xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

2df5218e49c9b42a48032a2f8b0a70abe590d7e1

5,354

py

Python

meteo.py

mhaberler/jumpvis

93b3b723d27aab7f3d4319cc91d06432022ddc6d

[ "MIT" ]

null

meteo.py

mhaberler/jumpvis

93b3b723d27aab7f3d4319cc91d06432022ddc6d

[ "MIT" ]

null

meteo.py

mhaberler/jumpvis

93b3b723d27aab7f3d4319cc91d06432022ddc6d

[ "MIT" ]

null

import logging import sys import xarray as xr # http://xarray.pydata.org/ from metpy.units import units import metpy.calc as mpcalc from czml3 import Packet from czml3.properties import ( # Billboard, # Clock, Color, # Label, # Point, # Material, # Model, # ViewFrom, # Orientation, # Path, # Position, PositionList, Polyline, SolidColorMaterial, # PolylineOutlineMaterial, PolylineArrowMaterial, # PolylineDashMaterial, # PolylineMaterial ) import seaborn as sns from geographiclib.constants import Constants from geographiclib.geodesic import Geodesic # https://stackoverflow.com/questions/33001420/find-destination-coordinates-given-starting-coordinates-bearing-and-distance # https://stackoverflow.com/a/33026930/2468365 # cartographicdegrees cartographicdegrees degrees meters def getEndpoint(lat1, lon1, bearing, distance): geod = Geodesic(Constants.WGS84_a, Constants.WGS84_f) d = geod.Direct(lat1, lon1, bearing, distance) return d['lat2'], d['lon2'] class Meteo: def __init__(self, netcdf=None, bbox=None, windcolors="viridis"): self.wind_colormap = sns.color_palette(windcolors, as_cmap=True) self.bbox = bbox if self.bbox: ds = xr.open_dataset(netcdf) self.ds = ds.where((ds.t.latitude > self.bbox['min_latitude']) & (ds.t.latitude < self.bbox['max_latitude']) & (ds.t.longitude > self.bbox['min_longitude']) & (ds.t.longitude < self.bbox['max_longitude']), drop=True) ds.close() else: self.ds = xr.open_dataset(netcdf) logging.debug("%s: data_vars %s", netcdf, ds.data_vars) logging.debug("valid_time %s", ds.valid_time) logging.debug("time %s", ds.time) logging.debug("step %s", ds.step) def czml_wind_vectors(self, layer): wind_packets = [] speed_scale = 100 for lat in self.ds.coords['latitude']: for lon in self.ds.coords['longitude']: u = self.ds.u.sel(latitude=lat, longitude=lon, generalVerticalLayer=layer) v = self.ds.v.sel(latitude=lat, longitude=lon, generalVerticalLayer=layer) w = self.ds.wz.sel(latitude=lat, longitude=lon, generalVerticalLayer=layer) t = self.ds.t.sel(latitude=lat, longitude=lon, generalVerticalLayer=layer) qv = self.ds.q.sel(latitude=lat, longitude=lon, generalVerticalLayer=layer) p = self.ds.pres.sel(latitude=lat, longitude=lon, generalVerticalLayer=layer) tempK = float(t) * units.K celsius = tempK.to('degC').magnitude # Dewpoint K dewpt = mpcalc.dewpoint_from_specific_humidity(qv, t, p) # relative humidity relhum = mpcalc.relative_humidity_from_dewpoint(t, dewpt) height = self.ds.h.sel( latitude=lat, longitude=lon, generalVerticalLayer=layer) ms = mpcalc.wind_speed(u, v) * units.kt wdir = mpcalc.wind_direction(u, v, convention='from') logging.debug("%f %f %f %3.1f %3.1f", float(lat), float( lon), float(height), ms.magnitude, wdir.magnitude) logging.debug("wind direction %f", wdir.magnitude) # radians logging.debug("wind speed %f", ms.magnitude) latend, lonend = getEndpoint(lat, lon, wdir. magnitude, ms.magnitude * speed_scale) plist = PositionList(cartographicDegrees=[float(lon), float(lat), float(height), lonend, latend, float(height)]) if True: # norm = matplotlib.colors.Normalize(vmin=10.0, vmax=20.0) # print(norm(15.0)) # 0.5 ratio = float(height)/6000. arrow_color = SolidColorMaterial(color=Color(rgbaf=self.wind_colormap(ratio))) else: arrow_rgba = [200, 200, 0, 255] arrow_color = SolidColorMaterial(color=Color(rgba=arrow_rgba)) mat = PolylineArrowMaterial(polylineArrow=arrow_color) pl = Polyline(width=5, #show=True, #clampToGround=False, material=mat, positions=plist) p3 = Packet(id="%4.fm wind %3.1fkt %3.0f' %3.1f°/%3.1f° rh %2.0f%%" % ( height, ms.magnitude, wdir.magnitude, celsius, dewpt.to('degC').magnitude,relhum*100), polyline=pl) wind_packets.append(p3) return wind_packets

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null

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null

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1

0

2df5f7eab6052f3e891da6a10f332205f7943272

4,616

py

Python

models/small_unet.py

ajithvallabai/Circle-U-Net

45733fea2f567837f890d6d93c503ad23b013715

[ "MIT" ]

1

2021-06-05T09:47:55.000Z

models/small_unet.py

ajithvallabai/Circle-U-Net

45733fea2f567837f890d6d93c503ad23b013715

[ "MIT" ]

null

models/small_unet.py

ajithvallabai/Circle-U-Net

45733fea2f567837f890d6d93c503ad23b013715

[ "MIT" ]

1

2021-09-19T21:17:27.000Z

from tensorflow.keras.layers import Input, Add, Dropout, Permute, add, concatenate, UpSampling2D from tensorflow.keras.layers import Convolution2D, ZeroPadding2D, MaxPooling2D, Cropping2D, Conv2D, BatchNormalization from tensorflow.compat.v1.layers import conv2d_transpose from tensorflow.keras.models import Model from tensorflow.keras.regularizers import l2 def UNet(n_filters=16, bn=True, dilation_rate=1): '''Validation Image data generator Inputs: n_filters - base convolution filters bn - flag to set batch normalization dilation_rate - convolution dilation rate Output: Unet keras Model ''' # Define input batch shape batch_shape = (256, 256, 3) inputs = Input(batch_shape=(5, 256, 256, 3)) print(inputs) conv1 = Conv2D(n_filters * 1, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(inputs) if bn: conv1 = BatchNormalization()(conv1) conv1 = Conv2D(n_filters * 1, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv1) if bn: conv1 = BatchNormalization()(conv1) pool1 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last')(conv1) conv2 = Conv2D(n_filters * 2, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(pool1) if bn: conv2 = BatchNormalization()(conv2) conv2 = Conv2D(n_filters * 2, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv2) if bn: conv2 = BatchNormalization()(conv2) pool2 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last')(conv2) conv3 = Conv2D(n_filters * 4, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(pool2) if bn: conv3 = BatchNormalization()(conv3) conv3 = Conv2D(n_filters * 4, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv3) if bn: conv3 = BatchNormalization()(conv3) pool3 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last')(conv3) conv4 = Conv2D(n_filters * 8, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(pool3) if bn: conv4 = BatchNormalization()(conv4) conv4 = Conv2D(n_filters * 8, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv4) if bn: conv4 = BatchNormalization()(conv4) pool4 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last')(conv4) conv5 = Conv2D(n_filters * 16, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(pool4) if bn: conv5 = BatchNormalization()(conv5) conv5 = Conv2D(n_filters * 16, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv5) if bn: conv5 = BatchNormalization()(conv5) up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=3) conv6 = Conv2D(n_filters * 8, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(up6) if bn: conv6 = BatchNormalization()(conv6) conv6 = Conv2D(n_filters * 8, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv6) if bn: conv6 = BatchNormalization()(conv6) up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3], axis=3) conv7 = Conv2D(n_filters * 4, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(up7) if bn: conv7 = BatchNormalization()(conv7) conv7 = Conv2D(n_filters * 4, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv7) if bn: conv7 = BatchNormalization()(conv7) up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2], axis=3) conv8 = Conv2D(n_filters * 2, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(up8) if bn: conv8 = BatchNormalization()(conv8) conv8 = Conv2D(n_filters * 2, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv8) if bn: conv8 = BatchNormalization()(conv8) up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1], axis=3) conv9 = Conv2D(n_filters * 1, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(up9) if bn: conv9 = BatchNormalization()(conv9) conv9 = Conv2D(n_filters * 1, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(conv9) if bn: conv9 = BatchNormalization()(conv9) conv10 = Conv2D(24, (1, 1), activation='softmax', padding='same', dilation_rate=dilation_rate)(conv9) model = Model(inputs=inputs, outputs=conv10) return model

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2df7323affd2ebec4b1620e5d4d2839848f89cdd

2,546

py

Python

ui/__init__.py

qenops/dGraph

b67c835bf60f1627a79d3e22183301f34431c5b3

[ "Apache-2.0" ]

1

2019-03-20T18:17:49.000Z

ui/__init__.py

qenops/dGraph

b67c835bf60f1627a79d3e22183301f34431c5b3

[ "Apache-2.0" ]

null

ui/__init__.py

qenops/dGraph

b67c835bf60f1627a79d3e22183301f34431c5b3

[ "Apache-2.0" ]

null

#!/usr/bin/env python # pylint: disable=bad-whitespace, line-too-long '''User interface submodule for dGraph scene description module based on glfw David Dunn Feb 2017 - created ALL UNITS ARE IN METRIC ie 1 cm = .01 www.qenops.com ''' __author__ = ('David Dunn') __version__ = '1.6' __all__ = [] from dGraph.ui import dglfw as fw from dGraph.ui.dglfw import * import numpy as np #from . import dglfw as fw #from .dglfw import * WINDOWSTACKS = {} # Each window can have 1 associated renderGraph WINDOWS = [] # should look at glfw Monitor objects class Display(object): ''' A class that defines the physical properties of a display AKA a monitor''' def __init__(self, name, monitor, bezel=None,location=(0.,0.,0.)): self.name = name self.classifier = 'display' self.resolution, self.colorDepth, self.fps = [np.array(a) for a in fw.get_video_mode(monitor)] self.glResolution = np.flipud(self.resolution) #print(self.resolution,self.colorDepth, self.fps) self.fps = 60 if self.fps == 59 else 30 if self.fps == 29 else self.fps # fix rounding down errors self.size = np.array(fw.get_monitor_physical_size(monitor))/1000. self.screenPosition = np.array(fw.get_monitor_pos(monitor)) self.bezel = None if bezel is None else np.array(bezel) self.location = None if location is None else np.array(location) # the top left corner of the display (not the bezel) @property def width(self): return self.resolution[0] @property def height(self): return self.resolution[1] def pixelSize(self): return self.size/self.resolution def resize_window_callback(window, w, h): ''' BROKEN - DON'T USE Need to figure out how to track this what is rederStack -> window relationship??? ''' renderGraph = WINDOWSTACKS[window] width = w if w > 1 else 2 height = h if h > 1 else 2 renderGraph._width = None renderGraph._height = None for cam in cameras: cam.setResolution((width/2, height)) # for binocular ??? for node in renderGraph: node.setup(renderGraph.width, renderGraph.height) def get_window_id(window): try: id = WINDOWS.index(window) except ValueError: id = len(WINDOWS) WINDOWS.append(window) return id def close_window(window): id = get_window_id(window) rg = WINDOWSTACKS.get(id, None) if rg is not None: rg.removeWindow(window) WINDOWS[id] = None fw.set_window_should_close(window, True)

33.064935

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0.423077

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0

null

0

null

0

1

0

2df84c3e6613791a1bd1619fefe6e749c4454933

1,855

py

Python

Server/src/quadradiusr_server/notification.py

kjarosh/QuadradiusR

2e55188bf9c9cd980ec6d11fce51830d0b4749d7

[ "MIT" ]

6

2022-02-08T11:16:39.000Z

2022-03-27T10:41:19.000Z

Server/src/quadradiusr_server/notification.py

kjarosh/QuadradiusR

2e55188bf9c9cd980ec6d11fce51830d0b4749d7

[ "MIT" ]

60

2022-02-08T10:33:36.000Z

2022-03-27T15:30:57.000Z

Server/src/quadradiusr_server/notification.py

kjarosh/QuadradiusR

2e55188bf9c9cd980ec6d11fce51830d0b4749d7

[ "MIT" ]

2

2022-02-11T12:50:39.000Z

2022-02-17T00:11:32.000Z

import abc import asyncio import fnmatch from abc import ABC from collections import defaultdict from dataclasses import dataclass from typing import Dict, List, Tuple @dataclass class Notification: topic: str subject_id: str data: dict class Handler(ABC): @abc.abstractmethod async def handle(self, notification: Notification): pass class NotificationService: def __init__(self) -> None: self.handlers: Dict[str, List[Tuple[str, Handler]]] = \ defaultdict(lambda: []) def register_handler( self, subject_id: str, topic: str, handler: Handler): self.handlers[subject_id].append((topic, handler)) def unregister_handler( self, subject_id: str, topic: str, handler: Handler): self.handlers[subject_id].remove((topic, handler)) def notify(self, notification: Notification): import asyncio asyncio.create_task(self.notify_now(notification)) async def notify_now(self, notification: Notification): handlers = set() for subject_id, tpl in self.handlers.items(): for topic, handler in tpl: if self._subject_matches(notification.subject_id, subject_id) and \ self._topic_matches(notification.topic, topic): handlers.add(handler) await asyncio.gather(*[h.handle(notification) for h in handlers]) def _subject_matches(self, subject_id: str, subject_id_wildcard: str): if subject_id_wildcard == '*': return True return subject_id == subject_id_wildcard def _topic_matches(self, topic: str, topic_wildcard: str): if topic_wildcard == '*': return True filtered = fnmatch.filter([topic], topic_wildcard) if filtered: return True

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null

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null

0

1

0

2dfab7efee4a5b2c160f01c920e607475e00542c

12,475

py

Python

pytorch_unet/processing/augments.py

mukeshmithrakumar/UNet

3f83f5116cd897293f1075f448703b75930707d5

[ "MIT" ]

11

2019-02-03T14:20:24.000Z

2021-06-28T15:18:59.000Z

pytorch_unet/processing/augments.py

mukeshmithrakumar/radnet

3f83f5116cd897293f1075f448703b75930707d5

[ "MIT" ]

null

pytorch_unet/processing/augments.py

mukeshmithrakumar/radnet

3f83f5116cd897293f1075f448703b75930707d5

[ "MIT" ]

2

2019-07-19T20:00:24.000Z

2020-02-18T04:49:49.000Z

import random import cv2 import numpy as np import torch from torchvision.transforms import RandomApply, Compose class PrepareImageAndMask(object): """Prepare images and masks like fixing channel numbers.""" def __call__(self, data): img = data['input'] img = img[:, :, :3] # max 3 channels img = img / 255 if 'mask' in data: mask = data['mask'] else: mask = np.zeros(img.shape[:2], dtype=img.dtype) data['input'] = img.astype(np.float32) data['mask'] = mask.astype(np.float32) return data def to_tensor(pic): if isinstance(pic, np.ndarray): # handle numpy array img = torch.from_numpy(pic.transpose((0, 1, 2))) # backward compatibility if isinstance(img, torch.ByteTensor): return img.float().div(255) else: return img class ConvertToTensor(object): """ Converts the image to tensor. Note: Modified from PyTorch vision ToTensor. Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] by calling the to_tensor function. """ def __call__(self, data): trans_images_arr = np.expand_dims(data['input'], axis=0) trans_labels_arr = np.expand_dims(data['mask'], axis=0) data['input'] = to_tensor(trans_images_arr) data['mask'] = to_tensor(trans_labels_arr) return data class ResizeToNxN(object): """Resize input images to rgb NxN and the masks into gray NxN. Note: uses cv2.INTER_LINEAR which implements bilinear interpolation for resizing. """ def __init__(self, n=128): self.n = n def __call__(self, data): n = self.n data['input'] = cv2.resize(data['input'], (n, n), interpolation=cv2.INTER_LINEAR) data['mask'] = cv2.resize(data['mask'], (n, n), interpolation=cv2.INTER_NEAREST) return data def compute_padding(h, w, n=128): if h % n == 0: dy0, dy1 = 0, 0 else: dy = n - h % n dy0 = dy // 2 dy1 = dy - dy0 if w % n == 0: dx0, dx1 = 0, 0 else: dx = n - w % n dx0 = dx // 2 dx1 = dx - dx0 return dy0, dy1, dx0, dx1 class PadToNxN(object): """Apply Pad to image size NxN using border reflection. Note: uses copyMakeBorder which and BORDER_REFLECT_101 which basically reflects the border of the image to pad. """ def __init__(self, n=128): self.n = n def __call__(self, data): n = self.n h, w = data['input'].shape[:2] dy0, dy1, dx0, dx1 = compute_padding(h, w, n) data['input'] = cv2.copyMakeBorder(data['input'], dy0, dy1, dx0, dx1, cv2.BORDER_REFLECT_101) data['mask'] = cv2.copyMakeBorder(data['mask'], dy0, dy1, dx0, dx1, cv2.BORDER_REFLECT_101) return data class HorizontalFlip(object): """Flip input and masks horizontally.""" def __call__(self, data): data['input'] = cv2.flip(data['input'], 1) data['mask'] = cv2.flip(data['mask'], 1) return data class BrightnessShift(object): """Applies Brightness shift to the images. Note: When changing the brightness of an image, a constant is added or subtracted from the luminnance of all sample values. Here we are shifting the histogram left (subtraction) or right (addition) by a max value. """ def __init__(self, max_value=0.1): self.max_value = max_value def __call__(self, data): img = data['input'] img += np.random.uniform(-self.max_value, self.max_value) data['input'] = np.clip(img, 0, 1) return data class BrightnessScaling(object): """Applies Brightness scaling to the images. Note: Brightness scaling scales the histogram by a max value. """ def __init__(self, max_value=0.08): self.max_value = max_value def __call__(self, data): img = data['input'] img *= np.random.uniform(1 - self.max_value, 1 + self.max_value) data['input'] = np.clip(img, 0, 1) return data class GammaChange(object): """Applies Gamma change to the images. Note: is a nonlinear operation used to encode and decode luminance values in images. """ def __init__(self, max_value=0.08): self.max_value = max_value def __call__(self, data): img = data['input'] img = img ** (1.0 / np.random.uniform(1 - self.max_value, 1 + self.max_value)) data['input'] = np.clip(img, 0, 1) return data def do_elastic_transform(image, mask, grid=10, distort=0.2): height, width = image.shape[:2] x_step = int(grid) xx = np.zeros(width, np.float32) prev = 0 for x in range(0, width, x_step): start = x end = x + x_step if end > width: end = width cur = width else: cur = prev + x_step * (1 + random.uniform(-distort, distort)) xx[start:end] = np.linspace(prev, cur, end - start) prev = cur y_step = int(grid) yy = np.zeros(height, np.float32) prev = 0 for y in range(0, height, y_step): start = y end = y + y_step if end > height: end = height cur = height else: cur = prev + y_step * (1 + random.uniform(-distort, distort)) yy[start:end] = np.linspace(prev, cur, end - start) prev = cur # grid map_x, map_y = np.meshgrid(xx, yy) map_x = map_x.astype(np.float32) map_y = map_y.astype(np.float32) image = cv2.remap(image, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,)) mask = cv2.remap(mask, map_x, map_y, interpolation=cv2.INTER_NEAREST, borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,)) # mask = (mask > 0.5).astype(np.float32) return image, mask class ElasticDeformation(object): """Applies Elastic deformation to the images. Note: Elastic deformation of images as described in [Simard2003]_ (with modifications). Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5 """ def __init__(self, grid=10, max_distort=0.15): self.grid = grid self.max_distort = max_distort def __call__(self, data): distort = np.random.uniform(0, self.max_distort) img, mask = do_elastic_transform(data['input'], data['mask'], self.grid, distort) data['input'] = img data['mask'] = mask return data def do_rotation_transform(image, mask, angle=0): height, width = image.shape[:2] cc = np.cos(angle / 180 * np.pi) ss = np.sin(angle / 180 * np.pi) rotate_matrix = np.array([[cc, -ss], [ss, cc]]) box0 = np.array([[0, 0], [width, 0], [width, height], [0, height], ], np.float32) box1 = box0 - np.array([width / 2, height / 2]) box1 = np.dot(box1, rotate_matrix.T) + np.array([width / 2, height / 2]) box0 = box0.astype(np.float32) box1 = box1.astype(np.float32) mat = cv2.getPerspectiveTransform(box0, box1) image = cv2.warpPerspective(image, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,)) mask = cv2.warpPerspective(mask, mat, (width, height), flags=cv2.INTER_NEAREST, borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,)) # mask = (mask > 0.5).astype(np.float32) return image, mask class Rotation(object): """Applies to the Rotation to the images. Note: Does rotation transformation. """ def __init__(self, max_angle=15): self.max_angle = max_angle def __call__(self, data): angle = np.random.uniform(-self.max_angle, self.max_angle) img, mask = do_rotation_transform(data['input'], data['mask'], angle) data['input'] = img data['mask'] = mask return data def do_horizontal_shear(image, mask, scale=0): height, width = image.shape[:2] dx = int(scale * width) box0 = np.array([[0, 0], [width, 0], [width, height], [0, height], ], np.float32) box1 = np.array([[+dx, 0], [width + dx, 0], [width - dx, height], [-dx, height], ], np.float32) box0 = box0.astype(np.float32) box1 = box1.astype(np.float32) mat = cv2.getPerspectiveTransform(box0, box1) image = cv2.warpPerspective(image, mat, (width, height), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,)) mask = cv2.warpPerspective(mask, mat, (width, height), flags=cv2.INTER_NEAREST, borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,)) # mask = (mask > 0.5).astype(np.float32) return image, mask class HorizontalShear(object): """Applies Horizontal Shear to the images. Note: horizontal shear (or shear parallel to the x axis) is a function that takes a generic point with coordinates (x,y) to the point (x+my,y); where m is a fixed parameter, called the shear factor. """ def __init__(self, max_scale=0.2): self.max_scale = max_scale def __call__(self, data): scale = np.random.uniform(-self.max_scale, self.max_scale) img, mask = do_horizontal_shear(data['input'], data['mask'], scale) data['input'] = img data['mask'] = mask return data class HWCtoCHW(object): """Converts HWC to CHW.""" def __call__(self, data): data['input'] = data['input'].transpose((2, 0, 1)) return data def augmentations(args): """Applies random augmentations for the input images based on the transform probability. Note: Many methods are taken from https://www.kaggle.com/c/tgs-salt-identification-challenge/discussion/63974. The user can specify between geometric, image or both types of transforms to the images since sometimes some transformations work well for certain datasets. :param args: image_size (int) : size of the image to be resized. transform_prob (float) : probability to apply transformations on the data. :return: a compose of transformations. """ augment_type = 'geometric' transform_prob = args.transform_prob if augment_type == 'geometric': geometric_transforms = Compose([RandomApply([HorizontalShear(max_scale=0.07)], p=transform_prob), RandomApply([Rotation(max_angle=15)], p=transform_prob), RandomApply([ElasticDeformation(max_distort=0.15)], p=transform_prob), ResizeToNxN(args.image_size), ConvertToTensor() ]) return geometric_transforms elif augment_type == 'image': brightness_transform = Compose([RandomApply([BrightnessShift(max_value=0.1)], p=transform_prob), RandomApply([BrightnessScaling(max_value=0.08)], p=transform_prob), RandomApply([GammaChange(max_value=0.08)], p=transform_prob), ResizeToNxN(args.image_size), ConvertToTensor() ]) return brightness_transform elif augment_type == 'both': both_transforms = Compose([RandomApply([HorizontalShear(max_scale=0.07)], p=transform_prob), RandomApply([Rotation(max_angle=15)], p=transform_prob), RandomApply([ElasticDeformation(max_distort=0.15)], p=transform_prob), RandomApply([BrightnessShift(max_value=0.1)], p=transform_prob), RandomApply([BrightnessScaling(max_value=0.08)], p=transform_prob), RandomApply([GammaChange(max_value=0.08)], p=transform_prob), ResizeToNxN(args.image_size), ConvertToTensor() ]) return both_transforms

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null

0

null

0

1

0

2dfb60993df01b905cc7b936dd1d08f5223caffa

21,391

py

Python

bdscan/classComponentList.py

matthewb66/test_python_action

c6c8ff47982584a8bc124d343094ff12aedcea09

[ "Apache-2.0" ]

null

bdscan/classComponentList.py

matthewb66/test_python_action

c6c8ff47982584a8bc124d343094ff12aedcea09

[ "Apache-2.0" ]

null

bdscan/classComponentList.py

matthewb66/test_python_action

c6c8ff47982584a8bc124d343094ff12aedcea09

[ "Apache-2.0" ]

null

import re import os # import shutil import sys import tempfile import hashlib # import sys import json from operator import itemgetter from bdscan import classComponent, classNugetComponent, classNpmComponent, classMavenComponent, classPyPiComponent, \ classConanComponent, classCargoComponent, classHexComponent, classGoLangComponent, classCondaComponent, \ classDartComponent from bdscan import utils, globals class ComponentList: md_directdeps_header = \ f"\n## SUMMARY Direct Dependencies with vulnerabilities:\n\n" \ f"| Direct Dependency | Changed | Num Direct Vulns | Max Direct Vuln Severity | Num Indirect Vulns | " \ f"Max Indirect Vuln Severity | Upgrade to |\n| --- | --- | --- | --- | --- | --- | --- |\n" md_comp_lic_hdr = \ "\n## SUMMARY License violations:\n\n" \ "| Parent | Child Component | License | Policy Violated | Direct Dep Changed |\n" \ "| --- | --- | --- | --- | --- |\n" def __init__(self): self.compids = [] self.components = [] def add(self, compid): if compid in self.compids: return self.components[self.compids.index(compid)] globals.printdebug(f"DEBUG: add(compid={compid})") arr = re.split('[/:]', compid) ns = arr[0] if ns == 'npmjs': component = classNpmComponent.NpmComponent(compid, arr[1], arr[2], ns) elif ns == 'nuget': component = classNugetComponent.NugetComponent(compid, arr[1], arr[2], ns) elif ns == 'maven': component = classMavenComponent.MavenComponent(compid, arr[1], arr[2], arr[3], ns) elif ns == 'pypi': component = classPyPiComponent.PyPiComponent(compid, arr[1], arr[2], ns) elif ns == 'conan': component = classConanComponent.ConanComponent(compid, arr[1], arr[2], ns) elif ns == 'crates': component = classCargoComponent.CargoComponent(compid, arr[1], arr[2], ns) elif ns == 'hex': component = classHexComponent.HexComponent(compid, arr[1], arr[2], ns) elif ns == 'golang': component = classGoLangComponent.GoLangComponent(compid, arr[1], arr[2], ns) elif ns == 'anaconda': component = classCondaComponent.CondaComponent(compid, arr[1], arr[2], ns) elif ns == 'dart': component = classDartComponent.DartComponent(compid, arr[1], arr[2], ns) else: component = classComponent.Component(compid, arr[1], arr[2], ns) raise ValueError(f'Unsupported package manager {ns}') self.components.append(component) self.compids.append(component.compid) return component def set_data_in_comp(self, compid, fieldname, data): if compid in self.compids: index = self.compids.index(compid) comp = self.components[index] return comp.set_data(fieldname, data) return False def add_origins_to_comp(self, compid, ver, data): if compid in self.compids: index = self.compids.index(compid) comp = self.components[index] comp.set_origins(ver, data) def get_component(self, compid): if compid in self.compids: return self.components[self.compids.index(compid)] return None def find_upgrade_versions(self, upgrade_major): for comp in self.components: comp.find_upgrade_versions(upgrade_major) def validate_upgrades(self): detect_jar = utils.get_detect_jar() bd_output_path = 'upgrade-tests' detect_connection_opts = [ f'--blackduck.url={globals.args.url}', f'--blackduck.api.token={globals.args.token}', "--detect.blackduck.scan.mode=RAPID", # "--detect.detector.buildless=true", # detect_connection_opts.append("--detect.maven.buildless.legacy.mode=false") f"--detect.output.path={bd_output_path}", "--detect.cleanup=false" ] if globals.args.trustcert: detect_connection_opts.append('--blackduck.trust.cert=true') max_upgrade_count = 0 for comp in self.components: if len(comp.potentialupgrades) > max_upgrade_count: max_upgrade_count = len(comp.potentialupgrades) upgrade_index = 0 while upgrade_index <= max_upgrade_count: print(f'BD-Scan-Action: Validating upgrades cycle {upgrade_index+1} ...') # dirname = "snps-upgrade-" + direct_name + "-" + direct_version dirname = tempfile.TemporaryDirectory() # os.mkdir(dirname) origdir = os.getcwd() os.chdir(dirname.name) test_upgrade_list = [] test_origdeps_list = [] for comp in self.components: # Do not process components in package managers not supported by direct upgrade guidance, but use # regular upgrade guidance if available if not comp.supports_direct_upgrades(): if globals.debug: print(f"DEBUG: Component {comp.name} via package manager {comp.pm} does not" "support direct upgrades, skipping") if comp.upgradeguidance and comp.upgradeguidance[0]: comp.goodupgrade = comp.upgradeguidance[0] elif comp.upgradeguidance and comp.upgradeguidance[1]: comp.goodupgrade = comp.upgradeguidance[1] continue if comp.goodupgrade == '' and len(comp.potentialupgrades) > upgrade_index: if comp.prepare_upgrade(upgrade_index): test_upgrade_list.append([comp.org, comp.name, comp.potentialupgrades[upgrade_index]]) globals.printdebug(f"Will test upgrade {comp.name}/{comp.version} to " f"{comp.potentialupgrades[upgrade_index]}") test_origdeps_list.append(comp.compid) if len(test_origdeps_list) == 0: os.chdir(origdir) dirname.cleanup() upgrade_index += 1 continue pm_list = [] for comp in self.components: if comp.pm not in pm_list and comp.compid in test_origdeps_list: pm_list.append(comp.pm) comp.finalise_upgrade() if len(pm_list) == 1 and pm_list[0] == 'maven' and \ "--detect.detector.buildless=true" not in detect_connection_opts: detect_connection_opts.append("--detect.detector.buildless=true") output = False if globals.debug > 0: output = True pvurl, projname, vername, retval = utils.run_detect(detect_jar, detect_connection_opts, output) if retval == 3: # Policy violation returned rapid_scan_data, dep_dict, direct_deps_vuln = utils.process_scan(bd_output_path, globals.bd) # process_scan(scan_folder, bd, baseline_comp_cache, incremental, upgrade_indirect): last_vulnerable_dirdeps = [] for vulndep in direct_deps_vuln.components: # # find comp in depver_list for upgradedep, origdep in zip(test_upgrade_list, test_origdeps_list): if upgradedep[1] == vulndep.name: # vulnerable_upgrade_list.append([origdep, upgradedep[2]]) last_vulnerable_dirdeps.append(origdep) break elif retval != 0: # Other Detect failure - no upgrades determined last_vulnerable_dirdeps = [] for upgradedep, origdep in zip(test_upgrade_list, test_origdeps_list): # vulnerable_upgrade_list.append([origdep, upgradedep[2]]) last_vulnerable_dirdeps.append(origdep) else: # Detect returned 0 # All tested upgrades not vulnerable last_vulnerable_dirdeps = [] for lcomp in self.components: if (lcomp.compid in test_origdeps_list and lcomp.compid not in last_vulnerable_dirdeps and len(lcomp.potentialupgrades) >= upgrade_index and lcomp.goodupgrade == ''): lcomp.set_data('goodupgrade', lcomp.potentialupgrades[upgrade_index]) os.chdir(origdir) dirname.cleanup() upgrade_index += 1 return def check_in_baselineproj(self, baseline_data): for basecomp in baseline_data: for baseorig in basecomp['origins']: if baseorig['externalNamespace'] != '': basecompid = f"{baseorig['externalNamespace']}:{baseorig['externalId']}" else: basecompid = baseorig['externalId'] if basecompid in self.compids: comp = self.get_component(basecompid) comp.set_data('inbaseline', True) break # def check_projfiles(self): # for comp in self.components: # package_file, package_line = comp.get_package_file() # if package_file == 'Unknown' or package_line <= 0: # # component doesn't exist in pkgfile - skip # continue # package_file = utils.remove_cwd_from_filename(package_file) # if package_file not in comp.projfiles: # comp.set_data('projfiles', package_file) # comp.set_data('projfilelines', package_line) def get_children(self, dep_dict): for comp in self.components: children = [] for alldep in dep_dict.keys(): if comp.compid in dep_dict[alldep]['directparents']: children.append(alldep) comp.set_data('children', children) def calc_vulns(self, rapid_scan_data): for comp in self.components: max_vuln_severity = 0 max_vuln_severity_children = 0 existing_vulns = [] existing_vulns_children = [] existing_lic_violations = [] existing_lic_violations_children = [] for rscanitem in rapid_scan_data['items']: child = False parent = False if rscanitem['componentIdentifier'] == comp.compid: parent = True else: for childid in comp.children: if rscanitem['componentIdentifier'] == childid: child = True break if not parent and not child: continue for vuln in rscanitem['policyViolationVulnerabilities']: # print(f"vuln={vuln}") parent_name = '-' parent_ver = '-' if parent: if vuln['name'] in existing_vulns: continue if max_vuln_severity < vuln['overallScore']: max_vuln_severity = vuln['overallScore'] elif child: if vuln['name'] in existing_vulns_children: continue if max_vuln_severity_children < vuln['overallScore']: max_vuln_severity_children = vuln['overallScore'] parent_name = comp.name parent_ver = comp.version child_ns, child_name, child_ver = comp.parse_compid(rscanitem['componentIdentifier']) desc = vuln['description'].replace('\n', ' ') if len(desc) > 200: desc = desc[:196] desc += ' ...' name = vuln['name'] link = f"{globals.args.url}/api/vulnerabilities/{name}/overview" vulnname = f'<a href="{link}" target="_blank">{name}</a>' if comp.inbaseline: changed = 'No' else: changed = 'Yes' vuln_item = [ f"{parent_name}/{parent_ver}", f"{child_name}/{child_ver}", vulnname, str(vuln['overallScore']), vuln['violatingPolicies'][0]['policyName'], desc, changed ] if parent and vuln['name'] not in existing_vulns: comp.add_vuln(name, vuln_item) comp.set_data('maxvulnscore', max_vuln_severity) if child and vuln['name'] not in existing_vulns_children: comp.add_child_vuln(name, vuln_item) comp.set_data('maxchildvulnscore', max_vuln_severity_children) # TODO: Revisit license violations for lic in rscanitem['policyViolationLicenses']: parent_name = '-' parent_ver = '-' if parent: print(f"lic={lic}") if lic['name'] in existing_lic_violations: continue #if max_vuln_severity < vuln['overallScore']: # max_vuln_severity = vuln['overallScore'] elif child: if lic['name'] in existing_lic_violations_children: continue #if max_vuln_severity_children < vuln['overallScore']: # max_vuln_severity_children = vuln['overallScore'] parent_name = comp.name parent_ver = comp.version child_ns, child_name, child_ver = comp.parse_compid(rscanitem['componentIdentifier']) name = lic['name'] # TODO: This link is not user friendly; follow to generate correct link link = lic['_meta']['href'] #link = f"{globals.args.url}/api/vulnerabilities/{name}/overview" licname = f'<a href="{link}" target="_blank">{name}</a>' if comp.inbaseline: changed = 'No' else: changed = 'Yes' lic_item = [ f"{parent_name}/{parent_ver}", f"{child_name}/{child_ver}", licname, lic['violatingPolicies'][0]['policyName'], changed ] if parent and lic['name'] not in existing_lic_violations: comp.add_lic_violation(name, lic_item) #comp.set_data('maxvulnscore', max_vuln_severity) if child and lic['name'] not in existing_lic_violations_children: comp.add_child_lic_violation(name, lic_item) #comp.set_data('maxchildvulnscore', max_vuln_severity_children) # Sort the tables # vuln_list = sorted(vuln_list, key=itemgetter(3), reverse=True) # vuln_list_children = sorted(vuln_list_children, key=itemgetter(3), reverse=True) return def write_sarif(self, sarif_file): if os.path.exists(sarif_file): os.remove(sarif_file) if os.path.exists(sarif_file): print(f'BD-Scan-Action: ERROR: Unable to write SARIF file {sarif_file}') return False sarif_result = [] sarif_tool_rule = [] for comp in self.components: # md_comp_vulns_table = comp.md_table() projfile = '' projfileline = 1 if len(comp.projfiles) > 0: projfile = comp.projfiles[0] if len(comp.projfilelines) > 0: projfileline = comp.projfilelines[0] sarif_result.append( { 'ruleId': comp.name, 'message': { 'text': comp.shorttext() }, 'locations': [ { 'physicalLocation': { 'artifactLocation': { 'uri': projfile, }, 'region': { 'startLine': projfileline, } } } ], 'partialFingerprints': { 'primaryLocationLineHash': hashlib.sha224(b"{compid}").hexdigest(), } } ) if comp.maxchildvulnscore >= 7 or comp.maxvulnscore >= 7: level = "error" elif comp.maxchildvulnscore >= 4 or comp.maxvulnscore >= 4: level = "warning" else: level = "note" if comp.goodupgrade != '': uhelp = f"{comp.longtext_md()}\n\nRecommended to upgrade to version {comp.goodupgrade}.\n\n" else: uhelp = f"{comp.longtext_md()}\n\nNo upgrade available at this time.\n\n" sarif_tool_rule.append( { 'id': comp.name, 'shortDescription': { 'text': comp.shorttext(), }, 'fullDescription': { 'text': comp.longtext(), }, 'help': { 'text': '', 'markdown': uhelp, }, 'defaultConfiguration': { 'level': level, }, 'properties': { 'tags': ["security"], 'security-severity': str(comp.maxvulnscore) } } ) code_security_scan_report = { '$schema': "https://raw.githubusercontent.com/oasis-tcs/sarif-spec/master/Schemata/sarif-schema-2.1.0.json", 'version': "2.1.0", 'runs': [ { 'tool': { 'driver': { 'name': 'Synopsys Black Duck', 'organization': 'Synopsys', 'version': globals.scan_utility_version, 'rules': sarif_tool_rule, } }, 'results': sarif_result, } ], } try: with open(sarif_file, "w") as fp: json.dump(code_security_scan_report, fp, indent=4) except Exception as e: print(f"BD-Scan-Action: ERROR: Unable to write to SARIF output file '{sarif_file} - '" + str(e)) return False return True def get_comments(self, incremental): md_main_table = [] md_comp_data_string = '' md_lic_table_string = '' for comp in self.components: if incremental and comp.inbaseline: continue if comp.get_num_vulns() > 0: md_main_table.append(comp.md_summary_table_row()) md_comp_data_string += f"\n### Direct Dependency: {comp.name}/{comp.version}" + comp.md_table() md_lic_table_string += comp.md_lic_table() # Sort main table here md_main_table = sorted(md_main_table, key=itemgetter(4), reverse=True) md_main_table = sorted(md_main_table, key=itemgetter(6), reverse=True) sep = ' | ' md_main_table_string = '' for row in md_main_table: md_main_table_string += '| ' + sep.join(row) + ' |\n' md_comments = '' if len(md_main_table) > 0: md_comments += self.md_directdeps_header + md_main_table_string if (len(md_lic_table_string) > 1): md_comments += self.md_comp_lic_hdr + md_lic_table_string if len(md_main_table) > 0: md_comments += '\n\nVulnerable Direct Dependencies listed below:\n\n' + md_comp_data_string return md_comments def print_upgrade_summary(self): print('\n------------------------------------------------------------------------------------') print('SUMMARY UPGRADE GUIDANCE:') for comp in self.components: if comp.goodupgrade != '': upg = f'Upgrade to {comp.goodupgrade}' else: upg = 'No Upgrade Available' print(f'- {comp.name}/{comp.version}: {upg}') print('------------------------------------------------------------------------------------\n') def supports_direct_upgrades(self): return False

42.867735

120

0.511757

2,018

21,391

5.238355

0.18781

0.022514

0.019866

0.013528

0.314824

0.262984

0.236118

0.217293

0.171602

0.137357

0

0.00616

0.385255

21,391

498

121

42.953815

0.797719

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0

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0

0.005076

0.149157

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false

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0.032995

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null

0

null

0

1

0

2dfc29d7013519f59daa6312e09bc8cdb96754d9

1,334

py

Python

apps/posts/models.py

DiceNameIsMy/starnavi-task

e2e8d20889d9b4d5cf02e332d88b7b9ec5f4aee4

[ "MIT" ]

1

2021-10-04T03:08:25.000Z

apps/posts/models.py

DiceNameIsMy/starnavi-task

e2e8d20889d9b4d5cf02e332d88b7b9ec5f4aee4

[ "MIT" ]

null

apps/posts/models.py

DiceNameIsMy/starnavi-task

e2e8d20889d9b4d5cf02e332d88b7b9ec5f4aee4

[ "MIT" ]

null

from django.db import models from django.contrib.auth import get_user_model from django.utils import timezone # Saving media locally is not a good practice # for a big social network actually. GoogleCloud or AWS # migth be a better alternative def get_user_image_path(instance, filename): return f'users/{instance.author}/posts/{instance.id}/{filename}' class Post(models.Model): author = models.ForeignKey( to=get_user_model(), null=True, on_delete=models.SET_NULL, related_name='posts' ) image = models.ImageField( upload_to=get_user_image_path, blank=True, null=True, ) text = models.CharField( max_length=8192, blank=True ) likes = models.ManyToManyField( to=get_user_model(), blank=True, through='Like' ) created_at = models.DateTimeField(default=timezone.now) updated_at = models.DateTimeField(auto_now=True) def count_likes(self): return len(self.likes.all()) class Like(models.Model): author = models.ForeignKey( get_user_model(), on_delete=models.CASCADE ) post = models.ForeignKey( Post, on_delete=models.CASCADE ) date = models.DateField(default=timezone.now) time = models.TimeField(default=timezone.now)

25.653846

68

0.664918

166

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5.192771

0.493976

0.048724

0.055684

0.037123

0.076566

0

0.003941

0.23913

1,334

51

69

26.156863

0.84532

0.096702

0

0.243902

0

0.052456

0.044963

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0.04878

false

0

0.073171

0.04878

0.463415

0

null

0

null

0

1

0

2dfd2b75b22705c1bffc7af8fe1e8acd1e0693c7

350

py

Python

ABC104/ABC104b.py

VolgaKurvar/AtCoder

21acb489f1594bbb1cdc64fbf8421d876b5b476d

[ "Unlicense" ]

null

ABC104/ABC104b.py

VolgaKurvar/AtCoder

21acb489f1594bbb1cdc64fbf8421d876b5b476d

[ "Unlicense" ]

null

ABC104/ABC104b.py

VolgaKurvar/AtCoder

21acb489f1594bbb1cdc64fbf8421d876b5b476d

[ "Unlicense" ]

null

# ABC104b import sys input = sys.stdin.readline sys.setrecursionlimit(10**6) s = input()[:-1] #print(s.count('C', 2, -1)) if (s[0] != 'A' or s.count('C', 2, -1) != 1): print('WA') # print('hi') exit() cPos = s.find('C', 2, -1) if (s[1:cPos].islower() and s[cPos + 1:].islower()): # print('hi') print('AC') exit() print('WA')

19.444444

52

0.525714

59

350

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0.048913

0.086957

0.146739

0

0.060498

0.197143

350

17

53

20.588235

0.594306

0.162857

0

0.333333

0

0.03125

0

1

0

false

0

0.083333

0

0.083333

0.25

0

null

0

null

0

1

0

9300b3516d09a5957e2dfc67d70aa1ba14f76b6f

2,761

py

Python

HMM_Construction_Scripts/s01-make_tree_dataset.py

dantaslab/resfams_update

982091818a299d316811fe98c7656762be7284fb

[ "MIT" ]

null

HMM_Construction_Scripts/s01-make_tree_dataset.py

dantaslab/resfams_update

982091818a299d316811fe98c7656762be7284fb

[ "MIT" ]

null

HMM_Construction_Scripts/s01-make_tree_dataset.py

dantaslab/resfams_update

982091818a299d316811fe98c7656762be7284fb

[ "MIT" ]

null

#!/usr/bin/env python """ File Name : s01-make_tree_dataset.py Author : Max Bernstein Created On : 2019-07-12 Last Modified : 2019-12-17 Description : A program to prep and rename sequences in order to make tree anlaysis easier Dependencies : py-biopython Usage : s01-make_tree_dataset.py --infile blactamA.fasta --blast blactamA_card_blast.txt --family blactamA --out_path path/to/output/directory/ CHANGE LOG : TODO : """ import sys import os import argparse import csv import re from Bio import SeqIO def main(argv): args = parse_arguments(argv) infile = args.infile blast_file = args.blast #retrieve blast data seqInfo = {} for record in SeqIO.parse(infile,'fasta'): array=[] for hit in blast_file: if record.id == hit[0] and hit[1] not in array: array.append(hit[1]) seqInfo[record.id] = array #renmane sequences seq_file = open("{}/{}_tree_dataset.faa".format(args.out_path,args.family), 'w+') mapping_file = open("{}/{}_tree_mappingFile.txt".format(args.out_path,args.family), 'w+') counter = 1 for seq,data in seqInfo.items(): outHeaders = [] seqHeader = seq.split("|") outSeq = "RF-" + str(counter) + "|" + record.id bHitCount = 1 for bData in data: outSeq = "RF-" + str(counter) + "-" + str(bHitCount) + "|" + seqHeader[-1] + "|" + bdata.split("|")[-1] outHeaders.append(outSeq) bHitCount+=1 if len(outHeaders) < 1: outHeaders.append(outSeq) for header in outHeaders: print(header + "\t" + record.id) mapping_file.write(header + "\t" + record.id + "\n") seq_file.write(">" + header + "\n") seq_file.write(str(record.seq) + "\n") counter+=1 def parse_arguments(argv): parser = argparse.ArgumentParser( prog = 'make_tree_dataset.py', description = 'A program to prep and rename sequences in order to make tree anlaysis easier') parser.add_argument( '-i', '--infile', help = 'path to input sequences to make trees', required = True ) parser.add_argument( '-b', '--blast', help = 'path to input blast file', required = True ) parser.add_argument( '-f', '--family', help = 'assumed resistance family of input sequences', required = True ) parser.add_argument( '-o', '-outpath', dest = 'out_path', help = 'Enter path to output directory' ) return parser.parse_args() if __name__=="__main__": main(sys.argv[1:])

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0.572619

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0.374622

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0.04407

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0.214517

0.13221

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0.016054

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2,761

106

116

26.04717

0.783014

0.202825

0

0.140625

0

0.167123

0.021918

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0.009434

0

1

0.03125

false

0

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0.015625

0

null

0

null

0

1

0

9301758f56b6b3c8022d921d1f16d3e4bc73189b

39,391

py

Python

modules/morsecraft.py

Crazy-Ginger/MOSAR

74f1a7ca1f17a90ede61f37d223a2ae4de6a1088

[ "MIT" ]

null

modules/morsecraft.py

Crazy-Ginger/MOSAR

74f1a7ca1f17a90ede61f37d223a2ae4de6a1088

[ "MIT" ]

null

modules/morsecraft.py

Crazy-Ginger/MOSAR

74f1a7ca1f17a90ede61f37d223a2ae4de6a1088

[ "MIT" ]

