fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f734a7810a7e04bf77d068a86ee4ed61a560a6e3

2,872

py

Python

bench_utils/timeit.py

drkostas/bench-utils

0ec976238175ee8137a54101e679f0faf58996b9

[ "Apache-2.0" ]

3

2021-12-22T14:29:17.000Z

2022-01-26T21:21:36.000Z

bench_utils/timeit.py

drkostas/bench-utils

0ec976238175ee8137a54101e679f0faf58996b9

[ "Apache-2.0" ]

null

bench_utils/timeit.py

drkostas/bench-utils

0ec976238175ee8137a54101e679f0faf58996b9

[ "Apache-2.0" ]

null

from contextlib import ContextDecorator from typing import Callable, IO, Union from functools import wraps from time import time from termcolor_logger import ColorLogger time_logger = ColorLogger('Timeit', 'white') class timeit(ContextDecorator): custom_print: str skip: bool total: Union[float, None] internal_only: bool file: IO def __init__(self, **kwargs): """Decorator/ContextManager for counting the execution times of functions and code blocks Args: custom_print: Custom print string Use {duration} to reference the running time. When used as decorator it can also be formatted using `func_name`, `args`, and {0}, {1}, .. to reference the function's first, second, ... argument. skip: If True, don't time this time. Suitable when inside loops file: Write the timing output to a file too """ self.total = None self.skip = False self.internal_only = False self.__dict__.update(kwargs) def __call__(self, func: Callable): """ This is called only when invoked as a decorator Args: func: The method to wrap """ @wraps(func) def timed(*args, **kwargs): with self._recreate_cm(): self.func_name = func.__name__ self.args = args self.kwargs = kwargs self.all_args = (*args, *kwargs.values()) if kwargs != {} else args return func(*args, **kwargs) return timed def __enter__(self, *args, **kwargs): if not self.skip: self.ts = time() return self def __exit__(self, type, value, traceback): if self.skip: return self.te = time() self.total = self.te - self.ts if hasattr(self, 'func_name'): if not hasattr(self, 'custom_print'): print_string = 'Func: {func_name!r} with args: {args!r} took: {duration:2.5f} sec(s)' else: print_string = self.custom_print time_logger.info(print_string.format(*self.args, func_name=self.func_name, args=self.all_args, duration=self.total, **self.kwargs)) else: if not hasattr(self, 'custom_print'): print_string = 'Code block took: {duration:2.5f} sec(s)' else: print_string = self.custom_print if hasattr(self, 'file'): self.file.write(print_string.format(duration=self.total)) if not self.internal_only: time_logger.info(print_string.format(duration=self.total))

35.02439

101

0.55188

from contextlib import ContextDecorator from typing import Callable, IO, Union from functools import wraps from time import time from termcolor_logger import ColorLogger time_logger = ColorLogger('Timeit', 'white') class timeit(ContextDecorator): custom_print: str skip: bool total: Union[float, None] internal_only: bool file: IO def __init__(self, **kwargs): self.total = None self.skip = False self.internal_only = False self.__dict__.update(kwargs) def __call__(self, func: Callable): @wraps(func) def timed(*args, **kwargs): with self._recreate_cm(): self.func_name = func.__name__ self.args = args self.kwargs = kwargs self.all_args = (*args, *kwargs.values()) if kwargs != {} else args return func(*args, **kwargs) return timed def __enter__(self, *args, **kwargs): if not self.skip: self.ts = time() return self def __exit__(self, type, value, traceback): if self.skip: return self.te = time() self.total = self.te - self.ts if hasattr(self, 'func_name'): if not hasattr(self, 'custom_print'): print_string = 'Func: {func_name!r} with args: {args!r} took: {duration:2.5f} sec(s)' else: print_string = self.custom_print time_logger.info(print_string.format(*self.args, func_name=self.func_name, args=self.all_args, duration=self.total, **self.kwargs)) else: if not hasattr(self, 'custom_print'): print_string = 'Code block took: {duration:2.5f} sec(s)' else: print_string = self.custom_print if hasattr(self, 'file'): self.file.write(print_string.format(duration=self.total)) if not self.internal_only: time_logger.info(print_string.format(duration=self.total))

true

f734a811ed32e5cecdcde508cdef13f526385529

120,801

py

Python

menpowidgets/base.py

apapaion/menpowidgets

237a39ddf4e65c57e8165f8a87f25a25f34d4698

[ "BSD-3-Clause" ]

null

menpowidgets/base.py

apapaion/menpowidgets

237a39ddf4e65c57e8165f8a87f25a25f34d4698

[ "BSD-3-Clause" ]

null

menpowidgets/base.py

apapaion/menpowidgets

237a39ddf4e65c57e8165f8a87f25a25f34d4698

[ "BSD-3-Clause" ]

null

from collections import Sized, OrderedDict import matplotlib.pyplot as plt from matplotlib import collections as mc import numpy as np import ipywidgets import IPython.display as ipydisplay from menpo.base import name_of_callable from menpo.image import MaskedImage, Image from menpo.image.base import _convert_patches_list_to_single_array from menpo.shape import TriMesh, ColouredTriMesh, TexturedTriMesh from menpo.visualize import print_dynamic from menpo.landmark import LandmarkManager from .options import (RendererOptionsWidget, TextPrintWidget, SaveMatplotlibFigureOptionsWidget, AnimationOptionsWidget, ImageOptionsWidget, LandmarkOptionsWidget, PlotMatplotlibOptionsWidget, PatchOptionsWidget, LinearModelParametersWidget, CameraSnapshotWidget, Shape2DOptionsWidget, Shape3DOptionsWidget, SaveMayaviFigureOptionsWidget, Mesh3DOptionsWidget) from .tools import LogoWidget, SwitchWidget from .utils import (extract_group_labels_from_landmarks, extract_groups_labels_from_image, render_image, render_patches) from .checks import check_n_parameters from .style import map_styles_to_hex_colours def menpowidgets_src_dir_path(): r""" The path to the top of the menpowidgets package. Useful for locating where the logos folder is stored. Returns ------- path : ``pathlib.Path`` The full path to the top of the Menpo package """ # to avoid cluttering the menpowidgets.base namespace from pathlib import Path import os.path return Path(os.path.abspath(__file__)).parent def visualize_shapes_2d(shapes, figure_size=(7, 7), browser_style='buttons', custom_info_callback=None): r""" Widget that allows browsing through a `list` of 2D shapes. The supported objects are: ================================== = Object ================================== = `menpo.shape.PointCloud` `menpo.shape.PointUndirectedGraph` `menpo.shape.PointDirectedGraph` `menpo.shape.PointTree` `menpo.shape.LabelledPointGraph` `menpo.shape.TriMesh` ================================== = Any instance of the above can be combined in the input `list`. Parameters ---------- shapes : `list` The `list` of objects to be visualized. It can contain a combination of ================================== = Object ================================== = `menpo.shape.PointCloud` `menpo.shape.PointUndirectedGraph` `menpo.shape.PointDirectedGraph` `menpo.shape.PointTree` `menpo.shape.LabelledPointGraph` `menpo.shape.TriMesh` ================================== = or subclasses of those. figure_size : (`int`, `int`), optional The initial size of the rendered figure. browser_style : ``{'buttons', 'slider'}``, optional It defines whether the selector of the objects will have the form of plus/minus buttons or a slider. custom_info_callback: `function` or ``None``, optional If not ``None``, it should be a function that accepts a 2D shape and returns a list of custom messages to be printed about it. Each custom message will be printed in a separate line. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that shapes is a list even with one member if not isinstance(shapes, Sized): shapes = [shapes] # Get the number of shapes n_shapes = len(shapes) # Define the styling options main_style = 'warning' # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # Get selected shape index i = shape_number_wid.selected_values if n_shapes > 1 else 0 # Create options dictionary options = dict() options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options['image_view'] = shape_options_wid.selected_values['image_view'] options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) # Correct options based on the type of the shape if hasattr(shapes[i], 'labels'): # If the shape is a LabelledPointUndirectedGraph ... # ...use the legend options options.update(renderer_options_wid.selected_values['legend']) # ...use with_labels options['with_labels'] = \ shape_options_wid.selected_values['with_labels'] # ...correct colours line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: idx = shapes[i].labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_face_colour.append(options['marker_face_colour'][idx]) marker_edge_colour.append(options['marker_edge_colour'][idx]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] # Get figure size new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) # Render shape with selected options save_figure_wid.renderer = shapes[i].view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size, **options) # Force rendering save_figure_wid.renderer.force_draw() # Update info text widget update_info(shapes[i], custom_info_callback=custom_info_callback) # Define function that updates the info text def update_info(shape, custom_info_callback=None): min_b, max_b = shape.bounds() rang = shape.range() cm = shape.centre() text_per_line = [ "> {}".format(name_of_callable(shape)), "> {} points".format(shape.n_points), "> Bounds: [{0:.1f}-{1:.1f}]W, [{2:.1f}-{3:.1f}]H".format( min_b[0], max_b[0], min_b[1], max_b[1]), "> Range: {0:.1f}W, {1:.1f}H".format(rang[0], rang[1]), "> Centre of mass: ({0:.1f}, {1:.1f})".format(cm[0], cm[1]), "> Norm: {0:.2f}".format(shape.norm())] if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(shape): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) # If the object is a LabelledPointUndirectedGraph, grab the labels labels = None if hasattr(shapes[0], 'labels'): labels = shapes[0].labels # Create widgets shape_options_wid = Shape2DOptionsWidget( labels=labels, render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], labels=None, axes_x_limits=0.1, axes_y_limits=0.1, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets if n_shapes > 1: # Define function that updates options' widgets state def update_widgets(change): # Get current shape and check if it has labels i = change['new'] labels = None if hasattr(shapes[i], 'labels'): labels = shapes[i].labels # Update shape options shape_options_wid.set_widget_state(labels=labels, allow_callback=True) # Shape selection slider index = {'min': 0, 'max': n_shapes-1, 'step': 1, 'index': 0} shape_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Shape', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, shape_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( [info_wid, shape_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Shape', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_shapes > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def visualize_shapes_3d(shapes, browser_style='buttons', custom_info_callback=None): r""" Widget that allows browsing through a `list` of 3D shapes. The supported objects are: ================================== Object ================================== `menpo.shape.PointCloud` `menpo.shape.PointUndirectedGraph` `menpo.shape.PointDirectedGraph` `menpo.shape.PointTree` `menpo.shape.LabelledPointGraph` ================================== Any instance of the above can be combined in the input `list`. Parameters ---------- shapes : `list` The `list` of objects to be visualized. It can contain a combination of ================================== Object ================================== `menpo.shape.PointCloud` `menpo.shape.PointUndirectedGraph` `menpo.shape.PointDirectedGraph` `menpo.shape.PointTree` `menpo.shape.LabelledPointGraph` ================================== or subclasses of those. browser_style : ``{'buttons', 'slider'}``, optional It defines whether the selector of the objects will have the form of plus/minus buttons or a slider. custom_info_callback: `function` or ``None``, optional If not ``None``, it should be a function that accepts a 2D shape and returns a list of custom messages to be printed about it. Each custom message will be printed in a separate line. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print_dynamic('Initializing...') # Make sure that shapes is a list even with one member if not isinstance(shapes, Sized): shapes = [shapes] # Get the number of shapes n_shapes = len(shapes) # Define the styling options main_style = 'warning' # Define render function def render_function(change): # Clear current figure save_figure_wid.renderer.clear_figure() ipydisplay.clear_output(wait=True) # Get selected shape index i = shape_number_wid.selected_values if n_shapes > 1 else 0 # Update info text widget update_info(shapes[i], custom_info_callback=custom_info_callback) # Create options dictionary options = dict() if isinstance(shapes[i], TriMesh): # Note that 3D TriMesh has a totally different set of options # compared to any other PointCloud or PointGraph. However, in order # for visualize_shapes_3d to support TriMeshes, we simply use the # options that are common. This means that most of the widget's # options will have no effect on rendering... options['mesh_type'] = 'wireframe' if shape_options_wid.selected_values['markers']['render_markers']: options['mesh_type'] = 'fancymesh' options['line_width'] = \ shape_options_wid.selected_values['lines']['line_width'] options['colour'] = \ shape_options_wid.selected_values['lines']['line_colour'][0] options['marker_style'] = \ shape_options_wid.selected_values['markers']['marker_style'] options['marker_size'] = \ shape_options_wid.selected_values['markers']['marker_size'] options['marker_resolution'] = \ shape_options_wid.selected_values['markers']['marker_resolution'] options['step'] = \ shape_options_wid.selected_values['markers']['step'] else: options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_mayavi']) # Correct options based on the type of the shape if hasattr(shapes[i], 'labels'): # If the shape is a LabelledPointUndirectedGraph ... # ...use with_labels options['with_labels'] = \ shape_options_wid.selected_values['with_labels'] # ...correct colours line_colour = [] marker_colour = [] for lbl in options['with_labels']: idx = shapes[i].labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_colour.append(options['marker_colour'][idx]) options['line_colour'] = line_colour options['marker_colour'] = marker_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_colour'] = options['marker_colour'][0] # Render shape with selected options save_figure_wid.renderer = shapes[i].view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, alpha=1.0, **options) # Force rendering save_figure_wid.renderer.force_draw() # Define function that updates the info text def update_info(shape, custom_info_callback=None): min_b, max_b = shape.bounds() rang = shape.range() cm = shape.centre() text_per_line = [ "> {}".format(name_of_callable(shape)), "> {} points".format(shape.n_points), "> Bounds: [{0:.1f}-{1:.1f}]X, [{2:.1f}-{3:.1f}]Y, " "[{4:.1f}-{5:.1f}]Z".format(min_b[0], max_b[0], min_b[1], max_b[1], min_b[2], max_b[2]), "> Range: {0:.1f}X, {1:.1f}Y, {2:.1f}Z".format(rang[0], rang[1], rang[2]), "> Centre of mass: ({0:.1f}X, {1:.1f}Y, {2:.1f}Z)".format( cm[0], cm[1], cm[2]), "> Norm: {0:.2f}".format(shape.norm())] if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(shape): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) # If the object is a LabelledPointUndirectedGraph, grab the labels labels = None if hasattr(shapes[0], 'labels'): labels = shapes[0].labels # Create widgets shape_options_wid = Shape3DOptionsWidget( labels=labels, render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['numbering_mayavi'], labels=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() # Group widgets if n_shapes > 1: # Define function that updates options' widgets state def update_widgets(change): # Get current shape and check if it has labels i = change['new'] labels = None if hasattr(shapes[i], 'labels'): labels = shapes[i].labels # Update shape options shape_options_wid.set_widget_state(labels=labels, allow_callback=True) # Shape selection slider index = {'min': 0, 'max': n_shapes-1, 'step': 1, 'index': 0} shape_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Shape', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, shape_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( [info_wid, shape_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Shape', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_shapes > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) print_dynamic('') def visualize_landmarks_2d(landmarks, figure_size=(7, 7), browser_style='buttons', custom_info_callback=None): r""" Widget that allows browsing through a `list` of `menpo.landmark.LandmarkManager` (or subclass) objects. The landmark managers can have a combination of different attributes, e.g. landmark groups and labels etc. Parameters ---------- landmarks : `list` of `menpo.landmark.LandmarkManager` or subclass The `list` of landmark managers to be visualized. figure_size : (`int`, `int`), optional The initial size of the rendered figure. browser_style : ``{'buttons', 'slider'}``, optional It defines whether the selector of the objects will have the form of plus/minus buttons or a slider. custom_info_callback: `function` or ``None``, optional If not None, it should be a function that accepts a landmark group and returns a list of custom messages to be printed per landmark group. Each custom message will be printed in a separate line. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that landmarks is a list even with one landmark manager member if isinstance(landmarks, LandmarkManager): landmarks = [landmarks] # Get the number of landmark managers n_landmarks = len(landmarks) # Define the styling options main_style = 'info' # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # get selected index and selected group i = landmark_number_wid.selected_values if n_landmarks > 1 else 0 g = landmark_options_wid.selected_values['landmarks']['group'] if landmark_options_wid.selected_values['landmarks']['render_landmarks']: # get shape shape = landmarks[i][g] # Create options dictionary options = dict() options.update(landmark_options_wid.selected_values['lines']) options.update(landmark_options_wid.selected_values['markers']) options['image_view'] = landmark_options_wid.selected_values['image_view'] options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) # Correct options based on the type of the shape if hasattr(shape, 'labels'): # If the shape is a LabelledPointUndirectedGraph ... # ...use the legend options options.update(renderer_options_wid.selected_values['legend']) # ...use with_labels options['with_labels'] = \ landmark_options_wid.selected_values['landmarks']['with_labels'] # ...correct colours line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: id = shape.labels.index(lbl) line_colour.append(options['line_colour'][id]) marker_face_colour.append(options['marker_face_colour'][id]) marker_edge_colour.append(options['marker_edge_colour'][id]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] # Get figure size new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) # Render shape with selected options save_figure_wid.renderer = shape.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size, **options) # Force rendering save_figure_wid.renderer.force_draw() else: ipydisplay.clear_output() # update info text widget update_info(landmarks[i], g, custom_info_callback=custom_info_callback) # Define function that updates the info text def update_info(landmarks, group, custom_info_callback=None): if group is not None: min_b, max_b = landmarks[group].bounds() rang = landmarks[group].range() cm = landmarks[group].centre() text_per_line = [ "> {} landmark points".format(landmarks[group].n_points), "> {}".format(name_of_callable(landmarks[group])), "> Bounds: [{0:.1f}-{1:.1f}]W, [{2:.1f}-{3:.1f}]H".format( min_b[0], max_b[0], min_b[1], max_b[1]), "> Range: {0:.1f}W, {1:.1f}H".format(rang[0], rang[1]), "> Centre of mass: ({0:.1f}, {1:.1f})".format(cm[0], cm[1]), "> Norm: {0:.2f}".format(landmarks[group].norm())] if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(landmarks[group]): text_per_line.append('> {}'.format(msg)) else: text_per_line = ["No landmarks available."] info_wid.set_widget_state(text_per_line=text_per_line) # Create widgets groups_keys, labels_keys = extract_group_labels_from_landmarks(landmarks[0]) first_label = labels_keys[0] if labels_keys else None landmark_options_wid = LandmarkOptionsWidget( group_keys=groups_keys, labels_keys=labels_keys, type='2D', render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], labels=first_label, axes_x_limits=0.1, axes_y_limits=0.1, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets if n_landmarks > 1: # Define function that updates options' widgets state def update_widgets(change): # Get new groups and labels i = landmark_number_wid.selected_values g_keys, l_keys = extract_group_labels_from_landmarks( landmarks[i]) # Update landmarks options landmark_options_wid.set_widget_state( group_keys=g_keys, labels_keys=l_keys, allow_callback=True) # Landmark selection slider index = {'min': 0, 'max': n_landmarks-1, 'step': 1, 'index': 0} landmark_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Shape', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, landmark_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( children=[info_wid, landmark_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Landmarks', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_landmarks > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def visualize_landmarks_3d(landmarks, browser_style='buttons', custom_info_callback=None): r""" Widget that allows browsing through a `list` of `menpo.landmark.LandmarkManager` (or subclass) objects. The landmark managers can have a combination of different attributes, e.g. landmark groups and labels etc. Parameters ---------- landmarks : `list` of `menpo.landmark.LandmarkManager` or subclass The `list` of landmark managers to be visualized. browser_style : ``{'buttons', 'slider'}``, optional It defines whether the selector of the objects will have the form of plus/minus buttons or a slider. custom_info_callback: `function` or ``None``, optional If not None, it should be a function that accepts a landmark group and returns a list of custom messages to be printed per landmark group. Each custom message will be printed in a separate line. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that landmarks is a list even with one landmark manager member if not isinstance(landmarks, list): landmarks = [landmarks] # Get the number of landmark managers n_landmarks = len(landmarks) # Define the styling options main_style = 'info' # Define render function def render_function(change): # Clear current figure save_figure_wid.renderer.clear_figure() ipydisplay.clear_output(wait=True) # get selected index and selected group i = landmark_number_wid.selected_values if n_landmarks > 1 else 0 g = landmark_options_wid.selected_values['landmarks']['group'] # update info text widget update_info(landmarks[i], g, custom_info_callback=custom_info_callback) if landmark_options_wid.selected_values['landmarks']['render_landmarks']: # get shape shape = landmarks[i][g] options = dict() if isinstance(shape, TriMesh): # Note that 3D TriMesh has a totally different set of options # compared to any other PointCloud or PointGraph. However, in # order for visualize_landmarks_3d to support TriMeshes, we # simply use the options that are common. This means that most # of the widget's options will have no effect on rendering... options['mesh_type'] = 'wireframe' if landmark_options_wid.selected_values['markers'][ 'render_markers']: options['mesh_type'] = 'fancymesh' options['line_width'] = \ landmark_options_wid.selected_values['lines']['line_width'] options['colour'] = \ landmark_options_wid.selected_values['lines']['line_colour'][0] options['marker_style'] = \ landmark_options_wid.selected_values['markers']['marker_style'] options['marker_size'] = \ landmark_options_wid.selected_values['markers']['marker_size'] options['marker_resolution'] = \ landmark_options_wid.selected_values['markers'][ 'marker_resolution'] options['step'] = \ landmark_options_wid.selected_values['markers']['step'] else: options.update(landmark_options_wid.selected_values['lines']) options.update(landmark_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_mayavi']) # Correct options based on the type of the shape if hasattr(shape, 'labels'): # If the shape is a LabelledPointUndirectedGraph ... # ...use with_labels options['with_labels'] = \ landmark_options_wid.selected_values['landmarks']['with_labels'] # ...correct colours line_colour = [] marker_colour = [] for lbl in options['with_labels']: idx = shape.labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_colour.append(options['marker_colour'][idx]) options['line_colour'] = line_colour options['marker_colour'] = marker_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_colour'] = options['marker_colour'][0] # Render shape with selected options save_figure_wid.renderer = shape.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, alpha=1.0, **options) # Force rendering save_figure_wid.renderer.force_draw() else: ipydisplay.clear_output() # Define function that updates the info text def update_info(landmarks, group, custom_info_callback=None): if group is not None: min_b, max_b = landmarks[group].bounds() rang = landmarks[group].range() cm = landmarks[group].centre() text_per_line = [ "> {} landmark points".format(landmarks[group].n_points), "> {}".format(name_of_callable(landmarks[group])), "> Bounds: [{0:.1f}-{1:.1f}]X, [{2:.1f}-{3:.1f}]Y, " "[{4:.1f}-{5:.1f}]Z".format( min_b[0], max_b[0], min_b[1], max_b[1], min_b[2], max_b[2]), "> Range: {0:.1f}X, {1:.1f}Y, {2:.1f}Z".format(rang[0], rang[1], rang[2]), "> Centre of mass: ({0:.1f}X, {1:.1f}Y, {2:.1f}Z)".format( cm[0], cm[1], cm[2]), "> Norm: {0:.2f}".format(landmarks[group].norm())] if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(landmarks[group]): text_per_line.append('> {}'.format(msg)) else: text_per_line = ["No landmarks available."] info_wid.set_widget_state(text_per_line=text_per_line) # Create widgets groups_keys, labels_keys = extract_group_labels_from_landmarks(landmarks[0]) first_label = labels_keys[0] if labels_keys else None landmark_options_wid = LandmarkOptionsWidget( group_keys=groups_keys, labels_keys=labels_keys, type='3D', render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['numbering_mayavi'], labels=first_label, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() # Group widgets if n_landmarks > 1: # Define function that updates options' widgets state def update_widgets(change): # Get new groups and labels i = landmark_number_wid.selected_values g_keys, l_keys = extract_group_labels_from_landmarks( landmarks[i]) # Update landmarks options landmark_options_wid.set_widget_state( group_keys=g_keys, labels_keys=l_keys, allow_callback=True) # Landmark selection slider index = {'min': 0, 'max': n_landmarks-1, 'step': 1, 'index': 0} landmark_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Shape', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, landmark_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( children=[info_wid, landmark_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Landmarks', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_landmarks > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) print_dynamic('') def visualize_meshes_3d(meshes, browser_style='buttons', custom_info_callback=None): r""" Widget that allows browsing through a `list` of 3D meshes. The supported objects are: ================================== Object ================================== `menpo.shape.TriMesh` `menpo.shape.ColouredTriMesdh` `menpo.shape.TexturedTriMesh` ================================== Any instance of the above can be combined in the input `list`. Parameters ---------- meshes : `list` The `list` of objects to be visualized. It can contain a combination of ================================== Object ================================== `menpo.shape.TriMesh` `menpo.shape.ColouredTriMesdh` `menpo.shape.TexturedTriMesh` ================================== or subclasses of those. browser_style : ``{'buttons', 'slider'}``, optional It defines whether the selector of the objects will have the form of plus/minus buttons or a slider. custom_info_callback: `function` or ``None``, optional If not ``None``, it should be a function that accepts a 3D mesh and returns a list of custom messages to be printed about it. Each custom message will be printed in a separate line. """ # Ensure that the code is being run inside a Jupyter kernel!! from menpowidgets.utils import verify_ipython_and_kernel verify_ipython_and_kernel() out = ipywidgets.Output() ipydisplay.display(out) with out: ipydisplay.clear_output(wait=True) print('Initializing...') # Make sure that meshes is a list even with one member if not isinstance(meshes, Sized): meshes = [meshes] # Get the number of meshes n_meshes = len(meshes) # Define the styling options main_style = 'warning' # Define render function def render_function(_): # Clear current figure save_figure_wid.renderer.clear_figure() with out: ipydisplay.clear_output(wait=True) # Get selected mesh index i = mesh_number_wid.selected_values if n_meshes > 1 else 0 # Update info text widget update_info(meshes[i], custom_info_callback=custom_info_callback) # Render instance save_figure_wid.renderer = meshes[i].view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, **mesh_options_wid.selected_values) # Force rendering save_figure_wid.renderer.force_draw() # Define function that updates the info text def update_info(mesh, custom_info_callback=None): min_b, max_b = mesh.bounds() rang = mesh.range() cm = mesh.centre() text_per_line = [ "> {}".format(name_of_callable(mesh)), "> {} points".format(mesh.n_points), "> Bounds: [{0:.1f}-{1:.1f}]X, [{2:.1f}-{3:.1f}]Y, " "[{4:.1f}-{5:.1f}]Z".format( min_b[0], max_b[0], min_b[1], max_b[1], min_b[2], max_b[2]), "> Range: {0:.1f}X, {1:.1f}Y, {2:.1f}Z".format(rang[0], rang[1], rang[2]), "> Centre of mass: ({0:.1f}X, {1:.1f}Y, {2:.1f}Z)".format( cm[0], cm[1], cm[2]), "> Norm: {0:.2f}".format(mesh.norm())] if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(mesh): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) # Create widgets mesh_options_wid = Mesh3DOptionsWidget( textured=(isinstance(meshes[0], ColouredTriMesh) or isinstance(meshes[0], TexturedTriMesh)), render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() # Group widgets if n_meshes > 1: # Define function that updates options' widgets state def update_widgets(change): i = change['new'] # Update shape options mesh_options_wid.set_widget_state( textured=(isinstance(meshes[i], ColouredTriMesh) or isinstance(meshes[i], TexturedTriMesh)), allow_callback=True) # selection slider index = {'min': 0, 'max': n_meshes-1, 'step': 1, 'index': 0} mesh_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Mesh', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, mesh_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab([info_wid, mesh_options_wid, save_figure_wid]) tab_titles = ['Info', 'Mesh', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_meshes > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) with out: print_dynamic('') def visualize_images(images, figure_size=(7, 7), browser_style='buttons', custom_info_callback=None): r""" Widget that allows browsing through a `list` of `menpo.image.Image` (or subclass) objects. The images can have a combination of different attributes, e.g. masked or not, landmarked or not, without multiple landmark groups and labels etc. Parameters ---------- images : `list` of `menpo.image.Image` or subclass The `list` of images to be visualized. figure_size : (`int`, `int`), optional The initial size of the rendered figure. browser_style : ``{'buttons', 'slider'}``, optional It defines whether the selector of the objects will have the form of plus/minus buttons or a slider. custom_info_callback: `function` or ``None``, optional If not None, it should be a function that accepts an image and returns a list of custom messages to be printed per image. Each custom message will be printed in a separate line. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that images is a list even with one member if not isinstance(images, Sized): images = [images] # Get the number of images n_images = len(images) # Define the styling options main_style = 'info' # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # get selected index and selected group i = image_number_wid.selected_values if n_images > 1 else 0 g = landmark_options_wid.selected_values['landmarks']['group'] # check if image is masked image_is_masked = isinstance(images[i], MaskedImage) # Create options dictionary options = dict() options.update(landmark_options_wid.selected_values['lines']) options.update(landmark_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) options.update(renderer_options_wid.selected_values['legend']) options.update(image_options_wid.selected_values) options.update(landmark_options_wid.selected_values['landmarks']) # Correct options based on the type of the shape if (images[i].has_landmarks and hasattr(images[i].landmarks[g], 'labels')): # If the shape is a LabelledPointUndirectedGraph ... # ...correct colours line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: id = images[i].landmarks[g].labels.index(lbl) line_colour.append(options['line_colour'][id]) marker_face_colour.append(options['marker_face_colour'][id]) marker_edge_colour.append(options['marker_edge_colour'][id]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] # Get figure size new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) # Render shape with selected options save_figure_wid.renderer = render_image( image=images[i], renderer=save_figure_wid.renderer, image_is_masked=image_is_masked, figure_size=new_figure_size, **options) # Update info update_info(images[i], image_is_masked, g, custom_info_callback=custom_info_callback) # Define function that updates the info text def update_info(img, image_is_masked, group, custom_info_callback=None): # Prepare masked (or non-masked) string masked_str = 'Masked Image' if image_is_masked else 'Image' # Get image path, if available path_str = img.path if hasattr(img, 'path') else 'No path available' # Create text lines text_per_line = [ "> {} of size {} with {} channel{}".format( masked_str, img._str_shape(), img.n_channels, 's' * (img.n_channels > 1)), "> Path: '{}'".format(path_str)] if image_is_masked: text_per_line.append( "> {} masked pixels (attached mask {:.1%} true)".format( img.n_true_pixels(), img.mask.proportion_true())) text_per_line.append("> min={:.3f}, max={:.3f}".format( img.pixels.min(), img.pixels.max())) if img.has_landmarks: text_per_line.append("> {} landmark points".format( img.landmarks[group].n_points)) if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(img): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) # Create widgets groups_keys, labels_keys = extract_groups_labels_from_image(images[0]) first_label = labels_keys[0] if labels_keys else None image_options_wid = ImageOptionsWidget( n_channels=images[0].n_channels, image_is_masked=isinstance(images[0], MaskedImage), render_function=render_function) landmark_options_wid = LandmarkOptionsWidget( group_keys=groups_keys, labels_keys=labels_keys, type='2D', render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], labels=first_label, axes_x_limits=None, axes_y_limits=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets if n_images > 1: # Define function that updates options' widgets state def update_widgets(change): # Get new groups and labels, then update landmark options i = image_number_wid.selected_values g_keys, l_keys = extract_groups_labels_from_image(images[i]) # Update landmarks options landmark_options_wid.set_widget_state( group_keys=g_keys, labels_keys=l_keys, allow_callback=False) # Update channels options image_options_wid.set_widget_state( n_channels=images[i].n_channels, image_is_masked=isinstance(images[i], MaskedImage), allow_callback=True) # Image selection slider index = {'min': 0, 'max': n_images-1, 'step': 1, 'index': 0} image_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Image', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, image_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( children=[info_wid, image_options_wid, landmark_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Image', 'Landmarks', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_images > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def visualize_patches(patches, patch_centers, figure_size=(7, 7), browser_style='buttons', custom_info_callback=None): r""" Widget that allows browsing through a `list` of patch-based images. The patches argument can have any of the two formats that are returned from the `extract_patches()` and `extract_patches_around_landmarks()` methods of `menpo.image.Image`. Specifically it can be: 1. ``(n_center, n_offset, self.n_channels, patch_shape)`` `ndarray` 2. `list` of ``n_center * n_offset`` `menpo.image.Image` objects The patches can have a combination of different attributes, e.g. number of centers, number of offsets, number of channels etc. Parameters ---------- patches : `list` The `list` of patch-based images to be visualized. It can consist of objects with any of the two formats that are returned from the `extract_patches()` and `extract_patches_around_landmarks()` methods. Specifically, it can either be an ``(n_center, n_offset, self.n_channels, patch_shape)`` `ndarray` or a `list` of ``n_center * n_offset`` `menpo.image.Image` objects. patch_centers : `list` of `menpo.shape.PointCloud` The centers to set the patches around. If the `list` has only one `menpo.shape.PointCloud` then this will be used for all patches members. Otherwise, it needs to have the same length as patches. figure_size : (`int`, `int`), optional The initial size of the rendered figure. browser_style : ``{'buttons', 'slider'}``, optional It defines whether the selector of the objects will have the form of plus/minus buttons or a slider. custom_info_callback: `function` or ``None``, optional If not None, it should be a function that accepts an image and returns a list of custom messages to be printed per image. Each custom message will be printed in a separate line. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that patches is a list even with one member if (isinstance(patches, list) and isinstance(patches[0], Image)) or \ not isinstance(patches, list): patches = [patches] # Make sure that patch_centers is a list even with one shape if not isinstance(patch_centers, list): patch_centers = [patch_centers] * len(patches) elif isinstance(patch_centers, list) and len(patch_centers) == 1: patch_centers *= len(patches) # Make sure all patch-based images are in the single array format for i in range(len(patches)): if isinstance(patches[i], list): patches[i] = _convert_patches_list_to_single_array( patches[i], patch_centers[i].n_points) # Get the number of patch_based images n_patches = len(patches) # Define the styling options main_style = 'info' # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # get selected index i = image_number_wid.selected_values if n_patches > 1 else 0 # Create options dictionary options = dict() options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) image_options = dict(image_options_wid.selected_values) del image_options['masked_enabled'] options.update(image_options) options.update(patch_options_wid.selected_values) options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] # Get figure size new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) # Render image with selected options save_figure_wid.renderer = render_patches( patches=patches[i], patch_centers=patch_centers[i], renderer=save_figure_wid.renderer, figure_size=new_figure_size, **options) # update info text widget update_info(patches[i], custom_info_callback=custom_info_callback) # Define function that updates the info text def update_info(ptchs, custom_info_callback=None): text_per_line = [ "> Patch-Based Image with {} patche{} and {} offset{}.".format( ptchs.shape[0], 's' * (ptchs.shape[0] > 1), ptchs.shape[1], 's' * (ptchs.shape[1] > 1)), "> Each patch has size {}H x {}W with {} channel{}.".format( ptchs.shape[3], ptchs.shape[4], ptchs.shape[2], 's' * (ptchs.shape[2] > 1)), "> min={:.3f}, max={:.3f}".format(ptchs.min(), ptchs.max())] if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(ptchs): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) # Create widgets shape_options_wid = Shape2DOptionsWidget( labels=None, render_function=None) shape_options_wid.line_options_wid.render_lines_switch.button_wid.value = False shape_options_wid.add_render_function(render_function) patch_options_wid = PatchOptionsWidget( n_patches=patches[0].shape[0], n_offsets=patches[0].shape[1], render_function=render_function) image_options_wid = ImageOptionsWidget( n_channels=patches[0].shape[2], image_is_masked=False, render_function=None) image_options_wid.interpolation_checkbox.button_wid.value = False image_options_wid.add_render_function(render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib'], labels=None, axes_x_limits=None, axes_y_limits=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets if n_patches > 1: # Define function that updates options' widgets state def update_widgets(change): # Selected object i = image_number_wid.selected_values # Update patch options patch_options_wid.set_widget_state( n_patches=patches[i].shape[0], n_offsets=patches[i].shape[1], allow_callback=False) # Update channels options image_options_wid.set_widget_state( n_channels=patches[i].shape[2], image_is_masked=False, allow_callback=True) # Image selection slider index = {'min': 0, 'max': n_patches-1, 'step': 1, 'index': 0} image_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Image', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, image_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( children=[info_wid, patch_options_wid, image_options_wid, shape_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Patches', 'Image', 'Shape', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_patches > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def plot_graph(x_axis, y_axis, legend_entries=None, figure_size=(9, 5)): r""" Widget that allows plotting various curves in a graph. The widget has options tabs regarding the graph and the renderer (lines, markers, legend, figure, axes, grid) and saving the figure to file. Parameters ---------- x_axis : `list` of `float` The values of the horizontal axis. Note that these values are common for all the curves. y_axis : `list` of `lists` of `float` A `list` that stores a `list` of values to be plotted for each curve. legend_entries : `list` or `str` or ``None``, optional The `list` of names that will appear on the legend for each curve. If ``None``, then the names format is ``curve {}.format(i)``. figure_size : (`int`, `int`), optional The initial size of the rendered figure. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() from menpo.visualize import plot_curve print('Initializing...') # Get number of curves to be plotted n_curves = len(y_axis) # Define the styling options main_style = 'danger' # Parse options if legend_entries is None: legend_entries = ["curve {}".format(i) for i in range(n_curves)] # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # plot with selected options opts = plot_wid.selected_values.copy() new_figure_size = ( plot_wid.selected_values['zoom'][0] * figure_size[0], plot_wid.selected_values['zoom'][1] * figure_size[1]) del opts['zoom'] save_figure_wid.renderer = plot_curve( x_axis=x_axis, y_axis=y_axis, figure_size=new_figure_size, figure_id=save_figure_wid.renderer.figure_id, new_figure=False, **opts) # show plot save_figure_wid.renderer.force_draw() # Create widgets plot_wid = PlotMatplotlibOptionsWidget( legend_entries=legend_entries, render_function=render_function) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets logo = LogoWidget(style=main_style) logo.layout.margin = '0px 10px 0px 0px' tmp_children = list(plot_wid.tab_box.children) tmp_children.append(save_figure_wid) plot_wid.tab_box.children = tmp_children plot_wid.tab_box.set_title(0, 'Labels') plot_wid.tab_box.set_title(1, 'Style') plot_wid.tab_box.set_title(2, 'Legend') plot_wid.tab_box.set_title(3, 'Axes') plot_wid.tab_box.set_title(4, 'Zoom') plot_wid.tab_box.set_title(5, 'Grid') plot_wid.tab_box.set_title(6, 'Export') # Display final widget wid = ipywidgets.HBox([logo, plot_wid]) wid.box_style = main_style wid.layout.border = '2px solid' + map_styles_to_hex_colours(main_style) plot_wid.container.border = '0px' final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def save_matplotlib_figure(renderer): r""" Widget that allows to save a figure, which was generated with Matplotlib, to file. Parameters ---------- renderer : `menpo.visualize.viewmatplotlib.MatplotlibRenderer` The Matplotlib renderer object. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() # Create sub-widgets logo_wid = LogoWidget() logo_wid.layout.margin = '0px 10px 0px 0px' save_figure_wid = SaveMatplotlibFigureOptionsWidget(renderer, style='warning') wid = ipywidgets.HBox([logo_wid, save_figure_wid]) # Display widget ipydisplay.display(wid) def save_mayavi_figure(renderer): r""" Widget that allows to save a figure, which was generated with Mayavi, to file. Parameters ---------- renderer : `menpo3d.visualize.viewmayavi.MayaviRenderer` The Mayavi renderer object. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() # Create sub-widgets logo_wid = LogoWidget() logo_wid.layout.margin = '0px 10px 0px 0px' save_figure_wid = SaveMayaviFigureOptionsWidget(renderer, style='warning') wid = ipywidgets.HBox([logo_wid, save_figure_wid]) # Display widget ipydisplay.display(wid) def visualize_shape_model_2d(shape_model, n_parameters=5, mode='multiple', parameters_bounds=(-3.0, 3.0), figure_size=(7, 7)): r""" Widget that allows the dynamic visualization of a multi-scale linear statistical 2D shape model. Parameters ---------- shape_model : `list` of `menpo.shape.PCAModel` or `subclass` The multi-scale shape model to be visualized. Note that each level can have different number of components. n_parameters : `int` or `list` of `int` or ``None``, optional The number of principal components to be used for the parameters sliders. If `int`, then the number of sliders per level is the minimum between `n_parameters` and the number of active components per level. If `list` of `int`, then a number of sliders is defined per level. If ``None``, all the active components per level will have a slider. mode : ``{'single', 'multiple'}``, optional If ``'single'``, then only a single slider is constructed along with a drop down menu. If ``'multiple'``, then a slider is constructed for each parameter. parameters_bounds : (`float`, `float`), optional The minimum and maximum bounds, in std units, for the sliders. figure_size : (`int`, `int`), optional The size of the plotted figures. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() from menpo.visualize.viewmatplotlib import (_set_axes_options, _parse_axes_limits) out = ipywidgets.Output() ipydisplay.display(out) with out: ipydisplay.clear_output(wait=True) print('Initializing...') # Make sure that shape_model is a list even with one member if not isinstance(shape_model, list): shape_model = [shape_model] # Get the number of levels (i.e. number of shape models) n_levels = len(shape_model) # Define the styling options main_style = 'warning' # Get the maximum number of components per level max_n_params = [sp.n_active_components for sp in shape_model] # Check the given number of parameters (the returned n_parameters is a list # of len n_scales) n_parameters = check_n_parameters(n_parameters, n_levels, max_n_params) # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered with out: ipydisplay.clear_output(wait=True) # Get selected level level = 0 if n_levels > 1: level = level_wid.value # Compute weights parameters = model_parameters_wid.selected_values weights = (parameters * shape_model[level].eigenvalues[:len(parameters)] ** 0.5) # Get the mean mean = shape_model[level].mean() # Create options dictionary options = dict() options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options['image_view'] = shape_options_wid.selected_values['image_view'] options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) # Correct options based on the type of the shape if hasattr(mean, 'labels'): # If the shape is a LabelledPointUndirectedGraph ... # ...use the legend options options.update(renderer_options_wid.selected_values['legend']) # ...use with_labels options['with_labels'] = \ shape_options_wid.selected_values['with_labels'] # ...correct colours line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: idx = mean.labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_face_colour.append(options['marker_face_colour'][idx]) marker_edge_colour.append(options['marker_edge_colour'][idx]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] # Get figure size new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) # Render with selected options if mode_wid.value == 1: # Deformation mode # Compute instance instance = shape_model[level].instance(weights) # Render mean shape if mean_wid.selected_values: save_figure_wid.renderer = mean.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=None, image_view=options['image_view'], render_lines=options['render_lines'], line_colour='yellow', line_style=options['line_style'], line_width=options['line_width'], render_markers=options['render_markers'], marker_style=options['marker_style'], marker_size=options['marker_size'], marker_face_colour='yellow', marker_edge_colour='yellow', marker_edge_width=options['marker_edge_width']) # Render instance save_figure_wid.renderer = instance.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size, **options) # Get instance range instance_range = instance.range() else: # Vectors mode # Compute instance instance_lower = shape_model[level].instance([-p for p in weights]) instance_upper = shape_model[level].instance(weights) # Render mean shape save_figure_wid.renderer = mean.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size, **options) # Render vectors ax = plt.gca() x_min = np.Inf y_min = np.Inf x_max = -np.Inf y_max = -np.Inf for p in range(mean.n_points): xm = mean.points[p, 0] ym = mean.points[p, 1] xl = instance_lower.points[p, 0] yl = instance_lower.points[p, 1] xu = instance_upper.points[p, 0] yu = instance_upper.points[p, 1] if options['image_view']: # image mode lines = [[(ym, xm), (yl, xl)], [(ym, xm), (yu, xu)]] else: # point cloud mode lines = [[(xm, ym), (xl, yl)], [(xm, ym), (xu, yu)]] lc = mc.LineCollection(lines, colors=('g', 'b'), linestyles='solid', linewidths=2) # update min, max y_min = np.min([y_min, xl, xu]) y_max = np.max([y_max, xl, xu]) x_min = np.min([x_min, yl, yu]) x_max = np.max([x_max, yl, yu]) # add collection ax.add_collection(lc) # parse axes limits axes_x_limits, axes_y_limits = _parse_axes_limits( x_min, x_max, y_min, y_max, options['axes_x_limits'], options['axes_y_limits']) _set_axes_options( ax, render_axes=options['render_axes'], inverted_y_axis=options['image_view'], axes_font_name=options['axes_font_name'], axes_font_size=options['axes_font_size'], axes_font_style=options['axes_font_style'], axes_font_weight=options['axes_font_weight'], axes_x_limits=axes_x_limits, axes_y_limits=axes_y_limits, axes_x_ticks=options['axes_x_ticks'], axes_y_ticks=options['axes_y_ticks']) # Get instance range instance_range = mean.range() # Force rendering save_figure_wid.renderer.force_draw() # Update info update_info(level, instance_range) # Define function that updates the info text def update_info(level, instance_range): text_per_line = [ "> Level {} out of {}".format(level + 1, n_levels), "> {} components in total".format(shape_model[level].n_components), "> {} active components".format( shape_model[level].n_active_components), "> {:.1f}% variance kept".format( shape_model[level].variance_ratio() * 100), "> Instance range: {:.1f} x {:.1f}".format(instance_range[0], instance_range[1]), "> {} landmark points, {} features".format( shape_model[level].mean().n_points, shape_model[level].n_features)] info_wid.set_widget_state(text_per_line=text_per_line) # Plot variance function def plot_variance(name): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # Get selected level level = level_wid.value if n_levels > 1 else 0 # Render new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * 10, renderer_options_wid.selected_values['zoom_one'] * 3) plt.subplot(121) save_figure_wid.renderer = shape_model[level].plot_eigenvalues_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False) plt.subplot(122) save_figure_wid.renderer = \ shape_model[level].plot_eigenvalues_cumulative_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size) save_figure_wid.renderer.force_draw() # Create widgets mode_dict = OrderedDict() mode_dict['Deformation'] = 1 mode_dict['Vectors'] = 2 mode_wid = ipywidgets.RadioButtons( options=mode_dict, description='Mode', value=1, layout=ipywidgets.Layout(width='6cm')) mode_wid.observe(render_function, names='value', type='change') mean_wid = SwitchWidget( selected_value=False, description='Render mean shape', description_location='right', switch_type='checkbox') mean_wid.observe(render_function, names='selected_values', type='change') # Function that controls mean shape checkbox visibility def mean_visible(change): if change['new'] == 1: mean_wid.button_wid.disabled = False else: mean_wid.button_wid.disabled = True mean_wid.set_widget_state(False, allow_callback=False) mode_wid.observe(mean_visible, names='value', type='change') model_parameters_wid = LinearModelParametersWidget( n_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_variance, animation_step=0.5, interval=0., loop_enabled=True, continuous_update=False) labels = None if hasattr(shape_model[0].mean(), 'labels'): labels = shape_model[0].mean().labels shape_options_wid = Shape2DOptionsWidget( labels=labels, render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], labels=None, axes_x_limits=0.1, axes_y_limits=0.1, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets if n_levels > 1: # Define function that updates options' widgets state def update_widgets(change): model_parameters_wid.set_widget_state( n_parameters=n_parameters[change['new']], params_str='Parameter ', allow_callback=True) # Create pyramid radiobuttons radio_str = OrderedDict() for l in range(n_levels): if l == 0: radio_str["Level {} (low)".format(l)] = l elif l == n_levels - 1: radio_str["Level {} (high)".format(l)] = l else: radio_str["Level {}".format(l)] = l level_wid = ipywidgets.RadioButtons( options=radio_str, description='Pyramid', value=n_levels-1, layout=ipywidgets.Layout(width='6cm')) level_wid.observe(update_widgets, names='value', type='change') level_wid.observe(render_function, names='value', type='change') radio_children = [level_wid, mode_wid, mean_wid] else: radio_children = [mode_wid, mean_wid] radio_wids = ipywidgets.VBox(radio_children) tmp_wid = ipywidgets.HBox([radio_wids, model_parameters_wid]) options_box = ipywidgets.Tab( children=[tmp_wid, shape_options_wid, renderer_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Shape', 'Renderer', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def visualize_shape_model_3d(shape_model, n_parameters=5, mode='multiple', parameters_bounds=(-15.0, 15.0)): r""" Widget that allows the dynamic visualization of a multi-scale linear statistical 3D shape model. Parameters ---------- shape_model : `list` of `menpo.shape.PCAModel` or `subclass` The multi-scale shape model to be visualized. Note that each level can have different number of components. n_parameters : `int` or `list` of `int` or ``None``, optional The number of principal components to be used for the parameters sliders. If `int`, then the number of sliders per level is the minimum between `n_parameters` and the number of active components per level. If `list` of `int`, then a number of sliders is defined per level. If ``None``, all the active components per level will have a slider. mode : ``{'single', 'multiple'}``, optional If ``'single'``, then only a single slider is constructed along with a drop down menu. If ``'multiple'``, then a slider is constructed for each parameter. parameters_bounds : (`float`, `float`), optional The minimum and maximum bounds, in std units, for the sliders. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() out = ipywidgets.Output() ipydisplay.display(out) with out: ipydisplay.clear_output(wait=True) print('Initializing...') # Make sure that shape_model is a list even with one member if not isinstance(shape_model, list): shape_model = [shape_model] # Get the number of levels (i.e. number of shape models) n_levels = len(shape_model) # Check if the model is TriMesh or any other 3D shape class is_trimesh = isinstance(shape_model[0].template_instance, TriMesh) # Define the styling options main_style = 'warning' # Get the maximum number of components per level max_n_params = [sp.n_active_components for sp in shape_model] # Check the given number of parameters (the returned n_parameters is a list # of len n_scales) n_parameters = check_n_parameters(n_parameters, n_levels, max_n_params) # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered save_figure_wid.renderer.clear_figure() with out: ipydisplay.clear_output(wait=True) # Get selected level level = 0 if n_levels > 1: level = level_wid.value # Compute weights parameters = model_parameters_wid.selected_values weights = (parameters * shape_model[level].eigenvalues[:len(parameters)] ** 0.5) # Compute instance instance = shape_model[level].instance(weights) # Create options dictionary options = dict() if is_trimesh: options.update(shape_options_wid.selected_values) else: options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_mayavi']) # Correct options based on the type of the shape if hasattr(instance, 'labels'): # If the shape is a LabelledPointUndirectedGraph ... # ...use with_labels options['with_labels'] = \ shape_options_wid.selected_values['with_labels'] # ...correct colours line_colour = [] marker_colour = [] for lbl in options['with_labels']: idx = instance.labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_colour.append(options['marker_colour'][idx]) options['line_colour'] = line_colour options['marker_colour'] = marker_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_colour'] = options['marker_colour'][0] # Update info update_info(level, instance.range()) # Render instance save_figure_wid.renderer = instance.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, **options) # Force rendering save_figure_wid.renderer.force_draw() # Define function that updates the info text def update_info(level, instance_range): text_per_line = [ "> Level {} out of {}".format(level + 1, n_levels), "> {} components in total".format(shape_model[level].n_components), "> {} active components".format( shape_model[level].n_active_components), "> {:.1f}% variance kept".format( shape_model[level].variance_ratio() * 100), "> Instance range: {:.1f} x {:.1f}".format(instance_range[0], instance_range[1]), "> {} points".format( shape_model[level].mean().n_points)] info_wid.set_widget_state(text_per_line=text_per_line) # Plot variance function def plot_variance(name): # Clear current figure, but wait until the generation of the new data # that will be rendered # Get selected level level = level_wid.value if n_levels > 1 else 0 # Render with out: ipydisplay.clear_output(wait=True) plt.subplot(121) shape_model[level].plot_eigenvalues_ratio() plt.subplot(122) shape_model[level].plot_eigenvalues_cumulative_ratio() plt.show() # Create widgets model_parameters_wid = LinearModelParametersWidget( n_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_variance, animation_step=0.5, interval=0., loop_enabled=True, continuous_update=False) if is_trimesh: shape_options_wid = Mesh3DOptionsWidget(textured=False, render_function=render_function) else: labels = None if hasattr(shape_model[0].mean(), 'labels'): labels = shape_model[0].mean().labels shape_options_wid = Shape3DOptionsWidget(labels=labels, render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['numbering_mayavi'], labels=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() # Group widgets if n_levels > 1: # Define function that updates options' widgets state def update_widgets(change): model_parameters_wid.set_widget_state( n_parameters=n_parameters[change['new']], params_str='Parameter ', allow_callback=True) # Create pyramid radiobuttons radio_str = OrderedDict() for l in range(n_levels): if l == 0: radio_str["Level {} (low)".format(l)] = l elif l == n_levels - 1: radio_str["Level {} (high)".format(l)] = l else: radio_str["Level {}".format(l)] = l level_wid = ipywidgets.RadioButtons( options=radio_str, description='Pyramid', value=n_levels-1, layout=ipywidgets.Layout(width='6cm')) level_wid.observe(update_widgets, names='value', type='change') level_wid.observe(render_function, names='value', type='change') tmp_wid = ipywidgets.HBox([level_wid, model_parameters_wid]) else: tmp_wid = ipywidgets.HBox(children=[model_parameters_wid]) if is_trimesh: options_box = ipywidgets.Tab( children=[tmp_wid, shape_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Mesh', 'Info', 'Export'] else: options_box = ipywidgets.Tab( children=[tmp_wid, shape_options_wid, renderer_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Shape', 'Renderer', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) with out: print_dynamic('') def visualize_appearance_model(appearance_model, n_parameters=5, mode='multiple', parameters_bounds=(-3.0, 3.0), figure_size=(7, 7)): r""" Widget that allows the dynamic visualization of a multi-scale linear statistical appearance model. Parameters ---------- appearance_model : `list` of `menpo.model.PCAModel` or subclass The multi-scale appearance model to be visualized. Note that each level can have different number of components. n_parameters : `int` or `list` of `int` or ``None``, optional The number of principal components to be used for the parameters sliders. If `int`, then the number of sliders per level is the minimum between `n_parameters` and the number of active components per level. If `list` of `int`, then a number of sliders is defined per level. If ``None``, all the active components per level will have a slider. mode : ``{'single', 'multiple'}``, optional If ``'single'``, then only a single slider is constructed along with a drop down menu. If ``'multiple'``, then a slider is constructed for each parameter. parameters_bounds : (`float`, `float`), optional The minimum and maximum bounds, in std units, for the sliders. figure_size : (`int`, `int`), optional The size of the plotted figures. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that appearance_model is a list even with one member if not isinstance(appearance_model, list): appearance_model = [appearance_model] # Get the number of levels (i.e. number of appearance models) n_levels = len(appearance_model) # Define the styling options main_style = 'success' # Get the maximum number of components per level max_n_params = [ap.n_active_components for ap in appearance_model] # Check the given number of parameters (the returned n_parameters is a list # of len n_scales) n_parameters = check_n_parameters(n_parameters, n_levels, max_n_params) # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # Get selected level level = level_wid.value if n_levels > 1 else 0 # Compute weights and instance parameters = model_parameters_wid.selected_values weights = (parameters * appearance_model[level].eigenvalues[:len(parameters)] ** 0.5) instance = appearance_model[level].instance(weights) image_is_masked = isinstance(instance, MaskedImage) g = landmark_options_wid.selected_values['landmarks']['group'] # Create options dictionary options = dict() options.update(landmark_options_wid.selected_values['lines']) options.update(landmark_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) options.update(renderer_options_wid.selected_values['legend']) options.update(image_options_wid.selected_values) options.update(landmark_options_wid.selected_values['landmarks']) # Correct options based on the type of the shape if (instance.has_landmarks and hasattr(instance.landmarks[g], 'labels')): # If the shape is a LabelledPointUndirectedGraph ... # ...correct colours line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: id = instance.landmarks[g].labels.index(lbl) line_colour.append(options['line_colour'][id]) marker_face_colour.append(options['marker_face_colour'][id]) marker_edge_colour.append(options['marker_edge_colour'][id]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] # Get figure size new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) # Render shape with selected options save_figure_wid.renderer = render_image( image=instance, renderer=save_figure_wid.renderer, image_is_masked=image_is_masked, figure_size=new_figure_size, **options) # Update info update_info(instance, level, g) # Define function that updates the info text def update_info(image, level, group): lvl_app_mod = appearance_model[level] lp = 0 if group is None else image.landmarks[group].n_points text_per_line = [ "> Level: {} out of {}.".format(level + 1, n_levels), "> {} components in total.".format(lvl_app_mod.n_components), "> {} active components.".format(lvl_app_mod.n_active_components), "> {:.1f}% variance kept.".format( lvl_app_mod.variance_ratio() * 100), "> Reference shape of size {} with {} channel{}.".format( image._str_shape(), image.n_channels, 's' * (image.n_channels > 1)), "> {} features.".format(lvl_app_mod.n_features), "> {} landmark points.".format(lp), "> Instance: min={:.3f}, max={:.3f}".format(image.pixels.min(), image.pixels.max())] info_wid.set_widget_state(text_per_line=text_per_line) # Plot variance function def plot_variance(name): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # Get selected level level = level_wid.value if n_levels > 1 else 0 # Render new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * 10, renderer_options_wid.selected_values['zoom_one'] * 3) plt.subplot(121) save_figure_wid.renderer = \ appearance_model[level].plot_eigenvalues_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False) plt.subplot(122) save_figure_wid.renderer = \ appearance_model[level].plot_eigenvalues_cumulative_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size) save_figure_wid.renderer.force_draw() # Create widgets model_parameters_wid = LinearModelParametersWidget( n_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_variance, animation_step=0.5, interval=0., loop_enabled=True, continuous_update=False) groups_keys, labels_keys = extract_groups_labels_from_image( appearance_model[0].mean()) image_options_wid = ImageOptionsWidget( n_channels=appearance_model[0].mean().n_channels, image_is_masked=isinstance(appearance_model[0].mean(), MaskedImage), render_function=render_function) landmark_options_wid = LandmarkOptionsWidget( group_keys=groups_keys, labels_keys=labels_keys, type='2D', render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], axes_x_limits=None, axes_y_limits=None, labels=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets tmp_children = [model_parameters_wid] if n_levels > 1: # Define function that updates options' widgets state def update_widgets(change): value = change['new'] # Update model parameters widget model_parameters_wid.set_widget_state( n_parameters[value], params_str='Parameter ', allow_callback=False) # Update landmarks options g_keys, l_keys = extract_groups_labels_from_image( appearance_model[value].mean()) landmark_options_wid.set_widget_state( group_keys=g_keys, labels_keys=l_keys, allow_callback=False) # Update channels options image_options_wid.set_widget_state( n_channels=appearance_model[value].mean().n_channels, image_is_masked=isinstance( appearance_model[value].mean(), MaskedImage), allow_callback=True) # Create pyramid radiobuttons radio_str = OrderedDict() for l in range(n_levels): if l == 0: radio_str["Level {} (low)".format(l)] = l elif l == n_levels - 1: radio_str["Level {} (high)".format(l)] = l else: radio_str["Level {}".format(l)] = l level_wid = ipywidgets.RadioButtons( options=radio_str, description='Pyramid', value=n_levels-1, layout=ipywidgets.Layout(width='6cm')) level_wid.observe(update_widgets, names='value', type='change') level_wid.observe(render_function, names='value', type='change') tmp_children.insert(0, level_wid) tmp_wid = ipywidgets.HBox(tmp_children) options_box = ipywidgets.Tab( children=[tmp_wid, image_options_wid, landmark_options_wid, renderer_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Image', 'Landmarks', 'Renderer', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def visualize_patch_appearance_model(appearance_model, centers, n_parameters=5, mode='multiple', parameters_bounds=(-3.0, 3.0), figure_size=(7, 7)): r""" Widget that allows the dynamic visualization of a multi-scale linear statistical patch-based appearance model. Parameters ---------- appearance_model : `list` of `menpo.model.PCAModel` or subclass The multi-scale patch-based appearance model to be visualized. Note that each level can have different number of components. centers : `list` of `menpo.shape.PointCloud` or subclass The centers to set the patches around. If the `list` has only one `menpo.shape.PointCloud` then this will be used for all appearance model levels. Otherwise, it needs to have the same length as `appearance_model`. n_parameters : `int` or `list` of `int` or ``None``, optional The number of principal components to be used for the parameters sliders. If `int`, then the number of sliders per level is the minimum between `n_parameters` and the number of active components per level. If `list` of `int`, then a number of sliders is defined per level. If ``None``, all the active components per level will have a slider. mode : ``{'single', 'multiple'}``, optional If ``'single'``, then only a single slider is constructed along with a drop down menu. If ``'multiple'``, then a slider is constructed for each parameter. parameters_bounds : (`float`, `float`), optional The minimum and maximum bounds, in std units, for the sliders. figure_size : (`int`, `int`), optional The size of the plotted figures. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that appearance_model is a list even with one member if not isinstance(appearance_model, list): appearance_model = [appearance_model] # Get the number of levels (i.e. number of appearance models) n_levels = len(appearance_model) # Make sure that centers is a list even with one pointcloud if not isinstance(centers, list): centers = [centers] * n_levels elif isinstance(centers, list) and len(centers) == 1: centers *= n_levels # Define the styling options main_style = 'success' # Get the maximum number of components per level max_n_params = [ap.n_active_components for ap in appearance_model] # Check the given number of parameters (the returned n_parameters is a list # of len n_scales) n_parameters = check_n_parameters(n_parameters, n_levels, max_n_params) # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # Get selected level level = level_wid.value if n_levels > 1 else 0 # Compute weights and instance parameters = model_parameters_wid.selected_values weights = (parameters * appearance_model[level].eigenvalues[:len(parameters)] ** 0.5) instance = appearance_model[level].instance(weights) # Create options dictionary options = dict() options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) image_options = dict(image_options_wid.selected_values) del image_options['masked_enabled'] options.update(image_options) options.update(patch_options_wid.selected_values) options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] # Get figure size new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) # Render image with selected options save_figure_wid.renderer = render_patches( patches=instance.pixels, patch_centers=centers[level], renderer=save_figure_wid.renderer, figure_size=new_figure_size, **options) # Update info update_info(instance, level) # Define function that updates the info text def update_info(image, level): lvl_app_mod = appearance_model[level] text_per_line = [ "> Level: {} out of {}.".format(level + 1, n_levels), "> {} components in total.".format(lvl_app_mod.n_components), "> {} active components.".format(lvl_app_mod.n_active_components), "> {:.1f}% variance kept.".format( lvl_app_mod.variance_ratio() * 100), "> Each patch has size {}H x {}W with {} channel{}.".format( image.pixels.shape[3], image.pixels.shape[4], image.pixels.shape[2], 's' * (image.pixels.shape[2] > 1)), "> {} features.".format(lvl_app_mod.n_features), "> {} landmark points.".format(image.pixels.shape[0]), "> Instance: min={:.3f}, max={:.3f}".format(image.pixels.min(), image.pixels.max())] info_wid.set_widget_state(text_per_line=text_per_line) # Plot variance function def plot_variance(name): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # Get selected level level = 0 if n_levels > 1: level = level_wid.value # Render new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * 10, renderer_options_wid.selected_values['zoom_one'] * 3) plt.subplot(121) save_figure_wid.renderer = \ appearance_model[level].plot_eigenvalues_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False) plt.subplot(122) save_figure_wid.renderer = \ appearance_model[level].plot_eigenvalues_cumulative_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size) save_figure_wid.renderer.force_draw() # Create widgets model_parameters_wid = LinearModelParametersWidget( n_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_variance, animation_step=0.5, interval=0., loop_enabled=True, continuous_update=False) shape_options_wid = Shape2DOptionsWidget( labels=None, render_function=None) shape_options_wid.line_options_wid.render_lines_switch.button_wid.value = False shape_options_wid.add_render_function(render_function) patch_options_wid = PatchOptionsWidget( n_patches=appearance_model[0].mean().pixels.shape[0], n_offsets=appearance_model[0].mean().pixels.shape[1], render_function=render_function) image_options_wid = ImageOptionsWidget( n_channels=appearance_model[0].mean().pixels.shape[2], image_is_masked=isinstance(appearance_model[0].mean(), MaskedImage), render_function=None) image_options_wid.interpolation_checkbox.button_wid.value = False image_options_wid.add_render_function(render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib'], labels=None, axes_x_limits=None, axes_y_limits=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets tmp_children = [model_parameters_wid] if n_levels > 1: # Define function that updates options' widgets state def update_widgets(change): value = change['new'] # Update model parameters widget model_parameters_wid.set_widget_state( n_parameters[value], params_str='Parameter ', allow_callback=False) # Update patch options patch_options_wid.set_widget_state( n_patches=appearance_model[value].mean().pixels.shape[0], n_offsets=appearance_model[value].mean().pixels.shape[1], allow_callback=False) # Update channels options image_options_wid.set_widget_state( n_channels=appearance_model[value].mean().pixels.shape[2], image_is_masked=isinstance(appearance_model[value].mean(), MaskedImage), allow_callback=True) # Define pyramid radiobuttons radio_str = OrderedDict() for l in range(n_levels): if l == 0: radio_str["Level {} (low)".format(l)] = l elif l == n_levels - 1: radio_str["Level {} (high)".format(l)] = l else: radio_str["Level {}".format(l)] = l level_wid = ipywidgets.RadioButtons( options=radio_str, description='Pyramid', value=n_levels-1, layout=ipywidgets.Layout(width='6cm')) level_wid.observe(update_widgets, names='value', type='change') level_wid.observe(render_function, names='value', type='change') tmp_children.insert(0, level_wid) tmp_wid = ipywidgets.HBox(tmp_children) options_box = ipywidgets.Tab( children=[tmp_wid, patch_options_wid, image_options_wid, shape_options_wid, renderer_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Patches', 'Channels', 'Shape', 'Renderer', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def visualize_morphable_model(mm, n_shape_parameters=5, n_texture_parameters=5, mode='multiple', parameters_bounds=(-15.0, 15.0)): r""" Widget that allows the dynamic visualization of a 3D Morphable Model. Parameters ---------- mm : `menpo3d.morhpablemodel.ColouredMorphableModel` or `subclass` The multi-scale 3D Morphable Model to be visualized. n_shape_parameters : `int` or `list` of `int` or ``None``, optional The number of principal components to be used for the shape parameters sliders. If `int`, then the number of sliders per level is the minimum between `n_parameters` and the number of active components per level. If `list` of `int`, then a number of sliders is defined per level. If ``None``, all the active components per level will have a slider. n_texture_parameters : `int` or `list` of `int` or ``None``, optional The number of principal components to be used for the tecture parameters sliders. If `int`, then the number of sliders per level is the minimum between `n_parameters` and the number of active components per level. If `list` of `int`, then a number of sliders is defined per level. If ``None``, all the active components per level will have a slider. mode : ``{'single', 'multiple'}``, optional If ``'single'``, then only a single slider is constructed along with a drop down menu. If ``'multiple'``, then a slider is constructed for each parameter. parameters_bounds : (`float`, `float`), optional The minimum and maximum bounds, in std units, for the sliders. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print_dynamic('Initializing...') # Define the styling options main_style = 'info' # Check the given number of parameters n_shape_parameters = check_n_parameters( n_shape_parameters, 1, [mm.shape_model.n_active_components]) n_texture_parameters = check_n_parameters( n_texture_parameters, 1, [mm.texture_model.n_active_components]) # Define render function def render_function(change): # Clear current figure save_figure_wid.renderer.clear_figure() ipydisplay.clear_output(wait=True) # Compute weights shape_weights = shape_model_parameters_wid.selected_values shape_weights = ( shape_weights * mm.shape_model.eigenvalues[:len(shape_weights)] ** 0.5) texture_weights = texture_model_parameters_wid.selected_values texture_weights = ( texture_weights * mm.texture_model.eigenvalues[:len(texture_weights)] ** 0.5) instance = mm.instance(shape_weights=shape_weights, texture_weights=texture_weights) # TODO: Is this really needed? instance = instance.clip_texture() # Update info update_info(mm, instance) # Render instance save_figure_wid.renderer = instance.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, **mesh_options_wid.selected_values) # Force rendering save_figure_wid.renderer.force_draw() # Define function that updates the info text def update_info(mm, instance): text_per_line = [ "> {} vertices, {} triangles".format(mm.n_vertices, mm.n_triangles), "> {} shape components ({:.2f}% of variance)".format( mm.shape_model.n_components, mm.shape_model.variance_ratio() * 100), "> {} texture channels".format(mm.n_channels), "> {} texture components ({:.2f}% of variance)".format( mm.texture_model.n_components, mm.texture_model.variance_ratio() * 100), "> Instance: min={:.3f} , max={:.3f}".format( instance.colours.min(), instance.colours.max())] info_wid.set_widget_state(text_per_line=text_per_line) # Plot shape variance function def plot_shape_variance(name): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # Render plt.subplot(121) mm.shape_model.plot_eigenvalues_ratio() plt.subplot(122) mm.shape_model.plot_eigenvalues_cumulative_ratio() plt.show() # Plot texture variance function def plot_texture_variance(name): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # Render plt.subplot(121) mm.texture_model.plot_eigenvalues_ratio() plt.subplot(122) mm.texture_model.plot_eigenvalues_cumulative_ratio() plt.show() # Create widgets shape_model_parameters_wid = LinearModelParametersWidget( n_shape_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_shape_variance, animation_step=0.5, interval=0., loop_enabled=True) texture_model_parameters_wid = LinearModelParametersWidget( n_texture_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_texture_variance, animation_step=0.5, interval=0., loop_enabled=True) mesh_options_wid = Mesh3DOptionsWidget(textured=True, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() # Group widgets model_parameters_wid = ipywidgets.HBox( [ipywidgets.Tab([shape_model_parameters_wid, texture_model_parameters_wid])]) model_parameters_wid.children[0].set_title(0, 'Shape') model_parameters_wid.children[0].set_title(1, 'Texture') options_box = ipywidgets.Tab([model_parameters_wid, mesh_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Mesh', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) print_dynamic('') def webcam_widget(canvas_width=640, hd=True, n_preview_windows=5): r""" Webcam widget for taking snapshots. The snapshots are dynamically previewed in a FIFO stack of thumbnails. Parameters ---------- canvas_width : `int`, optional The initial width of the rendered canvas. Note that this doesn't actually change the webcam resolution. It simply rescales the rendered image, as well as the size of the returned screenshots. hd : `bool`, optional If ``True``, then the webcam will be set to high definition (HD), i.e. 720 x 1280. Otherwise the default resolution will be used. n_preview_windows : `int`, optional The number of preview thumbnails that will be used as a FIFO stack to show the captured screenshots. It must be at least 4. Returns ------- snapshots : `list` of `menpo.image.Image` The list of captured images. """ # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() # Set update function images = [] def update(_): images.append(wid.selected_values[-1]) # Create widgets wid = CameraSnapshotWidget( canvas_width=canvas_width, hd=hd, n_preview_windows=n_preview_windows, preview_windows_margin=3, style='danger', preview_style='warning', render_function=update) wid.container.layout.border = ( '2px solid' + map_styles_to_hex_colours('danger')) # Display widget ipydisplay.display(wid) # Return return images

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from collections import Sized, OrderedDict import matplotlib.pyplot as plt from matplotlib import collections as mc import numpy as np import ipywidgets import IPython.display as ipydisplay from menpo.base import name_of_callable from menpo.image import MaskedImage, Image from menpo.image.base import _convert_patches_list_to_single_array from menpo.shape import TriMesh, ColouredTriMesh, TexturedTriMesh from menpo.visualize import print_dynamic from menpo.landmark import LandmarkManager from .options import (RendererOptionsWidget, TextPrintWidget, SaveMatplotlibFigureOptionsWidget, AnimationOptionsWidget, ImageOptionsWidget, LandmarkOptionsWidget, PlotMatplotlibOptionsWidget, PatchOptionsWidget, LinearModelParametersWidget, CameraSnapshotWidget, Shape2DOptionsWidget, Shape3DOptionsWidget, SaveMayaviFigureOptionsWidget, Mesh3DOptionsWidget) from .tools import LogoWidget, SwitchWidget from .utils import (extract_group_labels_from_landmarks, extract_groups_labels_from_image, render_image, render_patches) from .checks import check_n_parameters from .style import map_styles_to_hex_colours def menpowidgets_src_dir_path(): from pathlib import Path import os.path return Path(os.path.abspath(__file__)).parent def visualize_shapes_2d(shapes, figure_size=(7, 7), browser_style='buttons', custom_info_callback=None): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') if not isinstance(shapes, Sized): shapes = [shapes] n_shapes = len(shapes) main_style = 'warning' def render_function(change): ipydisplay.clear_output(wait=True) i = shape_number_wid.selected_values if n_shapes > 1 else 0 options = dict() options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options['image_view'] = shape_options_wid.selected_values['image_view'] options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) if hasattr(shapes[i], 'labels'): options.update(renderer_options_wid.selected_values['legend']) options['with_labels'] = \ shape_options_wid.selected_values['with_labels'] line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: idx = shapes[i].labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_face_colour.append(options['marker_face_colour'][idx]) marker_edge_colour.append(options['marker_edge_colour'][idx]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) save_figure_wid.renderer = shapes[i].view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size, **options) save_figure_wid.renderer.force_draw() update_info(shapes[i], custom_info_callback=custom_info_callback) def update_info(shape, custom_info_callback=None): min_b, max_b = shape.bounds() rang = shape.range() cm = shape.centre() text_per_line = [ "> {}".format(name_of_callable(shape)), "> {} points".format(shape.n_points), "> Bounds: [{0:.1f}-{1:.1f}]W, [{2:.1f}-{3:.1f}]H".format( min_b[0], max_b[0], min_b[1], max_b[1]), "> Range: {0:.1f}W, {1:.1f}H".format(rang[0], rang[1]), "> Centre of mass: ({0:.1f}, {1:.1f})".format(cm[0], cm[1]), "> Norm: {0:.2f}".format(shape.norm())] if custom_info_callback is not None: for msg in custom_info_callback(shape): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) labels = None if hasattr(shapes[0], 'labels'): labels = shapes[0].labels shape_options_wid = Shape2DOptionsWidget( labels=labels, render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], labels=None, axes_x_limits=0.1, axes_y_limits=0.1, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() if n_shapes > 1: def update_widgets(change): # Get current shape and check if it has labels i = change['new'] labels = None if hasattr(shapes[i], 'labels'): labels = shapes[i].labels # Update shape options shape_options_wid.set_widget_state(labels=labels, allow_callback=True) # Shape selection slider index = {'min': 0, 'max': n_shapes-1, 'step': 1, 'index': 0} shape_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Shape', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, shape_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( [info_wid, shape_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Shape', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_shapes > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) def visualize_shapes_3d(shapes, browser_style='buttons', custom_info_callback=None): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print_dynamic('Initializing...') if not isinstance(shapes, Sized): shapes = [shapes] n_shapes = len(shapes) main_style = 'warning' def render_function(change): save_figure_wid.renderer.clear_figure() ipydisplay.clear_output(wait=True) i = shape_number_wid.selected_values if n_shapes > 1 else 0 update_info(shapes[i], custom_info_callback=custom_info_callback) options = dict() if isinstance(shapes[i], TriMesh): # options will have no effect on rendering... options['mesh_type'] = 'wireframe' if shape_options_wid.selected_values['markers']['render_markers']: options['mesh_type'] = 'fancymesh' options['line_width'] = \ shape_options_wid.selected_values['lines']['line_width'] options['colour'] = \ shape_options_wid.selected_values['lines']['line_colour'][0] options['marker_style'] = \ shape_options_wid.selected_values['markers']['marker_style'] options['marker_size'] = \ shape_options_wid.selected_values['markers']['marker_size'] options['marker_resolution'] = \ shape_options_wid.selected_values['markers']['marker_resolution'] options['step'] = \ shape_options_wid.selected_values['markers']['step'] else: options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_mayavi']) # Correct options based on the type of the shape if hasattr(shapes[i], 'labels'): # If the shape is a LabelledPointUndirectedGraph ... # ...use with_labels options['with_labels'] = \ shape_options_wid.selected_values['with_labels'] # ...correct colours line_colour = [] marker_colour = [] for lbl in options['with_labels']: idx = shapes[i].labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_colour.append(options['marker_colour'][idx]) options['line_colour'] = line_colour options['marker_colour'] = marker_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_colour'] = options['marker_colour'][0] # Render shape with selected options save_figure_wid.renderer = shapes[i].view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, alpha=1.0, **options) # Force rendering save_figure_wid.renderer.force_draw() # Define function that updates the info text def update_info(shape, custom_info_callback=None): min_b, max_b = shape.bounds() rang = shape.range() cm = shape.centre() text_per_line = [ "> {}".format(name_of_callable(shape)), "> {} points".format(shape.n_points), "> Bounds: [{0:.1f}-{1:.1f}]X, [{2:.1f}-{3:.1f}]Y, " "[{4:.1f}-{5:.1f}]Z".format(min_b[0], max_b[0], min_b[1], max_b[1], min_b[2], max_b[2]), "> Range: {0:.1f}X, {1:.1f}Y, {2:.1f}Z".format(rang[0], rang[1], rang[2]), "> Centre of mass: ({0:.1f}X, {1:.1f}Y, {2:.1f}Z)".format( cm[0], cm[1], cm[2]), "> Norm: {0:.2f}".format(shape.norm())] if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(shape): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) # If the object is a LabelledPointUndirectedGraph, grab the labels labels = None if hasattr(shapes[0], 'labels'): labels = shapes[0].labels # Create widgets shape_options_wid = Shape3DOptionsWidget( labels=labels, render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['numbering_mayavi'], labels=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() # Group widgets if n_shapes > 1: # Define function that updates options' widgets state def update_widgets(change): i = change['new'] labels = None if hasattr(shapes[i], 'labels'): labels = shapes[i].labels shape_options_wid.set_widget_state(labels=labels, allow_callback=True) index = {'min': 0, 'max': n_shapes-1, 'step': 1, 'index': 0} shape_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Shape', loop_enabled=True, continuous_update=False) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, shape_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( [info_wid, shape_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Shape', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_shapes > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) print_dynamic('') def visualize_landmarks_2d(landmarks, figure_size=(7, 7), browser_style='buttons', custom_info_callback=None): # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that landmarks is a list even with one landmark manager member if isinstance(landmarks, LandmarkManager): landmarks = [landmarks] # Get the number of landmark managers n_landmarks = len(landmarks) # Define the styling options main_style = 'info' # Define render function def render_function(change): # Clear current figure, but wait until the generation of the new data # that will be rendered ipydisplay.clear_output(wait=True) # get selected index and selected group i = landmark_number_wid.selected_values if n_landmarks > 1 else 0 g = landmark_options_wid.selected_values['landmarks']['group'] if landmark_options_wid.selected_values['landmarks']['render_landmarks']: # get shape shape = landmarks[i][g] # Create options dictionary options = dict() options.update(landmark_options_wid.selected_values['lines']) options.update(landmark_options_wid.selected_values['markers']) options['image_view'] = landmark_options_wid.selected_values['image_view'] options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) # Correct options based on the type of the shape if hasattr(shape, 'labels'): # If the shape is a LabelledPointUndirectedGraph ... # ...use the legend options options.update(renderer_options_wid.selected_values['legend']) # ...use with_labels options['with_labels'] = \ landmark_options_wid.selected_values['landmarks']['with_labels'] # ...correct colours line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: id = shape.labels.index(lbl) line_colour.append(options['line_colour'][id]) marker_face_colour.append(options['marker_face_colour'][id]) marker_edge_colour.append(options['marker_edge_colour'][id]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: # If shape is PointCloud, TriMesh or PointGraph # ...correct colours options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] # Get figure size new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) # Render shape with selected options save_figure_wid.renderer = shape.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size, **options) # Force rendering save_figure_wid.renderer.force_draw() else: ipydisplay.clear_output() # update info text widget update_info(landmarks[i], g, custom_info_callback=custom_info_callback) # Define function that updates the info text def update_info(landmarks, group, custom_info_callback=None): if group is not None: min_b, max_b = landmarks[group].bounds() rang = landmarks[group].range() cm = landmarks[group].centre() text_per_line = [ "> {} landmark points".format(landmarks[group].n_points), "> {}".format(name_of_callable(landmarks[group])), "> Bounds: [{0:.1f}-{1:.1f}]W, [{2:.1f}-{3:.1f}]H".format( min_b[0], max_b[0], min_b[1], max_b[1]), "> Range: {0:.1f}W, {1:.1f}H".format(rang[0], rang[1]), "> Centre of mass: ({0:.1f}, {1:.1f})".format(cm[0], cm[1]), "> Norm: {0:.2f}".format(landmarks[group].norm())] if custom_info_callback is not None: # iterate over the list of messages returned by the callback # function and append them in the text_per_line. for msg in custom_info_callback(landmarks[group]): text_per_line.append('> {}'.format(msg)) else: text_per_line = ["No landmarks available."] info_wid.set_widget_state(text_per_line=text_per_line) # Create widgets groups_keys, labels_keys = extract_group_labels_from_landmarks(landmarks[0]) first_label = labels_keys[0] if labels_keys else None landmark_options_wid = LandmarkOptionsWidget( group_keys=groups_keys, labels_keys=labels_keys, type='2D', render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], labels=first_label, axes_x_limits=0.1, axes_y_limits=0.1, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() # Group widgets if n_landmarks > 1: # Define function that updates options' widgets state def update_widgets(change): i = landmark_number_wid.selected_values g_keys, l_keys = extract_group_labels_from_landmarks( landmarks[i]) landmark_options_wid.set_widget_state( group_keys=g_keys, labels_keys=l_keys, allow_callback=True) index = {'min': 0, 'max': n_landmarks-1, 'step': 1, 'index': 0} landmark_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Shape', loop_enabled=True, continuous_update=False) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, landmark_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( children=[info_wid, landmark_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Landmarks', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_landmarks > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) def visualize_landmarks_3d(landmarks, browser_style='buttons', custom_info_callback=None): # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') # Make sure that landmarks is a list even with one landmark manager member if not isinstance(landmarks, list): landmarks = [landmarks] # Get the number of landmark managers n_landmarks = len(landmarks) # Define the styling options main_style = 'info' # Define render function def render_function(change): # Clear current figure save_figure_wid.renderer.clear_figure() ipydisplay.clear_output(wait=True) # get selected index and selected group i = landmark_number_wid.selected_values if n_landmarks > 1 else 0 g = landmark_options_wid.selected_values['landmarks']['group'] # update info text widget update_info(landmarks[i], g, custom_info_callback=custom_info_callback) if landmark_options_wid.selected_values['landmarks']['render_landmarks']: # get shape shape = landmarks[i][g] options = dict() if isinstance(shape, TriMesh): # Note that 3D TriMesh has a totally different set of options # compared to any other PointCloud or PointGraph. However, in # order for visualize_landmarks_3d to support TriMeshes, we # simply use the options that are common. This means that most # of the widget's options will have no effect on rendering... options['mesh_type'] = 'wireframe' if landmark_options_wid.selected_values['markers'][ 'render_markers']: options['mesh_type'] = 'fancymesh' options['line_width'] = \ landmark_options_wid.selected_values['lines']['line_width'] options['colour'] = \ landmark_options_wid.selected_values['lines']['line_colour'][0] options['marker_style'] = \ landmark_options_wid.selected_values['markers']['marker_style'] options['marker_size'] = \ landmark_options_wid.selected_values['markers']['marker_size'] options['marker_resolution'] = \ landmark_options_wid.selected_values['markers'][ 'marker_resolution'] options['step'] = \ landmark_options_wid.selected_values['markers']['step'] else: options.update(landmark_options_wid.selected_values['lines']) options.update(landmark_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_mayavi']) if hasattr(shape, 'labels'): options['with_labels'] = \ landmark_options_wid.selected_values['landmarks']['with_labels'] line_colour = [] marker_colour = [] for lbl in options['with_labels']: idx = shape.labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_colour.append(options['marker_colour'][idx]) options['line_colour'] = line_colour options['marker_colour'] = marker_colour else: options['line_colour'] = options['line_colour'][0] options['marker_colour'] = options['marker_colour'][0] save_figure_wid.renderer = shape.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, alpha=1.0, **options) save_figure_wid.renderer.force_draw() else: ipydisplay.clear_output() def update_info(landmarks, group, custom_info_callback=None): if group is not None: min_b, max_b = landmarks[group].bounds() rang = landmarks[group].range() cm = landmarks[group].centre() text_per_line = [ "> {} landmark points".format(landmarks[group].n_points), "> {}".format(name_of_callable(landmarks[group])), "> Bounds: [{0:.1f}-{1:.1f}]X, [{2:.1f}-{3:.1f}]Y, " "[{4:.1f}-{5:.1f}]Z".format( min_b[0], max_b[0], min_b[1], max_b[1], min_b[2], max_b[2]), "> Range: {0:.1f}X, {1:.1f}Y, {2:.1f}Z".format(rang[0], rang[1], rang[2]), "> Centre of mass: ({0:.1f}X, {1:.1f}Y, {2:.1f}Z)".format( cm[0], cm[1], cm[2]), "> Norm: {0:.2f}".format(landmarks[group].norm())] if custom_info_callback is not None: for msg in custom_info_callback(landmarks[group]): text_per_line.append('> {}'.format(msg)) else: text_per_line = ["No landmarks available."] info_wid.set_widget_state(text_per_line=text_per_line) groups_keys, labels_keys = extract_group_labels_from_landmarks(landmarks[0]) first_label = labels_keys[0] if labels_keys else None landmark_options_wid = LandmarkOptionsWidget( group_keys=groups_keys, labels_keys=labels_keys, type='3D', render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['numbering_mayavi'], labels=first_label, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() if n_landmarks > 1: def update_widgets(change): # Get new groups and labels i = landmark_number_wid.selected_values g_keys, l_keys = extract_group_labels_from_landmarks( landmarks[i]) # Update landmarks options landmark_options_wid.set_widget_state( group_keys=g_keys, labels_keys=l_keys, allow_callback=True) # Landmark selection slider index = {'min': 0, 'max': n_landmarks-1, 'step': 1, 'index': 0} landmark_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Shape', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, landmark_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( children=[info_wid, landmark_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Landmarks', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_landmarks > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) print_dynamic('') def visualize_meshes_3d(meshes, browser_style='buttons', custom_info_callback=None): from menpowidgets.utils import verify_ipython_and_kernel verify_ipython_and_kernel() out = ipywidgets.Output() ipydisplay.display(out) with out: ipydisplay.clear_output(wait=True) print('Initializing...') if not isinstance(meshes, Sized): meshes = [meshes] n_meshes = len(meshes) main_style = 'warning' def render_function(_): save_figure_wid.renderer.clear_figure() with out: ipydisplay.clear_output(wait=True) i = mesh_number_wid.selected_values if n_meshes > 1 else 0 update_info(meshes[i], custom_info_callback=custom_info_callback) save_figure_wid.renderer = meshes[i].view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, **mesh_options_wid.selected_values) save_figure_wid.renderer.force_draw() def update_info(mesh, custom_info_callback=None): min_b, max_b = mesh.bounds() rang = mesh.range() cm = mesh.centre() text_per_line = [ "> {}".format(name_of_callable(mesh)), "> {} points".format(mesh.n_points), "> Bounds: [{0:.1f}-{1:.1f}]X, [{2:.1f}-{3:.1f}]Y, " "[{4:.1f}-{5:.1f}]Z".format( min_b[0], max_b[0], min_b[1], max_b[1], min_b[2], max_b[2]), "> Range: {0:.1f}X, {1:.1f}Y, {2:.1f}Z".format(rang[0], rang[1], rang[2]), "> Centre of mass: ({0:.1f}X, {1:.1f}Y, {2:.1f}Z)".format( cm[0], cm[1], cm[2]), "> Norm: {0:.2f}".format(mesh.norm())] if custom_info_callback is not None: for msg in custom_info_callback(mesh): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) mesh_options_wid = Mesh3DOptionsWidget( textured=(isinstance(meshes[0], ColouredTriMesh) or isinstance(meshes[0], TexturedTriMesh)), render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() if n_meshes > 1: def update_widgets(change): i = change['new'] # Update shape options mesh_options_wid.set_widget_state( textured=(isinstance(meshes[i], ColouredTriMesh) or isinstance(meshes[i], TexturedTriMesh)), allow_callback=True) # selection slider index = {'min': 0, 'max': n_meshes-1, 'step': 1, 'index': 0} mesh_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Mesh', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, mesh_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab([info_wid, mesh_options_wid, save_figure_wid]) tab_titles = ['Info', 'Mesh', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_meshes > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) with out: print_dynamic('') def visualize_images(images, figure_size=(7, 7), browser_style='buttons', custom_info_callback=None): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') if not isinstance(images, Sized): images = [images] n_images = len(images) main_style = 'info' def render_function(change): ipydisplay.clear_output(wait=True) i = image_number_wid.selected_values if n_images > 1 else 0 g = landmark_options_wid.selected_values['landmarks']['group'] image_is_masked = isinstance(images[i], MaskedImage) options = dict() options.update(landmark_options_wid.selected_values['lines']) options.update(landmark_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) options.update(renderer_options_wid.selected_values['legend']) options.update(image_options_wid.selected_values) options.update(landmark_options_wid.selected_values['landmarks']) if (images[i].has_landmarks and hasattr(images[i].landmarks[g], 'labels')): line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: id = images[i].landmarks[g].labels.index(lbl) line_colour.append(options['line_colour'][id]) marker_face_colour.append(options['marker_face_colour'][id]) marker_edge_colour.append(options['marker_edge_colour'][id]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) save_figure_wid.renderer = render_image( image=images[i], renderer=save_figure_wid.renderer, image_is_masked=image_is_masked, figure_size=new_figure_size, **options) update_info(images[i], image_is_masked, g, custom_info_callback=custom_info_callback) def update_info(img, image_is_masked, group, custom_info_callback=None): masked_str = 'Masked Image' if image_is_masked else 'Image' path_str = img.path if hasattr(img, 'path') else 'No path available' text_per_line = [ "> {} of size {} with {} channel{}".format( masked_str, img._str_shape(), img.n_channels, 's' * (img.n_channels > 1)), "> Path: '{}'".format(path_str)] if image_is_masked: text_per_line.append( "> {} masked pixels (attached mask {:.1%} true)".format( img.n_true_pixels(), img.mask.proportion_true())) text_per_line.append("> min={:.3f}, max={:.3f}".format( img.pixels.min(), img.pixels.max())) if img.has_landmarks: text_per_line.append("> {} landmark points".format( img.landmarks[group].n_points)) if custom_info_callback is not None: for msg in custom_info_callback(img): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) groups_keys, labels_keys = extract_groups_labels_from_image(images[0]) first_label = labels_keys[0] if labels_keys else None image_options_wid = ImageOptionsWidget( n_channels=images[0].n_channels, image_is_masked=isinstance(images[0], MaskedImage), render_function=render_function) landmark_options_wid = LandmarkOptionsWidget( group_keys=groups_keys, labels_keys=labels_keys, type='2D', render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], labels=first_label, axes_x_limits=None, axes_y_limits=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() if n_images > 1: def update_widgets(change): # Get new groups and labels, then update landmark options i = image_number_wid.selected_values g_keys, l_keys = extract_groups_labels_from_image(images[i]) # Update landmarks options landmark_options_wid.set_widget_state( group_keys=g_keys, labels_keys=l_keys, allow_callback=False) # Update channels options image_options_wid.set_widget_state( n_channels=images[i].n_channels, image_is_masked=isinstance(images[i], MaskedImage), allow_callback=True) # Image selection slider index = {'min': 0, 'max': n_images-1, 'step': 1, 'index': 0} image_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Image', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, image_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( children=[info_wid, image_options_wid, landmark_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Image', 'Landmarks', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_images > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) def visualize_patches(patches, patch_centers, figure_size=(7, 7), browser_style='buttons', custom_info_callback=None): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') if (isinstance(patches, list) and isinstance(patches[0], Image)) or \ not isinstance(patches, list): patches = [patches] if not isinstance(patch_centers, list): patch_centers = [patch_centers] * len(patches) elif isinstance(patch_centers, list) and len(patch_centers) == 1: patch_centers *= len(patches) for i in range(len(patches)): if isinstance(patches[i], list): patches[i] = _convert_patches_list_to_single_array( patches[i], patch_centers[i].n_points) n_patches = len(patches) main_style = 'info' def render_function(change): ipydisplay.clear_output(wait=True) i = image_number_wid.selected_values if n_patches > 1 else 0 options = dict() options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) image_options = dict(image_options_wid.selected_values) del image_options['masked_enabled'] options.update(image_options) options.update(patch_options_wid.selected_values) options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) save_figure_wid.renderer = render_patches( patches=patches[i], patch_centers=patch_centers[i], renderer=save_figure_wid.renderer, figure_size=new_figure_size, **options) update_info(patches[i], custom_info_callback=custom_info_callback) def update_info(ptchs, custom_info_callback=None): text_per_line = [ "> Patch-Based Image with {} patche{} and {} offset{}.".format( ptchs.shape[0], 's' * (ptchs.shape[0] > 1), ptchs.shape[1], 's' * (ptchs.shape[1] > 1)), "> Each patch has size {}H x {}W with {} channel{}.".format( ptchs.shape[3], ptchs.shape[4], ptchs.shape[2], 's' * (ptchs.shape[2] > 1)), "> min={:.3f}, max={:.3f}".format(ptchs.min(), ptchs.max())] if custom_info_callback is not None: for msg in custom_info_callback(ptchs): text_per_line.append('> {}'.format(msg)) info_wid.set_widget_state(text_per_line=text_per_line) shape_options_wid = Shape2DOptionsWidget( labels=None, render_function=None) shape_options_wid.line_options_wid.render_lines_switch.button_wid.value = False shape_options_wid.add_render_function(render_function) patch_options_wid = PatchOptionsWidget( n_patches=patches[0].shape[0], n_offsets=patches[0].shape[1], render_function=render_function) image_options_wid = ImageOptionsWidget( n_channels=patches[0].shape[2], image_is_masked=False, render_function=None) image_options_wid.interpolation_checkbox.button_wid.value = False image_options_wid.add_render_function(render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib'], labels=None, axes_x_limits=None, axes_y_limits=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() if n_patches > 1: def update_widgets(change): # Selected object i = image_number_wid.selected_values # Update patch options patch_options_wid.set_widget_state( n_patches=patches[i].shape[0], n_offsets=patches[i].shape[1], allow_callback=False) # Update channels options image_options_wid.set_widget_state( n_channels=patches[i].shape[2], image_is_masked=False, allow_callback=True) # Image selection slider index = {'min': 0, 'max': n_patches-1, 'step': 1, 'index': 0} image_number_wid = AnimationOptionsWidget( index, render_function=update_widgets, index_style=browser_style, interval=0.2, description='Image', loop_enabled=True, continuous_update=False) # Header widget logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' header_wid = ipywidgets.HBox([logo_wid, image_number_wid]) header_wid.layout.align_items = 'center' header_wid.layout.margin = '0px 0px 10px 0px' else: # Header widget header_wid = LogoWidget(style=main_style) header_wid.layout.margin = '0px 10px 0px 0px' options_box = ipywidgets.Tab( children=[info_wid, patch_options_wid, image_options_wid, shape_options_wid, renderer_options_wid, save_figure_wid]) tab_titles = ['Info', 'Patches', 'Image', 'Shape', 'Renderer', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) if n_patches > 1: wid = ipywidgets.VBox([header_wid, options_box]) else: wid = ipywidgets.HBox([header_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) def plot_graph(x_axis, y_axis, legend_entries=None, figure_size=(9, 5)): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() from menpo.visualize import plot_curve print('Initializing...') n_curves = len(y_axis) main_style = 'danger' if legend_entries is None: legend_entries = ["curve {}".format(i) for i in range(n_curves)] def render_function(change): ipydisplay.clear_output(wait=True) opts = plot_wid.selected_values.copy() new_figure_size = ( plot_wid.selected_values['zoom'][0] * figure_size[0], plot_wid.selected_values['zoom'][1] * figure_size[1]) del opts['zoom'] save_figure_wid.renderer = plot_curve( x_axis=x_axis, y_axis=y_axis, figure_size=new_figure_size, figure_id=save_figure_wid.renderer.figure_id, new_figure=False, **opts) save_figure_wid.renderer.force_draw() plot_wid = PlotMatplotlibOptionsWidget( legend_entries=legend_entries, render_function=render_function) save_figure_wid = SaveMatplotlibFigureOptionsWidget() logo = LogoWidget(style=main_style) logo.layout.margin = '0px 10px 0px 0px' tmp_children = list(plot_wid.tab_box.children) tmp_children.append(save_figure_wid) plot_wid.tab_box.children = tmp_children plot_wid.tab_box.set_title(0, 'Labels') plot_wid.tab_box.set_title(1, 'Style') plot_wid.tab_box.set_title(2, 'Legend') plot_wid.tab_box.set_title(3, 'Axes') plot_wid.tab_box.set_title(4, 'Zoom') plot_wid.tab_box.set_title(5, 'Grid') plot_wid.tab_box.set_title(6, 'Export') wid = ipywidgets.HBox([logo, plot_wid]) wid.box_style = main_style wid.layout.border = '2px solid' + map_styles_to_hex_colours(main_style) plot_wid.container.border = '0px' final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) def save_matplotlib_figure(renderer): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() logo_wid = LogoWidget() logo_wid.layout.margin = '0px 10px 0px 0px' save_figure_wid = SaveMatplotlibFigureOptionsWidget(renderer, style='warning') wid = ipywidgets.HBox([logo_wid, save_figure_wid]) ipydisplay.display(wid) def save_mayavi_figure(renderer): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() logo_wid = LogoWidget() logo_wid.layout.margin = '0px 10px 0px 0px' save_figure_wid = SaveMayaviFigureOptionsWidget(renderer, style='warning') wid = ipywidgets.HBox([logo_wid, save_figure_wid]) ipydisplay.display(wid) def visualize_shape_model_2d(shape_model, n_parameters=5, mode='multiple', parameters_bounds=(-3.0, 3.0), figure_size=(7, 7)): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() from menpo.visualize.viewmatplotlib import (_set_axes_options, _parse_axes_limits) out = ipywidgets.Output() ipydisplay.display(out) with out: ipydisplay.clear_output(wait=True) print('Initializing...') if not isinstance(shape_model, list): shape_model = [shape_model] n_levels = len(shape_model) main_style = 'warning' max_n_params = [sp.n_active_components for sp in shape_model] n_parameters = check_n_parameters(n_parameters, n_levels, max_n_params) def render_function(change): with out: ipydisplay.clear_output(wait=True) level = 0 if n_levels > 1: level = level_wid.value parameters = model_parameters_wid.selected_values weights = (parameters * shape_model[level].eigenvalues[:len(parameters)] ** 0.5) mean = shape_model[level].mean() options = dict() options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options['image_view'] = shape_options_wid.selected_values['image_view'] options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) if hasattr(mean, 'labels'): options.update(renderer_options_wid.selected_values['legend']) options['with_labels'] = \ shape_options_wid.selected_values['with_labels'] line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: idx = mean.labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_face_colour.append(options['marker_face_colour'][idx]) marker_edge_colour.append(options['marker_edge_colour'][idx]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) if mode_wid.value == 1: instance = shape_model[level].instance(weights) if mean_wid.selected_values: save_figure_wid.renderer = mean.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=None, image_view=options['image_view'], render_lines=options['render_lines'], line_colour='yellow', line_style=options['line_style'], line_width=options['line_width'], render_markers=options['render_markers'], marker_style=options['marker_style'], marker_size=options['marker_size'], marker_face_colour='yellow', marker_edge_colour='yellow', marker_edge_width=options['marker_edge_width']) save_figure_wid.renderer = instance.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size, **options) instance_range = instance.range() else: instance_lower = shape_model[level].instance([-p for p in weights]) instance_upper = shape_model[level].instance(weights) save_figure_wid.renderer = mean.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size, **options) ax = plt.gca() x_min = np.Inf y_min = np.Inf x_max = -np.Inf y_max = -np.Inf for p in range(mean.n_points): xm = mean.points[p, 0] ym = mean.points[p, 1] xl = instance_lower.points[p, 0] yl = instance_lower.points[p, 1] xu = instance_upper.points[p, 0] yu = instance_upper.points[p, 1] if options['image_view']: lines = [[(ym, xm), (yl, xl)], [(ym, xm), (yu, xu)]] else: lines = [[(xm, ym), (xl, yl)], [(xm, ym), (xu, yu)]] lc = mc.LineCollection(lines, colors=('g', 'b'), linestyles='solid', linewidths=2) y_min = np.min([y_min, xl, xu]) y_max = np.max([y_max, xl, xu]) x_min = np.min([x_min, yl, yu]) x_max = np.max([x_max, yl, yu]) ax.add_collection(lc) axes_x_limits, axes_y_limits = _parse_axes_limits( x_min, x_max, y_min, y_max, options['axes_x_limits'], options['axes_y_limits']) _set_axes_options( ax, render_axes=options['render_axes'], inverted_y_axis=options['image_view'], axes_font_name=options['axes_font_name'], axes_font_size=options['axes_font_size'], axes_font_style=options['axes_font_style'], axes_font_weight=options['axes_font_weight'], axes_x_limits=axes_x_limits, axes_y_limits=axes_y_limits, axes_x_ticks=options['axes_x_ticks'], axes_y_ticks=options['axes_y_ticks']) instance_range = mean.range() save_figure_wid.renderer.force_draw() update_info(level, instance_range) def update_info(level, instance_range): text_per_line = [ "> Level {} out of {}".format(level + 1, n_levels), "> {} components in total".format(shape_model[level].n_components), "> {} active components".format( shape_model[level].n_active_components), "> {:.1f}% variance kept".format( shape_model[level].variance_ratio() * 100), "> Instance range: {:.1f} x {:.1f}".format(instance_range[0], instance_range[1]), "> {} landmark points, {} features".format( shape_model[level].mean().n_points, shape_model[level].n_features)] info_wid.set_widget_state(text_per_line=text_per_line) def plot_variance(name): ipydisplay.clear_output(wait=True) level = level_wid.value if n_levels > 1 else 0 new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * 10, renderer_options_wid.selected_values['zoom_one'] * 3) plt.subplot(121) save_figure_wid.renderer = shape_model[level].plot_eigenvalues_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False) plt.subplot(122) save_figure_wid.renderer = \ shape_model[level].plot_eigenvalues_cumulative_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size) save_figure_wid.renderer.force_draw() mode_dict = OrderedDict() mode_dict['Deformation'] = 1 mode_dict['Vectors'] = 2 mode_wid = ipywidgets.RadioButtons( options=mode_dict, description='Mode', value=1, layout=ipywidgets.Layout(width='6cm')) mode_wid.observe(render_function, names='value', type='change') mean_wid = SwitchWidget( selected_value=False, description='Render mean shape', description_location='right', switch_type='checkbox') mean_wid.observe(render_function, names='selected_values', type='change') def mean_visible(change): if change['new'] == 1: mean_wid.button_wid.disabled = False else: mean_wid.button_wid.disabled = True mean_wid.set_widget_state(False, allow_callback=False) mode_wid.observe(mean_visible, names='value', type='change') model_parameters_wid = LinearModelParametersWidget( n_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_variance, animation_step=0.5, interval=0., loop_enabled=True, continuous_update=False) labels = None if hasattr(shape_model[0].mean(), 'labels'): labels = shape_model[0].mean().labels shape_options_wid = Shape2DOptionsWidget( labels=labels, render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], labels=None, axes_x_limits=0.1, axes_y_limits=0.1, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() if n_levels > 1: def update_widgets(change): model_parameters_wid.set_widget_state( n_parameters=n_parameters[change['new']], params_str='Parameter ', allow_callback=True) # Create pyramid radiobuttons radio_str = OrderedDict() for l in range(n_levels): if l == 0: radio_str["Level {} (low)".format(l)] = l elif l == n_levels - 1: radio_str["Level {} (high)".format(l)] = l else: radio_str["Level {}".format(l)] = l level_wid = ipywidgets.RadioButtons( options=radio_str, description='Pyramid', value=n_levels-1, layout=ipywidgets.Layout(width='6cm')) level_wid.observe(update_widgets, names='value', type='change') level_wid.observe(render_function, names='value', type='change') radio_children = [level_wid, mode_wid, mean_wid] else: radio_children = [mode_wid, mean_wid] radio_wids = ipywidgets.VBox(radio_children) tmp_wid = ipywidgets.HBox([radio_wids, model_parameters_wid]) options_box = ipywidgets.Tab( children=[tmp_wid, shape_options_wid, renderer_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Shape', 'Renderer', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) def visualize_shape_model_3d(shape_model, n_parameters=5, mode='multiple', parameters_bounds=(-15.0, 15.0)): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() out = ipywidgets.Output() ipydisplay.display(out) with out: ipydisplay.clear_output(wait=True) print('Initializing...') if not isinstance(shape_model, list): shape_model = [shape_model] n_levels = len(shape_model) is_trimesh = isinstance(shape_model[0].template_instance, TriMesh) main_style = 'warning' max_n_params = [sp.n_active_components for sp in shape_model] n_parameters = check_n_parameters(n_parameters, n_levels, max_n_params) def render_function(change): save_figure_wid.renderer.clear_figure() with out: ipydisplay.clear_output(wait=True) level = 0 if n_levels > 1: level = level_wid.value parameters = model_parameters_wid.selected_values weights = (parameters * shape_model[level].eigenvalues[:len(parameters)] ** 0.5) instance = shape_model[level].instance(weights) options = dict() if is_trimesh: options.update(shape_options_wid.selected_values) else: options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_mayavi']) if hasattr(instance, 'labels'): options['with_labels'] = \ shape_options_wid.selected_values['with_labels'] line_colour = [] marker_colour = [] for lbl in options['with_labels']: idx = instance.labels.index(lbl) line_colour.append(options['line_colour'][idx]) marker_colour.append(options['marker_colour'][idx]) options['line_colour'] = line_colour options['marker_colour'] = marker_colour else: options['line_colour'] = options['line_colour'][0] options['marker_colour'] = options['marker_colour'][0] update_info(level, instance.range()) save_figure_wid.renderer = instance.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, **options) save_figure_wid.renderer.force_draw() def update_info(level, instance_range): text_per_line = [ "> Level {} out of {}".format(level + 1, n_levels), "> {} components in total".format(shape_model[level].n_components), "> {} active components".format( shape_model[level].n_active_components), "> {:.1f}% variance kept".format( shape_model[level].variance_ratio() * 100), "> Instance range: {:.1f} x {:.1f}".format(instance_range[0], instance_range[1]), "> {} points".format( shape_model[level].mean().n_points)] info_wid.set_widget_state(text_per_line=text_per_line) def plot_variance(name): level = level_wid.value if n_levels > 1 else 0 with out: ipydisplay.clear_output(wait=True) plt.subplot(121) shape_model[level].plot_eigenvalues_ratio() plt.subplot(122) shape_model[level].plot_eigenvalues_cumulative_ratio() plt.show() model_parameters_wid = LinearModelParametersWidget( n_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_variance, animation_step=0.5, interval=0., loop_enabled=True, continuous_update=False) if is_trimesh: shape_options_wid = Mesh3DOptionsWidget(textured=False, render_function=render_function) else: labels = None if hasattr(shape_model[0].mean(), 'labels'): labels = shape_model[0].mean().labels shape_options_wid = Shape3DOptionsWidget(labels=labels, render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['numbering_mayavi'], labels=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() if n_levels > 1: def update_widgets(change): model_parameters_wid.set_widget_state( n_parameters=n_parameters[change['new']], params_str='Parameter ', allow_callback=True) # Create pyramid radiobuttons radio_str = OrderedDict() for l in range(n_levels): if l == 0: radio_str["Level {} (low)".format(l)] = l elif l == n_levels - 1: radio_str["Level {} (high)".format(l)] = l else: radio_str["Level {}".format(l)] = l level_wid = ipywidgets.RadioButtons( options=radio_str, description='Pyramid', value=n_levels-1, layout=ipywidgets.Layout(width='6cm')) level_wid.observe(update_widgets, names='value', type='change') level_wid.observe(render_function, names='value', type='change') tmp_wid = ipywidgets.HBox([level_wid, model_parameters_wid]) else: tmp_wid = ipywidgets.HBox(children=[model_parameters_wid]) if is_trimesh: options_box = ipywidgets.Tab( children=[tmp_wid, shape_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Mesh', 'Info', 'Export'] else: options_box = ipywidgets.Tab( children=[tmp_wid, shape_options_wid, renderer_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Shape', 'Renderer', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) with out: print_dynamic('') def visualize_appearance_model(appearance_model, n_parameters=5, mode='multiple', parameters_bounds=(-3.0, 3.0), figure_size=(7, 7)): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') if not isinstance(appearance_model, list): appearance_model = [appearance_model] n_levels = len(appearance_model) main_style = 'success' max_n_params = [ap.n_active_components for ap in appearance_model] n_parameters = check_n_parameters(n_parameters, n_levels, max_n_params) def render_function(change): ipydisplay.clear_output(wait=True) level = level_wid.value if n_levels > 1 else 0 parameters = model_parameters_wid.selected_values weights = (parameters * appearance_model[level].eigenvalues[:len(parameters)] ** 0.5) instance = appearance_model[level].instance(weights) image_is_masked = isinstance(instance, MaskedImage) g = landmark_options_wid.selected_values['landmarks']['group'] options = dict() options.update(landmark_options_wid.selected_values['lines']) options.update(landmark_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) options.update(renderer_options_wid.selected_values['legend']) options.update(image_options_wid.selected_values) options.update(landmark_options_wid.selected_values['landmarks']) if (instance.has_landmarks and hasattr(instance.landmarks[g], 'labels')): line_colour = [] marker_face_colour = [] marker_edge_colour = [] for lbl in options['with_labels']: id = instance.landmarks[g].labels.index(lbl) line_colour.append(options['line_colour'][id]) marker_face_colour.append(options['marker_face_colour'][id]) marker_edge_colour.append(options['marker_edge_colour'][id]) options['line_colour'] = line_colour options['marker_face_colour'] = marker_face_colour options['marker_edge_colour'] = marker_edge_colour else: options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) save_figure_wid.renderer = render_image( image=instance, renderer=save_figure_wid.renderer, image_is_masked=image_is_masked, figure_size=new_figure_size, **options) update_info(instance, level, g) def update_info(image, level, group): lvl_app_mod = appearance_model[level] lp = 0 if group is None else image.landmarks[group].n_points text_per_line = [ "> Level: {} out of {}.".format(level + 1, n_levels), "> {} components in total.".format(lvl_app_mod.n_components), "> {} active components.".format(lvl_app_mod.n_active_components), "> {:.1f}% variance kept.".format( lvl_app_mod.variance_ratio() * 100), "> Reference shape of size {} with {} channel{}.".format( image._str_shape(), image.n_channels, 's' * (image.n_channels > 1)), "> {} features.".format(lvl_app_mod.n_features), "> {} landmark points.".format(lp), "> Instance: min={:.3f}, max={:.3f}".format(image.pixels.min(), image.pixels.max())] info_wid.set_widget_state(text_per_line=text_per_line) def plot_variance(name): ipydisplay.clear_output(wait=True) level = level_wid.value if n_levels > 1 else 0 new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * 10, renderer_options_wid.selected_values['zoom_one'] * 3) plt.subplot(121) save_figure_wid.renderer = \ appearance_model[level].plot_eigenvalues_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False) plt.subplot(122) save_figure_wid.renderer = \ appearance_model[level].plot_eigenvalues_cumulative_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size) save_figure_wid.renderer.force_draw() model_parameters_wid = LinearModelParametersWidget( n_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_variance, animation_step=0.5, interval=0., loop_enabled=True, continuous_update=False) groups_keys, labels_keys = extract_groups_labels_from_image( appearance_model[0].mean()) image_options_wid = ImageOptionsWidget( n_channels=appearance_model[0].mean().n_channels, image_is_masked=isinstance(appearance_model[0].mean(), MaskedImage), render_function=render_function) landmark_options_wid = LandmarkOptionsWidget( group_keys=groups_keys, labels_keys=labels_keys, type='2D', render_function=render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib', 'legend'], axes_x_limits=None, axes_y_limits=None, labels=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() tmp_children = [model_parameters_wid] if n_levels > 1: def update_widgets(change): value = change['new'] # Update model parameters widget model_parameters_wid.set_widget_state( n_parameters[value], params_str='Parameter ', allow_callback=False) # Update landmarks options g_keys, l_keys = extract_groups_labels_from_image( appearance_model[value].mean()) landmark_options_wid.set_widget_state( group_keys=g_keys, labels_keys=l_keys, allow_callback=False) # Update channels options image_options_wid.set_widget_state( n_channels=appearance_model[value].mean().n_channels, image_is_masked=isinstance( appearance_model[value].mean(), MaskedImage), allow_callback=True) # Create pyramid radiobuttons radio_str = OrderedDict() for l in range(n_levels): if l == 0: radio_str["Level {} (low)".format(l)] = l elif l == n_levels - 1: radio_str["Level {} (high)".format(l)] = l else: radio_str["Level {}".format(l)] = l level_wid = ipywidgets.RadioButtons( options=radio_str, description='Pyramid', value=n_levels-1, layout=ipywidgets.Layout(width='6cm')) level_wid.observe(update_widgets, names='value', type='change') level_wid.observe(render_function, names='value', type='change') tmp_children.insert(0, level_wid) tmp_wid = ipywidgets.HBox(tmp_children) options_box = ipywidgets.Tab( children=[tmp_wid, image_options_wid, landmark_options_wid, renderer_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Image', 'Landmarks', 'Renderer', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) def visualize_patch_appearance_model(appearance_model, centers, n_parameters=5, mode='multiple', parameters_bounds=(-3.0, 3.0), figure_size=(7, 7)): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print('Initializing...') if not isinstance(appearance_model, list): appearance_model = [appearance_model] n_levels = len(appearance_model) if not isinstance(centers, list): centers = [centers] * n_levels elif isinstance(centers, list) and len(centers) == 1: centers *= n_levels main_style = 'success' max_n_params = [ap.n_active_components for ap in appearance_model] n_parameters = check_n_parameters(n_parameters, n_levels, max_n_params) def render_function(change): ipydisplay.clear_output(wait=True) level = level_wid.value if n_levels > 1 else 0 parameters = model_parameters_wid.selected_values weights = (parameters * appearance_model[level].eigenvalues[:len(parameters)] ** 0.5) instance = appearance_model[level].instance(weights) options = dict() options.update(shape_options_wid.selected_values['lines']) options.update(shape_options_wid.selected_values['markers']) options.update( renderer_options_wid.selected_values['numbering_matplotlib']) options.update(renderer_options_wid.selected_values['axes']) image_options = dict(image_options_wid.selected_values) del image_options['masked_enabled'] options.update(image_options) options.update(patch_options_wid.selected_values) options['line_colour'] = options['line_colour'][0] options['marker_face_colour'] = options['marker_face_colour'][0] options['marker_edge_colour'] = options['marker_edge_colour'][0] new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * figure_size[0], renderer_options_wid.selected_values['zoom_one'] * figure_size[1]) save_figure_wid.renderer = render_patches( patches=instance.pixels, patch_centers=centers[level], renderer=save_figure_wid.renderer, figure_size=new_figure_size, **options) update_info(instance, level) def update_info(image, level): lvl_app_mod = appearance_model[level] text_per_line = [ "> Level: {} out of {}.".format(level + 1, n_levels), "> {} components in total.".format(lvl_app_mod.n_components), "> {} active components.".format(lvl_app_mod.n_active_components), "> {:.1f}% variance kept.".format( lvl_app_mod.variance_ratio() * 100), "> Each patch has size {}H x {}W with {} channel{}.".format( image.pixels.shape[3], image.pixels.shape[4], image.pixels.shape[2], 's' * (image.pixels.shape[2] > 1)), "> {} features.".format(lvl_app_mod.n_features), "> {} landmark points.".format(image.pixels.shape[0]), "> Instance: min={:.3f}, max={:.3f}".format(image.pixels.min(), image.pixels.max())] info_wid.set_widget_state(text_per_line=text_per_line) def plot_variance(name): ipydisplay.clear_output(wait=True) level = 0 if n_levels > 1: level = level_wid.value new_figure_size = ( renderer_options_wid.selected_values['zoom_one'] * 10, renderer_options_wid.selected_values['zoom_one'] * 3) plt.subplot(121) save_figure_wid.renderer = \ appearance_model[level].plot_eigenvalues_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False) plt.subplot(122) save_figure_wid.renderer = \ appearance_model[level].plot_eigenvalues_cumulative_ratio( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, figure_size=new_figure_size) save_figure_wid.renderer.force_draw() model_parameters_wid = LinearModelParametersWidget( n_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_variance, animation_step=0.5, interval=0., loop_enabled=True, continuous_update=False) shape_options_wid = Shape2DOptionsWidget( labels=None, render_function=None) shape_options_wid.line_options_wid.render_lines_switch.button_wid.value = False shape_options_wid.add_render_function(render_function) patch_options_wid = PatchOptionsWidget( n_patches=appearance_model[0].mean().pixels.shape[0], n_offsets=appearance_model[0].mean().pixels.shape[1], render_function=render_function) image_options_wid = ImageOptionsWidget( n_channels=appearance_model[0].mean().pixels.shape[2], image_is_masked=isinstance(appearance_model[0].mean(), MaskedImage), render_function=None) image_options_wid.interpolation_checkbox.button_wid.value = False image_options_wid.add_render_function(render_function) renderer_options_wid = RendererOptionsWidget( options_tabs=['zoom_one', 'axes', 'numbering_matplotlib'], labels=None, axes_x_limits=None, axes_y_limits=None, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMatplotlibFigureOptionsWidget() tmp_children = [model_parameters_wid] if n_levels > 1: def update_widgets(change): value = change['new'] # Update model parameters widget model_parameters_wid.set_widget_state( n_parameters[value], params_str='Parameter ', allow_callback=False) # Update patch options patch_options_wid.set_widget_state( n_patches=appearance_model[value].mean().pixels.shape[0], n_offsets=appearance_model[value].mean().pixels.shape[1], allow_callback=False) # Update channels options image_options_wid.set_widget_state( n_channels=appearance_model[value].mean().pixels.shape[2], image_is_masked=isinstance(appearance_model[value].mean(), MaskedImage), allow_callback=True) # Define pyramid radiobuttons radio_str = OrderedDict() for l in range(n_levels): if l == 0: radio_str["Level {} (low)".format(l)] = l elif l == n_levels - 1: radio_str["Level {} (high)".format(l)] = l else: radio_str["Level {}".format(l)] = l level_wid = ipywidgets.RadioButtons( options=radio_str, description='Pyramid', value=n_levels-1, layout=ipywidgets.Layout(width='6cm')) level_wid.observe(update_widgets, names='value', type='change') level_wid.observe(render_function, names='value', type='change') tmp_children.insert(0, level_wid) tmp_wid = ipywidgets.HBox(tmp_children) options_box = ipywidgets.Tab( children=[tmp_wid, patch_options_wid, image_options_wid, shape_options_wid, renderer_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Patches', 'Channels', 'Shape', 'Renderer', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) # Set widget's style wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) render_function({}) def visualize_morphable_model(mm, n_shape_parameters=5, n_texture_parameters=5, mode='multiple', parameters_bounds=(-15.0, 15.0)): from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() print_dynamic('Initializing...') main_style = 'info' n_shape_parameters = check_n_parameters( n_shape_parameters, 1, [mm.shape_model.n_active_components]) n_texture_parameters = check_n_parameters( n_texture_parameters, 1, [mm.texture_model.n_active_components]) def render_function(change): save_figure_wid.renderer.clear_figure() ipydisplay.clear_output(wait=True) shape_weights = shape_model_parameters_wid.selected_values shape_weights = ( shape_weights * mm.shape_model.eigenvalues[:len(shape_weights)] ** 0.5) texture_weights = texture_model_parameters_wid.selected_values texture_weights = ( texture_weights * mm.texture_model.eigenvalues[:len(texture_weights)] ** 0.5) instance = mm.instance(shape_weights=shape_weights, texture_weights=texture_weights) instance = instance.clip_texture() update_info(mm, instance) save_figure_wid.renderer = instance.view( figure_id=save_figure_wid.renderer.figure_id, new_figure=False, **mesh_options_wid.selected_values) save_figure_wid.renderer.force_draw() def update_info(mm, instance): text_per_line = [ "> {} vertices, {} triangles".format(mm.n_vertices, mm.n_triangles), "> {} shape components ({:.2f}% of variance)".format( mm.shape_model.n_components, mm.shape_model.variance_ratio() * 100), "> {} texture channels".format(mm.n_channels), "> {} texture components ({:.2f}% of variance)".format( mm.texture_model.n_components, mm.texture_model.variance_ratio() * 100), "> Instance: min={:.3f} , max={:.3f}".format( instance.colours.min(), instance.colours.max())] info_wid.set_widget_state(text_per_line=text_per_line) def plot_shape_variance(name): ipydisplay.clear_output(wait=True) plt.subplot(121) mm.shape_model.plot_eigenvalues_ratio() plt.subplot(122) mm.shape_model.plot_eigenvalues_cumulative_ratio() plt.show() def plot_texture_variance(name): ipydisplay.clear_output(wait=True) plt.subplot(121) mm.texture_model.plot_eigenvalues_ratio() plt.subplot(122) mm.texture_model.plot_eigenvalues_cumulative_ratio() plt.show() shape_model_parameters_wid = LinearModelParametersWidget( n_shape_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_shape_variance, animation_step=0.5, interval=0., loop_enabled=True) texture_model_parameters_wid = LinearModelParametersWidget( n_texture_parameters[0], render_function, params_str='Parameter ', mode=mode, params_bounds=parameters_bounds, params_step=0.1, plot_variance_visible=True, plot_variance_function=plot_texture_variance, animation_step=0.5, interval=0., loop_enabled=True) mesh_options_wid = Mesh3DOptionsWidget(textured=True, render_function=render_function) info_wid = TextPrintWidget(text_per_line=['']) save_figure_wid = SaveMayaviFigureOptionsWidget() model_parameters_wid = ipywidgets.HBox( [ipywidgets.Tab([shape_model_parameters_wid, texture_model_parameters_wid])]) model_parameters_wid.children[0].set_title(0, 'Shape') model_parameters_wid.children[0].set_title(1, 'Texture') options_box = ipywidgets.Tab([model_parameters_wid, mesh_options_wid, info_wid, save_figure_wid]) tab_titles = ['Model', 'Mesh', 'Info', 'Export'] for (k, tl) in enumerate(tab_titles): options_box.set_title(k, tl) logo_wid = LogoWidget(style=main_style) logo_wid.layout.margin = '0px 10px 0px 0px' wid = ipywidgets.HBox([logo_wid, options_box]) wid.box_style = main_style wid.layout.border = '2px solid ' + map_styles_to_hex_colours(main_style) # Display final widget final_box = ipywidgets.Box([wid]) final_box.layout.display = 'flex' ipydisplay.display(final_box) # Trigger initial visualization render_function({}) print_dynamic('') def webcam_widget(canvas_width=640, hd=True, n_preview_windows=5): # Ensure that the code is being run inside a Jupyter kernel! from .utils import verify_ipython_and_kernel verify_ipython_and_kernel() # Set update function images = [] def update(_): images.append(wid.selected_values[-1]) # Create widgets wid = CameraSnapshotWidget( canvas_width=canvas_width, hd=hd, n_preview_windows=n_preview_windows, preview_windows_margin=3, style='danger', preview_style='warning', render_function=update) wid.container.layout.border = ( '2px solid' + map_styles_to_hex_colours('danger')) # Display widget ipydisplay.display(wid) # Return return images

true

f734a86b72c070548e64cd2ada2afc4598dd3bb2

758

py

Python

resources/lib/streamlink/plugins/rtlxl.py

rrosajp/script.module.streamlink-1

2ad5076c9c4c38288af94825064cc471da8142d8

[ "BSD-2-Clause" ]

1

2021-04-25T16:09:09.000Z

resources/lib/streamlink/plugins/rtlxl.py

rrosajp/script.module.streamlink-1

2ad5076c9c4c38288af94825064cc471da8142d8

[ "BSD-2-Clause" ]

null

resources/lib/streamlink/plugins/rtlxl.py

rrosajp/script.module.streamlink-1

2ad5076c9c4c38288af94825064cc471da8142d8

[ "BSD-2-Clause" ]

null

import re import json from streamlink.plugin import Plugin from streamlink.stream import HLSStream _url_re = re.compile(r"http(?:s)?://(?:\w+\.)?rtl.nl/video/(?P<uuid>.*?)\Z", re.IGNORECASE) class rtlxl(Plugin): @classmethod def can_handle_url(cls, url): return _url_re.match(url) def _get_streams(self): match = _url_re.match(self.url) uuid = match.group("uuid") videourlfeed = self.session.http.get( 'https://tm-videourlfeed.rtl.nl/api/url/{}?device=pc&drm&format=hls'.format(uuid) ).text videourlfeedjson = json.loads(videourlfeed) playlist_url = videourlfeedjson["url"] return HLSStream.parse_variant_playlist(self.session, playlist_url) __plugin__ = rtlxl

26.137931

93

0.666227

import re import json from streamlink.plugin import Plugin from streamlink.stream import HLSStream _url_re = re.compile(r"http(?:s)?://(?:\w+\.)?rtl.nl/video/(?P<uuid>.*?)\Z", re.IGNORECASE) class rtlxl(Plugin): @classmethod def can_handle_url(cls, url): return _url_re.match(url) def _get_streams(self): match = _url_re.match(self.url) uuid = match.group("uuid") videourlfeed = self.session.http.get( 'https://tm-videourlfeed.rtl.nl/api/url/{}?device=pc&drm&format=hls'.format(uuid) ).text videourlfeedjson = json.loads(videourlfeed) playlist_url = videourlfeedjson["url"] return HLSStream.parse_variant_playlist(self.session, playlist_url) __plugin__ = rtlxl

true

f734aab43acf3765f95a4b2ab60c56fad32168bf

1,577

py

Python

CodeSignal/Challenges/MZ/06_Quest_Efficiency_Item.py

Zubieta/CPP

fb4a3cbf2e4edcc590df15663cd28fb9ecab679c

[ "MIT" ]

8

2017-03-02T07:56:45.000Z

2021-08-07T20:20:19.000Z

CodeSignal/Challenges/MZ/06_Quest_Efficiency_Item.py

zubie7a/Algorithms

fb4a3cbf2e4edcc590df15663cd28fb9ecab679c

[ "MIT" ]

null

CodeSignal/Challenges/MZ/06_Quest_Efficiency_Item.py

zubie7a/Algorithms

fb4a3cbf2e4edcc590df15663cd28fb9ecab679c

[ "MIT" ]

1

2021-08-07T20:20:20.000Z

# https://app.codesignal.com/company-challenges/mz/zCYv3tuxRE4JajQNY def questEfficiencyItem(hours, points, time_for_quests): # Time is short, you want to complete as many quests as possible # but it's difficult to do so. So we want to maximize the points # we can obtain with quests in a given limited time. # hours: hours it takes to complete a quest # points: points each quest gives you # time_for_quests: the limit of time to do stuff. # Recursively, at each position, decide whether to take this quest # or not. This 'iteration' can be done since the order of the quests # doesn't matter so you can check from left to right whether to take # each one or not, generating unique combinations. def recursive(idx, score_acum, time_left): # Time ran out, acum with last step is invalid. if time_left < 0: return 0 # Time was precise, return until here. if time_left == 0: return score_acum # Ran out of quests to if idx == len(hours): return score_acum score = 0 hours_idx = hours[idx] points_idx = points[idx] # At each position decide whats better, whether to consume it or # advance to the next without consuming current. res_1 = recursive(idx + 1, score_acum + points_idx, time_left - hours_idx) res_2 = recursive(idx + 1, score_acum, time_left) return max(res_1, res_2) # Start with 0 accumulated points and all the time left. return recursive(0, 0, time_for_quests)

