approx, bbox, c, i, rbox)) elif i==2: if len(approx) < 8: continue circularity = 4 * math.pi * (area / (peri*peri)) if circular[0] < circularity < circular[1]: finalCountours.append((approx, bbox, c, i, rbox)) elif i==3: finalCountours.append((approx, bbox, c, i, rbox)) finalCountours = sorted(finalCountours, key=lambda x:x[1], reverse=True) if thick==0: thick = -1 for cont in finalCountours: cv2.drawContours(image, [cont[2]], -1, color, thick) return finalCountours, image def reorder(self, points): NewPoints = np.zeros_like(points) points = points.reshape((4,2)) add = points.sum(1) NewPoints[0] = points[np.argmin(add)] NewPoints[2] = points[np.argmax(add)] d_dx = np.diff(points, axis=1) NewPoints[1] = points[np.argmin(d_dx)] NewPoints[3] = points[np.argmax(d_dx)] return NewPoints def warpImg(self, image, points, size, pad=3): points = self.reorder(points) # if not size: w, h = points[1][0][0] - points[0][0][0], points[3][0][1]-points[0][0][1] sw,sh = w/size[0], h/size[1] # w,h = size pts1 = np.float32(points) pts2 = np.float32([[0,0], [w,0], [w,h], [0,h]]) matrix = cv2.getPerspectiveTransform(pts1, pts2) imgWarp = cv2.warpPerspective(image, matrix, (w,h)) imgWarp = imgWarp[pad:imgWarp.shape[0]-pad, pad:imgWarp.shape[1]-pad] #remove boundary return imgWarp, (sw,sh) def findDist(self, flag, pts, scale, unit, deci): unit_conv = 1 if unit[0]==0: unit_conv = 1 elif unit[0]==1: unit_conv = 10 elif unit[0]==2: unit_conv = 1000 if unit[1]==0: unit_conv /= 1 elif unit[1]==1: unit_conv /= 10 elif unit[1]==2: unit_conv /= 1000 def dist(pt1, pt2): return ((pt2[0] // scale[0] - pt1[0] // scale[0]) ** 2 + (pt2[1] // scale[1] - pt1[1] // scale[1]) ** 2) ** 0.5 # if flag==1: # rect pts = self.reorder(pts) if flag==1: #rect p1, p2, p3 = pts[0][0], pts[1][0], pts[3][0] else: p1, p2, p3 = pts[0], pts[1], pts[3] if p1[1]==p2[1]: newW = (p2[0]-p1[0])//scale[0] else: newW = dist(p1, p2) if p1[0]==p3[0]: newH = (p3[1]-p1[1])//scale[1] else: newH = dist(p1, p3) newW = newW*unit_conv newH = newH*unit_conv return "{:.{}f}".format(newW, deci), "{:.{}f}".format(newH, deci) def deviceList(self): index = 0 arr, res = [], [] while True: cap = cv2.VideoCapture(index) if not cap.read()[0]: break else: arr.append(str(index)) res.append((cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) cap.release() index += 1 return arr, res
2.5625
3
src/vimpdb/proxy.py
dtrckd/vimpdb
110
12791748
import os import socket import subprocess from vimpdb import config from vimpdb import errors def get_eggs_paths(): import vim_bridge vimpdb_path = config.get_package_path(errors.ReturnCodeError()) vim_bridge_path = config.get_package_path(vim_bridge.bridged) return ( os.path.dirname(vimpdb_path), os.path.dirname(vim_bridge_path), ) class Communicator(object): def __init__(self, script, server_name): self.script = script self.server_name = server_name def prepare_subprocess(self, *args): parts = self.script.split() parts.extend(args) return parts def _remote_expr(self, expr): parts = self.prepare_subprocess('--servername', self.server_name, "--remote-expr", expr) p = subprocess.Popen(parts, stdout=subprocess.PIPE) return_code = p.wait() if return_code: raise errors.RemoteUnavailable() child_stdout = p.stdout output = child_stdout.read() return output.strip() def _send(self, command): # add ':<BS>' to hide last keys sent in VIM command-line command = ''.join((command, ':<BS>')) parts = self.prepare_subprocess('--servername', self.server_name, "--remote-send", command) return_code = subprocess.call(parts) if return_code: raise errors.RemoteUnavailable() class ProxyToVim(object): """ use subprocess to launch Vim instance that use clientserver mode to communicate with Vim instance used for