2

2020-09-18T00:02:16.000Z

2021-02-22T23:42:30.000Z

#!/usr/bin/env python3.5 """Spacecraft made up of Modules used in conjunction with morse for simulation""" import math import operator as op import numpy as np import jsonpickle as pickler from .craftmodule import Module as Module from .scripts import modControl as modCon _authors_ = ["Mark A Post", "Rebecca Wardle"] _copyright_ = "Copyright 2020 Rebecca Wardle" _license_ = "MIT License" _credit_ = [ "Mark A Post", "Rebecca Wardle", "Robert Fitch", "Daniela Rus", "Zachary Butler", ] _version_ = "-0.1" class Spacecraft: """ A spacecraft class it stores a dictionary of modules and manages their connections and rearrangement terms: modules: cubesats (10cm x 10cm x 10cm) with a port on each face base_ports: these are the ports of the modules assuming the module has no rotation applied to it they are laid out so that 0->2, 1->3, 4->5 x axis passes through 0 -> 2 y axis passes through 3 -> 1 z axis passes through 5 -> 4 +---+ | 4 | +---+---+---+---+ | 0 | 3 | 2 | 1 | +---+---+---+---+ | 5 | +---+ """ def __get_coord_path(self, mod_path, final_port, clearance=None): """ pass a list of modules and the port the module will be connected to, returns a list of coordinates around the path :param mod_path: path of modules from the root to the module being relocated :param final_port: the port the module will be connected to :param clearance: (optional) how far to keep the module from the structure :returns: numpy array of floating point numbers that should be external to the modules TODO ---- find why it sometimes outputs duplicate coords for 3 or so lines as the first part refactor to remove some for loops take into account the orientation of the modules (will cause problems with different sized mods) """ if clearance is None: clearance = self.precision if type(mod_path) != list: mod_path = list(mod_path) # initial variables and conditions mod_path = mod_path[::-1] moving_mod = mod_path[0] path = np.array([np.round(self.modules[moving_mod].pos, 2)]) final_pos = self._get_new_position(mod_path[-1], moving_mod, final_port) moving_mod = self.modules[mod_path[0]] # get the direction of clearance to place the module clear of the structure # also ensures that the direction of movement is counter after 2nd connection # for j in range(2): # diff = np.round(list(map(op.sub, list(self.modules[mod_path[j]].pos), list(self.modules[mod_path[j+1]].pos))), 3) # for index in range(len(diff)): # if abs(diff[index]) >= 0.1: # axis_of_movement = index # if j != 0: # break # offset = np.round(clearance * np.sign(self.modules[mod_path[0]].pos[index] - self.modules[mod_path[1]].pos[index]), 4) # finds the vector the first connection moves in so the vector of clearance can be found diff = np.round(list(map(op.sub, list(self.modules[mod_path[0]].pos), list(self.modules[mod_path[1]].pos),)), 3) for index in range(len(diff)): if abs(diff[index]) >= 0.1: axis_of_movement = index offset = np.round(clearance * np.sign(self.modules[mod_path[0]].pos[index] - self.modules[mod_path[1]].pos[index]), 4) # if the path length is long enough ignores the first connection to find the next module if len(mod_path) > 2: diff = np.round(list(map(op.sub, list(self.modules[mod_path[1]].pos), list(self.modules[mod_path[2]].pos))), 3) for index in range(len(diff)): if abs(diff[index]) >= 0.1: axis_of_movement = index # gets the corners in the path # still using 0.1 for module size needs to be altered to take into account of module dimension # also doesn't account for module rotation for index in range(2, len(mod_path)): diff = np.round(list(map(op.sub, self.modules[mod_path[index - 1]].pos, self.modules[mod_path[index]].pos)), 3) if abs(diff[axis_of_movement]) > 0.1: path = np.concatenate([path, np.array([list(np.round(self.modules[mod_path[index - 1]].pos, 2))])]) for index in range(len(diff)): if abs(diff[index]) > clearance: axis_of_movement = index break # if a chain then add motions up and over the chain (not sure if necessary, chains don't seem to require it) if len(path) == 1: # creates 2 new modules to move the module around the chain over = np.array(np.array([final_pos])) path = np.concatenate((path, over)) else: # add the final destination to the path path = np.concatenate((path, np.array([final_pos]))) # make the first movement to place the module clear of the structure path[0][axis_of_movement] += offset mod_coords = np.array([self.modules[x].pos for x in mod_path]) # now with corners in mod_path, extrapolate external coordinates (seems to add a duplicate of the first movement for i in range(1, len(path)): # take the previous offset to ensure that clearance is maintained in that axis dims = [0, 1, 2] path[i][axis_of_movement] = path[i - 1][axis_of_movement] dims.remove(axis_of_movement) for dim in dims: # if no change from previous axis skip it if np.round(path[i][dim], 2) == np.round(path[i - 1][dim], 2): break axis_of_movement = dim # checks if the module is the last and if so just add the offset if i == len(path) - 1: path[i][dim] += np.round(clearance * np.sign(path[i][dim] - path[i - 1][dim]), 4) # adds the clearance else: try: cur_mod = mod_path[np.where(mod_coords.all() == path[i])[0][0]] except IndexError: print("%i coordinates don't appear to be related to another module in the path" % (path[i])) offset = np.round((clearance + self.modules[cur_mod].dims[dim] / 2 + moving_mod.dims[dim] / 2) * np.sign(path[i][dim] - path[i - 1][dim]), 4) path[i][dim] += offset path = np.concatenate((path, np.array([final_pos]))) # finally move the module around the last corner and onto the docking position for dim in range(len(path[-1])): if np.round(abs(path[-2][dim] - path[-1][dim]), 4) == clearance: path[-1][dim] = path[-2][dim] continue else: path[-1][dim] = final_pos[dim] path = np.concatenate((path, np.array([final_pos]))) final_mod_path = np.array(path) return np.round(final_mod_path, 2) def __get_isolated_mod(self, root): """ gets unconnected module from root and path from root to module according to BFS :param root: the root module in the rearrangement :returns: module key, list of module keys """ to_visit = [[root]] visited = set() while to_visit: path = to_visit.pop(0) current_node = str(path[-1]) to_return = True # TODO consider if this will skip anything if current_node in visited: continue # checks if current_node is only connected by 1 link if sum(x is None for x in self.modules[current_node].cons) == 5 and current_node != root: return current_node, path # add the children nodes in order elif current_node not in visited: for child in self.modules[current_node].cons: if child is not None and child not in visited: new_path = list(path) new_path.append(child) to_visit.append(new_path) to_return = False visited.add(current_node) # print(current_node, ": ", path, "\nvisited: ", visited, "\n") if to_return is True: return current_node, visited def __init__(self, tag_length=3, precision=0.01, is_goal=False): """ constructor :param tag_length: int, length of the tags at then end of the module names that descibe their speciality :param precision: float, general precision of the movements to be made :is_goal: bool, if the craft created is actually a goal (means that none of the movements actually exist) """ self._root = None self.modules = {} self.goal = None self.tag_len = tag_length self.precision = precision self.is_goal = is_goal def add_mod(self, new_id, position, size=(0.1, 0.1, 0.1), rotation=(0, 0, 0)): """ Add an unconnected module to the craft dictionary :param new_id: string, id of the new module :param position: tuple(floats), x, y, z coordinates of the module :param size: tuple(floats), x, y, z dimensions of the module :param rotation: tuple, rotation of the module in x, y, z (cartesian) """ position = np.round(position, 4) new_mod = Module(new_id, size, position) new_mod.type = new_id[-self.tag_len:] if not self._root: self._root = new_mod x = math.radians(rotation[0]) / 2 y = math.radians(rotation[1]) / 2 z = math.radians(rotation[2]) / 2 cos = math.cos sin = math.sin new_mod.rotation = [ cos(x) * cos(y) * cos(z) + sin(x) * sin(y) * sin(z), sin(x) * cos(y) * cos(z) - cos(x) * sin(y) * sin(z), cos(x) * sin(y) * cos(z) + sin(x) * cos(y) * sin(z), cos(x) * cos(y) * sin(z) - sin(x) * sin(y) * cos(z), ] self.modules[str(new_id)] = new_mod def set_rotation(self, mod_id, rotation): """" Set a module's rotation, cannot be done if the module is already connected to another :param mod_id: id of the module to rotate :param rotation: rotation of the module in x, y, z format :raises KeyError: raises an exception if already connected to structure """ if sum(x is None for x in self.modules[current_node].cons) != 6: raise KeyError("%s is connected to another module" % (mod_id)) x = math.radians(rotation[0]) / 2 y = math.radians(rotation[1]) / 2 z = math.radians(rotation[2]) / 2 cos = math.cos sin = math.sin self.modules[mod_id].rotation = [ cos(x) * cos(y) * cos(z) + sin(x) * sin(y) * sin(z), sin(x) * cos(y) * cos(z) - cos(x) * sin(y) * sin(z), cos(x) * sin(y) * cos(z) + sin(x) * cos(y) * sin(z), cos(x) * cos(y) * sin(z) - sin(x) * sin(y) * cos(z), ] # if rotation is added to modController uncomment to implement the effects on the simulator # modCon.set_rotation(rotation) def create_goal(self, add_mods=True, mod_root=False): """ creates a sub-object that can then be manipulated to set the goal state of the spacecraft :param add_mods: (optional) to add all the modules in the current craft :param mod_root: (optional) select a module to maintain position, if not set first module added is used """ self.goal = Spacecraft(self.tag_len, self.precision, is_goal=True) # adds the modules to the goal, preserving names, positions and dimensions if add_mods: for key in self.modules.keys(): self.goal.add_mod(str(key), self.modules[key].pos, self.modules[key].dims) # sets the root of the goal if mod_root: self.goal._root = mod_root else: self.goal._root, dump_path = self.__get_isolated_mod(next(iter(self.modules))) def _get_new_position(self, fixed_mod, moving_mod, port_id): """ finds the new positional coordinates of the module being moved :param fixed_mod: module that is not being moved :param moving_mod: module that is being moved to connect to the fixed module :param port_id: base port on the fixed module the moving module will be attached via :returns: tuple of x, y, z coords of new position for moving module """ fixed_mod = self.modules[fixed_mod] moving_mod = self.modules[moving_mod] # detect modules with more than one port per face (change offset) # first get x, y, z diffs to be added x_diff = (fixed_mod.dims[0] / 2) + (moving_mod.dims[0] / 2) y_diff = (fixed_mod.dims[1] / 2) + (moving_mod.dims[1] / 2) z_diff = (fixed_mod.dims[2] / 2) + (moving_mod.dims[2] / 2) # np.array allows port_id to index the correct offset ports = [ [-x_diff, 0, 0], [0, y_diff, 0], [x_diff, 0, 0], [0, -y_diff, 0], [0, 0, z_diff], [0, 0, -z_diff], ] # convert quaternions to rotation matrix which can be applied upon the ports q = fixed_mod.rotation rotation = np.array( [ [ 1 - 2 * (q[2] ** 2 + q[3] ** 2), 2 * (q[1] * q[2] - q[3] * q[0]), 2 * (q[1] * q[3] + q[2] * q[0]), 0, ], [ 2 * (q[1] * q[2] + q[3] * q[0]), 1 - 2 * (q[1] ** 2 + q[3] ** 2), 2 * (q[2] * q[3] - q[1] * q[0]), 0, ], [ 2 * (q[1] * q[3] - q[2] * q[0]), 2 * (q[2] * q[3] + q[1] * q[0]), 1 - 2 * (q[1] ** 2 + q[2] ** 2), 0, ], [0, 0, 0, 1], ] ) # select the port from port_id diff = np.array(ports[port_id] + [0]) # apply rotation matrix to get new direction of offset then add to fixed mod position return tuple(map(op.add, fixed_mod.pos, tuple(rotation.dot(diff))[:3])) def _check_adjacency(self, mod_a, mod_b): """ finds if 2 modules are adjacent to each other based on coordinates and dimensions :param mod_a: primary module key :param mod_b: secondary module key :returns: base port id if adjacent, false if not adjacent """ mod_a = self.modules[mod_a] mod_b = self.modules[mod_b] for i in range(len(mod_a.pos) * 2): # ensures that both directions of each dim are tested if i % 2 == 0: mul = 1 else: mul = -1 # sets only one dim to the offset the modules would be difference = [0] * len(mod_a.pos) difference[i % len(mod_a.pos)] = mul * ((mod_a.dims[i % 3] / 2) + (mod_b.dims[i % 3] / 2)) mod_position = tuple(map(op.add, tuple(mod_a.pos), tuple(difference))) # refactor into single line without for loop (using sum) to_return = True for j in range(len(mod_position)): if abs(mod_position[j] - mod_b.pos[j]) >= self.precision: to_return = False break if to_return: port_ids = [2, 3, 4, 0, 1, 5] return port_ids[i] return None def _check_chain(self, mod): """ calculate max length of chain around given module :param mod: module to check as origin of the chain :returns: the base port on which the chain starts, the number of modules contain in the chain """ max_length = 0 for port in self.modules[mod].cons: if port is not None: max_length += 1 # diff = list(map(op.sub, self.modules[mod].pos, self.modules[port].pos)) # cont = True def _get_port(self, mod, base_port): """ returns the actual port to connect modules with when passed mod and the port to connect without rotation :param mod: module which has the rotation checked :param base_port: the port without rotation (gets axis/direction in which port points) :returns: port that now points in the direction of the base ports :raises: ValueError """ base_direcs = np.array( [ [-1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 1, 0], [0, 0, -1, 0], ] ) direction = np.array(base_direcs[base_port]) q = self.modules[mod].rotation rotation = np.array( [ [ 1 - 2 * (q[2] ** 2 + q[3] ** 2), 2 * (q[1] * q[2] - q[3] * q[0]), 2 * (q[1] * q[3] + q[2] * q[0]), 0, ], [ 2 * (q[1] * q[2] + q[3] * q[0]), 1 - 2 * (q[1] ** 2 + q[3] ** 2), 2 * (q[2] * q[3] - q[1] * q[0]), 0, ], [ 2 * (q[1] * q[3] - q[2] * q[0]), 2 * (q[2] * q[3] + q[1] * q[0]), 1 - 2 * (q[1] ** 2 + q[2] ** 2), 0, ], [0, 0, 0, 1], ] ) rotated = np.round(base_direcs.dot(rotation)) # now check which port has the same vector as the base port for index in range(len(base_direcs)): if np.array_equal(rotated[index], direction): return index raise ValueError("Apperntly no ports point in that direction someone is wrong (blame the writer)") def connect(self, mod_a, mod_a_port, mod_b, mod_b_port): """ Connects the 2 passed modules with the specified ports also ensures that the modules are :param mod_a: first module key :param mod_a_port: port id to connect second module to :param mod_b: second module key :param mod_b_port: port id to connect first module to :raises: ValueError :raises: IndexError """ # checks the modules are not already connceted if self.modules[mod_a].cons[mod_a_port] == mod_b: if self.modules[mod_b].cons[mod_b_port] == mod_a: return # checks that the ports are not already in use try: if self.modules[mod_a].cons[mod_a_port] is not None: raise ValueError("The port %d on %s is already connected to %s" % (mod_a_port, mod_a, self.modules[mod_a].cons[mod_a_port])) except IndexError: raise IndexError("Port %d does not exist in this dimension" % (mod_a_port)) try: if self.modules[mod_b].cons[mod_b_port] is not None: raise ValueError("The port %d on %s is already in use" % (mod_b_port, mod_b)) except IndexError: raise IndexError("Port %d does not exist in this dimension" % (mod_b_port)) # give postitions to connected module if self.is_goal: if mod_a == self._root: self.modules[mod_b].pos = self._get_new_position(mod_a, mod_b, mod_a_port) elif mod_b == self._root: self.modules[mod_a].pos = self._get_new_position(mod_b, mod_a, mod_b_port) elif (self.modules[mod_a].pos is not None) and (self.modules[mod_b].pos is not None): # checks modules are next to each other if self._check_adjacency(mod_a, mod_b) is None: raise ValueError("Modules %s, %s are not adjecent" % (mod_a, mod_b)) elif self.modules[mod_a].pos is not None: self.modules[mod_b].pos = self._get_new_position(mod_a, mod_b, mod_a_port) elif self.modules[mod_b].pos is not None: self.modules[mod_a].pos = self._get_new_position(mod_b, mod_a, mod_b_port) self.modules[mod_a].cons[mod_a_port] = mod_b self.modules[mod_b].cons[mod_b_port] = mod_a # move the cubes to the correct positions # won't move modules already in place # checks that modules should actually be moved if not self.is_goal: # move the modules into position and ensure they are there self._move_mod(mod_a, self.modules[mod_a].pos) self._move_mod(mod_b, self.modules[mod_b].pos) # links the modules together modCon.link(mod_a, mod_b) def connect_all(self, mod_id): """ give a mod id, checks all adjacent positions and connects to any modules found there :param mod_id: module key to connect modules to """ if self.modules[mod_id].pos is None: raise IndexError("%s does not have a position so it not yet connected" % (mod_id)) for mod in self.modules: if mod != mod_id: adja = self._check_adjacency(mod_id, mod) if adja is not None: # use the returned port to get the actual ports to connect with and then connect the mods mod_a_port = self._get_port(mod, adja) # skips if the module is already connected at that port if self.modules[mod_id].cons[mod_a_port] == mod: continue base_cons = [2, 3, 0, 1, 5, 4] mod_b_port = self._get_port(mod, base_cons[adja]) self.connect(mod_id, mod_a_port, mod, mod_b_port) def disconnect(self, mod_id, port_id): """ Disconnects 2 modules connected together through a specific port on one and unlinks them both :param mod_id: primary module key which disconnected through :param port_id: port id to disconnect :raises: ValueError """ if self.modules[mod_id].cons[port_id] is None: raise ValueError("Port %d on module: %s is not connected" % (port_id, mod_id)) # unlinks modules # TODO investigate issues with unlinking modules (some modules prefer to unlink one way) modCon.unlink(mod_id, self.modules[mod_id].cons[port_id]) modCon.unlink(self.modules[mod_id].cons[port_id], mod_id) # disconnects port on other module flag = False for i in range(len(self.modules[self.modules[mod_id].cons[port_id]].cons)): if self.modules[self.modules[mod_id].cons[port_id]].cons[i] == mod_id: self.modules[self.modules[mod_id].cons[port_id]].cons[i] = None flag = True if not flag: print("Error Disconnect_all:") print(mod_id, ": ", self.modules[mod_id].cons) print(self.modules[mod_id].cons[port_id], ": ", self.modules[self.modules[mod_id].cons[port_id]].cons) print() # raise RuntimeError("%s was not connected to %s in both directions" % (mod_id, self.modules[mod_id].cons[port_id])) self.modules[mod_id].cons[port_id] = None def disconnect_all(self, mod_id): """ Loops through all connections of a given module and if connected runs disconnect """ # add a way to avoid disconnect from arm/tug for port_id in range(len(self.modules[mod_id].cons)): if self.modules[mod_id].cons[port_id] is not None: self.disconnect(mod_id, port_id) def _get_goal_order(self): """ Uses BFS to find the order of the goal structure :returns: linear array of modules """ root = self.goal._root to_visit = [root] visited = [] while to_visit: current_node = to_visit[0] visited.append(current_node) for child in self.goal.modules[current_node].cons: # broken? if child is not None and child not in to_visit and child not in visited: to_visit.append(child) to_visit.pop(0) return visited def _get_mod_path(self, root, goal): """ Dijkstra implementation finds path from root and goal as module ids :param root: root module key :param goal: goal module key :returns: list of module keys that form path from root to goal """ to_visit = {root} est_cost = {root: 0} final_cost = {} visited = set() back_track = {} while to_visit: current_node = None current_score = None for mod in to_visit: if current_node is None or est_cost[mod] < current_score: current_node = mod current_score = est_cost[mod] # checks if reached goal if current_node == goal: path = [current_node] while current_node in back_track: current_node = back_track[current_node] path.append(current_node) # if goal[-self._mod_type:] == path[0][-self._mod_type:]: path.reverse() return path to_visit.remove(current_node) visited.add(current_node) for neighbour in self.goal.modules[current_node].cons: if neighbour in visited: continue tmp_cost = est_cost[current_node] + 1 if neighbour not in to_visit: to_visit.add(neighbour) elif tmp_cost >= final_cost[neighbour]: continue back_track[neighbour] = current_node final_cost[neighbour] = tmp_cost est_cost[neighbour] = final_cost[neighbour] + 1 def import_from_json(self, file_name, goal=True): """ Decode a json file into a craft or craft goal :param file_name: file name :param goal: (optional) boolean :returns: (optional) new craft """ with open(file_name, "r") as file: data = file.read().replace("\n", "") if goal is False: new_craft = pickler.decode(data) try: new_craft.tag_len except AttributeError: new_craft.tag_len = 3 return new_craft else: self.goal = pickler.decode(data) def export_to_json(self, file_name): """ Exports current spacecraft as a json file :param file_name: file name to be outputted """ write_file = open(file_name + ".json", "w") write_file.write(pickler.encode(self)) def _move_mod(self, mod_id, dest, precision=None): """ Moves the module to dest (within precision) :param mod_id: module key :param dest: coordinates of the destination (x, y, z) :param precision: (optional) integer/float offset """ if precision is None: precision = self.precision cont = False loop_checker = 0 prev_x = 0 prev_y = 0 prev_z = 0 while cont is False: modCon.setDest(mod_id=mod_id, x=dest[0], y=dest[1], z=dest[2]) pose = modCon.getPose(mod_id) if round(prev_x - pose["x"], 3) == 0 and round(prev_y - pose["y"], 3) == 0 and round(prev_z - pose["z"], 3) == 0: loop_checker += 1 if loop_checker > 200: print("Failed to move: ", mod_id, " to: ", dest) return else: prev_x, prev_y, prev_z = pose["x"], pose["y"], pose["z"] if dest[0] - precision <= pose["x"] <= dest[0] + precision: if dest[1] - precision <= pose["y"] <= dest[1] + precision: if dest[2] - precision <= pose["z"] <= dest[2] + precision: self.modules[mod_id].pos = tuple(dest) cont = True def melt(self, root=None): """ Places all modules in a chain :param root: the module to rearrange all the other cubes around :returns: list of module keys in new order """ # get most extreme module or check passed module if root is None: root, dump_path = self.__get_isolated_mod(next(iter(self.modules))) else: if root not in self.modules: raise ValueError("%s is not a valid module" % (root)) good_root = False for port in self.modules[root].cons: if port is None: good_root = True if good_root is False: raise ValueError("%s is not a valid root" % (root)) # connect all modules together to ensure optimum paths for node in self.modules: self.connect_all(node) print("root: ", root, "\t", self.modules[root].cons) # find coords of free space next to root port_id = None for i in range(len(self.modules[root].cons)): if self.modules[root].cons[i] is None: port_id = i break if port_id is None: raise TypeError("port_id has not been set, check root validity") base_cons = [2, 3, 0, 1, 5, 4] # moves all modules into chain moved = [] to_move = set(self.modules.keys()) while len(to_move) != 0: # gets an isolated mod and the path of modules that connect it to the root current_node, current_path = self.__get_isolated_mod(root) print("Melting: ", current_node) # gets the path of coordinates for the module to travel along coord_path = self.__get_coord_path(current_path, base_cons[port_id]) # disconnect the module and move it self.disconnect_all(current_node) print(current_node, ": ", self.modules[current_node].cons) # modCon.setDest(current_node, x=2, y=2, z=2) # tmp = input() # move current node over path by getting positions outside of modules for coords in coord_path: self._move_mod(current_node, coords) # connect module to chain (1 needs to be replaced to take account of modules need to be in certain orientations) self.connect(current_node, self._get_port(current_node, base_cons[port_id]), root, port_id) moved.append(current_node) to_move.remove(current_node) root = current_node return moved def sort(self, current_order=None): """ Sorts the chain of modules into a chain with the modules in the order needed to be placed into the goal order :param current_order: (optional) module keys in current order of the chain """ # if no current order is passed, find it if current_order is None: end_mod, dump = self.__get_isolated_mod(next(iter(self.modules))) opposite_end, current_order = self.__get_isolated_mod(end_mod) del end_mod, dump, opposite_end if self.goal is None: raise TypeError("goal is not set and therefore cannot be achieved") # get order for goal then take only module types goal_order = self._get_goal_order() goal_order = [elem[-self.tag_len:] for elem in goal_order] final_places = {} if len(goal_order) != len(current_order): # handle this (write later) raise ValueError("Goal and spacecraft contain different number of modules") tmp_order = current_order.copy() # finds where each module type need to be moved for pos in range(len(goal_order)): try: index = [ idx for idx, s in enumerate(tmp_order) if goal_order[pos] in s ][0] except IndexError: raise IndexError("%s doesn't exist in craft" % (goal_order[pos])) final_places[tmp_order[index]] = pos del tmp_order[index] base_cons = [2, 3, 0, 1, 5, 4] # find the occupied ports and makes a list of the unused ones mid_mod = current_order[len(current_order) // 2] used = [] for port_id in range(len(self.modules[mid_mod].cons)): if self.modules[mid_mod].cons[port_id] is not None: used.append(port_id) if len(used) != 2: raise IndexError("The modules are not in a chain") unused = [0, 1, 2, 3, 4, 5] unused.remove(used[0]) unused.remove(used[1]) print("\nSplitting in 2") # splits the row in 2 current_order = [current_order] for i in range(len(current_order[0]) // 2): self.disconnect_all(current_order[0][0]) popped_mod = current_order[0].pop(0) path = current_order[0][-i-1::-1] + [popped_mod] # print("splitting: ", popped_mod) if i == 0: current_order.append([popped_mod]) else: current_order[1].insert(0, popped_mod) # moves the module to the new position # path currently moves the module towards nearest module (oops) path = self.__get_coord_path(path, unused[0]) path = np.unique(path, axis=0) # print(path, "\n") for coord in path: # print("moving to:", coord) self._move_mod(popped_mod, coord) # final_pose = modCon.getPose(popped_mod) # final_pose = [final_pose["x"]] + [final_pose["y"]] + [final_pose["z"]] # final_pose = np.round(final_pose, 3) # connects to row above/below # self.connect(popped_mod, unused[0], current_order[0][-i - 1], base_cons[unused[0]]) # connect to modules on it's own self.connect_all(popped_mod) # for testing: prints out mods and their connections # for mod in self.modules: # print(mod, ": ", self.modules[mod].cons) # remove the now used ports so that bubble sort only uses the remaining dimension unused.remove(base_cons[unused[0]]) unused.remove(unused[0]) print(goal_order) print(final_places) print(current_order[0]) print(current_order[1]) print("beginning bubble") tmp = input() # sort each row seperately (could run in parallel?) for sub_list in current_order: # sorts each row for i in range(len(sub_list) - 1): for j in range(0, len(sub_list) - i - 1): if final_places[sub_list[j]] > final_places[sub_list[j + 1]]: # final positions of each module pos1 = self.modules[sub_list[j]].pos pos2 = self.modules[sub_list[j + 1]].pos self.disconnect_all(sub_list[j + 1]) self.disconnect_all(sub_list[j]) # self.move_mod(sub_list[j],) # take first mod, get unused dimension # move the first mod up and then ontop of the second module # move the second mod along to pos of 1st # move 1st mod down into position self.connect_all(sub_list[j + 1]) self.connect_all(sub_list[j]) sub_list[j], sub_list[j + 1] = sub_list[j + 1], sub_list[j] # connect structure together for key in self.modules: self.connect_all(key) # merge sorted rows if final_places[current_order[0][0]] < final_places[current_order[1][0]]: root = current_order[0][0] del current_order[0][0] else: root = current_order[1][0] del current_order[1][0] if final_places[current_order[0][-1]] > final_places[current_order[1][-1]]: self.disconnect_all(root) self.connect(current_order[0][-1], 2, root, 0) else: self.disconnect_all(root) self.connect(current_order[1][-1], 2, root, 0) final_order = [root] while len(current_order[0]) > 0 and len(current_order[1]) > 0: if final_places[current_order[0][0]] < final_places[current_order[1][0]]: self.disconnect_all(current_order[0][0]) self.connect(root, 2, current_order[0][0], 0) root = current_order[0][0] del current_order[0][0] final_order.append(root) else: self.disconnect_all(current_order[1][0]) self.connect(root, 2, current_order[1][0], 0) root = current_order[1][0] del current_order[1][0] final_order.append(root) for mod in current_order[0]: self.disconnect_all(mod) self.connect(root, 2, mod, 0) root = mod final_order.append(root) for mod in current_order[1]: self.disconnect_all(mod) self.connect(root, 2, mod, 0) root = mod final_order.append(root) return final_order def grow(self, order): """ Rearranges a sorted module chain to form the goal structure :param order: module keys in current order of the chain """ for idx in range(len(order)): base_cons = [2, 3, 0, 1, 5, 4] mod_type = order[idx][-self.tag_len:] path = order[idx + 1:] if idx == 0: self.disconnect_all(order[idx]) self.connect(order[-1], 2, order[idx], 0) # self.goal.modules[order[idx].replace("_", "-")] = self.goal.modules.pop(order[idx]) # order[idx] = order[idx].replace("_", "-") continue path = path + self._get_mod_path(order[0], order[idx]) sucess = False last_mod = path[-2] for port in range(len(self.goal.modules[last_mod].cons)): if self.goal.modules[last_mod].cons[port] is None: continue elif (self.goal.modules[last_mod].cons[port][-self.tag_len:] == mod_type): self.disconnect_all(order[idx]) self.connect(order[idx], base_cons[port], path[-1], port) sucess = True self.display() if not sucess: raise ValueError("Growing failed. Sucess:", sucess)

38.280855

161

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0.036053

0.012972

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0.026362

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null

0

null

0

1

0

9304974cceaa6c0815b75d4d6551003da77ce3ad

5,625

py

Python

yumina/syosetu.py

jeffswt/yumina

cdb18dc97e38028f6866b98d3ae43bc375440836

[ "MIT" ]

3

2017-12-10T03:35:30.000Z

2018-12-15T23:13:28.000Z

yumina/syosetu.py

jeffswt/yumina

cdb18dc97e38028f6866b98d3ae43bc375440836

[ "MIT" ]

null

yumina/syosetu.py

jeffswt/yumina

cdb18dc97e38028f6866b98d3ae43bc375440836

[ "MIT" ]

null

import bs4 import json import os import re import requests import sqlite3 from . import renderer def get_webpage(*args, **kwargs): """ get_webpage(...) -- request webpage content / text """ return requests.get(*args, **kwargs).text.encode('ISO-8859-1').decode('utf-8') def map_num(s): """ map_num(str) -- change all full-width characters to half-width. """ s = s.replace('０', '0')\ .replace('１', '1')\ .replace('２', '2')\ .replace('３', '3')\ .replace('４', '4')\ .replace('５', '5')\ .replace('６', '6')\ .replace('７', '7')\ .replace('８', '8')\ .replace('９', '9')\ .replace('\u3000', ' ') return s def get_chapter_list(web_id): sel_1 = r'<div class="chapter_title">.*?</div>' sel_2 = r'<dd class="subtitle">\n<a href="/%s/\d+/">.*?</a>\n</dd>' % web_id q1 = map_num(get_webpage('http://ncode.syosetu.com/%s/' % web_id)) q2 = re.findall('(%s|%s)' % (sel_1, sel_2), q1) q3 = [] for i in q2: if re.findall(sel_1, i) != []: sel_3 = r'^<div class="chapter_title">第(\d+)章 (.*?)</div>$' j = int(re.sub(sel_3, r'\1', i)) k = re.sub(sel_3, r'\2', i) q3.append(('chapter_title', j, k)) else: sel_3 = r'^<dd class="subtitle">\n<a href="/%s/(\d+)/">(.*?)</a>\n</dd>$' % web_id k = int(re.sub(sel_3, r'\1', i)) l = re.sub(r'^[＃#].*? (.*?)$', r'\1', re.sub(sel_3, r'\2', i)) q3.append(('subtitle', k, l)) return q3 def get_chapter(web_id, chap_id): q1 = map_num(get_webpage('http://ncode.syosetu.com/%s/%d/' % (web_id, chap_id))) q2 = bs4.BeautifulSoup(q1, 'html5lib') q3 = q2.find_all(id='novel_honbun')[0].text # stylize paragraphs q3 = re.sub(r'\n +', r'\n', q3) q3 = re.sub(r'\n\n+', r'\n\n', q3) q3 = re.sub(r'(^\n+|\n+$)', r'', q3) # split into lines q4 = q3.split('\n') q5 = [] for i in q4: if re.findall(r'^ *$', i) != []: q5.append(('break',)) else: q5.append(('line', [('regular', i.replace(' ', ''))])) return q5 class SyosetuDatabase: def __init__(self, filename, syosetu_id, force_clear=False): found = os.path.exists(filename) self.base = sqlite3.connect(filename) self.cur = self.base.cursor() self.sid = syosetu_id if not found or force_clear: self.cur.execute("DROP TABLE IF EXISTS toc;") self.cur.execute("DROP TABLE IF EXISTS cont;") self.cur.execute(""" CREATE TABLE toc ( e_type TEXT, e_id INTEGER, e_title TEXT );"""); self.cur.execute(""" CREATE TABLE cont ( t_idx INTEGER, t_jpn JSONB, t_jpn_lit JSONB );"""); return def get_contents(self): q1 = [] for i in self.cur.execute("SELECT * FROM toc;"): q1.append((i[0], i[1], i[2])) return q1 def get_chapter_title(self, typ, num): for i in self.get_contents(): if i[0] == typ and i[1] == num: return i return (typ, num, '無題') def get_contents_chapters_id(self): q1 = [] for i in self.get_contents(): if i[0] == 'subtitle': q1.append(i[1]) return sorted(list(set(q1))) def update_contents(self): toc = get_chapter_list(self.sid) self.cur.execute("DELETE FROM toc;") for i in toc: self.cur.execute("INSERT INTO toc (e_type, e_id, e_title) VALUES (?, ?, ?)", (i[0], i[1], i[2])) return def get_chapter(self, chap_id): q1 = [] for i in self.cur.execute("SELECT * FROM cont WHERE t_idx = ?", (chap_id,)): q1.append(i) if q1 == []: return [] q = [[], []] for num in range(0, 2): for i in json.loads(q1[0][num + 1]): if i[0] == 'line': q[num].append(('line', list(tuple(i) for i in i[1]))) else: q[num].append(('break',)) return q[0], q[1] def has_chapter(self, chap_id): q1 = [] for i in self.cur.execute("SELECT * FROM cont WHERE t_idx = ?", (chap_id,)): q1.append(i) return q1 != [] def update_chapter(self, chap_id, phonogram_renderer=None): chap1 = get_chapter(self.sid, chap_id) cj1 = json.dumps(chap1) chap2 = renderer.phoneticize(chap1, phonogram_renderer=phonogram_renderer) cj2 = json.dumps(chap2) self.cur.execute("DELETE FROM cont WHERE t_idx = ?;", (chap_id,)) self.cur.execute("INSERT INTO cont (t_idx, t_jpn, t_jpn_lit) VALUES (?, ?, ?)", (chap_id, cj1, cj2)) return def update_all(self, phonogram_renderer=None, display_progress_bar=False): self.update_contents() self.commit() ch = self.get_contents_chapters_id() for i in ch: if not self.has_chapter(i): self.update_chapter(i, phonogram_renderer=phonogram_renderer) self.commit() if display_progress_bar: print('%s|%s\r' % (str(i).rjust(4), ('=' * int(i / len(ch) * 70)).ljust(70, '.')), end='') return def commit(self): self.base.commit() return def close(self): self.commit() self.base.close() return pass

35.377358

110

0.497778

764

5,625

3.534031

0.219895

0.031111

0.024444

0.018519

0.292593

0.221111

0.217407

0.177037

0.165926

0.11037

0

0.032326

0.323556

5,625

158

111

35.601266

0.677267

0.0272

0

0.176871

0

0.013605

0.19956

0.030603

0

1

0.102041

false

0.006803

0.047619

0

0.272109

0.006803

0

null

0

null

0

1

0

9305e2e159b395e28159191379c582290d786c32

2,112

py

Python

cogs/administrator.py

jagadeesh70/arose-discord-bot

de7b8c9d3c01e3028b5dd063c10a372d3a4a3225

[ "MIT" ]

null

cogs/administrator.py

jagadeesh70/arose-discord-bot

de7b8c9d3c01e3028b5dd063c10a372d3a4a3225

[ "MIT" ]

null

cogs/administrator.py

jagadeesh70/arose-discord-bot

de7b8c9d3c01e3028b5dd063c10a372d3a4a3225

[ "MIT" ]

null

import discord from discord.ext import commands import sys def mods_or_owner(): """ Check that the user has the correct role to execute a command """ def predicate(ctx): return commands.check_any(commands.is_owner(), commands.has_role("Moderator")) return commands.check(predicate) class Moderation(commands.Cog): def __init__(self, client): self.client = client @commands.command(help='''Just kick that kid''') @mods_or_owner() @commands.guild_only() @commands.has_permissions(kick_members=True) async def kick(self, ctx, member: discord.Member = None, reason: str = "Because you were bad. We kicked you."): if member is not None: await ctx.guild.kick(member, reason=reason) await ctx.send(f'**{member}** has been kicked....**reason: {reason}**') else: await ctx.send("Please specify user to kick via mention") @commands.command(help='''Just ban that notorious guy''') @mods_or_owner() @commands.guild_only() @commands.has_permissions(ban_members=True) async def ban(self, ctx, member: discord.Member = None, reason: str = "Because you are naughty. We banned you."): if member is not None: await ctx.guild.ban(member, reason=reason) else: await ctx.send("Please specify user to kick via mention") @commands.command(help='''unban the guy u banned :)''') @mods_or_owner() @commands.guild_only() @commands.has_permissions(ban_members=True) async def unban(self, ctx, member: str = "", reason: str = "You have been unbanned. Time is over. Please behave"): if member == "": await ctx.send("Please specify username as text") return bans = await ctx.guild.bans() for b in bans: if b.user.name == member: await ctx.guild.unban(b.user, reason=reason) await ctx.send("User was unbanned") return await ctx.send("User was not found in ban list.") def setup(client): client.add_cog(Moderation(client))

35.2

118

0.633996

281

2,112

4.676157

0.320285

0.060883

0.054795

0.043379

0.469559

0.394216

0.305936

0

0.247633

2,112

59

119

35.79661

0.826935

0.028883

0

0.347826

0

0.20344

0

1

0.086957

false

0

0.065217

0.021739

0.26087

0

null

0

null

0

1

0

9305fcafb01d19b524fa60ee8115fba60d45f909

815

py

Python

examples/Saxs_Cube/genCubeData.py

DomiDre/modelexp

1ec25f71e739dac27716f9a8637fa6ab067499b9

[ "MIT" ]

null

examples/Saxs_Cube/genCubeData.py

DomiDre/modelexp

1ec25f71e739dac27716f9a8637fa6ab067499b9

[ "MIT" ]

null

examples/Saxs_Cube/genCubeData.py

DomiDre/modelexp

1ec25f71e739dac27716f9a8637fa6ab067499b9

[ "MIT" ]

null

import modelexp from modelexp.experiments.sas import Saxs from modelexp.models.sas import Cube import numpy as np import random app = modelexp.Cli() app.setExperiment(Saxs) modelRef = app.setModel(Cube) modelRef.addModel(np.linspace(1e-2, 0.5, 300)) modelRef.setParam('a', 50) modelRef.setParam('sldCube', 45e-6) modelRef.setParam('sldSolvent', 10e-6) modelRef.setParam('sigA', 0.05) modelRef.setParam('i0', 1) modelRef.setParam('bg', 0) modelRef.calcModel() q = modelRef.getModelset(0).getDomain() I = modelRef.getModelset(0).getValues() sig_y = 0.05*I randomized_y = [] for i in range(len(I)): randomized_y.append(random.gauss(I[i], 0.10*I[i])) randomized_y = np.array(randomized_y) with open('saxsCubeData.xye', 'w') as f: for i in range(len(I)): f.write(f'{q[i]}\t{randomized_y[i]}\t{sig_y[i]}\n')

25.46875

55

0.722699

135

815

4.311111

0.466667

0.164948

0.061856

0.037801

0.051546

0

0.039617

0.10184

815

31

56

26.290323

0.755464

0

0.076923

0

0.100614

0.047853

0

1

0

false

0

0.192308

0

0.192308

0

null

0

null

0

1

0

930630b81faeff458c2406841098e8b0305b1be5

3,621

py

Python

common-scripts/msys2-binary-convert.py

derickl/conda-recipes

52df2d8fe92f2c18da8500cdc49b75a8f261f304

[ "BSD-3-Clause" ]

1

2015-01-30T05:25:29.000Z

common-scripts/msys2-binary-convert.py

derickl/conda-recipes

52df2d8fe92f2c18da8500cdc49b75a8f261f304

[ "BSD-3-Clause" ]

null

common-scripts/msys2-binary-convert.py

derickl/conda-recipes

52df2d8fe92f2c18da8500cdc49b75a8f261f304

[ "BSD-3-Clause" ]

null

import os from conda_build.metadata import MetaData import requests import hashlib import tarfile import tempfile from glob import glob from shutil import move, copy from os.path import join, normpath, dirname from os import makedirs, getenv from sys import exit import patch import re def get_tar_xz(url, md5): tmpdir = tempfile.mkdtemp() urlparts = requests.packages.urllib3.util.url.parse_url(url) fname = urlparts.path.split('/')[-1] sig = hashlib.md5() tmp_tar_xz = join(tmpdir, fname) if urlparts.scheme == 'file': path = re.compile('^file://').sub('', url).replace('/', os.sep) copy(path, tmp_tar_xz) with open(tmp_tar_xz, "rb") as tar_xz: for block in iter(lambda: tar_xz.read(1024), b""): sig.update(block) else: with open(tmp_tar_xz, 'wb') as tar_xz: response = requests.get(url, stream=True) for block in response.iter_content(1024): sig.update(block) tar_xz.write(block) if sig.hexdigest() != md5: print( 'ERROR: md5 sum mismatch expected %s, got %s' % (md5, sig.hexdigest())) exit(1) return tmp_tar_xz def main(): recipe_dir = os.environ["RECIPE_DIR"] conda_platform = 'win-32' if os.environ["ARCH"] == '32' else 'win-64' prefix = os.environ['PREFIX'] metadata = MetaData(recipe_dir) msys2_tar_xz_url = metadata.get_section( 'extra')['msys2-binaries'][conda_platform]['url'] msys2_md5 = metadata.get_section( 'extra')['msys2-binaries'][conda_platform]['md5'] mv_srcs_list = metadata.get_section( 'extra')['msys2-binaries'][conda_platform]['mv-srcs'] mv_dsts_list = metadata.get_section( 'extra')['msys2-binaries'][conda_platform]['mv-dsts'] msys2_tar_xz = get_tar_xz(msys2_tar_xz_url, msys2_md5) tar = tarfile.open(msys2_tar_xz, 'r|xz') tar.extractall(path=prefix) try: patches = metadata.get_section( 'extra')['msys2-binaries'][conda_platform]['patches'] except: patches = [] if len(patches): for patchname in patches: patchset = patch.fromfile(join(getenv('RECIPE_DIR'), patchname)) patchset.apply(1, root=prefix) # shutil is a bit funny (like mv) with regards to how it treats # the destination depending on whether it is an existing directory or not # (i.e. moving into that versus moving as that). # Therefore, the rules employed are: # 1. If mv_dst ends with a '/' it is a directory that you want mv_src # moved into. # 2. If mv_src has a wildcard, mv_dst is a directory that you want mv_src # moved into. # In these cases we makedirs(mv_dst) and then call move(mv_src, mv_dst) # .. otherwise we makedirs(dirname(mv_dst)) and call move(mv_src, mv_dst) # .. however, if no mv_srcs exist we don't makedirs at all. for mv_src, mv_dst in zip(mv_srcs_list, mv_dsts_list): mv_dst_definitely_dir = False mv_srcs = glob(join(prefix, normpath(mv_src))) if '*' in mv_src or mv_dst.endswith('/') or len(mv_srcs) > 1: mv_dst_definitely_dir = True if len(mv_srcs): mv_dst = join(prefix, normpath(mv_dst)) mv_dst_mkdir = mv_dst if not mv_dst_definitely_dir: mv_dst_mkdir = dirname(mv_dst_mkdir) try: makedirs(mv_dst_mkdir) except: pass for mv_src in mv_srcs: move(mv_src, mv_dst) tar.close() if __name__ == "__main__": main()

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517

3,621

4.181818

0.324952

0.043941

0.018501

0.053191

0.191027

0.16975

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0.085106

0

0.014937

0.260425

3,621

100

78

36.21

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false

0.012346

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0.012346

0

null

0

null

0

1

0

9306e30b393e2b459c7f4ab524c75866193a41b4

4,404

py

Python

cyolo_score_following/eval.py

CPJKU/cyolo_score_following

4b34947a9b7cc19a139ce3768eac6079aaff5cfe

[ "MIT" ]

7

2021-05-23T22:14:30.000Z

2022-03-07T16:46:18.000Z

cyolo_score_following/eval.py

CPJKU/cyolo_score_following

4b34947a9b7cc19a139ce3768eac6079aaff5cfe

[ "MIT" ]

null

cyolo_score_following/eval.py

CPJKU/cyolo_score_following

4b34947a9b7cc19a139ce3768eac6079aaff5cfe

[ "MIT" ]

3

2021-05-23T22:38:59.000Z

2021-12-02T19:07:01.000Z

import argparse import os import torch import numpy as np from cyolo_score_following.dataset import load_dataset, collate_wrapper, iterate_dataset, CLASS_MAPPING from cyolo_score_following.utils.data_utils import FPS from cyolo_score_following.models.yolo import load_pretrained_model from torch.utils.data import DataLoader if __name__ == '__main__': parser = argparse.ArgumentParser(description='Evaluation Script') parser.add_argument('--param_path', help='path to the stored network', type=str) parser.add_argument('--test_dirs', help='path to test dataset.', nargs='+') parser.add_argument('--only_onsets', help='only evaluate onset frames', default=False, action='store_true') parser.add_argument('--batch_size', help='batch size', type=int, default=32) parser.add_argument('--split_files', help='split file to only evaluate a subset from the test dirs', default=None, nargs='+') parser.add_argument('--scale_width', help='sheet image scale factor', type=float, default=416) parser.add_argument('--num_workers', default=4, type=int, help="number of parallel datapool worker") parser.add_argument('--load_audio', default=False, action='store_true', help="preload audio files for datapool") parser.add_argument('--print_piecewise', default=False, action='store_true', help="print statistics for each piece") parser.add_argument('--save_tag', default=None) parser.add_argument('--save_dir', type=str, default="") args = parser.parse_args() device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') network, criterion = load_pretrained_model(args.param_path) predict_sb = network.nc == 3 print(network) print(f"Putting model to {device}") network.to(device) network.eval() dataset = load_dataset(args.test_dirs, augment=False, scale_width=args.scale_width, split_files=args.split_files, only_onsets=args.only_onsets, load_audio=args.load_audio, predict_sb=predict_sb) dataloader = DataLoader(dataset=dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, collate_fn=collate_wrapper, pin_memory=True) stats = iterate_dataset(network, dataloader, optimizer=None, criterion=criterion, device=device) if args.save_tag is not None: with open(os.path.join(args.save_dir, args.save_tag + "_stats.npy"), "wb") as f: np.save(f, stats) ordering = [] max_str_len = 0 for piece in stats['piece_stats'].keys(): ordering.append((piece, np.mean(stats['piece_stats'][piece]['frame_diff']))) # store maximum string length for printing str_len = len(piece) if str_len > max_str_len: max_str_len = str_len ordering = sorted(ordering, key=lambda k: k[1], reverse=False) thresholds = [0.05, 0.1, 0.5, 1.0, 5.0] if args.print_piecewise: print("Piecewise frame tracking ratios") for piece, _ in ordering: piece_stat = stats['piece_stats'][piece] print(f"{piece}:") if 'frame_diff' in piece_stat: diffs = np.array(piece_stat['frame_diff']) diffs = diffs / FPS total = len(diffs) cumulative_percentage = [] for th in thresholds: cumulative_percentage.append(np.round(100 * np.sum(diffs <= th) / total, 1)) print("\tTracked Frame Ratios", cumulative_percentage) for value in CLASS_MAPPING.values(): if value + "_accuracy" in piece_stat: print(f"\t{value} Accuracy: {piece_stat[value + '_accuracy']:.3f}") print() for value in CLASS_MAPPING.values(): if value + "_accuracy" in stats: print(f'Average accuracy for {value}: {stats[value + "_accuracy"]:.3f}') frame_diffs = np.concatenate([piece_stats['frame_diff'] for piece_stats in stats['piece_stats'].values()]) / FPS total_frames = len(frame_diffs) ratio_str = "" print('Average frame tracking ratios:') for th in thresholds: ratio = np.sum(frame_diffs <= th) / total_frames percentage = np.round(100 * ratio, 1) ratio_str += f"& {ratio:.3f} " print(f'<= {th}: {percentage}') # string for latex table ratio_str += "\\\\" print(ratio_str)

40.40367

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0.660082

578

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0.292388

0.035522

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0.032293

0

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4,404

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121

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0.166667

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null

0

null

0

1

0

93074a881ac1b60007d9201c550e6e05a4b71ade

16,344

py

Python

segmentation_net/segmentation_class/segmentation_train.py

PeterJackNaylor/segmentation_net

9af94854a662d9529ca6f4bb774bf2603a434a3a

[ "MIT" ]

null

segmentation_net/segmentation_class/segmentation_train.py

PeterJackNaylor/segmentation_net

9af94854a662d9529ca6f4bb774bf2603a434a3a

[ "MIT" ]

null

segmentation_net/segmentation_class/segmentation_train.py

PeterJackNaylor/segmentation_net

9af94854a662d9529ca6f4bb774bf2603a434a3a

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """segnet package file tf_record Segmentation_base_class -> SegmentationInput -> SegmentationCompile -> SegmentationSummaries -> Segmentation_model_utils -> Segmentation_train """ from datetime import datetime from tqdm import trange from ..net_utils import ScoreRecorder from .segmentation_model_utils import * def verbose_range(beg, end, word, verbose, verbose_thresh): """Monitores the time in range with tqdm If verbose, use tqdm to take care of estimating end of training. Args: beg: integer, where to start iterating end: integer, where to end iteration (not included) word: string, to print in the displayed progress_bar verbose: integer, value of verbose given mostlikely by the object himself verbose_thresh: integer, will display progress bar if verbose > verbose_thresh Returns: An object on which you can iterate that can or not, depending on the value of verbose print a progress bar to the stdoutput. """ returned_range = None if verbose > verbose_thresh: returned_range = trange(beg, end, desc=word) else: returned_range = range(beg, end) return returned_range class SegmentationTrain(SegmentationModelUtils): def train(self, train_record, test_record=None, learning_rate=0.001, lr_procedure="1epoch", weight_decay=0.0005, batch_size=1, decay_ema=0.9999, k=0.96, n_epochs=10, early_stopping=3, loss_func=tf.nn.l2_loss, save_weights=True, new_log=None, num_parallele_batch=8, restore=False, track_variable="loss", track_training=False, tensorboard=True, save_best=True, return_best=False, decode=tf.float32): """ Trains the model on train record, optionnaly you can monitor the training by evaluation the test record Args: train_record: string, path to a tensorflow record file for training. test_record: string or None, if given, the model will be evaluated on the test data at every epoch. learning_rate: float (default: 0.001) Initial learning rate for the gradient descent update. lr_procedure : string (default: 10epoch) Will be perfome learning rate decay every 10 epochs. weight_decay : float (default: 0.0005) Initial value given to the weight decay, the loss is computed: loss = loss + weight_decay * sum(loss_func(W)) where W are training parameters of the model. batch_size : integer (default: 1) Size of batch to be feeded at each iterations. decay_ema : float (default: 0) if 0: ignored exponential moving average decay parameter to apply to weights over time for more robust convergence. k : float (default: 0.96) value by which the learning rate decays every update. n_epochs : integer (default: 10) number of epochs to perform early_stopping : integer, if 0 or None ignored, else the model will stop training if the tracked variable doesn't go in the right direction in under early_stopping epochs. loss_func : tensorflow function (default: l2_loss) to apply on the weights for the weight decay in the loss function. save_weights : bool (default: True) If to store the weigths new_log : string (default: None) if to save the model in a different folder then the one from which the variables were restored. num_parallele_batch : integer (default: 8) number of workers to use to perform paralelle computing. restore : bool (default: False) if too restore from the new_log given. track_variable : str (default: loss) which variable to track in order to perform early stopping. track_training : bool (default: False) if to track track_variable on the training data or on the test data. tensorboard : bool (default: True) if to monitor the model via tensorboard. save_best : bool (default: True) if to save the best model as last weights in case of early stopping or if there is a better possible model with respect to the test set. return_best : bool (default: True) if to return the best model in case of early stopping or if there is a better possible model with respect to the test set. decode: tensorflow function (default: tf.float32) how to decode the bytes in the tensorflow records for the input rgb data. Returns: An python dictionnary recaping the training and if present the test history. """ steps_in_epoch = max(ut.record_size(train_record) // batch_size, 1) test_steps = ut.record_size(test_record) //batch_size if test_record is not None else None max_steps = steps_in_epoch * n_epochs self.tensorboard = tensorboard if new_log is None: new_log = self.log else: check_or_create(new_log) stop_early = early_stopping is not None and early_stopping != 0 if not stop_early: early_stopping = 0 if early_stopping not in [0, 3]: ## this saver is to ensure that we can restore to the best weights at the end self.saver = self.saver_object(keep=early_stopping + 1, log=new_log, restore=restore) self.score_recorder = ScoreRecorder(self.saver, self.sess, new_log, stop_early=stop_early, lag=early_stopping) if not (k == 0 or k is None or lr_procedure is None or lr_procedure == ""): with tf.name_scope('learning_rate_scheduler'): lrs = self.learning_rate_scheduler(learning_rate, k, lr_procedure, steps_in_epoch) if self.verbose: msg = "learning_rate_scheduler added \ with initial_value = {}, k = {} \ and decrease every = {}" tqdm.write(msg.format(learning_rate, k, lr_procedure)) self.learning_rate = lrs else: lrs = learning_rate if self.verbose: tqdm.write("Learning_rate fixed to :{}".format(lrs)) if self.tensorboard: sw, ms, stw, mts = self.setup_summary(new_log, test_record) self.summary_writer = sw self.merged_summaries = ms if test_record: self.summary_test_writer = stw self.merged_summaries_test = mts if self.verbose: tqdm.write("summaries added") if weight_decay != 0: with tf.name_scope('regularization'): self.loss = self.regularize_model(self.loss, loss_func, weight_decay) if self.verbose: tqdm.write('regularization weight decay added: {}'.format(weight_decay)) with tf.name_scope('optimization'): opt = self.optimization(lrs, self.loss, self.training_variables) if decay_ema != 0 and decay_ema is not None: with tf.name_scope('exponential_moving_average'): training_op = self.exponential_moving_average(opt, self.training_variables, decay_ema) if self.verbose: tqdm.write("Exponential moving average added to prediction") else: training_op = opt with tf.name_scope('input_from_queue'): image_out, anno_out = self.setup_queues(train_record, test_record, batch_size, num_parallele_batch, decode=decode) # To plug in the queue to the main graph # with tf.control_dependencies([image_out, anno_out]): with tf.name_scope('queue_assigning'): # Control the dependency to allow the flow thought the data queues assign_rgb_to_queue = tf.assign(self.rgb_v, image_out, validate_shape=False) assign_lbl_to_queue = tf.assign(self.lbl_v, anno_out, validate_shape=False) assign_to_variable = [assign_rgb_to_queue, assign_lbl_to_queue] to_control = tf.tuple(assign_to_variable, control_inputs=[image_out, anno_out]) blank = tf.tuple([self.is_training], name=None, control_inputs=to_control) train_op = tf.tuple([training_op], name=None, control_inputs=to_control) self.init_uninit([]) begin_iter = 0 begin_epoch = begin_iter // steps_in_epoch last_epoch = begin_epoch + n_epochs last_iter = max_steps + begin_iter range_ = verbose_range(begin_iter, last_iter, "training ", self.verbose, 0) self.sess.run(blank) for step in range_: self.sess.run(train_op) if (step - begin_epoch + 1) % steps_in_epoch == 0 and (step - begin_epoch) != 0: # If we are at the end of an epoch epoch_number = step // steps_in_epoch if self.verbose: i = datetime.now() msg = i.strftime('[%Y/%m/%d %H:%M:%S]: ') msg += ' Epoch {} / {}'.format(epoch_number + 1, last_epoch) tqdm.write(msg) if save_weights: self.saver.save(self.sess, new_log + '/' + "model.ckpt", global_step=epoch_number + 1) dic_train_record = self.infer_train_step(epoch_number, control=to_control) self.score_recorder.diggest(epoch_number, dic_train_record) if test_record: self.sess.run(blank, feed_dict={self.is_training:False}) dic_test_record = self.infer_test_set(epoch_number, test_steps, during_training=True, control=to_control) self.sess.run(blank, feed_dict={self.is_training:True}) self.score_recorder.diggest(epoch_number, dic_test_record, train=False) if self.score_recorder.stop(track_variable, train_set=track_training): if self.verbose > 0: tqdm.write('stopping early') break if save_best: self.score_recorder.save_best(track_variable, save_weights, train_set=track_training) if return_best: # actually works when save best tqdm.write("restore_best NOT IMPLEMENTED") return self.score_recorder.all_tables() # ttt1, ttt2 = self.sess.run([test, self.conv1]) # ttt1, ttt2 = self.sess.run([test, self.conv1]) # import matplotlib.pylab as plt # f, axes = plt.subplots(nrows=9, ncols=ttt1[0].shape[0]) # for i in range(ttt1[0].shape[0]): # for j in range(8): # axes[j, i].imshow(ttt2[i,:,:,j].astype('uint8')) # axes[-1, i].imshow(ttt1[0][i,:,:].astype('uint8')) # ttt1, ttt2 = self.sess.run([test, self.conv1]) # ttt1, ttt2 = self.sess.run([test, self.conv1]) # fig, axes2 = plt.subplots(nrows=9, ncols=ttt1[0].shape[0]) # for i in range(ttt1[0].shape[0]): # for j in range(8): # axes2[j, i].imshow(ttt2[i,:,:,j].astype('uint8')) # axes2[-1, i].imshow(ttt1[0][i,:,:].astype('uint8')) # plt.show() # import pdb; pdb.set_trace() # size = self.sess.run([warm_up, warm_up2]) # tqdm.write(str(size[0])) # size = self.sess.run([warm_up, warm_up2]) # tqdm.write(str(size[0])) # self.sess.run(warm) # a, c, d, b, e, ff, ff3, ff2, ff1 = self.sess.run([test, self.probability, self.predictions, self.rgb_ph, self.lbl_ph, self.logit, self.conv3, self.conv2, self.conv1]) # self.label_int, # import matplotlib.pylab as plt # f, axes = plt.subplots(nrows=4, ncols=c.shape[0]); # if b.shape[1] == c.shape[1]: # dis = 0 # else: # dis = 92 # if c.shape[0] == 1: # axes[0].imshow(c[0,:,:,0]) # if dis== 0: # axes[1].imshow(b[0,:,:].astype('uint8')) # else: # axes[1].imshow(b[0,dis:-dis,dis:-dis].astype('uint8')) # axes[2].imshow(d[0,:,:]) # axes[3].imshow(e[0,:,:,0]) # #axes[4].imshow(entry[0,:,:,0]) # for j in range(5): # axes[j].axis('off') # else: # for i in range(c.shape[0]): # axes[0, i].imshow(c[i,:,:,0]) # if dis== 0: # axes[1, i].imshow(b[i,:,:].astype('uint8')) # else: # axes[1, i].imshow(b[i,dis:-dis,dis:-dis].astype('uint8')) # axes[2, i].imshow(d[i,:,:]) # axes[3, i].imshow(e[i,:,:,0]) # #axes[4, i].imshow(entry[i,:,:,0]) # for j in range(4): # axes[j, i].axis('off') # plt.savefig("train/train_{}.png".format(step)) # f, axes = plt.subplots(nrows=2, ncols=c.shape[0]); # if c.shape[0] == 1: # for j in range(2): # axes[j].imshow(ff[0,:,:,j]) # axes[j].axis('off') # else: # for i in range(c.shape[0]): # for j in range(2): # axes[j, i].imshow(ff[i,:,:,j]) # axes[j, i].axis('off') # plt.savefig("train/logit_{}.png".format(step)) # f, axes = plt.subplots(nrows=8, ncols=c.shape[0]); # if c.shape[0] == 1: # for j in range(8): # axes[j].imshow(ff3[0,:,:,j]) # axes[j].axis('off') # else: # for i in range(c.shape[0]): # for j in range(8): # axes[j, i].imshow(ff3[i,:,:,j]) # axes[j, i].axis('off') # plt.savefig("train/conv3_{}.png".format(step)) # f, axes = plt.subplots(nrows=8, ncols=c.shape[0]); # if c.shape[0] == 1: # for j in range(8): # axes[j].imshow(ff2[0,:,:,j]) # axes[j].axis('off') # else: # for i in range(c.shape[0]): # for j in range(8): # axes[j, i].imshow(ff2[i,:,:,j]) # axes[j, i].axis('off') # plt.savefig("train/conv2_{}.png".format(step)) # f, axes = plt.subplots(nrows=8, ncols=c.shape[0]); # if c.shape[0] == 1: # for j in range(8): # axes[j].imshow(ff1[0,:,:,j]) # axes[j].axis('off') # else: # for i in range(c.shape[0]): # for j in range(8): # axes[j, i].imshow(ff1[i,:,:,j]) # axes[j, i].axis('off') # plt.savefig("train/conv1_{}.png".format(step))

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0.031008

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0.069767

0

null

0

null

0

1

0

9307a12d7ab7ec9787a35aa2cc8ebd65e88b80b2

757

py

Python

examples/mlbasics_learn_to_add.py

JonathanRaiman/dali-cython-stub

e258469aeb1d4cb3e4cdf5c07e8948f461a038f1

[ "MIT" ]

7

2016-06-20T17:50:06.000Z

2019-12-13T17:27:46.000Z

examples/mlbasics_learn_to_add.py

JonathanRaiman/dali-cython

e258469aeb1d4cb3e4cdf5c07e8948f461a038f1

[ "MIT" ]

6

2015-08-04T07:25:38.000Z

2015-08-13T22:06:22.000Z

examples/mlbasics_learn_to_add.py

JonathanRaiman/dali-cython

e258469aeb1d4cb3e4cdf5c07e8948f461a038f1

[ "MIT" ]

2

2016-07-04T21:38:14.000Z

2016-08-31T02:53:19.000Z

from test_dali import Mat, random, MatOps, Graph num_examples = 100 example_size = 3 iterations = 150 lr = 0.01 X = random.uniform( 0.0, 1.0 / example_size, size=(num_examples, example_size) ) ones = Mat.ones((X.shape[1], 1)) Y = X.dot(ones) X = MatOps.consider_constant(X) Y = MatOps.consider_constant(Y) W = random.uniform(-1.0, 1.0, (example_size, 1)) print(repr(W)) for i in range(iterations): predY = X.dot(W) error = ((predY - Y) ** 2).sum() print(repr(error)) # line below can be replaced by simply error.grad() error.dw += 1 Graph.backward() # there are much nicer solvers in Dali, # but here we write out gradient descent # explicitly W.w -= W.dw * lr W.dw = 0 print(repr(W))

22.264706

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0.627477

124

757

3.758065

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0

0.04

0.12

0

null

0

null

0

1

0

930ea439321e8015beffc7c1651e06f8c268d480

1,628

py

Python

arakat-core/src/pipeline_generator/family_base/Join.py

sopaoglu/arakat

efa32fcc93076801cad24ab850ecdf9048a824e8

[ "Apache-2.0" ]

23

2018-08-18T17:32:40.000Z

2021-10-05T22:57:06.000Z

arakat-core/src/pipeline_generator/family_base/Join.py

sopaoglu/arakat

efa32fcc93076801cad24ab850ecdf9048a824e8

[ "Apache-2.0" ]

23

2018-09-22T08:47:07.000Z

2021-08-04T07:08:34.000Z

arakat-core/src/pipeline_generator/family_base/Join.py

sopaoglu/arakat

efa32fcc93076801cad24ab850ecdf9048a824e8

[ "Apache-2.0" ]

22

2018-08-17T10:33:31.000Z

2021-10-05T22:57:07.000Z

import os from src.domain.ErrorTypes import ErrorTypes from src.utils.code_generation import CodeGenerationUtils from src.validity import IncomingEdgeValidityChecker # Add other join options as well # How about join cascades # Add necessary checks for stream-stream, stream-batch joins... # Note that for stream-static joins, stream df must be on left. def generate_code(args): node = args["node"] requireds_info = args["requireds_info"] edges = args["edges"] checklist={"df_count": {2}, "model_count": {0}} error, extra= IncomingEdgeValidityChecker.check_validity(node["id"], requireds_info, edges, checklist) code=[] shared_function_set = set() if(error == ErrorTypes.NO_ERROR): df_names=__get_dfs_to_join(extra) code.extend(["df_" + node["id"] + "=" + df_names[0] + ".join(" + df_names[1] + ", " + CodeGenerationUtils.handle_primitive(node["parameters"]["join_column"]["value"]) + ")", os.linesep]) return code, shared_function_set, error def __get_dfs_to_join(extra): # IncomingEdgeValidityChecker return a sorted list of df info by the order given by user. # For now, we allow only two dataframes to be joined. However, we can handle a cascade of joins as well. # For this purpose, change node-specs of join to get more than one column name to join, and then generate each join-statement code... df_names=[] for elem in extra["dfs"]: if ("portion" in elem): df_names.append("df_" + elem["source_id"] + "[" + str(elem["portion"]) + "]") else: df_names.append("df_" + elem["source_id"]) return df_names

42.842105

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0.690418

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1,628

4.831111

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0.038638

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0.187346

1,628

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195

42.842105

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0.333333

0

null

0

null

0

1

0

930fa7bf77bac97e46a52354c3d1f9d8c81fb05f

464

py

Python

examples/python/cpu/tensors/ocean_auto_cast_01.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

27

2018-08-16T21:32:49.000Z

2021-11-30T10:31:08.000Z

examples/python/cpu/tensors/ocean_auto_cast_01.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

null

examples/python/cpu/tensors/ocean_auto_cast_01.py

kant/ocean-tensor-package

fb3fcff8bba7f4ef6cd8b8d02f0e1be1258da02d

[ "Apache-2.0" ]

13

2018-08-17T17:33:16.000Z

2021-11-30T10:31:09.000Z

## Automatic type case and broadcast import pyOcean_cpu as ocean import sys def exceptionMsg() : print("Expected error: %s" % str(sys.exc_info()[1])) def failTest(command) : try : eval(command) except : exceptionMsg() a = ocean.int16([1,2,3]) b = ocean.float([1]) print(a+b) ocean.setAutoTypecast(False) print(a+3) failTest("a+3.2") print(b+3) print(b+3.2) failTest("a+b") ocean.setAutoBroadcast(False) print(a+3) failTest("a+[3]")

13.647059

55

0.657328

73

464

4.150685

0.479452

0.026403

0.046205

0.079208

0.145215

0

0.03876

0.165948

464

33

56

14.060606

0.744186

0.071121

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false

0

0.095238

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0.190476

0.285714

0

null

0

null

0

1

0

9310e77db1e72d4d632caad266185a2b309d6ee4

2,140

py

Python

examples/stresstest_logger.py

felfel/logging-py

62e836da8f666286e190dfc1a4428eb04375d08c

[ "MIT" ]

2

2018-08-24T12:45:56.000Z

2020-02-23T07:59:34.000Z

examples/stresstest_logger.py

felfel/logging-py

62e836da8f666286e190dfc1a4428eb04375d08c

[ "MIT" ]

6

2018-07-10T11:43:09.000Z

2018-10-22T11:34:49.000Z

examples/stresstest_logger.py

felfel/logging-py

62e836da8f666286e190dfc1a4428eb04375d08c

[ "MIT" ]

1

2018-07-13T09:32:58.000Z

from loggingpy import Logger from loggingpy import BundlingHttpSink import time import random import logging import sys from examples import uris # you must provide these uri strings (just any uri that accepts requests.post(...) requests) if __name__ == "__main__": # Sumologic token url (just a basic string) sumoUri = uris.sumoUri # Logz.io token url (just a basic string) elasticUri = uris.elasticUri # these are two sinks of type BatchedHttpSink, which extend the logging.Handler class sumoSink = BundlingHttpSink('test_app', sumoUri) elasticSink = BundlingHttpSink('test_app', elasticUri) # however, you can use basic logging.Handler derived classes together with the ones here stdoutSink = logging.StreamHandler(sys.stdout) # configure the logger with a list of handlers to which it pushes the messages Logger.with_sinks([sumoSink, elasticSink, stdoutSink]) # get logger of context logger = Logger("Calculator") # this is just some basic code that generates different types of exceptions and then pushes different messages try: for i in range(0, 100000): try: div = random.randint(0, 20) x = 123 / div if div == 1: raise Exception("Because I can") logger.info(payload_type="MathOperation", message='You performed a division', payload={ "OperationType": "Division", "OperationDetails": { "Div": div, "Result": x } } ) except Exception as e: logger.fatal(message='What the fck just happened???', exception=e, payload_type="CalculationError") if i % 100 == 0: time.sleep(random.randint(10, 5000)/1000) if i % 500 == 0: print("Got " + str(i)) except BaseException as e: # this catch is required in order to shutdown the logger properly pass print("Flushing logger...") Logger.flush() print('...Done.')