43.805556

82

0.665821

def questEfficiencyItem(hours, points, time_for_quests): # we can obtain with quests in a given limited time. # hours: hours it takes to complete a quest # points: points each quest gives you # time_for_quests: the limit of time to do stuff. # Recursively, at each position, decide whether to take this quest # or not. This 'iteration' can be done since the order of the quests # doesn't matter so you can check from left to right whether to take def recursive(idx, score_acum, time_left): if time_left < 0: return 0 if time_left == 0: return score_acum if idx == len(hours): return score_acum score = 0 hours_idx = hours[idx] points_idx = points[idx] res_1 = recursive(idx + 1, score_acum + points_idx, time_left - hours_idx) res_2 = recursive(idx + 1, score_acum, time_left) return max(res_1, res_2) return recursive(0, 0, time_for_quests)

true

f734ab273b7cc42f0ec809fb4d69086c50c004da

4,047

py

Python

tests/_fixers/_stdlib.py

eldorplus/importlib

48047b7de74c0e75fecbc0b846864e523e57ecc6

[ "PSF-2.0", "BSD-2-Clause" ]

null

tests/_fixers/_stdlib.py

eldorplus/importlib

48047b7de74c0e75fecbc0b846864e523e57ecc6

[ "PSF-2.0", "BSD-2-Clause" ]

null

tests/_fixers/_stdlib.py

eldorplus/importlib

48047b7de74c0e75fecbc0b846864e523e57ecc6

[ "PSF-2.0", "BSD-2-Clause" ]

null

import os import shutil import sys import tempfile import types from importlib2._fixers import (swap, SimpleNamespace, new_class, _thread, builtins) from importlib2._fixers._modules import mod_from_ns def fix_builtins(builtins=builtins): sys.modules.setdefault('builtins', builtins) def fix_types(types=types): types.SimpleNamespace = SimpleNamespace types.new_class = new_class return types def fix_collections(): try: import collections.abc except ImportError: import collections collections.abc = collections sys.modules['collections.abc'] = collections def fix_tempfile(): if not hasattr(tempfile, 'TemporaryDirectory'): class TemporaryDirectory(object): def __init__(self): self.name = tempfile.mkdtemp() def __enter__(self): return self def __exit__(self, *args): shutil.rmtree(self.name, ignore_errors=True) tempfile.TemporaryDirectory = TemporaryDirectory def fix_os(os=os): if not hasattr(os, 'fsencode'): os.fsencode = lambda s: s if not hasattr(os, 'fsdecode'): os.fsdecode = lambda s: s def fix_thread(_thread=_thread): sys.modules['_thread'] = _thread if not hasattr(_thread, 'TIMEOUT_MAX'): _thread.TIMEOUT_MAX = 10 # XXX Make it accurate. if not hasattr(_thread, '_set_sentinel'): _thread._set_sentinel = lambda: _thread.allocate_lock() def inject_threading(): from . import threading sys.modules['threading'] = threading ################################################# # testing def fix_unittest(): import unittest # Add in unittest.TestCase.subTest. if not hasattr(unittest.TestCase, 'subTest'): from contextlib import contextmanager @contextmanager def subTest(self, *args, **kwargs): yield unittest.TestCase.subTest = subTest # Add in a fake unittest.mock. try: import unittest.mock except ImportError: def patched(obj, attr): def mocked(*args, **kwargs): try: exc = mocked.side_effect except AttributeError: return mocked.return_value else: raise exc return swap(obj, attr, mocked, pop=False) from importlib2 import _bootstrap mock = _bootstrap._new_module('unittest.mock') mock.__loader__ = _bootstrap.BuiltinImporter mock.__spec__ = _bootstrap.ModuleSpec(mock.__name__, mock.__loader__, origin=__file__) mock.patch = lambda: None mock.patch.object = patched sys.modules['unittest.mock'] = mock unittest.mock = mock def _format_obj(obj): if isinstance(obj, dict) and '__builtins__' in obj: refmod = mod_from_ns(obj) return ('<ns for module {!r} ({} {})>' ).format(obj['__name__'], refmod, id(refmod)) else: return '{} {}'.format(obj, id(obj)) def check_mod(module_name, mod=None, orig=None): if module_name is None: if mod is None: raise TypeError('missing module_name') module_name = mod.__name__ if module_name is None: raise ImportError('{!r}: mod.__name__ is None'.format(mod)) if mod is None: if module_name not in sys.modules: return mod = sys.modules[module_name] # Check the module. if module_name.startswith('importlib'): if not hasattr(mod, '_bootstrap'): try: f = mod._resolve_name except AttributeError: f = mod.ModuleSpec.__init__ bsname = f.__globals__['__name__'] assert bsname is not None, module_name def fix_support(support=None): if support is None: from tests import support if not hasattr(support, 'check_mod'): support.check_mod = check_mod

28.702128

77

0.604151

import os import shutil import sys import tempfile import types from importlib2._fixers import (swap, SimpleNamespace, new_class, _thread, builtins) from importlib2._fixers._modules import mod_from_ns def fix_builtins(builtins=builtins): sys.modules.setdefault('builtins', builtins) def fix_types(types=types): types.SimpleNamespace = SimpleNamespace types.new_class = new_class return types def fix_collections(): try: import collections.abc except ImportError: import collections collections.abc = collections sys.modules['collections.abc'] = collections def fix_tempfile(): if not hasattr(tempfile, 'TemporaryDirectory'): class TemporaryDirectory(object): def __init__(self): self.name = tempfile.mkdtemp() def __enter__(self): return self def __exit__(self, *args): shutil.rmtree(self.name, ignore_errors=True) tempfile.TemporaryDirectory = TemporaryDirectory def fix_os(os=os): if not hasattr(os, 'fsencode'): os.fsencode = lambda s: s if not hasattr(os, 'fsdecode'): os.fsdecode = lambda s: s def fix_thread(_thread=_thread): sys.modules['_thread'] = _thread if not hasattr(_thread, 'TIMEOUT_MAX'): _thread.TIMEOUT_MAX = 10 if not hasattr(_thread, '_set_sentinel'): _thread._set_sentinel = lambda: _thread.allocate_lock() def inject_threading(): from . import threading sys.modules['threading'] = threading __' in obj: refmod = mod_from_ns(obj) return ('<ns for module {!r} ({} {})>' ).format(obj['__name__'], refmod, id(refmod)) else: return '{} {}'.format(obj, id(obj)) def check_mod(module_name, mod=None, orig=None): if module_name is None: if mod is None: raise TypeError('missing module_name') module_name = mod.__name__ if module_name is None: raise ImportError('{!r}: mod.__name__ is None'.format(mod)) if mod is None: if module_name not in sys.modules: return mod = sys.modules[module_name] if module_name.startswith('importlib'): if not hasattr(mod, '_bootstrap'): try: f = mod._resolve_name except AttributeError: f = mod.ModuleSpec.__init__ bsname = f.__globals__['__name__'] assert bsname is not None, module_name def fix_support(support=None): if support is None: from tests import support if not hasattr(support, 'check_mod'): support.check_mod = check_mod

true

f734ac2fb9831a1d3787413031ed1eda99d96841

1,869

py

Python

daemons/build_tf_deploy_config.py

ucsc-cgp/cgp-data-store

1d583d654a07a14df0c566a66fb6ba574be36cbc

[ "MIT" ]

46

2017-03-24T15:56:09.000Z

2021-03-15T19:49:07.000Z

daemons/build_tf_deploy_config.py

ucsc-cgp/cgp-data-store

1d583d654a07a14df0c566a66fb6ba574be36cbc

[ "MIT" ]

1,799

2017-04-04T17:54:28.000Z

2020-11-19T12:30:13.000Z

daemons/build_tf_deploy_config.py

ucsc-cgp/cgp-data-store

1d583d654a07a14df0c566a66fb6ba574be36cbc

[ "MIT" ]

13

2017-03-27T23:49:35.000Z

2021-01-18T07:39:49.000Z

#!/usr/bin/env python """ This script generates Terraform scripting needed for daemons that deploy infrastructure. """ import os import glob import json import boto3 import argparse daemons_root = os.path.abspath(os.path.dirname(__file__)) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("daemon") args = parser.parse_args() env_vars_to_lambda = os.environ['EXPORT_ENV_VARS_TO_LAMBDA'].split() terraform_backend_template = """terraform {{ backend "s3" {{ bucket = "{bucket}" key = "dss-{daemon}-{stage}.tfstate" region = "{region}" {profile_setting} }} }} """ terraform_providers_template = """ provider aws {{ region = "{aws_region}" }} """ account_id = boto3.client("sts").get_caller_identity()['Account'] backend_bucket = os.environ['DSS_TERRAFORM_BACKEND_BUCKET_TEMPLATE'].format(account_id=account_id) terraform_variable_info = {'variable': dict()} for key in env_vars_to_lambda: terraform_variable_info['variable'][key] = { 'default': os.environ[key] } with open(os.path.join(daemons_root, args.daemon, "backend.tf"), "w") as fp: if os.environ.get('AWS_PROFILE'): profile = os.environ['AWS_PROFILE'] profile_setting = f'profile = "{profile}"' else: profile_setting = '' fp.write(terraform_backend_template.format( bucket=backend_bucket, daemon=args.daemon, stage=os.environ['DSS_DEPLOYMENT_STAGE'], region=os.environ['AWS_DEFAULT_REGION'], profile_setting=profile_setting, )) with open(os.path.join(daemons_root, args.daemon, "variables.tf"), "w") as fp: fp.write(json.dumps(terraform_variable_info, indent=2)) with open(os.path.join(daemons_root, args.daemon, "providers.tf"), "w") as fp: fp.write(terraform_providers_template.format( aws_region=os.environ['AWS_DEFAULT_REGION'], ))

26.7

98

0.702515

import os import glob import json import boto3 import argparse daemons_root = os.path.abspath(os.path.dirname(__file__)) parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("daemon") args = parser.parse_args() env_vars_to_lambda = os.environ['EXPORT_ENV_VARS_TO_LAMBDA'].split() terraform_backend_template = """terraform {{ backend "s3" {{ bucket = "{bucket}" key = "dss-{daemon}-{stage}.tfstate" region = "{region}" {profile_setting} }} }} """ terraform_providers_template = """ provider aws {{ region = "{aws_region}" }} """ account_id = boto3.client("sts").get_caller_identity()['Account'] backend_bucket = os.environ['DSS_TERRAFORM_BACKEND_BUCKET_TEMPLATE'].format(account_id=account_id) terraform_variable_info = {'variable': dict()} for key in env_vars_to_lambda: terraform_variable_info['variable'][key] = { 'default': os.environ[key] } with open(os.path.join(daemons_root, args.daemon, "backend.tf"), "w") as fp: if os.environ.get('AWS_PROFILE'): profile = os.environ['AWS_PROFILE'] profile_setting = f'profile = "{profile}"' else: profile_setting = '' fp.write(terraform_backend_template.format( bucket=backend_bucket, daemon=args.daemon, stage=os.environ['DSS_DEPLOYMENT_STAGE'], region=os.environ['AWS_DEFAULT_REGION'], profile_setting=profile_setting, )) with open(os.path.join(daemons_root, args.daemon, "variables.tf"), "w") as fp: fp.write(json.dumps(terraform_variable_info, indent=2)) with open(os.path.join(daemons_root, args.daemon, "providers.tf"), "w") as fp: fp.write(terraform_providers_template.format( aws_region=os.environ['AWS_DEFAULT_REGION'], ))

true

f734ac3225b00088002daa226213683a43e43d53

8,450

py

Python

view/__init__.py

mstrechen/pacman

fc9b609857476588fa269dff4acee744f51d9fbb

[ "MIT" ]

null

view/__init__.py

mstrechen/pacman

fc9b609857476588fa269dff4acee744f51d9fbb

[ "MIT" ]

null

view/__init__.py

mstrechen/pacman

fc9b609857476588fa269dff4acee744f51d9fbb

[ "MIT" ]

null

import typing as t import pygame from labyrinth.labyrinth import Labyrinth from view.banana import Banana from view.dot import Dot from view.ghost import Ghost from view.pacman import Pacman MAX_DISPLAY_WIDTH = 1000 MAX_DISPLAY_HEIGHT = 500 class View: cell_size: int sprites: t.Dict[str, pygame.sprite.Sprite] sprites_group: pygame.sprite.Group labyrinth: Labyrinth = None SMOOTH_MOVEMENT = ['pacman', 'ghosts'] SPRITES_Z_INDEX = { 'pacman': 0, 'target': -1, 'ghosts': 1, } REGULAR_SPRITES = ['pacman', 'target'] LIST_SPRITES = ['ghosts'] STATIC_SPRITES = ['dots'] def __init__(self): pygame.init() self.sprites_group = pygame.sprite.Group() self.display_info = pygame.display.Info() self.sprites: t.Dict[t.Union[pygame.sprite.Sprite, t.List[pygame.sprite.Sprite]]] = {} self.benchmarking = {} self.screen = pygame.display.set_mode([MAX_DISPLAY_WIDTH, MAX_DISPLAY_HEIGHT]) self.clock = pygame.time.Clock() def draw_labyrinth(self, labyrinth: t.Optional[Labyrinth] = None): self.labyrinth = labyrinth or self.labyrinth labyrinth = self.labyrinth cell_size = min( self.display_info.current_h // len(labyrinth.raw_img), self.display_info.current_w // len(labyrinth.raw_img[0]) ) self.screen = pygame.display.set_mode([cell_size * len(labyrinth.raw_img[0]), cell_size * len(labyrinth.raw_img)]) self.cell_size = cell_size for line_no, line in enumerate(labyrinth.raw_img): for char_no, char in enumerate(line): color = (0, 0, 0) if char == ' ' else (0, 0, 200) pygame.draw.rect( self.screen, color, (char_no * cell_size, line_no * cell_size, cell_size, cell_size) ) def set_initial_state(self, state: t.Dict[str, t.Any]): self.state = state if 'pacman' in state: x, y = state['pacman'] pacman = Pacman(size=self.cell_size) self.sprites['pacman'] = pacman pacman.rect.move_ip(y * self.cell_size, x * self.cell_size) if 'target' in state: x, y = state['target'] target = Banana(size=self.cell_size) self.sprites['target'] = target target.rect.move_ip(y * self.cell_size, x * self.cell_size) if 'ghosts' in state: self.sprites['ghosts'] = [ Ghost(size=self.cell_size) for _ in state['ghosts'] ] for i, (x, y) in enumerate(state['ghosts']): self.sprites['ghosts'][i].rect.move_ip(y * self.cell_size, x * self.cell_size) if 'dots' in state: self.sprites['dots'] = [ Dot(size=self.cell_size) for _ in state['dots'] ] for i, (x, y) in enumerate(state['dots']): sprite: Dot = self.sprites['dots'][i] sprite.rect.move_ip(y * self.cell_size + sprite.offset, x * self.cell_size + sprite.offset) self.sprites_group.add(*self.sorted_sprites) self.render() def update_state(self, state: t.Dict[str, t.Any], benchmarking: t.Dict[str, t.Any]): self.benchmarking = benchmarking self.update_rotations(self.state, state) steps = 5 if self.cell_size > 8 else 1 for int_state in self._generate_intermediate_states(steps, self.state, state): self.state.update(int_state) self.sync() self.render() self.state.update(state) self.sync() self.render() def sync(self): if 'pacman' in self.sprites: pacman = self.sprites['pacman'] x, y = self.state['pacman'] pacman.rect.update([y * self.cell_size, x * self.cell_size, self.cell_size, self.cell_size]) if 'target' in self.sprites: target = self.sprites['target'] x, y = self.state['target'] target.rect.update([y * self.cell_size, x * self.cell_size, self.cell_size, self.cell_size]) if 'ghosts' in self.sprites: for source, target in zip(self.state['ghosts'], self.sprites['ghosts']): x, y = source target.rect.update([y * self.cell_size, x * self.cell_size, self.cell_size, self.cell_size]) if 'dots' in self.state: self.sprites['dots'] = [ Dot(size=self.cell_size) for _ in self.state['dots'] ] for i, (x, y) in enumerate(self.state['dots']): sprite: Dot = self.sprites['dots'][i] sprite.rect.move_ip(y * self.cell_size + sprite.offset, x * self.cell_size + sprite.offset) def render(self): self.draw_labyrinth() self.draw_dots() self.sprites_group.draw(self.screen) self.show_benchmarking() pygame.display.flip() self.clock.tick(30) @property def sorted_sprites(self): res = sorted( [ (key, value) for key, value in self.sprites.items() if key in self.REGULAR_SPRITES ], key=lambda kv: self.SPRITES_Z_INDEX.get(kv[0]) ) res = list(map(lambda kv: kv[1], res)) for sprite_list in sorted( [ (key, value) for key, value in self.sprites.items() if key in self.LIST_SPRITES ], key=lambda kv: self.SPRITES_Z_INDEX.get(kv[0]) ): res += sprite_list[1] return res def update_rotations(self, from_state, to_state): pacman = self.sprites['pacman'] pacman.rotation = self._get_rotation(from_state['pacman'], to_state['pacman']) def show_benchmarking(self): font = pygame.font.Font(None, 36) for i, (key, value) in enumerate(self.benchmarking.items()): if isinstance(value, float): text = f'{key} : {value:.5f}' else: text = f'{key}: {value}' text = font.render(text, True, (0, 140, 0)) place = text.get_rect(topleft=(10, 10 + i * 36)) self.screen.blit(text, place) @classmethod def _generate_intermediate_states(cls, count: int, state_from: t.Dict[str, t.Any], state_to: t.Dict[str, t.Any])\ -> t.List[t.Dict[str, t.Any]]: # TODO: fix teleports - need some complex logic intermediate_states = [] for i in range(count): intermediate_state = {} for key, value in state_from.items(): if key not in cls.SMOOTH_MOVEMENT: intermediate_state[key] = value if i == 0 or key not in state_to else state_to[key] elif ( (key not in state_to) or (not isinstance(value, list) and not isinstance(value, tuple)) or len(value) != 2 ): intermediate_state[key] = value else: if key in cls.REGULAR_SPRITES: intermediate_state[key] = ( value[0] + (state_to[key][0] - value[0]) * i / count, value[1] + (state_to[key][1] - value[1]) * i / count ) else: intermediate_state[key] = [ ( source[0] + (dest[0] - source[0]) * i / count, source[1] + (dest[1] - source[1]) * i / count ) for source, dest in zip(value, state_to[key]) ] intermediate_states.append(intermediate_state) return intermediate_states @staticmethod def _get_rotation(from_xy, to_xy): x_f, y_f = from_xy x_t, y_t = to_xy if x_t < x_f: return 3 if x_t > x_f: return 1 if y_t < y_f: return 0 return 2 def draw_dots(self): if 'dots' not in self.sprites: return sprites = self.sprites['dots'] sprites_group = pygame.sprite.Group() sprites_group.add(*sprites) sprites_group.draw(self.screen)

38.235294

122

0.539053

import typing as t import pygame from labyrinth.labyrinth import Labyrinth from view.banana import Banana from view.dot import Dot from view.ghost import Ghost from view.pacman import Pacman MAX_DISPLAY_WIDTH = 1000 MAX_DISPLAY_HEIGHT = 500 class View: cell_size: int sprites: t.Dict[str, pygame.sprite.Sprite] sprites_group: pygame.sprite.Group labyrinth: Labyrinth = None SMOOTH_MOVEMENT = ['pacman', 'ghosts'] SPRITES_Z_INDEX = { 'pacman': 0, 'target': -1, 'ghosts': 1, } REGULAR_SPRITES = ['pacman', 'target'] LIST_SPRITES = ['ghosts'] STATIC_SPRITES = ['dots'] def __init__(self): pygame.init() self.sprites_group = pygame.sprite.Group() self.display_info = pygame.display.Info() self.sprites: t.Dict[t.Union[pygame.sprite.Sprite, t.List[pygame.sprite.Sprite]]] = {} self.benchmarking = {} self.screen = pygame.display.set_mode([MAX_DISPLAY_WIDTH, MAX_DISPLAY_HEIGHT]) self.clock = pygame.time.Clock() def draw_labyrinth(self, labyrinth: t.Optional[Labyrinth] = None): self.labyrinth = labyrinth or self.labyrinth labyrinth = self.labyrinth cell_size = min( self.display_info.current_h // len(labyrinth.raw_img), self.display_info.current_w // len(labyrinth.raw_img[0]) ) self.screen = pygame.display.set_mode([cell_size * len(labyrinth.raw_img[0]), cell_size * len(labyrinth.raw_img)]) self.cell_size = cell_size for line_no, line in enumerate(labyrinth.raw_img): for char_no, char in enumerate(line): color = (0, 0, 0) if char == ' ' else (0, 0, 200) pygame.draw.rect( self.screen, color, (char_no * cell_size, line_no * cell_size, cell_size, cell_size) ) def set_initial_state(self, state: t.Dict[str, t.Any]): self.state = state if 'pacman' in state: x, y = state['pacman'] pacman = Pacman(size=self.cell_size) self.sprites['pacman'] = pacman pacman.rect.move_ip(y * self.cell_size, x * self.cell_size) if 'target' in state: x, y = state['target'] target = Banana(size=self.cell_size) self.sprites['target'] = target target.rect.move_ip(y * self.cell_size, x * self.cell_size) if 'ghosts' in state: self.sprites['ghosts'] = [ Ghost(size=self.cell_size) for _ in state['ghosts'] ] for i, (x, y) in enumerate(state['ghosts']): self.sprites['ghosts'][i].rect.move_ip(y * self.cell_size, x * self.cell_size) if 'dots' in state: self.sprites['dots'] = [ Dot(size=self.cell_size) for _ in state['dots'] ] for i, (x, y) in enumerate(state['dots']): sprite: Dot = self.sprites['dots'][i] sprite.rect.move_ip(y * self.cell_size + sprite.offset, x * self.cell_size + sprite.offset) self.sprites_group.add(*self.sorted_sprites) self.render() def update_state(self, state: t.Dict[str, t.Any], benchmarking: t.Dict[str, t.Any]): self.benchmarking = benchmarking self.update_rotations(self.state, state) steps = 5 if self.cell_size > 8 else 1 for int_state in self._generate_intermediate_states(steps, self.state, state): self.state.update(int_state) self.sync() self.render() self.state.update(state) self.sync() self.render() def sync(self): if 'pacman' in self.sprites: pacman = self.sprites['pacman'] x, y = self.state['pacman'] pacman.rect.update([y * self.cell_size, x * self.cell_size, self.cell_size, self.cell_size]) if 'target' in self.sprites: target = self.sprites['target'] x, y = self.state['target'] target.rect.update([y * self.cell_size, x * self.cell_size, self.cell_size, self.cell_size]) if 'ghosts' in self.sprites: for source, target in zip(self.state['ghosts'], self.sprites['ghosts']): x, y = source target.rect.update([y * self.cell_size, x * self.cell_size, self.cell_size, self.cell_size]) if 'dots' in self.state: self.sprites['dots'] = [ Dot(size=self.cell_size) for _ in self.state['dots'] ] for i, (x, y) in enumerate(self.state['dots']): sprite: Dot = self.sprites['dots'][i] sprite.rect.move_ip(y * self.cell_size + sprite.offset, x * self.cell_size + sprite.offset) def render(self): self.draw_labyrinth() self.draw_dots() self.sprites_group.draw(self.screen) self.show_benchmarking() pygame.display.flip() self.clock.tick(30) @property def sorted_sprites(self): res = sorted( [ (key, value) for key, value in self.sprites.items() if key in self.REGULAR_SPRITES ], key=lambda kv: self.SPRITES_Z_INDEX.get(kv[0]) ) res = list(map(lambda kv: kv[1], res)) for sprite_list in sorted( [ (key, value) for key, value in self.sprites.items() if key in self.LIST_SPRITES ], key=lambda kv: self.SPRITES_Z_INDEX.get(kv[0]) ): res += sprite_list[1] return res def update_rotations(self, from_state, to_state): pacman = self.sprites['pacman'] pacman.rotation = self._get_rotation(from_state['pacman'], to_state['pacman']) def show_benchmarking(self): font = pygame.font.Font(None, 36) for i, (key, value) in enumerate(self.benchmarking.items()): if isinstance(value, float): text = f'{key} : {value:.5f}' else: text = f'{key}: {value}' text = font.render(text, True, (0, 140, 0)) place = text.get_rect(topleft=(10, 10 + i * 36)) self.screen.blit(text, place) @classmethod def _generate_intermediate_states(cls, count: int, state_from: t.Dict[str, t.Any], state_to: t.Dict[str, t.Any])\ -> t.List[t.Dict[str, t.Any]]: intermediate_states = [] for i in range(count): intermediate_state = {} for key, value in state_from.items(): if key not in cls.SMOOTH_MOVEMENT: intermediate_state[key] = value if i == 0 or key not in state_to else state_to[key] elif ( (key not in state_to) or (not isinstance(value, list) and not isinstance(value, tuple)) or len(value) != 2 ): intermediate_state[key] = value else: if key in cls.REGULAR_SPRITES: intermediate_state[key] = ( value[0] + (state_to[key][0] - value[0]) * i / count, value[1] + (state_to[key][1] - value[1]) * i / count ) else: intermediate_state[key] = [ ( source[0] + (dest[0] - source[0]) * i / count, source[1] + (dest[1] - source[1]) * i / count ) for source, dest in zip(value, state_to[key]) ] intermediate_states.append(intermediate_state) return intermediate_states @staticmethod def _get_rotation(from_xy, to_xy): x_f, y_f = from_xy x_t, y_t = to_xy if x_t < x_f: return 3 if x_t > x_f: return 1 if y_t < y_f: return 0 return 2 def draw_dots(self): if 'dots' not in self.sprites: return sprites = self.sprites['dots'] sprites_group = pygame.sprite.Group() sprites_group.add(*sprites) sprites_group.draw(self.screen)

true

f734ac4222c50c3f665746f358b2578f221a455c

4,696

py

Python

tools/utils/crawl_urls.py

p3t3r67x0/purple_jo

a58e89c8597aaa612b3f2784ff3259342b7b9fe6

[ "MIT" ]

8

2020-03-25T21:06:14.000Z

2022-02-17T03:22:06.000Z

tools/utils/crawl_urls.py

webtobesocial/purple_jo

a58e89c8597aaa612b3f2784ff3259342b7b9fe6

[ "MIT" ]

7

2021-03-31T19:31:44.000Z

2022-03-12T00:08:20.000Z

tools/utils/crawl_urls.py

webtobesocial/purple_jo

a58e89c8597aaa612b3f2784ff3259342b7b9fe6

[ "MIT" ]

3

2020-11-21T22:07:08.000Z

2021-11-03T09:10:49.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import re import time import requests import multiprocessing import argparse from lxml import html from urllib.parse import urljoin from urllib.parse import urlparse from fake_useragent import UserAgent from lxml.etree import ParserError from lxml.etree import XMLSyntaxError from requests.exceptions import Timeout from requests.exceptions import InvalidURL from requests.exceptions import InvalidSchema from requests.exceptions import MissingSchema from requests.exceptions import ConnectionError from requests.exceptions import ChunkedEncodingError from requests.exceptions import ContentDecodingError from requests.exceptions import TooManyRedirects from pymongo import MongoClient from pymongo.errors import DuplicateKeyError from pymongo.errors import AutoReconnect from pymongo.errors import WriteError from idna.core import IDNAError from datetime import datetime def check_mail(url): return re.match(r'\b[\w.+-]+?@[-_\w]+[.]+[-_.\w]+\b', url) def connect(host): return MongoClient('mongodb://{}:27017'.format(host)) def retrieve_domains(db_ip_data, skip, limit): return db_ip_data.dns.find({'domain_crawled': {'$exists': False}})[limit - skip:limit] def update_data(db_ip_data, domain): try: res = db_ip_data.dns.update_one({'domain': domain}, {'$set': {'domain_crawled': datetime.utcnow()}}, upsert=False) if res.modified_count > 0: print('INFO: domain {} crawled and updated with {} documents'.format(domain, res.modified_count)) except DuplicateKeyError: pass def add_urls(db_url_data, db_ip_data, url, domain): try: post = {'url': url.lower(), 'created': datetime.utcnow()} post_id = db_url_data.url.insert_one(post).inserted_id print(u'INFO: the url {} was added with the id {}'.format(url, post_id)) update_data(db_ip_data, domain) except AutoReconnect: time.sleep(30) except (DuplicateKeyError, WriteError) as e: print(e) def get_urls(db, ua, url): try: headers = {'User-Agent': ua.chrome} res = requests.get('http://{}'.format(url), timeout=1, headers=headers) content = res.text except (Timeout, ConnectionError, TooManyRedirects): return None except (IDNAError, InvalidURL, InvalidSchema, MissingSchema, ContentDecodingError, ChunkedEncodingError): return None try: doc = html.document_fromstring(content) except (ValueError, ParserError, XMLSyntaxError): return None links = doc.xpath('//a/@href') base_url = 'http://{}'.format(url) url_set = set() for link in links: link = link.lower().strip() if link.startswith('#') or link.startswith('+') or link.startswith('tel:') or link.startswith('javascript:') or link.startswith('mailto:'): continue elif link.startswith('/'): link = urljoin(base_url, link) elif link.startswith('?'): link = urljoin(base_url, link) elif link.startswith('..'): link = urljoin(base_url, link.replace('..', '')) if urlparse(link).netloc: url_set.add(link) print(url_set) return url_set def worker(host, skip, limit): client = connect(host) db_url_data = client.url_data db_ip_data = client.ip_data ua = UserAgent() try: domains = retrieve_domains(db_ip_data, limit, skip) except CursorNotFound: client.close() return for domain in domains: print(u'INFO: the domain {} is beeing processed'.format(domain['domain'])) links = get_urls(db, ua, domain['domain']) if links is not None and len(links) > 0: for link in links: add_urls(db_url_data, db_ip_data, link, domain['domain']) client.close() return def argparser(): parser = argparse.ArgumentParser() parser.add_argument('--worker', help='set worker count', type=int, required=True) parser.add_argument('--host', help='set the host', type=str, required=True) args = parser.parse_args() return args if __name__ == '__main__': args = argparser() client = connect(args.host) db = client.ip_data jobs = [] threads = args.worker amount = round(db.dns.estimated_document_count() / (threads + 50000)) limit = amount print(limit, amount) for f in range(threads): j = multiprocessing.Process(target=worker, args=(args.host, limit, amount)) jobs.append(j) j.start() limit = limit + amount for j in jobs: client.close() j.join() print('exitcode = {}'.format(j.exitcode))

28.460606

147

0.664608

import re import time import requests import multiprocessing import argparse from lxml import html from urllib.parse import urljoin from urllib.parse import urlparse from fake_useragent import UserAgent from lxml.etree import ParserError from lxml.etree import XMLSyntaxError from requests.exceptions import Timeout from requests.exceptions import InvalidURL from requests.exceptions import InvalidSchema from requests.exceptions import MissingSchema from requests.exceptions import ConnectionError from requests.exceptions import ChunkedEncodingError from requests.exceptions import ContentDecodingError from requests.exceptions import TooManyRedirects from pymongo import MongoClient from pymongo.errors import DuplicateKeyError from pymongo.errors import AutoReconnect from pymongo.errors import WriteError from idna.core import IDNAError from datetime import datetime def check_mail(url): return re.match(r'\b[\w.+-]+?@[-_\w]+[.]+[-_.\w]+\b', url) def connect(host): return MongoClient('mongodb://{}:27017'.format(host)) def retrieve_domains(db_ip_data, skip, limit): return db_ip_data.dns.find({'domain_crawled': {'$exists': False}})[limit - skip:limit] def update_data(db_ip_data, domain): try: res = db_ip_data.dns.update_one({'domain': domain}, {'$set': {'domain_crawled': datetime.utcnow()}}, upsert=False) if res.modified_count > 0: print('INFO: domain {} crawled and updated with {} documents'.format(domain, res.modified_count)) except DuplicateKeyError: pass def add_urls(db_url_data, db_ip_data, url, domain): try: post = {'url': url.lower(), 'created': datetime.utcnow()} post_id = db_url_data.url.insert_one(post).inserted_id print(u'INFO: the url {} was added with the id {}'.format(url, post_id)) update_data(db_ip_data, domain) except AutoReconnect: time.sleep(30) except (DuplicateKeyError, WriteError) as e: print(e) def get_urls(db, ua, url): try: headers = {'User-Agent': ua.chrome} res = requests.get('http://{}'.format(url), timeout=1, headers=headers) content = res.text except (Timeout, ConnectionError, TooManyRedirects): return None except (IDNAError, InvalidURL, InvalidSchema, MissingSchema, ContentDecodingError, ChunkedEncodingError): return None try: doc = html.document_fromstring(content) except (ValueError, ParserError, XMLSyntaxError): return None links = doc.xpath('//a/@href') base_url = 'http://{}'.format(url) url_set = set() for link in links: link = link.lower().strip() if link.startswith('#') or link.startswith('+') or link.startswith('tel:') or link.startswith('javascript:') or link.startswith('mailto:'): continue elif link.startswith('/'): link = urljoin(base_url, link) elif link.startswith('?'): link = urljoin(base_url, link) elif link.startswith('..'): link = urljoin(base_url, link.replace('..', '')) if urlparse(link).netloc: url_set.add(link) print(url_set) return url_set def worker(host, skip, limit): client = connect(host) db_url_data = client.url_data db_ip_data = client.ip_data ua = UserAgent() try: domains = retrieve_domains(db_ip_data, limit, skip) except CursorNotFound: client.close() return for domain in domains: print(u'INFO: the domain {} is beeing processed'.format(domain['domain'])) links = get_urls(db, ua, domain['domain']) if links is not None and len(links) > 0: for link in links: add_urls(db_url_data, db_ip_data, link, domain['domain']) client.close() return def argparser(): parser = argparse.ArgumentParser() parser.add_argument('--worker', help='set worker count', type=int, required=True) parser.add_argument('--host', help='set the host', type=str, required=True) args = parser.parse_args() return args if __name__ == '__main__': args = argparser() client = connect(args.host) db = client.ip_data jobs = [] threads = args.worker amount = round(db.dns.estimated_document_count() / (threads + 50000)) limit = amount print(limit, amount) for f in range(threads): j = multiprocessing.Process(target=worker, args=(args.host, limit, amount)) jobs.append(j) j.start() limit = limit + amount for j in jobs: client.close() j.join() print('exitcode = {}'.format(j.exitcode))

true

f734ae1b367f0cd2d5054b2947d084a9683c0171

2,106

py

Python

multilingual_t5/baseline_mr/baseline_mr.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/baseline_mr/baseline_mr.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/baseline_mr/baseline_mr.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

"""baseline_mr dataset.""" import tensorflow_datasets as tfds import tensorflow as tf # TODO(baseline_mr): Markdown description that will appear on the catalog page. _DESCRIPTION = """ Description is **formatted** as markdown. It should also contain any processing which has been applied (if any), (e.g. corrupted example skipped, images cropped,...): """ # TODO(baseline_mr): BibTeX citation _CITATION = """ """ class BaselineMr(tfds.core.GeneratorBasedBuilder): """DatasetBuilder for baseline_mr dataset.""" VERSION = tfds.core.Version('1.0.0') RELEASE_NOTES = { '1.0.0': 'Initial release.', } def _info(self) -> tfds.core.DatasetInfo: """Returns the dataset metadata.""" # TODO(baseline_mr): Specifies the tfds.core.DatasetInfo object return tfds.core.DatasetInfo( builder=self, description=_DESCRIPTION, features=tfds.features.FeaturesDict({ 'source': tfds.features.Text(), 'target': tfds.features.Text(), }), homepage='https://dataset-homepage/', citation=_CITATION, ) def _split_generators(self, dl_manager: tfds.download.DownloadManager): """Returns SplitGenerators.""" # TODO(baseline_mr): Downloads the data and defines the splits path = dl_manager.download_and_extract('https://storage.googleapis.com/ai4b-anuvaad-nmt/baselines/mT5/baseline_mr/strict-en-mr.zip') # TODO(baseline_mr): Returns the Dict[split names, Iterator[Key, Example]] return { 'train': self._generate_examples(source=path/'en-mr/train/train.mr', target=path/'en-mr/train/train.en'), 'validation': self._generate_examples(source=path/'en-mr/dev/dev.mr', target=path/'en-mr/dev/dev.en') } def _generate_examples(self, source, target): """Yields examples.""" # TODO(baseline_mr): Yields (key, example) tuples from the dataset src = tf.io.gfile.GFile(source, 'r').readlines() tgt = tf.io.gfile.GFile(target, 'r').readlines() for idx, row in enumerate(zip(src, tgt)): yield idx, { 'source': row[0], 'target': row[1], }

33.967742

136

0.672365

import tensorflow_datasets as tfds import tensorflow as tf _DESCRIPTION = """ Description is **formatted** as markdown. It should also contain any processing which has been applied (if any), (e.g. corrupted example skipped, images cropped,...): """ _CITATION = """ """ class BaselineMr(tfds.core.GeneratorBasedBuilder): VERSION = tfds.core.Version('1.0.0') RELEASE_NOTES = { '1.0.0': 'Initial release.', } def _info(self) -> tfds.core.DatasetInfo: return tfds.core.DatasetInfo( builder=self, description=_DESCRIPTION, features=tfds.features.FeaturesDict({ 'source': tfds.features.Text(), 'target': tfds.features.Text(), }), homepage='https://dataset-homepage/', citation=_CITATION, ) def _split_generators(self, dl_manager: tfds.download.DownloadManager): path = dl_manager.download_and_extract('https://storage.googleapis.com/ai4b-anuvaad-nmt/baselines/mT5/baseline_mr/strict-en-mr.zip') return { 'train': self._generate_examples(source=path/'en-mr/train/train.mr', target=path/'en-mr/train/train.en'), 'validation': self._generate_examples(source=path/'en-mr/dev/dev.mr', target=path/'en-mr/dev/dev.en') } def _generate_examples(self, source, target): src = tf.io.gfile.GFile(source, 'r').readlines() tgt = tf.io.gfile.GFile(target, 'r').readlines() for idx, row in enumerate(zip(src, tgt)): yield idx, { 'source': row[0], 'target': row[1], }

true

f734ae3edb3e3e64c8d186b6444c1c175228a9b2

4,769

py

Python

lib-dynload/_recordclass/lib/recordclass/test/test_litelist.py

tabulon-ext/dedupsqlfs

9dfbed17450e7f2a499a7381e0368d08ae3c700d

[ "MIT" ]

22

2015-04-09T09:00:00.000Z

2022-03-23T00:16:04.000Z

lib-dynload/_recordclass/lib/recordclass/test/test_litelist.py

tabulon-ext/dedupsqlfs

9dfbed17450e7f2a499a7381e0368d08ae3c700d

[ "MIT" ]

119

2015-02-11T21:39:27.000Z

2021-07-27T23:04:49.000Z

lib-dynload/_recordclass/lib/recordclass/test/test_litelist.py

tabulon-ext/dedupsqlfs

9dfbed17450e7f2a499a7381e0368d08ae3c700d

[ "MIT" ]

7

2016-03-16T11:53:45.000Z

2022-02-24T13:47:31.000Z

import unittest from recordclass import litelist import gc import pickle import sys class litelistTest(unittest.TestCase): def test_len(self): a = litelist([]) self.assertEqual(len(a), 0) a = litelist([1]) self.assertEqual(len(a), 1) def test_items(self): a = litelist([1,2,3]) self.assertEqual(a[0], 1) self.assertEqual(a[-1], 3) a[1] = 100 self.assertEqual(a[1], 100) def test_remove(self): a = litelist([1,2,3]) a.remove(2) self.assertEqual(a[0], 1) self.assertEqual(a[-1], 3) a = litelist([1,2,3]) a.remove(1) self.assertEqual(a[0], 2) self.assertEqual(a[-1], 3) a = litelist([1,2,3]) a.remove(3) self.assertEqual(a[0], 1) self.assertEqual(a[-1], 2) def test_gc(self): a = litelist([1,2,3]) self.assertEqual(sys.getsizeof(a), a.__sizeof__()) def test_append(self): a = litelist([]) a.append(1) self.assertEqual(a[0], 1) a.append(2) self.assertEqual(a[1], 2) a.append(3) self.assertEqual(a[2], 3) def test_extend1(self): a = litelist([]) a.extend([1,2,3]) self.assertEqual(a[0], 1) self.assertEqual(a[1], 2) self.assertEqual(a[2], 3) def test_extend2(self): a = litelist([1,2,3]) a.extend([4,5,6]) self.assertEqual(a[3], 4) self.assertEqual(a[4], 5) self.assertEqual(a[5], 6) def test_repr(self): a = litelist([]) self.assertEqual(repr(a), "litelist([])") a = litelist([1]) self.assertEqual(repr(a), "litelist([1])") a = litelist([1, 2]) self.assertEqual(repr(a), "litelist([1, 2])") def test_iter(self): a = litelist([1,2,3]) self.assertEqual(list(a), [1,2,3]) self.assertEqual(tuple(a), (1,2,3)) def test_iter2(self): from recordclass.litelist import litelistiter a = litelist([1,2,3]) self.assertTrue(isinstance(iter(a), litelistiter)) def test_getslice1(self): a = litelist([1,2,3]) self.assertEqual(len(a[1:1]), 0) self.assertEqual(repr(a[1:1]), "litelist([])") self.assertEqual(len(a[1:2]), 1) self.assertEqual(repr(a[1:2]), "litelist([2])") self.assertEqual(len(a[:-1]), 2) self.assertEqual(repr(a[:-1]), "litelist([1, 2])") def test_getslice2(self): a = litelist([1,2,3]) self.assertEqual(repr(a[:]), "litelist([1, 2, 3])") def test_setslice1(self): a = litelist([1,2,3]) a[1:1] = [] self.assertEqual(repr(a), "litelist([1, 2, 3])") def test_setslice2(self): a = litelist([1,2,3]) a[1:2] = [100] self.assertEqual(repr(a), "litelist([1, 100, 3])") def test_setslice3(self): a = litelist([1,2,3]) a[:-1] = [100,200] self.assertEqual(repr(a), "litelist([100, 200, 3])") def test_setslice4(self): a = litelist([1,2,3]) a[:] = [100,200,300] self.assertEqual(repr(a), "litelist([100, 200, 300])") def test_delitem1(self): a = litelist([1,2,3,4,5]) del a[1] self.assertEqual(repr(a), "litelist([1, 3, 4, 5])") def test_delitem2(self): a = litelist([1,2,3,4,5]) del a[0] self.assertEqual(repr(a), "litelist([2, 3, 4, 5])") def test_delitem3(self): a = litelist([1,2,3,4,5]) del a[4] self.assertEqual(repr(a), "litelist([1, 2, 3, 4])") def test_delitem4(self): a = litelist([1,2,3,4,5]) del a[-1] self.assertEqual(repr(a), "litelist([1, 2, 3, 4])") def test_iterator_pickle(self): # Userlist iterators don't support pickling yet since # they are based on generators. data = litelist([4, 5, 6, 7]) for proto in range(pickle.HIGHEST_PROTOCOL + 1): itorg = iter(data) d = pickle.dumps(itorg, proto) it = pickle.loads(d) self.assertEqual(type(itorg), type(it)) self.assertEqual(list(litelist(it)), list(data)) it = pickle.loads(d) next(it) d = pickle.dumps(it) self.assertEqual(list(litelist(it)), list(data[1:])) def test_refleak_on_assignemnt(self): a = 1 ll = litelist([a,2,3]) c = sys.getrefcount(a) b = ll[0] self.assertEqual(sys.getrefcount(a), c+1) ll[0] = None self.assertEqual(sys.getrefcount(a), c) def main(): suite = unittest.TestSuite() suite.addTest(unittest.makeSuite(litelistTest)) return suite

28.728916

66

0.528413

import unittest from recordclass import litelist import gc import pickle import sys class litelistTest(unittest.TestCase): def test_len(self): a = litelist([]) self.assertEqual(len(a), 0) a = litelist([1]) self.assertEqual(len(a), 1) def test_items(self): a = litelist([1,2,3]) self.assertEqual(a[0], 1) self.assertEqual(a[-1], 3) a[1] = 100 self.assertEqual(a[1], 100) def test_remove(self): a = litelist([1,2,3]) a.remove(2) self.assertEqual(a[0], 1) self.assertEqual(a[-1], 3) a = litelist([1,2,3]) a.remove(1) self.assertEqual(a[0], 2) self.assertEqual(a[-1], 3) a = litelist([1,2,3]) a.remove(3) self.assertEqual(a[0], 1) self.assertEqual(a[-1], 2) def test_gc(self): a = litelist([1,2,3]) self.assertEqual(sys.getsizeof(a), a.__sizeof__()) def test_append(self): a = litelist([]) a.append(1) self.assertEqual(a[0], 1) a.append(2) self.assertEqual(a[1], 2) a.append(3) self.assertEqual(a[2], 3) def test_extend1(self): a = litelist([]) a.extend([1,2,3]) self.assertEqual(a[0], 1) self.assertEqual(a[1], 2) self.assertEqual(a[2], 3) def test_extend2(self): a = litelist([1,2,3]) a.extend([4,5,6]) self.assertEqual(a[3], 4) self.assertEqual(a[4], 5) self.assertEqual(a[5], 6) def test_repr(self): a = litelist([]) self.assertEqual(repr(a), "litelist([])") a = litelist([1]) self.assertEqual(repr(a), "litelist([1])") a = litelist([1, 2]) self.assertEqual(repr(a), "litelist([1, 2])") def test_iter(self): a = litelist([1,2,3]) self.assertEqual(list(a), [1,2,3]) self.assertEqual(tuple(a), (1,2,3)) def test_iter2(self): from recordclass.litelist import litelistiter a = litelist([1,2,3]) self.assertTrue(isinstance(iter(a), litelistiter)) def test_getslice1(self): a = litelist([1,2,3]) self.assertEqual(len(a[1:1]), 0) self.assertEqual(repr(a[1:1]), "litelist([])") self.assertEqual(len(a[1:2]), 1) self.assertEqual(repr(a[1:2]), "litelist([2])") self.assertEqual(len(a[:-1]), 2) self.assertEqual(repr(a[:-1]), "litelist([1, 2])") def test_getslice2(self): a = litelist([1,2,3]) self.assertEqual(repr(a[:]), "litelist([1, 2, 3])") def test_setslice1(self): a = litelist([1,2,3]) a[1:1] = [] self.assertEqual(repr(a), "litelist([1, 2, 3])") def test_setslice2(self): a = litelist([1,2,3]) a[1:2] = [100] self.assertEqual(repr(a), "litelist([1, 100, 3])") def test_setslice3(self): a = litelist([1,2,3]) a[:-1] = [100,200] self.assertEqual(repr(a), "litelist([100, 200, 3])") def test_setslice4(self): a = litelist([1,2,3]) a[:] = [100,200,300] self.assertEqual(repr(a), "litelist([100, 200, 300])") def test_delitem1(self): a = litelist([1,2,3,4,5]) del a[1] self.assertEqual(repr(a), "litelist([1, 3, 4, 5])") def test_delitem2(self): a = litelist([1,2,3,4,5]) del a[0] self.assertEqual(repr(a), "litelist([2, 3, 4, 5])") def test_delitem3(self): a = litelist([1,2,3,4,5]) del a[4] self.assertEqual(repr(a), "litelist([1, 2, 3, 4])") def test_delitem4(self): a = litelist([1,2,3,4,5]) del a[-1] self.assertEqual(repr(a), "litelist([1, 2, 3, 4])") def test_iterator_pickle(self): # they are based on generators. data = litelist([4, 5, 6, 7]) for proto in range(pickle.HIGHEST_PROTOCOL + 1): itorg = iter(data) d = pickle.dumps(itorg, proto) it = pickle.loads(d) self.assertEqual(type(itorg), type(it)) self.assertEqual(list(litelist(it)), list(data)) it = pickle.loads(d) next(it) d = pickle.dumps(it) self.assertEqual(list(litelist(it)), list(data[1:])) def test_refleak_on_assignemnt(self): a = 1 ll = litelist([a,2,3]) c = sys.getrefcount(a) b = ll[0] self.assertEqual(sys.getrefcount(a), c+1) ll[0] = None self.assertEqual(sys.getrefcount(a), c) def main(): suite = unittest.TestSuite() suite.addTest(unittest.makeSuite(litelistTest)) return suite

true

f734afa1170846a895fc159dcf6c25886daf9a07

3,509

py

Python

gnes/preprocessor/video/shotdetect.py

dixiak/gnes

12513d29157a06bd22923717fd0c19a856f20193

[ "Apache-2.0" ]

null

gnes/preprocessor/video/shotdetect.py

dixiak/gnes

12513d29157a06bd22923717fd0c19a856f20193

[ "Apache-2.0" ]

null

gnes/preprocessor/video/shotdetect.py

dixiak/gnes

12513d29157a06bd22923717fd0c19a856f20193

[ "Apache-2.0" ]

null

# Tencent is pleased to support the open source community by making GNES available. # # Copyright (C) 2019 THL A29 Limited, a Tencent company. 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. from typing import List import numpy as np from ..base import BaseVideoPreprocessor from ..helper import compute_descriptor, compare_descriptor, detect_peak_boundary, compare_ecr from ..io_utils import video as video_util from ...proto import gnes_pb2, array2blob class ShotDetectPreprocessor(BaseVideoPreprocessor): store_args_kwargs = True def __init__(self, frame_size: str = '192:168', descriptor: str = 'block_hsv_histogram', distance_metric: str = 'bhattacharya', detect_method: str = 'threshold', frame_rate: int = 10, frame_num: int = -1, *args, **kwargs): super().__init__(*args, **kwargs) self.frame_size = frame_size self.descriptor = descriptor self.distance_metric = distance_metric self.detect_method = detect_method self.frame_rate = frame_rate self.frame_num = frame_num self._detector_kwargs = kwargs def detect_shots(self, frames: 'np.ndarray') -> List[List['np.ndarray']]: descriptors = [] for frame in frames: descriptor = compute_descriptor( frame, method=self.descriptor, **self._detector_kwargs) descriptors.append(descriptor) # compute distances between frames if self.distance_metric == 'edge_change_ration': dists = compare_ecr(descriptors) else: dists = [ compare_descriptor(pair[0], pair[1], self.distance_metric) for pair in zip(descriptors[:-1], descriptors[1:]) ] shot_bounds = detect_peak_boundary(dists, self.detect_method) shots = [] for ci in range(0, len(shot_bounds) - 1): shots.append(frames[shot_bounds[ci]:shot_bounds[ci + 1]]) return shots def apply(self, doc: 'gnes_pb2.Document') -> None: super().apply(doc) if doc.raw_bytes: all_frames = video_util.capture_frames( input_data=doc.raw_bytes, scale=self.frame_size, fps=self.frame_rate, vframes=self.frame_num) num_frames = len(all_frames) assert num_frames > 0 shots = self.detect_shots(all_frames) for ci, frames in enumerate(shots): c = doc.chunks.add() c.doc_id = doc.doc_id # chunk_data = np.concatenate(frames, axis=0) chunk_data = np.array(frames) c.blob.CopyFrom(array2blob(chunk_data)) c.offset = ci c.weight = len(frames) / num_frames else: self.logger.error('bad document: "raw_bytes" is empty!')

37.329787

94

0.620405

from typing import List import numpy as np from ..base import BaseVideoPreprocessor from ..helper import compute_descriptor, compare_descriptor, detect_peak_boundary, compare_ecr from ..io_utils import video as video_util from ...proto import gnes_pb2, array2blob class ShotDetectPreprocessor(BaseVideoPreprocessor): store_args_kwargs = True def __init__(self, frame_size: str = '192:168', descriptor: str = 'block_hsv_histogram', distance_metric: str = 'bhattacharya', detect_method: str = 'threshold', frame_rate: int = 10, frame_num: int = -1, *args, **kwargs): super().__init__(*args, **kwargs) self.frame_size = frame_size self.descriptor = descriptor self.distance_metric = distance_metric self.detect_method = detect_method self.frame_rate = frame_rate self.frame_num = frame_num self._detector_kwargs = kwargs def detect_shots(self, frames: 'np.ndarray') -> List[List['np.ndarray']]: descriptors = [] for frame in frames: descriptor = compute_descriptor( frame, method=self.descriptor, **self._detector_kwargs) descriptors.append(descriptor) if self.distance_metric == 'edge_change_ration': dists = compare_ecr(descriptors) else: dists = [ compare_descriptor(pair[0], pair[1], self.distance_metric) for pair in zip(descriptors[:-1], descriptors[1:]) ] shot_bounds = detect_peak_boundary(dists, self.detect_method) shots = [] for ci in range(0, len(shot_bounds) - 1): shots.append(frames[shot_bounds[ci]:shot_bounds[ci + 1]]) return shots def apply(self, doc: 'gnes_pb2.Document') -> None: super().apply(doc) if doc.raw_bytes: all_frames = video_util.capture_frames( input_data=doc.raw_bytes, scale=self.frame_size, fps=self.frame_rate, vframes=self.frame_num) num_frames = len(all_frames) assert num_frames > 0 shots = self.detect_shots(all_frames) for ci, frames in enumerate(shots): c = doc.chunks.add() c.doc_id = doc.doc_id chunk_data = np.array(frames) c.blob.CopyFrom(array2blob(chunk_data)) c.offset = ci c.weight = len(frames) / num_frames else: self.logger.error('bad document: "raw_bytes" is empty!')

true

f734afad97f1d8801b5285aa420eeebbfb4adc49

6,967

py

Python

src/flappy_bird_gym/envs/flappy_bird_env_simple.py

chokyzhou/gym-flappy-bird

ffe1089501f3e2e113a8868cd27480653dbe0ef7

[ "MIT" ]

null

src/flappy_bird_gym/envs/flappy_bird_env_simple.py

chokyzhou/gym-flappy-bird

ffe1089501f3e2e113a8868cd27480653dbe0ef7

[ "MIT" ]

null

src/flappy_bird_gym/envs/flappy_bird_env_simple.py

chokyzhou/gym-flappy-bird

ffe1089501f3e2e113a8868cd27480653dbe0ef7

[ "MIT" ]

null

# # Copyright (c) 2020 Gabriel Nogueira (Talendar) # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # ============================================================================== """ Implementation of a Flappy Bird OpenAI Gym environment that yields simple numerical information about the game's state as observations. """ from typing import Dict, Tuple, Optional, Union import gym import numpy as np import pygame from flappy_bird_gym.envs.game_logic import FlappyBirdLogic from flappy_bird_gym.envs.game_logic import PIPE_WIDTH, PIPE_HEIGHT from flappy_bird_gym.envs.game_logic import PLAYER_WIDTH, PLAYER_HEIGHT from flappy_bird_gym.envs.renderer import FlappyBirdRenderer class FlappyBirdEnvSimple(gym.Env): """ Flappy Bird Gym environment that yields simple observations. The observations yielded by this environment are simple numerical information about the game's state. Specifically, the observations are: * Horizontal distance to the next pipe; * Difference between the player's y position and the next hole's y position. The reward received by the agent in each step is equal to the score obtained by the agent in that step. A score point is obtained every time the bird passes a pipe. Args: screen_size (Tuple[int, int]): The screen's width and height. normalize_obs (bool): If `True`, the observations will be normalized before being returned. pipe_gap (int): Space between a lower and an upper pipe. bird_color (str): Color of the flappy bird. The currently available colors are "yellow", "blue" and "red". pipe_color (str): Color of the pipes. The currently available colors are "green" and "red". background (Optional[str]): Type of background image. The currently available types are "day" and "night". If `None`, no background will be drawn. """ metadata = {'render.modes': ['human']} def __init__(self, screen_size: Tuple[int, int] = (288, 512), normalize_obs: bool = True, pipe_gap: int = 100, bird_color: str = "yellow", pipe_color: str = "green", background: Optional[str] = "day") -> None: self.action_space = gym.spaces.Discrete(2) self.observation_space = gym.spaces.Box(-np.inf, np.inf, shape=(3,), dtype=np.float32) self._screen_size = screen_size self._normalize_obs = normalize_obs self._pipe_gap = pipe_gap self._game = None self._renderer = None self._bird_color = bird_color self._pipe_color = pipe_color self._bg_type = background def _get_observation(self): up_pipe = low_pipe = None h_dist = 0 for up_pipe, low_pipe in zip(self._game.upper_pipes, self._game.lower_pipes): h_dist = (low_pipe["x"] + PIPE_WIDTH / 2 - (self._game.player_x - PLAYER_WIDTH / 2)) h_dist += 3 # extra distance to compensate for the buggy hit-box if h_dist >= 0: break upper_pipe_y = up_pipe["y"] + PIPE_HEIGHT lower_pipe_y = low_pipe["y"] player_y = self._game.player_y y_vel = self._game.player_vel_y v_dist = (upper_pipe_y + lower_pipe_y) / 2 - (player_y + PLAYER_HEIGHT/2) if self._normalize_obs: h_dist /= self._screen_size[0] v_dist /= self._screen_size[1] return np.array([ h_dist, v_dist, y_vel, ]) def step(self, action: Union[FlappyBirdLogic.Actions, int], ) -> Tuple[np.ndarray, float, bool, Dict]: """ Given an action, updates the game state. Args: action (Union[FlappyBirdLogic.Actions, int]): The action taken by the agent. Zero (0) means "do nothing" and one (1) means "flap". Returns: A tuple containing, respectively: * an observation (horizontal distance to the next pipe; difference between the player's y position and the next hole's y position); * a reward (always 1); * a status report (`True` if the game is over and `False` otherwise); * an info dictionary. """ alive = self._game.update_state(action) obs = self._get_observation() reward = 1 done = not alive info = {"score": self._game.score} return obs, reward, done, info def reset(self): """ Resets the environment (starts a new game). """ self._game = FlappyBirdLogic(screen_size=self._screen_size, pipe_gap_size=self._pipe_gap) if self._renderer is not None: self._renderer.game = self._game return self._get_observation() def render(self, mode='human') -> None: """ Renders the next frame. """ if self._renderer is None: self._renderer = FlappyBirdRenderer(screen_size=self._screen_size, bird_color=self._bird_color, pipe_color=self._pipe_color, background=self._bg_type) self._renderer.game = self._game self._renderer.make_display() self._renderer.draw_surface(show_score=True) self._renderer.update_display() def close(self): """ Closes the environment. """ if self._renderer is not None: pygame.display.quit() self._renderer = None super().close()

39.140449

80

0.604564

from typing import Dict, Tuple, Optional, Union import gym import numpy as np import pygame from flappy_bird_gym.envs.game_logic import FlappyBirdLogic from flappy_bird_gym.envs.game_logic import PIPE_WIDTH, PIPE_HEIGHT from flappy_bird_gym.envs.game_logic import PLAYER_WIDTH, PLAYER_HEIGHT from flappy_bird_gym.envs.renderer import FlappyBirdRenderer class FlappyBirdEnvSimple(gym.Env): metadata = {'render.modes': ['human']} def __init__(self, screen_size: Tuple[int, int] = (288, 512), normalize_obs: bool = True, pipe_gap: int = 100, bird_color: str = "yellow", pipe_color: str = "green", background: Optional[str] = "day") -> None: self.action_space = gym.spaces.Discrete(2) self.observation_space = gym.spaces.Box(-np.inf, np.inf, shape=(3,), dtype=np.float32) self._screen_size = screen_size self._normalize_obs = normalize_obs self._pipe_gap = pipe_gap self._game = None self._renderer = None self._bird_color = bird_color self._pipe_color = pipe_color self._bg_type = background def _get_observation(self): up_pipe = low_pipe = None h_dist = 0 for up_pipe, low_pipe in zip(self._game.upper_pipes, self._game.lower_pipes): h_dist = (low_pipe["x"] + PIPE_WIDTH / 2 - (self._game.player_x - PLAYER_WIDTH / 2)) h_dist += 3 if h_dist >= 0: break upper_pipe_y = up_pipe["y"] + PIPE_HEIGHT lower_pipe_y = low_pipe["y"] player_y = self._game.player_y y_vel = self._game.player_vel_y v_dist = (upper_pipe_y + lower_pipe_y) / 2 - (player_y + PLAYER_HEIGHT/2) if self._normalize_obs: h_dist /= self._screen_size[0] v_dist /= self._screen_size[1] return np.array([ h_dist, v_dist, y_vel, ]) def step(self, action: Union[FlappyBirdLogic.Actions, int], ) -> Tuple[np.ndarray, float, bool, Dict]: alive = self._game.update_state(action) obs = self._get_observation() reward = 1 done = not alive info = {"score": self._game.score} return obs, reward, done, info def reset(self): self._game = FlappyBirdLogic(screen_size=self._screen_size, pipe_gap_size=self._pipe_gap) if self._renderer is not None: self._renderer.game = self._game return self._get_observation() def render(self, mode='human') -> None: if self._renderer is None: self._renderer = FlappyBirdRenderer(screen_size=self._screen_size, bird_color=self._bird_color, pipe_color=self._pipe_color, background=self._bg_type) self._renderer.game = self._game self._renderer.make_display() self._renderer.draw_surface(show_score=True) self._renderer.update_display() def close(self): if self._renderer is not None: pygame.display.quit() self._renderer = None super().close()

true

f734afd4fd2b3089c11565e92f2a38f6324a6e63

4,769

py

Python

pkg/ampcor/products/OffsetMap.py

aivazis/ampcor

a673e6fd12ac29086c88002ce999a8eabdf406cd

[ "BSD-2-Clause" ]

3

2018-12-16T14:16:51.000Z

2020-11-12T17:33:02.000Z

pkg/ampcor/products/OffsetMap.py

aivazis/ampcor

a673e6fd12ac29086c88002ce999a8eabdf406cd

[ "BSD-2-Clause" ]

null

pkg/ampcor/products/OffsetMap.py

aivazis/ampcor

a673e6fd12ac29086c88002ce999a8eabdf406cd

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- # # michael a.g. aïvázis <michael.aivazis@para-sim.com> # parasim # (c) 1998-2021 all rights reserved # # the framework import ampcor # the extension from ampcor.ext import ampcor as libampcor # declaration class OffsetMap(ampcor.flow.product, family="ampcor.products.offsets.offsets", implements=ampcor.specs.offsets): """ Access to the data of an offset map """ # public data shape = ampcor.properties.tuple(schema=ampcor.properties.int()) shape.default = (0,0) shape.doc = "the shape of the map" data = ampcor.properties.path() data.doc = "the path to my binary data" # public data @property def layout(self): """ Get my layout """ # ask the spec return self.spec.layout @property def bytesPerCell(self): """ Get the memory footprint of my cell """ # ask the spec return self.spec.bytesPerCell # protocol obligations @ampcor.export def cells(self): """ Compute the number of points """ # ask my spec; it knows return self.spec.cells @ampcor.export def bytes(self): """ Compute my memory footprint """ # ask my spec; it knows return self.spec.bytes @ampcor.export def slice(self, origin, shape): """ Grant access to a slice of data of the given {shape} starting at {origin} """ @ampcor.export def open(self, mode="r"): """ Map me over the contents of {filename} """ # unpack the shape shape = self.shape # attempt to try: # resolve the filename using the {vfs} uri = self.pyre_fileserver[self.data].uri # if that fails except Exception: # use the raw name uri = self.data # if we are opening in read-only mode if mode == "r": # make a const raster raster = ampcor.libampcor.OffsetsConstRaster(shape=shape, uri=uri) # if we are opening an existing one in read/write mode elif mode == "w": # make a modifiable raster raster = ampcor.libampcor.OffsetsRaster(shape=shape, uri=uri, new=False) # if we are creating one elif mode == "n": # make a new raster; careful: this deletes existing products raster = ampcor.libampcor.OffsetsRaster(shape=shape, uri=uri, new=True) # otherwise else: # grab the journal import journal # make a channel channel = journal.error("ampcor.products.slc") # and complain channel.line(f"unknown mode '{mode}'") channel.line(f" while opening '{uri}'") channel.line(f" in ampcor.products.OffsetMap.open();") channel.line(f" valid modes are: 'r', 'w', 'n'") channel.log() # just in case errors are non-fatal raster = None # attach the raster self.raster = raster # all done return self # meta-methods def __init__(self, **kwds): # chain up super().__init__(**kwds) # load my product spec self.spec = ampcor.libampcor.Offsets(shape=self.shape) # i get a raster after {open} self.raster = None # all done return def __getitem__(self, idx): """ Return the pair of correlated points stored at {index} """ # ask the raster return self.raster[idx] def __setitem__(self, idx, points): """ Establish a correlation between the reference and secondary {points} at {index} """ # delegate to the raster self.raster[idx] = points # all done return # framework hooks def pyre_traitModified(self, trait, new, old): """ Handle post construction configuration changes """ # when my shape changes if trait.name == "shape": # recompute my spec self.spec = ampcor.libampcor.Offsets(shape=self.shape) # all done return self # implementation details def show(self, indent, margin): """ Generate a report of my configuration """ # my info yield f"{margin}name: {self.pyre_name}" yield f"{margin}family: {self.pyre_family()}" yield f"{margin}data: {self.data}" yield f"{margin}shape: {self.shape}" yield f"{margin}points: {self.cells()}" yield f"{margin}footprint: {self.bytes()} bytes" # all done return # end of file

25.639785

91

0.556511

import ampcor from ampcor.ext import ampcor as libampcor class OffsetMap(ampcor.flow.product, family="ampcor.products.offsets.offsets", implements=ampcor.specs.offsets): shape = ampcor.properties.tuple(schema=ampcor.properties.int()) shape.default = (0,0) shape.doc = "the shape of the map" data = ampcor.properties.path() data.doc = "the path to my binary data" @property def layout(self): return self.spec.layout @property def bytesPerCell(self): return self.spec.bytesPerCell @ampcor.export def cells(self): return self.spec.cells @ampcor.export def bytes(self): return self.spec.bytes @ampcor.export def slice(self, origin, shape): @ampcor.export def open(self, mode="r"): shape = self.shape try: uri = self.pyre_fileserver[self.data].uri except Exception: uri = self.data if mode == "r": raster = ampcor.libampcor.OffsetsConstRaster(shape=shape, uri=uri) elif mode == "w": raster = ampcor.libampcor.OffsetsRaster(shape=shape, uri=uri, new=False) elif mode == "n": raster = ampcor.libampcor.OffsetsRaster(shape=shape, uri=uri, new=True) else: import journal channel = journal.error("ampcor.products.slc") channel.line(f"unknown mode '{mode}'") channel.line(f" while opening '{uri}'") channel.line(f" in ampcor.products.OffsetMap.open();") channel.line(f" valid modes are: 'r', 'w', 'n'") channel.log() raster = None self.raster = raster return self def __init__(self, **kwds): super().__init__(**kwds) self.spec = ampcor.libampcor.Offsets(shape=self.shape) self.raster = None return def __getitem__(self, idx): return self.raster[idx] def __setitem__(self, idx, points): self.raster[idx] = points return def pyre_traitModified(self, trait, new, old): if trait.name == "shape": self.spec = ampcor.libampcor.Offsets(shape=self.shape) return self def show(self, indent, margin): yield f"{margin}name: {self.pyre_name}" yield f"{margin}family: {self.pyre_family()}" yield f"{margin}data: {self.data}" yield f"{margin}shape: {self.shape}" yield f"{margin}points: {self.cells()}" yield f"{margin}footprint: {self.bytes()} bytes" return

true

f734b04a077be3478e1741e20f6f998dfde15758

2,542

py

Python

Competitive Programming/Blind 75 Must Do Leetcode/Maximum Subarray.py

shreejitverma/GeeksforGeeks

d7bcb166369fffa9a031a258e925b6aff8d44e6c

[ "MIT" ]

2

2022-02-18T05:14:28.000Z

2022-03-08T07:00:08.000Z

Competitive Programming/Blind 75 Must Do Leetcode/Maximum Subarray.py

shivaniverma1/Competitive-Programming-1

d7bcb166369fffa9a031a258e925b6aff8d44e6c

[ "MIT" ]

6

2022-01-13T04:31:04.000Z

2022-03-12T01:06:16.000Z

Competitive Programming/Blind 75 Must Do Leetcode/Maximum Subarray.py

shivaniverma1/Competitive-Programming-1

d7bcb166369fffa9a031a258e925b6aff8d44e6c

[ "MIT" ]

2

2022-02-14T19:53:53.000Z

2022-02-18T05:14:30.000Z

'''https://leetcode.com/problems/maximum-subarray/ 53. Maximum Subarray Easy 15507 728 Add to List Share Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum. A subarray is a contiguous part of an array. Example 1: Input: nums = [-2,1,-3,4,-1,2,1,-5,4] Output: 6 Explanation: [4,-1,2,1] has the largest sum = 6. Example 2: Input: nums = [1] Output: 1 Example 3: Input: nums = [5,4,-1,7,8] Output: 23 Constraints: 1 <= nums.length <= 105 -104 <= nums[i] <= 104 Follow up: If you have figured out the O(n) solution, try coding another solution using the divide and conquer approach, which is more subtle.''' class Solution: def maxSubArray(self, nums: List[int]) -> int: ans = nums[0] cur_sum = 0 for i in range(len(nums)): if cur_sum > 0: cur_sum += nums[i] else: cur_sum = nums[i] ans = max(ans, cur_sum) return ans # @lc code=end def brute_force(nums): max_sum = 0 for L in range(len(nums)): for R in range(L, len(nums)): cur_sum = 0 for i in range(L, R): cur_sum += nums[i] if cur_sum > max_sum: max_sum = cur_sum return max_sum def Devided_Conquer(nums, left, right): if left == right: return nums[left] # if nums[left] > 0 else 0 center = (left+right) // 2 max_left = Devided_Conquer(nums, left, center) max_right = Devided_Conquer(nums, center+1, right) left_Sum = 0 maxLeft_Sum = nums[center] for i in range(center-1, left-1, -1): left_Sum += nums[i] if left_Sum > maxLeft_Sum: maxLeft_Sum = left_Sum right_sum = 0 max_right_sum = nums[center+1] for i in range(center+2, right+1): right_sum += nums[i] if right_sum > max_right_sum: max_right_sum = right_sum print("max_left:{0}, max_right:{1} ".format(maxLeft_Sum, max_right_sum)) print("left:{0}, right:{1}, mid:{2}".format( max_left, max_right, maxLeft_Sum+max_right_sum)) return max(max_left, max_right, maxLeft_Sum+max_right_sum) def One_Pass(nums): max_sum = nums[0] this_sum = nums[0] for num in nums[1:]: this_sum = max(num, this_sum+num) if this_sum > max_sum: max_sum = this_sum return max_sum if __name__ == '__main__': nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4] print(One_Pass(nums))

22.900901

136

0.597168

class Solution: def maxSubArray(self, nums: List[int]) -> int: ans = nums[0] cur_sum = 0 for i in range(len(nums)): if cur_sum > 0: cur_sum += nums[i] else: cur_sum = nums[i] ans = max(ans, cur_sum) return ans def brute_force(nums): max_sum = 0 for L in range(len(nums)): for R in range(L, len(nums)): cur_sum = 0 for i in range(L, R): cur_sum += nums[i] if cur_sum > max_sum: max_sum = cur_sum return max_sum def Devided_Conquer(nums, left, right): if left == right: return nums[left] center = (left+right) // 2 max_left = Devided_Conquer(nums, left, center) max_right = Devided_Conquer(nums, center+1, right) left_Sum = 0 maxLeft_Sum = nums[center] for i in range(center-1, left-1, -1): left_Sum += nums[i] if left_Sum > maxLeft_Sum: maxLeft_Sum = left_Sum right_sum = 0 max_right_sum = nums[center+1] for i in range(center+2, right+1): right_sum += nums[i] if right_sum > max_right_sum: max_right_sum = right_sum print("max_left:{0}, max_right:{1} ".format(maxLeft_Sum, max_right_sum)) print("left:{0}, right:{1}, mid:{2}".format( max_left, max_right, maxLeft_Sum+max_right_sum)) return max(max_left, max_right, maxLeft_Sum+max_right_sum) def One_Pass(nums): max_sum = nums[0] this_sum = nums[0] for num in nums[1:]: this_sum = max(num, this_sum+num) if this_sum > max_sum: max_sum = this_sum return max_sum if __name__ == '__main__': nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4] print(One_Pass(nums))

true

f734b04aea02cf19e100ce06a3b561cc7a0bf7dc

7,054

py

Python

iot_services_sdk/sensor.py

sap-archive/iot-services-sdk

157e607b0c8b3a7b77836336aa31d89ebd8e9f86

[ "CNRI-Python" ]

4

2019-05-02T07:51:13.000Z

2019-09-25T12:14:06.000Z

iot_services_sdk/sensor.py

sap-archive/iot-services-sdk

157e607b0c8b3a7b77836336aa31d89ebd8e9f86

[ "CNRI-Python" ]

2

2019-09-13T15:36:32.000Z

2019-11-15T06:01:09.000Z

iot_services_sdk/sensor.py

sap-archive/iot-services-sdk

157e607b0c8b3a7b77836336aa31d89ebd8e9f86

[ "CNRI-Python" ]

1

2020-01-17T15:44:52.000Z

""" Author: Philipp Steinrötter (steinroe) """ from .tenant_iot_service import TenantIoTService from .utils import build_query from .response import Response class SensorService(TenantIoTService): def __init__(self, instance, user, password, tenant_id): """Instantiate SensorService object Arguments: instance {string} -- IoT Services instance user {string} -- IoT Services user password {string} -- IoT Services password tenant_id {string} -- Id of the tenant """ self.service = '/sensors' TenantIoTService.__init__( self, instance=instance, user=user, password=password, tenant_id=tenant_id ) def get_sensors(self, filters=None, orderby=None, asc=True, skip=None, top=None) -> Response: """The endpoint returns a list of sensors. Keyword Arguments: filters {list} -- This parameter allows clients to filter the collection for specific attributes. It is possible to filter by 'id’, 'deviceId’, 'name’, and 'alternateId’. The filters must be provided as a list of strings, e.q. ["name eq 'my-name'", "id eq '111'"]. (default: {None}) orderby {str} -- The attribute to order by. (default: {None}) asc {bool} -- Only considered if orderby is not none. Defines if the values should be ordered asc or desc. (default: {True}) skip {int} -- This parameter specifies the number of items in the queried collection which will be skipped and therefore included in the result set. (default: {None}) top {int} -- This parameter restricts the maximum number of items which will be returned by the request. (default: {None}) Returns: Response -- Response object """ query = build_query(filters=filters, orderby=orderby, asc=asc, skip=skip, top=top) return super().request_core(method='GET', service=self.service, query=query, accept_json=True) def get_sensor_count(self): """The endpoint returns the count of all sensors. Returns: Response -- Response object """ service = self.service + '/count' return super().request_core(method='GET', service=service, accept_json=True) def create_sensor(self, device_id: str, alternate_id: str, name: str, sensor_type_id: str) -> Response: """This endpoint is used to create a sensor. Arguments: device_id {str} -- Respective device ID for the sensor alternate_id {str} -- Alternate ID for the sensor name {str} -- Name for the sensor sensor_type_id {str} -- ID of the respective sensor type Returns: Response -- Response object """ headers = {'Content-Type': 'application/json'} payload = '{ "deviceId": "' + device_id + '", "alternateId": "' + alternate_id + '", "name": "' + name + '", "sensorTypeId": "' + sensor_type_id + '"}' return super().request_core(method='POST', service=self.service, headers=headers, payload=payload, accept_json=True) def delete_sensor(self, sensor_id: str) -> Response: """The endpoint is used to delete the sensor associated to the given id. Arguments: sensor_id {str} -- Unique identifier of a sensor Returns: Response -- Response object """ service = self.service + '/' + sensor_id return super().request_core(method='DELETE', service=service, accept_json=True) def get_sensor(self, sensor_id: str) -> Response: """The endpoint returns the sensor associated to the given id. Arguments: sensor_id {str} -- Unique identifier of a sensor Returns: Response -- Response object """ service = self.service + '/' + sensor_id return super().request_core(method='GET', service=service, accept_json=True) def update_sensor(self, sensor_id: str, name: str, sensor_type_id: str) -> Response: """This endpoint is used to update a sensor associated to the given id with details specified in the request body. Arguments: sensor_id {str} -- Unique identifier of a sensor name {str} -- Name of the sensor sensor_type_id {str} -- Respective sensor type ID Returns: Response -- [description] """ service = self.service + '/' + sensor_id headers = {'Content-Type': 'application/json'} payload = '{ "name" : "' + name + '", "sensorTypeId" : "' + sensor_type_id + '" }' return super().request_core(method='PUT', service=service, headers=headers, payload=payload, accept_json=True) def add_custom_property(self, sensor_id: str, key: str, value: str) -> Response: """The endpoint is used to add a custom property to the sensor associated to the given id. Arguments: sensor_id {str} -- Unique identifier of a sensor key {str} -- Key of the custom property value {str} -- Value of the custom property Returns: Response -- Response object """ service = self.service + '/' + sensor_id + '/customProperties' headers = {'Content-Type': 'application/json'} payload = '{ "key" : "' + key + '", "value" : "' + value + '" }' return super().request_core(method='POST', service=service, headers=headers, payload=payload, accept_json=True) def delete_custom_property(self, sensor_id: str, key: str) -> Response: """Delete a custom property from the sensor associated to the given id. Arguments: sensor_id {str} -- Unique identifier of a sensor key {str} -- Key of the custom property Returns: Response -- Response object """ service = self.service + '/' + sensor_id + '/customProperties/' + key return super().request_core(method='DELETE', service=service, accept_json=True) def update_custom_property(self, sensor_id: str, key: str, value: str) -> Response: """Updates a custom property of the sensor associated to the given id. The ‘key’ attribute cannot be modified. Arguments: sensor_id {str} -- Unique identifier of a sensor key {str} -- Key of the custom property value {str} -- The updated value of the custom property Returns: Response -- Response object """ service = self.service + '/' + sensor_id + '/customProperties/' + key headers = {'Content-Type': 'application/json'} payload = '{ "key" : "' + key + '", "value" : "' + value + '" }' return super().request_core(method='PUT', service=service, headers=headers, payload=payload, accept_json=True)

44.36478

294

0.601503

from .tenant_iot_service import TenantIoTService from .utils import build_query from .response import Response class SensorService(TenantIoTService): def __init__(self, instance, user, password, tenant_id): self.service = '/sensors' TenantIoTService.__init__( self, instance=instance, user=user, password=password, tenant_id=tenant_id ) def get_sensors(self, filters=None, orderby=None, asc=True, skip=None, top=None) -> Response: query = build_query(filters=filters, orderby=orderby, asc=asc, skip=skip, top=top) return super().request_core(method='GET', service=self.service, query=query, accept_json=True) def get_sensor_count(self): service = self.service + '/count' return super().request_core(method='GET', service=service, accept_json=True) def create_sensor(self, device_id: str, alternate_id: str, name: str, sensor_type_id: str) -> Response: headers = {'Content-Type': 'application/json'} payload = '{ "deviceId": "' + device_id + '", "alternateId": "' + alternate_id + '", "name": "' + name + '", "sensorTypeId": "' + sensor_type_id + '"}' return super().request_core(method='POST', service=self.service, headers=headers, payload=payload, accept_json=True) def delete_sensor(self, sensor_id: str) -> Response: service = self.service + '/' + sensor_id return super().request_core(method='DELETE', service=service, accept_json=True) def get_sensor(self, sensor_id: str) -> Response: service = self.service + '/' + sensor_id return super().request_core(method='GET', service=service, accept_json=True) def update_sensor(self, sensor_id: str, name: str, sensor_type_id: str) -> Response: service = self.service + '/' + sensor_id headers = {'Content-Type': 'application/json'} payload = '{ "name" : "' + name + '", "sensorTypeId" : "' + sensor_type_id + '" }' return super().request_core(method='PUT', service=service, headers=headers, payload=payload, accept_json=True) def add_custom_property(self, sensor_id: str, key: str, value: str) -> Response: service = self.service + '/' + sensor_id + '/customProperties' headers = {'Content-Type': 'application/json'} payload = '{ "key" : "' + key + '", "value" : "' + value + '" }' return super().request_core(method='POST', service=service, headers=headers, payload=payload, accept_json=True) def delete_custom_property(self, sensor_id: str, key: str) -> Response: service = self.service + '/' + sensor_id + '/customProperties/' + key return super().request_core(method='DELETE', service=service, accept_json=True) def update_custom_property(self, sensor_id: str, key: str, value: str) -> Response: service = self.service + '/' + sensor_id + '/customProperties/' + key headers = {'Content-Type': 'application/json'} payload = '{ "key" : "' + key + '", "value" : "' + value + '" }' return super().request_core(method='PUT', service=service, headers=headers, payload=payload, accept_json=True)

true

f734b0be4b39646d3f90ce887df36ee68dd8bc06

541

py

Python

users/models.py

DarkoR12/dafi-system

f923ea4273b04f7acc7016b2f7d03e51eb00b85b

[ "MIT" ]

null

users/models.py

DarkoR12/dafi-system

f923ea4273b04f7acc7016b2f7d03e51eb00b85b

[ "MIT" ]

null

users/models.py

DarkoR12/dafi-system

f923ea4273b04f7acc7016b2f7d03e51eb00b85b

[ "MIT" ]

null

from datetime import datetime from django.contrib.auth.models import AbstractUser from django.db import models def current_year(): return datetime.now().year class User(AbstractUser): telegram_user = models.CharField('usuario de telegram', max_length=64, blank=True) telegram_id = models.IntegerField('ID de telegram', blank=True, null=True) first_year = models.IntegerField('año de ingreso', default=current_year) def __str__(self): return '{} {} - {}'.format(self.first_name, self.last_name, self.email)

28.473684

86

0.733826

from datetime import datetime from django.contrib.auth.models import AbstractUser from django.db import models def current_year(): return datetime.now().year class User(AbstractUser): telegram_user = models.CharField('usuario de telegram', max_length=64, blank=True) telegram_id = models.IntegerField('ID de telegram', blank=True, null=True) first_year = models.IntegerField('año de ingreso', default=current_year) def __str__(self): return '{} {} - {}'.format(self.first_name, self.last_name, self.email)

true

f734b13f88fa172c6f4bf050b1997c8b370e45cb

15,421

py

Python

mistral/mistral/tests/unit/engine/test_default_engine.py

Toure/openstack_mistral_wip

1c3d028cb7c918de74a3cb018c84d6c5ee42e3f1

[ "Apache-2.0" ]

null

mistral/mistral/tests/unit/engine/test_default_engine.py

Toure/openstack_mistral_wip

1c3d028cb7c918de74a3cb018c84d6c5ee42e3f1

[ "Apache-2.0" ]

null

mistral/mistral/tests/unit/engine/test_default_engine.py

Toure/openstack_mistral_wip

1c3d028cb7c918de74a3cb018c84d6c5ee42e3f1

[ "Apache-2.0" ]

null

# Copyright 2014 - Mirantis, Inc. # Copyright 2015 - StackStorm, 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 datetime import mock from oslo_config import cfg from oslo_messaging.rpc import client as rpc_client from oslo_utils import uuidutils from mistral.db.v2 import api as db_api from mistral.db.v2.sqlalchemy import models from mistral.engine import default_engine as d_eng from mistral.engine.rpc_backend import rpc from mistral import exceptions as exc from mistral.services import workbooks as wb_service from mistral.tests.unit import base from mistral.tests.unit.engine import base as eng_test_base from mistral.workflow import states from mistral.workflow import utils as wf_utils # Use the set_default method to set value otherwise in certain test cases # the change in value is not permanent. cfg.CONF.set_default('auth_enable', False, group='pecan') WORKBOOK = """ --- version: '2.0' name: wb workflows: wf: type: reverse input: - param1: value1 - param2 tasks: task1: action: std.echo output=<% $.param1 %> publish: var: <% task(task1).result %> task2: action: std.echo output=<% $.param2 %> requires: [task1] """ DATETIME_FORMAT = '%Y-%m-%d %H:%M:%S.%f' ENVIRONMENT = { 'id': uuidutils.generate_uuid(), 'name': 'test', 'description': 'my test settings', 'variables': { 'key1': 'abc', 'key2': 123 }, 'scope': 'private', 'created_at': str(datetime.datetime.utcnow()), 'updated_at': str(datetime.datetime.utcnow()) } ENVIRONMENT_DB = models.Environment( id=ENVIRONMENT['id'], name=ENVIRONMENT['name'], description=ENVIRONMENT['description'], variables=ENVIRONMENT['variables'], scope=ENVIRONMENT['scope'], created_at=datetime.datetime.strptime(ENVIRONMENT['created_at'], DATETIME_FORMAT), updated_at=datetime.datetime.strptime(ENVIRONMENT['updated_at'], DATETIME_FORMAT) ) MOCK_ENVIRONMENT = mock.MagicMock(return_value=ENVIRONMENT_DB) MOCK_NOT_FOUND = mock.MagicMock(side_effect=exc.DBEntityNotFoundError()) @mock.patch.object(rpc, 'get_executor_client', mock.Mock()) class DefaultEngineTest(base.DbTestCase): def setUp(self): super(DefaultEngineTest, self).setUp() wb_service.create_workbook_v2(WORKBOOK) # Note: For purposes of this test we can easily use # simple magic mocks for engine and executor clients self.engine = d_eng.DefaultEngine() def test_start_workflow(self): wf_input = {'param1': 'Hey', 'param2': 'Hi'} # Start workflow. wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, 'my execution', task_name='task2' ) self.assertIsNotNone(wf_ex) self.assertEqual(states.RUNNING, wf_ex.state) self.assertEqual('my execution', wf_ex.description) self.assertIn('__execution', wf_ex.context) with db_api.transaction(): # Note: We need to reread execution to access related tasks. wf_ex = db_api.get_workflow_execution(wf_ex.id) task_execs = wf_ex.task_executions self.assertEqual(1, len(task_execs)) task_ex = task_execs[0] self.assertEqual('wb.wf', task_ex.workflow_name) self.assertEqual('task1', task_ex.name) self.assertEqual(states.RUNNING, task_ex.state) self.assertIsNotNone(task_ex.spec) self.assertDictEqual({}, task_ex.runtime_context) # Data Flow properties. action_execs = db_api.get_action_executions( task_execution_id=task_ex.id ) self.assertEqual(1, len(action_execs)) task_action_ex = action_execs[0] self.assertIsNotNone(task_action_ex) self.assertDictEqual({'output': 'Hey'}, task_action_ex.input) def test_start_workflow_with_input_default(self): wf_input = {'param2': 'value2'} # Start workflow. wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, task_name='task1' ) self.assertIsNotNone(wf_ex) self.assertEqual(states.RUNNING, wf_ex.state) self.assertIn('__execution', wf_ex.context) # Note: We need to reread execution to access related tasks. with db_api.transaction(): wf_ex = db_api.get_workflow_execution(wf_ex.id) task_execs = wf_ex.task_executions self.assertEqual(1, len(task_execs)) task_ex = task_execs[0] self.assertEqual('wb.wf', task_ex.workflow_name) self.assertEqual('task1', task_ex.name) self.assertEqual(states.RUNNING, task_ex.state) self.assertIsNotNone(task_ex.spec) self.assertDictEqual({}, task_ex.runtime_context) # Data Flow properties. action_execs = db_api.get_action_executions( task_execution_id=task_ex.id ) self.assertEqual(1, len(action_execs)) task_action_ex = action_execs[0] self.assertIsNotNone(task_action_ex) self.assertDictEqual({'output': 'value1'}, task_action_ex.input) def test_start_workflow_with_adhoc_env(self): wf_input = { 'param1': '<% env().key1 %>', 'param2': '<% env().key2 %>' } env = ENVIRONMENT['variables'] # Start workflow. wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, env=env, task_name='task2') self.assertIsNotNone(wf_ex) wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertDictEqual(wf_ex.params.get('env', {}), env) @mock.patch.object(db_api, "load_environment", MOCK_ENVIRONMENT) def test_start_workflow_with_saved_env(self): wf_input = { 'param1': '<% env().key1 %>', 'param2': '<% env().key2 %>' } env = ENVIRONMENT['variables'] # Start workflow. wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, env='test', task_name='task2' ) self.assertIsNotNone(wf_ex) wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertDictEqual(wf_ex.params.get('env', {}), env) @mock.patch.object(db_api, "get_environment", MOCK_NOT_FOUND) def test_start_workflow_env_not_found(self): e = self.assertRaises( exc.InputException, self.engine.start_workflow, 'wb.wf', { 'param1': '<% env().key1 %>', 'param2': 'some value' }, env='foo', task_name='task2' ) self.assertEqual("Environment is not found: foo", e.message) def test_start_workflow_with_env_type_error(self): e = self.assertRaises( exc.InputException, self.engine.start_workflow, 'wb.wf', { 'param1': '<% env().key1 %>', 'param2': 'some value' }, env=True, task_name='task2' ) self.assertIn( 'Unexpected value type for environment', e.message ) def test_start_workflow_missing_parameters(self): e = self.assertRaises( exc.InputException, self.engine.start_workflow, 'wb.wf', None, task_name='task2' ) self.assertIn("Invalid input", e.message) self.assertIn("missing=['param2']", e.message) def test_start_workflow_unexpected_parameters(self): e = self.assertRaises( exc.InputException, self.engine.start_workflow, 'wb.wf', { 'param1': 'Hey', 'param2': 'Hi', 'unexpected_param': 'val' }, task_name='task2' ) self.assertIn("Invalid input", e.message) self.assertIn("unexpected=['unexpected_param']", e.message) def test_on_action_complete(self): wf_input = {'param1': 'Hey', 'param2': 'Hi'} # Start workflow. wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, task_name='task2' ) self.assertIsNotNone(wf_ex) self.assertEqual(states.RUNNING, wf_ex.state) with db_api.transaction(): # Note: We need to reread execution to access related tasks. wf_ex = db_api.get_workflow_execution(wf_ex.id) task_execs = wf_ex.task_executions self.assertEqual(1, len(task_execs)) task1_ex = task_execs[0] self.assertEqual('task1', task1_ex.name) self.assertEqual(states.RUNNING, task1_ex.state) self.assertIsNotNone(task1_ex.spec) self.assertDictEqual({}, task1_ex.runtime_context) self.assertNotIn('__execution', task1_ex.in_context) action_execs = db_api.get_action_executions( task_execution_id=task1_ex.id ) self.assertEqual(1, len(action_execs)) task1_action_ex = action_execs[0] self.assertIsNotNone(task1_action_ex) self.assertDictEqual({'output': 'Hey'}, task1_action_ex.input) # Finish action of 'task1'. task1_action_ex = self.engine.on_action_complete( task1_action_ex.id, wf_utils.Result(data='Hey') ) self.assertIsInstance(task1_action_ex, models.ActionExecution) self.assertEqual('std.echo', task1_action_ex.name) self.assertEqual(states.SUCCESS, task1_action_ex.state) # Data Flow properties. task1_ex = db_api.get_task_execution(task1_ex.id) # Re-read the state. self.assertDictEqual({'var': 'Hey'}, task1_ex.published) self.assertDictEqual({'output': 'Hey'}, task1_action_ex.input) self.assertDictEqual({'result': 'Hey'}, task1_action_ex.output) with db_api.transaction(): wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertIsNotNone(wf_ex) self.assertEqual(states.RUNNING, wf_ex.state) task_execs = wf_ex.task_executions self.assertEqual(2, len(task_execs)) task2_ex = self._assert_single_item(task_execs, name='task2') self.assertEqual(states.RUNNING, task2_ex.state) action_execs = db_api.get_action_executions( task_execution_id=task2_ex.id ) self.assertEqual(1, len(action_execs)) task2_action_ex = action_execs[0] self.assertIsNotNone(task2_action_ex) self.assertDictEqual({'output': 'Hi'}, task2_action_ex.input) # Finish 'task2'. task2_action_ex = self.engine.on_action_complete( task2_action_ex.id, wf_utils.Result(data='Hi') ) with db_api.transaction(): wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertIsNotNone(wf_ex) task_execs = wf_ex.task_executions # Workflow completion check is done separate with scheduler # but scheduler doesn't start in this test (in fact, it's just # a DB test)so the workflow is expected to be in running state. self.assertEqual(states.RUNNING, wf_ex.state) self.assertIsInstance(task2_action_ex, models.ActionExecution) self.assertEqual('std.echo', task2_action_ex.name) self.assertEqual(states.SUCCESS, task2_action_ex.state) # Data Flow properties. self.assertDictEqual({'output': 'Hi'}, task2_action_ex.input) self.assertDictEqual({}, task2_ex.published) self.assertDictEqual({'output': 'Hi'}, task2_action_ex.input) self.assertDictEqual({'result': 'Hi'}, task2_action_ex.output) self.assertEqual(2, len(task_execs)) self._assert_single_item(task_execs, name='task1') self._assert_single_item(task_execs, name='task2') def test_stop_workflow_fail(self): # Start workflow. wf_ex = self.engine.start_workflow( 'wb.wf', { 'param1': 'Hey', 'param2': 'Hi' }, task_name="task2" ) # Re-read execution to access related tasks. wf_ex = db_api.get_workflow_execution(wf_ex.id) self.engine.stop_workflow(wf_ex.id, 'ERROR', "Stop this!") # Re-read from DB again wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertEqual('ERROR', wf_ex.state) self.assertEqual("Stop this!", wf_ex.state_info) def test_stop_workflow_succeed(self): # Start workflow. wf_ex = self.engine.start_workflow( 'wb.wf', { 'param1': 'Hey', 'param2': 'Hi' }, task_name="task2" ) # Re-read execution to access related tasks. wf_ex = db_api.get_workflow_execution(wf_ex.id) self.engine.stop_workflow(wf_ex.id, 'SUCCESS', "Like this, done") # Re-read from DB again wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertEqual('SUCCESS', wf_ex.state) self.assertEqual("Like this, done", wf_ex.state_info) def test_stop_workflow_bad_status(self): wf_ex = self.engine.start_workflow( 'wb.wf', { 'param1': 'Hey', 'param2': 'Hi' }, task_name="task2" ) # Re-read execution to access related tasks. wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertNotEqual( 'PAUSE', self.engine.stop_workflow(wf_ex.id, 'PAUSE') ) def test_resume_workflow(self): # TODO(akhmerov): Implement. pass class DefaultEngineWithTransportTest(eng_test_base.EngineTestCase): def test_engine_client_remote_error(self): mocked = mock.Mock() mocked.sync_call.side_effect = rpc_client.RemoteError( 'InputException', 'Input is wrong' ) self.engine_client._client = mocked self.assertRaises( exc.InputException, self.engine_client.start_workflow, 'some_wf', {}, 'some_description' ) def test_engine_client_remote_error_arbitrary(self): mocked = mock.Mock() mocked.sync_call.side_effect = KeyError('wrong key') self.engine_client._client = mocked exception = self.assertRaises( exc.MistralException, self.engine_client.start_workflow, 'some_wf', {}, 'some_description' ) self.assertIn('KeyError: wrong key', exception.message)

30.237255

79

0.611828

import datetime import mock from oslo_config import cfg from oslo_messaging.rpc import client as rpc_client from oslo_utils import uuidutils from mistral.db.v2 import api as db_api from mistral.db.v2.sqlalchemy import models from mistral.engine import default_engine as d_eng from mistral.engine.rpc_backend import rpc from mistral import exceptions as exc from mistral.services import workbooks as wb_service from mistral.tests.unit import base from mistral.tests.unit.engine import base as eng_test_base from mistral.workflow import states from mistral.workflow import utils as wf_utils cfg.CONF.set_default('auth_enable', False, group='pecan') WORKBOOK = """ --- version: '2.0' name: wb workflows: wf: type: reverse input: - param1: value1 - param2 tasks: task1: action: std.echo output=<% $.param1 %> publish: var: <% task(task1).result %> task2: action: std.echo output=<% $.param2 %> requires: [task1] """ DATETIME_FORMAT = '%Y-%m-%d %H:%M:%S.%f' ENVIRONMENT = { 'id': uuidutils.generate_uuid(), 'name': 'test', 'description': 'my test settings', 'variables': { 'key1': 'abc', 'key2': 123 }, 'scope': 'private', 'created_at': str(datetime.datetime.utcnow()), 'updated_at': str(datetime.datetime.utcnow()) } ENVIRONMENT_DB = models.Environment( id=ENVIRONMENT['id'], name=ENVIRONMENT['name'], description=ENVIRONMENT['description'], variables=ENVIRONMENT['variables'], scope=ENVIRONMENT['scope'], created_at=datetime.datetime.strptime(ENVIRONMENT['created_at'], DATETIME_FORMAT), updated_at=datetime.datetime.strptime(ENVIRONMENT['updated_at'], DATETIME_FORMAT) ) MOCK_ENVIRONMENT = mock.MagicMock(return_value=ENVIRONMENT_DB) MOCK_NOT_FOUND = mock.MagicMock(side_effect=exc.DBEntityNotFoundError()) @mock.patch.object(rpc, 'get_executor_client', mock.Mock()) class DefaultEngineTest(base.DbTestCase): def setUp(self): super(DefaultEngineTest, self).setUp() wb_service.create_workbook_v2(WORKBOOK) self.engine = d_eng.DefaultEngine() def test_start_workflow(self): wf_input = {'param1': 'Hey', 'param2': 'Hi'} wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, 'my execution', task_name='task2' ) self.assertIsNotNone(wf_ex) self.assertEqual(states.RUNNING, wf_ex.state) self.assertEqual('my execution', wf_ex.description) self.assertIn('__execution', wf_ex.context) with db_api.transaction(): wf_ex = db_api.get_workflow_execution(wf_ex.id) task_execs = wf_ex.task_executions self.assertEqual(1, len(task_execs)) task_ex = task_execs[0] self.assertEqual('wb.wf', task_ex.workflow_name) self.assertEqual('task1', task_ex.name) self.assertEqual(states.RUNNING, task_ex.state) self.assertIsNotNone(task_ex.spec) self.assertDictEqual({}, task_ex.runtime_context) action_execs = db_api.get_action_executions( task_execution_id=task_ex.id ) self.assertEqual(1, len(action_execs)) task_action_ex = action_execs[0] self.assertIsNotNone(task_action_ex) self.assertDictEqual({'output': 'Hey'}, task_action_ex.input) def test_start_workflow_with_input_default(self): wf_input = {'param2': 'value2'} wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, task_name='task1' ) self.assertIsNotNone(wf_ex) self.assertEqual(states.RUNNING, wf_ex.state) self.assertIn('__execution', wf_ex.context) with db_api.transaction(): wf_ex = db_api.get_workflow_execution(wf_ex.id) task_execs = wf_ex.task_executions self.assertEqual(1, len(task_execs)) task_ex = task_execs[0] self.assertEqual('wb.wf', task_ex.workflow_name) self.assertEqual('task1', task_ex.name) self.assertEqual(states.RUNNING, task_ex.state) self.assertIsNotNone(task_ex.spec) self.assertDictEqual({}, task_ex.runtime_context) action_execs = db_api.get_action_executions( task_execution_id=task_ex.id ) self.assertEqual(1, len(action_execs)) task_action_ex = action_execs[0] self.assertIsNotNone(task_action_ex) self.assertDictEqual({'output': 'value1'}, task_action_ex.input) def test_start_workflow_with_adhoc_env(self): wf_input = { 'param1': '<% env().key1 %>', 'param2': '<% env().key2 %>' } env = ENVIRONMENT['variables'] wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, env=env, task_name='task2') self.assertIsNotNone(wf_ex) wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertDictEqual(wf_ex.params.get('env', {}), env) @mock.patch.object(db_api, "load_environment", MOCK_ENVIRONMENT) def test_start_workflow_with_saved_env(self): wf_input = { 'param1': '<% env().key1 %>', 'param2': '<% env().key2 %>' } env = ENVIRONMENT['variables'] wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, env='test', task_name='task2' ) self.assertIsNotNone(wf_ex) wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertDictEqual(wf_ex.params.get('env', {}), env) @mock.patch.object(db_api, "get_environment", MOCK_NOT_FOUND) def test_start_workflow_env_not_found(self): e = self.assertRaises( exc.InputException, self.engine.start_workflow, 'wb.wf', { 'param1': '<% env().key1 %>', 'param2': 'some value' }, env='foo', task_name='task2' ) self.assertEqual("Environment is not found: foo", e.message) def test_start_workflow_with_env_type_error(self): e = self.assertRaises( exc.InputException, self.engine.start_workflow, 'wb.wf', { 'param1': '<% env().key1 %>', 'param2': 'some value' }, env=True, task_name='task2' ) self.assertIn( 'Unexpected value type for environment', e.message ) def test_start_workflow_missing_parameters(self): e = self.assertRaises( exc.InputException, self.engine.start_workflow, 'wb.wf', None, task_name='task2' ) self.assertIn("Invalid input", e.message) self.assertIn("missing=['param2']", e.message) def test_start_workflow_unexpected_parameters(self): e = self.assertRaises( exc.InputException, self.engine.start_workflow, 'wb.wf', { 'param1': 'Hey', 'param2': 'Hi', 'unexpected_param': 'val' }, task_name='task2' ) self.assertIn("Invalid input", e.message) self.assertIn("unexpected=['unexpected_param']", e.message) def test_on_action_complete(self): wf_input = {'param1': 'Hey', 'param2': 'Hi'} wf_ex = self.engine.start_workflow( 'wb.wf', wf_input, task_name='task2' ) self.assertIsNotNone(wf_ex) self.assertEqual(states.RUNNING, wf_ex.state) with db_api.transaction(): wf_ex = db_api.get_workflow_execution(wf_ex.id) task_execs = wf_ex.task_executions self.assertEqual(1, len(task_execs)) task1_ex = task_execs[0] self.assertEqual('task1', task1_ex.name) self.assertEqual(states.RUNNING, task1_ex.state) self.assertIsNotNone(task1_ex.spec) self.assertDictEqual({}, task1_ex.runtime_context) self.assertNotIn('__execution', task1_ex.in_context) action_execs = db_api.get_action_executions( task_execution_id=task1_ex.id ) self.assertEqual(1, len(action_execs)) task1_action_ex = action_execs[0] self.assertIsNotNone(task1_action_ex) self.assertDictEqual({'output': 'Hey'}, task1_action_ex.input) task1_action_ex = self.engine.on_action_complete( task1_action_ex.id, wf_utils.Result(data='Hey') ) self.assertIsInstance(task1_action_ex, models.ActionExecution) self.assertEqual('std.echo', task1_action_ex.name) self.assertEqual(states.SUCCESS, task1_action_ex.state) task1_ex = db_api.get_task_execution(task1_ex.id) self.assertDictEqual({'var': 'Hey'}, task1_ex.published) self.assertDictEqual({'output': 'Hey'}, task1_action_ex.input) self.assertDictEqual({'result': 'Hey'}, task1_action_ex.output) with db_api.transaction(): wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertIsNotNone(wf_ex) self.assertEqual(states.RUNNING, wf_ex.state) task_execs = wf_ex.task_executions self.assertEqual(2, len(task_execs)) task2_ex = self._assert_single_item(task_execs, name='task2') self.assertEqual(states.RUNNING, task2_ex.state) action_execs = db_api.get_action_executions( task_execution_id=task2_ex.id ) self.assertEqual(1, len(action_execs)) task2_action_ex = action_execs[0] self.assertIsNotNone(task2_action_ex) self.assertDictEqual({'output': 'Hi'}, task2_action_ex.input) task2_action_ex = self.engine.on_action_complete( task2_action_ex.id, wf_utils.Result(data='Hi') ) with db_api.transaction(): wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertIsNotNone(wf_ex) task_execs = wf_ex.task_executions self.assertEqual(states.RUNNING, wf_ex.state) self.assertIsInstance(task2_action_ex, models.ActionExecution) self.assertEqual('std.echo', task2_action_ex.name) self.assertEqual(states.SUCCESS, task2_action_ex.state) self.assertDictEqual({'output': 'Hi'}, task2_action_ex.input) self.assertDictEqual({}, task2_ex.published) self.assertDictEqual({'output': 'Hi'}, task2_action_ex.input) self.assertDictEqual({'result': 'Hi'}, task2_action_ex.output) self.assertEqual(2, len(task_execs)) self._assert_single_item(task_execs, name='task1') self._assert_single_item(task_execs, name='task2') def test_stop_workflow_fail(self): wf_ex = self.engine.start_workflow( 'wb.wf', { 'param1': 'Hey', 'param2': 'Hi' }, task_name="task2" ) wf_ex = db_api.get_workflow_execution(wf_ex.id) self.engine.stop_workflow(wf_ex.id, 'ERROR', "Stop this!") wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertEqual('ERROR', wf_ex.state) self.assertEqual("Stop this!", wf_ex.state_info) def test_stop_workflow_succeed(self): wf_ex = self.engine.start_workflow( 'wb.wf', { 'param1': 'Hey', 'param2': 'Hi' }, task_name="task2" ) wf_ex = db_api.get_workflow_execution(wf_ex.id) self.engine.stop_workflow(wf_ex.id, 'SUCCESS', "Like this, done") wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertEqual('SUCCESS', wf_ex.state) self.assertEqual("Like this, done", wf_ex.state_info) def test_stop_workflow_bad_status(self): wf_ex = self.engine.start_workflow( 'wb.wf', { 'param1': 'Hey', 'param2': 'Hi' }, task_name="task2" ) wf_ex = db_api.get_workflow_execution(wf_ex.id) self.assertNotEqual( 'PAUSE', self.engine.stop_workflow(wf_ex.id, 'PAUSE') ) def test_resume_workflow(self): pass class DefaultEngineWithTransportTest(eng_test_base.EngineTestCase): def test_engine_client_remote_error(self): mocked = mock.Mock() mocked.sync_call.side_effect = rpc_client.RemoteError( 'InputException', 'Input is wrong' ) self.engine_client._client = mocked self.assertRaises( exc.InputException, self.engine_client.start_workflow, 'some_wf', {}, 'some_description' ) def test_engine_client_remote_error_arbitrary(self): mocked = mock.Mock() mocked.sync_call.side_effect = KeyError('wrong key') self.engine_client._client = mocked exception = self.assertRaises( exc.MistralException, self.engine_client.start_workflow, 'some_wf', {}, 'some_description' ) self.assertIn('KeyError: wrong key', exception.message)

true

f734b394aa62228b86d84da1a5347056479a101d

1,575

py

Python

hsse_api/migrations/0032_auto_20181112_2011.py

JEpifanio90/HSSE_APIv2

7b8e8fee9cffa932884f979d24658f08726b656d

[ "MIT" ]

null

hsse_api/migrations/0032_auto_20181112_2011.py

JEpifanio90/HSSE_APIv2

7b8e8fee9cffa932884f979d24658f08726b656d

[ "MIT" ]

null

hsse_api/migrations/0032_auto_20181112_2011.py

JEpifanio90/HSSE_APIv2

7b8e8fee9cffa932884f979d24658f08726b656d

[ "MIT" ]

1

2018-12-07T03:43:32.000Z

# Generated by Django 2.1 on 2018-11-13 01:11 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('hsse_api', '0031_auto_20181111_1424'), ] operations = [ migrations.AlterField( model_name='auditinspection', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='employeecommunityactivity', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='environmentalindicator', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='monthlyreport', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='report', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='safetyactivity', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='site', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='user', name='created_on', field=models.DateField(auto_now_add=True), ), ]

29.166667

54

0.568889

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('hsse_api', '0031_auto_20181111_1424'), ] operations = [ migrations.AlterField( model_name='auditinspection', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='employeecommunityactivity', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='environmentalindicator', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='monthlyreport', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='report', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='safetyactivity', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='site', name='created_on', field=models.DateField(auto_now_add=True), ), migrations.AlterField( model_name='user', name='created_on', field=models.DateField(auto_now_add=True), ), ]

true

f734b4ee525907aaa86b2d3ec604fe9872b56b26

129

py

Python

sandbox/partner/partner/apps.py

ApsRajput/oscar-extend

8828d66b78ae139a73d5ca707c0fe0710eac890a

[ "BSD-3-Clause" ]

null

sandbox/partner/partner/apps.py

ApsRajput/oscar-extend

8828d66b78ae139a73d5ca707c0fe0710eac890a

[ "BSD-3-Clause" ]

null

sandbox/partner/partner/apps.py

ApsRajput/oscar-extend

8828d66b78ae139a73d5ca707c0fe0710eac890a

[ "BSD-3-Clause" ]

null

from __future__ import unicode_literals from django.apps import AppConfig class AccountConfig(AppConfig): name = 'account'

18.428571

39

0.79845

from __future__ import unicode_literals from django.apps import AppConfig class AccountConfig(AppConfig): name = 'account'

true

f734b5ebe426a14ff0e314fceef43f9af09ce111

680

py

Python

project/app.py

mapattacker/flask-serverless

9612b7cbc5157770d88f352e0676911658c4de9a

[ "Apache-2.0" ]

null

project/app.py

mapattacker/flask-serverless

9612b7cbc5157770d88f352e0676911658c4de9a

[ "Apache-2.0" ]

null

project/app.py

mapattacker/flask-serverless

9612b7cbc5157770d88f352e0676911658c4de9a

[ "Apache-2.0" ]

null

import pickle import traceback import numpy as np from flask import Flask, request from config import MODELPATH, DEBUG app = Flask(__name__) model = pickle.load(open(MODELPATH, 'rb')) @app.route("/predict", methods=["POST"]) def predict(): """{"input": [5.8, 2.8, 5.1, 2.4]}""" try: content = request.json sample = content["input"] sample = np.array(sample).reshape(1, -1) prediction = model.predict(sample).tolist()[0] return {"prediction": prediction} except Exception as e: tb = traceback.format_exc() return {"errorMessages": tb.replace("\n","")} if __name__ == "__main__": app.run(debug=DEBUG)

21.935484

54

0.623529

import pickle import traceback import numpy as np from flask import Flask, request from config import MODELPATH, DEBUG app = Flask(__name__) model = pickle.load(open(MODELPATH, 'rb')) @app.route("/predict", methods=["POST"]) def predict(): try: content = request.json sample = content["input"] sample = np.array(sample).reshape(1, -1) prediction = model.predict(sample).tolist()[0] return {"prediction": prediction} except Exception as e: tb = traceback.format_exc() return {"errorMessages": tb.replace("\n","")} if __name__ == "__main__": app.run(debug=DEBUG)

true

f734b60ccd490cc86dc23fd7c60141903b7a603e

1,049

py

Python

generate_refs.py

life4/gweb

396bcca71dd05e492b28ccbaa9fb3f9ae1c27502

[ "MIT" ]

185

2020-02-10T17:54:01.000Z

2022-03-23T06:12:03.000Z

generate_refs.py

life4/gweb

396bcca71dd05e492b28ccbaa9fb3f9ae1c27502

[ "MIT" ]

1

2020-10-05T06:56:38.000Z

generate_refs.py

life4/gweb

396bcca71dd05e492b28ccbaa9fb3f9ae1c27502

[ "MIT" ]

15

2020-10-02T17:32:50.000Z

2022-01-24T14:58:31.000Z

import re from collections import defaultdict from pathlib import Path base_url = 'https://developer.mozilla.org/en-US/docs/Web/API/' doc_base_url = 'https://pkg.go.dev/github.com/life4/gweb/{package}#{obj}' link = re.escape(f'// {base_url}') rex = re.compile(rf'(?:{link}([a-zA-Z/-]+))+\nfunc $[a-z]+ \*?([a-zA-Z]+)$ ([a-zA-Z]+)') refs: dict = defaultdict(list) for path in Path().glob('*/*.go'): content = path.read_text() for match in rex.findall(content): *links, struct, func = match for link in links: refs[link].append((path.parent.name, f'{struct}.{func}')) print(""" # Reference Below is the mapping of web API to gweb functions. This file is autogenerated, so some references may be missed. | Web API | gweb | | ------- | ---- | """.strip()) for ref, objects in sorted(refs.items()): url = base_url + ref ref = ref.replace('/', '.') for package, obj in objects: doc_url = doc_base_url.format(package=package, obj=obj) print(f'| [{ref}]({url}) | [{obj}]({doc_url}) |')

31.787879

90

0.614871

import re from collections import defaultdict from pathlib import Path base_url = 'https://developer.mozilla.org/en-US/docs/Web/API/' doc_base_url = 'https://pkg.go.dev/github.com/life4/gweb/{package}#{obj}' link = re.escape(f'// {base_url}') rex = re.compile(rf'(?:{link}([a-zA-Z/-]+))+\nfunc $[a-z]+ \*?([a-zA-Z]+)$ ([a-zA-Z]+)') refs: dict = defaultdict(list) for path in Path().glob('*/*.go'): content = path.read_text() for match in rex.findall(content): *links, struct, func = match for link in links: refs[link].append((path.parent.name, f'{struct}.{func}')) print(""" # Reference Below is the mapping of web API to gweb functions. This file is autogenerated, so some references may be missed. | Web API | gweb | | ------- | ---- | """.strip()) for ref, objects in sorted(refs.items()): url = base_url + ref ref = ref.replace('/', '.') for package, obj in objects: doc_url = doc_base_url.format(package=package, obj=obj) print(f'| [{ref}]({url}) | [{obj}]({doc_url}) |')

true

f734b703f0a22b5a0f6e899df15bfb9827e5dc49

1,999

py

Python

5_Quadrature Formulas/Algoritmi_Quadratura.py

LeonardoSaccotelli/Numerical-Calculus-Project

becb480a611c9a57416127f6b0289085fe180ee4

[ "MIT" ]

null

5_Quadrature Formulas/Algoritmi_Quadratura.py

LeonardoSaccotelli/Numerical-Calculus-Project

becb480a611c9a57416127f6b0289085fe180ee4

[ "MIT" ]

null

5_Quadrature Formulas/Algoritmi_Quadratura.py

LeonardoSaccotelli/Numerical-Calculus-Project

becb480a611c9a57416127f6b0289085fe180ee4

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Apr 7 17:58:09 2020 @author: Leonardo Saccotelli """ import numpy as np """ FORMULA DEI TRAPEZI Al metodo vengono passati: - la funzione integranda - l'estremo inferiore di integrazione - l'estremo superiore di integrazione """ def Trapezoid(f_x, a, b): #Calcolo l'integrale T = (b-a)*(f_x(a)+f_x(b))/2 return T """ FORMULA DEI TRAPEZI COMPOSTI Al metodo vengono passati: - la funzione integranda - l'estremo inferiore di integrazione - l'estremo superiore di integrazione - il numero di intervallini """ def CompositeTrapezoid(f_x, a, b, N): #Estrpolo N+1 intervalli equidistanti da [a,b] z = np.linspace(a,b,N+1) #Calcolo f_x() in ogni punto di z fz = f_x(z) S = 0 #Calcolo del trapezio composto for i in range(1,N): S = S + fz[i] TC = (fz[0] + 2*S + fz[N])*(b-a)/2/N return TC """ FORMULA DI SIMPSON Al metodo vengono passati: - la funzione integranda - l'estremo inferiore di integrazione - l'estremo superiore di integrazione """ def Simpson(f_x, a, b): #Calcolo l'integrale T = ((b-a)/6) * (f_x(a) +4 * f_x((b+a)/2) + f_x(b)) return T """ FORMULA DI SIMPSON COMPOSTA Al metodo vengono passati: - la funzione integranda - l'estremo inferiore di integrazione - l'estremo superiore di integrazione - il numero di intervalli """ def CompositeSimpson(f, a, b, N): #Genero n+1 intervallini in [a,b] z = np.linspace(a,b,N+1) #Calcolo f negli intervalli z fz = f(z) #Definisco le somme dispari e le somme pari S_d = 0 S_p = 0 #Definisco l'ampiezza dei singoli intervalli h = (b-a)/N #Calcolo le somme dispari for i in range(1,N,2): S_d = S_d + fz[i] #Calcolo le somme pari for i in range(2,N-1,2): S_p = S_p + fz[i] Tsc = (fz[0] + 4*S_d + 2*S_p + fz[N])*h/3 return Tsc

21.042105

55

0.595798

import numpy as np def Trapezoid(f_x, a, b): T = (b-a)*(f_x(a)+f_x(b))/2 return T def CompositeTrapezoid(f_x, a, b, N): #Estrpolo N+1 intervalli equidistanti da [a,b] z = np.linspace(a,b,N+1) #Calcolo f_x() in ogni punto di z fz = f_x(z) S = 0 #Calcolo del trapezio composto for i in range(1,N): S = S + fz[i] TC = (fz[0] + 2*S + fz[N])*(b-a)/2/N return TC def Simpson(f_x, a, b): #Calcolo l'integrale T = ((b-a)/6) * (f_x(a) +4 * f_x((b+a)/2) + f_x(b)) return T def CompositeSimpson(f, a, b, N): z = np.linspace(a,b,N+1) fz = f(z) S_d = 0 S_p = 0 h = (b-a)/N #Calcolo le somme dispari for i in range(1,N,2): S_d = S_d + fz[i] #Calcolo le somme pari for i in range(2,N-1,2): S_p = S_p + fz[i] Tsc = (fz[0] + 4*S_d + 2*S_p + fz[N])*h/3 return Tsc

true

f734b7c8a2993ef120151e812e8f2a1a91a1d8ae

6,204

py

Python

airbyte-integrations/connectors/source-facebook-marketing/unit_tests/test_client.py

aadityasinha-dotcom/airbyte

7f6dd9b6fc4288260532fe1beaf7901633f13c88

[ "MIT" ]

1

2021-11-24T17:36:54.000Z

airbyte-integrations/connectors/source-facebook-marketing/unit_tests/test_client.py

aadityasinha-dotcom/airbyte

7f6dd9b6fc4288260532fe1beaf7901633f13c88

[ "MIT" ]

2

2022-03-16T16:17:37.000Z

2022-03-24T19:00:41.000Z

airbyte-integrations/connectors/source-facebook-marketing/unit_tests/test_client.py

aadityasinha-dotcom/airbyte

7f6dd9b6fc4288260532fe1beaf7901633f13c88

[ "MIT" ]

null

# # Copyright (c) 2021 Airbyte, Inc., all rights reserved. # import json from datetime import datetime import pendulum import pytest from airbyte_cdk.models import SyncMode from facebook_business import FacebookAdsApi, FacebookSession from facebook_business.exceptions import FacebookRequestError from source_facebook_marketing.api import API from source_facebook_marketing.streams import AdCreatives, Campaigns FB_API_VERSION = FacebookAdsApi.API_VERSION @pytest.fixture(scope="session", name="account_id") def account_id_fixture(): return "unknown_account" @pytest.fixture(scope="session", name="some_config") def some_config_fixture(account_id): return {"start_date": "2021-01-23T00:00:00Z", "account_id": f"{account_id}", "access_token": "unknown_token"} @pytest.fixture(autouse=True) def mock_default_sleep_interval(mocker): mocker.patch("source_facebook_marketing.streams.common.DEFAULT_SLEEP_INTERVAL", return_value=pendulum.duration(seconds=5)) @pytest.fixture(name="api") def api_fixture(some_config, requests_mock, fb_account_response): api = API(account_id=some_config["account_id"], access_token=some_config["access_token"]) requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/me/adaccounts", [fb_account_response]) return api @pytest.fixture(name="fb_call_rate_response") def fb_call_rate_response_fixture(): error = { "message": ( "(#80000) There have been too many calls from this ad-account. Wait a bit and try again. " "For more info, please refer to https://developers.facebook.com/docs/graph-api/overview/rate-limiting." ), "type": "OAuthException", "code": 80000, "error_subcode": 2446079, "fbtrace_id": "this_is_fake_response", } headers = {"x-app-usage": json.dumps({"call_count": 28, "total_time": 25, "total_cputime": 25})} return { "json": { "error": error, }, "status_code": 400, "headers": headers, } @pytest.fixture(name="fb_account_response") def fb_account_response_fixture(account_id): return { "json": { "data": [ { "account_id": account_id, "id": f"act_{account_id}", } ], "paging": {"cursors": {"before": "MjM4NDYzMDYyMTcyNTAwNzEZD", "after": "MjM4NDYzMDYyMTcyNTAwNzEZD"}}, }, "status_code": 200, } class TestBackoff: def test_limit_reached(self, mocker, requests_mock, api, fb_call_rate_response, account_id): """Error once, check that we retry and not fail""" # turn Campaigns into non batch mode to test non batch logic mocker.patch.object(Campaigns, "use_batch", new_callable=mocker.PropertyMock, return_value=False) campaign_responses = [ fb_call_rate_response, { "json": {"data": [{"id": 1, "updated_time": "2020-09-25T00:00:00Z"}, {"id": 2, "updated_time": "2020-09-25T00:00:00Z"}]}, "status_code": 200, }, ] requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/act_{account_id}/campaigns", campaign_responses) requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/1/", [{"status_code": 200}]) requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/2/", [{"status_code": 200}]) stream = Campaigns(api=api, start_date=pendulum.now(), end_date=pendulum.now(), include_deleted=False) try: records = list(stream.read_records(sync_mode=SyncMode.full_refresh, stream_state={})) assert records except FacebookRequestError: pytest.fail("Call rate error has not being handled") def test_batch_limit_reached(self, requests_mock, api, fb_call_rate_response, account_id): """Error once, check that we retry and not fail""" responses = [ fb_call_rate_response, { "json": { "data": [ { "id": "123", "object_type": "SHARE", "status": "ACTIVE", }, { "id": "1234", "object_type": "SHARE", "status": "ACTIVE", }, ], "status_code": 200, } }, ] batch_responses = [ fb_call_rate_response, { "json": [ {"body": json.dumps({"name": "creative 1"}), "code": 200, "headers": {}}, {"body": json.dumps({"name": "creative 2"}), "code": 200, "headers": {}}, ] }, ] requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/act_{account_id}/adcreatives", responses) requests_mock.register_uri("POST", FacebookSession.GRAPH + f"/{FB_API_VERSION}/", batch_responses) stream = AdCreatives(api=api, include_deleted=False) records = list(stream.read_records(sync_mode=SyncMode.full_refresh, stream_state={})) assert records == [{"name": "creative 1"}, {"name": "creative 2"}] def test_server_error(self, requests_mock, api, account_id): """Error once, check that we retry and not fail""" responses = [ {"json": {"error": {}}, "status_code": 500}, { "json": {"data": [{"id": 1, "updated_time": "2020-09-25T00:00:00Z"}, {"id": 2, "updated_time": "2020-09-25T00:00:00Z"}]}, "status_code": 200, }, ] requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/act_{account_id}/campaigns", responses) with pytest.raises(FacebookRequestError): stream = Campaigns(api=api, start_date=datetime.now(), end_date=datetime.now(), include_deleted=False) list(stream.read_records(sync_mode=SyncMode.full_refresh, stream_state={}))

38.296296

137

0.598001

import json from datetime import datetime import pendulum import pytest from airbyte_cdk.models import SyncMode from facebook_business import FacebookAdsApi, FacebookSession from facebook_business.exceptions import FacebookRequestError from source_facebook_marketing.api import API from source_facebook_marketing.streams import AdCreatives, Campaigns FB_API_VERSION = FacebookAdsApi.API_VERSION @pytest.fixture(scope="session", name="account_id") def account_id_fixture(): return "unknown_account" @pytest.fixture(scope="session", name="some_config") def some_config_fixture(account_id): return {"start_date": "2021-01-23T00:00:00Z", "account_id": f"{account_id}", "access_token": "unknown_token"} @pytest.fixture(autouse=True) def mock_default_sleep_interval(mocker): mocker.patch("source_facebook_marketing.streams.common.DEFAULT_SLEEP_INTERVAL", return_value=pendulum.duration(seconds=5)) @pytest.fixture(name="api") def api_fixture(some_config, requests_mock, fb_account_response): api = API(account_id=some_config["account_id"], access_token=some_config["access_token"]) requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/me/adaccounts", [fb_account_response]) return api @pytest.fixture(name="fb_call_rate_response") def fb_call_rate_response_fixture(): error = { "message": ( "(#80000) There have been too many calls from this ad-account. Wait a bit and try again. " "For more info, please refer to https://developers.facebook.com/docs/graph-api/overview/rate-limiting." ), "type": "OAuthException", "code": 80000, "error_subcode": 2446079, "fbtrace_id": "this_is_fake_response", } headers = {"x-app-usage": json.dumps({"call_count": 28, "total_time": 25, "total_cputime": 25})} return { "json": { "error": error, }, "status_code": 400, "headers": headers, } @pytest.fixture(name="fb_account_response") def fb_account_response_fixture(account_id): return { "json": { "data": [ { "account_id": account_id, "id": f"act_{account_id}", } ], "paging": {"cursors": {"before": "MjM4NDYzMDYyMTcyNTAwNzEZD", "after": "MjM4NDYzMDYyMTcyNTAwNzEZD"}}, }, "status_code": 200, } class TestBackoff: def test_limit_reached(self, mocker, requests_mock, api, fb_call_rate_response, account_id): mocker.patch.object(Campaigns, "use_batch", new_callable=mocker.PropertyMock, return_value=False) campaign_responses = [ fb_call_rate_response, { "json": {"data": [{"id": 1, "updated_time": "2020-09-25T00:00:00Z"}, {"id": 2, "updated_time": "2020-09-25T00:00:00Z"}]}, "status_code": 200, }, ] requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/act_{account_id}/campaigns", campaign_responses) requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/1/", [{"status_code": 200}]) requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/2/", [{"status_code": 200}]) stream = Campaigns(api=api, start_date=pendulum.now(), end_date=pendulum.now(), include_deleted=False) try: records = list(stream.read_records(sync_mode=SyncMode.full_refresh, stream_state={})) assert records except FacebookRequestError: pytest.fail("Call rate error has not being handled") def test_batch_limit_reached(self, requests_mock, api, fb_call_rate_response, account_id): responses = [ fb_call_rate_response, { "json": { "data": [ { "id": "123", "object_type": "SHARE", "status": "ACTIVE", }, { "id": "1234", "object_type": "SHARE", "status": "ACTIVE", }, ], "status_code": 200, } }, ] batch_responses = [ fb_call_rate_response, { "json": [ {"body": json.dumps({"name": "creative 1"}), "code": 200, "headers": {}}, {"body": json.dumps({"name": "creative 2"}), "code": 200, "headers": {}}, ] }, ] requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/act_{account_id}/adcreatives", responses) requests_mock.register_uri("POST", FacebookSession.GRAPH + f"/{FB_API_VERSION}/", batch_responses) stream = AdCreatives(api=api, include_deleted=False) records = list(stream.read_records(sync_mode=SyncMode.full_refresh, stream_state={})) assert records == [{"name": "creative 1"}, {"name": "creative 2"}] def test_server_error(self, requests_mock, api, account_id): responses = [ {"json": {"error": {}}, "status_code": 500}, { "json": {"data": [{"id": 1, "updated_time": "2020-09-25T00:00:00Z"}, {"id": 2, "updated_time": "2020-09-25T00:00:00Z"}]}, "status_code": 200, }, ] requests_mock.register_uri("GET", FacebookSession.GRAPH + f"/{FB_API_VERSION}/act_{account_id}/campaigns", responses) with pytest.raises(FacebookRequestError): stream = Campaigns(api=api, start_date=datetime.now(), end_date=datetime.now(), include_deleted=False) list(stream.read_records(sync_mode=SyncMode.full_refresh, stream_state={}))

true

f734b7e22b114bcf1c5f0a73a9a92dfaa783b0aa

7,751

py

Python

bertviz/pytorch_pretrained_bert/file_utils.py

whaleloops/bertviz

3c6323fa3a3e03e7399a0ad6ab8463c320179323

[ "Apache-2.0" ]

58

2019-09-16T07:24:23.000Z

2021-12-30T09:04:38.000Z

bertviz/pytorch_pretrained_bert/file_utils.py

whaleloops/bertviz

3c6323fa3a3e03e7399a0ad6ab8463c320179323

[ "Apache-2.0" ]

4

2021-09-09T03:02:18.000Z

2022-03-24T13:55:55.000Z

bertviz/pytorch_pretrained_bert/file_utils.py

whaleloops/bertviz

3c6323fa3a3e03e7399a0ad6ab8463c320179323

[ "Apache-2.0" ]