debugging. """ def __init__(self, communicator): self.communicator = communicator def _send(self, command): self.communicator._send(command) config.logger.debug("sent: %s" % command) def _remote_expr(self, expr): return self.communicator._remote_expr(expr) def setupRemote(self): if not self.isRemoteSetup(): # source vimpdb.vim proxy_package_path = config.get_package_path(self) filename = os.path.join(proxy_package_path, "vimpdb.vim") command = "<C-\><C-N>:source %s<CR>" % filename self._send(command) for egg_path in get_eggs_paths(): self._send(':call PDB_setup_egg(%s)<CR>' % repr(egg_path)) self._send(':call PDB_init_controller()') def isRemoteSetup(self): status = self._expr("exists('*PDB_setup_egg')") return status == '1' def showFeedback(self, feedback): if not feedback: return feedback_list = feedback.splitlines() self.setupRemote() self._send(':call PDB_show_feedback(%s)<CR>' % repr(feedback_list)) def displayLocals(self, feedback): if not feedback: return feedback_list = feedback.splitlines() self.setupRemote() self._send(':call PDB_reset_watch()<CR>') for line in feedback_list: self._send(':call PDB_append_watch([%s])<CR>' % repr(line)) def showFileAtLine(self, filename, lineno): if os.path.exists(filename): self._showFileAtLine(filename, lineno) def _showFileAtLine(self, filename, lineno): # Windows compatibility: # Windows command-line does not play well with backslash in filename. # So turn backslash to slash; Vim knows how to translate them back. filename = filename.replace('\\', '/') self.setupRemote() self._send(':call PDB_show_file_at_line("%s", "%d")<CR>' % (filename, lineno)) def _expr(self, expr): config.logger.debug("expr: %s" % expr) result = self._remote_expr(expr) config.logger.debug("result: %s" % result) return result # code leftover from hacking # def getText(self, prompt): # self.setupRemote() # command = self._expr('PDB_get_command("%s")' % prompt) # return command class ProxyFromVim(object): BUFLEN = 512 socket_factory = socket.socket def __init__(self, port): self.socket_inactive = True self.port = port def bindSocket(self): if self.socket_inactive: self.socket = self.socket_factory( socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP) self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) self.socket.bind(('', self.port)) self.socket_inactive = False def closeSocket(self): if not self.socket_inactive: self.socket.close() self.socket_inactive = True def waitFor(self, pdb): self.bindSocket() (message, address) = self.socket.recvfrom(self.BUFLEN) config.logger.debug("command: %s" % message) return message # code leftover from hacking # def eat_stdin(self): # sys.stdout.write('-- Type Ctrl-D to continue --\n') # sys.stdout.flush() # sys.stdin.readlines()
2.359375
2
drf_file_management/urls.py
FJLendinez/drf-file-management
0
12791749
from django.urls import path, include from rest_framework import routers from drf_file_management.views import FileAPIView router = routers.SimpleRouter() router.register(r'file', FileAPIView) app_name = 'drf_file_management' urlpatterns = router.urls
1.625
2
alembic/versions/0aedc36acb3f_upgrade_to_2_0_0.py
goodtiding5/flask-track-usage
46
12791750
<gh_stars>10-100 """Upgrade to 2.0.0 Revision ID: <KEY> Revises: 0<PASSWORD> Create Date: 2018-04-25 09:39:38.879327 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '<KEY>' down_revision = '0<PASSWORD>' branch_labels = None depends_on = None def upgrade(): op.add_column('flask_usage', sa.Column('track_var', sa.String(128), nullable=True)) op.add_column('flask_usage', sa.Column('username', sa.String(128), nullable=True)) def downgrade(): op.drop_column('flask_usage', 'track_var') op.drop_column('flask_usage', 'username')
1.28125
1