33.968254

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5.240816

0.55102

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0.029595

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0.037383

0

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2,140

62

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0.851122

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0

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false

0.02439

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0

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0.073171

0

null

0

null

0

1

0

931746dd6e36cf03f12c48ba2eadffc6ec81a1e9

903

py

Python

single-results/parse_file.py

andrewevans0102/sensor-sampling-with-react-recharts

b5491ad30e8bb633c79ea3c6b6c51f70eb197f41

[ "MIT" ]

null

single-results/parse_file.py

andrewevans0102/sensor-sampling-with-react-recharts

b5491ad30e8bb633c79ea3c6b6c51f70eb197f41

[ "MIT" ]

null

single-results/parse_file.py

andrewevans0102/sensor-sampling-with-react-recharts

b5491ad30e8bb633c79ea3c6b6c51f70eb197f41

[ "MIT" ]

null

## write files originally copied from stack abuse at ## https://stackabuse.com/writing-files-using-python/ ## read files originally copied from stack abuse at ## https://stackabuse.com/reading-files-with-python/ def writeOutput(line): appendFilehandle = open('clean_history.log','a') appendFilehandle.write(line) appendFilehandle.close() # define the name of the file to read from filename = "working_history_daily.log" # open the file for reading readFilehandle = open(filename, 'r') while True: # read a single line line = readFilehandle.readline() if not line: break # # cleanup file so clean 24 hour blocks have been recorded if("03/18/2021" in line and "00" in line and "error" not in line and "Checksum" not in line and "DHT" not in line and "buffer" not in line): writeOutput(line) # close the pointer to that file readFilehandle.close()

33.444444

144

0.712071

130

903

4.923077

0.507692

0.05625

0.070313

0.05625

0.171875

0

0.016416

0.190476

903

27

145

33.444444

0.859097

0.415282

0

0.151751

0.048638

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false

0

0.076923

0

null

0

null

0

1

0

93190146b61f7d460199298bec31da8c98e4b313

2,528

py

Python

tests/test_multiple_pool.py

UT-Covid/compartmental_model_case_studies

324e2c92453c928e64c637d6e6fbe570fb714cdb

[ "BSD-3-Clause-Clear" ]

1

2021-02-04T14:59:32.000Z

tests/test_multiple_pool.py

UT-Covid/compartmental_model_case_studies

324e2c92453c928e64c637d6e6fbe570fb714cdb

[ "BSD-3-Clause-Clear" ]

null

tests/test_multiple_pool.py

UT-Covid/compartmental_model_case_studies

324e2c92453c928e64c637d6e6fbe570fb714cdb

[ "BSD-3-Clause-Clear" ]

null

import os import sys import pytest import pickle import numpy as np import xarray as xr from .pytest_utils import fp, md5sum, call_with_legacy_params, assert_objects_equal from SEIRcity.simulate.multiple_pool import multiple_pool from SEIRcity.simulate.multiple_serial import multiple_serial from SEIRcity.param import aggregate_params_and_data class TestPool(object): @pytest.mark.parametrize("legacy_pickle,yaml_fp", [ (fp("tests/data/multiple_serial_result1.pckl"), fp("tests/data/configs/multiple_reps_single_scenario5.yaml")), ]) def test_mp_can_return_xarray(self, legacy_pickle, yaml_fp, tmp_path): """Can generate same outputs as serial version. """ assert os.path.isfile(legacy_pickle) assert os.path.isfile(yaml_fp) config = aggregate_params_and_data(yaml_fp=yaml_fp) kwargs = { 'legacy_pickle': legacy_pickle, 'func': multiple_pool } kwargs['override_args'] = [config] legacy_result, new_result = call_with_legacy_params(**kwargs) assert isinstance(legacy_result.outcomes, xr.DataArray) assert isinstance(new_result.outcomes, xr.DataArray) assert legacy_result.outcomes.dims == new_result.outcomes.dims # print("legacy: ", legacy_result.outcomes) # print("new: ", new_result.outcomes) # print("legacy coords: ", legacy_result.outcomes.coords) # print("new coords: ", new_result.outcomes.coords) @pytest.mark.slow @pytest.mark.parametrize("legacy_pickle,yaml_fp", [ (fp("tests/data/multiple_serial_result6.pckl"), fp("tests/data/configs/multiple_scenario2.yaml")) ]) def test_mp_can_return_xarray_slow(self, legacy_pickle, yaml_fp): """same as above but slow""" assert os.path.isfile(legacy_pickle) assert os.path.isfile(yaml_fp) config = aggregate_params_and_data(yaml_fp=yaml_fp) legacy_result, new_result = call_with_legacy_params( legacy_pickle=legacy_pickle, func=multiple_pool, override_args=[config]) assert isinstance(legacy_result.outcomes, xr.DataArray) assert isinstance(new_result.outcomes, xr.DataArray) assert legacy_result.outcomes.dims == new_result.outcomes.dims # print("legacy: ", legacy_result.outcomes) # print("new: ", new_result.outcomes) print("legacy coords: ", legacy_result.outcomes.coords) print("new coords: ", new_result.outcomes.coords)

42.133333

83

0.697785

315

2,528

5.32381

0.234921

0.133572

0.095408

0.042934

0.711986

0.685748

0.64997

0.568873

0.519976

0

0.002478

0.201741

2,528

59

84

42.847458

0.828543

0.134098

0

0.347826

0

0.125633

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0

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1

0.043478

false

0

0.217391

0

0.282609

0.043478

0

null

0

null

0

1

0

931a12e506bc76c7a5c3ea40e50db2b8ec6b9495

3,668

py

Python

core/polyaxon/client/client.py

jjasonkal/polyaxon

8454b29b2b971b965de8a7bf63afdd48f07d6d53

[ "Apache-2.0" ]

null

core/polyaxon/client/client.py

jjasonkal/polyaxon

8454b29b2b971b965de8a7bf63afdd48f07d6d53

[ "Apache-2.0" ]

null

core/polyaxon/client/client.py

jjasonkal/polyaxon

8454b29b2b971b965de8a7bf63afdd48f07d6d53

[ "Apache-2.0" ]

null

#!/usr/bin/python # # Copyright 2018-2020 Polyaxon, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import polyaxon_sdk from polyaxon import settings from polyaxon.client.transport import Transport class PolyaxonClient: def __init__(self, config=None, token=None): self._config = config or settings.CLIENT_CONFIG self._config.token = token or settings.AUTH_CONFIG.token self._transport = None self.api_client = polyaxon_sdk.ApiClient( self.config.sdk_config, **self.config.client_header ) self._projects_v1 = None self._runs_v1 = None self._auth_v1 = None self._users_v1 = None self._versions_v1 = None self._agents_v1 = None self._components_v1 = None self._models_v1 = None def reset(self): self._transport = None self._projects_v1 = None self._runs_v1 = None self._auth_v1 = None self._users_v1 = None self._versions_v1 = None self._agents_v1 = None self._components_v1 = None self._models_v1 = None self.api_client = polyaxon_sdk.ApiClient( self.config.sdk_config, **self.config.client_header ) def set_health_check(self, url): self.transport.set_health_check(url) def unset_health_check(self, url): self.transport.unset_health_check(url) @property def transport(self): if not self._transport: self._transport = Transport(config=self.config) return self._transport @property def config(self): return self._config @property def projects_v1(self): if not self._projects_v1: self._projects_v1 = polyaxon_sdk.ProjectsV1Api(self.api_client) return self._projects_v1 @property def runs_v1(self): if not self._runs_v1: self._runs_v1 = polyaxon_sdk.RunsV1Api(self.api_client) return self._runs_v1 @property def auth_v1(self): if not self._auth_v1: self._auth_v1 = polyaxon_sdk.AuthV1Api(self.api_client) return self._auth_v1 @property def users_v1(self): if not self._users_v1: self._users_v1 = polyaxon_sdk.UsersV1Api(self.api_client) return self._users_v1 @property def versions_v1(self): if not self._versions_v1: self._versions_v1 = polyaxon_sdk.VersionsV1Api(self.api_client) return self._versions_v1 @property def agents_v1(self): if not self._agents_v1: self._agents_v1 = polyaxon_sdk.AgentsV1Api(self.api_client) return self._agents_v1 @property def components_v1(self): if not self._components_v1: self._components_v1 = polyaxon_sdk.HubComponentsV1Api(self.api_client) return self._components_v1 @property def models_v1(self): if not self._models_v1: self._models_v1 = polyaxon_sdk.HubModelsV1Api(self.api_client) return self._models_v1 def sanitize_for_serialization(self, value): return self.api_client.sanitize_for_serialization(value)

30.065574

82

0.67121

479

3,668

4.847599

0.235908

0.062016

0.064599

0.050388

0.335917

0.204996

0.178295

0

0.024863

0.254362

3,668

121

83

30.31405

0.824132

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0

0.372093

0

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0.174419

false

0

0.034884

0.023256

0.348837

0

null

0

null

0

1

0

931bcf76748e923ca4a633a3cbeb069fcf538af1

1,898

py

Python

metrics/rabbitmq.py

ONSdigital/ras-rm-metrics

16931ad6b479b6b30f4ba8934e79d8633ebd8032

[ "MIT" ]

null

metrics/rabbitmq.py

ONSdigital/ras-rm-metrics

16931ad6b479b6b30f4ba8934e79d8633ebd8032

[ "MIT" ]

1

2018-12-03T11:10:34.000Z

metrics/rabbitmq.py

ONSdigital/ras-rm-metrics

16931ad6b479b6b30f4ba8934e79d8633ebd8032

[ "MIT" ]

2

2018-08-23T15:39:25.000Z

2021-04-11T08:10:53.000Z

import json import cfenv import requests import logging from structlog import wrap_logger logger = wrap_logger(logging.getLogger(__name__)) class RabbitMQ: def __init__(self, cf_service_name): self.cf_service_name = cf_service_name self.services = self._get_services_from_cf() def log_metrics(self): for service_name, uri in self.services.items(): queues = self._fetch_metrics_for_all_queues(service_name, uri) for queue_metrics in queues: self._log_queue_metrics(service_name, queue_metrics) def _get_services_from_cf(self): return {s.name: s.credentials['http_api_uri'] for s in cfenv.AppEnv().services if cfenv.match_all(s.env, {'label': self.cf_service_name})} def _fetch_metrics_for_all_queues(self, service, uri): uri = uri.rstrip('/') + '/queues' response = requests.get(uri) if response.status_code != requests.codes.ok: logger.error( self._service_logger_name(service), f'GET {uri} - [{response.status_code}] - {response.text}') return [] return [self._prepare_metrics(service, metrics) for metrics in response.json()] def _prepare_metrics(self, service, raw): return { 'service': service, 'name': raw['name'], 'messages': raw['messages'] } def _log_queue_metrics(self, service_name, queue_metrics): logger.info( queue=self._queue_logger_name(service_name, queue_metrics['name']), messages=queue_metrics['messages']) def _queue_logger_name(self, service_name, queue_name): return f'{self._service_logger_name(service_name)}.{queue_name}' def _service_logger_name(self, service): return f'sdc.metrics.{self.cf_service_name}.{service}'

32.169492

79

0.643836

232

1,898

4.909483

0.232759

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0.057068

0.059701

0.122037

0

0.25079

1,898

58

80

32.724138

0.800985

0

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0.064278

0

1

0.186047

false

0

0.116279

0.093023

0.465116

0

null

0

null

0

1

0

931d4f107dcc3a3742cb0972921784cc9ef9d306

4,026

py

Python

Resnet/resnet.py

yt4766269/pytorch_zoo

fef877a15c3541771512e9f9489c3023aee20819

[ "Apache-2.0" ]

1

2021-07-22T02:56:13.000Z

Resnet/resnet.py

yt4766269/pytorch_zoo

fef877a15c3541771512e9f9489c3023aee20819

[ "Apache-2.0" ]

null

Resnet/resnet.py

yt4766269/pytorch_zoo

fef877a15c3541771512e9f9489c3023aee20819

[ "Apache-2.0" ]

null

import torch import torch.nn as nn import torch.nn.functional as function def conv3x3(in_features: int, out_features: int, stride: int = 1) -> nn.Conv2d: '''3x3 conv with padding''' return nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=3, stride=stride, padding=1) class BasicBlock(nn.Module): expansion = 1 def __init__(self, input_planes, planes, stride = 1, downsample = None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(in_features=input_planes, out_features=planes, stride = stride) self.bn1 = nn.BatchNorm2d(num_features=planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(in_features=planes, out_features=planes) self.bn2 = nn.BatchNorm2d(num_features=planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.conv2(x) x = self.bn2(x) if self.downsample is not None: residual = self.downsample(residual) x = x+residual out = self.relu(x) return out class Bottlenect(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride = 1, downsample = None): super(Bottlenect, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1,stride = stride, padding=0, bias=False) self.bn1 = nn.BatchNorm2d(num_features=planes) self.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride = stride, padding = 1, bias = False) self.bn2 = nn.BatchNorm2d(num_features=planes) self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias = False) self.bn3 = nn.BatchNorm2d(planes * self.expansion) self.downsample = downsample self.stride = stride def forward(self, x): residual = x x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.conv3(x) x = self.bn3(x) if self.downsample is not None: residual = self.downsample(residual) x = x + residual x = self.relu(x) return x class Resnet(nn.Module): def __init__(self, block:nn.Module, layer_num:int, num_class:int) -> None: super(Resnet, self).__init__() self.inplanes = 64 self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layer_num[0]) self.layer2 = self._make_layer(block, 128, layer_num[1], stride=2) self.layer3 = self._make_layer(block, 256, layer_num[2], stride=2) self.layer4 = self._make_layer(block, 512, layer_num[3], stride=2) self.avgpool = nn.AvgPool2d(kernel_size=7) self.fc = nn.Linear(512 * block.expansion, num_class) def _make_layer(self, block, planes, blocks, stride = 1) -> nn.Module: downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.inplanes, planes*block.expansion, kernel_size=1, stride=stride, bias = False), nn.BatchNorm2d(planes*block.expansion) ) layers = [] for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return x

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932488970519b338525f68c216759facdd4c7e7e

13,443

py

Python

applied_python/applied_python/lib/python2.7/site-packages/ansible/modules/extras/system/zfs.py

mith1979/ansible_automation

013dfa67c6d91720b787fadb21de574b6e023a26

[ "Apache-2.0" ]

1

2020-10-14T00:06:54.000Z

applied_python/applied_python/lib/python2.7/site-packages/ansible/modules/extras/system/zfs.py

mith1979/ansible_automation

013dfa67c6d91720b787fadb21de574b6e023a26

[ "Apache-2.0" ]

null

applied_python/applied_python/lib/python2.7/site-packages/ansible/modules/extras/system/zfs.py

mith1979/ansible_automation

013dfa67c6d91720b787fadb21de574b6e023a26

[ "Apache-2.0" ]

2

2015-08-06T07:45:48.000Z

2017-01-04T17:47:16.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # (c) 2013, Johan Wiren <johan.wiren.se@gmail.com> # # This file is part of Ansible # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # DOCUMENTATION = ''' --- module: zfs short_description: Manage zfs description: - Manages ZFS file systems on Solaris and FreeBSD. Can manage file systems, volumes and snapshots. See zfs(1M) for more information about the properties. version_added: "1.1" options: name: description: - File system, snapshot or volume name e.g. C(rpool/myfs) required: true state: description: - Whether to create (C(present)), or remove (C(absent)) a file system, snapshot or volume. required: true choices: [present, absent] aclinherit: description: - The aclinherit property. required: False choices: [discard,noallow,restricted,passthrough,passthrough-x] aclmode: description: - The aclmode property. required: False choices: [discard,groupmask,passthrough] atime: description: - The atime property. required: False choices: ['on','off'] canmount: description: - The canmount property. required: False choices: ['on','off','noauto'] casesensitivity: description: - The casesensitivity property. required: False choices: [sensitive,insensitive,mixed] checksum: description: - The checksum property. required: False choices: ['on','off',fletcher2,fletcher4,sha256] compression: description: - The compression property. required: False choices: ['on','off',lzjb,gzip,gzip-1,gzip-2,gzip-3,gzip-4,gzip-5,gzip-6,gzip-7,gzip-8,gzip-9,lz4,zle] copies: description: - The copies property. required: False choices: [1,2,3] dedup: description: - The dedup property. required: False choices: ['on','off'] devices: description: - The devices property. required: False choices: ['on','off'] exec: description: - The exec property. required: False choices: ['on','off'] jailed: description: - The jailed property. required: False choices: ['on','off'] logbias: description: - The logbias property. required: False choices: [latency,throughput] mountpoint: description: - The mountpoint property. required: False nbmand: description: - The nbmand property. required: False choices: ['on','off'] normalization: description: - The normalization property. required: False choices: [none,formC,formD,formKC,formKD] primarycache: description: - The primarycache property. required: False choices: [all,none,metadata] quota: description: - The quota property. required: False readonly: description: - The readonly property. required: False choices: ['on','off'] recordsize: description: - The recordsize property. required: False refquota: description: - The refquota property. required: False refreservation: description: - The refreservation property. required: False reservation: description: - The reservation property. required: False secondarycache: description: - The secondarycache property. required: False choices: [all,none,metadata] setuid: description: - The setuid property. required: False choices: ['on','off'] shareiscsi: description: - The shareiscsi property. required: False choices: ['on','off'] sharenfs: description: - The sharenfs property. required: False sharesmb: description: - The sharesmb property. required: False snapdir: description: - The snapdir property. required: False choices: [hidden,visible] sync: description: - The sync property. required: False choices: ['on','off'] utf8only: description: - The utf8only property. required: False choices: ['on','off'] volsize: description: - The volsize property. required: False volblocksize: description: - The volblocksize property. required: False vscan: description: - The vscan property. required: False choices: ['on','off'] xattr: description: - The xattr property. required: False choices: ['on','off'] zoned: description: - The zoned property. required: False choices: ['on','off'] author: Johan Wiren ''' EXAMPLES = ''' # Create a new file system called myfs in pool rpool - zfs: name=rpool/myfs state=present # Create a new volume called myvol in pool rpool. - zfs: name=rpool/myvol state=present volsize=10M # Create a snapshot of rpool/myfs file system. - zfs: name=rpool/myfs@mysnapshot state=present # Create a new file system called myfs2 with snapdir enabled - zfs: name=rpool/myfs2 state=present snapdir=enabled ''' import os class Zfs(object): def __init__(self, module, name, properties): self.module = module self.name = name self.properties = properties self.changed = False self.immutable_properties = [ 'casesensitivity', 'normalization', 'utf8only' ] def exists(self): cmd = [self.module.get_bin_path('zfs', True)] cmd.append('list') cmd.append('-t all') cmd.append(self.name) (rc, out, err) = self.module.run_command(' '.join(cmd)) if rc == 0: return True else: return False def create(self): if self.module.check_mode: self.changed = True return properties=self.properties volsize = properties.pop('volsize', None) volblocksize = properties.pop('volblocksize', None) if "@" in self.name: action = 'snapshot' else: action = 'create' cmd = [self.module.get_bin_path('zfs', True)] cmd.append(action) if volblocksize: cmd.append('-b %s' % volblocksize) if properties: for prop, value in properties.iteritems(): cmd.append('-o %s="%s"' % (prop, value)) if volsize: cmd.append('-V') cmd.append(volsize) cmd.append(self.name) (rc, err, out) = self.module.run_command(' '.join(cmd)) if rc == 0: self.changed=True else: self.module.fail_json(msg=out) def destroy(self): if self.module.check_mode: self.changed = True return cmd = [self.module.get_bin_path('zfs', True)] cmd.append('destroy') cmd.append(self.name) (rc, err, out) = self.module.run_command(' '.join(cmd)) if rc == 0: self.changed = True else: self.module.fail_json(msg=out) def set_property(self, prop, value): if self.module.check_mode: self.changed = True return cmd = self.module.get_bin_path('zfs', True) args = [cmd, 'set', prop + '=' + value, self.name] (rc, err, out) = self.module.run_command(args) if rc == 0: self.changed = True else: self.module.fail_json(msg=out) def set_properties_if_changed(self): current_properties = self.get_current_properties() for prop, value in self.properties.iteritems(): if current_properties[prop] != value: if prop in self.immutable_properties: self.module.fail_json(msg='Cannot change property %s after creation.' % prop) else: self.set_property(prop, value) def get_current_properties(self): def get_properties_by_name(propname): cmd = [self.module.get_bin_path('zfs', True)] cmd += ['get', '-H', propname, self.name] rc, out, err = self.module.run_command(cmd) return [l.split('\t')[1:3] for l in out.splitlines()] properties = dict(get_properties_by_name('all')) if 'share.*' in properties: # Some ZFS pools list the sharenfs and sharesmb properties # hierarchically as share.nfs and share.smb respectively. del properties['share.*'] for p, v in get_properties_by_name('share.all'): alias = p.replace('.', '') # share.nfs -> sharenfs (etc) properties[alias] = v return properties def run_command(self, cmd): progname = cmd[0] cmd[0] = module.get_bin_path(progname, True) return module.run_command(cmd) def main(): # FIXME: should use dict() constructor like other modules, required=False is default module = AnsibleModule( argument_spec = { 'name': {'required': True}, 'state': {'required': True, 'choices':['present', 'absent']}, 'aclinherit': {'required': False, 'choices':['discard', 'noallow', 'restricted', 'passthrough', 'passthrough-x']}, 'aclmode': {'required': False, 'choices':['discard', 'groupmask', 'passthrough']}, 'atime': {'required': False, 'choices':['on', 'off']}, 'canmount': {'required': False, 'choices':['on', 'off', 'noauto']}, 'casesensitivity': {'required': False, 'choices':['sensitive', 'insensitive', 'mixed']}, 'checksum': {'required': False, 'choices':['on', 'off', 'fletcher2', 'fletcher4', 'sha256']}, 'compression': {'required': False, 'choices':['on', 'off', 'lzjb', 'gzip', 'gzip-1', 'gzip-2', 'gzip-3', 'gzip-4', 'gzip-5', 'gzip-6', 'gzip-7', 'gzip-8', 'gzip-9', 'lz4', 'zle']}, 'copies': {'required': False, 'choices':['1', '2', '3']}, 'dedup': {'required': False, 'choices':['on', 'off']}, 'devices': {'required': False, 'choices':['on', 'off']}, 'exec': {'required': False, 'choices':['on', 'off']}, # Not supported #'groupquota': {'required': False}, 'jailed': {'required': False, 'choices':['on', 'off']}, 'logbias': {'required': False, 'choices':['latency', 'throughput']}, 'mountpoint': {'required': False}, 'nbmand': {'required': False, 'choices':['on', 'off']}, 'normalization': {'required': False, 'choices':['none', 'formC', 'formD', 'formKC', 'formKD']}, 'primarycache': {'required': False, 'choices':['all', 'none', 'metadata']}, 'quota': {'required': False}, 'readonly': {'required': False, 'choices':['on', 'off']}, 'recordsize': {'required': False}, 'refquota': {'required': False}, 'refreservation': {'required': False}, 'reservation': {'required': False}, 'secondarycache': {'required': False, 'choices':['all', 'none', 'metadata']}, 'setuid': {'required': False, 'choices':['on', 'off']}, 'shareiscsi': {'required': False, 'choices':['on', 'off']}, 'sharenfs': {'required': False}, 'sharesmb': {'required': False}, 'snapdir': {'required': False, 'choices':['hidden', 'visible']}, 'sync': {'required': False, 'choices':['on', 'off']}, # Not supported #'userquota': {'required': False}, 'utf8only': {'required': False, 'choices':['on', 'off']}, 'volsize': {'required': False}, 'volblocksize': {'required': False}, 'vscan': {'required': False, 'choices':['on', 'off']}, 'xattr': {'required': False, 'choices':['on', 'off']}, 'zoned': {'required': False, 'choices':['on', 'off']}, }, supports_check_mode=True ) state = module.params.pop('state') name = module.params.pop('name') # Get all valid zfs-properties properties = dict() for prop, value in module.params.iteritems(): if prop in ['CHECKMODE']: continue if value: properties[prop] = value result = {} result['name'] = name result['state'] = state zfs=Zfs(module, name, properties) if state == 'present': if zfs.exists(): zfs.set_properties_if_changed() else: zfs.create() elif state == 'absent': if zfs.exists(): zfs.destroy() result.update(zfs.properties) result['changed'] = zfs.changed module.exit_json(**result) # import module snippets from ansible.module_utils.basic import * main()

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null

0

null

0

1

0

93257115dcc4e6c8454010d2189516afc8ea523a

4,491

py

Python

stlearn/plotting/deconvolution_plot.py

duypham2108/dev_st

47adcfa5803eba7549b1185ec69d2317b386d9ff

[ "BSD-3-Clause" ]

null

stlearn/plotting/deconvolution_plot.py

duypham2108/dev_st

47adcfa5803eba7549b1185ec69d2317b386d9ff

[ "BSD-3-Clause" ]

null

stlearn/plotting/deconvolution_plot.py

duypham2108/dev_st

47adcfa5803eba7549b1185ec69d2317b386d9ff

[ "BSD-3-Clause" ]

null

from typing import Optional, Union from anndata import AnnData import matplotlib.pyplot as plt from matplotlib import cm import matplotlib as mpl import numpy as np def deconvolution_plot( adata: AnnData, library_id: str = None, use_label: str = "louvain", cluster: [int, str] = None, celltype: str = None, celltype_threshold: float = 0, data_alpha: float = 1.0, threshold: float = 0.0, cmap: str = "tab20", tissue_alpha: float = 1.0, title: str = None, spot_size: Union[float, int] = 10, show_axis: bool = False, show_legend: bool = True, cropped: bool = True, margin: int = 100, name: str = None, dpi: int = 150, output: str = None, copy: bool = False, ) -> Optional[AnnData]: """\ Clustering plot for sptial transcriptomics data. Also it has a function to display trajectory inference. Parameters ---------- adata Annotated data matrix. library_id Library id stored in AnnData. use_label Use label result of clustering method. list_cluster Choose set of clusters that will display in the plot. data_alpha Opacity of the spot. tissue_alpha Opacity of the tissue. cmap Color map to use. spot_size Size of the spot. show_axis Show axis or not. show_legend Show legend or not. show_trajectory Show the spatial trajectory or not. It requires stlearn.spatial.trajectory.pseudotimespace. show_subcluster Show subcluster or not. It requires stlearn.spatial.trajectory.global_level. name Name of the output figure file. dpi DPI of the output figure. output Save the figure as file or not. copy Return a copy instead of writing to adata. Returns ------- Nothing """ # plt.rcParams['figure.dpi'] = dpi imagecol = adata.obs["imagecol"] imagerow = adata.obs["imagerow"] fig, ax = plt.subplots() label = adata.obsm["deconvolution"].T tmp = label.sum(axis=1) label_filter = label.loc[tmp[tmp >= np.quantile(tmp, threshold)].index] if cluster is not None: base = adata.obs[adata.obs[use_label] == str(cluster)][["imagecol", "imagerow"]] else: base = adata.obs[["imagecol", "imagerow"]] if celltype is not None: base = base.loc[ adata.obs_names[adata.obsm["deconvolution"][celltype] > celltype_threshold] ] label_filter_ = label_filter[base.index] color_vals = list(range(0, len(label_filter_), 1)) my_norm = mpl.colors.Normalize(0, len(label_filter_)) my_cmap = mpl.cm.get_cmap(cmap, len(color_vals)) for i, xy in enumerate(base.values): _ = ax.pie( label_filter_.T.iloc[i].values, colors=my_cmap.colors, center=(xy[0], xy[1]), radius=spot_size, frame=True, ) ax.autoscale() if library_id is None: library_id = list(adata.uns["spatial"].keys())[0] image = adata.uns["spatial"][library_id]["images"][ adata.uns["spatial"][library_id]["use_quality"] ] ax_pie = fig.add_axes([0.5, -0.4, 0.03, 0.5]) def my_autopct(pct): return ("%1.0f%%" % pct) if pct >= 4 else "" ax_pie.pie( label_filter_.sum(axis=1), colors=my_cmap.colors, radius=5, frame=True, autopct=my_autopct, pctdistance=1.1, startangle=90, wedgeprops=dict(width=(2), edgecolor="w", antialiased=True), textprops={"fontsize": 5}, ) ax_pie.set_axis_off() ax_cb = fig.add_axes([0.9, 0.25, 0.03, 0.5], axisbelow=False) cb = mpl.colorbar.ColorbarBase(ax_cb, cmap=my_cmap, norm=my_norm, ticks=color_vals) cb.ax.tick_params(size=0) loc = np.array(color_vals) + 0.5 cb.set_ticks(loc) cb.set_ticklabels(label_filter_.index) cb.outline.set_visible(False) # Overlay the tissue image ax.imshow( image, alpha=1, zorder=-1, ) ax.axis("off") if cropped: ax.set_xlim(imagecol.min() - margin, imagecol.max() + margin) ax.set_ylim(imagerow.min() - margin, imagerow.max() + margin) ax.set_ylim(ax.get_ylim()[::-1]) # plt.gca().invert_yaxis() if name is None: name = use_label if output is not None: fig.savefig(output + "/" + name, dpi=dpi, bbox_inches="tight", pad_inches=0) plt.show()

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null

0

null

0

1

0

9326eba3e2a32b8391b62c5d9ad30ac89d990e0b

5,536

py

Python

g2p_train/train_and_export.py

gitter-badger/TensorVox

a87a326249e5e31f15c25c41458df01638fd5fd0

[ "MIT" ]

132

2020-08-14T04:37:23.000Z

2022-03-30T04:49:58.000Z

g2p_train/train_and_export.py

gitter-badger/TensorVox

a87a326249e5e31f15c25c41458df01638fd5fd0

[ "MIT" ]

7

2020-11-19T03:55:14.000Z

2022-03-18T00:54:58.000Z

g2p_train/train_and_export.py

gitter-badger/TensorVox

a87a326249e5e31f15c25c41458df01638fd5fd0

[ "MIT" ]

14

2020-08-16T10:25:14.000Z

2021-12-21T06:32:09.000Z

from tqdm import tqdm import os import argparse import tensorflow as tf import yaml import shutil global_max = 0 cumodel = None def safemkdir(dirn): if not os.path.isdir(dirn): os.mkdir(dirn) def preprocess(in_fname,char_phn_tok): words = list() phn = list() print("Opening file...") with open(in_fname,"r",encoding="utf-8") as f: for li in tqdm(f.readlines()): spl = li.strip().split("\t") if len(spl) > 1: words.append(spl[0].lower()) #convert to lowercase for re-exporting later phn.append(spl[1]) if char_phn_tok: print("Tokenizing phoneme strings in char level too") phntok = tf.keras.preprocessing.text.Tokenizer(lower=False,filters='"\t\n',char_level=char_phn_tok) txttok = tf.keras.preprocessing.text.Tokenizer(char_level=True) print("Fitting on texts...") phntok.fit_on_texts(phn) txttok.fit_on_texts(words) print("Converting to sequences") txtseqs = txttok.texts_to_sequences(words) phnseqs = phntok.texts_to_sequences(phn) txt_max = len(max(txtseqs, key=len)) phn_max = len(max(phnseqs,key=len)) global global_max global_max = max(txt_max,phn_max) print("Common padding index is " + str(global_max)) txtpadded = tf.keras.preprocessing.sequence.pad_sequences(txtseqs,padding="post",maxlen=global_max) phnpadded = tf.keras.preprocessing.sequence.pad_sequences(phnseqs,padding="post",maxlen=global_max) txtsize = len(txttok.word_index) phnsize = len(phntok.word_index) return txtpadded, phnpadded, txtsize, phnsize, phntok.word_index, txttok.word_index, words, phn def getmodel(input_shape, in_vocab_size, out_vocab_size,gru_size,in_lr): model = tf.keras.models.Sequential([tf.keras.layers.Embedding(in_vocab_size, gru_size, input_length=input_shape[1], input_shape=input_shape[1:]), tf.keras.layers.Bidirectional(tf.keras.layers.GRU(gru_size,input_shape=input_shape[1:],return_sequences=True)), tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(1024,activation="relu")), tf.keras.layers.Dropout(0.5), tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(out_vocab_size,activation="softmax"))]) model.compile(loss='sparse_categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(in_lr), metrics=['accuracy']) return model @tf.function( experimental_relax_shapes=True, input_signature=[ tf.TensorSpec([None], dtype=tf.int32, name="input_ids"), tf.TensorSpec([1,], dtype=tf.int32, name="input_len"), tf.TensorSpec([1,], dtype=tf.float32, name="input_temperature"), ], ) def callg2p(input_ids,input_len,input_temperature): #Generate padding pad = tf.zeros([global_max - input_len[0]],dtype=tf.int32) #Add padding to input_ids and reshape input_ids = tf.concat([input_ids,pad],0) input_ids = tf.reshape(input_ids,[-1,global_max]) #Predict pred = cumodel(input_ids) #Apply temperature predx = tf.squeeze(pred, 0) predx /= input_temperature #Select IDs retids = tf.random.categorical(predx, 1) #Remove padding bool_mask = tf.not_equal(retids, 0) phn_ids = tf.boolean_mask(retids, bool_mask) return tf.cast(phn_ids,tf.int32) def exportdict(indict,outf): f = open(outf,"w") for de in indict: f.write(de + "\t" + str(indict[de]) + "\n") f.close() def export_model(folname,in_model,in_phnwi,in_charwi): safemkdir(folname) exportdict(in_phnwi,os.path.join(folname,"phn2id.txt")) exportdict(in_charwi,os.path.join(folname,"char2id.txt")) print("Exporting model...") in_model.save(os.path.join(folname,"model"),save_format="tf",signatures=callg2p) def main(): parser = argparse.ArgumentParser(description="Train and export a G2P model") parser.add_argument( "--config-path", default="config/default.yaml", type=str, help="Path of config", ) parser.add_argument( "--dict-path", default="dict.txt", type=str, help="Path of dictionary", ) parser.add_argument( "--out-path", default="model1", type=str, help="Output path of model", ) parser.add_argument( "--char-tok-phn", action="store_true", help="Whether to tokenize phoneme strings by char. Turn this on if using IPA or some other phoneme with no spaces inbetween", ) args = parser.parse_args() txtpadded, phnpadded, txtsize, phnsize, phn_wi, txt_wi, words, phns = preprocess(args.dict_path,args.char_tok_phn) yf = open(args.config_path,"r") config = yaml.load(yf) yf.close() print("Finished preprocessing. Getting model") global cumodel cumodel = getmodel(txtpadded.shape,txtsize + 1,phnsize + 1,config["gru_dims"],config["learning_rate"]) x_train = txtpadded y_train = phnpadded print("Starting training...") cumodel.fit(x_train, y_train, batch_size=config["batch_size"], epochs=config["epochs"],validation_split=config["val_per"]) print("Starting export...") export_model(args.out_path,cumodel,phn_wi,txt_wi) print("Re-exporting dict...") outdict = open(os.path.join(args.out_path,"dict.txt"),"w",encoding="utf-8") for idx, w in enumerate(words): outdict.write(w + "\t" + phns[idx] + "\n") outdict.close() print("Done!") if __name__ == "__main__": main()

29.291005

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0.665643

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4.715232

0.313907

0.027528

0.029213

0.014326

0.123876

0.048315

0.025843

0

0.009232

0.197796

5,536

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0.792389

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0.085938

0

null

0

null

0

1

0

9328f45eec569820bd8aed41540890adb6c0a93e

1,299

py

Python

e2xgrader/preprocessors/overwritecells.py

mhwasil/e2xgrader

14c57c0b8e4bd7a689a9f98066c700b83818e954

[ "MIT" ]

null

e2xgrader/preprocessors/overwritecells.py

mhwasil/e2xgrader

14c57c0b8e4bd7a689a9f98066c700b83818e954

[ "MIT" ]

null

e2xgrader/preprocessors/overwritecells.py

mhwasil/e2xgrader

14c57c0b8e4bd7a689a9f98066c700b83818e954

[ "MIT" ]

null

import json from nbformat.notebooknode import NotebookNode from nbconvert.exporters.exporter import ResourcesDict from typing import Tuple from nbgrader.preprocessors import OverwriteCells as NbgraderOverwriteCells from ..utils.extra_cells import is_extra_cell class OverwriteCells(NbgraderOverwriteCells): def preprocess_cell(self, cell: NotebookNode, resources: ResourcesDict, cell_index: int ) -> Tuple[NotebookNode, ResourcesDict]: if not is_extra_cell(cell): return super().preprocess_cell(cell, resources, cell_index) grade_id = cell.metadata.get('nbgrader', {}).get('grade_id', None) if grade_id is None: return cell, resources try: source_cell = self.gradebook.find_source_cell( grade_id, self.notebook_id, self.assignment_id ) except MissingEntry: self.log.warning(f'Cell {grade_id} does not exist in database') del cell.metadata.nbgrader['grade_id'] return cell, resources cell.metadata.extended_cell.source = json.loads(source_cell.source) return cell, resources

35.108108

75

0.617398

133

1,299

5.864662

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0.315627

1,299

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76

35.108108

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false

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0.206897

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0.413793

0

null

0

null

0

1

0

932b0148fc6f49141d5f5ef655554fa68bc9e82e

1,462

py

Python

qtvscodestyle/examples/widget_gallery/ui/dock.py

Greatness7/QtVSCodeStyle

2654ca967c7ae5db3ce3fb46657ace9f1104f6b9

[ "MIT" ]

8

2021-10-04T00:21:25.000Z

2022-03-14T19:57:03.000Z

qtvscodestyle/examples/widget_gallery/ui/dock.py

Greatness7/QtVSCodeStyle

2654ca967c7ae5db3ce3fb46657ace9f1104f6b9

[ "MIT" ]

null

qtvscodestyle/examples/widget_gallery/ui/dock.py

Greatness7/QtVSCodeStyle

2654ca967c7ae5db3ce3fb46657ace9f1104f6b9

[ "MIT" ]

3

2021-11-15T23:58:33.000Z

2022-02-01T18:50:01.000Z

from qtvscodestyle.qtpy.QtCore import Qt from qtvscodestyle.qtpy.QtWidgets import QDockWidget, QMainWindow, QTextEdit class DockUI: def _setup_ui(self, main_win: QMainWindow) -> None: # Attribute left_dock = QDockWidget("Left dock") right_dock = QDockWidget("Right dock") top_dock = QDockWidget("Top dock") bottom_dock = QDockWidget("Bottom dock") docks = [left_dock, right_dock, top_dock, bottom_dock] # Setup ui left_dock.setWidget(QTextEdit("This is the left widget.")) right_dock.setWidget(QTextEdit("This is the right widget.")) top_dock.setWidget(QTextEdit("This is the top widget.")) bottom_dock.setWidget(QTextEdit("This is the bottom widget.")) for dock in docks: dock.setAllowedAreas( Qt.DockWidgetArea.LeftDockWidgetArea | Qt.DockWidgetArea.RightDockWidgetArea | Qt.DockWidgetArea.BottomDockWidgetArea | Qt.DockWidgetArea.TopDockWidgetArea ) # Layout main_win.setCentralWidget(QTextEdit("This is the central widget.")) main_win.addDockWidget(Qt.DockWidgetArea.LeftDockWidgetArea, left_dock) main_win.addDockWidget(Qt.DockWidgetArea.RightDockWidgetArea, right_dock) main_win.addDockWidget(Qt.DockWidgetArea.TopDockWidgetArea, top_dock) main_win.addDockWidget(Qt.DockWidgetArea.BottomDockWidgetArea, bottom_dock)

44.30303

83

0.688782

152

1,462

6.467105

0.282895

0.130214

0.076297

0.091556

0.284842

0.24822

0

0.22777

1,462

32

84

45.6875

0.870682

0.0171

0

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1

0.04

false

0

0.08

0

0.16

0

null

0

null

0

1

0

932c3dccec809da71964fb1b9ece1362885627e8

7,563

py

Python

interpretdl/interpreter/gradient_cam.py

Tyihou/InterpretDL

df8894f8703634df4bfcbdcc495a3d12b220028c

[ "Apache-2.0" ]

1

2021-03-11T02:38:51.000Z

interpretdl/interpreter/gradient_cam.py

Tyihou/InterpretDL

df8894f8703634df4bfcbdcc495a3d12b220028c

[ "Apache-2.0" ]

null

interpretdl/interpreter/gradient_cam.py

Tyihou/InterpretDL

df8894f8703634df4bfcbdcc495a3d12b220028c

[ "Apache-2.0" ]

null

import typing from typing import Any, Callable, List, Tuple, Union import numpy as np import os, sys from PIL import Image from .abc_interpreter import Interpreter from ..data_processor.readers import preprocess_image, read_image, restore_image, preprocess_inputs from ..data_processor.visualizer import visualize_heatmap class GradCAMInterpreter(Interpreter): """ Gradient CAM Interpreter. More details regarding the GradCAM method can be found in the original paper: https://arxiv.org/abs/1610.02391 """ def __init__(self, paddle_model, trained_model_path, use_cuda=True, model_input_shape=[3, 224, 224]) -> None: """ Initialize the GradCAMInterpreter. Args: paddle_model (callable): A user-defined function that gives access to model predictions. It takes the following arguments: - data: Data inputs. and outputs predictions. See the example at the end of ``interpret()``. trained_model_path (str): The pretrained model directory. use_cuda (bool, optional): Whether or not to use cuda. Default: True model_input_shape (list, optional): The input shape of the model. Default: [3, 224, 224] """ Interpreter.__init__(self) self.paddle_model = paddle_model self.trained_model_path = trained_model_path self.use_cuda = use_cuda self.model_input_shape = model_input_shape self.paddle_prepared = False def interpret(self, inputs, target_layer_name, labels=None, visual=True, save_path=None): """ Main function of the interpreter. Args: inputs (str or list of strs or numpy.ndarray): The input image filepath or a list of filepaths or numpy array of read images. target_layer_name (str): The target layer to calculate gradients. labels (list or tuple or numpy.ndarray, optional): The target labels to analyze. The number of labels should be equal to the number of images. If None, the most likely label for each image will be used. Default: None visual (bool, optional): Whether or not to visualize the processed image. Default: True save_path (str or list of strs or None, optional): The filepath(s) to save the processed image(s). If None, the image will not be saved. Default: None :return: interpretations/heatmap for each image :rtype: numpy.ndarray Example:: import interpretdl as it def paddle_model(data): import paddle.fluid as fluid class_num = 1000 model = ResNet50() logits = model.net(input=image_input, class_dim=class_num) probs = fluid.layers.softmax(logits, axis=-1) return probs gradcam = it.GradCAMInterpreter(paddle_model, "assets/ResNet50_pretrained",True) gradcam.interpret( 'assets/catdog.png', 'res5c.add.output.5.tmp_0', label=None, visual=True, save_path='assets/gradcam_test.jpg') """ imgs, data, save_path = preprocess_inputs(inputs, save_path, self.model_input_shape) self.target_layer_name = target_layer_name if not self.paddle_prepared: self._paddle_prepare() bsz = len(data) if labels is None: _, _, out = self.predict_fn( data, np.zeros( (bsz, 1), dtype='int64')) labels = np.argmax(out, axis=1) labels = np.array(labels).reshape((bsz, 1)) feature_map, gradients, _ = self.predict_fn(data, labels) f = np.array(feature_map) g = np.array(gradients) mean_g = np.mean(g, (2, 3)) heatmap = f.transpose([0, 2, 3, 1]) dim_array = np.ones((1, heatmap.ndim), int).ravel() dim_array[heatmap.ndim - 1] = -1 dim_array[0] = bsz heatmap = heatmap * mean_g.reshape(dim_array) heatmap = np.mean(heatmap, axis=-1) heatmap = np.maximum(heatmap, 0) heatmap_max = np.max(heatmap, axis=tuple(np.arange(1, heatmap.ndim))) heatmap /= heatmap_max.reshape((bsz, ) + (1, ) * (heatmap.ndim - 1)) for i in range(bsz): visualize_heatmap(heatmap[i], imgs[i], visual, save_path[i]) return heatmap def _paddle_prepare(self, predict_fn=None): if predict_fn is None: import paddle.fluid as fluid startup_prog = fluid.Program() main_program = fluid.Program() with fluid.program_guard(main_program, startup_prog): with fluid.unique_name.guard(): image_op = fluid.data( name='image', shape=[None] + self.model_input_shape, dtype='float32') label_op = fluid.layers.data( name='label', shape=[None, 1], dtype='int64') probs = self.paddle_model(image_op) if isinstance(probs, tuple): probs = probs[0] # manually switch the model to test mode for op in main_program.global_block().ops: if op.type == 'batch_norm': op._set_attr('use_global_stats', True) elif op.type == 'dropout': op._set_attr('dropout_prob', 0.0) # fetch the target layer trainable_vars = list(main_program.list_vars()) for v in trainable_vars: if v.name == self.target_layer_name: conv = v class_num = probs.shape[-1] one_hot = fluid.layers.one_hot(label_op, class_num) one_hot = fluid.layers.elementwise_mul(probs, one_hot) target_category_loss = fluid.layers.reduce_sum( one_hot, dim=1) # target_category_loss = - fluid.layers.cross_entropy(probs, label_op)[0] # add back-propagration p_g_list = fluid.backward.append_backward( target_category_loss) # calculate the gradients w.r.t. the target layer gradients_map = fluid.gradients(target_category_loss, conv)[0] if self.use_cuda: gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0)) place = fluid.CUDAPlace(gpu_id) else: place = fluid.CPUPlace() exe = fluid.Executor(place) fluid.io.load_persistables(exe, self.trained_model_path, main_program) def predict_fn(data, labels): feature_map, gradients, out = exe.run( main_program, feed={'image': data, 'label': labels}, fetch_list=[conv, gradients_map, probs]) return feature_map, gradients, out self.predict_fn = predict_fn self.paddle_prepared = True

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0

0.152381

0

null

0

null

0

1

0

932c64f215335ed9010eadefdd112aeb33d21c55

740

py

Python

examples/ec2_role_example/ec2_role_example.py

YuvalShaul/easyawslib

59284f8b408ccb5a1846f6c2a2982a0b7a5e28dd

[ "MIT" ]

null

examples/ec2_role_example/ec2_role_example.py

YuvalShaul/easyawslib

59284f8b408ccb5a1846f6c2a2982a0b7a5e28dd

[ "MIT" ]

null

examples/ec2_role_example/ec2_role_example.py

YuvalShaul/easyawslib

59284f8b408ccb5a1846f6c2a2982a0b7a5e28dd

[ "MIT" ]

1

2021-04-13T10:39:16.000Z

from easyaws.ec2_vm import Ec2Tool from easyaws.s3_bucket import S3Bucket def get_metadata_creds(): creds = Ec2Tool.get_credentials() print('metadata credentials:', creds) return creds def get_metadata_role_arn(): role_arn = Ec2Tool.get_role() print('role arn: ', role_arn) return role_arn def list_s3_buckets(): my_s3 = S3Bucket(bucket_name='my-first-bucket-84629694625') ans = my_s3.s3_client.list_buckets() print(ans) return ans def do_all(): region = 'us-east-1' aws_access_key_id, aws_secret_access_key, token = get_metadata_creds() role_arn = get_metadata_role_arn() try: list_s3_buckets() except Exception as e: print('Creds not good!!!', e) do_all()

23.870968

74

0.698649

108

740

4.462963

0.416667

0.10166

0.058091

0.074689

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31

75

23.870968

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false

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0.166667

0

null

0

null

0

1

0

932e61c9520a7b1646e51a8e609370789b6fc95a

972

py

Python

utils/set_user_to_trusted.py

DCGM/pero_ocr_web

e901027712827278f9ace914f6ccba16d3ac280f

[ "BSD-2-Clause" ]

2

2020-05-07T13:58:31.000Z

2021-01-27T09:33:07.000Z

utils/set_user_to_trusted.py

DCGM/pero_ocr_web

e901027712827278f9ace914f6ccba16d3ac280f

[ "BSD-2-Clause" ]

47

2019-09-17T19:20:07.000Z

2022-03-20T12:33:28.000Z

utils/set_user_to_trusted.py

DCGM/pero_ocr_web

e901027712827278f9ace914f6ccba16d3ac280f

[ "BSD-2-Clause" ]

1

2019-10-02T10:42:35.000Z

import sys from sqlalchemy import create_engine from sqlalchemy.orm import scoped_session, sessionmaker import argparse from app.db import User def parseargs(): parser = argparse.ArgumentParser() parser.add_argument('-d', '--database', type=str, required=True, help="Database.") parser.add_argument('-e', '--email', type=str, required=True, help="Email of user.") args = parser.parse_args() return args def main(): args = parseargs() database_url = 'sqlite:///' + args.database engine = create_engine(database_url, convert_unicode=True, connect_args={'check_same_thread': False}) db_session = scoped_session(sessionmaker(autocommit=False, autoflush=False, bind=engine)) user = db_session.query(User).filter(User.email == args.email).first() user.trusted = 1 db_session.commit() if __name__ == '__main__': sys.exit(main())

30.375

105

0.646091

113

972

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0.044702

0.082781

0.062914

0.076159

0

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0.230453

972

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0

0.347826

0

null

0

null

0

1

0

932e660d4a56e07ffab941905822671642e41cf2

2,025

py

Python

tests/test_backends/test_redis.py

theruziev/aio_pubsub

7629c6c5fe02218e5300fa11c6f0cf2beaf8aaf5

[ "MIT" ]

7

2019-06-11T12:39:39.000Z

2021-03-23T13:41:01.000Z

tests/test_backends/test_redis.py

hugokernel/aio_pubsub

992762bd316793d588de055075eedf70f2087870

[ "MIT" ]

150

2019-05-30T09:18:07.000Z

2022-02-04T17:21:17.000Z

tests/test_backends/test_redis.py

theruziev/aio_pubsub

7629c6c5fe02218e5300fa11c6f0cf2beaf8aaf5

[ "MIT" ]

3

2019-07-12T13:37:13.000Z

2021-02-20T20:53:12.000Z

import aioredis import pytest from aio_pubsub.backends.redis import RedisPubSub @pytest.fixture async def create_pub_sub_conn(): pub = await aioredis.create_redis("redis://localhost:6379/0?encoding=utf-8") sub = await aioredis.create_redis("redis://localhost:6379/0?encoding=utf-8") yield pub, sub pub.close() sub.close() @pytest.mark.asyncio async def test_subscriber_isinstance(create_pub_sub_conn): from aio_pubsub.backends.redis import RedisSubscriber pubsub = RedisPubSub(*create_pub_sub_conn) subscriber = await pubsub.subscribe("a_chan") assert isinstance(subscriber, RedisSubscriber) @pytest.mark.asyncio async def test_iteration_protocol(create_pub_sub_conn): pubsub = RedisPubSub(*create_pub_sub_conn) subscriber = await pubsub.subscribe("a_chan") await pubsub.publish("a_chan", "hello world!") subscriber = subscriber.__aiter__() assert await subscriber.__anext__() == "hello world!" @pytest.mark.asyncio async def test_pubsub(create_pub_sub_conn): pubsub = RedisPubSub(*create_pub_sub_conn) subscriber = await pubsub.subscribe("a_chan") await pubsub.publish("a_chan", "hello world!") await pubsub.publish("a_chan", "hello universe!") subscriber = subscriber.__aiter__() assert await subscriber.__anext__() == "hello world!" assert await subscriber.__anext__() == "hello universe!" @pytest.mark.asyncio async def test_not_subscribed_chan(create_pub_sub_conn): pubsub = RedisPubSub(*create_pub_sub_conn) subscriber_a_chan = await pubsub.subscribe("a_chan") subscriber_c_chan = await pubsub.subscribe("c_chan") await pubsub.publish("a_chan", "hello world!") await pubsub.publish("b_chan", "junk message") await pubsub.publish("c_chan", "hello universe!") subscriber_a_chan = subscriber_a_chan.__aiter__() subscriber_c_chan = subscriber_c_chan.__aiter__() assert await subscriber_a_chan.__anext__() == "hello world!" assert await subscriber_c_chan.__anext__() == "hello universe!"

35.526316

80

0.749136

263

2,025

5.380228

0.186312

0.042403

0.076325

0.101767

0.669965

0.64523

0.470671

0.384452

0

0.006885

0.139259

2,025

56

81

36.160714

0.804934

0

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0.142222

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0

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0.090909

0

null

0

null

0

1

0

932f2c5b9f0c92ca43e0d3230731f07ab383efe2

1,223

py

Python

financescraper/extractors.py

kpitzen/financescraper

cfe32af33e1903a5725813f3d604f56025b21634

[ "MIT" ]

1

2020-07-23T10:58:28.000Z

financescraper/extractors.py

kpitzen/financescraper

cfe32af33e1903a5725813f3d604f56025b21634

[ "MIT" ]

null

financescraper/extractors.py

kpitzen/financescraper

cfe32af33e1903a5725813f3d604f56025b21634

[ "MIT" ]

null

'''Contains classes and methods dedicated to scraping web finance data''' from json.decoder import JSONDecodeError import demjson import requests class BaseStockDataPump(): '''Base class for intake of stock data''' def __init__(self, url, stock_name, output_queue = None, chunk_size: int = 5): self._url = url self._data = None self._get_stock_data() self._stock_name = stock_name self._output_queue = output_queue self._chunk_size = chunk_size def _get_stock_data(self): data_request = requests.get(self._url) try: assert 'application/json' in data_request.headers['Content-Type'] except AssertionError: print(data_request) raise NotImplementedError('We require JSON returns!') try: request_data = data_request.json() except JSONDecodeError: request_data = data_request.text request_data = demjson.decode(request_data) self._data = request_data def feed_data(self): print('>>Feeding {} data...'.format(self._stock_name)) self._output_queue.put((self._stock_name, self._data)) self._output_queue.put('kill')

32.184211

82

0.654947

146

1,223

5.171233

0.410959

0.087417

0.051656

0.042384

0.063576

0

0.001095

0.253475

1,223

37

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33.054054

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0.071429

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0.107143

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0

0.107143

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0.25

0.071429

0

null

0

null

0

1

0

932f6e9ea473e0bd5ce0911e26562b22f5c7faf1

596

py

Python

backend/settings/prod.py

DataHack-CSCE606/django-vue-template

9dd1b1bf91223383938b844ed484de2d3b949a4d

[ "MIT" ]

null

backend/settings/prod.py

DataHack-CSCE606/django-vue-template

9dd1b1bf91223383938b844ed484de2d3b949a4d

[ "MIT" ]

1

2021-04-26T04:48:16.000Z

backend/settings/prod.py

DataHack-CSCE606/django-vue-template

9dd1b1bf91223383938b844ed484de2d3b949a4d

[ "MIT" ]

null

""" Production Settings """ import os import dj_database_url #import django_heroku from .dev import * ############ # DATABASE # ############ DATABASES = { 'default': dj_database_url.config( default=os.getenv('DATABASE_URL') ) } ############ # SECURITY # ############ DEBUG = bool(os.getenv('DJANGO_DEBUG', '')) SECRET_KEY = os.getenv('DJANGO_SECRET_KEY', SECRET_KEY) # Set to your Domain here (eg. 'django-vue-template-demo.herokuapp.com') ALLOWED_HOSTS = ['portfoliotradingassistant.herokuapp.com', 'localhost:8000', 'localhost:8080'] #django_heroku.settings(locals())

18.625

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0.654362

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0.015504

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596

31

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0.715116

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0.272727

0

null

0

null

0

1

0

932fdc8a4b8b337b35f8ecfe153137f737d06999

5,561

py

Python

backend/crud.py

jphacks/A_2111

22624f3f6bb4cc4eb40cc16a2113b7e860d5159e

[ "MIT" ]

8

2021-10-31T06:45:27.000Z

2021-11-30T04:33:17.000Z

backend/crud.py

jphacks/A_2111

22624f3f6bb4cc4eb40cc16a2113b7e860d5159e

[ "MIT" ]

55

2021-10-29T18:25:09.000Z

2022-02-27T19:42:48.000Z

backend/crud.py

jphacks/A_2111

22624f3f6bb4cc4eb40cc16a2113b7e860d5159e

[ "MIT" ]

5

2021-11-23T05:41:59.000Z

2021-12-20T02:20:19.000Z

from fastapi import HTTPException, status import os from uuid import uuid4 from firebase import db from firebase_admin import firestore import numpy as np # 全ての登録情報を取得 async def get_all_members(): docs = db.collection("members").stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) return data # 特定の登録情報を取得 async def get_member(uuid: str): docs = db.collection("members").where("uuid", "==", uuid).stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) if len(data) == 0: raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="このIDは見つかりません") return data # すべてのリレーション情報を取得 async def get_all_familiars(): docs = db.collection("familiars").stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) return data # 特定のリレーション情報を取得 async def get_familiar(uuid: str): docs = db.collection("familiars").where("start", "==", uuid).stream() docs2 = db.collection("familiars").where("end", "==", uuid).stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) for doc in docs2: post = {"id": doc.id, **doc.to_dict()} data.append(post) return data # メンバー登録 async def create_member(name: str, size: str, vector: str) -> str: size_width = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"] if size not in size_width: raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="1～10のいずれかの整数を半角で入力してください") uuid = str(uuid4()) doc_ref = db.collection("members").document() doc_ref.set({ "uuid": uuid, "name": name, "size": size, "vector": vector }) return uuid # リレーション登録 async def create_familiar(start: str, end: str): doc_ref = db.collection("familiars").document() doc_ref.set({ "start": start, "end": end }) return True # 既存のリレーションの有無を確認 async def existed_familiar(start: str, end: str): docs = db.collection("familiars").where("start", "==", start).where("end", "==", end).stream() docs2 = db.collection("familiars").where("start", "==", end).where("end", "==", start).stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) for doc in docs2: post = {"id": doc.id, **doc.to_dict()} data.append(post) if len(data) != 0: raise HTTPException(status_code=status.HTTP_409_CONFLICT, detail="このIDはすでに登録されています") return True # 登録情報を更新 async def update_member(uuid: str, name: str, size: str): size_width = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"] if size not in size_width: raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="1～10のいずれかの整数を半角で入力してください") docs = db.collection("members").where("uuid", "==", uuid).stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) if len(data) == 0: raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="あなたのIDが見つかりませんでした") doc_ref = db.collection("members").document(data[0]["id"]) result = doc_ref.update({"name": name, "size": size}) return result # 登録情報を削除 async def remove_member(uuid: str): docs = db.collection("members").where("uuid", "==", uuid).stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) if len(data) == 0: raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="あなたのIDが見つかりませんでした") result = db.collection("members").document(data[0]["id"]).delete() return result # 登録情報を削除した際、それに付随するリレーションも全て削除 async def remove_familiar_related_member(uuid: str): docs = db.collection("familiars").where("start", "==", uuid).stream() docs2 = db.collection("familiars").where("end", "==", uuid).stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) for doc in docs2: post = {"id": doc.id, **doc.to_dict()} data.append(post) i = 0 while True: if i > len(data) - 1: break db.collection("familiars").document(data[i]["id"]).delete() i += 1 return True # 特定のリレーションを削除 async def remove_familiar(start: str, end: str): docs = db.collection("familiars").where("start", "==", start).where("end", "==", end).stream() docs2 = db.collection("familiars").where("start", "==", end).where("end", "==", start).stream() data = [] for doc in docs: post = {"id": doc.id, **doc.to_dict()} data.append(post) for doc in docs2: post = {"id": doc.id, **doc.to_dict()} data.append(post) if len(data) == 0: raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="登録しているIDがありません") result = db.collection("familiars").document(data[0]["id"]).delete() return result # cos類似度を計算 async def cosine_similarity(a, b): return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) # もらったベクトルとDBに登録されているベクトルを照合 async def login(uuid: str, vector: list): already_registered_vector = db.collection("members").where("uuid", "==", uuid).stream() for vec in already_registered_vector: post = {"id": vec.id, **vec.to_dict()} cosine_result = await cosine_similarity(vector, post["vector"]) return cosine_result

30.387978

103

0.608344

724

5,561

4.569061

0.160221

0.039299

0.031439

0.043229

0.654172

0.631197

0.617594

0.570133

0

0.016099

0.218126

5,561

182

104

30.554945

0.74471

0.032368

0

0.625

0

0.089858

0.008949

0

1

0

false

0

0.044118

0

0.139706

0

null

0

null

0

1

0

933070cff398c58c442f2e8174797188b26d5e04

1,429

py

Python

modules/html.py

fagcinsk/pytools

772172451f0b27c7c09508698d24bee2ef40ddb5

[ "MIT" ]

1

2021-01-05T20:49:02.000Z

modules/html.py

fagcinsk/pytools

772172451f0b27c7c09508698d24bee2ef40ddb5

[ "MIT" ]

1

2021-01-13T20:15:02.000Z

2021-01-14T19:16:10.000Z

modules/html.py

fagcinsk/pytools

772172451f0b27c7c09508698d24bee2ef40ddb5

[ "MIT" ]

1

2021-01-05T13:59:08.000Z

class Html: """HTML utilities""" @staticmethod def ahrefs(url): """Get <a> hrefs related to domain""" from lib.pt_html import get_page_ahrefs for href in get_page_ahrefs(url): print(href) def sel(self, url, selector, fmt=None): """Shows some part of source by selector selector -- css selector, ex.: ul>li """ for res in self._soup(url).select(selector): if fmt == 'csv': for tr in res.find_all('tr'): print(tr.get_text(',')) return print(res.prettify()) def xpath(self, url, xpath): """Shows some part of source by xpath xpath -- ex.: //a/@href """ from lxml import etree for res in self._lxml(url).xpath(xpath): if isinstance(res, etree._ElementUnicodeResult): print(res) else: print(etree.tostring(res, pretty_print=True).decode()) def src(self, url): """Shows prettified html source,""" print(self._soup(url).prettify()) @staticmethod def _soup(url): from bs4 import BeautifulSoup from requests import get return BeautifulSoup(get(url).text, 'html.parser') @staticmethod def _lxml(url): from lxml import html from requests import get return html.fromstring(get(url).text)

28.019608

70

0.554934

171

1,429

4.555556

0.380117

0.057766

0.033376

0.038511

0.12837

0.05905

0

0.001048

0.3324

1,429

50

71

28.58

0.815514

0.148355

0

0.15625

0

0.014719

0

1

0.1875

false

0

0.1875

0

0.5

0.1875

0

null

0

null

0

1

0

9330b5b56e6e3955dd86252f7beed42138f64599

2,959

py

Python

loss.py

aadhithya/pytorch-yolo-v1

c362ab4305d22ccf1c0481f9b693e32bf50bd46e

[ "MIT" ]

null

loss.py

aadhithya/pytorch-yolo-v1

c362ab4305d22ccf1c0481f9b693e32bf50bd46e

[ "MIT" ]

null

loss.py

aadhithya/pytorch-yolo-v1

c362ab4305d22ccf1c0481f9b693e32bf50bd46e

[ "MIT" ]

null

from model import YOLOv1 import torch import torch.nn as nn class YOLOv1Loss(nn.Module): def __init__(self, S=7, B=2, C=20): """ __init__ initialize YOLOv1 Loss. Args: S (int, optional): split_size. Defaults to 7. B (int, optional): number of boxes. Defaults to 2. C (int, optional): number of classes. Defaults to 20. """ super().__init__() self.mse = nn.MSELoss(reduction="sum") self.S = S self.B = B self.C = C self.l_noobl = 0.5 self.l_coord = 5 def forward(self, predictions, target): predictions = predictions.reshape(-1, self.S, self.S, self.C + Self.B*5) iou_b1 = get_iou(predictions[...,21:25], target[...,21:25]) iou_b2 = get_iou(predictions[...,26:30], target[...,21:25]) ious = torch.stack([iou_b1, iou_b2], 0) _, max_iou = torch.max(ious, dim=0) exists_box = target[...,20].unsqueeze(3) # select target objectness.object # * Box Coordinates Loss # Select the bounding boxes with highest IoU box_predictions = exists_box * ( ( max_iou * predictions[..., 26:30] + (1 - max_iou) * predictions[..., 21:25] ) ) # Select targets which has an object box_targets = exists_box * target[...,21:25] box_predictions[...,2:4] = torch.sign(box_predictions[...,2:4]) * torch.sqrt( torch.abs(box_predictions[..., 2:4]) + 1e-6 ) box_targets[..., 2:4] = torch.sqrt(box_targets[..., 2:4]) box_loss = self.mse( torch.flatten(box_predictions, end_dim=-2), torch.flatten(box_targets, end_dim=-2) ) # * Object Losss pred_box = ( max_iou * predictions[..., 25:26] + (1-max_iou) * predictions[..., 20:21] ) object_loss = self.mse( torch.flatten(exists_box * pred_box), torch.flatten(exists_box * target[..., 20:21]) ) # * No Object Loss # For the first box no_boject_loss = self.mse( torch.flatten((1-max_iou) * predictions[...,20:21], start_dim=1), torch.flatten((1-max_iou) * target[...,20:21], start_dim=1) ) # For the second box no_boject_loss += self.mse( torch.flatten(max_iou * predictions[...,25:26], start_dim=1), torch.flatten(max_iou * target[...,20:21], start_dim=1) ) # * Class prediction Loss class_loss = self.mse( torch.flatten(exists_box * predictions[...,:20], end_dim=-2), torch.flatten(exists_box * target[...,:20], end_dim=-2) ) # * Total Loss loss = ( self.l_coord * box_loss + object_loss + self.l_noobl * no_boject_loss + class_loss ) return loss

31.147368

85

0.528219

370

2,959

4.032432

0.254054

0.080429

0.068365

0.053619

0.308311

0.190349

0.121984

0.079088

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0.054995

0.330179

2,959

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false

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0.04918

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0.114754

0

null

0

null

0

1

0

93310a9f4d736b657493fe8d8dcee72bcfd8191c

5,332

py

Python

Inverter.glyphsFilter/Contents/Resources/plugin.py

mekkablue/Inverter

8ed3ba6199c738c7621da7a6a223af2f6b021828

[ "Apache-2.0" ]

1

2015-01-12T10:24:58.000Z

Inverter.glyphsFilter/Contents/Resources/plugin.py

mekkablue/Inverter

8ed3ba6199c738c7621da7a6a223af2f6b021828

[ "Apache-2.0" ]