32

2019-07-30T17:47:21.000Z

2022-03-29T22:33:29.000Z

""" Utilities for working with the local dataset cache. This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp Copyright by the AllenNLP authors. """ import os import logging import shutil import tempfile import json from urllib.parse import urlparse from pathlib import Path from typing import Optional, Tuple, Union, IO, Callable, Set from hashlib import sha256 from functools import wraps from tqdm import tqdm import boto3 from botocore.exceptions import ClientError import requests logger = logging.getLogger(__name__) # pylint: disable=invalid-name PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', Path.home() / '.pytorch_pretrained_bert')) def url_to_filename(url: str, etag: str = None) -> str: """ Convert `url` into a hashed filename in a repeatable way. If `etag` is specified, append its hash to the url's, delimited by a period. """ url_bytes = url.encode('utf-8') url_hash = sha256(url_bytes) filename = url_hash.hexdigest() if etag: etag_bytes = etag.encode('utf-8') etag_hash = sha256(etag_bytes) filename += '.' + etag_hash.hexdigest() return filename def filename_to_url(filename: str, cache_dir: str = None) -> Tuple[str, str]: """ Return the url and etag (which may be ``None``) stored for `filename`. Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist. """ if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE cache_path = os.path.join(cache_dir, filename) if not os.path.exists(cache_path): raise FileNotFoundError("file {} not found".format(cache_path)) meta_path = cache_path + '.json' if not os.path.exists(meta_path): raise FileNotFoundError("file {} not found".format(meta_path)) with open(meta_path) as meta_file: metadata = json.load(meta_file) url = metadata['url'] etag = metadata['etag'] return url, etag def cached_path(url_or_filename: Union[str, Path], cache_dir: str = None) -> str: """ Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and then return the path. """ if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE if isinstance(url_or_filename, Path): url_or_filename = str(url_or_filename) parsed = urlparse(url_or_filename) if parsed.scheme in ('http', 'https', 's3'): # URL, so get it from the cache (downloading if necessary) return get_from_cache(url_or_filename, cache_dir) elif os.path.exists(url_or_filename): # File, and it exists. return url_or_filename elif parsed.scheme == '': # File, but it doesn't exist. raise FileNotFoundError("file {} not found".format(url_or_filename)) else: # Something unknown raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) def split_s3_path(url: str) -> Tuple[str, str]: """Split a full s3 path into the bucket name and path.""" parsed = urlparse(url) if not parsed.netloc or not parsed.path: raise ValueError("bad s3 path {}".format(url)) bucket_name = parsed.netloc s3_path = parsed.path # Remove '/' at beginning of path. if s3_path.startswith("/"): s3_path = s3_path[1:] return bucket_name, s3_path def s3_request(func: Callable): """ Wrapper function for s3 requests in order to create more helpful error messages. """ @wraps(func) def wrapper(url: str, *args, **kwargs): try: return func(url, *args, **kwargs) except ClientError as exc: if int(exc.response["Error"]["Code"]) == 404: raise FileNotFoundError("file {} not found".format(url)) else: raise return wrapper @s3_request def s3_etag(url: str) -> Optional[str]: """Check ETag on S3 object.""" s3_resource = boto3.resource("s3") bucket_name, s3_path = split_s3_path(url) s3_object = s3_resource.Object(bucket_name, s3_path) return s3_object.e_tag @s3_request def s3_get(url: str, temp_file: IO) -> None: """Pull a file directly from S3.""" s3_resource = boto3.resource("s3") bucket_name, s3_path = split_s3_path(url) s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file) def http_get(url: str, temp_file: IO) -> None: req = requests.get(url, stream=True) content_length = req.headers.get('Content-Length') total = int(content_length) if content_length is not None else None progress = tqdm(unit="B", total=total) for chunk in req.iter_content(chunk_size=1024): if chunk: # filter out keep-alive new chunks progress.update(len(chunk)) temp_file.write(chunk) progress.close() def get_from_cache(url: str, cache_dir: str = None) -> str: """ Given a URL, look for the corresponding dataset in the local cache. If it's not there, download it. Then return the path to the cached file. """ if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE os.makedirs(cache_dir, exist_ok=True) # Get eTag to add to filename, if it exists. if url.startswith("s3://"): etag = s3_etag(url) else: response = requests.head(url, allow_redirects=True) if response.status_code != 200: raise IOError("HEAD request failed for url {} with status code {}" .format(url, response.status_code)) etag = response.headers.get("ETag") filename = url_to_filename(url, etag) # get cache path to put the file cache_path = os.path.join(cache_dir, filename) if not os.path.exists(cache_path): # Download to temporary file, then copy to cache dir once finished. # Otherwise you get corrupt cache entries if the download gets interrupted. with tempfile.NamedTemporaryFile() as temp_file: logger.info("%s not found in cache, downloading to %s", url, temp_file.name) # GET file object if url.startswith("s3://"): s3_get(url, temp_file) else: http_get(url, temp_file) # we are copying the file before closing it, so flush to avoid truncation temp_file.flush() # shutil.copyfileobj() starts at the current position, so go to the start temp_file.seek(0) logger.info("copying %s to cache at %s", temp_file.name, cache_path) with open(cache_path, 'wb') as cache_file: shutil.copyfileobj(temp_file, cache_file) logger.info("creating metadata file for %s", cache_path) meta = {'url': url, 'etag': etag} meta_path = cache_path + '.json' with open(meta_path, 'w') as meta_file: json.dump(meta, meta_file) logger.info("removing temp file %s", temp_file.name) return cache_path def read_set_from_file(filename: str) -> Set[str]: ''' Extract a de-duped collection (set) of text from a file. Expected file format is one item per line. ''' collection = set() with open(filename, 'r') as file_: for line in file_: collection.add(line.rstrip()) return collection def get_file_extension(path: str, dot=True, lower: bool = True): ext = os.path.splitext(path)[1] ext = ext if dot else ext[1:] return ext.lower() if lower else ext

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98

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import os import logging import shutil import tempfile import json from urllib.parse import urlparse from pathlib import Path from typing import Optional, Tuple, Union, IO, Callable, Set from hashlib import sha256 from functools import wraps from tqdm import tqdm import boto3 from botocore.exceptions import ClientError import requests logger = logging.getLogger(__name__) PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', Path.home() / '.pytorch_pretrained_bert')) def url_to_filename(url: str, etag: str = None) -> str: url_bytes = url.encode('utf-8') url_hash = sha256(url_bytes) filename = url_hash.hexdigest() if etag: etag_bytes = etag.encode('utf-8') etag_hash = sha256(etag_bytes) filename += '.' + etag_hash.hexdigest() return filename def filename_to_url(filename: str, cache_dir: str = None) -> Tuple[str, str]: if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE cache_path = os.path.join(cache_dir, filename) if not os.path.exists(cache_path): raise FileNotFoundError("file {} not found".format(cache_path)) meta_path = cache_path + '.json' if not os.path.exists(meta_path): raise FileNotFoundError("file {} not found".format(meta_path)) with open(meta_path) as meta_file: metadata = json.load(meta_file) url = metadata['url'] etag = metadata['etag'] return url, etag def cached_path(url_or_filename: Union[str, Path], cache_dir: str = None) -> str: if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE if isinstance(url_or_filename, Path): url_or_filename = str(url_or_filename) parsed = urlparse(url_or_filename) if parsed.scheme in ('http', 'https', 's3'): return get_from_cache(url_or_filename, cache_dir) elif os.path.exists(url_or_filename): return url_or_filename elif parsed.scheme == '': raise FileNotFoundError("file {} not found".format(url_or_filename)) else: # Something unknown raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) def split_s3_path(url: str) -> Tuple[str, str]: parsed = urlparse(url) if not parsed.netloc or not parsed.path: raise ValueError("bad s3 path {}".format(url)) bucket_name = parsed.netloc s3_path = parsed.path # Remove '/' at beginning of path. if s3_path.startswith("/"): s3_path = s3_path[1:] return bucket_name, s3_path def s3_request(func: Callable): @wraps(func) def wrapper(url: str, *args, **kwargs): try: return func(url, *args, **kwargs) except ClientError as exc: if int(exc.response["Error"]["Code"]) == 404: raise FileNotFoundError("file {} not found".format(url)) else: raise return wrapper @s3_request def s3_etag(url: str) -> Optional[str]: s3_resource = boto3.resource("s3") bucket_name, s3_path = split_s3_path(url) s3_object = s3_resource.Object(bucket_name, s3_path) return s3_object.e_tag @s3_request def s3_get(url: str, temp_file: IO) -> None: s3_resource = boto3.resource("s3") bucket_name, s3_path = split_s3_path(url) s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file) def http_get(url: str, temp_file: IO) -> None: req = requests.get(url, stream=True) content_length = req.headers.get('Content-Length') total = int(content_length) if content_length is not None else None progress = tqdm(unit="B", total=total) for chunk in req.iter_content(chunk_size=1024): if chunk: # filter out keep-alive new chunks progress.update(len(chunk)) temp_file.write(chunk) progress.close() def get_from_cache(url: str, cache_dir: str = None) -> str: if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE os.makedirs(cache_dir, exist_ok=True) # Get eTag to add to filename, if it exists. if url.startswith("s3://"): etag = s3_etag(url) else: response = requests.head(url, allow_redirects=True) if response.status_code != 200: raise IOError("HEAD request failed for url {} with status code {}" .format(url, response.status_code)) etag = response.headers.get("ETag") filename = url_to_filename(url, etag) # get cache path to put the file cache_path = os.path.join(cache_dir, filename) if not os.path.exists(cache_path): # Download to temporary file, then copy to cache dir once finished. # Otherwise you get corrupt cache entries if the download gets interrupted. with tempfile.NamedTemporaryFile() as temp_file: logger.info("%s not found in cache, downloading to %s", url, temp_file.name) # GET file object if url.startswith("s3://"): s3_get(url, temp_file) else: http_get(url, temp_file) # we are copying the file before closing it, so flush to avoid truncation temp_file.flush() # shutil.copyfileobj() starts at the current position, so go to the start temp_file.seek(0) logger.info("copying %s to cache at %s", temp_file.name, cache_path) with open(cache_path, 'wb') as cache_file: shutil.copyfileobj(temp_file, cache_file) logger.info("creating metadata file for %s", cache_path) meta = {'url': url, 'etag': etag} meta_path = cache_path + '.json' with open(meta_path, 'w') as meta_file: json.dump(meta, meta_file) logger.info("removing temp file %s", temp_file.name) return cache_path def read_set_from_file(filename: str) -> Set[str]: collection = set() with open(filename, 'r') as file_: for line in file_: collection.add(line.rstrip()) return collection def get_file_extension(path: str, dot=True, lower: bool = True): ext = os.path.splitext(path)[1] ext = ext if dot else ext[1:] return ext.lower() if lower else ext

true

f734b81b788e1e3bc224c74ce6cddaa03e354b05

1,010

py

Python

bindings/python/tests/test_wrapper.py

kamino/dragonffi

3c983cc8c091d5472f7cdeab1b06dc3b1902e1be

[ "Apache-2.0" ]

523

2018-02-02T08:07:24.000Z

2022-03-21T15:44:39.000Z

bindings/python/tests/test_wrapper.py

kamino/dragonffi

3c983cc8c091d5472f7cdeab1b06dc3b1902e1be

[ "Apache-2.0" ]

28

2018-02-02T20:58:13.000Z

2022-02-06T15:03:41.000Z

bindings/python/tests/test_wrapper.py

kamino/dragonffi

3c983cc8c091d5472f7cdeab1b06dc3b1902e1be

[ "Apache-2.0" ]

28

2018-02-02T12:05:55.000Z

2021-09-16T21:05:05.000Z

# Copyright 2018 Adrien Guinet <adrien@guinet.me> # # 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 unittest import pydffi from common import DFFITest class WrapperTest(DFFITest): def test_wrapper(self): CU = self.FFI.compile(''' struct A { int a; int b; }; struct A init_A(int a, int b) { struct A ret = {a,b}; return ret; } ''') FTy = pydffi.typeof(CU.funcs.init_A) print(FTy.getWrapperLLVMStr("wrap")) if __name__ == '__main__': unittest.main()

27.297297

74

0.70099

import unittest import pydffi from common import DFFITest class WrapperTest(DFFITest): def test_wrapper(self): CU = self.FFI.compile(''' struct A { int a; int b; }; struct A init_A(int a, int b) { struct A ret = {a,b}; return ret; } ''') FTy = pydffi.typeof(CU.funcs.init_A) print(FTy.getWrapperLLVMStr("wrap")) if __name__ == '__main__': unittest.main()

true

f734b8650bc5950384be7f02d5e473f74c10c65c

7,897

py

Python

third_party/retdec-3.2/scripts/retdec-unpacker.py

Fimbure/icebox-1

0b81992a53e1b410955ca89bdb6f8169d6f2da86

[ "MIT" ]

521

2019-03-29T15:44:08.000Z

2022-03-22T09:46:19.000Z

third_party/retdec-3.2/scripts/retdec-unpacker.py

Fimbure/icebox-1

0b81992a53e1b410955ca89bdb6f8169d6f2da86

[ "MIT" ]

30

2019-06-04T17:00:49.000Z

2021-09-08T20:44:19.000Z

third_party/retdec-3.2/scripts/retdec-unpacker.py

Fimbure/icebox-1

0b81992a53e1b410955ca89bdb6f8169d6f2da86

[ "MIT" ]

99

2019-03-29T16:04:13.000Z

2022-03-28T16:59:34.000Z

#!/usr/bin/env python3 """ The script tries to unpack the given executable file by using any of the supported unpackers, which are at present: * generic unpacker * upx Required argument: * (packed) binary file Optional arguments: * desired name of unpacked file * use extended exit codes Returns: * 0 successfully unpacked """ import argparse import os import shutil import sys import importlib config = importlib.import_module('retdec-config') utils = importlib.import_module('retdec-utils') CmdRunner = utils.CmdRunner sys.stdout = utils.Unbuffered(sys.stdout) def parse_args(_args): parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('file', metavar='FILE', help='The input file.') parser.add_argument('-e', '--extended-exit-codes', dest='extended_exit_codes', action='store_true', help='Use more granular exit codes than just 0/1.') parser.add_argument('-o', '--output', dest='output', metavar='FILE', help='Output file (default: file-unpacked).') parser.add_argument('--max-memory', dest='max_memory', help='Limit the maximal memory of retdec-unpacker to N bytes.') parser.add_argument('--max-memory-half-ram', dest='max_memory_half_ram', action='store_true', help='Limit the maximal memory of retdec-unpacker to half of system RAM.') return parser.parse_args(_args) class Unpacker: RET_UNPACK_OK = 0 # 1 generic unpacker - nothing to do; upx succeeded (--extended-exit-codes only) RET_UNPACKER_NOTHING_TO_DO_OTHERS_OK = 1 # 2 not packed or unknown packer RET_NOTHING_TO_DO = 2 # 3 generic unpacker failed; upx succeeded (--extended-exit-codes only) RET_UNPACKER_FAILED_OTHERS_OK = 3 # 4 generic unpacker failed; upx not succeeded RET_UNPACKER_FAILED = 4 UNPACKER_EXIT_CODE_OK = 0 # 1 There was not found matching plugin. UNPACKER_EXIT_CODE_NOTHING_TO_DO = 1 # 2 At least one plugin failed at the unpacking of the file. UNPACKER_EXIT_CODE_UNPACKING_FAILED = 2 # 3 Error with preprocessing of input file before unpacking. UNPACKER_EXIT_CODE_PREPROCESSING_ERROR = 3 # UNPACKER_EXIT_CODE_OTHER = -1 def __init__(self, _args): self.args = parse_args(_args) self.input = '' self.output = '' self.log_output = False self.unpacker_output = '' def _check_arguments(self): """Check proper combination of input arguments. """ # Check whether the input file was specified. if self.args.file is None: utils.print_error('No input file was specified') return False if not os.access(self.args.file, os.R_OK): utils.print_error('The input file %s does not exist or is not readable' % self.args.file) return False # Conditional initialization. if not self.args.output: self.output = self.args.file + '-unpacked' else: self.output = self.args.output if self.args.max_memory is not None: try: int(self.args.max_memory) except ValueError: utils.print_error('Invalid value for --max-memory: %s (expected a positive integer)' % self.args.max_memory) return False # Convert to absolute paths. self.input = os.path.abspath(self.args.file) self.output = os.path.abspath(self.output) return True def _unpack(self, output): """Try to unpack the given file. """ unpacker_params = [self.input, '-o', output] if self.args.max_memory: unpacker_params.extend(['--max-memory', self.args.max_memory]) elif self.args.max_memory_half_ram: unpacker_params.append('--max-memory-half-ram') cmd = CmdRunner() self._print('\n##### Trying to unpack ' + self.input + ' into ' + output + ' by using generic unpacker...') out, unpacker_rc, _ = cmd.run_cmd([config.UNPACKER] + unpacker_params, buffer_output=True, print_run_msg=True) self._print(out) if unpacker_rc == self.UNPACKER_EXIT_CODE_OK: self._print('##### Unpacking by using generic unpacker: successfully unpacked') return self.unpacker_output, self.RET_UNPACK_OK elif unpacker_rc == self.UNPACKER_EXIT_CODE_NOTHING_TO_DO: self._print('##### Unpacking by using generic unpacker: nothing to do') else: # Do not return -> try the next unpacker self._print('##### Unpacking by using generic unpacker: failed') if utils.tool_exists('upx'): # Do not return -> try the next unpacker # Try to unpack via UPX self._print('\n##### Trying to unpack ' + self.input + ' into ' + output + ' by using UPX...') out, upx_rc, _ = cmd.run_cmd(['upx', '-d', self.input, '-o', output], buffer_output=True, discard_stdout=True, print_run_msg=True) self._print(out) if upx_rc == 0: self._print('##### Unpacking by using UPX: successfully unpacked') if self.args.extended_exit_codes: if unpacker_rc == self.UNPACKER_EXIT_CODE_NOTHING_TO_DO: return self.unpacker_output, self.RET_UNPACKER_NOTHING_TO_DO_OTHERS_OK elif unpacker_rc >= self.UNPACKER_EXIT_CODE_UNPACKING_FAILED: return self.unpacker_output, self.RET_UNPACKER_FAILED_OTHERS_OK else: return self.unpacker_output, self.RET_UNPACK_OK else: # We cannot distinguish whether upx failed or the input file was # not upx-packed self._print('##### Unpacking by using UPX: nothing to do') else: self._print('##### \'upx\' not available: nothing to do') if unpacker_rc >= self.UNPACKER_EXIT_CODE_UNPACKING_FAILED: return self.unpacker_output, self.RET_UNPACKER_FAILED else: return self.unpacker_output, self.RET_NOTHING_TO_DO def unpack_all(self, log_output=False): self.log_output = log_output if not self._check_arguments(): return '', self.UNPACKER_EXIT_CODE_OTHER res_rc = self.UNPACKER_EXIT_CODE_OTHER res_out = '' tmp_output = self.output + '.tmp' while True: unpacker_out, return_code = self._unpack(tmp_output) res_out += unpacker_out + '\n' if return_code == self.RET_UNPACK_OK or return_code == self.RET_UNPACKER_NOTHING_TO_DO_OTHERS_OK \ or return_code == self.RET_UNPACKER_FAILED_OTHERS_OK: res_rc = return_code shutil.move(tmp_output, self.output) self.input = self.output else: # Remove the temporary file, just in case some of the unpackers crashed # during unpacking and left it on the disk (e.g. upx). utils.remove_file_forced(tmp_output) break return (res_out, return_code) if res_rc == self.UNPACKER_EXIT_CODE_OTHER else (res_out, res_rc) def _print(self, line=''): if self.log_output: self.unpacker_output = self.unpacker_output + line else: print(line) if __name__ == '__main__': unpacker = Unpacker(sys.argv[1:]) _, rc = unpacker.unpack_all() sys.exit(rc)

36.391705

142

0.607193

import argparse import os import shutil import sys import importlib config = importlib.import_module('retdec-config') utils = importlib.import_module('retdec-utils') CmdRunner = utils.CmdRunner sys.stdout = utils.Unbuffered(sys.stdout) def parse_args(_args): parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('file', metavar='FILE', help='The input file.') parser.add_argument('-e', '--extended-exit-codes', dest='extended_exit_codes', action='store_true', help='Use more granular exit codes than just 0/1.') parser.add_argument('-o', '--output', dest='output', metavar='FILE', help='Output file (default: file-unpacked).') parser.add_argument('--max-memory', dest='max_memory', help='Limit the maximal memory of retdec-unpacker to N bytes.') parser.add_argument('--max-memory-half-ram', dest='max_memory_half_ram', action='store_true', help='Limit the maximal memory of retdec-unpacker to half of system RAM.') return parser.parse_args(_args) class Unpacker: RET_UNPACK_OK = 0 RET_UNPACKER_NOTHING_TO_DO_OTHERS_OK = 1 RET_NOTHING_TO_DO = 2 RET_UNPACKER_FAILED_OTHERS_OK = 3 RET_UNPACKER_FAILED = 4 UNPACKER_EXIT_CODE_OK = 0 UNPACKER_EXIT_CODE_NOTHING_TO_DO = 1 UNPACKER_EXIT_CODE_UNPACKING_FAILED = 2 UNPACKER_EXIT_CODE_PREPROCESSING_ERROR = 3 UNPACKER_EXIT_CODE_OTHER = -1 def __init__(self, _args): self.args = parse_args(_args) self.input = '' self.output = '' self.log_output = False self.unpacker_output = '' def _check_arguments(self): if self.args.file is None: utils.print_error('No input file was specified') return False if not os.access(self.args.file, os.R_OK): utils.print_error('The input file %s does not exist or is not readable' % self.args.file) return False if not self.args.output: self.output = self.args.file + '-unpacked' else: self.output = self.args.output if self.args.max_memory is not None: try: int(self.args.max_memory) except ValueError: utils.print_error('Invalid value for --max-memory: %s (expected a positive integer)' % self.args.max_memory) return False self.input = os.path.abspath(self.args.file) self.output = os.path.abspath(self.output) return True def _unpack(self, output): unpacker_params = [self.input, '-o', output] if self.args.max_memory: unpacker_params.extend(['--max-memory', self.args.max_memory]) elif self.args.max_memory_half_ram: unpacker_params.append('--max-memory-half-ram') cmd = CmdRunner() self._print('\n##### Trying to unpack ' + self.input + ' into ' + output + ' by using generic unpacker...') out, unpacker_rc, _ = cmd.run_cmd([config.UNPACKER] + unpacker_params, buffer_output=True, print_run_msg=True) self._print(out) if unpacker_rc == self.UNPACKER_EXIT_CODE_OK: self._print('##### Unpacking by using generic unpacker: successfully unpacked') return self.unpacker_output, self.RET_UNPACK_OK elif unpacker_rc == self.UNPACKER_EXIT_CODE_NOTHING_TO_DO: self._print('##### Unpacking by using generic unpacker: nothing to do') else: self._print('##### Unpacking by using generic unpacker: failed') if utils.tool_exists('upx'): self._print('\n##### Trying to unpack ' + self.input + ' into ' + output + ' by using UPX...') out, upx_rc, _ = cmd.run_cmd(['upx', '-d', self.input, '-o', output], buffer_output=True, discard_stdout=True, print_run_msg=True) self._print(out) if upx_rc == 0: self._print('##### Unpacking by using UPX: successfully unpacked') if self.args.extended_exit_codes: if unpacker_rc == self.UNPACKER_EXIT_CODE_NOTHING_TO_DO: return self.unpacker_output, self.RET_UNPACKER_NOTHING_TO_DO_OTHERS_OK elif unpacker_rc >= self.UNPACKER_EXIT_CODE_UNPACKING_FAILED: return self.unpacker_output, self.RET_UNPACKER_FAILED_OTHERS_OK else: return self.unpacker_output, self.RET_UNPACK_OK else: self._print('##### Unpacking by using UPX: nothing to do') else: self._print('##### \'upx\' not available: nothing to do') if unpacker_rc >= self.UNPACKER_EXIT_CODE_UNPACKING_FAILED: return self.unpacker_output, self.RET_UNPACKER_FAILED else: return self.unpacker_output, self.RET_NOTHING_TO_DO def unpack_all(self, log_output=False): self.log_output = log_output if not self._check_arguments(): return '', self.UNPACKER_EXIT_CODE_OTHER res_rc = self.UNPACKER_EXIT_CODE_OTHER res_out = '' tmp_output = self.output + '.tmp' while True: unpacker_out, return_code = self._unpack(tmp_output) res_out += unpacker_out + '\n' if return_code == self.RET_UNPACK_OK or return_code == self.RET_UNPACKER_NOTHING_TO_DO_OTHERS_OK \ or return_code == self.RET_UNPACKER_FAILED_OTHERS_OK: res_rc = return_code shutil.move(tmp_output, self.output) self.input = self.output else: utils.remove_file_forced(tmp_output) break return (res_out, return_code) if res_rc == self.UNPACKER_EXIT_CODE_OTHER else (res_out, res_rc) def _print(self, line=''): if self.log_output: self.unpacker_output = self.unpacker_output + line else: print(line) if __name__ == '__main__': unpacker = Unpacker(sys.argv[1:]) _, rc = unpacker.unpack_all() sys.exit(rc)

true

f734b8e77edfbc9b357701255382e772aa6650eb

4,072

py

Python

data/get_dataset.py

KristinaRay/english-arabic-nmt-bot

1e0baddc81b829b3ee1abe95143cdef5c1206dd2

[ "MIT" ]

1

2022-01-31T11:48:01.000Z

data/get_dataset.py

KristinaRay/english-arabic-nmt-bot

1e0baddc81b829b3ee1abe95143cdef5c1206dd2

[ "MIT" ]

null

data/get_dataset.py

KristinaRay/english-arabic-nmt-bot

1e0baddc81b829b3ee1abe95143cdef5c1206dd2

[ "MIT" ]

null

import os import tqdm import numpy as np import requests import youtokentome as yttm from argparse import ArgumentParser from zipfile import ZipFile from config import * from data.preprocessing import * from utils import * DATA_FILE_PATH = f'{DATA_PATH}/data.zip' DATA_URL = 'https://opus.nlpl.eu/download.php?f=OpenSubtitles/v2018/moses/ar-en.txt.zip' TRG_FILE_NAME = 'OpenSubtitles.ar-en.ar' SRC_FILE_NAME = 'OpenSubtitles.ar-en.en' TRG_SAMPLE_FILE_PATH = f'{DATA_PATH}/ar.txt' SRC_SAMPLE_FILE_PATH = f'{DATA_PATH}/en.txt' TRG_ORIG_FILE_PATH = f'{DATA_PATH}/{TRG_FILE_NAME}' SRC_ORIG_FILE_PATH = f'{DATA_PATH}/{SRC_FILE_NAME}' def fetch_dataset(data_url, data_path, data_file_path): """ Download data """ if not os.path.exists(data_path): os.makedirs(data_path) print("Dataset not found, downloading...") response = requests.get(data_url, stream=True) filename = data_url.split("/")[-1] total_size_in_bytes= int(response.headers.get('content-length', 0)) progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True) with open(data_file_path, 'wb') as file: for data in response.iter_content(1024): progress_bar.update(len(data)) file.write(data) progress_bar.close() log("Download complete") log("Extracting...") zip = ZipFile(DATA_FILE_PATH, "r") zip.extract(TRG_FILE_NAME, DATA_PATH) zip.extract(SRC_FILE_NAME, DATA_PATH) zip.close() log("Extracting complete") num_lines_ar = sum(1 for line in open(TRG_ORIG_FILE_PATH)) # number of lines in arabic file num_lines_en = sum(1 for line in open(SRC_ORIG_FILE_PATH)) # number of lines in english file assert num_lines_ar == num_lines_en, "Lost some data" assert os.path.exists(data_path) else: log('Datasets are found') def create_sample(sample_size, max_text_len): """ Clean data sample and remove duplicates """ log('Creating txt files for both languages...') num_lines_ar = sum(1 for line in open(TRG_ORIG_FILE_PATH)) sample_data_size = 2 * sample_size chosen_lines = set(np.random.choice(np.arange(num_lines_ar), size=sample_data_size, replace=False)) en_sub = open(SRC_ORIG_FILE_PATH, "r") ar_sub = open(TRG_ORIG_FILE_PATH, "r") unique_pairs = set() with open(SRC_TXT_FILE_PATH, "a+") as en, open(TRG_TXT_FILE_PATH, "a+") as ar: for idx, (en_line, ar_line) in enumerate(zip(en_sub, ar_sub)): if idx in chosen_lines: src = clean_en_text(en_line) trg = clean_ar_text(ar_line) if 2 < len(src) <= max_text_len and 2 < len(trg) < max_text_len: if ((src + trg) not in unique_pairs and (len(unique_pairs) < sample_size)): en.write(src) ar.write(trg) unique_pairs.add((src + trg)) elif len(unique_pairs) >= sample_size: break assert len(unique_pairs) == sample_size, "Not enough data" en_sub.close() ar_sub.close() en.close() ar.close() log("Done") log(f'Number of unique pairs of sentences: {len(unique_pairs)}') def main(): fetch_dataset(DATA_URL, DATA_PATH, DATA_FILE_PATH) parser = ArgumentParser() parser.add_argument("--sample_size", required=True, type=int, help='Number of the sentence pairs to prepare for the training') parser.add_argument("--max_text_len", required=True, type=int, help='Max character length of the sentences') args = parser.parse_args() create_sample(args.sample_size, args.max_text_len) log('Training tokenizers...') yttm.BPE.train(data=TRG_TXT_FILE_PATH, vocab_size=TRG_VOCAB_SIZE, model=TRG_TOKENIZER_PATH) yttm.BPE.train(data=SRC_TXT_FILE_PATH, vocab_size=SRC_VOCAB_SIZE, model=SRC_TOKENIZER_PATH) log("Done") if __name__ == "__main__": main()

37.357798

130

0.653242

import os import tqdm import numpy as np import requests import youtokentome as yttm from argparse import ArgumentParser from zipfile import ZipFile from config import * from data.preprocessing import * from utils import * DATA_FILE_PATH = f'{DATA_PATH}/data.zip' DATA_URL = 'https://opus.nlpl.eu/download.php?f=OpenSubtitles/v2018/moses/ar-en.txt.zip' TRG_FILE_NAME = 'OpenSubtitles.ar-en.ar' SRC_FILE_NAME = 'OpenSubtitles.ar-en.en' TRG_SAMPLE_FILE_PATH = f'{DATA_PATH}/ar.txt' SRC_SAMPLE_FILE_PATH = f'{DATA_PATH}/en.txt' TRG_ORIG_FILE_PATH = f'{DATA_PATH}/{TRG_FILE_NAME}' SRC_ORIG_FILE_PATH = f'{DATA_PATH}/{SRC_FILE_NAME}' def fetch_dataset(data_url, data_path, data_file_path): if not os.path.exists(data_path): os.makedirs(data_path) print("Dataset not found, downloading...") response = requests.get(data_url, stream=True) filename = data_url.split("/")[-1] total_size_in_bytes= int(response.headers.get('content-length', 0)) progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True) with open(data_file_path, 'wb') as file: for data in response.iter_content(1024): progress_bar.update(len(data)) file.write(data) progress_bar.close() log("Download complete") log("Extracting...") zip = ZipFile(DATA_FILE_PATH, "r") zip.extract(TRG_FILE_NAME, DATA_PATH) zip.extract(SRC_FILE_NAME, DATA_PATH) zip.close() log("Extracting complete") num_lines_ar = sum(1 for line in open(TRG_ORIG_FILE_PATH)) num_lines_en = sum(1 for line in open(SRC_ORIG_FILE_PATH)) assert num_lines_ar == num_lines_en, "Lost some data" assert os.path.exists(data_path) else: log('Datasets are found') def create_sample(sample_size, max_text_len): log('Creating txt files for both languages...') num_lines_ar = sum(1 for line in open(TRG_ORIG_FILE_PATH)) sample_data_size = 2 * sample_size chosen_lines = set(np.random.choice(np.arange(num_lines_ar), size=sample_data_size, replace=False)) en_sub = open(SRC_ORIG_FILE_PATH, "r") ar_sub = open(TRG_ORIG_FILE_PATH, "r") unique_pairs = set() with open(SRC_TXT_FILE_PATH, "a+") as en, open(TRG_TXT_FILE_PATH, "a+") as ar: for idx, (en_line, ar_line) in enumerate(zip(en_sub, ar_sub)): if idx in chosen_lines: src = clean_en_text(en_line) trg = clean_ar_text(ar_line) if 2 < len(src) <= max_text_len and 2 < len(trg) < max_text_len: if ((src + trg) not in unique_pairs and (len(unique_pairs) < sample_size)): en.write(src) ar.write(trg) unique_pairs.add((src + trg)) elif len(unique_pairs) >= sample_size: break assert len(unique_pairs) == sample_size, "Not enough data" en_sub.close() ar_sub.close() en.close() ar.close() log("Done") log(f'Number of unique pairs of sentences: {len(unique_pairs)}') def main(): fetch_dataset(DATA_URL, DATA_PATH, DATA_FILE_PATH) parser = ArgumentParser() parser.add_argument("--sample_size", required=True, type=int, help='Number of the sentence pairs to prepare for the training') parser.add_argument("--max_text_len", required=True, type=int, help='Max character length of the sentences') args = parser.parse_args() create_sample(args.sample_size, args.max_text_len) log('Training tokenizers...') yttm.BPE.train(data=TRG_TXT_FILE_PATH, vocab_size=TRG_VOCAB_SIZE, model=TRG_TOKENIZER_PATH) yttm.BPE.train(data=SRC_TXT_FILE_PATH, vocab_size=SRC_VOCAB_SIZE, model=SRC_TOKENIZER_PATH) log("Done") if __name__ == "__main__": main()

true

f734b98f59acd1949a167b610f57a6d37d0e9a9e

584

py

Python

codes/DBN.py

NoSRPKU/GradD

c74af028d4a93e2f645a61316a5c339ecf690bf1

[ "MIT" ]

null

codes/DBN.py

NoSRPKU/GradD

c74af028d4a93e2f645a61316a5c339ecf690bf1

[ "MIT" ]

null

codes/DBN.py

NoSRPKU/GradD

c74af028d4a93e2f645a61316a5c339ecf690bf1

[ "MIT" ]

null

import numpy import theano import theano.tensor as T from deeplearning import rbm class DBN(): def __init__(self, vsize=None, hsizes=[], lr=None, bsize=10, seed=123): assert vsize and hsizes and lr input = T.dmatrix('global_input') self.layers = [] for hsize in hsizes: r = rbm.RBM(input=input, vsize=vsize, hsize=hsize, bsize=bsize, lr=lr, seed=seed) self.layers.append(r) # configure inputs for subsequent layer input = self.layers[-1].hid vsize = hsize

23.36

75

0.583904

import numpy import theano import theano.tensor as T from deeplearning import rbm class DBN(): def __init__(self, vsize=None, hsizes=[], lr=None, bsize=10, seed=123): assert vsize and hsizes and lr input = T.dmatrix('global_input') self.layers = [] for hsize in hsizes: r = rbm.RBM(input=input, vsize=vsize, hsize=hsize, bsize=bsize, lr=lr, seed=seed) self.layers.append(r) input = self.layers[-1].hid vsize = hsize

true

f734ba16367e8b66e709a083cd83399a14c6eb48

1,169

py

Python

Model_codebase_2_flask.py

anmolmore/Chatbot-for-COVID-19-FAQ-using-Dialogflow

f80670e9ee67e18c790da85d49e9c9617753c6f8

[ "MIT" ]

1

2021-01-02T06:37:41.000Z

Model_codebase_2_flask.py

anmolmore/Chatbot-for-COVID-19-FAQ-using-Dialogflow

f80670e9ee67e18c790da85d49e9c9617753c6f8

[ "MIT" ]

null

Model_codebase_2_flask.py

anmolmore/Chatbot-for-COVID-19-FAQ-using-Dialogflow

f80670e9ee67e18c790da85d49e9c9617753c6f8

[ "MIT" ]

null

#11915010 Raghu Punnamraju #11915043 Anmol More #11915001 Sriganesh Balamurugan #11915052 Kapil Bindal import pandas as pd from ast import literal_eval from cdqa.utils.filters import filter_paragraphs from cdqa.utils.download import download_model, download_bnpp_data from cdqa.pipeline.cdqa_sklearn import QAPipeline #read the cleaned dataset and just take question and context for our model df = pd.read_csv('data/dataset_collected.csv', usecols=['question', 'context']) #convert paragraphs to a list df['paragraphs'] = df[df.columns[1:]].apply( lambda x: x.dropna().values.tolist(), axis=1) df.rename(columns={"question": "title"}, inplace=True) df.drop(columns='context', inplace=True) df.to_csv('df_corona.csv', index=False) #use a lighter pipleline model to build pipeline on top of it cdqa_pipeline = QAPipeline(reader='models/distilbert_qa.joblib') cdqa_pipeline.fit_retriever(df=df) print('Welcome to Corona Chatbot ! How can I help you ? ') print('Press enter twice to quit') while True: query = input() prediction = cdqa_pipeline.predict(query=query) print('Query : {}\n'.format(query)) print('Reply from Bot: {}\n'.format(prediction[0]))

32.472222

79

0.763901

import pandas as pd from ast import literal_eval from cdqa.utils.filters import filter_paragraphs from cdqa.utils.download import download_model, download_bnpp_data from cdqa.pipeline.cdqa_sklearn import QAPipeline df = pd.read_csv('data/dataset_collected.csv', usecols=['question', 'context']) df['paragraphs'] = df[df.columns[1:]].apply( lambda x: x.dropna().values.tolist(), axis=1) df.rename(columns={"question": "title"}, inplace=True) df.drop(columns='context', inplace=True) df.to_csv('df_corona.csv', index=False) cdqa_pipeline = QAPipeline(reader='models/distilbert_qa.joblib') cdqa_pipeline.fit_retriever(df=df) print('Welcome to Corona Chatbot ! How can I help you ? ') print('Press enter twice to quit') while True: query = input() prediction = cdqa_pipeline.predict(query=query) print('Query : {}\n'.format(query)) print('Reply from Bot: {}\n'.format(prediction[0]))

true

f734bb4107b6567f1e96ae7b325230b167696c84

1,053

py

Python

best_single_model/focal_loss.py

hellopikaqiu/AIchallenger_MachineReadingComprehension

03c8d4ab60f6ac9c7f777fd2c932cc01300b5c42

[ "MIT" ]

94

2018-12-17T09:12:48.000Z

2021-03-15T02:56:22.000Z

best_single_model/focal_loss.py

1120327383/AIchallenger2018_MachineReadingComprehension

03c8d4ab60f6ac9c7f777fd2c932cc01300b5c42

[ "MIT" ]

4

2019-03-10T03:20:18.000Z

2020-06-02T05:10:46.000Z

best_single_model/focal_loss.py

1120327383/AIchallenger2018_MachineReadingComprehension

03c8d4ab60f6ac9c7f777fd2c932cc01300b5c42

[ "MIT" ]

24

2018-12-17T09:13:49.000Z

2020-01-15T09:14:41.000Z

""" AI Challenger观点型问题阅读理解 focal_loss.py @author: yuhaitao """ # -*- coding:utf-8 -*- import tensorflow as tf def sparse_focal_loss(logits, labels, gamma=2): """ Computer focal loss for multi classification Args: labels: A int32 tensor of shape [batch_size]. logits: A float32 tensor of shape [batch_size,num_classes]. gamma: A scalar for focal loss gamma hyper-parameter. Returns: A tensor of the same shape as `lables` """ with tf.name_scope("focal_loss"): y_pred = tf.nn.softmax(logits, dim=-1) # [batch_size,num_classes] labels = tf.one_hot(labels, depth=y_pred.shape[1]) L = -labels * ((1 - y_pred)**gamma) * tf.log(y_pred) L = tf.reduce_sum(L, axis=1) return L ''' if __name__ == '__main__': labels = tf.constant([0, 1], name="labels") logits = tf.constant([[0.7, 0.2, 0.1], [0.6, 0.1, 0.3]], name="logits") a = tf.reduce_mean(sparse_focal_loss(logits, tf.stop_gradient(labels))) with tf.Session() as sess: print(sess.run(a))'''

29.25

75

0.62868

import tensorflow as tf def sparse_focal_loss(logits, labels, gamma=2): with tf.name_scope("focal_loss"): y_pred = tf.nn.softmax(logits, dim=-1) labels = tf.one_hot(labels, depth=y_pred.shape[1]) L = -labels * ((1 - y_pred)**gamma) * tf.log(y_pred) L = tf.reduce_sum(L, axis=1) return L

true

f734bc2a2a57c1eac2aa84d6d7661a74990feb17

72,645

py

Python

python/ccxt/bitmex.py

Joukahainen/ccxt

82823a85b96cee336853f0deb353474df2122b88

[ "MIT" ]

2

2022-03-10T15:21:49.000Z

2022-03-10T15:22:01.000Z

python/ccxt/bitmex.py

alimogh/ccxt

518ea1a6d212605aa19deed74991bc525470e5c9

[ "MIT" ]

4

2021-12-14T06:19:10.000Z

2022-03-19T02:39:29.000Z

python/ccxt/bitmex.py

alimogh/ccxt

518ea1a6d212605aa19deed74991bc525470e5c9

[ "MIT" ]

2

2022-03-08T20:43:26.000Z

2022-03-14T19:28:27.000Z

# -*- coding: utf-8 -*- # PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN: # https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code from ccxt.base.exchange import Exchange from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import BadRequest from ccxt.base.errors import BadSymbol from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import DDoSProtection from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.decimal_to_precision import TICK_SIZE from ccxt.base.precise import Precise class bitmex(Exchange): def describe(self): return self.deep_extend(super(bitmex, self).describe(), { 'id': 'bitmex', 'name': 'BitMEX', 'countries': ['SC'], # Seychelles 'version': 'v1', 'userAgent': None, 'rateLimit': 2000, 'pro': True, 'has': { 'CORS': None, 'spot': False, 'margin': False, 'swap': None, # has but not fully implemented 'future': None, # has but not fully implemented 'option': None, # has but not fully implemented 'cancelAllOrders': True, 'cancelOrder': True, 'cancelOrders': True, 'createOrder': True, 'editOrder': True, 'fetchBalance': True, 'fetchClosedOrders': True, 'fetchIndexOHLCV': False, 'fetchLedger': True, 'fetchLeverageTiers': False, 'fetchMarkets': True, 'fetchMarkOHLCV': False, 'fetchMyTrades': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchOrders': True, 'fetchPositions': True, 'fetchPremiumIndexOHLCV': False, 'fetchTicker': True, 'fetchTickers': True, 'fetchTrades': True, 'fetchTransactions': 'emulated', 'withdraw': True, }, 'timeframes': { '1m': '1m', '5m': '5m', '1h': '1h', '1d': '1d', }, 'urls': { 'test': { 'public': 'https://testnet.bitmex.com', 'private': 'https://testnet.bitmex.com', }, 'logo': 'https://user-images.githubusercontent.com/1294454/27766319-f653c6e6-5ed4-11e7-933d-f0bc3699ae8f.jpg', 'api': { 'public': 'https://www.bitmex.com', 'private': 'https://www.bitmex.com', }, 'www': 'https://www.bitmex.com', 'doc': [ 'https://www.bitmex.com/app/apiOverview', 'https://github.com/BitMEX/api-connectors/tree/master/official-http', ], 'fees': 'https://www.bitmex.com/app/fees', 'referral': 'https://www.bitmex.com/register/upZpOX', }, 'api': { 'public': { 'get': [ 'announcement', 'announcement/urgent', 'funding', 'instrument', 'instrument/active', 'instrument/activeAndIndices', 'instrument/activeIntervals', 'instrument/compositeIndex', 'instrument/indices', 'insurance', 'leaderboard', 'liquidation', 'orderBook', 'orderBook/L2', 'quote', 'quote/bucketed', 'schema', 'schema/websocketHelp', 'settlement', 'stats', 'stats/history', 'trade', 'trade/bucketed', ], }, 'private': { 'get': [ 'apiKey', 'chat', 'chat/channels', 'chat/connected', 'execution', 'execution/tradeHistory', 'notification', 'order', 'position', 'user', 'user/affiliateStatus', 'user/checkReferralCode', 'user/commission', 'user/depositAddress', 'user/executionHistory', 'user/margin', 'user/minWithdrawalFee', 'user/wallet', 'user/walletHistory', 'user/walletSummary', ], 'post': [ 'apiKey', 'apiKey/disable', 'apiKey/enable', 'chat', 'order', 'order/bulk', 'order/cancelAllAfter', 'order/closePosition', 'position/isolate', 'position/leverage', 'position/riskLimit', 'position/transferMargin', 'user/cancelWithdrawal', 'user/confirmEmail', 'user/confirmEnableTFA', 'user/confirmWithdrawal', 'user/disableTFA', 'user/logout', 'user/logoutAll', 'user/preferences', 'user/requestEnableTFA', 'user/requestWithdrawal', ], 'put': [ 'order', 'order/bulk', 'user', ], 'delete': [ 'apiKey', 'order', 'order/all', ], }, }, 'exceptions': { 'exact': { 'Invalid API Key.': AuthenticationError, 'This key is disabled.': PermissionDenied, 'Access Denied': PermissionDenied, 'Duplicate clOrdID': InvalidOrder, 'orderQty is invalid': InvalidOrder, 'Invalid price': InvalidOrder, 'Invalid stopPx for ordType': InvalidOrder, }, 'broad': { 'Signature not valid': AuthenticationError, 'overloaded': ExchangeNotAvailable, 'Account has insufficient Available Balance': InsufficientFunds, 'Service unavailable': ExchangeNotAvailable, # {"error":{"message":"Service unavailable","name":"HTTPError"}} 'Server Error': ExchangeError, # {"error":{"message":"Server Error","name":"HTTPError"}} 'Unable to cancel order due to existing state': InvalidOrder, }, }, 'precisionMode': TICK_SIZE, 'options': { # https://blog.bitmex.com/api_announcement/deprecation-of-api-nonce-header/ # https://github.com/ccxt/ccxt/issues/4789 'api-expires': 5, # in seconds 'fetchOHLCVOpenTimestamp': True, }, 'commonCurrencies': { 'USDt': 'USDT', 'XBt': 'BTC', 'XBT': 'BTC', }, }) def fetch_markets(self, params={}): response = self.publicGetInstrumentActiveAndIndices(params) # # { # "symbol": "LTCUSDT", # "rootSymbol": "LTC", # "state": "Open", # "typ": "FFWCSX", # "listing": "2021-11-10T04:00:00.000Z", # "front": "2021-11-10T04:00:00.000Z", # "expiry": null, # "settle": null, # "listedSettle": null, # "relistInterval": null, # "inverseLeg": "", # "sellLeg": "", # "buyLeg": "", # "optionStrikePcnt": null, # "optionStrikeRound": null, # "optionStrikePrice": null, # "optionMultiplier": null, # "positionCurrency": "LTC", # "underlying": "LTC", # "quoteCurrency": "USDT", # "underlyingSymbol": "LTCT=", # "reference": "BMEX", # "referenceSymbol": ".BLTCT", # "calcInterval": null, # "publishInterval": null, # "publishTime": null, # "maxOrderQty": 1000000000, # "maxPrice": 1000000, # "lotSize": 1000, # "tickSize": 0.01, # "multiplier": 100, # "settlCurrency": "USDt", # "underlyingToPositionMultiplier": 10000, # "underlyingToSettleMultiplier": null, # "quoteToSettleMultiplier": 1000000, # "isQuanto": False, # "isInverse": False, # "initMargin": 0.03, # "maintMargin": 0.015, # "riskLimit": 1000000000000, # "riskStep": 1000000000000, # "limit": null, # "capped": False, # "taxed": True, # "deleverage": True, # "makerFee": -0.0001, # "takerFee": 0.0005, # "settlementFee": 0, # "insuranceFee": 0, # "fundingBaseSymbol": ".LTCBON8H", # "fundingQuoteSymbol": ".USDTBON8H", # "fundingPremiumSymbol": ".LTCUSDTPI8H", # "fundingTimestamp": "2022-01-14T20:00:00.000Z", # "fundingInterval": "2000-01-01T08:00:00.000Z", # "fundingRate": 0.0001, # "indicativeFundingRate": 0.0001, # "rebalanceTimestamp": null, # "rebalanceInterval": null, # "openingTimestamp": "2022-01-14T17:00:00.000Z", # "closingTimestamp": "2022-01-14T18:00:00.000Z", # "sessionInterval": "2000-01-01T01:00:00.000Z", # "prevClosePrice": 138.511, # "limitDownPrice": null, # "limitUpPrice": null, # "bankruptLimitDownPrice": null, # "bankruptLimitUpPrice": null, # "prevTotalVolume": 12699024000, # "totalVolume": 12702160000, # "volume": 3136000, # "volume24h": 114251000, # "prevTotalTurnover": 232418052349000, # "totalTurnover": 232463353260000, # "turnover": 45300911000, # "turnover24h": 1604331340000, # "homeNotional24h": 11425.1, # "foreignNotional24h": 1604331.3400000003, # "prevPrice24h": 135.48, # "vwap": 140.42165, # "highPrice": 146.42, # "lowPrice": 135.08, # "lastPrice": 144.36, # "lastPriceProtected": 144.36, # "lastTickDirection": "MinusTick", # "lastChangePcnt": 0.0655, # "bidPrice": 143.75, # "midPrice": 143.855, # "askPrice": 143.96, # "impactBidPrice": 143.75, # "impactMidPrice": 143.855, # "impactAskPrice": 143.96, # "hasLiquidity": True, # "openInterest": 38103000, # "openValue": 547963053300, # "fairMethod": "FundingRate", # "fairBasisRate": 0.1095, # "fairBasis": 0.004, # "fairPrice": 143.811, # "markMethod": "FairPrice", # "markPrice": 143.811, # "indicativeTaxRate": null, # "indicativeSettlePrice": 143.807, # "optionUnderlyingPrice": null, # "settledPriceAdjustmentRate": null, # "settledPrice": null, # "timestamp": "2022-01-14T17:49:55.000Z" # } # result = [] for i in range(0, len(response)): market = response[i] id = self.safe_string(market, 'symbol') baseId = self.safe_string(market, 'underlying') quoteId = self.safe_string(market, 'quoteCurrency') settleId = self.safe_string(market, 'settlCurrency') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) settle = self.safe_currency_code(settleId) basequote = baseId + quoteId swap = (id == basequote) # 'positionCurrency' may be empty("", as Bitmex currently returns for ETHUSD) # so let's take the quote currency first and then adjust if needed type = None future = False prediction = False index = False symbol = base + '/' + quote + ':' + settle expiryDatetime = self.safe_string(market, 'expiry') expiry = self.parse8601(expiryDatetime) inverse = self.safe_value(market, 'isInverse') status = self.safe_string(market, 'state') active = status != 'Unlisted' if swap: type = 'swap' elif id.find('B_') >= 0: prediction = True type = 'prediction' symbol = id elif expiry is not None: future = True type = 'future' symbol = symbol + '-' + self.yymmdd(expiry) else: index = True type = 'index' symbol = id active = False positionId = self.safe_string_2(market, 'positionCurrency', 'quoteCurrency') position = self.safe_currency_code(positionId) positionIsQuote = (position == quote) maxOrderQty = self.safe_number(market, 'maxOrderQty') contract = not index initMargin = self.safe_string(market, 'initMargin', '1') maxLeverage = self.parse_number(Precise.string_div('1', initMargin)) result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'settle': settle, 'baseId': baseId, 'quoteId': quoteId, 'settleId': settleId, 'type': type, 'spot': False, 'margin': False, 'swap': swap, 'future': future, 'option': False, 'prediction': prediction, 'index': index, 'active': active, 'contract': contract, 'linear': not inverse if contract else None, 'inverse': inverse if contract else None, 'taker': self.safe_number(market, 'takerFee'), 'maker': self.safe_number(market, 'makerFee'), 'contractSize': self.safe_number(market, 'multiplier'), 'expiry': expiry, 'expiryDatetime': expiryDatetime, 'strike': self.safe_number(market, 'optionStrikePrice'), 'optionType': None, 'precision': { 'amount': self.safe_number(market, 'lotSize'), 'price': self.safe_number(market, 'tickSize'), }, 'limits': { 'leverage': { 'min': self.parse_number('1') if contract else None, 'max': maxLeverage if contract else None, }, 'amount': { 'min': None, 'max': None if positionIsQuote else maxOrderQty, }, 'price': { 'min': None, 'max': self.safe_number(market, 'maxPrice'), }, 'cost': { 'min': None, 'max': maxOrderQty if positionIsQuote else None, }, }, 'info': market, }) return result def parse_balance(self, response): # # [ # { # "account":1455728, # "currency":"XBt", # "riskLimit":1000000000000, # "prevState":"", # "state":"", # "action":"", # "amount":263542, # "pendingCredit":0, # "pendingDebit":0, # "confirmedDebit":0, # "prevRealisedPnl":0, # "prevUnrealisedPnl":0, # "grossComm":0, # "grossOpenCost":0, # "grossOpenPremium":0, # "grossExecCost":0, # "grossMarkValue":0, # "riskValue":0, # "taxableMargin":0, # "initMargin":0, # "maintMargin":0, # "sessionMargin":0, # "targetExcessMargin":0, # "varMargin":0, # "realisedPnl":0, # "unrealisedPnl":0, # "indicativeTax":0, # "unrealisedProfit":0, # "syntheticMargin":null, # "walletBalance":263542, # "marginBalance":263542, # "marginBalancePcnt":1, # "marginLeverage":0, # "marginUsedPcnt":0, # "excessMargin":263542, # "excessMarginPcnt":1, # "availableMargin":263542, # "withdrawableMargin":263542, # "timestamp":"2020-08-03T12:01:01.246Z", # "grossLastValue":0, # "commission":null # } # ] # result = {'info': response} for i in range(0, len(response)): balance = response[i] currencyId = self.safe_string(balance, 'currency') code = self.safe_currency_code(currencyId) account = self.account() free = self.safe_string(balance, 'availableMargin') total = self.safe_string(balance, 'marginBalance') if code == 'BTC': free = Precise.string_div(free, '1e8') total = Precise.string_div(total, '1e8') account['free'] = free account['total'] = total result[code] = account return self.safe_balance(result) def fetch_balance(self, params={}): self.load_markets() request = { 'currency': 'all', } response = self.privateGetUserMargin(self.extend(request, params)) # # [ # { # "account":1455728, # "currency":"XBt", # "riskLimit":1000000000000, # "prevState":"", # "state":"", # "action":"", # "amount":263542, # "pendingCredit":0, # "pendingDebit":0, # "confirmedDebit":0, # "prevRealisedPnl":0, # "prevUnrealisedPnl":0, # "grossComm":0, # "grossOpenCost":0, # "grossOpenPremium":0, # "grossExecCost":0, # "grossMarkValue":0, # "riskValue":0, # "taxableMargin":0, # "initMargin":0, # "maintMargin":0, # "sessionMargin":0, # "targetExcessMargin":0, # "varMargin":0, # "realisedPnl":0, # "unrealisedPnl":0, # "indicativeTax":0, # "unrealisedProfit":0, # "syntheticMargin":null, # "walletBalance":263542, # "marginBalance":263542, # "marginBalancePcnt":1, # "marginLeverage":0, # "marginUsedPcnt":0, # "excessMargin":263542, # "excessMarginPcnt":1, # "availableMargin":263542, # "withdrawableMargin":263542, # "timestamp":"2020-08-03T12:01:01.246Z", # "grossLastValue":0, # "commission":null # } # ] # return self.parse_balance(response) def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], } if limit is not None: request['depth'] = limit response = self.publicGetOrderBookL2(self.extend(request, params)) result = { 'symbol': symbol, 'bids': [], 'asks': [], 'timestamp': None, 'datetime': None, 'nonce': None, } for i in range(0, len(response)): order = response[i] side = 'asks' if (order['side'] == 'Sell') else 'bids' amount = self.safe_number(order, 'size') price = self.safe_number(order, 'price') # https://github.com/ccxt/ccxt/issues/4926 # https://github.com/ccxt/ccxt/issues/4927 # the exchange sometimes returns null price in the orderbook if price is not None: result[side].append([price, amount]) result['bids'] = self.sort_by(result['bids'], 0, True) result['asks'] = self.sort_by(result['asks'], 0) return result def fetch_order(self, id, symbol=None, params={}): filter = { 'filter': { 'orderID': id, }, } response = self.fetch_orders(symbol, None, None, self.deep_extend(filter, params)) numResults = len(response) if numResults == 1: return response[0] raise OrderNotFound(self.id + ': The order ' + id + ' not found.') def fetch_orders(self, symbol=None, since=None, limit=None, params={}): self.load_markets() market = None request = {} if symbol is not None: market = self.market(symbol) request['symbol'] = market['id'] if since is not None: request['startTime'] = self.iso8601(since) if limit is not None: request['count'] = limit request = self.deep_extend(request, params) # why the hassle? urlencode in python is kinda broken for nested dicts. # E.g. self.urlencode({"filter": {"open": True}}) will return "filter={'open':+True}" # Bitmex doesn't like that. Hence resorting to self hack. if 'filter' in request: request['filter'] = self.json(request['filter']) response = self.privateGetOrder(request) return self.parse_orders(response, market, since, limit) def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): request = { 'filter': { 'open': True, }, } return self.fetch_orders(symbol, since, limit, self.deep_extend(request, params)) def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): # Bitmex barfs if you set 'open': False in the filter... orders = self.fetch_orders(symbol, since, limit, params) return self.filter_by(orders, 'status', 'closed') def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): self.load_markets() market = None request = {} if symbol is not None: market = self.market(symbol) request['symbol'] = market['id'] if since is not None: request['startTime'] = self.iso8601(since) if limit is not None: request['count'] = limit request = self.deep_extend(request, params) # why the hassle? urlencode in python is kinda broken for nested dicts. # E.g. self.urlencode({"filter": {"open": True}}) will return "filter={'open':+True}" # Bitmex doesn't like that. Hence resorting to self hack. if 'filter' in request: request['filter'] = self.json(request['filter']) response = self.privateGetExecutionTradeHistory(request) # # [ # { # "execID": "string", # "orderID": "string", # "clOrdID": "string", # "clOrdLinkID": "string", # "account": 0, # "symbol": "string", # "side": "string", # "lastQty": 0, # "lastPx": 0, # "underlyingLastPx": 0, # "lastMkt": "string", # "lastLiquidityInd": "string", # "simpleOrderQty": 0, # "orderQty": 0, # "price": 0, # "displayQty": 0, # "stopPx": 0, # "pegOffsetValue": 0, # "pegPriceType": "string", # "currency": "string", # "settlCurrency": "string", # "execType": "string", # "ordType": "string", # "timeInForce": "string", # "execInst": "string", # "contingencyType": "string", # "exDestination": "string", # "ordStatus": "string", # "triggered": "string", # "workingIndicator": True, # "ordRejReason": "string", # "simpleLeavesQty": 0, # "leavesQty": 0, # "simpleCumQty": 0, # "cumQty": 0, # "avgPx": 0, # "commission": 0, # "tradePublishIndicator": "string", # "multiLegReportingType": "string", # "text": "string", # "trdMatchID": "string", # "execCost": 0, # "execComm": 0, # "homeNotional": 0, # "foreignNotional": 0, # "transactTime": "2019-03-05T12:47:02.762Z", # "timestamp": "2019-03-05T12:47:02.762Z" # } # ] # return self.parse_trades(response, market, since, limit) def parse_ledger_entry_type(self, type): types = { 'Withdrawal': 'transaction', 'RealisedPNL': 'margin', 'UnrealisedPNL': 'margin', 'Deposit': 'transaction', 'Transfer': 'transfer', 'AffiliatePayout': 'referral', } return self.safe_string(types, type, type) def parse_ledger_entry(self, item, currency=None): # # { # transactID: "69573da3-7744-5467-3207-89fd6efe7a47", # account: 24321, # currency: "XBt", # transactType: "Withdrawal", # "AffiliatePayout", "Transfer", "Deposit", "RealisedPNL", ... # amount: -1000000, # fee: 300000, # transactStatus: "Completed", # "Canceled", ... # address: "1Ex4fkF4NhQaQdRWNoYpqiPbDBbq18Kdd9", # tx: "3BMEX91ZhhKoWtsH9QRb5dNXnmnGpiEetA", # text: "", # transactTime: "2017-03-21T20:05:14.388Z", # walletBalance: 0, # balance after # marginBalance: null, # timestamp: "2017-03-22T13:09:23.514Z" # } # # ButMEX returns the unrealized pnl from the wallet history endpoint. # The unrealized pnl transaction has an empty timestamp. # It is not related to historical pnl it has status set to "Pending". # Therefore it's not a part of the history at all. # https://github.com/ccxt/ccxt/issues/6047 # # { # "transactID":"00000000-0000-0000-0000-000000000000", # "account":121210, # "currency":"XBt", # "transactType":"UnrealisedPNL", # "amount":-5508, # "fee":0, # "transactStatus":"Pending", # "address":"XBTUSD", # "tx":"", # "text":"", # "transactTime":null, # ←---------------------------- null # "walletBalance":139198767, # "marginBalance":139193259, # "timestamp":null # ←---------------------------- null # } # id = self.safe_string(item, 'transactID') account = self.safe_string(item, 'account') referenceId = self.safe_string(item, 'tx') referenceAccount = None type = self.parse_ledger_entry_type(self.safe_string(item, 'transactType')) currencyId = self.safe_string(item, 'currency') code = self.safe_currency_code(currencyId, currency) amount = self.safe_number(item, 'amount') if amount is not None: amount = amount / 100000000 timestamp = self.parse8601(self.safe_string(item, 'transactTime')) if timestamp is None: # https://github.com/ccxt/ccxt/issues/6047 # set the timestamp to zero, 1970 Jan 1 00:00:00 # for unrealized pnl and other transactions without a timestamp timestamp = 0 # see comments above feeCost = self.safe_number(item, 'fee', 0) if feeCost is not None: feeCost = feeCost / 100000000 fee = { 'cost': feeCost, 'currency': code, } after = self.safe_number(item, 'walletBalance') if after is not None: after = after / 100000000 before = self.sum(after, -amount) direction = None if amount < 0: direction = 'out' amount = abs(amount) else: direction = 'in' status = self.parse_transaction_status(self.safe_string(item, 'transactStatus')) return { 'id': id, 'info': item, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'direction': direction, 'account': account, 'referenceId': referenceId, 'referenceAccount': referenceAccount, 'type': type, 'currency': code, 'amount': amount, 'before': before, 'after': after, 'status': status, 'fee': fee, } def fetch_ledger(self, code=None, since=None, limit=None, params={}): self.load_markets() currency = None if code is not None: currency = self.currency(code) request = { # 'start': 123, } # # if since is not None: # # date-based pagination not supported # } # if limit is not None: request['count'] = limit response = self.privateGetUserWalletHistory(self.extend(request, params)) # # [ # { # transactID: "69573da3-7744-5467-3207-89fd6efe7a47", # account: 24321, # currency: "XBt", # transactType: "Withdrawal", # "AffiliatePayout", "Transfer", "Deposit", "RealisedPNL", ... # amount: -1000000, # fee: 300000, # transactStatus: "Completed", # "Canceled", ... # address: "1Ex4fkF4NhQaQdRWNoYpqiPbDBbq18Kdd9", # tx: "3BMEX91ZhhKoWtsH9QRb5dNXnmnGpiEetA", # text: "", # transactTime: "2017-03-21T20:05:14.388Z", # walletBalance: 0, # balance after # marginBalance: null, # timestamp: "2017-03-22T13:09:23.514Z" # } # ] # return self.parse_ledger(response, currency, since, limit) def fetch_transactions(self, code=None, since=None, limit=None, params={}): self.load_markets() request = { # 'start': 123, } # # if since is not None: # # date-based pagination not supported # } # if limit is not None: request['count'] = limit response = self.privateGetUserWalletHistory(self.extend(request, params)) transactions = self.filter_by_array(response, 'transactType', ['Withdrawal', 'Deposit'], False) currency = None if code is not None: currency = self.currency(code) return self.parse_transactions(transactions, currency, since, limit) def parse_transaction_status(self, status): statuses = { 'Canceled': 'canceled', 'Completed': 'ok', 'Pending': 'pending', } return self.safe_string(statuses, status, status) def parse_transaction(self, transaction, currency=None): # # { # 'transactID': 'ffe699c2-95ee-4c13-91f9-0faf41daec25', # 'account': 123456, # 'currency': 'XBt', # 'transactType': 'Withdrawal', # 'amount': -100100000, # 'fee': 100000, # 'transactStatus': 'Completed', # 'address': '385cR5DM96n1HvBDMzLHPYcw89fZAXULJP', # 'tx': '3BMEXabcdefghijklmnopqrstuvwxyz123', # 'text': '', # 'transactTime': '2019-01-02T01:00:00.000Z', # 'walletBalance': 99900000, # 'marginBalance': None, # 'timestamp': '2019-01-02T13:00:00.000Z' # } # id = self.safe_string(transaction, 'transactID') # For deposits, transactTime == timestamp # For withdrawals, transactTime is submission, timestamp is processed transactTime = self.parse8601(self.safe_string(transaction, 'transactTime')) timestamp = self.parse8601(self.safe_string(transaction, 'timestamp')) type = self.safe_string_lower(transaction, 'transactType') # Deposits have no from address or to address, withdrawals have both address = None addressFrom = None addressTo = None if type == 'withdrawal': address = self.safe_string(transaction, 'address') addressFrom = self.safe_string(transaction, 'tx') addressTo = address amountString = self.safe_string(transaction, 'amount') amountString = Precise.string_div(Precise.string_abs(amountString), '1e8') feeCostString = self.safe_string(transaction, 'fee') feeCostString = Precise.string_div(feeCostString, '1e8') fee = { 'cost': self.parse_number(feeCostString), 'currency': 'BTC', } status = self.safe_string(transaction, 'transactStatus') if status is not None: status = self.parse_transaction_status(status) return { 'info': transaction, 'id': id, 'txid': None, 'timestamp': transactTime, 'datetime': self.iso8601(transactTime), 'network': None, 'addressFrom': addressFrom, 'address': address, 'addressTo': addressTo, 'tagFrom': None, 'tag': None, 'tagTo': None, 'type': type, 'amount': self.parse_number(amountString), # BTC is the only currency on Bitmex 'currency': 'BTC', 'status': status, 'updated': timestamp, 'comment': None, 'fee': fee, } def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) if not market['active']: raise BadSymbol(self.id + ' fetchTicker() symbol ' + symbol + ' is not tradable') tickers = self.fetch_tickers([market['symbol']], params) ticker = self.safe_value(tickers, market['symbol']) if ticker is None: raise BadSymbol(self.id + ' fetchTicker() symbol ' + symbol + ' not found') return ticker def fetch_tickers(self, symbols=None, params={}): self.load_markets() response = self.publicGetInstrumentActiveAndIndices(params) result = {} for i in range(0, len(response)): ticker = self.parse_ticker(response[i]) symbol = self.safe_string(ticker, 'symbol') if symbol is not None: result[symbol] = ticker uniformSymbols = [] if symbols is not None: for i in range(0, len(symbols)): symbol = symbols[i] market = self.market(symbol) uniformSymbols.append(market['symbol']) return self.filter_by_array(result, 'symbol', uniformSymbols) def parse_ticker(self, ticker, market=None): # # { symbol: "ETHH19", # rootSymbol: "ETH", # state: "Open", # typ: "FFCCSX", # listing: "2018-12-17T04:00:00.000Z", # front: "2019-02-22T12:00:00.000Z", # expiry: "2019-03-29T12:00:00.000Z", # settle: "2019-03-29T12:00:00.000Z", # relistInterval: null, # inverseLeg: "", # sellLeg: "", # buyLeg: "", # optionStrikePcnt: null, # optionStrikeRound: null, # optionStrikePrice: null, # optionMultiplier: null, # positionCurrency: "ETH", # underlying: "ETH", # quoteCurrency: "XBT", # underlyingSymbol: "ETHXBT=", # reference: "BMEX", # referenceSymbol: ".BETHXBT30M", # calcInterval: null, # publishInterval: null, # publishTime: null, # maxOrderQty: 100000000, # maxPrice: 10, # lotSize: 1, # tickSize: 0.00001, # multiplier: 100000000, # settlCurrency: "XBt", # underlyingToPositionMultiplier: 1, # underlyingToSettleMultiplier: null, # quoteToSettleMultiplier: 100000000, # isQuanto: False, # isInverse: False, # initMargin: 0.02, # maintMargin: 0.01, # riskLimit: 5000000000, # riskStep: 5000000000, # limit: null, # capped: False, # taxed: True, # deleverage: True, # makerFee: -0.0005, # takerFee: 0.0025, # settlementFee: 0, # insuranceFee: 0, # fundingBaseSymbol: "", # fundingQuoteSymbol: "", # fundingPremiumSymbol: "", # fundingTimestamp: null, # fundingInterval: null, # fundingRate: null, # indicativeFundingRate: null, # rebalanceTimestamp: null, # rebalanceInterval: null, # openingTimestamp: "2019-02-13T08:00:00.000Z", # closingTimestamp: "2019-02-13T09:00:00.000Z", # sessionInterval: "2000-01-01T01:00:00.000Z", # prevClosePrice: 0.03347, # limitDownPrice: null, # limitUpPrice: null, # bankruptLimitDownPrice: null, # bankruptLimitUpPrice: null, # prevTotalVolume: 1386531, # totalVolume: 1387062, # volume: 531, # volume24h: 17118, # prevTotalTurnover: 4741294246000, # totalTurnover: 4743103466000, # turnover: 1809220000, # turnover24h: 57919845000, # homeNotional24h: 17118, # foreignNotional24h: 579.19845, # prevPrice24h: 0.03349, # vwap: 0.03383564, # highPrice: 0.03458, # lowPrice: 0.03329, # lastPrice: 0.03406, # lastPriceProtected: 0.03406, # lastTickDirection: "ZeroMinusTick", # lastChangePcnt: 0.017, # bidPrice: 0.03406, # midPrice: 0.034065, # askPrice: 0.03407, # impactBidPrice: 0.03406, # impactMidPrice: 0.034065, # impactAskPrice: 0.03407, # hasLiquidity: True, # openInterest: 83679, # openValue: 285010674000, # fairMethod: "ImpactMidPrice", # fairBasisRate: 0, # fairBasis: 0, # fairPrice: 0.03406, # markMethod: "FairPrice", # markPrice: 0.03406, # indicativeTaxRate: 0, # indicativeSettlePrice: 0.03406, # optionUnderlyingPrice: null, # settledPrice: null, # timestamp: "2019-02-13T08:40:30.000Z", # } # marketId = self.safe_string(ticker, 'symbol') symbol = self.safe_symbol(marketId, market) timestamp = self.parse8601(self.safe_string(ticker, 'timestamp')) open = self.safe_string(ticker, 'prevPrice24h') last = self.safe_string(ticker, 'lastPrice') return self.safe_ticker({ 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_string(ticker, 'highPrice'), 'low': self.safe_string(ticker, 'lowPrice'), 'bid': self.safe_string(ticker, 'bidPrice'), 'bidVolume': None, 'ask': self.safe_string(ticker, 'askPrice'), 'askVolume': None, 'vwap': self.safe_string(ticker, 'vwap'), 'open': open, 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': None, 'average': None, 'baseVolume': self.safe_string(ticker, 'homeNotional24h'), 'quoteVolume': self.safe_string(ticker, 'foreignNotional24h'), 'info': ticker, }, market, False) def parse_ohlcv(self, ohlcv, market=None): # # { # "timestamp":"2015-09-25T13:38:00.000Z", # "symbol":"XBTUSD", # "open":237.45, # "high":237.45, # "low":237.45, # "close":237.45, # "trades":0, # "volume":0, # "vwap":null, # "lastSize":null, # "turnover":0, # "homeNotional":0, # "foreignNotional":0 # } # return [ self.parse8601(self.safe_string(ohlcv, 'timestamp')), self.safe_number(ohlcv, 'open'), self.safe_number(ohlcv, 'high'), self.safe_number(ohlcv, 'low'), self.safe_number(ohlcv, 'close'), self.safe_number(ohlcv, 'volume'), ] def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): self.load_markets() # send JSON key/value pairs, such as {"key": "value"} # filter by individual fields and do advanced queries on timestamps # filter = {'key': 'value'} # send a bare series(e.g. XBU) to nearest expiring contract in that series # you can also send a timeframe, e.g. XBU:monthly # timeframes: daily, weekly, monthly, quarterly, and biquarterly market = self.market(symbol) request = { 'symbol': market['id'], 'binSize': self.timeframes[timeframe], 'partial': True, # True == include yet-incomplete current bins # 'filter': filter, # filter by individual fields and do advanced queries # 'columns': [], # will return all columns if omitted # 'start': 0, # starting point for results(wtf?) # 'reverse': False, # True == newest first # 'endTime': '', # ending date filter for results } if limit is not None: request['count'] = limit # default 100, max 500 duration = self.parse_timeframe(timeframe) * 1000 fetchOHLCVOpenTimestamp = self.safe_value(self.options, 'fetchOHLCVOpenTimestamp', True) # if since is not set, they will return candles starting from 2017-01-01 if since is not None: timestamp = since if fetchOHLCVOpenTimestamp: timestamp = self.sum(timestamp, duration) ymdhms = self.ymdhms(timestamp) request['startTime'] = ymdhms # starting date filter for results else: request['reverse'] = True response = self.publicGetTradeBucketed(self.extend(request, params)) # # [ # {"timestamp":"2015-09-25T13:38:00.000Z","symbol":"XBTUSD","open":237.45,"high":237.45,"low":237.45,"close":237.45,"trades":0,"volume":0,"vwap":null,"lastSize":null,"turnover":0,"homeNotional":0,"foreignNotional":0}, # {"timestamp":"2015-09-25T13:39:00.000Z","symbol":"XBTUSD","open":237.45,"high":237.45,"low":237.45,"close":237.45,"trades":0,"volume":0,"vwap":null,"lastSize":null,"turnover":0,"homeNotional":0,"foreignNotional":0}, # {"timestamp":"2015-09-25T13:40:00.000Z","symbol":"XBTUSD","open":237.45,"high":237.45,"low":237.45,"close":237.45,"trades":0,"volume":0,"vwap":null,"lastSize":null,"turnover":0,"homeNotional":0,"foreignNotional":0} # ] # result = self.parse_ohlcvs(response, market, timeframe, since, limit) if fetchOHLCVOpenTimestamp: # bitmex returns the candle's close timestamp - https://github.com/ccxt/ccxt/issues/4446 # we can emulate the open timestamp by shifting all the timestamps one place # so the previous close becomes the current open, and we drop the first candle for i in range(0, len(result)): result[i][0] = result[i][0] - duration return result def parse_trade(self, trade, market=None): # # fetchTrades(public) # # { # timestamp: '2018-08-28T00:00:02.735Z', # symbol: 'XBTUSD', # side: 'Buy', # size: 2000, # price: 6906.5, # tickDirection: 'PlusTick', # trdMatchID: 'b9a42432-0a46-6a2f-5ecc-c32e9ca4baf8', # grossValue: 28958000, # homeNotional: 0.28958, # foreignNotional: 2000 # } # # fetchMyTrades(private) # # { # "execID": "string", # "orderID": "string", # "clOrdID": "string", # "clOrdLinkID": "string", # "account": 0, # "symbol": "string", # "side": "string", # "lastQty": 0, # "lastPx": 0, # "underlyingLastPx": 0, # "lastMkt": "string", # "lastLiquidityInd": "string", # "simpleOrderQty": 0, # "orderQty": 0, # "price": 0, # "displayQty": 0, # "stopPx": 0, # "pegOffsetValue": 0, # "pegPriceType": "string", # "currency": "string", # "settlCurrency": "string", # "execType": "string", # "ordType": "string", # "timeInForce": "string", # "execInst": "string", # "contingencyType": "string", # "exDestination": "string", # "ordStatus": "string", # "triggered": "string", # "workingIndicator": True, # "ordRejReason": "string", # "simpleLeavesQty": 0, # "leavesQty": 0, # "simpleCumQty": 0, # "cumQty": 0, # "avgPx": 0, # "commission": 0, # "tradePublishIndicator": "string", # "multiLegReportingType": "string", # "text": "string", # "trdMatchID": "string", # "execCost": 0, # "execComm": 0, # "homeNotional": 0, # "foreignNotional": 0, # "transactTime": "2019-03-05T12:47:02.762Z", # "timestamp": "2019-03-05T12:47:02.762Z" # } # timestamp = self.parse8601(self.safe_string(trade, 'timestamp')) priceString = self.safe_string_2(trade, 'avgPx', 'price') amountString = self.safe_string_2(trade, 'size', 'lastQty') execCost = self.safe_string(trade, 'execCost') costString = Precise.string_div(Precise.string_abs(execCost), '1e8') id = self.safe_string(trade, 'trdMatchID') order = self.safe_string(trade, 'orderID') side = self.safe_string_lower(trade, 'side') # price * amount doesn't work for all symbols(e.g. XBT, ETH) fee = None feeCostString = Precise.string_div(self.safe_string(trade, 'execComm'), '1e8') if feeCostString is not None: currencyId = self.safe_string(trade, 'settlCurrency') feeCurrencyCode = self.safe_currency_code(currencyId) feeRateString = self.safe_string(trade, 'commission') fee = { 'cost': feeCostString, 'currency': feeCurrencyCode, 'rate': feeRateString, } # Trade or Funding execType = self.safe_string(trade, 'execType') takerOrMaker = None if feeCostString is not None and execType == 'Trade': takerOrMaker = 'maker' if Precise.string_lt(feeCostString, '0') else 'taker' marketId = self.safe_string(trade, 'symbol') symbol = self.safe_symbol(marketId, market) type = self.safe_string_lower(trade, 'ordType') return self.safe_trade({ 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': id, 'order': order, 'type': type, 'takerOrMaker': takerOrMaker, 'side': side, 'price': priceString, 'cost': costString, 'amount': amountString, 'fee': fee, }, market) def parse_order_status(self, status): statuses = { 'New': 'open', 'PartiallyFilled': 'open', 'Filled': 'closed', 'DoneForDay': 'open', 'Canceled': 'canceled', 'PendingCancel': 'open', 'PendingNew': 'open', 'Rejected': 'rejected', 'Expired': 'expired', 'Stopped': 'open', 'Untriggered': 'open', 'Triggered': 'open', } return self.safe_string(statuses, status, status) def parse_time_in_force(self, timeInForce): timeInForces = { 'Day': 'Day', 'GoodTillCancel': 'GTC', 'ImmediateOrCancel': 'IOC', 'FillOrKill': 'FOK', } return self.safe_string(timeInForces, timeInForce, timeInForce) def parse_order(self, order, market=None): # # { # "orderID":"56222c7a-9956-413a-82cf-99f4812c214b", # "clOrdID":"", # "clOrdLinkID":"", # "account":1455728, # "symbol":"XBTUSD", # "side":"Sell", # "simpleOrderQty":null, # "orderQty":1, # "price":40000, # "displayQty":null, # "stopPx":null, # "pegOffsetValue":null, # "pegPriceType":"", # "currency":"USD", # "settlCurrency":"XBt", # "ordType":"Limit", # "timeInForce":"GoodTillCancel", # "execInst":"", # "contingencyType":"", # "exDestination":"XBME", # "ordStatus":"New", # "triggered":"", # "workingIndicator":true, # "ordRejReason":"", # "simpleLeavesQty":null, # "leavesQty":1, # "simpleCumQty":null, # "cumQty":0, # "avgPx":null, # "multiLegReportingType":"SingleSecurity", # "text":"Submitted via API.", # "transactTime":"2021-01-02T21:38:49.246Z", # "timestamp":"2021-01-02T21:38:49.246Z" # } # status = self.parse_order_status(self.safe_string(order, 'ordStatus')) marketId = self.safe_string(order, 'symbol') symbol = self.safe_symbol(marketId, market) timestamp = self.parse8601(self.safe_string(order, 'timestamp')) lastTradeTimestamp = self.parse8601(self.safe_string(order, 'transactTime')) price = self.safe_string(order, 'price') amount = self.safe_string(order, 'orderQty') filled = self.safe_string(order, 'cumQty', 0.0) average = self.safe_string(order, 'avgPx') id = self.safe_string(order, 'orderID') type = self.safe_string_lower(order, 'ordType') side = self.safe_string_lower(order, 'side') clientOrderId = self.safe_string(order, 'clOrdID') timeInForce = self.parse_time_in_force(self.safe_string(order, 'timeInForce')) stopPrice = self.safe_number(order, 'stopPx') execInst = self.safe_string(order, 'execInst') postOnly = (execInst == 'ParticipateDoNotInitiate') return self.safe_order({ 'info': order, 'id': id, 'clientOrderId': clientOrderId, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': lastTradeTimestamp, 'symbol': symbol, 'type': type, 'timeInForce': timeInForce, 'postOnly': postOnly, 'side': side, 'price': price, 'stopPrice': stopPrice, 'amount': amount, 'cost': None, 'average': average, 'filled': filled, 'remaining': None, 'status': status, 'fee': None, 'trades': None, }, market) def fetch_trades(self, symbol, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], } if since is not None: request['startTime'] = self.iso8601(since) else: # by default reverse=false, i.e. trades are fetched since the time of market inception(year 2015 for XBTUSD) request['reverse'] = True if limit is not None: request['count'] = limit response = self.publicGetTrade(self.extend(request, params)) # # [ # { # timestamp: '2018-08-28T00:00:02.735Z', # symbol: 'XBTUSD', # side: 'Buy', # size: 2000, # price: 6906.5, # tickDirection: 'PlusTick', # trdMatchID: 'b9a42432-0a46-6a2f-5ecc-c32e9ca4baf8', # grossValue: 28958000, # homeNotional: 0.28958, # foreignNotional: 2000 # }, # { # timestamp: '2018-08-28T00:00:03.778Z', # symbol: 'XBTUSD', # side: 'Sell', # size: 1000, # price: 6906, # tickDirection: 'MinusTick', # trdMatchID: '0d4f1682-5270-a800-569b-4a0eb92db97c', # grossValue: 14480000, # homeNotional: 0.1448, # foreignNotional: 1000 # }, # ] # return self.parse_trades(response, market, since, limit) def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() market = self.market(symbol) orderType = self.capitalize(type) request = { 'symbol': market['id'], 'side': self.capitalize(side), 'orderQty': float(self.amount_to_precision(symbol, amount)), 'ordType': orderType, } if (orderType == 'Stop') or (orderType == 'StopLimit') or (orderType == 'MarketIfTouched') or (orderType == 'LimitIfTouched'): stopPrice = self.safe_number_2(params, 'stopPx', 'stopPrice') if stopPrice is None: raise ArgumentsRequired(self.id + ' createOrder() requires a stopPx or stopPrice parameter for the ' + orderType + ' order type') else: request['stopPx'] = float(self.price_to_precision(symbol, stopPrice)) params = self.omit(params, ['stopPx', 'stopPrice']) if (orderType == 'Limit') or (orderType == 'StopLimit') or (orderType == 'LimitIfTouched'): request['price'] = float(self.price_to_precision(symbol, price)) clientOrderId = self.safe_string_2(params, 'clOrdID', 'clientOrderId') if clientOrderId is not None: request['clOrdID'] = clientOrderId params = self.omit(params, ['clOrdID', 'clientOrderId']) response = self.privatePostOrder(self.extend(request, params)) return self.parse_order(response, market) def edit_order(self, id, symbol, type, side, amount=None, price=None, params={}): self.load_markets() request = {} origClOrdID = self.safe_string_2(params, 'origClOrdID', 'clientOrderId') if origClOrdID is not None: request['origClOrdID'] = origClOrdID clientOrderId = self.safe_string(params, 'clOrdID', 'clientOrderId') if clientOrderId is not None: request['clOrdID'] = clientOrderId params = self.omit(params, ['origClOrdID', 'clOrdID', 'clientOrderId']) else: request['orderID'] = id if amount is not None: request['orderQty'] = amount if price is not None: request['price'] = price response = self.privatePutOrder(self.extend(request, params)) return self.parse_order(response) def cancel_order(self, id, symbol=None, params={}): self.load_markets() # https://github.com/ccxt/ccxt/issues/6507 clientOrderId = self.safe_value_2(params, 'clOrdID', 'clientOrderId') request = {} if clientOrderId is None: request['orderID'] = id else: request['clOrdID'] = clientOrderId params = self.omit(params, ['clOrdID', 'clientOrderId']) response = self.privateDeleteOrder(self.extend(request, params)) order = self.safe_value(response, 0, {}) error = self.safe_string(order, 'error') if error is not None: if error.find('Unable to cancel order due to existing state') >= 0: raise OrderNotFound(self.id + ' cancelOrder() failed: ' + error) return self.parse_order(order) def cancel_orders(self, ids, symbol=None, params={}): return self.cancel_order(ids, symbol, params) def cancel_all_orders(self, symbol=None, params={}): self.load_markets() request = {} market = None if symbol is not None: market = self.market(symbol) request['symbol'] = market['id'] response = self.privateDeleteOrderAll(self.extend(request, params)) # # [ # { # "orderID": "string", # "clOrdID": "string", # "clOrdLinkID": "string", # "account": 0, # "symbol": "string", # "side": "string", # "simpleOrderQty": 0, # "orderQty": 0, # "price": 0, # "displayQty": 0, # "stopPx": 0, # "pegOffsetValue": 0, # "pegPriceType": "string", # "currency": "string", # "settlCurrency": "string", # "ordType": "string", # "timeInForce": "string", # "execInst": "string", # "contingencyType": "string", # "exDestination": "string", # "ordStatus": "string", # "triggered": "string", # "workingIndicator": True, # "ordRejReason": "string", # "simpleLeavesQty": 0, # "leavesQty": 0, # "simpleCumQty": 0, # "cumQty": 0, # "avgPx": 0, # "multiLegReportingType": "string", # "text": "string", # "transactTime": "2020-06-01T09:36:35.290Z", # "timestamp": "2020-06-01T09:36:35.290Z" # } # ] # return self.parse_orders(response, market) def fetch_positions(self, symbols=None, params={}): self.load_markets() response = self.privateGetPosition(params) # [ # { # "account": 0, # "symbol": "string", # "currency": "string", # "underlying": "string", # "quoteCurrency": "string", # "commission": 0, # "initMarginReq": 0, # "maintMarginReq": 0, # "riskLimit": 0, # "leverage": 0, # "crossMargin": True, # "deleveragePercentile": 0, # "rebalancedPnl": 0, # "prevRealisedPnl": 0, # "prevUnrealisedPnl": 0, # "prevClosePrice": 0, # "openingTimestamp": "2020-11-09T06:53:59.892Z", # "openingQty": 0, # "openingCost": 0, # "openingComm": 0, # "openOrderBuyQty": 0, # "openOrderBuyCost": 0, # "openOrderBuyPremium": 0, # "openOrderSellQty": 0, # "openOrderSellCost": 0, # "openOrderSellPremium": 0, # "execBuyQty": 0, # "execBuyCost": 0, # "execSellQty": 0, # "execSellCost": 0, # "execQty": 0, # "execCost": 0, # "execComm": 0, # "currentTimestamp": "2020-11-09T06:53:59.893Z", # "currentQty": 0, # "currentCost": 0, # "currentComm": 0, # "realisedCost": 0, # "unrealisedCost": 0, # "grossOpenCost": 0, # "grossOpenPremium": 0, # "grossExecCost": 0, # "isOpen": True, # "markPrice": 0, # "markValue": 0, # "riskValue": 0, # "homeNotional": 0, # "foreignNotional": 0, # "posState": "string", # "posCost": 0, # "posCost2": 0, # "posCross": 0, # "posInit": 0, # "posComm": 0, # "posLoss": 0, # "posMargin": 0, # "posMaint": 0, # "posAllowance": 0, # "taxableMargin": 0, # "initMargin": 0, # "maintMargin": 0, # "sessionMargin": 0, # "targetExcessMargin": 0, # "varMargin": 0, # "realisedGrossPnl": 0, # "realisedTax": 0, # "realisedPnl": 0, # "unrealisedGrossPnl": 0, # "longBankrupt": 0, # "shortBankrupt": 0, # "taxBase": 0, # "indicativeTaxRate": 0, # "indicativeTax": 0, # "unrealisedTax": 0, # "unrealisedPnl": 0, # "unrealisedPnlPcnt": 0, # "unrealisedRoePcnt": 0, # "simpleQty": 0, # "simpleCost": 0, # "simpleValue": 0, # "simplePnl": 0, # "simplePnlPcnt": 0, # "avgCostPrice": 0, # "avgEntryPrice": 0, # "breakEvenPrice": 0, # "marginCallPrice": 0, # "liquidationPrice": 0, # "bankruptPrice": 0, # "timestamp": "2020-11-09T06:53:59.894Z", # "lastPrice": 0, # "lastValue": 0 # } # ] # # todo unify parsePosition/parsePositions return response def is_fiat(self, currency): if currency == 'EUR': return True if currency == 'PLN': return True return False def withdraw(self, code, amount, address, tag=None, params={}): tag, params = self.handle_withdraw_tag_and_params(tag, params) self.check_address(address) self.load_markets() # currency = self.currency(code) if code != 'BTC': raise ExchangeError(self.id + ' supoprts BTC withdrawals only, other currencies coming soon...') request = { 'currency': 'XBt', # temporarily 'amount': amount, 'address': address, # 'otpToken': '123456', # requires if two-factor auth(OTP) is enabled # 'fee': 0.001, # bitcoin network fee } response = self.privatePostUserRequestWithdrawal(self.extend(request, params)) return { 'info': response, 'id': self.safe_string(response, 'transactID'), } def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody): if response is None: return if code == 429: raise DDoSProtection(self.id + ' ' + body) if code >= 400: error = self.safe_value(response, 'error', {}) message = self.safe_string(error, 'message') feedback = self.id + ' ' + body self.throw_exactly_matched_exception(self.exceptions['exact'], message, feedback) self.throw_broadly_matched_exception(self.exceptions['broad'], message, feedback) if code == 400: raise BadRequest(feedback) raise ExchangeError(feedback) # unknown message def nonce(self): return self.milliseconds() def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): query = '/api/' + self.version + '/' + path if method == 'GET': if params: query += '?' + self.urlencode(params) else: format = self.safe_string(params, '_format') if format is not None: query += '?' + self.urlencode({'_format': format}) params = self.omit(params, '_format') url = self.urls['api'][api] + query if api == 'private': self.check_required_credentials() auth = method + query expires = self.safe_integer(self.options, 'api-expires') headers = { 'Content-Type': 'application/json', 'api-key': self.apiKey, } expires = self.sum(self.seconds(), expires) expires = str(expires) auth += expires headers['api-expires'] = expires if method == 'POST' or method == 'PUT' or method == 'DELETE': if params: body = self.json(params) auth += body headers['api-signature'] = self.hmac(self.encode(auth), self.encode(self.secret)) return {'url': url, 'method': method, 'body': body, 'headers': headers}

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ge import Exchange from ccxt.base.errors import ExchangeError from ccxt.base.errors import AuthenticationError from ccxt.base.errors import PermissionDenied from ccxt.base.errors import ArgumentsRequired from ccxt.base.errors import BadRequest from ccxt.base.errors import BadSymbol from ccxt.base.errors import InsufficientFunds from ccxt.base.errors import InvalidOrder from ccxt.base.errors import OrderNotFound from ccxt.base.errors import DDoSProtection from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.decimal_to_precision import TICK_SIZE from ccxt.base.precise import Precise class bitmex(Exchange): def describe(self): return self.deep_extend(super(bitmex, self).describe(), { 'id': 'bitmex', 'name': 'BitMEX', 'countries': ['SC'], 'version': 'v1', 'userAgent': None, 'rateLimit': 2000, 'pro': True, 'has': { 'CORS': None, 'spot': False, 'margin': False, 'swap': None, 'future': None, 'option': None, 'cancelAllOrders': True, 'cancelOrder': True, 'cancelOrders': True, 'createOrder': True, 'editOrder': True, 'fetchBalance': True, 'fetchClosedOrders': True, 'fetchIndexOHLCV': False, 'fetchLedger': True, 'fetchLeverageTiers': False, 'fetchMarkets': True, 'fetchMarkOHLCV': False, 'fetchMyTrades': True, 'fetchOHLCV': True, 'fetchOpenOrders': True, 'fetchOrder': True, 'fetchOrderBook': True, 'fetchOrders': True, 'fetchPositions': True, 'fetchPremiumIndexOHLCV': False, 'fetchTicker': True, 'fetchTickers': True, 'fetchTrades': True, 'fetchTransactions': 'emulated', 'withdraw': True, }, 'timeframes': { '1m': '1m', '5m': '5m', '1h': '1h', '1d': '1d', }, 'urls': { 'test': { 'public': 'https://testnet.bitmex.com', 'private': 'https://testnet.bitmex.com', }, 'logo': 'https://user-images.githubusercontent.com/1294454/27766319-f653c6e6-5ed4-11e7-933d-f0bc3699ae8f.jpg', 'api': { 'public': 'https://www.bitmex.com', 'private': 'https://www.bitmex.com', }, 'www': 'https://www.bitmex.com', 'doc': [ 'https://www.bitmex.com/app/apiOverview', 'https://github.com/BitMEX/api-connectors/tree/master/official-http', ], 'fees': 'https://www.bitmex.com/app/fees', 'referral': 'https://www.bitmex.com/register/upZpOX', }, 'api': { 'public': { 'get': [ 'announcement', 'announcement/urgent', 'funding', 'instrument', 'instrument/active', 'instrument/activeAndIndices', 'instrument/activeIntervals', 'instrument/compositeIndex', 'instrument/indices', 'insurance', 'leaderboard', 'liquidation', 'orderBook', 'orderBook/L2', 'quote', 'quote/bucketed', 'schema', 'schema/websocketHelp', 'settlement', 'stats', 'stats/history', 'trade', 'trade/bucketed', ], }, 'private': { 'get': [ 'apiKey', 'chat', 'chat/channels', 'chat/connected', 'execution', 'execution/tradeHistory', 'notification', 'order', 'position', 'user', 'user/affiliateStatus', 'user/checkReferralCode', 'user/commission', 'user/depositAddress', 'user/executionHistory', 'user/margin', 'user/minWithdrawalFee', 'user/wallet', 'user/walletHistory', 'user/walletSummary', ], 'post': [ 'apiKey', 'apiKey/disable', 'apiKey/enable', 'chat', 'order', 'order/bulk', 'order/cancelAllAfter', 'order/closePosition', 'position/isolate', 'position/leverage', 'position/riskLimit', 'position/transferMargin', 'user/cancelWithdrawal', 'user/confirmEmail', 'user/confirmEnableTFA', 'user/confirmWithdrawal', 'user/disableTFA', 'user/logout', 'user/logoutAll', 'user/preferences', 'user/requestEnableTFA', 'user/requestWithdrawal', ], 'put': [ 'order', 'order/bulk', 'user', ], 'delete': [ 'apiKey', 'order', 'order/all', ], }, }, 'exceptions': { 'exact': { 'Invalid API Key.': AuthenticationError, 'This key is disabled.': PermissionDenied, 'Access Denied': PermissionDenied, 'Duplicate clOrdID': InvalidOrder, 'orderQty is invalid': InvalidOrder, 'Invalid price': InvalidOrder, 'Invalid stopPx for ordType': InvalidOrder, }, 'broad': { 'Signature not valid': AuthenticationError, 'overloaded': ExchangeNotAvailable, 'Account has insufficient Available Balance': InsufficientFunds, 'Service unavailable': ExchangeNotAvailable, 'Server Error': ExchangeError, 'Unable to cancel order due to existing state': InvalidOrder, }, }, 'precisionMode': TICK_SIZE, 'options': { 'api-expires': 5, 'fetchOHLCVOpenTimestamp': True, }, 'commonCurrencies': { 'USDt': 'USDT', 'XBt': 'BTC', 'XBT': 'BTC', }, }) def fetch_markets(self, params={}): response = self.publicGetInstrumentActiveAndIndices(params) result = [] for i in range(0, len(response)): market = response[i] id = self.safe_string(market, 'symbol') baseId = self.safe_string(market, 'underlying') quoteId = self.safe_string(market, 'quoteCurrency') settleId = self.safe_string(market, 'settlCurrency') base = self.safe_currency_code(baseId) quote = self.safe_currency_code(quoteId) settle = self.safe_currency_code(settleId) basequote = baseId + quoteId swap = (id == basequote) type = None future = False prediction = False index = False symbol = base + '/' + quote + ':' + settle expiryDatetime = self.safe_string(market, 'expiry') expiry = self.parse8601(expiryDatetime) inverse = self.safe_value(market, 'isInverse') status = self.safe_string(market, 'state') active = status != 'Unlisted' if swap: type = 'swap' elif id.find('B_') >= 0: prediction = True type = 'prediction' symbol = id elif expiry is not None: future = True type = 'future' symbol = symbol + '-' + self.yymmdd(expiry) else: index = True type = 'index' symbol = id active = False positionId = self.safe_string_2(market, 'positionCurrency', 'quoteCurrency') position = self.safe_currency_code(positionId) positionIsQuote = (position == quote) maxOrderQty = self.safe_number(market, 'maxOrderQty') contract = not index initMargin = self.safe_string(market, 'initMargin', '1') maxLeverage = self.parse_number(Precise.string_div('1', initMargin)) result.append({ 'id': id, 'symbol': symbol, 'base': base, 'quote': quote, 'settle': settle, 'baseId': baseId, 'quoteId': quoteId, 'settleId': settleId, 'type': type, 'spot': False, 'margin': False, 'swap': swap, 'future': future, 'option': False, 'prediction': prediction, 'index': index, 'active': active, 'contract': contract, 'linear': not inverse if contract else None, 'inverse': inverse if contract else None, 'taker': self.safe_number(market, 'takerFee'), 'maker': self.safe_number(market, 'makerFee'), 'contractSize': self.safe_number(market, 'multiplier'), 'expiry': expiry, 'expiryDatetime': expiryDatetime, 'strike': self.safe_number(market, 'optionStrikePrice'), 'optionType': None, 'precision': { 'amount': self.safe_number(market, 'lotSize'), 'price': self.safe_number(market, 'tickSize'), }, 'limits': { 'leverage': { 'min': self.parse_number('1') if contract else None, 'max': maxLeverage if contract else None, }, 'amount': { 'min': None, 'max': None if positionIsQuote else maxOrderQty, }, 'price': { 'min': None, 'max': self.safe_number(market, 'maxPrice'), }, 'cost': { 'min': None, 'max': maxOrderQty if positionIsQuote else None, }, }, 'info': market, }) return result def parse_balance(self, response): # # [ # { # "account":1455728, # "currency":"XBt", # "riskLimit":1000000000000, # "prevState":"", # "state":"", # "action":"", # "amount":263542, # "pendingCredit":0, # "pendingDebit":0, # "confirmedDebit":0, # "prevRealisedPnl":0, # "prevUnrealisedPnl":0, # "grossComm":0, # "grossOpenCost":0, # "grossOpenPremium":0, # "grossExecCost":0, # "grossMarkValue":0, # "riskValue":0, # "taxableMargin":0, # "initMargin":0, # "maintMargin":0, # "sessionMargin":0, # "targetExcessMargin":0, # "varMargin":0, # "realisedPnl":0, # "unrealisedPnl":0, # "indicativeTax":0, # "unrealisedProfit":0, # "syntheticMargin":null, # "walletBalance":263542, # "marginBalance":263542, # "marginBalancePcnt":1, # "marginLeverage":0, # "marginUsedPcnt":0, # "excessMargin":263542, # "excessMarginPcnt":1, # "availableMargin":263542, # "withdrawableMargin":263542, # "timestamp":"2020-08-03T12:01:01.246Z", # "grossLastValue":0, # "commission":null # } # ] # result = {'info': response} for i in range(0, len(response)): balance = response[i] currencyId = self.safe_string(balance, 'currency') code = self.safe_currency_code(currencyId) account = self.account() free = self.safe_string(balance, 'availableMargin') total = self.safe_string(balance, 'marginBalance') if code == 'BTC': free = Precise.string_div(free, '1e8') total = Precise.string_div(total, '1e8') account['free'] = free account['total'] = total result[code] = account return self.safe_balance(result) def fetch_balance(self, params={}): self.load_markets() request = { 'currency': 'all', } response = self.privateGetUserMargin(self.extend(request, params)) # # [ # { # "account":1455728, # "currency":"XBt", # "riskLimit":1000000000000, # "prevState":"", # "state":"", # "action":"", # "amount":263542, # "pendingCredit":0, # "pendingDebit":0, # "confirmedDebit":0, # "prevRealisedPnl":0, # "prevUnrealisedPnl":0, # "grossComm":0, # "grossOpenCost":0, # "grossOpenPremium":0, # "grossExecCost":0, # "grossMarkValue":0, # "riskValue":0, # "taxableMargin":0, # "initMargin":0, # "maintMargin":0, # "sessionMargin":0, # "targetExcessMargin":0, # "varMargin":0, # "realisedPnl":0, # "unrealisedPnl":0, # "indicativeTax":0, # "unrealisedProfit":0, # "syntheticMargin":null, # "walletBalance":263542, # "marginBalance":263542, # "marginBalancePcnt":1, # "marginLeverage":0, # "marginUsedPcnt":0, # "excessMargin":263542, # "excessMarginPcnt":1, # "availableMargin":263542, # "withdrawableMargin":263542, # "timestamp":"2020-08-03T12:01:01.246Z", # "grossLastValue":0, # "commission":null # } # ] # return self.parse_balance(response) def fetch_order_book(self, symbol, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], } if limit is not None: request['depth'] = limit response = self.publicGetOrderBookL2(self.extend(request, params)) result = { 'symbol': symbol, 'bids': [], 'asks': [], 'timestamp': None, 'datetime': None, 'nonce': None, } for i in range(0, len(response)): order = response[i] side = 'asks' if (order['side'] == 'Sell') else 'bids' amount = self.safe_number(order, 'size') price = self.safe_number(order, 'price') # https://github.com/ccxt/ccxt/issues/4926 # https://github.com/ccxt/ccxt/issues/4927 # the exchange sometimes returns null price in the orderbook if price is not None: result[side].append([price, amount]) result['bids'] = self.sort_by(result['bids'], 0, True) result['asks'] = self.sort_by(result['asks'], 0) return result def fetch_order(self, id, symbol=None, params={}): filter = { 'filter': { 'orderID': id, }, } response = self.fetch_orders(symbol, None, None, self.deep_extend(filter, params)) numResults = len(response) if numResults == 1: return response[0] raise OrderNotFound(self.id + ': The order ' + id + ' not found.') def fetch_orders(self, symbol=None, since=None, limit=None, params={}): self.load_markets() market = None request = {} if symbol is not None: market = self.market(symbol) request['symbol'] = market['id'] if since is not None: request['startTime'] = self.iso8601(since) if limit is not None: request['count'] = limit request = self.deep_extend(request, params) # why the hassle? urlencode in python is kinda broken for nested dicts. # E.g. self.urlencode({"filter": {"open": True}}) will return "filter={'open':+True}" # Bitmex doesn't like that. Hence resorting to self hack. if 'filter' in request: request['filter'] = self.json(request['filter']) response = self.privateGetOrder(request) return self.parse_orders(response, market, since, limit) def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}): request = { 'filter': { 'open': True, }, } return self.fetch_orders(symbol, since, limit, self.deep_extend(request, params)) def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}): orders = self.fetch_orders(symbol, since, limit, params) return self.filter_by(orders, 'status', 'closed') def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}): self.load_markets() market = None request = {} if symbol is not None: market = self.market(symbol) request['symbol'] = market['id'] if since is not None: request['startTime'] = self.iso8601(since) if limit is not None: request['count'] = limit request = self.deep_extend(request, params) if 'filter' in request: request['filter'] = self.json(request['filter']) response = self.privateGetExecutionTradeHistory(request) # # [ # { # "execID": "string", # "orderID": "string", # "clOrdID": "string", # "clOrdLinkID": "string", # "account": 0, # "symbol": "string", # "side": "string", # "lastQty": 0, # "lastPx": 0, # "underlyingLastPx": 0, # "lastMkt": "string", # "lastLiquidityInd": "string", # "simpleOrderQty": 0, # "orderQty": 0, # "price": 0, # "displayQty": 0, # "stopPx": 0, # "pegOffsetValue": 0, # "pegPriceType": "string", # "currency": "string", # "settlCurrency": "string", # "execType": "string", # "ordType": "string", # "timeInForce": "string", # "execInst": "string", # "contingencyType": "string", # "exDestination": "string", # "ordStatus": "string", # "triggered": "string", # "workingIndicator": True, # "ordRejReason": "string", # "simpleLeavesQty": 0, # "leavesQty": 0, # "simpleCumQty": 0, # "cumQty": 0, # "avgPx": 0, # "commission": 0, # "tradePublishIndicator": "string", # "multiLegReportingType": "string", # "text": "string", # "trdMatchID": "string", # "execCost": 0, # "execComm": 0, # "homeNotional": 0, # "foreignNotional": 0, # "transactTime": "2019-03-05T12:47:02.762Z", # "timestamp": "2019-03-05T12:47:02.762Z" # } # ] # return self.parse_trades(response, market, since, limit) def parse_ledger_entry_type(self, type): types = { 'Withdrawal': 'transaction', 'RealisedPNL': 'margin', 'UnrealisedPNL': 'margin', 'Deposit': 'transaction', 'Transfer': 'transfer', 'AffiliatePayout': 'referral', } return self.safe_string(types, type, type) def parse_ledger_entry(self, item, currency=None): # # { # transactID: "69573da3-7744-5467-3207-89fd6efe7a47", # account: 24321, # currency: "XBt", # transactType: "Withdrawal", # "AffiliatePayout", "Transfer", "Deposit", "RealisedPNL", ... # amount: -1000000, # fee: 300000, # transactStatus: "Completed", # "Canceled", ... # address: "1Ex4fkF4NhQaQdRWNoYpqiPbDBbq18Kdd9", # tx: "3BMEX91ZhhKoWtsH9QRb5dNXnmnGpiEetA", # text: "", # transactTime: "2017-03-21T20:05:14.388Z", # walletBalance: 0, # balance after # marginBalance: null, # timestamp: "2017-03-22T13:09:23.514Z" # } # # ButMEX returns the unrealized pnl from the wallet history endpoint. # The unrealized pnl transaction has an empty timestamp. # It is not related to historical pnl it has status set to "Pending". # Therefore it's not a part of the history at all. ing(item, 'transactID') account = self.safe_string(item, 'account') referenceId = self.safe_string(item, 'tx') referenceAccount = None type = self.parse_ledger_entry_type(self.safe_string(item, 'transactType')) currencyId = self.safe_string(item, 'currency') code = self.safe_currency_code(currencyId, currency) amount = self.safe_number(item, 'amount') if amount is not None: amount = amount / 100000000 timestamp = self.parse8601(self.safe_string(item, 'transactTime')) if timestamp is None: timestamp = 0 feeCost = self.safe_number(item, 'fee', 0) if feeCost is not None: feeCost = feeCost / 100000000 fee = { 'cost': feeCost, 'currency': code, } after = self.safe_number(item, 'walletBalance') if after is not None: after = after / 100000000 before = self.sum(after, -amount) direction = None if amount < 0: direction = 'out' amount = abs(amount) else: direction = 'in' status = self.parse_transaction_status(self.safe_string(item, 'transactStatus')) return { 'id': id, 'info': item, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'direction': direction, 'account': account, 'referenceId': referenceId, 'referenceAccount': referenceAccount, 'type': type, 'currency': code, 'amount': amount, 'before': before, 'after': after, 'status': status, 'fee': fee, } def fetch_ledger(self, code=None, since=None, limit=None, params={}): self.load_markets() currency = None if code is not None: currency = self.currency(code) request = { } s not None: request['count'] = limit response = self.privateGetUserWalletHistory(self.extend(request, params)) return self.parse_ledger(response, currency, since, limit) def fetch_transactions(self, code=None, since=None, limit=None, params={}): self.load_markets() request = { } s not None: request['count'] = limit response = self.privateGetUserWalletHistory(self.extend(request, params)) transactions = self.filter_by_array(response, 'transactType', ['Withdrawal', 'Deposit'], False) currency = None if code is not None: currency = self.currency(code) return self.parse_transactions(transactions, currency, since, limit) def parse_transaction_status(self, status): statuses = { 'Canceled': 'canceled', 'Completed': 'ok', 'Pending': 'pending', } return self.safe_string(statuses, status, status) def parse_transaction(self, transaction, currency=None): id = self.safe_string(transaction, 'transactID') transactTime = self.parse8601(self.safe_string(transaction, 'transactTime')) timestamp = self.parse8601(self.safe_string(transaction, 'timestamp')) type = self.safe_string_lower(transaction, 'transactType') address = None addressFrom = None addressTo = None if type == 'withdrawal': address = self.safe_string(transaction, 'address') addressFrom = self.safe_string(transaction, 'tx') addressTo = address amountString = self.safe_string(transaction, 'amount') amountString = Precise.string_div(Precise.string_abs(amountString), '1e8') feeCostString = self.safe_string(transaction, 'fee') feeCostString = Precise.string_div(feeCostString, '1e8') fee = { 'cost': self.parse_number(feeCostString), 'currency': 'BTC', } status = self.safe_string(transaction, 'transactStatus') if status is not None: status = self.parse_transaction_status(status) return { 'info': transaction, 'id': id, 'txid': None, 'timestamp': transactTime, 'datetime': self.iso8601(transactTime), 'network': None, 'addressFrom': addressFrom, 'address': address, 'addressTo': addressTo, 'tagFrom': None, 'tag': None, 'tagTo': None, 'type': type, 'amount': self.parse_number(amountString), 'currency': 'BTC', 'status': status, 'updated': timestamp, 'comment': None, 'fee': fee, } def fetch_ticker(self, symbol, params={}): self.load_markets() market = self.market(symbol) if not market['active']: raise BadSymbol(self.id + ' fetchTicker() symbol ' + symbol + ' is not tradable') tickers = self.fetch_tickers([market['symbol']], params) ticker = self.safe_value(tickers, market['symbol']) if ticker is None: raise BadSymbol(self.id + ' fetchTicker() symbol ' + symbol + ' not found') return ticker def fetch_tickers(self, symbols=None, params={}): self.load_markets() response = self.publicGetInstrumentActiveAndIndices(params) result = {} for i in range(0, len(response)): ticker = self.parse_ticker(response[i]) symbol = self.safe_string(ticker, 'symbol') if symbol is not None: result[symbol] = ticker uniformSymbols = [] if symbols is not None: for i in range(0, len(symbols)): symbol = symbols[i] market = self.market(symbol) uniformSymbols.append(market['symbol']) return self.filter_by_array(result, 'symbol', uniformSymbols) def parse_ticker(self, ticker, market=None): marketId = self.safe_string(ticker, 'symbol') symbol = self.safe_symbol(marketId, market) timestamp = self.parse8601(self.safe_string(ticker, 'timestamp')) open = self.safe_string(ticker, 'prevPrice24h') last = self.safe_string(ticker, 'lastPrice') return self.safe_ticker({ 'symbol': symbol, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'high': self.safe_string(ticker, 'highPrice'), 'low': self.safe_string(ticker, 'lowPrice'), 'bid': self.safe_string(ticker, 'bidPrice'), 'bidVolume': None, 'ask': self.safe_string(ticker, 'askPrice'), 'askVolume': None, 'vwap': self.safe_string(ticker, 'vwap'), 'open': open, 'close': last, 'last': last, 'previousClose': None, 'change': None, 'percentage': None, 'average': None, 'baseVolume': self.safe_string(ticker, 'homeNotional24h'), 'quoteVolume': self.safe_string(ticker, 'foreignNotional24h'), 'info': ticker, }, market, False) def parse_ohlcv(self, ohlcv, market=None): return [ self.parse8601(self.safe_string(ohlcv, 'timestamp')), self.safe_number(ohlcv, 'open'), self.safe_number(ohlcv, 'high'), self.safe_number(ohlcv, 'low'), self.safe_number(ohlcv, 'close'), self.safe_number(ohlcv, 'volume'), ] def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], 'binSize': self.timeframes[timeframe], 'partial': True, rame) * 1000 fetchOHLCVOpenTimestamp = self.safe_value(self.options, 'fetchOHLCVOpenTimestamp', True) if since is not None: timestamp = since if fetchOHLCVOpenTimestamp: timestamp = self.sum(timestamp, duration) ymdhms = self.ymdhms(timestamp) request['startTime'] = ymdhms else: request['reverse'] = True response = self.publicGetTradeBucketed(self.extend(request, params)) result = self.parse_ohlcvs(response, market, timeframe, since, limit) if fetchOHLCVOpenTimestamp: # we can emulate the open timestamp by shifting all the timestamps one place # so the previous close becomes the current open, and we drop the first candle for i in range(0, len(result)): result[i][0] = result[i][0] - duration return result def parse_trade(self, trade, market=None): # # fetchTrades(public) # # { # timestamp: '2018-08-28T00:00:02.735Z', # symbol: 'XBTUSD', # side: 'Buy', # size: 2000, # price: 6906.5, # tickDirection: 'PlusTick', # trdMatchID: 'b9a42432-0a46-6a2f-5ecc-c32e9ca4baf8', # grossValue: 28958000, # homeNotional: 0.28958, # foreignNotional: 2000 # } # # fetchMyTrades(private) # # { # "execID": "string", # "orderID": "string", # "clOrdID": "string", # "clOrdLinkID": "string", # "account": 0, # "symbol": "string", # "side": "string", # "lastQty": 0, # "lastPx": 0, # "underlyingLastPx": 0, # "lastMkt": "string", # "lastLiquidityInd": "string", # "simpleOrderQty": 0, # "orderQty": 0, # "price": 0, # "displayQty": 0, # "stopPx": 0, # "pegOffsetValue": 0, # "pegPriceType": "string", # "currency": "string", # "settlCurrency": "string", # "execType": "string", # "ordType": "string", # "timeInForce": "string", # "execInst": "string", # "contingencyType": "string", # "exDestination": "string", # "ordStatus": "string", # "triggered": "string", # "workingIndicator": True, # "ordRejReason": "string", # "simpleLeavesQty": 0, # "leavesQty": 0, # "simpleCumQty": 0, # "cumQty": 0, # "avgPx": 0, # "commission": 0, # "tradePublishIndicator": "string", # "multiLegReportingType": "string", # "text": "string", # "trdMatchID": "string", # "execCost": 0, # "execComm": 0, # "homeNotional": 0, # "foreignNotional": 0, # "transactTime": "2019-03-05T12:47:02.762Z", # "timestamp": "2019-03-05T12:47:02.762Z" # } # timestamp = self.parse8601(self.safe_string(trade, 'timestamp')) priceString = self.safe_string_2(trade, 'avgPx', 'price') amountString = self.safe_string_2(trade, 'size', 'lastQty') execCost = self.safe_string(trade, 'execCost') costString = Precise.string_div(Precise.string_abs(execCost), '1e8') id = self.safe_string(trade, 'trdMatchID') order = self.safe_string(trade, 'orderID') side = self.safe_string_lower(trade, 'side') # price * amount doesn't work for all symbols(e.g. XBT, ETH) fee = None feeCostString = Precise.string_div(self.safe_string(trade, 'execComm'), '1e8') if feeCostString is not None: currencyId = self.safe_string(trade, 'settlCurrency') feeCurrencyCode = self.safe_currency_code(currencyId) feeRateString = self.safe_string(trade, 'commission') fee = { 'cost': feeCostString, 'currency': feeCurrencyCode, 'rate': feeRateString, } execType = self.safe_string(trade, 'execType') takerOrMaker = None if feeCostString is not None and execType == 'Trade': takerOrMaker = 'maker' if Precise.string_lt(feeCostString, '0') else 'taker' marketId = self.safe_string(trade, 'symbol') symbol = self.safe_symbol(marketId, market) type = self.safe_string_lower(trade, 'ordType') return self.safe_trade({ 'info': trade, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'symbol': symbol, 'id': id, 'order': order, 'type': type, 'takerOrMaker': takerOrMaker, 'side': side, 'price': priceString, 'cost': costString, 'amount': amountString, 'fee': fee, }, market) def parse_order_status(self, status): statuses = { 'New': 'open', 'PartiallyFilled': 'open', 'Filled': 'closed', 'DoneForDay': 'open', 'Canceled': 'canceled', 'PendingCancel': 'open', 'PendingNew': 'open', 'Rejected': 'rejected', 'Expired': 'expired', 'Stopped': 'open', 'Untriggered': 'open', 'Triggered': 'open', } return self.safe_string(statuses, status, status) def parse_time_in_force(self, timeInForce): timeInForces = { 'Day': 'Day', 'GoodTillCancel': 'GTC', 'ImmediateOrCancel': 'IOC', 'FillOrKill': 'FOK', } return self.safe_string(timeInForces, timeInForce, timeInForce) def parse_order(self, order, market=None): status = self.parse_order_status(self.safe_string(order, 'ordStatus')) marketId = self.safe_string(order, 'symbol') symbol = self.safe_symbol(marketId, market) timestamp = self.parse8601(self.safe_string(order, 'timestamp')) lastTradeTimestamp = self.parse8601(self.safe_string(order, 'transactTime')) price = self.safe_string(order, 'price') amount = self.safe_string(order, 'orderQty') filled = self.safe_string(order, 'cumQty', 0.0) average = self.safe_string(order, 'avgPx') id = self.safe_string(order, 'orderID') type = self.safe_string_lower(order, 'ordType') side = self.safe_string_lower(order, 'side') clientOrderId = self.safe_string(order, 'clOrdID') timeInForce = self.parse_time_in_force(self.safe_string(order, 'timeInForce')) stopPrice = self.safe_number(order, 'stopPx') execInst = self.safe_string(order, 'execInst') postOnly = (execInst == 'ParticipateDoNotInitiate') return self.safe_order({ 'info': order, 'id': id, 'clientOrderId': clientOrderId, 'timestamp': timestamp, 'datetime': self.iso8601(timestamp), 'lastTradeTimestamp': lastTradeTimestamp, 'symbol': symbol, 'type': type, 'timeInForce': timeInForce, 'postOnly': postOnly, 'side': side, 'price': price, 'stopPrice': stopPrice, 'amount': amount, 'cost': None, 'average': average, 'filled': filled, 'remaining': None, 'status': status, 'fee': None, 'trades': None, }, market) def fetch_trades(self, symbol, since=None, limit=None, params={}): self.load_markets() market = self.market(symbol) request = { 'symbol': market['id'], } if since is not None: request['startTime'] = self.iso8601(since) else: request['reverse'] = True if limit is not None: request['count'] = limit response = self.publicGetTrade(self.extend(request, params)) return self.parse_trades(response, market, since, limit) def create_order(self, symbol, type, side, amount, price=None, params={}): self.load_markets() market = self.market(symbol) orderType = self.capitalize(type) request = { 'symbol': market['id'], 'side': self.capitalize(side), 'orderQty': float(self.amount_to_precision(symbol, amount)), 'ordType': orderType, } if (orderType == 'Stop') or (orderType == 'StopLimit') or (orderType == 'MarketIfTouched') or (orderType == 'LimitIfTouched'): stopPrice = self.safe_number_2(params, 'stopPx', 'stopPrice') if stopPrice is None: raise ArgumentsRequired(self.id + ' createOrder() requires a stopPx or stopPrice parameter for the ' + orderType + ' order type') else: request['stopPx'] = float(self.price_to_precision(symbol, stopPrice)) params = self.omit(params, ['stopPx', 'stopPrice']) if (orderType == 'Limit') or (orderType == 'StopLimit') or (orderType == 'LimitIfTouched'): request['price'] = float(self.price_to_precision(symbol, price)) clientOrderId = self.safe_string_2(params, 'clOrdID', 'clientOrderId') if clientOrderId is not None: request['clOrdID'] = clientOrderId params = self.omit(params, ['clOrdID', 'clientOrderId']) response = self.privatePostOrder(self.extend(request, params)) return self.parse_order(response, market) def edit_order(self, id, symbol, type, side, amount=None, price=None, params={}): self.load_markets() request = {} origClOrdID = self.safe_string_2(params, 'origClOrdID', 'clientOrderId') if origClOrdID is not None: request['origClOrdID'] = origClOrdID clientOrderId = self.safe_string(params, 'clOrdID', 'clientOrderId') if clientOrderId is not None: request['clOrdID'] = clientOrderId params = self.omit(params, ['origClOrdID', 'clOrdID', 'clientOrderId']) else: request['orderID'] = id if amount is not None: request['orderQty'] = amount if price is not None: request['price'] = price response = self.privatePutOrder(self.extend(request, params)) return self.parse_order(response) def cancel_order(self, id, symbol=None, params={}): self.load_markets() clientOrderId = self.safe_value_2(params, 'clOrdID', 'clientOrderId') request = {} if clientOrderId is None: request['orderID'] = id else: request['clOrdID'] = clientOrderId params = self.omit(params, ['clOrdID', 'clientOrderId']) response = self.privateDeleteOrder(self.extend(request, params)) order = self.safe_value(response, 0, {}) error = self.safe_string(order, 'error') if error is not None: if error.find('Unable to cancel order due to existing state') >= 0: raise OrderNotFound(self.id + ' cancelOrder() failed: ' + error) return self.parse_order(order) def cancel_orders(self, ids, symbol=None, params={}): return self.cancel_order(ids, symbol, params) def cancel_all_orders(self, symbol=None, params={}): self.load_markets() request = {} market = None if symbol is not None: market = self.market(symbol) request['symbol'] = market['id'] response = self.privateDeleteOrderAll(self.extend(request, params)) return self.parse_orders(response, market) def fetch_positions(self, symbols=None, params={}): self.load_markets() response = self.privateGetPosition(params) return response def is_fiat(self, currency): if currency == 'EUR': return True if currency == 'PLN': return True return False def withdraw(self, code, amount, address, tag=None, params={}): tag, params = self.handle_withdraw_tag_and_params(tag, params) self.check_address(address) self.load_markets() if code != 'BTC': raise ExchangeError(self.id + ' supoprts BTC withdrawals only, other currencies coming soon...') request = { 'currency': 'XBt', 'amount': amount, 'address': address, uestWithdrawal(self.extend(request, params)) return { 'info': response, 'id': self.safe_string(response, 'transactID'), } def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody): if response is None: return if code == 429: raise DDoSProtection(self.id + ' ' + body) if code >= 400: error = self.safe_value(response, 'error', {}) message = self.safe_string(error, 'message') feedback = self.id + ' ' + body self.throw_exactly_matched_exception(self.exceptions['exact'], message, feedback) self.throw_broadly_matched_exception(self.exceptions['broad'], message, feedback) if code == 400: raise BadRequest(feedback) raise ExchangeError(feedback) def nonce(self): return self.milliseconds() def sign(self, path, api='public', method='GET', params={}, headers=None, body=None): query = '/api/' + self.version + '/' + path if method == 'GET': if params: query += '?' + self.urlencode(params) else: format = self.safe_string(params, '_format') if format is not None: query += '?' + self.urlencode({'_format': format}) params = self.omit(params, '_format') url = self.urls['api'][api] + query if api == 'private': self.check_required_credentials() auth = method + query expires = self.safe_integer(self.options, 'api-expires') headers = { 'Content-Type': 'application/json', 'api-key': self.apiKey, } expires = self.sum(self.seconds(), expires) expires = str(expires) auth += expires headers['api-expires'] = expires if method == 'POST' or method == 'PUT' or method == 'DELETE': if params: body = self.json(params) auth += body headers['api-signature'] = self.hmac(self.encode(auth), self.encode(self.secret)) return {'url': url, 'method': method, 'body': body, 'headers': headers}

true

f734bd64143a5c11cb97eb8dad029aafd37b792e

951

py

Python

setup.py

DavidNKraemer/WordleBot

856108445ae881edf71d0e4360ec3219c9ed9fe4

[ "MIT" ]

1

2022-02-11T00:22:14.000Z

setup.py

DavidNKraemer/WordleBot

856108445ae881edf71d0e4360ec3219c9ed9fe4

[ "MIT" ]

1

2022-02-07T02:35:49.000Z

2022-02-15T14:27:57.000Z

setup.py

DavidNKraemer/Gym-Wordle

856108445ae881edf71d0e4360ec3219c9ed9fe4

[ "MIT" ]

null

from setuptools import setup, find_packages with open('README.md', 'r', encoding='utf-8') as fh: long_description = fh.read() setup( name='gym_wordle', version='0.1.3', author='David Kraemer', author_email='david.kraemer@stonybrook.edu', description='OpenAI gym environment for training agents on Wordle', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/DavidNKraemer/Gym-Wordle', packages=find_packages( include=[ 'gym_wordle', 'gym_wordle.*' ] ), package_data={ 'gym_wordle': ['dictionary/*'] }, python_requires='>=3.7', classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], install_requires=[ 'numpy>=1.20', 'gym==0.19', 'sty==1.0', ], )

26.416667

71

0.599369

from setuptools import setup, find_packages with open('README.md', 'r', encoding='utf-8') as fh: long_description = fh.read() setup( name='gym_wordle', version='0.1.3', author='David Kraemer', author_email='david.kraemer@stonybrook.edu', description='OpenAI gym environment for training agents on Wordle', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/DavidNKraemer/Gym-Wordle', packages=find_packages( include=[ 'gym_wordle', 'gym_wordle.*' ] ), package_data={ 'gym_wordle': ['dictionary/*'] }, python_requires='>=3.7', classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], install_requires=[ 'numpy>=1.20', 'gym==0.19', 'sty==1.0', ], )

true

f734bda982fdfb5c124c2601234d24204182ffb0

4,957

py

Python

pywikibot/editor.py

valhallasw/pywikibot-core

32a8c3c1298a5cb077381fe202daefde82c1c5d3

[ "MIT" ]

null

pywikibot/editor.py

valhallasw/pywikibot-core

32a8c3c1298a5cb077381fe202daefde82c1c5d3

[ "MIT" ]

null

pywikibot/editor.py

valhallasw/pywikibot-core

32a8c3c1298a5cb077381fe202daefde82c1c5d3

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- """Text editor class for your favourite editor.""" from __future__ import unicode_literals # # (C) Gerrit Holl, 2004 # (C) Pywikibot team, 2004-2015 # # Distributed under the terms of the MIT license. # __version__ = '$Id: f734bda982fdfb5c124c2601234d24204182ffb0 $' # import codecs import os import subprocess import tempfile import pywikibot from pywikibot import config from pywikibot.tools import deprecated try: from pywikibot.userinterfaces import gui # noqa except ImportError as e: gui = e class TextEditor(object): """Text editor.""" def _command(self, file_name, text, jump_index=None): """Return editor selected in user-config.py.""" if jump_index: # Some editors make it possible to mark occurrences of substrings, # or to jump to the line of the first occurrence. # TODO: Find a better solution than hardcoding these, e.g. a config # option. line = text[:jump_index].count('\n') column = jump_index - (text[:jump_index].rfind('\n') + 1) else: line = column = 0 # Linux editors. We use startswith() because some users might use # parameters. if config.editor.startswith('kate'): command = ['-l', '%i' % (line + 1), '-c', '%i' % (column + 1)] elif config.editor.startswith('gedit'): command = ['+%i' % (line + 1)] # seems not to support columns elif config.editor.startswith('emacs'): command = ['+%i' % (line + 1)] # seems not to support columns elif config.editor.startswith('jedit'): command = ['+line:%i' % (line + 1)] # seems not to support columns elif config.editor.startswith('vim'): command = ['+%i' % (line + 1)] # seems not to support columns elif config.editor.startswith('nano'): command = ['+%i,%i' % (line + 1, column + 1)] # Windows editors elif config.editor.lower().endswith('notepad++.exe'): command = ['-n%i' % (line + 1)] # seems not to support columns else: command = [] # See T102465 for problems relating to using config.editor unparsed. command = [config.editor] + command + [file_name] pywikibot.log(u'Running editor: %s' % TextEditor._concat(command)) return command @staticmethod def _concat(command): return ' '.join("'{0}'".format(part) if ' ' in part else part for part in command) @deprecated('_command (should not be used from the outside)') def command(self, tempFilename, text, jumpIndex=None): """Return editor selected in user-config.py.""" return TextEditor._concat(self._command(tempFilename, text, jumpIndex)) def edit(self, text, jumpIndex=None, highlight=None): """ Call the editor and thus allows the user to change the text. Halts the thread's operation until the editor is closed. @param text: the text to be edited @type text: unicode @param jumpIndex: position at which to put the caret @type jumpIndex: int @param highlight: each occurrence of this substring will be highlighted @type highlight: unicode @return: the modified text, or None if the user didn't save the text file in his text editor @rtype: unicode or None """ if config.editor: tempFilename = '%s.%s' % (tempfile.mkstemp()[1], config.editor_filename_extension) try: with codecs.open(tempFilename, 'w', encoding=config.editor_encoding) as tempFile: tempFile.write(text) creationDate = os.stat(tempFilename).st_mtime subprocess.call(self._command(tempFilename, text, jumpIndex)) lastChangeDate = os.stat(tempFilename).st_mtime if lastChangeDate == creationDate: # Nothing changed return None else: with codecs.open(tempFilename, 'r', encoding=config.editor_encoding) as temp_file: newcontent = temp_file.read() return newcontent finally: os.unlink(tempFilename) if isinstance(gui, ImportError): raise pywikibot.Error( 'Could not load GUI modules: %s\nNo editor available.\n' 'Set your favourite editor in user-config.py "editor", ' 'or install python packages tkinter and idlelib, which ' 'are typically part of Python but may be packaged separately ' 'on your platform.\n' % gui) return pywikibot.ui.editText(text, jumpIndex=jumpIndex, highlight=highlight)