2

2016-01-29T16:56:59.000Z

2018-01-01T14:51:21.000Z

Inverter.glyphsFilter/Contents/Resources/plugin.py

mekkablue/Inverter

8ed3ba6199c738c7621da7a6a223af2f6b021828

[ "Apache-2.0" ]

1

2017-12-30T21:20:14.000Z

# encoding: utf-8 from __future__ import division, print_function, unicode_literals ########################################################################################################### # # # Filter with dialog Plugin # # Read the docs: # https://github.com/schriftgestalt/GlyphsSDK/tree/master/Python%20Templates/Filter%20with%20Dialog # # For help on the use of Interface Builder: # https://github.com/schriftgestalt/GlyphsSDK/tree/master/Python%20Templates # # ########################################################################################################### import objc from GlyphsApp import * from GlyphsApp.plugins import * from AppKit import NSAffineTransform, NSPoint from math import tan, pi class Inverter(FilterWithDialog): dialog = objc.IBOutlet() topEdgeField = objc.IBOutlet() bottomEdgeField = objc.IBOutlet() overlapField = objc.IBOutlet() @objc.python_method def settings(self): self.menuName = Glyphs.localize({ 'en': 'Inverter', 'de': 'Umkehren', 'fr': 'Inverter', 'es': 'Invertar', 'it': 'Invertire', 'pt': 'Inverter', }) self.actionButtonLabel = Glyphs.localize({ 'en': 'Invert', 'de': 'Umkehren', 'fr': 'Inverter', 'es': 'Invertar', 'it': 'Invertire', 'pt': 'Inverter', }) Glyphs.registerDefault( "com.mekkablue.Inverter.topEdge", 800.0 ) Glyphs.registerDefault( "com.mekkablue.Inverter.bottomEdge", -200.0 ) Glyphs.registerDefault( "com.mekkablue.Inverter.overlap", 5.0 ) # Load dialog from .nib (without .extension) self.loadNib('IBdialog', __file__) # On dialog show @objc.python_method def start(self): # Set value of text field self.topEdgeField.setFloatValue_( Glyphs.defaults['com.mekkablue.Inverter.topEdge'] ) self.bottomEdgeField.setFloatValue_( Glyphs.defaults['com.mekkablue.Inverter.bottomEdge'] ) self.overlapField.setFloatValue_( Glyphs.defaults['com.mekkablue.Inverter.overlap'] ) self.topEdgeField.becomeFirstResponder() @objc.IBAction def setTopEdge_( self, sender ): # Store value coming in from dialog Glyphs.defaults['com.mekkablue.Inverter.topEdge'] = sender.floatValue() # Trigger redraw self.update() @objc.IBAction def setBottomEdge_( self, sender ): Glyphs.defaults['com.mekkablue.Inverter.bottomEdge'] = sender.floatValue() self.update() @objc.IBAction def setOverlap_( self, sender ): Glyphs.defaults['com.mekkablue.Inverter.overlap'] = sender.floatValue() self.update() @objc.python_method def pathRect( self, bottomLeft, topRight, italicAngle=0.0, downShift=0.0 ): try: # coordinates of rectangle: myCoordinates = ( NSPoint( bottomLeft.x, bottomLeft.y ), NSPoint( topRight.x, bottomLeft.y ), NSPoint( topRight.x, topRight.y ), NSPoint( bottomLeft.x, topRight.y ) ) # build the path: rectangle = GSPath() for thisPoint in myCoordinates: newNode = GSNode() newNode.type = 1 # GSLINE newNode.position = thisPoint rectangle.nodes.append( newNode ) rectangle.closed = True # skew if there is an italic angle: if not italicAngle == 0.0: # calculate & build skew transformation: skewTangens = tan( italicAngle/180*pi ) skew = NSAffineTransform.transform() skew.setTransformStruct_( (1.0, 0.0, skewTangens, 1.0, 0.0, downShift) ) skew.translateXBy_yBy_( 0.0, -downShift ) # apply transformation to points of rectangle: for thisNode in rectangle.nodes: thisNode.position = skew.transformPoint_( thisNode.position ) return rectangle except Exception as e: import traceback print(traceback.format_exc()) print("pathRect: %s" % str(e)) @objc.python_method def filter(self, layer, inEditView, customParameters): topEdge = float( Glyphs.defaults['com.mekkablue.Inverter.topEdge'] ) bottomEdge = float( Glyphs.defaults['com.mekkablue.Inverter.bottomEdge'] ) overlap = float( Glyphs.defaults['com.mekkablue.Inverter.overlap'] ) # Called on font export, override with values from customParameters: if 'top' in customParameters: topEdge = customParameters['top'] if 'bottom' in customParameters: bottomEdge = customParameters['bottom'] if 'overlap' in customParameters: overlap = customParameters['overlap'] # upper and lower edges of rectangle: bottomLeft = NSPoint( -overlap, bottomEdge ) topRight = NSPoint( layer.width+overlap, topEdge ) # check italic angle and skew origin: thisMaster = layer.associatedFontMaster() skewAngle = thisMaster.italicAngle halfXHeight = thisMaster.xHeight * 0.5 # build the rectangle path: rectangle = self.pathRect( bottomLeft, topRight, skewAngle, halfXHeight ) # add it to the decomposed glyph: if rectangle: layer.decomposeComponents() layer.removeOverlap() try: # GLYPHS 3 layer.shapes.append( rectangle ) except: # GLYPHS 2 layer.paths.append( rectangle ) layer.correctPathDirection() @objc.python_method def generateCustomParameter( self ): return "%s; top:%s; bottom:%s; overlap:%s" % ( self.__class__.__name__, Glyphs.defaults['com.mekkablue.Inverter.topEdge'], Glyphs.defaults['com.mekkablue.Inverter.bottomEdge'], Glyphs.defaults['com.mekkablue.Inverter.overlap'], ) @objc.python_method def __file__(self): """Please leave this method unchanged""" return __file__

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0.153788

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0.109078

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0.078261

false

0

0.06087

0.008696

0.208696

0.026087

0

null

0

null

0

1

0

9336657b701c8f38490d58496d625f32174dfce9

1,832

py

Python

rlrunner/termination/dynamic_tc.py

PriestTheBeast/RLRunner

0626508a8133b67947afc1039c30b4fd512656a2

[ "MIT" ]

8

2020-07-06T19:32:30.000Z

2020-08-11T05:50:32.000Z

rlrunner/termination/dynamic_tc.py

PriestTheBeast/RLRunner

0626508a8133b67947afc1039c30b4fd512656a2

[ "MIT" ]

null

rlrunner/termination/dynamic_tc.py

PriestTheBeast/RLRunner

0626508a8133b67947afc1039c30b4fd512656a2

[ "MIT" ]

null

from rlrunner.termination.base_termination_condition import BaseTerminationCondition from collections import deque class DynamicTC(BaseTerminationCondition): """ This is a more complex and dynamic termination condition It will see if there has been sufficient progress in the last X episodes and if not it will assume the agent has stopped learning and terminate the run """ def __init__(self, epi_interval_for_progress=50, nr_exploits_in_interval=10): super().__init__() self.epi_interval_for_progress = epi_interval_for_progress self.nr_exploits_in_interval = nr_exploits_in_interval # this will calculate how frequent the exploit episodes will be to match the requirements wanted # in the default case it will be 50//10 = 5, so in every 5 episodes one of them will be an exploit episode self.exploit_every_x_epi = self.epi_interval_for_progress // self.nr_exploits_in_interval # info about the progress in the last X episodes self.info = deque(maxlen=self.nr_exploits_in_interval) self.cumulative_rewards = 0 def is_exploit_episode(self, episode_number): return episode_number % self.exploit_every_x_epi == 0 def update_info(self, episode_number, transition): # It will be more precise to measure the progress only from exploit episodes if self.is_exploit_episode(episode_number): _, _, reward, _, done = transition self.cumulative_rewards += reward if done: self.info.append(self.cumulative_rewards) self.cumulative_rewards = 0 def check_termination(self, episode_number): # that "3" reward difference is kinda hardcoded for the simple_env reward function # but you get the point if episode_number > self.epi_interval_for_progress: avg = sum(self.info) / len(self.info) best_value = max(self.info) if best_value - avg < 3: return True return False

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108

0.782205

276

1,832

4.945652

0.384058

0.057143

0.051282

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0.067399

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0.158843

1,832

46

109

39.826087

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0.34607

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false

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0.076923

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0.384615

0

null

0

null

0

1

0

933cb13bc7fe5bd1b62885cb8b25ce8a810ed468

2,082

py

Python

Packs/CrowdStrikeFalcon/Scripts/ReadNetstatFile/ReadNetstatFile.py

jrauen/content

81a92be1cbb053a5f26a6f325eff3afc0ca840e0

[ "MIT" ]

null

Packs/CrowdStrikeFalcon/Scripts/ReadNetstatFile/ReadNetstatFile.py

jrauen/content

81a92be1cbb053a5f26a6f325eff3afc0ca840e0

[ "MIT" ]

40

2022-03-03T07:34:00.000Z

2022-03-31T07:38:35.000Z

Packs/CrowdStrikeFalcon/Scripts/ReadNetstatFile/ReadNetstatFile.py

jrauen/content

81a92be1cbb053a5f26a6f325eff3afc0ca840e0

[ "MIT" ]

null

from CommonServerPython import * COMMAND_NAME = 'netstat' def get_netstat_file_name(command_files): if command_files and isinstance(command_files, dict): netstat_files = command_files.get(COMMAND_NAME, []) if netstat_files: if isinstance(netstat_files, list): # we want to get the last file name return netstat_files[len(netstat_files) - 1].get('Filename') elif isinstance(netstat_files, dict): return netstat_files.get('Filename') # type:ignore def get_file_name_from_context() -> str: crowdstrike_context = demisto.context().get('CrowdStrike', {}) all_command_files = [] if isinstance(crowdstrike_context, list): for ctx in crowdstrike_context: if cmd_ctx := ctx.get('Command'): all_command_files.append(cmd_ctx) elif isinstance(crowdstrike_context, dict) and (cmd_ctx := crowdstrike_context.get('Command')): all_command_files.append(cmd_ctx) for command_file in all_command_files[::-1]: # get last file in context if file_name := get_netstat_file_name(command_file): return file_name return "" def get_file_entry_id(file_name): file_entry_id = "" if file_name: entries = demisto.executeCommand('getEntries', {}) for entry in entries: file_entry = demisto.get(entry, 'File') is_correct_file = file_name.lower() == file_entry.lower() if is_correct_file: file_entry_id = entry['ID'] break return file_entry_id def get_file_content(file_entry_id): if file_entry_id: res = execute_command('getFilePath', {'id': file_entry_id}) file_path = res.get('path') with open(file_path, 'r') as f: file_content = f.read() return file_content def main(): file_name = get_file_name_from_context() if file_name: demisto.results(get_file_content(get_file_entry_id(file_name))) if __name__ in ('__main__', '__builtin__', 'builtins'): main()

33.580645

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0.650336

265

2,082

4.735849

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0.082869

0.07012

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0.196016

0.094024

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2,082

61

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0

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0.106383

false

0

0.021277

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0

null

0

null

0

1

0

93448c13d9f2db38fbc78aac40bc93d67e0581e9

5,241

py

Python

src/app/bert.py

korney3/ARES_RVision_Hack

86b53b5e9c5495e988951cc3a11afe61c883d2a6

[ "MIT" ]

1

2021-09-08T16:17:32.000Z

src/app/bert.py

korney3/ARES_RVision_Hack

86b53b5e9c5495e988951cc3a11afe61c883d2a6

[ "MIT" ]

null

src/app/bert.py

korney3/ARES_RVision_Hack

86b53b5e9c5495e988951cc3a11afe61c883d2a6

[ "MIT" ]

3

2021-03-31T09:11:59.000Z

2021-08-18T07:18:51.000Z

import pandas as pd import glob from tqdm import tqdm, trange from nltk.tokenize import sent_tokenize, word_tokenize import numpy as np import json import os import requests from flask import Flask, request, Response from flask_cors import CORS from requests import Request, Session import json import transformers from transformers import BertForTokenClassification, AdamW import torch from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler from transformers import BertTokenizer, BertConfig from keras.preprocessing.sequence import pad_sequences from sklearn.model_selection import train_test_split from transformers import get_linear_schedule_with_warmup from sklearn.metrics import accuracy_score from sklearn.metrics import f1_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt import seaborn as sns app = Flask(__name__) cors = CORS(app) @app.route(('/annotate_text')) def annotate_text(): data = request.args.get('text', default='APT', type=str) text = [] for para in data.strip().split('\n\n'): para = ' '.join(para.strip().replace("\n", " ").split()) if para!='': text.extend(sent_tokenize(para)) annotation = [] for test_sentence in text: prev_label='O' tokenized_sentence = tokenizer.encode(test_sentence) input_ids = torch.tensor([tokenized_sentence])#.cuda() with torch.no_grad(): output = model(input_ids) label_indices = np.argmax(output[0].to('cpu').numpy(), axis=2) tokens = tokenizer.convert_ids_to_tokens(input_ids.to('cpu').numpy()[0]) new_tokens, new_labels = [], [] for token, label_idx in zip(tokens, label_indices[0]): if token.startswith("##"): new_tokens[-1] = new_tokens[-1] + token[2:] else: new_labels.append(tag_values[label_idx]) new_tokens.append(token) from nltk import pos_tag from nltk.tree import Tree from nltk.chunk import conlltags2tree tokens = new_tokens tags = new_labels # tag each token with pos pos_tags = [pos for token, pos in pos_tag(tokens)] # convert the BIO / IOB tags to tree conlltags = [(token, pos, tg) for token, pos, tg in zip(tokens, pos_tags, tags)] ne_tree = conlltags2tree(conlltags) # parse the tree to get our original text original_text = [] for subtree in ne_tree: # checking for 'O' tags if type(subtree) == Tree: original_label = subtree.label() original_string = " ".join([token for token, pos in subtree.leaves()]) if (original_string!='[CLS]' and original_string!='[SEP]'): if original_label==prev_label: original_text.append(original_string) else: original_text.append('<'+original_label.upper()+'>'+original_string) prev_label = original_label elif type(subtree)==tuple: if (subtree[0]!='[CLS]' and subtree[0]!='[SEP]'): if prev_label!='O': original_text[-1]+='</'+original_label.upper()+'>' prev_label='O' original_text.append(subtree[0]) annotation+=[tokenizer.convert_tokens_to_string(original_text)] json_string = json.dumps({'parse':'\n'.join(annotation),'f1_macro':macro_f1[-1], 'prec_macro':macro_prec[-1], 'rec_macro':macro_rec[-1]}, ensure_ascii=False) response = Response(json_string, content_type="application/json; charset=utf-8") return '\n'.join(annotation) @app.route(('/kill_flask')) def kill_flask(): raise ValueError('Server was killed') if __name__ == '__main__': import nltk nltk.download('punkt') nltk.download('averaged_perceptron_tagger') DATA_PATH = '../../data/processed/' LOG_PATH = '../../models/BERT_baseline/' with open(os.path.join(LOG_PATH,'macro_prec.txt'),'r') as f: macro_prec = f.read().strip().split('\n') with open(os.path.join(LOG_PATH,'macro_rec.txt'),'r') as f: macro_rec = f.read().strip().split('\n') with open(os.path.join(LOG_PATH,'macro_f1.txt'),'r') as f: macro_f1 = f.read().strip().split('\n') tokenizer = BertTokenizer.from_pretrained(os.path.join(LOG_PATH), do_lower_case=False) model = BertForTokenClassification.from_pretrained(LOG_PATH) tag_values = ['B-identity', 'I-malware', 'B-org', 'B-industry', 'I-org', 'I-city', 'I-user', 'B-software', 'I-cve', 'B-file', 'I-mitre_attack', 'B-theat_actor', 'I-appdata', 'B-ioc', 'B-mitre_attack', 'B-cve', 'B-technique', 'B-name', 'I-technique', 'I-program', 'I-tool', 'B-user', 'B-major', 'B-city', 'B-appdata', 'I-identity', 'I-ioc', 'O', 'B-timestamp', 'B-pid', 'B-program', 'I-name', 'I-country', 'I-campaign', 'I-local', 'B-country', 'B-campaign', 'B-local', 'I-windows', 'B-attack_pattern', 'B-excel', 'B-n', 'I-timestamp', 'I-software', 'I-industry', 'B-update', 'B-threat_actor', 'B-tool', 'I-type', 'B-windows', 'I-file', 'B-malware', 'B-type', 'I-input', 'B-input', 'I-threat_actor', 'PAD'] app.run(host='0.0.0.0', port=5002)

26.336683

159

0.655218

717

5,241

4.616457

0.304045

0.025378

0.02719

0.036254

0.106042

0.036858

0.027795

0

0.007091

0.192711

5,241

198

160

26.469697

0.77523

0.024423

0

0.038217

0

0.149961

0.014487

0

1

0.012739

false

0

0.197452

0

0.216561

0

null

0

null

0

1

0

9348323aeab818c58eea18c65f534561d0ac86e0

1,026

py

Python

codewars-python/Kingdoms-E-2-The-curse-(simplified).py

fmelihh/competitive-programming-solutions

c15c2f7d90153f35f9bd9ffcea20ac921564eacf

[ "MIT" ]

2

2021-09-06T22:13:12.000Z

2021-11-22T08:50:04.000Z

codewars-python/Kingdoms-E-2-The-curse-(simplified).py

fmelihh/competitive-programming-solutions

c15c2f7d90153f35f9bd9ffcea20ac921564eacf

[ "MIT" ]

null

codewars-python/Kingdoms-E-2-The-curse-(simplified).py

fmelihh/competitive-programming-solutions

c15c2f7d90153f35f9bd9ffcea20ac921564eacf

[ "MIT" ]

null

#https://www.codewars.com/kata/6159dda246a119001a7de465/train/python def translate(s, voc): s = s.split(' ') output = [] for speech in s: for vocabulary in voc: real_speech = speech_decoder(speech, vocabulary) if real_speech: output.append(real_speech) return ' '.join(output) def speech_decoder(speech, vocabulary): copy_speech = speech length_raw_string = len(copy_speech.translate(str.maketrans('','','?!,.'))) if length_raw_string != len(vocabulary): return False i = 0 output = [] for word in speech: if word == '*': output.append(vocabulary[i]) i += 1 continue if word in '?!,.': output.append(word) continue if word == vocabulary[i]: output.append(vocabulary[i]) else: return False i += 1 result = ''.join(output) output.clear() return result

19

79

0.531189

108

1,026

4.944444

0.37037

0.089888

0.071161

0.108614

0

0.03012

0.352827

1,026

53

80

19.358491

0.774096

0.065302

0

0.3125

0

0.011494

0

1

0.0625

false

0

0.1875

0

null

0

null

0

1

0

934873d20743eecdf15b1e37621d9c7a3c1fcae4

3,878

py

Python

locs/datasets/charged_data.py

mkofinas/locs

4cb0ab9e989ebfee42d1d2850bdf3360336b5c1c

[ "MIT" ]

16

2021-11-04T07:57:58.000Z

2022-03-01T17:45:32.000Z

locs/datasets/charged_data.py

mkofinas/locs

4cb0ab9e989ebfee42d1d2850bdf3360336b5c1c

[ "MIT" ]

null

locs/datasets/charged_data.py

mkofinas/locs

4cb0ab9e989ebfee42d1d2850bdf3360336b5c1c

[ "MIT" ]

null

import os import numpy as np import torch from torch.utils.data import Dataset class ChargedData(Dataset): def __init__(self, data_path, mode, params): self.mode = mode self.data_path = data_path if self.mode == 'train': path = os.path.join(data_path, 'train_feats') edge_path = os.path.join(data_path, 'train_edges') elif self.mode == 'val': path = os.path.join(data_path, 'valid_feats') edge_path = os.path.join(data_path, 'valid_edges') elif self.mode == 'test': path = os.path.join(data_path, 'test_feats') edge_path = os.path.join(data_path, 'test_edges') self.feats = torch.load(path) self.edges = torch.load(edge_path) self.same_norm = params['same_data_norm'] self.symmetric_norm = params['symmetric_data_norm'] self.no_norm = params['no_data_norm'] self.vel_norm_norm = params['vel_norm_norm'] if not self.no_norm: self._normalize_data() def _normalize_data(self): train_data = torch.load(os.path.join(self.data_path, 'train_feats')) if self.same_norm: self.feat_max = train_data.max() self.feat_min = train_data.min() self.feats = (self.feats - self.feat_min)*2/(self.feat_max-self.feat_min) - 1 elif self.vel_norm_norm: self.vel_norm_max = np.linalg.norm(train_data[..., 3:], axis=-1).max() self.feats[..., :3] = self.feats[..., :3] / self.vel_norm_max self.feats[..., 3:] = self.feats[..., 3:] / self.vel_norm_max else: if self.symmetric_norm: self.loc_max = train_data[:, :, :, :3].abs().max() self.loc_min = -self.loc_max self.vel_max = train_data[:, :, :, 3:].abs().max() self.vel_min = -self.vel_max else: self.loc_max = train_data[:, :, :, :3].max() self.loc_min = train_data[:, :, :, :3].min() self.vel_max = train_data[:, :, :, 3:].max() self.vel_min = train_data[:, :, :, 3:].min() self.feats[:,:,:, :3] = (self.feats[:,:,:,:3]-self.loc_min)*2/(self.loc_max - self.loc_min) - 1 self.feats[:,:,:,3:] = (self.feats[:,:,:,3:]-self.vel_min)*2/(self.vel_max-self.vel_min)-1 def unnormalize(self, data): if self.no_norm: return data.numpy() elif self.same_norm: return (data + 1) * (self.feat_max - self.feat_min) / 2. + self.feat_min elif self.vel_norm_norm: result1 = data[..., :3] * self.vel_norm_max result2 = data[..., 3:] * self.vel_norm_max return np.concatenate([result1, result2], axis=-1) else: result1 = (data[:, :, :, :3] + 1) * (self.loc_max - self.loc_min) / 2. + self.loc_min result2 = (data[:, :, :, 3:] + 1) * (self.vel_max - self.vel_min) / 2. + self.vel_min return np.concatenate([result1, result2], axis=-1) def torch_unnormalize(self, data): if self.no_norm: return data elif self.same_norm: return (data + 1) * (self.feat_max - self.feat_min) / 2. + self.feat_min elif self.vel_norm_norm: result1 = data[..., :3] * self.vel_norm_max result2 = data[..., 3:] * self.vel_norm_max return torch.cat([result1, result2], axis=-1) else: result1 = (data[:, :, :, :3] + 1) * (self.loc_max - self.loc_min) / 2. + self.loc_min result2 = (data[:, :, :, 3:] + 1) * (self.vel_max - self.vel_min) / 2. + self.vel_min return torch.cat([result1, result2], axis=-1) def __getitem__(self, idx): return {'inputs': self.feats[idx], 'edges': self.edges[idx]} def __len__(self): return len(self.feats)

44.574713

107

0.550542

524

3,878

3.832061

0.108779

0.087151

0.060259

0.055777

0.616036

0.603586

0.511454

0.38994

0.330179

0.289343

0

0.022399

0.28623

3,878

86

108

45.093023

0.703035

0

0.320513

0

0.040227

0

1

0.076923

false

0

0.051282

0.025641

0.269231

0

null

0

null

0

1

0

934b67cb1527f74ff3f23c604c7293d16bac3821

30,891

py

Python

acq4/util/database/database.py

aleonlein/acq4

4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555

[ "MIT" ]

1

2020-06-04T17:04:53.000Z

acq4/util/database/database.py

aleonlein/acq4

4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555

[ "MIT" ]

24

2016-09-27T17:25:24.000Z

2017-03-02T21:00:11.000Z

acq4/util/database/database.py

sensapex/acq4

9561ba73caff42c609bd02270527858433862ad8

[ "MIT" ]

4

2016-10-19T06:39:36.000Z

2019-09-30T21:06:45.000Z

# -*- coding: utf-8 -*- from __future__ import print_function import numpy as np import pickle, re, os import acq4.Manager import collections import acq4.util.functions as functions import acq4.util.advancedTypes as advancedTypes import acq4.util.debug as debug from acq4.util import Qt import six from six.moves import range import sqlite3 class SqliteDatabase: """Encapsulates an SQLITE database to add more features. Arbitrary SQL may be executed by calling the db object directly, eg: db('select * from table') Using the select() and insert() methods will do automatic type conversions and allows any picklable objects to be directly stored in BLOB type columns. (it is not necessarily safe to store pickled objects in TEXT columns) NOTE: Data types in SQLITE work differently than in most other DBs--each value may take any type regardless of the type specified by its column. """ def __init__(self, fileName=':memory:'): ## decide on an appropriate name for this connection. ## For file connections, the name should always be the name of the file ## to avoid opening more than one connection to the same file. if fileName != ':memory:': fileName = os.path.abspath(fileName) self._connectionName = fileName self.db = sqlite3.connect(self._connectionName) self.db.row_factory = sqlite3.Row self.db.isolation_level = None self.tables = None self._transactions = [] self._readTableList() def close(self): if self.db is None: return self.db.close() self.db = None ## no need to remove the connection entirely. #import gc #gc.collect() ## try to convince python to clean up the db immediately so we can remove the connection #Qt.QSqlDatabase.removeDatabase(self._connectionName) def exe(self, cmd, data=None, batch=False, toDict=True, toArray=False): """Execute an SQL query. If data is provided, it should be a list of dicts and each will be bound to the query and executed sequentially. Returns the query object. Arguments: cmd - The SQL query to execute data - List of dicts, one per record to be processed For each record, data is bound to the query by key name {"key1": "value1"} => ":key1"="value1" batch - If True, then all input data is processed in a single execution. In this case, data must be provided as a dict-of-lists or record array. toDict - If True, return a list-of-dicts representation of the query results toArray - If True, return a record array representation of the query results """ p = debug.Profiler('SqliteDatabase.exe', disabled=True) p.mark('Command: %s' % cmd) if data is None: cur = self.db.execute(cmd) p.mark("Executed with no data") else: data = TableData(data) res = [] if batch: cur = self.db.executemany(cmd, data.__iter__()) else: for d in data: p.mark("bound values for record") self.db.execute(cmd, d) p.mark("executed with data") if cmd is not None: if str(cmd)[:6].lower() == 'create': self.tables = None ## clear table cache if toArray: ret = self._queryToArray(cur) elif toDict: ret = self._queryToDict(cur) else: ret = cur p.finish() return ret def __call__(self, *args, **kargs): return self.exe(*args, **kargs) def select(self, table, columns='*', where=None, sql='', toDict=True, toArray=False, distinct=False, limit=None, offset=None): """ Construct and execute a SELECT statement, returning the results. ============== ================================================================ **Arguments:** table The name of the table from which to read data columns (list or str) List of column names to read from table. The default is '*', which reads all columns If *columns* is given as a string, it is inserted verbatim into the SQL command. If *columns* is given as a list, it is converted to a string of comma-separated, quoted names. where Optional dict of {column: value} pairs. only results where column=value will be returned distinct (bool) If true, omit all redundant results limit (int) Limit the number of results that may be returned (best used with offset argument) offset (int) Omit a certain number of results from the beginning of the list sql Optional string to be appended to the SQL query (will be inserted before limit/offset arguments) toDict If True, return a list-of-dicts (this is the default) toArray if True, return a numpy record array ============== ================================================================ """ p = debug.Profiler("SqliteDatabase.select", disabled=True) if columns != '*': #if isinstance(columns, six.string_types): #columns = columns.split(',') if not isinstance(columns, six.string_types): qf = [] for f in columns: if f == '*': qf.append(f) else: qf.append('"'+f+'"') columns = ','.join(qf) #columns = quoteList(columns) whereStr = self._buildWhereClause(where, table) distinct = "distinct" if (distinct is True) else "" limit = ("limit %d" % limit) if (limit is not None) else "" offset = ("offset %d" % offset) if (offset is not None) else "" cmd = "SELECT %s %s FROM %s %s %s %s %s" % (distinct, columns, table, whereStr, sql, limit, offset) p.mark("generated command") q = self.exe(cmd, toDict=toDict, toArray=toArray) p.finish() return q def iterSelect(self, *args, **kargs): """ Return a generator that iterates through the results of a select query using limit/offset arguments. This is useful for select queries that would otherwise return a very large list of results. All arguments are passed through to select(). By default, limit=1000 and offset=0. Note that if you specify limit or offset, they MUST be given as keyword arguments. """ if 'chunkSize' in kargs: ## for compatibility with iterInsert kargs['limit'] = kargs['chunkSize'] del kargs['chunkSize'] if 'offset' not in kargs: kargs['offset'] = 0 if 'limit' not in kargs: kargs['limit'] = 1000 while True: res = self.select(*args, **kargs) if res is None or len(res) == 0: break yield res kargs['offset'] += kargs['limit'] def insert(self, table, records=None, replaceOnConflict=False, ignoreExtraColumns=False, **args): """Insert records (a dict or list of dicts) into table. If records is None, a single record may be specified via keyword arguments. ==================== ======================================= **Arguments:** table Name of the table to insert into records Data to insert. May be a variety of formats: numpy record array, list of dicts, dict of lists, dict of values (single record) replaceOnConflict If True, inserts that conflict with pre-existing data will overwrite the pre-existing data. This occurs, for example, when a column has a 'unique' constraint. ignoreExtraColumns If True, ignore any extra columns in the data that do not exist in the table ==================== ======================================= """ for n,nmax in self.iterInsert(table=table, records=records, replaceOnConflict=replaceOnConflict, ignoreExtraColumns=ignoreExtraColumns, chunkAll=True, **args): pass def iterInsert(self, table, records=None, replaceOnConflict=False, ignoreExtraColumns=False, chunkSize=500, chunkAll=False, **args): """ Iteratively insert chunks of data into a table while yielding a tuple (n, max) indicating progress. This *must* be used inside a for loop:: for n,nmax in db.iterInsert(table, data): print("Insert %d%% complete" % (100. * n / nmax)) Use the chunkSize argument to determine how many records are inserted per iteration. See insert() for a description of all other options. """ p = debug.Profiler("SqliteDatabase.insert", disabled=True) if records is None: records = [args] #if type(records) is not list: #records = [records] if len(records) == 0: return ret = [] with self.transaction(): ## Rememember that _prepareData may change the number of columns! records = TableData(self._prepareData(table, records, ignoreUnknownColumns=ignoreExtraColumns, batch=True)) p.mark("prepared data") columns = list(records.keys()) insert = "INSERT" if replaceOnConflict: insert += " OR REPLACE" #print "Insert:", columns cmd = "%s INTO %s (%s) VALUES (%s)" % (insert, table, quoteList(columns), ','.join([':'+f for f in columns])) numRecs = len(records) if chunkAll: ## insert all records in one go. self.exe(cmd, records, batch=True) yield (numRecs, numRecs) return chunkSize = int(chunkSize) ## just make sure offset = 0 i = 0 while offset < len(records): #print len(columns), len(records[0]), len(self.tableSchema(table)) chunk = records[offset:offset+chunkSize] self.exe(cmd, chunk, batch=True) offset += len(chunk) yield (offset, numRecs) p.mark("Transaction done") p.finish() def delete(self, table, where): with self.transaction(): whereStr = self._buildWhereClause(where, table) cmd = "DELETE FROM %s %s" % (table, whereStr) return self(cmd) def update(self, table, vals, where=None, rowid=None, sql=''): """Update records in the DB. Arguments: vals: dict of {column: value} pairs where: SQL clause specifying rows to update rowid: int row IDs. Used instead of 'where' sql: SQL string to append to end of statement""" if rowid is not None: if where is not None: raise Exception("'where' and 'rowid' are mutually exclusive arguments.") where = {'rowid': rowid} with self.transaction(): whereStr = self._buildWhereClause(where, table) setStr = ', '.join(['"%s"=:%s' % (k, k) for k in vals]) cmd = "UPDATE %s SET %s %s %s" % (table, setStr, whereStr, sql) data = self._prepareData(table, [vals], batch=True) return self(cmd, data, batch=True) def transaction(self, name=None): """Return an enterable Transaction instance. Use thusly:: with db.transaction(): db.doStuff() db.doMoreStuff() If an exception is raised while the transaction is active, all changes will be rolled back. Note that wrapping multiple database operations in a transaction can *greatly* increase performance. """ return Transaction(self, name) def lastInsertRow(self): q = self("select last_insert_rowid()") return list(q[0].values())[0] def replace(self, *args, **kargs): return self.insert(*args, replaceOnConflict=True, **kargs) def createTable(self, table, columns, sql=""): """Create a table in the database. table: (str) the name of the table to create columns: (list) a list of tuples (name, type, constraints) defining columns in the table. all 3 elements othe tuple are strings; constraints are optional. Types may be any string, but are typically int, real, text, or blob. (see sqlite 'CREATE TABLE') """ #print "create table", table, ', '.join(columns) columns = parseColumnDefs(columns) columnStr = [] for name, conf in columns.items(): columnStr.append('"%s" %s %s' % (name, conf['Type'], conf.get('Constraints', ''))) columnStr = ','.join(columnStr) self('CREATE TABLE "%s" (%s) %s' % (table, columnStr, sql)) self._readTableList() def createIndex(self, table, columns, ifNotExist=True): """ Create an index on table (columns) *columns* may be the name of a single column or a list of column names. (see sqlite 'CREATE INDEX') """ ine = "IF NOT EXISTS" if ifNotExist else "" if isinstance(columns, six.string_types): columns = [columns] name = table + '__' + '_'.join(columns) colStr = quoteList(columns) cmd = 'CREATE INDEX %s "%s" ON "%s" (%s)' % (ine, name, table, colStr) self(cmd) def addColumn(self, table, colName, colType, constraints=None): """ Add a column to a table. """ if constraints is None: constraints = '' self('ALTER TABLE "%s" ADD COLUMN "%s" %s %s' % (table, colName, colType, constraints)) self.tables = None def listTables(self): """ Return a list of the names of tables in the DB. """ if self.tables is None: self._readTableList() return list(self.tables.keys()) def removeTable(self, table): self('DROP TABLE "%s"' % table) def hasTable(self, table): self.listTables() ## make sure table list has been generated return table in self.tables ## this is a case-insensitive operation def tableSchema(self, table): """ Return a dict {'columnName': 'type', ...} for the specified table. """ if self.tables is None: self._readTableList() return self.tables[table].copy() ## this is a case-insensitive operation def tableLength(self, table): return self('select count(*) from "%s"' % table)[0]['count(*)'] def _buildWhereClause(self, where, table): if where is None or len(where) == 0: return '' where = self._prepareData(table, where)[0] conds = [] for k,v in where.items(): if isinstance(v, six.string_types): conds.append('"%s"=\'%s\'' % (k, v)) else: conds.append('"%s"=%s' % (k,v)) whereStr = "WHERE " + " AND ".join(conds) return whereStr def _prepareData(self, table, data, ignoreUnknownColumns=False, batch=False): ## Massage data so it is ready for insert into the DB. (internal use only) ## - data destined for BLOB columns is pickled ## - numerical columns convert to int or float ## - text columns convert to unicode ## converters may be a dict of {'columnName': function} ## that overrides the default conversion funcitons. ## Returns a dict-of-lists if batch=True, otherwise list-of-dicts data = TableData(data) converters = {} ## determine the conversion functions to use for each column. schema = self.tableSchema(table) for k in schema: if k in converters: continue typ = schema[k].lower() if typ == 'blob': converters[k] = lambda obj: buffer(pickle.dumps(obj)) elif typ == 'int': converters[k] = int elif typ == 'real': converters[k] = float elif typ == 'text': converters[k] = str else: converters[k] = lambda obj: obj if batch: newData = dict([(k,[]) for k in data.columnNames() if not (ignoreUnknownColumns and (k not in schema))]) else: newData = [] for rec in data: newRec = {} for k in rec: if k not in schema: if ignoreUnknownColumns: continue #if addUnknownColumns: ## Is this just a bad idea? #dtyp = self.suggestColumnType(rec[k]) #self.addColumn(table, k, dtyp) if rec[k] is None: newRec[k] = None else: try: newRec[k] = converters[k](rec[k]) except: newRec[k] = rec[k] if k.lower() != 'rowid': if k not in schema: raise Exception("Column '%s' not present in table '%s'" % (k, table)) print("Warning: Setting %s column %s.%s with type %s" % (schema[k], table, k, str(type(rec[k])))) if batch: for k in newData: newData[k].append(newRec.get(k, None)) else: newData.append(newRec) #print "new data:", newData return newData def _queryToDict(self, q): prof = debug.Profiler("_queryToDict", disabled=True) res = [] for rec in q: res.append(self._readRecord(rec)) return res def _queryToArray(self, q): prof = debug.Profiler("_queryToArray", disabled=True) recs = self._queryToDict(q) prof.mark("got records") if len(recs) < 1: #return np.array([]) ## need to return empty array *with correct columns*, but this is very difficult, so just return None return None rec1 = recs[0] dtype = functions.suggestRecordDType(rec1, singleRecord=True) #print rec1, dtype arr = np.empty(len(recs), dtype=dtype) arr[0] = tuple(rec1.values()) for i in range(1, len(recs)): arr[i] = tuple(recs[i].values()) prof.mark('converted to array') prof.finish() return arr def _readRecord(self, rec): prof = debug.Profiler("_readRecord", disabled=True) data = collections.OrderedDict() names = list(rec.keys()) for i in range(len(rec)): val = rec[i] name = names[i] ## Unpickle byte arrays into their original objects. ## (Hopefully they were stored as pickled data in the first place!) if isinstance(val, buffer): val = pickle.loads(str(val)) data[name] = val prof.finish() return data def _readTableList(self): """Reads the schema for each table, extracting the column names and types.""" names = self("select name from sqlite_master where type='table' or type='view'") tables = advancedTypes.CaselessDict() for table in names: table = table['name'] columns = advancedTypes.CaselessDict() recs = self('PRAGMA table_info(%s)' % table) for rec in recs: columns[rec['name']] = rec['type'] tables[table] = columns self.tables = tables def quoteList(strns): """Given a list of strings, return a single string like '"string1", "string2",...' Note: in SQLite, double quotes are for escaping table and column names; single quotes are for string literals. """ return ','.join(['"'+s+'"' for s in strns]) class Transaction: """See SQLiteDatabase.transaction()""" def __init__(self, db, name=None): self.db = db self.name = name def __enter__(self): if self.name is None: self.name = 'transaction%d' % len(self.db._transactions) self.db('SAVEPOINT %s' % self.name) self.db._transactions.append(self) def __exit__(self, exc_type, exc_value, traceback): if exc_type is None: self.db('RELEASE SAVEPOINT %s' % self.name) else: try: self.db('ROLLBACK TRANSACTION TO %s' % self.name) self.db.tables = None ## make sure we are forced to re-read the table list after the rollback. except Exception: print("WARNING: Error occurred during transaction and rollback failed.") if self.db._transactions[-1] is not self: print(self, self.db._transactions) raise Exception('Tried to exit transaction before another nested transaction has finished.') self.db._transactions.pop(-1) class TableData: """ Class for presenting multiple forms of tabular data through a consistent interface. May contain: - numpy record array - list-of-dicts (all dicts are _not_ required to have the same keys) - dict-of-lists - dict (single record) Note: if all the values in this record are lists, it will be interpreted as multiple records Data can be accessed and modified by column, by row, or by value data[columnName] # returns list or array data[rowId] # returns dict or ordereddict data[columnName, rowId] = value data[columnName] = [value, value, ...] data[rowId] = {columnName: value, ...} """ def __init__(self, data): self.data = data if isinstance(data, np.ndarray): self.mode = 'array' elif isinstance(data, list): self.mode = 'list' elif isinstance(data, dict): types = set(map(type, list(data.values()))) ## dict may be a dict-of-lists or a single record types -= set([list, np.ndarray]) ## if dict contains any non-sequence values, it is probably a single record. if len(types) != 0: self.data = [self.data] self.mode = 'list' else: self.mode = 'dict' elif isinstance(data, TableData) or 'TableData' in str(type(data)): self.data = data.data self.mode = data.mode else: raise Exception("Cannot create TableData from object '%s' (type='%s')" % (str(data), type(data))) for fn in ['__getitem__', '__setitem__']: setattr(self, fn, getattr(self, '_TableData'+fn+self.mode)) self.copy = getattr(self, 'copy_' + self.mode) def originalData(self): return self.data def toArray(self): if self.mode == 'array': return self.data if len(self) < 1: #return np.array([]) ## need to return empty array *with correct columns*, but this is very difficult, so just return None return None rec1 = self[0] #dtype = functions.suggestRecordDType(self) ## Need to look through all data before deciding on dtype. ## It is not sufficient to look at just the first record, ## nor to look at the column types. types = {k:set() for k in self.keys()} for rec in self: for k,v in rec.items(): types[k].add(type(v)) dtype = [] for k in self.keys(): t = types[k] if t == set([float]) or t == set([float, type(None)]): dtype.append((k, float)) elif t == set([int]): dtype.append((k, int)) else: dtype.append((k, object)) #print rec1, dtype arr = np.empty(len(self), dtype=dtype) arr[0] = tuple(rec1.values()) for i in range(1, len(self)): arr[i] = tuple(self[i].values()) return arr def __getitem__array(self, arg): if isinstance(arg, six.string_types): return self.data[arg] elif isinstance(arg, int): return collections.OrderedDict([(k, self.data[k][arg]) for k in self.columnNames()]) elif isinstance(arg, tuple): return self.data[arg[0]][arg[1]] elif isinstance(arg, slice): return TableData(self.data[arg]) else: raise Exception("Cannot index TableData with object '%s' (type='%s')" % (str(arg), type(arg))) def __getitem__list(self, arg): if isinstance(arg, six.string_types): return [d.get(arg, None) for d in self.data] elif isinstance(arg, int): return self.data[arg] elif isinstance(arg, tuple): arg = self._orderArgs(arg) return self.data[arg[0]][arg[1]] elif isinstance(arg, slice): return TableData(self.data[arg]) else: raise Exception("Cannot index TableData with object '%s' (type='%s')" % (str(arg), type(arg))) def __getitem__dict(self, arg): if isinstance(arg, six.string_types): return self.data[arg] elif isinstance(arg, int): return collections.OrderedDict([(k, v[arg]) for k, v in self.data.items()]) elif isinstance(arg, tuple): arg = self._orderArgs(arg) return self.data[arg[1]][arg[0]] elif isinstance(arg, slice): return TableData(collections.OrderedDict([(k, v[arg]) for k, v in self.data.items()])) else: raise Exception("Cannot index TableData with object '%s' (type='%s')" % (str(arg), type(arg))) def __setitem__array(self, arg, val): if isinstance(arg, tuple): self.data[arg[0]][arg[1]] = val else: self.data[arg] = val def __setitem__list(self, arg, val): if isinstance(arg, six.string_types): if len(val) != len(self.data): raise Exception("Values (%d) and data set (%d) are not the same length." % (len(val), len(self.data))) for i, rec in enumerate(self.data): rec[arg] = val[i] elif isinstance(arg, int): self.data[arg] = val elif isinstance(arg, tuple): arg = self._orderArgs(arg) self.data[arg[0]][arg[1]] = val else: raise TypeError(type(arg)) def __setitem__dict(self, arg, val): if isinstance(arg, six.string_types): if len(val) != len(self.data[arg]): raise Exception("Values (%d) and data set (%d) are not the same length." % (len(val), len(self.data[arg]))) self.data[arg] = val elif isinstance(arg, int): for k in self.data: self.data[k][arg] = val[k] elif isinstance(arg, tuple): arg = self._orderArgs(arg) self.data[arg[1]][arg[0]] = val else: raise TypeError(type(arg)) def _orderArgs(self, args): ## return args in (int, str) order if isinstance(args[0], six.string_types): return (args[1], args[0]) else: return args def copy_array(self): return TableData(self.data.copy()) def copy_list(self): return TableData([rec.copy() for rec in self.data]) def copy_dict(self): return TableData({k:v[:] for k,v in self.data.items()}) def __iter__(self): for i in range(len(self)): yield self[i] def __len__(self): if self.mode == 'array' or self.mode == 'list': return len(self.data) else: return max(list(map(len, self.data.values()))) def columnNames(self): """returns column names in no particular order""" if self.mode == 'array': return self.data.dtype.names elif self.mode == 'list': if len(self.data) == 0: return [] return list(self.data[0].keys()) ## all records must have all keys. #names = set() #for row in self.data: #names.update(row.keys()) #return list(names) elif self.mode == 'dict': return list(self.data.keys()) def keys(self): return self.columnNames() def parseColumnDefs(defs, keyOrder=None): """ Translate a few different forms of column definitions into a single common format. These formats are accepted for all methods which request column definitions (createTable, checkTable, etc) list of tuples: [(name, type, <constraints>), ...] dict of strings: {name: type, ...} dict of tuples: {name: (type, <constraints>), ...} dict of dicts: {name: {'Type': type, ...}, ...} Returns dict of dicts as the common format. """ if keyOrder is None: keyOrder = ['Type', 'Constraints'] def isSequence(x): return isinstance(x, list) or isinstance(x, tuple) def toDict(args): d = collections.OrderedDict() for i,v in enumerate(args): d[keyOrder[i]] = v if i >= len(keyOrder) - 1: break return d if isSequence(defs) and all(map(isSequence, defs)): return collections.OrderedDict([(c[0], toDict(c[1:])) for c in defs]) if isinstance(defs, dict): ret = collections.OrderedDict() for k, v in defs.items(): if isSequence(v): ret[k] = toDict(v) elif isinstance(v, dict): ret[k] = v elif isinstance(v, six.string_types): ret[k] = {'Type': v} else: raise Exception("Invalid column-list specification: %s" % str(defs)) return ret else: raise Exception("Invalid column-list specification: %s" % str(defs))

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934b94904687667744b39ca9e00a4c7308a9eefb

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py

Python

HighLevelAnalyzer.py

mikeITMattersMost/saleae_si443x_decoder

00693ca7580f449355bc3f83f8f48038684e34b4

[ "MIT" ]

null

HighLevelAnalyzer.py

mikeITMattersMost/saleae_si443x_decoder

00693ca7580f449355bc3f83f8f48038684e34b4

[ "MIT" ]

null

HighLevelAnalyzer.py

mikeITMattersMost/saleae_si443x_decoder

00693ca7580f449355bc3f83f8f48038684e34b4

[ "MIT" ]

null

from typing import Iterable, Optional, Union from saleae.analyzers import HighLevelAnalyzer, AnalyzerFrame, StringSetting, NumberSetting, ChoicesSetting si_registers = { 0x00: "Device Type (R)", 0x01: "Device Version (R)", 0x02: "Device Status (R)", 0x03: "Interrupt Status 1 (R)", 0x04: "Interrupt Status 2 (R)", 0x05: "Interrupt Enable 1 (R/W)", 0x06: "Interrupt Enable 2 (R/W)", 0x07: "Operating & Function Control 1 (R/W)", 0x08: "Operating & Function Control 2 (R/W)", 0x09: "Crystal Oscillator Load Capacitance (R/W)", 0x0A: "Microcontroller Output Clock (R/W)", 0x0B: "GPIO0 Configuration (R/W)", 0x0C: "GPIO1 Configuration (R/W)", 0x0D: "GPIO2 Configuration (R/W)", 0x0E: "I/O Port Configuration (R/W)", 0x0F: "ADC Configuration (R/W)", 0x10: "ADC Sensor Amplifier Offset (R/W)", 0x11: "ADC Value (R)", 0x12: "Temperature Sensor Control (R/W)", 0x13: "Temperature Value Offset (R/W)", 0x14: "Wake-Up Timer Period 1 (R/W)", 0x15: "Wake-Up Timer Period 2 (R/W)", 0x16: "Wake-Up Timer Period 3 (R/W)", 0x17: "Wake-Up Timer Value 1 (R)", 0x18: "Wake-Up Timer Value 2 (R)", 0x19: "Low-Duty Cycle Mode Duration (R/W)", 0x1A: "Low Battery Detector Threshold (R/W)", 0x1B: "Battery Voltage Level (R)", 0x1C: "IF Filter Bandwidth (R/W)", 0x1D: "AFC Loop Gearshift Override (R/W)", 0x1E: "AFC Timing Control (R/W)", 0x1F: "Clock Recovery Gearshift Override (R/W)", 0x20: "Clock Recovery Oversampling Ratio (R/W)", 0x21: "Clock Recovery Offset 2 (R/W)", 0x22: "Clock Recovery Offset 1 (R/W)", 0x23: "Clock Recovery Offset 0 (R/W)", 0x24: "Clock Recovery Timing Loop Gain 1 (R/W)", 0x25: "Clock Recovery Timing Loop Gain 0 (R/W)", 0x26: "Received Signal Strength Indicator (R)", 0x27: "RSSI Threshold for Clear Channel Indicator (R/W)", 0x28: "Antenna Diversity Register 1 (R)", 0x29: "Antenna Diversity Register 2 (R)", 0x2A: "AFC Limiter (R/W)", 0x2B: "AFC Correction Read (R)", 0x2C: "OOK Counter Value 1 (R/W)", 0x2D: "OOK Counter Value 2 (R/W)", 0x2E: "Slicer Peak Hold (R/W)", 0x2F: "Reserved (0x2F)", 0x30: "Data Access Control (R/W)", 0x31: "EzMAC status 0 (R)", 0x32: "Header Control 1 (R/W)", 0x33: "Header Control 2 (R/W)", 0x34: "Preamble Length (R/W)", 0x35: "Preamble Detection Control (R/W)", 0x36: "Sync Word 3 (R/W)", 0x37: "Sync Word 2 (R/W)", 0x38: "Sync Word 1 (R/W)", 0x39: "Sync Word 0 (R/W)", 0x3A: "Transmit Header 3 (R/W)", 0x3B: "Transmit Header 2 (R/W)", 0x3C: "Transmit Header 1 (R/W)", 0x3D: "Transmit Header 0 (R/W)", 0x3E: "Transmit Packet Length (R/W)", 0x3F: "Check Header 3 (R/W)", 0x40: "Check Header 2 (R/W)", 0x41: "Check Header 1 (R/W)", 0x42: "Check Header 0 (R/W)", 0x43: "Header Enable 3 (R/W)", 0x44: "Header Enable 2 (R/W)", 0x45: "Header Enable 1 (R/W)", 0x46: "Header Enable 0 (R/W)", 0x47: "Received Header 3 (R)", 0x48: "Received Header 2 (R)", 0x49: "Received Header 1 (R)", 0x4A: "Received Header 0 (R)", 0x4B: "Received Packet Length (R)", 0x4C: "Reserved (0x4C)", 0x4D: "Reserved (0x4D)", 0x4E: "Reserved (0x4E)", 0x4F: "ADC8 Control (R/W)", 0x50: "Reserved (0x50)", 0x51: "Reserved (0x51)", 0x52: "Reserved (0x52)", 0x53: "Reserved (0x53)", 0x54: "Reserved (0x54)", 0x55: "Reserved (0x55)", 0x56: "Reserved (0x56)", 0x57: "Reserved (0x57)", 0x58: "Reserved (0x58)", 0x59: "Reserved (0x59)", 0x5A: "Reserved (0x5A)", 0x5B: "Reserved (0x5B)", 0x5C: "Reserved (0x5C)", 0x5D: "Reserved (0x5D)", 0x5E: "Reserved (0x5E)", 0x5F: "Reserved (0x5F)", 0x60: "Channel Filter Coefficient Address (R/W)", 0x61: "Reserved (0x61)", 0x62: "Crystal Oscillator/Control Test (R/W)", 0x63: "Reserved (0x63)", 0x64: "Reserved (0x64)", 0x65: "Reserved (0x65)", 0x66: "Reserved (0x66)", 0x67: "Reserved (0x67)", 0x68: "Reserved (0x68)", 0x69: "AGC Override 1 (R/W)", 0x6A: "Reserved (0x6A)", 0x6B: "Reserved (0x6B)", 0x6C: "Reserved (0x6C)", 0x6D: "TX Power (R/W)", 0x6E: "TX Data Rate 1 (R/W)", 0x6F: "TX Data Rate 0 (R/W)", 0x70: "Modulation Mode Control 1 (R/W)", 0x71: "Modulation Mode Control 2 (R/W)", 0x72: "Frequency Deviation (R/W)", 0x73: "Frequency Offset 1 (R/W)", 0x74: "Frequency Offset 2 (R/W)", 0x75: "Frequency Band Select (R/W)", 0x76: "Nominal Carrier Frequency 1 (R/W)", 0x77: "Nominal Carrier Frequency 0 (R/W)", 0x78: "Reserved (0x78)", 0x79: "Frequency Hopping Channel Select (R/W)", 0x7A: "Frequency Hopping Step Size (R/W)", 0x7B: "Reserved (0x7B)", 0x7C: "TX FIFO Control 1 (R/W)", 0x7D: "TX FIFO Control 2 (R/W)", 0x7E: "RX FIFO Control (R/W)", 0x7F: "FIFO Access (R/W)" } def get_register_name(register_addr: int) -> str: try: return si_registers[register_addr] except KeyError: return "UNKNOWN_REGISTER" class Hla(HighLevelAnalyzer): result_types = { "si_address": {"format": "{{data.rw}} {{data.reg}} {{data.value}}"}, "si_read": {"format": "{{data.rw}} {{data.reg}} {{data.value}}"}, "si_write": {"format": "{{data.rw}} {{data.reg}} {{data.value}}"}, } def __init__(self): # Previous frame type # https://support.saleae.com/extensions/analyzer-frame-types/spi-analyzer self._previous_type: str = "" # current address self._address: Optional[int] = None # current access type self._rw: str = "" def decode(self, frame: AnalyzerFrame) -> Optional[Union[Iterable[AnalyzerFrame], AnalyzerFrame]]: """ Decode frames. """ is_first_byte: bool = self._previous_type == "enable" self._previous_type: str = frame.type if frame.type != "result": return None mosi: bytes = frame.data["mosi"] miso: bytes = frame.data["miso"] #print("mosi bytes: ", mosi) #print("miso bytes: ", miso) if is_first_byte: try: self._address = mosi[0] except IndexError: return None self._rw = "Write" if self._address & 0x80 != 0 else "Read" # normalize the address, removing the read/write bit self._address &= 0x7F return AnalyzerFrame( "si_address", start_time=frame.start_time, end_time=frame.end_time, data={"reg": get_register_name(self._address), "rw": self._rw, "value": "reg_"+f"0x{self._address:02X}"}, ) else: if self._rw.lower() == "write": try: byte = mosi[0] except IndexError: return None else: try: byte = miso[0] except IndexError: return None ret = AnalyzerFrame( "si_"+self._rw.lower(), start_time=frame.start_time, end_time=frame.end_time, data={ "reg": get_register_name(self._address), "rw": self._rw, "value": self._rw.lower()+"-> "+f"0x{byte:02X}" + " = ASCII "+chr(byte), }, ) if self._address != 0xFF: # FIFO self._address += 1 self._address &= 0x7F return ret

33.55

132

0.578377

978

7,381

4.306748

0.322086

0.036562

0.012108

0.009497

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0.090312

0.257418

7,381

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33.703196

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0.459019

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false

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0.078125

0

null

0

null

0

1

0

934d55cab0f4fe65121120a091896d64ce49a6d5

3,719

py

Python

tests/test_block_until_url.py

aniruddha2000/init

fe2a32d2736c359a6911cc22bc42007ac97c5b10

[ "BSD-3-Clause" ]

3

2017-10-13T18:40:37.000Z

2020-02-05T07:36:04.000Z

tests/test_block_until_url.py

aniruddha2000/init

fe2a32d2736c359a6911cc22bc42007ac97c5b10

[ "BSD-3-Clause" ]

null

tests/test_block_until_url.py

aniruddha2000/init

fe2a32d2736c359a6911cc22bc42007ac97c5b10

[ "BSD-3-Clause" ]

5

2017-03-07T03:53:55.000Z

2020-08-12T13:11:17.000Z

#!/usr/bin/python2.7 # Copyright (c) 2013 The CoreOS Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import BaseHTTPServer import os import select import signal import subprocess import threading import time import unittest script_path = os.path.abspath('%s/../../bin/block-until-url' % __file__) class UsageTestCase(unittest.TestCase): def test_no_url(self): proc = subprocess.Popen([script_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = proc.communicate() self.assertEquals(proc.returncode, 1) self.assertEquals(out, '') self.assertIn('invalid url', err) def test_invalid_url(self): proc = subprocess.Popen([script_path, 'fooshizzle'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = proc.communicate() self.assertEquals(proc.returncode, 1) self.assertEquals(out, '') self.assertIn('invalid url', err) class TestRequestHandler(BaseHTTPServer.BaseHTTPRequestHandler): def send_test_data(self): if self.path == '/ok': ok_data = 'OK!\n' self.send_response(200) self.send_header('Content-type', 'text/plain') self.send_header('Content-Length', str(len(ok_data))) if self.command != 'HEAD': self.wfile.write(ok_data) elif self.path == '/404': self.send_error(404) else: # send nothing so curl fails pass def do_GET(self): self.send_test_data() def do_HEAD(self): self.send_test_data() def log_message(self, format, *args): pass class HttpTestCase(unittest.TestCase): def setUp(self): self.server = BaseHTTPServer.HTTPServer( ('localhost', 0), TestRequestHandler) self.server_url = 'http://%s:%s' % self.server.server_address server_thread = threading.Thread(target=self.server.serve_forever) server_thread.daemon = True server_thread.start() def tearDown(self): self.server.shutdown() def test_quick_ok(self): proc = subprocess.Popen([script_path, '%s/ok' % self.server_url], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = proc.communicate() self.assertEquals(proc.returncode, 0) self.assertEquals(out, '') self.assertEquals(err, '') def test_quick_404(self): proc = subprocess.Popen([script_path, '%s/404' % self.server_url], stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = proc.communicate() self.assertEquals(proc.returncode, 0) self.assertEquals(out, '') self.assertEquals(err, '') def test_timeout(self): proc = subprocess.Popen([script_path, '%s/bogus' % self.server_url], bufsize=4096, stdout=subprocess.PIPE, stderr=subprocess.PIPE) timeout = time.time() + 2 # kill after 2 seconds while time.time() < timeout: time.sleep(0.1) self.assertIs(proc.poll(), None, 'script terminated early!') proc.terminate() out, err = proc.communicate() self.assertEquals(proc.returncode, -signal.SIGTERM) self.assertEquals(out, '') self.assertEquals(err, '') if __name__ == '__main__': unittest.main()

32.622807

76

0.579726

402

3,719

5.233831

0.350746

0.098859

0.042776

0.054658

0.437738

0.378802

0.296103

0.271863

0

0.013194

0.307072

3,719

113

77

32.911504

0.80326

0.0605

0

0.37931

0

0.052752

0.008028

0

0.183908

1

0.126437

false

0.022989

0.091954

0

0.252874

0

null

0

null

0

1

0

93502650f25d22a96c9c76f56f389213f7e2df2e

1,883

py

Python

fasttest.py

godkillok/node2vec

17b65e1adad01e3881078bc6e9d9eb34e671a296

[ "MIT" ]

1

2019-12-09T09:14:11.000Z

fasttest.py

godkillok/node2vec

17b65e1adad01e3881078bc6e9d9eb34e671a296

[ "MIT" ]

null

fasttest.py

godkillok/node2vec

17b65e1adad01e3881078bc6e9d9eb34e671a296

[ "MIT" ]

null

import os import json from collections import defaultdict corpus_folder="/data/tanggp/all_text_data_text_pipe/eval_golden" tags_dic=defaultdict(int) co=0 node=defaultdict(int) tags_text=[] for root, _, files in os.walk(corpus_folder): for file in files: raw_corpus_file_path = os.path.join(root, file) with open(raw_corpus_file_path,"r",encoding="utf8") as f: lines=f.readlines() for li in lines: li=json.loads(li) tags=li.get("tags",[]) tags=[ta.lower().strip() for ta in tags] tags=sorted(tags) tags_text.append(' ;'.join(tags)) with open("/data/tanggp/tmp/tags_text",'w',encoding="utf8") as f: for ta in tags_text: f.writelines(ta+'\n') import fastText as ft FASTTEXT_SOFTWARE = '/data/tanggp/fastText-0.1.0' #os.system("cd {} && ./fasttext skipgram -input /data/tanggp/tmp/tags_text -dim 100 -output /data/tanggp/tmp/tags_w2v".format(FASTTEXT_SOFTWARE)) FAST_TEXT_MODEL_PATH='/data/tanggp/tmp/tags_w2v.bin' model = ft.load_model(FAST_TEXT_MODEL_PATH) sentor_vetor_list=[] with open("/data/tanggp/tmp/in_node2vec", "r", encoding="utf8") as f: in_node2vec=f.readlines() for text in in_node2vec: try: text=text.strip() sentor_vetor_array=model.get_sentence_vector(text) sentor_vetor = ','.join([str(w) for w in list(sentor_vetor_array)]) sentor_vetor_list.append(sentor_vetor) except Exception as e: print("wrong text ---{}".format(text)) print(e) with open("/data/tanggp/tmp/w2vec_fast_id", "w", encoding="utf8") as f: for i,text in enumerate(in_node2vec): text=text.strip() f.writelines(text+'\t'+str(i) + '\n') with open("/data/tanggp/tmp/w2vec_embed", "w", encoding="utf8") as f: for i in sentor_vetor_list: f.writelines(i+'\n')

33.625

145

0.650558

283

1,883

4.144876

0.310954

0.076726

0.077579

0.063939

0.216539

0.094629

0.034101

0

0.013324

0.202868

1,883

55

146

34.236364

0.768155

0.076474

0

0.044444

0

0.156682

0.124424

0

1

0

false

0

0.088889

0

0.088889

0.044444

0

null

0

null

0

1

0

935806f4923d4948dc0c54a63cff487df1a38a40

4,205

py

Python

simulations/structure_function_xray_sims.py

st--/Mrk_335

4e9afd0f9b1904ac11209220d4f6896d9be33a0d

[ "MIT" ]

12

2021-03-11T21:27:34.000Z

2022-01-03T09:37:04.000Z

simulations/structure_function_xray_sims.py

st--/Mrk_335

4e9afd0f9b1904ac11209220d4f6896d9be33a0d

[ "MIT" ]

null

simulations/structure_function_xray_sims.py

st--/Mrk_335

4e9afd0f9b1904ac11209220d4f6896d9be33a0d

[ "MIT" ]

2

2021-03-20T22:29:28.000Z

2021-10-01T03:12:39.000Z

# Copyright Ryan-Rhys Griffiths 2021 # Author: Ryan-Rhys Griffiths """ Comparison of GP-interpolated X-ray and true structure functions where the GP interpolated structure functions are computed following the introduction of gaps into lightcurves. """ import numpy as np from matplotlib import pyplot as plt from simulation_utils import load_sim_data from structure_function_utils import compute_gp_structure_function TIMINGS_FILE = '../processed_data/xray_simulations/x_ray_sim_times.pickle' GAPPED_FILE = 'sim_curves/xray_lightcurves.dat' GROUND_TRUTH_FILE = 'sim_curves/xray_lightcurves_no_gaps.dat' resolution = 5.3 nsims = 1000 # number of simulated curves i.e length of gapped_file kernel = 'Matern' # ['Matern', 'RQ'] f_plot = False if __name__ == '__main__': if kernel == 'Matern': tag = 'Matern_12' else: tag = 'Rational Quadratic' # Load the times for gap points, times for full curves, count rates for gap points and count rates for full curves # Matrix because second dimension corresponds to nsims. time, test_times, gapped_count_rates_matrix, ground_truth_count_rates_matrix = load_sim_data(TIMINGS_FILE, GAPPED_FILE, GROUND_TRUTH_FILE) for i in range(0, 15): # file handle for GP lightcurve handle = f'SF_xray_samples_{tag} Kernel_iteration_{i}.txt' gapped_count_rates = np.reshape(gapped_count_rates_matrix[i, :], (-1, 1)) count_rates = np.reshape(ground_truth_count_rates_matrix[i, :], (-1, 1)) gp_count_rates = np.reshape(np.loadtxt(fname=f'SF_samples/xray/{handle}'), (-1, 1)) gapped_tao_plot, gapped_structure_function_vals = compute_gp_structure_function(gapped_count_rates, time, resolution=resolution) ground_truth_tao_plot, ground_truth_structure_function_vals = compute_gp_structure_function(count_rates, test_times, resolution=resolution) gp_tao_plot, gp_structure_function_vals = compute_gp_structure_function(gp_count_rates, test_times, resolution=resolution) np.savetxt(f'saved_sf_values/xray/_gapped_tao_plot_{i}.txt', gapped_tao_plot, fmt='%.15f') np.savetxt(f'saved_sf_values/xray/gapped_structure_function_vals_{i}.txt', gapped_structure_function_vals, fmt='%.15f') np.savetxt(f'saved_sf_values/xray/{kernel}_gp_tao_plot_{i}.txt', gp_tao_plot, fmt='%.15f') np.savetxt(f'saved_sf_values/xray/ground_truth_structure_function_vals_{i}.txt', ground_truth_structure_function_vals, fmt='%.15f') np.savetxt(f'saved_sf_values/xray/ground_truth_tao_plot_{i}.txt', ground_truth_tao_plot, fmt='%.15f') np.savetxt(f'saved_sf_values/xray/{kernel}_gp_structure_function_vals_{i}.txt', gp_structure_function_vals, fmt='%.15f') if f_plot: fig, ax = plt.subplots(1) plt.scatter(gapped_tao_plot, gapped_structure_function_vals, s=10, marker='+', label='Gapped') plt.scatter(ground_truth_tao_plot, ground_truth_structure_function_vals, s=10, marker='+', label='Ground Truth') plt.xscale('log') plt.yscale('log') plt.xlabel(r'$\tau$' + ' (days)') plt.ylabel('SF') plt.xlim([10, 700]) plt.title('X-ray Gapped Structure Function') plt.tight_layout() plt.legend() plt.savefig(f'SF_sims_figures/xray/gapped_structure_function_{i}') plt.close() fig, ax = plt.subplots(1) plt.scatter(gp_tao_plot, gp_structure_function_vals, s=10, marker='+', label='GP') plt.scatter(ground_truth_tao_plot, ground_truth_structure_function_vals, s=10, marker='+', label='Ground Truth') plt.xscale('log') plt.yscale('log') plt.xlabel(r'$\tau$' + ' (days)') plt.ylabel('SF') plt.xlim([10, 700]) plt.title(f'X-ray GP {kernel} Structure Function') plt.tight_layout() plt.legend() plt.savefig(f'SF_sims_figures/xray/gp_{kernel}_structure_function_{i}') plt.close()

48.895349

147

0.670392

565

4,205

4.649558

0.244248

0.142368

0.103921

0.03426

0.564903

0.466311

0.422155

0.306433

0.291587

0.272554

0

0.016183

0.221165

4,205

85

148

49.470588

0.785954

0.120571

0

0.333333

0

0.232356

0.171824

0

1

0

false

0

0.066667

0

0.066667

0

null

0

null

0

1

0

935b2e44b7b2c45351f62e76c25c2670f2799100

3,223

py

Python

3-2.TextLSTM/TextLSTM-Torch.py

aserron/nlp-tutorial

299dc4369a3c9597b5ac2042c606afe7da67b72f

[ "MIT" ]

5

2020-08-28T02:45:56.000Z

2021-11-23T07:03:52.000Z

3-2.TextLSTM/TextLSTM-Torch.py

aserron/nlp-tutorial

299dc4369a3c9597b5ac2042c606afe7da67b72f

[ "MIT" ]

null

3-2.TextLSTM/TextLSTM-Torch.py

aserron/nlp-tutorial

299dc4369a3c9597b5ac2042c606afe7da67b72f

[ "MIT" ]

5

2020-09-28T01:13:22.000Z

2021-05-21T01:13:47.000Z

''' code by Tae Hwan Jung(Jeff Jung) @graykode ''' import numpy as np import torch import torch.nn as nn import torch.optim as optim import sys dtype = torch.FloatTensor char_arr = [c for c in 'abcdefghijklmnopqrstuvwxyz'] word_dict = {n: i for i, n in enumerate(char_arr)} number_dict = {i: w for i, w in enumerate(char_arr)} n_class = len(word_dict) # number of class(=number of vocab) seq_data = ['make', 'need', 'coal', 'word', 'love', 'hate', 'live', 'home', 'hash', 'star'] # TextLSTM Parameters n_step = 3 n_hidden = 128 def make_batch(seq_data): input_batch, target_batch = [], [] for seq in seq_data: input = [word_dict[n] for n in seq[:-1]] # 'm', 'a' , 'k' is input target = word_dict[seq[-1]] # 'e' is target input_batch.append(np.eye(n_class)[input]) target_batch.append(target) return torch.Tensor(input_batch), torch.LongTensor(target_batch) class TextLSTM(nn.Module): # MOSTLY THE SAME EXCEPT FOR THE USAGE OF THE CELL STATE def __init__(self): super(TextLSTM, self).__init__() self.lstm = nn.LSTM(input_size=n_class, hidden_size=n_hidden) self.W = nn.Parameter(torch.randn([n_hidden, n_class]).type(dtype)) self.b = nn.Parameter(torch.randn([n_class]).type(dtype)) def forward(self, hidden_state, cell_state, X): input = X.transpose(0, 1) # X : [n_step, batch_size, n_class] # hidden_state: [num_layers(=1) * num_directions(=1), batch_size, n_hidden] # cell_state: [num_layers(=1) * num_directions(=1), batch_size, n_hidden] # hidden_state_size: the same with the RNN - hidden state # cell_state_size: the same with the RNN - hidden state outputs, (_, _) = self.lstm(input, (hidden_state, cell_state)) # outputs: [n_step, batch_size, num_directions(=1) * n_hidden] # outputs_size: the same with the RNN - outputs outputs = outputs[-1] # [batch_size, n_hidden] model = torch.mm(outputs, self.W) + self.b # model : [batch_size, n_class] return model input_batch, target_batch = make_batch(seq_data) print("*"*30) print("input_batch_size:", input_batch.size()) print("*"*30) print("target_batch_size:", target_batch.size()) print("*"*30) model = TextLSTM() criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) for name, param in model.named_parameters(): print("PARAM: ", name, param.size()) print("-"*30) # Training for epoch in range(1000): optimizer.zero_grad() hidden_state = torch.zeros(1, len(input_batch), n_hidden) cell_state = torch.zeros(1, len(input_batch), n_hidden) output = model(hidden_state, cell_state, input_batch) loss = criterion(output, target_batch) if (epoch + 1) % 100 == 0: print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss)) loss.backward() optimizer.step() inputs = [sen[:3] for sen in seq_data] hidden_state_t = torch.zeros(1, len(input_batch), n_hidden) cell_state_t = torch.zeros(1, len(input_batch), n_hidden) predict = model(hidden_state_t, cell_state_t, input_batch).data.max(1, keepdim=True)[1] print(inputs, '->', [number_dict[n.item()] for n in predict.squeeze()])

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0.15

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null

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1

0

935bc89978bfdfe24ff5ac32092e0b5efcbf9ef8

5,366

py

Python

userbot/modules/filemanager.py

elevenrin/WeebProject

68e3b342afb7fa55293652f458d7366289856f38

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

userbot/modules/filemanager.py

elevenrin/WeebProject

68e3b342afb7fa55293652f458d7366289856f38

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

userbot/modules/filemanager.py

elevenrin/WeebProject

68e3b342afb7fa55293652f458d7366289856f38

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

# Credits to Userge for Remove and Rename import io import os import os.path import shutil import time from os.path import dirname, exists, isdir, isfile, join from shutil import rmtree from userbot import CMD_HELP from userbot.events import register from userbot.utils import humanbytes MAX_MESSAGE_SIZE_LIMIT = 4095 @register(outgoing=True, pattern=r"^\.ls ?(.*)") async def lst(event): if event.fwd_from: return cat = event.pattern_match.group(1) path = cat if cat else os.getcwd() if not exists(path): await event.edit( f"`Tidak ada direktori atau file seperti itu dengan nama` **{cat}**, `periksa lagi!`" ) return if isdir(path): if cat: msg = "`Folder dan file di `{}` :\n\n".format(path) else: msg = "`Folder dan file di direktori saat ini` :\n\n" lists = os.listdir(path) files = "" folders = "" for contents in sorted(lists): catpath = path + "/" + contents if not isdir(catpath): size = os.stat(catpath).st_size if contents.endswith((".mp3", ".flac", ".wav", ".m4a")): files += "🎵 " elif contents.endswith((".opus")): files += "🎙 " elif contents.endswith( (".mkv", ".mp4", ".webm", ".avi", ".mov", ".flv") ): files += "🎞 " elif contents.endswith( (".zip", ".tar", ".tar.gz", ".rar", ".7z", ".xz") ): files += "🗜 " elif contents.endswith( (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".ico", ".webp") ): files += "🖼 " elif contents.endswith((".exe", ".deb")): files += "⚙️ " elif contents.endswith((".iso", ".img")): files += "💿 " elif contents.endswith((".apk", ".xapk")): files += "📱 " elif contents.endswith((".py")): files += "🐍 " else: files += "📄 " files += f"`{contents}` - __{humanbytes(size)}__\n" else: folders += f"📁 `{contents}`\n" msg = msg + folders + files if files or folders else msg + "__direktori kosong__" else: size = os.stat(path).st_size msg = "The details of given file :\n\n" if path.endswith((".mp3", ".flac", ".wav", ".m4a")): mode = "🎵 " elif path.endswith((".opus")): mode = "🎙 " elif path.endswith((".mkv", ".mp4", ".webm", ".avi", ".mov", ".flv")): mode = "🎞 " elif path.endswith((".zip", ".tar", ".tar.gz", ".rar", ".7z", ".xz")): mode = "🗜 " elif path.endswith((".jpg", ".jpeg", ".png", ".gif", ".bmp", ".ico", ".webp")): mode = "🖼 " elif path.endswith((".exe", ".deb")): mode = "⚙️ " elif path.endswith((".iso", ".img")): mode = "💿 " elif path.endswith((".apk", ".xapk")): mode = "📱 " elif path.endswith((".py")): mode = "🐍 " else: mode = "📄 " time.ctime(os.path.getctime(path)) time2 = time.ctime(os.path.getmtime(path)) time3 = time.ctime(os.path.getatime(path)) msg += f"**Lokasi** : `{path}`\n" msg += f"**Ikon** : `{mode}`\n" msg += f"**Ukuran** : `{humanbytes(size)}`\n" msg += f"**Waktu terakhir diubah** : `{time2}`\n" msg += f"**Waktu terakhir diakses** : `{time3}`" if len(msg) > MAX_MESSAGE_SIZE_LIMIT: with io.BytesIO(str.encode(msg)) as out_file: out_file.name = "ls.txt" await event.client.send_file( event.chat_id, out_file, force_document=True, allow_cache=False, caption=path, ) await event.delete() else: await event.edit(msg) @register(outgoing=True, pattern=r"^\.rm ?(.*)") async def rmove(event): """Removing Directory/File""" cat = event.pattern_match.group(1) if not cat: await event.edit("`Jalur file tidak ada!`") return if not exists(cat): await event.edit("`Jalur file tidak ada!`") return if isfile(cat): os.remove(cat) else: rmtree(cat) await event.edit(f"**{cat}** `dihapus!`") @register(outgoing=True, pattern=r"^\.rn ([^|]+)\|([^|]+)") async def rname(event): """Renaming Directory/File""" cat = str(event.pattern_match.group(1)).strip() new_name = str(event.pattern_match.group(2)).strip() if not exists(cat): await event.edit(f"`Jalur file` : **{cat}** `tidak ada!`") return new_path = join(dirname(cat), new_name) shutil.move(cat, new_path) await event.edit(f"`Nama diganti dari` **{cat}** `menjadi` **{new_path}**") CMD_HELP.update( { "file": "`.ls [direktori]`" "\n➥ Dapatkan daftar file di dalam direktori." "\n\n`.rm [direktori/file]`" "\n➥ Hapus file atau direktori." "\n\n`.rn [direktori/file] | [nama baru]`" "\n➥ Mengubah nama file atau direktori." } )

33.962025

98

0.479873

607

5,366

4.227348

0.314662

0.035074

0.062354

0.034295

0.252143

0.132892

0.112627

0.077163

0.032736

0

0.005664

0.341968

5,366

157

99

34.178344

0.713679

0.007268

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0

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false

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0.105634

0

null

0

null

0

1

0

935d0c7ba01171407513e36657e2685947f91c56

1,946

py

Python

test/test_input_data.py

simonpf/qprof

3a501ca7dc694a3455be928453afa13c236ad492

[ "MIT" ]

1

2020-09-19T12:00:57.000Z

test/test_input_data.py

simonpf/qprof

3a501ca7dc694a3455be928453afa13c236ad492

[ "MIT" ]

null

test/test_input_data.py

simonpf/qprof

3a501ca7dc694a3455be928453afa13c236ad492

[ "MIT" ]

null

""" Tests for the qprof.input_data module. """ import numpy as np import pytest from qprof.models import get_normalizer, get_model from qprof.input_data import BinInputData NETCDF4_AVAILABLE = False try: import netCDF4 NETCDF4_AVAILABLE = True except ImportError: pass def test_bin_input_data(test_data): """ Ensure that bin data is correctly converted to retrieval input. """ normalizer = get_normalizer() input_file = BinInputData(test_data["bin_file"], normalizer) batch = input_file.get_batch() batch = normalizer.invert(batch) bin_data = input_file.bin_file.handle assert np.all(np.isclose(batch[:, :3], bin_data["brightness_temperatures"][:, :3])) assert np.all(np.isclose(batch[:, 15], bin_data["two_meter_temperature"])) assert np.all(np.isclose(batch[:, 16], bin_data["total_column_water_vapor"])) st = np.where(batch[:, 17:36])[1] assert np.all(np.isclose(st, input_file.bin_file.surface_type)) at = np.where(batch[:, 36:])[1] assert np.all(np.isclose(at, input_file.bin_file.airmass_type)) @pytest.mark.skipif(not NETCDF4_AVAILABLE, reason="netCDF4 package missing.") def test_bin_retrieval(test_data): """ Ensure that bin data is correctly converted to retrieval input. """ normalizer = get_normalizer() model = get_model() input_file = test_data["bin_file"] folder = input_file.parent input_data = BinInputData(input_file, normalizer) results = input_data.run_retrieval(model) retrieval_file = input_data.write_retrieval_results(folder, results) assert retrieval_file.name[:-2] == input_file.name[:-3] data = netCDF4.Dataset(retrieval_file) y = data["truth"][:].data assert np.all(np.isclose(input_data.bin_file.handle["surface_precip"], y))

29.044776

78

0.661357

251

1,946

4.896414

0.322709

0.065907

0.053702

0.063466

0.251424

0.235151

0.174125

0.136697

0

0.014579

0.224563

1,946

66

79

29.484848

0.799867

0.085303

0

0.097561

0

0.072947

0.039058

0

0.170732

1

0.04878

false

0.02439

0.146341

0

0.195122

0

null

0

null

0

1

0

9366ef921a5f620eac2a027b90f615edf00495a8

57,075

py

Python

vmware_nsx/common/config.py

salv-orlando/vmware-nsx

6ad0d595aa8099004eb6dd5ff62c7a91b0e11dfd

[ "Apache-2.0" ]

null

vmware_nsx/common/config.py

salv-orlando/vmware-nsx

6ad0d595aa8099004eb6dd5ff62c7a91b0e11dfd

[ "Apache-2.0" ]

null

vmware_nsx/common/config.py

salv-orlando/vmware-nsx

6ad0d595aa8099004eb6dd5ff62c7a91b0e11dfd

[ "Apache-2.0" ]

null

# Copyright 2012 VMware, Inc. # # 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 oslo_config import cfg from oslo_config import types from oslo_log import log as logging from neutron.conf.db import l3_hamode_db from vmware_nsx._i18n import _ from vmware_nsx.common import exceptions as nsx_exc from vmware_nsx.common import nsxv_constants from vmware_nsx.dvs import dvs_utils from vmware_nsx.extensions import projectpluginmap from vmware_nsx.extensions import routersize LOG = logging.getLogger(__name__) DEFAULT_VDR_TRANSIT_NETWORK = "169.254.2.0/28" DEFAULT_PLR_ADDRESS = "169.254.2.3" class AgentModes(object): AGENT = 'agent' AGENTLESS = 'agentless' COMBINED = 'combined' class MetadataModes(object): DIRECT = 'access_network' INDIRECT = 'dhcp_host_route' class ReplicationModes(object): SERVICE = 'service' SOURCE = 'source' base_opts = [ cfg.IntOpt('max_lp_per_bridged_ls', default=5000, deprecated_group='NVP', help=_("Maximum number of ports of a logical switch on a " "bridged transport zone. The recommended value for " "this parameter varies with NSX version.\nPlease use:\n" "NSX 2.x -> 64\nNSX 3.0, 3.1 -> 5000\n" "NSX 3.2 -> 10000")), cfg.IntOpt('max_lp_per_overlay_ls', default=256, deprecated_group='NVP', help=_("Maximum number of ports of a logical switch on an " "overlay transport zone")), cfg.IntOpt('concurrent_connections', default=10, deprecated_group='NVP', help=_("Maximum concurrent connections to each NSX " "controller.")), cfg.IntOpt('nsx_gen_timeout', default=-1, deprecated_name='nvp_gen_timeout', deprecated_group='NVP', help=_("Number of seconds a generation id should be valid for " "(default -1 meaning do not time out)")), cfg.StrOpt('metadata_mode', default=MetadataModes.DIRECT, deprecated_group='NVP', help=_("If set to access_network this enables a dedicated " "connection to the metadata proxy for metadata server " "access via Neutron router. If set to dhcp_host_route " "this enables host route injection via the dhcp agent. " "This option is only useful if running on a host that " "does not support namespaces otherwise access_network " "should be used.")), cfg.StrOpt('default_transport_type', default='stt', deprecated_group='NVP', help=_("The default network tranport type to use (stt, gre, " "bridge, ipsec_gre, or ipsec_stt)")), cfg.StrOpt('agent_mode', default=AgentModes.AGENT, deprecated_group='NVP', help=_("Specifies in which mode the plugin needs to operate " "in order to provide DHCP and metadata proxy services " "to tenant instances. If 'agent' is chosen (default) " "the NSX plugin relies on external RPC agents (i.e. " "dhcp and metadata agents) to provide such services. " "In this mode, the plugin supports API extensions " "'agent' and 'dhcp_agent_scheduler'. If 'agentless' " "is chosen (experimental in Icehouse), the plugin will " "use NSX logical services for DHCP and metadata proxy. " "This simplifies the deployment model for Neutron, in " "that the plugin no longer requires the RPC agents to " "operate. When 'agentless' is chosen, the config option " "metadata_mode becomes ineffective. The 'agentless' " "mode works only on NSX 4.1. Furthermore, a 'combined' " "mode is also provided and is used to support existing " "deployments that want to adopt the agentless mode. " "With this mode, existing networks keep being served by " "the existing infrastructure (thus preserving backward " "compatibility, whereas new networks will be served by " "the new infrastructure. Migration tools are provided " "to 'move' one network from one model to another; with " "agent_mode set to 'combined', option " "'network_auto_schedule' in neutron.conf is ignored, as " "new networks will no longer be scheduled to existing " "dhcp agents.")), cfg.StrOpt('replication_mode', default=ReplicationModes.SERVICE, choices=(ReplicationModes.SERVICE, ReplicationModes.SOURCE), help=_("Specifies which mode packet replication should be done " "in. If set to service a service node is required in " "order to perform packet replication. This can also be " "set to source if one wants replication to be performed " "locally (NOTE: usually only useful for testing if one " "does not want to deploy a service node). In order to " "leverage distributed routers, replication_mode should " "be set to 'service'.")), cfg.FloatOpt('qos_peak_bw_multiplier', default=2.0, min=1.0, help=_("The QoS rules peak bandwidth value will be the " "configured maximum bandwidth of the QoS rule, " "multiplied by this value. Value must be bigger than" " 1")), ] connection_opts = [ cfg.StrOpt('nsx_user', default='admin', deprecated_name='nvp_user', help=_('User name for NSX controllers in this cluster')), cfg.StrOpt('nsx_password', default='admin', deprecated_name='nvp_password', secret=True, help=_('Password for NSX controllers in this cluster')), cfg.IntOpt('http_timeout', default=75, help=_('Time before aborting a request on an ' 'unresponsive controller (Seconds)')), cfg.IntOpt('retries', default=2, help=_('Maximum number of times a particular request ' 'should be retried')), cfg.IntOpt('redirects', default=2, help=_('Maximum number of times a redirect response ' 'should be followed')), cfg.ListOpt('nsx_controllers', default=[], deprecated_name='nvp_controllers', help=_('Comma-separated list of NSX controller ' 'endpoints (<ip>:<port>). When port is omitted, ' '443 is assumed. This option MUST be specified. ' 'e.g.: aa.bb.cc.dd, ee.ff.gg.hh.ee:80')), cfg.IntOpt('conn_idle_timeout', default=900, help=_('Reconnect connection to nsx if not used within this ' 'amount of time.')), ] cluster_opts = [ cfg.StrOpt('default_tz_uuid', help=_("This is uuid of the default NSX Transport zone that " "will be used for creating tunneled isolated " "\"Neutron\" networks. It needs to be created in NSX " "before starting Neutron with the nsx plugin.")), cfg.StrOpt('default_l3_gw_service_uuid', help=_("(Optional) UUID of the NSX L3 Gateway " "service which will be used for implementing routers " "and floating IPs")), cfg.StrOpt('default_l2_gw_service_uuid', help=_("(Optional) UUID of the NSX L2 Gateway service " "which will be used by default for network gateways")), cfg.StrOpt('default_service_cluster_uuid', help=_("(Optional) UUID of the Service Cluster which will " "be used by logical services like dhcp and metadata")), cfg.StrOpt('nsx_default_interface_name', default='breth0', deprecated_name='default_interface_name', help=_("Name of the interface on a L2 Gateway transport node " "which should be used by default when setting up a " "network connection")), ] nsx_common_opts = [ cfg.StrOpt('nsx_l2gw_driver', help=_("Specify the class path for the Layer 2 gateway " "backend driver (i.e. NSX-T/NSX-V). This field will be " "used when a L2 Gateway service plugin is configured.")), cfg.StrOpt('locking_coordinator_url', help=_("(Optional) URL for distributed locking coordination " "resource for lock manager. This value is passed as a " "parameter to tooz coordinator. By default, value is " "None and oslo_concurrency is used for single-node " "lock management.")), cfg.BoolOpt('api_replay_mode', default=False, help=_("If true, the server then allows the caller to " "specify the id of resources. This should only " "be enabled in order to allow one to migrate an " "existing install of neutron to a new VMWare plugin.")), cfg.ListOpt('nsx_extension_drivers', default=[], help=_("An ordered list of extension driver " "entrypoints to be loaded from the " "vmware_nsx.extension_drivers namespace.")), cfg.StrOpt('smtp_gateway', help=_("(Optional) IP address of SMTP gateway to use for" "admin warnings.")), cfg.StrOpt('smtp_from_addr', help=_("(Optional) email address to use for outgoing admin" "notifications.")), cfg.ListOpt('snmp_to_list', default=[], help=_("(Optional) List of email addresses for " "notifications.")), cfg.IntOpt('octavia_stats_interval', default=10, help=_("Interval in seconds for Octavia statistics reporting. " "0 means no reporting")), ] nsx_v3_and_p = [ cfg.ListOpt('nsx_api_user', default=['admin'], help=_('User names for the NSX managers')), cfg.ListOpt('nsx_api_password', default=['default'], secret=True, help=_('Passwords for the NSX managers')), cfg.ListOpt('nsx_api_managers', default=[], help=_("IP address of one or more NSX managers separated " "by commas. The IP address should be of the form:\n" "[<scheme>://]<ip_address>[:<port>]\nIf scheme is not " "provided https is used. If port is not provided port " "80 is used for http and port 443 for https.")), cfg.BoolOpt('nsx_use_client_auth', default=False, help=_("Use client certificate in NSX manager " "authentication")), cfg.StrOpt('nsx_client_cert_file', default='', help=_("File to contain client certificate and private key")), cfg.StrOpt('nsx_client_cert_pk_password', default="", secret=True, help=_("password for private key encryption")), cfg.StrOpt('nsx_client_cert_storage', default='nsx-db', choices=['nsx-db', 'none'], help=_("Storage type for client certificate sensitive data")), cfg.IntOpt('retries', default=10, help=_('Maximum number of times to retry API requests upon ' 'stale revision errors.')), cfg.ListOpt('ca_file', help=_('Specify a CA bundle files to use in verifying the NSX ' 'Managers server certificate. This option is ignored ' 'if "insecure" is set to True. If "insecure" is set to ' 'False and ca_file is unset, the system root CAs will ' 'be used to verify the server certificate.')), cfg.BoolOpt('insecure', default=True, help=_('If true, the NSX Manager server certificate is not ' 'verified. If false the CA bundle specified via ' '"ca_file" will be used or if unsest the default ' 'system root CAs will be used.')), cfg.IntOpt('http_timeout', default=10, help=_('The time in seconds before aborting a HTTP connection ' 'to a NSX manager.')), cfg.IntOpt('http_read_timeout', default=180, help=_('The time in seconds before aborting a HTTP read ' 'response from a NSX manager.')), cfg.IntOpt('http_retries', default=3, help=_('Maximum number of times to retry a HTTP connection.')), cfg.IntOpt('concurrent_connections', default=10, help=_("Maximum concurrent connections to each NSX " "manager.")), cfg.IntOpt('conn_idle_timeout', default=10, help=_("The amount of time in seconds to wait before ensuring " "connectivity to the NSX manager if no manager " "connection has been used.")), cfg.IntOpt('redirects', default=2, help=_('Number of times a HTTP redirect should be followed.')), cfg.BoolOpt('log_security_groups_blocked_traffic', default=False, help=_("(Optional) Indicates whether distributed-firewall " "rule for security-groups blocked traffic is logged.")), cfg.BoolOpt('log_security_groups_allowed_traffic', default=False, help=_("(Optional) Indicates whether distributed-firewall " "security-groups rules are logged.")), cfg.ListOpt('network_vlan_ranges', default=[], help=_("List of <TZ UUID>:<vlan_min>:<vlan_max> " "specifying Transport Zone UUID usable for VLAN " "provider networks, as well as ranges of VLAN " "tags on each available for allocation to networks.")), cfg.ListOpt('availability_zones', default=[], help=_('Optional parameter defining the networks availability ' 'zones names for the native dhcp configuration. The ' 'configuration of each zone will be under a group ' 'names [az:<name>]')), cfg.StrOpt('metadata_proxy', help=_("This is the name or UUID of the NSX Metadata Proxy " "that will be used to enable native metadata service. " "It needs to be created in NSX before starting Neutron " "with the NSX plugin.")), cfg.StrOpt('native_metadata_route', default="169.254.169.254/31", help=_("The metadata route used for native metadata proxy " "service.")), cfg.BoolOpt('windows_metadata_route', default=True, help=_("Inject a route for allowing windows guest access NSX " "native metadata proxy service")), cfg.StrOpt('dns_domain', default='openstacklocal', help=_("Domain to use for building the hostnames.")), cfg.ListOpt('nameservers', default=[], help=_("List of nameservers to configure for the DHCP " "binding entries. These will be used if there are no " "nameservers defined on the subnet.")), cfg.StrOpt('edge_cluster', help=_("(Optional) Specifying an edge cluster for Tier1 " "routers to connect other that the one connected to" " the Tier0 router")), cfg.ListOpt('transit_networks', default=['100.64.0.0/16', 'fc3d:e3c3:7b93::/48'], help=_("List of transit networks used by NSX tier0 routers. " "Neutron subnets will not be allowed to use those " "cidrs")), cfg.BoolOpt('init_objects_by_tags', default=False, help=_("When True, the configured transport zones, router and " "profiles will be found by tags on the NSX. The scope " "of the tag will be the value of search_objects_" "scope. The value of the search tag will be the name " "configured in each respective configuration.")), cfg.StrOpt('search_objects_scope', help=_("This is the scope of the tag that will be used for " "finding the objects uuids on the NSX during plugin " "init.")), cfg.IntOpt('dhcp_lease_time', default=86400, help=_("DHCP default lease time.")), cfg.BoolOpt('support_nsx_port_tagging', default=False, help=_("If true, adding neutron tags to ports will also add " "tags on the NSX logical ports. This feature requires " "oslo_messaging_notifications driver to be " "configured.")), ] nsx_v3_opts = nsx_v3_and_p + [ cfg.StrOpt('dhcp_profile', help=_("This is the name or UUID of the NSX DHCP Profile " "that will be used to enable native DHCP service. It " "needs to be created in NSX before starting Neutron " "with the NSX plugin")), cfg.StrOpt('default_overlay_tz', help=_("This is the name or UUID of the default NSX overlay " "transport zone that will be used for creating " "tunneled isolated Neutron networks. It needs to be " "created in NSX before starting Neutron with the NSX " "plugin.")), cfg.StrOpt('default_vlan_tz', help=_("(Optional) Only required when creating VLAN or flat " "provider networks. Name or UUID of default NSX VLAN " "transport zone that will be used for bridging between " "Neutron networks, if no physical network has been " "specified")), cfg.StrOpt('default_bridge_cluster', deprecated_for_removal=True, help=_("(Optional) Name or UUID of the default NSX bridge " "cluster that will be used to perform L2 gateway " "bridging between VXLAN and VLAN networks. If default " "bridge cluster UUID is not specified, admin will have " "to manually create a L2 gateway corresponding to a " "NSX Bridge Cluster using L2 gateway APIs. This field " "must be specified on one of the active neutron " "servers only.")), cfg.StrOpt('default_bridge_endpoint_profile', help=_("(Optional) Name or UUID of the default NSX bridge " "endpoint profile that will be used to perform L2 " "bridging between networks in the NSX fabric and " "VLANs external to NSX. If not specified, operators " "will need to explicitly create a layer-2 gateway in " "Neutron using the L2 gateway APIs.")), cfg.StrOpt('default_tier0_router', help=_("Name or UUID of the default tier0 router that will be " "used for connecting to tier1 logical routers and " "configuring external networks")), cfg.IntOpt('number_of_nested_groups', default=8, help=_("(Optional) The number of nested groups which are used " "by the plugin, each Neutron security-groups is added " "to one nested group, and each nested group can contain " "as maximum as 500 security-groups, therefore, the " "maximum number of security groups that can be created " "is 500 * number_of_nested_groups. The default is 8 " "nested groups, which allows a maximum of 4k " "security-groups, to allow creation of more " "security-groups, modify this figure.")), cfg.StrOpt('metadata_mode', default=MetadataModes.DIRECT, help=_("If set to access_network this enables a dedicated " "connection to the metadata proxy for metadata server " "access via Neutron router. If set to dhcp_host_route " "this enables host route injection via the dhcp agent. " "This option is only useful if running on a host that " "does not support namespaces otherwise access_network " "should be used.")), cfg.BoolOpt('metadata_on_demand', default=False, help=_("If true, an internal metadata network will be created " "for a router only when the router is attached to a " "DHCP-disabled subnet.")), cfg.BoolOpt('native_dhcp_metadata', default=True, help=_("If true, DHCP and metadata proxy services will be " "provided by NSX backend.")), cfg.ListOpt('switching_profiles', default=[], help=_("Optional parameter defining a list switching profiles " "uuids that will be attached to all neutron created " "nsx ports.")), cfg.BoolOpt('ens_support', default=False, help=_("(Optional) Indicates whether ENS transport zones can " "be used")), cfg.BoolOpt('disable_port_security_for_ens', # This flag was relevant only for NSX version that did not # support ENS with security features deprecated_for_removal=True, default=False, help=_("When True, port security will be set to False for " "newly created ENS networks and ports, overriding " "user settings")), cfg.StrOpt('dhcp_relay_service', help=_("(Optional) This is the name or UUID of the NSX dhcp " "relay service that will be used to enable DHCP relay " "on router ports.")), cfg.ListOpt('housekeeping_jobs', default=['orphaned_dhcp_server', 'orphaned_logical_switch', 'orphaned_logical_router', 'mismatch_logical_port', 'orphaned_firewall_section'], help=_("List of the enabled housekeeping jobs")), cfg.ListOpt('housekeeping_readonly_jobs', default=[], help=_("List of housekeeping jobs which are enabled in read " "only mode")), cfg.BoolOpt('housekeeping_readonly', default=True, help=_("Housekeeping will only warn about breakage.")), ] nsx_p_opts = nsx_v3_and_p + [ cfg.StrOpt('dhcp_profile', help=_("This is the name or UUID of the NSX DHCP Profile, " "or the name or ID of the Policy DHCP server config " "that will be used to enable native DHCP service. It " "needs to be created in NSX before starting Neutron " "with the NSX plugin")), cfg.StrOpt('default_tier0_router', help=_("Name or UUID of the default tier0 router that will be " "used for connecting to tier1 logical routers and " "configuring external networks. If only one tier0 " " router is present on backend, it will be assumed " "as default unless this value is provided")), cfg.StrOpt('default_overlay_tz', help=_("This is the name or UUID of the default NSX overlay " "transport zone that will be used for creating " "tunneled isolated Neutron networks. It needs to be " "created in NSX before starting Neutron with the NSX " "plugin. If only one overlay transport zone is present " "on backend, it will be assumed as default unless this " "value is provided")), cfg.StrOpt('default_vlan_tz', help=_("(Optional) Only required when creating VLAN or flat " "provider networks. Name or UUID of default NSX VLAN " "transport zone that will be used for bridging between " "Neutron networks, if no physical network has been " "specified. If only one VLAN transport zone is present " "on backend, it will be assumed as default unless this " "value is provided")), cfg.StrOpt('waf_profile', deprecated_for_removal=True, help=_("(Optional) Name or UUID of the default WAF profile to " "be attached to L7 loadbalancer listeners")), cfg.BoolOpt('allow_passthrough', default=True, help=_("If True, use nsx manager api for cases which are not " "supported by the policy manager api")), cfg.IntOpt('realization_max_attempts', default=50, help=_("(Optional) Maximum number of times to retry while " "waiting for a resource to be realized")), cfg.IntOpt('realization_wait_sec', default=1.0, help=_("(Optional) Number of seconds to wait between attempts " "for a resource to be realized")), cfg.BoolOpt('firewall_match_internal_addr', default=True, help=_("If True, edge firewall rules will match internal " "addresses. Else they will match the external " "addresses")), ] DEFAULT_STATUS_CHECK_INTERVAL = 2000 DEFAULT_MINIMUM_POOLED_EDGES = 1 DEFAULT_MAXIMUM_POOLED_EDGES = 3 DEFAULT_MAXIMUM_TUNNELS_PER_VNIC = 20 nsxv_opts = [ cfg.StrOpt('user', default='admin', help=_('User name for NSXv manager')), cfg.StrOpt('password', default='default', secret=True, help=_('Password for NSXv manager')), cfg.StrOpt('manager_uri', help=_('URL for NSXv manager')), cfg.StrOpt('ca_file', help=_('Specify a CA bundle file to use in verifying the NSXv ' 'server certificate.')), cfg.BoolOpt('insecure', default=True, help=_('If true, the NSXv server certificate is not verified. ' 'If false, then the default CA truststore is used for ' 'verification. This option is ignored if "ca_file" is ' 'set.')), cfg.ListOpt('cluster_moid', default=[], help=_('(Required) Parameter listing the IDs of the clusters ' 'which are used by OpenStack.')), cfg.StrOpt('datacenter_moid', help=_('Required parameter identifying the ID of datacenter ' 'to deploy NSX Edges')), cfg.StrOpt('deployment_container_id', help=_('Optional parameter identifying the ID of datastore to ' 'deploy NSX Edges')), cfg.StrOpt('resource_pool_id', help=_('Optional parameter identifying the ID of resource to ' 'deploy NSX Edges')), cfg.ListOpt('availability_zones', default=[], help=_('Optional parameter defining the availability zones ' 'names for deploying NSX Edges. The configuration of ' 'each zone will be under a group names [az:<name>]')), cfg.StrOpt('datastore_id', help=_('Optional parameter identifying the ID of datastore to ' 'deploy NSX Edges')), cfg.StrOpt('ha_datastore_id', help=_('Optional parameter identifying the ID of datastore to ' 'deploy NSX Edges in addition to data_store_id in case' 'edge_ha is True')), cfg.BoolOpt('ha_placement_random', default=False, help=_('When True and in case edge_ha is True, half of the ' 'edges will be placed in the primary datastore as ' 'active and the other half will be placed in the ' 'ha_datastore')), cfg.ListOpt('edge_host_groups', default=[], help=_('(Optional) If edge HA is used then this will ensure ' 'that active/backup edges are placed in the listed ' 'host groups. At least 2 predefined host groups need ' 'to be configured.')), cfg.StrOpt('external_network', help=_('(Required) Network ID for physical network ' 'connectivity')), cfg.IntOpt('task_status_check_interval', default=DEFAULT_STATUS_CHECK_INTERVAL, help=_("(Optional) Asynchronous task status check interval. " "Default is 2000 (millisecond)")), cfg.StrOpt('vdn_scope_id', help=_('(Optional) Network scope ID for VXLAN virtual wires')), cfg.StrOpt('dvs_id', help=_('(Optional) DVS MoRef ID for DVS connected to ' 'Management / Edge cluster')), cfg.IntOpt('maximum_tunnels_per_vnic', default=DEFAULT_MAXIMUM_TUNNELS_PER_VNIC, min=1, max=110, help=_('(Optional) Maximum number of sub interfaces supported ' 'per vnic in edge.')), cfg.ListOpt('backup_edge_pool', default=['service:compact:4:10', 'vdr:compact:4:10'], help=_("Defines edge pool's management range with the format: " "<edge_type>:[edge_size]:<min_edges>:<max_edges>." "edge_type: service,vdr. " "edge_size: compact, large, xlarge, quadlarge " "and default is compact. By default, edge pool manager " "would manage service edge with compact size " "and distributed edge with compact size as following: " "service:compact:4:10,vdr:compact:" "4:10")), cfg.IntOpt('retries', default=20, help=_('Maximum number of API retries on endpoint.')), cfg.StrOpt('mgt_net_moid', help=_('(Optional) Portgroup MoRef ID for metadata proxy ' 'management network')), cfg.ListOpt('mgt_net_proxy_ips', default=[], help=_('(Optional) Comma separated list of management network ' 'IP addresses for metadata proxy.')), cfg.StrOpt('mgt_net_proxy_netmask', help=_("(Optional) Management network netmask for metadata " "proxy.")), cfg.StrOpt('mgt_net_default_gateway', help=_("(Optional) Management network default gateway for " "metadata proxy.")), cfg.ListOpt('nova_metadata_ips', default=[], help=_("(Optional) IP addresses used by Nova metadata " "service.")), cfg.PortOpt('nova_metadata_port', default=8775, help=_("(Optional) TCP Port used by Nova metadata server.")), cfg.StrOpt('metadata_shared_secret', secret=True, help=_("(Optional) Shared secret to sign metadata requests.")), cfg.BoolOpt('metadata_insecure', default=True, help=_("(Optional) If True, the end to end connection for " "metadata service is not verified. If False, the " "default CA truststore is used for verification.")), cfg.StrOpt('metadata_nova_client_cert', help=_('(Optional) Client certificate to use when metadata ' 'connection is to be verified. If not provided, ' 'a self signed certificate will be used.')), cfg.StrOpt('metadata_nova_client_priv_key', help=_("(Optional) Private key of client certificate.")), cfg.BoolOpt('spoofguard_enabled', default=True, help=_("(Optional) If True then plugin will use NSXV " "spoofguard component for port-security feature.")), cfg.BoolOpt('use_exclude_list', default=True, help=_("(Optional) If True then plugin will use NSXV exclude " "list component when port security is disabled and " "spoofguard is enabled.")), cfg.ListOpt('tenant_router_types', default=['shared', 'distributed', 'exclusive'], help=_("Ordered list of router_types to allocate as tenant " "routers. It limits the router types that the Nsxv " "can support for tenants:\ndistributed: router is " "supported by distributed edge at the backend.\n" "shared: multiple routers share the same service " "edge at the backend.\nexclusive: router exclusively " "occupies one service edge at the backend.\nNsxv would " "select the first available router type from " "tenant_router_types list if router-type is not " "specified. If the tenant defines the router type with " "'--distributed','--router_type exclusive' or " "'--router_type shared', Nsxv would verify that the " "router type is in tenant_router_types. Admin supports " "all these three router types.")), cfg.StrOpt('edge_appliance_user', secret=True, help=_("(Optional) Username to configure for Edge appliance " "login.")), cfg.StrOpt('edge_appliance_password', secret=True, help=_("(Optional) Password to configure for Edge appliance " "login.")), cfg.IntOpt('dhcp_lease_time', default=86400, help=_("(Optional) DHCP default lease time.")), cfg.BoolOpt('metadata_initializer', default=True, help=_("If True, the server instance will attempt to " "initialize the metadata infrastructure")), cfg.ListOpt('metadata_service_allowed_ports', item_type=types.Port(), default=[], help=_('List of tcp ports, to be allowed access to the ' 'metadata proxy, in addition to the default ' '80,443,8775 tcp ports')), cfg.BoolOpt('edge_ha', default=False, help=_("(Optional) Enable HA for NSX Edges.")), cfg.StrOpt('exclusive_router_appliance_size', default="compact", choices=routersize.VALID_EDGE_SIZES, help=_("(Optional) Edge appliance size to be used for creating " "exclusive router. Valid values: " "['compact', 'large', 'xlarge', 'quadlarge']. This " "exclusive_router_appliance_size will be picked up if " "--router-size parameter is not specified while doing " "neutron router-create")), cfg.StrOpt('shared_router_appliance_size', default="compact", choices=routersize.VALID_EDGE_SIZES, help=_("(Optional) Edge appliance size to be used for creating " "shared router edge. Valid values: " "['compact', 'large', 'xlarge', 'quadlarge'].")), cfg.StrOpt('dns_search_domain', help=_("(Optional) Use this search domain if there is no " "search domain configured on the subnet.")), cfg.ListOpt('nameservers', default=[], help=_('List of nameservers to configure for the DHCP binding ' 'entries. These will be used if there are no ' 'nameservers defined on the subnet.')), cfg.BoolOpt('use_dvs_features', default=False, help=_('If True, dvs features will be supported which ' 'involves configuring the dvs backing nsx_v directly. ' 'If False, only features exposed via nsx_v will be ' 'supported')), cfg.BoolOpt('log_security_groups_blocked_traffic', default=False, help=_("(Optional) Indicates whether distributed-firewall " "rule for security-groups blocked traffic is logged.")), cfg.BoolOpt('log_security_groups_allowed_traffic', default=False, help=_("(Optional) Indicates whether distributed-firewall " "security-groups allowed traffic is logged.")), cfg.StrOpt('service_insertion_profile_id', help=_("(Optional) The profile id of the redirect firewall " "rules that will be used for the Service Insertion " "feature.")), cfg.BoolOpt('service_insertion_redirect_all', default=False, help=_("(Optional) If set to True, the plugin will create " "a redirect rule to send all the traffic to the " "security partner")), cfg.BoolOpt('use_nsx_policies', default=False, help=_("If set to True, the plugin will use NSX policies " "in the neutron security groups.")), cfg.StrOpt('default_policy_id', help=_("(Optional) If use_nsx_policies is True, this policy " "will be used as the default policy for new tenants.")), cfg.BoolOpt('allow_tenant_rules_with_policy', default=False, help=_("(Optional) If use_nsx_policies is True, this value " "will determine if a tenants can add rules to their " "security groups.")), cfg.StrOpt('vdr_transit_network', default=DEFAULT_VDR_TRANSIT_NETWORK, help=_("(Optional) Sets the network address for distributed " "router TLR-PLR connectivity, with " "<network IP>/<prefix> syntax")), cfg.BoolOpt('bind_floatingip_to_all_interfaces', default=False, help=_("If set to False, router will associate floating ip " "with external interface of only, thus denying " "connectivity between hosts on same network via " "their floating ips. If True, floating ip will " "be associated with all router interfaces.")), cfg.BoolOpt('exclusive_dhcp_edge', default=False, help=_("(Optional) Have exclusive DHCP edge per network.")), cfg.IntOpt('bgp_neighbour_hold_down_timer', default=4, help=_("(Optional) Set the interval (Seconds) for BGP " "neighbour hold down time.")), cfg.IntOpt('bgp_neighbour_keep_alive_timer', default=1, help=_("(Optional) Set the interval (Seconds) for BGP " "neighbour keep alive time.")), cfg.IntOpt('ecmp_wait_time', default=2, help=_("(Optional) Set the wait time (Seconds) between " "enablement of ECMP.")), cfg.ListOpt('network_vlan_ranges', default=[], help=_("List of <DVS MoRef ID>:<vlan_min>:<vlan_max> " "specifying DVS MoRef ID usable for VLAN provider " "networks, as well as ranges of VLAN tags on each " "available for allocation to networks.")), cfg.IntOpt('nsx_transaction_timeout', default=240, help=_("Timeout interval for NSX backend transactions.")), cfg.BoolOpt('share_edges_between_tenants', default=True, help=_("If False, different tenants will not use the same " "DHCP edge or router edge.")), cfg.ListOpt('housekeeping_jobs', default=['error_dhcp_edge', 'error_backup_edge'], help=_("List of the enabled housekeeping jobs")), cfg.ListOpt('housekeeping_readonly_jobs', default=[], help=_("List of housekeeping jobs which are enabled in read " "only mode")), cfg.BoolOpt('housekeeping_readonly', default=True, help=_("Housekeeping will only warn about breakage.")), cfg.BoolOpt('use_default_block_all', default=False, help=_("Use default block all rule when no security groups " "are set on a port and port security is enabled")), cfg.BoolOpt('use_routers_as_lbaas_platform', default=False, help=_("Use subnet's exclusive router as a platform for " "LBaaS")), cfg.BoolOpt('allow_multiple_ip_addresses', default=False, help=_("Allow associating multiple IPs to VMs " "without spoofguard limitations")), cfg.StrOpt('nsx_sg_name_format', default='%(name)s (%(id)s)', help=_("(Optional) Format for the NSX name of an openstack " "security group")), cfg.BoolOpt('init_validation', default=True, help=_("Set to False to skip plugin init validation")), cfg.BoolOpt('loadbalancer_pool_transparency', default=False, help=_("Create LBaaS pools with transparent mode on. Use with " "use_routers_as_lbaas_platform enabled")), cfg.ListOpt('default_edge_size', default=[], help=_("(Optional) Defines the default edge size for router, " "dhcp and loadbalancer edges with the format: " "<purpose>:<edge_size>. " "purpose: router, dhcp, lb. " "edge_size: compact, large, xlarge, quadlarge")), ] # define the configuration of each NSX-V availability zone. # the list of expected zones is under nsxv group: availability_zones # Note: if any of the optional arguments is missing - the global one will be # used instead. nsxv_az_opts = [ cfg.StrOpt('resource_pool_id', help=_('Identifying the ID of resource to deploy NSX Edges')), cfg.StrOpt('datastore_id', help=_('Identifying the ID of datastore to deploy NSX Edges')), cfg.BoolOpt('edge_ha', default=False, help=_("(Optional) Enable HA for NSX Edges.")), cfg.StrOpt('ha_datastore_id', help=_('Optional parameter identifying the ID of datastore to ' 'deploy NSX Edges in addition to data_store_id in case' 'edge_ha is True')), cfg.BoolOpt('ha_placement_random', help=_('When True and in case edge_ha is True, half of the ' 'edges will be placed in the primary datastore as ' 'active and the other half will be placed in the ' 'ha_datastore. If this value is not set, the global ' 'one will be used')), cfg.ListOpt('edge_host_groups', default=[], help=_('(Optional) If edge HA is used then this will ensure ' 'that active/backup edges are placed in the listed ' 'host groups. At least 2 predefined host groups need ' 'to be configured.')), cfg.StrOpt('datacenter_moid', help=_('(Optional) Identifying the ID of datacenter to deploy ' 'NSX Edges')), cfg.ListOpt('backup_edge_pool', help=_("(Optional) Defines edge pool's management range for " "the availability zone. If not defined, the global one " "will be used")), cfg.StrOpt('mgt_net_moid', help=_('(Optional) Portgroup MoRef ID for metadata proxy ' 'management network')), cfg.ListOpt('mgt_net_proxy_ips', default=[], help=_('(Optional) Comma separated list of management network ' 'IP addresses for metadata proxy.')), cfg.StrOpt('mgt_net_proxy_netmask', help=_("(Optional) Management network netmask for metadata " "proxy.")), cfg.StrOpt('mgt_net_default_gateway', help=_("(Optional) Management network default gateway for " "metadata proxy.")), cfg.StrOpt('external_network', help=_('(Optional) Network ID for physical network ' 'connectivity')), cfg.StrOpt('vdn_scope_id', help=_('(Optional) Network scope ID for VXLAN virtual wires')), cfg.StrOpt('dvs_id', help=_('(Optional) DVS MoRef ID for DVS connected to ' 'Management / Edge cluster')), cfg.BoolOpt('exclusive_dhcp_edge', default=False, help=_("(Optional) Have exclusive DHCP edge per network.")), cfg.BoolOpt('bind_floatingip_to_all_interfaces', default=False, help=_("If set to False, router will associate floating ip " "with external interface of only, thus denying " "connectivity between hosts on same network via " "their floating ips. If True, floating ip will " "be associated with all router interfaces.")), ] # define the configuration of each NSX-V3 availability zone. # the list of expected zones is under nsx_v3 group: availability_zones # Note: if any of the optional arguments is missing - the global one will be # used instead. nsx_v3_and_p_az_opts = [ cfg.StrOpt('metadata_proxy', help=_("The name or UUID of the NSX Metadata Proxy " "that will be used to enable native metadata service. " "It needs to be created in NSX before starting Neutron " "with the NSX plugin.")), cfg.StrOpt('dhcp_profile', help=_("The name or UUID of the NSX DHCP Profile " "that will be used to enable native DHCP service. It " "needs to be created in NSX before starting Neutron " "with the NSX plugin")), cfg.StrOpt('native_metadata_route', help=_("(Optional) The metadata route used for native metadata " "proxy service.")), cfg.StrOpt('dns_domain', help=_("(Optional) Domain to use for building the hostnames.")), cfg.ListOpt('nameservers', help=_("(Optional) List of nameservers to configure for the " "DHCP binding entries. These will be used if there are " "no nameservers defined on the subnet.")), cfg.StrOpt('default_overlay_tz', help=_("(Optional) This is the name or UUID of the default NSX " "overlay transport zone that will be used for creating " "tunneled isolated Neutron networks. It needs to be " "created in NSX before starting Neutron with the NSX " "plugin.")), cfg.StrOpt('default_vlan_tz', help=_("(Optional) Only required when creating VLAN or flat " "provider networks. Name or UUID of default NSX VLAN " "transport zone that will be used for bridging between " "Neutron networks, if no physical network has been " "specified")), cfg.StrOpt('default_tier0_router', help=_("Name or UUID of the default tier0 router that will be " "used for connecting to tier1 logical routers and " "configuring external networks")), cfg.StrOpt('edge_cluster', help=_("(Optional) Specifying an edge cluster for Tier1 " "routers to connect other that the one connected to" " the Tier0 router")), ] nsxv3_az_opts = nsx_v3_and_p_az_opts + [ cfg.ListOpt('switching_profiles', help=_("(Optional) list switching profiles uuids that will be " "attached to all neutron created nsx ports.")), cfg.StrOpt('dhcp_relay_service', help=_("(Optional) This is the name or UUID of the NSX dhcp " "relay service that will be used to enable DHCP relay " "on router ports.")), ] nsxp_az_opts = nsx_v3_and_p_az_opts nsx_tvd_opts = [ cfg.ListOpt('nsx_v_extension_drivers', default=[], help=_("An ordered list of NSX-V extension driver " "entrypoints to be loaded from the " "vmware_nsx.extension_drivers namespace.")), cfg.ListOpt('nsx_v3_extension_drivers', default=[], help=_("An ordered list of NSX-T extension driver " "entrypoints to be loaded from the " "vmware_nsx.extension_drivers namespace.")), cfg.ListOpt('dvs_extension_drivers', default=[], help=_("An ordered list of DVS extension driver " "entrypoints to be loaded from the " "vmware_nsx.extension_drivers namespace.")), cfg.StrOpt('default_plugin', default=projectpluginmap.NsxPlugins.NSX_T, choices=projectpluginmap.VALID_TYPES, help=_("The default plugin that will be used for new projects " "that were not added to the projects plugin mapping.")), cfg.ListOpt('enabled_plugins', default=[projectpluginmap.NsxPlugins.NSX_T, projectpluginmap.NsxPlugins.NSX_V, projectpluginmap.NsxPlugins.DVS], help=_("The list of plugins that the TVD core plugin will " "load")), cfg.ListOpt('nsx_v_default_availability_zones', default=[], help=_("The default availability zones that will be used for " "NSX-V networks and routers creation under the TVD " "plugin.")), cfg.ListOpt('nsx_v3_default_availability_zones', default=[], help=_("The default availability zones that will be used for " "NSX-V3 networks and routers creation under the TVD " "plugin.")), cfg.IntOpt('init_retries', default=3, help=_('Maximum number of times a particular plugin ' 'initialization should be retried')), ] # Register the configuration options cfg.CONF.register_opts(connection_opts) cfg.CONF.register_opts(cluster_opts) cfg.CONF.register_opts(nsx_common_opts) cfg.CONF.register_opts(nsx_p_opts, group="nsx_p") cfg.CONF.register_opts(nsx_v3_opts, group="nsx_v3") cfg.CONF.register_opts(nsxv_opts, group="nsxv") cfg.CONF.register_opts(nsx_tvd_opts, group="nsx_tvd") cfg.CONF.register_opts(base_opts, group="NSX") # register l3_ha config opts. This is due to commit # a7c633dc8e8a67e65e558ecbdf9ea8efc5468251 cfg.CONF.register_opts(l3_hamode_db.L3_HA_OPTS) def _register_nsx_azs(conf, availability_zones, az_opts): # first verify that the availability zones are in the format of a # list of names. The old format was a list of values for each az, # separated with ':' if not availability_zones or len(availability_zones[0].split(':')) > 1: return for az in availability_zones: az_group = 'az:%s' % az conf.register_group(cfg.OptGroup( name=az_group, title="Configuration for availability zone %s" % az)) conf.register_opts(az_opts, group=az_group) # register a group for each nsxv/v3 availability zones def register_nsxv_azs(conf, availability_zones): _register_nsx_azs(conf, availability_zones, nsxv_az_opts) def register_nsxv3_azs(conf, availability_zones): _register_nsx_azs(conf, availability_zones, nsxv3_az_opts) def register_nsxp_azs(conf, availability_zones): _register_nsx_azs(conf, availability_zones, nsxp_az_opts) register_nsxv_azs(cfg.CONF, cfg.CONF.nsxv.availability_zones) register_nsxv3_azs(cfg.CONF, cfg.CONF.nsx_v3.availability_zones) register_nsxp_azs(cfg.CONF, cfg.CONF.nsx_p.availability_zones) def _get_nsx_az_opts(az, opts): az_info = {} group = 'az:%s' % az if group not in cfg.CONF: raise nsx_exc.NsxInvalidConfiguration( opt_name=group, opt_value='None', reason=(_("Configuration group \'%s\' must be defined") % group)) for opt in opts: az_info[opt.name] = cfg.CONF[group][opt.name] return az_info def get_nsxv_az_opts(az): return _get_nsx_az_opts(az, nsxv_az_opts) def get_nsxv3_az_opts(az): return _get_nsx_az_opts(az, nsxv3_az_opts) def get_nsxp_az_opts(az): return _get_nsx_az_opts(az, nsxp_az_opts) def validate_nsxv_config_options(): if (cfg.CONF.nsxv.manager_uri is None or cfg.CONF.nsxv.user is None or cfg.CONF.nsxv.password is None): error = _("manager_uri, user, and password must be configured!") raise nsx_exc.NsxPluginException(err_msg=error) if cfg.CONF.nsxv.dvs_id is None: LOG.warning("dvs_id must be configured to support VLANs!") if cfg.CONF.nsxv.vdn_scope_id is None: LOG.warning("vdn_scope_id must be configured to support VXLANs!") if cfg.CONF.nsxv.use_dvs_features and not dvs_utils.dvs_is_enabled( dvs_id=cfg.CONF.nsxv.dvs_id): error = _("dvs host/vcenter credentials must be defined to use " "dvs features") raise nsx_exc.NsxPluginException(err_msg=error) for purpose_def in cfg.CONF.nsxv.default_edge_size: (p, s) = purpose_def.split(':') if p not in ['lb', 'router', 'dhcp']: error = _('Invalid service edge purpose %s') % p raise nsx_exc.NsxPluginException(err_msg=error) if s not in nsxv_constants.VALID_EDGE_SIZE: error = _('Invalid service edge size %s') % s raise nsx_exc.NsxPluginException(err_msg=error) def validate_nsx_config_options(): if cfg.CONF.nsx_extension_drivers: error = _("nsx_extension_drivers should not be configured!") raise nsx_exc.NsxPluginException(err_msg=error)

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null

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null

0

1

0

9368cd3c71110a578984ff436503f5560c25fa4f

3,933

py

Python

ico/kyc.py

miohtama/Smart-Contracts

8892e85d1c75994871a0fa14eb8c03016db39d88

[ "Apache-2.0" ]

1,148

2017-03-28T08:41:32.000Z

2019-01-26T13:39:39.000Z

ico/kyc.py

miohtama/Smart-Contracts

8892e85d1c75994871a0fa14eb8c03016db39d88

[ "Apache-2.0" ]