39.031496

84

0.586443

from __future__ import unicode_literals __version__ = '$Id: f734bda982fdfb5c124c2601234d24204182ffb0 $' import codecs import os import subprocess import tempfile import pywikibot from pywikibot import config from pywikibot.tools import deprecated try: from pywikibot.userinterfaces import gui except ImportError as e: gui = e class TextEditor(object): def _command(self, file_name, text, jump_index=None): if jump_index: line = text[:jump_index].count('\n') column = jump_index - (text[:jump_index].rfind('\n') + 1) else: line = column = 0 if config.editor.startswith('kate'): command = ['-l', '%i' % (line + 1), '-c', '%i' % (column + 1)] elif config.editor.startswith('gedit'): command = ['+%i' % (line + 1)] elif config.editor.startswith('emacs'): command = ['+%i' % (line + 1)] elif config.editor.startswith('jedit'): command = ['+line:%i' % (line + 1)] elif config.editor.startswith('vim'): command = ['+%i' % (line + 1)] elif config.editor.startswith('nano'): command = ['+%i,%i' % (line + 1, column + 1)] elif config.editor.lower().endswith('notepad++.exe'): command = ['-n%i' % (line + 1)] else: command = [] command = [config.editor] + command + [file_name] pywikibot.log(u'Running editor: %s' % TextEditor._concat(command)) return command @staticmethod def _concat(command): return ' '.join("'{0}'".format(part) if ' ' in part else part for part in command) @deprecated('_command (should not be used from the outside)') def command(self, tempFilename, text, jumpIndex=None): return TextEditor._concat(self._command(tempFilename, text, jumpIndex)) def edit(self, text, jumpIndex=None, highlight=None): if config.editor: tempFilename = '%s.%s' % (tempfile.mkstemp()[1], config.editor_filename_extension) try: with codecs.open(tempFilename, 'w', encoding=config.editor_encoding) as tempFile: tempFile.write(text) creationDate = os.stat(tempFilename).st_mtime subprocess.call(self._command(tempFilename, text, jumpIndex)) lastChangeDate = os.stat(tempFilename).st_mtime if lastChangeDate == creationDate: return None else: with codecs.open(tempFilename, 'r', encoding=config.editor_encoding) as temp_file: newcontent = temp_file.read() return newcontent finally: os.unlink(tempFilename) if isinstance(gui, ImportError): raise pywikibot.Error( 'Could not load GUI modules: %s\nNo editor available.\n' 'Set your favourite editor in user-config.py "editor", ' 'or install python packages tkinter and idlelib, which ' 'are typically part of Python but may be packaged separately ' 'on your platform.\n' % gui) return pywikibot.ui.editText(text, jumpIndex=jumpIndex, highlight=highlight)

true

f734bee389dc8207340f0fc6a99ba2f167fd79e7

29,991

py

Python

flax_models/t5x/train.py

muell-monster/google-research

04d2024f4723bc4be3d639a668c19fb1f6a31478

[ "Apache-2.0" ]

1

2020-12-25T01:18:50.000Z

flax_models/t5x/train.py

thomascherickal/google-research

294a888bbb6678ac255c6422fd703c325cbb0772

[ "Apache-2.0" ]

null

flax_models/t5x/train.py

thomascherickal/google-research

294a888bbb6678ac255c6422fd703c325cbb0772

[ "Apache-2.0" ]

1

2021-09-27T03:17:14.000Z

# coding=utf-8 # Copyright 2020 The Google Research Authors. # # 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. """This script pre-trains or fine-tunes a Transformer using the T5 data pipeline.""" from concurrent.futures import thread import functools import importlib import os from typing import Any, Mapping, Sequence, Tuple from absl import app from absl import flags from absl import logging # Set Linen to add profiling information when constructing Modules. # Must be set before flax imports. # pylint:disable=g-import-not-at-top os.environ['FLAX_PROFILE'] = 'true' from flax import linen as nn from flax import optim from flax.metrics import tensorboard from flax.training import checkpoints from flax.training import common_utils import jax from jax import lax from jax import random from jax.interpreters.sharded_jit import sharded_jit import jax.numpy as jnp import ml_collections from ml_collections import config_flags import numpy as np import t5 from t5x import checkpoint_importer from t5x import input_pipeline from t5x import models from t5x import partitions from t5x import train_lib import tensorflow as tf # pylint:disable=g-long-lambda FLAGS = flags.FLAGS CFG = None PyTreeDef = type(jax.tree_structure(None)) TransformerConfig = models.TransformerConfig jax.config.parse_flags_with_absl() flags.DEFINE_string( 'model_dir', default=None, help='Directory to store model data.') flags.DEFINE_string( 'data_dir', default=None, help='Tensorflow datasets directory.') config_flags.DEFINE_config_file( name='config', default='configs/t5_small_glue.py', help_string='training config file.') ConfigDict = ml_collections.ConfigDict def get_configs( config ): """Get train, eval, and predict model configs. Args: config: The config dict for the experiment. Returns: A triple (train_config, eval_config, predict_config). """ train_config = TransformerConfig( vocab_size=config.vocab_size, output_vocab_size=config.vocab_size, share_embeddings=config.share_embeddings, logits_via_embedding=config.logits_via_embedding, dtype=jnp.bfloat16 if config.use_bfloat16 else jnp.float32, emb_dim=config.emb_dim, num_heads=config.num_heads, num_layers=config.num_layers, qkv_dim=config.qkv_dim, mlp_dim=config.mlp_dim, mlp_activations=config.mlp_activations, position_embeddings='relative', relative_attention_num_buckets=config.relative_attention_num_buckets, relative_attention_max_distance=config.relative_attention_max_distance, max_len=max(config.max_input_length, config.max_target_length, config.max_eval_input_length, config.max_eval_target_length), dropout_rate=config.dropout_rate, attention_dropout_rate=config.attention_dropout_rate, deterministic=False, decode=False, kernel_init=nn.initializers.xavier_uniform(), bias_init=nn.initializers.normal(stddev=1e-6)) eval_config = train_config.replace(deterministic=True) # pytype: disable=attribute-error predict_config = train_config.replace( # pytype: disable=attribute-error deterministic=True, decode=True, max_decode_len=config.max_eval_target_length) return (train_config, eval_config, predict_config) def get_initial_params(rng, config, transformer_config, optimizer_def): """Get the initial parameter tree.""" input_shape = (config.batch_size, CFG.max_input_length) target_shape = (config.batch_size, CFG.max_target_length) initial_variables = models.Transformer(transformer_config).init( rng, jnp.ones(input_shape, jnp.float32), jnp.ones(target_shape, jnp.float32)) # apply an optimizer to the parameters return optimizer_def.create(initial_variables['params']) def main(argv): global CFG CFG = FLAGS.config if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') # Guarantee that the JAX bfloat16 extension is used rather than TF bfloat16. _ = np.array(jnp.array([1.0], dtype=jnp.bfloat16)) # Use hardware RNG for bernoulli randoms in dropout mask creation. if CFG.hardware_rng: models.set_hardware_bernoulli() if 'module_import' in CFG and CFG.module_import: for module in CFG.module_import: importlib.import_module(module) if 'additional_task_cache_dirs' in CFG and CFG.additional_task_cache_dirs: t5.data.add_global_cache_dirs(CFG.additional_task_cache_dirs) num_partitions = CFG.num_partitions topology = train_lib.compute_multihost_topology(num_partitions) batch_size = CFG.batch_size eval_batch_size = CFG.eval_batch_size per_replica_set_eval_batch_size = eval_batch_size // topology.num_replica_sets if batch_size % topology.num_replicas: raise ValueError('Batch size must be divisible by the number of replicas.') steps_per_epoch = CFG.steps_per_epoch logging.info('steps per epoch: %d', steps_per_epoch) broadcast = functools.partial( train_lib.broadcast, num_replicas=topology.per_replica_set_num_replicas, num_partitions=topology.per_host_num_partitions, devices=topology.this_host_device_assignment) if jax.host_id() == 0: tf.io.gfile.makedirs(FLAGS.model_dir) tf.io.gfile.copy(FLAGS['config'].config_filename, os.path.join(FLAGS.model_dir, 'config.py'), overwrite=True) train_summary_writer = tensorboard.SummaryWriter( os.path.join(FLAGS.model_dir, 'train')) eval_summary_writer = tensorboard.SummaryWriter( os.path.join(FLAGS.model_dir, 'eval')) else: train_summary_writer = None eval_summary_writer = None # Write summaries in background thread to avoid blocking on device sync if CFG.infeed: # Infeed is currently synchronous, so do it in a background thread too infeed_pool = thread.ThreadPoolExecutor(jax.local_device_count(), 'infeed') (train_ds, eval_ds), eval_cache = input_pipeline.get_datasets_and_cache( CFG, topology.num_replica_sets, topology.replica_set_id, topology.per_replica_set_host_id) vocab = input_pipeline.get_vocabulary(CFG.mixture_or_task_name) encoder = vocab.tf_tokenizer eos_id = vocab.tokenizer.eos_id() def decode_tokens(toks, eos_id = eos_id, max_id = 32000): """Decode tokens back to unicode.""" del eos_id # TODO(levskaya): T5 doesn't seem to emit EOS tokens? double check this # is the best decoding function or just switch to using tf_decode. # valid_toks = toks[:np.argmax(toks == eos_id) + 1].astype(np.int32) valid_toks = toks.astype(np.int32) valid_toks[valid_toks >= max_id] = 3 return encoder.detokenize(valid_toks).numpy().decode('utf-8') logging.info('Initializing model, optimizer, and step functions.') train_config, eval_config, predict_config = get_configs(CFG) rng = random.PRNGKey(CFG.random_seed) rng, init_rng = random.split(rng) # This is used for infeed conversion from feature dict <--> tuple train_keys = [ 'inputs', 'targets', 'inputs_position', 'targets_position', 'inputs_segmentation', 'targets_segmentation' ] device_train_input_shape = tuple([ (batch_size // topology.num_replicas, CFG.max_input_length if 'inputs' in k else CFG.max_target_length) for k in train_keys ]) learning_rate_fn = train_lib.create_learning_rate_scheduler( factors=CFG.schedule, base_learning_rate=CFG.learning_rate, warmup_steps=CFG.warmup_steps) # First, we only abstractly initialize the optimizer and model parameters, # since the parameters may not even fit in device memory! # TODO(jekbradbury): make optimizer_defs compare by value so it can be created # in get_initial_params without causing pytree incompatibility optimizer_def = optim.Adafactor( CFG.learning_rate, decay_rate=0.8, step_offset=CFG.step_offset) initialize_params_fn = functools.partial( get_initial_params, config=CFG, transformer_config=eval_config, optimizer_def=optimizer_def) optimizer = jax.eval_shape(initialize_params_fn, init_rng) # tuple-like pytree leaves for global_arg_shapes optimizer_shapes = jax.tree_map(lambda x: partitions.Spec(*x.shape), optimizer) # Build parameter partition annotations for preserving partitions from train # to eval. if num_partitions > 1: optimizer_partitions = optimizer.restore_state( partitions.set_partitions(num_partitions, optimizer.state_dict())) per_host_optimizer_partitions = optimizer.restore_state( partitions.set_partitions(topology.per_host_num_partitions, optimizer.state_dict())) # Restore unreplicated optimizer + model state from last checkpoint. # TODO(jekbradbury,levskaya): implement sharded native checkpoint/restore existing_checkpoint_found = False if CFG.restore_checkpoints: existing_checkpoint_found = train_lib.checkpoint_exists(FLAGS.model_dir) optimizer = checkpoints.restore_checkpoint(FLAGS.model_dir, optimizer) # Import a pretrained-T5 checkpoint only if we didn't import a local # "native" checkpoint (e.g. due to resuming a pre-empted finetuning run.) # TODO(jekbradbury,levskaya): implement sharded T5 checkpoint/restore if CFG.restore_t5_checkpoint and not existing_checkpoint_found: optimizer = checkpoint_importer.restore_from_t5_checkpoint( optimizer, CFG.restore_t5_checkpoint) if CFG.restore_t5_checkpoint or existing_checkpoint_found: if num_partitions > 1: # Until checkpoint/restore is sharded, the restored checkpoint is global # and we need to slice each sharded parameter into the chunk containing # only the partitions that are present on this host. def per_host_chunk(x, spec): if spec is None or spec is x: # unsharded or not a parameter return x if spec[0] == 1: dim_size = x.shape[1] elif spec[1] == 1: dim_size = x.shape[0] else: raise NotImplementedError() chunk_size = ( dim_size * topology.per_host_num_partitions // num_partitions) lower = topology.per_replica_set_host_id * chunk_size upper = (topology.per_replica_set_host_id + 1) * chunk_size if spec[0] == 1: return x[:, lower:upper] else: return x[lower:upper] optimizer = jax.tree_multimap(per_host_chunk, optimizer, optimizer_partitions) else: # If pretraining and no checkpoint imported, we jit the (sharded-) init # function to minimize fragmentation. We use the same pmap(sharded_jit) # setup as the training step/loop to initialize everything "in-place" and # avoid communication or OOM. if num_partitions > 1: initialize_params_fn = sharded_jit( initialize_params_fn, in_parts=None, local_in_parts=None, out_parts=optimizer_partitions, local_out_parts=per_host_optimizer_partitions, # devices=one_replica_device_assignment, ) initialize_params_fn = jax.pmap( initialize_params_fn, 'batch', in_axes=0, axis_size=topology.num_replicas, devices=topology.device_assignment) init_rng = broadcast(init_rng) optimizer = initialize_params_fn(init_rng) # We maintain the optimizer in unbroadcasted form (i.e. with no leading # replica axis). This is equivalent to the as-yet-nonexistent pmap kwarg # out_axes=None. optimizer = train_lib.unbroadcast(optimizer) else: optimizer = jax.jit(initialize_params_fn)(init_rng) # --------------------------------------------------------------------------- # Compile multidevice versions of train/eval/predict step and cache init fn. # --------------------------------------------------------------------------- # We can use either a single train-step for a host training loop: # train_step(optimizer, batch, prev_metrics, dropout_rng, **kwargs) # --> new_optimizer, metrics, new_dropout_rng def p_train_step(optimizer, batch, prev_metrics, dropout_rng): return train_lib.train_step( optimizer, batch, prev_metrics, dropout_rng, config=train_config, learning_rate_fn=learning_rate_fn, num_microbatches=CFG.microbatches, label_smoothing=CFG.label_smoothing, z_loss=CFG.z_loss, use_bfloat16=CFG.use_bfloat16) if num_partitions > 1: p_train_step = sharded_jit( p_train_step, in_parts=(optimizer_partitions, None, None, None), local_in_parts=(per_host_optimizer_partitions, None, None, None), out_parts=(optimizer_partitions, None, None), local_out_parts=(per_host_optimizer_partitions, None, None)) # TODO(levskaya): the in_axes spec below might be wrong, double-check. p_train_step = jax.pmap( p_train_step, axis_name='batch', in_axes=(None, 0, 0, 0), donate_argnums=(0,), global_arg_shapes=(optimizer_shapes, None, None, None), axis_size=topology.num_replicas, devices=topology.device_assignment) # pytype: disable=wrong-arg-types # OR, we use an on-device loop that feeds the training step via infeed queue. def device_train_loop_cond( args ): """Stopping criterion for on-device loop.""" _, _, _, _, step, epoch = args return step // steps_per_epoch == epoch def device_train_loop_body( args ): """On-device loop body.""" optimizer, dropout_rngs, metrics, token, step, epoch = args # Ordering input data from infeed requires threading a symbolic token # through the computation. input_data, token = lax.infeed( token, shape=tuple( [jax.ShapedArray(s, jnp.int32) for s in device_train_input_shape])) # Rebuild input dict from infeed data tuple. batch = {k: v for k, v in zip(train_keys, input_data)} # Run the train_step function and return the loop state. optimizer, metrics, dropout_rngs = train_lib.train_step( optimizer, batch, metrics, dropout_rngs, train_config, learning_rate_fn, num_microbatches=CFG.microbatches, label_smoothing=CFG.label_smoothing, z_loss=CFG.z_loss) step += 1 return optimizer, dropout_rngs, metrics, token, step, epoch def device_train_loop(optimizer, dropout_rngs, metrics, step, epoch): # Create symbolic token for threading infeed data. token = lax.create_token(step) # Run on-device loop. optimizer, dropout_rngs, metrics, _, step, _ = lax.while_loop( device_train_loop_cond, device_train_loop_body, (optimizer, dropout_rngs, metrics, token, step, epoch)) return optimizer, dropout_rngs, metrics, step if num_partitions > 1: device_train_loop = sharded_jit( device_train_loop, in_parts=(optimizer_partitions, None, None, None, None), local_in_parts=(per_host_optimizer_partitions, None, None, None, None), out_parts=(optimizer_partitions, None, None, None), local_out_parts=(per_host_optimizer_partitions, None, None, None)) p_train_epoch = jax.pmap( device_train_loop, axis_name='batch', in_axes=(None, 0, 0, None, None), donate_argnums=(0,), global_arg_shapes=(optimizer_shapes, None, None, None, None), axis_size=topology.num_replicas, devices=topology.device_assignment) # pytype: disable=wrong-arg-types # Reduction psum for metric data. def p_allreduce_metrics(x): return lax.psum(x, axis_name='batch') if num_partitions > 1: p_allreduce_metrics = sharded_jit( p_allreduce_metrics, in_parts=None, local_in_parts=None, out_parts=None, local_out_parts=None, num_partitions=num_partitions, local_num_partitions=topology.per_host_num_partitions) p_allreduce_metrics = jax.pmap( p_allreduce_metrics, axis_name='batch', global_arg_shapes=None, axis_size=topology.num_replicas, devices=topology.device_assignment) # Training evaluation computation. # eval_step(params, batch, config, label_smoothing=0.0) --> metrics def p_eval_step(params, batch): return train_lib.eval_step( params, batch, config=eval_config, label_smoothing=CFG.label_smoothing) if num_partitions > 1: p_eval_step = sharded_jit( p_eval_step, in_parts=(optimizer_partitions.target, None), local_in_parts=(per_host_optimizer_partitions.target, None), out_parts=None, local_out_parts=None) p_eval_step = jax.pmap( p_eval_step, axis_name='batch', in_axes=(None, 0), global_arg_shapes=(optimizer_shapes.target, None), axis_size=topology.num_replicas, devices=topology.device_assignment) # pytype: disable=wrong-arg-types # Fast autoregressive decoding loop. # For inference and model evaluation. # predict_step(inputs, params, # eos_id, max_decode_len, config, beam_size=4) --> beam_seqs def p_pred_step(inputs, params): return train_lib.predict_step(inputs, params, eos_id, CFG.max_eval_target_length, predict_config, CFG.beam_size) if num_partitions > 1: p_pred_step = sharded_jit( p_pred_step, in_parts=(None, optimizer_partitions.target), local_in_parts=(None, per_host_optimizer_partitions.target), out_parts=None, local_out_parts=None) p_pred_step = jax.pmap( p_pred_step, axis_name='batch', in_axes=(0, None), global_arg_shapes=(None, optimizer_shapes.target), axis_size=topology.num_replicas, devices=topology.device_assignment) # pytype: disable=wrong-arg-types # --------------------------------------------------------------------------- # Main Train Loop # --------------------------------------------------------------------------- # We init the first set of dropout PRNG keys, but update it afterwards inside # the main pmap'd training update for performance. # There should be a unique dropout key for each replica represented on this # host, but the key should be the same for the same replica on other hosts. # Again, this is what the replica set abstraction is for. dropout_rngs = random.split( random.fold_in(rng, topology.replica_set_id), topology.per_replica_set_num_replicas) # restore step from last checkpoint host_step = int(optimizer.state.step) empty_metrics = broadcast({ 'loss': 0.0, 'accuracy': 0.0, 'learning_rate': 0.0, 'denominator': 0.0 }) if CFG.infeed: # TODO(jekbradbury): support something like this for the Python-loop case logging.info('Precompiling training loop and moving optimizer to device.') optimizer, _, metrics, _ = p_train_epoch(optimizer, dropout_rngs, empty_metrics, jnp.array(0, dtype=jnp.int32), 1) optimizer = train_lib.unbroadcast(optimizer) metrics['loss'].block_until_ready() logging.info('Starting training loop.') local_devices = jax.local_devices() device_step = broadcast(host_step) first_epoch = host_step // steps_per_epoch # Main Loop over "epochs". train_iter = train_ds.as_numpy_iterator() for epoch in range(first_epoch, first_epoch + CFG.num_epochs): metrics = empty_metrics # NOTE: 'optimizer' is unbroadcast by construction at initialization or # when loading a checkpoint. It is maintained in 'unbroadcast' state to # enable the XLA cross-replica sharding optimization. The broadcasting is # handled automatically by the pmap'd functions that use it. # Gather all task evaluation metrics. logging.info('Evaluating tasks.') if epoch == first_epoch + 1: train_lib.sync_devices() for task in eval_cache.tasks: logging.info('Evaluating task %s', task.name) all_predicted, all_bs = [], [] for pred_batch in eval_cache.preprocessed_examples[task.name]: # Handle final odd-sized batch by padding instead of dropping it. input_batch, unpadded_batch_size = train_lib.pad_batch_to_size( pred_batch['inputs'], per_replica_set_eval_batch_size) all_bs.append(unpadded_batch_size) # Split batch dimensions for pmap. input_batch = jax.tree_map( lambda x: x.reshape( (topology.per_replica_set_num_replicas, -1) + x.shape[1:]), input_batch) # Run fast inference on batch. all_predicted.append(p_pred_step(input_batch, optimizer.target)) # Pad out the number of batches so each host has the same number. max_host_batch_number = np.max( eval_cache.preprocessed_batch_sizes[task.name]) batch_shortfall = max_host_batch_number - len(all_predicted) if batch_shortfall > 0: # TODO(levskaya): Fix for case of entirely empty all_predicted. # To make sure the cross-host barriers work, we run the program the same # number of times on all hosts. The results of this call is ignored, and # the predictions are populated with zeros instead. p_pred_step(input_batch, optimizer.target) # Dummy call. all_predicted.extend([jnp.zeros_like(all_predicted[0])] * batch_shortfall) all_bs.extend([0] * batch_shortfall) all_predicted = jnp.concatenate(all_predicted) all_bs = jnp.array(all_bs) # Collect all batches from across hosts and reverse sharding. all_predicted = train_lib.host_allgather( all_predicted, topology.num_replica_sets, topology.replica_set_id, topology.per_replica_set_host_id == 0) seqlength = all_predicted.shape[-1] total_examples = np.sum( train_lib.host_allgather(all_bs, topology.num_replica_sets, topology.replica_set_id, topology.per_replica_set_host_id == 0)) del all_bs assert total_examples == len(eval_cache.examples[task.name]), ( 'Total number of batches incorrect for task %s.' % task.name) # De-shard the collected predicted tokens and remove padding. all_predicted = np.transpose(all_predicted, (1, 2, 0, 3)).reshape( -1, seqlength)[:total_examples] # We now run the post-processing and metric-fns on a single host. if jax.host_id() == 0: assert eval_summary_writer raw_predictions = [] for tokens in all_predicted: raw_predictions.append(decode_tokens(tokens)) # post-process predictions for metric fns predictions = [ task.postprocess_fn(p, example=ex) for p, ex in zip(raw_predictions, eval_cache.examples[task.name]) ] for metric_fn in task.metric_fns: scores = metric_fn(eval_cache.targets[task.name], predictions) for metric_name, metric_value in scores.items(): tag = f'eval/{task.name}/{metric_name}' eval_summary_writer.scalar(tag, metric_value, host_step) logging.info('EVAL %s at step %d: %.3f', tag, host_step, metric_value) eval_summary_writer.flush() # Save text samples for tensorboard. exemplars = '' for n in np.random.choice(np.arange(len(predictions)), 8): tgt_txt = tf.compat.as_text( eval_cache.examples[task.name][n]['targets_plaintext']) pred_txt = raw_predictions[n] exemplars += (f'{eval_cache.inputs[task.name][n]}\n\n' f'target: {tgt_txt}\n\n' f'prediction: {pred_txt}\n\n') eval_summary_writer.text(f'{task.name} samples', exemplars, host_step) eval_summary_writer.flush() # Take an Xprof trace after the first loop has compiled everything. if epoch == first_epoch + 1: train_lib.sync_devices() # For on-device loop, we launch the computation before feeding data. logging.info('BEGIN Train loop.') if CFG.infeed: optimizer, dropout_rngs, metrics, device_step = p_train_epoch( optimizer, dropout_rngs, metrics, train_lib.unbroadcast(device_step), epoch) optimizer = train_lib.unbroadcast(optimizer) # Epoch loop. while int(host_step // steps_per_epoch) == epoch: batch = next(train_iter) batch = jax.tree_map( lambda x: x.reshape( (topology.per_replica_set_num_replicas, -1) + x.shape[1:]), batch) # Feed the on-device training loop. if CFG.infeed: for i, device in enumerate(local_devices): # When using infeed to provide data to the computation, we're on our # own for feeding the right values to the right devices. Each device # should get the minibatch corresponding to its replica, a slice of # the larger batch corresponding to the host's replica set. if device.platform == 'tpu': device_coords = (*device.coords, device.id % 2) else: device_coords = (device.host_id, i) per_replica_set_device_coords = tuple( dc % prsm for dc, prsm in zip(device_coords, topology.per_replica_set_mesh)) per_replica_set_replica_coords = tuple( prsdc // prm for prsdc, prm in zip(per_replica_set_device_coords, topology.per_replica_mesh)) per_replica_set_replica_id = 0 for prsm, prm, prsrc in zip(topology.per_replica_set_mesh, topology.per_replica_mesh, per_replica_set_replica_coords): per_replica_set_replica_id = ( per_replica_set_replica_id * prsm // prm + prsrc) input_tuple = tuple( [batch[k][per_replica_set_replica_id] for k in train_keys]) # Safety check: infeed does not check shape or types but requires # them to agree with on-device spec, otherwise the queue and program # stalls. tuple_shapes = jax.tree_map(jnp.shape, input_tuple) tuple_dtypes = jax.tree_map(lambda x: x.dtype, input_tuple) assert tuple_shapes == device_train_input_shape, ( 'infeed shape error %s != %s' % (tuple_shapes, device_train_input_shape)) assert tuple(set(tuple_dtypes)) == (jnp.int32,), \ ('infeed dtype error %s not all of type %s' % ( tuple_dtypes, jnp.int32)) infeed_pool.submit( functools.partial(device.transfer_to_infeed, input_tuple)) # Host training loop. else: optimizer, metrics, dropout_rngs = p_train_step(optimizer, batch, metrics, dropout_rngs) optimizer = train_lib.unbroadcast(optimizer) host_step += 1 logging.info('END Train loop.') # Maybe save a checkpoint on one host. if (CFG.save_checkpoints and epoch % CFG.checkpoint_freq == CFG.checkpoint_freq - 1 and jax.host_id() == 0): checkpoints.save_checkpoint(FLAGS.model_dir, optimizer, host_step) # Gather training metrics. metrics = p_allreduce_metrics(metrics) metrics = jax.tree_map(lambda x: jax.device_get(x[0]), metrics) denominator = metrics.pop('denominator') summary = jax.tree_map(lambda x: x / denominator, metrics) # pylint: disable=cell-var-from-loop logging.info('train in step: %s, %s', host_step, summary) if jax.host_id() == 0: assert train_summary_writer for key, val in summary.items(): train_summary_writer.scalar(key, val, host_step) train_summary_writer.flush() # Gather training evaluation metrics. logging.info('Gathering training evaluation metrics.') eval_metrics = [] eval_iter = eval_ds.as_numpy_iterator() for _, eval_batch in zip(range(CFG.num_eval_steps), eval_iter): eval_batch = jax.tree_map( lambda x: x.reshape( (topology.per_replica_set_num_replicas, -1) + x.shape[1:]), eval_batch) metrics = p_eval_step(optimizer.target, eval_batch) eval_metrics.append(metrics) # average metrics across devices eval_metrics = p_allreduce_metrics(eval_metrics) eval_metrics = common_utils.get_metrics(eval_metrics) # average metrics across steps eval_metrics = jax.tree_map(np.sum, eval_metrics) eval_denominator = eval_metrics.pop('denominator') eval_summary = jax.tree_map( lambda x: x / eval_denominator, # pylint: disable=cell-var-from-loop eval_metrics) logging.info('eval in step: %s, %s', host_step, eval_summary) if jax.host_id() == 0: assert eval_summary_writer for key, val in eval_summary.items(): eval_summary_writer.scalar(key, val, host_step) eval_summary_writer.flush() # Wait until computations are done before exiting logging.info('Finished.') train_lib.sync_devices() # Shut down the infeed threadpool. if CFG.infeed: infeed_pool.shutdown() if __name__ == '__main__': app.run(main)

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100

0.681038

from concurrent.futures import thread import functools import importlib import os from typing import Any, Mapping, Sequence, Tuple from absl import app from absl import flags from absl import logging os.environ['FLAX_PROFILE'] = 'true' from flax import linen as nn from flax import optim from flax.metrics import tensorboard from flax.training import checkpoints from flax.training import common_utils import jax from jax import lax from jax import random from jax.interpreters.sharded_jit import sharded_jit import jax.numpy as jnp import ml_collections from ml_collections import config_flags import numpy as np import t5 from t5x import checkpoint_importer from t5x import input_pipeline from t5x import models from t5x import partitions from t5x import train_lib import tensorflow as tf FLAGS = flags.FLAGS CFG = None PyTreeDef = type(jax.tree_structure(None)) TransformerConfig = models.TransformerConfig jax.config.parse_flags_with_absl() flags.DEFINE_string( 'model_dir', default=None, help='Directory to store model data.') flags.DEFINE_string( 'data_dir', default=None, help='Tensorflow datasets directory.') config_flags.DEFINE_config_file( name='config', default='configs/t5_small_glue.py', help_string='training config file.') ConfigDict = ml_collections.ConfigDict def get_configs( config ): train_config = TransformerConfig( vocab_size=config.vocab_size, output_vocab_size=config.vocab_size, share_embeddings=config.share_embeddings, logits_via_embedding=config.logits_via_embedding, dtype=jnp.bfloat16 if config.use_bfloat16 else jnp.float32, emb_dim=config.emb_dim, num_heads=config.num_heads, num_layers=config.num_layers, qkv_dim=config.qkv_dim, mlp_dim=config.mlp_dim, mlp_activations=config.mlp_activations, position_embeddings='relative', relative_attention_num_buckets=config.relative_attention_num_buckets, relative_attention_max_distance=config.relative_attention_max_distance, max_len=max(config.max_input_length, config.max_target_length, config.max_eval_input_length, config.max_eval_target_length), dropout_rate=config.dropout_rate, attention_dropout_rate=config.attention_dropout_rate, deterministic=False, decode=False, kernel_init=nn.initializers.xavier_uniform(), bias_init=nn.initializers.normal(stddev=1e-6)) eval_config = train_config.replace(deterministic=True) predict_config = train_config.replace( deterministic=True, decode=True, max_decode_len=config.max_eval_target_length) return (train_config, eval_config, predict_config) def get_initial_params(rng, config, transformer_config, optimizer_def): input_shape = (config.batch_size, CFG.max_input_length) target_shape = (config.batch_size, CFG.max_target_length) initial_variables = models.Transformer(transformer_config).init( rng, jnp.ones(input_shape, jnp.float32), jnp.ones(target_shape, jnp.float32)) return optimizer_def.create(initial_variables['params']) def main(argv): global CFG CFG = FLAGS.config if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') _ = np.array(jnp.array([1.0], dtype=jnp.bfloat16)) if CFG.hardware_rng: models.set_hardware_bernoulli() if 'module_import' in CFG and CFG.module_import: for module in CFG.module_import: importlib.import_module(module) if 'additional_task_cache_dirs' in CFG and CFG.additional_task_cache_dirs: t5.data.add_global_cache_dirs(CFG.additional_task_cache_dirs) num_partitions = CFG.num_partitions topology = train_lib.compute_multihost_topology(num_partitions) batch_size = CFG.batch_size eval_batch_size = CFG.eval_batch_size per_replica_set_eval_batch_size = eval_batch_size // topology.num_replica_sets if batch_size % topology.num_replicas: raise ValueError('Batch size must be divisible by the number of replicas.') steps_per_epoch = CFG.steps_per_epoch logging.info('steps per epoch: %d', steps_per_epoch) broadcast = functools.partial( train_lib.broadcast, num_replicas=topology.per_replica_set_num_replicas, num_partitions=topology.per_host_num_partitions, devices=topology.this_host_device_assignment) if jax.host_id() == 0: tf.io.gfile.makedirs(FLAGS.model_dir) tf.io.gfile.copy(FLAGS['config'].config_filename, os.path.join(FLAGS.model_dir, 'config.py'), overwrite=True) train_summary_writer = tensorboard.SummaryWriter( os.path.join(FLAGS.model_dir, 'train')) eval_summary_writer = tensorboard.SummaryWriter( os.path.join(FLAGS.model_dir, 'eval')) else: train_summary_writer = None eval_summary_writer = None if CFG.infeed: infeed_pool = thread.ThreadPoolExecutor(jax.local_device_count(), 'infeed') (train_ds, eval_ds), eval_cache = input_pipeline.get_datasets_and_cache( CFG, topology.num_replica_sets, topology.replica_set_id, topology.per_replica_set_host_id) vocab = input_pipeline.get_vocabulary(CFG.mixture_or_task_name) encoder = vocab.tf_tokenizer eos_id = vocab.tokenizer.eos_id() def decode_tokens(toks, eos_id = eos_id, max_id = 32000): del eos_id # is the best decoding function or just switch to using tf_decode. # valid_toks = toks[:np.argmax(toks == eos_id) + 1].astype(np.int32) valid_toks = toks.astype(np.int32) valid_toks[valid_toks >= max_id] = 3 return encoder.detokenize(valid_toks).numpy().decode('utf-8') logging.info('Initializing model, optimizer, and step functions.') train_config, eval_config, predict_config = get_configs(CFG) rng = random.PRNGKey(CFG.random_seed) rng, init_rng = random.split(rng) # This is used for infeed conversion from feature dict <--> tuple train_keys = [ 'inputs', 'targets', 'inputs_position', 'targets_position', 'inputs_segmentation', 'targets_segmentation' ] device_train_input_shape = tuple([ (batch_size // topology.num_replicas, CFG.max_input_length if 'inputs' in k else CFG.max_target_length) for k in train_keys ]) learning_rate_fn = train_lib.create_learning_rate_scheduler( factors=CFG.schedule, base_learning_rate=CFG.learning_rate, warmup_steps=CFG.warmup_steps) # First, we only abstractly initialize the optimizer and model parameters, # since the parameters may not even fit in device memory! # TODO(jekbradbury): make optimizer_defs compare by value so it can be created # in get_initial_params without causing pytree incompatibility optimizer_def = optim.Adafactor( CFG.learning_rate, decay_rate=0.8, step_offset=CFG.step_offset) initialize_params_fn = functools.partial( get_initial_params, config=CFG, transformer_config=eval_config, optimizer_def=optimizer_def) optimizer = jax.eval_shape(initialize_params_fn, init_rng) # tuple-like pytree leaves for global_arg_shapes optimizer_shapes = jax.tree_map(lambda x: partitions.Spec(*x.shape), optimizer) # Build parameter partition annotations for preserving partitions from train # to eval. if num_partitions > 1: optimizer_partitions = optimizer.restore_state( partitions.set_partitions(num_partitions, optimizer.state_dict())) per_host_optimizer_partitions = optimizer.restore_state( partitions.set_partitions(topology.per_host_num_partitions, optimizer.state_dict())) # Restore unreplicated optimizer + model state from last checkpoint. # TODO(jekbradbury,levskaya): implement sharded native checkpoint/restore existing_checkpoint_found = False if CFG.restore_checkpoints: existing_checkpoint_found = train_lib.checkpoint_exists(FLAGS.model_dir) optimizer = checkpoints.restore_checkpoint(FLAGS.model_dir, optimizer) # Import a pretrained-T5 checkpoint only if we didn't import a local if CFG.restore_t5_checkpoint and not existing_checkpoint_found: optimizer = checkpoint_importer.restore_from_t5_checkpoint( optimizer, CFG.restore_t5_checkpoint) if CFG.restore_t5_checkpoint or existing_checkpoint_found: if num_partitions > 1: def per_host_chunk(x, spec): if spec is None or spec is x: return x if spec[0] == 1: dim_size = x.shape[1] elif spec[1] == 1: dim_size = x.shape[0] else: raise NotImplementedError() chunk_size = ( dim_size * topology.per_host_num_partitions // num_partitions) lower = topology.per_replica_set_host_id * chunk_size upper = (topology.per_replica_set_host_id + 1) * chunk_size if spec[0] == 1: return x[:, lower:upper] else: return x[lower:upper] optimizer = jax.tree_multimap(per_host_chunk, optimizer, optimizer_partitions) else: if num_partitions > 1: initialize_params_fn = sharded_jit( initialize_params_fn, in_parts=None, local_in_parts=None, out_parts=optimizer_partitions, local_out_parts=per_host_optimizer_partitions, ) initialize_params_fn = jax.pmap( initialize_params_fn, 'batch', in_axes=0, axis_size=topology.num_replicas, devices=topology.device_assignment) init_rng = broadcast(init_rng) optimizer = initialize_params_fn(init_rng) optimizer = train_lib.unbroadcast(optimizer) else: optimizer = jax.jit(initialize_params_fn)(init_rng) def p_train_step(optimizer, batch, prev_metrics, dropout_rng): return train_lib.train_step( optimizer, batch, prev_metrics, dropout_rng, config=train_config, learning_rate_fn=learning_rate_fn, num_microbatches=CFG.microbatches, label_smoothing=CFG.label_smoothing, z_loss=CFG.z_loss, use_bfloat16=CFG.use_bfloat16) if num_partitions > 1: p_train_step = sharded_jit( p_train_step, in_parts=(optimizer_partitions, None, None, None), local_in_parts=(per_host_optimizer_partitions, None, None, None), out_parts=(optimizer_partitions, None, None), local_out_parts=(per_host_optimizer_partitions, None, None)) p_train_step = jax.pmap( p_train_step, axis_name='batch', in_axes=(None, 0, 0, 0), donate_argnums=(0,), global_arg_shapes=(optimizer_shapes, None, None, None), axis_size=topology.num_replicas, devices=topology.device_assignment) def device_train_loop_cond( args ): _, _, _, _, step, epoch = args return step // steps_per_epoch == epoch def device_train_loop_body( args ): optimizer, dropout_rngs, metrics, token, step, epoch = args input_data, token = lax.infeed( token, shape=tuple( [jax.ShapedArray(s, jnp.int32) for s in device_train_input_shape])) batch = {k: v for k, v in zip(train_keys, input_data)} optimizer, metrics, dropout_rngs = train_lib.train_step( optimizer, batch, metrics, dropout_rngs, train_config, learning_rate_fn, num_microbatches=CFG.microbatches, label_smoothing=CFG.label_smoothing, z_loss=CFG.z_loss) step += 1 return optimizer, dropout_rngs, metrics, token, step, epoch def device_train_loop(optimizer, dropout_rngs, metrics, step, epoch): token = lax.create_token(step) optimizer, dropout_rngs, metrics, _, step, _ = lax.while_loop( device_train_loop_cond, device_train_loop_body, (optimizer, dropout_rngs, metrics, token, step, epoch)) return optimizer, dropout_rngs, metrics, step if num_partitions > 1: device_train_loop = sharded_jit( device_train_loop, in_parts=(optimizer_partitions, None, None, None, None), local_in_parts=(per_host_optimizer_partitions, None, None, None, None), out_parts=(optimizer_partitions, None, None, None), local_out_parts=(per_host_optimizer_partitions, None, None, None)) p_train_epoch = jax.pmap( device_train_loop, axis_name='batch', in_axes=(None, 0, 0, None, None), donate_argnums=(0,), global_arg_shapes=(optimizer_shapes, None, None, None, None), axis_size=topology.num_replicas, devices=topology.device_assignment) def p_allreduce_metrics(x): return lax.psum(x, axis_name='batch') if num_partitions > 1: p_allreduce_metrics = sharded_jit( p_allreduce_metrics, in_parts=None, local_in_parts=None, out_parts=None, local_out_parts=None, num_partitions=num_partitions, local_num_partitions=topology.per_host_num_partitions) p_allreduce_metrics = jax.pmap( p_allreduce_metrics, axis_name='batch', global_arg_shapes=None, axis_size=topology.num_replicas, devices=topology.device_assignment) def p_eval_step(params, batch): return train_lib.eval_step( params, batch, config=eval_config, label_smoothing=CFG.label_smoothing) if num_partitions > 1: p_eval_step = sharded_jit( p_eval_step, in_parts=(optimizer_partitions.target, None), local_in_parts=(per_host_optimizer_partitions.target, None), out_parts=None, local_out_parts=None) p_eval_step = jax.pmap( p_eval_step, axis_name='batch', in_axes=(None, 0), global_arg_shapes=(optimizer_shapes.target, None), axis_size=topology.num_replicas, devices=topology.device_assignment) def p_pred_step(inputs, params): return train_lib.predict_step(inputs, params, eos_id, CFG.max_eval_target_length, predict_config, CFG.beam_size) if num_partitions > 1: p_pred_step = sharded_jit( p_pred_step, in_parts=(None, optimizer_partitions.target), local_in_parts=(None, per_host_optimizer_partitions.target), out_parts=None, local_out_parts=None) p_pred_step = jax.pmap( p_pred_step, axis_name='batch', in_axes=(0, None), global_arg_shapes=(None, optimizer_shapes.target), axis_size=topology.num_replicas, devices=topology.device_assignment) # There should be a unique dropout key for each replica represented on this # host, but the key should be the same for the same replica on other hosts. # Again, this is what the replica set abstraction is for. dropout_rngs = random.split( random.fold_in(rng, topology.replica_set_id), topology.per_replica_set_num_replicas) # restore step from last checkpoint host_step = int(optimizer.state.step) empty_metrics = broadcast({ 'loss': 0.0, 'accuracy': 0.0, 'learning_rate': 0.0, 'denominator': 0.0 }) if CFG.infeed: # TODO(jekbradbury): support something like this for the Python-loop case logging.info('Precompiling training loop and moving optimizer to device.') optimizer, _, metrics, _ = p_train_epoch(optimizer, dropout_rngs, empty_metrics, jnp.array(0, dtype=jnp.int32), 1) optimizer = train_lib.unbroadcast(optimizer) metrics['loss'].block_until_ready() logging.info('Starting training loop.') local_devices = jax.local_devices() device_step = broadcast(host_step) first_epoch = host_step // steps_per_epoch # Main Loop over "epochs". train_iter = train_ds.as_numpy_iterator() for epoch in range(first_epoch, first_epoch + CFG.num_epochs): metrics = empty_metrics # NOTE: 'optimizer' is unbroadcast by construction at initialization or # when loading a checkpoint. It is maintained in 'unbroadcast' state to # enable the XLA cross-replica sharding optimization. The broadcasting is # handled automatically by the pmap'd functions that use it. logging.info('Evaluating tasks.') if epoch == first_epoch + 1: train_lib.sync_devices() for task in eval_cache.tasks: logging.info('Evaluating task %s', task.name) all_predicted, all_bs = [], [] for pred_batch in eval_cache.preprocessed_examples[task.name]: input_batch, unpadded_batch_size = train_lib.pad_batch_to_size( pred_batch['inputs'], per_replica_set_eval_batch_size) all_bs.append(unpadded_batch_size) input_batch = jax.tree_map( lambda x: x.reshape( (topology.per_replica_set_num_replicas, -1) + x.shape[1:]), input_batch) all_predicted.append(p_pred_step(input_batch, optimizer.target)) max_host_batch_number = np.max( eval_cache.preprocessed_batch_sizes[task.name]) batch_shortfall = max_host_batch_number - len(all_predicted) if batch_shortfall > 0: p_pred_step(input_batch, optimizer.target) all_predicted.extend([jnp.zeros_like(all_predicted[0])] * batch_shortfall) all_bs.extend([0] * batch_shortfall) all_predicted = jnp.concatenate(all_predicted) all_bs = jnp.array(all_bs) all_predicted = train_lib.host_allgather( all_predicted, topology.num_replica_sets, topology.replica_set_id, topology.per_replica_set_host_id == 0) seqlength = all_predicted.shape[-1] total_examples = np.sum( train_lib.host_allgather(all_bs, topology.num_replica_sets, topology.replica_set_id, topology.per_replica_set_host_id == 0)) del all_bs assert total_examples == len(eval_cache.examples[task.name]), ( 'Total number of batches incorrect for task %s.' % task.name) all_predicted = np.transpose(all_predicted, (1, 2, 0, 3)).reshape( -1, seqlength)[:total_examples] if jax.host_id() == 0: assert eval_summary_writer raw_predictions = [] for tokens in all_predicted: raw_predictions.append(decode_tokens(tokens)) predictions = [ task.postprocess_fn(p, example=ex) for p, ex in zip(raw_predictions, eval_cache.examples[task.name]) ] for metric_fn in task.metric_fns: scores = metric_fn(eval_cache.targets[task.name], predictions) for metric_name, metric_value in scores.items(): tag = f'eval/{task.name}/{metric_name}' eval_summary_writer.scalar(tag, metric_value, host_step) logging.info('EVAL %s at step %d: %.3f', tag, host_step, metric_value) eval_summary_writer.flush() exemplars = '' for n in np.random.choice(np.arange(len(predictions)), 8): tgt_txt = tf.compat.as_text( eval_cache.examples[task.name][n]['targets_plaintext']) pred_txt = raw_predictions[n] exemplars += (f'{eval_cache.inputs[task.name][n]}\n\n' f'target: {tgt_txt}\n\n' f'prediction: {pred_txt}\n\n') eval_summary_writer.text(f'{task.name} samples', exemplars, host_step) eval_summary_writer.flush() if epoch == first_epoch + 1: train_lib.sync_devices() logging.info('BEGIN Train loop.') if CFG.infeed: optimizer, dropout_rngs, metrics, device_step = p_train_epoch( optimizer, dropout_rngs, metrics, train_lib.unbroadcast(device_step), epoch) optimizer = train_lib.unbroadcast(optimizer) while int(host_step // steps_per_epoch) == epoch: batch = next(train_iter) batch = jax.tree_map( lambda x: x.reshape( (topology.per_replica_set_num_replicas, -1) + x.shape[1:]), batch) if CFG.infeed: for i, device in enumerate(local_devices): # own for feeding the right values to the right devices. Each device # should get the minibatch corresponding to its replica, a slice of # the larger batch corresponding to the host's replica set. if device.platform == 'tpu': device_coords = (*device.coords, device.id % 2) else: device_coords = (device.host_id, i) per_replica_set_device_coords = tuple( dc % prsm for dc, prsm in zip(device_coords, topology.per_replica_set_mesh)) per_replica_set_replica_coords = tuple( prsdc // prm for prsdc, prm in zip(per_replica_set_device_coords, topology.per_replica_mesh)) per_replica_set_replica_id = 0 for prsm, prm, prsrc in zip(topology.per_replica_set_mesh, topology.per_replica_mesh, per_replica_set_replica_coords): per_replica_set_replica_id = ( per_replica_set_replica_id * prsm // prm + prsrc) input_tuple = tuple( [batch[k][per_replica_set_replica_id] for k in train_keys]) tuple_shapes = jax.tree_map(jnp.shape, input_tuple) tuple_dtypes = jax.tree_map(lambda x: x.dtype, input_tuple) assert tuple_shapes == device_train_input_shape, ( 'infeed shape error %s != %s' % (tuple_shapes, device_train_input_shape)) assert tuple(set(tuple_dtypes)) == (jnp.int32,), \ ('infeed dtype error %s not all of type %s' % ( tuple_dtypes, jnp.int32)) infeed_pool.submit( functools.partial(device.transfer_to_infeed, input_tuple)) else: optimizer, metrics, dropout_rngs = p_train_step(optimizer, batch, metrics, dropout_rngs) optimizer = train_lib.unbroadcast(optimizer) host_step += 1 logging.info('END Train loop.') if (CFG.save_checkpoints and epoch % CFG.checkpoint_freq == CFG.checkpoint_freq - 1 and jax.host_id() == 0): checkpoints.save_checkpoint(FLAGS.model_dir, optimizer, host_step) metrics = p_allreduce_metrics(metrics) metrics = jax.tree_map(lambda x: jax.device_get(x[0]), metrics) denominator = metrics.pop('denominator') summary = jax.tree_map(lambda x: x / denominator, metrics) logging.info('train in step: %s, %s', host_step, summary) if jax.host_id() == 0: assert train_summary_writer for key, val in summary.items(): train_summary_writer.scalar(key, val, host_step) train_summary_writer.flush() logging.info('Gathering training evaluation metrics.') eval_metrics = [] eval_iter = eval_ds.as_numpy_iterator() for _, eval_batch in zip(range(CFG.num_eval_steps), eval_iter): eval_batch = jax.tree_map( lambda x: x.reshape( (topology.per_replica_set_num_replicas, -1) + x.shape[1:]), eval_batch) metrics = p_eval_step(optimizer.target, eval_batch) eval_metrics.append(metrics) eval_metrics = p_allreduce_metrics(eval_metrics) eval_metrics = common_utils.get_metrics(eval_metrics) eval_metrics = jax.tree_map(np.sum, eval_metrics) eval_denominator = eval_metrics.pop('denominator') eval_summary = jax.tree_map( lambda x: x / eval_denominator, eval_metrics) logging.info('eval in step: %s, %s', host_step, eval_summary) if jax.host_id() == 0: assert eval_summary_writer for key, val in eval_summary.items(): eval_summary_writer.scalar(key, val, host_step) eval_summary_writer.flush() logging.info('Finished.') train_lib.sync_devices() if CFG.infeed: infeed_pool.shutdown() if __name__ == '__main__': app.run(main)

true

f734bfd8dda478c4a821955f2b8d8d088ca859db

47,394

py

Python

botcity/core/bot.py

lf2a/botcity-framework-core-python-2

eaa073a4f0b5099b0684400533f7427e001648e5

[ "Apache-2.0" ]

null

botcity/core/bot.py

lf2a/botcity-framework-core-python-2

eaa073a4f0b5099b0684400533f7427e001648e5

[ "Apache-2.0" ]

null

botcity/core/bot.py

lf2a/botcity-framework-core-python-2

eaa073a4f0b5099b0684400533f7427e001648e5

[ "Apache-2.0" ]

null

import os import functools import multiprocessing import platform import random import subprocess import time import webbrowser import pyautogui import pyperclip from PIL import Image from botcity.base import BaseBot, State from botcity.base.utils import is_retina, only_if_element from . import config, os_compat try: from botcity.maestro import BotMaestroSDK MAESTRO_AVAILABLE = True except ImportError: MAESTRO_AVAILABLE = False class DesktopBot(BaseBot): """ Base class for Desktop Bots. Users must implement the `action` method in their classes. Attributes: state (State): The internal state of this bot. maestro (BotMaestroSDK): an instance to interact with the BotMaestro server. """ def __init__(self): super().__init__() self.state = State() self.maestro = BotMaestroSDK() if MAESTRO_AVAILABLE else None self._interval = 0.005 if platform.system() == "Darwin" else 0.0 # For parity with Java self.addImage = self.add_image self.getImageFromMap = self.get_image_from_map self.getLastElement = self.get_last_element self.getScreenShot = self.get_screenshot self.screenCut = self.screen_cut self.saveScreenshot = self.save_screenshot self.getCoordinates = self.get_element_coords self.getElementCoords = self.get_element_coords self.getElementCoordsCentered = self.get_element_coords_centered self.find = self.find_until self.findUntil = self.find_until self.findText = self.find_text self.findLastUntil = self.find_until # Java API compatibility self.clickOn = self.click_on self.getLastX = self.get_last_x self.getLastY = self.get_last_y self.mouseMove = self.mouse_move self.clickAt = self.click_at self.doubleclick = self.double_click self.doubleClick = self.double_click self.doubleClickRelative = self.double_click_relative self.tripleClick = self.triple_click self.tripleClickRelative = self.triple_click_relative self.scrollDown = self.scroll_down self.scrollUp = self.scroll_up self.moveTo = self.mouse_move self.moveRelative = self.move_relative self.moveRandom = self.move_random self.moveAndClick = self.click self.rightClick = self.right_click self.rightClickAt = self.right_click_at self.rightClickRelative = self.right_click_relative self.moveAndRightClick = self.right_click pyperclip.determine_clipboard() ########## # Display ########## def add_image(self, label, path): """ Add an image into the state image map. Args: label (str): The image identifier path (str): The path for the image on disk """ self.state.map_images[label] = path def get_image_from_map(self, label): """ Return an image from teh state image map. Args: label (str): The image identifier Returns: Image: The Image object """ path = self.state.map_images.get(label) if not path: raise KeyError('Invalid label for image map.') img = Image.open(path) return img def find_multiple(self, labels, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, best=True, grayscale=False): """ Find multiple elements defined by label on screen until a timeout happens. Args: labels (list): A list of image identifiers x (int, optional): Search region start position x. Defaults to 0. y (int, optional): Search region start position y. Defaults to 0. width (int, optional): Search region width. Defaults to screen width. height (int, optional): Search region height. Defaults to screen height. threshold (int, optional): The threshold to be applied when doing grayscale search. Defaults to None. matching (float, optional): The matching index ranging from 0 to 1. Defaults to 0.9. waiting_time (int, optional): Maximum wait time (ms) to search for a hit. Defaults to 10000ms (10s). best (bool, optional): Whether or not to keep looking until the best matching is found. Defaults to True. grayscale (bool, optional): Whether or not to convert to grayscale before searching. Defaults to False. Returns: results (dict): A dictionary in which the key is the label and value are the element coordinates in a NamedTuple. """ def _to_dict(lbs, elems): return {k: v for k, v in zip(lbs, elems)} screen_w, screen_h = pyautogui.size() x = x or 0 y = y or 0 w = width or screen_w h = height or screen_h region = (x, y, w, h) results = [None] * len(labels) paths = [self._search_image_file(la) for la in labels] if threshold: # TODO: Figure out how we should do threshold print('Threshold not yet supported') if not best: # TODO: Implement best=False. print('Warning: Ignoring best=False for now. It will be supported in the future.') start_time = time.time() n_cpus = multiprocessing.cpu_count() - 1 while True: elapsed_time = (time.time() - start_time) * 1000 if elapsed_time > waiting_time: return _to_dict(labels, results) haystack = pyautogui.screenshot() helper = functools.partial(self._find_multiple_helper, haystack, region, matching, grayscale) with multiprocessing.Pool(processes=n_cpus) as pool: results = pool.map(helper, paths) results = [self._fix_retina_element(r) for r in results] if None in results: continue else: return _to_dict(labels, results) def _fix_retina_element(self, ele): if not is_retina(): return ele if ele is not None: if is_retina(): ele = ele._replace(left=ele.left / 2.0, top=ele.top / 2.0) return ele def _find_multiple_helper(self, haystack, region, confidence, grayscale, needle): ele = pyautogui.locate(needle, haystack, region=region, confidence=confidence, grayscale=grayscale) return ele def find(self, label, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, best=True, grayscale=False): """ Find an element defined by label on screen until a timeout happens. Args: label (str): The image identifier x (int, optional): Search region start position x. Defaults to 0. y (int, optional): Search region start position y. Defaults to 0. width (int, optional): Search region width. Defaults to screen width. height (int, optional): Search region height. Defaults to screen height. threshold (int, optional): The threshold to be applied when doing grayscale search. Defaults to None. matching (float, optional): The matching index ranging from 0 to 1. Defaults to 0.9. waiting_time (int, optional): Maximum wait time (ms) to search for a hit. Defaults to 10000ms (10s). best (bool, optional): Whether or not to keep looking until the best matching is found. Defaults to True. grayscale (bool, optional): Whether or not to convert to grayscale before searching. Defaults to False. Returns: element (NamedTuple): The element coordinates. None if not found. """ return self.find_until(label, x=x, y=y, width=width, height=height, threshold=threshold, matching=matching, waiting_time=waiting_time, best=best, grayscale=grayscale) def find_until(self, label, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, best=True, grayscale=False): """ Find an element defined by label on screen until a timeout happens. Args: label (str): The image identifier x (int, optional): Search region start position x. Defaults to 0. y (int, optional): Search region start position y. Defaults to 0. width (int, optional): Search region width. Defaults to screen width. height (int, optional): Search region height. Defaults to screen height. threshold (int, optional): The threshold to be applied when doing grayscale search. Defaults to None. matching (float, optional): The matching index ranging from 0 to 1. Defaults to 0.9. waiting_time (int, optional): Maximum wait time (ms) to search for a hit. Defaults to 10000ms (10s). best (bool, optional): Whether or not to keep looking until the best matching is found. Defaults to True. grayscale (bool, optional): Whether or not to convert to grayscale before searching. Defaults to False. Returns: element (NamedTuple): The element coordinates. None if not found. """ self.state.element = None screen_w, screen_h = pyautogui.size() x = x or 0 y = y or 0 w = width or screen_w h = height or screen_h region = (x, y, w, h) element_path = self._search_image_file(label) if threshold: # TODO: Figure out how we should do threshold print('Threshold not yet supported') if not best: # TODO: Implement best=False. print('Warning: Ignoring best=False for now. It will be supported in the future.') start_time = time.time() while True: elapsed_time = (time.time() - start_time) * 1000 if elapsed_time > waiting_time: return None ele = pyautogui.locateOnScreen(element_path, region=region, confidence=matching, grayscale=grayscale) if ele is not None: if is_retina(): ele = ele._replace(left=ele.left / 2.0, top=ele.top / 2.0) self.state.element = ele return ele def find_all(self, label, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, grayscale=False): """ Find all elements defined by label on screen until a timeout happens. Args: label (str): The image identifier x (int, optional): Search region start position x. Defaults to 0. y (int, optional): Search region start position y. Defaults to 0. width (int, optional): Search region width. Defaults to screen width. height (int, optional): Search region height. Defaults to screen height. threshold (int, optional): The threshold to be applied when doing grayscale search. Defaults to None. matching (float, optional): The matching index ranging from 0 to 1. Defaults to 0.9. waiting_time (int, optional): Maximum wait time (ms) to search for a hit. Defaults to 10000ms (10s). grayscale (bool, optional): Whether or not to convert to grayscale before searching. Defaults to False. Returns: elements (collections.Iterable[NamedTuple]): A generator with all element coordinates fount. None if not found. """ def deduplicate(elems): def find_same(item, items): x_start = item.left x_end = item.left + item.width y_start = item.top y_end = item.top + item.height similars = [] for itm in items: if itm == item: continue if (itm.left >= x_start and itm.left < x_end)\ and (itm.top >= y_start and itm.top < y_end): similars.append(itm) continue return similars index = 0 while True: try: dups = find_same(elems[index], elems[index:]) for d in dups: elems.remove(d) index += 1 except IndexError: break return elems self.state.element = None screen_w, screen_h = pyautogui.size() x = x or 0 y = y or 0 w = width or screen_w h = height or screen_h region = (x, y, w, h) element_path = self._search_image_file(label) if threshold: # TODO: Figure out how we should do threshold print('Threshold not yet supported') start_time = time.time() while True: elapsed_time = (time.time() - start_time) * 1000 if elapsed_time > waiting_time: return None eles = pyautogui.locateAllOnScreen(element_path, region=region, confidence=matching, grayscale=grayscale) if not eles: continue eles = deduplicate(list(eles)) for ele in eles: if ele is not None: if is_retina(): ele = ele._replace(left=ele.left / 2.0, top=ele.top / 2.0) self.state.element = ele yield ele break def find_text(self, label, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, best=True): """ Find an element defined by label on screen until a timeout happens. Args: label (str): The image identifier x (int, optional): Search region start position x. Defaults to 0. y (int, optional): Search region start position y. Defaults to 0. width (int, optional): Search region width. Defaults to screen width. height (int, optional): Search region height. Defaults to screen height. threshold (int, optional): The threshold to be applied when doing grayscale search. Defaults to None. matching (float, optional): The matching index ranging from 0 to 1. Defaults to 0.9. waiting_time (int, optional): Maximum wait time (ms) to search for a hit. Defaults to 10000ms (10s). best (bool, optional): Whether or not to keep looking until the best matching is found. Defaults to True. Returns: element (NamedTuple): The element coordinates. None if not found. """ return self.find_until(label, x, y, width, height, threshold=threshold, matching=matching, waiting_time=waiting_time, best=best, grayscale=True) def get_last_element(self): """ Return the last element found. Returns: element (NamedTuple): The element coordinates (left, top, width, height) """ return self.state.element def display_size(self): """ Returns the display size in pixels. Returns: size (Tuple): The screen dimension (width and height) in pixels. """ screen_size = pyautogui.size() return screen_size.width, screen_size.height def screenshot(self, filepath=None, region=None): """ Capture a screenshot. Args: filepath (str, optional): The filepath in which to save the screenshot. Defaults to None. region (tuple, optional): Bounding box containing left, top, width and height to crop screenshot. Returns: Image: The screenshot Image object """ img = pyautogui.screenshot(filepath, region) return img def get_screenshot(self, filepath=None, region=None): """ Capture a screenshot. Args: filepath (str, optional): The filepath in which to save the screenshot. Defaults to None. region (tuple, optional): Bounding box containing left, top, width and height to crop screenshot. Returns: Image: The screenshot Image object """ return self.screenshot(filepath, region) def screen_cut(self, x, y, width=None, height=None): """ Capture a screenshot from a region of the screen. Args: x (int): region start position x y (int): region start position y width (int): region width height (int): region height Returns: Image: The screenshot Image object """ screen_size = pyautogui.size() x = x or 0 y = y or 0 width = width or screen_size.width height = height or screen_size.height img = pyautogui.screenshot(region=(x, y, width, height)) return img def save_screenshot(self, path): """ Saves a screenshot in a given path. Args: path (str): The filepath in which to save the screenshot """ pyautogui.screenshot(path) def get_element_coords(self, label, x=None, y=None, width=None, height=None, matching=0.9, best=True): """ Find an element defined by label on screen and returns its coordinates. Args: label (str): The image identifier x (int, optional): X (Left) coordinate of the search area. y (int, optional): Y (Top) coordinate of the search area. width (int, optional): Width of the search area. height (int, optional): Height of the search area. matching (float, optional): Minimum score to consider a match in the element image recognition process. Defaults to 0.9. best (bool, optional): Whether or not to search for the best value. If False the method returns on the first find. Defaults to True. Returns: coords (Tuple): A tuple containing the x and y coordinates for the element. """ self.state.element = None screen_size = pyautogui.size() x = x or 0 y = y or 0 width = width or screen_size.width height = height or screen_size.height region = (x, y, width, height) if not best: print('Warning: Ignoring best=False for now. It will be supported in the future.') ele = pyautogui.locateOnScreen(self._search_image_file(label), region=region, confidence=matching) if ele is None: return None, None if is_retina(): ele = ele._replace(left=ele.left / 2.0, top=ele.top / 2.0) self.state.element = ele return ele.left, ele.top def get_element_coords_centered(self, label, x=None, y=None, width=None, height=None, matching=0.9, best=True): """ Find an element defined by label on screen and returns its centered coordinates. Args: label (str): The image identifier x (int, optional): X (Left) coordinate of the search area. y (int, optional): Y (Top) coordinate of the search area. width (int, optional): Width of the search area. height (int, optional): Height of the search area. matching (float, optional): Minimum score to consider a match in the element image recognition process. Defaults to 0.9. best (bool, optional): Whether or not to search for the best value. If False the method returns on the first find. Defaults to True. Returns: coords (Tuple): A tuple containing the x and y coordinates for the center of the element. """ self.get_element_coords(label, x, y, width, height, matching, best) return self.state.center() ######### # Browser ######### def browse(self, url, location=0): """ Invoke the default browser passing an URL Args: url (str): The URL to be visited. location (int): If possible, open url in a location determined by new: * 0: the same browser window (the default) * 1: a new browser window * 2: a new browser page ("tab") Returns: bool: Whether or not the request was successful """ status = webbrowser.open(url, location) return status ####### # Mouse ####### def click_on(self, label): """ Click on the element. Args: label (str): The image identifier """ x, y = self.get_element_coords_centered(label) if None in (x, y): raise ValueError(f'Element not available. Cannot find {label}.') os_compat.click(x, y) def get_last_x(self): """ Get the last X position for the mouse. Returns: x (int): The last x position for the mouse. """ return pyautogui.position().x def get_last_y(self): """ Get the last Y position for the mouse. Returns: y (int): The last y position for the mouse. """ return pyautogui.position().y def mouse_move(self, x, y): """ Mouse the move to the coordinate defined by x and y Args: x (int): The X coordinate y (int): The Y coordinate """ pyautogui.moveTo(x, y) def click_at(self, x, y): """ Click at the coordinate defined by x and y Args: x (int): The X coordinate y (int): The Y coordinate """ os_compat.click(x, y) @only_if_element def click(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, clicks=1, interval_between_clicks=0, button='left'): """ Click on the last found element. Args: wait_after (int, optional): Interval to wait after clicking on the element. clicks (int, optional): Number of times to click. Defaults to 1. interval_between_clicks (int, optional): The interval between clicks in ms. Defaults to 0. button (str, optional): One of 'left', 'right', 'middle'. Defaults to 'left' """ x, y = self.state.center() os_compat.click(x, y, clicks=clicks, button=button, interval=interval_between_clicks/1000.0) self.sleep(wait_after) @only_if_element def click_relative(self, x, y, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, clicks=1, interval_between_clicks=0, button='left'): """ Click Relative on the last found element. Args: x (int): Horizontal offset y (int): Vertical offset wait_after (int, optional): Interval to wait after clicking on the element. clicks (int, optional): Number of times to click. Defaults to 1. interval_between_clicks (int, optional): The interval between clicks in ms. Defaults to 0. button (str, optional): One of 'left', 'right', 'middle'. Defaults to 'left' """ x = self.state.x() + x y = self.state.y() + y os_compat.click(x, y, clicks=clicks, button=button, interval=interval_between_clicks/1000.0) self.sleep(wait_after) @only_if_element def double_click(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): """ Double Click on the last found element. Args: wait_after (int, optional): Interval to wait after clicking on the element. """ self.click(wait_after=wait_after, clicks=2) @only_if_element def double_click_relative(self, x, y, interval_between_clicks=0, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): """ Double Click Relative on the last found element. Args: x (int): Horizontal offset y (int): Vertical offset interval_between_clicks (int, optional): The interval between clicks in ms. Defaults to 0. wait_after (int, optional): Interval to wait after clicking on the element. """ self.click_relative(x, y, wait_after=wait_after, clicks=2, interval_between_clicks=interval_between_clicks) @only_if_element def triple_click(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): """ Triple Click on the last found element. Args: wait_after (int, optional): Interval to wait after clicking on the element. """ self.click(wait_after=wait_after, clicks=3) @only_if_element def triple_click_relative(self, x, y, interval_between_clicks=0, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): """ Triple Click Relative on the last found element. Args: x (int): Horizontal offset y (int): Vertical offset interval_between_clicks (int, optional): The interval between clicks in ms. Defaults to 0. wait_after (int, optional): Interval to wait after clicking on the element. """ self.click_relative(x, y, wait_after=wait_after, clicks=3, interval_between_clicks=interval_between_clicks) def mouse_down(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, button='left'): """ Holds down the requested mouse button. Args: wait_after (int, optional): Interval to wait after clicking on the element. button (str, optional): One of 'left', 'right', 'middle'. Defaults to 'left' """ pyautogui.mouseDown(button=button) self.sleep(wait_after) def mouse_up(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, button='left'): """ Releases the requested mouse button. Args: wait_after (int, optional): Interval to wait after clicking on the element. button (str, optional): One of 'left', 'right', 'middle'. Defaults to 'left' """ pyautogui.mouseUp(button=button) self.sleep(wait_after) def scroll_down(self, clicks): """ Scroll Down n clicks Args: clicks (int): Number of times to scroll down. """ pyautogui.scroll(-1 * clicks) def scroll_up(self, clicks): """ Scroll Up n clicks Args: clicks (int): Number of times to scroll up. """ pyautogui.scroll(clicks) @only_if_element def move(self): """ Move to the center position of last found item. """ x, y = self.state.center() pyautogui.moveTo(x, y) def move_relative(self, x, y): """ Move the mouse relative to its current position. Args: x (int): Horizontal offset y (int): Vertical offset """ x = self.get_last_x() + x y = self.get_last_y() + y pyautogui.moveTo(x, y) def move_random(self, range_x, range_y): """ Move randomly along the given x, y range. Args: range_x (int): Horizontal range range_y (int): Vertical range """ x = int(random.random() * range_x) y = int(random.random() * range_y) pyautogui.moveTo(x, y) @only_if_element def right_click(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, clicks=1, interval_between_clicks=0): """ Right click on the last found element. Args: wait_after (int, optional): Interval to wait after clicking on the element. clicks (int, optional): Number of times to click. Defaults to 1. interval_between_clicks (int, optional): The interval between clicks in ms. Defaults to 0. """ x, y = self.state.center() os_compat.click(x, y, clicks=clicks, button='right', interval=interval_between_clicks/1000.0) self.sleep(wait_after) def right_click_at(self, x, y): """ Right click at the coordinate defined by x and y Args: x (int): The X coordinate y (int): The Y coordinate """ os_compat.click(x, y, button='right') @only_if_element def right_click_relative(self, x, y, interval_between_clicks=0, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): """ Right Click Relative on the last found element. Args: x (int): Horizontal offset y (int): Vertical offset interval_between_clicks (int, optional): The interval between clicks in ms. Defaults to 0. wait_after (int, optional): Interval to wait after clicking on the element. """ self.click_relative(x, y, wait_after=wait_after, clicks=3, interval_between_clicks=interval_between_clicks, button='right') ########## # Keyboard ########## def type_key(self, text, interval=0): """ Type a text char by char (individual key events). Args: text (str): text to be typed. interval (int, optional): interval (ms) between each key press. Defaults to 0 """ self.kb_type(text=text, interval=interval/1000.0) def kb_type(self, text, interval=0): """ Type a text char by char (individual key events). Args: text (str): text to be typed. interval (int, optional): interval (ms) between each key press. Defaults to 0 """ pyautogui.write(text, interval=interval/1000.0) self.sleep(config.DEFAULT_SLEEP_AFTER_ACTION) def paste(self, text=None, wait=0): """ Paste content from the clipboard. Args: text (str, optional): The text to be pasted. Defaults to None wait (int, optional): Wait interval (ms) after task """ if text: pyperclip.copy(text) self.control_v() def copy_to_clipboard(self, text, wait=0): """ Copy content to the clipboard. Args: text (str): The text to be copied. wait (int, optional): Wait interval (ms) after task """ pyperclip.copy(text) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def tab(self, wait=0): """ Press key Tab Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('tab') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def enter(self, wait=0): """ Press key Enter Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('enter') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def key_right(self, wait=0): """ Press key Right Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('right') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def key_enter(self, wait=0): """ Press key Enter Args: wait (int, optional): Wait interval (ms) after task """ self.enter(wait) def key_end(self, wait=0): """ Press key End Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('end') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def key_esc(self, wait=0): """ Press key Esc Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('esc') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def _key_fx(self, idx, wait=0): """ Press key F[idx] where idx is a value from 1 to 12 Args: idx (int): F key index from 1 to 12 wait (int, optional): Wait interval (ms) after task """ pyautogui.press(f'f{idx}') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def key_f1(self, wait=0): self._key_fx(1, wait=wait) def key_f2(self, wait=0): self._key_fx(2, wait=wait) def key_f3(self, wait=0): self._key_fx(3, wait=wait) def key_f4(self, wait=0): self._key_fx(4, wait=wait) def key_f5(self, wait=0): self._key_fx(5, wait=wait) def key_f6(self, wait=0): self._key_fx(6, wait=wait) def key_f7(self, wait=0): self._key_fx(7, wait=wait) def key_f8(self, wait=0): self._key_fx(8, wait=wait) def key_f9(self, wait=0): self._key_fx(9, wait=wait) def key_f10(self, wait=0): self._key_fx(10, wait=wait) def key_f11(self, wait=0): self._key_fx(11, wait=wait) def key_f12(self, wait=0): self._key_fx(12, wait=wait) def hold_shift(self, wait=0): """ Hold key Shift Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.keyDown('shift') self.sleep(wait) def release_shift(self): """ Release key Shift. This method needs to be invoked after holding Shift or similar. """ pyautogui.keyUp('shift') def alt_space(self, wait=0): """ Press keys Alt+Space Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.hotkey('alt', 'space', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def maximize_window(self): """ Shortcut to maximize window on Windows OS. """ self.alt_space() self.sleep(1000) pyautogui.press('x') def type_keys_with_interval(self, interval, keys): """ Press a sequence of keys. Hold the keys in the specific order and releases them. Args: interval (int): Interval (ms) in which to press and release keys keys (list): List of keys to be pressed """ pyautogui.hotkey(*keys, interval=interval/1000.0) def type_keys(self, keys): """ Press a sequence of keys. Hold the keys in the specific order and releases them. Args: keys (list): List of keys to be pressed """ self.type_keys_with_interval(100, keys) def alt_e(self, wait=0): """ Press keys Alt+E Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.hotkey('alt', 'e', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def alt_r(self, wait=0): """ Press keys Alt+R Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.hotkey('alt', 'r', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def alt_f(self, wait=0): """ Press keys Alt+F Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.hotkey('alt', 'f', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def alt_u(self, wait=0): """ Press keys Alt+U Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.hotkey('alt', 'u', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def alt_f4(self, wait=0): """ Press keys Alt+F4 Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.hotkey('alt', 'f4', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_c(self, wait=0): """ Press keys CTRL+C Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'c', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) return self.get_clipboard() def control_v(self, wait=0): """ Press keys CTRL+V Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'v', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_a(self, wait=0): """ Press keys CTRL+A Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'a', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_f(self, wait=0): """ Press keys CTRL+F Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'f', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_p(self, wait=0): """ Press keys CTRL+P Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'p', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_u(self, wait=0): """ Press keys CTRL+U Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'u', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_r(self, wait=0): """ Press keys CTRL+R Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'r', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_t(self, wait=0): """ Press keys CTRL+T Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 't', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_end(self, wait=0): """ Press keys CTRL+End Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'end', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_home(self, wait=0): """ Press keys CTRL+Home Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'home', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_w(self, wait=0): """ Press keys CTRL+W Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'w', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_shift_p(self, wait=0): """ Press keys CTRL+Shift+P Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'shift', 'p', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_shift_j(self, wait=0): """ Press keys CTRL+Shift+J Args: wait (int, optional): Wait interval (ms) after task """ key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'shift', 'j', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def shift_tab(self, wait=0): """ Press keys Shift+Tab Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.hotkey('shift', 'tab', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def get_clipboard(self): """ Get the current content in the clipboard. Returns: text (str): Current clipboard content """ return pyperclip.paste() def type_left(self, wait=0): """ Press Left key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('left') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def type_right(self, wait=0): """ Press Right key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('right') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def type_down(self, wait=0): """ Press Down key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('down') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def type_up(self, wait=0): """ Press Up key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('up') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def type_windows(self, wait=0): """ Press Win logo key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('win') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def page_up(self, wait=0): """ Press Page Up key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('pageup') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def page_down(self, wait=0): """ Press Page Down key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('pagedown') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def space(self, wait=0): """ Press Space key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('space') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def backspace(self, wait=0): """ Press Backspace key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('backspace') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def delete(self, wait=0): """ Press Delete key Args: wait (int, optional): Wait interval (ms) after task """ pyautogui.press('delete') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) ###### # Misc ###### def wait_for_file(self, path, timeout=10000): """ Invoke the system handler to open the given file. Args: path (str): The path for the file to be executed timeout (int, optional): Maximum wait time (ms) to search for a hit. Defaults to 10000ms (10s). Returns status (bool): Whether or not the file was available before the timeout """ start_time = time.time() while True: elapsed_time = (time.time() - start_time) * 1000 if elapsed_time > timeout: return False if os.path.isfile(path) and os.access(path, os.R_OK): return True self.sleep(config.DEFAULT_SLEEP_AFTER_ACTION) def execute(self, file_path): """ Invoke the system handler to open the given file. Args: file_path (str): The path for the file to be executed """ if platform.system() == "Windows": os.startfile(file_path) else: subprocess.Popen(file_path.split(" ")) def wait(self, interval): """ Wait / Sleep for a given interval. Args: interval (int): Interval in milliseconds """ time.sleep(interval / 1000.0) def sleep(self, interval): """ Wait / Sleep for a given interval. Args: interval (int): Interval in milliseconds """ self.wait(interval)

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import os import functools import multiprocessing import platform import random import subprocess import time import webbrowser import pyautogui import pyperclip from PIL import Image from botcity.base import BaseBot, State from botcity.base.utils import is_retina, only_if_element from . import config, os_compat try: from botcity.maestro import BotMaestroSDK MAESTRO_AVAILABLE = True except ImportError: MAESTRO_AVAILABLE = False class DesktopBot(BaseBot): def __init__(self): super().__init__() self.state = State() self.maestro = BotMaestroSDK() if MAESTRO_AVAILABLE else None self._interval = 0.005 if platform.system() == "Darwin" else 0.0 self.addImage = self.add_image self.getImageFromMap = self.get_image_from_map self.getLastElement = self.get_last_element self.getScreenShot = self.get_screenshot self.screenCut = self.screen_cut self.saveScreenshot = self.save_screenshot self.getCoordinates = self.get_element_coords self.getElementCoords = self.get_element_coords self.getElementCoordsCentered = self.get_element_coords_centered self.find = self.find_until self.findUntil = self.find_until self.findText = self.find_text self.findLastUntil = self.find_until self.clickOn = self.click_on self.getLastX = self.get_last_x self.getLastY = self.get_last_y self.mouseMove = self.mouse_move self.clickAt = self.click_at self.doubleclick = self.double_click self.doubleClick = self.double_click self.doubleClickRelative = self.double_click_relative self.tripleClick = self.triple_click self.tripleClickRelative = self.triple_click_relative self.scrollDown = self.scroll_down self.scrollUp = self.scroll_up self.moveTo = self.mouse_move self.moveRelative = self.move_relative self.moveRandom = self.move_random self.moveAndClick = self.click self.rightClick = self.right_click self.rightClickAt = self.right_click_at self.rightClickRelative = self.right_click_relative self.moveAndRightClick = self.right_click pyperclip.determine_clipboard() ath def get_image_from_map(self, label): path = self.state.map_images.get(label) if not path: raise KeyError('Invalid label for image map.') img = Image.open(path) return img def find_multiple(self, labels, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, best=True, grayscale=False): def _to_dict(lbs, elems): return {k: v for k, v in zip(lbs, elems)} screen_w, screen_h = pyautogui.size() x = x or 0 y = y or 0 w = width or screen_w h = height or screen_h region = (x, y, w, h) results = [None] * len(labels) paths = [self._search_image_file(la) for la in labels] if threshold: print('Threshold not yet supported') if not best: print('Warning: Ignoring best=False for now. It will be supported in the future.') start_time = time.time() n_cpus = multiprocessing.cpu_count() - 1 while True: elapsed_time = (time.time() - start_time) * 1000 if elapsed_time > waiting_time: return _to_dict(labels, results) haystack = pyautogui.screenshot() helper = functools.partial(self._find_multiple_helper, haystack, region, matching, grayscale) with multiprocessing.Pool(processes=n_cpus) as pool: results = pool.map(helper, paths) results = [self._fix_retina_element(r) for r in results] if None in results: continue else: return _to_dict(labels, results) def _fix_retina_element(self, ele): if not is_retina(): return ele if ele is not None: if is_retina(): ele = ele._replace(left=ele.left / 2.0, top=ele.top / 2.0) return ele def _find_multiple_helper(self, haystack, region, confidence, grayscale, needle): ele = pyautogui.locate(needle, haystack, region=region, confidence=confidence, grayscale=grayscale) return ele def find(self, label, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, best=True, grayscale=False): return self.find_until(label, x=x, y=y, width=width, height=height, threshold=threshold, matching=matching, waiting_time=waiting_time, best=best, grayscale=grayscale) def find_until(self, label, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, best=True, grayscale=False): self.state.element = None screen_w, screen_h = pyautogui.size() x = x or 0 y = y or 0 w = width or screen_w h = height or screen_h region = (x, y, w, h) element_path = self._search_image_file(label) if threshold: print('Threshold not yet supported') if not best: print('Warning: Ignoring best=False for now. It will be supported in the future.') start_time = time.time() while True: elapsed_time = (time.time() - start_time) * 1000 if elapsed_time > waiting_time: return None ele = pyautogui.locateOnScreen(element_path, region=region, confidence=matching, grayscale=grayscale) if ele is not None: if is_retina(): ele = ele._replace(left=ele.left / 2.0, top=ele.top / 2.0) self.state.element = ele return ele def find_all(self, label, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, grayscale=False): def deduplicate(elems): def find_same(item, items): x_start = item.left x_end = item.left + item.width y_start = item.top y_end = item.top + item.height similars = [] for itm in items: if itm == item: continue if (itm.left >= x_start and itm.left < x_end)\ and (itm.top >= y_start and itm.top < y_end): similars.append(itm) continue return similars index = 0 while True: try: dups = find_same(elems[index], elems[index:]) for d in dups: elems.remove(d) index += 1 except IndexError: break return elems self.state.element = None screen_w, screen_h = pyautogui.size() x = x or 0 y = y or 0 w = width or screen_w h = height or screen_h region = (x, y, w, h) element_path = self._search_image_file(label) if threshold: print('Threshold not yet supported') start_time = time.time() while True: elapsed_time = (time.time() - start_time) * 1000 if elapsed_time > waiting_time: return None eles = pyautogui.locateAllOnScreen(element_path, region=region, confidence=matching, grayscale=grayscale) if not eles: continue eles = deduplicate(list(eles)) for ele in eles: if ele is not None: if is_retina(): ele = ele._replace(left=ele.left / 2.0, top=ele.top / 2.0) self.state.element = ele yield ele break def find_text(self, label, x=None, y=None, width=None, height=None, *, threshold=None, matching=0.9, waiting_time=10000, best=True): return self.find_until(label, x, y, width, height, threshold=threshold, matching=matching, waiting_time=waiting_time, best=best, grayscale=True) def get_last_element(self): return self.state.element def display_size(self): screen_size = pyautogui.size() return screen_size.width, screen_size.height def screenshot(self, filepath=None, region=None): img = pyautogui.screenshot(filepath, region) return img def get_screenshot(self, filepath=None, region=None): return self.screenshot(filepath, region) def screen_cut(self, x, y, width=None, height=None): screen_size = pyautogui.size() x = x or 0 y = y or 0 width = width or screen_size.width height = height or screen_size.height img = pyautogui.screenshot(region=(x, y, width, height)) return img def save_screenshot(self, path): pyautogui.screenshot(path) def get_element_coords(self, label, x=None, y=None, width=None, height=None, matching=0.9, best=True): self.state.element = None screen_size = pyautogui.size() x = x or 0 y = y or 0 width = width or screen_size.width height = height or screen_size.height region = (x, y, width, height) if not best: print('Warning: Ignoring best=False for now. It will be supported in the future.') ele = pyautogui.locateOnScreen(self._search_image_file(label), region=region, confidence=matching) if ele is None: return None, None if is_retina(): ele = ele._replace(left=ele.left / 2.0, top=ele.top / 2.0) self.state.element = ele return ele.left, ele.top def get_element_coords_centered(self, label, x=None, y=None, width=None, height=None, matching=0.9, best=True): self.get_element_coords(label, x, y, width, height, matching, best) return self.state.center() rowser.open(url, location) return status x, y = self.get_element_coords_centered(label) if None in (x, y): raise ValueError(f'Element not available. Cannot find {label}.') os_compat.click(x, y) def get_last_x(self): return pyautogui.position().x def get_last_y(self): return pyautogui.position().y def mouse_move(self, x, y): pyautogui.moveTo(x, y) def click_at(self, x, y): os_compat.click(x, y) @only_if_element def click(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, clicks=1, interval_between_clicks=0, button='left'): x, y = self.state.center() os_compat.click(x, y, clicks=clicks, button=button, interval=interval_between_clicks/1000.0) self.sleep(wait_after) @only_if_element def click_relative(self, x, y, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, clicks=1, interval_between_clicks=0, button='left'): x = self.state.x() + x y = self.state.y() + y os_compat.click(x, y, clicks=clicks, button=button, interval=interval_between_clicks/1000.0) self.sleep(wait_after) @only_if_element def double_click(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): self.click(wait_after=wait_after, clicks=2) @only_if_element def double_click_relative(self, x, y, interval_between_clicks=0, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): self.click_relative(x, y, wait_after=wait_after, clicks=2, interval_between_clicks=interval_between_clicks) @only_if_element def triple_click(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): self.click(wait_after=wait_after, clicks=3) @only_if_element def triple_click_relative(self, x, y, interval_between_clicks=0, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): self.click_relative(x, y, wait_after=wait_after, clicks=3, interval_between_clicks=interval_between_clicks) def mouse_down(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, button='left'): pyautogui.mouseDown(button=button) self.sleep(wait_after) def mouse_up(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, button='left'): pyautogui.mouseUp(button=button) self.sleep(wait_after) def scroll_down(self, clicks): pyautogui.scroll(-1 * clicks) def scroll_up(self, clicks): pyautogui.scroll(clicks) @only_if_element def move(self): x, y = self.state.center() pyautogui.moveTo(x, y) def move_relative(self, x, y): x = self.get_last_x() + x y = self.get_last_y() + y pyautogui.moveTo(x, y) def move_random(self, range_x, range_y): x = int(random.random() * range_x) y = int(random.random() * range_y) pyautogui.moveTo(x, y) @only_if_element def right_click(self, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION, *, clicks=1, interval_between_clicks=0): x, y = self.state.center() os_compat.click(x, y, clicks=clicks, button='right', interval=interval_between_clicks/1000.0) self.sleep(wait_after) def right_click_at(self, x, y): os_compat.click(x, y, button='right') @only_if_element def right_click_relative(self, x, y, interval_between_clicks=0, wait_after=config.DEFAULT_SLEEP_AFTER_ACTION): self.click_relative(x, y, wait_after=wait_after, clicks=3, interval_between_clicks=interval_between_clicks, button='right') rval=interval/1000.0) def kb_type(self, text, interval=0): pyautogui.write(text, interval=interval/1000.0) self.sleep(config.DEFAULT_SLEEP_AFTER_ACTION) def paste(self, text=None, wait=0): if text: pyperclip.copy(text) self.control_v() def copy_to_clipboard(self, text, wait=0): pyperclip.copy(text) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def tab(self, wait=0): pyautogui.press('tab') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def enter(self, wait=0): pyautogui.press('enter') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def key_right(self, wait=0): pyautogui.press('right') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def key_enter(self, wait=0): self.enter(wait) def key_end(self, wait=0): pyautogui.press('end') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def key_esc(self, wait=0): pyautogui.press('esc') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def _key_fx(self, idx, wait=0): pyautogui.press(f'f{idx}') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def key_f1(self, wait=0): self._key_fx(1, wait=wait) def key_f2(self, wait=0): self._key_fx(2, wait=wait) def key_f3(self, wait=0): self._key_fx(3, wait=wait) def key_f4(self, wait=0): self._key_fx(4, wait=wait) def key_f5(self, wait=0): self._key_fx(5, wait=wait) def key_f6(self, wait=0): self._key_fx(6, wait=wait) def key_f7(self, wait=0): self._key_fx(7, wait=wait) def key_f8(self, wait=0): self._key_fx(8, wait=wait) def key_f9(self, wait=0): self._key_fx(9, wait=wait) def key_f10(self, wait=0): self._key_fx(10, wait=wait) def key_f11(self, wait=0): self._key_fx(11, wait=wait) def key_f12(self, wait=0): self._key_fx(12, wait=wait) def hold_shift(self, wait=0): pyautogui.keyDown('shift') self.sleep(wait) def release_shift(self): pyautogui.keyUp('shift') def alt_space(self, wait=0): pyautogui.hotkey('alt', 'space', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def maximize_window(self): self.alt_space() self.sleep(1000) pyautogui.press('x') def type_keys_with_interval(self, interval, keys): pyautogui.hotkey(*keys, interval=interval/1000.0) def type_keys(self, keys): self.type_keys_with_interval(100, keys) def alt_e(self, wait=0): pyautogui.hotkey('alt', 'e', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def alt_r(self, wait=0): pyautogui.hotkey('alt', 'r', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def alt_f(self, wait=0): pyautogui.hotkey('alt', 'f', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def alt_u(self, wait=0): pyautogui.hotkey('alt', 'u', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def alt_f4(self, wait=0): pyautogui.hotkey('alt', 'f4', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_c(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'c', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) return self.get_clipboard() def control_v(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'v', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_a(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'a', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_f(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'f', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_p(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'p', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_u(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'u', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_r(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'r', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_t(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 't', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_end(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'end', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_home(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'home', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_w(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'w', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_shift_p(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'shift', 'p', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def control_shift_j(self, wait=0): key = 'ctrl' if platform.system() == 'Darwin': key = 'command' pyautogui.hotkey(key, 'shift', 'j', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def shift_tab(self, wait=0): pyautogui.hotkey('shift', 'tab', interval=self._interval) delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def get_clipboard(self): return pyperclip.paste() def type_left(self, wait=0): pyautogui.press('left') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def type_right(self, wait=0): pyautogui.press('right') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def type_down(self, wait=0): pyautogui.press('down') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def type_up(self, wait=0): pyautogui.press('up') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def type_windows(self, wait=0): pyautogui.press('win') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def page_up(self, wait=0): pyautogui.press('pageup') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def page_down(self, wait=0): pyautogui.press('pagedown') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def space(self, wait=0): pyautogui.press('space') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def backspace(self, wait=0): pyautogui.press('backspace') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) def delete(self, wait=0): pyautogui.press('delete') delay = max(0, wait or config.DEFAULT_SLEEP_AFTER_ACTION) self.sleep(delay) ile(self, path, timeout=10000): start_time = time.time() while True: elapsed_time = (time.time() - start_time) * 1000 if elapsed_time > timeout: return False if os.path.isfile(path) and os.access(path, os.R_OK): return True self.sleep(config.DEFAULT_SLEEP_AFTER_ACTION) def execute(self, file_path): if platform.system() == "Windows": os.startfile(file_path) else: subprocess.Popen(file_path.split(" ")) def wait(self, interval): time.sleep(interval / 1000.0) def sleep(self, interval): self.wait(interval)

true

f734c01585f2ae4014ad72f125f7748b1f3b18d9

4,174

py

Python

aleph/tests/test_collections_api.py

gavinrozzi/aleph

a8e3d10ec34b0d0a05b4daf3fdd2d09b96928b35

[ "MIT" ]

null

aleph/tests/test_collections_api.py

gavinrozzi/aleph

a8e3d10ec34b0d0a05b4daf3fdd2d09b96928b35

[ "MIT" ]

null

aleph/tests/test_collections_api.py

gavinrozzi/aleph

a8e3d10ec34b0d0a05b4daf3fdd2d09b96928b35

[ "MIT" ]

null

import json from aleph.core import db from aleph.model import Entity from aleph.tests.util import TestCase class CollectionsApiTestCase(TestCase): def setUp(self): super(CollectionsApiTestCase, self).setUp() self.rolex = self.create_user(foreign_id='user_3') self.col = self.create_collection( label='Test Collection', foreign_id='test_coll_entities_api', category='leak', countries=[] ) self.ent = Entity.create({ 'schema': 'Person', 'name': 'Winnie the Pooh', }, self.col) db.session.add(self.ent) db.session.commit() def test_index(self): res = self.client.get('/api/2/collections') assert res.status_code == 200, res assert res.json['total'] == 0, res.json _, headers = self.login(is_admin=True) res = self.client.get('/api/2/collections', headers=headers) assert res.status_code == 200, res assert res.json['total'] == 1, res.json def test_view(self): res = self.client.get('/api/2/collections/%s' % self.col.id) assert res.status_code == 403, res _, headers = self.login(is_admin=True) res = self.client.get('/api/2/collections/%s' % self.col.id, headers=headers) assert res.status_code == 200, res assert 'test_coll' in res.json['foreign_id'], res.json assert 'Winnie' not in res.json['label'], res.json def test_sitemap(self): self.update_index() url = '/api/2/collections/%s/sitemap.xml' % self.col.id res = self.client.get(url) assert res.status_code == 403, res self.grant_publish(self.col) res = self.client.get(url) assert res.status_code == 200, res data = res.data.decode('utf-8') assert self.ent.id in data, data def test_rdf(self): url = '/api/2/collections/%s/rdf' % self.col.id res = self.client.get(url) assert res.status_code == 403, res self.grant_publish(self.col) res = self.client.get(url) assert res.status_code == 200, res def test_update_valid(self): _, headers = self.login(is_admin=True) url = '/api/2/collections/%s' % self.col.id res = self.client.get(url, headers=headers) assert res.status_code == 200, res data = res.json data['label'] = 'Collected Collection' res = self.client.post(url, data=json.dumps(data), headers=headers, content_type='application/json') assert res.status_code == 200, res.json assert 'Collected' in res.json['label'], res.json def test_update_no_label(self): _, headers = self.login(is_admin=True) url = '/api/2/collections/%s' % self.col.id res = self.client.get(url, headers=headers) data = res.json data['label'] = '' res = self.client.post(url, data=json.dumps(data), headers=headers, content_type='application/json') assert res.status_code == 400, res.json res = self.client.get(url, headers=headers) data = res.json data['category'] = 'banana' res = self.client.post(url, data=json.dumps(data), headers=headers, content_type='application/json') assert res.status_code == 400, res.json def test_delete(self): _, headers = self.login(is_admin=True) url = '/api/2/collections/%s' % self.col.id res = self.client.get(url, headers=headers) assert res.status_code == 200, res res = self.client.delete(url, headers=headers) assert res.status_code == 204, res res = self.client.get(url, headers=headers) assert res.status_code == 404, res

36.938053

68

0.546957

import json from aleph.core import db from aleph.model import Entity from aleph.tests.util import TestCase class CollectionsApiTestCase(TestCase): def setUp(self): super(CollectionsApiTestCase, self).setUp() self.rolex = self.create_user(foreign_id='user_3') self.col = self.create_collection( label='Test Collection', foreign_id='test_coll_entities_api', category='leak', countries=[] ) self.ent = Entity.create({ 'schema': 'Person', 'name': 'Winnie the Pooh', }, self.col) db.session.add(self.ent) db.session.commit() def test_index(self): res = self.client.get('/api/2/collections') assert res.status_code == 200, res assert res.json['total'] == 0, res.json _, headers = self.login(is_admin=True) res = self.client.get('/api/2/collections', headers=headers) assert res.status_code == 200, res assert res.json['total'] == 1, res.json def test_view(self): res = self.client.get('/api/2/collections/%s' % self.col.id) assert res.status_code == 403, res _, headers = self.login(is_admin=True) res = self.client.get('/api/2/collections/%s' % self.col.id, headers=headers) assert res.status_code == 200, res assert 'test_coll' in res.json['foreign_id'], res.json assert 'Winnie' not in res.json['label'], res.json def test_sitemap(self): self.update_index() url = '/api/2/collections/%s/sitemap.xml' % self.col.id res = self.client.get(url) assert res.status_code == 403, res self.grant_publish(self.col) res = self.client.get(url) assert res.status_code == 200, res data = res.data.decode('utf-8') assert self.ent.id in data, data def test_rdf(self): url = '/api/2/collections/%s/rdf' % self.col.id res = self.client.get(url) assert res.status_code == 403, res self.grant_publish(self.col) res = self.client.get(url) assert res.status_code == 200, res def test_update_valid(self): _, headers = self.login(is_admin=True) url = '/api/2/collections/%s' % self.col.id res = self.client.get(url, headers=headers) assert res.status_code == 200, res data = res.json data['label'] = 'Collected Collection' res = self.client.post(url, data=json.dumps(data), headers=headers, content_type='application/json') assert res.status_code == 200, res.json assert 'Collected' in res.json['label'], res.json def test_update_no_label(self): _, headers = self.login(is_admin=True) url = '/api/2/collections/%s' % self.col.id res = self.client.get(url, headers=headers) data = res.json data['label'] = '' res = self.client.post(url, data=json.dumps(data), headers=headers, content_type='application/json') assert res.status_code == 400, res.json res = self.client.get(url, headers=headers) data = res.json data['category'] = 'banana' res = self.client.post(url, data=json.dumps(data), headers=headers, content_type='application/json') assert res.status_code == 400, res.json def test_delete(self): _, headers = self.login(is_admin=True) url = '/api/2/collections/%s' % self.col.id res = self.client.get(url, headers=headers) assert res.status_code == 200, res res = self.client.delete(url, headers=headers) assert res.status_code == 204, res res = self.client.get(url, headers=headers) assert res.status_code == 404, res

true

f734c05d8d611e2e78373da2d10eb3ba7f634a15

18,048

py

Python

scripts/icml_2018_experiment.py

davidinouye/destructive-deep-learning

632add7a9731347e050d271ceebb24251e1d8e01

[ "BSD-3-Clause" ]

21

2018-06-19T21:23:32.000Z

2021-03-03T03:29:00.000Z

scripts/icml_2018_experiment.py

davidinouye/destructive-deep-learning

632add7a9731347e050d271ceebb24251e1d8e01

[ "BSD-3-Clause" ]

39

2018-06-19T16:45:51.000Z

2020-09-15T12:53:20.000Z

scripts/icml_2018_experiment.py

davidinouye/destructive-deep-learning

632add7a9731347e050d271ceebb24251e1d8e01

[ "BSD-3-Clause" ]

5

2018-10-01T22:58:32.000Z

2019-11-12T20:41:16.000Z

"""ICML 2018 experiment for MNIST and CIFAR-10.""" import argparse import logging import os import subprocess import sys import time import warnings import numpy as np import scipy.stats # Needed for standard error of the mean scipy.stats.sem from sklearn.base import clone from sklearn.decomposition import PCA # Add the directory of this script sys.path.append(os.path.dirname(os.path.realpath(__file__))) # noqa E402 # Add directory for ddl library sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')) # noqa E402 # isort:imports-firstparty from ddl.base import CompositeDestructor from ddl.deep import DeepDestructorCV from ddl.externals.mlpack import MlpackDensityTreeEstimator from ddl.independent import IndependentDensity, IndependentDestructor, IndependentInverseCdf from ddl.linear import BestLinearReconstructionDestructor from ddl.local import FeatureGroupsDestructor, ImageFeaturePairs from ddl.tree import TreeDensity, TreeDestructor from ddl.univariate import HistogramUnivariateDensity, ScipyUnivariateDensity from maf_data import CIFAR10_ALPHA, MNIST_ALPHA, get_maf_data try: import cPickle as pickle except ImportError: import pickle logger = logging.getLogger(__name__) def run_experiment(data_name, model_name, model_kwargs=None): """ Parameters ---------- data_name : model_name : model_kwargs : Returns ------- """ if model_kwargs is None: model_kwargs = {} # Setup experiment_filename = model_kwargs['experiment_filename'] experiment_label = model_kwargs['experiment_label'] _setup_loggers(experiment_filename) try: git_hash = subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'] ).decode('ascii')[:-1] except subprocess.CalledProcessError: git_hash = 'unknown' logger.debug('Current git hash = %s' % git_hash) # Load data logger.debug('Loading data for %s' % experiment_label) data_dict = get_maf_data(data_name) X_train, X_validation, X_test = ( data_dict['X_train'], data_dict['X_validation'], data_dict['X_test']) n_train, n_validation, n_test = (_X.shape[0] for _X in (X_train, X_validation, X_test)) # Setup cv and refit parameters X_train_val = np.vstack((X_train, X_validation)) model_kwargs['cv'] = [(np.arange(n_train), n_train + np.arange(n_validation))] model_kwargs['refit'] = False # Load model deep_destructor = _get_model(data_name, model_name, model_kwargs=model_kwargs) # Fit destructor logger.debug('Starting training for %s' % experiment_label) start_time = time.time() deep_destructor.fit(X_train_val, y=None, X_test=X_test) train_time = time.time() - start_time logger.debug('Finished training for %s' % experiment_label) logger.debug('%s: Time to train = %g s or %g minutes or %g hours' % (experiment_label, train_time, train_time / 60, train_time / 60 / 60)) # Get test score start_time = time.time() test_scores = deep_destructor.score_samples(X_test) score_time = time.time() - start_time test_score = np.mean(test_scores) test_score_stderr = scipy.stats.sem(test_scores) logger.debug('%s: Final test score=%g with std_err=%g computed in %g s' % (experiment_label, float(test_score), test_score_stderr, score_time)) date_time_completed = time.strftime("%Y_%m_%d-%H_%M_%S") logger.debug('Date/time completed (just before saving): %s' % date_time_completed) # Prepare results in dictionary result_dict = dict( # Data statistics data_name=data_name, n_features=X_train.shape[1], n_train=n_train, n_validation=n_validation, n_test=n_test, # Model destructor=deep_destructor, model_name=model_name, model_kwargs=model_kwargs, # Time train_time=train_time, score_time=score_time, date_time_completed=date_time_completed, # Test scores test_score=test_score, test_score_stderr=test_score_stderr, test_scores=test_scores, git_hash=git_hash, ) # Save results to pickle file with open(experiment_filename + '.pkl', 'wb') as f: pickle.dump(result_dict, f) logger.debug('%s: Saved results to file %s' % (experiment_label, experiment_filename)) return result_dict def load_experiment_results(data_name, model_name=None, model_kwargs=None, notebook=False): """ Parameters ---------- data_name : model_name : model_kwargs : notebook : Returns ------- """ experiment_filename, _ = _get_experiment_filename_and_label(data_name, model_name=model_name, model_kwargs=model_kwargs) if notebook: experiment_filename = os.path.join('..', experiment_filename) with open(experiment_filename + '.pkl', 'rb') as f: result_dict = pickle.load(file=f) logger.debug('Loaded results from file %s' % experiment_filename) return result_dict def _get_model(data_name, model_name, model_kwargs): if 'is_test' not in model_kwargs: model_kwargs['is_test'] = False # Init destructor is shared with all models init_destructor = CompositeDestructor( destructors=[ _get_inverse_logit_destructor(data_name), IndependentDestructor( independent_density=IndependentDensity( univariate_estimators=HistogramUnivariateDensity( bins=256, bounds=[0, 1], alpha=1) ) ) ], random_state=0, ) # Setup canonical destructor for various models if model_name == 'deep-copula': deep_stop_tol = 0.001 canonical_destructor = _get_copula_destructor() else: deep_stop_tol = 0.0001 n_jobs = model_kwargs['n_jobs'] # Get pair estimators (i.e. pairs of pixels in a spiral pattern) pair_estimators = _get_pair_estimators(data_name, n_uniq_dir=8) # Setup the local/pair destructor pair_canonical_destructor = _get_pair_canonical_destructor(model_name) # Setup a list of canonical destructors that destroy in each pixel direction canonical_destructor = [ FeatureGroupsDestructor( groups_estimator=pair_estimator, group_canonical_destructor=clone(pair_canonical_destructor), n_jobs=n_jobs ) for pair_estimator in pair_estimators ] # Shared DeepDestructorCV return DeepDestructorCV( init_destructor=init_destructor, canonical_destructor=canonical_destructor, stop_tol=deep_stop_tol, # Either n_extend or max_canonical_destructors must be None n_extend=1, cv=model_kwargs['cv'], refit=model_kwargs['refit'], silent=False, log_prefix='', random_state=0, # Set maximum number of layers (None for infinite) max_canonical_destructors=None if not model_kwargs['is_test'] else 1, ) def _get_inverse_logit_destructor(data_name): if data_name == 'mnist': alpha = MNIST_ALPHA elif data_name == 'cifar10': alpha = CIFAR10_ALPHA else: raise ValueError('dataset should either be mnist or cifar10') inverse_logit = CompositeDestructor( destructors=[ IndependentDestructor( independent_density=IndependentDensity( univariate_estimators=ScipyUnivariateDensity( scipy_rv=scipy.stats.logistic, scipy_fit_kwargs=dict(floc=0, fscale=1) ) ) ), IndependentDestructor( independent_density=IndependentDensity( univariate_estimators=ScipyUnivariateDensity( scipy_rv=scipy.stats.uniform, scipy_fit_kwargs=dict(floc=alpha, fscale=1 - 2 * alpha) ) ) ) ] ) return inverse_logit def _get_copula_destructor(hist_kwargs=None): if hist_kwargs is None: hist_kwargs = dict(bins=40, bounds=[0, 1], alpha=100) return CompositeDestructor( destructors=[ IndependentDestructor( independent_density=IndependentDensity( univariate_estimators=HistogramUnivariateDensity(**hist_kwargs) ) ), IndependentInverseCdf(), BestLinearReconstructionDestructor( linear_estimator=PCA(), destructor=IndependentDestructor(), linear_projector_kwargs=dict(fit_bias=False), ) ], random_state=0, ) def _get_pair_canonical_destructor(model_name): if model_name == 'image-pairs-tree': return TreeDestructor( tree_density=TreeDensity( tree_estimator=MlpackDensityTreeEstimator( max_depth=None, min_samples_leaf=100, max_leaf_nodes=50, ), get_tree=None, node_destructor=None, uniform_weight=0.5, ) ) elif model_name == 'image-pairs-copula': return _get_copula_destructor() else: raise ValueError('Invalid model name "%s"') def _get_pair_estimators(data_name, n_uniq_dir): """Returns `n_uniq_dir` pair estimators in a spiral pattern.""" def _generate_pixel_circle(radius=1): cur = radius * np.array([1, 1]) # Start in top right d = [cur] for step in np.array([[0, -1], [-1, 0], [0, 1], [1, 0]]): for i in range(2 * radius): cur = cur + step d.append(cur) d.pop(-1) # remove last that is a repeat def _rotate(a, n): return a[n:] + a[:n] return _rotate(d, radius) # Rotate to make directly east the first direction def _generate_pixel_spiral(n_spirals=2): d = [] for i in range(n_spirals): d.extend(_generate_pixel_circle(radius=i + 1)) return d directions = np.array(_generate_pixel_spiral(n_spirals=10)) if data_name == 'mnist': directions = directions[:n_uniq_dir] return [ ImageFeaturePairs( image_shape=(28, 28), relative_position=r, init_offset=(0, 0), step=(1, 0), wrap=True ) for r in directions ] elif data_name == 'cifar10': # Make 3d coordinates directions = [(d2[0], d2[1], 0) for d2 in directions[:n_uniq_dir]] init_offset = [(0, 0, 0) for _ in directions] # Handle color channels directions.extend([(0, 0, 1), (0, 0, 1), (0, 0, 1)]) init_offset.extend([(0, 0, 0), (0, 0, 1), (0, 0, 2)]) return [ ImageFeaturePairs( image_shape=(32, 32, 3), relative_position=r, init_offset=io, step=(1, 0, 0), wrap=True ) for r, io in zip(directions, init_offset) ] else: raise RuntimeError('Only mnist and cifar10 are supported') def _setup_loggers(experiment_filename): # Setup log file and console to have same format log_formatter = logging.Formatter( fmt='%(asctime)s:%(levelname)s:%(name)s:%(process)d: %(message)s') log_file = logging.FileHandler(experiment_filename + '.log') log_file.setFormatter(log_formatter) console_handler = logging.StreamHandler(sys.stdout) console_handler.setFormatter(log_formatter) # Add handlers to root logger root_logger = logging.getLogger() root_logger.addHandler(console_handler) root_logger.addHandler(log_file) # Adjust settings for loggers logging.captureWarnings(True) logging.getLogger().setLevel(logging.DEBUG) logging.getLogger('ddl').setLevel(logging.DEBUG) def _get_experiment_filename_and_label(data_name, model_name=None, model_kwargs=None): if model_kwargs is None: model_kwargs = {} data_dir = os.path.join( os.path.dirname(os.path.realpath(__file__)), '..', 'data', 'results') try: os.makedirs(data_dir) except OSError: pass arg_str = '_'.join(['%s-%s' % (k, str(v)) for k, v in model_kwargs.items()]) arg_str = arg_str.replace('.', '_') if len(arg_str) > 0: arg_str = '_' + arg_str filename = ('data-%s_model-%s%s' % (str(data_name), str(model_name), arg_str)) pickle_filename = os.path.join(data_dir, filename) arg_str = ', '.join(['%s=%s' % (k, str(v)) for k, v in model_kwargs.items()]) if len(arg_str) > 0: arg_str = ', ' + arg_str experiment_label = '(data=%s, model=%s%s)' % (data_name, str(model_name), arg_str) return pickle_filename, experiment_label # Add fast sanity-check tests for mnist dataset try: # noinspection PyPackageRequirements import pytest except ImportError: pass else: @pytest.mark.parametrize( 'model_name', # 'image-pairs-tree' not needed since covered by other tests ['deep-copula', 'image-pairs-copula'] ) def test_mnist_experiment(model_name): data_name = 'mnist' model_kwargs = dict(is_test=True, n_jobs=1) model_kwargs['experiment_filename'], model_kwargs[ 'experiment_label'] = _get_experiment_filename_and_label( data_name, model_name=model_name, model_kwargs=model_kwargs) result_dict = run_experiment(data_name, model_name, model_kwargs=model_kwargs) # Check if test likelihood/score is as expected _model_names = ['deep-copula', 'image-pairs-copula', 'image-pairs-tree'] expected_test_scores = [-1.060270463188296844e+03, -1.155477974922050180e+03, -1.134326498390250208e+03] ind = _model_names.index(model_name) assert (np.abs(expected_test_scores[ind] - result_dict['test_score']) / np.abs(expected_test_scores[ind]) < 1e-15) if __name__ == '__main__': # Parse args all_data_names = ['mnist', 'cifar10'] all_model_names = ['deep-copula', 'image-pairs-copula', 'image-pairs-tree'] parser = argparse.ArgumentParser(description='Sets up and/or runs MAF experiments.') parser.add_argument( '--model_names', default=','.join(all_model_names), help='One or more model names separated by commas from the list %s' % str(all_model_names)) parser.add_argument( '--data_names', default=','.join(all_data_names), help='One or more data names separated by commas from the list %s' % str(all_data_names)) parser.add_argument( '--parallel_subprocesses', default=False, type=bool, help='Whether to use parallel subprocesses for each (model, data) experiment ' 'pair or run directly (default is False).') parser.add_argument( '--n_jobs', default=1, type=int, help='Number of parallel jobs to use for image-pairs models (default is 1).') args = parser.parse_args() print('Parsed args = %s' % str(args)) print('----------------------') # Run experiments _model_kwargs = vars(args).copy() # Extract model_kwargs as dictionary model_names = _model_kwargs.pop('model_names').split(',') data_names = _model_kwargs.pop('data_names').split(',') is_parallel = _model_kwargs.pop('parallel_subprocesses') processes = [] for _data_name in data_names: # Make sure data has already been cached get_maf_data(_data_name) for _model_name in model_names: _model_kwargs['experiment_filename'], _model_kwargs[ 'experiment_label'] = _get_experiment_filename_and_label( _data_name, model_name=_model_name, model_kwargs=_model_kwargs) if not is_parallel: # Just run the experiment directly try: run_experiment(_data_name, _model_name, _model_kwargs) except RuntimeError as e: if 'mlpack' not in str(e).lower(): raise e else: warnings.warn('Skipping %s because of error "%s"' % (_model_name, str(e))) else: # Generate script to run experiment in parallel in separate subprocesses script_str = ( 'import os\n' 'os.chdir(\'%s\')\n' 'from icml_2018_experiment import run_experiment\n' 'run_experiment(\'%s\', \'%s\', model_kwargs=%s)\n' ) % ( os.path.dirname(os.path.realpath(__file__)), _data_name, _model_name, str(_model_kwargs) ) echo_args = ['echo', '-e', script_str] # Launch subprocess which can run in parallel DEVNULL = open(os.devnull, 'w') echo = subprocess.Popen(['echo', '-e', script_str], stdout=subprocess.PIPE) python = subprocess.Popen(['python'], stdin=echo.stdout, stdout=DEVNULL) processes.append(echo) processes.append(python) print('Started subprocess for experiment %s' % _model_kwargs['experiment_label']) print( ' Appending to end of log file %s.log' % _model_kwargs['experiment_filename']) # Remove filenames and labels for next round _model_kwargs.pop('experiment_filename') _model_kwargs.pop('experiment_label') if is_parallel: # Wait for all processes to finish print('Waiting for all subprocesses to finish') for p in processes: p.wait() print('All subprocesses finished!')

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import argparse import logging import os import subprocess import sys import time import warnings import numpy as np import scipy.stats from sklearn.base import clone from sklearn.decomposition import PCA sys.path.append(os.path.dirname(os.path.realpath(__file__))) sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')) from ddl.base import CompositeDestructor from ddl.deep import DeepDestructorCV from ddl.externals.mlpack import MlpackDensityTreeEstimator from ddl.independent import IndependentDensity, IndependentDestructor, IndependentInverseCdf from ddl.linear import BestLinearReconstructionDestructor from ddl.local import FeatureGroupsDestructor, ImageFeaturePairs from ddl.tree import TreeDensity, TreeDestructor from ddl.univariate import HistogramUnivariateDensity, ScipyUnivariateDensity from maf_data import CIFAR10_ALPHA, MNIST_ALPHA, get_maf_data try: import cPickle as pickle except ImportError: import pickle logger = logging.getLogger(__name__) def run_experiment(data_name, model_name, model_kwargs=None): if model_kwargs is None: model_kwargs = {} experiment_filename = model_kwargs['experiment_filename'] experiment_label = model_kwargs['experiment_label'] _setup_loggers(experiment_filename) try: git_hash = subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'] ).decode('ascii')[:-1] except subprocess.CalledProcessError: git_hash = 'unknown' logger.debug('Current git hash = %s' % git_hash) logger.debug('Loading data for %s' % experiment_label) data_dict = get_maf_data(data_name) X_train, X_validation, X_test = ( data_dict['X_train'], data_dict['X_validation'], data_dict['X_test']) n_train, n_validation, n_test = (_X.shape[0] for _X in (X_train, X_validation, X_test)) X_train_val = np.vstack((X_train, X_validation)) model_kwargs['cv'] = [(np.arange(n_train), n_train + np.arange(n_validation))] model_kwargs['refit'] = False deep_destructor = _get_model(data_name, model_name, model_kwargs=model_kwargs) logger.debug('Starting training for %s' % experiment_label) start_time = time.time() deep_destructor.fit(X_train_val, y=None, X_test=X_test) train_time = time.time() - start_time logger.debug('Finished training for %s' % experiment_label) logger.debug('%s: Time to train = %g s or %g minutes or %g hours' % (experiment_label, train_time, train_time / 60, train_time / 60 / 60)) start_time = time.time() test_scores = deep_destructor.score_samples(X_test) score_time = time.time() - start_time test_score = np.mean(test_scores) test_score_stderr = scipy.stats.sem(test_scores) logger.debug('%s: Final test score=%g with std_err=%g computed in %g s' % (experiment_label, float(test_score), test_score_stderr, score_time)) date_time_completed = time.strftime("%Y_%m_%d-%H_%M_%S") logger.debug('Date/time completed (just before saving): %s' % date_time_completed) result_dict = dict( data_name=data_name, n_features=X_train.shape[1], n_train=n_train, n_validation=n_validation, n_test=n_test, destructor=deep_destructor, model_name=model_name, model_kwargs=model_kwargs, train_time=train_time, score_time=score_time, date_time_completed=date_time_completed, test_score=test_score, test_score_stderr=test_score_stderr, test_scores=test_scores, git_hash=git_hash, ) with open(experiment_filename + '.pkl', 'wb') as f: pickle.dump(result_dict, f) logger.debug('%s: Saved results to file %s' % (experiment_label, experiment_filename)) return result_dict def load_experiment_results(data_name, model_name=None, model_kwargs=None, notebook=False): experiment_filename, _ = _get_experiment_filename_and_label(data_name, model_name=model_name, model_kwargs=model_kwargs) if notebook: experiment_filename = os.path.join('..', experiment_filename) with open(experiment_filename + '.pkl', 'rb') as f: result_dict = pickle.load(file=f) logger.debug('Loaded results from file %s' % experiment_filename) return result_dict def _get_model(data_name, model_name, model_kwargs): if 'is_test' not in model_kwargs: model_kwargs['is_test'] = False init_destructor = CompositeDestructor( destructors=[ _get_inverse_logit_destructor(data_name), IndependentDestructor( independent_density=IndependentDensity( univariate_estimators=HistogramUnivariateDensity( bins=256, bounds=[0, 1], alpha=1) ) ) ], random_state=0, ) if model_name == 'deep-copula': deep_stop_tol = 0.001 canonical_destructor = _get_copula_destructor() else: deep_stop_tol = 0.0001 n_jobs = model_kwargs['n_jobs'] pair_estimators = _get_pair_estimators(data_name, n_uniq_dir=8) pair_canonical_destructor = _get_pair_canonical_destructor(model_name) canonical_destructor = [ FeatureGroupsDestructor( groups_estimator=pair_estimator, group_canonical_destructor=clone(pair_canonical_destructor), n_jobs=n_jobs ) for pair_estimator in pair_estimators ] return DeepDestructorCV( init_destructor=init_destructor, canonical_destructor=canonical_destructor, stop_tol=deep_stop_tol, n_extend=1, cv=model_kwargs['cv'], refit=model_kwargs['refit'], silent=False, log_prefix='', random_state=0, max_canonical_destructors=None if not model_kwargs['is_test'] else 1, ) def _get_inverse_logit_destructor(data_name): if data_name == 'mnist': alpha = MNIST_ALPHA elif data_name == 'cifar10': alpha = CIFAR10_ALPHA else: raise ValueError('dataset should either be mnist or cifar10') inverse_logit = CompositeDestructor( destructors=[ IndependentDestructor( independent_density=IndependentDensity( univariate_estimators=ScipyUnivariateDensity( scipy_rv=scipy.stats.logistic, scipy_fit_kwargs=dict(floc=0, fscale=1) ) ) ), IndependentDestructor( independent_density=IndependentDensity( univariate_estimators=ScipyUnivariateDensity( scipy_rv=scipy.stats.uniform, scipy_fit_kwargs=dict(floc=alpha, fscale=1 - 2 * alpha) ) ) ) ] ) return inverse_logit def _get_copula_destructor(hist_kwargs=None): if hist_kwargs is None: hist_kwargs = dict(bins=40, bounds=[0, 1], alpha=100) return CompositeDestructor( destructors=[ IndependentDestructor( independent_density=IndependentDensity( univariate_estimators=HistogramUnivariateDensity(**hist_kwargs) ) ), IndependentInverseCdf(), BestLinearReconstructionDestructor( linear_estimator=PCA(), destructor=IndependentDestructor(), linear_projector_kwargs=dict(fit_bias=False), ) ], random_state=0, ) def _get_pair_canonical_destructor(model_name): if model_name == 'image-pairs-tree': return TreeDestructor( tree_density=TreeDensity( tree_estimator=MlpackDensityTreeEstimator( max_depth=None, min_samples_leaf=100, max_leaf_nodes=50, ), get_tree=None, node_destructor=None, uniform_weight=0.5, ) ) elif model_name == 'image-pairs-copula': return _get_copula_destructor() else: raise ValueError('Invalid model name "%s"') def _get_pair_estimators(data_name, n_uniq_dir): def _generate_pixel_circle(radius=1): cur = radius * np.array([1, 1]) d = [cur] for step in np.array([[0, -1], [-1, 0], [0, 1], [1, 0]]): for i in range(2 * radius): cur = cur + step d.append(cur) d.pop(-1) def _rotate(a, n): return a[n:] + a[:n] return _rotate(d, radius) def _generate_pixel_spiral(n_spirals=2): d = [] for i in range(n_spirals): d.extend(_generate_pixel_circle(radius=i + 1)) return d directions = np.array(_generate_pixel_spiral(n_spirals=10)) if data_name == 'mnist': directions = directions[:n_uniq_dir] return [ ImageFeaturePairs( image_shape=(28, 28), relative_position=r, init_offset=(0, 0), step=(1, 0), wrap=True ) for r in directions ] elif data_name == 'cifar10': directions = [(d2[0], d2[1], 0) for d2 in directions[:n_uniq_dir]] init_offset = [(0, 0, 0) for _ in directions] directions.extend([(0, 0, 1), (0, 0, 1), (0, 0, 1)]) init_offset.extend([(0, 0, 0), (0, 0, 1), (0, 0, 2)]) return [ ImageFeaturePairs( image_shape=(32, 32, 3), relative_position=r, init_offset=io, step=(1, 0, 0), wrap=True ) for r, io in zip(directions, init_offset) ] else: raise RuntimeError('Only mnist and cifar10 are supported') def _setup_loggers(experiment_filename): log_formatter = logging.Formatter( fmt='%(asctime)s:%(levelname)s:%(name)s:%(process)d: %(message)s') log_file = logging.FileHandler(experiment_filename + '.log') log_file.setFormatter(log_formatter) console_handler = logging.StreamHandler(sys.stdout) console_handler.setFormatter(log_formatter) root_logger = logging.getLogger() root_logger.addHandler(console_handler) root_logger.addHandler(log_file) logging.captureWarnings(True) logging.getLogger().setLevel(logging.DEBUG) logging.getLogger('ddl').setLevel(logging.DEBUG) def _get_experiment_filename_and_label(data_name, model_name=None, model_kwargs=None): if model_kwargs is None: model_kwargs = {} data_dir = os.path.join( os.path.dirname(os.path.realpath(__file__)), '..', 'data', 'results') try: os.makedirs(data_dir) except OSError: pass arg_str = '_'.join(['%s-%s' % (k, str(v)) for k, v in model_kwargs.items()]) arg_str = arg_str.replace('.', '_') if len(arg_str) > 0: arg_str = '_' + arg_str filename = ('data-%s_model-%s%s' % (str(data_name), str(model_name), arg_str)) pickle_filename = os.path.join(data_dir, filename) arg_str = ', '.join(['%s=%s' % (k, str(v)) for k, v in model_kwargs.items()]) if len(arg_str) > 0: arg_str = ', ' + arg_str experiment_label = '(data=%s, model=%s%s)' % (data_name, str(model_name), arg_str) return pickle_filename, experiment_label try: import pytest except ImportError: pass else: @pytest.mark.parametrize( 'model_name', ['deep-copula', 'image-pairs-copula'] ) def test_mnist_experiment(model_name): data_name = 'mnist' model_kwargs = dict(is_test=True, n_jobs=1) model_kwargs['experiment_filename'], model_kwargs[ 'experiment_label'] = _get_experiment_filename_and_label( data_name, model_name=model_name, model_kwargs=model_kwargs) result_dict = run_experiment(data_name, model_name, model_kwargs=model_kwargs) _model_names = ['deep-copula', 'image-pairs-copula', 'image-pairs-tree'] expected_test_scores = [-1.060270463188296844e+03, -1.155477974922050180e+03, -1.134326498390250208e+03] ind = _model_names.index(model_name) assert (np.abs(expected_test_scores[ind] - result_dict['test_score']) / np.abs(expected_test_scores[ind]) < 1e-15) if __name__ == '__main__': all_data_names = ['mnist', 'cifar10'] all_model_names = ['deep-copula', 'image-pairs-copula', 'image-pairs-tree'] parser = argparse.ArgumentParser(description='Sets up and/or runs MAF experiments.') parser.add_argument( '--model_names', default=','.join(all_model_names), help='One or more model names separated by commas from the list %s' % str(all_model_names)) parser.add_argument( '--data_names', default=','.join(all_data_names), help='One or more data names separated by commas from the list %s' % str(all_data_names)) parser.add_argument( '--parallel_subprocesses', default=False, type=bool, help='Whether to use parallel subprocesses for each (model, data) experiment ' 'pair or run directly (default is False).') parser.add_argument( '--n_jobs', default=1, type=int, help='Number of parallel jobs to use for image-pairs models (default is 1).') args = parser.parse_args() print('Parsed args = %s' % str(args)) print('----------------------') _model_kwargs = vars(args).copy() model_names = _model_kwargs.pop('model_names').split(',') data_names = _model_kwargs.pop('data_names').split(',') is_parallel = _model_kwargs.pop('parallel_subprocesses') processes = [] for _data_name in data_names: get_maf_data(_data_name) for _model_name in model_names: _model_kwargs['experiment_filename'], _model_kwargs[ 'experiment_label'] = _get_experiment_filename_and_label( _data_name, model_name=_model_name, model_kwargs=_model_kwargs) if not is_parallel: try: run_experiment(_data_name, _model_name, _model_kwargs) except RuntimeError as e: if 'mlpack' not in str(e).lower(): raise e else: warnings.warn('Skipping %s because of error "%s"' % (_model_name, str(e))) else: script_str = ( 'import os\n' 'os.chdir(\'%s\')\n' 'from icml_2018_experiment import run_experiment\n' 'run_experiment(\'%s\', \'%s\', model_kwargs=%s)\n' ) % ( os.path.dirname(os.path.realpath(__file__)), _data_name, _model_name, str(_model_kwargs) ) echo_args = ['echo', '-e', script_str] DEVNULL = open(os.devnull, 'w') echo = subprocess.Popen(['echo', '-e', script_str], stdout=subprocess.PIPE) python = subprocess.Popen(['python'], stdin=echo.stdout, stdout=DEVNULL) processes.append(echo) processes.append(python) print('Started subprocess for experiment %s' % _model_kwargs['experiment_label']) print( ' Appending to end of log file %s.log' % _model_kwargs['experiment_filename']) _model_kwargs.pop('experiment_filename') _model_kwargs.pop('experiment_label') if is_parallel: print('Waiting for all subprocesses to finish') for p in processes: p.wait() print('All subprocesses finished!')

true

f734c2997557807df9822443ec26bb97e6121bc8

8,265

py

Python

samples/pubsub.py

KernelGamut32/aws-iot-device-sdk-python-v2

1eb65095261c431c8fc6f1168ec490a5fc87ed34

[ "Apache-2.0" ]

null

samples/pubsub.py

KernelGamut32/aws-iot-device-sdk-python-v2

1eb65095261c431c8fc6f1168ec490a5fc87ed34

[ "Apache-2.0" ]

null

samples/pubsub.py

KernelGamut32/aws-iot-device-sdk-python-v2

1eb65095261c431c8fc6f1168ec490a5fc87ed34

[ "Apache-2.0" ]

null

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0. import argparse from awscrt import io, mqtt, auth, http from awsiot import mqtt_connection_builder import sys import threading import time from uuid import uuid4 import json # This sample uses the Message Broker for AWS IoT to send and receive messages # through an MQTT connection. On startup, the device connects to the server, # subscribes to a topic, and begins publishing messages to that topic. # The device should receive those same messages back from the message broker, # since it is subscribed to that same topic. parser = argparse.ArgumentParser(description="Send and receive messages through and MQTT connection.") parser.add_argument('--endpoint', required=True, help="Your AWS IoT custom endpoint, not including a port. " + "Ex: \"abcd123456wxyz-ats.iot.us-east-1.amazonaws.com\"") parser.add_argument('--cert', help="File path to your client certificate, in PEM format.") parser.add_argument('--key', help="File path to your private key, in PEM format.") parser.add_argument('--root-ca', help="File path to root certificate authority, in PEM format. " + "Necessary if MQTT server uses a certificate that's not already in " + "your trust store.") parser.add_argument('--client-id', default="test-" + str(uuid4()), help="Client ID for MQTT connection.") parser.add_argument('--topic', default="test/topic", help="Topic to subscribe to, and publish messages to.") parser.add_argument('--message', default="Hello World!", help="Message to publish. " + "Specify empty string to publish nothing.") parser.add_argument('--count', default=10, type=int, help="Number of messages to publish/receive before exiting. " + "Specify 0 to run forever.") parser.add_argument('--use-websocket', default=False, action='store_true', help="To use a websocket instead of raw mqtt. If you " + "specify this option you must specify a region for signing, you can also enable proxy mode.") parser.add_argument('--signing-region', default='us-east-1', help="If you specify --use-web-socket, this " + "is the region that will be used for computing the Sigv4 signature") parser.add_argument('--proxy-host', help="Hostname for proxy to connect to. Note: if you use this feature, " + "you will likely need to set --root-ca to the ca for your proxy.") parser.add_argument('--proxy-port', type=int, default=8080, help="Port for proxy to connect to.") parser.add_argument('--verbosity', choices=[x.name for x in io.LogLevel], default=io.LogLevel.NoLogs.name, help='Logging level') parser.add_argument('--interval', type=int, default=1) parser.add_argument('--devicename', default='') # Using globals to simplify sample code args = parser.parse_args() io.init_logging(getattr(io.LogLevel, args.verbosity), 'stderr') received_count = 0 received_all_event = threading.Event() # Callback when connection is accidentally lost. def on_connection_interrupted(connection, error, **kwargs): print("Connection interrupted. error: {}".format(error)) # Callback when an interrupted connection is re-established. def on_connection_resumed(connection, return_code, session_present, **kwargs): print("Connection resumed. return_code: {} session_present: {}".format(return_code, session_present)) if return_code == mqtt.ConnectReturnCode.ACCEPTED and not session_present: print("Session did not persist. Resubscribing to existing topics...") resubscribe_future, _ = connection.resubscribe_existing_topics() # Cannot synchronously wait for resubscribe result because we're on the connection's event-loop thread, # evaluate result with a callback instead. resubscribe_future.add_done_callback(on_resubscribe_complete) def on_resubscribe_complete(resubscribe_future): resubscribe_results = resubscribe_future.result() print("Resubscribe results: {}".format(resubscribe_results)) for topic, qos in resubscribe_results['topics']: if qos is None: sys.exit("Server rejected resubscribe to topic: {}".format(topic)) # Callback when the subscribed topic receives a message def on_message_received(topic, payload, dup, qos, retain, **kwargs): print("Received message from topic '{}': {}".format(topic, payload)) global received_count received_count += 1 if received_count == args.count: received_all_event.set() if __name__ == '__main__': # Spin up resources event_loop_group = io.EventLoopGroup(1) host_resolver = io.DefaultHostResolver(event_loop_group) client_bootstrap = io.ClientBootstrap(event_loop_group, host_resolver) if args.use_websocket == True: proxy_options = None if (args.proxy_host): proxy_options = http.HttpProxyOptions(host_name=args.proxy_host, port=args.proxy_port) credentials_provider = auth.AwsCredentialsProvider.new_default_chain(client_bootstrap) mqtt_connection = mqtt_connection_builder.websockets_with_default_aws_signing( endpoint=args.endpoint, client_bootstrap=client_bootstrap, region=args.signing_region, credentials_provider=credentials_provider, websocket_proxy_options=proxy_options, ca_filepath=args.root_ca, on_connection_interrupted=on_connection_interrupted, on_connection_resumed=on_connection_resumed, client_id=args.client_id, clean_session=False, keep_alive_secs=6) else: mqtt_connection = mqtt_connection_builder.mtls_from_path( endpoint=args.endpoint, cert_filepath=args.cert, pri_key_filepath=args.key, client_bootstrap=client_bootstrap, ca_filepath=args.root_ca, on_connection_interrupted=on_connection_interrupted, on_connection_resumed=on_connection_resumed, client_id=args.client_id, clean_session=False, keep_alive_secs=6) print("Connecting to {} with client ID '{}'...".format( args.endpoint, args.client_id)) connect_future = mqtt_connection.connect() # Future.result() waits until a result is available connect_future.result() print("Connected!") # Subscribe print("Subscribing to topic '{}'...".format(args.topic)) subscribe_future, packet_id = mqtt_connection.subscribe( topic=args.topic, qos=mqtt.QoS.AT_LEAST_ONCE, callback=on_message_received) subscribe_result = subscribe_future.result() print("Subscribed with {}".format(str(subscribe_result['qos']))) # Publish message to server desired number of times. # This step is skipped if message is blank. # This step loops forever if count was set to 0. if args.message: if args.count == 0: print ("Sending messages until program killed") else: print ("Sending {} message(s)".format(args.count)) publish_count = 1 while (publish_count <= args.count) or (args.count == 0): jsonMessage = {"device_name": args.devicename, "data": {"temperature": 79.5, "humidity": 0.45} } jsonData = json.dumps(jsonMessage) print("Publishing message to topic '{}': {}".format(args.topic, jsonData)) mqtt_connection.publish( topic=args.topic, payload=jsonData, qos=mqtt.QoS.AT_LEAST_ONCE) time.sleep(args.interval) publish_count += 1 # Wait for all messages to be received. # This waits forever if count was set to 0. if args.count != 0 and not received_all_event.is_set(): print("Waiting for all messages to be received...") received_all_event.wait() print("{} message(s) received.".format(received_count)) # Disconnect print("Disconnecting...") disconnect_future = mqtt_connection.disconnect() disconnect_future.result() print("Disconnected!")