117

2017-03-31T07:31:22.000Z

2019-01-14T16:14:49.000Z

ico/kyc.py

miohtama/Smart-Contracts

8892e85d1c75994871a0fa14eb8c03016db39d88

[ "Apache-2.0" ]

494

2017-03-30T23:11:45.000Z

2019-01-29T17:41:37.000Z

"""AML data passing helpers.""" from binascii import hexlify from uuid import UUID from eth_utils import is_checksum_address def pack_kyc_dataframe(whitelisted_address: str, customer_id: UUID, min_eth_10k: int, max_eth_10k: int) -> bytes: """Pack KYC information to the smart contract. See KYCPayloadDeserializer for the matching Solidity code. .. note :: In a long term this will be deprecated in the behalf of the function below. :param whitelisted_address: Must be whitelisted address in a Ethereum checksummed format :param customer_id: Customer id as UUIDv8 :param min_eth: Min investment for this customer. Expressed as the parts of 1/10000. :param max_eth: Max investment for this customer. Expressed as the parts of 1/10000. :return: """ assert is_checksum_address(whitelisted_address) assert isinstance(customer_id, UUID) assert type(min_eth_10k) == int assert type(max_eth_10k) == int addr_value = int(whitelisted_address, 16) addr_b = addr_value.to_bytes(20, byteorder="big") # Ethereum address is 20 bytes customer_b = customer_id.bytes min_b = min_eth_10k.to_bytes(4, byteorder="big") max_b = max_eth_10k.to_bytes(4, byteorder="big") data = addr_b + customer_b + min_b + max_b assert len(data) == 44, "Got length: {}".format(len(data)) return data def pack_kyc_pricing_dataframe(whitelisted_address: str, customer_id: UUID, min_eth_10k: int, max_eth_10k: int, pricing_info: int) -> bytes: """Pack KYC presale information to the smart contract. Same as above, but with pricing info included. See KYCPayloadDeserializer for the matching Solidity code. :param whitelisted_address: Must be whitelisted address in a Ethereum checksummed format :param customer_id: Customer id as UUIDv8 :param min_eth: Min investment for this customer. Expressed as the parts of 1/10000. :param max_eth: Max investment for this customer. Expressed as the parts of 1/10000. :param pricing_info: Tier identifier or directly one token price in wei. :return: Raw bytes to send to the contract as a function argument """ assert is_checksum_address(whitelisted_address) assert isinstance(customer_id, UUID) assert type(min_eth_10k) == int assert type(max_eth_10k) == int assert type(pricing_info) == int addr_value = int(whitelisted_address, 16) addr_b = addr_value.to_bytes(20, byteorder="big") # Ethereum address is 20 bytes customer_b = customer_id.bytes min_b = min_eth_10k.to_bytes(4, byteorder="big") max_b = max_eth_10k.to_bytes(4, byteorder="big") pricing_data = pricing_info.to_bytes(32, byteorder="big") data = addr_b + customer_b + min_b + max_b + pricing_data assert len(data) == 76, "Got length: {}".format(len(data)) return data def unpack_kyc_pricing_dataframe(b: bytes) -> dict: """Unpack a KYC payloda for diagnostics purposes. Useful to troubleshoot live transactions. Grab the transaction hex data from Etherscan, starting on [5], make it a single string and use this function to see what parameters where given to the user. Example:: import binascii from ico.kyc import unpack_kyc_pricing_dataframe h = "83dcb...40000000000000000000000000000000000000000000000000000000000000001" b = binascii.unhexlify(h) unpack_kyc_pricing_dataframe(b) """ assert len(b) == 76, "Got byte array of length: {}".format(len(b)) addr_value = b[0:20] customer_id = b[20:36] min_b = b[36:40] max_b = b[40:44] pricing_data = b[44:76] return { "address": "0x" + hexlify(addr_value).decode("ascii"), "customer_id": UUID(int=int(hexlify(customer_id), 16)), "min_payment_eth": int(hexlify(min_b), 16) / 10000.0, "max_payment_eth": int(hexlify(max_b), 16) / 10000.0, "pricing_data": int(hexlify(pricing_data), 16), }

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null

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936974027f31a8b9f8b800f37c91ea5285dd409f

1,227

py

Python

model_example/3_Feature.py

kn45/ml-flow

d7869a55ef50ddbb28f23572b78b010ae3cec7b9

[ "MIT" ]

1

2016-08-01T09:26:36.000Z

model_example/3_Feature.py

kn45/ml-flow

d7869a55ef50ddbb28f23572b78b010ae3cec7b9

[ "MIT" ]

1

2016-10-17T07:00:54.000Z

2016-10-17T10:00:59.000Z

model_example/3_Feature.py

kn45/ml-flow

d7869a55ef50ddbb28f23572b78b010ae3cec7b9

[ "MIT" ]

null

#!/usr/bin/env python import sys import numpy as np from mlfutil import * port_encoder = None def init(): global port_encoder port_encoder = PortEncoder() port_encoder.init() def build_feat(): infile = sys.argv[1] outfile = sys.argv[2] fo = open(outfile, 'w') data = None with open(infile) as f: data = np.array([l.rstrip('\r\n').split('\t') for l in f.readlines()]) data_size = len(data) for nr, inst in enumerate(data): feats = [] label = inst[0] uid = inst[1] pclass = inst[2] # number name = inst[3] # string sex = inst[4] # cat age = inst[5] # number sbisp = inst[6] # number parch = inst[7] # number ticket = inst[8] # string fare = inst[9] # number cabin = inst[10] # string port = inst[11] # cat feats += [pclass] feats += sex_encoder(sex) feats += [age] feats += [sbisp] feats += [parch] feats += [fare] feats += port_encoder.encode(port) print >> fo, '\t'.join(map(str, [label] + feats)) draw_progress(nr, data_size-1) if __name__ == '__main__': init() build_feat()

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null

0

null

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936978c1dc18be1c7e31b93d94d87e740f03c54a

764

py

Python

api/permissions.py

GeRDI-Project/HarvesterControlCenter

ce161a31a6510ae28ffa68b8e0fd43c42060cb07

[ "Apache-2.0" ]

null

api/permissions.py

GeRDI-Project/HarvesterControlCenter

ce161a31a6510ae28ffa68b8e0fd43c42060cb07

[ "Apache-2.0" ]

9

2020-01-07T12:40:26.000Z

2021-09-22T18:00:38.000Z

api/permissions.py

GeRDI-Project/HarvesterControlCenter

ce161a31a6510ae28ffa68b8e0fd43c42060cb07

[ "Apache-2.0" ]

null

""" Permission Module """ from rest_framework import permissions from .models import Harvester __author__ = "Jan Frömberg" __copyright__ = "Copyright 2018, GeRDI Project" __credits__ = ["Jan Frömberg"] __license__ = "Apache 2.0" __maintainer__ = "Jan Frömberg" __email__ = "jan.froemberg@tu-dresden.de" class IsOwner(permissions.BasePermission): """Custom permission class to allow only harvester owners to edit them.""" def has_object_permission(self, request, view, obj): """Return True if permission is granted to the harvester owner.""" if isinstance(obj, Harvester): return obj.owner == request.user # Write permissions are only allowed to the owner of the harvester. return obj.owner == request.user

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null

0

null

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1

0

936b51f5e479541a9901ff8c24f9c022fbc9749d

1,435

py

Python

augment.py

luminide/example-darkmatter

69c538ac7ec9f2b35d90a7e53503050369c9a4fd

[ "BSD-3-Clause" ]

null

augment.py

luminide/example-darkmatter

69c538ac7ec9f2b35d90a7e53503050369c9a4fd

[ "BSD-3-Clause" ]

null

augment.py

luminide/example-darkmatter

69c538ac7ec9f2b35d90a7e53503050369c9a4fd

[ "BSD-3-Clause" ]

null

import albumentations as A from albumentations.pytorch import ToTensorV2 import cv2 def make_augmenters(conf): p = conf.aug_prob crop_size = round(conf.image_size*conf.crop_size) aug_list = [ A.ShiftScaleRotate( shift_limit=0.0625, scale_limit=0.2, rotate_limit=25, interpolation=cv2.INTER_AREA, p=p), A.RandomCrop(height=crop_size, width=crop_size, always_apply=True), A.Flip(p=0.5*p), A.OneOf([ A.MotionBlur(p=0.2*p), A.MedianBlur(blur_limit=3, p=0.1*p), A.Blur(blur_limit=3, p=0.1*p), ], p=0.2*p), A.Perspective(p=0.2*p), ] if conf.strong_aug: aug_list.extend([ A.GaussNoise(p=0.2*p), A.OneOf([ A.OpticalDistortion(p=0.3*p), A.GridDistortion(p=0.1*p), A.PiecewiseAffine(p=0.3*p), ], p=0.2*p), A.OneOf([ A.CLAHE(clip_limit=2, p=0.2*p), A.Sharpen(p=0.2*p), A.Emboss(p=0.2*p), A.RandomBrightnessContrast(p=0.2*p), ], p=0.3*p), ]) aug_list.extend([ A.Normalize(), ToTensorV2() ]) train_aug = A.Compose(aug_list) test_aug = A.Compose([ A.CenterCrop(height=crop_size, width=crop_size), A.Normalize(), ToTensorV2() ]) return train_aug, test_aug

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null

0

null

0

1

0

936e5c25ad6da90719ceebc9df8fa4a67ac1a358

946

py

Python

setup.py

AjithRamachandran/yamm

6bb0f878022e39d262ff57d068b6ba2c84484a7c

[ "MIT" ]

null

setup.py

AjithRamachandran/yamm

6bb0f878022e39d262ff57d068b6ba2c84484a7c

[ "MIT" ]

null

setup.py

AjithRamachandran/yamm

6bb0f878022e39d262ff57d068b6ba2c84484a7c

[ "MIT" ]

null

VERSION='0.9dev0' from setuptools import setup, Extension with open("README.md", "r") as doc: long_description = doc.read() doc.close() yammpy = Extension('yammpy', sources=['yammpy/yammpy.c'], include_dirs=['yammpy/include']) setup( name="yammpy", version=VERSION, author="Ajith Ramachandran", author_email="ajithar204@gmail.com", description="Yet Another Math Module", long_description=long_description, long_description_content_type='text/markdown', url="https://github.com/AjithRamachandran/yamm", keywords='Mathematics', license='MIT', packages=['yammpy'], tests_require=['unittest'], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: Implementation :: CPython", ], ext_modules=[yammpy], python_requires='>=3.7', )

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0.035714

0

null

0

null

0

1

0

936fd0cbca4a4d6d5d56baf7e7d2399c7e26fb3a

3,194

py

Python

main.py

shellrazer/Project_1_kaikeba

77dbadfdd8d57b8b588d7895ba645fb676132f8a

[ "MIT" ]

8

2019-11-10T02:52:04.000Z

2020-06-03T02:53:28.000Z

main.py

shellrazer/test

77dbadfdd8d57b8b588d7895ba645fb676132f8a

[ "MIT" ]

null

main.py

shellrazer/test

77dbadfdd8d57b8b588d7895ba645fb676132f8a

[ "MIT" ]

4

2019-12-26T07:41:28.000Z

2020-06-03T02:53:37.000Z

import os import argparse from data_loader import pip_data from train import train_test_interface from predict import predict def main(): parser = argparse.ArgumentParser() # parameters defined for in pip_data parser.add_argument("--mode", help="pip_data, train, test or predict", default="predict", type=str) parser.add_argument("--data_dir", help="Data Folder", default="./data", type=str) parser.add_argument("--max_df", help="tfidf term: max frequency to keep in vocab", default=0.75, type=float) parser.add_argument("--min_df", help="tfidf term: min counts to keep in vocab", default=2, type=int) parser.add_argument("--min_tfidf", help="tfidf term: min tfidf to keep in vocab", default=0.1, type=float) parser.add_argument("--embedding_size", default=256, help="Words embeddings dimension", type=int) # parameters defined for train and test model parser.add_argument("--max_lens", default=[98,100,34,103], help="a list of max lens for merged_train_test,train_X,train_y,test_X", nargs='+', type=int) parser.add_argument("--batch_sz", default=128, help="batch size", type=int) parser.add_argument("--test_percent", default=0.05, help="proportion of test samples", type=float) # encoder is bidirectional gru_unit/2 for one direction parser.add_argument("--gru_units", default=512, help="Encode and decode GRU cell units number", type=int) parser.add_argument("--att_units", default=64, help="attention weights", type=int) parser.add_argument("--learning_rate", default=0.001, help="Learning rate", type=float) parser.add_argument("--clipvalue", default=2.0, help="gradient clip value", type=float) parser.add_argument("--checkpoint_dir", help="Checkpoint directory", default='./training_checkpoints', type=str) parser.add_argument("--save_chkp_epoch", help="Checkpoint save every # epoch", default=5, type=int) parser.add_argument("--use_checkpoint", help="for train and test, restore from checkpoint?", default=True, type=bool) parser.add_argument("--train_epoch", help="train epoch", default=15, type=int) parser.add_argument("--cov_loss_wt", help="coverage loss weight", default=0.5, type=float) # parameters defined for predict parser.add_argument("--max_len_y", default=40, help="max words of the predicted abstract", type=int) parser.add_argument("--min_len_y", default=5, help="min words of the predicted abstract", type=int) parser.add_argument("--beam_size", default=3, help="beam size for beam search", type=int) parser.add_argument("--prediction_path", help="Path to save prediction results", default="./prediction.txt", type=str) args = parser.parse_args() params = vars(args) print(params) assert params["mode"] in ["pip_data","train", "test", "predict"], "The mode must be pip_data, train, test or predict" assert os.path.exists(params["data_dir"]), "data_dir doesn't exist" if params["mode"] == "pip_data": pip_data(params) elif params["mode"] in ['train','test']: train_test_interface(params) elif params["mode"] == "predict": predict(params) if __name__ == "__main__": main()

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0.023256

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null

0

null

0

1

0

936fe72d3a34941c53f0ba058f48f1a592adec38

2,226

py

Python

amplify/agent/collectors/plus/meta.py

dp92987/nginx-amplify-agent

1b2eed6eab52a82f35974928d75044451b4bedaf

[ "BSD-2-Clause" ]

308

2015-11-17T13:15:33.000Z

2022-03-24T12:03:40.000Z

amplify/agent/collectors/plus/meta.py

dp92987/nginx-amplify-agent

1b2eed6eab52a82f35974928d75044451b4bedaf

[ "BSD-2-Clause" ]

211

2015-11-16T15:27:41.000Z

2022-03-28T16:20:15.000Z

amplify/agent/collectors/plus/meta.py

dp92987/nginx-amplify-agent

1b2eed6eab52a82f35974928d75044451b4bedaf

[ "BSD-2-Clause" ]

80

2015-11-16T18:20:30.000Z

2022-03-02T12:47:56.000Z

# -*- coding: utf-8 -*- from amplify.agent.common.context import context from amplify.agent.collectors.abstract import AbstractMetaCollector from amplify.agent.objects.plus.api import TYPE_MAP __author__ = "Grant Hulegaard" __copyright__ = "Copyright (C) Nginx, Inc. All rights reserved." __license__ = "" __maintainer__ = "Grant Hulegaard" __email__ = "grant.hulegaard@nginx.com" class PlusStatusObjectMetaCollector(AbstractMetaCollector): short_name = 'status_meta' def __init__(self, **kwargs): super(PlusStatusObjectMetaCollector, self).__init__(**kwargs) self.register( self.version ) @property def default_meta(self): zone = self.object.type if self.object.type != 'server_zone' else 'status_zone' meta = { 'type': self.object.type_template % zone, 'local_name': self.object.local_name, 'local_id': self.object.local_id, 'root_uuid': context.uuid, 'hostname': context.app_config['credentials']['imagename'] or context.hostname, 'version': None } return meta def version(self): parent = context.objects.find_parent(obj=self.object) self.meta['version'] = parent.version if parent else None class PlusApiObjectMetaCollector(AbstractMetaCollector): short_name = 'api_meta' def __init__(self, **kwargs): super(PlusApiObjectMetaCollector, self).__init__(**kwargs) self.register( self.version ) @property def default_meta(self): mapped_type = TYPE_MAP.get(self.object.type, self.object.type) zone = mapped_type if mapped_type != 'server_zone' else 'status_zone' meta = { 'type': self.object.type_template % zone, 'local_name': self.object.local_name, 'local_id': self.object.local_id, 'root_uuid': context.uuid, 'hostname': context.app_config['credentials']['imagename'] or context.hostname, 'version': None } return meta def version(self): parent = context.objects.find_parent(obj=self.object) self.meta['version'] = parent.version if parent else None

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0.283019

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null

0

null

0

1

0

9370fd3d005a703571a18f0178253fd113e0b6e4

3,679

py

Python

tests/boxes/annotations/test_vatic.py

WildbookOrg/wbia-deprecate-tpl-brambox

9aa6a69f706d0653a65520c696a7cd66715b6a37

[ "MIT" ]

2

2019-03-23T03:14:11.000Z

2019-11-21T07:16:13.000Z

tests/boxes/annotations/test_vatic.py

WildbookOrg/wbia-deprecate-tpl-brambox

9aa6a69f706d0653a65520c696a7cd66715b6a37

[ "MIT" ]

null

tests/boxes/annotations/test_vatic.py

WildbookOrg/wbia-deprecate-tpl-brambox

9aa6a69f706d0653a65520c696a7cd66715b6a37

[ "MIT" ]

1

2021-12-01T03:04:53.000Z

# -*- coding: utf-8 -*- import unittest from brambox.boxes.annotations.annotation import Annotation from brambox.boxes.annotations import VaticAnnotation, VaticParser vatic_string = """-1 0 0 0 0 0 0 0 0 ? -1 0 0 0 0 0 0 0 0 ? -1 0 0 0 0 0 0 0 0 person -1 0 0 0 0 1 0 0 0 person""" class TestVaticAnnotation(unittest.TestCase): def setUp(self): self.anno = VaticAnnotation() def tearDown(self): pass def test_serialize(self): """ test if major fields: label, x, y, w, h, object_id, frame_nr are serialized """ frame_nr = 100 self.anno.class_label = 'person' self.anno.object_id = 3 self.anno.x_top_left = 13 self.anno.y_top_left = 14 self.anno.width = 15 self.anno.height = 16 string = self.anno.serialize(frame_nr) self.assertEqual(string, '3 13 14 28 30 100 0 0 0 person') def test_serialize_round(self): """ test if serialize rounds the x,y,w,h values correctly """ self.anno.x_top_left = 12.8 self.anno.y_top_left = 14.4 self.anno.width = 14.56 self.anno.height = 16.1 string = self.anno.serialize() self.assertEqual(string, '-1 13 14 27 30 0 0 0 0 ?') def test_serialize_occluded(self): """ test if occluded flag is serialized """ self.anno.occluded = 1 string = self.anno.serialize() self.assertEqual(string, '-1 0 0 0 0 0 0 1 0 ?') def test_serialize_lost(self): """ test if lost flag is serialized """ self.anno.lost = 1 string = self.anno.serialize() self.assertEqual(string, '-1 0 0 0 0 0 1 0 0 ?') def test_deserialize(self): """ test if major fields: label, x, y, w, h, object_id, frame_nr are processed """ string = '3 13 14 28 30 100 0 0 0 person' self.anno.deserialize(string) self.assertEqual(self.anno.object_id, 3) self.assertAlmostEqual(self.anno.x_top_left, 13) self.assertAlmostEqual(self.anno.y_top_left, 14) self.assertAlmostEqual(self.anno.width, 15) self.assertAlmostEqual(self.anno.height, 16) self.assertFalse(self.anno.lost) self.assertFalse(self.anno.occluded) self.assertEqual(self.anno.class_label, 'person') def test_deserialize_occluded(self): """ test if occluded flag is processed """ string = '-1 0 0 0 0 0 0 1 0 ?' self.anno.deserialize(string) self.assertTrue(self.anno.occluded) def test_deserialize_lost(self): """ test if lost flag is processed """ string = '-1 0 0 0 0 0 1 0 0 ?' self.anno.deserialize(string) self.assertTrue(self.anno.lost) class TestVaticParser(unittest.TestCase): def setUp(self): self.parser = VaticParser() def tearDown(self): pass def test_serialize(self): """ test if basic serialize works """ testanno1 = Annotation() testanno2 = Annotation() testanno2.class_label = 'person' obj = {} obj['0'] = [testanno1, testanno1, testanno2] obj['1'] = [testanno2] string = self.parser.serialize(obj) self.assertEqual(string, vatic_string) def test_deserialize(self): """ test if basic deserialize works """ obj = self.parser.deserialize(vatic_string) self.assertEqual(type(obj), dict) self.assertEqual(len(obj), 2) self.assertEqual(len(obj['0']), 3) self.assertEqual(len(obj['1']), 1) self.assertEqual(obj['0'][0].class_label, '') self.assertEqual(obj['1'][0].class_label, 'person') if __name__ == '__main__': unittest.main()

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0

0.225

0

null

0

null

0

1

0

93713b953d367685ba52811607ebf1403fcfbaeb

2,307

py

Python

tests/test_supplemental.py

silnrsi/langtags

d65e9640031915f46d4bee38032fde82175bcfcf

[ "MIT" ]

7

2019-01-19T04:17:58.000Z

2021-08-05T14:56:18.000Z

tests/test_supplemental.py

silnrsi/langtags

d65e9640031915f46d4bee38032fde82175bcfcf

[ "MIT" ]

8

2018-10-29T20:56:28.000Z

2022-03-25T23:51:14.000Z

tests/test_supplemental.py

silnrsi/langtags

d65e9640031915f46d4bee38032fde82175bcfcf

[ "MIT" ]

1

2019-01-19T04:17:48.000Z

#!/usr/bin/python # -*- encoding: utf-8 import unittest, os, re, json from xml.etree import ElementTree as et from palaso.sldr.langtags_full import LangTags, LangTag from itertools import product langtagjson = os.path.join(os.path.dirname(__file__), '..', 'pub', 'langtags.json') exceptions = set(["aii-Cyrl"]) class Supplemental(unittest.TestCase): ''' Tests alltags.txt for discrepencies against likelySubtags.xml ''' def setUp(self): with open(langtagjson, "r") as inf: self.data = json.load(inf) self.ltags = {} for j in self.data: if j['tag'].startswith("_"): continue self.ltags[j['tag']] = j self.ltags[j['full']] = j if 'tags' in j: for t in j['tags']: self.ltags[t] = j thisdir = os.path.dirname(__file__) self.doc = et.parse(os.path.join(thisdir, "supplementalData.xml")) def test_languageData(self): failures = [] for e in self.doc.findall('./languageData/language'): lang = e.get('type') if lang == "und": continue scripts = e.get('scripts', '').split(' ') regions = e.get('territories', '').split(' ') for s in scripts: tag = lang + ("-" + s if len(s) else "") if tag not in self.ltags: failures.append(tag) continue for r in regions: if not len(r): continue t = tag + "-" + r if t in self.ltags: continue if r not in self.ltags[tag].get('regions', []): failures.append(t) if len(failures): self.fail("Missing tags from supplemental Data" + str(failures)) def test_names(self): for r in self.data: if r['tag'].startswith("_"): continue if 'names' in r: if any(x == u'↑↑↑' for x in r['names']): self.fail("Inherited names item in " + str(r['tag'])) if 'name' in r: if r['name'] == u'↑↑↑': self.fail("Inherited name in " + str(['tag']))

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0

0.148148

0

null

0

null

0

1

0

9372409c6e9d457358e3990049706136e6ed0a9a

5,178

py

Python

trafficmonitor/gui/secondary.py

Sumanth007/Traffic-Monitor

2623f5c03a362b14415620528f05a91aba960374

[ "MIT" ]

null

trafficmonitor/gui/secondary.py

Sumanth007/Traffic-Monitor

2623f5c03a362b14415620528f05a91aba960374

[ "MIT" ]

1

2022-03-22T21:21:19.000Z

trafficmonitor/gui/secondary.py

SumanthTirumale/Traffic-Monitor

2623f5c03a362b14415620528f05a91aba960374

[ "MIT" ]

null

from pathlib import Path from PyQt5.QtWidgets import QDialog, QLineEdit, QCheckBox, QPushButton, QApplication from PyQt5.QtWidgets import QLabel, QMessageBox, QHBoxLayout, QFormLayout from PyQt5.QtGui import QIcon from PyQt5.Qt import Qt from trafficmonitor.helper_functions import create_path, ping class Secondary(QDialog): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.setWindowTitle("Traffic Monitor") self.image_path = str(Path(__file__).absolute().parent.parent/"images") self.setWindowIcon(QIcon(str(Path(self.image_path)/"logo.ico"))) self.resize(400, 200) self.center() # initialize values self.path = create_path() self.data = {} # initialize all widgets self.edit_execution_name = QLineEdit() self.edit_ip_address = QLineEdit() self.edit_proxy_address = QLineEdit("127.0.0.1") self.edit_proxy_port = QLineEdit("9090") self.check_box_upstream_proxy = QCheckBox("Enable Upstream proxy") self.button_start = QPushButton("Start") self.bind_signals() self.check_upstream_proxy() self.init_ui() def center(self): """Method to center the QMainWindow""" frame_gm = self.frameGeometry() screen = QApplication.desktop().screenNumber(QApplication.desktop().cursor().pos()) center_point = QApplication.desktop().screenGeometry(screen).center() frame_gm.moveCenter(center_point) self.move(frame_gm.topLeft()) def init_ui(self): form_layout = QFormLayout() horizontal_box1 = QHBoxLayout() horizontal_box2 = QHBoxLayout() form_layout.addRow(QLabel("Execution Name: "), self.edit_execution_name) form_layout.addRow(QLabel("Host Address: "), self.edit_ip_address) horizontal_box1.addStretch() horizontal_box1.addWidget(self.check_box_upstream_proxy) horizontal_box1.addStretch() form_layout.addRow(horizontal_box1) form_layout.addRow(QLabel("Proxy Address: "), self.edit_proxy_address) form_layout.addRow(QLabel("Proxy Port: "), self.edit_proxy_port) horizontal_box2.addStretch() horizontal_box2.addWidget(self.button_start) horizontal_box2.addStretch() form_layout.addRow(horizontal_box2) self.setLayout(form_layout) self.setWindowModality(Qt.ApplicationModal) self.show() def bind_signals(self): self.check_box_upstream_proxy.stateChanged.connect(self.check_upstream_proxy) self.button_start.clicked.connect(self.evt_button_start) def check_upstream_proxy(self): if self.check_box_upstream_proxy.isChecked(): self.edit_proxy_address.setDisabled(False) self.edit_proxy_port.setDisabled(False) else: self.edit_proxy_address.setDisabled(True) self.edit_proxy_port.setDisabled(True) def evt_button_start(self): execution_name = self.edit_execution_name.text() ip_address = self.edit_ip_address.text() proxy_address = self.edit_proxy_address.text() proxy_port = self.edit_proxy_port.text() empty_values = [None, ''] # validate the values if execution_name not in empty_values: is_file_exists = Path(f"{self.path}/{execution_name}.db") if not is_file_exists.exists(): if ip_address not in empty_values: if ping(ip_address): if self.check_box_upstream_proxy.isChecked(): if proxy_address not in empty_values: if proxy_port not in empty_values: self.data['UPSTREAM_PROXY_IP'] = proxy_address self.data['UPSTREAM_PROXY_PORT'] = proxy_port self.data['EXECUTION_NAME'] = execution_name self.data['IP_ADDRESS'] = ip_address self.close() else: QMessageBox.information(self, "Warning", "Please enter proxy port") else: QMessageBox.information(self, "Warning", "Please enter proxy address") else: self.data['EXECUTION_NAME'] = execution_name self.data['IP_ADDRESS'] = ip_address self.close() else: QMessageBox.information(self, "Warning", f"'{ip_address} is unreachable!!'") else: QMessageBox.information(self, "Warning", "Please enter host address") else: QMessageBox.information(self, "Warning", f"'{execution_name}' already exists!!") else: QMessageBox.information(self, "Warning", "Please enter execution name")

39.830769

104

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531

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5.546139

0.242938

0.043464

0.044143

0.061121

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0

null

0

null

0

1

0

9373925e0184fcb2bd8e59c3fee2f516b9d8c7c9

1,350

py

Python

fly.py

anyaevostinar/FlyYeastModel

22a81a30407cc20dd3491c558cd4266d9a73870f

[ "MIT" ]

null

fly.py

anyaevostinar/FlyYeastModel

22a81a30407cc20dd3491c558cd4266d9a73870f

[ "MIT" ]

null

fly.py

anyaevostinar/FlyYeastModel

22a81a30407cc20dd3491c558cd4266d9a73870f

[ "MIT" ]

1

2015-05-27T19:00:14.000Z

""" A class library to model fly behavior. """ from random import * WORLD_SIZE = 200 class Fly(object): '''Doc string''' def __init__(self, location): #A list for holding the collection of yeast spores in the fly gut self.stomach = [] self.location = location self.fitness = 0 def move(self, yeast_pop): self.location += randint(0, WORLD_SIZE/4) if self.location >= WORLD_SIZE: self.location = self.location - WORLD_SIZE if not yeast_pop[self.location] and len(self.stomach): hatched = self.stomach.pop() hatched.is_spore = False yeast_pop[self.location] = hatched def eat(self, yeast): if yeast.is_spore: self.stomach.append(yeast) else: self.fitness += 1 def reproduce(self): new_loc=round(gauss(self.location, 2),0) if new_loc < 0: new_loc = 0 elif new_loc > WORLD_SIZE-1: new_loc = WORLD_SIZE-1 #how best to keep fly location within yeast world?? return Fly(new_loc) def update(self, yeast_pop): if yeast_pop[self.location]: self.eat(yeast_pop[self.location]) if self.fitness == 50: return self.reproduce() else: self.move(yeast_pop)

27.55102

73

0.577037

175

1,350

4.308571

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0.04244

0

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0.325926

1,350

48

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0

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0

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false

0

0.029412

0

0.264706

0

null

0

null

0

1

0

9375c927126dada2e11408b96c12b7feda3db9d6

18,074

py

Python

lib/googlecloudsdk/compute/subcommands/instances/create.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

null

lib/googlecloudsdk/compute/subcommands/instances/create.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

null

lib/googlecloudsdk/compute/subcommands/instances/create.py

IsaacHuang/google-cloud-sdk

52afa5d1a75dff08f4f5380c5cccc015bf796ca5

[ "Apache-2.0" ]

null

# Copyright 2014 Google Inc. All Rights Reserved. """Command for creating instances.""" import collections from googlecloudsdk.calliope import exceptions from googlecloudsdk.compute.lib import addresses_utils from googlecloudsdk.compute.lib import base_classes from googlecloudsdk.compute.lib import constants from googlecloudsdk.compute.lib import image_utils from googlecloudsdk.compute.lib import instance_utils from googlecloudsdk.compute.lib import metadata_utils from googlecloudsdk.compute.lib import request_helper from googlecloudsdk.compute.lib import utils from googlecloudsdk.compute.lib import windows_password from googlecloudsdk.compute.lib import zone_utils from googlecloudsdk.core import log DISK_METAVAR = ( 'name=NAME [mode={ro,rw}] [boot={yes,no}] [device-name=DEVICE_NAME] ' '[auto-delete={yes,no}]') class Create(base_classes.BaseAsyncCreator, image_utils.ImageExpander, addresses_utils.AddressExpander, zone_utils.ZoneResourceFetcher): """Create Google Compute Engine virtual machine instances.""" @staticmethod def Args(parser): metadata_utils.AddMetadataArgs(parser) instance_utils.AddDiskArgs(parser) instance_utils.AddLocalSsdArgs(parser) instance_utils.AddImageArgs(parser) instance_utils.AddCanIpForwardArgs(parser) instance_utils.AddAddressArgs(parser, instances=True) instance_utils.AddMachineTypeArgs(parser) instance_utils.AddMaintenancePolicyArgs(parser) instance_utils.AddNetworkArgs(parser) instance_utils.AddNoRestartOnFailureArgs(parser) instance_utils.AddScopeArgs(parser) instance_utils.AddTagsArgs(parser) parser.add_argument( '--description', help='Specifies a textual description of the instances.') parser.add_argument( 'names', metavar='NAME', nargs='+', help='The names of the instances to create.') utils.AddZoneFlag( parser, resource_type='instances', operation_type='create') @property def service(self): return self.compute.instances @property def method(self): return 'Insert' @property def resource_type(self): return 'instances' def ValidateLocalSsdFlags(self, args): for local_ssd in args.local_ssd or []: interface = local_ssd.get('interface') if interface and interface not in instance_utils.LOCAL_SSD_INTERFACES: raise exceptions.ToolException( 'Unexpected local SSD interface: [{given}]. ' 'Legal values are [{ok}].' .format(given=interface, ok=', '.join(instance_utils.LOCAL_SSD_INTERFACES))) def ValidateDiskFlags(self, args): """Validates the values of all disk-related flags.""" boot_disk_specified = False for disk in args.disk or []: disk_name = disk.get('name') if not disk_name: raise exceptions.ToolException( '[name] is missing in [--disk]. [--disk] value must be of the form ' '[{0}].'.format(DISK_METAVAR)) mode_value = disk.get('mode') if mode_value and mode_value not in ('rw', 'ro'): raise exceptions.ToolException( 'Value for [mode] in [--disk] must be [rw] or [ro], not [{0}].' .format(mode_value)) # Ensures that the user is not trying to attach a read-write # disk to more than one instance. if len(args.names) > 1 and mode_value == 'rw': raise exceptions.ToolException( 'Cannot attach disk [{0}] in read-write mode to more than one ' 'instance.'.format(disk_name)) boot_value = disk.get('boot') if boot_value and boot_value not in ('yes', 'no'): raise exceptions.ToolException( 'Value for [boot] in [--disk] must be [yes] or [no], not [{0}].' .format(boot_value)) auto_delete_value = disk.get('auto-delete') if auto_delete_value and auto_delete_value not in ['yes', 'no']: raise exceptions.ToolException( 'Value for [auto-delete] in [--disk] must be [yes] or [no], not ' '[{0}].'.format(auto_delete_value)) # If this is a boot disk and we have already seen a boot disk, # we need to fail because only one boot disk can be attached. if boot_value == 'yes': if boot_disk_specified: raise exceptions.ToolException( 'Each instance can have exactly one boot disk. At least two ' 'boot disks were specified through [--disk].') else: boot_disk_specified = True if args.image and boot_disk_specified: raise exceptions.ToolException( 'Each instance can have exactly one boot disk. One boot disk ' 'was specified through [--disk] and another through [--image].') if boot_disk_specified: if args.boot_disk_device_name: raise exceptions.ToolException( '[--boot-disk-device-name] can only be used when creating a new ' 'boot disk.') if args.boot_disk_type: raise exceptions.ToolException( '[--boot-disk-type] can only be used when creating a new boot ' 'disk.') if args.boot_disk_size: raise exceptions.ToolException( '[--boot-disk-size] can only be used when creating a new boot ' 'disk.') if args.no_boot_disk_auto_delete: raise exceptions.ToolException( '[--no-boot-disk-auto-delete] can only be used when creating a ' 'new boot disk.') def UseExistingBootDisk(self, args): """Returns True if the user has specified an existing boot disk.""" return any(disk.get('boot') == 'yes' for disk in args.disk or []) def CreatePersistentAttachedDiskMessages(self, args, instance_ref): """Returns a list of AttachedDisk messages and the boot disk's reference.""" disks = [] boot_disk_ref = None for disk in args.disk or []: name = disk['name'] # Resolves the mode. mode_value = disk.get('mode', 'rw') if mode_value == 'rw': mode = self.messages.AttachedDisk.ModeValueValuesEnum.READ_WRITE else: mode = self.messages.AttachedDisk.ModeValueValuesEnum.READ_ONLY boot = disk.get('boot') == 'yes' auto_delete = disk.get('auto-delete') == 'yes' disk_ref = self.CreateZonalReference( name, instance_ref.zone, resource_type='disks') if boot: boot_disk_ref = disk_ref attached_disk = self.messages.AttachedDisk( autoDelete=auto_delete, boot=boot, deviceName=disk.get('device-name'), mode=mode, source=disk_ref.SelfLink(), type=self.messages.AttachedDisk.TypeValueValuesEnum.PERSISTENT) # The boot disk must end up at index 0. if boot: disks = [attached_disk] + disks else: disks.append(attached_disk) return disks, boot_disk_ref def CreateLocalSsdMessage(self, zone, device_name, interface): disk_type_ref = self.CreateZonalReference('local-ssd', zone, resource_type='diskTypes') maybe_interface_enum = ( self.messages.AttachedDisk.InterfaceValueValuesEnum(interface) if interface else None) return self.messages.AttachedDisk( type=self.messages.AttachedDisk.TypeValueValuesEnum.SCRATCH, autoDelete=True, deviceName=device_name, interface=maybe_interface_enum, mode=self.messages.AttachedDisk.ModeValueValuesEnum.READ_WRITE, initializeParams=self.messages.AttachedDiskInitializeParams( diskType=disk_type_ref.SelfLink()), ) def CreateDefaultBootAttachedDiskMessage( self, args, boot_disk_size_gb, image_uri, instance_ref): """Returns an AttachedDisk message for creating a new boot disk.""" if args.boot_disk_type: disk_type_ref = self.CreateZonalReference( args.boot_disk_type, instance_ref.zone, resource_type='diskTypes') disk_type_uri = disk_type_ref.SelfLink() else: disk_type_ref = None disk_type_uri = None return self.messages.AttachedDisk( autoDelete=not args.no_boot_disk_auto_delete, boot=True, deviceName=args.boot_disk_device_name, initializeParams=self.messages.AttachedDiskInitializeParams( sourceImage=image_uri, diskSizeGb=boot_disk_size_gb, diskType=disk_type_uri), mode=self.messages.AttachedDisk.ModeValueValuesEnum.READ_WRITE, type=self.messages.AttachedDisk.TypeValueValuesEnum.PERSISTENT) def FetchDiskResources(self, disk_refs): """Returns a list of disk resources corresponding to the disk references.""" requests = [] for disk_ref in disk_refs: requests.append(( self.compute.disks, 'Get', self.messages.ComputeDisksGetRequest( disk=disk_ref.Name(), project=disk_ref.project, zone=disk_ref.zone))) errors = [] res = list(request_helper.MakeRequests( requests=requests, http=self.http, batch_url=self.batch_url, errors=errors, custom_get_requests=None)) if errors: utils.RaiseToolException( errors, error_message='Could not fetch some boot disks:') return res def CreateServiceAccountMessages(self, args): """Returns a list of ServiceAccount messages corresponding to --scopes.""" if args.no_scopes: scopes = [] else: scopes = args.scopes or constants.DEFAULT_SCOPES accounts_to_scopes = collections.defaultdict(list) for scope in scopes: parts = scope.split('=') if len(parts) == 1: account = 'default' scope_uri = scope elif len(parts) == 2: account, scope_uri = parts else: raise exceptions.ToolException( '[{0}] is an illegal value for [--scopes]. Values must be of the ' 'form [SCOPE] or [ACCOUNT=SCOPE].'.format(scope)) # Expands the scope if the user provided an alias like # "compute-rw". scope_uri = constants.SCOPES.get(scope_uri, scope_uri) accounts_to_scopes[account].append(scope_uri) res = [] for account, scopes in sorted(accounts_to_scopes.iteritems()): res.append(self.messages.ServiceAccount( email=account, scopes=sorted(scopes))) return res def CreateNetworkInterfaceMessage(self, args, instance_refs): """Returns a new NetworkInterface message.""" network_ref = self.CreateGlobalReference( args.network, resource_type='networks') network_interface = self.messages.NetworkInterface( network=network_ref.SelfLink()) if not args.no_address: access_config = self.messages.AccessConfig( name=constants.DEFAULT_ACCESS_CONFIG_NAME, type=self.messages.AccessConfig.TypeValueValuesEnum.ONE_TO_ONE_NAT) # If the user provided an external IP, populate the access # config with it. if len(instance_refs) == 1: region = utils.ZoneNameToRegionName(instance_refs[0].zone) address = self.ExpandAddressFlag(args, region) if address: access_config.natIP = address network_interface.accessConfigs = [access_config] return network_interface def CreateRequests(self, args): self.ValidateDiskFlags(args) self.ValidateLocalSsdFlags(args) if args.maintenance_policy: on_host_maintenance = ( self.messages.Scheduling.OnHostMaintenanceValueValuesEnum( args.maintenance_policy)) else: on_host_maintenance = None scheduling = self.messages.Scheduling( automaticRestart=not args.no_restart_on_failure, onHostMaintenance=on_host_maintenance) service_accounts = self.CreateServiceAccountMessages(args) if args.tags: tags = self.messages.Tags(items=args.tags) else: tags = None metadata = metadata_utils.ConstructMetadataMessage( self.messages, metadata=args.metadata, metadata_from_file=args.metadata_from_file) # If the user already provided an initial Windows password and # username through metadata, then there is no need to check # whether the image or the boot disk is Windows. windows_username_present = False windows_password_present = False for kv in metadata.items: if kv.key == constants.INITIAL_WINDOWS_USER_METADATA_KEY_NAME: windows_username_present = True if kv.key == constants.INITIAL_WINDOWS_PASSWORD_METADATA_KEY_NAME: windows_password_present = True check_for_windows_image = (not windows_username_present or not windows_password_present) boot_disk_size_gb = utils.BytesToGb(args.boot_disk_size) utils.WarnIfDiskSizeIsTooSmall(boot_disk_size_gb, args.boot_disk_type) instance_refs = self.CreateZonalReferences(args.names, args.zone) # Check if the zone is deprecated or has maintenance coming. self.WarnForZonalCreation(instance_refs) network_interface = self.CreateNetworkInterfaceMessage(args, instance_refs) # The element at index i is the machine type URI for instance # i. We build this list here because we want to delay work that # requires API calls as much as possible. This leads to a better # user experience because the tool can fail fast upon a spelling # mistake instead of delaying the user by making API calls whose # purpose has already been rendered moot by the spelling mistake. machine_type_uris = [] for instance_ref in instance_refs: machine_type_uris.append(self.CreateZonalReference( args.machine_type, instance_ref.zone, resource_type='machineTypes').SelfLink()) create_boot_disk = not self.UseExistingBootDisk(args) add_windows_credentials_to_metadata = False if create_boot_disk: image_uri, image_resource = self.ExpandImageFlag( args, return_image_resource=check_for_windows_image) if (check_for_windows_image and image_utils.HasWindowsLicense(image_resource, self.resources)): log.debug('[%s] is a Windows image.', image_resource.selfLink) add_windows_credentials_to_metadata = True else: image_uri = None # A list of lists where the element at index i contains a list of # disk messages that should be set for the instance at index i. disks_messages = [] # A mapping of zone to boot disk references for all existing boot # disks that are being attached. existing_boot_disks = {} for instance_ref in instance_refs: persistent_disks, boot_disk_ref = ( self.CreatePersistentAttachedDiskMessages(args, instance_ref)) local_ssds = [ self.CreateLocalSsdMessage( instance_ref.zone, x.get('device-name'), x.get('interface')) for x in args.local_ssd or []] if create_boot_disk: boot_disk = self.CreateDefaultBootAttachedDiskMessage( args, boot_disk_size_gb, image_uri, instance_ref) persistent_disks = [boot_disk] + persistent_disks else: existing_boot_disks[boot_disk_ref.zone] = boot_disk_ref disks_messages.append(persistent_disks + local_ssds) # Now for every existing boot disk being attached, we have to # figure out whether it has a Windows license. if check_for_windows_image and existing_boot_disks: # Sorts the disk references by zone, so the code behaves # deterministically. disk_resources = self.FetchDiskResources( disk_ref for _, disk_ref in sorted(existing_boot_disks.iteritems())) for disk_resource in disk_resources: if image_utils.HasWindowsLicense(disk_resource, self.resources): log.debug('[%s] has a Windows image.', disk_resource.selfLink) add_windows_credentials_to_metadata = True if add_windows_credentials_to_metadata: if not windows_username_present: username = self.project.split(':')[-1][ :constants.MAX_WINDOWS_USERNAME_LENGTH] metadata.items.append(self.messages.Metadata.ItemsValueListEntry( key=constants.INITIAL_WINDOWS_USER_METADATA_KEY_NAME, value=username)) if not windows_password_present: metadata.items.append(self.messages.Metadata.ItemsValueListEntry( key=constants.INITIAL_WINDOWS_PASSWORD_METADATA_KEY_NAME, value=windows_password.Generate())) requests = [] for instance_ref, machine_type_uri, disks in zip( instance_refs, machine_type_uris, disks_messages): requests.append(self.messages.ComputeInstancesInsertRequest( instance=self.messages.Instance( canIpForward=args.can_ip_forward, disks=disks, description=args.description, machineType=machine_type_uri, metadata=metadata, name=instance_ref.Name(), networkInterfaces=[network_interface], serviceAccounts=service_accounts, scheduling=scheduling, tags=tags, ), project=self.project, zone=instance_ref.zone)) return requests Create.detailed_help = { 'brief': 'Create Compute Engine virtual machine instances', 'DESCRIPTION': """\ *{command}* facilitates the creation of Google Compute Engine virtual machines. For example, running: $ {command} example-instance-1 example-instance-2 example-instance-3 --zone us-central1-a will create three instances called 'example-instance-1', 'example-instance-2', and 'example-instance-3' in the ``us-central1-a'' zone. For more examples, refer to the *EXAMPLES* section below. """, 'EXAMPLES': """\ To create an instance with the latest ``Red Hat Enterprise Linux 6'' image available, run: $ {command} example-instance --image rhel-6 --zone us-central1-a """, }

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0.023631

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0.00813

0.105691

0

null

0

null

0

1

0

9376ac585073afdf1faa0ba83d55f23d93bb24fe

6,215

py

Python

messidge/broker/identity.py

RantyDave/messidge

ada4dfb1f4df5bcbe3c0920fdf4c75b030624c88

[ "BSD-2-Clause" ]

1

2017-10-26T00:09:49.000Z

messidge/broker/identity.py

20ft/messidge

ada4dfb1f4df5bcbe3c0920fdf4c75b030624c88

[ "BSD-2-Clause" ]

null

messidge/broker/identity.py

20ft/messidge

ada4dfb1f4df5bcbe3c0920fdf4c75b030624c88

[ "BSD-2-Clause" ]

null

# Copyright (c) 2017 David Preece - davep@polymath.tech, All rights reserved. # # Permission to use, copy, modify, and/or distribute this software for any # purpose with or without fee is hereby granted. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. import logging import os import shortuuid from threading import Thread from bottle import Bottle, run, request from litecache.cache import SqlCache ident_init = """ CREATE TABLE nodes (pk TEXT NOT NULL UNIQUE, json BLOB); CREATE TABLE users (pk TEXT NOT NULL UNIQUE, email TEXT NOT NULL, json BLOB); CREATE TABLE pending (token TEXT NOT NULL UNIQUE, email TEXT NOT NULL); """ class Identity: """A default provider of identity and configuration""" def __init__(self, directory="~"): """Construct the identity database if it's not there. :param directory: The directory in which to place the database (identity.sqlite3)""" self.db = SqlCache(os.path.expanduser(directory), 'identity', ident_init) def stop(self): """Stop the background (SqlCache) thread before closing""" logging.debug("Closing Identity") self.db.close() def create_pending_user(self, email) -> str: """Registers the intention for someone to become a registered user :param email: email address of the user. :return: confirmation token to give to the user.""" token = shortuuid.uuid() self.db.mutate("INSERT INTO pending (token, email) VALUES (?, ?)", (token, email)) return token def pending_users_for_token(self, token) -> []: """Return the pending users for the given token (may well be zero). :param token: the token a user was given in order to be able to confirm their account. :return: The list of pending users for that token.""" return self.db.query("SELECT email FROM pending WHERE token=?", (token,)) def register_user(self, pk_b64: str, email: str, config: str): """Registers a user as being valid. :param pk_b64: The user's primary key - base64 encoded string. :param email: The user's email address. :param config: A json description of any configuration to be associated with the user.""" self.db.mutate("DELETE FROM pending WHERE email=?", (email,)) self.db.mutate("INSERT INTO users (pk, email, json) VALUES (?, ?, ?)", (pk_b64, email, config)) def user_config_from_db(self, pk_b64: str) -> (str, str): # is used to check for presence in the db, too """Returns the json configuration for a user. :param pk_b64: The user's primary key - base64 encoded string. :return: A tuple of email address and the json configuration.""" return self.db.query_one("SELECT email, json FROM users WHERE pk=?", (pk_b64,), "Unknown user") def raise_for_no_user(self, email: str): """Raises an error if this email address does not have an account. :param email: email address of the user.""" self.db.query_one("SELECT * FROM users WHERE email=?", (email,), "no validated account") def register_node(self, pk_b64: str, config: str): """Writes a node's configuration into the database. :param pk_b64: The node's primary key - base64 encoded string. :param config: A json description of any configuration to be associated with the node.""" self.db.mutate("INSERT INTO nodes (pk, json) VALUES (?, ?)", (pk_b64, config)) def node_config_from_db(self, pk_b64: str) -> str: """Returns the json configuration of a node. :param pk_b64: The node's primary key - base64 encoded string. :return: The json configuration for the node.""" return self.db.query_one("SELECT json FROM nodes WHERE pk=?", (pk_b64,), "Unknown node")[0] confirmation_server = Bottle() class AccountConfirmationServer(Thread): """A simple HTTP server for confirming accounts""" # has single use tokens so no real need to SSL identity = None pk = None port = None def __init__(self, identity, keys, port): super().__init__(name=str("Account Confirmation Server"), daemon=True) AccountConfirmationServer.identity = identity AccountConfirmationServer.pk = keys.public AccountConfirmationServer.port = port self.start() def stop(self): logging.debug("Stopping AccountConfirmationServer") confirmation_server.close() @staticmethod @confirmation_server.route('/', method='POST') def account(): # de-HTTP the request try: token, user_pk = request.body.read().decode().split() except: logging.warning("Off-spec request to account creation server: " + request.body.read().decode()) return None # valid token? pending_records = AccountConfirmationServer.identity.pending_users_for_token(token) if len(pending_records) == 0: logging.warning("An attempt was made to confirm an account with an incorrect token: " + token) return "Fail: this token is either incorrect or has been used already." user_email = pending_records[0][0] # all good AccountConfirmationServer.identity.register_user(user_pk, user_email, "{}") logging.info("Confirmed an account for: " + user_email) return AccountConfirmationServer.pk def run(self): try: logging.info("Started account confirmation server: 0.0.0.0:" + str(AccountConfirmationServer.port)) run(app=confirmation_server, host='0.0.0.0', port=AccountConfirmationServer.port, quiet=True) except OSError: logging.critical("Could not bind account confirmation server, exiting") exit(1)

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0

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0.43662

0

null

0

null

0

1

0

937afaaa0ef96d58eb433c0d7ddeed51e90f25a2

9,947

py

Python

src/cc_catalog_airflow/dags/common/storage/media.py

sarayourfriend/openverse-catalog

b12ba815de782032f72ffa4f5620cfc8de8c84bd

[ "MIT" ]

null

src/cc_catalog_airflow/dags/common/storage/media.py

sarayourfriend/openverse-catalog

b12ba815de782032f72ffa4f5620cfc8de8c84bd

[ "MIT" ]

null

src/cc_catalog_airflow/dags/common/storage/media.py

sarayourfriend/openverse-catalog

b12ba815de782032f72ffa4f5620cfc8de8c84bd

[ "MIT" ]

null

import abc from datetime import datetime import logging import os from typing import Optional, Union from common.licenses.licenses import is_valid_license_info from common.storage import util logger = logging.getLogger(__name__) # Filter out tags that exactly match these terms. All terms should be # lowercase. TAG_BLACKLIST = {"no person", "squareformat"} # Filter out tags that contain the following terms. All entrees should be # lowercase. TAG_CONTAINS_BLACKLIST = { "flickriosapp", "uploaded", ":", "=", "cc0", "by", "by-nc", "by-nd", "by-sa", "by-nc-nd", "by-nc-sa", "pdm", } COMMON_CRAWL = 'commoncrawl' PROVIDER_API = 'provider_api' class MediaStore(metaclass=abc.ABCMeta): """ An abstract base class that stores media information from a given provider. Optional init arguments: provider: String marking the provider in the `media` (`image`, `audio` etc) table of the DB. output_file: String giving a temporary .tsv filename (*not* the full path) where the media info should be stored. output_dir: String giving a path where `output_file` should be placed. buffer_length: Integer giving the maximum number of media information rows to store in memory before writing them to disk. """ def __init__( self, provider: Optional[str] = None, output_file: Optional[str] = None, output_dir: Optional[str] = None, buffer_length: int = 100, media_type: Optional[str] = "generic", ): logger.info(f"Initialized {media_type} MediaStore" f" with provider {provider}") self.media_type = media_type self._media_buffer = [] self._total_items = 0 self._PROVIDER = provider self._BUFFER_LENGTH = buffer_length self._NOW = datetime.now() self._OUTPUT_PATH = self._initialize_output_path( output_dir, output_file, provider, ) self.columns = None def save_item(self, media) -> None: """ Appends item data to the buffer as a tsv row, only if data is valid. Args: media: a namedtuple with validated media metadata """ tsv_row = self._create_tsv_row(media) if tsv_row: self._media_buffer.append(tsv_row) self._total_items += 1 if len(self._media_buffer) >= self._BUFFER_LENGTH: self._flush_buffer() @abc.abstractmethod def add_item(self, **kwargs): """ Abstract method to clean the item data and add it to the store """ pass def clean_media_metadata(self, **media_data) -> Optional[dict]: """ Cleans and enriches the base media metadata common for all media types. Even though we clean license info in the provider API scripts, we validate it here, too, to make sure we don't have invalid license information in the database. Returns a dictionary: media_type-specific fields are untouched, and for common metadata we: - validate `license_info` - enrich `metadata`, - replace `raw_tags` with enriched `tags`, - validate `source`, - add `provider`, - add `filesize` (with value of None) Returns None if license is invalid """ if ( media_data['license_info'].license is None or not is_valid_license_info(media_data['license_info']) ): logger.debug("Discarding media due to invalid license") return None media_data['source'] = util.get_source( media_data.get('source'), self._PROVIDER ) # Add ingestion_type column value based on `source`. # The implementation is based on `ingestion_column` if media_data.get('ingestion_type') is None: if media_data['source'] == 'commoncrawl': media_data['ingestion_type'] = 'commoncrawl' else: media_data['ingestion_type'] = 'provider_api' media_data['tags'] = self._enrich_tags( media_data.pop('raw_tags', None) ) media_data['meta_data'] = self._enrich_meta_data( media_data.pop('meta_data', None), media_data['license_info'].url, media_data['license_info'].raw_url, ) media_data['license_'] = media_data['license_info'].license media_data['license_version'] = media_data['license_info'].version media_data.pop('license_info', None) media_data['provider'] = self._PROVIDER media_data['filesize'] = None return media_data def commit(self): """Writes all remaining media items in the buffer to disk.""" self._flush_buffer() return self.total_items def _initialize_output_path( self, output_dir: Optional[str], output_file: Optional[str], provider: str, ) -> str: """Creates the path for the tsv file. If output_dir and output_file ar not given, the following filename is used: `/tmp/{provider_name}_{media_type}_{timestamp}.tsv` Returns: Path of the tsv file to write media data pulled from providers """ if output_dir is None: logger.info("No given output directory. " "Using OUTPUT_DIR from environment.") output_dir = os.getenv("OUTPUT_DIR") if output_dir is None: logger.warning( "OUTPUT_DIR is not set in the environment. " "Output will go to /tmp." ) output_dir = "/tmp" if output_file is not None: output_file = str(output_file) else: datetime_string = datetime.strftime( self._NOW, '%Y%m%d%H%M%S') output_file = ( f"{provider}_{self.media_type}" f"_{datetime_string}.tsv" ) output_path = os.path.join(output_dir, output_file) logger.info(f"Output path: {output_path}") return output_path @property def total_items(self): """Get total items for directly using in scripts.""" return self._total_items def _create_tsv_row(self, item): row_length = len(self.columns) prepared_strings = [ self.columns[i].prepare_string(item[i]) for i in range(row_length) ] logger.debug(f"Prepared strings list:\n{prepared_strings}") for i in range(row_length): if self.columns[i].REQUIRED and prepared_strings[i] is None: logger.warning(f"Row missing required {self.columns[i].NAME}") return None else: return ( "\t".join( [s if s is not None else "\\N" for s in prepared_strings]) + "\n" ) def _flush_buffer(self) -> int: buffer_length = len(self._media_buffer) if buffer_length > 0: logger.info(f"Writing {buffer_length} lines from buffer to disk.") with open(self._OUTPUT_PATH, "a") as f: f.writelines(self._media_buffer) self._media_buffer = [] logger.debug( f"Total Media Items Processed so far: {self._total_items}" ) else: logger.debug("Empty buffer! Nothing to write.") return buffer_length @staticmethod def _tag_blacklisted(tag: Union[str, dict]) -> bool: """ Tag is banned or contains a banned substring. :param tag: the tag to be verified against the blacklist :return: true if tag is blacklisted, else returns false """ if type(tag) == dict: # check if the tag is already enriched tag = tag.get("name") if tag in TAG_BLACKLIST: return True for blacklisted_substring in TAG_CONTAINS_BLACKLIST: if blacklisted_substring in tag: return True return False @staticmethod def _enrich_meta_data( meta_data, license_url, raw_license_url) -> dict: """ Makes sure that meta_data is a dictionary, and contains license_url and raw_license_url """ if type(meta_data) != dict: logger.debug(f"`meta_data` is not a dictionary: {meta_data}") enriched_meta_data = { "license_url": license_url, "raw_license_url": raw_license_url, } else: enriched_meta_data = meta_data enriched_meta_data.update( license_url=license_url, raw_license_url=raw_license_url ) return enriched_meta_data def _enrich_tags(self, raw_tags) -> Optional[list]: """Takes a list of tags and adds provider information to them Args: raw_tags: List of strings or dictionaries Returns: A list of 'enriched' tags: {"name": "tag_name", "provider": self._PROVIDER} """ if type(raw_tags) != list: logger.debug("`tags` is not a list.") return None else: return [ self._format_raw_tag(tag) for tag in raw_tags if not self._tag_blacklisted(tag) ] def _format_raw_tag(self, tag): if type(tag) == dict and tag.get("name") and tag.get("provider"): logger.debug(f"Tag already enriched: {tag}") return tag else: logger.debug(f"Enriching tag: {tag}") return {"name": tag, "provider": self._PROVIDER}

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0.190722

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null

0

null

0

1

0

937ca990f86faab5de19956b6f0ea3640d942692

1,864

py

Python

files/scrapli/app_async.py

cremsburg/SRX-get-security-zones

6bd0522b956f2e69d7d86a01ffd57802c392a666

[ "Apache-2.0" ]

null

files/scrapli/app_async.py

cremsburg/SRX-get-security-zones

6bd0522b956f2e69d7d86a01ffd57802c392a666

[ "Apache-2.0" ]

null

files/scrapli/app_async.py

cremsburg/SRX-get-security-zones

6bd0522b956f2e69d7d86a01ffd57802c392a666

[ "Apache-2.0" ]

2

2021-10-30T00:55:47.000Z

2021-11-16T16:20:54.000Z

import asyncio import xmltodict from scrapli_netconf.driver import AsyncNetconfDriver from scrapli.logging import enable_basic_logging from jinja2 import Environment, FileSystemLoader # Enable logging. Create a log file in the current directory. enable_basic_logging(file=True, level="debug") GALVESTON = { "host": "192.168.105.137", "auth_username": "scrapli", "auth_password": "juniper123", "auth_strict_key": False, "transport": "asyncssh" } SANANTONIO = { "host": "192.168.105.146", "auth_username": "scrapli", "auth_password": "juniper123", "auth_strict_key": False, "transport": "asyncssh" } DEVICES = [GALVESTON, SANANTONIO] RPC = """ <get-zones-information> </get-zones-information> """ # jinja2 parameters env = Environment(loader=FileSystemLoader('templates'),trim_blocks=True) template = env.get_template('test.j2') # async function to open a connection and return the output of our RPC async def gather_security_zones(device): conn = AsyncNetconfDriver(**device) await conn.open() result = await conn.rpc(filter_=RPC) await conn.close() return result # primary function async def main(): """Function to gather coroutines, await them and print results""" coroutines = [gather_security_zones(device) for device in DEVICES] results = await asyncio.gather(*coroutines) for each in results: reply_as_dict = xmltodict.parse(each.result) security_zones = reply_as_dict["rpc-reply"]["zones-information"]["zones-security"] # template output with jinja2 and save to file output_from_parsed_template = template.render(security_zones=security_zones) with open(f"./output/{each.host}.yaml", "w") as fh: fh.write(output_from_parsed_template) if __name__ == "__main__": asyncio.get_event_loop().run_until_complete(main())

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0.136364

0

null

0

null

0

1

0

937ea31fc0d7d3bea616809be52b9e4bc8a4e803

5,245

py

Python

hwsushy/hwsushy/tests/unit/resources/test_base.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

null

hwsushy/hwsushy/tests/unit/resources/test_base.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

null

hwsushy/hwsushy/tests/unit/resources/test_base.py

saintifly/Server_Manage_Plugin

ae272e7e3ca065236cc7bc86c296ff9eb83f1bb9

[ "Apache-2.0" ]

null

# Copyright 2017 Red Hat, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock from hwsushy import exceptions from hwsushy.resources import base as resource_base from hwsushy.tests.unit import base class BaseResouce(resource_base.ResourceBase): def _parse_attributes(self): pass class ResourceBaseTestCase(base.TestCase): def setUp(self): super(ResourceBaseTestCase, self).setUp() self.conn = mock.Mock() self.base_resource = BaseResouce(connector=self.conn, path='/Foo', redfish_version='1.0.2') # refresh() is called in the constructor self.conn.reset_mock() def test_refresh(self): self.base_resource.refresh() self.conn.get.assert_called_once_with(path='/Foo') def test_refresh_http_error_reraised(self): self.conn.get.side_effect = exceptions.HTTPError( method='GET', url='http://foo.bar:8000/redfish/v1', error='boom', status_code=404) self.assertRaises(exceptions.ResourceNotFoundError, self.base_resource.refresh) self.conn.get.assert_called_once_with(path='/Foo') def test_refresh_resource_not_found(self): self.conn.get.side_effect = exceptions.HTTPError( method='GET', url='http://foo.bar:8000/redfish/v1', error='boom', status_code=400) self.assertRaises(exceptions.HTTPError, self.base_resource.refresh) self.conn.get.assert_called_once_with(path='/Foo') class TestResouce(resource_base.ResourceBase): """A concrete Test Resource to test against""" def __init__(self, connector, identity, redfish_version=None): """Ctor of TestResouce :param connector: A Connector instance :param identity: The id of the Resource :param redfish_version: The version of RedFish. Used to construct the object according to schema of the given version. """ super(TestResouce, self).__init__(connector, 'Fakes/%s' % identity, redfish_version) self.identity = identity def _parse_attributes(self): pass class TestResouceCollection(resource_base.ResourceCollectionBase): """A concrete Test Resource Collection to test against""" @property def _resource_type(self): return TestResouce def __init__(self, connector, redfish_version=None): """Ctor of TestResourceCollection :param connector: A Connector instance :param redfish_version: The version of RedFish. Used to construct the object according to schema of the given version. """ super(TestResouceCollection, self).__init__(connector, 'Fakes', redfish_version) class ResourceCollectionBaseTestCase(base.TestCase): def setUp(self): super(ResourceCollectionBaseTestCase, self).setUp() self.conn = mock.MagicMock() self.test_resource_collection = TestResouceCollection( self.conn, redfish_version='1.0.x') self.conn.reset_mock() def test_get_member(self): # | GIVEN | # setting a valid member identity self.test_resource_collection.members_identities = ('1',) # | WHEN | result = self.test_resource_collection.get_member('1') # | THEN | self.assertTrue(isinstance(result, TestResouce)) self.assertEqual('1', result.identity) self.assertEqual('1.0.x', result.redfish_version) def test_get_member_for_invalid_id(self): # | GIVEN | # setting a valid member identity self.test_resource_collection.members_identities = ('1',) self.conn.get.side_effect = exceptions.HTTPError( method='GET', url='http://foo.bar:8000/redfish/v1/Fakes/2', error='boom', status_code=404) # | WHEN & THEN | self.assertRaises(exceptions.ResourceNotFoundError, self.test_resource_collection.get_member, '2') self.conn.get.assert_called_once_with(path='Fakes/2') def test_get_members(self): # | GIVEN | # setting some valid member paths member_ids = ('1', '2') self.test_resource_collection.members_identities = member_ids # | WHEN | result = self.test_resource_collection.get_members() # | THEN | self.assertTrue(isinstance(result, list)) for val in result: self.assertTrue(isinstance(val, TestResouce)) self.assertTrue(val.identity in member_ids) self.assertEqual('1.0.x', val.redfish_version)

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0.031194

0.052789

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0.263647

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0

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5,245

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false

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0.310811

0

null

0

null

0

1

0

9380c40a504e6bed441a01b8170bbbf1c938fe3e

2,719

py

Python

main.py

Tiggax/SoundSnake

9d841fff431d37beeb30e73276f645bf422987bb

[ "MIT" ]

null

main.py

Tiggax/SoundSnake

9d841fff431d37beeb30e73276f645bf422987bb

[ "MIT" ]

null

main.py

Tiggax/SoundSnake

9d841fff431d37beeb30e73276f645bf422987bb

[ "MIT" ]

null

# This Python file uses the following encoding: utf-8 from gui.uis.windows.main_window.functions_main_window import * import sys import os from qt_core import * from gui.core.json_settings import Settings from gui.uis.windows.main_window import * from gui.widgets import * os.environ["QT_FONT_DPI"] = "96" class MainWindow(QMainWindow): def __init__(self): super().__init__() # Load widgets from "gui\uis\main_window\ui_main.py" self.ui = UI_MainWindow() self.ui.setup_ui(self) # LOAD SETTINGS settings = Settings() self.settings = settings.items # SETUP MAIN WINDOW self.hide_grips = True # Show/Hide resize grips SetupMainWindow.setup_gui(self) # SHOW MAIN WINDOW self.show() # LEFT MENU BTN IS CLICKED # Run function when btn is clicked # Check funtion by object name / btn_id # /////////////////////////////////////////////////////////////// def btn_clicked(self): # GET BT CLICKED btn = SetupMainWindow.setup_btns(self) # click events #HOME if btn.objectName() == "btn_home": self.ui.left_menu.select_only_one(btn.objectName()) MainFunctions.set_page(self, self.ui.load_pages.page_1) #Search if btn.objectName() == "btn_search": self.ui.left_menu.select_only_one(btn.objectName()) MainFunctions.set_page(self, self.ui.load_pages.page_2) #Settings if btn.objectName() == "btn_settings": self.ui.left_menu.select_only_one(btn.objectName()) MainFunctions.set_page(self, self.ui.load_pages.page_3) # TITLE BAR MENU # /////////////////////////////////////////////////////////////// # SETTINGS TITLE BAR # LEFT MENU BTN IS RELEASED # Run function when btn is released # Check funtion by object name / btn_id # /////////////////////////////////////////////////////////////// def btn_released(self): # GET BT CLICKED btn = SetupMainWindow.setup_btns(self) # DEBUG print(f"Button {btn.objectName()}, released!") # RESIZE EVENT # /////////////////////////////////////////////////////////////// def resizeEvent(self, event): SetupMainWindow.resize_grips(self) # MOUSE CLICK EVENTS # /////////////////////////////////////////////////////////////// def mousePressEvent(self, event): # SET DRAG POS WINDOW self.dragPos = event.globalPos() if __name__ == "__main__": app = QApplication(sys.argv) app.setWindowIcon(QIcon("icon.ico")) window = MainWindow() sys.exit(app.exec())

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4.822742

0.344482

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0.037448

0.351595

0.323856

0.286408

0.172677

0

0.002933

0.247517

2,719

95

74

28.621053

0.701857

0.309305

0

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0

0.051379

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1

0.121951

false

0

0.170732

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0.317073

0.02439

0

null

0

null

0

1

0

93814b0f89541fdaac833798b0100b3575df0b96

6,158

py

Python

checkCardReaders.py

hansliss/IECTools

96a348f6488eaf3a1263646c77862b0f9a68294b

[ "BSD-2-Clause" ]

null

checkCardReaders.py

hansliss/IECTools

96a348f6488eaf3a1263646c77862b0f9a68294b

[ "BSD-2-Clause" ]

null

checkCardReaders.py

hansliss/IECTools

96a348f6488eaf3a1263646c77862b0f9a68294b

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 """checkCardReaders.py: Use a local MySQL database to keep track of card readers and produce a 'changes' JSon document.""" __author__ = "Hans Liss" __copyright__ = "Copyright 2020, Hans Liss" __license__ = "BSD 2-Clause License" __version__ = "1.1" __maintainer__ = "Hans Liss" __email__ = "Hans@Liss.nu" __status__ = "Example code" from zeep import Client import MySQLdb import uuid import sys import datetime import configparser import json import argparse ## Read command-line parameters and configuration file parser = argparse.ArgumentParser(description='Find changes in CardReaders') parser.add_argument('-c', '--configfile', required=True, help='path to configuration file') parser.add_argument('-i', '--instance', required=True, help='name of the instance to use from the config file') parser.add_argument('-l', '--logprefix', help='prefix for log files. Datestamp will be added') parser.add_argument('-f', '--force', action='store_true', help='register changes even when deletes exceeds 200') args = parser.parse_args() config = configparser.ConfigParser() config.read(args.configfile) if args.logprefix is not None: logfile = open(datetime.date.today().strftime(args.logprefix + "%Y-%m-%d"), "a") def log(str): logfile.write(datetime.datetime.now().strftime("%H:%M:%S\t") + str + "\n") else: def log(str): pass wsdl = config[args.instance]['wsdl'] endpoint = config[args.instance]['endpoint'] sessiontoken = config[args.instance]['sessiontoken'] # We do some fairly ugly string concatenation to create SQL queries below. readerFields = ['Id', 'ParentFolderPath', 'Name', 'Description', 'AccessPointId', 'CardReaderType', 'SecurityLevel'] try: conn = MySQLdb.connect( host = config[args.instance]['db_host'], port = 3365, user = config[args.instance]['db_user'], password = config[args.instance]['db_password'], database = config[args.instance]['db_db'], ) dbCursor = conn.cursor() dbCursor.execute("CREATE TEMPORARY TABLE readersTemp like readers") except MySQLdb.Error as e: print(f"Error connecting to MySQL Platform: {e}") sys.exit(1) ## Create a SOAP client and from that, create a new service with the correct endpoint client = Client(wsdl) client.service._binding_options['address'] = endpoint ## Request data should contain whatever is in the 'request' subdocument within the ## SOAP request XML request_data={'request' : {'SessionToken' : uuid.UUID('{' + sessiontoken + '}'), 'MessageId' : uuid.uuid4(), 'PageSize' : 100}} done=False pageNo=0 doneCount=0 while (not done): request_data['request']['PageIndex']=pageNo ## Call the method and get a response object try: response=client.service.GetCardReadersList(**request_data) except: e = sys.exc_info()[0] print(f"SOAP Error: {e}") sys.exit(1) totalCount = response.TotalCount batch = [] for reader in response.Results.__values__['CardReaderModel']: values = [] for fieldName in readerFields: values.append(reader[fieldName]) batch.append(values) try: queryString = "INSERT INTO readersTemp (" first = True for fieldName in readerFields: if(first): first = False else: queryString += "," queryString += fieldName queryString += ") values (%s,%s,%s,%s,%s,%s,%s)" dbCursor.executemany(queryString, batch) except MySQLdb.Error as e: print(f"Error on insert: {e}") sys.exit(1) doneCount = doneCount + len(response.Results.__values__['CardReaderModel']) pageNo = pageNo + 1 #print("Done %d out of %d, at page %d" % (doneCount, totalCount, pageNo)) if doneCount >= totalCount: done = True deleted = []; added = []; modified = []; # These are deleted dbCursor.execute('SELECT r.Id from readers r left join readersTemp rt on rt.Id = r.Id where rt.Id IS NULL') for row in dbCursor: deleted.append(row[0]) log("Deleted: %d" % row[0]) # select all from readersTemp left join readers fieldListRt = "" fieldListR = "" fieldListChanged = "" first = True for fieldName in readerFields: if(first): first = False else: fieldListRt += "," fieldListR += "," fieldListChanged += " OR " fieldListRt += "rt." + fieldName fieldListR += "r." + fieldName fieldListChanged += "NOT ( rt." + fieldName + " <=> r." + fieldName + ")" queryString = "SELECT " + fieldListRt + "," + fieldListR + " from readersTemp rt left join readers r on rt.Id = r.Id where r.Id IS NULL or " + fieldListChanged dbCursor.execute(queryString) for row in dbCursor: # If right side of JOIN is null, the reader has been added if row[len(readerFields)] is None: reader = {} for i in range(len(readerFields)): reader[readerFields[i]] = row[i] added.append(reader) log("Added: %d" % row[0]) else: reader = {} reader['Id'] = row[0] for i in range(len(readerFields)): if row[i] != row[i + len(readerFields)]: reader[readerFields[i]] = row[i] log("Modified: %d field %s changed from \"%s\" to \"%s\"" % (row[0], readerFields[i], row[i + len(readerFields)], row[i])) modified.append(reader) if len(deleted) < 200 or args.force: if len(deleted) > 0 or len(added) > 0 or len(modified) > 0: dbCursor.execute("DELETE FROM readers") dbCursor.execute("INSERT INTO readers SELECT * FROM readersTemp") conn.commit() update={} update['timestamp'] = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%fZ") update['deleted'] = deleted update['added'] = added update['modified'] = modified print(json.dumps(update, indent=2)) else: print("The number of deletes is large (%d) and the -f flag was not given. Doing nothing." % len(deleted)) conn.close()

34.402235

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0.034722

0

null

0

null

0

1

0

9383005dd0c349133f874e23088ec259d85b45a7

8,460

py

Python

final/180401040.py

yigitcanustek/blm2010

2e86dab3fc225a7679b6c660fb01902423476a94

[ "Unlicense" ]

2

2020-05-20T19:25:37.000Z

2021-04-01T21:26:54.000Z

final/180401040.py

yigitcanustek/blm2010

2e86dab3fc225a7679b6c660fb01902423476a94

[ "Unlicense" ]

15

2020-05-18T14:53:18.000Z

2020-06-26T09:20:50.000Z

final/180401040.py

yigitcanustek/blm2010

2e86dab3fc225a7679b6c660fb01902423476a94

[ "Unlicense" ]

155

2020-04-28T16:14:38.000Z

2020-06-26T09:46:59.000Z

# ad-soyad : Ramazan AYDIN --- numara : 180401040 print("\n") """ Bu python kodunda ; parametre olarak verilen dosyadaki (veriler.txt) verilerin sırasıyla 1,2,3,4,5,6. dereceden polinoma yakınlaştırarak bu polinomlardan hangisinin en az hata ile sonucu bulduğunu hesaplayacağiz. Tespit ettiğiniz polinomunun a ( öğrenci numarasının son rakamı) ile b (dosyanın satır sayısı) arasındaki integrali hesaplayacağız . Aynı integrali veriler.txt dosyasındaki verileri kullanarak hesaplayacağız. Son olarak hesapladığımız bu 2 integralin sonuclarının farklı çıkmasının nedenini yorum.txt dosyasında açıklayacağız. """ # veriler.txt dosyasına gider ve oradaki verilerin tamamını okur. *********** with open("veriler.txt", "r", encoding='utf-8') as file: dizi = [] #dizi adında bir liste oluşturduk. for i in file.read().split(): dizi.append(int(i)) #veriler.txt dosyasından okuduğumuz verileri diziye ekledik. #Dizinin boyutunu tutması için bir fonksiyon oluşturduk. def size(dizi): return len(dizi) n = size(dizi) # n'ye dizinin boyutunu atadık # kare matris oluşturma def karematris(calculateMATRIS): dizi_m = calculateMATRIS.copy() column = len(calculateMATRIS[0]) line = len(calculateMATRIS) # left triangular matris hesaplama for s in range(line - 1): for y in range(line - 1 - s): multiplier = dizi_m[y][s] / dizi_m[y + 1][s] for b in range(column): dizi_m[y][b] += -multiplier * dizi_m[y + 1][b] # diagonal matris for m in range(line - 1, 0, -1): for n in range(line - 1, line - 1 - m, -1): multiplier = dizi_m[n][m] / dizi_m[n - 1][m] for h in range(column): dizi_m[n][h] += -multiplier * dizi_m[n - 1][h] cozum = [] for s in range(line - 1, -1, -1): x = dizi_m[s][line] / dizi_m[s][line - s - 1] cozum.append(x) return cozum # toplam y değerlerini hesapladık def totalY(dizi): y = sum(dizi) return y totalyi = totalY(dizi) # xi toplamları tutan dizi def totalxi(n): total_x_kare = [] #total_x_kare adında bir dizi olusturduk. for j in range(1, 13, 1): kare_x = 0 #kare_x in ilk değerini 0 a eşitledik. for p in range(n): kare_x += (p + 1) ** j #dizideki eleman sayısı kadar, x lerin karelerini hesaplattık total_x_kare.append(kare_x) total_x_kare.insert(0, n) #hesaplattığımız değerleri diziye ekledik return total_x_kare # bütün polinomların (1,6) x^ derecelerini tutan dizi def xiyiToplam(n, dizi, totalY): derece_x_totaly = [] for j in range(1, 7, 1): #6 polinom olduğu için 6 kere döndürdük. deger = 0 for eleman in range(n): deger += (eleman + 1) ** j * dizi[eleman] derece_x_totaly.append(deger) derece_x_totaly.insert(0, totalY) return derece_x_totaly """ 6. polinoma kadar gittiğimiz için 7 tane a değerin oluşacak; bu yüzden m=8 e kadar döngümüzü çalıştıracağız a0, a1, a2, a3, a4, a5, a6, a7 """ def value_of_a(n, dizi, m=8): cozum = [] total_x_kare = totalxi(n) y = totalY(dizi) derece_x_totaly = xiyiToplam(n, dizi, y) for x in range(2, m, 1): yenidizi = [] for i in range(x): yenidizi.append([]) for j in range(x): yenidizi[i].append(total_x_kare[j + i]) yenidizi[i].append(derece_x_totaly[i]) if (i == x - 1): cozum.append(karematris(yenidizi)) yenidizi.clear() return cozum """ deger_a dizisi ; n. derece polinomun a değerlerini bir dizi olarak tutar """ deger_a = value_of_a(n, dizi) """ Korelasyon, iki değişken arasında doğrusal bir ilişkiyi ifade eder. Korelasyon katsayısı ise değişkenler arasındaki ilişkiyi göstermek için kullanılan bir değerdir. Korelasyon katsayısı; ** 1′e yaklaştıkça iki değişken arasında aynı yöndeki ilişki artar.Değişkenlerden biri artarken diğeri de artar. ** -1′e yaklaştıkça iki değişen arasında ters yönde ilişki artar. Değişkenlerden biri artarken diğeri azalır. ** 0’a yaklaştıkça iki değişken arasındaki ilişki azalır. Ödevde aynı yönde artan veriler üzerinde işlem yaptığımız için korelasyon katsayısı 1'e en yakın olan polinomu, en uygun polinom olarak alacağız. """ """ korelasyon değerlerini hesaplayacagız """ def Hata_Hesaplama(x, dizi, n, totalY): S_R = 0 S_T = 0 y = totalY / n size = len(x) for i in range(n): gecici = 0 for j in range(size): if j == 0: gecici += x[j] else: gecici += x[j] * (i + 1) ** j S_R += (dizi[i] - gecici) ** 2 S_T += (dizi[i] - y) ** 2 r = ((S_T - S_R) / S_T) ** (1 / 2) return r """ hesapladığımız korelasyon değerleri içinde 1 en yakın kolerasyon değerini bulmalıyız ve bunu döndürmeliyiz """ ''' Bu fonksiyonumda , elde ettiğimiz korelasyon değerlerini bir dizi oluşturup ,bu dizi içerisinde tutacağız. ''' KorelasyonValue = [] for i in deger_a: e = Hata_Hesaplama(i,dizi,n , totalyi) KorelasyonValue.append(e) def en_iyi_kolerasyon(dizi): # Bu fonksiyon 1'e en yakın olan kolerasyon değerini döndürür sirali_dizi = sorted(dizi) # sorted fonksiyonu ile diziyi sıraladık. biggest = sirali_dizi[-1] b = 1 while (biggest != dizi[b - 1]): b = b + 1 return b, biggest sayici, en_iyi_korelasyon_degeri = en_iyi_kolerasyon(KorelasyonValue) print("Sonucu en düşük hata payi ile hesaplayan polinomun derecesi : ", sayici) print("Sonucu en düşük hata payi ile hesaplayan polinomun Korelasyon değeri : ", en_iyi_korelasyon_degeri) print("\n") polinom = deger_a[sayici - 1] def fonksiyon(w , polinom1 = polinom ): u = polinom1 total_value = 0 for i in range(len(u)): total_value += u[i] * (w ** i) return total_value """ Bu fonksiyonda 2.soruyu cevaplayacağız. Integrali tespit ettiğimiz en iyi korelasyon değerine sahip polinomu kullanarak hesapladık ve sonucu ekrana yazdırdık """ def polinom_ile_integral_hesaplama(n): # okul numaram 180401040 olduğu için son rakamı 0 . Bu yüzden a =10 alacağız. a = 10 b = n deltax = 0.001 integral = 0 size = int((b - a) / deltax) for i in range(size): integral += deltax * (fonksiyon(a) + fonksiyon(a + deltax)) / 2 a += deltax print("Polinom kullanarak hesaplanan sonuc : ", integral) """ Bu fonksiyonda 3.soruyu cevaplayacağız. Integrali veriler.txt dosyasındaki verileri kullanarak (polinomu kullanmadan) hesaplayıp bu sonucu da ekrana yazdırdık. """ def veriler_ile_integral_hesaplama(n, dizi): # 180401040 (0) . a değerini 10 olarak alacağız. a = 10 b = n integral = 0 for i in range(a - 1, b - 1): integral += (dizi[i] + dizi[i + 1]) / 2 print("Veriler kullanılarak hesaplanan sonuc : ", integral) """ yorum.txt dosyasının içerisinde hesapladimiz 2 integral değerininde neden farklı sonuçlar verdiğini açıklayacağız. """ def yorumlarim(): with open("180401040_yorum.txt", "w", encoding='utf-8') as dosya : dosya.write("ad - soyad : Ramazan AYDIN \n") dosya.write("NUMARA : 180401040 \n") dosya.write(" Hesaplamalarımda yamuk metodunu kullandım.\n ") dosya.write("Hesapladigimiz 2 integral değeri de öngördüğümüz gibi birbirinden farkli çikmistir. \n") dosya.write(" Bunun nedeni ; \n") dosya.write("İntegral Hesabi yapılırken , verilen polinomu küçük dikdörtgenlere bölerek ve bunların alanlarını toplayarak hesaplamaya çalışırız. \n ") dosya.write("Deltax(dikdörtgenin eni) değerini ne kadar küçültürsek ,işleme katılacak alan sayısı artar ve bulacağımız değer o kadar gerçeğe yakın olur.\n ") dosya.write("Ancak bu iki integral arasındaki farkın temel sebebi , birinci integrali polinom haline getirirken \n ") dosya.write("belirli bir korelasyon sayısına göre polinoma yaklaştırmamızdandır.\n ") dosya.write("Bu sebepten, deltax değerlerini eşit aldığımızda bile sonuç farklı olur. \n ") polinom_ile_integral_hesaplama(n) veriler_ile_integral_hesaplama(n, dizi) yorumlarim()

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0.0112

0.11723

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null

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null

0

1

0

93843a5470b274737095d437c24a1bad935e4e23

1,757

py

Python

hailo_model_zoo/utils/downloader.py

markgrobman/hailo_model_zoo

2ea72272ed2debd7f6bee7c4a65bd41de57ec9cf

[ "MIT" ]

2

2021-07-20T15:09:51.000Z

2021-11-17T11:05:02.000Z

hailo_model_zoo/utils/downloader.py

markgrobman/hailo_model_zoo

2ea72272ed2debd7f6bee7c4a65bd41de57ec9cf

[ "MIT" ]

null

hailo_model_zoo/utils/downloader.py

markgrobman/hailo_model_zoo

2ea72272ed2debd7f6bee7c4a65bd41de57ec9cf

[ "MIT" ]

null

""" model files downloader Usage: >>> from downloader import download >>> from logging import getLogger >>> model_files_dir = '.' >>> hailo_storage = 'https://hailo-modelzoo-pub.s3.eu-west-2.amazonaws.com/' >>> model_path = 'Classification/mobilenet_v1/pretrained/mobilenet_v1_1_0_224.ckpt.zip' >>> download(hailo_storage+model_path, model_files_dir, getLogger()) """ import logging import zipfile from pathlib import Path from requests import get from tqdm.auto import tqdm from typing import Union def _download(url: str, dst: Path) -> None: resp = get(url, allow_redirects=True, stream=True) with dst.open('wb') as fout: with tqdm( desc=dst.name, miniters=1, total=int(resp.headers.get('content-length', 0)), unit='B', unit_divisor=1024, unit_scale=True, ) as progress_bar: for chunk in resp.iter_content(chunk_size=4096): fout.write(chunk) progress_bar.update(len(chunk)) def download(url: str, dst_dir: Union[str, Path], logger: logging.Logger) -> str: """downloads a file from given url, and returns the downloaded file name""" dst_dir.mkdir(parents=True, exist_ok=True) dst = Path(dst_dir) / Path(url).name if not(dst.exists() and dst.is_file()): logger.debug(f'downloading {url} into {dst_dir}') _download(url, dst) else: logger.debug(f'{dst.name} already exists inside {dst_dir}. Skipping download') if len(list(Path('/'.join(dst.parts[:-1])).iterdir())) == 1: logger.debug(f'unzipping {dst} into {dst_dir}') with zipfile.ZipFile(dst, 'r') as zip_fp: zip_fp.extractall(dst_dir) return dst.name

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null

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null

0

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fa730a81c19db224339c86a776c2dccf38b63307

3,059

py

Python

tests/test_resource_hh.py

ollis-nwcouncil/NRELWindData

bc5146ea5577e4ab1b86587d15b783fd302f3895

[ "BSD-3-Clause" ]

null

tests/test_resource_hh.py

ollis-nwcouncil/NRELWindData

bc5146ea5577e4ab1b86587d15b783fd302f3895

[ "BSD-3-Clause" ]

null

tests/test_resource_hh.py

ollis-nwcouncil/NRELWindData

bc5146ea5577e4ab1b86587d15b783fd302f3895

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ pytests for resource handlers with a single hub height """ import numpy as np import os import pytest from rex.renewable_resource import WindResource from rex import TESTDATADIR def test_single_hh(): """Test that resource with data at a single hub height will always return the data at that hub height (and also return a warning)""" h5 = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_2012_incomplete_1.h5') with WindResource(h5) as wind: # Existing datasets are P0m and T80m assert np.array_equal(wind['pressure_80m'], wind['pressure_0m']) assert np.array_equal(wind['temperature_10m'], wind['temperature_80m']) def test_check_hh(): """Test that check hub height method will return the hh at the single windspeed""" h5 = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_2012_incomplete_2.h5') msg = ('Wind resource method _check_hub_height() failed! Should have ' 'returned 100 because theres only windspeed at 100m') with WindResource(h5) as wind: assert (wind._check_hub_height(120) == 100), msg def test_sam_df_hh(): """Test that if there's only windspeed at one HH, all data is returned from that hh """ h5 = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_2012_incomplete_2.h5') with WindResource(h5) as wind: sam_df = wind._get_SAM_df('pressure_80m', 0) arr1 = wind['pressure_100m', :, 0] * 9.86923e-6 arr2 = sam_df['pressure_100m'].values msg1 = ('Error: pressure should have been loaded at 100m ' 'b/c there is only windspeed at 100m.') assert np.array_equal(arr1, arr2), msg1 def test_preload_sam_hh(): """Test the preload_SAM method with a single hub height windspeed in res. In this case, all variables should be loaded at the single windspeed hh """ h5 = os.path.join(TESTDATADIR, 'wtk/ri_100_wtk_2012_incomplete_2.h5') sites = slice(0, 200) hub_heights = 80 SAM_res = WindResource.preload_SAM(h5, sites, hub_heights) with WindResource(h5) as wind: p = wind['pressure_100m'] * 9.86923e-6 t = wind['temperature_100m'] msg1 = ('Error: pressure should have been loaded at 100m ' 'b/c there is only windspeed at 100m.') msg2 = ('Error: temperature should have been loaded at 100m ' 'b/c there is only windspeed at 100m.') assert np.allclose(SAM_res['pressure', :, :].values, p), msg1 assert np.allclose(SAM_res['temperature', :, :].values, t), msg2 def execute_pytest(capture='all', flags='-rapP'): """Execute module as pytest with detailed summary report. Parameters ---------- capture : str Log or stdout/stderr capture option. ex: log (only logger), all (includes stdout/stderr) flags : str Which tests to show logs and results for. """ fname = os.path.basename(__file__) pytest.main(['-q', '--show-capture={}'.format(capture), fname, flags]) if __name__ == '__main__': execute_pytest()

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0.227273

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null

0

null

0

1

0

fa763da6a9be5df8b6085a6402d291d454f9e88f

2,482

py

Python

qdb_cloudwatch/cloudwatch.py

bureau14/qdb-cloudwatch-exporter

ef6e433f7a2085a01db341122315c0612ce97354

[ "BSD-3-Clause" ]

null

qdb_cloudwatch/cloudwatch.py

bureau14/qdb-cloudwatch-exporter

ef6e433f7a2085a01db341122315c0612ce97354

[ "BSD-3-Clause" ]

null

qdb_cloudwatch/cloudwatch.py

bureau14/qdb-cloudwatch-exporter

ef6e433f7a2085a01db341122315c0612ce97354

[ "BSD-3-Clause" ]

null

import boto3 def _get_client(): return boto3.client('cloudwatch') def _metric_suffix(s): return s.rsplit('.', 1)[1] def _coerce_metric(k, v): if (k.startswith('cpu.')): # We don't expose CPU metrics through Cloudwatch, as this is already collected # by the regular metrics. return None elif (k == 'license.memory'): return ('Bytes', float(v)) sufx = _metric_suffix(k) if sufx == 'total_ns': return ('Microseconds', float(v) / 1000) elif (sufx == 'duration_us' or sufx == 'time_us'): return ('Microseconds', float(v)) elif (sufx == 'remaining_days'): return ('Seconds', float(v * 86400)) elif (sufx.startswith('bytes') or sufx.endswith('bytes')): return ('Bytes', float(v)) elif (sufx.endswith('count')): return ('Count', float(v)) else: print('unknown suffix: ', sufx, ', k: ', k, ', v: ', v) return ('None', float(v)) def _to_metric(k, v): try: x = _coerce_metric(k, v) if x: (u, v_) = x return {'MetricName': k, 'Value': v_, 'Unit': u} except: return None def _qdb_to_cloudwatch(stats): # We want to flatten all metrics into a tuple of 3 items: # - node_id # - user_id # - measurement ret = list() for node_id,xs in stats.items(): for user_id,xs_ in xs['by_uid'].items(): dims = [{'Name': 'UserId', 'Value': str(user_id)}, {'Name': 'NodeId', 'Value': str(node_id)}] for k,v in xs_.items(): m = _to_metric(k, v) if m: m['Dimensions'] = dims ret.append(m) dims = [{'Name': 'NodeId', 'Value': str(node_id)}] for k,v in xs['cumulative'].items(): m = _to_metric(k, v) if m: m['Dimensions'] = dims ret.append(m) return ret def push_stats(stats, namespace): client = _get_client() stats_ = _qdb_to_cloudwatch(stats) metrics_per_req = 20 metrics = [stats_[i:i+metrics_per_req] for i in range(0, len(stats_), metrics_per_req)] for metric in metrics: response = client.put_metric_data(Namespace=namespace, MetricData=metric) print("Pushed {} metrics".format(len(stats_)))

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2,482

4.063123

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0

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2,482

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false

0

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0.030769

0

null

0

null

0

1

0

fa7b2b7882cb8f5c695df5b6229adb9f484bbb44

812

py

Python

tests/test_grid.py

alexras/boomslang

62b6dc3a183fd8686b165c4abdb55d10d537b4ab

[ "BSD-3-Clause" ]

4

2015-02-24T06:50:08.000Z

2020-08-08T03:23:32.000Z

tests/test_grid.py

alexras/boomslang

62b6dc3a183fd8686b165c4abdb55d10d537b4ab

[ "BSD-3-Clause" ]

13

2017-07-17T15:52:09.000Z

tests/test_grid.py

alexras/boomslang

62b6dc3a183fd8686b165c4abdb55d10d537b4ab

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python from boomslang import Line, Plot from ImageComparisonTestCase import ImageComparisonTestCase import unittest class GridTest(ImageComparisonTestCase, unittest.TestCase): def __init__(self, testCaseName): super(GridTest,self).__init__(testCaseName) self.imageName = "grid.png" def constructImage(self): plot = Plot() line = Line() line.yValues = [25, 40, 30, 23, 10, 50] line.xValues = range(len(line.yValues)) plot.add(line) plot.xLabel = "X Label" plot.yLabel = "Y Label" plot.yLimits = (0, 60) plot.grid = True plot.save(self.imageName) ImageComparisonTestCase.register(GridTest) if __name__ == "__main__": test = GridTest("testImageComparison") test.constructImage()

24.606061

59

0.657635

87

812

5.954023

0.574713

0.030888

0

0.024077

0.232759

812

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25.375

0.807384

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false

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0.272727

0

null

0

null

0

1

0

fa7c44876b17f83341951ab9a347d918c59e572a

1,496

py

Python

ppb_timing.py

ironfroggy/ppb_timing

4add69342d7e78146c99a604957129161d97dfed

[ "MIT" ]

null

ppb_timing.py

ironfroggy/ppb_timing

4add69342d7e78146c99a604957129161d97dfed

[ "MIT" ]

null

ppb_timing.py

ironfroggy/ppb_timing

4add69342d7e78146c99a604957129161d97dfed

[ "MIT" ]

null

from dataclasses import dataclass from types import FunctionType from typing import Optional import ppb from ppb.systemslib import System from ppb.utils import get_time @dataclass class Timer: end_time: float callback: FunctionType repeating: float = 0 clear: bool = False until: float = None def __hash__(self): return hash(id(self)) def cancel(self): self.clear = True class Timers(System): timers = set() @classmethod def delay(cls, seconds, func): t = Timer(get_time() + seconds, func) cls.timers.add(t) return t @classmethod def repeat(cls, seconds, func, until=None): n = get_time() t = Timer(n + seconds, func, repeating=seconds, until=n + until) cls.timers.add(t) return t @classmethod def on_idle(cls, idle, signal): clear = [] for t in list(cls.timers): if t.clear: clear.append(t) else: now = get_time() if now >= t.end_time: if t.until is None or t.until > now: t.callback() if t.repeating > 0: if t.until is None or t.until > now: t.end_time += t.repeating continue clear.append(t) for t in clear: cls.timers.remove(t) delay = Timers.delay repeat = Timers.repeat

23.746032

72

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181

1,496

4.337017

0.320442

0.035669

0.033121

0.152866

0.066242

0

0.002162

0.381684

1,496

62

73

24.129032

0.846486

0

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0

1

0.1

false

0

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0.44

0

null

0

null

0

1

0

fa7f5aafe91ba7d0f8bafd8e2dd6804a61920b1e

1,974

py

Python

molot/context.py

sydspace/molot

61541817a189af7340c5fa171c78bbfba1c07836

[ "MIT" ]

3

2019-08-28T23:46:45.000Z

2019-10-03T05:46:32.000Z

molot/context.py

sydspace/molot

61541817a189af7340c5fa171c78bbfba1c07836

[ "MIT" ]

null

molot/context.py

sydspace/molot

61541817a189af7340c5fa171c78bbfba1c07836

[ "MIT" ]

null

import os import shutil import logging import urllib.request import subprocess from molot.builder import git_hash class Context: """Base context with common operations.""" def ensure_dir(self, path: str, keep_files: bool): """Ensures directory path exists. Arguments: path {str} -- Directory path. keep_files {bool} -- Keep existing files if already exists if true. """ if os.path.exists(path): if not keep_files: shutil.rmtree(path) os.makedirs(path) else: os.makedirs(path) def download_files( self, file_urls: dict, out_path: str, ignore_existing: bool = False ): """Downloads files into target directory. Arguments: file_urls {dict} -- Dict of filename => url for download. out_path {str} -- Output directory path. Keyword Arguments: ignore_existing {bool} -- Ignores existing files and re-downloads if true. (default: {False}) """ if not ignore_existing: existing_files = os.listdir(out_path) for f in existing_files: if f in file_urls: logging.info("Already exists %s", f) file_urls.pop(f, None) else: os.remove(os.path.join(out_path, f)) for filename in file_urls: url = file_urls[filename] logging.info("Downloading %s", url) urllib.request.urlretrieve(url, os.path.join(out_path, filename)) def add_git_hash(self, out_path: str): """Adds Git hash to output. Arguments: out_path {str} -- Output directory path. """ output = git_hash() logging.info("Writing Git hash %s", output) out_file_path = os.path.join(out_path, "git-hash") with open(out_file_path, "w") as file: file.write(output)

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0.572948

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4.637131

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0.036397

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0.099181

0.052775

0

0.33232

1,974

66

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0.085714

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0

0.171429

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0.285714

0

null

0

null

0

1

0

fa7fad14befa168a6dc9f22fdd5717589f466dc3

2,720

py

Python

process.py

ravsa/data_scraping

e7684030e1ff65537fc337f21057053df0e6add0

[ "Apache-2.0" ]

2

2018-02-18T18:48:44.000Z

2018-02-22T13:21:21.000Z

process.py

ravsa/data_scraping

e7684030e1ff65537fc337f21057053df0e6add0

[ "Apache-2.0" ]

1

2018-02-21T17:27:22.000Z

process.py

ravsa/data_scraping

e7684030e1ff65537fc337f21057053df0e6add0

[ "Apache-2.0" ]

2

2018-06-14T06:09:29.000Z

2019-01-08T18:41:19.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from base_functions import BaseFunctions class SpringIO(BaseFunctions): def __init__(self): self.sub_eco = 'spring' self._output_file = None super().__init__(self.sub_eco) def process(self): for item in self._config.get('dependencies', []): for version, content in item.items(): print("VERSION: ", version) for group, dependencies in content.items(): pkg_query = '&style=' + \ '&style='.join([dep['id'] for dep in dependencies]) resp = self.get_query_result(version, pkg_query) self.processed_data[group] += [dict(pkg, **{'categories': group, 'version': version}) for pkg in resp] print("GROUP: ", group) def __str__(self): return self.sub_eco class VertxIO(BaseFunctions): def __init__(self): self.sub_eco = 'vertx' self._output_file = None super().__init__(self.sub_eco) def process(self): for version in self._versions: print("VERSION: ", version) for item in self._config.get('dependencies', []): group = item.get('category') items = item.get('items') pkg_query = ','.join([it['artifactId'] for it in items if it.get('artifactId')]) resp = self.get_query_result(version, pkg_query) self.processed_data[group] += [dict(pkg, **{'categories': group, 'version': version }) for pkg in resp] print("GROUP: ", group) def __str__(self): return self.sub_eco class WildflyIO(BaseFunctions): def __init__(self): self.sub_eco = 'wildfly' self._output_file = None super().__init__(self.sub_eco) def process(self): for item in self._config.get('dependencies', []): group = item.get('category') pkg_query = '&d=' + \ ('&d='.join([frac['artifactId'] for frac in item.get('fractions')])) resp = self.get_query_result('', pkg_query) self.processed_data[group] += [dict(pkg, **{'categories': group}) for pkg in resp] print("GROUP: ", group) def __str__(self): return self.sub_eco

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0.086207

0

null

0

null

0

1

0

fa7fd567c727259e88fdafdda00e4c3ee2228b36

3,923

py

Python

recipes/lightgbm/all/conanfile.py

maksim-0/conan-center-index

4bf032cd73ed8f7bfe379dcd463430ec145b9e80

[ "MIT" ]

null

recipes/lightgbm/all/conanfile.py

maksim-0/conan-center-index

4bf032cd73ed8f7bfe379dcd463430ec145b9e80

[ "MIT" ]

null

recipes/lightgbm/all/conanfile.py

maksim-0/conan-center-index

4bf032cd73ed8f7bfe379dcd463430ec145b9e80

[ "MIT" ]

null

from conans import CMake, ConanFile, tools from conan.tools.microsoft import is_msvc import functools required_conan_version = ">=1.33.0" class LightGBMConan(ConanFile): name = "lightgbm" description = "A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks." topics = ("machine-learning", "boosting") url = "https://github.com/conan-io/conan-center-index" homepage = "https://github.com/microsoft/LightGBM" license = "MIT" settings = "os", "arch", "compiler", "build_type" exports_sources = ["CMakeLists.txt", "patches/**"] generators = "cmake", "cmake_find_package" options = { "shared": [True, False], "fPIC": [True, False], "with_openmp": [True, False] } default_options = { "shared": False, "fPIC": True, "with_openmp": True } @property def _source_subfolder(self): return "source_subfolder" def config_options(self): if self.settings.os == "Windows": del self.options.fPIC def configure(self): if self.options.shared: del self.options.fPIC def requirements(self): self.requires("eigen/3.4.0") self.requires("fast_double_parser/0.6.0") self.requires("fmt/8.1.1") if self.options.with_openmp and self.settings.compiler in ("clang", "apple-clang"): self.requires("llvm-openmp/11.1.0") def validate(self): if self.settings.compiler.get_safe("cppstd"): tools.check_min_cppstd(self, 11) def source(self): tools.get(**self.conan_data["sources"][self.version], destination=self._source_subfolder, strip_root=True) def _patch_sources(self): for patch in self.conan_data.get("patches", {}).get(self.version, []): tools.patch(**patch) @functools.lru_cache(1) def _configure_cmake(self): cmake = CMake(self) cmake.definitions["BUILD_STATIC_LIB"] = not self.options.shared cmake.definitions["USE_DEBUG"] = self.settings.build_type == "Debug" cmake.definitions["USE_OPENMP"] = self.options.with_openmp if self.settings.os == "Macos": cmake.definitions["APPLE_OUTPUT_DYLIB"] = True cmake.configure() return cmake def build(self): self._patch_sources() cmake = self._configure_cmake() cmake.build() def package(self): self.copy("LICENSE", dst="licenses", src=self._source_subfolder) cmake = self._configure_cmake() cmake.install() def package_info(self): self.cpp_info.set_property("cmake_file_name", "LightGBM") self.cpp_info.set_property("cmake_target_name", "LightGBM::LightGBM") # TODO: to remove in conan v2 once cmake_find_package* generators removed self.cpp_info.names["cmake_find_package"] = "LightGBM" self.cpp_info.names["cmake_find_package_multi"] = "LightGBM" self.cpp_info.libs = ["lib_lightgbm"] if is_msvc(self) else ["_lightgbm"] if self.settings.os == "Windows": self.cpp_info.system_libs.extend(["ws2_32", "iphlpapi"]) elif self.settings.os == "Linux": self.cpp_info.system_libs.append("pthread") if not self.options.shared and self.options.with_openmp: if is_msvc(self): openmp_flags = ["-openmp"] elif self.settings.compiler == "gcc": openmp_flags = ["-fopenmp"] elif self.settings.compiler in ("clang", "apple-clang"): openmp_flags = ["-Xpreprocessor", "-fopenmp"] else: openmp_flags = [] self.cpp_info.exelinkflags.extend(openmp_flags) self.cpp_info.sharedlinkflags.extend(openmp_flags)

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3,923

5.124464

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0.045226

0.041457

0.020101

0.19598

0.080402

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0

0.008059

0.240887

3,923

105

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37.361905

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0.018098

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0.225195

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false

0

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0.011494

0.321839

0

null

0

null

0

1

0

fa8744aeb897b1dd532ef4bcf6032ca2b74ba54a

2,574

py

Python

src/letter_classifier/take_pic.py

tomoya777773/KB_1815

fb71711113cc79cc93809f96799dfb1ba9f5b1ed

[ "MIT" ]

2

2018-10-18T17:38:49.000Z

2018-10-21T09:57:43.000Z

src/letter_classifier/take_pic.py

tomoya777773/KB_1815

fb71711113cc79cc93809f96799dfb1ba9f5b1ed

[ "MIT" ]

null

src/letter_classifier/take_pic.py

tomoya777773/KB_1815

fb71711113cc79cc93809f96799dfb1ba9f5b1ed

[ "MIT" ]

3

2018-10-20T03:33:07.000Z

2018-10-28T08:12:58.000Z

from argparse import ArgumentParser from pathlib import Path import time import numpy as np import cv2 from datetime import datetime import requests DIFF_THRESHOLD = 30 DEFFAULT_SLEEP = 1 LINE_ENDPOINT = 'https://uketori.herokuapp.com/important' VISION_ENDPOINT = 'https://southcentralus.api.cognitive.microsoft.com/customvision/v2.0/Prediction/2d6dff05-36fb-493e-a387-1093bbbb175b/image' TMP_DIR = Path('../../src/public/images/') vision_headers = { 'Prediction-Key': '', 'Content-Type': 'application/octet-stream', 'Prediction-Key': '4fdd8e3729b04880af66cdb52d0b5c73', } line_headers = {'Content-Type': 'application/json'} IMPORTANT_TAG = 'important' NOT_IMPORTANT_TAG = 'not_important' def detect_diff(img_before, img_after, diff_threthold=DIFF_THRESHOLD): ''' detect difference between two images. :param img_before: image to be compared. :param img_after: image to compare with. :return: two image is differ or not. ''' gray_before = cv2.cvtColor(img_before, cv2.COLOR_RGB2GRAY) gray_after = cv2.cvtColor(img_after, cv2.COLOR_RGB2GRAY) (width, height) = gray_before.shape pix_num = width * height diff = cv2.absdiff(gray_before, gray_after) mean_diff = np.sum(diff) / pix_num return mean_diff > diff_threthold def main(cam_device=0): if not TMP_DIR.exists(): TMP_DIR.mkdir(parents=True) cap = cv2.VideoCapture(cam_device) frame = None while True: frame_before = frame ret, frame = cap.read() if frame_before is None: frame_before = frame cv2.imshow('frame', frame) diff = detect_diff(frame_before, frame) if diff: print(True) now_time = datetime.now().strftime('%Y%m%d%H%M%S') target_name = '%s/%s.jpg' % (str(TMP_DIR), now_time) cv2.imwrite(target_name, frame) r =requests.post( VISION_ENDPOINT, data=open(target_name, "rb"), headers=vision_headers ).json() results = r['predictions'] result_tag = results[0]['tagName'] print(result_tag) if result_tag == IMPORTANT_TAG: payload = {'result': '%s.jpg' % now_time} requests.post(LINE_ENDPOINT, data=json.dumps(payload), header=line_headers) time.sleep(DEFFAULT_SLEEP) if __name__ == '__main__': parser = ArgumentParser() parser.add_argument('--cam_device', type=int, default=0) args = parser.parse_args() main(args.cam_device)

28.285714

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320

2,574

5.034375

0.425

0.014898

0.029795

0

0.02889

0.233489

2,574

90

143

28.6

0.787633

0.060995

0

0.032258

0

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0.171201

0.033487

0

1

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false

0

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0.225806

0.032258

0

null

0

null

0

1

0

fa88dadbcf6756dea815702084c2d113a57d337c

7,286

py

Python

aerosols/mie.py

loic-rossi/python-titan-aerosols

6c55be3c54e8078844b3268943d8451e378742d4

[ "MIT" ]

1

2020-11-25T03:12:57.000Z

aerosols/mie.py

loic-rossi/python-titan-aerosols

6c55be3c54e8078844b3268943d8451e378742d4

[ "MIT" ]

1

2021-12-10T17:15:26.000Z

2021-12-10T17:47:45.000Z

aerosols/mie.py

loic-rossi/python-titan-aerosols

6c55be3c54e8078844b3268943d8451e378742d4

[ "MIT" ]

1

2021-12-10T15:48:05.000Z

"""Mie module.""" import numpy as np NANG = 91 NMXX = 150e3 def mie_bohren_huffman(x, refrel, nang=NANG): """ Compute mie scattering based on Bohren and Huffman theory Parameters ---------- x: float Size parameter = k*radius = 2π/λ * radius (λ is the wavelength in the medium around the scatterers). refrel: float Refraction index (n in complex form for example: 1.5 + 0.02i. nang: int, optional Number of angles for S1 and S2 function in range from 0 to π/2. Returns ------- S1, S2: numpy.ndarray Function which correspond to the (complex) phase functions. Qext: Extinction efficiency. Qsca: Scattering efficiency. Qback: Backscatter efficiency. gsca: Asymmetry parameter. Raises ------ ValueError If the input argument are outside the validity range. Note ---- This file is converted from [mie.m](http://atol.ucsd.edu/scatlib/index.htm) Bohren and Huffman originally published the code in their book on light scattering. Source: http://scatterlib.googlecode.com/files/bhmie_herbert_kaiser_july2012.py """ # pylint: disable=too-many-locals if nang > 1_000: raise ValueError(f"Require NANG = {nang} <= 1000") if nang < 2: raise ValueError( f"Require NANG = {nang} > 1 in order to calculate scattering intensities") ang = .5 * np.pi / (nang - 1) mu = np.cos(np.arange(0, nang, 1) * ang) # Series expansion terminated after NSTOP terms # Logarithmic derivatives calculated from NMX on down xstop = x + 4 * np.power(x, 1 / 3) + 2 # xstop = x + 4 * np.power(x, 1/3) + 10 # Old form ymod = abs(x * refrel) nmx = np.fix(max(xstop, ymod) + 15) # BTD experiment 91/1/15: add one more term to series and compare results # NMX = AMAX1(XSTOP, YMOD) + 16 # test: compute 7001 wavelen > hs between .0001 and 1000 micron # for a = 1.0 micron SiC grain. When NMX increased by 1, only a single # computed number changed (out of 4*7001) and it only changed by 1/8387 # Conclusion: we are indeed retaining enough terms in series! if nmx > NMXX: raise ValueError(f"nmx = {nmx} > NMXX = {NMXX} for |m|x = {ymod}") s1_1 = np.zeros(nang, dtype=np.complex128) s1_2 = np.zeros(nang, dtype=np.complex128) s2_1 = np.zeros(nang, dtype=np.complex128) s2_2 = np.zeros(nang, dtype=np.complex128) pi = np.zeros(nang, dtype=np.complex128) tau = np.zeros(nang, dtype=np.complex128) pi0 = np.zeros(nang, dtype=np.complex128) pi1 = np.ones(nang, dtype=np.complex128) # Logarithmic derivative D(J) calculated by downward recurrence # beginning with initial value (0,0) at J = NMX nn = int(nmx) - 1 d = np.zeros(nn + 1, dtype=np.complex128) for n in range(0, nn): en = (nmx - n) / (x * refrel) d[nn - n - 1] = en - 1 / (d[nn - n] + en) # Riccati-Bessel functions with real argument X # calculated by upward recurrence an, bn = None, None psi0 = np.cos(x) psi1 = np.sin(x) chi0 = -np.sin(x) chi1 = np.cos(x) xi1 = psi1 - chi1 * 1j qsca = 0 gsca = 0 p = -1 nstop = int(xstop) for n in range(0, nstop): en = n + 1 fn = (2 * en + 1) / (en * (en + 1)) # for given N, PSI = psi_n CHI = chi_n # PSI1 = psi_{n-1} CHI1 = chi_{n-1} # PSI0 = psi_{n-2} CHI0 = chi_{n-2} # Calculate psi_n and chi_n psi = (2 * en - 1) * psi1 / x - psi0 chi = (2 * en - 1) * chi1 / x - chi0 xi = psi - chi * 1j # Store previous values of AN and BN for use # in computation of g=<np.cos(theta)> if n > 0: an1 = an bn1 = bn # Compute AN and BN: an = (d[n] / refrel + en / x) * psi - psi1 an = an / ((d[n] / refrel + en / x) * xi - xi1) bn = (refrel * d[n] + en / x) * psi - psi1 bn = bn / ((refrel * d[n] + en / x) * xi - xi1) # Augment sums for Qsca and g=<np.cos(theta)> qsca += (2 * en + 1) * (abs(an) ** 2 + abs(bn) ** 2) gsca += ((2 * en + 1) / (en * (en + 1))) * \ (np.real(an) * np.real(bn) + np.imag(an) * np.imag(bn)) if n > 0: gsca += ((en - 1) * (en + 1) / en) * \ (np.real(an1) * np.real(an) + np.imag(an1) * np.imag(an) + np.real(bn1) * np.real(bn) + np.imag(bn1) * np.imag(bn)) # Now calculate scattering intensity pattern # First do angles from 0 to 90 pi = np.copy(pi1) tau = en * mu * pi - (en + 1) * pi0 s1_1 += fn * (an * pi + bn * tau) s2_1 += fn * (an * tau + bn * pi) # Now do angles greater than 90 using PI and TAU from # angles less than 90. # P=1 for N=1,3,...% P=-1 for N=2,4,... # remember that we have to reverse the order of the elements # of the second part of s1 and s2 after the calculation p = -p s1_2 += fn * p * (an * pi - bn * tau) s2_2 += fn * p * (bn * pi - an * tau) psi0 = psi1 psi1 = psi chi0 = chi1 chi1 = chi xi1 = psi1 - chi1 * 1j # Compute pi_n for next value of n # For each angle J, compute pi_n+1 # from PI = pi_n , PI0 = pi_n-1 pi1 = ((2 * en + 1) * mu * pi - (en + 1) * pi0) / en pi0 = np.copy(pi) # Have summed sufficient terms. # Now compute QSCA, QEXT, QBACK and GSCA # We have to reverse the order of the elements of the second part of s1 and s2 s1 = np.concatenate((s1_1, s1_2[-2::-1])) s2 = np.concatenate((s2_1, s2_2[-2::-1])) gsca = 2 * gsca / qsca qsca = 2 / x ** 2 * qsca qext = 4 / x ** 2 * np.real(s1[0]) # More common definition of the backscattering efficiency, # so that the backscattering cross section really # has dimension of length squared qback = 4 * (abs(s1[2 * (nang - 1)]) / x) ** 2 # qback = ((abs( s1[2 * nang - 2])/x )**2 )/np.pi # Old form return s1, s2, qext, qsca, qback, gsca def mie(wvln, nr, ni, r, nang=NANG): """Compute Mie cross-sections and phase function based on Bohren and Huffman theory. Parameters ---------- wvln: float Wavelength (m). nr: float Particle real optical index. ni: float Particle real imaginary index. r: float Particle radius (m). nang: int, optional Number of angles for the phase function (range from 0 to π/2) Returns ------- qsct: float Scattering cross section (m^-2). qext: float Extinction cross section (m^-2). qabs: float Absorption cross section (m^-2). gg: float Asymmetry parameter. theta: numpy.ndarray Phase function angles (radians). P: numpy.ndarray Phase function. """ Xm = 2 * np.pi * r / wvln s1, s2, Qe, Qs, _, gg = mie_bohren_huffman(Xm, complex(nr, ni), nang) qsct = Qs * np.pi * r ** 2 qext = Qe * np.pi * r ** 2 qabs = qext - qsct S11 = .5 * (abs(s2) ** 2 + abs(s1) ** 2) theta = np.linspace(0, np.pi, len(s1)) norm = .5 * np.trapz(S11 * np.sin(theta), x=theta) P = S11 / norm return qsct, qext, qabs, gg, theta, P

30.742616

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null

0

null

0

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0

fa8b02af1478c1f96b1b0bc909f7b0a37212823b

2,361

py

Python

edx_course_team_api/views.py

ibm-skills-network/edx-course-team-api

662a756f61047bd2f1c10ede2feb6f1a24c2717d

[ "MIT" ]

null

edx_course_team_api/views.py

ibm-skills-network/edx-course-team-api

662a756f61047bd2f1c10ede2feb6f1a24c2717d

[ "MIT" ]

null

edx_course_team_api/views.py

ibm-skills-network/edx-course-team-api

662a756f61047bd2f1c10ede2feb6f1a24c2717d

[ "MIT" ]

null

import logging import json from rest_framework.views import APIView from rest_framework.response import Response from rest_framework.exceptions import ParseError from rest_framework import status from rest_framework.authentication import BasicAuthentication from rest_framework.permissions import IsAuthenticated from django.contrib.auth.models import User # edx imports from student import auth from student.models import CourseEnrollment from student.roles import CourseInstructorRole, CourseStaffRole from opaque_keys.edx.keys import CourseKey from cms.djangoapps.contentstore.views.user import _course_team_user log = logging.getLogger(__name__) USERNAME = 'admin' # the user who will be associated with new courses ROLE_TYPE_MAPPINGS = { "staff": CourseStaffRole, "instructor": CourseInstructorRole } ROLE_OPTIONS = list(ROLE_TYPE_MAPPINGS.keys()) class CourseView(APIView): authentication_classes = [BasicAuthentication] permission_classes = [IsAuthenticated] def post(self, request, course_key_string): course_key = CourseKey.from_string(course_key_string) email = request.data.get("email", None) if not email: msg = { "error": "Missing parameter 'email' in body." } log.info(msg) raise ParseError(msg) role = request.data.get("role", None) if not role: msg = { "error": "Missing parameter 'role' in body." } log.info(msg) raise ParseError(msg) if role not in ROLE_OPTIONS: msg = { "error": "Parameter 'role' has to be one of '{}'".format(ROLE_OPTIONS) } log.info(msg) raise ParseError(msg) try: user = User.objects.get(email=email) except Exception: # pylint: disable=broad-except msg = { "error": "Could not find user by email address '{email}'".format(email=email) } return Response(msg, 404) role_type = ROLE_TYPE_MAPPINGS.get(role)(course_key) auth.add_users(request.user, role_type, user) CourseEnrollment.enroll(user, course_key) msg = "'{email}' is granted '{role}' to '{course_key}'".format(email=email, role=role, course_key=course_key) log.info(msg) return Response({'message': "User is added to {}.".format(course_key)})

33.253521

117

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5.54417

0.371025

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0.06501

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0.061185

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0

0.001639

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2,361

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118

33.728571

0.855738

0.037696

0

0.132075

0

0.120811

0

1

0.018868

false

0

0.264151

0

0.377358

0

null

0

null

0

1

0

fa8fc5d3e55cdf2ceecce40af28055451e8b8a11

927

py

Python

Markov.py

unrealTOM/MC

5a4cdf1ee11ef3d438f24dd38e894731103448ac

[ "MIT" ]

4

2020-04-11T09:54:27.000Z

2021-08-18T07:06:52.000Z

Markov.py

unrealTOM/MC

5a4cdf1ee11ef3d438f24dd38e894731103448ac

[ "MIT" ]

null

Markov.py

unrealTOM/MC

5a4cdf1ee11ef3d438f24dd38e894731103448ac

[ "MIT" ]

5

2019-01-22T03:47:17.000Z

2022-02-14T18:09:07.000Z

# https://zhuanlan.zhihu.com/p/25610149 import numpy as np import matplotlib.pyplot as plt import math def p(x): #standard normal mu=0 sigma=1 return 1/(math.pi*2)**0.5/sigma*np.exp(-(x-mu)**2/2/sigma**2) #uniform proposal distribution on [-4,4] def q(x): #uniform return np.array([0.125 for i in range(len(x))]) x = np.linspace(-4,4,500) M = 3.5 N=1000 #number of samples needed i = 1 count = 0 X = np.array([]) while i < N: u = np.random.rand(10) #evaluate 10 each loop x = (np.random.rand(10)-0.5)*8 res = u < p(x)/q(x)/M if any(res): X = np.hstack((X,x[res])) i+=len(x[res]) count+=10 count -= len(X) - 1000 X=X[:1000] x = np.linspace(-4,4,500) plt.plot(x,p(x)) plt.hist(X,bins=100,density=True) plt.title('Rejection Sampling') plt.show() plt.savefig('result.png', dpi=100) print (N/count) #proportion of raw sample used

21.55814

66

0.594391

172

927

3.203488

0.482558

0.027223

0.039927

0.043557

0.058076

0

0.091413

0.221143

927

42

67

22.071429

0.671745

0.185545

0

0.0625

0

0.039716

0

1

0.0625

false

0

0.09375

0.03125

0.21875

0.03125

0

null

0

null

0

1

0

fa92751ba744314eb889ed22b1f78c58218f77cd

4,573

py

Python

utils/awrams/utils/messaging/general.py

Zac-HD/awra_cms

ebc51df859ee665d936cf9600ea29dc8e45321d7

[ "NetCDF" ]

2

2020-05-10T05:27:16.000Z

2021-01-20T02:14:23.000Z

utils/awrams/utils/messaging/general.py

Zac-HD/awra_cms

ebc51df859ee665d936cf9600ea29dc8e45321d7

[ "NetCDF" ]

null

utils/awrams/utils/messaging/general.py

Zac-HD/awra_cms

ebc51df859ee665d936cf9600ea29dc8e45321d7

[ "NetCDF" ]