45.916667

116

0.684453

import argparse from awscrt import io, mqtt, auth, http from awsiot import mqtt_connection_builder import sys import threading import time from uuid import uuid4 import json parser = argparse.ArgumentParser(description="Send and receive messages through and MQTT connection.") parser.add_argument('--endpoint', required=True, help="Your AWS IoT custom endpoint, not including a port. " + "Ex: \"abcd123456wxyz-ats.iot.us-east-1.amazonaws.com\"") parser.add_argument('--cert', help="File path to your client certificate, in PEM format.") parser.add_argument('--key', help="File path to your private key, in PEM format.") parser.add_argument('--root-ca', help="File path to root certificate authority, in PEM format. " + "Necessary if MQTT server uses a certificate that's not already in " + "your trust store.") parser.add_argument('--client-id', default="test-" + str(uuid4()), help="Client ID for MQTT connection.") parser.add_argument('--topic', default="test/topic", help="Topic to subscribe to, and publish messages to.") parser.add_argument('--message', default="Hello World!", help="Message to publish. " + "Specify empty string to publish nothing.") parser.add_argument('--count', default=10, type=int, help="Number of messages to publish/receive before exiting. " + "Specify 0 to run forever.") parser.add_argument('--use-websocket', default=False, action='store_true', help="To use a websocket instead of raw mqtt. If you " + "specify this option you must specify a region for signing, you can also enable proxy mode.") parser.add_argument('--signing-region', default='us-east-1', help="If you specify --use-web-socket, this " + "is the region that will be used for computing the Sigv4 signature") parser.add_argument('--proxy-host', help="Hostname for proxy to connect to. Note: if you use this feature, " + "you will likely need to set --root-ca to the ca for your proxy.") parser.add_argument('--proxy-port', type=int, default=8080, help="Port for proxy to connect to.") parser.add_argument('--verbosity', choices=[x.name for x in io.LogLevel], default=io.LogLevel.NoLogs.name, help='Logging level') parser.add_argument('--interval', type=int, default=1) parser.add_argument('--devicename', default='') # Using globals to simplify sample code args = parser.parse_args() io.init_logging(getattr(io.LogLevel, args.verbosity), 'stderr') received_count = 0 received_all_event = threading.Event() # Callback when connection is accidentally lost. def on_connection_interrupted(connection, error, **kwargs): print("Connection interrupted. error: {}".format(error)) # Callback when an interrupted connection is re-established. def on_connection_resumed(connection, return_code, session_present, **kwargs): print("Connection resumed. return_code: {} session_present: {}".format(return_code, session_present)) if return_code == mqtt.ConnectReturnCode.ACCEPTED and not session_present: print("Session did not persist. Resubscribing to existing topics...") resubscribe_future, _ = connection.resubscribe_existing_topics() # Cannot synchronously wait for resubscribe result because we're on the connection's event-loop thread, # evaluate result with a callback instead. resubscribe_future.add_done_callback(on_resubscribe_complete) def on_resubscribe_complete(resubscribe_future): resubscribe_results = resubscribe_future.result() print("Resubscribe results: {}".format(resubscribe_results)) for topic, qos in resubscribe_results['topics']: if qos is None: sys.exit("Server rejected resubscribe to topic: {}".format(topic)) # Callback when the subscribed topic receives a message def on_message_received(topic, payload, dup, qos, retain, **kwargs): print("Received message from topic '{}': {}".format(topic, payload)) global received_count received_count += 1 if received_count == args.count: received_all_event.set() if __name__ == '__main__': # Spin up resources event_loop_group = io.EventLoopGroup(1) host_resolver = io.DefaultHostResolver(event_loop_group) client_bootstrap = io.ClientBootstrap(event_loop_group, host_resolver) if args.use_websocket == True: proxy_options = None if (args.proxy_host): proxy_options = http.HttpProxyOptions(host_name=args.proxy_host, port=args.proxy_port) credentials_provider = auth.AwsCredentialsProvider.new_default_chain(client_bootstrap) mqtt_connection = mqtt_connection_builder.websockets_with_default_aws_signing( endpoint=args.endpoint, client_bootstrap=client_bootstrap, region=args.signing_region, credentials_provider=credentials_provider, websocket_proxy_options=proxy_options, ca_filepath=args.root_ca, on_connection_interrupted=on_connection_interrupted, on_connection_resumed=on_connection_resumed, client_id=args.client_id, clean_session=False, keep_alive_secs=6) else: mqtt_connection = mqtt_connection_builder.mtls_from_path( endpoint=args.endpoint, cert_filepath=args.cert, pri_key_filepath=args.key, client_bootstrap=client_bootstrap, ca_filepath=args.root_ca, on_connection_interrupted=on_connection_interrupted, on_connection_resumed=on_connection_resumed, client_id=args.client_id, clean_session=False, keep_alive_secs=6) print("Connecting to {} with client ID '{}'...".format( args.endpoint, args.client_id)) connect_future = mqtt_connection.connect() # Future.result() waits until a result is available connect_future.result() print("Connected!") # Subscribe print("Subscribing to topic '{}'...".format(args.topic)) subscribe_future, packet_id = mqtt_connection.subscribe( topic=args.topic, qos=mqtt.QoS.AT_LEAST_ONCE, callback=on_message_received) subscribe_result = subscribe_future.result() print("Subscribed with {}".format(str(subscribe_result['qos']))) # Publish message to server desired number of times. # This step is skipped if message is blank. # This step loops forever if count was set to 0. if args.message: if args.count == 0: print ("Sending messages until program killed") else: print ("Sending {} message(s)".format(args.count)) publish_count = 1 while (publish_count <= args.count) or (args.count == 0): jsonMessage = {"device_name": args.devicename, "data": {"temperature": 79.5, "humidity": 0.45} } jsonData = json.dumps(jsonMessage) print("Publishing message to topic '{}': {}".format(args.topic, jsonData)) mqtt_connection.publish( topic=args.topic, payload=jsonData, qos=mqtt.QoS.AT_LEAST_ONCE) time.sleep(args.interval) publish_count += 1 # Wait for all messages to be received. # This waits forever if count was set to 0. if args.count != 0 and not received_all_event.is_set(): print("Waiting for all messages to be received...") received_all_event.wait() print("{} message(s) received.".format(received_count)) # Disconnect print("Disconnecting...") disconnect_future = mqtt_connection.disconnect() disconnect_future.result() print("Disconnected!")

true

f734c331e3defaefc8932cce380c4f0de759d187

24,299

py

Python

pandas/tests/scalar/timestamp/test_timestamp.py

guitargeek/pandas

a6c1f6cccee6bbccfb29488a94664ed07db024d9

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause" ]

1

2022-01-07T12:43:15.000Z

pandas/tests/scalar/timestamp/test_timestamp.py

guitargeek/pandas

a6c1f6cccee6bbccfb29488a94664ed07db024d9

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause" ]

null

pandas/tests/scalar/timestamp/test_timestamp.py

guitargeek/pandas

a6c1f6cccee6bbccfb29488a94664ed07db024d9

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause" ]

null

""" test the scalar Timestamp """ import calendar from datetime import ( datetime, timedelta, ) import locale import pickle import unicodedata from dateutil.tz import tzutc import numpy as np import pytest import pytz from pytz import ( timezone, utc, ) from pandas._libs.tslibs.timezones import ( dateutil_gettz as gettz, get_timezone, ) import pandas.util._test_decorators as td from pandas import ( NaT, Timedelta, Timestamp, ) import pandas._testing as tm from pandas.tseries import offsets class TestTimestampProperties: def test_freq_deprecation(self): # GH#41586 msg = "The 'freq' argument in Timestamp is deprecated" with tm.assert_produces_warning(FutureWarning, match=msg): # warning issued at construction ts = Timestamp("2021-06-01", freq="D") ts2 = Timestamp("2021-06-01", freq="B") msg = "Timestamp.freq is deprecated" with tm.assert_produces_warning(FutureWarning, match=msg): # warning issued at attribute lookup ts.freq for per in ["month", "quarter", "year"]: for side in ["start", "end"]: attr = f"is_{per}_{side}" with tm.assert_produces_warning(FutureWarning, match=msg): getattr(ts2, attr) # is_(month|quarter|year)_(start|end) does _not_ issue a warning # with freq="D" bc the result will be unaffected by the deprecation with tm.assert_produces_warning(None): getattr(ts, attr) @pytest.mark.filterwarnings("ignore:The 'freq' argument:FutureWarning") @pytest.mark.filterwarnings("ignore:Timestamp.freq is deprecated:FutureWarning") def test_properties_business(self): ts = Timestamp("2017-10-01", freq="B") control = Timestamp("2017-10-01") assert ts.dayofweek == 6 assert ts.day_of_week == 6 assert not ts.is_month_start # not a weekday assert not ts.freq.is_month_start(ts) assert ts.freq.is_month_start(ts + Timedelta(days=1)) assert not ts.is_quarter_start # not a weekday assert not ts.freq.is_quarter_start(ts) assert ts.freq.is_quarter_start(ts + Timedelta(days=1)) # Control case: non-business is month/qtr start assert control.is_month_start assert control.is_quarter_start ts = Timestamp("2017-09-30", freq="B") control = Timestamp("2017-09-30") assert ts.dayofweek == 5 assert ts.day_of_week == 5 assert not ts.is_month_end # not a weekday assert not ts.freq.is_month_end(ts) assert ts.freq.is_month_end(ts - Timedelta(days=1)) assert not ts.is_quarter_end # not a weekday assert not ts.freq.is_quarter_end(ts) assert ts.freq.is_quarter_end(ts - Timedelta(days=1)) # Control case: non-business is month/qtr start assert control.is_month_end assert control.is_quarter_end @pytest.mark.parametrize( "attr, expected", [ ["year", 2014], ["month", 12], ["day", 31], ["hour", 23], ["minute", 59], ["second", 0], ["microsecond", 0], ["nanosecond", 0], ["dayofweek", 2], ["day_of_week", 2], ["quarter", 4], ["dayofyear", 365], ["day_of_year", 365], ["week", 1], ["daysinmonth", 31], ], ) @pytest.mark.parametrize("tz", [None, "US/Eastern"]) def test_fields(self, attr, expected, tz): # GH 10050 # GH 13303 ts = Timestamp("2014-12-31 23:59:00", tz=tz) result = getattr(ts, attr) # that we are int like assert isinstance(result, int) assert result == expected @pytest.mark.parametrize("tz", [None, "US/Eastern"]) def test_millisecond_raises(self, tz): ts = Timestamp("2014-12-31 23:59:00", tz=tz) msg = "'Timestamp' object has no attribute 'millisecond'" with pytest.raises(AttributeError, match=msg): ts.millisecond @pytest.mark.parametrize( "start", ["is_month_start", "is_quarter_start", "is_year_start"] ) @pytest.mark.parametrize("tz", [None, "US/Eastern"]) def test_is_start(self, start, tz): ts = Timestamp("2014-01-01 00:00:00", tz=tz) assert getattr(ts, start) @pytest.mark.parametrize("end", ["is_month_end", "is_year_end", "is_quarter_end"]) @pytest.mark.parametrize("tz", [None, "US/Eastern"]) def test_is_end(self, end, tz): ts = Timestamp("2014-12-31 23:59:59", tz=tz) assert getattr(ts, end) # GH 12806 @pytest.mark.parametrize( "data", [Timestamp("2017-08-28 23:00:00"), Timestamp("2017-08-28 23:00:00", tz="EST")], ) # error: Unsupported operand types for + ("List[None]" and "List[str]") @pytest.mark.parametrize( "time_locale", [None] + (tm.get_locales() or []) # type: ignore[operator] ) def test_names(self, data, time_locale): # GH 17354 # Test .day_name(), .month_name if time_locale is None: expected_day = "Monday" expected_month = "August" else: with tm.set_locale(time_locale, locale.LC_TIME): expected_day = calendar.day_name[0].capitalize() expected_month = calendar.month_name[8].capitalize() result_day = data.day_name(time_locale) result_month = data.month_name(time_locale) # Work around https://github.com/pandas-dev/pandas/issues/22342 # different normalizations expected_day = unicodedata.normalize("NFD", expected_day) expected_month = unicodedata.normalize("NFD", expected_month) result_day = unicodedata.normalize("NFD", result_day) result_month = unicodedata.normalize("NFD", result_month) assert result_day == expected_day assert result_month == expected_month # Test NaT nan_ts = Timestamp(NaT) assert np.isnan(nan_ts.day_name(time_locale)) assert np.isnan(nan_ts.month_name(time_locale)) def test_is_leap_year(self, tz_naive_fixture): tz = tz_naive_fixture # GH 13727 dt = Timestamp("2000-01-01 00:00:00", tz=tz) assert dt.is_leap_year assert isinstance(dt.is_leap_year, bool) dt = Timestamp("1999-01-01 00:00:00", tz=tz) assert not dt.is_leap_year dt = Timestamp("2004-01-01 00:00:00", tz=tz) assert dt.is_leap_year dt = Timestamp("2100-01-01 00:00:00", tz=tz) assert not dt.is_leap_year def test_woy_boundary(self): # make sure weeks at year boundaries are correct d = datetime(2013, 12, 31) result = Timestamp(d).week expected = 1 # ISO standard assert result == expected d = datetime(2008, 12, 28) result = Timestamp(d).week expected = 52 # ISO standard assert result == expected d = datetime(2009, 12, 31) result = Timestamp(d).week expected = 53 # ISO standard assert result == expected d = datetime(2010, 1, 1) result = Timestamp(d).week expected = 53 # ISO standard assert result == expected d = datetime(2010, 1, 3) result = Timestamp(d).week expected = 53 # ISO standard assert result == expected result = np.array( [ Timestamp(datetime(*args)).week for args in [(2000, 1, 1), (2000, 1, 2), (2005, 1, 1), (2005, 1, 2)] ] ) assert (result == [52, 52, 53, 53]).all() def test_resolution(self): # GH#21336, GH#21365 dt = Timestamp("2100-01-01 00:00:00") assert dt.resolution == Timedelta(nanoseconds=1) # Check that the attribute is available on the class, mirroring # the stdlib datetime behavior assert Timestamp.resolution == Timedelta(nanoseconds=1) class TestTimestamp: def test_tz(self): tstr = "2014-02-01 09:00" ts = Timestamp(tstr) local = ts.tz_localize("Asia/Tokyo") assert local.hour == 9 assert local == Timestamp(tstr, tz="Asia/Tokyo") conv = local.tz_convert("US/Eastern") assert conv == Timestamp("2014-01-31 19:00", tz="US/Eastern") assert conv.hour == 19 # preserves nanosecond ts = Timestamp(tstr) + offsets.Nano(5) local = ts.tz_localize("Asia/Tokyo") assert local.hour == 9 assert local.nanosecond == 5 conv = local.tz_convert("US/Eastern") assert conv.nanosecond == 5 assert conv.hour == 19 def test_utc_z_designator(self): assert get_timezone(Timestamp("2014-11-02 01:00Z").tzinfo) is utc def test_asm8(self): np.random.seed(7_960_929) ns = [Timestamp.min.value, Timestamp.max.value, 1000] for n in ns: assert ( Timestamp(n).asm8.view("i8") == np.datetime64(n, "ns").view("i8") == n ) assert Timestamp("nat").asm8.view("i8") == np.datetime64("nat", "ns").view("i8") def test_class_ops_pytz(self): def compare(x, y): assert int((Timestamp(x).value - Timestamp(y).value) / 1e9) == 0 compare(Timestamp.now(), datetime.now()) compare(Timestamp.now("UTC"), datetime.now(timezone("UTC"))) compare(Timestamp.utcnow(), datetime.utcnow()) compare(Timestamp.today(), datetime.today()) current_time = calendar.timegm(datetime.now().utctimetuple()) msg = "timezone-aware Timestamp with UTC" with tm.assert_produces_warning(FutureWarning, match=msg): # GH#22451 ts_utc = Timestamp.utcfromtimestamp(current_time) compare( ts_utc, datetime.utcfromtimestamp(current_time), ) compare( Timestamp.fromtimestamp(current_time), datetime.fromtimestamp(current_time) ) compare( # Support tz kwarg in Timestamp.fromtimestamp Timestamp.fromtimestamp(current_time, "UTC"), datetime.fromtimestamp(current_time, utc), ) compare( # Support tz kwarg in Timestamp.fromtimestamp Timestamp.fromtimestamp(current_time, tz="UTC"), datetime.fromtimestamp(current_time, utc), ) date_component = datetime.utcnow() time_component = (date_component + timedelta(minutes=10)).time() compare( Timestamp.combine(date_component, time_component), datetime.combine(date_component, time_component), ) def test_class_ops_dateutil(self): def compare(x, y): assert ( int( np.round(Timestamp(x).value / 1e9) - np.round(Timestamp(y).value / 1e9) ) == 0 ) compare(Timestamp.now(), datetime.now()) compare(Timestamp.now("UTC"), datetime.now(tzutc())) compare(Timestamp.utcnow(), datetime.utcnow()) compare(Timestamp.today(), datetime.today()) current_time = calendar.timegm(datetime.now().utctimetuple()) msg = "timezone-aware Timestamp with UTC" with tm.assert_produces_warning(FutureWarning, match=msg): # GH#22451 ts_utc = Timestamp.utcfromtimestamp(current_time) compare( ts_utc, datetime.utcfromtimestamp(current_time), ) compare( Timestamp.fromtimestamp(current_time), datetime.fromtimestamp(current_time) ) date_component = datetime.utcnow() time_component = (date_component + timedelta(minutes=10)).time() compare( Timestamp.combine(date_component, time_component), datetime.combine(date_component, time_component), ) def test_basics_nanos(self): val = np.int64(946_684_800_000_000_000).view("M8[ns]") stamp = Timestamp(val.view("i8") + 500) assert stamp.year == 2000 assert stamp.month == 1 assert stamp.microsecond == 0 assert stamp.nanosecond == 500 # GH 14415 val = np.iinfo(np.int64).min + 80_000_000_000_000 stamp = Timestamp(val) assert stamp.year == 1677 assert stamp.month == 9 assert stamp.day == 21 assert stamp.microsecond == 145224 assert stamp.nanosecond == 192 @pytest.mark.parametrize( "value, check_kwargs", [ [946688461000000000, {}], [946688461000000000 / 1000, {"unit": "us"}], [946688461000000000 / 1_000_000, {"unit": "ms"}], [946688461000000000 / 1_000_000_000, {"unit": "s"}], [10957, {"unit": "D", "h": 0}], [ (946688461000000000 + 500000) / 1000000000, {"unit": "s", "us": 499, "ns": 964}, ], [ (946688461000000000 + 500000000) / 1000000000, {"unit": "s", "us": 500000}, ], [(946688461000000000 + 500000) / 1000000, {"unit": "ms", "us": 500}], [(946688461000000000 + 500000) / 1000, {"unit": "us", "us": 500}], [(946688461000000000 + 500000000) / 1000000, {"unit": "ms", "us": 500000}], [946688461000000000 / 1000.0 + 5, {"unit": "us", "us": 5}], [946688461000000000 / 1000.0 + 5000, {"unit": "us", "us": 5000}], [946688461000000000 / 1000000.0 + 0.5, {"unit": "ms", "us": 500}], [946688461000000000 / 1000000.0 + 0.005, {"unit": "ms", "us": 5, "ns": 5}], [946688461000000000 / 1000000000.0 + 0.5, {"unit": "s", "us": 500000}], [10957 + 0.5, {"unit": "D", "h": 12}], ], ) def test_unit(self, value, check_kwargs): def check(value, unit=None, h=1, s=1, us=0, ns=0): stamp = Timestamp(value, unit=unit) assert stamp.year == 2000 assert stamp.month == 1 assert stamp.day == 1 assert stamp.hour == h if unit != "D": assert stamp.minute == 1 assert stamp.second == s assert stamp.microsecond == us else: assert stamp.minute == 0 assert stamp.second == 0 assert stamp.microsecond == 0 assert stamp.nanosecond == ns check(value, **check_kwargs) def test_roundtrip(self): # test value to string and back conversions # further test accessors base = Timestamp("20140101 00:00:00") result = Timestamp(base.value + Timedelta("5ms").value) assert result == Timestamp(f"{base}.005000") assert result.microsecond == 5000 result = Timestamp(base.value + Timedelta("5us").value) assert result == Timestamp(f"{base}.000005") assert result.microsecond == 5 result = Timestamp(base.value + Timedelta("5ns").value) assert result == Timestamp(f"{base}.000000005") assert result.nanosecond == 5 assert result.microsecond == 0 result = Timestamp(base.value + Timedelta("6ms 5us").value) assert result == Timestamp(f"{base}.006005") assert result.microsecond == 5 + 6 * 1000 result = Timestamp(base.value + Timedelta("200ms 5us").value) assert result == Timestamp(f"{base}.200005") assert result.microsecond == 5 + 200 * 1000 def test_hash_equivalent(self): d = {datetime(2011, 1, 1): 5} stamp = Timestamp(datetime(2011, 1, 1)) assert d[stamp] == 5 @pytest.mark.parametrize( "timezone, year, month, day, hour", [["America/Chicago", 2013, 11, 3, 1], ["America/Santiago", 2021, 4, 3, 23]], ) def test_hash_timestamp_with_fold(self, timezone, year, month, day, hour): # see gh-33931 test_timezone = gettz(timezone) transition_1 = Timestamp( year=year, month=month, day=day, hour=hour, minute=0, fold=0, tzinfo=test_timezone, ) transition_2 = Timestamp( year=year, month=month, day=day, hour=hour, minute=0, fold=1, tzinfo=test_timezone, ) assert hash(transition_1) == hash(transition_2) def test_tz_conversion_freq(self, tz_naive_fixture): # GH25241 with tm.assert_produces_warning(FutureWarning, match="freq"): t1 = Timestamp("2019-01-01 10:00", freq="H") assert t1.tz_localize(tz=tz_naive_fixture).freq == t1.freq with tm.assert_produces_warning(FutureWarning, match="freq"): t2 = Timestamp("2019-01-02 12:00", tz="UTC", freq="T") assert t2.tz_convert(tz="UTC").freq == t2.freq def test_pickle_freq_no_warning(self): # GH#41949 we don't want a warning on unpickling with tm.assert_produces_warning(FutureWarning, match="freq"): ts = Timestamp("2019-01-01 10:00", freq="H") out = pickle.dumps(ts) with tm.assert_produces_warning(None): res = pickle.loads(out) assert res._freq == ts._freq class TestTimestampNsOperations: def test_nanosecond_string_parsing(self): ts = Timestamp("2013-05-01 07:15:45.123456789") # GH 7878 expected_repr = "2013-05-01 07:15:45.123456789" expected_value = 1_367_392_545_123_456_789 assert ts.value == expected_value assert expected_repr in repr(ts) ts = Timestamp("2013-05-01 07:15:45.123456789+09:00", tz="Asia/Tokyo") assert ts.value == expected_value - 9 * 3600 * 1_000_000_000 assert expected_repr in repr(ts) ts = Timestamp("2013-05-01 07:15:45.123456789", tz="UTC") assert ts.value == expected_value assert expected_repr in repr(ts) ts = Timestamp("2013-05-01 07:15:45.123456789", tz="US/Eastern") assert ts.value == expected_value + 4 * 3600 * 1_000_000_000 assert expected_repr in repr(ts) # GH 10041 ts = Timestamp("20130501T071545.123456789") assert ts.value == expected_value assert expected_repr in repr(ts) def test_nanosecond_timestamp(self): # GH 7610 expected = 1_293_840_000_000_000_005 t = Timestamp("2011-01-01") + offsets.Nano(5) assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')" assert t.value == expected assert t.nanosecond == 5 t = Timestamp(t) assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')" assert t.value == expected assert t.nanosecond == 5 t = Timestamp("2011-01-01 00:00:00.000000005") assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')" assert t.value == expected assert t.nanosecond == 5 expected = 1_293_840_000_000_000_010 t = t + offsets.Nano(5) assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')" assert t.value == expected assert t.nanosecond == 10 t = Timestamp(t) assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')" assert t.value == expected assert t.nanosecond == 10 t = Timestamp("2011-01-01 00:00:00.000000010") assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')" assert t.value == expected assert t.nanosecond == 10 class TestTimestampToJulianDate: def test_compare_1700(self): r = Timestamp("1700-06-23").to_julian_date() assert r == 2_342_145.5 def test_compare_2000(self): r = Timestamp("2000-04-12").to_julian_date() assert r == 2_451_646.5 def test_compare_2100(self): r = Timestamp("2100-08-12").to_julian_date() assert r == 2_488_292.5 def test_compare_hour01(self): r = Timestamp("2000-08-12T01:00:00").to_julian_date() assert r == 2_451_768.5416666666666666 def test_compare_hour13(self): r = Timestamp("2000-08-12T13:00:00").to_julian_date() assert r == 2_451_769.0416666666666666 class TestTimestampConversion: def test_conversion(self): # GH#9255 ts = Timestamp("2000-01-01") result = ts.to_pydatetime() expected = datetime(2000, 1, 1) assert result == expected assert type(result) == type(expected) result = ts.to_datetime64() expected = np.datetime64(ts.value, "ns") assert result == expected assert type(result) == type(expected) assert result.dtype == expected.dtype def test_to_pydatetime_nonzero_nano(self): ts = Timestamp("2011-01-01 9:00:00.123456789") # Warn the user of data loss (nanoseconds). with tm.assert_produces_warning(UserWarning): expected = datetime(2011, 1, 1, 9, 0, 0, 123456) result = ts.to_pydatetime() assert result == expected def test_timestamp_to_datetime(self): stamp = Timestamp("20090415", tz="US/Eastern") dtval = stamp.to_pydatetime() assert stamp == dtval assert stamp.tzinfo == dtval.tzinfo def test_timestamp_to_datetime_dateutil(self): stamp = Timestamp("20090415", tz="dateutil/US/Eastern") dtval = stamp.to_pydatetime() assert stamp == dtval assert stamp.tzinfo == dtval.tzinfo def test_timestamp_to_datetime_explicit_pytz(self): stamp = Timestamp("20090415", tz=pytz.timezone("US/Eastern")) dtval = stamp.to_pydatetime() assert stamp == dtval assert stamp.tzinfo == dtval.tzinfo @td.skip_if_windows def test_timestamp_to_datetime_explicit_dateutil(self): stamp = Timestamp("20090415", tz=gettz("US/Eastern")) dtval = stamp.to_pydatetime() assert stamp == dtval assert stamp.tzinfo == dtval.tzinfo def test_to_datetime_bijective(self): # Ensure that converting to datetime and back only loses precision # by going from nanoseconds to microseconds. exp_warning = None if Timestamp.max.nanosecond == 0 else UserWarning with tm.assert_produces_warning(exp_warning): pydt_max = Timestamp.max.to_pydatetime() assert Timestamp(pydt_max).value / 1000 == Timestamp.max.value / 1000 exp_warning = None if Timestamp.min.nanosecond == 0 else UserWarning with tm.assert_produces_warning(exp_warning): pydt_min = Timestamp.min.to_pydatetime() # The next assertion can be enabled once GH#39221 is merged # assert pydt_min < Timestamp.min # this is bc nanos are dropped tdus = timedelta(microseconds=1) assert pydt_min + tdus > Timestamp.min assert Timestamp(pydt_min + tdus).value / 1000 == Timestamp.min.value / 1000 def test_to_period_tz_warning(self): # GH#21333 make sure a warning is issued when timezone # info is lost ts = Timestamp("2009-04-15 16:17:18", tz="US/Eastern") with tm.assert_produces_warning(UserWarning): # warning that timezone info will be lost ts.to_period("D") def test_to_numpy_alias(self): # GH 24653: alias .to_numpy() for scalars ts = Timestamp(datetime.now()) assert ts.to_datetime64() == ts.to_numpy() # GH#44460 msg = "dtype and copy arguments are ignored" with pytest.raises(ValueError, match=msg): ts.to_numpy("M8[s]") with pytest.raises(ValueError, match=msg): ts.to_numpy(copy=True) class SubDatetime(datetime): pass @pytest.mark.parametrize( "lh,rh", [ (SubDatetime(2000, 1, 1), Timedelta(hours=1)), (Timedelta(hours=1), SubDatetime(2000, 1, 1)), ], ) def test_dt_subclass_add_timedelta(lh, rh): # GH#25851 # ensure that subclassed datetime works for # Timedelta operations result = lh + rh expected = SubDatetime(2000, 1, 1, 1) assert result == expected

35.318314

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0.59381

import calendar from datetime import ( datetime, timedelta, ) import locale import pickle import unicodedata from dateutil.tz import tzutc import numpy as np import pytest import pytz from pytz import ( timezone, utc, ) from pandas._libs.tslibs.timezones import ( dateutil_gettz as gettz, get_timezone, ) import pandas.util._test_decorators as td from pandas import ( NaT, Timedelta, Timestamp, ) import pandas._testing as tm from pandas.tseries import offsets class TestTimestampProperties: def test_freq_deprecation(self): msg = "The 'freq' argument in Timestamp is deprecated" with tm.assert_produces_warning(FutureWarning, match=msg): ts = Timestamp("2021-06-01", freq="D") ts2 = Timestamp("2021-06-01", freq="B") msg = "Timestamp.freq is deprecated" with tm.assert_produces_warning(FutureWarning, match=msg): ts.freq for per in ["month", "quarter", "year"]: for side in ["start", "end"]: attr = f"is_{per}_{side}" with tm.assert_produces_warning(FutureWarning, match=msg): getattr(ts2, attr) with tm.assert_produces_warning(None): getattr(ts, attr) @pytest.mark.filterwarnings("ignore:The 'freq' argument:FutureWarning") @pytest.mark.filterwarnings("ignore:Timestamp.freq is deprecated:FutureWarning") def test_properties_business(self): ts = Timestamp("2017-10-01", freq="B") control = Timestamp("2017-10-01") assert ts.dayofweek == 6 assert ts.day_of_week == 6 assert not ts.is_month_start assert not ts.freq.is_month_start(ts) assert ts.freq.is_month_start(ts + Timedelta(days=1)) assert not ts.is_quarter_start assert not ts.freq.is_quarter_start(ts) assert ts.freq.is_quarter_start(ts + Timedelta(days=1)) assert control.is_month_start assert control.is_quarter_start ts = Timestamp("2017-09-30", freq="B") control = Timestamp("2017-09-30") assert ts.dayofweek == 5 assert ts.day_of_week == 5 assert not ts.is_month_end assert not ts.freq.is_month_end(ts) assert ts.freq.is_month_end(ts - Timedelta(days=1)) assert not ts.is_quarter_end assert not ts.freq.is_quarter_end(ts) assert ts.freq.is_quarter_end(ts - Timedelta(days=1)) assert control.is_month_end assert control.is_quarter_end @pytest.mark.parametrize( "attr, expected", [ ["year", 2014], ["month", 12], ["day", 31], ["hour", 23], ["minute", 59], ["second", 0], ["microsecond", 0], ["nanosecond", 0], ["dayofweek", 2], ["day_of_week", 2], ["quarter", 4], ["dayofyear", 365], ["day_of_year", 365], ["week", 1], ["daysinmonth", 31], ], ) @pytest.mark.parametrize("tz", [None, "US/Eastern"]) def test_fields(self, attr, expected, tz): ts = Timestamp("2014-12-31 23:59:00", tz=tz) result = getattr(ts, attr) assert isinstance(result, int) assert result == expected @pytest.mark.parametrize("tz", [None, "US/Eastern"]) def test_millisecond_raises(self, tz): ts = Timestamp("2014-12-31 23:59:00", tz=tz) msg = "'Timestamp' object has no attribute 'millisecond'" with pytest.raises(AttributeError, match=msg): ts.millisecond @pytest.mark.parametrize( "start", ["is_month_start", "is_quarter_start", "is_year_start"] ) @pytest.mark.parametrize("tz", [None, "US/Eastern"]) def test_is_start(self, start, tz): ts = Timestamp("2014-01-01 00:00:00", tz=tz) assert getattr(ts, start) @pytest.mark.parametrize("end", ["is_month_end", "is_year_end", "is_quarter_end"]) @pytest.mark.parametrize("tz", [None, "US/Eastern"]) def test_is_end(self, end, tz): ts = Timestamp("2014-12-31 23:59:59", tz=tz) assert getattr(ts, end) @pytest.mark.parametrize( "data", [Timestamp("2017-08-28 23:00:00"), Timestamp("2017-08-28 23:00:00", tz="EST")], ) @pytest.mark.parametrize( "time_locale", [None] + (tm.get_locales() or []) ) def test_names(self, data, time_locale): if time_locale is None: expected_day = "Monday" expected_month = "August" else: with tm.set_locale(time_locale, locale.LC_TIME): expected_day = calendar.day_name[0].capitalize() expected_month = calendar.month_name[8].capitalize() result_day = data.day_name(time_locale) result_month = data.month_name(time_locale) expected_day = unicodedata.normalize("NFD", expected_day) expected_month = unicodedata.normalize("NFD", expected_month) result_day = unicodedata.normalize("NFD", result_day) result_month = unicodedata.normalize("NFD", result_month) assert result_day == expected_day assert result_month == expected_month nan_ts = Timestamp(NaT) assert np.isnan(nan_ts.day_name(time_locale)) assert np.isnan(nan_ts.month_name(time_locale)) def test_is_leap_year(self, tz_naive_fixture): tz = tz_naive_fixture dt = Timestamp("2000-01-01 00:00:00", tz=tz) assert dt.is_leap_year assert isinstance(dt.is_leap_year, bool) dt = Timestamp("1999-01-01 00:00:00", tz=tz) assert not dt.is_leap_year dt = Timestamp("2004-01-01 00:00:00", tz=tz) assert dt.is_leap_year dt = Timestamp("2100-01-01 00:00:00", tz=tz) assert not dt.is_leap_year def test_woy_boundary(self): d = datetime(2013, 12, 31) result = Timestamp(d).week expected = 1 assert result == expected d = datetime(2008, 12, 28) result = Timestamp(d).week expected = 52 assert result == expected d = datetime(2009, 12, 31) result = Timestamp(d).week expected = 53 assert result == expected d = datetime(2010, 1, 1) result = Timestamp(d).week expected = 53 assert result == expected d = datetime(2010, 1, 3) result = Timestamp(d).week expected = 53 assert result == expected result = np.array( [ Timestamp(datetime(*args)).week for args in [(2000, 1, 1), (2000, 1, 2), (2005, 1, 1), (2005, 1, 2)] ] ) assert (result == [52, 52, 53, 53]).all() def test_resolution(self): p("2100-01-01 00:00:00") assert dt.resolution == Timedelta(nanoseconds=1) assert Timestamp.resolution == Timedelta(nanoseconds=1) class TestTimestamp: def test_tz(self): tstr = "2014-02-01 09:00" ts = Timestamp(tstr) local = ts.tz_localize("Asia/Tokyo") assert local.hour == 9 assert local == Timestamp(tstr, tz="Asia/Tokyo") conv = local.tz_convert("US/Eastern") assert conv == Timestamp("2014-01-31 19:00", tz="US/Eastern") assert conv.hour == 19 ts = Timestamp(tstr) + offsets.Nano(5) local = ts.tz_localize("Asia/Tokyo") assert local.hour == 9 assert local.nanosecond == 5 conv = local.tz_convert("US/Eastern") assert conv.nanosecond == 5 assert conv.hour == 19 def test_utc_z_designator(self): assert get_timezone(Timestamp("2014-11-02 01:00Z").tzinfo) is utc def test_asm8(self): np.random.seed(7_960_929) ns = [Timestamp.min.value, Timestamp.max.value, 1000] for n in ns: assert ( Timestamp(n).asm8.view("i8") == np.datetime64(n, "ns").view("i8") == n ) assert Timestamp("nat").asm8.view("i8") == np.datetime64("nat", "ns").view("i8") def test_class_ops_pytz(self): def compare(x, y): assert int((Timestamp(x).value - Timestamp(y).value) / 1e9) == 0 compare(Timestamp.now(), datetime.now()) compare(Timestamp.now("UTC"), datetime.now(timezone("UTC"))) compare(Timestamp.utcnow(), datetime.utcnow()) compare(Timestamp.today(), datetime.today()) current_time = calendar.timegm(datetime.now().utctimetuple()) msg = "timezone-aware Timestamp with UTC" with tm.assert_produces_warning(FutureWarning, match=msg): ts_utc = Timestamp.utcfromtimestamp(current_time) compare( ts_utc, datetime.utcfromtimestamp(current_time), ) compare( Timestamp.fromtimestamp(current_time), datetime.fromtimestamp(current_time) ) compare( Timestamp.fromtimestamp(current_time, "UTC"), datetime.fromtimestamp(current_time, utc), ) compare( Timestamp.fromtimestamp(current_time, tz="UTC"), datetime.fromtimestamp(current_time, utc), ) date_component = datetime.utcnow() time_component = (date_component + timedelta(minutes=10)).time() compare( Timestamp.combine(date_component, time_component), datetime.combine(date_component, time_component), ) def test_class_ops_dateutil(self): def compare(x, y): assert ( int( np.round(Timestamp(x).value / 1e9) - np.round(Timestamp(y).value / 1e9) ) == 0 ) compare(Timestamp.now(), datetime.now()) compare(Timestamp.now("UTC"), datetime.now(tzutc())) compare(Timestamp.utcnow(), datetime.utcnow()) compare(Timestamp.today(), datetime.today()) current_time = calendar.timegm(datetime.now().utctimetuple()) msg = "timezone-aware Timestamp with UTC" with tm.assert_produces_warning(FutureWarning, match=msg): ts_utc = Timestamp.utcfromtimestamp(current_time) compare( ts_utc, datetime.utcfromtimestamp(current_time), ) compare( Timestamp.fromtimestamp(current_time), datetime.fromtimestamp(current_time) ) date_component = datetime.utcnow() time_component = (date_component + timedelta(minutes=10)).time() compare( Timestamp.combine(date_component, time_component), datetime.combine(date_component, time_component), ) def test_basics_nanos(self): val = np.int64(946_684_800_000_000_000).view("M8[ns]") stamp = Timestamp(val.view("i8") + 500) assert stamp.year == 2000 assert stamp.month == 1 assert stamp.microsecond == 0 assert stamp.nanosecond == 500 val = np.iinfo(np.int64).min + 80_000_000_000_000 stamp = Timestamp(val) assert stamp.year == 1677 assert stamp.month == 9 assert stamp.day == 21 assert stamp.microsecond == 145224 assert stamp.nanosecond == 192 @pytest.mark.parametrize( "value, check_kwargs", [ [946688461000000000, {}], [946688461000000000 / 1000, {"unit": "us"}], [946688461000000000 / 1_000_000, {"unit": "ms"}], [946688461000000000 / 1_000_000_000, {"unit": "s"}], [10957, {"unit": "D", "h": 0}], [ (946688461000000000 + 500000) / 1000000000, {"unit": "s", "us": 499, "ns": 964}, ], [ (946688461000000000 + 500000000) / 1000000000, {"unit": "s", "us": 500000}, ], [(946688461000000000 + 500000) / 1000000, {"unit": "ms", "us": 500}], [(946688461000000000 + 500000) / 1000, {"unit": "us", "us": 500}], [(946688461000000000 + 500000000) / 1000000, {"unit": "ms", "us": 500000}], [946688461000000000 / 1000.0 + 5, {"unit": "us", "us": 5}], [946688461000000000 / 1000.0 + 5000, {"unit": "us", "us": 5000}], [946688461000000000 / 1000000.0 + 0.5, {"unit": "ms", "us": 500}], [946688461000000000 / 1000000.0 + 0.005, {"unit": "ms", "us": 5, "ns": 5}], [946688461000000000 / 1000000000.0 + 0.5, {"unit": "s", "us": 500000}], [10957 + 0.5, {"unit": "D", "h": 12}], ], ) def test_unit(self, value, check_kwargs): def check(value, unit=None, h=1, s=1, us=0, ns=0): stamp = Timestamp(value, unit=unit) assert stamp.year == 2000 assert stamp.month == 1 assert stamp.day == 1 assert stamp.hour == h if unit != "D": assert stamp.minute == 1 assert stamp.second == s assert stamp.microsecond == us else: assert stamp.minute == 0 assert stamp.second == 0 assert stamp.microsecond == 0 assert stamp.nanosecond == ns check(value, **check_kwargs) def test_roundtrip(self): base = Timestamp("20140101 00:00:00") result = Timestamp(base.value + Timedelta("5ms").value) assert result == Timestamp(f"{base}.005000") assert result.microsecond == 5000 result = Timestamp(base.value + Timedelta("5us").value) assert result == Timestamp(f"{base}.000005") assert result.microsecond == 5 result = Timestamp(base.value + Timedelta("5ns").value) assert result == Timestamp(f"{base}.000000005") assert result.nanosecond == 5 assert result.microsecond == 0 result = Timestamp(base.value + Timedelta("6ms 5us").value) assert result == Timestamp(f"{base}.006005") assert result.microsecond == 5 + 6 * 1000 result = Timestamp(base.value + Timedelta("200ms 5us").value) assert result == Timestamp(f"{base}.200005") assert result.microsecond == 5 + 200 * 1000 def test_hash_equivalent(self): d = {datetime(2011, 1, 1): 5} stamp = Timestamp(datetime(2011, 1, 1)) assert d[stamp] == 5 @pytest.mark.parametrize( "timezone, year, month, day, hour", [["America/Chicago", 2013, 11, 3, 1], ["America/Santiago", 2021, 4, 3, 23]], ) def test_hash_timestamp_with_fold(self, timezone, year, month, day, hour): test_timezone = gettz(timezone) transition_1 = Timestamp( year=year, month=month, day=day, hour=hour, minute=0, fold=0, tzinfo=test_timezone, ) transition_2 = Timestamp( year=year, month=month, day=day, hour=hour, minute=0, fold=1, tzinfo=test_timezone, ) assert hash(transition_1) == hash(transition_2) def test_tz_conversion_freq(self, tz_naive_fixture): with tm.assert_produces_warning(FutureWarning, match="freq"): t1 = Timestamp("2019-01-01 10:00", freq="H") assert t1.tz_localize(tz=tz_naive_fixture).freq == t1.freq with tm.assert_produces_warning(FutureWarning, match="freq"): t2 = Timestamp("2019-01-02 12:00", tz="UTC", freq="T") assert t2.tz_convert(tz="UTC").freq == t2.freq def test_pickle_freq_no_warning(self): ureWarning, match="freq"): ts = Timestamp("2019-01-01 10:00", freq="H") out = pickle.dumps(ts) with tm.assert_produces_warning(None): res = pickle.loads(out) assert res._freq == ts._freq class TestTimestampNsOperations: def test_nanosecond_string_parsing(self): ts = Timestamp("2013-05-01 07:15:45.123456789") # GH 7878 expected_repr = "2013-05-01 07:15:45.123456789" expected_value = 1_367_392_545_123_456_789 assert ts.value == expected_value assert expected_repr in repr(ts) ts = Timestamp("2013-05-01 07:15:45.123456789+09:00", tz="Asia/Tokyo") assert ts.value == expected_value - 9 * 3600 * 1_000_000_000 assert expected_repr in repr(ts) ts = Timestamp("2013-05-01 07:15:45.123456789", tz="UTC") assert ts.value == expected_value assert expected_repr in repr(ts) ts = Timestamp("2013-05-01 07:15:45.123456789", tz="US/Eastern") assert ts.value == expected_value + 4 * 3600 * 1_000_000_000 assert expected_repr in repr(ts) # GH 10041 ts = Timestamp("20130501T071545.123456789") assert ts.value == expected_value assert expected_repr in repr(ts) def test_nanosecond_timestamp(self): # GH 7610 expected = 1_293_840_000_000_000_005 t = Timestamp("2011-01-01") + offsets.Nano(5) assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')" assert t.value == expected assert t.nanosecond == 5 t = Timestamp(t) assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')" assert t.value == expected assert t.nanosecond == 5 t = Timestamp("2011-01-01 00:00:00.000000005") assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')" assert t.value == expected assert t.nanosecond == 5 expected = 1_293_840_000_000_000_010 t = t + offsets.Nano(5) assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')" assert t.value == expected assert t.nanosecond == 10 t = Timestamp(t) assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')" assert t.value == expected assert t.nanosecond == 10 t = Timestamp("2011-01-01 00:00:00.000000010") assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')" assert t.value == expected assert t.nanosecond == 10 class TestTimestampToJulianDate: def test_compare_1700(self): r = Timestamp("1700-06-23").to_julian_date() assert r == 2_342_145.5 def test_compare_2000(self): r = Timestamp("2000-04-12").to_julian_date() assert r == 2_451_646.5 def test_compare_2100(self): r = Timestamp("2100-08-12").to_julian_date() assert r == 2_488_292.5 def test_compare_hour01(self): r = Timestamp("2000-08-12T01:00:00").to_julian_date() assert r == 2_451_768.5416666666666666 def test_compare_hour13(self): r = Timestamp("2000-08-12T13:00:00").to_julian_date() assert r == 2_451_769.0416666666666666 class TestTimestampConversion: def test_conversion(self): # GH#9255 ts = Timestamp("2000-01-01") result = ts.to_pydatetime() expected = datetime(2000, 1, 1) assert result == expected assert type(result) == type(expected) result = ts.to_datetime64() expected = np.datetime64(ts.value, "ns") assert result == expected assert type(result) == type(expected) assert result.dtype == expected.dtype def test_to_pydatetime_nonzero_nano(self): ts = Timestamp("2011-01-01 9:00:00.123456789") # Warn the user of data loss (nanoseconds). with tm.assert_produces_warning(UserWarning): expected = datetime(2011, 1, 1, 9, 0, 0, 123456) result = ts.to_pydatetime() assert result == expected def test_timestamp_to_datetime(self): stamp = Timestamp("20090415", tz="US/Eastern") dtval = stamp.to_pydatetime() assert stamp == dtval assert stamp.tzinfo == dtval.tzinfo def test_timestamp_to_datetime_dateutil(self): stamp = Timestamp("20090415", tz="dateutil/US/Eastern") dtval = stamp.to_pydatetime() assert stamp == dtval assert stamp.tzinfo == dtval.tzinfo def test_timestamp_to_datetime_explicit_pytz(self): stamp = Timestamp("20090415", tz=pytz.timezone("US/Eastern")) dtval = stamp.to_pydatetime() assert stamp == dtval assert stamp.tzinfo == dtval.tzinfo @td.skip_if_windows def test_timestamp_to_datetime_explicit_dateutil(self): stamp = Timestamp("20090415", tz=gettz("US/Eastern")) dtval = stamp.to_pydatetime() assert stamp == dtval assert stamp.tzinfo == dtval.tzinfo def test_to_datetime_bijective(self): # Ensure that converting to datetime and back only loses precision # by going from nanoseconds to microseconds. exp_warning = None if Timestamp.max.nanosecond == 0 else UserWarning with tm.assert_produces_warning(exp_warning): pydt_max = Timestamp.max.to_pydatetime() assert Timestamp(pydt_max).value / 1000 == Timestamp.max.value / 1000 exp_warning = None if Timestamp.min.nanosecond == 0 else UserWarning with tm.assert_produces_warning(exp_warning): pydt_min = Timestamp.min.to_pydatetime() # The next assertion can be enabled once GH#39221 is merged # assert pydt_min < Timestamp.min # this is bc nanos are dropped tdus = timedelta(microseconds=1) assert pydt_min + tdus > Timestamp.min assert Timestamp(pydt_min + tdus).value / 1000 == Timestamp.min.value / 1000 def test_to_period_tz_warning(self): # GH#21333 make sure a warning is issued when timezone # info is lost ts = Timestamp("2009-04-15 16:17:18", tz="US/Eastern") with tm.assert_produces_warning(UserWarning): # warning that timezone info will be lost ts.to_period("D") def test_to_numpy_alias(self): # GH 24653: alias .to_numpy() for scalars ts = Timestamp(datetime.now()) assert ts.to_datetime64() == ts.to_numpy() # GH#44460 msg = "dtype and copy arguments are ignored" with pytest.raises(ValueError, match=msg): ts.to_numpy("M8[s]") with pytest.raises(ValueError, match=msg): ts.to_numpy(copy=True) class SubDatetime(datetime): pass @pytest.mark.parametrize( "lh,rh", [ (SubDatetime(2000, 1, 1), Timedelta(hours=1)), (Timedelta(hours=1), SubDatetime(2000, 1, 1)), ], ) def test_dt_subclass_add_timedelta(lh, rh): # GH#25851 # ensure that subclassed datetime works for # Timedelta operations result = lh + rh expected = SubDatetime(2000, 1, 1, 1) assert result == expected

true

f734c4c022c88b66d3fcb77381bcb51601a9d8af

992

py

Python

backend/web_item.py

ldrozdz/Webforming

ded27ee72da1753b4f3d83e8a4abda1884528a8b

[ "Apache-2.0" ]

null

backend/web_item.py

ldrozdz/Webforming

ded27ee72da1753b4f3d83e8a4abda1884528a8b

[ "Apache-2.0" ]

null

backend/web_item.py

ldrozdz/Webforming

ded27ee72da1753b4f3d83e8a4abda1884528a8b

[ "Apache-2.0" ]

null

class WebItem(object): def __init__(self): self.post_id = None self.parent_id = None self.thread_starter_id = None self.post_url = None self.site_url = None self.source_id = None self.type = None self.hash = None self.post_date = None self.parsed_post_date = None self.crawl_date = None self.post_title = None self.post_author = None self.post_author_id = None self.post_author_reputation = None self.content = None self.parent_title = None self.parent_author = None self.parent_author_id = None self.parent_url = None self.thread_starter_title = None self.thread_starter_author = None self.thread_starter_url = None self.page_category = None self.sub_category = None self.notes = None self.language = None self.likes = None self.dislikes = None self.children = None self.facebooked = None self.tweeted = None self.linkedinned = None self.gplussed = None

27.555556

38

0.680444

class WebItem(object): def __init__(self): self.post_id = None self.parent_id = None self.thread_starter_id = None self.post_url = None self.site_url = None self.source_id = None self.type = None self.hash = None self.post_date = None self.parsed_post_date = None self.crawl_date = None self.post_title = None self.post_author = None self.post_author_id = None self.post_author_reputation = None self.content = None self.parent_title = None self.parent_author = None self.parent_author_id = None self.parent_url = None self.thread_starter_title = None self.thread_starter_author = None self.thread_starter_url = None self.page_category = None self.sub_category = None self.notes = None self.language = None self.likes = None self.dislikes = None self.children = None self.facebooked = None self.tweeted = None self.linkedinned = None self.gplussed = None

true

f734c5a16a4fb88f97b1cf255529a1b8023df50f

1,216

py

Python

_unittests/ut_packaged/test_diff.py

sdpython/pymyinstall

72b3a56a29def0694e34ccae910bf288a95cf4a5

[ "MIT" ]

8

2015-08-24T21:01:49.000Z

2018-01-04T06:34:51.000Z

_unittests/ut_packaged/test_diff.py

sdpython/pymyinstall

72b3a56a29def0694e34ccae910bf288a95cf4a5

[ "MIT" ]

66

2015-06-14T22:04:58.000Z

2021-11-11T13:46:03.000Z

_unittests/ut_packaged/test_diff.py

sdpython/pymyinstall

72b3a56a29def0694e34ccae910bf288a95cf4a5

[ "MIT" ]

5

2016-09-13T18:14:46.000Z

2021-08-23T12:03:28.000Z

""" @brief test log(time=200s) """ import unittest from pyquickhelper.loghelper import fLOG from pyquickhelper.pycode import ExtTestCase from pymyinstall.packaged import small_set class TestDifference(ExtTestCase): def test_diff(self): fLOG( __file__, self._testMethodName, OutputPrint=__name__ == "__main__") name = set(_.name for _ in small_set()) keep = [] for mod in small_set(): if mod.name not in name: keep.append(mod) self.assertGreater(len(keep), 0) for mod in keep: if mod.mname is None: fLOG( "ModuleInstall('{0}', '{1}'),".format(mod.name, mod.kind)) else: fLOG("ModuleInstall('{0}', '{1}', mname='{2}'),".format( mod.name, mod.kind, mod.mname)) def test_diff2(self): fLOG( __file__, self._testMethodName, OutputPrint=__name__ == "__main__") res = small_set() count = {} for mod in res: count[mod.name] = 1 self.assertIn("coverage", count) if __name__ == "__main__": unittest.main()

25.87234

78

0.535362

import unittest from pyquickhelper.loghelper import fLOG from pyquickhelper.pycode import ExtTestCase from pymyinstall.packaged import small_set class TestDifference(ExtTestCase): def test_diff(self): fLOG( __file__, self._testMethodName, OutputPrint=__name__ == "__main__") name = set(_.name for _ in small_set()) keep = [] for mod in small_set(): if mod.name not in name: keep.append(mod) self.assertGreater(len(keep), 0) for mod in keep: if mod.mname is None: fLOG( "ModuleInstall('{0}', '{1}'),".format(mod.name, mod.kind)) else: fLOG("ModuleInstall('{0}', '{1}', mname='{2}'),".format( mod.name, mod.kind, mod.mname)) def test_diff2(self): fLOG( __file__, self._testMethodName, OutputPrint=__name__ == "__main__") res = small_set() count = {} for mod in res: count[mod.name] = 1 self.assertIn("coverage", count) if __name__ == "__main__": unittest.main()

true

f734c5b8ff6599d9412f3021b918a8bb7dc23ee8

553

py

Python

bot/cogs/guilds/sheeptrainer.py

issuemeaname/rammus-discord-bot

23bcb02af11b8b764d75fa974149164f0086c1ea

[ "MIT" ]

1

2020-07-07T05:02:40.000Z

bot/cogs/guilds/sheeptrainer.py

issuemeaname/rammus-discord-bot

23bcb02af11b8b764d75fa974149164f0086c1ea

[ "MIT" ]

null

bot/cogs/guilds/sheeptrainer.py

issuemeaname/rammus-discord-bot

23bcb02af11b8b764d75fa974149164f0086c1ea

[ "MIT" ]

2

2019-02-07T18:26:43.000Z

2021-07-04T16:58:41.000Z

# import discord from discord.ext import commands import bot.checks class Sheeptrainer(commands.Cog, command_attrs={"hidden": True}): _GUILD = 296463400064647168 def __init__(self, bot): self.bot = bot @bot.checks.in_guild(_GUILD) async def cog_check(self, ctx): print("a") return True @commands.command(usage="{0}part 8") async def part(self, ctx, number: int = None): if number == 8: await ctx.send("Have ur balls removed") def setup(bot): bot.add_cog(Sheeptrainer(bot))

21.269231

65

0.641953

from discord.ext import commands import bot.checks class Sheeptrainer(commands.Cog, command_attrs={"hidden": True}): _GUILD = 296463400064647168 def __init__(self, bot): self.bot = bot @bot.checks.in_guild(_GUILD) async def cog_check(self, ctx): print("a") return True @commands.command(usage="{0}part 8") async def part(self, ctx, number: int = None): if number == 8: await ctx.send("Have ur balls removed") def setup(bot): bot.add_cog(Sheeptrainer(bot))

true

f734c651842c525dc107baa1fc6c1a5c794093b3

1,458

py

Python

setup.py

pyarnold/Mailpile

a7c0a0c6257da167207200f3b214b0e66bb93a10

[ "Apache-2.0" ]

2

2017-02-03T07:00:57.000Z

2020-12-18T01:07:34.000Z

setup.py

cz8s/Mailpile

a7c0a0c6257da167207200f3b214b0e66bb93a10

[ "Apache-2.0" ]

null

setup.py

cz8s/Mailpile

a7c0a0c6257da167207200f3b214b0e66bb93a10

[ "Apache-2.0" ]

null

#!/usr/bin/env python2 from datetime import date from setuptools import setup, find_packages import os import re from glob import glob APPVER = ( line.strip() for line in open('mailpile/defaults.py', 'r') if re.match(r'^APPVER\s*=', line) ).next().split('"')[1] try: # This borks sdist. os.remove('.SELF') except: pass data_files = [] # Copy static UI files for dir, dirs, files in os.walk('static'): data_files.append((dir, [os.path.join(dir, file_) for file_ in files])) # Copy translation files for dir, dirs, files in os.walk('locale'): data_files.append((dir, [os.path.join(dir, file_) for file_ in files])) setup( name="mailpile", version=APPVER.replace('github', 'dev'+date.today().isoformat().replace('-', '')), license="AGPLv3+", author="Bjarni R. Einarsson", author_email="bre@klaki.net", url="http://www.mailpile.is/", description="""\ Mailpile is a personal tool for searching and indexing e-mail.""", long_description="""\ Mailpile is a tool for building and maintaining a tagging search engine for a personal collection of e-mail. It can be used as a simple web-mail client. """, packages=find_packages(), data_files=data_files, install_requires=[ 'lxml>=2.3.2', 'jinja2', 'spambayes>=1.1b1' ], entry_points={ 'console_scripts': [ 'mailpile = mailpile.__main__:main' ]}, )

25.578947

76

0.633059

from datetime import date from setuptools import setup, find_packages import os import re from glob import glob APPVER = ( line.strip() for line in open('mailpile/defaults.py', 'r') if re.match(r'^APPVER\s*=', line) ).next().split('"')[1] try: # This borks sdist. os.remove('.SELF') except: pass data_files = [] # Copy static UI files for dir, dirs, files in os.walk('static'): data_files.append((dir, [os.path.join(dir, file_) for file_ in files])) # Copy translation files for dir, dirs, files in os.walk('locale'): data_files.append((dir, [os.path.join(dir, file_) for file_ in files])) setup( name="mailpile", version=APPVER.replace('github', 'dev'+date.today().isoformat().replace('-', '')), license="AGPLv3+", author="Bjarni R. Einarsson", author_email="bre@klaki.net", url="http://www.mailpile.is/", description="""\ Mailpile is a personal tool for searching and indexing e-mail.""", long_description="""\ Mailpile is a tool for building and maintaining a tagging search engine for a personal collection of e-mail. It can be used as a simple web-mail client. """, packages=find_packages(), data_files=data_files, install_requires=[ 'lxml>=2.3.2', 'jinja2', 'spambayes>=1.1b1' ], entry_points={ 'console_scripts': [ 'mailpile = mailpile.__main__:main' ]}, )

true

f734c7ad29376309ca2140ef6f5ab05ea0760b35

3,037

py

Python

Code/main.py

Orelbenr/acoustic-fencing

2d8c6121c915d2f12fae3c9d776e6339f028e35a

[ "MIT" ]

null

Code/main.py

Orelbenr/acoustic-fencing

2d8c6121c915d2f12fae3c9d776e6339f028e35a

[ "MIT" ]

null

Code/main.py

Orelbenr/acoustic-fencing

2d8c6121c915d2f12fae3c9d776e6339f028e35a

[ "MIT" ]

null

import sys from train import train from separate import separate import os from os.path import join as pjoin import logging from datetime import datetime def get_logger(logger_name, file_name): logger = logging.getLogger(logger_name) file_handler = logging.FileHandler(file_name) stream_handler = logging.StreamHandler(sys.stdout) formatter = logging.Formatter('%(message)s') file_handler.setFormatter(formatter) stream_handler.setFormatter(formatter) logger.addHandler(file_handler) logger.addHandler(stream_handler) logger.setLevel(logging.INFO) return logger def main(): # directories output_dir = '../output' # Folder to save outputs (Created by program) train_dir = '../data/TRAIN' # Directory contiaing train set - .wav files (Created by user) test_dir = '../data/TEST' # Directory contiaing test set - .wav files (Created by user) rir_dir = '../RIRs' # Directory of Simulation rirs .mat file (Created by user) runs = [ # run1 {'run_name': 'demo_run', 'test_out_name': 'test_results', 'rir_name': 'demo', 'micN': 9, 'zoneN': 2, 'spN': 2, 'batch': 128, 'lr': 1e-3, 'perm_skip': 0, 'seg_len': 100, 'epochs': 30, 'sc_step': 10, 'sc_gamma': 0.5, 'train': True, 'test': True, 'files2save': 5, 'evaluate': True, 'is_simulation': True, 'old_model': None} # run2 ... ] for i, run in enumerate(runs): # create required directories cur_out_dir = pjoin(output_dir, run['run_name']) os.makedirs(cur_out_dir, exist_ok=True) train_rir = pjoin(rir_dir, 'train_' + run['rir_name'] + '.mat') test_rir = pjoin(rir_dir, 'test_' + run['rir_name'] + '.mat') model_path = pjoin(cur_out_dir, 'trained_model', 'unet_model.pt') # created by train test_out_dir = pjoin(cur_out_dir, run['test_out_name']) # logging logger = get_logger(logger_name='my_logger', file_name=pjoin(cur_out_dir, 'log.txt')) now = datetime.now() logger.info('Run {}/{} Started - {}'.format(i, len(runs), now.strftime("%d/%m/%Y %H:%M:%S"))) if run['train']: train(cur_out_dir, train_dir, train_rir, mic_num=run['micN'], zone_num=run['zoneN'], sp_num=run['spN'], batch_size=run['batch'], perm_skip=run['perm_skip'], seg_len=run['seg_len'], learning_rate=run['lr'], num_epochs=run['epochs'], sched_step_size=run['sc_step'], sched_gamma=run['sc_gamma'], is_simulation=run['is_simulation'], old_model=run['old_model']) if run['test']: separate(test_out_dir, test_dir, test_rir, model_path, mic_num=run['micN'], zone_num=run['zoneN'], sp_num=run['spN'], perm_skip=run['perm_skip'], seg_len=run['seg_len'], save_num=run['files2save'], is_evaluated=run['evaluate'], is_simulation=run['is_simulation']) logger.info('\nProgram Finished Successfully. Yey!') logger.info('-'*30) if __name__ == '__main__': main()

43.385714

131

0.638459

import sys from train import train from separate import separate import os from os.path import join as pjoin import logging from datetime import datetime def get_logger(logger_name, file_name): logger = logging.getLogger(logger_name) file_handler = logging.FileHandler(file_name) stream_handler = logging.StreamHandler(sys.stdout) formatter = logging.Formatter('%(message)s') file_handler.setFormatter(formatter) stream_handler.setFormatter(formatter) logger.addHandler(file_handler) logger.addHandler(stream_handler) logger.setLevel(logging.INFO) return logger def main(): output_dir = '../output' train_dir = '../data/TRAIN' test_dir = '../data/TEST' rir_dir = '../RIRs' runs = [ {'run_name': 'demo_run', 'test_out_name': 'test_results', 'rir_name': 'demo', 'micN': 9, 'zoneN': 2, 'spN': 2, 'batch': 128, 'lr': 1e-3, 'perm_skip': 0, 'seg_len': 100, 'epochs': 30, 'sc_step': 10, 'sc_gamma': 0.5, 'train': True, 'test': True, 'files2save': 5, 'evaluate': True, 'is_simulation': True, 'old_model': None} ] for i, run in enumerate(runs): cur_out_dir = pjoin(output_dir, run['run_name']) os.makedirs(cur_out_dir, exist_ok=True) train_rir = pjoin(rir_dir, 'train_' + run['rir_name'] + '.mat') test_rir = pjoin(rir_dir, 'test_' + run['rir_name'] + '.mat') model_path = pjoin(cur_out_dir, 'trained_model', 'unet_model.pt') test_out_dir = pjoin(cur_out_dir, run['test_out_name']) logger = get_logger(logger_name='my_logger', file_name=pjoin(cur_out_dir, 'log.txt')) now = datetime.now() logger.info('Run {}/{} Started - {}'.format(i, len(runs), now.strftime("%d/%m/%Y %H:%M:%S"))) if run['train']: train(cur_out_dir, train_dir, train_rir, mic_num=run['micN'], zone_num=run['zoneN'], sp_num=run['spN'], batch_size=run['batch'], perm_skip=run['perm_skip'], seg_len=run['seg_len'], learning_rate=run['lr'], num_epochs=run['epochs'], sched_step_size=run['sc_step'], sched_gamma=run['sc_gamma'], is_simulation=run['is_simulation'], old_model=run['old_model']) if run['test']: separate(test_out_dir, test_dir, test_rir, model_path, mic_num=run['micN'], zone_num=run['zoneN'], sp_num=run['spN'], perm_skip=run['perm_skip'], seg_len=run['seg_len'], save_num=run['files2save'], is_evaluated=run['evaluate'], is_simulation=run['is_simulation']) logger.info('\nProgram Finished Successfully. Yey!') logger.info('-'*30) if __name__ == '__main__': main()

true

f734ca6ded8fd6b46244078e8a884dc84331652f

18,494

py

Python

lasagne/tests/layers/test_conv.py

JeffreyDF/Lasagne

6dd88f5fada20768087f29ae89cbd83980fe0a4e

[ "MIT" ]

60

2015-01-29T21:54:04.000Z

2019-11-12T07:38:15.000Z

lasagne/tests/layers/test_conv.py

JeffreyDF/Lasagne

6dd88f5fada20768087f29ae89cbd83980fe0a4e

[ "MIT" ]

5

2015-06-15T00:21:47.000Z

2017-09-14T10:24:40.000Z

lasagne/tests/layers/test_conv.py

JeffreyDF/Lasagne

6dd88f5fada20768087f29ae89cbd83980fe0a4e

[ "MIT" ]

20

2015-04-28T00:21:41.000Z

2019-09-16T01:10:37.000Z

import numpy as np import pytest import importlib import theano import lasagne from lasagne.utils import floatX, as_tuple def conv2d(input, kernel, pad): """Execute a 2D convolution. Parameters ---------- input : numpy array kernel : numpy array pad : {0, 'valid', 'same', 'full'} Returns ------- numpy array """ if pad not in ['valid', 'same', 'full']: pad = as_tuple(pad, 2, int) input = np.pad(input, ((0, 0), (0, 0), (pad[0], pad[0]), (pad[1], pad[1])), mode='constant') pad = 'valid' output = np.zeros((input.shape[0], kernel.shape[0], input.shape[2] + kernel.shape[2] - 1, input.shape[3] + kernel.shape[3] - 1, )) for i in range(kernel.shape[2]): for j in range(kernel.shape[3]): k = kernel[:, :, i, j][:, :, np.newaxis, np.newaxis] output[:, :, i:i + input.shape[2], j:j + input.shape[3]] += (input[:, np.newaxis] * k).sum(2) if pad == 'valid': trim = (kernel.shape[2] - 1, kernel.shape[3] - 1) output = output[:, :, trim[0]:-trim[0] or None, trim[1]:-trim[1] or None] elif pad == 'same': shift_x = (kernel.shape[2] - 1) // 2 shift_y = (kernel.shape[3] - 1) // 2 output = output[:, :, shift_x:input.shape[2] + shift_x, shift_y:input.shape[3] + shift_y] return output def conv2d_test_sets(): def _convert(input, kernel, output, kwargs): return [theano.shared(floatX(input)), floatX(kernel), output, kwargs] for pad in [0, 'full', 'same']: for stride in [1, 2, 3]: for filter_size in [1, 3]: if stride > filter_size: continue input = np.random.random((3, 1, 16, 23)) kernel = np.random.random((16, 1, filter_size, filter_size)) output = conv2d(input, kernel, pad=pad) output = output[:, :, ::stride, ::stride] yield _convert(input, kernel, output, {'pad': pad, 'stride': stride }) # bias-less case input = np.random.random((3, 1, 16, 23)) kernel = np.random.random((16, 1, 3, 3)) output = conv2d(input, kernel, pad='valid') yield _convert(input, kernel, output, {'b': None}) # pad='valid' case yield _convert(input, kernel, output, {'pad': 'valid'}) def conv1d(input, kernel, pad): if pad not in ['valid', 'same', 'full']: input = np.pad(input, ((0, 0), (0, 0), (int(pad), int(pad))), mode='constant') pad = 'valid' output = [] for b in input: temp = [] for c in kernel: temp.append( np.convolve(b[0, :], c[0, :], mode=pad)) output.append(temp) return np.array(output) def conv1d_test_sets(): def _convert(input, kernel, output, kwargs): return [theano.shared(floatX(input)), floatX(kernel), output, kwargs] for pad in [0, 1, 2, 'full', 'same']: for stride in [1, 2, 3]: for filter_size in [1, 3]: if stride > filter_size: continue input = np.random.random((3, 1, 23)) kernel = np.random.random((16, 1, filter_size)) output = conv1d(input, kernel, pad) output = output[:, :, ::stride] yield _convert(input, kernel, output, {'pad': pad, 'stride': stride, }) # bias-less case input = np.random.random((3, 1, 23)) kernel = np.random.random((16, 1, 3)) output = conv1d(input, kernel, pad='valid') yield _convert(input, kernel, output, {'b': None}) # pad='valid' case yield _convert(input, kernel, output, {'pad': 'valid'}) def test_conv_output_length(): from lasagne.layers.conv import conv_output_length assert conv_output_length(13, 5, 3, 'valid') == 3 assert conv_output_length(13, 5, 3, 0) == 3 assert conv_output_length(13, 5, 3, 'full') == 6 assert conv_output_length(13, 5, 3, 'same') == 5 assert conv_output_length(13, 5, 3, 2) == 5 with pytest.raises(ValueError) as exc: conv_output_length(13, 5, 3, '_nonexistent_mode') assert "Invalid pad: " in exc.value.args[0] @pytest.fixture def DummyInputLayer(): def factory(shape): from lasagne.layers.input import InputLayer return InputLayer(shape) return factory class TestConv1DLayer: @pytest.mark.parametrize( "input, kernel, output, kwargs", list(conv1d_test_sets())) @pytest.mark.parametrize("extra_kwargs", [ {}, {'untie_biases': True}, ]) def test_defaults(self, DummyInputLayer, input, kernel, output, kwargs, extra_kwargs): kwargs.update(extra_kwargs) b, c, w = input.shape.eval() input_layer = DummyInputLayer((b, c, w)) try: from lasagne.layers.conv import Conv1DLayer layer = Conv1DLayer( input_layer, num_filters=kernel.shape[0], filter_size=kernel.shape[2], W=kernel, **kwargs ) actual = layer.get_output_for(input).eval() assert actual.shape == output.shape assert actual.shape == layer.output_shape assert np.allclose(actual, output) except NotImplementedError: pass def test_init_none_nonlinearity_bias(self, DummyInputLayer): from lasagne.layers.conv import Conv1DLayer input_layer = DummyInputLayer((1, 2, 3)) layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(3,), nonlinearity=None, b=None) assert layer.nonlinearity == lasagne.nonlinearities.identity assert layer.b is None def test_invalid_pad(self, DummyInputLayer): from lasagne.layers.conv import Conv1DLayer input_layer = DummyInputLayer((1, 2, 3)) with pytest.raises(TypeError) as exc: layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(3,), pad='_nonexistent_mode') assert "iterable of int" in exc.value.args[0] with pytest.raises(NotImplementedError) as exc: layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(4,), pad='same') assert "requires odd filter size" in exc.value.args[0] class TestConv2DLayerImplementations: @pytest.fixture( params=[ ('lasagne.layers', 'Conv2DLayer', {}), ('lasagne.layers.cuda_convnet', 'Conv2DCCLayer', {'flip_filters': True}), ('lasagne.layers.corrmm', 'Conv2DMMLayer', {'flip_filters': True}), ('lasagne.layers.dnn', 'Conv2DDNNLayer', {'flip_filters': True}), ], ) def Conv2DImpl(self, request): impl_module_name, impl_name, impl_default_kwargs = request.param try: mod = importlib.import_module(impl_module_name) except ImportError: pytest.skip("{} not available".format(impl_module_name)) impl = getattr(mod, impl_name) def wrapper(*args, **kwargs): kwargs2 = impl_default_kwargs.copy() kwargs2.update(kwargs) return impl(*args, **kwargs2) wrapper.__name__ = impl_name return wrapper @pytest.mark.parametrize( "input, kernel, output, kwargs", list(conv2d_test_sets())) @pytest.mark.parametrize("extra_kwargs", [ {}, {'untie_biases': True}, ]) def test_defaults(self, Conv2DImpl, DummyInputLayer, input, kernel, output, kwargs, extra_kwargs): kwargs.update(extra_kwargs) b, c, h, w = input.shape.eval() input_layer = DummyInputLayer((b, c, h, w)) try: layer = Conv2DImpl( input_layer, num_filters=kernel.shape[0], filter_size=kernel.shape[2:], W=kernel, **kwargs ) actual = layer.get_output_for(input).eval() assert actual.shape == output.shape assert actual.shape == layer.output_shape assert np.allclose(actual, output) except NotImplementedError: pytest.skip() @pytest.mark.parametrize( "input, kernel, output, kwargs", list(conv2d_test_sets())) def test_with_nones(self, Conv2DImpl, DummyInputLayer, input, kernel, output, kwargs): b, c, h, w = input.shape.eval() input_layer = DummyInputLayer((None, c, None, None)) try: layer = Conv2DImpl( input_layer, num_filters=kernel.shape[0], filter_size=kernel.shape[2:], W=kernel, **kwargs ) actual = layer.get_output_for(input).eval() assert layer.output_shape == (None, kernel.shape[0], None, None) assert actual.shape == output.shape assert np.allclose(actual, output) except NotImplementedError: pytest.skip() def test_init_none_nonlinearity_bias(self, Conv2DImpl, DummyInputLayer): input_layer = DummyInputLayer((1, 2, 3, 3)) layer = Conv2DImpl(input_layer, num_filters=16, filter_size=(3, 3), nonlinearity=None, b=None) assert layer.nonlinearity == lasagne.nonlinearities.identity assert layer.b is None def test_invalid_pad(self, Conv2DImpl, DummyInputLayer): input_layer = DummyInputLayer((1, 2, 3)) with pytest.raises(TypeError) as exc: layer = Conv2DImpl(input_layer, num_filters=16, filter_size=(3, 3), pad='_nonexistent_mode') assert "iterable of int" in exc.value.args[0] with pytest.raises(NotImplementedError) as exc: layer = Conv2DImpl(input_layer, num_filters=16, filter_size=(4, 4), pad='same') assert "requires odd filter size" in exc.value.args[0] def test_get_params(self, Conv2DImpl, DummyInputLayer): input_layer = DummyInputLayer((128, 3, 32, 32)) layer = Conv2DImpl(input_layer, num_filters=16, filter_size=(3, 3)) assert layer.get_params() == [layer.W, layer.b] assert layer.get_params(regularizable=False) == [layer.b] assert layer.get_params(regularizable=True) == [layer.W] assert layer.get_params(trainable=True) == [layer.W, layer.b] assert layer.get_params(trainable=False) == [] assert layer.get_params(_nonexistent_tag=True) == [] assert layer.get_params(_nonexistent_tag=False) == [layer.W, layer.b] class TestConv2DDNNLayer: def test_import_without_gpu_or_cudnn_raises(self): from theano.sandbox.cuda import dnn if theano.config.device.startswith("gpu") and dnn.dnn_available(): pytest.skip() else: with pytest.raises(ImportError): import lasagne.layers.dnn def test_pad(self, DummyInputLayer): try: from lasagne.layers.dnn import Conv2DDNNLayer except ImportError: pytest.skip("dnn not available") input_layer = DummyInputLayer((1, 2, 3, 3)) layer = Conv2DDNNLayer(input_layer, num_filters=4, filter_size=(3, 3), pad=(3, 3)) assert layer.output_shape == (1, 4, 7, 7) class TestConv2DMMLayer: def test_import_without_gpu_raises(self): if theano.config.device.startswith("gpu"): pytest.skip() else: with pytest.raises(ImportError): import lasagne.layers.corrmm def test_pad(self, DummyInputLayer): try: from lasagne.layers.corrmm import Conv2DMMLayer except ImportError: pytest.skip("corrmm not available") input_layer = DummyInputLayer((1, 2, 3, 3)) layer = Conv2DMMLayer(input_layer, num_filters=4, filter_size=(3, 3), pad=(3, 3)) assert layer.output_shape == (1, 4, 7, 7) class TestConv2DCCLayer: def test_import_without_gpu_raises(self): if theano.config.device.startswith("gpu"): pytest.skip() else: with pytest.raises(ImportError): import lasagne.layers.cuda_convnet def test_unsupported_settings(self, DummyInputLayer): try: from lasagne.layers.cuda_convnet import Conv2DCCLayer except ImportError: pytest.skip("cuda_convnet not available") input_layer = DummyInputLayer((128, 3, 32, 32)) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 5)) assert ("Conv2DCCLayer only supports square filters" in exc.value.args[0]) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), stride=(1, 2)) assert ("Conv2DCCLayer only supports square strides" in exc.value.args[0]) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=15, filter_size=(3, 3)) assert ("Conv2DCCLayer requires num_filters to be a multiple of 16" in exc.value.args[0]) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), pad=(1, 2)) assert ("Conv2DCCLayer only supports square padding" in exc.value.args[0]) input_layer = DummyInputLayer((128, 7, 32, 32)) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3)) assert ("Conv2DCCLayer requires the number of input channels to be " "1, 2, 3 or a multiple of 4" in exc.value.args[0]) def test_pad(self, DummyInputLayer): try: from lasagne.layers.cuda_convnet import Conv2DCCLayer except ImportError: pytest.skip("cuda_convnet not available") input_layer = DummyInputLayer((128, 3, 32, 32)) layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), pad=(3, 3)) assert layer.output_shape == (128, 16, 36, 36) def test_dimshuffle_false_shapes(self, DummyInputLayer): try: from lasagne.layers.cuda_convnet import Conv2DCCLayer except ImportError: pytest.skip("cuda_convnet not available") input_layer = DummyInputLayer((4, 32, 32, 128)) # c01b instead of bc01 layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), dimshuffle=False) assert layer.W.get_value().shape == (4, 3, 3, 16) assert layer.b.get_value().shape == (16,) layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), dimshuffle=False, untie_biases=True) assert layer.W.get_value().shape == (4, 3, 3, 16) assert layer.b.get_value().shape == (16, 30, 30) def test_dimshuffle_false_get_output_for(self, DummyInputLayer): try: from lasagne.layers.cuda_convnet import Conv2DCCLayer except ImportError: pytest.skip("cuda_convnet not available") # this implementation is tested against FilterActs instead of # theano.tensor.nnet.conv.conv2d because using the latter leads to # numerical precision errors. from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs filter_acts = FilterActs(stride=1, pad=0, partial_sum=1) input = theano.shared(floatX(np.random.random((4, 5, 5, 8)))) kernel = theano.shared(floatX(np.random.random((4, 3, 3, 16)))) input_layer = DummyInputLayer((4, 5, 5, 8)) # c01b instead of bc01 layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), dimshuffle=False, W=kernel, b=None, nonlinearity=None) output = np.array(filter_acts(input, kernel).eval()) actual = layer.get_output_for(input).eval() actual = np.array(actual) assert actual.shape == output.shape assert actual.shape == layer.output_shape assert np.allclose(actual, output) class TestShuffleLayers: def test_bc01_to_c01b(self): from lasagne.layers.input import InputLayer try: from lasagne.layers.cuda_convnet import ShuffleBC01ToC01BLayer except ImportError: pytest.skip("cuda_convnet not available") input_layer = InputLayer((1, 2, 3, 4)) layer = ShuffleBC01ToC01BLayer(input_layer) assert layer.output_shape == (2, 3, 4, 1) input = floatX(np.random.random((1, 2, 3, 4))) output = input.transpose(1, 2, 3, 0) actual = layer.get_output_for(theano.shared(input)).eval() assert np.allclose(output, actual) def test_c01b_to_bc01(self): from lasagne.layers.input import InputLayer try: from lasagne.layers.cuda_convnet import ShuffleC01BToBC01Layer except ImportError: pytest.skip("cuda_convnet not available") input_layer = InputLayer((1, 2, 3, 4)) layer = ShuffleC01BToBC01Layer(input_layer) assert layer.output_shape == (4, 1, 2, 3) input = floatX(np.random.random((1, 2, 3, 4))) output = input.transpose(3, 0, 1, 2) actual = layer.get_output_for(theano.shared(input)).eval() assert np.allclose(output, actual)

37.513185

79

0.568779

import numpy as np import pytest import importlib import theano import lasagne from lasagne.utils import floatX, as_tuple def conv2d(input, kernel, pad): if pad not in ['valid', 'same', 'full']: pad = as_tuple(pad, 2, int) input = np.pad(input, ((0, 0), (0, 0), (pad[0], pad[0]), (pad[1], pad[1])), mode='constant') pad = 'valid' output = np.zeros((input.shape[0], kernel.shape[0], input.shape[2] + kernel.shape[2] - 1, input.shape[3] + kernel.shape[3] - 1, )) for i in range(kernel.shape[2]): for j in range(kernel.shape[3]): k = kernel[:, :, i, j][:, :, np.newaxis, np.newaxis] output[:, :, i:i + input.shape[2], j:j + input.shape[3]] += (input[:, np.newaxis] * k).sum(2) if pad == 'valid': trim = (kernel.shape[2] - 1, kernel.shape[3] - 1) output = output[:, :, trim[0]:-trim[0] or None, trim[1]:-trim[1] or None] elif pad == 'same': shift_x = (kernel.shape[2] - 1) // 2 shift_y = (kernel.shape[3] - 1) // 2 output = output[:, :, shift_x:input.shape[2] + shift_x, shift_y:input.shape[3] + shift_y] return output def conv2d_test_sets(): def _convert(input, kernel, output, kwargs): return [theano.shared(floatX(input)), floatX(kernel), output, kwargs] for pad in [0, 'full', 'same']: for stride in [1, 2, 3]: for filter_size in [1, 3]: if stride > filter_size: continue input = np.random.random((3, 1, 16, 23)) kernel = np.random.random((16, 1, filter_size, filter_size)) output = conv2d(input, kernel, pad=pad) output = output[:, :, ::stride, ::stride] yield _convert(input, kernel, output, {'pad': pad, 'stride': stride }) input = np.random.random((3, 1, 16, 23)) kernel = np.random.random((16, 1, 3, 3)) output = conv2d(input, kernel, pad='valid') yield _convert(input, kernel, output, {'b': None}) yield _convert(input, kernel, output, {'pad': 'valid'}) def conv1d(input, kernel, pad): if pad not in ['valid', 'same', 'full']: input = np.pad(input, ((0, 0), (0, 0), (int(pad), int(pad))), mode='constant') pad = 'valid' output = [] for b in input: temp = [] for c in kernel: temp.append( np.convolve(b[0, :], c[0, :], mode=pad)) output.append(temp) return np.array(output) def conv1d_test_sets(): def _convert(input, kernel, output, kwargs): return [theano.shared(floatX(input)), floatX(kernel), output, kwargs] for pad in [0, 1, 2, 'full', 'same']: for stride in [1, 2, 3]: for filter_size in [1, 3]: if stride > filter_size: continue input = np.random.random((3, 1, 23)) kernel = np.random.random((16, 1, filter_size)) output = conv1d(input, kernel, pad) output = output[:, :, ::stride] yield _convert(input, kernel, output, {'pad': pad, 'stride': stride, }) input = np.random.random((3, 1, 23)) kernel = np.random.random((16, 1, 3)) output = conv1d(input, kernel, pad='valid') yield _convert(input, kernel, output, {'b': None}) yield _convert(input, kernel, output, {'pad': 'valid'}) def test_conv_output_length(): from lasagne.layers.conv import conv_output_length assert conv_output_length(13, 5, 3, 'valid') == 3 assert conv_output_length(13, 5, 3, 0) == 3 assert conv_output_length(13, 5, 3, 'full') == 6 assert conv_output_length(13, 5, 3, 'same') == 5 assert conv_output_length(13, 5, 3, 2) == 5 with pytest.raises(ValueError) as exc: conv_output_length(13, 5, 3, '_nonexistent_mode') assert "Invalid pad: " in exc.value.args[0] @pytest.fixture def DummyInputLayer(): def factory(shape): from lasagne.layers.input import InputLayer return InputLayer(shape) return factory class TestConv1DLayer: @pytest.mark.parametrize( "input, kernel, output, kwargs", list(conv1d_test_sets())) @pytest.mark.parametrize("extra_kwargs", [ {}, {'untie_biases': True}, ]) def test_defaults(self, DummyInputLayer, input, kernel, output, kwargs, extra_kwargs): kwargs.update(extra_kwargs) b, c, w = input.shape.eval() input_layer = DummyInputLayer((b, c, w)) try: from lasagne.layers.conv import Conv1DLayer layer = Conv1DLayer( input_layer, num_filters=kernel.shape[0], filter_size=kernel.shape[2], W=kernel, **kwargs ) actual = layer.get_output_for(input).eval() assert actual.shape == output.shape assert actual.shape == layer.output_shape assert np.allclose(actual, output) except NotImplementedError: pass def test_init_none_nonlinearity_bias(self, DummyInputLayer): from lasagne.layers.conv import Conv1DLayer input_layer = DummyInputLayer((1, 2, 3)) layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(3,), nonlinearity=None, b=None) assert layer.nonlinearity == lasagne.nonlinearities.identity assert layer.b is None def test_invalid_pad(self, DummyInputLayer): from lasagne.layers.conv import Conv1DLayer input_layer = DummyInputLayer((1, 2, 3)) with pytest.raises(TypeError) as exc: layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(3,), pad='_nonexistent_mode') assert "iterable of int" in exc.value.args[0] with pytest.raises(NotImplementedError) as exc: layer = Conv1DLayer(input_layer, num_filters=16, filter_size=(4,), pad='same') assert "requires odd filter size" in exc.value.args[0] class TestConv2DLayerImplementations: @pytest.fixture( params=[ ('lasagne.layers', 'Conv2DLayer', {}), ('lasagne.layers.cuda_convnet', 'Conv2DCCLayer', {'flip_filters': True}), ('lasagne.layers.corrmm', 'Conv2DMMLayer', {'flip_filters': True}), ('lasagne.layers.dnn', 'Conv2DDNNLayer', {'flip_filters': True}), ], ) def Conv2DImpl(self, request): impl_module_name, impl_name, impl_default_kwargs = request.param try: mod = importlib.import_module(impl_module_name) except ImportError: pytest.skip("{} not available".format(impl_module_name)) impl = getattr(mod, impl_name) def wrapper(*args, **kwargs): kwargs2 = impl_default_kwargs.copy() kwargs2.update(kwargs) return impl(*args, **kwargs2) wrapper.__name__ = impl_name return wrapper @pytest.mark.parametrize( "input, kernel, output, kwargs", list(conv2d_test_sets())) @pytest.mark.parametrize("extra_kwargs", [ {}, {'untie_biases': True}, ]) def test_defaults(self, Conv2DImpl, DummyInputLayer, input, kernel, output, kwargs, extra_kwargs): kwargs.update(extra_kwargs) b, c, h, w = input.shape.eval() input_layer = DummyInputLayer((b, c, h, w)) try: layer = Conv2DImpl( input_layer, num_filters=kernel.shape[0], filter_size=kernel.shape[2:], W=kernel, **kwargs ) actual = layer.get_output_for(input).eval() assert actual.shape == output.shape assert actual.shape == layer.output_shape assert np.allclose(actual, output) except NotImplementedError: pytest.skip() @pytest.mark.parametrize( "input, kernel, output, kwargs", list(conv2d_test_sets())) def test_with_nones(self, Conv2DImpl, DummyInputLayer, input, kernel, output, kwargs): b, c, h, w = input.shape.eval() input_layer = DummyInputLayer((None, c, None, None)) try: layer = Conv2DImpl( input_layer, num_filters=kernel.shape[0], filter_size=kernel.shape[2:], W=kernel, **kwargs ) actual = layer.get_output_for(input).eval() assert layer.output_shape == (None, kernel.shape[0], None, None) assert actual.shape == output.shape assert np.allclose(actual, output) except NotImplementedError: pytest.skip() def test_init_none_nonlinearity_bias(self, Conv2DImpl, DummyInputLayer): input_layer = DummyInputLayer((1, 2, 3, 3)) layer = Conv2DImpl(input_layer, num_filters=16, filter_size=(3, 3), nonlinearity=None, b=None) assert layer.nonlinearity == lasagne.nonlinearities.identity assert layer.b is None def test_invalid_pad(self, Conv2DImpl, DummyInputLayer): input_layer = DummyInputLayer((1, 2, 3)) with pytest.raises(TypeError) as exc: layer = Conv2DImpl(input_layer, num_filters=16, filter_size=(3, 3), pad='_nonexistent_mode') assert "iterable of int" in exc.value.args[0] with pytest.raises(NotImplementedError) as exc: layer = Conv2DImpl(input_layer, num_filters=16, filter_size=(4, 4), pad='same') assert "requires odd filter size" in exc.value.args[0] def test_get_params(self, Conv2DImpl, DummyInputLayer): input_layer = DummyInputLayer((128, 3, 32, 32)) layer = Conv2DImpl(input_layer, num_filters=16, filter_size=(3, 3)) assert layer.get_params() == [layer.W, layer.b] assert layer.get_params(regularizable=False) == [layer.b] assert layer.get_params(regularizable=True) == [layer.W] assert layer.get_params(trainable=True) == [layer.W, layer.b] assert layer.get_params(trainable=False) == [] assert layer.get_params(_nonexistent_tag=True) == [] assert layer.get_params(_nonexistent_tag=False) == [layer.W, layer.b] class TestConv2DDNNLayer: def test_import_without_gpu_or_cudnn_raises(self): from theano.sandbox.cuda import dnn if theano.config.device.startswith("gpu") and dnn.dnn_available(): pytest.skip() else: with pytest.raises(ImportError): import lasagne.layers.dnn def test_pad(self, DummyInputLayer): try: from lasagne.layers.dnn import Conv2DDNNLayer except ImportError: pytest.skip("dnn not available") input_layer = DummyInputLayer((1, 2, 3, 3)) layer = Conv2DDNNLayer(input_layer, num_filters=4, filter_size=(3, 3), pad=(3, 3)) assert layer.output_shape == (1, 4, 7, 7) class TestConv2DMMLayer: def test_import_without_gpu_raises(self): if theano.config.device.startswith("gpu"): pytest.skip() else: with pytest.raises(ImportError): import lasagne.layers.corrmm def test_pad(self, DummyInputLayer): try: from lasagne.layers.corrmm import Conv2DMMLayer except ImportError: pytest.skip("corrmm not available") input_layer = DummyInputLayer((1, 2, 3, 3)) layer = Conv2DMMLayer(input_layer, num_filters=4, filter_size=(3, 3), pad=(3, 3)) assert layer.output_shape == (1, 4, 7, 7) class TestConv2DCCLayer: def test_import_without_gpu_raises(self): if theano.config.device.startswith("gpu"): pytest.skip() else: with pytest.raises(ImportError): import lasagne.layers.cuda_convnet def test_unsupported_settings(self, DummyInputLayer): try: from lasagne.layers.cuda_convnet import Conv2DCCLayer except ImportError: pytest.skip("cuda_convnet not available") input_layer = DummyInputLayer((128, 3, 32, 32)) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 5)) assert ("Conv2DCCLayer only supports square filters" in exc.value.args[0]) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), stride=(1, 2)) assert ("Conv2DCCLayer only supports square strides" in exc.value.args[0]) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=15, filter_size=(3, 3)) assert ("Conv2DCCLayer requires num_filters to be a multiple of 16" in exc.value.args[0]) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), pad=(1, 2)) assert ("Conv2DCCLayer only supports square padding" in exc.value.args[0]) input_layer = DummyInputLayer((128, 7, 32, 32)) with pytest.raises(RuntimeError) as exc: layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3)) assert ("Conv2DCCLayer requires the number of input channels to be " "1, 2, 3 or a multiple of 4" in exc.value.args[0]) def test_pad(self, DummyInputLayer): try: from lasagne.layers.cuda_convnet import Conv2DCCLayer except ImportError: pytest.skip("cuda_convnet not available") input_layer = DummyInputLayer((128, 3, 32, 32)) layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), pad=(3, 3)) assert layer.output_shape == (128, 16, 36, 36) def test_dimshuffle_false_shapes(self, DummyInputLayer): try: from lasagne.layers.cuda_convnet import Conv2DCCLayer except ImportError: pytest.skip("cuda_convnet not available") input_layer = DummyInputLayer((4, 32, 32, 128)) layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), dimshuffle=False) assert layer.W.get_value().shape == (4, 3, 3, 16) assert layer.b.get_value().shape == (16,) layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), dimshuffle=False, untie_biases=True) assert layer.W.get_value().shape == (4, 3, 3, 16) assert layer.b.get_value().shape == (16, 30, 30) def test_dimshuffle_false_get_output_for(self, DummyInputLayer): try: from lasagne.layers.cuda_convnet import Conv2DCCLayer except ImportError: pytest.skip("cuda_convnet not available") from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs filter_acts = FilterActs(stride=1, pad=0, partial_sum=1) input = theano.shared(floatX(np.random.random((4, 5, 5, 8)))) kernel = theano.shared(floatX(np.random.random((4, 3, 3, 16)))) input_layer = DummyInputLayer((4, 5, 5, 8)) layer = Conv2DCCLayer(input_layer, num_filters=16, filter_size=(3, 3), dimshuffle=False, W=kernel, b=None, nonlinearity=None) output = np.array(filter_acts(input, kernel).eval()) actual = layer.get_output_for(input).eval() actual = np.array(actual) assert actual.shape == output.shape assert actual.shape == layer.output_shape assert np.allclose(actual, output) class TestShuffleLayers: def test_bc01_to_c01b(self): from lasagne.layers.input import InputLayer try: from lasagne.layers.cuda_convnet import ShuffleBC01ToC01BLayer except ImportError: pytest.skip("cuda_convnet not available") input_layer = InputLayer((1, 2, 3, 4)) layer = ShuffleBC01ToC01BLayer(input_layer) assert layer.output_shape == (2, 3, 4, 1) input = floatX(np.random.random((1, 2, 3, 4))) output = input.transpose(1, 2, 3, 0) actual = layer.get_output_for(theano.shared(input)).eval() assert np.allclose(output, actual) def test_c01b_to_bc01(self): from lasagne.layers.input import InputLayer try: from lasagne.layers.cuda_convnet import ShuffleC01BToBC01Layer except ImportError: pytest.skip("cuda_convnet not available") input_layer = InputLayer((1, 2, 3, 4)) layer = ShuffleC01BToBC01Layer(input_layer) assert layer.output_shape == (4, 1, 2, 3) input = floatX(np.random.random((1, 2, 3, 4))) output = input.transpose(3, 0, 1, 2) actual = layer.get_output_for(theano.shared(input)).eval() assert np.allclose(output, actual)

true

f734caef5c5d450dabd683c8724747521c21a4ff

21,738

py

Python

tensorflow/python/distribute/tpu_values.py

EricLi404/tensorflow

23759800d89f7b5362c338d9a3fd72a6810c3e22

[ "Apache-2.0" ]

74

2020-07-06T17:11:39.000Z

2022-01-28T06:31:28.000Z

tensorflow/python/distribute/tpu_values.py

EricLi404/tensorflow

23759800d89f7b5362c338d9a3fd72a6810c3e22

[ "Apache-2.0" ]

9

2020-10-13T23:25:29.000Z

2022-02-10T06:54:48.000Z

tensorflow/python/distribute/tpu_values.py

EricLi404/tensorflow

23759800d89f7b5362c338d9a3fd72a6810c3e22

[ "Apache-2.0" ]

12

2020-07-08T07:27:17.000Z

2021-12-27T08:54:27.000Z

# Copyright 2020 The TensorFlow Authors. 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. # ============================================================================== """Various classes representing TPU distributed values. Note that the tests are in values_test.py . """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import contextlib from tensorflow.python.distribute import packed_distributed_variable as packed from tensorflow.python.distribute import values from tensorflow.python.distribute import values_util from tensorflow.python.eager import context from tensorflow.python.eager import tape from tensorflow.python.framework import ops from tensorflow.python.ops import gen_resource_variable_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.tpu import tpu @contextlib.contextmanager def _maybe_enter_graph(tensor): # Note: might have an eager tensor but not be executing eagerly when # building functions. if (context.executing_eagerly() or isinstance(tensor, ops.EagerTensor) or ops.has_default_graph()): yield else: with tensor.graph.as_default(): yield @contextlib.contextmanager def _maybe_on_device(var): # Add a device scope for packed variables. if isinstance(var, packed.PackedVarAndDevice): with ops.device(var.device): yield else: yield def _make_raw_assign_fn(raw_assign_fn): # pylint: disable=missing-docstring def assign_fn(var, value, use_locking=False, name=None, read_value=True): # pylint: disable=missing-docstring del use_locking # Unused. handle = var.handle with _maybe_enter_graph(handle), _maybe_on_device(var): op = raw_assign_fn( handle, ops.convert_to_tensor(value, dtype=var.dtype), name=name) with ops.control_dependencies([op]): return var._read_variable_op() if read_value else op # pylint: disable=protected-access return assign_fn class TPUVariableMixin(object): """Mixin for TPU variables.""" def __init__(self, *args, **kwargs): super(TPUVariableMixin, self).__init__(*args, **kwargs) # Handle ID is needed for `get_replicated_var_handle` to cache the variables # correctly since in eager mode different variables can have the same name. if ops.executing_eagerly_outside_functions(): self._handle_id = self._common_name + "_" + str(id(self._primary)) else: self._handle_id = self._common_name def __getattr__(self, name): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self).__getattr__(name) else: raise AttributeError( "'{}' not accessible within a TPU context.".format(name)) def get(self): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self).get() else: raise NotImplementedError( "`TPUVariableMixin.get()` is not supported within a TPU context.") def _get_as_operand(self): return self.read_value() def _is_mirrored(self): raise NotImplementedError( "`TPUVariableMixin._is_mirrored()` must be implemented by subclasses.") @property def handle(self): """The handle by which this variable can be accessed.""" # If we're in a tpu.rewrite(), return the replicated handle. tpu_context = enclosing_tpu_context() if tpu_context is None or context.executing_eagerly(): return self._get_on_device_or_primary().handle else: is_packed = self._packed_var is not None val = self._values if is_packed: val = [self._packed_var] return tpu_context.get_replicated_var_handle(self._handle_id, val, self._is_mirrored(), is_packed) @property def device(self): return self.handle.device def _read_variable_op(self): """Reads the value of this variable.""" if self.trainable: tape.variable_accessed(self) handle = self.handle if getattr(handle, "is_packed", False): # Add a device scope for a packed variable handle. with ops.device(self._get_on_device_or_primary().device): return gen_resource_variable_ops.read_variable_op(handle, self.dtype) else: return gen_resource_variable_ops.read_variable_op(handle, self.dtype) def read_value(self): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self).read_value() else: return self._read_variable_op() def value(self): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self).value() else: return self._read_variable_op() def _as_graph_element(self): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self)._as_graph_element() # pylint: disable=protected-access else: return None @property def op(self): if values_util.is_saving_non_distributed(): return self._primary.op return values.DistributedVarOp(self._primary.op.name, self._primary.op.graph, self._primary.op.traceback, self._primary.op.type) def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False): """Converts a variable to a tensor.""" # pylint: disable=protected-access if enclosing_tpu_context() is None: return super(TPUVariableMixin, self)._dense_var_to_tensor( dtype=dtype, name=name, as_ref=as_ref) # pylint: enable=protected-access elif dtype is not None and dtype != self.dtype: return math_ops.cast(self.read_value(), dtype) else: return self.handle if as_ref else self.read_value() def enclosing_tpu_context(): """Returns the TPUReplicateContext, which exists inside a tpu.rewrite().""" graph = ops.get_default_graph() while graph is not None: # pylint: disable=protected-access context_ = graph._get_control_flow_context() # pylint: enable=protected-access while context_ is not None: if isinstance(context_, tpu.TPUReplicateContext): return context_ context_ = context_.outer_context # This may be a FuncGraph due to defuns or v2 control flow. We need to # find the original graph with the XLAControlFlowContext. graph = getattr(graph, "outer_graph", None) return None class TPUDistributedVariable(TPUVariableMixin, values.DistributedVariable): """DistributedVariable subclass for TPUStrategy.""" def _is_mirrored(self): self._policy._is_mirrored() # pylint: disable=protected-access def assign_sub(self, value, use_locking=False, name=None, read_value=True): if values_util.is_saving_non_distributed(): return self._primary.assign_sub(value, use_locking, name, read_value) return self._policy.assign_sub( self, value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(self, value, use_locking=False, name=None, read_value=True): if values_util.is_saving_non_distributed(): return self._primary.assign_add(value, use_locking, name, read_value) return self._policy.assign_add( self, value, use_locking=use_locking, name=name, read_value=read_value) def assign(self, value, use_locking=False, name=None, read_value=True): if values_util.is_saving_non_distributed(): return self._primary.assign(value, use_locking, name, read_value) return self._policy.assign( self, value, use_locking=use_locking, name=name, read_value=read_value) def scatter_sub(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_sub(sparse_delta, use_locking, name) return self._policy.scatter_sub( self, sparse_delta, use_locking=use_locking, name=name) def scatter_add(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_add(sparse_delta, use_locking, name) return self._policy.scatter_add( self, sparse_delta, use_locking=use_locking, name=name) def scatter_mul(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_mul(sparse_delta, use_locking, name) return self._policy.scatter_mul( self, sparse_delta, use_locking=use_locking, name=name) def scatter_div(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_div(sparse_delta, use_locking, name) return self._policy.scatter_div( self, sparse_delta, use_locking=use_locking, name=name) def scatter_min(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_min(sparse_delta, use_locking, name) return self._policy.scatter_min( self, sparse_delta, use_locking=use_locking, name=name) def scatter_max(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_max(sparse_delta, use_locking, name) return self._policy.scatter_max( self, sparse_delta, use_locking=use_locking, name=name) def scatter_update(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_update(sparse_delta, use_locking, name) return self._policy.scatter_update( self, sparse_delta, use_locking=use_locking, name=name) class TPUMirroredVariable(TPUVariableMixin, values.MirroredVariable): """Holds a map from replica to TPU variables whose values are kept in sync.""" def assign_sub(self, value, use_locking=False, name=None, read_value=True): if (enclosing_tpu_context() and self.aggregation == variable_scope.VariableAggregation.NONE): return _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op)( self, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign_sub(self, value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(self, value, use_locking=False, name=None, read_value=True): if (enclosing_tpu_context() and self.aggregation == variable_scope.VariableAggregation.NONE): return _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op)( self, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign_add(self, value, use_locking=use_locking, name=name, read_value=read_value) def assign(self, value, use_locking=False, name=None, read_value=True): if (enclosing_tpu_context() and self.aggregation == variable_scope.VariableAggregation.NONE): return _make_raw_assign_fn( gen_resource_variable_ops.assign_variable_op)( self, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign(self, value, use_locking=use_locking, name=name, read_value=read_value) def scatter_sub(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_sub(*args, **kwargs) raise NotImplementedError def scatter_add(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_add(*args, **kwargs) raise NotImplementedError def scatter_max(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_max(*args, **kwargs) raise NotImplementedError def scatter_min(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_min(*args, **kwargs) raise NotImplementedError def scatter_mul(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_mul(*args, **kwargs) raise NotImplementedError def scatter_div(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_div(*args, **kwargs) raise NotImplementedError def scatter_update(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_update(*args, **kwargs) raise NotImplementedError def _is_mirrored(self): return True class TPUSyncOnReadVariable(TPUVariableMixin, values.SyncOnReadVariable): """Holds a map from replica to variables whose values are reduced on save.""" def assign_sub(self, *args, **kwargs): if enclosing_tpu_context() is None: return values.SyncOnReadVariable.assign_sub(self, *args, **kwargs) else: return _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op)(self, *args, **kwargs) def assign_add(self, *args, **kwargs): if enclosing_tpu_context() is None: return values.SyncOnReadVariable.assign_add(self, *args, **kwargs) else: return _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op)(self, *args, **kwargs) def assign(self, *args, **kwargs): if enclosing_tpu_context() is None: return values.SyncOnReadVariable.assign(self, *args, **kwargs) else: return _make_raw_assign_fn(gen_resource_variable_ops.assign_variable_op)( self, *args, **kwargs) def _is_mirrored(self): return False # Common method between AutoPolicy, OnWrite and Mirrored variables. def assign_sub(var, value, use_locking=False, name=None, read_value=True): assign_sub_fn = _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op) return var._update( # pylint: disable=protected-access update_fn=assign_sub_fn, value=value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(var, value, use_locking=False, name=None, read_value=True): assign_add_fn = _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op) return var._update( # pylint: disable=protected-access update_fn=assign_add_fn, value=value, use_locking=use_locking, name=name, read_value=read_value) def assign(var, value, use_locking=False, name=None, read_value=True): assign_fn = _make_raw_assign_fn( gen_resource_variable_ops.assign_variable_op) return var._update( # pylint: disable=protected-access update_fn=assign_fn, value=value, use_locking=use_locking, name=name, read_value=read_value) class TPUAutoPolicy(values.AutoPolicy): """Policy defined for `tf.VariableSynchronization.AUTO` synchronization. This policy is created when `synchronization` is set to `tf.VariableSynchronization.AUTO` and `aggregation` is set to `tf.VariableAggregation.NONE` when creating a `tf.Variable` in `tf.distribute` scope. """ def assign_sub(self, var, value, use_locking=False, name=None, read_value=True): if enclosing_tpu_context(): return _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op)( var, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign_sub(var, value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(self, var, value, use_locking=False, name=None, read_value=True): if enclosing_tpu_context(): return _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op)( var, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign_add(var, value, use_locking=use_locking, name=name, read_value=read_value) def assign(self, var, value, use_locking=False, name=None, read_value=True): if enclosing_tpu_context(): return _make_raw_assign_fn( gen_resource_variable_ops.assign_variable_op)( var, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign(var, value, use_locking=use_locking, name=name, read_value=read_value) def scatter_sub(self, *args, **kwargs): raise NotImplementedError def scatter_add(self, *args, **kwargs): raise NotImplementedError def scatter_max(self, *args, **kwargs): raise NotImplementedError def scatter_min(self, *args, **kwargs): raise NotImplementedError def scatter_mul(self, *args, **kwargs): raise NotImplementedError def scatter_div(self, *args, **kwargs): raise NotImplementedError def scatter_update(self, *args, **kwargs): raise NotImplementedError def _is_mirrored(self): return True class TPUOnWritePolicy(values.OnWritePolicy): """Policy defined for `tf.VariableSynchronization.ON_WRITE` synchronization. This policy is created when the following `synchronization` and `aggregation` parameters are specified when creating a `tf.Variable` in `tf.distribute` scope: * `synchronization` is equal to `tf.VariableSynchronization.AUTO` and aggregation can be any of the following `tf.VariableAggregation` enum values such as `SUM`, `MEAN` or `ONLY_FIRST_REPLICA`. * `synchronization` is equal to `tf.VariableSynchronization.ON_WRITE` and aggregation can be any of the following `tf.VariableAggregation` enum values such as `NONE`, `SUM`, `MEAN` or `ONLY_FIRST_REPLICA`. """ def assign_sub(self, var, value, use_locking=False, name=None, read_value=True): return assign_sub(var, value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(self, var, value, use_locking=False, name=None, read_value=True): return assign_add(var, value, use_locking=use_locking, name=name, read_value=read_value) def assign(self, var, value, use_locking=False, name=None, read_value=True): return assign(var, value, use_locking=use_locking, name=name, read_value=read_value) def scatter_sub(self, *args, **kwargs): raise NotImplementedError def scatter_add(self, *args, **kwargs): raise NotImplementedError def scatter_max(self, *args, **kwargs): raise NotImplementedError def scatter_min(self, *args, **kwargs): raise NotImplementedError def scatter_mul(self, *args, **kwargs): raise NotImplementedError def scatter_div(self, *args, **kwargs): raise NotImplementedError def scatter_update(self, *args, **kwargs): raise NotImplementedError def _is_mirrored(self): return True class TPUOnReadPolicy(values.OnReadPolicy): """Policy defined for `tf.VariableSynchronization.ON_READ` synchronization. This policy is created when `synchronization` is set to `tf.VariableSynchronization.ON_READ` and `aggregation` is set to any of the values allowed by the `tf.VariableAggregation` enum such as `NONE`, `SUM`, `MEAN` or `ONLY_FIRST_REPLICA`when creating a `tf.Variable` in `tf.distribute` scope. """ def assign_sub(self, var, *args, **kwargs): if enclosing_tpu_context() is None: return super(TPUOnReadPolicy, self).assign_sub(var, *args, **kwargs) else: return _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op)(var, *args, **kwargs) def assign_add(self, var, *args, **kwargs): if enclosing_tpu_context() is None: return super(TPUOnReadPolicy, self).assign_add(var, *args, **kwargs) else: return _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op)(var, *args, **kwargs) def assign(self, var, *args, **kwargs): if enclosing_tpu_context() is None: return super(TPUOnReadPolicy, self).assign(var, *args, **kwargs) else: return _make_raw_assign_fn(gen_resource_variable_ops.assign_variable_op)( var, *args, **kwargs) def _is_mirrored(self): return False def scatter_sub(self, *args, **kwargs): raise NotImplementedError def scatter_add(self, *args, **kwargs): raise NotImplementedError def scatter_max(self, *args, **kwargs): raise NotImplementedError def scatter_min(self, *args, **kwargs): raise NotImplementedError def scatter_mul(self, *args, **kwargs): raise NotImplementedError def scatter_div(self, *args, **kwargs): raise NotImplementedError def scatter_update(self, *args, **kwargs): raise NotImplementedError

36.351171

112

0.699006

from __future__ import absolute_import from __future__ import division from __future__ import print_function import contextlib from tensorflow.python.distribute import packed_distributed_variable as packed from tensorflow.python.distribute import values from tensorflow.python.distribute import values_util from tensorflow.python.eager import context from tensorflow.python.eager import tape from tensorflow.python.framework import ops from tensorflow.python.ops import gen_resource_variable_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import variable_scope from tensorflow.python.tpu import tpu @contextlib.contextmanager def _maybe_enter_graph(tensor): if (context.executing_eagerly() or isinstance(tensor, ops.EagerTensor) or ops.has_default_graph()): yield else: with tensor.graph.as_default(): yield @contextlib.contextmanager def _maybe_on_device(var): if isinstance(var, packed.PackedVarAndDevice): with ops.device(var.device): yield else: yield def _make_raw_assign_fn(raw_assign_fn): def assign_fn(var, value, use_locking=False, name=None, read_value=True): del use_locking handle = var.handle with _maybe_enter_graph(handle), _maybe_on_device(var): op = raw_assign_fn( handle, ops.convert_to_tensor(value, dtype=var.dtype), name=name) with ops.control_dependencies([op]): return var._read_variable_op() if read_value else op return assign_fn class TPUVariableMixin(object): def __init__(self, *args, **kwargs): super(TPUVariableMixin, self).__init__(*args, **kwargs) if ops.executing_eagerly_outside_functions(): self._handle_id = self._common_name + "_" + str(id(self._primary)) else: self._handle_id = self._common_name def __getattr__(self, name): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self).__getattr__(name) else: raise AttributeError( "'{}' not accessible within a TPU context.".format(name)) def get(self): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self).get() else: raise NotImplementedError( "`TPUVariableMixin.get()` is not supported within a TPU context.") def _get_as_operand(self): return self.read_value() def _is_mirrored(self): raise NotImplementedError( "`TPUVariableMixin._is_mirrored()` must be implemented by subclasses.") @property def handle(self): tpu_context = enclosing_tpu_context() if tpu_context is None or context.executing_eagerly(): return self._get_on_device_or_primary().handle else: is_packed = self._packed_var is not None val = self._values if is_packed: val = [self._packed_var] return tpu_context.get_replicated_var_handle(self._handle_id, val, self._is_mirrored(), is_packed) @property def device(self): return self.handle.device def _read_variable_op(self): if self.trainable: tape.variable_accessed(self) handle = self.handle if getattr(handle, "is_packed", False): # Add a device scope for a packed variable handle. with ops.device(self._get_on_device_or_primary().device): return gen_resource_variable_ops.read_variable_op(handle, self.dtype) else: return gen_resource_variable_ops.read_variable_op(handle, self.dtype) def read_value(self): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self).read_value() else: return self._read_variable_op() def value(self): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self).value() else: return self._read_variable_op() def _as_graph_element(self): if enclosing_tpu_context() is None: return super(TPUVariableMixin, self)._as_graph_element() # pylint: disable=protected-access else: return None @property def op(self): if values_util.is_saving_non_distributed(): return self._primary.op return values.DistributedVarOp(self._primary.op.name, self._primary.op.graph, self._primary.op.traceback, self._primary.op.type) def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False): # pylint: disable=protected-access if enclosing_tpu_context() is None: return super(TPUVariableMixin, self)._dense_var_to_tensor( dtype=dtype, name=name, as_ref=as_ref) # pylint: enable=protected-access elif dtype is not None and dtype != self.dtype: return math_ops.cast(self.read_value(), dtype) else: return self.handle if as_ref else self.read_value() def enclosing_tpu_context(): graph = ops.get_default_graph() while graph is not None: # pylint: disable=protected-access context_ = graph._get_control_flow_context() # pylint: enable=protected-access while context_ is not None: if isinstance(context_, tpu.TPUReplicateContext): return context_ context_ = context_.outer_context # This may be a FuncGraph due to defuns or v2 control flow. We need to # find the original graph with the XLAControlFlowContext. graph = getattr(graph, "outer_graph", None) return None class TPUDistributedVariable(TPUVariableMixin, values.DistributedVariable): def _is_mirrored(self): self._policy._is_mirrored() # pylint: disable=protected-access def assign_sub(self, value, use_locking=False, name=None, read_value=True): if values_util.is_saving_non_distributed(): return self._primary.assign_sub(value, use_locking, name, read_value) return self._policy.assign_sub( self, value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(self, value, use_locking=False, name=None, read_value=True): if values_util.is_saving_non_distributed(): return self._primary.assign_add(value, use_locking, name, read_value) return self._policy.assign_add( self, value, use_locking=use_locking, name=name, read_value=read_value) def assign(self, value, use_locking=False, name=None, read_value=True): if values_util.is_saving_non_distributed(): return self._primary.assign(value, use_locking, name, read_value) return self._policy.assign( self, value, use_locking=use_locking, name=name, read_value=read_value) def scatter_sub(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_sub(sparse_delta, use_locking, name) return self._policy.scatter_sub( self, sparse_delta, use_locking=use_locking, name=name) def scatter_add(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_add(sparse_delta, use_locking, name) return self._policy.scatter_add( self, sparse_delta, use_locking=use_locking, name=name) def scatter_mul(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_mul(sparse_delta, use_locking, name) return self._policy.scatter_mul( self, sparse_delta, use_locking=use_locking, name=name) def scatter_div(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_div(sparse_delta, use_locking, name) return self._policy.scatter_div( self, sparse_delta, use_locking=use_locking, name=name) def scatter_min(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_min(sparse_delta, use_locking, name) return self._policy.scatter_min( self, sparse_delta, use_locking=use_locking, name=name) def scatter_max(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_max(sparse_delta, use_locking, name) return self._policy.scatter_max( self, sparse_delta, use_locking=use_locking, name=name) def scatter_update(self, sparse_delta, use_locking=False, name=None): if values_util.is_saving_non_distributed(): return self._primary.scatter_update(sparse_delta, use_locking, name) return self._policy.scatter_update( self, sparse_delta, use_locking=use_locking, name=name) class TPUMirroredVariable(TPUVariableMixin, values.MirroredVariable): def assign_sub(self, value, use_locking=False, name=None, read_value=True): if (enclosing_tpu_context() and self.aggregation == variable_scope.VariableAggregation.NONE): return _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op)( self, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign_sub(self, value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(self, value, use_locking=False, name=None, read_value=True): if (enclosing_tpu_context() and self.aggregation == variable_scope.VariableAggregation.NONE): return _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op)( self, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign_add(self, value, use_locking=use_locking, name=name, read_value=read_value) def assign(self, value, use_locking=False, name=None, read_value=True): if (enclosing_tpu_context() and self.aggregation == variable_scope.VariableAggregation.NONE): return _make_raw_assign_fn( gen_resource_variable_ops.assign_variable_op)( self, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign(self, value, use_locking=use_locking, name=name, read_value=read_value) def scatter_sub(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_sub(*args, **kwargs) raise NotImplementedError def scatter_add(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_add(*args, **kwargs) raise NotImplementedError def scatter_max(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_max(*args, **kwargs) raise NotImplementedError def scatter_min(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_min(*args, **kwargs) raise NotImplementedError def scatter_mul(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_mul(*args, **kwargs) raise NotImplementedError def scatter_div(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_div(*args, **kwargs) raise NotImplementedError def scatter_update(self, *args, **kwargs): if values_util.is_saving_non_distributed(): return self._primary.scatter_update(*args, **kwargs) raise NotImplementedError def _is_mirrored(self): return True class TPUSyncOnReadVariable(TPUVariableMixin, values.SyncOnReadVariable): def assign_sub(self, *args, **kwargs): if enclosing_tpu_context() is None: return values.SyncOnReadVariable.assign_sub(self, *args, **kwargs) else: return _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op)(self, *args, **kwargs) def assign_add(self, *args, **kwargs): if enclosing_tpu_context() is None: return values.SyncOnReadVariable.assign_add(self, *args, **kwargs) else: return _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op)(self, *args, **kwargs) def assign(self, *args, **kwargs): if enclosing_tpu_context() is None: return values.SyncOnReadVariable.assign(self, *args, **kwargs) else: return _make_raw_assign_fn(gen_resource_variable_ops.assign_variable_op)( self, *args, **kwargs) def _is_mirrored(self): return False # Common method between AutoPolicy, OnWrite and Mirrored variables. def assign_sub(var, value, use_locking=False, name=None, read_value=True): assign_sub_fn = _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op) return var._update( # pylint: disable=protected-access update_fn=assign_sub_fn, value=value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(var, value, use_locking=False, name=None, read_value=True): assign_add_fn = _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op) return var._update( # pylint: disable=protected-access update_fn=assign_add_fn, value=value, use_locking=use_locking, name=name, read_value=read_value) def assign(var, value, use_locking=False, name=None, read_value=True): assign_fn = _make_raw_assign_fn( gen_resource_variable_ops.assign_variable_op) return var._update( # pylint: disable=protected-access update_fn=assign_fn, value=value, use_locking=use_locking, name=name, read_value=read_value) class TPUAutoPolicy(values.AutoPolicy): def assign_sub(self, var, value, use_locking=False, name=None, read_value=True): if enclosing_tpu_context(): return _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op)( var, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign_sub(var, value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(self, var, value, use_locking=False, name=None, read_value=True): if enclosing_tpu_context(): return _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op)( var, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign_add(var, value, use_locking=use_locking, name=name, read_value=read_value) def assign(self, var, value, use_locking=False, name=None, read_value=True): if enclosing_tpu_context(): return _make_raw_assign_fn( gen_resource_variable_ops.assign_variable_op)( var, value=value, use_locking=use_locking, name=name, read_value=read_value) return assign(var, value, use_locking=use_locking, name=name, read_value=read_value) def scatter_sub(self, *args, **kwargs): raise NotImplementedError def scatter_add(self, *args, **kwargs): raise NotImplementedError def scatter_max(self, *args, **kwargs): raise NotImplementedError def scatter_min(self, *args, **kwargs): raise NotImplementedError def scatter_mul(self, *args, **kwargs): raise NotImplementedError def scatter_div(self, *args, **kwargs): raise NotImplementedError def scatter_update(self, *args, **kwargs): raise NotImplementedError def _is_mirrored(self): return True class TPUOnWritePolicy(values.OnWritePolicy): def assign_sub(self, var, value, use_locking=False, name=None, read_value=True): return assign_sub(var, value, use_locking=use_locking, name=name, read_value=read_value) def assign_add(self, var, value, use_locking=False, name=None, read_value=True): return assign_add(var, value, use_locking=use_locking, name=name, read_value=read_value) def assign(self, var, value, use_locking=False, name=None, read_value=True): return assign(var, value, use_locking=use_locking, name=name, read_value=read_value) def scatter_sub(self, *args, **kwargs): raise NotImplementedError def scatter_add(self, *args, **kwargs): raise NotImplementedError def scatter_max(self, *args, **kwargs): raise NotImplementedError def scatter_min(self, *args, **kwargs): raise NotImplementedError def scatter_mul(self, *args, **kwargs): raise NotImplementedError def scatter_div(self, *args, **kwargs): raise NotImplementedError def scatter_update(self, *args, **kwargs): raise NotImplementedError def _is_mirrored(self): return True class TPUOnReadPolicy(values.OnReadPolicy): def assign_sub(self, var, *args, **kwargs): if enclosing_tpu_context() is None: return super(TPUOnReadPolicy, self).assign_sub(var, *args, **kwargs) else: return _make_raw_assign_fn( gen_resource_variable_ops.assign_sub_variable_op)(var, *args, **kwargs) def assign_add(self, var, *args, **kwargs): if enclosing_tpu_context() is None: return super(TPUOnReadPolicy, self).assign_add(var, *args, **kwargs) else: return _make_raw_assign_fn( gen_resource_variable_ops.assign_add_variable_op)(var, *args, **kwargs) def assign(self, var, *args, **kwargs): if enclosing_tpu_context() is None: return super(TPUOnReadPolicy, self).assign(var, *args, **kwargs) else: return _make_raw_assign_fn(gen_resource_variable_ops.assign_variable_op)( var, *args, **kwargs) def _is_mirrored(self): return False def scatter_sub(self, *args, **kwargs): raise NotImplementedError def scatter_add(self, *args, **kwargs): raise NotImplementedError def scatter_max(self, *args, **kwargs): raise NotImplementedError def scatter_min(self, *args, **kwargs): raise NotImplementedError def scatter_mul(self, *args, **kwargs): raise NotImplementedError def scatter_div(self, *args, **kwargs): raise NotImplementedError def scatter_update(self, *args, **kwargs): raise NotImplementedError

true

f734cb4823063b2767bd39361c8b9837fb410f99

2,294

py

Python

Exercise-3/sensor_stick/src/sensor_stick/features.py

antoszy/RoboND-perception-exercises

8c725e77316162ae485ccee94085fd2314be7ae0

[ "MIT" ]

null

Exercise-3/sensor_stick/src/sensor_stick/features.py

antoszy/RoboND-perception-exercises

8c725e77316162ae485ccee94085fd2314be7ae0

[ "MIT" ]

null

Exercise-3/sensor_stick/src/sensor_stick/features.py

antoszy/RoboND-perception-exercises

8c725e77316162ae485ccee94085fd2314be7ae0

[ "MIT" ]

null

import matplotlib.colors import matplotlib.pyplot as plt import numpy as np from pcl_helper import * nbinscol = 32 nbinsnor = 20 def rgb_to_hsv(rgb_list): rgb_normalized = [1.0*rgb_list[0]/255, 1.0*rgb_list[1]/255, 1.0*rgb_list[2]/255] hsv_normalized = matplotlib.colors.rgb_to_hsv([[rgb_normalized]])[0][0] return hsv_normalized def compute_color_histograms(cloud, using_hsv=False): # Compute histograms for the clusters point_colors_list = [] # Step through each point in the point cloud for point in pc2.read_points(cloud, skip_nans=True): rgb_list = float_to_rgb(point[3]) if using_hsv: point_colors_list.append(rgb_to_hsv(rgb_list) * 255) else: point_colors_list.append(rgb_list) # Populate lists with color values channel_1_vals = [] channel_2_vals = [] channel_3_vals = [] for color in point_colors_list: channel_1_vals.append(color[0]) channel_2_vals.append(color[1]) channel_3_vals.append(color[2]) # TODO: Compute histograms hist_1 = np.histogram(channel_1_vals, bins = nbinscol, range = (0, 256)) hist_2 = np.histogram(channel_2_vals, bins = nbinscol, range = (0, 256)) hist_3 = np.histogram(channel_3_vals, bins = nbinscol, range = (0, 256)) # TODO: Concatenate and normalize the histograms features = np.concatenate((hist_1[0],hist_2[0],hist_3[0])).astype(np.float64) normed_features = features/np.sum(features) return normed_features def compute_normal_histograms(normal_cloud): norm_x_vals = [] norm_y_vals = [] norm_z_vals = [] for norm_component in pc2.read_points(normal_cloud, field_names = ('normal_x', 'normal_y', 'normal_z'), skip_nans=True): norm_x_vals.append(norm_component[0]) norm_y_vals.append(norm_component[1]) norm_z_vals.append(norm_component[2]) # TODO: Compute histograms of normal values (just like with color) hist_1 = np.histogram(norm_x_vals, bins = nbinsnor, range = (0, 256)) hist_2 = np.histogram(norm_y_vals, bins = nbinsnor, range = (0, 256)) hist_3 = np.histogram(norm_z_vals, bins = nbinsnor, range = (0, 256)) # TODO: Concatenate and normalize the histograms features = np.concatenate((hist_1[0],hist_2[0],hist_3[0])).astype(np.float64) normed_features = features/np.sum(features) return normed_features

32.309859

84

0.725806

import matplotlib.colors import matplotlib.pyplot as plt import numpy as np from pcl_helper import * nbinscol = 32 nbinsnor = 20 def rgb_to_hsv(rgb_list): rgb_normalized = [1.0*rgb_list[0]/255, 1.0*rgb_list[1]/255, 1.0*rgb_list[2]/255] hsv_normalized = matplotlib.colors.rgb_to_hsv([[rgb_normalized]])[0][0] return hsv_normalized def compute_color_histograms(cloud, using_hsv=False): point_colors_list = [] for point in pc2.read_points(cloud, skip_nans=True): rgb_list = float_to_rgb(point[3]) if using_hsv: point_colors_list.append(rgb_to_hsv(rgb_list) * 255) else: point_colors_list.append(rgb_list) channel_1_vals = [] channel_2_vals = [] channel_3_vals = [] for color in point_colors_list: channel_1_vals.append(color[0]) channel_2_vals.append(color[1]) channel_3_vals.append(color[2]) hist_1 = np.histogram(channel_1_vals, bins = nbinscol, range = (0, 256)) hist_2 = np.histogram(channel_2_vals, bins = nbinscol, range = (0, 256)) hist_3 = np.histogram(channel_3_vals, bins = nbinscol, range = (0, 256)) features = np.concatenate((hist_1[0],hist_2[0],hist_3[0])).astype(np.float64) normed_features = features/np.sum(features) return normed_features def compute_normal_histograms(normal_cloud): norm_x_vals = [] norm_y_vals = [] norm_z_vals = [] for norm_component in pc2.read_points(normal_cloud, field_names = ('normal_x', 'normal_y', 'normal_z'), skip_nans=True): norm_x_vals.append(norm_component[0]) norm_y_vals.append(norm_component[1]) norm_z_vals.append(norm_component[2]) hist_1 = np.histogram(norm_x_vals, bins = nbinsnor, range = (0, 256)) hist_2 = np.histogram(norm_y_vals, bins = nbinsnor, range = (0, 256)) hist_3 = np.histogram(norm_z_vals, bins = nbinsnor, range = (0, 256)) features = np.concatenate((hist_1[0],hist_2[0],hist_3[0])).astype(np.float64) normed_features = features/np.sum(features) return normed_features

true

f734cb94f1c618b92f07fe70c5d48199f6e127ba

2,737

py

Python

.Config/FslBuildGen/Xml/SubPackageSupportConfig.py

alejandrolozano2/OpenGL_DemoFramework

5fd85f05c98cc3d0c0a68bac438035df8cabaee7

[ "MIT", "BSD-3-Clause" ]

3

2019-01-19T20:21:24.000Z

2021-08-10T02:11:32.000Z

.Config/FslBuildGen/Xml/SubPackageSupportConfig.py

alejandrolozano2/OpenGL_DemoFramework

5fd85f05c98cc3d0c0a68bac438035df8cabaee7

[ "MIT", "BSD-3-Clause" ]

null

.Config/FslBuildGen/Xml/SubPackageSupportConfig.py

alejandrolozano2/OpenGL_DemoFramework

5fd85f05c98cc3d0c0a68bac438035df8cabaee7

[ "MIT", "BSD-3-Clause" ]

1

2021-08-10T02:11:33.000Z

#!/usr/bin/env python3 #**************************************************************************************************************************************************** # Copyright (c) 2014 Freescale Semiconductor, Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the Freescale Semiconductor, Inc. nor the names of # its contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. # IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, # INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE # OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # #**************************************************************************************************************************************************** from FslBuildGen.DataTypes import PackageType from FslBuildGen.DataTypes import SubPackageSupport class SubPackageSupportConfig(object): def __init__(self, packageType: int, subPackageSupport: int) -> None: super(SubPackageSupportConfig, self).__init__() self.Type = packageType # type: int self.Support = subPackageSupport # type: int self.AllowSubPackages = self.__AllowSubPackages(packageType, subPackageSupport) # type: bool def __AllowSubPackages(self, packageType: int, subPackageSupport: int) -> bool: return subPackageSupport == SubPackageSupport.Enabled or (subPackageSupport == SubPackageSupport.ExecutableOnly and packageType == PackageType.Executable)

58.234043

162

0.656924

from FslBuildGen.DataTypes import PackageType from FslBuildGen.DataTypes import SubPackageSupport class SubPackageSupportConfig(object): def __init__(self, packageType: int, subPackageSupport: int) -> None: super(SubPackageSupportConfig, self).__init__() self.Type = packageType self.Support = subPackageSupport self.AllowSubPackages = self.__AllowSubPackages(packageType, subPackageSupport) def __AllowSubPackages(self, packageType: int, subPackageSupport: int) -> bool: return subPackageSupport == SubPackageSupport.Enabled or (subPackageSupport == SubPackageSupport.ExecutableOnly and packageType == PackageType.Executable)

true

f734cd4cfbc6e0bc534a4349136cc39749e30092

8,442

py

Python

magnum/objects/x509keypair.py

mjbrewer/testIndex

420dc071d4240a89b6f266e8d2575cedb39bfea0

[ "Apache-2.0" ]

null

magnum/objects/x509keypair.py

mjbrewer/testIndex

420dc071d4240a89b6f266e8d2575cedb39bfea0

[ "Apache-2.0" ]

null

magnum/objects/x509keypair.py

mjbrewer/testIndex

420dc071d4240a89b6f266e8d2575cedb39bfea0

[ "Apache-2.0" ]

null

# coding=utf-8 # # # 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_versionedobjects import fields from magnum.common import exception from magnum.common import utils from magnum.db import api as dbapi from magnum.objects import base @base.MagnumObjectRegistry.register class X509KeyPair(base.MagnumPersistentObject, base.MagnumObject, base.MagnumObjectDictCompat): # Version 1.0: Initial version VERSION = '1.0' dbapi = dbapi.get_instance() fields = { 'id': fields.IntegerField(), 'uuid': fields.UUIDField(nullable=True), 'name': fields.StringField(nullable=True), 'bay_uuid': fields.StringField(nullable=True), 'ca_cert': fields.StringField(nullable=True), 'certificate': fields.StringField(nullable=True), 'private_key': fields.StringField(nullable=True), 'project_id': fields.StringField(nullable=True), 'user_id': fields.StringField(nullable=True), } @staticmethod def _from_db_object(x509keypair, db_x509keypair): """Converts a database entity to a formal object.""" for field in x509keypair.fields: x509keypair[field] = db_x509keypair[field] x509keypair.obj_reset_changes() return x509keypair @staticmethod def _from_db_object_list(db_objects, cls, context): """Converts a list of database entities to a list of formal objects.""" return [X509KeyPair._from_db_object(cls(context), obj) for obj in db_objects] @base.remotable_classmethod def get(cls, context, x509keypair_id): """Find a x509keypair_id based on its id or uuid and return a X509KeyPair object. :param x509keypair_id: the id *or* uuid of a x509keypair. :returns: a :class:`X509KeyPair` object. """ if utils.is_int_like(x509keypair_id): return cls.get_by_id(context, x509keypair_id) elif utils.is_uuid_like(x509keypair_id): return cls.get_by_uuid(context, x509keypair_id) else: raise exception.InvalidIdentity(identity=x509keypair_id) @base.remotable_classmethod def get_by_id(cls, context, x509keypair_id): """Find a x509keypair based on its integer id and return a X509KeyPair object. :param x509keypair_id: the id of a x509keypair. :returns: a :class:`X509KeyPair` object. """ db_x509keypair = cls.dbapi.get_x509keypair_by_id(context, x509keypair_id) x509keypair = X509KeyPair._from_db_object(cls(context), db_x509keypair) return x509keypair @base.remotable_classmethod def get_by_uuid(cls, context, uuid): """Find a x509keypair based on uuid and return a :class:`X509KeyPair` object. :param uuid: the uuid of a x509keypair. :param context: Security context :returns: a :class:`X509KeyPair` object. """ db_x509keypair = cls.dbapi.get_x509keypair_by_uuid(context, uuid) x509keypair = X509KeyPair._from_db_object(cls(context), db_x509keypair) return x509keypair @base.remotable_classmethod def get_by_name(cls, context, name): """Find a x509keypair based on name and return a X509KeyPair object. :param name: the logical name of a x509keypair. :param context: Security context :returns: a :class:`X509KeyPair` object. """ db_x509keypair = cls.dbapi.get_x509keypair_by_name(context, name) x509keypair = X509KeyPair._from_db_object(cls(context), db_x509keypair) return x509keypair @base.remotable_classmethod def list(cls, context, limit=None, marker=None, sort_key=None, sort_dir=None, filters=None): """Return a list of X509KeyPair objects. :param context: Security context. :param limit: maximum number of resources to return in a single result. :param marker: pagination marker for large data sets. :param sort_key: column to sort results by. :param sort_dir: direction to sort. "asc" or "desc". :param filters: filter dict, can include 'x509keypairmodel_id', 'name', 'node_count', 'stack_id', 'api_address', 'node_addresses', 'project_id', 'user_id', 'status'(should be a status list). :returns: a list of :class:`X509KeyPair` object. """ db_x509keypairs = cls.dbapi.get_x509keypair_list(context, limit=limit, marker=marker, sort_key=sort_key, sort_dir=sort_dir, filters=filters) return X509KeyPair._from_db_object_list(db_x509keypairs, cls, context) @base.remotable def create(self, context=None): """Create a X509KeyPair record in the DB. :param context: Security context. NOTE: This should only be used internally by the indirection_api. Unfortunately, RPC requires context as the first argument, even though we don't use it. A context should be set when instantiating the object, e.g.: X509KeyPair(context) """ values = self.obj_get_changes() db_x509keypair = self.dbapi.create_x509keypair(values) self._from_db_object(self, db_x509keypair) @base.remotable def destroy(self, context=None): """Delete the X509KeyPair from the DB. :param context: Security context. NOTE: This should only be used internally by the indirection_api. Unfortunately, RPC requires context as the first argument, even though we don't use it. A context should be set when instantiating the object, e.g.: X509KeyPair(context) """ self.dbapi.destroy_x509keypair(self.uuid) self.obj_reset_changes() @base.remotable def save(self, context=None): """Save updates to this X509KeyPair. Updates will be made column by column based on the result of self.what_changed(). :param context: Security context. NOTE: This should only be used internally by the indirection_api. Unfortunately, RPC requires context as the first argument, even though we don't use it. A context should be set when instantiating the object, e.g.: X509KeyPair(context) """ updates = self.obj_get_changes() self.dbapi.update_x509keypair(self.uuid, updates) self.obj_reset_changes() @base.remotable def refresh(self, context=None): """Loads updates for this X509KeyPair. Loads a x509keypair with the same uuid from the database and checks for updated attributes. Updates are applied from the loaded x509keypair column by column, if there are any updates. :param context: Security context. NOTE: This should only be used internally by the indirection_api. Unfortunately, RPC requires context as the first argument, even though we don't use it. A context should be set when instantiating the object, e.g.: X509KeyPair(context) """ current = self.__class__.get_by_uuid(self._context, uuid=self.uuid) for field in self.fields: if self.obj_attr_is_set(field) and self[field] != current[field]: self[field] = current[field]

41.586207

85

0.622957

from oslo_versionedobjects import fields from magnum.common import exception from magnum.common import utils from magnum.db import api as dbapi from magnum.objects import base @base.MagnumObjectRegistry.register class X509KeyPair(base.MagnumPersistentObject, base.MagnumObject, base.MagnumObjectDictCompat): VERSION = '1.0' dbapi = dbapi.get_instance() fields = { 'id': fields.IntegerField(), 'uuid': fields.UUIDField(nullable=True), 'name': fields.StringField(nullable=True), 'bay_uuid': fields.StringField(nullable=True), 'ca_cert': fields.StringField(nullable=True), 'certificate': fields.StringField(nullable=True), 'private_key': fields.StringField(nullable=True), 'project_id': fields.StringField(nullable=True), 'user_id': fields.StringField(nullable=True), } @staticmethod def _from_db_object(x509keypair, db_x509keypair): for field in x509keypair.fields: x509keypair[field] = db_x509keypair[field] x509keypair.obj_reset_changes() return x509keypair @staticmethod def _from_db_object_list(db_objects, cls, context): return [X509KeyPair._from_db_object(cls(context), obj) for obj in db_objects] @base.remotable_classmethod def get(cls, context, x509keypair_id): if utils.is_int_like(x509keypair_id): return cls.get_by_id(context, x509keypair_id) elif utils.is_uuid_like(x509keypair_id): return cls.get_by_uuid(context, x509keypair_id) else: raise exception.InvalidIdentity(identity=x509keypair_id) @base.remotable_classmethod def get_by_id(cls, context, x509keypair_id): db_x509keypair = cls.dbapi.get_x509keypair_by_id(context, x509keypair_id) x509keypair = X509KeyPair._from_db_object(cls(context), db_x509keypair) return x509keypair @base.remotable_classmethod def get_by_uuid(cls, context, uuid): db_x509keypair = cls.dbapi.get_x509keypair_by_uuid(context, uuid) x509keypair = X509KeyPair._from_db_object(cls(context), db_x509keypair) return x509keypair @base.remotable_classmethod def get_by_name(cls, context, name): db_x509keypair = cls.dbapi.get_x509keypair_by_name(context, name) x509keypair = X509KeyPair._from_db_object(cls(context), db_x509keypair) return x509keypair @base.remotable_classmethod def list(cls, context, limit=None, marker=None, sort_key=None, sort_dir=None, filters=None): db_x509keypairs = cls.dbapi.get_x509keypair_list(context, limit=limit, marker=marker, sort_key=sort_key, sort_dir=sort_dir, filters=filters) return X509KeyPair._from_db_object_list(db_x509keypairs, cls, context) @base.remotable def create(self, context=None): values = self.obj_get_changes() db_x509keypair = self.dbapi.create_x509keypair(values) self._from_db_object(self, db_x509keypair) @base.remotable def destroy(self, context=None): self.dbapi.destroy_x509keypair(self.uuid) self.obj_reset_changes() @base.remotable def save(self, context=None): updates = self.obj_get_changes() self.dbapi.update_x509keypair(self.uuid, updates) self.obj_reset_changes() @base.remotable def refresh(self, context=None): current = self.__class__.get_by_uuid(self._context, uuid=self.uuid) for field in self.fields: if self.obj_attr_is_set(field) and self[field] != current[field]: self[field] = current[field]

true

f734cdbc4784f67ad314325d7468e7791ca4a293

2,093

py

Python

modules/cluster_xyz.py

fonsecag/TBD

d4cb9dec96e753708950e223f921a29ab7bcb9dd

[ "MIT" ]

6

2021-03-04T06:58:11.000Z

2022-01-30T02:28:48.000Z

modules/cluster_xyz.py

fonsecag/TBD

d4cb9dec96e753708950e223f921a29ab7bcb9dd

[ "MIT" ]

null

modules/cluster_xyz.py

fonsecag/TBD

d4cb9dec96e753708950e223f921a29ab7bcb9dd

[ "MIT" ]

null

from run import MainHandler from .cluster import ClusterHandler from util import * class ClusterXYZHandler(ClusterHandler): def __init__(self, args, **kwargs): super().__init__(args, **kwargs) self.n_stages = self.n_main_stages + self.n_substages n_substages = ClusterHandler.n_substages # one more than ClusterHandler def run_command(self): super().run_command() def save_cluster_xyz(self): z = self.call_para("R_to_xyz", "z", args=[self, self.dataset]) self.z = z dir_path = os.path.join(self.storage_dir, "cluster_xyz") if not os.path.exists(dir_path): os.mkdir(dir_path) var_index_R = self.call_para("R_to_xyz", "var_index_R") R = self.vars[var_index_R] var_index_F = self.call_para("R_to_xyz", "var_index_F") F = self.vars[var_index_F] var_index_E = self.call_para("R_to_xyz", "var_index_E") E = self.vars[var_index_E] cl_ind = self.cluster_indices for i in range(len(cl_ind)): cl = np.array(cl_ind[i], dtype=np.int64) self.save_xyz_index(i, R[cl], F[cl], E[cl]) def save_xyz_index(self, i, R, F, E): file_name = f"cluster_{i}.xyz" path = os.path.join(self.storage_dir, "cluster_xyz", file_name) file = open(path, "w+") for j in range(len(R)): r_j, f_j, e_j = R[j], F[j], E[j] s = self.RFE_to_xyz_single(r_j, f_j, e_j) file.write(s) file.close() # Energy=-620726.002662 Properties=species:S:1:pos:R:3:forces:R:3 def RFE_to_xyz_single(self, R, F, E): z = self.z s = f"{len(z)}\n" s += f"{E[0]:.5e}\n" for i in range(0, len(R), 3): s += f"{z[i//3]:<3}{R[i]:<13.5e}{R[i+1]:<13.5e}{R[i+2]:<13.5e}" s += f"{F[i]:<13.5e}{F[i+1]:<13.5e}{F[i+2]:<13.5e}\n" return s def save_command(self): super().save_command() from time import time t0 = time() self.save_cluster_xyz() print(f"Took {time() - t0} seconds")

29.9

77

0.572862

from run import MainHandler from .cluster import ClusterHandler from util import * class ClusterXYZHandler(ClusterHandler): def __init__(self, args, **kwargs): super().__init__(args, **kwargs) self.n_stages = self.n_main_stages + self.n_substages n_substages = ClusterHandler.n_substages def run_command(self): super().run_command() def save_cluster_xyz(self): z = self.call_para("R_to_xyz", "z", args=[self, self.dataset]) self.z = z dir_path = os.path.join(self.storage_dir, "cluster_xyz") if not os.path.exists(dir_path): os.mkdir(dir_path) var_index_R = self.call_para("R_to_xyz", "var_index_R") R = self.vars[var_index_R] var_index_F = self.call_para("R_to_xyz", "var_index_F") F = self.vars[var_index_F] var_index_E = self.call_para("R_to_xyz", "var_index_E") E = self.vars[var_index_E] cl_ind = self.cluster_indices for i in range(len(cl_ind)): cl = np.array(cl_ind[i], dtype=np.int64) self.save_xyz_index(i, R[cl], F[cl], E[cl]) def save_xyz_index(self, i, R, F, E): file_name = f"cluster_{i}.xyz" path = os.path.join(self.storage_dir, "cluster_xyz", file_name) file = open(path, "w+") for j in range(len(R)): r_j, f_j, e_j = R[j], F[j], E[j] s = self.RFE_to_xyz_single(r_j, f_j, e_j) file.write(s) file.close() def RFE_to_xyz_single(self, R, F, E): z = self.z s = f"{len(z)}\n" s += f"{E[0]:.5e}\n" for i in range(0, len(R), 3): s += f"{z[i//3]:<3}{R[i]:<13.5e}{R[i+1]:<13.5e}{R[i+2]:<13.5e}" s += f"{F[i]:<13.5e}{F[i+1]:<13.5e}{F[i+2]:<13.5e}\n" return s def save_command(self): super().save_command() from time import time t0 = time() self.save_cluster_xyz() print(f"Took {time() - t0} seconds")

true

f734ce2104b99a837e3511b1075b8cf719ab35b6

482

py

Python

pyxrf/db_config/hxn_db_config.py

andrewmkiss/PyXRF

61de2029c255f77279ba5bc3896107c1a2e4212f

[ "BSD-3-Clause" ]

19

2016-05-25T21:40:41.000Z

2022-01-19T01:58:15.000Z

pyxrf/db_config/hxn_db_config.py

andrewmkiss/PyXRF

61de2029c255f77279ba5bc3896107c1a2e4212f

[ "BSD-3-Clause" ]

90

2016-01-11T17:22:05.000Z

2021-12-02T15:59:58.000Z

pyxrf/db_config/hxn_db_config.py

andrewmkiss/PyXRF

61de2029c255f77279ba5bc3896107c1a2e4212f

[ "BSD-3-Clause" ]

22

2016-10-16T17:19:19.000Z

2022-02-18T21:45:08.000Z

try: from databroker.v0 import Broker except ModuleNotFoundError: from databroker import Broker from hxntools.handlers.xspress3 import Xspress3HDF5Handler from hxntools.handlers.timepix import TimepixHDF5Handler db = Broker.named("hxn") # db_analysis = Broker.named('hxn_analysis') db.reg.register_handler(Xspress3HDF5Handler.HANDLER_NAME, Xspress3HDF5Handler, overwrite=True) db.reg.register_handler(TimepixHDF5Handler._handler_name, TimepixHDF5Handler, overwrite=True)

34.428571

94

0.8361

try: from databroker.v0 import Broker except ModuleNotFoundError: from databroker import Broker from hxntools.handlers.xspress3 import Xspress3HDF5Handler from hxntools.handlers.timepix import TimepixHDF5Handler db = Broker.named("hxn") db.reg.register_handler(Xspress3HDF5Handler.HANDLER_NAME, Xspress3HDF5Handler, overwrite=True) db.reg.register_handler(TimepixHDF5Handler._handler_name, TimepixHDF5Handler, overwrite=True)

true

f734ce5df7ac9a87c9b31d2c6162e94261ab7b86

1,031

bzl

Python

examples/test.bzl

Aghassi/rules_nodejs

3eb42603c440f7e8496f2e6812337eb47827ff6a

[ "Apache-2.0" ]

1

2021-06-20T18:37:14.000Z

examples/test.bzl

Aghassi/rules_nodejs

3eb42603c440f7e8496f2e6812337eb47827ff6a

[ "Apache-2.0" ]

12

2020-04-06T21:50:34.000Z

2022-03-25T18:06:41.000Z

examples/test.bzl

Aghassi/rules_nodejs

3eb42603c440f7e8496f2e6812337eb47827ff6a

[ "Apache-2.0" ]

1

2018-03-07T13:48:39.000Z

"Define a convenience macro for examples integration testing" load("@build_bazel_rules_nodejs//internal/bazel_integration_test:bazel_integration_test.bzl", "rules_nodejs_integration_test") load("//:tools/defaults.bzl", "codeowners") def example_integration_test(name, owners = [], **kwargs): "Set defaults for the bazel_integration_test common to our examples" dirname = name[len("examples_"):] native.filegroup( name = "_%s_sources" % name, srcs = native.glob( [ "%s/*" % dirname, "%s/**/*" % dirname, ], exclude = ["%s/node_modules/**" % dirname], ), ) if len(owners): codeowners( name = "OWNERS." + name, teams = owners, pattern = dirname + "/**", ) rules_nodejs_integration_test( name = name, tags = kwargs.pop("tags", []) + ["examples"], workspace_files = kwargs.pop("workspace_files", "_%s_sources" % name), **kwargs )

33.258065

126

0.57129

load("@build_bazel_rules_nodejs//internal/bazel_integration_test:bazel_integration_test.bzl", "rules_nodejs_integration_test") load("//:tools/defaults.bzl", "codeowners") def example_integration_test(name, owners = [], **kwargs): dirname = name[len("examples_"):] native.filegroup( name = "_%s_sources" % name, srcs = native.glob( [ "%s/*" % dirname, "%s/**/*" % dirname, ], exclude = ["%s/node_modules/**" % dirname], ), ) if len(owners): codeowners( name = "OWNERS." + name, teams = owners, pattern = dirname + "/**", ) rules_nodejs_integration_test( name = name, tags = kwargs.pop("tags", []) + ["examples"], workspace_files = kwargs.pop("workspace_files", "_%s_sources" % name), **kwargs )

true

f734ce7aa3455146cd42a264d89384c7c91362c6

701

py

Python

send_sms.py

bthaman/noaa_precip

7c1b33760584fb4591e10eeb59e5904df48a17d0

[ "Python-2.0", "OLDAP-2.7" ]

null

send_sms.py

bthaman/noaa_precip

7c1b33760584fb4591e10eeb59e5904df48a17d0

[ "Python-2.0", "OLDAP-2.7" ]

null

send_sms.py

bthaman/noaa_precip

7c1b33760584fb4591e10eeb59e5904df48a17d0

[ "Python-2.0", "OLDAP-2.7" ]

null

# we import the Twilio client from the dependency we just installed # from twilio.rest import TwilioRestClient from twilio.rest import Client def send_text(message): # the following line needs your Twilio Account SID and Auth Token client = Client("AC3e84e9cae2390af9a661c1ab35955444", "4a8bf26cb30107ec85d98f6bf1182522") # change the "from_" number to your Twilio number and the "to" number # to the phone number you signed up for Twilio with, or upgrade your # account to send SMS to any phone number client.messages.create(to="+15129146948", from_="+17372105122", body=message) if __name__ == '__main__': send_text('Hello, this is a test.')

43.8125

93

0.723252

from twilio.rest import Client def send_text(message): client = Client("AC3e84e9cae2390af9a661c1ab35955444", "4a8bf26cb30107ec85d98f6bf1182522") client.messages.create(to="+15129146948", from_="+17372105122", body=message) if __name__ == '__main__': send_text('Hello, this is a test.')

true

f734cefe7c40ffddafb7ca7ca8a4bd256d52efd5

840

py

Python

tests/v1/test_event_alert_type.py

MichaelTROEHLER/datadog-api-client-python

12c46626622fb1277bb1e172753b342c671348bd

[ "Apache-2.0" ]

null

tests/v1/test_event_alert_type.py

MichaelTROEHLER/datadog-api-client-python

12c46626622fb1277bb1e172753b342c671348bd

[ "Apache-2.0" ]

null

tests/v1/test_event_alert_type.py

MichaelTROEHLER/datadog-api-client-python

12c46626622fb1277bb1e172753b342c671348bd

[ "Apache-2.0" ]

null

# coding: utf-8 # Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License. # This product includes software developed at Datadog (https://www.datadoghq.com/). # Copyright 2019-Present Datadog, Inc. from __future__ import absolute_import import sys import unittest import datadog_api_client.v1 from datadog_api_client.v1.model.event_alert_type import EventAlertType class TestEventAlertType(unittest.TestCase): """EventAlertType unit test stubs""" def setUp(self): pass def tearDown(self): pass def testEventAlertType(self): """Test EventAlertType""" # FIXME: construct object with mandatory attributes with example values # model = EventAlertType() # noqa: E501 pass if __name__ == '__main__': unittest.main()

24.705882

108

0.72381

from __future__ import absolute_import import sys import unittest import datadog_api_client.v1 from datadog_api_client.v1.model.event_alert_type import EventAlertType class TestEventAlertType(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testEventAlertType(self): s if __name__ == '__main__': unittest.main()

true

f734cfda1c639c31c88584c85e5043ba791d021a

3,000

py

Python

english/data_processing/lessons/code/vslide1.py

hrutkabence/tutorials

bd76294860804aee8ecda5e1445464506bf02ee0

[ "CC0-1.0" ]

null

english/data_processing/lessons/code/vslide1.py

hrutkabence/tutorials

bd76294860804aee8ecda5e1445464506bf02ee0

[ "CC0-1.0" ]

null

english/data_processing/lessons/code/vslide1.py

hrutkabence/tutorials

bd76294860804aee8ecda5e1445464506bf02ee0

[ "CC0-1.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import sys from math import hypot, atan2, sin, cos, pi, degrees import numpy as np from matplotlib import pyplot as plt def vplain(x1, y1, x2, y2): """ set up line equation vp[0] * x + vp[1] * y + vp[2] = 0 x1, y1 - horizontal coordinates of the start point of the section x2, y2 - horizontal coordinates of the end point of the section returns a numpy array with coefficients of the vertical plane """ vp = np.zeros((3,)) vp[0] = y1 - y2 vp[1] = x2 - x1 vp[2] = x1 * y2 - x2 * y1 vp = vp / hypot(vp[0], vp[1]) # normalize return vp def section(pc, x1, y1, x2, y2, tol): """ Select point from a point cloud near to a line pc - point cloud in a numpy array x1, y1 - horizontal coordinates of the start point of the section x2, y2 - horizontal coordinates of the end point of the section tol - tolerance distance from the section returns a numpy array with points near to the section """ pc1 = pc.copy() pc1[:, 2] = 1 # change to homogenous coordinates vp = vplain(x1, y1, x2, y2) # equation of vertical plain sec = pc[np.abs(np.dot(pc1, vp)) < tol] # select points close to the section return sec def tr(e1, n1, e2, n2): """ set up transformation matrix for homogenous coordinates Parameters: e1, n1 - start point of the section line e2, n2 - end point of the section section line returns the transformation matrix """ de = e2 - e1 dn = n2 - n1 a = atan2(dn, de) ca = cos(a) sa = sin(a) return np.dot(np.array([[1, 0, 0], [0, 1, 0], [-e1, -n1, 1]]), np.array([[ca, -sa, 0], [sa, ca, 0], [0, 0, 1]])) if __name__ == "__main__": if len(sys.argv) < 7: pc = np.loadtxt('lidar.txt', delimiter=',') ;# load point cloud x1 = 548060.0 y1 = 5129130.0 x2 = 549850.0 y2 = 5129030.0 #x1 = 549400 #y1 = 5128900 #x2 = 549200 #y2 = 5129300 tol = 1.0 else: pc = np.loadtxt(sys.argv[1], delimiter=',') ;# load point cloud x1 = float(sys.argv[2]) y1 = float(sys.argv[3]) x2 = float(sys.argv[4]) y2 = float(sys.argv[5]) tol = float(sys.argv[6]) # set up equation for vertical plain a * x + b * y + c = 0 vp = vplain(x1, y1, x2, y2) sec = section(pc,x1,y1,x2,y2,tol) # transformation matrix trm = tr(x1, y1, x2, y2) if abs(np.dot(np.array([x1, y1, 1]), trm)[1]) > 1e-5 or \ abs(np.dot(np.array([x2, y2, 1]), trm)[1]) > 1e-5: print("tr error") # make a copy of section points for homogenous transformation pc1 = sec.copy() pc1[:, 2] = 1 pc1 = np.dot(pc1, trm) # rotate points into the section plain pc1[:, 2] = sec[:, 2] # copy back elevations to transformed points plt.plot(pc1[:,0], pc1[:,2], 'o') plt.xlabel('chainage (m)') plt.ylabel('elevation (m)') plt.axis('equal') plt.grid('on') plt.show()

30.927835

80

0.576667

import sys from math import hypot, atan2, sin, cos, pi, degrees import numpy as np from matplotlib import pyplot as plt def vplain(x1, y1, x2, y2): vp = np.zeros((3,)) vp[0] = y1 - y2 vp[1] = x2 - x1 vp[2] = x1 * y2 - x2 * y1 vp = vp / hypot(vp[0], vp[1]) return vp def section(pc, x1, y1, x2, y2, tol): pc1 = pc.copy() pc1[:, 2] = 1 vp = vplain(x1, y1, x2, y2) sec = pc[np.abs(np.dot(pc1, vp)) < tol] return sec def tr(e1, n1, e2, n2): de = e2 - e1 dn = n2 - n1 a = atan2(dn, de) ca = cos(a) sa = sin(a) return np.dot(np.array([[1, 0, 0], [0, 1, 0], [-e1, -n1, 1]]), np.array([[ca, -sa, 0], [sa, ca, 0], [0, 0, 1]])) if __name__ == "__main__": if len(sys.argv) < 7: pc = np.loadtxt('lidar.txt', delimiter=',') ; x1 = 548060.0 y1 = 5129130.0 x2 = 549850.0 y2 = 5129030.0 tol = 1.0 else: pc = np.loadtxt(sys.argv[1], delimiter=',') ; x1 = float(sys.argv[2]) y1 = float(sys.argv[3]) x2 = float(sys.argv[4]) y2 = float(sys.argv[5]) tol = float(sys.argv[6]) vp = vplain(x1, y1, x2, y2) sec = section(pc,x1,y1,x2,y2,tol) trm = tr(x1, y1, x2, y2) if abs(np.dot(np.array([x1, y1, 1]), trm)[1]) > 1e-5 or \ abs(np.dot(np.array([x2, y2, 1]), trm)[1]) > 1e-5: print("tr error") pc1 = sec.copy() pc1[:, 2] = 1 pc1 = np.dot(pc1, trm) pc1[:, 2] = sec[:, 2] plt.plot(pc1[:,0], pc1[:,2], 'o') plt.xlabel('chainage (m)') plt.ylabel('elevation (m)') plt.axis('equal') plt.grid('on') plt.show()

true

f734d00b0261768c9a37cb2968bbdc5343c8ba0c

1,292

py

Python

demo_guided.py

MrJohnsson77/bat-country

894af13bd777ab8aa989baf520db074b97cfad9a

[ "MIT" ]

274

2015-07-06T14:29:09.000Z

2022-02-06T18:06:14.000Z

demo_guided.py

amsimoes/bat-country

c0d29a0b32c196ca3d4c40fbaf960432b507e8bb

[ "MIT" ]

11

2015-07-13T23:54:15.000Z

2022-03-11T23:11:38.000Z

demo_guided.py

amsimoes/bat-country

c0d29a0b32c196ca3d4c40fbaf960432b507e8bb

[ "MIT" ]

68

2015-07-06T15:16:53.000Z

2021-08-09T19:03:31.000Z

# USAGE # python demo_guided.py --base-model $CAFFE_ROOT/models/bvlc_googlenet \ # --image initial_images/clouds.jpg \ # --guide-image initial_images/seed_images/starry_night.jpg \ # --output examples/output/seeded/clouds_and_starry_night.jpg # import the necessary packages from batcountry import BatCountry from PIL import Image import numpy as np import argparse # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-b", "--base-model", required=True, help="base model path") ap.add_argument("-l", "--layer", type=str, default="inception_4c/output", help="layer of CNN to use") ap.add_argument("-i", "--image", required=True, help="path to base image") ap.add_argument("-g", "--guide-image", required=True, help="path to guide image") ap.add_argument("-o", "--output", required=True, help="path to output image") args = ap.parse_args() # we can't stop here... bc = BatCountry(args.base_model) features = bc.prepare_guide(Image.open(args.guide_image), end=args.layer) image = bc.dream(np.float32(Image.open(args.image)), end=args.layer, iter_n=20, objective_fn=BatCountry.guided_objective, objective_features=features,) bc.cleanup() # write the output image to file result = Image.fromarray(np.uint8(image)) result.save(args.output)

39.151515

81

0.755418

from batcountry import BatCountry from PIL import Image import numpy as np import argparse ap = argparse.ArgumentParser() ap.add_argument("-b", "--base-model", required=True, help="base model path") ap.add_argument("-l", "--layer", type=str, default="inception_4c/output", help="layer of CNN to use") ap.add_argument("-i", "--image", required=True, help="path to base image") ap.add_argument("-g", "--guide-image", required=True, help="path to guide image") ap.add_argument("-o", "--output", required=True, help="path to output image") args = ap.parse_args() bc = BatCountry(args.base_model) features = bc.prepare_guide(Image.open(args.guide_image), end=args.layer) image = bc.dream(np.float32(Image.open(args.image)), end=args.layer, iter_n=20, objective_fn=BatCountry.guided_objective, objective_features=features,) bc.cleanup() # write the output image to file result = Image.fromarray(np.uint8(image)) result.save(args.output)

true

f734d00b18af99f6b7b1b6eeaf82cc30c32aef02

3,245

py

Python

polling_stations/apps/data_collection/management/commands/import_harborough.py

mtravis/UK-Polling-Stations

26e0331dc29253dc436a0462ffaa01e974c5dc52

[ "BSD-3-Clause" ]

null

polling_stations/apps/data_collection/management/commands/import_harborough.py

mtravis/UK-Polling-Stations

26e0331dc29253dc436a0462ffaa01e974c5dc52

[ "BSD-3-Clause" ]

null

polling_stations/apps/data_collection/management/commands/import_harborough.py

mtravis/UK-Polling-Stations

26e0331dc29253dc436a0462ffaa01e974c5dc52

[ "BSD-3-Clause" ]

null

from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter class Command(BaseXpressDemocracyClubCsvImporter): council_id = "E07000131" addresses_name = ( "local.2019-05-02/Version 1/Democracy_Club__02May2019 Harborough DC.tsv" ) stations_name = ( "local.2019-05-02/Version 1/Democracy_Club__02May2019 Harborough DC.tsv" ) elections = ["local.2019-05-02"] csv_delimiter = "\t" def address_record_to_dict(self, record): rec = super().address_record_to_dict(record) uprn = record.property_urn.strip().lstrip("0") if uprn == "200003741884": rec["postcode"] = "LE14 2QY" if ( record.addressline1.strip() == "69 Main Street" and record.addressline2.strip() == "Great Bowden" and record.addressline3.strip() == "Market Harborough, Leics" ): rec["postcode"] = "LE16 7HD" rec["accept_suggestion"] = False if uprn in [ "100030474314", # LE79DE -> LE79DP : Grange Barn, Loddington Road, Tilton on the Hill, Leicester "100030474315", # LE79DE -> LE79DP : Grange Yard, Loddington Road, Tilton on the Hill, Leicester "200003741317", # LE79DE -> LE79DP : Robin A Tiptoe Farm, Loddington Road, Tilton on the Hill, Leicester "200003742237", # LE79XE -> LE79XB : Ash Tree Cottage, Launde Road, Loddington, Leicester "100030477785", # LE96PU -> LE96PW : 102 Station Road, Broughton Astley, Leics ]: rec["accept_suggestion"] = True if uprn in [ "200003741417", # LE79YE -> LE79FN : Park Farm, Uppingham Road, Skeffington, Leicester "200003737159", # LE175EA -> LE175RA : Hillcrest Farm, Frolesworth Road, Leire, Lutterworth, Leics "200003737160", # LE175EA -> LE175RA : Mount Pleasant, Frolesworth Road, Leire, Lutterworth, Leics "100032072508", # LE88AQ -> LE88AN : Wayside, Arnesby Road, Fleckney, Leicestershire "100030493011", # LE167SZ -> LE167SX : The Old Rectory, Stonton Road, Church Langton, Market Harborough, Leics "200003739029", # LE167RU -> LE167RT : Hunters Lodge, Main Street, Gumley, Market Harborough, Leics "100030480043", # LE174RU -> LE174RX : Toll Gate Cottage, Bitteswell Road, Lutterworth, Leics "10034458557", # LE175LE -> LE174LE : The Milking Parlour Boston Lodge, Lutterworth Road, Gilmorton, Lutterworth, Leics "200003744797", # LE175PL -> LE175RZ : Ewe Cottage Gilmorton Lodge, Kimcote Road, Gilmorton, Lutterworth, Leics "100030493741", # LE167TT -> LE167TX : Birchtree Farm, Welham Road, Thorpe Langton, Leics "200003742100", # LE174LH -> LE174LR : The Mere, Mere Road, Bitteswell, Lutterworth, Leics "200003741377", # LE79XL -> LE79XJ : 3 Fiddlers Green, Uppingham Road, East Norton, Leicester "200003741379", # LE79XL -> LE79XJ : 2 Fiddlers Green, Uppingham Road, East Norton, Leicester "200003741382", # LE79XL -> LE79XJ : 1 Fiddlers Green, Uppingham Road, East Norton, Leicester ]: rec["accept_suggestion"] = False return rec

55.948276

132

0.642527

from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter class Command(BaseXpressDemocracyClubCsvImporter): council_id = "E07000131" addresses_name = ( "local.2019-05-02/Version 1/Democracy_Club__02May2019 Harborough DC.tsv" ) stations_name = ( "local.2019-05-02/Version 1/Democracy_Club__02May2019 Harborough DC.tsv" ) elections = ["local.2019-05-02"] csv_delimiter = "\t" def address_record_to_dict(self, record): rec = super().address_record_to_dict(record) uprn = record.property_urn.strip().lstrip("0") if uprn == "200003741884": rec["postcode"] = "LE14 2QY" if ( record.addressline1.strip() == "69 Main Street" and record.addressline2.strip() == "Great Bowden" and record.addressline3.strip() == "Market Harborough, Leics" ): rec["postcode"] = "LE16 7HD" rec["accept_suggestion"] = False if uprn in [ "100030474314", "100030474315", "200003741317", "200003742237", "100030477785", ]: rec["accept_suggestion"] = True if uprn in [ "200003741417", "200003737159", "200003737160", "100032072508", "100030493011", "200003739029", "100030480043", "10034458557", "200003744797", "100030493741", "200003742100", "200003741377", "200003741379", "200003741382", ]: rec["accept_suggestion"] = False return rec

true

f734d0ce5e31881671bde02a0d35ed0eb21415f4

2,857

py

Python

data/transcoder_evaluation_gfg/python/STOOGE_SORT.py

mxl1n/CodeGen

e5101dd5c5e9c3720c70c80f78b18f13e118335a

[ "MIT" ]

241

2021-07-20T08:35:20.000Z

2022-03-31T02:39:08.000Z

data/transcoder_evaluation_gfg/python/STOOGE_SORT.py

mxl1n/CodeGen

e5101dd5c5e9c3720c70c80f78b18f13e118335a

[ "MIT" ]

49

2021-07-22T23:18:42.000Z

2022-03-24T09:15:26.000Z

data/transcoder_evaluation_gfg/python/STOOGE_SORT.py

mxl1n/CodeGen

e5101dd5c5e9c3720c70c80f78b18f13e118335a

[ "MIT" ]

71

2021-07-21T05:17:52.000Z

2022-03-29T23:49:28.000Z

# Copyright (c) 2019-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # def f_gold ( arr , l , h ) : if l >= h : return if arr [ l ] > arr [ h ] : t = arr [ l ] arr [ l ] = arr [ h ] arr [ h ] = t if h - l + 1 > 2 : t = ( int ) ( ( h - l + 1 ) / 3 ) f_gold ( arr , l , ( h - t ) ) f_gold ( arr , l + t , ( h ) ) f_gold ( arr , l , ( h - t ) ) #TOFILL if __name__ == '__main__': param = [ ([6, 25, 42, 52, 53, 54, 58, 66, 67, 70],6,6,), ([-13, -98, 50, -63, 48, 3, -76, 12, -35, 93, 29, 17, 16, 5, -97, -54, -45, -25],16,14,), ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],28,24,), ([7, 49, 26, 33, 48, 79, 2, 71, 32, 4, 20, 36],9,10,), ([88],0,0,), ([1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],20,28,), ([2, 2, 4, 5, 7, 12, 12, 14, 14, 16, 17, 29, 29, 31, 32, 39, 41, 47, 48, 49, 51, 54, 58, 58, 59, 60, 73, 78, 80, 81, 82, 83, 84, 85, 90, 95, 97, 99, 99],28,29,), ([-31, -55, 6, 37, 77, 61, 0, 46, -91, -38, 85, -71, 25, 14, 53, 43, 34],15,11,), ([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],12,17,), ([77, 68, 78, 97, 92, 52, 37, 8, 44, 98, 5, 69, 31, 45, 9, 32, 33, 67, 30, 76, 29, 3, 90, 57, 30, 9, 26, 2, 62, 3, 46, 68, 25, 51, 13, 44, 35, 55],27,20,) ] filled_function_param = [ ([6, 25, 42, 52, 53, 54, 58, 66, 67, 70],6,6,), ([-13, -98, 50, -63, 48, 3, -76, 12, -35, 93, 29, 17, 16, 5, -97, -54, -45, -25],16,14,), ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],28,24,), ([7, 49, 26, 33, 48, 79, 2, 71, 32, 4, 20, 36],9,10,), ([88],0,0,), ([1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],20,28,), ([2, 2, 4, 5, 7, 12, 12, 14, 14, 16, 17, 29, 29, 31, 32, 39, 41, 47, 48, 49, 51, 54, 58, 58, 59, 60, 73, 78, 80, 81, 82, 83, 84, 85, 90, 95, 97, 99, 99],28,29,), ([-31, -55, 6, 37, 77, 61, 0, 46, -91, -38, 85, -71, 25, 14, 53, 43, 34],15,11,), ([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],12,17,), ([77, 68, 78, 97, 92, 52, 37, 8, 44, 98, 5, 69, 31, 45, 9, 32, 33, 67, 30, 76, 29, 3, 90, 57, 30, 9, 26, 2, 62, 3, 46, 68, 25, 51, 13, 44, 35, 55],27,20,) ] n_success = 0 for i, parameters_set in enumerate(param): f_filled(*(filled_function_param[i])) f_gold(*parameters_set) if parameters_set == filled_function_param[i]: n_success+=1 print("#Results: %i, %i" % (n_success, len(param)))

52.907407

165

0.431222

def f_gold ( arr , l , h ) : if l >= h : return if arr [ l ] > arr [ h ] : t = arr [ l ] arr [ l ] = arr [ h ] arr [ h ] = t if h - l + 1 > 2 : t = ( int ) ( ( h - l + 1 ) / 3 ) f_gold ( arr , l , ( h - t ) ) f_gold ( arr , l + t , ( h ) ) f_gold ( arr , l , ( h - t ) ) if __name__ == '__main__': param = [ ([6, 25, 42, 52, 53, 54, 58, 66, 67, 70],6,6,), ([-13, -98, 50, -63, 48, 3, -76, 12, -35, 93, 29, 17, 16, 5, -97, -54, -45, -25],16,14,), ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],28,24,), ([7, 49, 26, 33, 48, 79, 2, 71, 32, 4, 20, 36],9,10,), ([88],0,0,), ([1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],20,28,), ([2, 2, 4, 5, 7, 12, 12, 14, 14, 16, 17, 29, 29, 31, 32, 39, 41, 47, 48, 49, 51, 54, 58, 58, 59, 60, 73, 78, 80, 81, 82, 83, 84, 85, 90, 95, 97, 99, 99],28,29,), ([-31, -55, 6, 37, 77, 61, 0, 46, -91, -38, 85, -71, 25, 14, 53, 43, 34],15,11,), ([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],12,17,), ([77, 68, 78, 97, 92, 52, 37, 8, 44, 98, 5, 69, 31, 45, 9, 32, 33, 67, 30, 76, 29, 3, 90, 57, 30, 9, 26, 2, 62, 3, 46, 68, 25, 51, 13, 44, 35, 55],27,20,) ] filled_function_param = [ ([6, 25, 42, 52, 53, 54, 58, 66, 67, 70],6,6,), ([-13, -98, 50, -63, 48, 3, -76, 12, -35, 93, 29, 17, 16, 5, -97, -54, -45, -25],16,14,), ([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],28,24,), ([7, 49, 26, 33, 48, 79, 2, 71, 32, 4, 20, 36],9,10,), ([88],0,0,), ([1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],20,28,), ([2, 2, 4, 5, 7, 12, 12, 14, 14, 16, 17, 29, 29, 31, 32, 39, 41, 47, 48, 49, 51, 54, 58, 58, 59, 60, 73, 78, 80, 81, 82, 83, 84, 85, 90, 95, 97, 99, 99],28,29,), ([-31, -55, 6, 37, 77, 61, 0, 46, -91, -38, 85, -71, 25, 14, 53, 43, 34],15,11,), ([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],12,17,), ([77, 68, 78, 97, 92, 52, 37, 8, 44, 98, 5, 69, 31, 45, 9, 32, 33, 67, 30, 76, 29, 3, 90, 57, 30, 9, 26, 2, 62, 3, 46, 68, 25, 51, 13, 44, 35, 55],27,20,) ] n_success = 0 for i, parameters_set in enumerate(param): f_filled(*(filled_function_param[i])) f_gold(*parameters_set) if parameters_set == filled_function_param[i]: n_success+=1 print("#Results: %i, %i" % (n_success, len(param)))

true

f734d1983e0e270444f39c71128b6e3bc933d71c

1,442

py

Python

aliyun-python-sdk-linkwan/aliyunsdklinkwan/request/v20190301/GetLocalConfigSyncTaskRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

1,001

2015-07-24T01:32:41.000Z

2022-03-25T01:28:18.000Z

aliyun-python-sdk-linkwan/aliyunsdklinkwan/request/v20190301/GetLocalConfigSyncTaskRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

363

2015-10-20T03:15:00.000Z

2022-03-08T12:26:19.000Z

aliyun-python-sdk-linkwan/aliyunsdklinkwan/request/v20190301/GetLocalConfigSyncTaskRequest.py

yndu13/aliyun-openapi-python-sdk

12ace4fb39fe2fb0e3927a4b1b43ee4872da43f5

[ "Apache-2.0" ]

682

2015-09-22T07:19:02.000Z

2022-03-22T09:51:46.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdklinkwan.endpoint import endpoint_data class GetLocalConfigSyncTaskRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'LinkWAN', '2019-03-01', 'GetLocalConfigSyncTask','linkwan') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_TaskId(self): return self.get_query_params().get('TaskId') def set_TaskId(self,TaskId): self.add_query_param('TaskId',TaskId)

37.947368

89

0.768377

from aliyunsdkcore.request import RpcRequest from aliyunsdklinkwan.endpoint import endpoint_data class GetLocalConfigSyncTaskRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'LinkWAN', '2019-03-01', 'GetLocalConfigSyncTask','linkwan') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_TaskId(self): return self.get_query_params().get('TaskId') def set_TaskId(self,TaskId): self.add_query_param('TaskId',TaskId)

true

f734d1e49cc32d7b6f5465291abd73c61b5e8638

1,175

py

Python

File.py

jessedeveloperinvestor/Multiple-Jesse-Projects

f66a6eed79117fd005668f79e0bee3d8ac76e2b8

[ "Apache-2.0" ]

null

File.py

jessedeveloperinvestor/Multiple-Jesse-Projects

f66a6eed79117fd005668f79e0bee3d8ac76e2b8

[ "Apache-2.0" ]

null

File.py

jessedeveloperinvestor/Multiple-Jesse-Projects

f66a6eed79117fd005668f79e0bee3d8ac76e2b8

[ "Apache-2.0" ]

null

y={0:{'Jesse Leite',23,'Best computer Engineer'},1:{'Lana Rhoades',22,'Best pleasure professional'}} with open('file.txt', 'w') as file: file.write("Python and Jesse built this file!") with open('file.txt', 'a') as file: file.write('\nJesse\n'+str(y)+'\n'+str(y)) inf=[] with open('file.data', 'r') as file: for row in file.readlines(): inf.append(row.split(',')) data=[] with open('file.txt', 'r') as file: a=file.read()+'\n' with open('file.data', 'w') as file: file.write(str(y)) with open('file.data', 'r') as file: for row in file.readlines(): data.append(row.split(',')) aa=inf[0][0]+inf[0][1]+inf[0][2] ab=str(aa) a0=data[0][0]+data[0][1]+data[0][2] a1=' Data from .TXT file:\n\n'+str(a0)+'\n\n----------------------------------------------------------\n Data from .DATA file:\n\n'+ab a2=a1.translate({ord(i): None for i in "["}) a3=a2.translate({ord(i): None for i in "]"}) a4=a3.translate({ord(i): None for i in ","}) a5=a4.translate({ord(i): None for i in "{"}) a6=a5.translate({ord(i): None for i in "}"}) a7=a6.translate({ord(i): None for i in ":"}) a8=a7.translate({ord(i): '\n' for i in "'"}) print(a8)

35.606061

147

0.560851

y={0:{'Jesse Leite',23,'Best computer Engineer'},1:{'Lana Rhoades',22,'Best pleasure professional'}} with open('file.txt', 'w') as file: file.write("Python and Jesse built this file!") with open('file.txt', 'a') as file: file.write('\nJesse\n'+str(y)+'\n'+str(y)) inf=[] with open('file.data', 'r') as file: for row in file.readlines(): inf.append(row.split(',')) data=[] with open('file.txt', 'r') as file: a=file.read()+'\n' with open('file.data', 'w') as file: file.write(str(y)) with open('file.data', 'r') as file: for row in file.readlines(): data.append(row.split(',')) aa=inf[0][0]+inf[0][1]+inf[0][2] ab=str(aa) a0=data[0][0]+data[0][1]+data[0][2] a1=' Data from .TXT file:\n\n'+str(a0)+'\n\n----------------------------------------------------------\n Data from .DATA file:\n\n'+ab a2=a1.translate({ord(i): None for i in "["}) a3=a2.translate({ord(i): None for i in "]"}) a4=a3.translate({ord(i): None for i in ","}) a5=a4.translate({ord(i): None for i in "{"}) a6=a5.translate({ord(i): None for i in "}"}) a7=a6.translate({ord(i): None for i in ":"}) a8=a7.translate({ord(i): '\n' for i in "'"}) print(a8)

true

f734d1f2191f58f7a8e4dabb4fc3ba6dbc11ad51

158

py

Python

depth/core/evaluation/__init__.py

zhyever/Monocular-Depth-Estimation-Toolbox

c591b9711321450387ffa7322ec1db9a340347c2

[ "Apache-2.0" ]

21

2022-03-12T01:42:05.000Z

2022-03-31T17:01:45.000Z

depth/core/evaluation/__init__.py

zhyever/Monocular-Depth-Estimation-Toolbox

c591b9711321450387ffa7322ec1db9a340347c2

[ "Apache-2.0" ]

2

2022-03-29T10:50:33.000Z

2022-03-30T10:40:53.000Z

depth/core/evaluation/__init__.py

zhyever/Monocular-Depth-Estimation-Toolbox

c591b9711321450387ffa7322ec1db9a340347c2

[ "Apache-2.0" ]

3

2022-03-26T11:52:44.000Z

2022-03-30T21:24:16.000Z

52.666667

63

0.829114

from .metrics import metrics, eval_metrics, pre_eval_to_metrics from .eval_hooks import EvalHook, DistEvalHook

true

f734d27754536095a51f7b1e9025b5c86e2bfe24

3,601

py

Python

amplimap/coverage.py

koelling/amplimap

cbd5b7b8c2f703982d8964a3c77bd350a47f08a6

[ "Apache-2.0" ]

11

2019-04-08T15:41:50.000Z

2020-11-04T14:25:19.000Z

amplimap/coverage.py

koelling/amplimap

cbd5b7b8c2f703982d8964a3c77bd350a47f08a6

[ "Apache-2.0" ]

5

2018-05-23T10:30:18.000Z

2021-06-29T09:33:17.000Z

amplimap/coverage.py

koelling/amplimap

cbd5b7b8c2f703982d8964a3c77bd350a47f08a6

[ "Apache-2.0" ]

9

2019-04-08T15:16:04.000Z

2021-06-09T15:14:09.000Z

# -*- coding: utf-8 -*- """ This module contains methods for processing and aggregating coverage files generated by ``bedtools``. """ import pandas as pd import numpy as np import re import os from .reader import read_sample_info cov_cols = ['Target', 'min_coverage', 'sum_coverage', 'basepairs', 'cov_per_bp', 'fraction_zero_coverage', 'fraction_10x_coverage', 'fraction_30x_coverage'] cov_cols_dtypes = dict(zip(cov_cols, [str, int, int, int, float, float])) def fraction_zero_coverage(coverage): """Calculate fraction of bases with coverage 0.""" return 1.0 * (coverage == 0).sum() / len(coverage) def fraction_10x_coverage(coverage): """Calculate fraction of bases with coverage 10 or more.""" return 1.0 * (coverage >= 10).sum() / len(coverage) def fraction_30x_coverage(coverage): """Calculate fraction of bases with coverage 30 or more.""" return 1.0 * (coverage >= 30).sum() / len(coverage) def process_file(input: str, output: str): """Read raw bedtools coverage file, calculate summary statistics and output them as CSV file. Args: input: path to a bedtools coverage file output: path to the summary CSV file """ # read bedtools output depth = pd.read_csv(input, sep='\t', names = ['chr', 'start_0', 'end', 'id', 'score', 'strand', 'position', 'coverage'], low_memory=False) # summarize summary = depth.groupby('id').aggregate({'coverage': [np.min, np.sum, len, np.mean, fraction_zero_coverage, fraction_10x_coverage, fraction_30x_coverage]}) # make id index into normal column, then reset column names summary.reset_index(level=0, inplace=True) summary.columns = cov_cols # write file summary.to_csv(output, index = False) def aggregate(input, output): """Read coverage summary files and create aggregate files. Args: input: dict containing 'csvs', the list of csvs fils to aggregate, and optionally 'sample_info', a table with additional sample annotation output: dict containing paths for output files: merged, min_coverage, cov_per_bp, fraction_zero_coverage """ # load sample information table sample_info = None if 'sample_info' in input and len(input['sample_info']) > 0: sample_info = read_sample_info(input['sample_info'][0]) merged = None for file in input['csvs']: sname = os.path.basename(file) sname = re.sub(r'\.coverage\.csv$', '', sname) print('Reading', file, 'for', sname, '...') df = pd.read_csv(file, index_col = False, dtype = cov_cols_dtypes) df['Sample'] = sname print(sname, 'coverage data shape:', str(df.shape)) if merged is None: merged = df else: merged = merged.append(df, ignore_index = True) assert merged is not None, \ '\n\nABORTED: Did not find any coverage data!\n\n' print('Merged data shape:', str(merged.shape)) print(merged.head()) print('Duplicated:') print(merged[merged.duplicated(['Target', 'Sample'], keep=False)]) if sample_info is not None: merged = merged.join(sample_info, on = ['Sample', 'Target'], how = 'left') # make matrices for column in ['min_coverage', 'cov_per_bp', 'fraction_zero_coverage']: pivoted = merged.pivot(index='Target', columns='Sample', values=column) print('Made pivot table for', column, ' with shape', str(pivoted.shape)) pivoted.to_csv(output[column]) print(output[column]) # output full merged data set merged.to_csv(output['merged'], index = False)

36.744898

159

0.663149

import pandas as pd import numpy as np import re import os from .reader import read_sample_info cov_cols = ['Target', 'min_coverage', 'sum_coverage', 'basepairs', 'cov_per_bp', 'fraction_zero_coverage', 'fraction_10x_coverage', 'fraction_30x_coverage'] cov_cols_dtypes = dict(zip(cov_cols, [str, int, int, int, float, float])) def fraction_zero_coverage(coverage): return 1.0 * (coverage == 0).sum() / len(coverage) def fraction_10x_coverage(coverage): return 1.0 * (coverage >= 10).sum() / len(coverage) def fraction_30x_coverage(coverage): return 1.0 * (coverage >= 30).sum() / len(coverage) def process_file(input: str, output: str): depth = pd.read_csv(input, sep='\t', names = ['chr', 'start_0', 'end', 'id', 'score', 'strand', 'position', 'coverage'], low_memory=False) summary = depth.groupby('id').aggregate({'coverage': [np.min, np.sum, len, np.mean, fraction_zero_coverage, fraction_10x_coverage, fraction_30x_coverage]}) summary.reset_index(level=0, inplace=True) summary.columns = cov_cols summary.to_csv(output, index = False) def aggregate(input, output): sample_info = None if 'sample_info' in input and len(input['sample_info']) > 0: sample_info = read_sample_info(input['sample_info'][0]) merged = None for file in input['csvs']: sname = os.path.basename(file) sname = re.sub(r'\.coverage\.csv$', '', sname) print('Reading', file, 'for', sname, '...') df = pd.read_csv(file, index_col = False, dtype = cov_cols_dtypes) df['Sample'] = sname print(sname, 'coverage data shape:', str(df.shape)) if merged is None: merged = df else: merged = merged.append(df, ignore_index = True) assert merged is not None, \ '\n\nABORTED: Did not find any coverage data!\n\n' print('Merged data shape:', str(merged.shape)) print(merged.head()) print('Duplicated:') print(merged[merged.duplicated(['Target', 'Sample'], keep=False)]) if sample_info is not None: merged = merged.join(sample_info, on = ['Sample', 'Target'], how = 'left') for column in ['min_coverage', 'cov_per_bp', 'fraction_zero_coverage']: pivoted = merged.pivot(index='Target', columns='Sample', values=column) print('Made pivot table for', column, ' with shape', str(pivoted.shape)) pivoted.to_csv(output[column]) print(output[column]) merged.to_csv(output['merged'], index = False)

true

f734d2d7c65ce3567f24d550214f74a82b5809d1

4,413

py

Python

src/streamlink/plugins/vimeo.py

kyldery/streamlink

ef36240408c194a543557fb31e4535b0426ec153

[ "BSD-2-Clause" ]

1

2022-02-25T20:14:03.000Z

src/streamlink/plugins/vimeo.py

kyldery/streamlink

ef36240408c194a543557fb31e4535b0426ec153

[ "BSD-2-Clause" ]

null

src/streamlink/plugins/vimeo.py

kyldery/streamlink

ef36240408c194a543557fb31e4535b0426ec153

[ "BSD-2-Clause" ]

12

2022-01-30T23:34:18.000Z

2022-03-26T17:09:43.000Z

""" $description Global live streaming and video hosting social platform. $url vimeo.com $type live, vod $notes Password protected streams are not supported """ import logging import re from html import unescape as html_unescape from urllib.parse import urlparse from streamlink.plugin import Plugin, PluginArgument, PluginArguments, pluginmatcher from streamlink.plugin.api import validate from streamlink.stream.dash import DASHStream from streamlink.stream.ffmpegmux import MuxedStream from streamlink.stream.hls import HLSStream from streamlink.stream.http import HTTPStream log = logging.getLogger(__name__) @pluginmatcher(re.compile( r"https?://(player\.vimeo\.com/video/\d+|(www\.)?vimeo\.com/.+)" )) class Vimeo(Plugin): _config_url_re = re.compile(r'(?:"config_url"|\bdata-config-url)\s*[:=]\s*(".+?")') _config_re = re.compile(r"var\s+config\s*=\s*({.+?})\s*;") _config_url_schema = validate.Schema( validate.transform(_config_url_re.search), validate.any( None, validate.Schema( validate.get(1), validate.parse_json(), validate.transform(html_unescape), validate.url(), ), ), ) _config_schema = validate.Schema( validate.parse_json(), { "request": { "files": { validate.optional("dash"): {"cdns": {validate.text: {"url": validate.url()}}}, validate.optional("hls"): {"cdns": {validate.text: {"url": validate.url()}}}, validate.optional("progressive"): validate.all( [{"url": validate.url(), "quality": validate.text}] ), }, validate.optional("text_tracks"): validate.all( [{"url": validate.text, "lang": validate.text}] ), } }, ) _player_schema = validate.Schema( validate.transform(_config_re.search), validate.any(None, validate.Schema(validate.get(1), _config_schema)), ) arguments = PluginArguments( PluginArgument("mux-subtitles", is_global=True) ) def _get_streams(self): if "player.vimeo.com" in self.url: data = self.session.http.get(self.url, schema=self._player_schema) else: api_url = self.session.http.get(self.url, schema=self._config_url_schema) if not api_url: return data = self.session.http.get(api_url, schema=self._config_schema) videos = data["request"]["files"] streams = [] for stream_type in ("hls", "dash"): if stream_type not in videos: continue for _, video_data in videos[stream_type]["cdns"].items(): log.trace("{0!r}".format(video_data)) url = video_data.get("url") if stream_type == "hls": for stream in HLSStream.parse_variant_playlist(self.session, url).items(): streams.append(stream) elif stream_type == "dash": p = urlparse(url) if p.path.endswith("dash.mpd"): # LIVE url = self.session.http.get(url).json()["url"] elif p.path.endswith("master.json"): # VOD url = url.replace("master.json", "master.mpd") else: log.error("Unsupported DASH path: {0}".format(p.path)) continue for stream in DASHStream.parse_manifest(self.session, url).items(): streams.append(stream) for stream in videos.get("progressive", []): streams.append((stream["quality"], HTTPStream(self.session, stream["url"]))) if self.get_option("mux_subtitles") and data["request"].get("text_tracks"): substreams = { s["lang"]: HTTPStream(self.session, "https://vimeo.com" + s["url"]) for s in data["request"]["text_tracks"] } for quality, stream in streams: yield quality, MuxedStream(self.session, stream, subtitles=substreams) else: for stream in streams: yield stream __plugin__ = Vimeo

37.084034

98

0.554498

import logging import re from html import unescape as html_unescape from urllib.parse import urlparse from streamlink.plugin import Plugin, PluginArgument, PluginArguments, pluginmatcher from streamlink.plugin.api import validate from streamlink.stream.dash import DASHStream from streamlink.stream.ffmpegmux import MuxedStream from streamlink.stream.hls import HLSStream from streamlink.stream.http import HTTPStream log = logging.getLogger(__name__) @pluginmatcher(re.compile( r"https?://(player\.vimeo\.com/video/\d+|(www\.)?vimeo\.com/.+)" )) class Vimeo(Plugin): _config_url_re = re.compile(r'(?:"config_url"|\bdata-config-url)\s*[:=]\s*(".+?")') _config_re = re.compile(r"var\s+config\s*=\s*({.+?})\s*;") _config_url_schema = validate.Schema( validate.transform(_config_url_re.search), validate.any( None, validate.Schema( validate.get(1), validate.parse_json(), validate.transform(html_unescape), validate.url(), ), ), ) _config_schema = validate.Schema( validate.parse_json(), { "request": { "files": { validate.optional("dash"): {"cdns": {validate.text: {"url": validate.url()}}}, validate.optional("hls"): {"cdns": {validate.text: {"url": validate.url()}}}, validate.optional("progressive"): validate.all( [{"url": validate.url(), "quality": validate.text}] ), }, validate.optional("text_tracks"): validate.all( [{"url": validate.text, "lang": validate.text}] ), } }, ) _player_schema = validate.Schema( validate.transform(_config_re.search), validate.any(None, validate.Schema(validate.get(1), _config_schema)), ) arguments = PluginArguments( PluginArgument("mux-subtitles", is_global=True) ) def _get_streams(self): if "player.vimeo.com" in self.url: data = self.session.http.get(self.url, schema=self._player_schema) else: api_url = self.session.http.get(self.url, schema=self._config_url_schema) if not api_url: return data = self.session.http.get(api_url, schema=self._config_schema) videos = data["request"]["files"] streams = [] for stream_type in ("hls", "dash"): if stream_type not in videos: continue for _, video_data in videos[stream_type]["cdns"].items(): log.trace("{0!r}".format(video_data)) url = video_data.get("url") if stream_type == "hls": for stream in HLSStream.parse_variant_playlist(self.session, url).items(): streams.append(stream) elif stream_type == "dash": p = urlparse(url) if p.path.endswith("dash.mpd"): url = self.session.http.get(url).json()["url"] elif p.path.endswith("master.json"): url = url.replace("master.json", "master.mpd") else: log.error("Unsupported DASH path: {0}".format(p.path)) continue for stream in DASHStream.parse_manifest(self.session, url).items(): streams.append(stream) for stream in videos.get("progressive", []): streams.append((stream["quality"], HTTPStream(self.session, stream["url"]))) if self.get_option("mux_subtitles") and data["request"].get("text_tracks"): substreams = { s["lang"]: HTTPStream(self.session, "https://vimeo.com" + s["url"]) for s in data["request"]["text_tracks"] } for quality, stream in streams: yield quality, MuxedStream(self.session, stream, subtitles=substreams) else: for stream in streams: yield stream __plugin__ = Vimeo

true

f734d363117a1d24b35c8edb306662ee55c567e1

625

py

Python

flatland/database/population/node/diagram_type_instances.py

lelandstarr/flatland-model-diagram-editor

dfbd10d80542359c6951d7b039a5a4e3da2a0f50

[ "MIT" ]

10

2021-01-03T16:47:34.000Z

2022-03-30T18:47:07.000Z

flatland/database/population/node/diagram_type_instances.py

lelandstarr/flatland-model-diagram-editor

dfbd10d80542359c6951d7b039a5a4e3da2a0f50

[ "MIT" ]

91

2021-01-09T02:14:13.000Z

2022-02-24T10:24:10.000Z

flatland/database/population/node/diagram_type_instances.py

lelandstarr/flatland-model-diagram-editor

dfbd10d80542359c6951d7b039a5a4e3da2a0f50

[ "MIT" ]

1

2021-01-13T22:13:19.000Z

""" diagram_type_instances.py """ population = [ {'Name': 'class', 'Abbreviation': 'CD', 'About': 'Show data, logic and constraints in a domain'}, {'Name': 'state machine', 'Abbreviation': 'SMD', 'About': 'lifecycle of a class or assigner relationship'}, {'Name': 'class collaboration', 'Abbreviation': 'CCD', 'About': 'Shows interactions among classes and external entities in a domain'}, {'Name': 'domain', 'Abbreviation': 'DD', 'About': 'Illustrates your logical subject matter "stack" ' + 'as a network of platform independent domains with requirements dependencies'} ]

39.0625

92

0.6512

population = [ {'Name': 'class', 'Abbreviation': 'CD', 'About': 'Show data, logic and constraints in a domain'}, {'Name': 'state machine', 'Abbreviation': 'SMD', 'About': 'lifecycle of a class or assigner relationship'}, {'Name': 'class collaboration', 'Abbreviation': 'CCD', 'About': 'Shows interactions among classes and external entities in a domain'}, {'Name': 'domain', 'Abbreviation': 'DD', 'About': 'Illustrates your logical subject matter "stack" ' + 'as a network of platform independent domains with requirements dependencies'} ]

true

f734d3c25a06993d61e3d706c674c3425718395c

12,885

py

Python

python/paddle/fluid/tests/unittests/xpu/test_softmax_with_cross_entropy_op_xpu.py

joey12300/Paddle

59102c6dcd2def3091f5c37816354ac69d669809

[ "Apache-2.0" ]

1

2020-12-03T05:11:47.000Z

python/paddle/fluid/tests/unittests/xpu/test_softmax_with_cross_entropy_op_xpu.py

joey12300/Paddle

59102c6dcd2def3091f5c37816354ac69d669809

[ "Apache-2.0" ]

null

python/paddle/fluid/tests/unittests/xpu/test_softmax_with_cross_entropy_op_xpu.py

joey12300/Paddle

59102c6dcd2def3091f5c37816354ac69d669809

[ "Apache-2.0" ]

null

# Copyright (c) 2020 PaddlePaddle Authors. 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. from __future__ import print_function import sys sys.path.append("..") from test_softmax_op import stable_softmax from op_test import OpTest import paddle.fluid.core as core import paddle import unittest import numpy as np def cross_entropy(softmax, label, soft_label, axis, ignore_index=-1): if soft_label: return (-label * np.log(softmax)).sum(axis=axis, keepdims=True) shape = softmax.shape axis %= len(shape) n = int(np.prod(shape[:axis])) axis_dim = shape[axis] remain = int(np.prod(shape[axis + 1:])) softmax_reshape = softmax.reshape((n, axis_dim, remain)) label_reshape = label.reshape((n, 1, remain)) result = np.zeros_like(label_reshape, dtype=softmax.dtype) for i in range(n): for j in range(remain): lbl = label_reshape[i, 0, j] if lbl != ignore_index: result[i, 0, j] -= np.log(softmax_reshape[i, lbl, j]) return result.reshape(label.shape) class TestSoftmaxWithCrossEntropyOp(OpTest): """ Test softmax with cross entropy operator with discreate one-hot labels. """ def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = False self.soft_label = False self.dtype = np.float32 self.axis = -1 self.ignore_index = -1 self.shape = [41, 37] self.use_xpu = True def setUp(self): self.initParams() logits = getattr( self, "logits", np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)) softmax = np.apply_along_axis(stable_softmax, self.axis, logits) if self.soft_label: labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype) labels /= np.sum(labels, axis=self.axis, keepdims=True) else: axis_dim = self.shape[self.axis] self.shape[self.axis] = 1 labels = np.random.randint(0, axis_dim, self.shape, dtype="int64") loss = cross_entropy(softmax, labels, self.soft_label, self.axis, self.ignore_index) self.inputs = {"Logits": logits, "Label": labels} self.outputs = { "Softmax": softmax.astype(self.dtype), "Loss": loss.astype(self.dtype) } self.attrs = { "numeric_stable_mode": self.numeric_stable_mode, "soft_label": self.soft_label, } if self.ignore_index >= 0: self.attrs['ignore_index'] = self.ignore_index if self.axis != -1: self.attrs['axis'] = self.axis def test_check_output(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_output_with_place(place, atol=1e-2) def test_check_grad(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place( place, ["Logits"], "Loss", max_relative_error=0.2) class TestXPUSoftmaxWithCrossEntropyOp(TestSoftmaxWithCrossEntropyOp): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [3, 5, 7, 11] self.axis = -1 self.ignore_index = -1 self.dtype = np.float32 self.use_xpu = True def test_check_output(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_output_with_place(place, atol=1e-2) def test_check_grad(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place( place, ["Logits"], "Loss", max_relative_error=0.2) class TestXPUSoftmaxWithCrossEntropyOp2(TestXPUSoftmaxWithCrossEntropyOp): """ Test softmax with cross entropy operator with soft labels. """ def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = True self.dtype = np.float32 self.axis = -1 self.ignore_index = -1 self.shape = [41, 37] self.use_xpu = True def test_check_output(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_output_with_place(place, atol=1e-2) def test_check_grad(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place( place, ["Logits"], "Loss", max_relative_error=0.2) class TestXPUSoftmaxWithCrossEntropyOp3(TestXPUSoftmaxWithCrossEntropyOp): """ Test softmax with cross entropy operator with ignore_index. """ def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [41, 37] self.ignore_index = 5 self.axis = -1 self.dtype = np.float32 # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpAxis1(TestXPUSoftmaxWithCrossEntropyOp): # """ # Test softmax with cross entropy operator with discreate one-hot labels. # Given axis != -1 # """ # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = False # self.dtype = np.float32 # self.axis = 0 # self.ignore_index = -1 # self.shape = [3, 5, 7, 11] # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpAxis2(TestXPUSoftmaxWithCrossEntropyOp): # """ # Test softmax with cross entropy operator with discreate one-hot labels. # Given axis != -1 # """ # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = False # self.dtype = np.float32 # self.axis = 1 # self.ignore_index = -1 # self.shape = [3, 5, 7, 11] # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpAxis3(TestXPUSoftmaxWithCrossEntropyOp): # """ # Test softmax with cross entropy operator with discreate one-hot labels. # Given axis != -1 # """ # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = False # self.dtype = np.float32 # self.axis = 2 # self.ignore_index = -1 # self.shape = [3, 5, 7, 11] class TestXPUSoftmaxWithCrossEntropyOpAxis4(TestXPUSoftmaxWithCrossEntropyOp): """ Test softmax with cross entropy operator with discreate one-hot labels. Given axis != -1 """ def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.dtype = np.float32 self.axis = 3 self.ignore_index = -1 self.shape = [3, 5, 7, 11] class TestXPUSoftmaxWithCrossEntropyOpAxisDimEqualOne( TestXPUSoftmaxWithCrossEntropyOp): """ Test softmax with cross entropy operator with discreate one-hot labels. Given axis != -1 """ def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.dtype = np.float32 self.axis = -1 self.ignore_index = -1 self.shape = [3, 5, 7, 1] # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpSoftLabelAxis1( # TestXPUSoftmaxWithCrossEntropyOp): # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = True # self.shape = [3, 5, 7, 11] # self.axis = 0 # self.ignore_index = -1 # self.dtype = np.float32 # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpSoftLabelAxis2( # TestXPUSoftmaxWithCrossEntropyOp2): # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = True # self.shape = [3, 5, 7, 11] # self.axis = 1 # self.ignore_index = -1 # self.dtype = np.float32 # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpSoftLabelAxis3( # TestXPUSoftmaxWithCrossEntropyOp2): # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = True # self.shape = [3, 5, 7, 11] # self.axis = 2 # self.ignore_index = -1 # self.dtype = np.float32 class TestXPUSoftmaxWithCrossEntropyOpSoftLabelAxis4( TestXPUSoftmaxWithCrossEntropyOp2): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = True self.shape = [3, 5, 7, 11] self.axis = 3 self.ignore_index = -1 self.dtype = np.float32 # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpIgnoreIndexNoCudnnAxis1( # TestXPUSoftmaxWithCrossEntropyOp3): # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = False # self.shape = [3, 5, 7, 11] # self.ignore_index = 1 # self.axis = 0 # self.dtype = np.float32 # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpIgnoreIndexNoCudnnAxis2( # TestXPUSoftmaxWithCrossEntropyOp3): # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = False # self.shape = [3, 5, 7, 11] # self.ignore_index = 0 # self.axis = 1 # self.dtype = np.float32 # xpu only support axis = rank -1 # class TestXPUSoftmaxWithCrossEntropyOpIgnoreIndexNoCudnnAxis3( # TestXPUSoftmaxWithCrossEntropyOp3): # def initParams(self): # self.op_type = "softmax_with_cross_entropy" # self.numeric_stable_mode = True # self.soft_label = False # self.shape = [3, 5, 7, 11] # self.ignore_index = 3 # self.axis = 2 # self.dtype = np.float32 class TestXPUSoftmaxWithCrossEntropyOpIgnoreIndexNoCudnnAxis4( TestXPUSoftmaxWithCrossEntropyOp3): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [3, 5, 7, 11] self.ignore_index = 3 self.axis = 3 self.dtype = np.float32 class TestXPUSoftmaxWithCrossEntropyOpBoundary0( TestXPUSoftmaxWithCrossEntropyOp): """ Test stable softmax with cross entropy operator will not product INF with small logits value. """ def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [3, 5, 7, 11] self.axis = -1 self.ignore_index = -1 self.dtype = np.float32 self.logits = np.full(self.shape, -500.0).astype(self.dtype) class TestXPUSoftmaxWithCrossEntropyOpBoundary1( TestXPUSoftmaxWithCrossEntropyOp): """ Test stable softmax with cross entropy operator will not product INF with small logits value. """ def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [3, 5, 7, 11] self.axis = -1 self.ignore_index = -1 self.dtype = np.float32 self.logits = np.full(self.shape, 1000.0).astype(self.dtype) self.logits[:, :, 0, :] = -1000.0 if __name__ == "__main__": unittest.main()

32.374372

80

0.632906

from __future__ import print_function import sys sys.path.append("..") from test_softmax_op import stable_softmax from op_test import OpTest import paddle.fluid.core as core import paddle import unittest import numpy as np def cross_entropy(softmax, label, soft_label, axis, ignore_index=-1): if soft_label: return (-label * np.log(softmax)).sum(axis=axis, keepdims=True) shape = softmax.shape axis %= len(shape) n = int(np.prod(shape[:axis])) axis_dim = shape[axis] remain = int(np.prod(shape[axis + 1:])) softmax_reshape = softmax.reshape((n, axis_dim, remain)) label_reshape = label.reshape((n, 1, remain)) result = np.zeros_like(label_reshape, dtype=softmax.dtype) for i in range(n): for j in range(remain): lbl = label_reshape[i, 0, j] if lbl != ignore_index: result[i, 0, j] -= np.log(softmax_reshape[i, lbl, j]) return result.reshape(label.shape) class TestSoftmaxWithCrossEntropyOp(OpTest): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = False self.soft_label = False self.dtype = np.float32 self.axis = -1 self.ignore_index = -1 self.shape = [41, 37] self.use_xpu = True def setUp(self): self.initParams() logits = getattr( self, "logits", np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype)) softmax = np.apply_along_axis(stable_softmax, self.axis, logits) if self.soft_label: labels = np.random.uniform(0.1, 1.0, self.shape).astype(self.dtype) labels /= np.sum(labels, axis=self.axis, keepdims=True) else: axis_dim = self.shape[self.axis] self.shape[self.axis] = 1 labels = np.random.randint(0, axis_dim, self.shape, dtype="int64") loss = cross_entropy(softmax, labels, self.soft_label, self.axis, self.ignore_index) self.inputs = {"Logits": logits, "Label": labels} self.outputs = { "Softmax": softmax.astype(self.dtype), "Loss": loss.astype(self.dtype) } self.attrs = { "numeric_stable_mode": self.numeric_stable_mode, "soft_label": self.soft_label, } if self.ignore_index >= 0: self.attrs['ignore_index'] = self.ignore_index if self.axis != -1: self.attrs['axis'] = self.axis def test_check_output(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_output_with_place(place, atol=1e-2) def test_check_grad(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place( place, ["Logits"], "Loss", max_relative_error=0.2) class TestXPUSoftmaxWithCrossEntropyOp(TestSoftmaxWithCrossEntropyOp): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [3, 5, 7, 11] self.axis = -1 self.ignore_index = -1 self.dtype = np.float32 self.use_xpu = True def test_check_output(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_output_with_place(place, atol=1e-2) def test_check_grad(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place( place, ["Logits"], "Loss", max_relative_error=0.2) class TestXPUSoftmaxWithCrossEntropyOp2(TestXPUSoftmaxWithCrossEntropyOp): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = True self.dtype = np.float32 self.axis = -1 self.ignore_index = -1 self.shape = [41, 37] self.use_xpu = True def test_check_output(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_output_with_place(place, atol=1e-2) def test_check_grad(self): if paddle.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place( place, ["Logits"], "Loss", max_relative_error=0.2) class TestXPUSoftmaxWithCrossEntropyOp3(TestXPUSoftmaxWithCrossEntropyOp): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [41, 37] self.ignore_index = 5 self.axis = -1 self.dtype = np.float32 # Test softmax with cross entropy operator with discreate one-hot labels. # Given axis != -1 # """ # Test softmax with cross entropy operator with discreate one-hot labels. # Given axis != -1 # """ # Test softmax with cross entropy operator with discreate one-hot labels. # Given axis != -1 # """ class TestXPUSoftmaxWithCrossEntropyOpAxis4(TestXPUSoftmaxWithCrossEntropyOp): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.dtype = np.float32 self.axis = 3 self.ignore_index = -1 self.shape = [3, 5, 7, 11] class TestXPUSoftmaxWithCrossEntropyOpAxisDimEqualOne( TestXPUSoftmaxWithCrossEntropyOp): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.dtype = np.float32 self.axis = -1 self.ignore_index = -1 self.shape = [3, 5, 7, 1] class TestXPUSoftmaxWithCrossEntropyOpSoftLabelAxis4( TestXPUSoftmaxWithCrossEntropyOp2): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = True self.shape = [3, 5, 7, 11] self.axis = 3 self.ignore_index = -1 self.dtype = np.float32 class TestXPUSoftmaxWithCrossEntropyOpIgnoreIndexNoCudnnAxis4( TestXPUSoftmaxWithCrossEntropyOp3): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [3, 5, 7, 11] self.ignore_index = 3 self.axis = 3 self.dtype = np.float32 class TestXPUSoftmaxWithCrossEntropyOpBoundary0( TestXPUSoftmaxWithCrossEntropyOp): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [3, 5, 7, 11] self.axis = -1 self.ignore_index = -1 self.dtype = np.float32 self.logits = np.full(self.shape, -500.0).astype(self.dtype) class TestXPUSoftmaxWithCrossEntropyOpBoundary1( TestXPUSoftmaxWithCrossEntropyOp): def initParams(self): self.op_type = "softmax_with_cross_entropy" self.numeric_stable_mode = True self.soft_label = False self.shape = [3, 5, 7, 11] self.axis = -1 self.ignore_index = -1 self.dtype = np.float32 self.logits = np.full(self.shape, 1000.0).astype(self.dtype) self.logits[:, :, 0, :] = -1000.0 if __name__ == "__main__": unittest.main()

true

f734d4b1af0cc7fc43f7f8c7fe07948454d387eb

5,473

py

Python

tensorflow/python/kernel_tests/math_ops/argmax_op_test.py

computationalartist/tensorflow

b89cf636c412abdff53b3e8f201bde671c92209d

[ "Apache-2.0" ]

190,993

2015-11-09T13:17:30.000Z

2022-03-31T23:05:27.000Z

tensorflow/python/kernel_tests/math_ops/argmax_op_test.py

computationalartist/tensorflow

b89cf636c412abdff53b3e8f201bde671c92209d

[ "Apache-2.0" ]

48,461

2015-11-09T14:21:11.000Z

2022-03-31T23:17:33.000Z

tensorflow/python/kernel_tests/math_ops/argmax_op_test.py

computationalartist/tensorflow

b89cf636c412abdff53b3e8f201bde671c92209d

[ "Apache-2.0" ]

104,981

2015-11-09T13:40:17.000Z

2022-03-31T19:51:54.000Z

# Copyright 2015 The TensorFlow Authors. 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. # ============================================================================== """Tests for tensorflow.ops.argmax_op.""" import functools import numpy as np from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import test class ArgMaxTest(test.TestCase): def _testArg(self, method, x, axis, expected_values, use_gpu=False, expected_err_re=None): with self.session(use_gpu=use_gpu): ans = method(x, axis=axis) if expected_err_re is None: tf_ans = self.evaluate(ans) # Defaults to int64 output. self.assertEqual(np.int64, tf_ans.dtype) self.assertAllEqual(tf_ans, expected_values) self.assertShapeEqual(expected_values, ans) else: with self.assertRaisesOpError(expected_err_re): self.evaluate(ans) def _testBothArg(self, method, x, axis, expected_values, expected_err_re=None): self._testArg(method, x, axis, expected_values, True, expected_err_re) # Compilation time is too large with XLA/CPU autojit. if not test_util.is_xla_enabled(): self._testArg(method, x, axis, expected_values, False, expected_err_re) def _testBasic(self, dtype): x = np.arange(200, dtype=np.float32).astype(dtype) np.random.shuffle(x) # Check that argmin and argmax match numpy along the primary axis self._testBothArg(math_ops.argmax, x, 0, x.argmax()) self._testBothArg(math_ops.argmin, x, 0, x.argmin()) def _testTieBreaking(self, dtype): x = np.zeros(200, dtype=dtype) # Check that argmin and argmax match numpy along the primary axis for # breaking ties. self._testBothArg(math_ops.argmax, x, 0, x.argmax()) self._testBothArg(math_ops.argmin, x, 0, x.argmin()) # Check that argmin and argmax match numpy along axis=1 for # breaking ties. x = np.array([[0, 0, 1, 1], [1, 1, 0, 0], [0, 1, 0, 1]], dtype=dtype) self._testBothArg(math_ops.argmax, x, 1, x.argmax(axis=1)) self._testBothArg(math_ops.argmin, x, 1, x.argmin(axis=1)) def _testDim(self, dtype): shape = (3, 2, 4, 5, 6, 3, 7) x = np.arange( functools.reduce(lambda x, y: x * y, shape), dtype=np.float32).astype(dtype) np.random.shuffle(x) x = x.reshape(shape) # Check that argmin and argmax match numpy along all axes for axis in range(-7, 7): self._testBothArg(math_ops.argmax, x, axis, x.argmax(axis)) self._testBothArg(math_ops.argmin, x, axis, x.argmin(axis)) def testFloat(self): self._testBasic(np.float32) self._testTieBreaking(np.float32) self._testDim(np.float32) def testFloatInt32Output(self): x = np.asarray(100 * np.random.randn(200), dtype=np.float32) expected_values = x.argmax() with self.session(): ans = math_ops.argmax(x, axis=0, output_type=dtypes.int32) tf_ans = self.evaluate(ans) self.assertEqual(np.int32, tf_ans.dtype) # The values are equal when comparing int32 to int64 because # the values don't have a range that exceeds 32-bit integers. self.assertAllEqual(tf_ans, expected_values) expected_values = x.argmin() with self.session(): ans = math_ops.argmin(x, axis=0, output_type=dtypes.int32) tf_ans = self.evaluate(ans) self.assertEqual(np.int32, tf_ans.dtype) self.assertAllEqual(tf_ans, expected_values) def testDouble(self): self._testBasic(np.float64) self._testTieBreaking(np.float64) self._testDim(np.float64) def testInt32(self): self._testBasic(np.int32) self._testTieBreaking(np.int32) self._testDim(np.int32) def testInt64(self): self._testBasic(np.int64) self._testTieBreaking(np.int64) self._testDim(np.int64) def testBool(self): self._testBasic(np.bool_) self._testTieBreaking(np.bool_) self._testDim(np.bool_) def testEmpty(self): with self.cached_session(): for op in math_ops.argmin, math_ops.argmax: with self.assertRaisesOpError( r"Reduction axis 0 is empty in shape \[0\]"): op([], 0).eval() @test_util.run_deprecated_v1 def testDefaultAxis(self): with self.cached_session(): for op in math_ops.argmin, math_ops.argmax: ans = op([1]).eval() self.assertAllEqual(ans, 0) @test_util.run_deprecated_v1 def testOutputEmpty(self): with self.cached_session(): for op in math_ops.argmin, math_ops.argmax: ret = op(array_ops.zeros(shape=[1, 0, 2]), axis=-1).eval() self.assertEqual(ret.shape, (1, 0)) if __name__ == "__main__": test.main()

34.20625

80

0.661794

import functools import numpy as np from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import test class ArgMaxTest(test.TestCase): def _testArg(self, method, x, axis, expected_values, use_gpu=False, expected_err_re=None): with self.session(use_gpu=use_gpu): ans = method(x, axis=axis) if expected_err_re is None: tf_ans = self.evaluate(ans) self.assertEqual(np.int64, tf_ans.dtype) self.assertAllEqual(tf_ans, expected_values) self.assertShapeEqual(expected_values, ans) else: with self.assertRaisesOpError(expected_err_re): self.evaluate(ans) def _testBothArg(self, method, x, axis, expected_values, expected_err_re=None): self._testArg(method, x, axis, expected_values, True, expected_err_re) if not test_util.is_xla_enabled(): self._testArg(method, x, axis, expected_values, False, expected_err_re) def _testBasic(self, dtype): x = np.arange(200, dtype=np.float32).astype(dtype) np.random.shuffle(x) self._testBothArg(math_ops.argmax, x, 0, x.argmax()) self._testBothArg(math_ops.argmin, x, 0, x.argmin()) def _testTieBreaking(self, dtype): x = np.zeros(200, dtype=dtype) self._testBothArg(math_ops.argmax, x, 0, x.argmax()) self._testBothArg(math_ops.argmin, x, 0, x.argmin()) x = np.array([[0, 0, 1, 1], [1, 1, 0, 0], [0, 1, 0, 1]], dtype=dtype) self._testBothArg(math_ops.argmax, x, 1, x.argmax(axis=1)) self._testBothArg(math_ops.argmin, x, 1, x.argmin(axis=1)) def _testDim(self, dtype): shape = (3, 2, 4, 5, 6, 3, 7) x = np.arange( functools.reduce(lambda x, y: x * y, shape), dtype=np.float32).astype(dtype) np.random.shuffle(x) x = x.reshape(shape) for axis in range(-7, 7): self._testBothArg(math_ops.argmax, x, axis, x.argmax(axis)) self._testBothArg(math_ops.argmin, x, axis, x.argmin(axis)) def testFloat(self): self._testBasic(np.float32) self._testTieBreaking(np.float32) self._testDim(np.float32) def testFloatInt32Output(self): x = np.asarray(100 * np.random.randn(200), dtype=np.float32) expected_values = x.argmax() with self.session(): ans = math_ops.argmax(x, axis=0, output_type=dtypes.int32) tf_ans = self.evaluate(ans) self.assertEqual(np.int32, tf_ans.dtype) self.assertAllEqual(tf_ans, expected_values) expected_values = x.argmin() with self.session(): ans = math_ops.argmin(x, axis=0, output_type=dtypes.int32) tf_ans = self.evaluate(ans) self.assertEqual(np.int32, tf_ans.dtype) self.assertAllEqual(tf_ans, expected_values) def testDouble(self): self._testBasic(np.float64) self._testTieBreaking(np.float64) self._testDim(np.float64) def testInt32(self): self._testBasic(np.int32) self._testTieBreaking(np.int32) self._testDim(np.int32) def testInt64(self): self._testBasic(np.int64) self._testTieBreaking(np.int64) self._testDim(np.int64) def testBool(self): self._testBasic(np.bool_) self._testTieBreaking(np.bool_) self._testDim(np.bool_) def testEmpty(self): with self.cached_session(): for op in math_ops.argmin, math_ops.argmax: with self.assertRaisesOpError( r"Reduction axis 0 is empty in shape \[0\]"): op([], 0).eval() @test_util.run_deprecated_v1 def testDefaultAxis(self): with self.cached_session(): for op in math_ops.argmin, math_ops.argmax: ans = op([1]).eval() self.assertAllEqual(ans, 0) @test_util.run_deprecated_v1 def testOutputEmpty(self): with self.cached_session(): for op in math_ops.argmin, math_ops.argmax: ret = op(array_ops.zeros(shape=[1, 0, 2]), axis=-1).eval() self.assertEqual(ret.shape, (1, 0)) if __name__ == "__main__": test.main()

true

f734d54a61d5c8de8595c92a6ae6ea4360716489

14,386

py

Python

pyscf/grad/casci.py

QuESt-Calculator/pyscf

0ed03633b699505c7278f1eb501342667d0aa910

[ "Apache-2.0" ]

501

2018-12-06T23:48:17.000Z

2022-03-31T11:53:18.000Z

pyscf/grad/casci.py

QuESt-Calculator/pyscf

0ed03633b699505c7278f1eb501342667d0aa910

[ "Apache-2.0" ]

710

2018-11-26T22:04:52.000Z

2022-03-30T03:53:12.000Z

pyscf/grad/casci.py

QuESt-Calculator/pyscf

0ed03633b699505c7278f1eb501342667d0aa910

[ "Apache-2.0" ]

273

2018-11-26T10:10:24.000Z

2022-03-30T12:25:28.000Z

#!/usr/bin/env python # Copyright 2014-2020 The PySCF Developers. 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. # # Author: Qiming Sun <osirpt.sun@gmail.com> # ''' CASCI analytical nuclear gradients Ref. J. Comput. Chem., 5, 589 ''' import sys from functools import reduce import numpy from pyscf import lib from pyscf import ao2mo from pyscf.lib import logger from pyscf.grad import rhf as rhf_grad from pyscf.grad.mp2 import _shell_prange from pyscf.scf import cphf if sys.version_info < (3,): RANGE_TYPE = list else: RANGE_TYPE = range def grad_elec(mc_grad, mo_coeff=None, ci=None, atmlst=None, verbose=None): mc = mc_grad.base if mo_coeff is None: mo_coeff = mc._scf.mo_coeff if ci is None: ci = mc.ci time0 = logger.process_clock(), logger.perf_counter() log = logger.new_logger(mc_grad, verbose) mol = mc_grad.mol ncore = mc.ncore ncas = mc.ncas nocc = ncore + ncas nelecas = mc.nelecas nao, nmo = mo_coeff.shape nao_pair = nao * (nao+1) // 2 mo_energy = mc._scf.mo_energy mo_occ = mo_coeff[:,:nocc] mo_core = mo_coeff[:,:ncore] mo_cas = mo_coeff[:,ncore:nocc] neleca, nelecb = mol.nelec assert(neleca == nelecb) orbo = mo_coeff[:,:neleca] orbv = mo_coeff[:,neleca:] casdm1, casdm2 = mc.fcisolver.make_rdm12(ci, ncas, nelecas) dm_core = numpy.dot(mo_core, mo_core.T) * 2 dm_cas = reduce(numpy.dot, (mo_cas, casdm1, mo_cas.T)) aapa = ao2mo.kernel(mol, (mo_cas, mo_cas, mo_coeff, mo_cas), compact=False) aapa = aapa.reshape(ncas,ncas,nmo,ncas) vj, vk = mc._scf.get_jk(mol, (dm_core, dm_cas)) h1 = mc.get_hcore() vhf_c = vj[0] - vk[0] * .5 vhf_a = vj[1] - vk[1] * .5 # Imat = h1_{pi} gamma1_{iq} + h2_{pijk} gamma_{iqkj} Imat = numpy.zeros((nmo,nmo)) Imat[:,:nocc] = reduce(numpy.dot, (mo_coeff.T, h1 + vhf_c + vhf_a, mo_occ)) * 2 Imat[:,ncore:nocc] = reduce(numpy.dot, (mo_coeff.T, h1 + vhf_c, mo_cas, casdm1)) Imat[:,ncore:nocc] += lib.einsum('uviw,vuwt->it', aapa, casdm2) aapa = vj = vk = vhf_c = vhf_a = h1 = None ee = mo_energy[:,None] - mo_energy zvec = numpy.zeros_like(Imat) zvec[:ncore,ncore:neleca] = Imat[:ncore,ncore:neleca] / -ee[:ncore,ncore:neleca] zvec[ncore:neleca,:ncore] = Imat[ncore:neleca,:ncore] / -ee[ncore:neleca,:ncore] zvec[nocc:,neleca:nocc] = Imat[nocc:,neleca:nocc] / -ee[nocc:,neleca:nocc] zvec[neleca:nocc,nocc:] = Imat[neleca:nocc,nocc:] / -ee[neleca:nocc,nocc:] zvec_ao = reduce(numpy.dot, (mo_coeff, zvec+zvec.T, mo_coeff.T)) vhf = mc._scf.get_veff(mol, zvec_ao) * 2 xvo = reduce(numpy.dot, (orbv.T, vhf, orbo)) xvo += Imat[neleca:,:neleca] - Imat[:neleca,neleca:].T def fvind(x): x = x.reshape(xvo.shape) dm = reduce(numpy.dot, (orbv, x, orbo.T)) v = mc._scf.get_veff(mol, dm + dm.T) v = reduce(numpy.dot, (orbv.T, v, orbo)) return v * 2 dm1resp = cphf.solve(fvind, mo_energy, mc._scf.mo_occ, xvo, max_cycle=30)[0] zvec[neleca:,:neleca] = dm1resp zeta = numpy.einsum('ij,j->ij', zvec, mo_energy) zeta = reduce(numpy.dot, (mo_coeff, zeta, mo_coeff.T)) zvec_ao = reduce(numpy.dot, (mo_coeff, zvec+zvec.T, mo_coeff.T)) p1 = numpy.dot(mo_coeff[:,:neleca], mo_coeff[:,:neleca].T) vhf_s1occ = reduce(numpy.dot, (p1, mc._scf.get_veff(mol, zvec_ao), p1)) Imat[:ncore,ncore:neleca] = 0 Imat[ncore:neleca,:ncore] = 0 Imat[nocc:,neleca:nocc] = 0 Imat[neleca:nocc,nocc:] = 0 Imat[neleca:,:neleca] = Imat[:neleca,neleca:].T im1 = reduce(numpy.dot, (mo_coeff, Imat, mo_coeff.T)) casci_dm1 = dm_core + dm_cas hf_dm1 = mc._scf.make_rdm1(mo_coeff, mc._scf.mo_occ) hcore_deriv = mc_grad.hcore_generator(mol) s1 = mc_grad.get_ovlp(mol) diag_idx = numpy.arange(nao) diag_idx = diag_idx * (diag_idx+1) // 2 + diag_idx casdm2_cc = casdm2 + casdm2.transpose(0,1,3,2) dm2buf = ao2mo._ao2mo.nr_e2(casdm2_cc.reshape(ncas**2,ncas**2), mo_cas.T, (0, nao, 0, nao)).reshape(ncas**2,nao,nao) dm2buf = lib.pack_tril(dm2buf) dm2buf[:,diag_idx] *= .5 dm2buf = dm2buf.reshape(ncas,ncas,nao_pair) casdm2 = casdm2_cc = None if atmlst is None: atmlst = range(mol.natm) aoslices = mol.aoslice_by_atom() de = numpy.zeros((len(atmlst),3)) max_memory = mc_grad.max_memory - lib.current_memory()[0] blksize = int(max_memory*.9e6/8 / ((aoslices[:,3]-aoslices[:,2]).max()*nao_pair)) blksize = min(nao, max(2, blksize)) for k, ia in enumerate(atmlst): shl0, shl1, p0, p1 = aoslices[ia] h1ao = hcore_deriv(ia) de[k] += numpy.einsum('xij,ij->x', h1ao, casci_dm1) de[k] += numpy.einsum('xij,ij->x', h1ao, zvec_ao) q1 = 0 for b0, b1, nf in _shell_prange(mol, 0, mol.nbas, blksize): q0, q1 = q1, q1 + nf dm2_ao = lib.einsum('ijw,pi,qj->pqw', dm2buf, mo_cas[p0:p1], mo_cas[q0:q1]) shls_slice = (shl0,shl1,b0,b1,0,mol.nbas,0,mol.nbas) eri1 = mol.intor('int2e_ip1', comp=3, aosym='s2kl', shls_slice=shls_slice).reshape(3,p1-p0,nf,nao_pair) de[k] -= numpy.einsum('xijw,ijw->x', eri1, dm2_ao) * 2 for i in range(3): eri1tmp = lib.unpack_tril(eri1[i].reshape((p1-p0)*nf,-1)) eri1tmp = eri1tmp.reshape(p1-p0,nf,nao,nao) de[k,i] -= numpy.einsum('ijkl,ij,kl', eri1tmp, hf_dm1[p0:p1,q0:q1], zvec_ao) * 2 de[k,i] -= numpy.einsum('ijkl,kl,ij', eri1tmp, hf_dm1, zvec_ao[p0:p1,q0:q1]) * 2 de[k,i] += numpy.einsum('ijkl,il,kj', eri1tmp, hf_dm1[p0:p1], zvec_ao[q0:q1]) de[k,i] += numpy.einsum('ijkl,jk,il', eri1tmp, hf_dm1[q0:q1], zvec_ao[p0:p1]) #:vhf1c, vhf1a = mc_grad.get_veff(mol, (dm_core, dm_cas)) #:de[k] += numpy.einsum('xij,ij->x', vhf1c[:,p0:p1], casci_dm1[p0:p1]) * 2 #:de[k] += numpy.einsum('xij,ij->x', vhf1a[:,p0:p1], dm_core[p0:p1]) * 2 de[k,i] -= numpy.einsum('ijkl,lk,ij', eri1tmp, dm_core[q0:q1], casci_dm1[p0:p1]) * 2 de[k,i] += numpy.einsum('ijkl,jk,il', eri1tmp, dm_core[q0:q1], casci_dm1[p0:p1]) de[k,i] -= numpy.einsum('ijkl,lk,ij', eri1tmp, dm_cas[q0:q1], dm_core[p0:p1]) * 2 de[k,i] += numpy.einsum('ijkl,jk,il', eri1tmp, dm_cas[q0:q1], dm_core[p0:p1]) eri1 = eri1tmp = None de[k] -= numpy.einsum('xij,ij->x', s1[:,p0:p1], im1[p0:p1]) de[k] -= numpy.einsum('xij,ji->x', s1[:,p0:p1], im1[:,p0:p1]) de[k] -= numpy.einsum('xij,ij->x', s1[:,p0:p1], zeta[p0:p1]) * 2 de[k] -= numpy.einsum('xij,ji->x', s1[:,p0:p1], zeta[:,p0:p1]) * 2 de[k] -= numpy.einsum('xij,ij->x', s1[:,p0:p1], vhf_s1occ[p0:p1]) * 2 de[k] -= numpy.einsum('xij,ji->x', s1[:,p0:p1], vhf_s1occ[:,p0:p1]) * 2 log.timer('CASCI nuclear gradients', *time0) return de def as_scanner(mcscf_grad, state=None): '''Generating a nuclear gradients scanner/solver (for geometry optimizer). The returned solver is a function. This function requires one argument "mol" as input and returns energy and first order nuclear derivatives. The solver will automatically use the results of last calculation as the initial guess of the new calculation. All parameters assigned in the nuc-grad object and SCF object (DIIS, conv_tol, max_memory etc) are automatically applied in the solver. Note scanner has side effects. It may change many underlying objects (_scf, with_df, with_x2c, ...) during calculation. Examples: >>> from pyscf import gto, scf, mcscf >>> mol = gto.M(atom='N 0 0 0; N 0 0 1.1', verbose=0) >>> mc_grad_scanner = mcscf.CASCI(scf.RHF(mol), 4, 4).nuc_grad_method().as_scanner() >>> etot, grad = mc_grad_scanner(gto.M(atom='N 0 0 0; N 0 0 1.1')) >>> etot, grad = mc_grad_scanner(gto.M(atom='N 0 0 0; N 0 0 1.5')) ''' from pyscf import gto from pyscf.mcscf.addons import StateAverageMCSCFSolver if isinstance(mcscf_grad, lib.GradScanner): return mcscf_grad if (state is not None and isinstance(mcscf_grad.base, StateAverageMCSCFSolver)): raise RuntimeError('State-Average MCSCF Gradients does not support ' 'state-specific nuclear gradients.') logger.info(mcscf_grad, 'Create scanner for %s', mcscf_grad.__class__) class CASCI_GradScanner(mcscf_grad.__class__, lib.GradScanner): def __init__(self, g): lib.GradScanner.__init__(self, g) def __call__(self, mol_or_geom, state=state, **kwargs): if isinstance(mol_or_geom, gto.Mole): mol = mol_or_geom else: mol = self.mol.set_geom_(mol_or_geom, inplace=False) if state is None: state = self.state mc_scanner = self.base # TODO: Check root flip e_tot = mc_scanner(mol) ci = mc_scanner.ci if isinstance(mc_scanner, StateAverageMCSCFSolver): e_tot = mc_scanner.e_average elif not isinstance(e_tot, float): if state >= mc_scanner.fcisolver.nroots: raise ValueError('State ID greater than the number of CASCI roots') e_tot = e_tot[state] # target at a specific state, to avoid overwriting self.state # in self.kernel ci = ci[state] self.mol = mol de = self.kernel(ci=ci, state=state, **kwargs) return e_tot, de return CASCI_GradScanner(mcscf_grad) class Gradients(rhf_grad.GradientsMixin): '''Non-relativistic restricted Hartree-Fock gradients''' def __init__(self, mc): from pyscf.mcscf.addons import StateAverageMCSCFSolver if isinstance(mc, StateAverageMCSCFSolver): self.state = None # not a specific state else: self.state = 0 # of which the gradients to be computed. rhf_grad.GradientsMixin.__init__(self, mc) def dump_flags(self, verbose=None): log = logger.new_logger(self, verbose) log.info('\n') if not self.base.converged: log.warn('Ground state %s not converged', self.base.__class__) log.info('******** %s for %s ********', self.__class__, self.base.__class__) if self.state is None: weights = self.base.weights log.info('State-average gradients over %d states with weights %s', len(weights), weights) elif self.state != 0 and self.base.fcisolver.nroots > 1: log.info('State ID = %d', self.state) log.info('max_memory %d MB (current use %d MB)', self.max_memory, lib.current_memory()[0]) return self grad_elec = grad_elec def kernel(self, mo_coeff=None, ci=None, atmlst=None, state=None, verbose=None): log = logger.new_logger(self, verbose) if ci is None: ci = self.base.ci if self.state is None: # state average MCSCF calculations assert(state is None) elif isinstance(ci, (list, tuple, RANGE_TYPE)): if state is None: state = self.state else: self.state = state ci = ci[state] log.info('Multiple roots are found in CASCI solver. ' 'Nuclear gradients of root %d are computed.', state) if atmlst is None: atmlst = self.atmlst else: self.atmlst = atmlst if self.verbose >= logger.WARN: self.check_sanity() if self.verbose >= logger.INFO: self.dump_flags() de = self.grad_elec(mo_coeff, ci, atmlst, log) self.de = de = de + self.grad_nuc(atmlst=atmlst) if self.mol.symmetry: self.de = self.symmetrize(self.de, atmlst) self._finalize() return self.de # Initialize hcore_deriv with the underlying SCF object because some # extensions (e.g. x2c, QM/MM, solvent) modifies the SCF object only. def hcore_generator(self, mol=None): mf_grad = self.base._scf.nuc_grad_method() return mf_grad.hcore_generator(mol) # Calling the underlying SCF nuclear gradients because it may be modified # by external modules (e.g. QM/MM, solvent) def grad_nuc(self, mol=None, atmlst=None): mf_grad = self.base._scf.nuc_grad_method() return mf_grad.grad_nuc(mol, atmlst) def _finalize(self): if self.verbose >= logger.NOTE: if self.state is None: logger.note(self, '--------- %s gradients ----------', self.base.__class__.__name__) else: logger.note(self, '--------- %s gradients for state %d ----------', self.base.__class__.__name__, self.state) self._write(self.mol, self.de, self.atmlst) logger.note(self, '----------------------------------------------') as_scanner = as_scanner Grad = Gradients from pyscf import mcscf mcscf.casci.CASCI.Gradients = lib.class_as_method(Gradients) if __name__ == '__main__': from pyscf import gto from pyscf import scf from pyscf import mcscf mol = gto.Mole() mol.atom = 'N 0 0 0; N 0 0 1.2; H 1 1 0; H 1 1 1.2' mol.build() mf = scf.RHF(mol).run(conv_tol=1e-14) mc = mcscf.CASCI(mf, 4, 4).run() g1 = mc.Gradients().kernel() print(lib.finger(g1) - -0.066025991364829367) mcs = mc.as_scanner() mol.set_geom_('N 0 0 0; N 0 0 1.201; H 1 1 0; H 1 1 1.2') e1 = mcs(mol) mol.set_geom_('N 0 0 0; N 0 0 1.199; H 1 1 0; H 1 1 1.2') e2 = mcs(mol) print(g1[1,2], (e1-e2)/0.002*lib.param.BOHR)

39.740331

100

0.605172

import sys from functools import reduce import numpy from pyscf import lib from pyscf import ao2mo from pyscf.lib import logger from pyscf.grad import rhf as rhf_grad from pyscf.grad.mp2 import _shell_prange from pyscf.scf import cphf if sys.version_info < (3,): RANGE_TYPE = list else: RANGE_TYPE = range def grad_elec(mc_grad, mo_coeff=None, ci=None, atmlst=None, verbose=None): mc = mc_grad.base if mo_coeff is None: mo_coeff = mc._scf.mo_coeff if ci is None: ci = mc.ci time0 = logger.process_clock(), logger.perf_counter() log = logger.new_logger(mc_grad, verbose) mol = mc_grad.mol ncore = mc.ncore ncas = mc.ncas nocc = ncore + ncas nelecas = mc.nelecas nao, nmo = mo_coeff.shape nao_pair = nao * (nao+1) // 2 mo_energy = mc._scf.mo_energy mo_occ = mo_coeff[:,:nocc] mo_core = mo_coeff[:,:ncore] mo_cas = mo_coeff[:,ncore:nocc] neleca, nelecb = mol.nelec assert(neleca == nelecb) orbo = mo_coeff[:,:neleca] orbv = mo_coeff[:,neleca:] casdm1, casdm2 = mc.fcisolver.make_rdm12(ci, ncas, nelecas) dm_core = numpy.dot(mo_core, mo_core.T) * 2 dm_cas = reduce(numpy.dot, (mo_cas, casdm1, mo_cas.T)) aapa = ao2mo.kernel(mol, (mo_cas, mo_cas, mo_coeff, mo_cas), compact=False) aapa = aapa.reshape(ncas,ncas,nmo,ncas) vj, vk = mc._scf.get_jk(mol, (dm_core, dm_cas)) h1 = mc.get_hcore() vhf_c = vj[0] - vk[0] * .5 vhf_a = vj[1] - vk[1] * .5 Imat = numpy.zeros((nmo,nmo)) Imat[:,:nocc] = reduce(numpy.dot, (mo_coeff.T, h1 + vhf_c + vhf_a, mo_occ)) * 2 Imat[:,ncore:nocc] = reduce(numpy.dot, (mo_coeff.T, h1 + vhf_c, mo_cas, casdm1)) Imat[:,ncore:nocc] += lib.einsum('uviw,vuwt->it', aapa, casdm2) aapa = vj = vk = vhf_c = vhf_a = h1 = None ee = mo_energy[:,None] - mo_energy zvec = numpy.zeros_like(Imat) zvec[:ncore,ncore:neleca] = Imat[:ncore,ncore:neleca] / -ee[:ncore,ncore:neleca] zvec[ncore:neleca,:ncore] = Imat[ncore:neleca,:ncore] / -ee[ncore:neleca,:ncore] zvec[nocc:,neleca:nocc] = Imat[nocc:,neleca:nocc] / -ee[nocc:,neleca:nocc] zvec[neleca:nocc,nocc:] = Imat[neleca:nocc,nocc:] / -ee[neleca:nocc,nocc:] zvec_ao = reduce(numpy.dot, (mo_coeff, zvec+zvec.T, mo_coeff.T)) vhf = mc._scf.get_veff(mol, zvec_ao) * 2 xvo = reduce(numpy.dot, (orbv.T, vhf, orbo)) xvo += Imat[neleca:,:neleca] - Imat[:neleca,neleca:].T def fvind(x): x = x.reshape(xvo.shape) dm = reduce(numpy.dot, (orbv, x, orbo.T)) v = mc._scf.get_veff(mol, dm + dm.T) v = reduce(numpy.dot, (orbv.T, v, orbo)) return v * 2 dm1resp = cphf.solve(fvind, mo_energy, mc._scf.mo_occ, xvo, max_cycle=30)[0] zvec[neleca:,:neleca] = dm1resp zeta = numpy.einsum('ij,j->ij', zvec, mo_energy) zeta = reduce(numpy.dot, (mo_coeff, zeta, mo_coeff.T)) zvec_ao = reduce(numpy.dot, (mo_coeff, zvec+zvec.T, mo_coeff.T)) p1 = numpy.dot(mo_coeff[:,:neleca], mo_coeff[:,:neleca].T) vhf_s1occ = reduce(numpy.dot, (p1, mc._scf.get_veff(mol, zvec_ao), p1)) Imat[:ncore,ncore:neleca] = 0 Imat[ncore:neleca,:ncore] = 0 Imat[nocc:,neleca:nocc] = 0 Imat[neleca:nocc,nocc:] = 0 Imat[neleca:,:neleca] = Imat[:neleca,neleca:].T im1 = reduce(numpy.dot, (mo_coeff, Imat, mo_coeff.T)) casci_dm1 = dm_core + dm_cas hf_dm1 = mc._scf.make_rdm1(mo_coeff, mc._scf.mo_occ) hcore_deriv = mc_grad.hcore_generator(mol) s1 = mc_grad.get_ovlp(mol) diag_idx = numpy.arange(nao) diag_idx = diag_idx * (diag_idx+1) // 2 + diag_idx casdm2_cc = casdm2 + casdm2.transpose(0,1,3,2) dm2buf = ao2mo._ao2mo.nr_e2(casdm2_cc.reshape(ncas**2,ncas**2), mo_cas.T, (0, nao, 0, nao)).reshape(ncas**2,nao,nao) dm2buf = lib.pack_tril(dm2buf) dm2buf[:,diag_idx] *= .5 dm2buf = dm2buf.reshape(ncas,ncas,nao_pair) casdm2 = casdm2_cc = None if atmlst is None: atmlst = range(mol.natm) aoslices = mol.aoslice_by_atom() de = numpy.zeros((len(atmlst),3)) max_memory = mc_grad.max_memory - lib.current_memory()[0] blksize = int(max_memory*.9e6/8 / ((aoslices[:,3]-aoslices[:,2]).max()*nao_pair)) blksize = min(nao, max(2, blksize)) for k, ia in enumerate(atmlst): shl0, shl1, p0, p1 = aoslices[ia] h1ao = hcore_deriv(ia) de[k] += numpy.einsum('xij,ij->x', h1ao, casci_dm1) de[k] += numpy.einsum('xij,ij->x', h1ao, zvec_ao) q1 = 0 for b0, b1, nf in _shell_prange(mol, 0, mol.nbas, blksize): q0, q1 = q1, q1 + nf dm2_ao = lib.einsum('ijw,pi,qj->pqw', dm2buf, mo_cas[p0:p1], mo_cas[q0:q1]) shls_slice = (shl0,shl1,b0,b1,0,mol.nbas,0,mol.nbas) eri1 = mol.intor('int2e_ip1', comp=3, aosym='s2kl', shls_slice=shls_slice).reshape(3,p1-p0,nf,nao_pair) de[k] -= numpy.einsum('xijw,ijw->x', eri1, dm2_ao) * 2 for i in range(3): eri1tmp = lib.unpack_tril(eri1[i].reshape((p1-p0)*nf,-1)) eri1tmp = eri1tmp.reshape(p1-p0,nf,nao,nao) de[k,i] -= numpy.einsum('ijkl,ij,kl', eri1tmp, hf_dm1[p0:p1,q0:q1], zvec_ao) * 2 de[k,i] -= numpy.einsum('ijkl,kl,ij', eri1tmp, hf_dm1, zvec_ao[p0:p1,q0:q1]) * 2 de[k,i] += numpy.einsum('ijkl,il,kj', eri1tmp, hf_dm1[p0:p1], zvec_ao[q0:q1]) de[k,i] += numpy.einsum('ijkl,jk,il', eri1tmp, hf_dm1[q0:q1], zvec_ao[p0:p1]) de[k,i] -= numpy.einsum('ijkl,lk,ij', eri1tmp, dm_core[q0:q1], casci_dm1[p0:p1]) * 2 de[k,i] += numpy.einsum('ijkl,jk,il', eri1tmp, dm_core[q0:q1], casci_dm1[p0:p1]) de[k,i] -= numpy.einsum('ijkl,lk,ij', eri1tmp, dm_cas[q0:q1], dm_core[p0:p1]) * 2 de[k,i] += numpy.einsum('ijkl,jk,il', eri1tmp, dm_cas[q0:q1], dm_core[p0:p1]) eri1 = eri1tmp = None de[k] -= numpy.einsum('xij,ij->x', s1[:,p0:p1], im1[p0:p1]) de[k] -= numpy.einsum('xij,ji->x', s1[:,p0:p1], im1[:,p0:p1]) de[k] -= numpy.einsum('xij,ij->x', s1[:,p0:p1], zeta[p0:p1]) * 2 de[k] -= numpy.einsum('xij,ji->x', s1[:,p0:p1], zeta[:,p0:p1]) * 2 de[k] -= numpy.einsum('xij,ij->x', s1[:,p0:p1], vhf_s1occ[p0:p1]) * 2 de[k] -= numpy.einsum('xij,ji->x', s1[:,p0:p1], vhf_s1occ[:,p0:p1]) * 2 log.timer('CASCI nuclear gradients', *time0) return de def as_scanner(mcscf_grad, state=None): from pyscf import gto from pyscf.mcscf.addons import StateAverageMCSCFSolver if isinstance(mcscf_grad, lib.GradScanner): return mcscf_grad if (state is not None and isinstance(mcscf_grad.base, StateAverageMCSCFSolver)): raise RuntimeError('State-Average MCSCF Gradients does not support ' 'state-specific nuclear gradients.') logger.info(mcscf_grad, 'Create scanner for %s', mcscf_grad.__class__) class CASCI_GradScanner(mcscf_grad.__class__, lib.GradScanner): def __init__(self, g): lib.GradScanner.__init__(self, g) def __call__(self, mol_or_geom, state=state, **kwargs): if isinstance(mol_or_geom, gto.Mole): mol = mol_or_geom else: mol = self.mol.set_geom_(mol_or_geom, inplace=False) if state is None: state = self.state mc_scanner = self.base e_tot = mc_scanner(mol) ci = mc_scanner.ci if isinstance(mc_scanner, StateAverageMCSCFSolver): e_tot = mc_scanner.e_average elif not isinstance(e_tot, float): if state >= mc_scanner.fcisolver.nroots: raise ValueError('State ID greater than the number of CASCI roots') e_tot = e_tot[state] ci = ci[state] self.mol = mol de = self.kernel(ci=ci, state=state, **kwargs) return e_tot, de return CASCI_GradScanner(mcscf_grad) class Gradients(rhf_grad.GradientsMixin): def __init__(self, mc): from pyscf.mcscf.addons import StateAverageMCSCFSolver if isinstance(mc, StateAverageMCSCFSolver): self.state = None else: self.state = 0 rhf_grad.GradientsMixin.__init__(self, mc) def dump_flags(self, verbose=None): log = logger.new_logger(self, verbose) log.info('\n') if not self.base.converged: log.warn('Ground state %s not converged', self.base.__class__) log.info('******** %s for %s ********', self.__class__, self.base.__class__) if self.state is None: weights = self.base.weights log.info('State-average gradients over %d states with weights %s', len(weights), weights) elif self.state != 0 and self.base.fcisolver.nroots > 1: log.info('State ID = %d', self.state) log.info('max_memory %d MB (current use %d MB)', self.max_memory, lib.current_memory()[0]) return self grad_elec = grad_elec def kernel(self, mo_coeff=None, ci=None, atmlst=None, state=None, verbose=None): log = logger.new_logger(self, verbose) if ci is None: ci = self.base.ci if self.state is None: assert(state is None) elif isinstance(ci, (list, tuple, RANGE_TYPE)): if state is None: state = self.state else: self.state = state ci = ci[state] log.info('Multiple roots are found in CASCI solver. ' 'Nuclear gradients of root %d are computed.', state) if atmlst is None: atmlst = self.atmlst else: self.atmlst = atmlst if self.verbose >= logger.WARN: self.check_sanity() if self.verbose >= logger.INFO: self.dump_flags() de = self.grad_elec(mo_coeff, ci, atmlst, log) self.de = de = de + self.grad_nuc(atmlst=atmlst) if self.mol.symmetry: self.de = self.symmetrize(self.de, atmlst) self._finalize() return self.de def hcore_generator(self, mol=None): mf_grad = self.base._scf.nuc_grad_method() return mf_grad.hcore_generator(mol) def grad_nuc(self, mol=None, atmlst=None): mf_grad = self.base._scf.nuc_grad_method() return mf_grad.grad_nuc(mol, atmlst) def _finalize(self): if self.verbose >= logger.NOTE: if self.state is None: logger.note(self, '--------- %s gradients ----------', self.base.__class__.__name__) else: logger.note(self, '--------- %s gradients for state %d ----------', self.base.__class__.__name__, self.state) self._write(self.mol, self.de, self.atmlst) logger.note(self, '----------------------------------------------') as_scanner = as_scanner Grad = Gradients from pyscf import mcscf mcscf.casci.CASCI.Gradients = lib.class_as_method(Gradients) if __name__ == '__main__': from pyscf import gto from pyscf import scf from pyscf import mcscf mol = gto.Mole() mol.atom = 'N 0 0 0; N 0 0 1.2; H 1 1 0; H 1 1 1.2' mol.build() mf = scf.RHF(mol).run(conv_tol=1e-14) mc = mcscf.CASCI(mf, 4, 4).run() g1 = mc.Gradients().kernel() print(lib.finger(g1) - -0.066025991364829367) mcs = mc.as_scanner() mol.set_geom_('N 0 0 0; N 0 0 1.201; H 1 1 0; H 1 1 1.2') e1 = mcs(mol) mol.set_geom_('N 0 0 0; N 0 0 1.199; H 1 1 0; H 1 1 1.2') e2 = mcs(mol) print(g1[1,2], (e1-e2)/0.002*lib.param.BOHR)

true

f734d5b2ccd738ec641aa3b8d778120e95269d71

18,438

py

Python

keras/utils/layer_utils_test.py

tsheaff/keras

ee227dda766d769b7499a5549e8ed77b5e88105b

[ "Apache-2.0" ]

1

2020-02-02T04:43:33.000Z

keras/utils/layer_utils_test.py

tsheaff/keras

ee227dda766d769b7499a5549e8ed77b5e88105b

[ "Apache-2.0" ]

1

2022-03-31T16:40:51.000Z

keras/utils/layer_utils_test.py

tsheaff/keras

ee227dda766d769b7499a5549e8ed77b5e88105b

[ "Apache-2.0" ]

1

2020-12-13T22:14:48.000Z

# Copyright 2020 The TensorFlow Authors. 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. # ============================================================================== """Tests for layer_utils.""" import keras import tensorflow.compat.v2 as tf import collections import contextlib import multiprocessing.dummy import os import pickle import shutil import sys import time import timeit import numpy as np from keras.utils import io_utils from keras.utils import layer_utils _PICKLEABLE_CALL_COUNT = collections.Counter() class MyPickleableObject(tf.__internal__.tracking.AutoTrackable): """Needed for InterfaceTests.test_property_cache_serialization. This class must be at the top level. This is a constraint of pickle, unrelated to `cached_per_instance`. """ @property @layer_utils.cached_per_instance def my_id(self): _PICKLEABLE_CALL_COUNT[self] += 1 return id(self) class LayerUtilsTest(tf.test.TestCase): def test_print_summary(self): model = keras.Sequential() model.add( keras.layers.Conv2D( filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv')) model.add(keras.layers.Flatten(name='flat')) model.add(keras.layers.Dense(5, name='dense')) file_name = 'model_1.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary(model, print_fn=print_to_file) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() self.assertEqual(len(lines), 15) except ImportError: pass def test_print_summary_without_print_fn(self): model = keras.Sequential([ keras.layers.Dense(5, input_shape=(10,), name='dense')]) io_utils.enable_interactive_logging() with self.captureWritesToStream(sys.stdout) as printed: layer_utils.print_summary(model) self.assertIn('dense (Dense)', printed.contents()) def test_print_summary_expand_nested(self): shape = (None, None, 3) def make_model(): x = inputs = keras.Input(shape) x = keras.layers.Conv2D(3, 1)(x) x = keras.layers.BatchNormalization()(x) return keras.Model(inputs, x) x = inner_inputs = keras.Input(shape) x = make_model()(x) inner_model = keras.Model(inner_inputs, x) inputs = keras.Input(shape) model = keras.Model(inputs, inner_model(inputs)) file_name = 'model_2.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary( model, print_fn=print_to_file, expand_nested=True) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() check_str = ( 'Model: "model_2"\n' '_________________________________________________________________\n' ' Layer (type) Output Shape Param # \n' '=================================================================\n' ' input_3 (InputLayer) [(None, None, None, 3)] 0 \n' ' \n' ' model_1 (Functional) (None, None, None, 3) 24 \n' '|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n' '| input_1 (InputLayer) [(None, None, None, 3)] 0 |\n' '| |\n' '| model (Functional) (None, None, None, 3) 24 |\n' '||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n' '|| input_2 (InputLayer) [(None, None, None, 3)] 0 ||\n' '|| ||\n' '|| conv2d (Conv2D) (None, None, None, 3) 12 ||\n' '|| ||\n' '|| batch_normalization (BatchN (None, None, None, 3) 12 ||\n' '|| ormalization) ||\n' '|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n' '¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n' '=================================================================\n' 'Total params: 24\n' 'Trainable params: 18\n' 'Non-trainable params: 6\n' '_________________________________________________________________\n') fin_str = '' for line in lines: fin_str += line self.assertIn(fin_str, check_str) self.assertEqual(len(lines), 25) except ImportError: pass def test_summary_subclass_model_expand_nested(self): class Sequential(keras.Model): def __init__(self, *args): super(Sequential, self).__init__() self.module_list = list(args) if args else [] def call(self, x): for module in self.module_list: x = module(x) return x class Block(keras.Model): def __init__(self): super(Block, self).__init__() self.module = Sequential( keras.layers.Dense(10), keras.layers.Dense(10), ) def call(self, input_tensor): x = self.module(input_tensor) return x class Base(keras.Model): def __init__(self): super(Base, self).__init__() self.module = Sequential(Block(), Block()) def call(self, input_tensor): x = self.module(input_tensor) y = self.module(x) return x, y class Network(keras.Model): def __init__(self): super(Network, self).__init__() self.child = Base() def call(self, inputs): return self.child(inputs) net = Network() inputs = keras.Input(shape=(10,)) outputs = net(inputs) model = keras.models.Model(inputs=inputs, outputs=outputs) file_name = 'model_3.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary( model, line_length=120, print_fn=print_to_file, expand_nested=True) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() # The output content are slightly different for the input shapes between # v1 and v2. if tf.__internal__.tf2.enabled(): self.assertEqual(len(lines), 39) else: self.assertEqual(len(lines), 40) except ImportError: pass def test_print_summary_show_trainable(self): model = keras.Sequential(name='trainable') untrained = keras.layers.Conv2D( filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv') model.add(untrained) model.add(keras.layers.Flatten(name='flat')) model.add(keras.layers.Dense(5, name='dense')) untrained.trainable = False file_name = 'model_4.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary( model, print_fn=print_to_file, show_trainable=True) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() check_str = ( 'Model: ' '"trainable"\n____________________________________________________________________________\n' ' Layer (type) Output Shape Param # ' 'Trainable ' '\n============================================================================\n' ' conv (Conv2D) (None, 2, 3, 2) 62 N' ' \n' ' ' '\n flat (Flatten) (None, 12) 0 ' 'Y \n' ' ' '\n dense (Dense) (None, 5) 65 ' 'Y \n' ' ' '\n============================================================================\nTotal' ' params: 127\nTrainable params: 65\nNon-trainable params: ' '62\n____________________________________________________________________________\n' '____________________________________________________________________________\n' ) fin_str = '' for line in lines: fin_str += line self.assertIn(fin_str, check_str) self.assertEqual(len(lines), 15) except ImportError: pass def test_print_summary_expand_nested_show_trainable(self): shape = (None, None, 3) def make_model(): x = inputs = keras.Input(shape, name='input2') untrainable = keras.layers.Conv2D(3, 1) untrainable.trainable = False x = untrainable(x) x = keras.layers.BatchNormalization()(x) return keras.Model(inputs, x) x = inner_inputs = keras.Input(shape, name='input1') x = make_model()(x) inner_model = keras.Model(inner_inputs, x) inputs = keras.Input(shape, name='input3') model = keras.Model(inputs, inner_model(inputs)) file_name = 'model_6.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary( model, print_fn=print_to_file, expand_nested=True, show_trainable=True) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() check_str = ( 'Model: ' '"model_2"\n____________________________________________________________________________\n' ' Layer (type) Output Shape Param # ' 'Trainable ' '\n============================================================================\n' ' input3 (InputLayer) [(None, None, None, 3)] 0 Y' ' \n' ' ' '\n model_1 (Functional) (None, None, None, 3) 24 ' 'Y ' '\n|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n|' ' input1 (InputLayer) [(None, None, None, 3)] 0 Y' ' |\n|' ' ' '|\n| model (Functional) (None, None, None, 3) 24 ' 'Y ' '|\n||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n||' ' input2 (InputLayer) [(None, None, None, 3)] 0 Y' ' ||\n||' ' ' '||\n|| conv2d (Conv2D) (None, None, None, 3) 12 ' 'N ||\n||' ' ' '||\n|| batch_normalization (BatchN (None, None, None, 3) 12 ' 'Y ||\n|| ormalization)' ' ' '||\n|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n============================================================================\nTotal' ' params: 24\nTrainable params: 6\nNon-trainable params: ' '18\n____________________________________________________________________________\n' '____________________________________________________________________________\n' ) fin_str = '' for line in lines: fin_str += line self.assertIn(fin_str, check_str) self.assertEqual(len(lines), 25) except ImportError: pass def test_property_cache(self): test_counter = collections.Counter() class MyObject(tf.__internal__.tracking.AutoTrackable): def __init__(self): super(MyObject, self).__init__() self._frozen = True def __setattr__(self, key, value): """Enforce that cache does not set attribute on MyObject.""" if getattr(self, '_frozen', False): raise ValueError('Cannot mutate when frozen.') return super(MyObject, self).__setattr__(key, value) @property @layer_utils.cached_per_instance def test_property(self): test_counter[id(self)] += 1 return id(self) first_object = MyObject() second_object = MyObject() # Make sure the objects return the correct values self.assertEqual(first_object.test_property, id(first_object)) self.assertEqual(second_object.test_property, id(second_object)) # Make sure the cache does not share across objects self.assertNotEqual(first_object.test_property, second_object.test_property) # Check again (Now the values should be cached.) self.assertEqual(first_object.test_property, id(first_object)) self.assertEqual(second_object.test_property, id(second_object)) # Count the function calls to make sure the cache is actually being used. self.assertAllEqual(tuple(test_counter.values()), (1, 1)) def test_property_cache_threaded(self): call_count = collections.Counter() class MyObject(tf.__internal__.tracking.AutoTrackable): @property @layer_utils.cached_per_instance def test_property(self): # Random sleeps to ensure that the execution thread changes # mid-computation. call_count['test_property'] += 1 time.sleep(np.random.random() + 1.) # Use a RandomState which is seeded off the instance's id (the mod is # because numpy limits the range of seeds) to ensure that an instance # returns the same value in different threads, but different instances # return different values. return int(np.random.RandomState(id(self) % (2 ** 31)).randint(2 ** 16)) def get_test_property(self, _): """Function provided to .map for threading test.""" return self.test_property # Test that multiple threads return the same value. This requires that # the underlying function is repeatable, as cached_property makes no attempt # to prioritize the first call. test_obj = MyObject() with contextlib.closing(multiprocessing.dummy.Pool(32)) as pool: # Intentionally make a large pool (even when there are only a small number # of cpus) to ensure that the runtime switches threads. results = pool.map(test_obj.get_test_property, range(64)) self.assertEqual(len(set(results)), 1) # Make sure we actually are testing threaded behavior. self.assertGreater(call_count['test_property'], 1) # Make sure new threads still cache hit. with contextlib.closing(multiprocessing.dummy.Pool(2)) as pool: start_time = timeit.default_timer() # Don't time pool instantiation. results = pool.map(test_obj.get_test_property, range(4)) total_time = timeit.default_timer() - start_time # Note(taylorrobie): The reason that it is safe to time a unit test is that # a cache hit will be << 1 second, and a cache miss is # guaranteed to be >= 1 second. Empirically confirmed by # 100,000 runs with no flakes. self.assertLess(total_time, 0.95) def test_property_cache_serialization(self): # Reset call count. .keys() must be wrapped in a list, because otherwise we # would mutate the iterator while iterating. for k in list(_PICKLEABLE_CALL_COUNT.keys()): _PICKLEABLE_CALL_COUNT.pop(k) first_instance = MyPickleableObject() self.assertEqual(id(first_instance), first_instance.my_id) # Test that we can pickle and un-pickle second_instance = pickle.loads(pickle.dumps(first_instance)) self.assertEqual(id(second_instance), second_instance.my_id) self.assertNotEqual(first_instance.my_id, second_instance.my_id) # Make sure de-serialized object uses the cache. self.assertEqual(_PICKLEABLE_CALL_COUNT[second_instance], 1) # Make sure the decorator cache is not being serialized with the object. expected_size = len(pickle.dumps(second_instance)) for _ in range(5): # Add some more entries to the cache. _ = MyPickleableObject().my_id self.assertEqual(len(_PICKLEABLE_CALL_COUNT), 7) size_check_instance = MyPickleableObject() _ = size_check_instance.my_id self.assertEqual(expected_size, len(pickle.dumps(size_check_instance))) if __name__ == '__main__': tf.test.main()