2

2019-12-26T13:36:44.000Z

2020-03-24T12:23:23.000Z

from io import StringIO import traceback import numpy as np import zmq import errno import uuid import logging from awrams.utils.metatypes import ObjectDict as o import subprocess class Chunk: def __init__(self,x,y): self.x = x self.y = y self.shape = (1,self.y.stop - self.y.start) def __repr__(self): return str((self.x,self.y)) def contains(self,cell): if cell[0] == self.x: if cell[1] >= self.y.start and cell[1] < self.y.stop: return True return False def idx(self,cell): ''' Warning - this doesn't check for validity, only passes back what it thinks is a local index ''' return cell[1]-self.y.start NULL_CHUNK = Chunk(-1,slice(-1,-1)) def gen_ipc_handle(prefix='awra'): suffix = uuid.uuid4().hex return "ipc:///tmp/" + prefix + '_' + suffix def send_array(socket, A, flags=0, copy=False, track=True): """send a numpy array with metadata""" md = dict( dtype = str(A.dtype), shape = A.shape, ) socket.send_pyobj(md, flags|zmq.SNDMORE) return socket.send(A, flags, copy=copy, track=track) def recv_array(socket, flags=0, copy=False, track=True): """recv a numpy array""" md = socket.recv_pyobj(flags=flags) msg = socket.recv(flags=flags, copy=copy, track=track) buf = msg A = np.frombuffer(buf, dtype=md['dtype']) return A.reshape(md['shape']) def message(subject,**kwargs): m = dict(subject=subject,content=dict()) m['content'].update(kwargs) return m def get_traceback(): import sys sio = StringIO() traceback.print_exc(file=sio) tb_text = sio.getvalue() tb = sys.exc_info()[2] while 1: if not tb.tb_next: break tb = tb.tb_next frame = tb.tb_frame tb_text += "Locals:\n" for key, value in list(frame.f_locals.items()): if key.startswith('__'): tb_text += '%s (skipped)\n'%(key) continue try: lstr = " %s = %s" % (key,value) except: lstr = " %s (unprintable)" % (key) tb_text += lstr + '\n' return tb_text ''' Support classes and methods ''' class ZMQLogger(logging.Handler): def __init__(self, socket, *args, **kwargs): logging.Handler.__init__(self, *args, **kwargs) self.socket = socket def emit(self,record): ''' Send a message to the socket ''' try: self.socket.send_pyobj(message('log_message',content=record.getMessage(),level=record.levelno)) except zmq.ZMQError as e: if e.errno == errno.EINTR: #+++ System interrupt, retry self.emit(record) else: raise class MPLogger: def __init__(self,queue): self.queue = queue def write(self,msg): self.queue.put(message('log_message',content=msg)) def flush(self): pass class QueingLogHandler(logging.Handler): def __init__(self, queue, *args, **kwargs): logging.Handler.__init__(self, *args, **kwargs) self.queue = queue def emit(self,record): self.queue.put(message('log_message',content=record.getMessage(),level=record.levelno)) class QueuedLogCollector(object): def __init__(self,queue): self.reset() self.queue = queue def reset(self): self.messages = { 'debug': [], 'info': [], 'warning': [], 'error': [], 'critical': [], } def harvest(self): while not self.queue.empty(): record = self.queue.get() self.messages[record.levelname.lower()].append(record.getMessage()) def configure_logging_to_zmq_client(channel): return _configure_client_logging(ZMQLogger(channel),True) def configure_logging_to_mp_client(queue): return _configure_client_logging(QueingLogHandler(queue)) def _configure_client_logging(handler,format=False): from awrams.utils.settings import LOGFORMAT #pylint: disable=no-name-in-module import logging import awrams.utils.awrams_log logger = awrams.utils.awrams_log.establish_logging() #client_logger = log_writer #handler = logging.StreamHandler(client_logger) #if format: # handler.setFormatter(logging.Formatter(LOGFORMAT)) logger.addHandler(handler) return handler def term(msg): ''' Terminator handler for Managed listeners ''' return 1 def term_print(msg): print(msg) return 1

25.405556

107

0.609009

578

4,573

4.676471

0.32526

0.033296

0.020348

0.011099

0.173881

0.112468

0.09471

0.076952

0

0.004452

0.263285

4,573

179

108

25.547486

0.797863

0.089657

0

0.121951

0

0.03679

0

1

0.195122

false

0.00813

0.105691

0.02439

0.455285

0.03252

0

null

0

null

0

1

0

fa933e3a2220b0a9dd004d7e37da742b1bfa7167

1,941

py

Python

backend/apps/users/admin.py

abodacs/django-fullstack-biolerplate

87e8618638eb801fd061c34da9365ff50bebdf77

[ "MIT" ]

null

backend/apps/users/admin.py

abodacs/django-fullstack-biolerplate

87e8618638eb801fd061c34da9365ff50bebdf77

[ "MIT" ]

null

backend/apps/users/admin.py

abodacs/django-fullstack-biolerplate

87e8618638eb801fd061c34da9365ff50bebdf77

[ "MIT" ]

null

from django.contrib import admin from django.contrib.auth import admin as auth_admin from django.contrib.auth import get_user_model from django.urls import reverse from django.utils.html import format_html, urlencode from django.utils.translation import ugettext_lazy as _ from apps.projects.models import Case from apps.users.forms import EnvoyChangeForm, UserChangeForm, UserCreationForm from .models import Envoy User = get_user_model() @admin.register(User) class UserAdmin(auth_admin.UserAdmin): form = UserChangeForm add_form = UserCreationForm fieldsets = ( (None, {"fields": ("username", "password")}), (_("Personal info"), {"fields": ("name", "type",)}), (_("Permissions"), {"fields": ("is_active", "is_superuser",),}), (_("Important dates"), {"fields": ("last_login",)}), ) list_filter = ( "is_active", "type", ) list_display = [ "username", "name", "type", ] search_fields = ["name"] @admin.register(Envoy) class EnvoyAdmin(auth_admin.UserAdmin): form = EnvoyChangeForm add_form = UserCreationForm list_filter = ("is_active",) fieldsets = ( (None, {"fields": ("username", "password")}), (_("Personal info"), {"fields": ("name", "type", "areas_in_charge", "mobile",)}), (_("Permissions"), {"fields": ("is_active",)}), (_("Important dates"), {"fields": ("last_login",)}), ) list_display = [ "username", "name", "type", "mobile", "show_cases_number", ] search_fields = ["name"] def show_cases_number(self, obj): url = reverse("admin:projects_case_changelist") + "?" + urlencode({"envoy_id": f"{obj.id}"}) count = Case.objects.filter(envoy=obj).only("id").count() return format_html('<a href="{}">{} Cases</a>', url, count) show_cases_number.short_description = _("Cases Number")

29.861538

100

0.619268

207

1,941

5.589372

0.376812

0.051858

0.04408

0.038029

0.26102

0.217805

0.105445

0

0.214323

1,941

64

101

30.328125

0.758689

0

0.351852

0

0.214323

0.015456

0

1

0.018519

false

0.037037

0.203704

0

0.5

0

null

0

null

0

1

0

fa936cb125d27698927a89093b09a059c5d97cd9

931

py

Python

gfootball/agent.py

level-antoine/football

516f63da0ea4696f4c8b6668c65ac4b20385a8fa

[ "Apache-2.0" ]

null

gfootball/agent.py

level-antoine/football

516f63da0ea4696f4c8b6668c65ac4b20385a8fa

[ "Apache-2.0" ]

null

gfootball/agent.py

level-antoine/football

516f63da0ea4696f4c8b6668c65ac4b20385a8fa

[ "Apache-2.0" ]

1

2022-03-02T14:01:00.000Z

import time import gfootball.env as football_env import random class Agent: def __init__(self): pass if __name__ == '__main__': env = football_env.create_environment(env_name='1_vs_1_easy', representation='extracted', render=True) state = env.reset() action_simple = football_env.observation_preprocessing.football_action_set.action_set_dict["simple"] obs = env.reset() while True: action = random.choice(action_simple) observation, reward, done, info = env.step(action) print('-----------------------------------------') i = 1 for obs in observation: print(i) print(obs) i += 1 time.sleep(1000000000) print(reward) print(done) print(info) print('-----------------------------------------') if done: env.reset() env.close()

23.275

106

0.531686

96

931

4.875

0.489583

0.070513

0

0.021212

0.291085

931

40

107

23.275

0.687879

0

0.071429

0

0.124464

0.087983

0

1

0.035714

false

0.035714

0.107143

0

0.178571

0.25

0

null

0

null

0

1

0

fa94064062b6a06f6e48c481bf507660eeb96adc

2,724

py

Python

utils.py

reutwerber/DeepLearningHW

89d1459139b8da294a303dbbe98fc4a48882fb56

[ "MIT" ]

null

utils.py

reutwerber/DeepLearningHW

89d1459139b8da294a303dbbe98fc4a48882fb56

[ "MIT" ]

null

utils.py

reutwerber/DeepLearningHW

89d1459139b8da294a303dbbe98fc4a48882fb56

[ "MIT" ]

null

import numpy as np import seaborn as sns import matplotlib.pyplot as plt # plot edible / poisonous def basic_plots(data): plt.figure(figsize=(15, 8)) fig, ax = plt.subplots() sns.countplot(x=data['odor'], hue=data['is-edible'], palette=['black', 'blue'], data=data) plt.ylabel('Number of Mushrooms') plt.legend(title=None, labels=['Poisonous', 'Edible']) plt.title("Edible vs. Poisonous, Sorted by Odor") plt.savefig("edible_vs_poisonous2_odor.png") new_data = data.loc[data["odor"] != "n"] new_data = new_data.loc[data["odor"] != "f"] # print(data["odor"].value_counts()) # there is only 1 value with odor m, so we will not use it. new_data = new_data.loc[data["odor"] != "m"] fig, ax = plt.subplots() plt.title('Odors: Edible vs Poisonous, Partial Results') sns.countplot(x=new_data['odor'], hue=new_data['is-edible'], palette=['black', 'blue'], data=new_data) plt.ylabel('Number of Mushrooms') plt.legend(title=None, labels=['Poisonous', 'Edible']) plt.savefig("edible_vs_poisonous3_odor_partial.png") def check_veil_type_is_zero(df): flag = False for item in df['veil-type']: if item != 0: flag = True print(item) print(flag) def plot_corr(df): # Correlation plt.figure(figsize=(14, 12)) sns.heatmap(df.corr(), linewidths=.1, cmap="Blues", annot=True, annot_kws={"size": 7}) plt.yticks(rotation=0) plt.savefig("corr.png", format='png', dpi=400, bbox_inches='tight') def plot_feature_importance(features_list, feature_importance): sorted_idx = np.argsort(feature_importance) plt.figure(figsize=(15, 8)) plt.barh(range(len(sorted_idx)), feature_importance[sorted_idx], align='center', color="blue") plt.yticks(range(len(sorted_idx)), features_list[sorted_idx], fontsize=12) plt.xlabel('Importance') plt.title('Attribute importance') plt.savefig("featureimp.png", format='png', dpi=500, bbox_inches='tight') def plot_confusion_matrix(cm, title): x_axis_labels = odor_labels y_axis_labels = x_axis_labels f, ax = plt.subplots(figsize=(7, 7)) hm_plot = sns.heatmap(cm, annot=True, linewidths=0.2, linecolor="black", fmt=".0f", ax=ax, cmap="Blues", xticklabels=x_axis_labels, yticklabels=y_axis_labels) hm_plot.set_xticklabels(hm_plot.get_xmajorticklabels(), fontsize=9) hm_plot.set_yticklabels(hm_plot.get_ymajorticklabels(), fontsize=9) plt.xlabel("PREDICTED LABEL") plt.ylabel("TRUE LABEL") plt.title(title) plt.savefig(title+'.png', format='png', dpi=500, bbox_inches='tight') odor_labels = ["almond", "anise", "creosote", "fishy", "foul", "none", "pungent", "spicy"]

37.833333

106

0.669971

389

2,724

4.534704

0.367609

0.031746

0.027211

0.02551

0.221655

0.183673

0.119048

0.081633

0

0.015432

0.167401

2,724

72

107

37.833333

0.762346

0.04699

0

0.148148

0

0.178241

0.025463

0

1

0.092593

false

0

0.148148

0

0.240741

0.037037

0

null

0

null

0

1

0

fa942d0bf0283b06c38a2f2f91cc6d29defda50a

3,499

py

Python

fdisk.py

lypant/diskutils

8ebc84dcb9bedd591b4d4484c9858fb71eaaa709

[ "MIT" ]

null

fdisk.py

lypant/diskutils

8ebc84dcb9bedd591b4d4484c9858fb71eaaa709

[ "MIT" ]

null

fdisk.py

lypant/diskutils

8ebc84dcb9bedd591b4d4484c9858fb71eaaa709

[ "MIT" ]

null

#!/usr/bin/env python3 class Fdisk(): ''' Composes bash command string capable of creating partitions for given disk ''' def __init__(self, disk): self.disk = disk self.query = '' self.partitionsCount = 0 def addPrimaryPartition(self, size, type): self.partitionsCount += 1 # Add new partition... self.query += 'n\n' # ...primary one... self.query += 'p\n' # ...pick default number... self.query += '\n' # ...choose default starting sector... self.query += '\n' # ...set partition size self.query += '%s\n' % size # Set new partition type... self.query += 't\n' # ...for partitons other than 1 it is necessary to give the partition number if self.partitionsCount != 1: self.query += '\n' # Use default number # ...set partition type self.query += '%s\n' % type def addExtendedPartition(self): ''' Assumption - 3 primary partitions already exist ''' # Add new partition... self.query += 'n\n' # ...extended one - necessary to select explicitly self.query += 'e\n' # ...pick partition number - done automatically for partition >=4 # ...choose default starting sector... self.query += '\n' # ...set partition size self.query += '\n' # Take all available space for container partition # No need to select partition type explicitly def addLogicalPartition(self, size, type): ''' Assumption - 3 primary and fourth extended partitions already exist ''' # Add new partition... self.query += 'n\n' # ...no need to select partition number explicitly # ...choose default starting sector... self.query += '\n' # ...set partition size self.query += '%s\n' % size # Set new partition type self.query += 't\n' # ...choose partition number... self.query += '\n' # Use default number # ...set partition type self.query += '%s\n' % type def setPartitionBootable(self, partitionNumber): # Toggle bootable flag of a partition... self.query += 'a\n' if self.partitionsCount != 1: self.query += '%s\n' % str(partitionNumber) def writePartitionTable(self): self.query += 'w\n' def getBashCommandString(self): # TODO Check if "cat and EOFs" are needed when using python return 'cat <<-EOF | fdisk {disk}\n{query}EOF'.format( disk=self.disk, query=self.query) if __name__ == '__main__': fd = Fdisk('/dev/sda') # fd.addPrimaryPartition('+128M', '83') # fd.addPrimaryPartition('+80G', '83') # fd.addPrimaryPartition('+6G', '82') # fd.setPartitionBootable(1) # fd.writePartitionTable() # fd.addPrimaryPartition('+128M', '83') # fd.setPartitionBootable(1) # fd.addPrimaryPartition('+80G', '83') # fd.addPrimaryPartition('+6G', '82') # fd.writePartitionTable() fd.addPrimaryPartition('+128M', '83') fd.addPrimaryPartition('+80G', '83') fd.addPrimaryPartition('+6G', '82') fd.addExtendedPartition() fd.addLogicalPartition('+20G', '83') fd.addLogicalPartition('+20G', '83') fd.addLogicalPartition('', '83') fd.setPartitionBootable(1) fd.writePartitionTable() print(fd.getBashCommandString())

31.809091

84

0.573021

373

3,499

5.343164

0.294906

0.103864

0.050176

0.027597

0.52283

0.455595

0.392373

0.344706

0.318113

0

0.023191

0.285224

3,499

109

85

32.100917

0.773691

0.402972

0

0.4

0

0.07333

0

0.009174

0

1

0.14

false

0

0.02

0.18

0.02

0

null

0

null

0

1

0

fa9aadc524b7e245b90d1790419586ddf6c23a3b

329

py

Python

Hackerearth Set/PalindromicCiphers.py

Siddharth2016/PYTHON3_prog

9dfa258d87f5b00779d39d9de9a49c1c6cea06be

[ "MIT" ]

2

2019-02-26T14:06:53.000Z

2019-02-27T17:13:01.000Z

Hackerearth Set/PalindromicCiphers.py

Siddharth2016/PYTHON3_prog

9dfa258d87f5b00779d39d9de9a49c1c6cea06be

[ "MIT" ]

null

Hackerearth Set/PalindromicCiphers.py

Siddharth2016/PYTHON3_prog

9dfa258d87f5b00779d39d9de9a49c1c6cea06be

[ "MIT" ]

2

2017-12-26T07:59:57.000Z

2018-06-24T03:35:05.000Z

#PALINDROMIC CIPHERS def value(s): i = 0 prod = 1 for i in range(len(s)): prod = prod * (ord(s[i])-96) return prod test = int(input()) for i in range(test): string = str(input()) stringr = string[::-1] if string == stringr: print("Palindrome") else: print(value(string))

18.277778

36

0.544073

46

329

3.891304

0.565217

0.022346

0.067039

0.122905

0

0.021739

0.300912

329

17

37

19.352941

0.756522

0.057751

0

0.032362

0

1

0.071429

false

0

0.142857

0

null

0

null

0

1

0

fa9c90bfa6f98eb6da005c0a9634a34f33cec44f

2,897

py

Python

pytorch_disco/nets/metric_learner.py

YunchuZhang/Visually-Grounded-Library-of-Behaviors-for-Generalizing-Manipulation-Across-Objects-Configurations-

896afda942dfc04e4aaad2ee751c32df1eb17913

[ "MIT" ]

1

2022-03-14T22:25:17.000Z

pytorch_disco/nets/metric_learner.py

YunchuZhang/Visually-Grounded-Library-of-Behaviors

896afda942dfc04e4aaad2ee751c32df1eb17913

[ "MIT" ]

null

pytorch_disco/nets/metric_learner.py

YunchuZhang/Visually-Grounded-Library-of-Behaviors

896afda942dfc04e4aaad2ee751c32df1eb17913

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from archs.encoder3D import SimpleEncoder3D import numpy as np class MetricLearner(nn.Module): def __init__(self, num_objects=150, repr_dim=128): super(MetricLearner, self).__init__() self.pred_dim = repr_dim # We need a Siamese style network self.encoder1 = SimpleEncoder3D(in_channel=32, pred_dim=128, chans=32).cuda() self.encoder2 = SimpleEncoder3D(in_channel=32, pred_dim=128, chans=32).cuda() # This size (first dimension) could be num_objects (if we want to learn feature per object) [seems correct to me] # num_tasks or even completely independent num_clusters (how to choose idx key in this case?) self.repr = nn.Embedding(num_objects, repr_dim).cuda() # Initialize weights to extremely small values with mean 0 and std 0.001 self.repr.weight.data.normal_(mean=0,std=0.001) # We assume that inputs have both positive labels and negative labels # We assume that inputs consists of one positive sample and B-1 negative samples def forward(self, feat_tensors, labels): # We assume that positive sample is at index 0 # And other samples are negative samples # Inputs is (1, H, W, B, C) # Output is (1, F) pos_feat = self.encoder1(feat_tensors[0].unsqueeze(0)) # Inputs is (B-1, H, W, B, C) # Output is (B-1, F) neg_feat = self.encoder2(feat_tensors[1:]) # feats is (B, F) feats = torch.cat([pos_feat, neg_feat], dim=0) if len(feats.shape) > 2: feats = torch.squeeze(feats) import ipdb; ipdb.set_trace() # We need to normalize the output of encoders norm = feats.norm(p=2, dim=1, keepdim=True) feats_normalized = feats.div(norm) loss = self.compute_loss(feats_normalized, labels, loss_type="n-class") return loss, feats_normalized # Note: feats contains both negative and positive feats # feats[0] belongs to the positive sample # labels[0] is the key of positive sample # we are learning embedding per object. We should use object labels and not object category labels here! def compute_loss(self, feats, object_labels, loss_type, temp=1): loss = 0 if loss_type == "n-class": import ipdb; ipdb.set_trace() indices = self.repr(object_labels) assert len(feats) == 2, "feats tensor should have shape (batch_size, pred_dim)" prod = torch.bmm(feats.view(-1, 1, self.pred_dim), indices.view(-1, self.pred_dim, 1))/temp max_val = torch.max(prod) prod = prod - max_val softmax = torch.div(torch.exp(prod[0]), torch.sum(torch.exp(prod))) loss = -1 * torch.log(softmax) loss = torch.squeeze(loss) return loss

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0.351351

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null

0

null

0

1

0

fa9d616c56bfe36fe6cf8e62f1d946df70dc71a6

528

py

Python

script/run_default_lstm.py

datadrivenempathy/who-wrote-this-training

66ccb65837de24d004b70d39a1e8522a7d1e184f

[ "MIT" ]

1

2019-08-24T04:21:15.000Z

script/run_default_lstm.py

datadrivenempathy/who-wrote-this-training

66ccb65837de24d004b70d39a1e8522a7d1e184f

[ "MIT" ]

8

2020-01-28T22:42:36.000Z

2022-02-10T00:21:57.000Z

script/run_default_lstm.py

datadrivenempathy/who-wrote-this-training

66ccb65837de24d004b70d39a1e8522a7d1e184f

[ "MIT" ]

null

import harness_util harness_factory = harness_util.TemplateHarnessFactory() config = { 'corpusCol': 'description', 'lstmSize': 64, 'dropoutRate': 0, 'kernelRegPenalty': 0.01, 'method': 'sequence', 'numWords': 2000, 'sourceCol': 'source', 'sourceIdCol': 'sourceId', 'sourceIdVectorCol': 'sourceIdVector', 'tokenVectorCol': 'tokenVector', 'tokensCol': 'tokens', 'maxSeqLen': 50 } harness = harness_factory.build(config) harness.run('who-wrote-this', 'desc-lstm-size-2', config)

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null

0

null

0

1

0

fa9fa525a3a637ac331509b0f67a63c258937a06

9,124

py

Python

pycloud/pycloud/model/message.py

SEI-AMS/pycloud

62764e9d2aae280e019306e3b151b7218bf82f4d

[ "MIT" ]

14

2015-08-20T11:54:56.000Z

2018-05-23T21:07:44.000Z

pycloud/pycloud/model/message.py

SEI-TAS/pycloud

62764e9d2aae280e019306e3b151b7218bf82f4d

[ "MIT" ]

10

2015-10-17T07:33:54.000Z

2018-04-27T20:50:52.000Z

pycloud/pycloud/model/message.py

SEI-AMS/pycloud

62764e9d2aae280e019306e3b151b7218bf82f4d

[ "MIT" ]

8

2016-03-31T07:04:26.000Z

2018-04-09T18:08:10.000Z

# KVM-based Discoverable Cloudlet (KD-Cloudlet) # Copyright (c) 2015 Carnegie Mellon University. # All Rights Reserved. # # THIS SOFTWARE IS PROVIDED "AS IS," WITH NO WARRANTIES WHATSOEVER. CARNEGIE MELLON UNIVERSITY EXPRESSLY DISCLAIMS TO THE FULLEST EXTENT PERMITTEDBY LAW ALL EXPRESS, IMPLIED, AND STATUTORY WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT OF PROPRIETARY RIGHTS. # # Released under a modified BSD license, please see license.txt for full terms. # DM-0002138 # # KD-Cloudlet includes and/or makes use of the following Third-Party Software subject to their own licenses: # MiniMongo # Copyright (c) 2010-2014, Steve Lacy # All rights reserved. Released under BSD license. # https://github.com/MiniMongo/minimongo/blob/master/LICENSE # # Bootstrap # Copyright (c) 2011-2015 Twitter, Inc. # Released under the MIT License # https://github.com/twbs/bootstrap/blob/master/LICENSE # # jQuery JavaScript Library v1.11.0 # http://jquery.com/ # Includes Sizzle.js # http://sizzlejs.com/ # Copyright 2005, 2014 jQuery Foundation, Inc. and other contributors # Released under the MIT license # http://jquery.org/license import datetime from pycloud.pycloud.mongo import Model, ObjectID ################################################################################################################ # Represents a mailbox where messages to devices can be put. ################################################################################################################ class DeviceMessage(Model): # Meta class is needed so that minimongo can map this class onto the database. class Meta: collection = "messages" external = ['device_id', 'service_id', 'message', 'params'] mapping = { } ################################################################################################################ # Constructor. ################################################################################################################ def __init__(self, *args, **kwargs): self.device_id = None self.service_id = None self.message = None self.params = {} self.datetime = datetime.datetime.now() self.read = False super(DeviceMessage, self).__init__(*args, **kwargs) ################################################################################################################ # Locate a CommandMailbox by its ID. ################################################################################################################ @staticmethod def by_id(item_id=None): record_id = item_id if not isinstance(record_id, ObjectID): # noinspection PyBroadException try: record_id = ObjectID(record_id) except: return None return DeviceMessage.find_one({'_id': record_id}) ################################################################################################################ # Locate a device by its pubilc device ID ################################################################################################################ # noinspection PyBroadException @staticmethod def by_device_id(did=None): messages = [] try: message_cursor = DeviceMessage.find({'device_id': did}) for message in message_cursor: messages.append(message) except: pass return messages ################################################################################################################ # Locate a device by its pubilc device ID ################################################################################################################ # noinspection PyBroadException @staticmethod def unread_by_device_id(device_id, service_id): messages = [] try: message_cursor = DeviceMessage.find({'device_id': device_id, 'service_id': service_id, 'read': False}) for message in message_cursor: return_message = {} return_message['device_id'] = message['device_id'] return_message['service_id'] = message['service_id'] return_message['message'] = message['message'] return_message['params'] = message['params'] messages.append(return_message) except: pass return messages ################################################################################################################ # ################################################################################################################ # noinspection PyBroadException @staticmethod def mark_all_as_read(device_id, service_id): messages = [] try: message_cursor = DeviceMessage.find({'device_id': device_id, 'service_id': service_id, 'read': False}) for message in message_cursor: message['read'] = True message.save() except: pass return messages ################################################################################################################ # Cleanly and safely gets a CommandMailbox and removes it from the database. ################################################################################################################ @staticmethod def find_and_remove(item_id): # Find the right app and remove it. find_and_modify will only return the document with matching id. return DeviceMessage.find_and_modify(query={'_id': item_id}, remove=True) ################################################################################################################ # Removes all messages of a certain type for a given device. ################################################################################################################ # noinspection PyBroadException @staticmethod def clear_all_messages(device_id, message_string): messages = DeviceMessage.find({'device_id': device_id, 'message': message_string}) for message in messages: DeviceMessage.find_and_remove(message._id) ################################################################################################################ # Particular message used to notify a device that it has to create a certain wifi profile and store credentials. ################################################################################################################ class AddTrustedCloudletDeviceMessage(DeviceMessage): MESSAGE = 'add-trusted-cloudlet' ################################################################################################################ # Constructor. ################################################################################################################ def __init__(self, paired_device_data_bundle, *args, **kwargs): super(AddTrustedCloudletDeviceMessage, self).__init__(*args, **kwargs) self.message = self.MESSAGE self.params = paired_device_data_bundle.__dict__ ################################################################################################################ # ################################################################################################################ # noinspection PyBroadException @staticmethod def clear_messages(device_id): AddTrustedCloudletDeviceMessage.clear_all_messages(device_id, AddTrustedCloudletDeviceMessage.MESSAGE) ################################################################################################################ # Particular message used to notify a device that it has to move to a new cloudlet. ################################################################################################################ class ConnectToNewCloudletMessage(DeviceMessage): MESSAGE = 'move-to-new-cloudlet-network' ################################################################################################################ # Constructor. ################################################################################################################ def __init__(self, paired_device_data_bundle, *args, **kwargs): super(ConnectToNewCloudletMessage, self).__init__(*args, **kwargs) self.message = self.MESSAGE self.params = paired_device_data_bundle.__dict__ ################################################################################################################ # ################################################################################################################ # noinspection PyBroadException @staticmethod def clear_messages(device_id): ConnectToNewCloudletMessage.clear_all_messages(device_id, ConnectToNewCloudletMessage.MESSAGE)

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0.284091

0

null

0

null

0

1

0

faa05b817f60195f3c141ce531008fcb9fb675a7

4,473

py

Python

BioExp/clusters/clusters.py

MiRL-IITM/BioExp

d121661bac7ae2d8c1bed7a52e9a0f550f446baa

[ "MIT" ]

null

BioExp/clusters/clusters.py

MiRL-IITM/BioExp

d121661bac7ae2d8c1bed7a52e9a0f550f446baa

[ "MIT" ]

null

BioExp/clusters/clusters.py

MiRL-IITM/BioExp

d121661bac7ae2d8c1bed7a52e9a0f550f446baa

[ "MIT" ]

null

import matplotlib matplotlib.use('Agg') import keras import numpy as np import tensorflow as tf import os from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram from sklearn.cluster import AgglomerativeClustering class Cluster(): """ A class for conducting an cluster study on a trained keras model instance """ def __init__(self, model, weights_pth, layer_name, max_clusters = None): """ model : keras model architecture (keras.models.Model) weights_pth : saved weights path (str) metric : metric to compare prediction with gt, for example dice, CE layer_name : name of the layer which needs to be ablated test_img : test image used for ablation max_clusters: maximum number of clusters """ self.model = model self.weights = weights_pth self.layer = layer_name self.layer_idx = 0 for idx, layer in enumerate(self.model.layers): if layer.name == self.layer: self.layer_idx = idx def get_distances(self, X, model, mode='l2'): """ """ distances = [] weights = [] children=model.children_ dims = (X.shape[1],1) distCache = {} weightCache = {} for childs in children: c1 = X[childs[0]].reshape(dims) c2 = X[childs[1]].reshape(dims) c1Dist = 0 c1W = 1 c2Dist = 0 c2W = 1 if childs[0] in distCache.keys(): c1Dist = distCache[childs[0]] c1W = weightCache[childs[0]] if childs[1] in distCache.keys(): c2Dist = distCache[childs[1]] c2W = weightCache[childs[1]] d = np.linalg.norm(c1-c2) # d = np.squeeze(np.dot(c1.T, c2)/ (np.linalg.norm(c1)*np.linalg.norm(c2))) cc = ((c1W*c1)+(c2W*c2))/(c1W+c2W) X = np.vstack((X,cc.T)) newChild_id = X.shape[0]-1 # How to deal with a higher level cluster merge with lower distance: if mode=='l2': # Increase the higher level cluster size suing an l2 norm added_dist = ((c1Dist**2+c2Dist**2)**0.5) dNew = (d**2 + added_dist**2)**0.5 elif mode == 'max': # If the previrous clusters had higher distance, use that one dNew = max(d,c1Dist,c2Dist) elif mode == 'cosine': dNew = np.squeeze(np.dot(c1Dist, c2Dist)/ (np.linalg.norm(c1Dist)*np.linalg.norm(c2Dist))) elif mode == 'actual': # Plot the actual distance. dNew = d wNew = (c1W + c2W) distCache[newChild_id] = dNew weightCache[newChild_id] = wNew distances.append(dNew) weights.append(wNew) return distances, weights def plot_dendrogram(self, X, model, threshold=.7): """ """ # Create linkage matrix and then plot the dendrogram distance, weight = self.get_distances(X,model) linkage_matrix = np.column_stack([model.children_, distance, weight]).astype(float) threshold = threshold*np.max(distance) sorted_ = linkage_matrix[np.argsort(distance)] splitnode = np.max(sorted_[sorted_[:, 2] > threshold][0, (0,1)]) level = np.log((-.5*splitnode)/(1.*X.shape[0]) + 1.)/np.log(.5) nclusters = int(np.round((1.*X.shape[0])/(2.**level))) ### New .... model = AgglomerativeClustering(n_clusters=nclusters).fit(X) distance, weight = self.get_distances(X, model) linkage_matrix = np.column_stack([model.children_, distance, weight]).astype(float) labels = model.labels_ print ("===========================", nclusters, threshold, np.max(distance), np.unique(labels), [sum(labels == i) for i in np.unique(labels)]) # Plot the corresponding dendrogram return linkage_matrix, labels def get_clusters(self, threshold=0.5, save_path = None): """ Does clustering on feature space save_path : path to save dendrogram image threshold : fraction of max distance to cluster """ layer_weights = np.array(self.model.layers[self.layer_idx].get_weights()) shape = layer_weights[0].shape X = layer_weights[0].reshape(layer_weights[0].shape[-1], -1) position = np.linspace(0, X.shape[-1], X.shape[-1]) X = X + position[None, :] model = AgglomerativeClustering().fit(X) # plot the top three levels of the dendrogram linkage_matrix, labels = self.plot_dendrogram(X, model, threshold = threshold) if save_path: os.makedirs(save_path, exist_ok=True) plt.figure(figsize=(20, 10)) plt.title('Hierarchical Clustering Dendrogram') R = dendrogram(linkage_matrix, truncate_mode='level') plt.xlabel("Number of points in node (or index of point if no parenthesis).") plt.savefig(os.path.join(save_path, '{}_dendrogram.png'.format(self.layer))) return labels

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0.011494

0

null

0

null

0

1

0

faa2a166815fc698776e953f5e5ffa0e59a2da75

1,032

py

Python

UFAL/PAA/BellmanFord/10557UVAXYZZY.py

NelsonGomesNeto/ProgramC

e743b1b869f58f7f3022d18bac00c5e0b078562e

[ "MIT" ]

3

2018-12-18T13:39:42.000Z

2021-06-23T18:05:18.000Z

UFAL/PAA/BellmanFord/10557UVAXYZZY.py

NelsonGomesNeto/ProgramC

e743b1b869f58f7f3022d18bac00c5e0b078562e

[ "MIT" ]

1

2018-11-02T21:32:40.000Z

2018-11-02T22:47:12.000Z

UFAL/PAA/BellmanFord/10557UVAXYZZY.py

NelsonGomesNeto/ProgramC

e743b1b869f58f7f3022d18bac00c5e0b078562e

[ "MIT" ]

6

2018-10-27T14:07:52.000Z

2019-11-14T13:49:29.000Z

# For some unknown reason... UVA keeps return RunTimeError for Python is this question... DEBUG = 0 inf = 2**33 while (True): rooms = -2 while (rooms == -2): try: rooms = int(input()) except EOFError: break except ValueError: continue if (rooms == -1): break if (rooms != 0): preGraph = [[] for i in range(rooms + 1)] energy = [0] * (rooms + 1) for i in range(1, rooms + 1): line = list(map(int, input().split())) energy[i] = line[0] for j in range(line[1]): preGraph[i] += [line[j + 2]] graph = [[] for i in range(rooms + 1)] for i in range(1, rooms + 1): for u in preGraph[i]: graph[i] += [[u, energy[u]]] cost = [inf] * (rooms + 1) #bellmanFord(graph, cost, path, loop, 1) canWin = 1 #if (DEBUG): print("path", path, "cost", cost) if (canWin): print("winnable") else: print("hopeless")

25.170732

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131

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null

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null

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1

0

faa38c67c94b04c9192f47b8ef3a9d3344c206b5

3,531

py

Python

src/heartbeat/auth.py

pbs/django-heartbeat

886e5b47c730498816c1458bb1f7e99e622afa9d

[ "MIT" ]

29

2016-02-04T12:50:13.000Z

2022-01-17T13:57:56.000Z

src/heartbeat/auth.py

pbs/django-heartbeat

886e5b47c730498816c1458bb1f7e99e622afa9d

[ "MIT" ]

2

2016-04-08T14:39:14.000Z

2020-07-09T06:55:12.000Z

src/heartbeat/auth.py

pbs/django-heartbeat

886e5b47c730498816c1458bb1f7e99e622afa9d

[ "MIT" ]

10

2016-02-26T19:34:26.000Z

2020-01-27T20:16:00.000Z

import re import base64 import logging from functools import wraps from ipaddress import ip_address, ip_network from .settings import HEARTBEAT from django.http import HttpResponse from django.core.exceptions import ImproperlyConfigured logging.basicConfig( format='%(levelname)s - %(message)s', level=logging.INFO) logger = logging.getLogger(__name__) def auth(func): @wraps(func) def _decorator(request, *args, **kwargs): auth = get_auth() if auth.get('disable', False) is True: return func(request, *args, **kwargs) if 'authorized_ips' in auth: ip = get_client_ip(request) if is_authorized(ip, auth['authorized_ips']): return func(request, *args, **kwargs) prepare_credentials(auth) if request.META.get('HTTP_AUTHORIZATION'): authmeth, auth = request.META['HTTP_AUTHORIZATION'].split(' ') if authmeth.lower() == 'basic': auth = base64.b64decode(auth).decode('utf-8') username, password = auth.split(':') if (username == HEARTBEAT['auth']['username'] and password == HEARTBEAT['auth']['password']): return func(request, *args, **kwargs) response = HttpResponse( "Authentication failed", status=401) response['WWW-Authenticate'] = 'Basic realm="Welcome to 1337"' return response return _decorator def get_auth(): auth = HEARTBEAT.get('auth') if not auth: raise ImproperlyConfigured('Missing auth configuration for heartbeat') return auth def prepare_credentials(auth): if not all([auth.get('username'), auth.get('password')]): raise ImproperlyConfigured( 'Username or password missing from auth configuration ' 'for heartbeat') def get_access_route(request): meta = request.META return ( meta.get('HTTP_X_FORWARDED_FOR') or meta.get('REMOTE_ADDR') ).split(',') def get_client_ip(request): access_route = get_access_route(request) if len(access_route) == 1: return access_route[0] expression = """ (^(?!(?:[0-9]{1,3}\.){3}[0-9]{1,3}$).*$)| # will match non valid ipV4 (^127\.0\.0\.1)| # will match 127.0.0.1 (^10\.)| # will match 10.0.0.0 - 10.255.255.255 IP-s (^172\.1[6-9]\.)| # will match 172.16.0.0 - 172.19.255.255 IP-s (^172\.2[0-9]\.)| # will match 172.20.0.0 - 172.29.255.255 IP-s (^172\.3[0-1]\.)| # will match 172.30.0.0 - 172.31.255.255 IP-s (^192\.168\.) # will match 192.168.0.0 - 192.168.255.255 IP-s """ regex = re.compile(expression, re.X) for ip in access_route: if not ip: # it's possible that the first value from X_FORWARDED_FOR # will be null, so we need to pass that value continue if regex.search(ip): continue else: return ip def is_authorized(ip, authorized_ips): ip = ip_address(ip) for item in authorized_ips: try: if ip == ip_address(item): return True except ValueError: try: if ip in ip_network(item): return True except ValueError: logger.warn('The "authorized_ip" list (settings.HEARTBEAT)' 'contains an item that is neither an ip address ' 'nor an ip network: {}'.format(item))

33.311321

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0

0.302326

0

null

0

null

0

1

0

faa6fad861b4fa4374ccfff188630051bf80c8fc

14,578

py

Python

saleor/dashboard/order/forms.py

djlowes/russell-westbark

7c3857e8a35b3bd0fea575ef0031360a1c5c0254

[ "BSD-3-Clause" ]

null

saleor/dashboard/order/forms.py

djlowes/russell-westbark

7c3857e8a35b3bd0fea575ef0031360a1c5c0254

[ "BSD-3-Clause" ]

null

saleor/dashboard/order/forms.py

djlowes/russell-westbark

7c3857e8a35b3bd0fea575ef0031360a1c5c0254

[ "BSD-3-Clause" ]

null

from django import forms from django.conf import settings from django.core.validators import MinValueValidator from django.urls import reverse_lazy from django.utils.translation import npgettext_lazy, pgettext_lazy from django_prices.forms import MoneyField from payments import PaymentError, PaymentStatus from ...account.i18n import ( AddressForm as StorefrontAddressForm, PossiblePhoneNumberFormField) from ...cart.forms import QuantityField from ...core.exceptions import InsufficientStock from ...discount.utils import decrease_voucher_usage from ...order.emails import send_note_confirmation from ...order.models import Fulfillment, FulfillmentLine, OrderLine, OrderNote from ...order.utils import ( add_variant_to_order, cancel_fulfillment, cancel_order, change_order_line_quantity, merge_duplicates_into_order_line, recalculate_order) from ...product.models import Product, ProductVariant, Stock from ...product.utils import allocate_stock, deallocate_stock from ..forms import AjaxSelect2ChoiceField from ..widgets import PhonePrefixWidget from .utils import fulfill_order_line class OrderNoteForm(forms.ModelForm): class Meta: model = OrderNote fields = ['content', 'is_public'] widgets = { 'content': forms.Textarea()} labels = { 'content': pgettext_lazy('Order note', 'Note'), 'is_public': pgettext_lazy( 'Allow customers to see note toggle', 'Customer can see this note')} def send_confirmation_email(self): order = self.instance.order send_note_confirmation.delay(order.pk) class ManagePaymentForm(forms.Form): amount = MoneyField( label=pgettext_lazy( 'Payment management form (capture, refund, release)', 'Amount'), max_digits=12, decimal_places=2, currency=settings.DEFAULT_CURRENCY) def __init__(self, *args, **kwargs): self.payment = kwargs.pop('payment') super().__init__(*args, **kwargs) def clean(self): if self.payment.status != self.clean_status: raise forms.ValidationError(self.clean_error) def payment_error(self, message): self.add_error( None, pgettext_lazy( 'Payment form error', 'Payment gateway error: %s') % message) def try_payment_action(self, action): money = self.cleaned_data['amount'] try: action(money.amount) except (PaymentError, ValueError) as e: self.payment_error(str(e)) return False return True class CapturePaymentForm(ManagePaymentForm): clean_status = PaymentStatus.PREAUTH clean_error = pgettext_lazy('Payment form error', 'Only pre-authorized payments can be captured') def capture(self): return self.try_payment_action(self.payment.capture) class RefundPaymentForm(ManagePaymentForm): clean_status = PaymentStatus.CONFIRMED clean_error = pgettext_lazy('Payment form error', 'Only confirmed payments can be refunded') def refund(self): return self.try_payment_action(self.payment.refund) class ReleasePaymentForm(forms.Form): def __init__(self, *args, **kwargs): self.payment = kwargs.pop('payment') super().__init__(*args, **kwargs) def clean(self): if self.payment.status != PaymentStatus.PREAUTH: raise forms.ValidationError( pgettext_lazy( 'Payment form error', 'Only pre-authorized payments can be released')) def payment_error(self, message): self.add_error( None, pgettext_lazy( 'Payment form error', 'Payment gateway error: %s') % message) def release(self): try: self.payment.release() except (PaymentError, ValueError) as e: self.payment_error(str(e)) return False return True class CancelOrderLineForm(forms.Form): def __init__(self, *args, **kwargs): self.line = kwargs.pop('line') super().__init__(*args, **kwargs) def cancel_line(self): if self.line.stock: deallocate_stock(self.line.stock, self.line.quantity) order = self.line.order self.line.delete() recalculate_order(order) class ChangeQuantityForm(forms.ModelForm): quantity = QuantityField( validators=[MinValueValidator(1)]) class Meta: model = OrderLine fields = ['quantity'] labels = { 'quantity': pgettext_lazy( 'Integer number', 'Quantity')} def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.initial_quantity = self.instance.quantity self.fields['quantity'].initial = self.initial_quantity def clean_quantity(self): quantity = self.cleaned_data['quantity'] delta = quantity - self.initial_quantity stock = self.instance.stock if stock and delta > stock.quantity_available: raise forms.ValidationError( npgettext_lazy( 'Change quantity form error', 'Only %(remaining)d remaining in stock.', 'Only %(remaining)d remaining in stock.', 'remaining') % { 'remaining': ( self.initial_quantity + stock.quantity_available)}) return quantity def save(self): quantity = self.cleaned_data['quantity'] stock = self.instance.stock if stock is not None: # update stock allocation delta = quantity - self.initial_quantity allocate_stock(stock, delta) change_order_line_quantity(self.instance, quantity) recalculate_order(self.instance.order) return self.instance class CancelOrderForm(forms.Form): """Allow canceling an entire order. Deallocate or increase corresponding stocks for each order line. """ restock = forms.BooleanField(initial=True, required=False) def __init__(self, *args, **kwargs): self.order = kwargs.pop('order') super().__init__(*args, **kwargs) self.fields['restock'].label = npgettext_lazy( 'Cancel order form action', 'Restock %(quantity)d item', 'Restock %(quantity)d items', 'quantity') % {'quantity': self.order.get_total_quantity()} def clean(self): data = super().clean() if not self.order.can_cancel(): raise forms.ValidationError( pgettext_lazy( 'Cancel order form error', "This order can't be cancelled")) return data def cancel_order(self): cancel_order(self.order, self.cleaned_data.get('restock')) class CancelFulfillmentForm(forms.Form): """Allow canceling an entire fulfillment. Increase corresponding stocks for each fulfillment line. """ restock = forms.BooleanField(initial=True, required=False) def __init__(self, *args, **kwargs): self.fulfillment = kwargs.pop('fulfillment') super().__init__(*args, **kwargs) self.fields['restock'].label = npgettext_lazy( 'Cancel fulfillment form action', 'Restock %(quantity)d item', 'Restock %(quantity)d items', 'quantity') % {'quantity': self.fulfillment.get_total_quantity()} def clean(self): data = super().clean() if not self.fulfillment.can_edit(): raise forms.ValidationError( pgettext_lazy( 'Cancel fulfillment form error', 'This fulfillment can\'t be canceled')) return data def cancel_fulfillment(self): cancel_fulfillment(self.fulfillment, self.cleaned_data.get('restock')) class FulfillmentTrackingNumberForm(forms.ModelForm): """Update tracking number in fulfillment group.""" send_mail = forms.BooleanField( initial=True, required=False, label=pgettext_lazy( 'Send mail to customer', 'Send notification email to customer')) class Meta: model = Fulfillment fields = ['tracking_number'] labels = { 'tracking_number': pgettext_lazy( 'Fulfillment record', 'Tracking number')} class RemoveVoucherForm(forms.Form): def __init__(self, *args, **kwargs): self.order = kwargs.pop('order') super().__init__(*args, **kwargs) def clean(self): data = super().clean() if not self.order.voucher: raise forms.ValidationError( pgettext_lazy( 'Remove voucher form error', 'This order has no voucher')) return data def remove_voucher(self): self.order.discount_amount = 0 self.order.discount_name = '' decrease_voucher_usage(self.order.voucher) self.order.voucher = None recalculate_order(self.order) PAYMENT_STATUS_CHOICES = ( [('', pgettext_lazy('Payment status field value', 'All'))] + PaymentStatus.CHOICES) class PaymentFilterForm(forms.Form): status = forms.ChoiceField(choices=PAYMENT_STATUS_CHOICES) class StockChoiceField(forms.ModelChoiceField): def label_from_instance(self, obj): return obj.location.name class ChangeStockForm(forms.ModelForm): stock = StockChoiceField(queryset=Stock.objects.none()) class Meta: model = OrderLine fields = ['stock'] labels = { 'stock': pgettext_lazy( 'Stock record', 'Stock')} def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) sku = self.instance.product_sku self.fields['stock'].queryset = Stock.objects.filter(variant__sku=sku) self.old_stock = self.instance.stock def clean_stock(self): stock = self.cleaned_data['stock'] if stock and stock.quantity_available < self.instance.quantity: raise forms.ValidationError( pgettext_lazy( 'Change stock form error', 'Only %(remaining)d remaining in this stock.') % { 'remaining': stock.quantity_available}) return stock def save(self, commit=True): quantity = self.instance.quantity stock = self.instance.stock self.instance.stock_location = ( stock.location.name if stock.location else '') if self.old_stock: deallocate_stock(self.old_stock, quantity) allocate_stock(stock, quantity) super().save(commit) merge_duplicates_into_order_line(self.instance) return self.instance class AddVariantToOrderForm(forms.Form): """Allow adding lines with given quantity to an order.""" variant = AjaxSelect2ChoiceField( queryset=ProductVariant.objects.filter( product__in=Product.objects.available_products()), fetch_data_url=reverse_lazy('dashboard:ajax-available-variants')) quantity = QuantityField( label=pgettext_lazy( 'Add variant to order form label', 'Quantity'), validators=[MinValueValidator(1)]) def __init__(self, *args, **kwargs): self.order = kwargs.pop('order') self.discounts = kwargs.pop('discounts') super().__init__(*args, **kwargs) def clean(self): """Check if given quantity is available in stocks.""" cleaned_data = super().clean() variant = cleaned_data.get('variant') quantity = cleaned_data.get('quantity') if variant and quantity is not None: try: variant.check_quantity(quantity) except InsufficientStock as e: error = forms.ValidationError( pgettext_lazy( 'Add item form error', 'Could not add item. ' 'Only %(remaining)d remaining in stock.' % {'remaining': e.item.get_stock_quantity()})) self.add_error('quantity', error) return cleaned_data def save(self): """Add variant to order. Updates stocks and order. """ variant = self.cleaned_data.get('variant') quantity = self.cleaned_data.get('quantity') add_variant_to_order( self.order, variant, quantity, self.discounts) recalculate_order(self.order) class AddressForm(StorefrontAddressForm): phone = PossiblePhoneNumberFormField( widget=PhonePrefixWidget, required=False) class FulfillmentForm(forms.ModelForm): """Create fulfillment group for a given order.""" send_mail = forms.BooleanField( initial=True, required=False, label=pgettext_lazy( 'Send mail to customer', 'Send shipment details to your customer now')) class Meta: model = Fulfillment fields = ['tracking_number'] labels = { 'tracking_number': pgettext_lazy( 'Order tracking number', 'Tracking number')} def __init__(self, *args, **kwargs): order = kwargs.pop('order') super().__init__(*args, **kwargs) self.instance.order = order class BaseFulfillmentLineFormSet(forms.BaseModelFormSet): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) for form in self.forms: form.empty_permitted = False class FulfillmentLineForm(forms.ModelForm): """Fulfill order line with given quantity by decreasing stock.""" class Meta: model = FulfillmentLine fields = ['order_line', 'quantity'] def clean_quantity(self): quantity = self.cleaned_data.get('quantity') order_line = self.cleaned_data.get('order_line') if quantity > order_line.quantity_unfulfilled: raise forms.ValidationError(npgettext_lazy( 'Fulfill order line form error', '%(quantity)d item remaining to fulfill.', '%(quantity)d items remaining to fulfill.', 'quantity') % { 'quantity': order_line.quantity_unfulfilled, 'order_line': order_line}) return quantity def save(self, commit=True): fulfill_order_line(self.instance.order_line, self.instance.quantity) return super().save(commit)

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null

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null

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0

fab51fc18a5b7d1bb4f84ed9d207bcf5e8fe01fb

9,740

py

Python

mina-telegram-alert.py

Makalfo/mina-telegram-alert

3a18b16528e43385300110f6893c6deab2ad5512

[ "MIT" ]

1

2022-01-03T13:55:36.000Z

mina-telegram-alert.py

Makalfo/mina-telegram-alert

3a18b16528e43385300110f6893c6deab2ad5512

[ "MIT" ]

null

mina-telegram-alert.py

Makalfo/mina-telegram-alert

3a18b16528e43385300110f6893c6deab2ad5512

[ "MIT" ]

null

#!/usr/bin/env python3 import requests, configparser import os import pandas as pd import json import base58 import time import urllib.request from google.cloud import bigquery pd.options.mode.chained_assignment = None # Mina constants MINA_DECIMALS = 1 / 1000000000 SLEEP_TIME = 60 class MinaTelegram(): def __init__( self, config_file='config.ini' ): # read the config and setup telegram self.name = os.uname()[1] self.read_config( config_file ) self.setup_telegram() self.public_key = self.config['Mina']['public_key'] self.client = bigquery.Client() # transaction self.recieved = 0 self.sent = 0 # set the bigquery variable os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = self.config['BigQuery']['credentials'] # get the provider info self.providers = self.get_providers( ) # hello message self.send( f'{self.name}: Hello from Mina Watcher!' ) while True: # obtain blocks blocks = self.get_blocks( self.config['Mina']['last_block'] ) # check if the block is empty if blocks.empty: print( f'Empty Blocks - Sleeping for {SLEEP_TIME}') time.sleep( SLEEP_TIME ) continue block_list = blocks['blockheight'].unique() block_list.sort() # save the datetime blocks['datetime'] = blocks['datetime'].apply(lambda x : pd.to_datetime(str(x))) blocks['receivedtime'] = blocks['receivedtime'].apply(lambda x : pd.to_datetime(str(x))) blocks['date'] = blocks['datetime'].dt.date blocks['time'] = blocks['datetime'].dt.time blocks['received_date'] = blocks['receivedtime'].dt.date blocks['received_time'] = blocks['receivedtime'].dt.time blocks['delta_time'] = blocks['receivedtime'] - blocks['datetime'] blocks['delta_time'] = blocks['delta_time'].apply(lambda x : x.total_seconds()) # parse the blocks for blockheight in block_list: #print( f'Parsing blockheight: {blockheight}') # obtain all the blocks of the block height blocks_of_height = blocks.loc[blocks['blockheight'] == blockheight] self.parse_blocks( blocks_of_height ) # save / update the config file self.config['Mina']['last_block'] = str( blocks['blockheight'].max() ) with open( config_file, 'w') as configfile: self.config.write(configfile) print( f'Sleeping for {SLEEP_TIME}') time.sleep( SLEEP_TIME ) def read_config( self, config_file ): ''' Read the configuration file ''' config = configparser.ConfigParser() config.read( config_file ) self.config = config def setup_telegram( self ): ''' Setup telegram ''' self.telegram_token = self.config['Telegram']['telegram_token'] self.telegram_chat_id = self.config['Telegram']['telegram_chat_id'] def send( self, msg ): ''' Send telegram message ''' requests.post( f'https://api.telegram.org/bot{self.telegram_token}/sendMessage?chat_id={self.telegram_chat_id}&text={msg}' ) print( msg ) def get_blocks( self, target_blockheight ): '''Get the blocks''' query = """ SELECT blockheight, creator, canonical, datetime, receivedtime, transactions, statehash, FROM minaexplorer.archive.blocks WHERE blockheight > %s ORDER BY blockheight DESC""" % target_blockheight query_job = self.client.query(query) iterator = query_job.result() rows = list(iterator) # if the query returns no data, return empty dataframe if len( rows ) == 0: return pd.DataFrame() # Transform the rows into a nice pandas dataframe df = pd.DataFrame(data=[list(x.values()) for x in rows], columns=list(rows[0].keys())) df.drop_duplicates(subset=['statehash']) df.sort_values(by=['blockheight'], inplace=True) return df def get_providers( self ): '''get provider list''' output = dict() # staketab providers with urllib.request.urlopen(self.config['Providers']['staketab']) as url: data = json.loads(url.read().decode()) for provider in data['staking_providers']: output[ provider['provider_address'] ] = provider['provider_title'] # Mina Foundation mf_data = self.get_csv( self.config['Providers']['mina_foundation'] ) for idx, address in enumerate(mf_data): output[ address ] = f'Mina Foundation {idx}' # O1 Labs mf_data = self.get_csv( self.config['Providers']['o1_labs'] ) for idx, address in enumerate(mf_data): output[ address ] = f'O1 Labs {idx}' # unofficial accounts unofficial = pd.read_csv( self.config['Providers']['unofficial'] ) for idx, row in unofficial.iterrows(): output[ row['address'] ] = row['identity'] return output def get_csv( self, url ): '''return the csv as a list''' req = urllib.request.Request( url ) req.add_header('User-Agent', 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:77.0) Gecko/20100101 Firefox/77.0') content = urllib.request.urlopen(req) data = pd.read_csv(content, header=None) return list( data[0] ) def get_provider( self, address ): '''return the provider name if it is in the provider dictionary''' if address in self.providers.keys(): address = self.providers[ address ] return address def parse_blocks( self, blocks ): '''parse the blocks of the same blockheight''' # parse the canonical blocks.sort_values(by=['canonical'], inplace=True, ascending=False) for index, block in blocks.iterrows(): self.parse_block( block ) def parse_block( self, block ): '''parse the block''' # canonical flag canonical = 'Canonical' if block.canonical == True else 'Non-Canonical' # parse the transactions transactions = self.parse_transactions( block.transactions ) # check if the creator of the block is the public_key if block.creator == self.public_key: self.send( f"{canonical} {block.blockheight}: Created Block - {self.get_provider( transactions['coinbase_receiver'] )} Received {transactions['coinbase_reward']} at { block.date } { block.time } [ { block.delta_time } ]" ) # check the transactions if canonical == 'Canonical': for transaction in transactions['user_commands']: if self.public_key in [transaction['from'], transaction['to']]: # if it is a delegation, omit the amount if transaction['kind'] == 'STAKE_DELEGATION': self.send( f"{canonical} {block.blockheight}: Stake Delegation from {self.get_provider( transaction['from'] )} to {self.get_provider( transaction['to'])} [{transaction['memo'].strip()}] at { block.date } { block.time }" ) else: self.send( f"{canonical} {block.blockheight}: {transaction['kind'].capitalize()} from {self.get_provider( transaction['from'] )} to {self.get_provider( transaction['to'])} for {transaction['amount']} [{transaction['memo'].strip()}] at { block.date } { block.time }" ) if transaction['kind'] == 'PAYMENT': if transaction['from'] == self.public_key: self.sent += transaction['amount'] else: self.recieved += transaction['amount'] # Send how much has gone through the account so far # self.send( f'Sent: {self.sent}\tReceived: {self.recieved}') def parse_transactions( self, transactions ): '''parse transactions''' if transactions['feetransfer'] == None or transactions['usercommands'] == None: return { 'coinbase_reward' : 0, 'coinbase_receiver' : '', 'user_commands' : [] } output = {} # parse the rewards output['coinbase_reward'] = transactions['coinbase'] * MINA_DECIMALS output['coinbase_receiver']= transactions['coinbasereceiveraccount']['publickey'] output['fee'] = [] # parse the fee transfers for fee_transfer in json.loads( transactions['feetransfer'] ): output['fee'].append( fee_transfer['fee'] * MINA_DECIMALS ) # user commands output['user_commands'] = [] for user_command in json.loads( transactions['usercommands'] ): user_tx = { 'from' : user_command['from'], 'to' : user_command['to'], 'amount': round( user_command['amount'] * MINA_DECIMALS, 4 ), 'fee' : round( user_command['fee'] * MINA_DECIMALS, 4), 'kind' : user_command['kind'], 'memo': self.decode_memo( user_command['memo'] ) } output['user_commands'].append( user_tx ) return output def decode_memo( self, memo ): '''decode the memo''' decoded = base58.b58decode( memo )[2:-4] return decoded.decode("utf-8", "strict") mina_bot = MinaTelegram()

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291

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9,740

5.253561

0.242165

0.025307

0.016269

0.0188

0.12256

0.110629

0.092191

0.051338

0.039046

0

0.008326

0.297125

9,740

237

292

41.097046

0.799737

0.114784

0

0.053333

0

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0.250796

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0

1

0.08

false

0

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0.02

0

null

0

null

0

1

0

fab6e383e6d4d6588e9129e39975cf189d7be4a2

1,935

py

Python

genetic_algorithm/Prepare_Data_Functions.py

pozzo-research-group/HEAD

98572c691d0dbef4da19a719427a4b946937e342

[ "MIT" ]

1

2022-03-31T04:29:54.000Z

genetic_algorithm/Prepare_Data_Functions.py

pozzo-research-group/HEAD

98572c691d0dbef4da19a719427a4b946937e342

[ "MIT" ]

null

genetic_algorithm/Prepare_Data_Functions.py

pozzo-research-group/HEAD

98572c691d0dbef4da19a719427a4b946937e342

[ "MIT" ]

null

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import preprocessing def load_df(directory): df = pd.read_excel(directory) return df def subtract_baseline(df, baseline): x = df.values cols = df.columns baseline_col = df[baseline].values for i in range(1,x.shape[1]): x[:,i] = x[:,i] - baseline_col df = pd.DataFrame(x, columns = cols) return df def delete_rows(df, rows_delete): x = df.values cols = df.columns x = x[rows_delete:,:] df = pd.DataFrame(x, columns = cols) return df def normalize_df(df): x = df.values #returns a numpy array cols = df.columns min_max_scaler = preprocessing.MinMaxScaler() x_scaled = min_max_scaler.fit_transform(x) x_scaled = np.hstack((x[:,0].reshape(-1,1),x_scaled[:,1:])) df = pd.DataFrame(x_scaled, columns = cols) return df def plot_single_graph(df, column): fig, ax = plt.subplots(figsize=(8,5)) ax.plot(df['Wavelength'], df[column]) ax.set_xlabel('Wavelength (nm)') ax.set_ylabel('Absorbance') def find_max_wavelength(df, column): array = np.vstack((df['Wavelength'], df[column])).T sorted_array = array[np.argsort(array[:, 1])] max_wavelength = sorted_array[-1,0] return max_wavelength def plot_all_spectra_multiple(df): for i in range(1, len(df.columns)): plot_single_graph(df.columns[i]) def plot_all_spectra_single(df): for i in range(1, len(df.columns)): plt.plot(df['Wavelength'], df[df.columns[i]]) plt.xlabel('Wavelength (nm)') plt.ylabel('Absorbance') def plot_some_spectra_single(df, cols): plt.figure(figsize=(8,5)) for i in range(0, len(cols)): plt.plot(df['Wavelength'], df[cols[i]]) plt.xlabel('Wavelength (nm)') plt.ylabel('Absorbance') plt.xlim([400,800]) # plt.legend()

29.318182

63

0.630491

284

1,935

4.158451

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0.053345

0.037257

0.276037

0.211685

0.174428

0.104996

0

0.014716

0.22739

1,935

66

64

29.318182

0.775251

0.017571

0

0.333333

0

0.060526

0

1

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false

0

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0.333333

0

null

0

null

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1

0