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import keras import tensorflow.compat.v2 as tf import collections import contextlib import multiprocessing.dummy import os import pickle import shutil import sys import time import timeit import numpy as np from keras.utils import io_utils from keras.utils import layer_utils _PICKLEABLE_CALL_COUNT = collections.Counter() class MyPickleableObject(tf.__internal__.tracking.AutoTrackable): @property @layer_utils.cached_per_instance def my_id(self): _PICKLEABLE_CALL_COUNT[self] += 1 return id(self) class LayerUtilsTest(tf.test.TestCase): def test_print_summary(self): model = keras.Sequential() model.add( keras.layers.Conv2D( filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv')) model.add(keras.layers.Flatten(name='flat')) model.add(keras.layers.Dense(5, name='dense')) file_name = 'model_1.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary(model, print_fn=print_to_file) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() self.assertEqual(len(lines), 15) except ImportError: pass def test_print_summary_without_print_fn(self): model = keras.Sequential([ keras.layers.Dense(5, input_shape=(10,), name='dense')]) io_utils.enable_interactive_logging() with self.captureWritesToStream(sys.stdout) as printed: layer_utils.print_summary(model) self.assertIn('dense (Dense)', printed.contents()) def test_print_summary_expand_nested(self): shape = (None, None, 3) def make_model(): x = inputs = keras.Input(shape) x = keras.layers.Conv2D(3, 1)(x) x = keras.layers.BatchNormalization()(x) return keras.Model(inputs, x) x = inner_inputs = keras.Input(shape) x = make_model()(x) inner_model = keras.Model(inner_inputs, x) inputs = keras.Input(shape) model = keras.Model(inputs, inner_model(inputs)) file_name = 'model_2.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary( model, print_fn=print_to_file, expand_nested=True) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() check_str = ( 'Model: "model_2"\n' '_________________________________________________________________\n' ' Layer (type) Output Shape Param # \n' '=================================================================\n' ' input_3 (InputLayer) [(None, None, None, 3)] 0 \n' ' \n' ' model_1 (Functional) (None, None, None, 3) 24 \n' '|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n' '| input_1 (InputLayer) [(None, None, None, 3)] 0 |\n' '| |\n' '| model (Functional) (None, None, None, 3) 24 |\n' '||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n' '|| input_2 (InputLayer) [(None, None, None, 3)] 0 ||\n' '|| ||\n' '|| conv2d (Conv2D) (None, None, None, 3) 12 ||\n' '|| ||\n' '|| batch_normalization (BatchN (None, None, None, 3) 12 ||\n' '|| ormalization) ||\n' '|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n' '¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n' '=================================================================\n' 'Total params: 24\n' 'Trainable params: 18\n' 'Non-trainable params: 6\n' '_________________________________________________________________\n') fin_str = '' for line in lines: fin_str += line self.assertIn(fin_str, check_str) self.assertEqual(len(lines), 25) except ImportError: pass def test_summary_subclass_model_expand_nested(self): class Sequential(keras.Model): def __init__(self, *args): super(Sequential, self).__init__() self.module_list = list(args) if args else [] def call(self, x): for module in self.module_list: x = module(x) return x class Block(keras.Model): def __init__(self): super(Block, self).__init__() self.module = Sequential( keras.layers.Dense(10), keras.layers.Dense(10), ) def call(self, input_tensor): x = self.module(input_tensor) return x class Base(keras.Model): def __init__(self): super(Base, self).__init__() self.module = Sequential(Block(), Block()) def call(self, input_tensor): x = self.module(input_tensor) y = self.module(x) return x, y class Network(keras.Model): def __init__(self): super(Network, self).__init__() self.child = Base() def call(self, inputs): return self.child(inputs) net = Network() inputs = keras.Input(shape=(10,)) outputs = net(inputs) model = keras.models.Model(inputs=inputs, outputs=outputs) file_name = 'model_3.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary( model, line_length=120, print_fn=print_to_file, expand_nested=True) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() if tf.__internal__.tf2.enabled(): self.assertEqual(len(lines), 39) else: self.assertEqual(len(lines), 40) except ImportError: pass def test_print_summary_show_trainable(self): model = keras.Sequential(name='trainable') untrained = keras.layers.Conv2D( filters=2, kernel_size=(2, 3), input_shape=(3, 5, 5), name='conv') model.add(untrained) model.add(keras.layers.Flatten(name='flat')) model.add(keras.layers.Dense(5, name='dense')) untrained.trainable = False file_name = 'model_4.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary( model, print_fn=print_to_file, show_trainable=True) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() check_str = ( 'Model: ' '"trainable"\n____________________________________________________________________________\n' ' Layer (type) Output Shape Param # ' 'Trainable ' '\n============================================================================\n' ' conv (Conv2D) (None, 2, 3, 2) 62 N' ' \n' ' ' '\n flat (Flatten) (None, 12) 0 ' 'Y \n' ' ' '\n dense (Dense) (None, 5) 65 ' 'Y \n' ' ' '\n============================================================================\nTotal' ' params: 127\nTrainable params: 65\nNon-trainable params: ' '62\n____________________________________________________________________________\n' '____________________________________________________________________________\n' ) fin_str = '' for line in lines: fin_str += line self.assertIn(fin_str, check_str) self.assertEqual(len(lines), 15) except ImportError: pass def test_print_summary_expand_nested_show_trainable(self): shape = (None, None, 3) def make_model(): x = inputs = keras.Input(shape, name='input2') untrainable = keras.layers.Conv2D(3, 1) untrainable.trainable = False x = untrainable(x) x = keras.layers.BatchNormalization()(x) return keras.Model(inputs, x) x = inner_inputs = keras.Input(shape, name='input1') x = make_model()(x) inner_model = keras.Model(inner_inputs, x) inputs = keras.Input(shape, name='input3') model = keras.Model(inputs, inner_model(inputs)) file_name = 'model_6.txt' temp_dir = self.get_temp_dir() self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True) fpath = os.path.join(temp_dir, file_name) writer = open(fpath, 'w') def print_to_file(text): print(text, file=writer) try: layer_utils.print_summary( model, print_fn=print_to_file, expand_nested=True, show_trainable=True) self.assertTrue(tf.io.gfile.exists(fpath)) writer.close() reader = open(fpath, 'r') lines = reader.readlines() reader.close() check_str = ( 'Model: ' '"model_2"\n____________________________________________________________________________\n' ' Layer (type) Output Shape Param # ' 'Trainable ' '\n============================================================================\n' ' input3 (InputLayer) [(None, None, None, 3)] 0 Y' ' \n' ' ' '\n model_1 (Functional) (None, None, None, 3) 24 ' 'Y ' '\n|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n|' ' input1 (InputLayer) [(None, None, None, 3)] 0 Y' ' |\n|' ' ' '|\n| model (Functional) (None, None, None, 3) 24 ' 'Y ' '|\n||¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯||\n||' ' input2 (InputLayer) [(None, None, None, 3)] 0 Y' ' ||\n||' ' ' '||\n|| conv2d (Conv2D) (None, None, None, 3) 12 ' 'N ||\n||' ' ' '||\n|| batch_normalization (BatchN (None, None, None, 3) 12 ' 'Y ||\n|| ormalization)' ' ' '||\n|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯|\n¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\n============================================================================\nTotal' ' params: 24\nTrainable params: 6\nNon-trainable params: ' '18\n____________________________________________________________________________\n' '____________________________________________________________________________\n' ) fin_str = '' for line in lines: fin_str += line self.assertIn(fin_str, check_str) self.assertEqual(len(lines), 25) except ImportError: pass def test_property_cache(self): test_counter = collections.Counter() class MyObject(tf.__internal__.tracking.AutoTrackable): def __init__(self): super(MyObject, self).__init__() self._frozen = True def __setattr__(self, key, value): if getattr(self, '_frozen', False): raise ValueError('Cannot mutate when frozen.') return super(MyObject, self).__setattr__(key, value) @property @layer_utils.cached_per_instance def test_property(self): test_counter[id(self)] += 1 return id(self) first_object = MyObject() second_object = MyObject() self.assertEqual(first_object.test_property, id(first_object)) self.assertEqual(second_object.test_property, id(second_object)) self.assertNotEqual(first_object.test_property, second_object.test_property) self.assertEqual(first_object.test_property, id(first_object)) self.assertEqual(second_object.test_property, id(second_object)) self.assertAllEqual(tuple(test_counter.values()), (1, 1)) def test_property_cache_threaded(self): call_count = collections.Counter() class MyObject(tf.__internal__.tracking.AutoTrackable): @property @layer_utils.cached_per_instance def test_property(self): call_count['test_property'] += 1 time.sleep(np.random.random() + 1.) # because numpy limits the range of seeds) to ensure that an instance # returns the same value in different threads, but different instances # return different values. return int(np.random.RandomState(id(self) % (2 ** 31)).randint(2 ** 16)) def get_test_property(self, _): return self.test_property # Test that multiple threads return the same value. This requires that # the underlying function is repeatable, as cached_property makes no attempt # to prioritize the first call. test_obj = MyObject() with contextlib.closing(multiprocessing.dummy.Pool(32)) as pool: # Intentionally make a large pool (even when there are only a small number # of cpus) to ensure that the runtime switches threads. results = pool.map(test_obj.get_test_property, range(64)) self.assertEqual(len(set(results)), 1) # Make sure we actually are testing threaded behavior. self.assertGreater(call_count['test_property'], 1) # Make sure new threads still cache hit. with contextlib.closing(multiprocessing.dummy.Pool(2)) as pool: start_time = timeit.default_timer() # Don't time pool instantiation. results = pool.map(test_obj.get_test_property, range(4)) total_time = timeit.default_timer() - start_time self.assertLess(total_time, 0.95) def test_property_cache_serialization(self): for k in list(_PICKLEABLE_CALL_COUNT.keys()): _PICKLEABLE_CALL_COUNT.pop(k) first_instance = MyPickleableObject() self.assertEqual(id(first_instance), first_instance.my_id) second_instance = pickle.loads(pickle.dumps(first_instance)) self.assertEqual(id(second_instance), second_instance.my_id) self.assertNotEqual(first_instance.my_id, second_instance.my_id) self.assertEqual(_PICKLEABLE_CALL_COUNT[second_instance], 1) expected_size = len(pickle.dumps(second_instance)) for _ in range(5): _ = MyPickleableObject().my_id self.assertEqual(len(_PICKLEABLE_CALL_COUNT), 7) size_check_instance = MyPickleableObject() _ = size_check_instance.my_id self.assertEqual(expected_size, len(pickle.dumps(size_check_instance))) if __name__ == '__main__': tf.test.main()

true

f734d64a2d3fe42732aa1ee1e867be66cb0f7bf3

47,046

py

Python

ranger/config_ranger/commands_full.py

nexayq/dot_files

473614b8ecdb4efb3ae7098ef3d8687da0585260

[ "MIT" ]

null

ranger/config_ranger/commands_full.py

nexayq/dot_files

473614b8ecdb4efb3ae7098ef3d8687da0585260

[ "MIT" ]

null

ranger/config_ranger/commands_full.py

nexayq/dot_files

473614b8ecdb4efb3ae7098ef3d8687da0585260

[ "MIT" ]

2

2016-10-24T08:36:03.000Z

2018-09-09T15:32:21.000Z

# -*- coding: utf-8 -*- # This file is part of ranger, the console file manager. # This configuration file is licensed under the same terms as ranger. # =================================================================== # # NOTE: If you copied this file to ~/.config/ranger/commands_full.py, # then it will NOT be loaded by ranger, and only serve as a reference. # # =================================================================== # This file contains ranger's commands. # It's all in python; lines beginning with # are comments. # # Note that additional commands are automatically generated from the methods # of the class ranger.core.actions.Actions. # # You can customize commands in the file ~/.config/ranger/commands.py. # It has the same syntax as this file. In fact, you can just copy this # file there with `ranger --copy-config=commands' and make your modifications. # But make sure you update your configs when you update ranger. # # =================================================================== # Every class defined here which is a subclass of `Command' will be used as a # command in ranger. Several methods are defined to interface with ranger: # execute(): called when the command is executed. # cancel(): called when closing the console. # tab(tabnum): called when <TAB> is pressed. # quick(): called after each keypress. # # tab() argument tabnum is 1 for <TAB> and -1 for <S-TAB> by default # # The return values for tab() can be either: # None: There is no tab completion # A string: Change the console to this string # A list/tuple/generator: cycle through every item in it # # The return value for quick() can be: # False: Nothing happens # True: Execute the command afterwards # # The return value for execute() and cancel() doesn't matter. # # =================================================================== # Commands have certain attributes and methods that facilitate parsing of # the arguments: # # self.line: The whole line that was written in the console. # self.args: A list of all (space-separated) arguments to the command. # self.quantifier: If this command was mapped to the key "X" and # the user pressed 6X, self.quantifier will be 6. # self.arg(n): The n-th argument, or an empty string if it doesn't exist. # self.rest(n): The n-th argument plus everything that followed. For example, # if the command was "search foo bar a b c", rest(2) will be "bar a b c" # self.start(n): Anything before the n-th argument. For example, if the # command was "search foo bar a b c", start(2) will be "search foo" # # =================================================================== # And this is a little reference for common ranger functions and objects: # # self.fm: A reference to the "fm" object which contains most information # about ranger. # self.fm.notify(string): Print the given string on the screen. # self.fm.notify(string, bad=True): Print the given string in RED. # self.fm.reload_cwd(): Reload the current working directory. # self.fm.thisdir: The current working directory. (A File object.) # self.fm.thisfile: The current file. (A File object too.) # self.fm.thistab.get_selection(): A list of all selected files. # self.fm.execute_console(string): Execute the string as a ranger command. # self.fm.open_console(string): Open the console with the given string # already typed in for you. # self.fm.move(direction): Moves the cursor in the given direction, which # can be something like down=3, up=5, right=1, left=1, to=6, ... # # File objects (for example self.fm.thisfile) have these useful attributes and # methods: # # cf.path: The path to the file. # cf.basename: The base name only. # cf.load_content(): Force a loading of the directories content (which # obviously works with directories only) # cf.is_directory: True/False depending on whether it's a directory. # # For advanced commands it is unavoidable to dive a bit into the source code # of ranger. # =================================================================== from ranger.api.commands import * class alias(Command): """:alias <newcommand> <oldcommand> Copies the oldcommand as newcommand. """ context = 'browser' resolve_macros = False def execute(self): if not self.arg(1) or not self.arg(2): self.fm.notify('Syntax: alias <newcommand> <oldcommand>', bad=True) else: self.fm.commands.alias(self.arg(1), self.rest(2)) class echo(Command): """:echo <text> Display the text in the statusbar. """ def execute(self): self.fm.notify(self.rest(1)) class cd(Command): """:cd [-r] <dirname> The cd command changes the directory. The command 'cd -' is equivalent to typing ``. Using the option "-r" will get you to the real path. """ def execute(self): import os.path if self.arg(1) == '-r': self.shift() destination = os.path.realpath(self.rest(1)) if os.path.isfile(destination): self.fm.select_file(destination) return else: destination = self.rest(1) if not destination: destination = '~' if destination == '-': self.fm.enter_bookmark('`') else: self.fm.cd(destination) def tab(self, tabnum): import os from os.path import dirname, basename, expanduser, join cwd = self.fm.thisdir.path rel_dest = self.rest(1) bookmarks = [v.path for v in self.fm.bookmarks.dct.values() if rel_dest in v.path ] # expand the tilde into the user directory if rel_dest.startswith('~'): rel_dest = expanduser(rel_dest) # define some shortcuts abs_dest = join(cwd, rel_dest) abs_dirname = dirname(abs_dest) rel_basename = basename(rel_dest) rel_dirname = dirname(rel_dest) try: # are we at the end of a directory? if rel_dest.endswith('/') or rel_dest == '': _, dirnames, _ = next(os.walk(abs_dest)) # are we in the middle of the filename? else: _, dirnames, _ = next(os.walk(abs_dirname)) dirnames = [dn for dn in dirnames \ if dn.startswith(rel_basename)] except (OSError, StopIteration): # os.walk found nothing pass else: dirnames.sort() if self.fm.settings.cd_bookmarks: dirnames = bookmarks + dirnames # no results, return None if len(dirnames) == 0: return # one result. since it must be a directory, append a slash. if len(dirnames) == 1: return self.start(1) + join(rel_dirname, dirnames[0]) + '/' # more than one result. append no slash, so the user can # manually type in the slash to advance into that directory return (self.start(1) + join(rel_dirname, dirname) for dirname in dirnames) class chain(Command): """:chain <command1>; <command2>; ... Calls multiple commands at once, separated by semicolons. """ def execute(self): for command in self.rest(1).split(";"): self.fm.execute_console(command) class shell(Command): escape_macros_for_shell = True def execute(self): if self.arg(1) and self.arg(1)[0] == '-': flags = self.arg(1)[1:] command = self.rest(2) else: flags = '' command = self.rest(1) if command: self.fm.execute_command(command, flags=flags) def tab(self, tabnum): from ranger.ext.get_executables import get_executables if self.arg(1) and self.arg(1)[0] == '-': command = self.rest(2) else: command = self.rest(1) start = self.line[0:len(self.line) - len(command)] try: position_of_last_space = command.rindex(" ") except ValueError: return (start + program + ' ' for program \ in get_executables() if program.startswith(command)) if position_of_last_space == len(command) - 1: selection = self.fm.thistab.get_selection() if len(selection) == 1: return self.line + selection[0].shell_escaped_basename + ' ' else: return self.line + '%s ' else: before_word, start_of_word = self.line.rsplit(' ', 1) return (before_word + ' ' + file.shell_escaped_basename \ for file in self.fm.thisdir.files or [] \ if file.shell_escaped_basename.startswith(start_of_word)) class open_with(Command): def execute(self): app, flags, mode = self._get_app_flags_mode(self.rest(1)) self.fm.execute_file( files = [f for f in self.fm.thistab.get_selection()], app = app, flags = flags, mode = mode) def tab(self, tabnum): return self._tab_through_executables() def _get_app_flags_mode(self, string): """Extracts the application, flags and mode from a string. examples: "mplayer f 1" => ("mplayer", "f", 1) "aunpack 4" => ("aunpack", "", 4) "p" => ("", "p", 0) "" => None """ app = '' flags = '' mode = 0 split = string.split() if len(split) == 0: pass elif len(split) == 1: part = split[0] if self._is_app(part): app = part elif self._is_flags(part): flags = part elif self._is_mode(part): mode = part elif len(split) == 2: part0 = split[0] part1 = split[1] if self._is_app(part0): app = part0 if self._is_flags(part1): flags = part1 elif self._is_mode(part1): mode = part1 elif self._is_flags(part0): flags = part0 if self._is_mode(part1): mode = part1 elif self._is_mode(part0): mode = part0 if self._is_flags(part1): flags = part1 elif len(split) >= 3: part0 = split[0] part1 = split[1] part2 = split[2] if self._is_app(part0): app = part0 if self._is_flags(part1): flags = part1 if self._is_mode(part2): mode = part2 elif self._is_mode(part1): mode = part1 if self._is_flags(part2): flags = part2 elif self._is_flags(part0): flags = part0 if self._is_mode(part1): mode = part1 elif self._is_mode(part0): mode = part0 if self._is_flags(part1): flags = part1 return app, flags, int(mode) def _is_app(self, arg): return not self._is_flags(arg) and not arg.isdigit() def _is_flags(self, arg): from ranger.core.runner import ALLOWED_FLAGS return all(x in ALLOWED_FLAGS for x in arg) def _is_mode(self, arg): return all(x in '0123456789' for x in arg) class set_(Command): """:set <option name>=<python expression> Gives an option a new value. """ name = 'set' # don't override the builtin set class def execute(self): name = self.arg(1) name, value, _ = self.parse_setting_line() self.fm.set_option_from_string(name, value) def tab(self, tabnum): from ranger.gui.colorscheme import get_all_colorschemes name, value, name_done = self.parse_setting_line() settings = self.fm.settings if not name: return sorted(self.firstpart + setting for setting in settings) if not value and not name_done: return sorted(self.firstpart + setting for setting in settings \ if setting.startswith(name)) if not value: # Cycle through colorschemes when name, but no value is specified if name == "colorscheme": return sorted(self.firstpart + colorscheme for colorscheme \ in get_all_colorschemes()) return self.firstpart + str(settings[name]) if bool in settings.types_of(name): if 'true'.startswith(value.lower()): return self.firstpart + 'True' if 'false'.startswith(value.lower()): return self.firstpart + 'False' # Tab complete colorscheme values if incomplete value is present if name == "colorscheme": return sorted(self.firstpart + colorscheme for colorscheme \ in get_all_colorschemes() if colorscheme.startswith(value)) class setlocal(set_): """:setlocal path=<regular expression> <option name>=<python expression> Gives an option a new value. """ PATH_RE = re.compile(r'^\s*path="?(.*?)"?\s*$') def execute(self): import os.path match = self.PATH_RE.match(self.arg(1)) if match: path = os.path.normpath(os.path.expanduser(match.group(1))) self.shift() elif self.fm.thisdir: path = self.fm.thisdir.path else: path = None if path: name = self.arg(1) name, value, _ = self.parse_setting_line() self.fm.set_option_from_string(name, value, localpath=path) class setintag(setlocal): """:setintag <tag or tags> <option name>=<option value> Sets an option for directories that are tagged with a specific tag. """ def execute(self): tags = self.arg(1) self.shift() name, value, _ = self.parse_setting_line() self.fm.set_option_from_string(name, value, tags=tags) class default_linemode(Command): def execute(self): import re from ranger.container.fsobject import FileSystemObject if len(self.args) < 2: self.fm.notify("Usage: default_linemode [path=<regexp> | tag=<tag(s)>] <linemode>", bad=True) # Extract options like "path=..." or "tag=..." from the command line arg1 = self.arg(1) method = "always" argument = None if arg1.startswith("path="): method = "path" argument = re.compile(arg1[5:]) self.shift() elif arg1.startswith("tag="): method = "tag" argument = arg1[4:] self.shift() # Extract and validate the line mode from the command line linemode = self.rest(1) if linemode not in FileSystemObject.linemode_dict: self.fm.notify("Invalid linemode: %s; should be %s" % (linemode, "/".join(FileSystemObject.linemode_dict)), bad=True) # Add the prepared entry to the fm.default_linemodes entry = [method, argument, linemode] self.fm.default_linemodes.appendleft(entry) # Redraw the columns if hasattr(self.fm.ui, "browser"): for col in self.fm.ui.browser.columns: col.need_redraw = True def tab(self, tabnum): mode = self.arg(1) return (self.arg(0) + " " + linemode for linemode in self.fm.thisfile.linemode_dict.keys() if linemode.startswith(self.arg(1))) class quit(Command): """:quit Closes the current tab. If there is only one tab, quit the program. """ def execute(self): if len(self.fm.tabs) <= 1: self.fm.exit() self.fm.tab_close() class quitall(Command): """:quitall Quits the program immediately. """ def execute(self): self.fm.exit() class quit_bang(quitall): """:quit! Quits the program immediately. """ name = 'quit!' allow_abbrev = False class terminal(Command): """:terminal Spawns an "x-terminal-emulator" starting in the current directory. """ def execute(self): import os from ranger.ext.get_executables import get_executables command = os.environ.get('TERMCMD', os.environ.get('TERM')) if command not in get_executables(): command = 'x-terminal-emulator' if command not in get_executables(): command = 'xterm' self.fm.run(command, flags='f') class delete(Command): """:delete Tries to delete the selection or the files passed in arguments (if any). The arguments use a shell-like escaping. "Selection" is defined as all the "marked files" (by default, you can mark files with space or v). If there are no marked files, use the "current file" (where the cursor is) When attempting to delete non-empty directories or multiple marked files, it will require a confirmation. """ allow_abbrev = False escape_macros_for_shell = True def execute(self): import os import shlex from functools import partial from ranger.container.file import File def is_directory_with_files(f): import os.path return (os.path.isdir(f) and not os.path.islink(f) \ and len(os.listdir(f)) > 0) if self.rest(1): files = shlex.split(self.rest(1)) many_files = (len(files) > 1 or is_directory_with_files(files[0])) else: cwd = self.fm.thisdir cf = self.fm.thisfile if not cwd or not cf: self.fm.notify("Error: no file selected for deletion!", bad=True) return # relative_path used for a user-friendly output in the confirmation. files = [f.relative_path for f in self.fm.thistab.get_selection()] many_files = (cwd.marked_items or is_directory_with_files(cf.path)) confirm = self.fm.settings.confirm_on_delete if confirm != 'never' and (confirm != 'multiple' or many_files): filename_list = files self.fm.ui.console.ask("Confirm deletion of: %s (y/N)" % ', '.join(files), partial(self._question_callback, files), ('n', 'N', 'y', 'Y')) else: # no need for a confirmation, just delete self.fm.delete(files) def tab(self, tabnum): return self._tab_directory_content() def _question_callback(self, files, answer): if answer == 'y' or answer == 'Y': self.fm.delete(files) class mark_tag(Command): """:mark_tag [<tags>] Mark all tags that are tagged with either of the given tags. When leaving out the tag argument, all tagged files are marked. """ do_mark = True def execute(self): cwd = self.fm.thisdir tags = self.rest(1).replace(" ","") if not self.fm.tags or not cwd.files: return for fileobj in cwd.files: try: tag = self.fm.tags.tags[fileobj.realpath] except KeyError: continue if not tags or tag in tags: cwd.mark_item(fileobj, val=self.do_mark) self.fm.ui.status.need_redraw = True self.fm.ui.need_redraw = True class console(Command): """:console <command> Open the console with the given command. """ def execute(self): position = None if self.arg(1)[0:2] == '-p': try: position = int(self.arg(1)[2:]) self.shift() except: pass self.fm.open_console(self.rest(1), position=position) class load_copy_buffer(Command): """:load_copy_buffer Load the copy buffer from confdir/copy_buffer """ copy_buffer_filename = 'copy_buffer' def execute(self): from ranger.container.file import File from os.path import exists try: fname = self.fm.confpath(self.copy_buffer_filename) f = open(fname, 'r') except: return self.fm.notify("Cannot open %s" % \ (fname or self.copy_buffer_filename), bad=True) self.fm.copy_buffer = set(File(g) \ for g in f.read().split("\n") if exists(g)) f.close() self.fm.ui.redraw_main_column() class save_copy_buffer(Command): """:save_copy_buffer Save the copy buffer to confdir/copy_buffer """ copy_buffer_filename = 'copy_buffer' def execute(self): fname = None try: fname = self.fm.confpath(self.copy_buffer_filename) f = open(fname, 'w') except: return self.fm.notify("Cannot open %s" % \ (fname or self.copy_buffer_filename), bad=True) f.write("\n".join(f.path for f in self.fm.copy_buffer)) f.close() class unmark_tag(mark_tag): """:unmark_tag [<tags>] Unmark all tags that are tagged with either of the given tags. When leaving out the tag argument, all tagged files are unmarked. """ do_mark = False class mkdir(Command): """:mkdir <dirname> Creates a directory with the name <dirname>. """ def execute(self): from os.path import join, expanduser, lexists from os import makedirs dirname = join(self.fm.thisdir.path, expanduser(self.rest(1))) if not lexists(dirname): makedirs(dirname) else: self.fm.notify("file/directory exists!", bad=True) def tab(self, tabnum): return self._tab_directory_content() class touch(Command): """:touch <fname> Creates a file with the name <fname>. """ def execute(self): from os.path import join, expanduser, lexists fname = join(self.fm.thisdir.path, expanduser(self.rest(1))) if not lexists(fname): open(fname, 'a').close() else: self.fm.notify("file/directory exists!", bad=True) def tab(self, tabnum): return self._tab_directory_content() class edit(Command): """:edit <filename> Opens the specified file in vim """ def execute(self): if not self.arg(1): self.fm.edit_file(self.fm.thisfile.path) else: self.fm.edit_file(self.rest(1)) def tab(self, tabnum): return self._tab_directory_content() class eval_(Command): """:eval [-q] <python code> Evaluates the python code. `fm' is a reference to the FM instance. To display text, use the function `p'. Examples: :eval fm :eval len(fm.directories) :eval p("Hello World!") """ name = 'eval' resolve_macros = False def execute(self): if self.arg(1) == '-q': code = self.rest(2) quiet = True else: code = self.rest(1) quiet = False import ranger global cmd, fm, p, quantifier fm = self.fm cmd = self.fm.execute_console p = fm.notify quantifier = self.quantifier try: try: result = eval(code) except SyntaxError: exec(code) else: if result and not quiet: p(result) except Exception as err: p(err) class rename(Command): """:rename <newname> Changes the name of the currently highlighted file to <newname> """ def execute(self): from ranger.container.file import File from os import access new_name = self.rest(1) tagged = {} old_name = self.fm.thisfile.relative_path for f in self.fm.tags.tags: if str(f).startswith(self.fm.thisfile.path): tagged[f] = self.fm.tags.tags[f] self.fm.tags.remove(f) if not new_name: return self.fm.notify('Syntax: rename <newname>', bad=True) if new_name == old_name: return if access(new_name, os.F_OK): return self.fm.notify("Can't rename: file already exists!", bad=True) if self.fm.rename(self.fm.thisfile, new_name): f = File(new_name) # Update bookmarks that were pointing on the previous name obsoletebookmarks = [b for b in self.fm.bookmarks if b[1].path == self.fm.thisfile] if obsoletebookmarks: for key, _ in obsoletebookmarks: self.fm.bookmarks[key] = f self.fm.bookmarks.update_if_outdated() self.fm.thisdir.pointed_obj = f self.fm.thisfile = f for t in tagged: self.fm.tags.tags[t.replace(old_name,new_name)] = tagged[t] self.fm.tags.dump() def tab(self, tabnum): return self._tab_directory_content() class rename_append(Command): """:rename_append Creates an open_console for the rename command, automatically placing the cursor before the file extension. """ def execute(self): cf = self.fm.thisfile path = cf.relative_path.replace("%", "%%") if path.find('.') != 0 and path.rfind('.') != -1 and not cf.is_directory: self.fm.open_console('rename ' + path, position=(7 + path.rfind('.'))) else: self.fm.open_console('rename ' + path) class chmod(Command): """:chmod <octal number> Sets the permissions of the selection to the octal number. The octal number is between 0 and 777. The digits specify the permissions for the user, the group and others. A 1 permits execution, a 2 permits writing, a 4 permits reading. Add those numbers to combine them. So a 7 permits everything. """ def execute(self): mode = self.rest(1) if not mode: mode = str(self.quantifier) try: mode = int(mode, 8) if mode < 0 or mode > 0o777: raise ValueError except ValueError: self.fm.notify("Need an octal number between 0 and 777!", bad=True) return for file in self.fm.thistab.get_selection(): try: os.chmod(file.path, mode) except Exception as ex: self.fm.notify(ex) try: # reloading directory. maybe its better to reload the selected # files only. self.fm.thisdir.load_content() except: pass class bulkrename(Command): """:bulkrename This command opens a list of selected files in an external editor. After you edit and save the file, it will generate a shell script which does bulk renaming according to the changes you did in the file. This shell script is opened in an editor for you to review. After you close it, it will be executed. """ def execute(self): import sys import tempfile from ranger.container.file import File from ranger.ext.shell_escape import shell_escape as esc py3 = sys.version_info[0] >= 3 # Create and edit the file list filenames = [f.relative_path for f in self.fm.thistab.get_selection()] listfile = tempfile.NamedTemporaryFile(delete=False) listpath = listfile.name if py3: listfile.write("\n".join(filenames).encode("utf-8")) else: listfile.write("\n".join(filenames)) listfile.close() self.fm.execute_file([File(listpath)], app='editor') listfile = open(listpath, 'r') new_filenames = listfile.read().split("\n") listfile.close() os.unlink(listpath) if all(a == b for a, b in zip(filenames, new_filenames)): self.fm.notify("No renaming to be done!") return # Generate script cmdfile = tempfile.NamedTemporaryFile() script_lines = [] script_lines.append("# This file will be executed when you close the editor.\n") script_lines.append("# Please double-check everything, clear the file to abort.\n") script_lines.extend("mv -vi -- %s %s\n" % (esc(old), esc(new)) \ for old, new in zip(filenames, new_filenames) if old != new) script_content = "".join(script_lines) if py3: cmdfile.write(script_content.encode("utf-8")) else: cmdfile.write(script_content) cmdfile.flush() # Open the script and let the user review it, then check if the script # was modified by the user self.fm.execute_file([File(cmdfile.name)], app='editor') cmdfile.seek(0) script_was_edited = (script_content != cmdfile.read()) # Do the renaming self.fm.run(['/bin/sh', cmdfile.name], flags='w') cmdfile.close() # Retag the files, but only if the script wasn't changed during review, # because only then we know which are the source and destination files. if not script_was_edited: tags_changed = False for old, new in zip(filenames, new_filenames): if old != new: oldpath = self.fm.thisdir.path + '/' + old newpath = self.fm.thisdir.path + '/' + new if oldpath in self.fm.tags: old_tag = self.fm.tags.tags[oldpath] self.fm.tags.remove(oldpath) self.fm.tags.tags[newpath] = old_tag tags_changed = True if tags_changed: self.fm.tags.dump() else: fm.notify("files have not been retagged") class relink(Command): """:relink <newpath> Changes the linked path of the currently highlighted symlink to <newpath> """ def execute(self): from ranger.container.file import File new_path = self.rest(1) cf = self.fm.thisfile if not new_path: return self.fm.notify('Syntax: relink <newpath>', bad=True) if not cf.is_link: return self.fm.notify('%s is not a symlink!' % cf.relative_path, bad=True) if new_path == os.readlink(cf.path): return try: os.remove(cf.path) os.symlink(new_path, cf.path) except OSError as err: self.fm.notify(err) self.fm.reset() self.fm.thisdir.pointed_obj = cf self.fm.thisfile = cf def tab(self, tabnum): if not self.rest(1): return self.line+os.readlink(self.fm.thisfile.path) else: return self._tab_directory_content() class help_(Command): """:help Display ranger's manual page. """ name = 'help' def execute(self): def callback(answer): if answer == "q": return elif answer == "m": self.fm.display_help() elif answer == "c": self.fm.dump_commands() elif answer == "k": self.fm.dump_keybindings() elif answer == "s": self.fm.dump_settings() c = self.fm.ui.console.ask("View [m]an page, [k]ey bindings," " [c]ommands or [s]ettings? (press q to abort)", callback, list("mkcsq") + [chr(27)]) class copymap(Command): """:copymap <keys> <newkeys1> [<newkeys2>...] Copies a "browser" keybinding from <keys> to <newkeys> """ context = 'browser' def execute(self): if not self.arg(1) or not self.arg(2): return self.fm.notify("Not enough arguments", bad=True) for arg in self.args[2:]: self.fm.ui.keymaps.copy(self.context, self.arg(1), arg) class copypmap(copymap): """:copypmap <keys> <newkeys1> [<newkeys2>...] Copies a "pager" keybinding from <keys> to <newkeys> """ context = 'pager' class copycmap(copymap): """:copycmap <keys> <newkeys1> [<newkeys2>...] Copies a "console" keybinding from <keys> to <newkeys> """ context = 'console' class copytmap(copymap): """:copycmap <keys> <newkeys1> [<newkeys2>...] Copies a "taskview" keybinding from <keys> to <newkeys> """ context = 'taskview' class unmap(Command): """:unmap <keys> [<keys2>, ...] Remove the given "browser" mappings """ context = 'browser' def execute(self): for arg in self.args[1:]: self.fm.ui.keymaps.unbind(self.context, arg) class cunmap(unmap): """:cunmap <keys> [<keys2>, ...] Remove the given "console" mappings """ context = 'browser' class punmap(unmap): """:punmap <keys> [<keys2>, ...] Remove the given "pager" mappings """ context = 'pager' class tunmap(unmap): """:tunmap <keys> [<keys2>, ...] Remove the given "taskview" mappings """ context = 'taskview' class map_(Command): """:map <keysequence> <command> Maps a command to a keysequence in the "browser" context. Example: map j move down map J move down 10 """ name = 'map' context = 'browser' resolve_macros = False def execute(self): if not self.arg(1) or not self.arg(2): return self.fm.notify("Not enough arguments", bad=True) self.fm.ui.keymaps.bind(self.context, self.arg(1), self.rest(2)) class cmap(map_): """:cmap <keysequence> <command> Maps a command to a keysequence in the "console" context. Example: cmap <ESC> console_close cmap <C-x> console_type test """ context = 'console' class tmap(map_): """:tmap <keysequence> <command> Maps a command to a keysequence in the "taskview" context. """ context = 'taskview' class pmap(map_): """:pmap <keysequence> <command> Maps a command to a keysequence in the "pager" context. """ context = 'pager' class scout(Command): """:scout [-FLAGS] <pattern> Swiss army knife command for searching, traveling and filtering files. The command takes various flags as arguments which can be used to influence its behaviour: -a = automatically open a file on unambiguous match -e = open the selected file when pressing enter -f = filter files that match the current search pattern -g = interpret pattern as a glob pattern -i = ignore the letter case of the files -k = keep the console open when changing a directory with the command -l = letter skipping; e.g. allow "rdme" to match the file "readme" -m = mark the matching files after pressing enter -M = unmark the matching files after pressing enter -p = permanent filter: hide non-matching files after pressing enter -s = smart case; like -i unless pattern contains upper case letters -t = apply filter and search pattern as you type -v = inverts the match Multiple flags can be combined. For example, ":scout -gpt" would create a :filter-like command using globbing. """ AUTO_OPEN = 'a' OPEN_ON_ENTER = 'e' FILTER = 'f' SM_GLOB = 'g' IGNORE_CASE = 'i' KEEP_OPEN = 'k' SM_LETTERSKIP = 'l' MARK = 'm' UNMARK = 'M' PERM_FILTER = 'p' SM_REGEX = 'r' SMART_CASE = 's' AS_YOU_TYPE = 't' INVERT = 'v' def __init__(self, *args, **kws): Command.__init__(self, *args, **kws) self._regex = None self.flags, self.pattern = self.parse_flags() def execute(self): thisdir = self.fm.thisdir flags = self.flags pattern = self.pattern regex = self._build_regex() count = self._count(move=True) self.fm.thistab.last_search = regex self.fm.set_search_method(order="search") if (self.MARK in flags or self.UNMARK in flags) and thisdir.files: value = flags.find(self.MARK) > flags.find(self.UNMARK) if self.FILTER in flags: for f in thisdir.files: thisdir.mark_item(f, value) else: for f in thisdir.files: if regex.search(f.relative_path): thisdir.mark_item(f, value) if self.PERM_FILTER in flags: thisdir.filter = regex if pattern else None # clean up: self.cancel() if self.OPEN_ON_ENTER in flags or \ self.AUTO_OPEN in flags and count == 1: if os.path.exists(pattern): self.fm.cd(pattern) else: self.fm.move(right=1) if self.KEEP_OPEN in flags and thisdir != self.fm.thisdir: # reopen the console: if not pattern: self.fm.open_console(self.line) else: self.fm.open_console(self.line[0:-len(pattern)]) if self.quickly_executed and thisdir != self.fm.thisdir and pattern != "..": self.fm.block_input(0.5) def cancel(self): self.fm.thisdir.temporary_filter = None self.fm.thisdir.refilter() def quick(self): asyoutype = self.AS_YOU_TYPE in self.flags if self.FILTER in self.flags: self.fm.thisdir.temporary_filter = self._build_regex() if self.PERM_FILTER in self.flags and asyoutype: self.fm.thisdir.filter = self._build_regex() if self.FILTER in self.flags or self.PERM_FILTER in self.flags: self.fm.thisdir.refilter() if self._count(move=asyoutype) == 1 and self.AUTO_OPEN in self.flags: return True return False def tab(self, tabnum): self._count(move=True, offset=tabnum) def _build_regex(self): if self._regex is not None: return self._regex frmat = "%s" flags = self.flags pattern = self.pattern if pattern == ".": return re.compile("") # Handle carets at start and dollar signs at end separately if pattern.startswith('^'): pattern = pattern[1:] frmat = "^" + frmat if pattern.endswith('$'): pattern = pattern[:-1] frmat += "$" # Apply one of the search methods if self.SM_REGEX in flags: regex = pattern elif self.SM_GLOB in flags: regex = re.escape(pattern).replace("\\*", ".*").replace("\\?", ".") elif self.SM_LETTERSKIP in flags: regex = ".*".join(re.escape(c) for c in pattern) else: regex = re.escape(pattern) regex = frmat % regex # Invert regular expression if necessary if self.INVERT in flags: regex = "^(?:(?!%s).)*$" % regex # Compile Regular Expression options = re.LOCALE | re.UNICODE if self.IGNORE_CASE in flags or self.SMART_CASE in flags and \ pattern.islower(): options |= re.IGNORECASE try: self._regex = re.compile(regex, options) except: self._regex = re.compile("") return self._regex def _count(self, move=False, offset=0): count = 0 cwd = self.fm.thisdir pattern = self.pattern if not pattern or not cwd.files: return 0 if pattern == '.': return 0 if pattern == '..': return 1 deq = deque(cwd.files) deq.rotate(-cwd.pointer - offset) i = offset regex = self._build_regex() for fsobj in deq: if regex.search(fsobj.relative_path): count += 1 if move and count == 1: cwd.move(to=(cwd.pointer + i) % len(cwd.files)) self.fm.thisfile = cwd.pointed_obj if count > 1: return count i += 1 return count == 1 class filter_inode_type(Command): """ :filter_inode_type [dfl] Displays only the files of specified inode type. Parameters can be combined. d display directories f display files l display links """ FILTER_DIRS = 'd' FILTER_FILES = 'f' FILTER_LINKS = 'l' def execute(self): if not self.arg(1): self.fm.thisdir.inode_type_filter = None else: self.fm.thisdir.inode_type_filter = lambda file: ( True if ((self.FILTER_DIRS in self.arg(1) and file.is_directory) or (self.FILTER_FILES in self.arg(1) and file.is_file and not file.is_link) or (self.FILTER_LINKS in self.arg(1) and file.is_link)) else False) self.fm.thisdir.refilter() class grep(Command): """:grep <string> Looks for a string in all marked files or directories """ def execute(self): if self.rest(1): action = ['grep', '--line-number'] action.extend(['-e', self.rest(1), '-r']) action.extend(f.path for f in self.fm.thistab.get_selection()) self.fm.execute_command(action, flags='p') # Version control commands # -------------------------------- class stage(Command): """ :stage Stage selected files for the corresponding version control system """ def execute(self): from ranger.ext.vcs import VcsError filelist = [f.path for f in self.fm.thistab.get_selection()] self.fm.thisdir.vcs_outdated = True # for f in self.fm.thistab.get_selection(): # f.vcs_outdated = True try: self.fm.thisdir.vcs.add(filelist) except VcsError: self.fm.notify("Could not stage files.") self.fm.reload_cwd() class unstage(Command): """ :unstage Unstage selected files for the corresponding version control system """ def execute(self): from ranger.ext.vcs import VcsError filelist = [f.path for f in self.fm.thistab.get_selection()] self.fm.thisdir.vcs_outdated = True # for f in self.fm.thistab.get_selection(): # f.vcs_outdated = True try: self.fm.thisdir.vcs.reset(filelist) except VcsError: self.fm.notify("Could not unstage files.") self.fm.reload_cwd() class diff(Command): """ :diff Displays a diff of selected files against the last committed version """ def execute(self): from ranger.ext.vcs import VcsError import tempfile L = self.fm.thistab.get_selection() if len(L) == 0: return filelist = [f.path for f in L] vcs = L[0].vcs diff = vcs.get_raw_diff(filelist=filelist) if len(diff.strip()) > 0: tmp = tempfile.NamedTemporaryFile() tmp.write(diff.encode('utf-8')) tmp.flush() pager = os.environ.get('PAGER', ranger.DEFAULT_PAGER) self.fm.run([pager, tmp.name]) else: raise Exception("diff is empty") class log(Command): """ :log Displays the log of the current repo or files """ def execute(self): from ranger.ext.vcs import VcsError import tempfile L = self.fm.thistab.get_selection() if len(L) == 0: return filelist = [f.path for f in L] vcs = L[0].vcs log = vcs.get_raw_log(filelist=filelist) tmp = tempfile.NamedTemporaryFile() tmp.write(log.encode('utf-8')) tmp.flush() pager = os.environ.get('PAGER', ranger.DEFAULT_PAGER) self.fm.run([pager, tmp.name]) class flat(Command): """ :flat <level> Flattens the directory view up to the specified level. -1 fully flattened 0 remove flattened view """ def execute(self): try: level = self.rest(1) level = int(level) except ValueError: level = self.quantifier if level < -1: self.fm.notify("Need an integer number (-1, 0, 1, ...)", bad=True) self.fm.thisdir.unload() self.fm.thisdir.flat = level self.fm.thisdir.load_content() # Metadata commands # -------------------------------- class prompt_metadata(Command): """ :prompt_metadata <key1> [<key2> [<key3> ...]] Prompt the user to input metadata for multiple keys in a row. """ _command_name = "meta" _console_chain = None def execute(self): prompt_metadata._console_chain = self.args[1:] self._process_command_stack() def _process_command_stack(self): if prompt_metadata._console_chain: key = prompt_metadata._console_chain.pop() self._fill_console(key) else: for col in self.fm.ui.browser.columns: col.need_redraw = True def _fill_console(self, key): metadata = self.fm.metadata.get_metadata(self.fm.thisfile.path) if key in metadata and metadata[key]: existing_value = metadata[key] else: existing_value = "" text = "%s %s %s" % (self._command_name, key, existing_value) self.fm.open_console(text, position=len(text)) class meta(prompt_metadata): """ :meta <key> [<value>] Change metadata of a file. Deletes the key if value is empty. """ def execute(self): key = self.arg(1) value = self.rest(1) update_dict = dict() update_dict[key] = self.rest(2) selection = self.fm.thistab.get_selection() for f in selection: self.fm.metadata.set_metadata(f.path, update_dict) self._process_command_stack() def tab(self, tabnum): key = self.arg(1) metadata = self.fm.metadata.get_metadata(self.fm.thisfile.path) if key in metadata and metadata[key]: return [" ".join([self.arg(0), self.arg(1), metadata[key]])] else: return [self.arg(0) + " " + key for key in sorted(metadata) if key.startswith(self.arg(1))] class linemode(default_linemode): """ :linemode <mode> Change what is displayed as a filename. - "mode" may be any of the defined linemodes (see: ranger.core.linemode). "normal" is mapped to "filename". """ def execute(self): mode = self.arg(1) if mode == "normal": mode = DEFAULT_LINEMODE if mode not in self.fm.thisfile.linemode_dict: self.fm.notify("Unhandled linemode: `%s'" % mode, bad=True) return self.fm.thisdir._set_linemode_of_children(mode) # Ask the browsercolumns to redraw for col in self.fm.ui.browser.columns: col.need_redraw = True

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# It's all in python; lines beginning with # But make sure you update your configs when you update ranger. # # =================================================================== # Every class defined here which is a subclass of `Command' will be used as a # # =================================================================== # Commands have certain attributes and methods that facilitate parsing of # the arguments: # # self.line: The whole line that was written in the console. # self.args: A list of all (space-separated) arguments to the command. # self.quantifier: If this command was mapped to the key "X" and # the user pressed 6X, self.quantifier will be 6. # self.arg(n): The n-th argument, or an empty string if it doesn't exist. # # For advanced commands it is unavoidable to dive a bit into the source code # of ranger. # =================================================================== from ranger.api.commands import * class alias(Command): context = 'browser' resolve_macros = False def execute(self): if not self.arg(1) or not self.arg(2): self.fm.notify('Syntax: alias <newcommand> <oldcommand>', bad=True) else: self.fm.commands.alias(self.arg(1), self.rest(2)) class echo(Command): def execute(self): self.fm.notify(self.rest(1)) class cd(Command): def execute(self): import os.path if self.arg(1) == '-r': self.shift() destination = os.path.realpath(self.rest(1)) if os.path.isfile(destination): self.fm.select_file(destination) return else: destination = self.rest(1) if not destination: destination = '~' if destination == '-': self.fm.enter_bookmark('`') else: self.fm.cd(destination) def tab(self, tabnum): import os from os.path import dirname, basename, expanduser, join cwd = self.fm.thisdir.path rel_dest = self.rest(1) bookmarks = [v.path for v in self.fm.bookmarks.dct.values() if rel_dest in v.path ] # expand the tilde into the user directory if rel_dest.startswith('~'): rel_dest = expanduser(rel_dest) # define some shortcuts abs_dest = join(cwd, rel_dest) abs_dirname = dirname(abs_dest) rel_basename = basename(rel_dest) rel_dirname = dirname(rel_dest) try: # are we at the end of a directory? if rel_dest.endswith('/') or rel_dest == '': _, dirnames, _ = next(os.walk(abs_dest)) # are we in the middle of the filename? else: _, dirnames, _ = next(os.walk(abs_dirname)) dirnames = [dn for dn in dirnames \ if dn.startswith(rel_basename)] except (OSError, StopIteration): # os.walk found nothing pass else: dirnames.sort() if self.fm.settings.cd_bookmarks: dirnames = bookmarks + dirnames # no results, return None if len(dirnames) == 0: return # one result. since it must be a directory, append a slash. if len(dirnames) == 1: return self.start(1) + join(rel_dirname, dirnames[0]) + '/' # more than one result. append no slash, so the user can # manually type in the slash to advance into that directory return (self.start(1) + join(rel_dirname, dirname) for dirname in dirnames) class chain(Command): def execute(self): for command in self.rest(1).split(";"): self.fm.execute_console(command) class shell(Command): escape_macros_for_shell = True def execute(self): if self.arg(1) and self.arg(1)[0] == '-': flags = self.arg(1)[1:] command = self.rest(2) else: flags = '' command = self.rest(1) if command: self.fm.execute_command(command, flags=flags) def tab(self, tabnum): from ranger.ext.get_executables import get_executables if self.arg(1) and self.arg(1)[0] == '-': command = self.rest(2) else: command = self.rest(1) start = self.line[0:len(self.line) - len(command)] try: position_of_last_space = command.rindex(" ") except ValueError: return (start + program + ' ' for program \ in get_executables() if program.startswith(command)) if position_of_last_space == len(command) - 1: selection = self.fm.thistab.get_selection() if len(selection) == 1: return self.line + selection[0].shell_escaped_basename + ' ' else: return self.line + '%s ' else: before_word, start_of_word = self.line.rsplit(' ', 1) return (before_word + ' ' + file.shell_escaped_basename \ for file in self.fm.thisdir.files or [] \ if file.shell_escaped_basename.startswith(start_of_word)) class open_with(Command): def execute(self): app, flags, mode = self._get_app_flags_mode(self.rest(1)) self.fm.execute_file( files = [f for f in self.fm.thistab.get_selection()], app = app, flags = flags, mode = mode) def tab(self, tabnum): return self._tab_through_executables() def _get_app_flags_mode(self, string): app = '' flags = '' mode = 0 split = string.split() if len(split) == 0: pass elif len(split) == 1: part = split[0] if self._is_app(part): app = part elif self._is_flags(part): flags = part elif self._is_mode(part): mode = part elif len(split) == 2: part0 = split[0] part1 = split[1] if self._is_app(part0): app = part0 if self._is_flags(part1): flags = part1 elif self._is_mode(part1): mode = part1 elif self._is_flags(part0): flags = part0 if self._is_mode(part1): mode = part1 elif self._is_mode(part0): mode = part0 if self._is_flags(part1): flags = part1 elif len(split) >= 3: part0 = split[0] part1 = split[1] part2 = split[2] if self._is_app(part0): app = part0 if self._is_flags(part1): flags = part1 if self._is_mode(part2): mode = part2 elif self._is_mode(part1): mode = part1 if self._is_flags(part2): flags = part2 elif self._is_flags(part0): flags = part0 if self._is_mode(part1): mode = part1 elif self._is_mode(part0): mode = part0 if self._is_flags(part1): flags = part1 return app, flags, int(mode) def _is_app(self, arg): return not self._is_flags(arg) and not arg.isdigit() def _is_flags(self, arg): from ranger.core.runner import ALLOWED_FLAGS return all(x in ALLOWED_FLAGS for x in arg) def _is_mode(self, arg): return all(x in '0123456789' for x in arg) class set_(Command): name = 'set' # don't override the builtin set class def execute(self): name = self.arg(1) name, value, _ = self.parse_setting_line() self.fm.set_option_from_string(name, value) def tab(self, tabnum): from ranger.gui.colorscheme import get_all_colorschemes name, value, name_done = self.parse_setting_line() settings = self.fm.settings if not name: return sorted(self.firstpart + setting for setting in settings) if not value and not name_done: return sorted(self.firstpart + setting for setting in settings \ if setting.startswith(name)) if not value: if name == "colorscheme": return sorted(self.firstpart + colorscheme for colorscheme \ in get_all_colorschemes()) return self.firstpart + str(settings[name]) if bool in settings.types_of(name): if 'true'.startswith(value.lower()): return self.firstpart + 'True' if 'false'.startswith(value.lower()): return self.firstpart + 'False' if name == "colorscheme": return sorted(self.firstpart + colorscheme for colorscheme \ in get_all_colorschemes() if colorscheme.startswith(value)) class setlocal(set_): PATH_RE = re.compile(r'^\s*path="?(.*?)"?\s*$') def execute(self): import os.path match = self.PATH_RE.match(self.arg(1)) if match: path = os.path.normpath(os.path.expanduser(match.group(1))) self.shift() elif self.fm.thisdir: path = self.fm.thisdir.path else: path = None if path: name = self.arg(1) name, value, _ = self.parse_setting_line() self.fm.set_option_from_string(name, value, localpath=path) class setintag(setlocal): def execute(self): tags = self.arg(1) self.shift() name, value, _ = self.parse_setting_line() self.fm.set_option_from_string(name, value, tags=tags) class default_linemode(Command): def execute(self): import re from ranger.container.fsobject import FileSystemObject if len(self.args) < 2: self.fm.notify("Usage: default_linemode [path=<regexp> | tag=<tag(s)>] <linemode>", bad=True) arg1 = self.arg(1) method = "always" argument = None if arg1.startswith("path="): method = "path" argument = re.compile(arg1[5:]) self.shift() elif arg1.startswith("tag="): method = "tag" argument = arg1[4:] self.shift() linemode = self.rest(1) if linemode not in FileSystemObject.linemode_dict: self.fm.notify("Invalid linemode: %s; should be %s" % (linemode, "/".join(FileSystemObject.linemode_dict)), bad=True) entry = [method, argument, linemode] self.fm.default_linemodes.appendleft(entry) if hasattr(self.fm.ui, "browser"): for col in self.fm.ui.browser.columns: col.need_redraw = True def tab(self, tabnum): mode = self.arg(1) return (self.arg(0) + " " + linemode for linemode in self.fm.thisfile.linemode_dict.keys() if linemode.startswith(self.arg(1))) class quit(Command): def execute(self): if len(self.fm.tabs) <= 1: self.fm.exit() self.fm.tab_close() class quitall(Command): def execute(self): self.fm.exit() class quit_bang(quitall): name = 'quit!' allow_abbrev = False class terminal(Command): def execute(self): import os from ranger.ext.get_executables import get_executables command = os.environ.get('TERMCMD', os.environ.get('TERM')) if command not in get_executables(): command = 'x-terminal-emulator' if command not in get_executables(): command = 'xterm' self.fm.run(command, flags='f') class delete(Command): allow_abbrev = False escape_macros_for_shell = True def execute(self): import os import shlex from functools import partial from ranger.container.file import File def is_directory_with_files(f): import os.path return (os.path.isdir(f) and not os.path.islink(f) \ and len(os.listdir(f)) > 0) if self.rest(1): files = shlex.split(self.rest(1)) many_files = (len(files) > 1 or is_directory_with_files(files[0])) else: cwd = self.fm.thisdir cf = self.fm.thisfile if not cwd or not cf: self.fm.notify("Error: no file selected for deletion!", bad=True) return files = [f.relative_path for f in self.fm.thistab.get_selection()] many_files = (cwd.marked_items or is_directory_with_files(cf.path)) confirm = self.fm.settings.confirm_on_delete if confirm != 'never' and (confirm != 'multiple' or many_files): filename_list = files self.fm.ui.console.ask("Confirm deletion of: %s (y/N)" % ', '.join(files), partial(self._question_callback, files), ('n', 'N', 'y', 'Y')) else: self.fm.delete(files) def tab(self, tabnum): return self._tab_directory_content() def _question_callback(self, files, answer): if answer == 'y' or answer == 'Y': self.fm.delete(files) class mark_tag(Command): do_mark = True def execute(self): cwd = self.fm.thisdir tags = self.rest(1).replace(" ","") if not self.fm.tags or not cwd.files: return for fileobj in cwd.files: try: tag = self.fm.tags.tags[fileobj.realpath] except KeyError: continue if not tags or tag in tags: cwd.mark_item(fileobj, val=self.do_mark) self.fm.ui.status.need_redraw = True self.fm.ui.need_redraw = True class console(Command): def execute(self): position = None if self.arg(1)[0:2] == '-p': try: position = int(self.arg(1)[2:]) self.shift() except: pass self.fm.open_console(self.rest(1), position=position) class load_copy_buffer(Command): copy_buffer_filename = 'copy_buffer' def execute(self): from ranger.container.file import File from os.path import exists try: fname = self.fm.confpath(self.copy_buffer_filename) f = open(fname, 'r') except: return self.fm.notify("Cannot open %s" % \ (fname or self.copy_buffer_filename), bad=True) self.fm.copy_buffer = set(File(g) \ for g in f.read().split("\n") if exists(g)) f.close() self.fm.ui.redraw_main_column() class save_copy_buffer(Command): copy_buffer_filename = 'copy_buffer' def execute(self): fname = None try: fname = self.fm.confpath(self.copy_buffer_filename) f = open(fname, 'w') except: return self.fm.notify("Cannot open %s" % \ (fname or self.copy_buffer_filename), bad=True) f.write("\n".join(f.path for f in self.fm.copy_buffer)) f.close() class unmark_tag(mark_tag): do_mark = False class mkdir(Command): def execute(self): from os.path import join, expanduser, lexists from os import makedirs dirname = join(self.fm.thisdir.path, expanduser(self.rest(1))) if not lexists(dirname): makedirs(dirname) else: self.fm.notify("file/directory exists!", bad=True) def tab(self, tabnum): return self._tab_directory_content() class touch(Command): def execute(self): from os.path import join, expanduser, lexists fname = join(self.fm.thisdir.path, expanduser(self.rest(1))) if not lexists(fname): open(fname, 'a').close() else: self.fm.notify("file/directory exists!", bad=True) def tab(self, tabnum): return self._tab_directory_content() class edit(Command): def execute(self): if not self.arg(1): self.fm.edit_file(self.fm.thisfile.path) else: self.fm.edit_file(self.rest(1)) def tab(self, tabnum): return self._tab_directory_content() class eval_(Command): name = 'eval' resolve_macros = False def execute(self): if self.arg(1) == '-q': code = self.rest(2) quiet = True else: code = self.rest(1) quiet = False import ranger global cmd, fm, p, quantifier fm = self.fm cmd = self.fm.execute_console p = fm.notify quantifier = self.quantifier try: try: result = eval(code) except SyntaxError: exec(code) else: if result and not quiet: p(result) except Exception as err: p(err) class rename(Command): def execute(self): from ranger.container.file import File from os import access new_name = self.rest(1) tagged = {} old_name = self.fm.thisfile.relative_path for f in self.fm.tags.tags: if str(f).startswith(self.fm.thisfile.path): tagged[f] = self.fm.tags.tags[f] self.fm.tags.remove(f) if not new_name: return self.fm.notify('Syntax: rename <newname>', bad=True) if new_name == old_name: return if access(new_name, os.F_OK): return self.fm.notify("Can't rename: file already exists!", bad=True) if self.fm.rename(self.fm.thisfile, new_name): f = File(new_name) # Update bookmarks that were pointing on the previous name obsoletebookmarks = [b for b in self.fm.bookmarks if b[1].path == self.fm.thisfile] if obsoletebookmarks: for key, _ in obsoletebookmarks: self.fm.bookmarks[key] = f self.fm.bookmarks.update_if_outdated() self.fm.thisdir.pointed_obj = f self.fm.thisfile = f for t in tagged: self.fm.tags.tags[t.replace(old_name,new_name)] = tagged[t] self.fm.tags.dump() def tab(self, tabnum): return self._tab_directory_content() class rename_append(Command): def execute(self): cf = self.fm.thisfile path = cf.relative_path.replace("%", "%%") if path.find('.') != 0 and path.rfind('.') != -1 and not cf.is_directory: self.fm.open_console('rename ' + path, position=(7 + path.rfind('.'))) else: self.fm.open_console('rename ' + path) class chmod(Command): def execute(self): mode = self.rest(1) if not mode: mode = str(self.quantifier) try: mode = int(mode, 8) if mode < 0 or mode > 0o777: raise ValueError except ValueError: self.fm.notify("Need an octal number between 0 and 777!", bad=True) return for file in self.fm.thistab.get_selection(): try: os.chmod(file.path, mode) except Exception as ex: self.fm.notify(ex) try: # reloading directory. maybe its better to reload the selected # files only. self.fm.thisdir.load_content() except: pass class bulkrename(Command): def execute(self): import sys import tempfile from ranger.container.file import File from ranger.ext.shell_escape import shell_escape as esc py3 = sys.version_info[0] >= 3 # Create and edit the file list filenames = [f.relative_path for f in self.fm.thistab.get_selection()] listfile = tempfile.NamedTemporaryFile(delete=False) listpath = listfile.name if py3: listfile.write("\n".join(filenames).encode("utf-8")) else: listfile.write("\n".join(filenames)) listfile.close() self.fm.execute_file([File(listpath)], app='editor') listfile = open(listpath, 'r') new_filenames = listfile.read().split("\n") listfile.close() os.unlink(listpath) if all(a == b for a, b in zip(filenames, new_filenames)): self.fm.notify("No renaming to be done!") return # Generate script cmdfile = tempfile.NamedTemporaryFile() script_lines = [] script_lines.append("# This file will be executed when you close the editor.\n") script_lines.append("# Please double-check everything, clear the file to abort.\n") script_lines.extend("mv -vi -- %s %s\n" % (esc(old), esc(new)) \ for old, new in zip(filenames, new_filenames) if old != new) script_content = "".join(script_lines) if py3: cmdfile.write(script_content.encode("utf-8")) else: cmdfile.write(script_content) cmdfile.flush() # Open the script and let the user review it, then check if the script # was modified by the user self.fm.execute_file([File(cmdfile.name)], app='editor') cmdfile.seek(0) script_was_edited = (script_content != cmdfile.read()) # Do the renaming self.fm.run(['/bin/sh', cmdfile.name], flags='w') cmdfile.close() # Retag the files, but only if the script wasn't changed during review, if not script_was_edited: tags_changed = False for old, new in zip(filenames, new_filenames): if old != new: oldpath = self.fm.thisdir.path + '/' + old newpath = self.fm.thisdir.path + '/' + new if oldpath in self.fm.tags: old_tag = self.fm.tags.tags[oldpath] self.fm.tags.remove(oldpath) self.fm.tags.tags[newpath] = old_tag tags_changed = True if tags_changed: self.fm.tags.dump() else: fm.notify("files have not been retagged") class relink(Command): def execute(self): from ranger.container.file import File new_path = self.rest(1) cf = self.fm.thisfile if not new_path: return self.fm.notify('Syntax: relink <newpath>', bad=True) if not cf.is_link: return self.fm.notify('%s is not a symlink!' % cf.relative_path, bad=True) if new_path == os.readlink(cf.path): return try: os.remove(cf.path) os.symlink(new_path, cf.path) except OSError as err: self.fm.notify(err) self.fm.reset() self.fm.thisdir.pointed_obj = cf self.fm.thisfile = cf def tab(self, tabnum): if not self.rest(1): return self.line+os.readlink(self.fm.thisfile.path) else: return self._tab_directory_content() class help_(Command): name = 'help' def execute(self): def callback(answer): if answer == "q": return elif answer == "m": self.fm.display_help() elif answer == "c": self.fm.dump_commands() elif answer == "k": self.fm.dump_keybindings() elif answer == "s": self.fm.dump_settings() c = self.fm.ui.console.ask("View [m]an page, [k]ey bindings," " [c]ommands or [s]ettings? (press q to abort)", callback, list("mkcsq") + [chr(27)]) class copymap(Command): context = 'browser' def execute(self): if not self.arg(1) or not self.arg(2): return self.fm.notify("Not enough arguments", bad=True) for arg in self.args[2:]: self.fm.ui.keymaps.copy(self.context, self.arg(1), arg) class copypmap(copymap): context = 'pager' class copycmap(copymap): context = 'console' class copytmap(copymap): context = 'taskview' class unmap(Command): context = 'browser' def execute(self): for arg in self.args[1:]: self.fm.ui.keymaps.unbind(self.context, arg) class cunmap(unmap): context = 'browser' class punmap(unmap): context = 'pager' class tunmap(unmap): context = 'taskview' class map_(Command): name = 'map' context = 'browser' resolve_macros = False def execute(self): if not self.arg(1) or not self.arg(2): return self.fm.notify("Not enough arguments", bad=True) self.fm.ui.keymaps.bind(self.context, self.arg(1), self.rest(2)) class cmap(map_): context = 'console' class tmap(map_): context = 'taskview' class pmap(map_): context = 'pager' class scout(Command): AUTO_OPEN = 'a' OPEN_ON_ENTER = 'e' FILTER = 'f' SM_GLOB = 'g' IGNORE_CASE = 'i' KEEP_OPEN = 'k' SM_LETTERSKIP = 'l' MARK = 'm' UNMARK = 'M' PERM_FILTER = 'p' SM_REGEX = 'r' SMART_CASE = 's' AS_YOU_TYPE = 't' INVERT = 'v' def __init__(self, *args, **kws): Command.__init__(self, *args, **kws) self._regex = None self.flags, self.pattern = self.parse_flags() def execute(self): thisdir = self.fm.thisdir flags = self.flags pattern = self.pattern regex = self._build_regex() count = self._count(move=True) self.fm.thistab.last_search = regex self.fm.set_search_method(order="search") if (self.MARK in flags or self.UNMARK in flags) and thisdir.files: value = flags.find(self.MARK) > flags.find(self.UNMARK) if self.FILTER in flags: for f in thisdir.files: thisdir.mark_item(f, value) else: for f in thisdir.files: if regex.search(f.relative_path): thisdir.mark_item(f, value) if self.PERM_FILTER in flags: thisdir.filter = regex if pattern else None self.cancel() if self.OPEN_ON_ENTER in flags or \ self.AUTO_OPEN in flags and count == 1: if os.path.exists(pattern): self.fm.cd(pattern) else: self.fm.move(right=1) if self.KEEP_OPEN in flags and thisdir != self.fm.thisdir: if not pattern: self.fm.open_console(self.line) else: self.fm.open_console(self.line[0:-len(pattern)]) if self.quickly_executed and thisdir != self.fm.thisdir and pattern != "..": self.fm.block_input(0.5) def cancel(self): self.fm.thisdir.temporary_filter = None self.fm.thisdir.refilter() def quick(self): asyoutype = self.AS_YOU_TYPE in self.flags if self.FILTER in self.flags: self.fm.thisdir.temporary_filter = self._build_regex() if self.PERM_FILTER in self.flags and asyoutype: self.fm.thisdir.filter = self._build_regex() if self.FILTER in self.flags or self.PERM_FILTER in self.flags: self.fm.thisdir.refilter() if self._count(move=asyoutype) == 1 and self.AUTO_OPEN in self.flags: return True return False def tab(self, tabnum): self._count(move=True, offset=tabnum) def _build_regex(self): if self._regex is not None: return self._regex frmat = "%s" flags = self.flags pattern = self.pattern if pattern == ".": return re.compile("") if pattern.startswith('^'): pattern = pattern[1:] frmat = "^" + frmat if pattern.endswith('$'): pattern = pattern[:-1] frmat += "$" if self.SM_REGEX in flags: regex = pattern elif self.SM_GLOB in flags: regex = re.escape(pattern).replace("\\*", ".*").replace("\\?", ".") elif self.SM_LETTERSKIP in flags: regex = ".*".join(re.escape(c) for c in pattern) else: regex = re.escape(pattern) regex = frmat % regex if self.INVERT in flags: regex = "^(?:(?!%s).)*$" % regex options = re.LOCALE | re.UNICODE if self.IGNORE_CASE in flags or self.SMART_CASE in flags and \ pattern.islower(): options |= re.IGNORECASE try: self._regex = re.compile(regex, options) except: self._regex = re.compile("") return self._regex def _count(self, move=False, offset=0): count = 0 cwd = self.fm.thisdir pattern = self.pattern if not pattern or not cwd.files: return 0 if pattern == '.': return 0 if pattern == '..': return 1 deq = deque(cwd.files) deq.rotate(-cwd.pointer - offset) i = offset regex = self._build_regex() for fsobj in deq: if regex.search(fsobj.relative_path): count += 1 if move and count == 1: cwd.move(to=(cwd.pointer + i) % len(cwd.files)) self.fm.thisfile = cwd.pointed_obj if count > 1: return count i += 1 return count == 1 class filter_inode_type(Command): FILTER_DIRS = 'd' FILTER_FILES = 'f' FILTER_LINKS = 'l' def execute(self): if not self.arg(1): self.fm.thisdir.inode_type_filter = None else: self.fm.thisdir.inode_type_filter = lambda file: ( True if ((self.FILTER_DIRS in self.arg(1) and file.is_directory) or (self.FILTER_FILES in self.arg(1) and file.is_file and not file.is_link) or (self.FILTER_LINKS in self.arg(1) and file.is_link)) else False) self.fm.thisdir.refilter() class grep(Command): def execute(self): if self.rest(1): action = ['grep', '--line-number'] action.extend(['-e', self.rest(1), '-r']) action.extend(f.path for f in self.fm.thistab.get_selection()) self.fm.execute_command(action, flags='p') class stage(Command): def execute(self): from ranger.ext.vcs import VcsError filelist = [f.path for f in self.fm.thistab.get_selection()] self.fm.thisdir.vcs_outdated = True try: self.fm.thisdir.vcs.add(filelist) except VcsError: self.fm.notify("Could not stage files.") self.fm.reload_cwd() class unstage(Command): def execute(self): from ranger.ext.vcs import VcsError filelist = [f.path for f in self.fm.thistab.get_selection()] self.fm.thisdir.vcs_outdated = True try: self.fm.thisdir.vcs.reset(filelist) except VcsError: self.fm.notify("Could not unstage files.") self.fm.reload_cwd() class diff(Command): def execute(self): from ranger.ext.vcs import VcsError import tempfile L = self.fm.thistab.get_selection() if len(L) == 0: return filelist = [f.path for f in L] vcs = L[0].vcs diff = vcs.get_raw_diff(filelist=filelist) if len(diff.strip()) > 0: tmp = tempfile.NamedTemporaryFile() tmp.write(diff.encode('utf-8')) tmp.flush() pager = os.environ.get('PAGER', ranger.DEFAULT_PAGER) self.fm.run([pager, tmp.name]) else: raise Exception("diff is empty") class log(Command): def execute(self): from ranger.ext.vcs import VcsError import tempfile L = self.fm.thistab.get_selection() if len(L) == 0: return filelist = [f.path for f in L] vcs = L[0].vcs log = vcs.get_raw_log(filelist=filelist) tmp = tempfile.NamedTemporaryFile() tmp.write(log.encode('utf-8')) tmp.flush() pager = os.environ.get('PAGER', ranger.DEFAULT_PAGER) self.fm.run([pager, tmp.name]) class flat(Command): def execute(self): try: level = self.rest(1) level = int(level) except ValueError: level = self.quantifier if level < -1: self.fm.notify("Need an integer number (-1, 0, 1, ...)", bad=True) self.fm.thisdir.unload() self.fm.thisdir.flat = level self.fm.thisdir.load_content() class prompt_metadata(Command): _command_name = "meta" _console_chain = None def execute(self): prompt_metadata._console_chain = self.args[1:] self._process_command_stack() def _process_command_stack(self): if prompt_metadata._console_chain: key = prompt_metadata._console_chain.pop() self._fill_console(key) else: for col in self.fm.ui.browser.columns: col.need_redraw = True def _fill_console(self, key): metadata = self.fm.metadata.get_metadata(self.fm.thisfile.path) if key in metadata and metadata[key]: existing_value = metadata[key] else: existing_value = "" text = "%s %s %s" % (self._command_name, key, existing_value) self.fm.open_console(text, position=len(text)) class meta(prompt_metadata): def execute(self): key = self.arg(1) value = self.rest(1) update_dict = dict() update_dict[key] = self.rest(2) selection = self.fm.thistab.get_selection() for f in selection: self.fm.metadata.set_metadata(f.path, update_dict) self._process_command_stack() def tab(self, tabnum): key = self.arg(1) metadata = self.fm.metadata.get_metadata(self.fm.thisfile.path) if key in metadata and metadata[key]: return [" ".join([self.arg(0), self.arg(1), metadata[key]])] else: return [self.arg(0) + " " + key for key in sorted(metadata) if key.startswith(self.arg(1))] class linemode(default_linemode): def execute(self): mode = self.arg(1) if mode == "normal": mode = DEFAULT_LINEMODE if mode not in self.fm.thisfile.linemode_dict: self.fm.notify("Unhandled linemode: `%s'" % mode, bad=True) return self.fm.thisdir._set_linemode_of_children(mode) # Ask the browsercolumns to redraw for col in self.fm.ui.browser.columns: col.need_redraw = True

true

f734d8142392b73526b2bc0c75d4fbafc3612f58

3,661

py

Python

python_modules/dagster/setup.py

davemasino/dagster

cec365242853579c7100bfd87a9ee4f36bdd8344

[ "Apache-2.0" ]

null

python_modules/dagster/setup.py

davemasino/dagster

cec365242853579c7100bfd87a9ee4f36bdd8344

[ "Apache-2.0" ]

null

python_modules/dagster/setup.py

davemasino/dagster

cec365242853579c7100bfd87a9ee4f36bdd8344

[ "Apache-2.0" ]

null

from setuptools import find_packages, setup def long_description(): return """ ## Dagster Dagster is a data orchestrator for machine learning, analytics, and ETL. Dagster lets you define pipelines in terms of the data flow between reusable, logical components, then test locally and run anywhere. With a unified view of pipelines and the assets they produce, Dagster can schedule and orchestrate Pandas, Spark, SQL, or anything else that Python can invoke. Dagster is designed for data platform engineers, data engineers, and full-stack data scientists. Building a data platform with Dagster makes your stakeholders more independent and your systems more robust. Developing data pipelines with Dagster makes testing easier and deploying faster. """.strip() def get_version(): version = {} with open("dagster/version.py") as fp: exec(fp.read(), version) # pylint: disable=W0122 return version["__version__"] if __name__ == "__main__": setup( name="dagster", version=get_version(), author="Elementl", author_email="hello@elementl.com", license="Apache-2.0", description="A data orchestrator for machine learning, analytics, and ETL.", long_description=long_description(), long_description_content_type="text/markdown", url="https://github.com/dagster-io/dagster", classifiers=[ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", ], packages=find_packages(exclude=["dagster_tests"]), package_data={ "dagster": [ "dagster/core/storage/event_log/sqlite/alembic/*", "dagster/core/storage/runs/sqlite/alembic/*", "dagster/core/storage/schedules/sqlite/alembic/*", "dagster/grpc/protos/*", ] }, include_package_data=True, install_requires=[ "future", # cli "click>=5.0", "coloredlogs>=6.1, <=14.0", "PyYAML", # core (not explicitly expressed atm) "alembic>=1.2.1", "croniter>=0.3.34", "grpcio>=1.32.0", # ensure version we require is >= that with which we generated the grpc code (set in dev-requirements) "grpcio-health-checking>=1.32.0", "pendulum==1.4.4", # pinned to match airflow, can upgrade to 2.0 once airflow 1.10.13 is released "protobuf>=3.13.0", # ensure version we require is >= that with which we generated the proto code (set in dev-requirements) "pyrsistent>=0.14.8", "python-dateutil", "requests", "rx<=1.6.1", # 3.0 was a breaking change. No py2 compatability as well. "six", "tabulate", "tqdm", "sqlalchemy>=1.0", "toposort>=1.0", "watchdog>=0.8.3", 'psutil >= 1.0; platform_system=="Windows"', # https://github.com/mhammond/pywin32/issues/1439 'pywin32 != 226; platform_system=="Windows"', "pytz", "docstring-parser==0.7.1", ], extras_require={"docker": ["docker"],}, entry_points={ "console_scripts": [ "dagster = dagster.cli:main", "dagster-scheduler = dagster.scheduler.cli:main", "dagster-daemon = dagster.daemon.cli:main", ] }, )

39.365591

136

0.589183

from setuptools import find_packages, setup def long_description(): return """ ## Dagster Dagster is a data orchestrator for machine learning, analytics, and ETL. Dagster lets you define pipelines in terms of the data flow between reusable, logical components, then test locally and run anywhere. With a unified view of pipelines and the assets they produce, Dagster can schedule and orchestrate Pandas, Spark, SQL, or anything else that Python can invoke. Dagster is designed for data platform engineers, data engineers, and full-stack data scientists. Building a data platform with Dagster makes your stakeholders more independent and your systems more robust. Developing data pipelines with Dagster makes testing easier and deploying faster. """.strip() def get_version(): version = {} with open("dagster/version.py") as fp: exec(fp.read(), version) return version["__version__"] if __name__ == "__main__": setup( name="dagster", version=get_version(), author="Elementl", author_email="hello@elementl.com", license="Apache-2.0", description="A data orchestrator for machine learning, analytics, and ETL.", long_description=long_description(), long_description_content_type="text/markdown", url="https://github.com/dagster-io/dagster", classifiers=[ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", ], packages=find_packages(exclude=["dagster_tests"]), package_data={ "dagster": [ "dagster/core/storage/event_log/sqlite/alembic/*", "dagster/core/storage/runs/sqlite/alembic/*", "dagster/core/storage/schedules/sqlite/alembic/*", "dagster/grpc/protos/*", ] }, include_package_data=True, install_requires=[ "future", "click>=5.0", "coloredlogs>=6.1, <=14.0", "PyYAML", "alembic>=1.2.1", "croniter>=0.3.34", "grpcio>=1.32.0", "grpcio-health-checking>=1.32.0", "pendulum==1.4.4", "protobuf>=3.13.0", "pyrsistent>=0.14.8", "python-dateutil", "requests", "rx<=1.6.1", "six", "tabulate", "tqdm", "sqlalchemy>=1.0", "toposort>=1.0", "watchdog>=0.8.3", 'psutil >= 1.0; platform_system=="Windows"', 'pywin32 != 226; platform_system=="Windows"', "pytz", "docstring-parser==0.7.1", ], extras_require={"docker": ["docker"],}, entry_points={ "console_scripts": [ "dagster = dagster.cli:main", "dagster-scheduler = dagster.scheduler.cli:main", "dagster-daemon = dagster.daemon.cli:main", ] }, )

true

f734d818565dc62e225a1b3a231c734bfd459388

1,802

py

Python

pre_tokenize.py

vyraun/awesome-align

9871098b94be589f45c505a9732e943fa409cf4d

[ "BSD-3-Clause" ]

null

pre_tokenize.py

vyraun/awesome-align

9871098b94be589f45c505a9732e943fa409cf4d

[ "BSD-3-Clause" ]

null

pre_tokenize.py

vyraun/awesome-align

9871098b94be589f45c505a9732e943fa409cf4d

[ "BSD-3-Clause" ]

null

import argparse import random import itertools import os import tempfile import warnings warnings.filterwarnings("ignore") from awesome_align.tokenization_bert import BasicTokenizer def main(): parser = argparse.ArgumentParser() parser.add_argument( "--data_file", default=None, type=str, required=True, help="The input data file (a text file)." ) parser.add_argument( "--output_file", default=None, type=str, required=False, help="The output data file to write (a text file)." ) args = parser.parse_args() file_path=args.data_file write_path=args.output_file if write_path == None: stdout = True else: w_f = open(write_path, "w") stdout=False # Default Arguments for Cased Multlingual tokenizer = BasicTokenizer() assert os.path.isfile(file_path) examples = [] with open(file_path, encoding="utf-8") as f: for idx, line in enumerate(f.readlines()): if len(line) == 0 or line.isspace() or not len(line.split(' ||| ')) == 2: raise ValueError(f'Line {idx+1} is not in the correct format!') src, tgt = line.split(' ||| ') if src.rstrip() == '' or tgt.rstrip() == '': raise ValueError(f'Line {idx+1} is not in the correct format!') sent_src, sent_tgt = src.strip() , tgt.strip() token_src, token_tgt = tokenizer.tokenize(sent_src) , tokenizer.tokenize(sent_tgt) token_src_string, token_tgt_string = ' '.join([t for t in token_src]) , ' '.join([t for t in token_tgt]) if stdout: print(token_src_string + ' ||| ' + token_tgt_string) else: w_f.write(token_src_string + ' ||| ' + token_tgt_string + '\n') if stdout==False: w_f.close() if __name__ == "__main__": main()

30.033333

116

0.630411

import argparse import random import itertools import os import tempfile import warnings warnings.filterwarnings("ignore") from awesome_align.tokenization_bert import BasicTokenizer def main(): parser = argparse.ArgumentParser() parser.add_argument( "--data_file", default=None, type=str, required=True, help="The input data file (a text file)." ) parser.add_argument( "--output_file", default=None, type=str, required=False, help="The output data file to write (a text file)." ) args = parser.parse_args() file_path=args.data_file write_path=args.output_file if write_path == None: stdout = True else: w_f = open(write_path, "w") stdout=False tokenizer = BasicTokenizer() assert os.path.isfile(file_path) examples = [] with open(file_path, encoding="utf-8") as f: for idx, line in enumerate(f.readlines()): if len(line) == 0 or line.isspace() or not len(line.split(' ||| ')) == 2: raise ValueError(f'Line {idx+1} is not in the correct format!') src, tgt = line.split(' ||| ') if src.rstrip() == '' or tgt.rstrip() == '': raise ValueError(f'Line {idx+1} is not in the correct format!') sent_src, sent_tgt = src.strip() , tgt.strip() token_src, token_tgt = tokenizer.tokenize(sent_src) , tokenizer.tokenize(sent_tgt) token_src_string, token_tgt_string = ' '.join([t for t in token_src]) , ' '.join([t for t in token_tgt]) if stdout: print(token_src_string + ' ||| ' + token_tgt_string) else: w_f.write(token_src_string + ' ||| ' + token_tgt_string + '\n') if stdout==False: w_f.close() if __name__ == "__main__": main()

true

f734d8a1c4282ee4aefbee1bf86ab99face93065

930

py

Python

py3server/swagger_server/test/test_evidence_controller.py

lhannest/pythonBeaconServerStub

3fee2505f5f7afda9184277b5f6308ff05832e35

[ "MIT" ]

null

py3server/swagger_server/test/test_evidence_controller.py

lhannest/pythonBeaconServerStub

3fee2505f5f7afda9184277b5f6308ff05832e35

[ "MIT" ]

null

py3server/swagger_server/test/test_evidence_controller.py

lhannest/pythonBeaconServerStub

3fee2505f5f7afda9184277b5f6308ff05832e35

[ "MIT" ]

null

# coding: utf-8 from __future__ import absolute_import from swagger_server.models.inline_response2004 import InlineResponse2004 from . import BaseTestCase from six import BytesIO from flask import json class TestEvidenceController(BaseTestCase): """ EvidenceController integration test stubs """ def test_get_evidence(self): """ Test case for get_evidence """ query_string = [('keywords', 'keywords_example'), ('pageNumber', 56), ('pageSize', 56)] response = self.client.open('/api/evidence/{statementId}'.format(statementId='statementId_example'), method='GET', query_string=query_string) self.assert200(response, "Response body is : " + response.data.decode('utf-8')) if __name__ == '__main__': import unittest unittest.main()

29.0625

108

0.611828

from __future__ import absolute_import from swagger_server.models.inline_response2004 import InlineResponse2004 from . import BaseTestCase from six import BytesIO from flask import json class TestEvidenceController(BaseTestCase): def test_get_evidence(self): query_string = [('keywords', 'keywords_example'), ('pageNumber', 56), ('pageSize', 56)] response = self.client.open('/api/evidence/{statementId}'.format(statementId='statementId_example'), method='GET', query_string=query_string) self.assert200(response, "Response body is : " + response.data.decode('utf-8')) if __name__ == '__main__': import unittest unittest.main()

true

f734d8a2dc5aaa3965dc9b461bc243e1cfe6a270

1,081

py

Python

recipes/cpp-peglib/0.x.x/conanfile.py

rockandsalt/conan-center-index

d739adcec3e4dd4c250eff559ceb738e420673dd

[ "MIT" ]

562

2019-09-04T12:23:43.000Z

2022-03-29T16:41:43.000Z

recipes/cpp-peglib/0.x.x/conanfile.py

rockandsalt/conan-center-index

d739adcec3e4dd4c250eff559ceb738e420673dd

[ "MIT" ]

9,799

2019-09-04T12:02:11.000Z

2022-03-31T23:55:45.000Z

recipes/cpp-peglib/0.x.x/conanfile.py

rockandsalt/conan-center-index

d739adcec3e4dd4c250eff559ceb738e420673dd

[ "MIT" ]

1,126

2019-09-04T11:57:46.000Z

2022-03-31T16:43:38.000Z

from conans import ConanFile, tools required_conan_version = ">=1.33.0" class CpppeglibConan(ConanFile): name = "cpp-peglib" description = "A single file C++11 header-only PEG (Parsing Expression Grammars) library." license = "MIT" topics = ("conan", "cpp-peglib", "peg", "parser", "header-only") homepage = "https://github.com/yhirose/cpp-peglib" url = "https://github.com/conan-io/conan-center-index" settings = "os" no_copy_source = True @property def _source_subfolder(self): return "source_subfolder" def package_id(self): self.info.header_only() def source(self): tools.get(**self.conan_data["sources"][self.version], destination=self._source_subfolder, strip_root=True) def package(self): self.copy("LICENSE", dst="licenses", src=self._source_subfolder) self.copy("peglib.h", dst="include", src=self._source_subfolder) def package_info(self): if self.settings.os in ["Linux", "FreeBSD"]: self.cpp_info.system_libs.append("pthread")

31.794118

94

0.655874

from conans import ConanFile, tools required_conan_version = ">=1.33.0" class CpppeglibConan(ConanFile): name = "cpp-peglib" description = "A single file C++11 header-only PEG (Parsing Expression Grammars) library." license = "MIT" topics = ("conan", "cpp-peglib", "peg", "parser", "header-only") homepage = "https://github.com/yhirose/cpp-peglib" url = "https://github.com/conan-io/conan-center-index" settings = "os" no_copy_source = True @property def _source_subfolder(self): return "source_subfolder" def package_id(self): self.info.header_only() def source(self): tools.get(**self.conan_data["sources"][self.version], destination=self._source_subfolder, strip_root=True) def package(self): self.copy("LICENSE", dst="licenses", src=self._source_subfolder) self.copy("peglib.h", dst="include", src=self._source_subfolder) def package_info(self): if self.settings.os in ["Linux", "FreeBSD"]: self.cpp_info.system_libs.append("pthread")

true

f734da1f0b7a9ab56219579fceb1f161ba893075

14,951

py

Python

tests/cli/test_rasa_train.py

chaneyjd/rasa

104a9591fc10b96eaa7fe402b6d64ca652b7ebe2

[ "Apache-2.0" ]

null

tests/cli/test_rasa_train.py

chaneyjd/rasa

104a9591fc10b96eaa7fe402b6d64ca652b7ebe2

[ "Apache-2.0" ]

52

2020-10-03T11:58:14.000Z

2022-02-01T13:17:52.000Z

tests/cli/test_rasa_train.py

chaneyjd/rasa

104a9591fc10b96eaa7fe402b6d64ca652b7ebe2

[ "Apache-2.0" ]

null

import os import tempfile from pathlib import Path import pytest from typing import Callable from _pytest.pytester import RunResult import rasa.shared.utils.io from rasa import model from rasa.nlu.model import Metadata from rasa.shared.nlu.training_data import training_data # noinspection PyProtectedMember from rasa.cli.train import _get_valid_config from rasa.shared.constants import ( CONFIG_MANDATORY_KEYS_CORE, CONFIG_MANDATORY_KEYS_NLU, CONFIG_MANDATORY_KEYS, ) import rasa.utils.io as io_utils def test_train(run_in_simple_project: Callable[..., RunResult]): temp_dir = os.getcwd() run_in_simple_project( "train", "-c", "config.yml", "-d", "domain.yml", "--data", "data", "--out", "train_models", "--fixed-model-name", "test-model", ) assert os.path.exists(os.path.join(temp_dir, "train_models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "train_models")) assert len(files) == 1 assert os.path.basename(files[0]) == "test-model.tar.gz" model_dir = model.get_model("train_models") assert model_dir is not None metadata = Metadata.load(os.path.join(model_dir, "nlu")) assert metadata.get("training_data") is None assert not os.path.exists( os.path.join(model_dir, "nlu", training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH) ) def test_train_persist_nlu_data(run_in_simple_project: Callable[..., RunResult]): temp_dir = os.getcwd() run_in_simple_project( "train", "-c", "config.yml", "-d", "domain.yml", "--data", "data", "--out", "train_models", "--fixed-model-name", "test-model", "--persist-nlu-data", ) assert os.path.exists(os.path.join(temp_dir, "train_models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "train_models")) assert len(files) == 1 assert os.path.basename(files[0]) == "test-model.tar.gz" model_dir = model.get_model("train_models") assert model_dir is not None metadata = Metadata.load(os.path.join(model_dir, "nlu")) assert metadata.get("training_data") is not None assert os.path.exists( os.path.join(model_dir, "nlu", training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH) ) def test_train_core_compare(run_in_simple_project: Callable[..., RunResult]): temp_dir = os.getcwd() rasa.shared.utils.io.write_yaml( { "language": "en", "pipeline": "supervised_embeddings", "policies": [{"name": "MemoizationPolicy"}], }, "config_1.yml", ) rasa.shared.utils.io.write_yaml( { "language": "en", "pipeline": "supervised_embeddings", "policies": [{"name": "MemoizationPolicy"}], }, "config_2.yml", ) run_in_simple_project( "train", "core", "-c", "config_1.yml", "config_2.yml", "--stories", "data/stories.yml", "--out", "core_comparison_results", "--runs", "2", "--percentages", "25", "75", "--augmentation", "5", ) assert os.path.exists(os.path.join(temp_dir, "core_comparison_results")) run_directories = rasa.shared.utils.io.list_subdirectories( os.path.join(temp_dir, "core_comparison_results") ) assert len(run_directories) == 2 model_files = rasa.shared.utils.io.list_files( os.path.join(temp_dir, "core_comparison_results", run_directories[0]) ) assert len(model_files) == 4 assert model_files[0].endswith("tar.gz") def test_train_no_domain_exists( run_in_simple_project: Callable[..., RunResult] ) -> None: os.remove("domain.yml") run_in_simple_project( "train", "-c", "config.yml", "--data", "data", "--out", "train_models_no_domain", "--fixed-model-name", "nlu-model-only", ) assert os.path.exists("train_models_no_domain") files = rasa.shared.utils.io.list_files("train_models_no_domain") assert len(files) == 1 trained_model_path = "train_models_no_domain/nlu-model-only.tar.gz" unpacked = model.unpack_model(trained_model_path) metadata_path = os.path.join(unpacked, "nlu", "metadata.json") assert os.path.exists(metadata_path) def test_train_skip_on_model_not_changed( run_in_simple_project_with_model: Callable[..., RunResult] ): temp_dir = os.getcwd() assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 file_name = files[0] run_in_simple_project_with_model("train") assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 assert file_name == files[0] def test_train_force(run_in_simple_project_with_model: Callable[..., RunResult]): temp_dir = os.getcwd() assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 run_in_simple_project_with_model("train", "--force") assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 2 def test_train_with_only_nlu_data(run_in_simple_project: Callable[..., RunResult]): temp_dir = Path.cwd() for core_file in ["stories.yml", "rules.yml"]: assert (temp_dir / "data" / core_file).exists() (temp_dir / "data" / core_file).unlink() run_in_simple_project("train", "--fixed-model-name", "test-model") assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 assert os.path.basename(files[0]) == "test-model.tar.gz" def test_train_with_only_core_data(run_in_simple_project: Callable[..., RunResult]): temp_dir = os.getcwd() assert os.path.exists(os.path.join(temp_dir, "data/nlu.yml")) os.remove(os.path.join(temp_dir, "data/nlu.yml")) run_in_simple_project("train", "--fixed-model-name", "test-model") assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 assert os.path.basename(files[0]) == "test-model.tar.gz" def test_train_core(run_in_simple_project: Callable[..., RunResult]): run_in_simple_project( "train", "core", "-c", "config.yml", "-d", "domain.yml", "--stories", "data", "--out", "train_rasa_models", "--fixed-model-name", "rasa-model", ) assert os.path.exists("train_rasa_models/rasa-model.tar.gz") assert os.path.isfile("train_rasa_models/rasa-model.tar.gz") def test_train_core_no_domain_exists(run_in_simple_project: Callable[..., RunResult]): os.remove("domain.yml") run_in_simple_project( "train", "core", "--config", "config.yml", "--domain", "domain1.yml", "--stories", "data", "--out", "train_rasa_models_no_domain", "--fixed-model-name", "rasa-model", ) assert not os.path.exists("train_rasa_models_no_domain/rasa-model.tar.gz") assert not os.path.isfile("train_rasa_models_no_domain/rasa-model.tar.gz") def test_train_nlu(run_in_simple_project: Callable[..., RunResult]): run_in_simple_project( "train", "nlu", "-c", "config.yml", "--nlu", "data/nlu.md", "--out", "train_models", ) assert os.path.exists("train_models") files = rasa.shared.utils.io.list_files("train_models") assert len(files) == 1 assert os.path.basename(files[0]).startswith("nlu-") model_dir = model.get_model("train_models") assert model_dir is not None metadata = Metadata.load(os.path.join(model_dir, "nlu")) assert metadata.get("training_data") is None assert not os.path.exists( os.path.join(model_dir, "nlu", training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH) ) def test_train_nlu_persist_nlu_data( run_in_simple_project: Callable[..., RunResult] ) -> None: run_in_simple_project( "train", "nlu", "-c", "config.yml", "--nlu", "data/nlu.md", "--out", "train_models", "--persist-nlu-data", ) assert os.path.exists("train_models") files = rasa.shared.utils.io.list_files("train_models") assert len(files) == 1 assert os.path.basename(files[0]).startswith("nlu-") model_dir = model.get_model("train_models") assert model_dir is not None metadata = Metadata.load(os.path.join(model_dir, "nlu")) assert metadata.get("training_data") is not None assert os.path.exists( os.path.join(model_dir, "nlu", training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH) ) def test_train_help(run): output = run("train", "--help") help_text = """usage: rasa train [-h] [-v] [-vv] [--quiet] [--data DATA [DATA ...]] [-c CONFIG] [-d DOMAIN] [--out OUT] [--augmentation AUGMENTATION] [--debug-plots] [--num-threads NUM_THREADS] [--fixed-model-name FIXED_MODEL_NAME] [--persist-nlu-data] [--force] {core,nlu} ...""" lines = help_text.split("\n") # expected help text lines should appear somewhere in the output printed_help = set(output.outlines) for line in lines: assert line in printed_help def test_train_nlu_help(run: Callable[..., RunResult]): output = run("train", "nlu", "--help") help_text = """usage: rasa train nlu [-h] [-v] [-vv] [--quiet] [-c CONFIG] [--out OUT] [-u NLU] [--num-threads NUM_THREADS] [--fixed-model-name FIXED_MODEL_NAME] [--persist-nlu-data]""" lines = help_text.split("\n") # expected help text lines should appear somewhere in the output printed_help = set(output.outlines) for line in lines: assert line in printed_help def test_train_core_help(run: Callable[..., RunResult]): output = run("train", "core", "--help") help_text = """usage: rasa train core [-h] [-v] [-vv] [--quiet] [-s STORIES] [-d DOMAIN] [-c CONFIG [CONFIG ...]] [--out OUT] [--augmentation AUGMENTATION] [--debug-plots] [--force] [--fixed-model-name FIXED_MODEL_NAME] [--percentages [PERCENTAGES [PERCENTAGES ...]]] [--runs RUNS]""" lines = help_text.split("\n") # expected help text lines should appear somewhere in the output printed_help = set(output.outlines) for line in lines: assert line in printed_help @pytest.mark.parametrize( "parameters", [ { "config_data": {"language": "en", "pipeline": "supervised"}, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS_CORE, "error": False, }, { "config_data": { "language": "en", "pipeline": "supervised", "policies": None, }, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS_CORE, "error": False, }, { "config_data": {}, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS, "error": True, }, { "config_data": { "policies": ["TEDPolicy", "FallbackPolicy"], "imports": "other-folder", }, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS_NLU, "error": True, }, { "config_data": None, "default_config": { "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS_NLU, "error": True, }, { "config_data": None, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS, "error": False, }, { "config_data": None, "default_config": {"language": "en", "pipeline": "supervised"}, "mandatory_keys": CONFIG_MANDATORY_KEYS_CORE, "error": False, }, { "config_data": None, "default_config": None, "mandatory_keys": CONFIG_MANDATORY_KEYS, "error": True, }, ], ) def test_get_valid_config(parameters): config_path = None if parameters["config_data"] is not None: config_path = os.path.join(tempfile.mkdtemp(), "config.yml") rasa.shared.utils.io.write_yaml(parameters["config_data"], config_path) default_config_path = None if parameters["default_config"] is not None: default_config_path = os.path.join(tempfile.mkdtemp(), "default-config.yml") rasa.shared.utils.io.write_yaml( parameters["default_config"], default_config_path ) if parameters["error"]: with pytest.raises(SystemExit): _get_valid_config(config_path, parameters["mandatory_keys"]) else: config_path = _get_valid_config( config_path, parameters["mandatory_keys"], default_config_path ) config_data = rasa.shared.utils.io.read_yaml_file(config_path) for k in parameters["mandatory_keys"]: assert k in config_data def test_get_valid_config_with_non_existing_file(): with pytest.raises(SystemExit): _get_valid_config("non-existing-file.yml", CONFIG_MANDATORY_KEYS)

30.826804

92

0.590596

import os import tempfile from pathlib import Path import pytest from typing import Callable from _pytest.pytester import RunResult import rasa.shared.utils.io from rasa import model from rasa.nlu.model import Metadata from rasa.shared.nlu.training_data import training_data from rasa.cli.train import _get_valid_config from rasa.shared.constants import ( CONFIG_MANDATORY_KEYS_CORE, CONFIG_MANDATORY_KEYS_NLU, CONFIG_MANDATORY_KEYS, ) import rasa.utils.io as io_utils def test_train(run_in_simple_project: Callable[..., RunResult]): temp_dir = os.getcwd() run_in_simple_project( "train", "-c", "config.yml", "-d", "domain.yml", "--data", "data", "--out", "train_models", "--fixed-model-name", "test-model", ) assert os.path.exists(os.path.join(temp_dir, "train_models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "train_models")) assert len(files) == 1 assert os.path.basename(files[0]) == "test-model.tar.gz" model_dir = model.get_model("train_models") assert model_dir is not None metadata = Metadata.load(os.path.join(model_dir, "nlu")) assert metadata.get("training_data") is None assert not os.path.exists( os.path.join(model_dir, "nlu", training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH) ) def test_train_persist_nlu_data(run_in_simple_project: Callable[..., RunResult]): temp_dir = os.getcwd() run_in_simple_project( "train", "-c", "config.yml", "-d", "domain.yml", "--data", "data", "--out", "train_models", "--fixed-model-name", "test-model", "--persist-nlu-data", ) assert os.path.exists(os.path.join(temp_dir, "train_models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "train_models")) assert len(files) == 1 assert os.path.basename(files[0]) == "test-model.tar.gz" model_dir = model.get_model("train_models") assert model_dir is not None metadata = Metadata.load(os.path.join(model_dir, "nlu")) assert metadata.get("training_data") is not None assert os.path.exists( os.path.join(model_dir, "nlu", training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH) ) def test_train_core_compare(run_in_simple_project: Callable[..., RunResult]): temp_dir = os.getcwd() rasa.shared.utils.io.write_yaml( { "language": "en", "pipeline": "supervised_embeddings", "policies": [{"name": "MemoizationPolicy"}], }, "config_1.yml", ) rasa.shared.utils.io.write_yaml( { "language": "en", "pipeline": "supervised_embeddings", "policies": [{"name": "MemoizationPolicy"}], }, "config_2.yml", ) run_in_simple_project( "train", "core", "-c", "config_1.yml", "config_2.yml", "--stories", "data/stories.yml", "--out", "core_comparison_results", "--runs", "2", "--percentages", "25", "75", "--augmentation", "5", ) assert os.path.exists(os.path.join(temp_dir, "core_comparison_results")) run_directories = rasa.shared.utils.io.list_subdirectories( os.path.join(temp_dir, "core_comparison_results") ) assert len(run_directories) == 2 model_files = rasa.shared.utils.io.list_files( os.path.join(temp_dir, "core_comparison_results", run_directories[0]) ) assert len(model_files) == 4 assert model_files[0].endswith("tar.gz") def test_train_no_domain_exists( run_in_simple_project: Callable[..., RunResult] ) -> None: os.remove("domain.yml") run_in_simple_project( "train", "-c", "config.yml", "--data", "data", "--out", "train_models_no_domain", "--fixed-model-name", "nlu-model-only", ) assert os.path.exists("train_models_no_domain") files = rasa.shared.utils.io.list_files("train_models_no_domain") assert len(files) == 1 trained_model_path = "train_models_no_domain/nlu-model-only.tar.gz" unpacked = model.unpack_model(trained_model_path) metadata_path = os.path.join(unpacked, "nlu", "metadata.json") assert os.path.exists(metadata_path) def test_train_skip_on_model_not_changed( run_in_simple_project_with_model: Callable[..., RunResult] ): temp_dir = os.getcwd() assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 file_name = files[0] run_in_simple_project_with_model("train") assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 assert file_name == files[0] def test_train_force(run_in_simple_project_with_model: Callable[..., RunResult]): temp_dir = os.getcwd() assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 run_in_simple_project_with_model("train", "--force") assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 2 def test_train_with_only_nlu_data(run_in_simple_project: Callable[..., RunResult]): temp_dir = Path.cwd() for core_file in ["stories.yml", "rules.yml"]: assert (temp_dir / "data" / core_file).exists() (temp_dir / "data" / core_file).unlink() run_in_simple_project("train", "--fixed-model-name", "test-model") assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 assert os.path.basename(files[0]) == "test-model.tar.gz" def test_train_with_only_core_data(run_in_simple_project: Callable[..., RunResult]): temp_dir = os.getcwd() assert os.path.exists(os.path.join(temp_dir, "data/nlu.yml")) os.remove(os.path.join(temp_dir, "data/nlu.yml")) run_in_simple_project("train", "--fixed-model-name", "test-model") assert os.path.exists(os.path.join(temp_dir, "models")) files = rasa.shared.utils.io.list_files(os.path.join(temp_dir, "models")) assert len(files) == 1 assert os.path.basename(files[0]) == "test-model.tar.gz" def test_train_core(run_in_simple_project: Callable[..., RunResult]): run_in_simple_project( "train", "core", "-c", "config.yml", "-d", "domain.yml", "--stories", "data", "--out", "train_rasa_models", "--fixed-model-name", "rasa-model", ) assert os.path.exists("train_rasa_models/rasa-model.tar.gz") assert os.path.isfile("train_rasa_models/rasa-model.tar.gz") def test_train_core_no_domain_exists(run_in_simple_project: Callable[..., RunResult]): os.remove("domain.yml") run_in_simple_project( "train", "core", "--config", "config.yml", "--domain", "domain1.yml", "--stories", "data", "--out", "train_rasa_models_no_domain", "--fixed-model-name", "rasa-model", ) assert not os.path.exists("train_rasa_models_no_domain/rasa-model.tar.gz") assert not os.path.isfile("train_rasa_models_no_domain/rasa-model.tar.gz") def test_train_nlu(run_in_simple_project: Callable[..., RunResult]): run_in_simple_project( "train", "nlu", "-c", "config.yml", "--nlu", "data/nlu.md", "--out", "train_models", ) assert os.path.exists("train_models") files = rasa.shared.utils.io.list_files("train_models") assert len(files) == 1 assert os.path.basename(files[0]).startswith("nlu-") model_dir = model.get_model("train_models") assert model_dir is not None metadata = Metadata.load(os.path.join(model_dir, "nlu")) assert metadata.get("training_data") is None assert not os.path.exists( os.path.join(model_dir, "nlu", training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH) ) def test_train_nlu_persist_nlu_data( run_in_simple_project: Callable[..., RunResult] ) -> None: run_in_simple_project( "train", "nlu", "-c", "config.yml", "--nlu", "data/nlu.md", "--out", "train_models", "--persist-nlu-data", ) assert os.path.exists("train_models") files = rasa.shared.utils.io.list_files("train_models") assert len(files) == 1 assert os.path.basename(files[0]).startswith("nlu-") model_dir = model.get_model("train_models") assert model_dir is not None metadata = Metadata.load(os.path.join(model_dir, "nlu")) assert metadata.get("training_data") is not None assert os.path.exists( os.path.join(model_dir, "nlu", training_data.DEFAULT_TRAINING_DATA_OUTPUT_PATH) ) def test_train_help(run): output = run("train", "--help") help_text = """usage: rasa train [-h] [-v] [-vv] [--quiet] [--data DATA [DATA ...]] [-c CONFIG] [-d DOMAIN] [--out OUT] [--augmentation AUGMENTATION] [--debug-plots] [--num-threads NUM_THREADS] [--fixed-model-name FIXED_MODEL_NAME] [--persist-nlu-data] [--force] {core,nlu} ...""" lines = help_text.split("\n") printed_help = set(output.outlines) for line in lines: assert line in printed_help def test_train_nlu_help(run: Callable[..., RunResult]): output = run("train", "nlu", "--help") help_text = """usage: rasa train nlu [-h] [-v] [-vv] [--quiet] [-c CONFIG] [--out OUT] [-u NLU] [--num-threads NUM_THREADS] [--fixed-model-name FIXED_MODEL_NAME] [--persist-nlu-data]""" lines = help_text.split("\n") printed_help = set(output.outlines) for line in lines: assert line in printed_help def test_train_core_help(run: Callable[..., RunResult]): output = run("train", "core", "--help") help_text = """usage: rasa train core [-h] [-v] [-vv] [--quiet] [-s STORIES] [-d DOMAIN] [-c CONFIG [CONFIG ...]] [--out OUT] [--augmentation AUGMENTATION] [--debug-plots] [--force] [--fixed-model-name FIXED_MODEL_NAME] [--percentages [PERCENTAGES [PERCENTAGES ...]]] [--runs RUNS]""" lines = help_text.split("\n") printed_help = set(output.outlines) for line in lines: assert line in printed_help @pytest.mark.parametrize( "parameters", [ { "config_data": {"language": "en", "pipeline": "supervised"}, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS_CORE, "error": False, }, { "config_data": { "language": "en", "pipeline": "supervised", "policies": None, }, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS_CORE, "error": False, }, { "config_data": {}, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS, "error": True, }, { "config_data": { "policies": ["TEDPolicy", "FallbackPolicy"], "imports": "other-folder", }, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS_NLU, "error": True, }, { "config_data": None, "default_config": { "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS_NLU, "error": True, }, { "config_data": None, "default_config": { "language": "en", "pipeline": "supervised", "policies": ["TEDPolicy", "FallbackPolicy"], }, "mandatory_keys": CONFIG_MANDATORY_KEYS, "error": False, }, { "config_data": None, "default_config": {"language": "en", "pipeline": "supervised"}, "mandatory_keys": CONFIG_MANDATORY_KEYS_CORE, "error": False, }, { "config_data": None, "default_config": None, "mandatory_keys": CONFIG_MANDATORY_KEYS, "error": True, }, ], ) def test_get_valid_config(parameters): config_path = None if parameters["config_data"] is not None: config_path = os.path.join(tempfile.mkdtemp(), "config.yml") rasa.shared.utils.io.write_yaml(parameters["config_data"], config_path) default_config_path = None if parameters["default_config"] is not None: default_config_path = os.path.join(tempfile.mkdtemp(), "default-config.yml") rasa.shared.utils.io.write_yaml( parameters["default_config"], default_config_path ) if parameters["error"]: with pytest.raises(SystemExit): _get_valid_config(config_path, parameters["mandatory_keys"]) else: config_path = _get_valid_config( config_path, parameters["mandatory_keys"], default_config_path ) config_data = rasa.shared.utils.io.read_yaml_file(config_path) for k in parameters["mandatory_keys"]: assert k in config_data def test_get_valid_config_with_non_existing_file(): with pytest.raises(SystemExit): _get_valid_config("non-existing-file.yml", CONFIG_MANDATORY_KEYS)

true

f734da6aa6ab9faacbf6cc70f08d939bab727772

146

py

Python

old/03/08.py

systemquant/book-pandas-for-finance

90b7eb9be1de20a12ae72b9bb5d51424a979b174

[ "MIT" ]

10

2021-02-04T12:49:56.000Z

2022-03-26T11:28:11.000Z

old/03/08.py

systemquant/book-pandas-for-finance

90b7eb9be1de20a12ae72b9bb5d51424a979b174

[ "MIT" ]

1

2022-03-24T03:47:14.000Z

2022-03-24T03:54:52.000Z

old/03/08.py

systemquant/book-pandas-for-finance

90b7eb9be1de20a12ae72b9bb5d51424a979b174

[ "MIT" ]

4

2021-07-17T16:50:15.000Z

2022-03-22T05:55:34.000Z

from pandas import Series data = [1000, 2000, 3000] index = ["메로나", "구구콘", "하겐다즈"] s = Series(data=data, index=index) print(s.loc['메로나':'구구콘'])

18.25

34

0.636986

from pandas import Series data = [1000, 2000, 3000] index = ["메로나", "구구콘", "하겐다즈"] s = Series(data=data, index=index) print(s.loc['메로나':'구구콘'])

true

f734daed1be6b52d4591d0e3cbfdf1517893f84d

5,975

py

Python

proper_forms/fields/field.py

jpsca/hyperform

d5c450ad8684a853fed26f8c2606877151125a9e

[ "MIT" ]

2

2020-09-30T22:41:00.000Z

2020-12-04T16:47:17.000Z

proper_forms/fields/field.py

jpsca/hyperform

d5c450ad8684a853fed26f8c2606877151125a9e

[ "MIT" ]

null

proper_forms/fields/field.py

jpsca/hyperform

d5c450ad8684a853fed26f8c2606877151125a9e

[ "MIT" ]

null

import re from .field_renderable import FieldRenderable __all__ = ("Field", ) default_error_messages = { "required": "This field is required.", "type": "Invalid type.", "min_num": "You need at least {num} values.", "max_num": "You can have at most {num} values.", } HARD_MAX_NUM = 1000 class Field(FieldRenderable): r""" Arguments are: *validators, name=None, required=False, strict=True, error_messages=None, prepare=None, clean=None, collection (bool): This field takes an open number of values of the same kind. For example, a list of comma separated tags or email addresses. sep (str): If `collection` is True, string to separate each value (default is ","). Ignored otherwise multiple=False, min_num=None, max_num=None, **extra """ __slots__ = ( "validators", "name", "required", "strict", "error_messages", "multiple", "min_num", "max_num", "collection", "sep", "extra", ) object_value = None input_values = None input_type = "text" error = None error_value = None updated = False def __init__( self, *validators, name=None, required=False, strict=True, error_messages=None, multiple=False, min_num=None, max_num=None, collection=False, sep=",", prepare=None, clean=None, **extra ): self.validators = validators self.name = name or "" self.required = required self.strict = strict self.min_num = min_num if max_num is not None: max_num = min(max_num, HARD_MAX_NUM) self.max_num = max_num self.error_messages = error_messages or {} self.collection = collection if collection: self.sep = sep multiple = False self.multiple = multiple self.custom_prepare = prepare self.custom_clean = clean self.extra = extra def load_data(self, input_values=None, object_value=None): self.input_values = input_values self.object_value = object_value @property def values(self): if self.input_values: return self.input_values if self.object_value: return (self.custom_prepare or self.prepare)(self.object_value) return [] @property def value(self): return self.values[0] if self.values else "" def get_value(self, index=0): if self.values and index < len(self.values): return self.values[index] return "" def prepare(self, object_value): return [object_value] def validate(self): self._reset() values = [str(value).strip() for value in self.input_values or []] if not values: if self.required: self._set_error("required") return None values = self._pre(values) pyvalues = self._typecast_values(values) if self.error: return None # Typecasting with `strict=False` could've emptied the values without erroring. # An empty string is only an error if the field is required if (not pyvalues or pyvalues[0] == "") and self.required: self._set_error("required") return None self._validate_values(pyvalues) if self.error: return None pyvalue = self._post(pyvalues) if self.custom_clean: pyvalue = self.custom_clean(pyvalue) self.updated = pyvalue != self.object_value return pyvalue def type(self, value, **kwargs): return str(value) # Private def _reset(self): self.error = None self.error_value = None self.updated = False def _pre(self, values): if self.collection: rxsep = r"\s*%s\s*" % re.escape(self.sep.strip()) all_values = [] for value in values: all_values += re.split(rxsep, value) return all_values return values def _post(self, values): if self.collection: return self.sep.join(values) elif self.multiple: return values else: return values[0] if values else None def _typecast_values(self, values): pyvalues = [] for value in values: try: pyvalue = self.type(value, **self.extra) except (ValueError, TypeError, IndexError): pyvalue = None if pyvalue is None: if self.strict: self._set_error("type") self.error_value = value return continue # pragma: no cover pyvalues.append(pyvalue) return pyvalues def _validate_values(self, pyvalues): num_values = len(pyvalues) if self.min_num is not None and self.min_num > num_values: self._set_error("min_num", num=self.min_num) return if self.max_num is not None and self.max_num < num_values: self._set_error("max_num", num=self.max_num) return for validator in self.validators: message = "Invalid value" valid = validator(pyvalues) if valid not in (True, False): valid, message = valid if not valid: self.error = message return def _set_error(self, name, **kwargs): msg = self.error_messages.get(name) or default_error_messages.get(name, "") for key, repl in kwargs.items(): msg = msg.replace("{" + key + "}", str(repl)) self.error = msg or name

25.105042

87

0.554477

import re from .field_renderable import FieldRenderable __all__ = ("Field", ) default_error_messages = { "required": "This field is required.", "type": "Invalid type.", "min_num": "You need at least {num} values.", "max_num": "You can have at most {num} values.", } HARD_MAX_NUM = 1000 class Field(FieldRenderable): __slots__ = ( "validators", "name", "required", "strict", "error_messages", "multiple", "min_num", "max_num", "collection", "sep", "extra", ) object_value = None input_values = None input_type = "text" error = None error_value = None updated = False def __init__( self, *validators, name=None, required=False, strict=True, error_messages=None, multiple=False, min_num=None, max_num=None, collection=False, sep=",", prepare=None, clean=None, **extra ): self.validators = validators self.name = name or "" self.required = required self.strict = strict self.min_num = min_num if max_num is not None: max_num = min(max_num, HARD_MAX_NUM) self.max_num = max_num self.error_messages = error_messages or {} self.collection = collection if collection: self.sep = sep multiple = False self.multiple = multiple self.custom_prepare = prepare self.custom_clean = clean self.extra = extra def load_data(self, input_values=None, object_value=None): self.input_values = input_values self.object_value = object_value @property def values(self): if self.input_values: return self.input_values if self.object_value: return (self.custom_prepare or self.prepare)(self.object_value) return [] @property def value(self): return self.values[0] if self.values else "" def get_value(self, index=0): if self.values and index < len(self.values): return self.values[index] return "" def prepare(self, object_value): return [object_value] def validate(self): self._reset() values = [str(value).strip() for value in self.input_values or []] if not values: if self.required: self._set_error("required") return None values = self._pre(values) pyvalues = self._typecast_values(values) if self.error: return None # An empty string is only an error if the field is required if (not pyvalues or pyvalues[0] == "") and self.required: self._set_error("required") return None self._validate_values(pyvalues) if self.error: return None pyvalue = self._post(pyvalues) if self.custom_clean: pyvalue = self.custom_clean(pyvalue) self.updated = pyvalue != self.object_value return pyvalue def type(self, value, **kwargs): return str(value) # Private def _reset(self): self.error = None self.error_value = None self.updated = False def _pre(self, values): if self.collection: rxsep = r"\s*%s\s*" % re.escape(self.sep.strip()) all_values = [] for value in values: all_values += re.split(rxsep, value) return all_values return values def _post(self, values): if self.collection: return self.sep.join(values) elif self.multiple: return values else: return values[0] if values else None def _typecast_values(self, values): pyvalues = [] for value in values: try: pyvalue = self.type(value, **self.extra) except (ValueError, TypeError, IndexError): pyvalue = None if pyvalue is None: if self.strict: self._set_error("type") self.error_value = value return continue # pragma: no cover pyvalues.append(pyvalue) return pyvalues def _validate_values(self, pyvalues): num_values = len(pyvalues) if self.min_num is not None and self.min_num > num_values: self._set_error("min_num", num=self.min_num) return if self.max_num is not None and self.max_num < num_values: self._set_error("max_num", num=self.max_num) return for validator in self.validators: message = "Invalid value" valid = validator(pyvalues) if valid not in (True, False): valid, message = valid if not valid: self.error = message return def _set_error(self, name, **kwargs): msg = self.error_messages.get(name) or default_error_messages.get(name, "") for key, repl in kwargs.items(): msg = msg.replace("{" + key + "}", str(repl)) self.error = msg or name

true

f734dbc58f6d9f310e99ffbb6ea2299976b3b377

1,078

py

Python

src/genie/libs/parser/iosxe/tests/ShowPlatformSoftwareFedSwitchActivePtpInterfaceInterface/cli/equal/golden_output1_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowPlatformSoftwareFedSwitchActivePtpInterfaceInterface/cli/equal/golden_output1_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowPlatformSoftwareFedSwitchActivePtpInterfaceInterface/cli/equal/golden_output1_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

null

expected_output = { "interface":{ "if_id":"2a", "clock_mode":"BOUNDARY CLOCK", "delay_mechanism":"End-to-End", "delay_req_seq_num":0, "domain_value":0, "log_mean_delay_interval":0, "log_mean_sync_interval":0, "num_info":{ "num_delay_requests_received":0, "num_delay_requests_transmitted":0, "num_delay_responses_received":0, "num_delay_responses_transmitted":0, "num_followup_messages_received":0, "num_followup_messages_transmitted":1710, "num_sync_messages_received":0, "num_sync_messages_transmitted":1710 }, "port_info":{ "clock_identity":"34:ED:1B:FF:FE:7D:F2:80", "mac_address":"34:ED:1B:7D:F2:A1", "mode":1, "number":33, "port_enabled":"TRUE", "state":"MASTER" }, "profile_type":"DEFAULT", "ptp_info":{ "ptp_vlan_id":0, "ptp_vlan_is_valid":"FALSE", "version":2 }, "ptt_port_enabled":"TRUE", "sync_seq_num":44364, "tag_native_vlan":"FALSE" } }

27.641026

51

0.592764

expected_output = { "interface":{ "if_id":"2a", "clock_mode":"BOUNDARY CLOCK", "delay_mechanism":"End-to-End", "delay_req_seq_num":0, "domain_value":0, "log_mean_delay_interval":0, "log_mean_sync_interval":0, "num_info":{ "num_delay_requests_received":0, "num_delay_requests_transmitted":0, "num_delay_responses_received":0, "num_delay_responses_transmitted":0, "num_followup_messages_received":0, "num_followup_messages_transmitted":1710, "num_sync_messages_received":0, "num_sync_messages_transmitted":1710 }, "port_info":{ "clock_identity":"34:ED:1B:FF:FE:7D:F2:80", "mac_address":"34:ED:1B:7D:F2:A1", "mode":1, "number":33, "port_enabled":"TRUE", "state":"MASTER" }, "profile_type":"DEFAULT", "ptp_info":{ "ptp_vlan_id":0, "ptp_vlan_is_valid":"FALSE", "version":2 }, "ptt_port_enabled":"TRUE", "sync_seq_num":44364, "tag_native_vlan":"FALSE" } }

true

f734dca4d057e52d82e93c9c2cb00c6a673f1488

1,303

py

Python

azure-mgmt-compute/azure/mgmt/compute/v2016_04_30_preview/models/virtual_machine_scale_set_instance_view_statuses_summary.py

v-Ajnava/azure-sdk-for-python

a1f6f80eb5869c5b710e8bfb66146546697e2a6f

[ "MIT" ]

4

2016-06-17T23:25:29.000Z

2022-03-30T22:37:45.000Z

azure-mgmt-compute/azure/mgmt/compute/v2016_04_30_preview/models/virtual_machine_scale_set_instance_view_statuses_summary.py

v-Ajnava/azure-sdk-for-python

a1f6f80eb5869c5b710e8bfb66146546697e2a6f

[ "MIT" ]

2

2016-09-30T21:40:24.000Z

2017-11-10T18:16:18.000Z

azure-mgmt-compute/azure/mgmt/compute/v2016_04_30_preview/models/virtual_machine_scale_set_instance_view_statuses_summary.py

v-Ajnava/azure-sdk-for-python

a1f6f80eb5869c5b710e8bfb66146546697e2a6f

[ "MIT" ]

3

2016-05-03T20:49:46.000Z

2017-10-05T21:05:27.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class VirtualMachineScaleSetInstanceViewStatusesSummary(Model): """Instance view statuses summary for virtual machines of a virtual machine scale set. Variables are only populated by the server, and will be ignored when sending a request. :ivar statuses_summary: The extensions information. :vartype statuses_summary: list[~azure.mgmt.compute.v2016_04_30_preview.models.VirtualMachineStatusCodeCount] """ _validation = { 'statuses_summary': {'readonly': True}, } _attribute_map = { 'statuses_summary': {'key': 'statusesSummary', 'type': '[VirtualMachineStatusCodeCount]'}, } def __init__(self): super(VirtualMachineScaleSetInstanceViewStatusesSummary, self).__init__() self.statuses_summary = None

34.289474

98

0.649271

from msrest.serialization import Model class VirtualMachineScaleSetInstanceViewStatusesSummary(Model): _validation = { 'statuses_summary': {'readonly': True}, } _attribute_map = { 'statuses_summary': {'key': 'statusesSummary', 'type': '[VirtualMachineStatusCodeCount]'}, } def __init__(self): super(VirtualMachineScaleSetInstanceViewStatusesSummary, self).__init__() self.statuses_summary = None

true

f734dd1ef6c0c61d40522bf0bb879127537e3d4f

1,272

py

Python

exporter/myapp.py

zeshahid/websitecheck_exporter

282e5f19d9e778479bbf9e00b468e1313060acba

[ "MIT" ]

null

exporter/myapp.py

zeshahid/websitecheck_exporter

282e5f19d9e778479bbf9e00b468e1313060acba

[ "MIT" ]

null

exporter/myapp.py

zeshahid/websitecheck_exporter

282e5f19d9e778479bbf9e00b468e1313060acba

[ "MIT" ]

null

from typing import Counter from prometheus_client import start_http_server, Summary, Counter, Gauge ,__all__ ,Histogram import random import time from requests import get import requests from requests.api import post urls =["https://httpstat.us/503","https://httpstat.us/200"] sitestatus = Gauge('sample_external_url_up', 'site status check', ['endpoint']) response_time = Gauge('sample_external_url_response_ms', 'Response Time in milliseconds', ['endpoint']) # Create a metric to track time spent and requests made. def response_request (url): response = requests.get(url) response_time.labels(endpoint=a).set(response.elapsed.total_seconds()) # response_time2.labels(endpoint=a).observe(response.elapsed.total_seconds()) # Create a metric to for status def request_state (a): response = requests.get(a) if response.status_code == 503: sitestatus.labels(endpoint=a).set(0) elif response.status_code == 200: sitestatus.labels(endpoint=a).set(1) if __name__ == '__main__': # Start up the server to expose the metrics. start_http_server(8000) # Generate some requests. while True: for a in urls: request_state (a) response_request (a)

34.378378

103

0.699686

from typing import Counter from prometheus_client import start_http_server, Summary, Counter, Gauge ,__all__ ,Histogram import random import time from requests import get import requests from requests.api import post urls =["https://httpstat.us/503","https://httpstat.us/200"] sitestatus = Gauge('sample_external_url_up', 'site status check', ['endpoint']) response_time = Gauge('sample_external_url_response_ms', 'Response Time in milliseconds', ['endpoint']) def response_request (url): response = requests.get(url) response_time.labels(endpoint=a).set(response.elapsed.total_seconds()) def request_state (a): response = requests.get(a) if response.status_code == 503: sitestatus.labels(endpoint=a).set(0) elif response.status_code == 200: sitestatus.labels(endpoint=a).set(1) if __name__ == '__main__': start_http_server(8000) while True: for a in urls: request_state (a) response_request (a)

true

f734dd86bad4fa348424eff36dc67fc52e519284

32

py

Python

HRM.py

asheu96/bme590hrm

c5d3b494149859a38f75452d1e61f6fc1c8bbea3

[ "MIT" ]

null

HRM.py

asheu96/bme590hrm

c5d3b494149859a38f75452d1e61f6fc1c8bbea3

[ "MIT" ]

null

HRM.py

asheu96/bme590hrm

c5d3b494149859a38f75452d1e61f6fc1c8bbea3

[ "MIT" ]

null

# module for heart rate monitor

16

31

0.78125

true

f734ddcdfbef18b57cb25db07dab576af78ed413

2,141

py

Python

services.py

vida18electronic/barcode

265318d893a9dabc71231424252b2476926577ec

[ "Apache-2.0" ]

null

services.py

vida18electronic/barcode

265318d893a9dabc71231424252b2476926577ec

[ "Apache-2.0" ]

null

services.py

vida18electronic/barcode

265318d893a9dabc71231424252b2476926577ec

[ "Apache-2.0" ]

null

import json import requests import sys import os import socket import fcntl import struct #info_device={"MAC_ADDRESS":"xx","IP_ADDRESS":"xxx","BLOCK_ID":"01","STOP_ID":"xxx"} #info_buncher={"BUNCHER_ID":"xx","BUNCH_ID":"xx","COMPOSITION_ID":"xx","TUB_ID":"xx","TxR":"xx","GR":"xx","VARIETY_ID":"xx","BLOCK_ID":"xx"} def getMAC(interface='wlan0'): # Return the MAC address of the specified interface try: str = open('/sys/class/net/%s/address' %interface).read() except: str = "00:00:00:00:00:00" return str[0:17] def get_local_ip_address(target): ipaddr = '' try: s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.connect((target, 8000)) ipaddr = s.getsockname()[0] s.close() except: pass return ipaddr def savedevice(info_device): mac=str(getMAC()) mac=mac.upper() ip=str(get_local_ip_address('10.0.1.1')) ip=ip.upper() info_device["MAC_ADDRESS"]=mac info_device["IP_ADDRESS"]=ip #print(getMAC) hostname="http://tfsnew.vida18.com:8078/api/devices" #responde = os.system("ping -c 1 "+hostname) r=requests.post(hostname,json=info_device) if str(r.json())== '1': return True else: return False def savebunch(info_buncher,info_device): mac=str(getMAC()) mac=mac.upper() info_device["MAC_ADDRESS"]=mac info_total={"MAC_ADDRESS":info_device["MAC_ADDRESS"], "STOP_ID":'S'+str(info_device["STOP_ID"]), "BLOCK_ID":'BK'+str(info_device["BLOCK_ID"]), "BUNCHER_ID":'B'+str(info_buncher["BUNCHER_ID"]), "BUNCH_ID":'R'+str(info_buncher["BUNCH_ID"]), "COMPOSITION_ID":'CM'+str(info_buncher["COMPOSITION_ID"]), "SORTER_ID":'C'+str(info_buncher["BUNCHER_ID"]), "TUB_ID":'T'+str(info_buncher["TUB_ID"]), "VARIETY_ID":'V'+str(info_buncher["VARIETY_ID"]) } hostname="http://tfsnew.vida18.com:8078/api/tracking" r=requests.post(hostname,json=info_total) if str(r.json())== '1': return True else: return False #print(savebunch(info_buncher,info_device))

29.328767

140

0.627744

import json import requests import sys import os import socket import fcntl import struct def getMAC(interface='wlan0'): try: str = open('/sys/class/net/%s/address' %interface).read() except: str = "00:00:00:00:00:00" return str[0:17] def get_local_ip_address(target): ipaddr = '' try: s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.connect((target, 8000)) ipaddr = s.getsockname()[0] s.close() except: pass return ipaddr def savedevice(info_device): mac=str(getMAC()) mac=mac.upper() ip=str(get_local_ip_address('10.0.1.1')) ip=ip.upper() info_device["MAC_ADDRESS"]=mac info_device["IP_ADDRESS"]=ip hostname="http://tfsnew.vida18.com:8078/api/devices" r=requests.post(hostname,json=info_device) if str(r.json())== '1': return True else: return False def savebunch(info_buncher,info_device): mac=str(getMAC()) mac=mac.upper() info_device["MAC_ADDRESS"]=mac info_total={"MAC_ADDRESS":info_device["MAC_ADDRESS"], "STOP_ID":'S'+str(info_device["STOP_ID"]), "BLOCK_ID":'BK'+str(info_device["BLOCK_ID"]), "BUNCHER_ID":'B'+str(info_buncher["BUNCHER_ID"]), "BUNCH_ID":'R'+str(info_buncher["BUNCH_ID"]), "COMPOSITION_ID":'CM'+str(info_buncher["COMPOSITION_ID"]), "SORTER_ID":'C'+str(info_buncher["BUNCHER_ID"]), "TUB_ID":'T'+str(info_buncher["TUB_ID"]), "VARIETY_ID":'V'+str(info_buncher["VARIETY_ID"]) } hostname="http://tfsnew.vida18.com:8078/api/tracking" r=requests.post(hostname,json=info_total) if str(r.json())== '1': return True else: return False

true

f734ddfee0c273d99cb4d87ab366012e453afc2e

7,323

py

Python

tests/test_memory_leak.py

fintzd/rasa

6359be5509c7d87cd29c2ab5149bc45e843fea85

[ "Apache-2.0" ]

9,701

2019-04-16T15:46:27.000Z

2022-03-31T11:52:18.000Z

tests/test_memory_leak.py

fintzd/rasa

6359be5509c7d87cd29c2ab5149bc45e843fea85

[ "Apache-2.0" ]

6,420

2019-04-16T15:58:22.000Z

2022-03-31T17:54:35.000Z

tests/test_memory_leak.py

fintzd/rasa

6359be5509c7d87cd29c2ab5149bc45e843fea85

[ "Apache-2.0" ]

3,063

2019-04-16T15:23:52.000Z

2022-03-31T00:01:12.000Z

import abc import json import subprocess import sys import tempfile import time from pathlib import Path from typing import Text, List, Tuple, Optional, Union import memory_profiler import psutil import pytest import rasa import rasa.shared.utils.io PROFILING_INTERVAL = 0.1 # Enable this to plot the results locally WRITE_RESULTS_TO_DISK = False def _custom_default_config( tmp_path: Union[Path, Text], epochs: int, max_history: Optional[int] = -1 ) -> Text: # Override default config to use custom amount of epochs default_config = Path("rasa", "shared", "importers", "default_config.yml") config = rasa.shared.utils.io.read_yaml_file(default_config) for model_part, items in config.items(): for item in items: if "epochs" in item: item["epochs"] = epochs if "max_history" in item and max_history != -1: item["max_history"] = None config_for_test = Path(tmp_path) / "test_config.yml" rasa.shared.utils.io.write_yaml(config, config_for_test) return str(config_for_test) class MemoryLeakTest(abc.ABC): """Generic template for memory leak tests.""" @property def max_memory_threshold_mb(self) -> float: return 1000 @pytest.fixture @abc.abstractmethod def name_for_dumped_files(self) -> Text: raise NotImplementedError @abc.abstractmethod def function_to_profile(self) -> None: raise NotImplementedError @pytest.mark.timeout(720, func_only=True) def test_for_memory_leak( self, name_for_dumped_files: Text, tmp_path: Path, ) -> None: # Run as separate process to avoid other things affecting the memory usage. # Unfortunately `memory-profiler` doesn't work properly with # `multiprocessing.Process` as it can't handle the process exit process = subprocess.Popen( [ sys.executable, "-c", ( f"from {__name__} import {self.__class__.__name__}; " f"t = {self.__class__.__name__}();" f"t.function_to_profile()" ), ], # Force TensorFlow to use CPU so we can track the memory usage env={"CUDA_VISIBLE_DEVICES": "-1"}, ) # Wait until process is running to avoid race conditions with the memory # profiling while not psutil.pid_exists(process.pid): time.sleep(0.01) results = memory_profiler.memory_usage( process, interval=PROFILING_INTERVAL, include_children=True, timestamps=True, ) # `memory-profiler` sometimes adds `None` values at the end which we don't need results = [ memory_timestamp for memory_timestamp in results if memory_timestamp is not None ] if WRITE_RESULTS_TO_DISK: self._write_results(name_for_dumped_files, results) max_memory_usage = max(results, key=lambda memory_time: memory_time[0])[0] assert max_memory_usage < self.max_memory_threshold_mb @staticmethod def _write_results(base_name: Text, results: List[Tuple[float]]) -> None: mprof_plot = Path(f"{base_name}_plot.txt") mprof_results = Path(f"{base_name}_raw.json") # plot this via `mprof plot mprof_result.txt` with open(mprof_plot, "w") as f: for memory, timestamp in results: f.write(f"MEM {memory:.6f} {timestamp:.4f}\n") # dump result as json to be able analyze them without re-running the test with open(mprof_results, "w") as f: f.write(json.dumps(results)) class TestNLULeakManyEpochs(MemoryLeakTest): """Tests for memory leaks in NLU components when training with many epochs.""" @property def epochs(self) -> int: return 30 @property def max_memory_threshold_mb(self) -> float: return 2200 def function_to_profile(self) -> None: import rasa.model_training with tempfile.TemporaryDirectory() as temp_dir: rasa.model_training.train_nlu( _custom_default_config(temp_dir, epochs=self.epochs), Path("data", "test_nlu_no_responses", "sara_nlu_data.yml"), output=temp_dir, ) @pytest.fixture() def name_for_dumped_files(self) -> Text: return ( f"memory_usage_rasa_nlu_{rasa.__version__}_" f"epochs{self.epochs}_training_runs1" ) class TestCoreLeakManyEpochs(MemoryLeakTest): """Tests for memory leaks in Core policies when training with many epochs.""" @property def epochs(self) -> int: return 200 @property def max_memory_threshold_mb(self) -> float: return 2000 def function_to_profile(self) -> None: import rasa.model_training with tempfile.TemporaryDirectory() as temp_dir: rasa.model_training.train_core( "data/test_domains/default_with_slots.yml", _custom_default_config(temp_dir, epochs=self.epochs, max_history=None), "data/test_yaml_stories/stories_defaultdomain.yml", output=temp_dir, additional_arguments={"augmentation_factor": 20}, ) @pytest.fixture() def name_for_dumped_files(self) -> Text: return ( f"memory_usage_rasa_core_{rasa.__version__}_" f"epochs{self.epochs}_training_runs1" ) class TestCRFDenseFeaturesLeak(MemoryLeakTest): """Tests for memory leaks in NLU the CRF when using dense features.""" @property def epochs(self) -> int: return 1 @property def max_memory_threshold_mb(self) -> float: return 1600 def function_to_profile(self) -> None: import rasa.model_training config = { "pipeline": [ {"name": "SpacyNLP"}, {"name": "SpacyTokenizer"}, {"name": "SpacyFeaturizer"}, { "name": "CRFEntityExtractor", "features": [ ["pos", "pos2"], [ "bias", "prefix5", "prefix2", "suffix5", "suffix3", "suffix2", "pos", "pos2", "digit", "text_dense_features", ], ["pos", "pos2"], ], }, ] } with tempfile.TemporaryDirectory() as temp_dir: config_for_test = Path(temp_dir) / "test_config.yml" rasa.shared.utils.io.write_yaml(config, config_for_test) rasa.model_training.train_nlu( str(config_for_test), str(Path("data", "test_nlu_no_responses", "sara_nlu_data.yml")), output=temp_dir, ) @pytest.fixture() def name_for_dumped_files(self) -> Text: return f"memory_usage_rasa_nlu_crf_dense_{rasa.__version__}_"

31.294872

87

0.583777

import abc import json import subprocess import sys import tempfile import time from pathlib import Path from typing import Text, List, Tuple, Optional, Union import memory_profiler import psutil import pytest import rasa import rasa.shared.utils.io PROFILING_INTERVAL = 0.1 WRITE_RESULTS_TO_DISK = False def _custom_default_config( tmp_path: Union[Path, Text], epochs: int, max_history: Optional[int] = -1 ) -> Text: default_config = Path("rasa", "shared", "importers", "default_config.yml") config = rasa.shared.utils.io.read_yaml_file(default_config) for model_part, items in config.items(): for item in items: if "epochs" in item: item["epochs"] = epochs if "max_history" in item and max_history != -1: item["max_history"] = None config_for_test = Path(tmp_path) / "test_config.yml" rasa.shared.utils.io.write_yaml(config, config_for_test) return str(config_for_test) class MemoryLeakTest(abc.ABC): @property def max_memory_threshold_mb(self) -> float: return 1000 @pytest.fixture @abc.abstractmethod def name_for_dumped_files(self) -> Text: raise NotImplementedError @abc.abstractmethod def function_to_profile(self) -> None: raise NotImplementedError @pytest.mark.timeout(720, func_only=True) def test_for_memory_leak( self, name_for_dumped_files: Text, tmp_path: Path, ) -> None: # `multiprocessing.Process` as it can't handle the process exit process = subprocess.Popen( [ sys.executable, "-c", ( f"from {__name__} import {self.__class__.__name__}; " f"t = {self.__class__.__name__}();" f"t.function_to_profile()" ), ], env={"CUDA_VISIBLE_DEVICES": "-1"}, ) while not psutil.pid_exists(process.pid): time.sleep(0.01) results = memory_profiler.memory_usage( process, interval=PROFILING_INTERVAL, include_children=True, timestamps=True, ) results = [ memory_timestamp for memory_timestamp in results if memory_timestamp is not None ] if WRITE_RESULTS_TO_DISK: self._write_results(name_for_dumped_files, results) max_memory_usage = max(results, key=lambda memory_time: memory_time[0])[0] assert max_memory_usage < self.max_memory_threshold_mb @staticmethod def _write_results(base_name: Text, results: List[Tuple[float]]) -> None: mprof_plot = Path(f"{base_name}_plot.txt") mprof_results = Path(f"{base_name}_raw.json") # plot this via `mprof plot mprof_result.txt` with open(mprof_plot, "w") as f: for memory, timestamp in results: f.write(f"MEM {memory:.6f} {timestamp:.4f}\n") # dump result as json to be able analyze them without re-running the test with open(mprof_results, "w") as f: f.write(json.dumps(results)) class TestNLULeakManyEpochs(MemoryLeakTest): @property def epochs(self) -> int: return 30 @property def max_memory_threshold_mb(self) -> float: return 2200 def function_to_profile(self) -> None: import rasa.model_training with tempfile.TemporaryDirectory() as temp_dir: rasa.model_training.train_nlu( _custom_default_config(temp_dir, epochs=self.epochs), Path("data", "test_nlu_no_responses", "sara_nlu_data.yml"), output=temp_dir, ) @pytest.fixture() def name_for_dumped_files(self) -> Text: return ( f"memory_usage_rasa_nlu_{rasa.__version__}_" f"epochs{self.epochs}_training_runs1" ) class TestCoreLeakManyEpochs(MemoryLeakTest): @property def epochs(self) -> int: return 200 @property def max_memory_threshold_mb(self) -> float: return 2000 def function_to_profile(self) -> None: import rasa.model_training with tempfile.TemporaryDirectory() as temp_dir: rasa.model_training.train_core( "data/test_domains/default_with_slots.yml", _custom_default_config(temp_dir, epochs=self.epochs, max_history=None), "data/test_yaml_stories/stories_defaultdomain.yml", output=temp_dir, additional_arguments={"augmentation_factor": 20}, ) @pytest.fixture() def name_for_dumped_files(self) -> Text: return ( f"memory_usage_rasa_core_{rasa.__version__}_" f"epochs{self.epochs}_training_runs1" ) class TestCRFDenseFeaturesLeak(MemoryLeakTest): @property def epochs(self) -> int: return 1 @property def max_memory_threshold_mb(self) -> float: return 1600 def function_to_profile(self) -> None: import rasa.model_training config = { "pipeline": [ {"name": "SpacyNLP"}, {"name": "SpacyTokenizer"}, {"name": "SpacyFeaturizer"}, { "name": "CRFEntityExtractor", "features": [ ["pos", "pos2"], [ "bias", "prefix5", "prefix2", "suffix5", "suffix3", "suffix2", "pos", "pos2", "digit", "text_dense_features", ], ["pos", "pos2"], ], }, ] } with tempfile.TemporaryDirectory() as temp_dir: config_for_test = Path(temp_dir) / "test_config.yml" rasa.shared.utils.io.write_yaml(config, config_for_test) rasa.model_training.train_nlu( str(config_for_test), str(Path("data", "test_nlu_no_responses", "sara_nlu_data.yml")), output=temp_dir, ) @pytest.fixture() def name_for_dumped_files(self) -> Text: return f"memory_usage_rasa_nlu_crf_dense_{rasa.__version__}_"

true

f734dead270c5ec9243064030981190070a9e1f6

83

py

Python

FusionIIIT/applications/hostel_management/urls.py

suyash-code/Fusion-op

eb1242d184482cd6585988028afb182b37abc25c

[ "bzip2-1.0.6" ]

null

FusionIIIT/applications/hostel_management/urls.py

suyash-code/Fusion-op

eb1242d184482cd6585988028afb182b37abc25c

[ "bzip2-1.0.6" ]

null

FusionIIIT/applications/hostel_management/urls.py

suyash-code/Fusion-op

eb1242d184482cd6585988028afb182b37abc25c

[ "bzip2-1.0.6" ]

3

2021-04-19T14:26:01.000Z

2021-04-22T13:34:48.000Z

from django.conf.urls import url app_name = 'hostel_management' urlpatterns = []

13.833333

32

0.759036

from django.conf.urls import url app_name = 'hostel_management' urlpatterns = []

true

f734ded5634d3062fbab77a54c8816eaf43cb21c

327

py

Python

setup.py

PyUnchained/pickle_storage

c0a978701ae59a9feeb3e14026ff0b2353b2e7f5

[ "MIT" ]

null

setup.py

PyUnchained/pickle_storage

c0a978701ae59a9feeb3e14026ff0b2353b2e7f5

[ "MIT" ]

null

setup.py

PyUnchained/pickle_storage

c0a978701ae59a9feeb3e14026ff0b2353b2e7f5

[ "MIT" ]

null

#!/usr/bin/env python from setuptools import setup, find_packages setup(name='pickle-storage', version='0.1', description='Python Pickle-Based Data Storage', author='Tatenda Tambo', author_email='tatendatambo@gmail.com', packages=find_packages(), install_requires=['wrapt>=1.12.1'] )

27.25

53

0.669725

from setuptools import setup, find_packages setup(name='pickle-storage', version='0.1', description='Python Pickle-Based Data Storage', author='Tatenda Tambo', author_email='tatendatambo@gmail.com', packages=find_packages(), install_requires=['wrapt>=1.12.1'] )

true

f734dee5533468bd89adc7a9869f9458710bc490

5,368

py

Python

models.py

vidursatija/SongWCT

c892c2833ff9f85cfb31788babf016699c5eec8f

[ "MIT" ]

null

models.py

vidursatija/SongWCT

c892c2833ff9f85cfb31788babf016699c5eec8f

[ "MIT" ]

null

models.py

vidursatija/SongWCT

c892c2833ff9f85cfb31788babf016699c5eec8f

[ "MIT" ]

null

import torch import torch.nn as nn try: from torch.hub import load_state_dict_from_url except ImportError: from torch.utils.model_zoo import load_url as load_state_dict_from_url from torchsummary import summary import numpy as np class X_Enc(nn.Module): def __init__(self, layers, num_classes=1000, init_weights=True): super(X_Enc, self).__init__() self.features = nn.Sequential(*layers) # layers print(self.features) if init_weights: self._initialize_weights() def forward(self, x): all_maxpools = [] for l in self.features: if isinstance(l, nn.MaxPool1d) == False: x = l(x) else: x, pool_indices = l(x) all_maxpools.append(pool_indices) return x, all_maxpools def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv1d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) def make_layers_enc(cfg): layers = [] conv_layers = [] in_channels = cfg[0] cfg = cfg[1:] for v in cfg: if v == 'M': layers += conv_layers # [nn.Sequential(*conv_layers)] conv_layers = [] layers += [nn.MaxPool1d(kernel_size=2, stride=2, return_indices=True)] else: conv1d = nn.Conv1d(in_channels, v, kernel_size=3, padding=1) conv_layers += [conv1d, nn.ReLU(inplace=True)] in_channels = v if len(conv_layers) > 0: layers += conv_layers # [nn.Sequential(*conv_layers)] return layers configs_enc = [ [128, 128], [128, 128, 128, 'M', 256], [128, 128, 128, 'M', 256, 256, 'M', 512], [128, 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512] ] configs_dec = [ [128, 128], [256, 128, 'M', 128, 128], [512, 256, 'M', 256, 128, 'M', 128, 128], [512, 512, 'M', 512, 256, 'M', 256, 128, 'M', 128, 128] ] def encoder(x, pretrained_path=None, **kwargs): if pretrained_path is not None: kwargs['init_weights'] = False model = X_Enc(make_layers_enc(configs_enc[x-1]), **kwargs) if pretrained_path is not None: model.load_state_dict(torch.load(pretrained_path), strict=False) return model class X_Dec(nn.Module): def __init__(self, layers, num_classes=1000, init_weights=True): super(X_Dec, self).__init__() self.layers = nn.Sequential(*layers) print(self.layers) if init_weights: self._initialize_weights() def forward(self, x, all_maxpools): ct = -1 for l in self.layers: if isinstance(l, nn.MaxUnpool1d) == False: x = l(x) else: x = l(x, all_maxpools[ct]) ct -= 1 return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.ConvTranspose1d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm1d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) def make_layers_dec(cfg): layers = [] conv_layers = [] in_channels = cfg[0] cfg = cfg[1:] for i, v in enumerate(cfg): if v == 'M': layers += conv_layers # [nn.Sequential(*conv_layers)] conv_layers = [] layers += [nn.MaxUnpool1d(kernel_size=2, stride=2)] else: conv1d = nn.ConvTranspose1d(in_channels, v, kernel_size=3, padding=1) if i != len(cfg) - 1: conv_layers += [conv1d, nn.ReLU(inplace=True)] else: conv_layers += [conv1d] in_channels = v if len(conv_layers) > 0: layers += conv_layers # [nn.Sequential(*conv_layers)] return layers def decoder(x, pretrained_path=None, **kwargs): if pretrained_path is not None: kwargs['init_weights'] = False model = X_Dec(make_layers_dec(configs_dec[x-1]), **kwargs) if pretrained_path is not None: model.load_state_dict(torch.load(pretrained_path), strict=False) return model if __name__ == '__main__': device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # PyTorch v0.4.0 encoder = vgg16_enc(x=3, pretrained=True) # .to(device) for k in encoder.state_dict(): print(k) summary(encoder, (3, 224, 224), device="cpu") z, all_maxpools = encoder(torch.from_numpy(np.zeros([1, 3, 224, 224])).float()) decoder = vgg16_dec(x=3, pretrained=False) # .to(device) for k in decoder.state_dict(): print(k) x_rebuild = decoder(z, all_maxpools) # summary(decoder, (256, 56, 56), device="cpu")

32.731707

91

0.575075

import torch import torch.nn as nn try: from torch.hub import load_state_dict_from_url except ImportError: from torch.utils.model_zoo import load_url as load_state_dict_from_url from torchsummary import summary import numpy as np class X_Enc(nn.Module): def __init__(self, layers, num_classes=1000, init_weights=True): super(X_Enc, self).__init__() self.features = nn.Sequential(*layers) print(self.features) if init_weights: self._initialize_weights() def forward(self, x): all_maxpools = [] for l in self.features: if isinstance(l, nn.MaxPool1d) == False: x = l(x) else: x, pool_indices = l(x) all_maxpools.append(pool_indices) return x, all_maxpools def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv1d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) def make_layers_enc(cfg): layers = [] conv_layers = [] in_channels = cfg[0] cfg = cfg[1:] for v in cfg: if v == 'M': layers += conv_layers conv_layers = [] layers += [nn.MaxPool1d(kernel_size=2, stride=2, return_indices=True)] else: conv1d = nn.Conv1d(in_channels, v, kernel_size=3, padding=1) conv_layers += [conv1d, nn.ReLU(inplace=True)] in_channels = v if len(conv_layers) > 0: layers += conv_layers return layers configs_enc = [ [128, 128], [128, 128, 128, 'M', 256], [128, 128, 128, 'M', 256, 256, 'M', 512], [128, 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512] ] configs_dec = [ [128, 128], [256, 128, 'M', 128, 128], [512, 256, 'M', 256, 128, 'M', 128, 128], [512, 512, 'M', 512, 256, 'M', 256, 128, 'M', 128, 128] ] def encoder(x, pretrained_path=None, **kwargs): if pretrained_path is not None: kwargs['init_weights'] = False model = X_Enc(make_layers_enc(configs_enc[x-1]), **kwargs) if pretrained_path is not None: model.load_state_dict(torch.load(pretrained_path), strict=False) return model class X_Dec(nn.Module): def __init__(self, layers, num_classes=1000, init_weights=True): super(X_Dec, self).__init__() self.layers = nn.Sequential(*layers) print(self.layers) if init_weights: self._initialize_weights() def forward(self, x, all_maxpools): ct = -1 for l in self.layers: if isinstance(l, nn.MaxUnpool1d) == False: x = l(x) else: x = l(x, all_maxpools[ct]) ct -= 1 return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.ConvTranspose1d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm1d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) def make_layers_dec(cfg): layers = [] conv_layers = [] in_channels = cfg[0] cfg = cfg[1:] for i, v in enumerate(cfg): if v == 'M': layers += conv_layers conv_layers = [] layers += [nn.MaxUnpool1d(kernel_size=2, stride=2)] else: conv1d = nn.ConvTranspose1d(in_channels, v, kernel_size=3, padding=1) if i != len(cfg) - 1: conv_layers += [conv1d, nn.ReLU(inplace=True)] else: conv_layers += [conv1d] in_channels = v if len(conv_layers) > 0: layers += conv_layers return layers def decoder(x, pretrained_path=None, **kwargs): if pretrained_path is not None: kwargs['init_weights'] = False model = X_Dec(make_layers_dec(configs_dec[x-1]), **kwargs) if pretrained_path is not None: model.load_state_dict(torch.load(pretrained_path), strict=False) return model if __name__ == '__main__': device = torch.device("cuda" if torch.cuda.is_available() else "cpu") encoder = vgg16_enc(x=3, pretrained=True) for k in encoder.state_dict(): print(k) summary(encoder, (3, 224, 224), device="cpu") z, all_maxpools = encoder(torch.from_numpy(np.zeros([1, 3, 224, 224])).float()) decoder = vgg16_dec(x=3, pretrained=False) for k in decoder.state_dict(): print(k) x_rebuild = decoder(z, all_maxpools)

true

f734df57e7a00474f077f260bc9b03ce6ece41c0

492

py

Python

apps/gradings/migrations/0001_initial.py

cloudartisan/dojomaster

9d5efa0345c659636f8d8b556302d0d7bb2055a8

[ "MIT" ]

1

2019-02-21T14:47:31.000Z

apps/gradings/migrations/0001_initial.py

cloudartisan/dojomaster

9d5efa0345c659636f8d8b556302d0d7bb2055a8

[ "MIT" ]

null

apps/gradings/migrations/0001_initial.py

cloudartisan/dojomaster

9d5efa0345c659636f8d8b556302d0d7bb2055a8

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.1 on 2016-09-30 23:43 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Grading', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), ]

21.391304

114

0.599593

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Grading', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), ]

true

f734df86fba52bb0e5b0075c1880a25fc79ec783

113

py

Python

Python/pythonProject/exercise/ex047.py

JoaoMoreira2002/Linguagens-de-programacao

b91a902188428238a567c8f52b2ac9028378c4df

[ "MIT" ]

null

Python/pythonProject/exercise/ex047.py

JoaoMoreira2002/Linguagens-de-programacao

b91a902188428238a567c8f52b2ac9028378c4df

[ "MIT" ]

null

Python/pythonProject/exercise/ex047.py

JoaoMoreira2002/Linguagens-de-programacao

b91a902188428238a567c8f52b2ac9028378c4df

[ "MIT" ]

null

for x in range(0, 11): for c in range(0, 11): print(x, 'x', c, '= {}'.format(x * c)) print('\t')

22.6

46

0.442478

for x in range(0, 11): for c in range(0, 11): print(x, 'x', c, '= {}'.format(x * c)) print('\t')

true

f734e032d4a7eeaed6fa44b9a554d7707bbd9ef9

6,038

py

Python

reading_data.py

eembees/solar_flares

9022f92c0577efaf06d7425002995e4fa4df74b4

[ "MIT" ]

null

reading_data.py

eembees/solar_flares

9022f92c0577efaf06d7425002995e4fa4df74b4

[ "MIT" ]

null

reading_data.py

eembees/solar_flares

9022f92c0577efaf06d7425002995e4fa4df74b4

[ "MIT" ]

null

from pathlib import Path import ijson import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) from json import JSONDecoder, JSONDecodeError # for reading the JSON data files import re # for regular expressions import os # for os related operations from sklearn.preprocessing import maxabs_scale def decode_obj(line, pos=0, decoder=JSONDecoder()): no_white_space_regex = re.compile(r'[^\s]') while True: match = no_white_space_regex.search(line, pos) if not match: return pos = match.start() try: obj, pos = decoder.raw_decode(line, pos) except JSONDecodeError as err: print('Oops! something went wrong. Error: {}'.format(err)) yield obj def get_obj_with_last_n_val(line, n): obj = next(decode_obj(line)) # type:dict id = obj['id'] class_label = obj['classNum'] data = pd.DataFrame.from_dict(obj['values']) # type:pd.DataFrame data.set_index(data.index.astype(int), inplace=True) last_n_indices = np.arange(0, 60)[-n:] data = data.loc[last_n_indices] return {'id': id, 'classType': class_label, 'values': data} def get_obj_with_all(line): obj = next(decode_obj(line)) # type:dict id = obj['id'] try: class_label = obj['classNum'] except KeyError: class_label = None data = pd.DataFrame.from_dict(obj['values']) # type:pd.DataFrame data.set_index(data.index.astype(int), inplace=True) # last_n_indices = np.arange(0, 60)[-n:] # data = data.loc[last_n_indices] return {'id': id, 'classType': class_label, 'values': data} def read_json_data_to_df(file_path: Path): """ Generates a dataframe by concatenating the last values of each multi-variate time series. This method is designed as an example to show how a json object can be converted into a csv file. :param data_dir: the path to the data directory. :param file_name: name of the file to be read, with the extension. :return: the generated dataframe. """ all_df, labels, ids = [], [], [] with open(file_path, 'r') as infile: # Open the file for reading for line in infile: # Each 'line' is one MVTS with its single label (0 or 1). obj = get_obj_with_all(line) all_df.append(obj['values']) labels.append(obj['classType']) ids.append(obj['id']) print(type(obj)) print(obj['values']) print(type(obj['values'])) # df = exit() df = pd.concat(all_df).reset_index(drop=True) df = df.assign(LABEL=pd.Series(labels)) df = df.assign(ID=pd.Series(ids)) df.set_index([pd.Index(ids)]) # Uncomment if you want to save this as CSV # df.to_csv(file_name + '_last_vals.csv', index=False) return df def read_json_data_to_arr(file_path: Path): """ Generates a dataframe by concatenating the last values of each multi-variate time series. This method is designed as an example to show how a json object can be converted into a csv file. :param data_dir: the path to the data directory. :param file_name: name of the file to be read, with the extension. :return: the generated dataframe. """ all_df, labels, ids = [], [], [] with open(file_path, 'r') as infile: # Open the file for reading for line in infile: # Each 'line' is one MVTS with its single label (0 or 1). obj = get_obj_with_all(line) # if obj['id'] < 100: df = obj['values'].sort_index() # remove anything 2 std dev from the mean df = df.mask(df.sub(df.mean()).div(df.std()).abs().gt(2)) # do interpolation of variables df = df.interpolate(method='linear', extrapolate=False) df = df.fillna(method='ffill').fillna(method='bfill').fillna(0.0) all_df.append(df.values) labels.append(obj['classType']) ids.append(obj['id']) all_df = np.array(all_df) labels = np.array(labels) ids = np.array(ids) return all_df, labels, ids def save_DF_to_NPZ(fp: Path, out_dir): fo = out_dir / fp.with_suffix('.npz').name # fo_k = Path(str(fo).replace(('.npz', '_keys.npz'))) df = pd.read_json(fp, lines=True) np.savez(fo, df=df, keys=df.keys, index=df.index) pass def save_arr_to_npz(arr: np.ndarray, labels: np.ndarray, ids: np.ndarray, fo: Path): np.savez(fo, data=arr, labels=labels, index=ids) pass def load_npz_file(path: Path, return_ids = False): a = np.load(path) X = a['data'] if np.any(np.isnan(X)): X = np.nan_to_num(X) try: y = a['labels'] except KeyError: y = None except ValueError: y = None if return_ids: try: ids = a['ids'] except KeyError: ids = None except ValueError: ids = None return X, y, ids else: return X, y def save_y_preds(y_index: np.ndarray, y_pred: np.ndarray, fo: Path): np.savez(fo, index=y_index, labels=y_pred) pass def preprocess_data(X, scaler=maxabs_scale): shap = X.shape # print(shap[1:]) if shap[1:] != (60, 25): raise ValueError('Data shape wrong') for i, x_i in enumerate(X): x_i_t = np.zeros_like(x_i.transpose()) for j, series in enumerate(x_i.transpose()): series = scaler(series) x_i_t[j] = series X[i] = x_i_t.transpose() return X if __name__ == '__main__': data_dir = Path('/Users/mag/PycharmProjects/solar_flares/input/') out_dir = Path('/Users/mag/PycharmProjects/solar_flares/input/npz') # out_dir = Path('./input/npz') file_paths = list(data_dir.glob('test*.json')) print(file_paths) for fp in file_paths: fo = out_dir / fp.with_suffix('.npz').name all_df, labels, ids = read_json_data_to_arr(fp) save_arr_to_npz(all_df, labels, ids, fo)

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86

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from pathlib import Path import ijson import numpy as np import pandas as pd from json import JSONDecoder, JSONDecodeError import re import os from sklearn.preprocessing import maxabs_scale def decode_obj(line, pos=0, decoder=JSONDecoder()): no_white_space_regex = re.compile(r'[^\s]') while True: match = no_white_space_regex.search(line, pos) if not match: return pos = match.start() try: obj, pos = decoder.raw_decode(line, pos) except JSONDecodeError as err: print('Oops! something went wrong. Error: {}'.format(err)) yield obj def get_obj_with_last_n_val(line, n): obj = next(decode_obj(line)) id = obj['id'] class_label = obj['classNum'] data = pd.DataFrame.from_dict(obj['values']) data.set_index(data.index.astype(int), inplace=True) last_n_indices = np.arange(0, 60)[-n:] data = data.loc[last_n_indices] return {'id': id, 'classType': class_label, 'values': data} def get_obj_with_all(line): obj = next(decode_obj(line)) id = obj['id'] try: class_label = obj['classNum'] except KeyError: class_label = None data = pd.DataFrame.from_dict(obj['values']) data.set_index(data.index.astype(int), inplace=True) return {'id': id, 'classType': class_label, 'values': data} def read_json_data_to_df(file_path: Path): all_df, labels, ids = [], [], [] with open(file_path, 'r') as infile: for line in infile: obj = get_obj_with_all(line) all_df.append(obj['values']) labels.append(obj['classType']) ids.append(obj['id']) print(type(obj)) print(obj['values']) print(type(obj['values'])) exit() df = pd.concat(all_df).reset_index(drop=True) df = df.assign(LABEL=pd.Series(labels)) df = df.assign(ID=pd.Series(ids)) df.set_index([pd.Index(ids)]) return df def read_json_data_to_arr(file_path: Path): all_df, labels, ids = [], [], [] with open(file_path, 'r') as infile: for line in infile: obj = get_obj_with_all(line) df = obj['values'].sort_index() df = df.mask(df.sub(df.mean()).div(df.std()).abs().gt(2)) df = df.interpolate(method='linear', extrapolate=False) df = df.fillna(method='ffill').fillna(method='bfill').fillna(0.0) all_df.append(df.values) labels.append(obj['classType']) ids.append(obj['id']) all_df = np.array(all_df) labels = np.array(labels) ids = np.array(ids) return all_df, labels, ids def save_DF_to_NPZ(fp: Path, out_dir): fo = out_dir / fp.with_suffix('.npz').name df = pd.read_json(fp, lines=True) np.savez(fo, df=df, keys=df.keys, index=df.index) pass def save_arr_to_npz(arr: np.ndarray, labels: np.ndarray, ids: np.ndarray, fo: Path): np.savez(fo, data=arr, labels=labels, index=ids) pass def load_npz_file(path: Path, return_ids = False): a = np.load(path) X = a['data'] if np.any(np.isnan(X)): X = np.nan_to_num(X) try: y = a['labels'] except KeyError: y = None except ValueError: y = None if return_ids: try: ids = a['ids'] except KeyError: ids = None except ValueError: ids = None return X, y, ids else: return X, y def save_y_preds(y_index: np.ndarray, y_pred: np.ndarray, fo: Path): np.savez(fo, index=y_index, labels=y_pred) pass def preprocess_data(X, scaler=maxabs_scale): shap = X.shape if shap[1:] != (60, 25): raise ValueError('Data shape wrong') for i, x_i in enumerate(X): x_i_t = np.zeros_like(x_i.transpose()) for j, series in enumerate(x_i.transpose()): series = scaler(series) x_i_t[j] = series X[i] = x_i_t.transpose() return X if __name__ == '__main__': data_dir = Path('/Users/mag/PycharmProjects/solar_flares/input/') out_dir = Path('/Users/mag/PycharmProjects/solar_flares/input/npz') file_paths = list(data_dir.glob('test*.json')) print(file_paths) for fp in file_paths: fo = out_dir / fp.with_suffix('.npz').name all_df, labels, ids = read_json_data_to_arr(fp) save_arr_to_npz(all_df, labels, ids, fo)

true

f734e09b76bc265ceb72504136dbcb9a86e98111

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py

Python

CORE/engines/constraint.py

geoffreynyaga/ostrich-project

157cd7a3c3d9014e31ef21ca21de43f04d039997

[ "MIT" ]

15

2017-11-08T10:03:26.000Z

2021-12-21T07:02:44.000Z

CORE/engines/constraint.py

geoffreynyaga/ostrich-project

157cd7a3c3d9014e31ef21ca21de43f04d039997

[ "MIT" ]

9

2020-01-17T15:09:22.000Z

2022-03-25T19:02:05.000Z

CORE/engines/constraint.py

geoffreynyaga/ostrich-project

157cd7a3c3d9014e31ef21ca21de43f04d039997

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding:utf-8 -*- ################################################################################## # File: c:\Projects\KENYA ONE PROJECT\CORE\engines\constraint.py # # Project: c:\Projects\KENYA ONE PROJECT\CORE\engines # # Created Date: Thursday, January 9th 2020, 8:56:55 pm # # Author: Geoffrey Nyaga Kinyua ( <info@geoffreynyaga.com> ) # # ----- # # Last Modified: Thursday January 9th 2020 8:56:55 pm # # Modified By: Geoffrey Nyaga Kinyua ( <info@geoffreynyaga.com> ) # # ----- # # MIT License # # # # Copyright (c) 2020 KENYA ONE PROJECT # # # # Permission is hereby granted, free of charge, to any person obtaining a copy of# # this software and associated documentation files (the "Software"), to deal in # # the Software without restriction, including without limitation the rights to # # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies # # of the Software, and to permit persons to whom the Software is furnished to do # # so, subject to the following conditions: # # # # The above copyright notice and this permission notice shall be included in all # # copies or substantial portions of the Software. # # # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # # SOFTWARE. # # ----- # # Copyright (c) 2020 KENYA ONE PROJECT # ################################################################################## import sys sys.path.append("../") from CORE.API.db_API import write_to_db, read_from_db import numpy as np # type: ignore import matplotlib.pylab as plt # type: ignore a = np.arange(50) ws = np.arange(10, 35, 0.01) cdmin: float = 0.025 write_to_db("cdMin", cdmin) do = read_from_db("rhoSL") dalt = read_from_db("altitudeDensity") # AAAAA k = read_from_db("k") # v = read_from_db('cruiseSpeed') * 1.688 v: float = 140 * 1.688 # AAAAA qcruise = 0.5 * dalt * v ** 2 # dynamic pressure at cruise qtakeoff = 0.5 * do * v ** 2 # dynamic pressure at take-off turnangle = 40 # turn angle loadfactor = 1 / (np.cos(turnangle)) # loadfactor twturn = ( qcruise * ((cdmin / ws) + (k * (loadfactor / qcruise) ** 2) * ws) * (v * 5850 / (0.8 * 550 * 0.6604)) ) # rate of climb roc = read_from_db("rateOfClimb") * 3.28 * 60 # rate of climb ft/min #AAAAAAA # Vy=sqrt((2/do)*ws * sqrt( k/(3*cdmin) )) Vy = 150 Vv = roc / 60 qclimb = 0.5 * do * (Vy ** 2) twclimb = ( (Vv / Vy) + ((qclimb / ws) * cdmin) + ((qclimb / ws) * cdmin) + ((k / qclimb) * ws) ) * (Vy * 5850 / (0.6 * 550)) # ground run Sg: int = 1000 # ground run ft Vlof: float = 70 * 1.688 clto: float = 1.4670 u: float = 0.04 cdto = 0.03 q1 = 0.5 * do * (Vlof / np.sqrt(2)) ** 2 twtakeoff = ( ((Vlof ** 2) / (2 * 32.174 * Sg)) + ((q1 * cdto) / ws) + u * (1 - (q1 * clto / ws)) ) * (Vlof * 5850 / (0.6 * 550)) # cruise altitude twcruise = (((qcruise * cdmin) / ws) + ((k / qcruise) * ws)) * ( v * 5850 / (0.6 * 550 * 0.6604) ) # service ceiling twservceiling = ( (1.668 / np.sqrt((2 * ws / dalt) * np.sqrt(k / (3 * cdmin)))) + (4 * np.sqrt(k * cdmin / 3)) ) * ((v * 5850) / (0.7 * 550 * 0.6604)) plt.plot(ws, twclimb, label="climb") plt.plot(ws, twturn, label="turn") plt.plot(ws, twtakeoff, label="Takeoff") plt.plot(ws, twservceiling, label="Service Ceiling") plt.plot(ws, twcruise, label="cruise") plotWS = read_from_db("WS") plt.axvline(x=plotWS) ################################ plt.legend(loc="upper left") if __name__ == "__main__": plt.show() def find_nearest(array, value): idx = (np.abs(array - value)).argmin() return idx # print(find_nearest(ws, plotWS)) myidx = find_nearest(ws, plotWS) # cruiseidx = (twcruise[myidx]) # takeoffidx = twtakeoff[myidx] # climbidx = twclimb[myidx] # turnidx = twturn[myidx] # ceilingidx = twservceiling[myidx] # print([cruiseidx,takeoffidx,climbidx,turnidx,ceilingidx]) def point(): cruiseidx = twcruise[myidx] takeoffidx = twtakeoff[myidx] climbidx = twclimb[myidx] turnidx = twturn[myidx] ceilingidx = twservceiling[myidx] # print([cruiseidx,takeoffidx,climbidx,turnidx,ceilingidx]) # print (cruiseidx,"cruiseidx") x = np.array([cruiseidx, takeoffidx, climbidx, turnidx, ceilingidx]) idx = x.argmax() return x[idx] finalBHP = point() # print ( finalBHP,"BHP") write_to_db("finalBHP", finalBHP) S = (read_from_db("finalMTOW")) / (plotWS * 10.57) write_to_db("S", S)

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88

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true

f734e0e3ddd024eab587865ded77a0ef1faf2c77

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py

Python

DEPRECATED_PYTHON_SRC/component/_brz_win.py

17701253801/firefly-proxy

153e0d343d93d68a803bba6b89497f7bc2b96bc8

[ "BSD-2-Clause" ]

5,895

2015-01-01T14:33:17.000Z

2022-03-19T03:08:48.000Z

DEPRECATED_PYTHON_SRC/component/_brz_win.py

BIGMONK/firefly-proxy

60161af7b239ab400d39a23b61ab312f84b94570

[ "BSD-2-Clause" ]

626

2015-01-07T22:09:26.000Z

2022-03-24T01:56:50.000Z

DEPRECATED_PYTHON_SRC/component/_brz_win.py

BIGMONK/firefly-proxy

60161af7b239ab400d39a23b61ab312f84b94570

[ "BSD-2-Clause" ]

1,145

2015-01-04T06:50:54.000Z

2022-03-15T13:12:17.000Z

# Copied from https://github.com/downloadam/client/blob/master/client/registry/win.py import logging import sys import os from contextlib import contextmanager import subprocess import _winreg as winreg from _winreg import HKEY_CLASSES_ROOT, HKEY_LOCAL_MACHINE, HKEY_CURRENT_USER, \ KEY_QUERY_VALUE, REG_SZ, KEY_ALL_ACCESS, KEY_WRITE, KEY_CREATE_SUB_KEY, KEY_SET_VALUE log = logging.getLogger(__name__) @contextmanager def open_key(hkey, *args): key = winreg.OpenKeyEx(hkey, *args) yield key winreg.CloseKey(key) @contextmanager def create_key(hkey, subkey): key = winreg.CreateKey(hkey, subkey) yield key winreg.CloseKey(key) def read_reg_key(hkey, subkey, name=""): try: with open_key(hkey, subkey, 0, KEY_QUERY_VALUE) as k: return winreg.QueryValueEx(k, name) except WindowsError as e: errno, message = e.args if errno != 2: raise e return (None, None) def write_reg_key(hkey, subkey, name, value): try: with open_key(hkey, subkey, 0, KEY_ALL_ACCESS) as k: winreg.SetValueEx(k, name, 0, value[0], value[1]) return True except WindowsError as e: errno, message = e.args if errno != 2: raise e return False def enum_reg_keys(hkey, subkey): with open_key(hkey, subkey) as k: i = 0 while True: try: name = winreg.EnumKey(k, i) except: break yield name i += 1 def _parse_browser_path(path): try: if path.startswith('"'): path = path[1:].split('"', 1)[0] return path except: return None def get_default_browser(): result = _parse_browser_path(read_reg_key(HKEY_CURRENT_USER, 'Software\\Classes\\http\\shell\\open\\command')[0]) if result is None: result = _parse_browser_path(read_reg_key(HKEY_CLASSES_ROOT, 'http\\shell\\open\\command')[0]) return result def get_browser_path(key): result = _parse_browser_path(read_reg_key(HKEY_CURRENT_USER, 'Software\\Clients\\StartMenuInternet\\{}\\shell\\open\\command'.format(key))[0]) if result is None: result = _parse_browser_path(read_reg_key(HKEY_LOCAL_MACHINE, 'Software\\Clients\\StartMenuInternet\\{}\\shell\\open\\command'.format(key))[0]) return result def iterate_browsers(default=None): if default is None: default = get_default_browser() or '' default = default.lower() ignore = set() for hkey in (HKEY_CURRENT_USER, HKEY_LOCAL_MACHINE): try: enum = list(enum_reg_keys(hkey, 'Software\\Clients\\StartMenuInternet')) except WindowsError: # key not exists or something? continue for key in enum: if key in ignore: continue ignore.add(key) path = get_browser_path(key) if not path: continue if not os.path.exists(path): continue if key == 'IEXPLORE.EXE': try: version = int(read_reg_key(hkey, 'Software\\Microsoft\\Internet Explorer', 'Version')[0].split('.', 1)[0]) except AttributeError: # this maybe happens, don't know why. assume IE is outdated version = 0 if version < 9: outdated = True else: outdated = False elif key == 'OperaStable': outdated = True else: outdated = False yield key.lower(), path, path.lower() == default, outdated old_ie_settings = {} def resume_ie_settings(): global old_ie_settings key = HKEY_CURRENT_USER subkey = 'Software\\Microsoft\\Windows\\CurrentVersion\\Internet Settings' for (name, value) in old_ie_settings.items(): write_reg_key(key, subkey, name, value) def launch_ie(executable, url, rootdir, proxy_type, proxy_ip, proxy_port): global old_ie_settings key = HKEY_CURRENT_USER subkey = 'Software\\Microsoft\\Windows\\CurrentVersion\\Internet Settings' new_values = { 'ProxyEnable' : (4, 1), 'ProxyOverride' : (1, u'*.local;<local>'), 'ProxyServer' : (1, u'%s:%d' % (proxy_ip, proxy_port)), } for (name, _) in new_values.items(): (reg_value, reg_type) = read_reg_key(key, subkey, name) if reg_value is not None: old_ie_settings[name] = (reg_type, reg_value) write_reg_key(key, subkey, name, new_values[name]) cmdline = [ executable, url, ] cmdline = [s.encode(sys.getfilesystemencoding()) for s in cmdline] return subprocess.Popen(cmdline) def launch_ie_tab(executable, url, rootdir): cmdline = [ executable, url, ] cmdline = [s.encode(sys.getfilesystemencoding()) for s in cmdline] return subprocess.Popen(cmdline)

33.006623

151

0.611758

import logging import sys import os from contextlib import contextmanager import subprocess import _winreg as winreg from _winreg import HKEY_CLASSES_ROOT, HKEY_LOCAL_MACHINE, HKEY_CURRENT_USER, \ KEY_QUERY_VALUE, REG_SZ, KEY_ALL_ACCESS, KEY_WRITE, KEY_CREATE_SUB_KEY, KEY_SET_VALUE log = logging.getLogger(__name__) @contextmanager def open_key(hkey, *args): key = winreg.OpenKeyEx(hkey, *args) yield key winreg.CloseKey(key) @contextmanager def create_key(hkey, subkey): key = winreg.CreateKey(hkey, subkey) yield key winreg.CloseKey(key) def read_reg_key(hkey, subkey, name=""): try: with open_key(hkey, subkey, 0, KEY_QUERY_VALUE) as k: return winreg.QueryValueEx(k, name) except WindowsError as e: errno, message = e.args if errno != 2: raise e return (None, None) def write_reg_key(hkey, subkey, name, value): try: with open_key(hkey, subkey, 0, KEY_ALL_ACCESS) as k: winreg.SetValueEx(k, name, 0, value[0], value[1]) return True except WindowsError as e: errno, message = e.args if errno != 2: raise e return False def enum_reg_keys(hkey, subkey): with open_key(hkey, subkey) as k: i = 0 while True: try: name = winreg.EnumKey(k, i) except: break yield name i += 1 def _parse_browser_path(path): try: if path.startswith('"'): path = path[1:].split('"', 1)[0] return path except: return None def get_default_browser(): result = _parse_browser_path(read_reg_key(HKEY_CURRENT_USER, 'Software\\Classes\\http\\shell\\open\\command')[0]) if result is None: result = _parse_browser_path(read_reg_key(HKEY_CLASSES_ROOT, 'http\\shell\\open\\command')[0]) return result def get_browser_path(key): result = _parse_browser_path(read_reg_key(HKEY_CURRENT_USER, 'Software\\Clients\\StartMenuInternet\\{}\\shell\\open\\command'.format(key))[0]) if result is None: result = _parse_browser_path(read_reg_key(HKEY_LOCAL_MACHINE, 'Software\\Clients\\StartMenuInternet\\{}\\shell\\open\\command'.format(key))[0]) return result def iterate_browsers(default=None): if default is None: default = get_default_browser() or '' default = default.lower() ignore = set() for hkey in (HKEY_CURRENT_USER, HKEY_LOCAL_MACHINE): try: enum = list(enum_reg_keys(hkey, 'Software\\Clients\\StartMenuInternet')) except WindowsError: continue for key in enum: if key in ignore: continue ignore.add(key) path = get_browser_path(key) if not path: continue if not os.path.exists(path): continue if key == 'IEXPLORE.EXE': try: version = int(read_reg_key(hkey, 'Software\\Microsoft\\Internet Explorer', 'Version')[0].split('.', 1)[0]) except AttributeError: version = 0 if version < 9: outdated = True else: outdated = False elif key == 'OperaStable': outdated = True else: outdated = False yield key.lower(), path, path.lower() == default, outdated old_ie_settings = {} def resume_ie_settings(): global old_ie_settings key = HKEY_CURRENT_USER subkey = 'Software\\Microsoft\\Windows\\CurrentVersion\\Internet Settings' for (name, value) in old_ie_settings.items(): write_reg_key(key, subkey, name, value) def launch_ie(executable, url, rootdir, proxy_type, proxy_ip, proxy_port): global old_ie_settings key = HKEY_CURRENT_USER subkey = 'Software\\Microsoft\\Windows\\CurrentVersion\\Internet Settings' new_values = { 'ProxyEnable' : (4, 1), 'ProxyOverride' : (1, u'*.local;<local>'), 'ProxyServer' : (1, u'%s:%d' % (proxy_ip, proxy_port)), } for (name, _) in new_values.items(): (reg_value, reg_type) = read_reg_key(key, subkey, name) if reg_value is not None: old_ie_settings[name] = (reg_type, reg_value) write_reg_key(key, subkey, name, new_values[name]) cmdline = [ executable, url, ] cmdline = [s.encode(sys.getfilesystemencoding()) for s in cmdline] return subprocess.Popen(cmdline) def launch_ie_tab(executable, url, rootdir): cmdline = [ executable, url, ] cmdline = [s.encode(sys.getfilesystemencoding()) for s in cmdline] return subprocess.Popen(cmdline)

true

f734e2ae995f4f0b1438b926ddce732f5c03bcb5

2,949

py

Python

students/k3342/laboratory_works/Shaidullina_Regina/laboratory_work_1/leaderboard/migrations/0001_initial.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

10

2020-03-20T09:06:12.000Z

2021-07-27T13:06:02.000Z

students/k3342/laboratory_works/Shaidullina_Regina/laboratory_work_1/leaderboard/migrations/0001_initial.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

134

2020-03-23T09:47:48.000Z

2022-03-12T01:05:19.000Z

students/k3342/laboratory_works/Shaidullina_Regina/laboratory_work_1/leaderboard/migrations/0001_initial.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

71

2020-03-20T12:45:56.000Z

2021-10-31T19:22:25.000Z

# Generated by Django 3.0.4 on 2020-04-18 12:38 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Car', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('car_number', models.CharField(max_length=6)), ('description', models.CharField(max_length=100)), ], options={ 'db_table': 'Car', }, ), migrations.CreateModel( name='Team', fields=[ ('name', models.CharField(max_length=30, primary_key=True, serialize=False)), ('country', models.CharField(max_length=30)), ('number_of_racers', models.IntegerField()), ], options={ 'db_table': 'Team', }, ), migrations.CreateModel( name='Racer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('surname', models.CharField(max_length=30)), ('name', models.CharField(max_length=30)), ('middlename', models.CharField(max_length=30)), ('description', models.CharField(max_length=100)), ('experience', models.CharField(max_length=30)), ('racer_class', models.CharField(choices=[('A', 'A'), ('B', 'B'), ('C', 'C'), ('D', 'D')], max_length=1)), ('car', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='leaderboard.Car')), ('team_name', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='leaderboard.Team')), ], options={ 'db_table': 'Racer', }, ), migrations.CreateModel( name='Race', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=30)), ('category', models.CharField(choices=[('OW', 'Open-wheel racing'), ('TC', 'Touring car racing'), ('SpC', 'Sports car racing'), ('PC', 'Production-car racing'), ('OM', 'One-make racing'), ('TAS', 'Time Attack Series'), ('StC', 'Stock car racing'), ('R', 'Rallying'), ('D', 'Drag racing'), ('OR', 'Off-road racing'), ('K', 'Kart racing'), ('H', 'Historical racing'), ('Other', 'Other')], max_length=5)), ('date', models.DateField()), ('winner', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='leaderboard.Racer')), ], options={ 'db_table': 'Race', }, ), ]

43.367647

418

0.528654

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Car', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('car_number', models.CharField(max_length=6)), ('description', models.CharField(max_length=100)), ], options={ 'db_table': 'Car', }, ), migrations.CreateModel( name='Team', fields=[ ('name', models.CharField(max_length=30, primary_key=True, serialize=False)), ('country', models.CharField(max_length=30)), ('number_of_racers', models.IntegerField()), ], options={ 'db_table': 'Team', }, ), migrations.CreateModel( name='Racer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('surname', models.CharField(max_length=30)), ('name', models.CharField(max_length=30)), ('middlename', models.CharField(max_length=30)), ('description', models.CharField(max_length=100)), ('experience', models.CharField(max_length=30)), ('racer_class', models.CharField(choices=[('A', 'A'), ('B', 'B'), ('C', 'C'), ('D', 'D')], max_length=1)), ('car', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='leaderboard.Car')), ('team_name', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='leaderboard.Team')), ], options={ 'db_table': 'Racer', }, ), migrations.CreateModel( name='Race', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=30)), ('category', models.CharField(choices=[('OW', 'Open-wheel racing'), ('TC', 'Touring car racing'), ('SpC', 'Sports car racing'), ('PC', 'Production-car racing'), ('OM', 'One-make racing'), ('TAS', 'Time Attack Series'), ('StC', 'Stock car racing'), ('R', 'Rallying'), ('D', 'Drag racing'), ('OR', 'Off-road racing'), ('K', 'Kart racing'), ('H', 'Historical racing'), ('Other', 'Other')], max_length=5)), ('date', models.DateField()), ('winner', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='leaderboard.Racer')), ], options={ 'db_table': 'Race', }, ), ]

true

f734e32ec87c1cc2ff0c847206e45f81f0a44682

5,052

py

Python

kubernetes/client/models/v1beta1_allowed_host_path.py

lp67/python

33c5ea9835356410ce4a9fa54a02c6a2a22143c6

[ "Apache-2.0" ]

null

kubernetes/client/models/v1beta1_allowed_host_path.py

lp67/python

33c5ea9835356410ce4a9fa54a02c6a2a22143c6

[ "Apache-2.0" ]

4

2019-11-19T10:33:47.000Z

2022-03-01T03:33:52.000Z

kubernetes/client/models/v1beta1_allowed_host_path.py

mohramadan911/PythonClientAPI

5d111812c81b7a573ac8661d1aec60bb97072412

[ "Apache-2.0" ]

2

2021-08-10T16:35:31.000Z

2021-09-14T04:53:06.000Z

# coding: utf-8 """ Kubernetes No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: release-1.20 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from kubernetes.client.configuration import Configuration class V1beta1AllowedHostPath(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'path_prefix': 'str', 'read_only': 'bool' } attribute_map = { 'path_prefix': 'pathPrefix', 'read_only': 'readOnly' } def __init__(self, path_prefix=None, read_only=None, local_vars_configuration=None): # noqa: E501 """V1beta1AllowedHostPath - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._path_prefix = None self._read_only = None self.discriminator = None if path_prefix is not None: self.path_prefix = path_prefix if read_only is not None: self.read_only = read_only @property def path_prefix(self): """Gets the path_prefix of this V1beta1AllowedHostPath. # noqa: E501 pathPrefix is the path prefix that the host volume must match. It does not support `*`. Trailing slashes are trimmed when validating the path prefix with a host path. Examples: `/foo` would allow `/foo`, `/foo/` and `/foo/bar` `/foo` would not allow `/food` or `/etc/foo` # noqa: E501 :return: The path_prefix of this V1beta1AllowedHostPath. # noqa: E501 :rtype: str """ return self._path_prefix @path_prefix.setter def path_prefix(self, path_prefix): """Sets the path_prefix of this V1beta1AllowedHostPath. pathPrefix is the path prefix that the host volume must match. It does not support `*`. Trailing slashes are trimmed when validating the path prefix with a host path. Examples: `/foo` would allow `/foo`, `/foo/` and `/foo/bar` `/foo` would not allow `/food` or `/etc/foo` # noqa: E501 :param path_prefix: The path_prefix of this V1beta1AllowedHostPath. # noqa: E501 :type: str """ self._path_prefix = path_prefix @property def read_only(self): """Gets the read_only of this V1beta1AllowedHostPath. # noqa: E501 when set to true, will allow host volumes matching the pathPrefix only if all volume mounts are readOnly. # noqa: E501 :return: The read_only of this V1beta1AllowedHostPath. # noqa: E501 :rtype: bool """ return self._read_only @read_only.setter def read_only(self, read_only): """Sets the read_only of this V1beta1AllowedHostPath. when set to true, will allow host volumes matching the pathPrefix only if all volume mounts are readOnly. # noqa: E501 :param read_only: The read_only of this V1beta1AllowedHostPath. # noqa: E501 :type: bool """ self._read_only = read_only def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, V1beta1AllowedHostPath): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, V1beta1AllowedHostPath): return True return self.to_dict() != other.to_dict()

33.456954

294

0.615796

import pprint import re import six from kubernetes.client.configuration import Configuration class V1beta1AllowedHostPath(object): openapi_types = { 'path_prefix': 'str', 'read_only': 'bool' } attribute_map = { 'path_prefix': 'pathPrefix', 'read_only': 'readOnly' } def __init__(self, path_prefix=None, read_only=None, local_vars_configuration=None): if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._path_prefix = None self._read_only = None self.discriminator = None if path_prefix is not None: self.path_prefix = path_prefix if read_only is not None: self.read_only = read_only @property def path_prefix(self): return self._path_prefix @path_prefix.setter def path_prefix(self, path_prefix): self._path_prefix = path_prefix @property def read_only(self): return self._read_only @read_only.setter def read_only(self, read_only): self._read_only = read_only def to_dict(self): result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, V1beta1AllowedHostPath): return False return self.to_dict() == other.to_dict() def __ne__(self, other): if not isinstance(other, V1beta1AllowedHostPath): return True return self.to_dict() != other.to_dict()

true

f734e3a1ce3a24d57c1f838699b9ec656d8a897c

7,219

py

Python

src/antidote/_constants.py

Finistere/antidote

97751e0e6a1b8bd638a1c33212345c7a84ad97b8

[ "MIT" ]

52

2017-12-17T19:52:37.000Z

2022-03-29T10:24:04.000Z

src/antidote/_constants.py

Finistere/antidote

97751e0e6a1b8bd638a1c33212345c7a84ad97b8

[ "MIT" ]

32

2018-11-02T08:49:16.000Z

2022-03-25T22:23:30.000Z

src/antidote/_constants.py

Finistere/antidote

97751e0e6a1b8bd638a1c33212345c7a84ad97b8

[ "MIT" ]

5

2019-05-17T18:26:14.000Z

2021-12-25T23:13:31.000Z

from typing import (Any, Dict, Generic, Hashable, Optional, TYPE_CHECKING, Tuple, Type, TypeVar, Union, cast, overload) from ._compatibility.typing import final, Protocol from ._internal import API from ._internal.utils import AbstractMeta, Default, FinalImmutable, FinalMeta, debug_repr from ._internal.utils.immutable import Immutable, ImmutableGenericMeta from ._providers.lazy import Lazy from .core import Container, DependencyDebug, DependencyValue, Scope T = TypeVar('T') if TYPE_CHECKING: from .constants import Constants # TODO: Once Python 3.6 support drops, fix this. # We're lying to Mypy here. That's not how the actual descriptor, even though it's # somewhat close. But inheriting Generic implies not being final anymore in Python 3.6, # until PEP 560, and internally there's no need for Generic. class Const(Generic[T]): __slots__ = () @overload def __get__(self, # noqa: E704 instance: 'Constants', owner: 'Type[Constants]') -> T: ... # pragma: no cover @overload def __get__(self, # noqa: E704 instance: None, owner: 'Type[Constants]') -> 'Const[T]': ... # pragma: no cover def __get__(self, instance: 'Optional[Constants]', owner: 'Type[Constants]') -> object: # pragma: no cover pass @API.private @final class MakeConst(metaclass=FinalMeta): def __call__(self, __arg: Optional[object] = None, *, default: Any = Default.sentinel) -> Const[object]: # Not true yet, but will be changed by ConstantsMeta return cast(Const[object], LazyConstToDo(__arg, None, default)) def __getitem__(self, tpe: Type[T]) -> 'MakeTypedConst[T]': return MakeTypedConst(tpe) @API.private @final class MakeTypedConst(Immutable, Generic[T], metaclass=ImmutableGenericMeta): __slots__ = ('__type',) __type: Type[T] def __call__(self, __arg: Optional[object] = None, *, default: Union[T, Default] = Default.sentinel) -> Const[T]: if not isinstance(default, (self.__type, Default)): raise TypeError(f"default is not an instance of {self.__type}, " f"but {type(default)}") # Not true yet, but will be changed by ConstantsMeta return cast(Const[T], LazyConstToDo(__arg, self.__type, default)) @API.private @final class LazyConstToDo(FinalImmutable): __slots__ = ('arg', 'type_', 'default') arg: Optional[object] type_: Optional[type] default: object @API.private class ConstantsMeta(AbstractMeta): def __new__(mcs: 'Type[ConstantsMeta]', name: str, bases: Tuple[type, ...], namespace: Dict[str, object], **kwargs: object ) -> 'ConstantsMeta': cls = cast( ConstantsMeta, super().__new__(mcs, name, bases, namespace, **kwargs) # type: ignore ) if not kwargs.get('abstract'): _configure_constants(cls) return cls @API.private def _configure_constants(cls: ConstantsMeta) -> None: from .constants import Constants from .service import service conf = getattr(cls, '__antidote__', None) if not isinstance(conf, Constants.Conf): raise TypeError(f"Constants configuration (__antidote__) is expected to be a " f"{Constants.Conf}, not a {type(conf)}") cls = service(cls, singleton=True, wiring=conf.wiring) for name, v in list(cls.__dict__.items()): if isinstance(v, LazyConstToDo): setattr(cls, name, LazyConstDescriptor( name=name, dependency=cls, method_name=Constants.provide_const.__name__, arg=v.arg, default=v.default, type_=v.type_ or object, auto_cast=v.type_ is not None and v.type_ in conf.auto_cast)) @API.private @final class LazyConstDescriptor(FinalImmutable): __slots__ = ('name', 'dependency', 'method_name', 'arg', 'default', 'type_', 'auto_cast', '_cache') name: str dependency: Hashable method_name: str arg: object default: object type_: type auto_cast: bool _cache: str def __init__(self, *, name: str, dependency: Hashable, method_name: str, arg: object, default: object, type_: type, auto_cast: bool ): assert isinstance(default, (Default, type_)) super().__init__( name=name, dependency=dependency, method_name=method_name, arg=arg, default=default, type_=type_, auto_cast=auto_cast, _cache=f"__antidote_dependency_{hex(id(self))}" ) def __get__(self, instance: object, owner: type) -> object: if instance is None: try: return getattr(owner, self._cache) except AttributeError: dependency = LazyConst(self) setattr(owner, self._cache, dependency) return dependency try: value = getattr(instance, self.method_name)(name=self.name, arg=self.arg) except LookupError: if self.default is not Default.sentinel: return self.default raise if self.auto_cast: value = self.type_(value) if not isinstance(value, self.type_): raise TypeError(f"Constant {self.name} is not an instance of {self.type_}, " f"but {type(value)}") return value @API.private @final class LazyConst(FinalImmutable, Lazy): __slots__ = ('descriptor',) descriptor: LazyConstDescriptor def __init__(self, descriptor: LazyConstDescriptor) -> None: super().__init__(descriptor=descriptor) def debug_info(self) -> DependencyDebug: descriptor = cast(LazyConstDescriptor, self.descriptor) cls = cast(type, descriptor.dependency) return DependencyDebug(f"{debug_repr(cls)}.{descriptor.name}", scope=Scope.singleton(), # TODO: Would be great if the first argument of the method # didn't show as unknown as it's always provided. wired=[getattr(cls, descriptor.method_name), cls]) def provide(self, container: Container) -> DependencyValue: # TODO: Waiting for a fix: https://github.com/python/mypy/issues/6910 descriptor = cast(LazyConstDescriptor, self.descriptor) return DependencyValue( descriptor.__get__( container.get(descriptor.dependency), None # type: ignore ), scope=Scope.singleton() )

33.892019

89

0.575842

from typing import (Any, Dict, Generic, Hashable, Optional, TYPE_CHECKING, Tuple, Type, TypeVar, Union, cast, overload) from ._compatibility.typing import final, Protocol from ._internal import API from ._internal.utils import AbstractMeta, Default, FinalImmutable, FinalMeta, debug_repr from ._internal.utils.immutable import Immutable, ImmutableGenericMeta from ._providers.lazy import Lazy from .core import Container, DependencyDebug, DependencyValue, Scope T = TypeVar('T') if TYPE_CHECKING: from .constants import Constants # somewhat close. But inheriting Generic implies not being final anymore in Python 3.6, # until PEP 560, and internally there's no need for Generic. class Const(Generic[T]): __slots__ = () @overload def __get__(self, instance: 'Constants', owner: 'Type[Constants]') -> T: ... @overload def __get__(self, instance: None, owner: 'Type[Constants]') -> 'Const[T]': ... def __get__(self, instance: 'Optional[Constants]', owner: 'Type[Constants]') -> object: pass @API.private @final class MakeConst(metaclass=FinalMeta): def __call__(self, __arg: Optional[object] = None, *, default: Any = Default.sentinel) -> Const[object]: return cast(Const[object], LazyConstToDo(__arg, None, default)) def __getitem__(self, tpe: Type[T]) -> 'MakeTypedConst[T]': return MakeTypedConst(tpe) @API.private @final class MakeTypedConst(Immutable, Generic[T], metaclass=ImmutableGenericMeta): __slots__ = ('__type',) __type: Type[T] def __call__(self, __arg: Optional[object] = None, *, default: Union[T, Default] = Default.sentinel) -> Const[T]: if not isinstance(default, (self.__type, Default)): raise TypeError(f"default is not an instance of {self.__type}, " f"but {type(default)}") return cast(Const[T], LazyConstToDo(__arg, self.__type, default)) @API.private @final class LazyConstToDo(FinalImmutable): __slots__ = ('arg', 'type_', 'default') arg: Optional[object] type_: Optional[type] default: object @API.private class ConstantsMeta(AbstractMeta): def __new__(mcs: 'Type[ConstantsMeta]', name: str, bases: Tuple[type, ...], namespace: Dict[str, object], **kwargs: object ) -> 'ConstantsMeta': cls = cast( ConstantsMeta, super().__new__(mcs, name, bases, namespace, **kwargs) ) if not kwargs.get('abstract'): _configure_constants(cls) return cls @API.private def _configure_constants(cls: ConstantsMeta) -> None: from .constants import Constants from .service import service conf = getattr(cls, '__antidote__', None) if not isinstance(conf, Constants.Conf): raise TypeError(f"Constants configuration (__antidote__) is expected to be a " f"{Constants.Conf}, not a {type(conf)}") cls = service(cls, singleton=True, wiring=conf.wiring) for name, v in list(cls.__dict__.items()): if isinstance(v, LazyConstToDo): setattr(cls, name, LazyConstDescriptor( name=name, dependency=cls, method_name=Constants.provide_const.__name__, arg=v.arg, default=v.default, type_=v.type_ or object, auto_cast=v.type_ is not None and v.type_ in conf.auto_cast)) @API.private @final class LazyConstDescriptor(FinalImmutable): __slots__ = ('name', 'dependency', 'method_name', 'arg', 'default', 'type_', 'auto_cast', '_cache') name: str dependency: Hashable method_name: str arg: object default: object type_: type auto_cast: bool _cache: str def __init__(self, *, name: str, dependency: Hashable, method_name: str, arg: object, default: object, type_: type, auto_cast: bool ): assert isinstance(default, (Default, type_)) super().__init__( name=name, dependency=dependency, method_name=method_name, arg=arg, default=default, type_=type_, auto_cast=auto_cast, _cache=f"__antidote_dependency_{hex(id(self))}" ) def __get__(self, instance: object, owner: type) -> object: if instance is None: try: return getattr(owner, self._cache) except AttributeError: dependency = LazyConst(self) setattr(owner, self._cache, dependency) return dependency try: value = getattr(instance, self.method_name)(name=self.name, arg=self.arg) except LookupError: if self.default is not Default.sentinel: return self.default raise if self.auto_cast: value = self.type_(value) if not isinstance(value, self.type_): raise TypeError(f"Constant {self.name} is not an instance of {self.type_}, " f"but {type(value)}") return value @API.private @final class LazyConst(FinalImmutable, Lazy): __slots__ = ('descriptor',) descriptor: LazyConstDescriptor def __init__(self, descriptor: LazyConstDescriptor) -> None: super().__init__(descriptor=descriptor) def debug_info(self) -> DependencyDebug: descriptor = cast(LazyConstDescriptor, self.descriptor) cls = cast(type, descriptor.dependency) return DependencyDebug(f"{debug_repr(cls)}.{descriptor.name}", scope=Scope.singleton(), wired=[getattr(cls, descriptor.method_name), cls]) def provide(self, container: Container) -> DependencyValue: descriptor = cast(LazyConstDescriptor, self.descriptor) return DependencyValue( descriptor.__get__( container.get(descriptor.dependency), None ), scope=Scope.singleton() )

true

f734e42e8c40b73c89d6a81190560d99c06b2daa

582

py

Python

plot.py

nkuxx161/baseline-SR

c4caf06c5a5a88d7f8e27069018316b319f0913b

[ "MIT" ]

null

plot.py

nkuxx161/baseline-SR

c4caf06c5a5a88d7f8e27069018316b319f0913b

[ "MIT" ]

null

plot.py

nkuxx161/baseline-SR

c4caf06c5a5a88d7f8e27069018316b319f0913b

[ "MIT" ]

null

import pandas as pd import os curve_name = '5_k7' data = pd.read_csv(os.path.join('result', curve_name+'.csv')) timestamp = data['timestamp'] value = data['value'] mag = data['mag'] isAnomaly = data['isAnomaly'] import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt plt.subplot(3, 1, 1) plt.plot(timestamp, value) plt.title('value') plt.subplot(3, 1, 2) plt.plot(timestamp, mag) plt.title('mag') plt.subplot(3, 1, 3) plt.plot(timestamp, isAnomaly) plt.title('isAnomaly') plt.savefig(os.path.join('./images', 'SR_'+curve_name+'.png')) plt.show() plt.close()

19.4

62

0.704467

import pandas as pd import os curve_name = '5_k7' data = pd.read_csv(os.path.join('result', curve_name+'.csv')) timestamp = data['timestamp'] value = data['value'] mag = data['mag'] isAnomaly = data['isAnomaly'] import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt plt.subplot(3, 1, 1) plt.plot(timestamp, value) plt.title('value') plt.subplot(3, 1, 2) plt.plot(timestamp, mag) plt.title('mag') plt.subplot(3, 1, 3) plt.plot(timestamp, isAnomaly) plt.title('isAnomaly') plt.savefig(os.path.join('./images', 'SR_'+curve_name+'.png')) plt.show() plt.close()

true

f734e53915eb41c00db1f0b9283810f28578ae28

1,048

py

Python

troc/apps/record/migrations/0001_initial.py

Windfarer/species2

15849c5805621410f3e8c26d27213f9bcf483fd1

[ "MIT" ]

1

2020-01-02T11:50:50.000Z

troc/apps/record/migrations/0001_initial.py

Windfarer/species2

15849c5805621410f3e8c26d27213f9bcf483fd1

[ "MIT" ]

5

2019-12-15T07:43:46.000Z

2022-02-26T17:47:26.000Z

troc/apps/record/migrations/0001_initial.py

Windfarer/species2

15849c5805621410f3e8c26d27213f9bcf483fd1

[ "MIT" ]

1

2020-06-13T02:25:42.000Z

# Generated by Django 2.2.5 on 2019-10-07 08:27 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): initial = True dependencies = [ ('species', '0002_auto_20190908_0902'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Record', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(default=django.utils.timezone.now)), ('updated_at', models.DateTimeField(default=django.utils.timezone.now)), ('species', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='species.Species')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

34.933333

118

0.655534

from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): initial = True dependencies = [ ('species', '0002_auto_20190908_0902'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Record', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(default=django.utils.timezone.now)), ('updated_at', models.DateTimeField(default=django.utils.timezone.now)), ('species', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='species.Species')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

true

f734e66795d03e7b634e13c3a1bbf25ad1e1bcbd

20,783

py

Python

models/_sources/model_trainer_c4d127b7cc8008ff2c0c849733ead6e1.py

catniplab/ML-music-analysis

793d54ed16166fbcd9acf4eec24998892334e064

[ "MIT" ]

null

models/_sources/model_trainer_c4d127b7cc8008ff2c0c849733ead6e1.py

catniplab/ML-music-analysis

793d54ed16166fbcd9acf4eec24998892334e064

[ "MIT" ]

null

models/_sources/model_trainer_c4d127b7cc8008ff2c0c849733ead6e1.py

catniplab/ML-music-analysis

793d54ed16166fbcd9acf4eec24998892334e064

[ "MIT" ]

1

2021-12-01T22:57:56.000Z

""" This script creates an instance of a sacred experiment and defines default configurations for training a neural network or a regression model. """ from src.neural_nets.models import get_model from src.neural_nets.load_data import get_loader from src.neural_nets.metrics import MaskedBCE, Accuracy, compute_acc, compute_loss import src.regression.logistic_regression as reg import os import numpy as np import torch import torch.nn as nn import torch.optim as optim import torchsso.optim as soptim import torch.nn.functional as F import random from torch.utils.data import DataLoader from sacred import Experiment from torch import Tensor, device from copy import deepcopy from time import sleep from tqdm import tqdm from typing import List from itertools import product # create a new sacred experiment whose name is an integer ex = Experiment(name=str(random.randint(0, 1000000))) # default configurations @ex.config def cfg(): # system cuda = torch.cuda.is_available() gpu = 0 base_dir = os.getcwd() # supported datasets # JSB_Chorales (short) # Nottingham (medium) # Piano_midi (long) # MuseData (extra long) dataset = "JSB_Chorales" # training num_epochs = 150 batch_size = 128 # mask some low notes and some high notes because they never show up low_off_notes = 0 high_off_notes = 88 lr = 0.001 decay = 1.0 optmzr = "SGD" regularization = 0.0 # hyperparameter search do_hpsearch = False learning_rates = 10**np.linspace(-2, -4, 5) decays = 1 - np.linspace(0, 0.1, num=5) regularizations = 10**np.linspace(-2, -4, num=5) hps_epochs = 50 # Supported architectures # REGRESSION # LDS # TANH architecture = 'LDS' readout = 'linear' gradient_clipping = 1 jit = False # not fully implemented # for regression lag = 1 window = 1 # for neural networks input_size = 88 hidden_size = 300 num_layers = 1 output_size = 88 # see models.py and initialization.py for details init = 'default' scale = 1.0 parity = None # see models.py t_distrib = torch.distributions.Uniform(0, 0.75) path = 'results/77/final_state_dict.pt' # when to save state dictionaries save_init_model = True save_final_model = True save_every_epoch = False # detect backprop anomalies detect_anomaly = False # give all random number generators the same seed def _seed_all(_seed) -> None: torch.manual_seed(_seed) np.random.seed(_seed) random.seed(_seed) # this context is used when we are running things on the cpu class NullContext(object): def __init__(self): pass def __enter__(self): pass def __exit__(self, type, value, traceback): pass # this function simply trains regression models and logs the results # see regression.trainer for details @ex.capture def sklearn_experiment(dataset: str, save_dir: str, num_epochs: int, high_off_notes: int, low_off_notes: int, lag: int, window: int, _seed, _log, _run): """ :param dataset: name of the dataset to be used :save_dir: temporary directory where artifacts are being stored :lag: how many time steps into the future the regression model is to predict :window: how many time steps the regression model is to take into account :param _seed: sacred random seed :param _log: sacred object used to output to the command line :param _run: sacred object used to monitor the runtime """ num_notes = high_off_notes - low_off_notes models = reg.train_models(dataset, num_epochs, low_off_notes, high_off_notes, _seed, lag=lag, window=window) coefs = np.zeros((num_notes, num_notes*window)) intercepts = np.zeros(num_notes*window) for i in range(num_notes): model = models[i] # if there were no notes played for this channel, a model won't be trained # simply save all parameters as -1 to discourage the note from being played if model == None: coefs[i] = -1 intercepts[i] = -1 else: coefs[i] = model.coef_ intercepts[i] = model.intercept_ np.save(save_dir + 'coefs.npy', coefs) np.save(save_dir + 'intercepts.npy', intercepts) _run.add_artifact(save_dir + 'coefs.npy') _run.add_artifact(save_dir + 'intercepts.npy') train_loss = reg.compute_loss(models, dataset, 'traindata', low_off_notes, high_off_notes, lag=lag, window=window) test_loss = reg.compute_loss(models, dataset, 'testdata', low_off_notes, high_off_notes, lag=lag, window=window) valid_loss = reg.compute_loss(models, dataset, 'validdata', low_off_notes, high_off_notes, lag=lag, window=window) _run.log_scalar('trainLoss', train_loss) _run.log_scalar('testLoss', test_loss) _run.log_scalar('validLoss', valid_loss) train_acc = reg.compute_accuracy(models, dataset, 'traindata', low_off_notes, high_off_notes, lag=lag, window=window) test_acc = reg.compute_accuracy(models, dataset, 'testdata', low_off_notes, high_off_notes, lag=lag, window=window) valid_acc = reg.compute_accuracy(models, dataset, 'validdata', low_off_notes, high_off_notes, lag=lag, window=window) _run.log_scalar('trainAccuracy', train_acc) _run.log_scalar('testAccuracy', test_acc) _run.log_scalar('validAccuracy', valid_acc) # a single optimization step @ex.capture def train_iter(device: device, cuda_device: torch.cuda.device, input_tensor: Tensor, target: Tensor, mask: Tensor, model: nn.Module, loss_fcn: nn.Module, optimizer: optim.Optimizer, save_every_epoch: bool, save_dir: str, train_loader: DataLoader, test_loader: DataLoader, valid_loader: DataLoader, low_off_notes: int, high_off_notes: int, _log, _run, logging=True): input_tensor = input_tensor.to(device) # number of songs in this batch N = input_tensor.shape[0] output, hidden_tensors = model(input_tensor) loss = loss_fcn(output, target, mask, model)/N optimizer.zero_grad() loss.backward() optimizer.step() # use sacred to log training loss and accuracy if logging: train_acc = compute_acc(model, train_loader, low=low_off_notes, high=high_off_notes) _run.log_scalar("trainLoss", loss.cpu().detach().item()) _run.log_scalar("trainAccuracy", train_acc) # save a copy of the model and make sacred remember it each epoch if save_every_epoch and logging: sd = deepcopy(model.state_dict()) torch.save(init_sd, save_dir + 'state_dict_' + str(epoch) + '.pt') _run.add_artifact(save_dir + 'state_dict_' + str(epoch) + '.pt') # train a neural network # returns the final loss and accuracy on the training, testing, and validation sets @ex.capture def pytorch_train_loop(cuda: bool, model_dict: dict, initializer: dict, train_loader: DataLoader, test_loader: DataLoader, valid_loader: DataLoader, low_off_notes: int, high_off_notes: int, optmzr: str, lr: float, decay: float, regularization: float, num_epochs: int, save_dir: str, save_init_model, save_every_epoch, save_final_model, _seed, _log, _run, logging=True): # construct and initialize the model model = get_model(model_dict, initializer, cuda) # save a copy of the initial model and make sacred remember it if save_init_model and logging: init_sd = deepcopy(model.state_dict()) torch.save(init_sd, save_dir + 'initial_state_dict.pt') _run.add_artifact(save_dir + 'initial_state_dict.pt') # if we are on cuda we construct the device and run everything on it cuda_device = NullContext() device = torch.device('cpu') if cuda: dev_name = 'cuda:' + str(gpu) cuda_device = torch.cuda.device(dev_name) device = torch.device(dev_name) model = model.to(device) with cuda_device: # see metrics.py loss_fcn = MaskedBCE(regularization, low_off_notes=low_off_notes, high_off_notes=high_off_notes) # compute the metrics before training and log them if logging: train_loss = compute_loss(loss_fcn, model, train_loader) test_loss = compute_loss(loss_fcn, model, test_loader) val_loss = compute_loss(loss_fcn, model, valid_loader) _run.log_scalar("trainLoss", train_loss) _run.log_scalar("testLoss", test_loss) _run.log_scalar("validLoss", val_loss) train_acc = compute_acc(model, train_loader, low=low_off_notes, high=high_off_notes) test_acc = compute_acc(model, test_loader, low=low_off_notes, high=high_off_notes) val_acc = compute_acc(model, valid_loader, low=low_off_notes, high=high_off_notes) _run.log_scalar("trainAccuracy", train_acc) _run.log_scalar("testAccuracy", test_acc) _run.log_scalar("validAccuracy", val_acc) # construct the optimizer optimizer = None if optmzr == "SGD": optimizer = optim.SGD(model.parameters(), lr=lr) elif optmzr == "Adam": optimizer = optim.Adam(model.parameters(), lr=lr) elif optmzr == "RMSprop": optimizer = optim.RMSprop(model.parameters(), lr=lr) else: raise ValueError("Optimizer {} not recognized.".format(optmzr)) # learning rate decay scheduler = None scheduler = optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: decay**epoch) # begin training loop for epoch in tqdm(range(num_epochs)): for input_tensor, target, mask in train_loader: train_iter(device, cuda_device, input_tensor, target, mask, model, loss_fcn, optimizer, save_every_epoch, save_dir, train_loader, test_loader, valid_loader, low_off_notes, high_off_notes, _log, _run, logging=logging) # learning rate decay scheduler.step() # use sacred to log testing and validation loss and accuracy if logging: test_loss = compute_loss(loss_fcn, model, test_loader) val_loss = compute_loss(loss_fcn, model, valid_loader) test_acc = compute_acc(model, test_loader, low=low_off_notes, high=high_off_notes) val_acc = compute_acc(model, valid_loader, low=low_off_notes, high=high_off_notes) _run.log_scalar("testLoss", test_loss) _run.log_scalar("validLoss", val_loss) _run.log_scalar("testAccuracy", test_acc) _run.log_scalar("validAccuracy", val_acc) # save a copy of the trained model and make sacred remember it if save_final_model and logging: fin_sd = deepcopy(model.state_dict()) torch.save(fin_sd, save_dir + 'final_state_dict.pt') _run.add_artifact(save_dir + 'final_state_dict.pt') # recompute the metrics so that this function can return them train_loss = compute_loss(loss_fcn, model, train_loader) test_loss = compute_loss(loss_fcn, model, test_loader) val_loss = compute_loss(loss_fcn, model, valid_loader) train_acc = compute_acc(model, train_loader, low=low_off_notes, high=high_off_notes) test_acc = compute_acc(model, test_loader, low=low_off_notes, high=high_off_notes) val_acc = compute_acc(model, valid_loader, low=low_off_notes, high=high_off_notes) return ((train_loss, test_loss, val_loss), (train_acc, test_acc, val_acc)) # main function @ex.automain def train_loop(cuda, gpu, base_dir, dataset, num_epochs, batch_size, low_off_notes, high_off_notes, lr, decay, optmzr, regularization, do_hpsearch, learning_rates, decays, regularizations, hps_epochs, architecture, readout, gradient_clipping, jit, lag, window, input_size, hidden_size, num_layers, output_size, detect_anomaly, init, scale, parity, t_distrib, path, save_init_model, save_final_model, save_every_epoch, _seed, _log, _run): # save artifacts to a temporary directory that gets erased when the experiment is over save_dir = base_dir + '/tmp_' + str(_seed) os.system('mkdir ' + save_dir) save_dir += '/' # give all random number generators the same seed _seed_all(_seed) sklearn_program = architecture == 'REGRESSION' # regression models and neural networks are trained very differently if sklearn_program: sklearn_experiment(dataset, save_dir, num_epochs, high_off_notes, low_off_notes, lag, window, _seed, _log, _run) # run a pytorch program else: model_dict = {'architecture': architecture, 'readout': readout, 'gradient_clipping': gradient_clipping, 'jit': jit, 'lag': lag, 'window': window, 'input_size': input_size, 'hidden_size': hidden_size, 'num_layers': num_layers, 'output_size': output_size } initializer = {'init': init, 'scale': scale, 'parity': parity, 't_distrib': t_distrib, 'path': path, 'low_off_notes': low_off_notes, 'high_off_notes': high_off_notes } # if we are debugging we may want to detect autograd anomalies torch.autograd.set_detect_anomaly(detect_anomaly) # construct the pytorch data loaders train_loader, test_loader, valid_loader = get_loader(dataset, batch_size) # standard training loop if not do_hpsearch: # the training loop function returns the metrics achieved at the end of training # they will be logged by default, no need to do anything with them here metrics = pytorch_train_loop(cuda, model_dict, initializer, train_loader, test_loader, valid_loader, low_off_notes, high_off_notes, optmzr, lr, decay, regularization, num_epochs, save_dir, save_init_model, save_every_epoch, save_final_model, _seed, _log, _run) # only goal here is to find the best hyper parameters else: min_test_loss = float('inf') best_lr = 0 best_dcay = 0 best_reg = 0 hyperparams = product(learning_rates, decays, regularizations) for rate, dcay, reg in hyperparams: # train a model with the given hyperparameters # don't log anything, otherwise we will have a ridiculous amount of extraneous info metrics = pytorch_train_loop(cuda, model_dict, initializer, train_loader, test_loader, valid_loader, optmzr, rate, dcay, reg, hps_epochs, save_dir, save_init_model, save_every_epoch, save_final_model, _seed, _log, _run, logging=False) # loss is first index, test set is second index test_loss = metrics[0][1] # compare loss against other hyperparams and update if necessary if test_loss == test_loss and test_loss < min_test_loss: min_test_loss = test_loss best_lr = rate best_dcay = dcay best_reg = reg # record the best hyperparameters _run.log_scalar("learning_rate", best_lr) _run.log_scalar("decay", best_dcay) _run.log_scalar("regularization", best_reg) # wait a second then remove the temporary directory used for storing artifacts sleep(1) os.system('rm -r ' + save_dir)

35.405451

142

0.505172

from src.neural_nets.models import get_model from src.neural_nets.load_data import get_loader from src.neural_nets.metrics import MaskedBCE, Accuracy, compute_acc, compute_loss import src.regression.logistic_regression as reg import os import numpy as np import torch import torch.nn as nn import torch.optim as optim import torchsso.optim as soptim import torch.nn.functional as F import random from torch.utils.data import DataLoader from sacred import Experiment from torch import Tensor, device from copy import deepcopy from time import sleep from tqdm import tqdm from typing import List from itertools import product ex = Experiment(name=str(random.randint(0, 1000000))) @ex.config def cfg(): cuda = torch.cuda.is_available() gpu = 0 base_dir = os.getcwd() dataset = "JSB_Chorales" num_epochs = 150 batch_size = 128 low_off_notes = 0 high_off_notes = 88 lr = 0.001 decay = 1.0 optmzr = "SGD" regularization = 0.0 do_hpsearch = False learning_rates = 10**np.linspace(-2, -4, 5) decays = 1 - np.linspace(0, 0.1, num=5) regularizations = 10**np.linspace(-2, -4, num=5) hps_epochs = 50 architecture = 'LDS' readout = 'linear' gradient_clipping = 1 jit = False lag = 1 window = 1 input_size = 88 hidden_size = 300 num_layers = 1 output_size = 88 init = 'default' scale = 1.0 parity = None t_distrib = torch.distributions.Uniform(0, 0.75) path = 'results/77/final_state_dict.pt' save_init_model = True save_final_model = True save_every_epoch = False detect_anomaly = False def _seed_all(_seed) -> None: torch.manual_seed(_seed) np.random.seed(_seed) random.seed(_seed) class NullContext(object): def __init__(self): pass def __enter__(self): pass def __exit__(self, type, value, traceback): pass @ex.capture def sklearn_experiment(dataset: str, save_dir: str, num_epochs: int, high_off_notes: int, low_off_notes: int, lag: int, window: int, _seed, _log, _run): num_notes = high_off_notes - low_off_notes models = reg.train_models(dataset, num_epochs, low_off_notes, high_off_notes, _seed, lag=lag, window=window) coefs = np.zeros((num_notes, num_notes*window)) intercepts = np.zeros(num_notes*window) for i in range(num_notes): model = models[i] # simply save all parameters as -1 to discourage the note from being played if model == None: coefs[i] = -1 intercepts[i] = -1 else: coefs[i] = model.coef_ intercepts[i] = model.intercept_ np.save(save_dir + 'coefs.npy', coefs) np.save(save_dir + 'intercepts.npy', intercepts) _run.add_artifact(save_dir + 'coefs.npy') _run.add_artifact(save_dir + 'intercepts.npy') train_loss = reg.compute_loss(models, dataset, 'traindata', low_off_notes, high_off_notes, lag=lag, window=window) test_loss = reg.compute_loss(models, dataset, 'testdata', low_off_notes, high_off_notes, lag=lag, window=window) valid_loss = reg.compute_loss(models, dataset, 'validdata', low_off_notes, high_off_notes, lag=lag, window=window) _run.log_scalar('trainLoss', train_loss) _run.log_scalar('testLoss', test_loss) _run.log_scalar('validLoss', valid_loss) train_acc = reg.compute_accuracy(models, dataset, 'traindata', low_off_notes, high_off_notes, lag=lag, window=window) test_acc = reg.compute_accuracy(models, dataset, 'testdata', low_off_notes, high_off_notes, lag=lag, window=window) valid_acc = reg.compute_accuracy(models, dataset, 'validdata', low_off_notes, high_off_notes, lag=lag, window=window) _run.log_scalar('trainAccuracy', train_acc) _run.log_scalar('testAccuracy', test_acc) _run.log_scalar('validAccuracy', valid_acc) # a single optimization step @ex.capture def train_iter(device: device, cuda_device: torch.cuda.device, input_tensor: Tensor, target: Tensor, mask: Tensor, model: nn.Module, loss_fcn: nn.Module, optimizer: optim.Optimizer, save_every_epoch: bool, save_dir: str, train_loader: DataLoader, test_loader: DataLoader, valid_loader: DataLoader, low_off_notes: int, high_off_notes: int, _log, _run, logging=True): input_tensor = input_tensor.to(device) # number of songs in this batch N = input_tensor.shape[0] output, hidden_tensors = model(input_tensor) loss = loss_fcn(output, target, mask, model)/N optimizer.zero_grad() loss.backward() optimizer.step() # use sacred to log training loss and accuracy if logging: train_acc = compute_acc(model, train_loader, low=low_off_notes, high=high_off_notes) _run.log_scalar("trainLoss", loss.cpu().detach().item()) _run.log_scalar("trainAccuracy", train_acc) # save a copy of the model and make sacred remember it each epoch if save_every_epoch and logging: sd = deepcopy(model.state_dict()) torch.save(init_sd, save_dir + 'state_dict_' + str(epoch) + '.pt') _run.add_artifact(save_dir + 'state_dict_' + str(epoch) + '.pt') # train a neural network # returns the final loss and accuracy on the training, testing, and validation sets @ex.capture def pytorch_train_loop(cuda: bool, model_dict: dict, initializer: dict, train_loader: DataLoader, test_loader: DataLoader, valid_loader: DataLoader, low_off_notes: int, high_off_notes: int, optmzr: str, lr: float, decay: float, regularization: float, num_epochs: int, save_dir: str, save_init_model, save_every_epoch, save_final_model, _seed, _log, _run, logging=True): # construct and initialize the model model = get_model(model_dict, initializer, cuda) # save a copy of the initial model and make sacred remember it if save_init_model and logging: init_sd = deepcopy(model.state_dict()) torch.save(init_sd, save_dir + 'initial_state_dict.pt') _run.add_artifact(save_dir + 'initial_state_dict.pt') # if we are on cuda we construct the device and run everything on it cuda_device = NullContext() device = torch.device('cpu') if cuda: dev_name = 'cuda:' + str(gpu) cuda_device = torch.cuda.device(dev_name) device = torch.device(dev_name) model = model.to(device) with cuda_device: # see metrics.py loss_fcn = MaskedBCE(regularization, low_off_notes=low_off_notes, high_off_notes=high_off_notes) # compute the metrics before training and log them if logging: train_loss = compute_loss(loss_fcn, model, train_loader) test_loss = compute_loss(loss_fcn, model, test_loader) val_loss = compute_loss(loss_fcn, model, valid_loader) _run.log_scalar("trainLoss", train_loss) _run.log_scalar("testLoss", test_loss) _run.log_scalar("validLoss", val_loss) train_acc = compute_acc(model, train_loader, low=low_off_notes, high=high_off_notes) test_acc = compute_acc(model, test_loader, low=low_off_notes, high=high_off_notes) val_acc = compute_acc(model, valid_loader, low=low_off_notes, high=high_off_notes) _run.log_scalar("trainAccuracy", train_acc) _run.log_scalar("testAccuracy", test_acc) _run.log_scalar("validAccuracy", val_acc) # construct the optimizer optimizer = None if optmzr == "SGD": optimizer = optim.SGD(model.parameters(), lr=lr) elif optmzr == "Adam": optimizer = optim.Adam(model.parameters(), lr=lr) elif optmzr == "RMSprop": optimizer = optim.RMSprop(model.parameters(), lr=lr) else: raise ValueError("Optimizer {} not recognized.".format(optmzr)) # learning rate decay scheduler = None scheduler = optim.lr_scheduler.LambdaLR(optimizer, lambda epoch: decay**epoch) # begin training loop for epoch in tqdm(range(num_epochs)): for input_tensor, target, mask in train_loader: train_iter(device, cuda_device, input_tensor, target, mask, model, loss_fcn, optimizer, save_every_epoch, save_dir, train_loader, test_loader, valid_loader, low_off_notes, high_off_notes, _log, _run, logging=logging) # learning rate decay scheduler.step() # use sacred to log testing and validation loss and accuracy if logging: test_loss = compute_loss(loss_fcn, model, test_loader) val_loss = compute_loss(loss_fcn, model, valid_loader) test_acc = compute_acc(model, test_loader, low=low_off_notes, high=high_off_notes) val_acc = compute_acc(model, valid_loader, low=low_off_notes, high=high_off_notes) _run.log_scalar("testLoss", test_loss) _run.log_scalar("validLoss", val_loss) _run.log_scalar("testAccuracy", test_acc) _run.log_scalar("validAccuracy", val_acc) # save a copy of the trained model and make sacred remember it if save_final_model and logging: fin_sd = deepcopy(model.state_dict()) torch.save(fin_sd, save_dir + 'final_state_dict.pt') _run.add_artifact(save_dir + 'final_state_dict.pt') # recompute the metrics so that this function can return them train_loss = compute_loss(loss_fcn, model, train_loader) test_loss = compute_loss(loss_fcn, model, test_loader) val_loss = compute_loss(loss_fcn, model, valid_loader) train_acc = compute_acc(model, train_loader, low=low_off_notes, high=high_off_notes) test_acc = compute_acc(model, test_loader, low=low_off_notes, high=high_off_notes) val_acc = compute_acc(model, valid_loader, low=low_off_notes, high=high_off_notes) return ((train_loss, test_loss, val_loss), (train_acc, test_acc, val_acc)) # main function @ex.automain def train_loop(cuda, gpu, base_dir, dataset, num_epochs, batch_size, low_off_notes, high_off_notes, lr, decay, optmzr, regularization, do_hpsearch, learning_rates, decays, regularizations, hps_epochs, architecture, readout, gradient_clipping, jit, lag, window, input_size, hidden_size, num_layers, output_size, detect_anomaly, init, scale, parity, t_distrib, path, save_init_model, save_final_model, save_every_epoch, _seed, _log, _run): # save artifacts to a temporary directory that gets erased when the experiment is over save_dir = base_dir + '/tmp_' + str(_seed) os.system('mkdir ' + save_dir) save_dir += '/' # give all random number generators the same seed _seed_all(_seed) sklearn_program = architecture == 'REGRESSION' # regression models and neural networks are trained very differently if sklearn_program: sklearn_experiment(dataset, save_dir, num_epochs, high_off_notes, low_off_notes, lag, window, _seed, _log, _run) # run a pytorch program else: model_dict = {'architecture': architecture, 'readout': readout, 'gradient_clipping': gradient_clipping, 'jit': jit, 'lag': lag, 'window': window, 'input_size': input_size, 'hidden_size': hidden_size, 'num_layers': num_layers, 'output_size': output_size } initializer = {'init': init, 'scale': scale, 'parity': parity, 't_distrib': t_distrib, 'path': path, 'low_off_notes': low_off_notes, 'high_off_notes': high_off_notes } # if we are debugging we may want to detect autograd anomalies torch.autograd.set_detect_anomaly(detect_anomaly) # construct the pytorch data loaders train_loader, test_loader, valid_loader = get_loader(dataset, batch_size) # standard training loop if not do_hpsearch: # the training loop function returns the metrics achieved at the end of training # they will be logged by default, no need to do anything with them here metrics = pytorch_train_loop(cuda, model_dict, initializer, train_loader, test_loader, valid_loader, low_off_notes, high_off_notes, optmzr, lr, decay, regularization, num_epochs, save_dir, save_init_model, save_every_epoch, save_final_model, _seed, _log, _run) # only goal here is to find the best hyper parameters else: min_test_loss = float('inf') best_lr = 0 best_dcay = 0 best_reg = 0 hyperparams = product(learning_rates, decays, regularizations) for rate, dcay, reg in hyperparams: # train a model with the given hyperparameters # don't log anything, otherwise we will have a ridiculous amount of extraneous info metrics = pytorch_train_loop(cuda, model_dict, initializer, train_loader, test_loader, valid_loader, optmzr, rate, dcay, reg, hps_epochs, save_dir, save_init_model, save_every_epoch, save_final_model, _seed, _log, _run, logging=False) test_loss = metrics[0][1] if test_loss == test_loss and test_loss < min_test_loss: min_test_loss = test_loss best_lr = rate best_dcay = dcay best_reg = reg _run.log_scalar("learning_rate", best_lr) _run.log_scalar("decay", best_dcay) _run.log_scalar("regularization", best_reg) sleep(1) os.system('rm -r ' + save_dir)

true

f734e6fd78fd01730a7c42ee39a5882d91564b45

9,026

py

Python

test/ut/tools/annotation/testcase/usercode/mnist.py

dutxubo/nni

c16f4e1c89b54b8b80661ef0072433d255ad2d24

[ "MIT" ]

9,680

2019-05-07T01:42:30.000Z

2022-03-31T16:48:33.000Z

test/ut/tools/annotation/testcase/usercode/mnist.py

dutxubo/nni

c16f4e1c89b54b8b80661ef0072433d255ad2d24

[ "MIT" ]

1,957

2019-05-06T21:44:21.000Z

2022-03-31T09:21:53.000Z

test/ut/tools/annotation/testcase/usercode/mnist.py

dutxubo/nni

c16f4e1c89b54b8b80661ef0072433d255ad2d24

[ "MIT" ]

1,571

2019-05-07T06:42:55.000Z

2022-03-31T03:19:24.000Z

# -*- encoding:utf8 -*- """A deep MNIST classifier using convolutional layers. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import logging import math import tempfile import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data logger = logging.getLogger('mnist') FLAGS = None class MnistNetwork(object): def __init__(self, channel_1_num = 32, channel_2_num = 64, conv_size = 5, hidden_size = 1024, pool_size = 2, learning_rate = 0.0001, x_dim = 784, y_dim = 10): self.channel_1_num = channel_1_num self.channel_2_num = channel_2_num '''@nni.variable(nni.choice(2,3,5,7),name=self.conv_size)''' self.conv_size = conv_size '''@nni.variable(nni.choice(124,512,1024),name=self.hidden_size)''' self.hidden_size = hidden_size self.pool_size = pool_size '''@nni.variable(nni.randint(2,3,5),name=self.learning_rate)''' self.learning_rate = learning_rate self.x_dim = x_dim self.y_dim = y_dim def build_network(self): self.x = tf.placeholder(tf.float32, [None, self.x_dim], name = 'input_x') self.y = tf.placeholder(tf.float32, [None, self.y_dim], name = 'input_y') self.keep_prob = tf.placeholder(tf.float32, name = 'keep_prob') # Reshape to use within a convolutional neural net. # Last dimension is for "features" - there is only one here, since images are # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc. with tf.name_scope('reshape'): try: input_dim = int(math.sqrt(self.x_dim)) except: #print('input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim)) logger.debug('input dim cannot be sqrt and reshape. input dim: ', str(self.x_dim)) raise x_image = tf.reshape(self.x, [-1, input_dim, input_dim, 1]) # First convolutional layer - maps one grayscale image to 32 feature maps. with tf.name_scope('conv1'): W_conv1 = weight_variable([self.conv_size, self.conv_size, 1, self.channel_1_num]) b_conv1 = bias_variable([self.channel_1_num]) """@nni.function_choice(tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1),tf.nn.sigmoid(conv2d(x_image, W_conv1) + b_conv1),tf.nn.tanh(conv2d(x_image, W_conv1) + b_conv1),name=tf.nn.relu)""" h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # Pooling layer - downsamples by 2X. with tf.name_scope('pool1'): """@nni.function_choice(max_pool(h_conv1, self.pool_size),avg_pool(h_conv1, self.pool_size),name=max_pool)""" h_pool1 = max_pool(h_conv1, self.pool_size) # Second convolutional layer -- maps 32 feature maps to 64. with tf.name_scope('conv2'): W_conv2 = weight_variable([self.conv_size, self.conv_size, self.channel_1_num, self.channel_2_num]) b_conv2 = bias_variable([self.channel_2_num]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # Second pooling layer. with tf.name_scope('pool2'): #"""@nni.dynamic(input={cnn_block:1, concat:2},function_choice={"cnn_block":(x,nni.choice([3,4])),"cnn_block":(x),"concat":(x,y)},limit={"cnn_block.input":[concat,input],"concat.input":[this.depth-1,this.depth-3,this.depth-5],"graph.width":[1]})""" h_pool2 = max_pool(h_conv2, self.pool_size) # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image # is down to 7x7x64 feature maps -- maps this to 1024 features. last_dim = int(input_dim / (self.pool_size * self.pool_size)) with tf.name_scope('fc1'): W_fc1 = weight_variable([last_dim * last_dim * self.channel_2_num, self.hidden_size]) b_fc1 = bias_variable([self.hidden_size]) h_pool2_flat = tf.reshape(h_pool2, [-1, last_dim * last_dim * self.channel_2_num]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # Dropout - controls the complexity of the model, prevents co-adaptation of features. with tf.name_scope('dropout'): h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob) # Map the 1024 features to 10 classes, one for each digit with tf.name_scope('fc2'): W_fc2 = weight_variable([self.hidden_size, self.y_dim]) b_fc2 = bias_variable([self.y_dim]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 with tf.name_scope('loss'): cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.y, logits = y_conv)) with tf.name_scope('adam_optimizer'): self.train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(cross_entropy) with tf.name_scope('accuracy'): correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(self.y, 1)) self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) return def conv2d(x, W): """conv2d returns a 2d convolution layer with full stride.""" return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool(x, pool_size): """max_pool downsamples a feature map by 2X.""" return tf.nn.max_pool(x, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME') def avg_pool(x,pool_size): return tf.nn.avg_pool(x, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME') def weight_variable(shape): """weight_variable generates a weight variable of a given shape.""" initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): """bias_variable generates a bias variable of a given shape.""" initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def main(): # Import data data_dir= '/tmp/tensorflow/mnist/input_data' mnist = input_data.read_data_sets(data_dir, one_hot=True) logger.debug('Mnist download data done.') # Create the model # Build the graph for the deep net mnist_network = MnistNetwork() mnist_network.build_network() logger.debug('Mnist build network done.') # Write log graph_location = tempfile.mkdtemp() logger.debug('Saving graph to: %s', graph_location) # print('Saving graph to: %s' % graph_location) train_writer = tf.summary.FileWriter(graph_location) train_writer.add_graph(tf.get_default_graph()) test_acc = 0.0 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) batch_num=200 for i in range(batch_num): '''@nni.variable(nni.choice(50,250,500),name=batch_size)''' batch_size=50 batch = mnist.train.next_batch(batch_size) '''@nni.variable(nni.choice(1,5),name=dropout_rate)''' dropout_rate=0.5 mnist_network.train_step.run(feed_dict={mnist_network.x: batch[0], mnist_network.y: batch[1], mnist_network.keep_prob: dropout_rate}) if i % 100 == 0: #train_accuracy = mnist_network.accuracy.eval(feed_dict={ # mnist_network.x: batch[0], mnist_network.y: batch[1], mnist_network.keep_prob: params['dropout_rate']}) #print('step %d, training accuracy %g' % (i, train_accuracy)) test_acc = mnist_network.accuracy.eval(feed_dict={ mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels, mnist_network.keep_prob: 1.0}) '''@nni.report_intermediate_result(test_acc)''' test_acc = mnist_network.accuracy.eval(feed_dict={ mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels, mnist_network.keep_prob: 1.0}) '''@nni.report_final_result(test_acc)''' def generate_default_params(): params = {'data_dir': '/tmp/tensorflow/mnist/input_data', 'dropout_rate': 0.5, 'channel_1_num': 32, 'channel_2_num': 64, 'conv_size': 5, 'pool_size': 2, 'hidden_size': 1024, 'batch_size': 50, 'batch_num': 200, 'learning_rate': 1e-4} return params if __name__ == '__main__': # run command: python mnist.py --init_file_path ./init.json #FLAGS, unparsed = parse_command() #original_params = parse_init_json(FLAGS.init_file_path, {}) #pipe_interface.set_params_to_env() '''@nni.get_next_parameter()''' try: params = generate_default_params() logger.debug('params') logger.debug('params update') main() except: logger.exception('Got some exception in while loop in mnist.py') raise

42.980952

260

0.634611

from __future__ import absolute_import from __future__ import division from __future__ import print_function import logging import math import tempfile import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data logger = logging.getLogger('mnist') FLAGS = None class MnistNetwork(object): def __init__(self, channel_1_num = 32, channel_2_num = 64, conv_size = 5, hidden_size = 1024, pool_size = 2, learning_rate = 0.0001, x_dim = 784, y_dim = 10): self.channel_1_num = channel_1_num self.channel_2_num = channel_2_num self.conv_size = conv_size self.hidden_size = hidden_size self.pool_size = pool_size self.learning_rate = learning_rate self.x_dim = x_dim self.y_dim = y_dim def build_network(self): self.x = tf.placeholder(tf.float32, [None, self.x_dim], name = 'input_x') self.y = tf.placeholder(tf.float32, [None, self.y_dim], name = 'input_y') self.keep_prob = tf.placeholder(tf.float32, name = 'keep_prob') with tf.name_scope('reshape'): try: input_dim = int(math.sqrt(self.x_dim)) except: logger.debug('input dim cannot be sqrt and reshape. input dim: ', str(self.x_dim)) raise x_image = tf.reshape(self.x, [-1, input_dim, input_dim, 1]) with tf.name_scope('conv1'): W_conv1 = weight_variable([self.conv_size, self.conv_size, 1, self.channel_1_num]) b_conv1 = bias_variable([self.channel_1_num]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) with tf.name_scope('pool1'): h_pool1 = max_pool(h_conv1, self.pool_size) with tf.name_scope('conv2'): W_conv2 = weight_variable([self.conv_size, self.conv_size, self.channel_1_num, self.channel_2_num]) b_conv2 = bias_variable([self.channel_2_num]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) with tf.name_scope('pool2'): h_pool2 = max_pool(h_conv2, self.pool_size) last_dim = int(input_dim / (self.pool_size * self.pool_size)) with tf.name_scope('fc1'): W_fc1 = weight_variable([last_dim * last_dim * self.channel_2_num, self.hidden_size]) b_fc1 = bias_variable([self.hidden_size]) h_pool2_flat = tf.reshape(h_pool2, [-1, last_dim * last_dim * self.channel_2_num]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) with tf.name_scope('dropout'): h_fc1_drop = tf.nn.dropout(h_fc1, self.keep_prob) with tf.name_scope('fc2'): W_fc2 = weight_variable([self.hidden_size, self.y_dim]) b_fc2 = bias_variable([self.y_dim]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 with tf.name_scope('loss'): cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = self.y, logits = y_conv)) with tf.name_scope('adam_optimizer'): self.train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(cross_entropy) with tf.name_scope('accuracy'): correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(self.y, 1)) self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) return def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool(x, pool_size): return tf.nn.max_pool(x, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME') def avg_pool(x,pool_size): return tf.nn.avg_pool(x, ksize=[1, pool_size, pool_size, 1], strides=[1, pool_size, pool_size, 1], padding='SAME') def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def main(): data_dir= '/tmp/tensorflow/mnist/input_data' mnist = input_data.read_data_sets(data_dir, one_hot=True) logger.debug('Mnist download data done.') mnist_network = MnistNetwork() mnist_network.build_network() logger.debug('Mnist build network done.') graph_location = tempfile.mkdtemp() logger.debug('Saving graph to: %s', graph_location) train_writer = tf.summary.FileWriter(graph_location) train_writer.add_graph(tf.get_default_graph()) test_acc = 0.0 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) batch_num=200 for i in range(batch_num): batch_size=50 batch = mnist.train.next_batch(batch_size) dropout_rate=0.5 mnist_network.train_step.run(feed_dict={mnist_network.x: batch[0], mnist_network.y: batch[1], mnist_network.keep_prob: dropout_rate}) if i % 100 == 0: test_acc = mnist_network.accuracy.eval(feed_dict={ mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels, mnist_network.keep_prob: 1.0}) test_acc = mnist_network.accuracy.eval(feed_dict={ mnist_network.x: mnist.test.images, mnist_network.y: mnist.test.labels, mnist_network.keep_prob: 1.0}) def generate_default_params(): params = {'data_dir': '/tmp/tensorflow/mnist/input_data', 'dropout_rate': 0.5, 'channel_1_num': 32, 'channel_2_num': 64, 'conv_size': 5, 'pool_size': 2, 'hidden_size': 1024, 'batch_size': 50, 'batch_num': 200, 'learning_rate': 1e-4} return params if __name__ == '__main__': try: params = generate_default_params() logger.debug('params') logger.debug('params update') main() except: logger.exception('Got some exception in while loop in mnist.py') raise

true

f734e7ac84c8e5d7674c3e443378662983d9d4e5

386

py

Python

demos/python/python_musl/demo.py

jessehui/occlum

8a5f3033881c090340d678f2aecdca4ac6355bf4

[ "BSD-3-Clause-Clear" ]

928

2019-07-04T12:00:04.000Z

2022-03-30T02:45:06.000Z

demos/python/python_musl/demo.py

jessehui/occlum

8a5f3033881c090340d678f2aecdca4ac6355bf4

[ "BSD-3-Clause-Clear" ]

333

2019-07-01T07:42:49.000Z

2022-03-31T10:53:19.000Z

demos/python/python_musl/demo.py

jessehui/occlum

8a5f3033881c090340d678f2aecdca4ac6355bf4

[ "BSD-3-Clause-Clear" ]

161

2019-07-19T03:46:44.000Z

2022-03-28T02:16:31.000Z

import pandas as pd import numpy as np from sklearn.datasets import dump_svmlight_file df1 = pd.read_csv("./dataset/input_label.csv") df2 = pd.read_csv("./dataset/input.csv") res = pd.merge(df1, df2, how='left', left_on='id', right_on='id') X = res[np.setdiff1d(res.columns,['label','id'])] y = res.label dump_svmlight_file(X,y,'/host/smvlight.dat',zero_based=True,multilabel=False)

29.692308

77

0.733161

import pandas as pd import numpy as np from sklearn.datasets import dump_svmlight_file df1 = pd.read_csv("./dataset/input_label.csv") df2 = pd.read_csv("./dataset/input.csv") res = pd.merge(df1, df2, how='left', left_on='id', right_on='id') X = res[np.setdiff1d(res.columns,['label','id'])] y = res.label dump_svmlight_file(X,y,'/host/smvlight.dat',zero_based=True,multilabel=False)

true

f734ea1c07870fb34749ffbc4233d57c196b4351

8,912

py

Python

pgAdmin/pgadmin4/web/pgadmin/tools/sqleditor/tests/test_encoding_charset.py

WeilerWebServices/PostgreSQL

ae594ed077bebbad1be3c1d95c38b7c2c2683e8c

[ "PostgreSQL" ]

null

pgAdmin/pgadmin4/web/pgadmin/tools/sqleditor/tests/test_encoding_charset.py

WeilerWebServices/PostgreSQL

ae594ed077bebbad1be3c1d95c38b7c2c2683e8c

[ "PostgreSQL" ]

null

pgAdmin/pgadmin4/web/pgadmin/tools/sqleditor/tests/test_encoding_charset.py

WeilerWebServices/PostgreSQL

ae594ed077bebbad1be3c1d95c38b7c2c2683e8c

[ "PostgreSQL" ]

null

# -*- coding: utf-8 -*- ########################################################################## # # pgAdmin 4 - PostgreSQL Tools # # Copyright (C) 2013 - 2020, The pgAdmin Development Team # This software is released under the PostgreSQL Licence # ########################################################################## from pgadmin.utils.route import BaseTestGenerator from pgadmin.browser.server_groups.servers.databases.tests import utils as \ database_utils from regression.python_test_utils import test_utils import json from pgadmin.utils import server_utils import random class TestEncodingCharset(BaseTestGenerator): """ This class validates character support in pgAdmin4 for different PostgresDB encodings """ skip_on_database = ['gpdb'] scenarios = [ ( 'With Encoding UTF8', dict( db_encoding='UTF8', lc_collate='C', test_str='A' )), ( 'With Encoding EUC_CN', dict( db_encoding='EUC_CN', lc_collate='C', test_str='A' )), ( 'With Encoding SQL_ASCII', dict( db_encoding='SQL_ASCII', lc_collate='C', test_str='Tif' )), ( 'With Encoding LATIN1', dict( db_encoding='LATIN1', lc_collate='C', test_str='äöüßÑ' )), ( 'With Encoding LATIN2', dict( db_encoding='LATIN2', lc_collate='C', test_str='§' )), ( 'With Encoding LATIN9', dict( db_encoding='LATIN9', lc_collate='C', test_str='äöüß' )), ( 'With Encoding EUC_JIS_2004', dict( db_encoding='EUC_JIS_2004', lc_collate='C', test_str='じんぼはりんごをたべる' )), ( 'With Encoding WIN1256', dict( db_encoding='WIN1256', lc_collate='C', test_str='صباح الخير' )), ( 'With Encoding WIN866', dict( db_encoding='WIN866', lc_collate='C', test_str='Альтернативная' )), ( 'With Encoding WIN874', dict( db_encoding='WIN874', lc_collate='C', test_str='กลิ่นหอม' )), ( 'With Encoding WIN1250', dict( db_encoding='WIN1250', lc_collate='C', test_str='ŔÁÄÇ' )), ( 'With Encoding WIN1251', dict( db_encoding='WIN1251', lc_collate='C', test_str='ЖИЙЮ' )), ( 'With Encoding WIN1252', dict( db_encoding='WIN1252', lc_collate='C', test_str='ÆØÙü' )), ( 'With Encoding WIN1253', dict( db_encoding='WIN1253', lc_collate='C', test_str='ΨΪμΫ' )), ( 'With Encoding WIN1254', dict( db_encoding='WIN1254', lc_collate='C', test_str='ĞğØŠ' )), ( 'With Encoding WIN1255', dict( db_encoding='WIN1255', lc_collate='C', test_str='₪¥©¾' )), ( 'With Encoding WIN1256', dict( db_encoding='WIN1256', lc_collate='C', test_str='بؤغق' )), ( 'With Encoding WIN1257', dict( db_encoding='WIN1257', lc_collate='C', test_str='‰ķģž' )), ( 'With Encoding WIN1258', dict( db_encoding='WIN1258', lc_collate='C', test_str='₫SHYÑđ' )), ( 'With Encoding EUC_CN', dict( db_encoding='EUC_CN', lc_collate='C', test_str='汉字不灭' )), ( 'With Encoding EUC_JP', dict( db_encoding='EUC_JP', lc_collate='C', test_str='での日本' )), ( 'With Encoding EUC_KR', dict( db_encoding='EUC_KR', lc_collate='C', test_str='ㄱㄲㄴㄷ' )), ( 'With Encoding EUC_TW', dict( db_encoding='EUC_TW', lc_collate='C', test_str='中文' )), ( 'With Encoding ISO_8859_5', dict( db_encoding='ISO_8859_5', lc_collate='C', test_str='ЁЎФЮ' )), ( 'With Encoding ISO_8859_6', dict( db_encoding='ISO_8859_6', lc_collate='C', test_str='العَرَبِيَّة' )), ( 'With Encoding ISO_8859_7', dict( db_encoding='ISO_8859_7', lc_collate='C', test_str='ελληνικά' )), ( 'With Encoding ISO_8859_8', dict( db_encoding='ISO_8859_8', lc_collate='C', test_str='דבא' )), ( 'With Encoding KOI8R', dict( db_encoding='KOI8R', lc_collate='C', test_str='Альтернативная' )), ( 'With Encoding KOI8U', dict( db_encoding='KOI8U', lc_collate='C', test_str='українська' )), ] def setUp(self): self.encode_db_name = 'encoding_' + self.db_encoding + \ str(random.randint(10000, 65535)) self.encode_sid = self.server_information['server_id'] server_con = server_utils.connect_server(self, self.encode_sid) if hasattr(self, 'skip_on_database'): if 'data' in server_con and 'type' in server_con['data']: if server_con['data']['type'] in self.skip_on_database: self.skipTest('cannot run in: %s' % server_con['data']['type']) self.encode_did = test_utils.create_database( self.server, self.encode_db_name, (self.db_encoding, self.lc_collate)) def runTest(self): db_con = database_utils.connect_database(self, test_utils.SERVER_GROUP, self.encode_sid, self.encode_did) if not db_con["info"] == "Database connected.": raise Exception("Could not connect to the database.") # Initialize query tool self.trans_id = str(random.randint(1, 9999999)) url = '/datagrid/initialize/query_tool/{0}/{1}/{2}/{3}'\ .format(self.trans_id, test_utils.SERVER_GROUP, self.encode_sid, self.encode_did) response = self.tester.post(url) self.assertEqual(response.status_code, 200) # Check character url = "/sqleditor/query_tool/start/{0}".format(self.trans_id) sql = "select E'{0}';".format(self.test_str) response = self.tester.post(url, data=json.dumps({"sql": sql}), content_type='html/json') self.assertEqual(response.status_code, 200) url = '/sqleditor/poll/{0}'.format(self.trans_id) response = self.tester.get(url) self.assertEqual(response.status_code, 200) response_data = json.loads(response.data.decode('utf-8')) self.assertEqual(response_data['data']['rows_fetched_to'], 1) result = response_data['data']['result'][0][0] self.assertEqual(result, self.test_str) database_utils.disconnect_database(self, self.encode_sid, self.encode_did) def tearDown(self): main_conn = test_utils.get_db_connection( self.server['db'], self.server['username'], self.server['db_password'], self.server['host'], self.server['port'], self.server['sslmode'] ) test_utils.drop_database(main_conn, self.encode_db_name)

30.520548

76

0.439408

With Encoding KOI8R', dict( db_encoding='KOI8R', lc_collate='C', test_str='Альтернативная' )), ( 'With Encoding KOI8U', dict( db_encoding='KOI8U', lc_collate='C', test_str='українська' )), ] def setUp(self): self.encode_db_name = 'encoding_' + self.db_encoding + \ str(random.randint(10000, 65535)) self.encode_sid = self.server_information['server_id'] server_con = server_utils.connect_server(self, self.encode_sid) if hasattr(self, 'skip_on_database'): if 'data' in server_con and 'type' in server_con['data']: if server_con['data']['type'] in self.skip_on_database: self.skipTest('cannot run in: %s' % server_con['data']['type']) self.encode_did = test_utils.create_database( self.server, self.encode_db_name, (self.db_encoding, self.lc_collate)) def runTest(self): db_con = database_utils.connect_database(self, test_utils.SERVER_GROUP, self.encode_sid, self.encode_did) if not db_con["info"] == "Database connected.": raise Exception("Could not connect to the database.") self.trans_id = str(random.randint(1, 9999999)) url = '/datagrid/initialize/query_tool/{0}/{1}/{2}/{3}'\ .format(self.trans_id, test_utils.SERVER_GROUP, self.encode_sid, self.encode_did) response = self.tester.post(url) self.assertEqual(response.status_code, 200) url = "/sqleditor/query_tool/start/{0}".format(self.trans_id) sql = "select E'{0}';".format(self.test_str) response = self.tester.post(url, data=json.dumps({"sql": sql}), content_type='html/json') self.assertEqual(response.status_code, 200) url = '/sqleditor/poll/{0}'.format(self.trans_id) response = self.tester.get(url) self.assertEqual(response.status_code, 200) response_data = json.loads(response.data.decode('utf-8')) self.assertEqual(response_data['data']['rows_fetched_to'], 1) result = response_data['data']['result'][0][0] self.assertEqual(result, self.test_str) database_utils.disconnect_database(self, self.encode_sid, self.encode_did) def tearDown(self): main_conn = test_utils.get_db_connection( self.server['db'], self.server['username'], self.server['db_password'], self.server['host'], self.server['port'], self.server['sslmode'] ) test_utils.drop_database(main_conn, self.encode_db_name)

true

f734ea202c7b17c7d2628d4129edacee942ccbf4

1,324

py

Python

oscar/apps/dashboard/ranges/app.py

endgame/django-oscar

e5d78436e20b55902537a6cc82edf4e22568f9d6

[ "BSD-3-Clause" ]

null

oscar/apps/dashboard/ranges/app.py

endgame/django-oscar

e5d78436e20b55902537a6cc82edf4e22568f9d6

[ "BSD-3-Clause" ]

null

oscar/apps/dashboard/ranges/app.py

endgame/django-oscar

e5d78436e20b55902537a6cc82edf4e22568f9d6

[ "BSD-3-Clause" ]

1

2019-07-10T06:32:14.000Z

from django.conf.urls import patterns, url from django.contrib.admin.views.decorators import staff_member_required from django.utils.translation import ugettext_lazy as _ from oscar.core.application import Application from oscar.apps.dashboard.ranges import views from oscar.apps.dashboard.nav import register, Node node = Node(_('Ranges'), 'dashboard:range-list') register(node, 70) class RangeDashboardApplication(Application): name = None list_view = views.RangeListView create_view = views.RangeCreateView update_view = views.RangeUpdateView delete_view = views.RangeDeleteView products_view = views.RangeProductListView def get_urls(self): urlpatterns = patterns('', url(r'^$', self.list_view.as_view(), name='range-list'), url(r'^create/$', self.create_view.as_view(), name='range-create'), url(r'^(?P<pk>\d+)/$', self.update_view.as_view(), name='range-update'), url(r'^(?P<pk>\d+)/delete/$', self.delete_view.as_view(), name='range-delete'), url(r'^(?P<pk>\d+)/products/$', self.products_view.as_view(), name='range-products'), ) return self.post_process_urls(urlpatterns) def get_url_decorator(self, url_name): return staff_member_required application = RangeDashboardApplication()

35.783784

97

0.700906

from django.conf.urls import patterns, url from django.contrib.admin.views.decorators import staff_member_required from django.utils.translation import ugettext_lazy as _ from oscar.core.application import Application from oscar.apps.dashboard.ranges import views from oscar.apps.dashboard.nav import register, Node node = Node(_('Ranges'), 'dashboard:range-list') register(node, 70) class RangeDashboardApplication(Application): name = None list_view = views.RangeListView create_view = views.RangeCreateView update_view = views.RangeUpdateView delete_view = views.RangeDeleteView products_view = views.RangeProductListView def get_urls(self): urlpatterns = patterns('', url(r'^$', self.list_view.as_view(), name='range-list'), url(r'^create/$', self.create_view.as_view(), name='range-create'), url(r'^(?P<pk>\d+)/$', self.update_view.as_view(), name='range-update'), url(r'^(?P<pk>\d+)/delete/$', self.delete_view.as_view(), name='range-delete'), url(r'^(?P<pk>\d+)/products/$', self.products_view.as_view(), name='range-products'), ) return self.post_process_urls(urlpatterns) def get_url_decorator(self, url_name): return staff_member_required application = RangeDashboardApplication()

true