xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

98463e119070853e4ff482c858d10b20b3eec8fb

3,332

py

Python

lib/tri_declarative/shortcut.py

TriOptima/tri.declarative

e1972287f7b00ab335cc6c9a8384d5a39232171e

[ "BSD-3-Clause" ]

15

2016-02-09T18:07:30.000Z

2021-11-08T09:05:40.000Z

lib/tri_declarative/shortcut.py

TriOptima/tri.declarative

e1972287f7b00ab335cc6c9a8384d5a39232171e

[ "BSD-3-Clause" ]

7

2016-02-08T12:07:13.000Z

2020-10-08T06:51:06.000Z

lib/tri_declarative/shortcut.py

TriOptima/tri.declarative

e1972287f7b00ab335cc6c9a8384d5a39232171e

[ "BSD-3-Clause" ]

7

2016-01-06T09:29:11.000Z

2021-04-07T09:35:20.000Z

import functools from .declarative import get_members from .dispatch import dispatch from .namespace import ( Namespace, setdefaults_path, ) # This is just a marker class for declaring shortcuts, and later for collecting them class Shortcut(Namespace): shortcut = True # decorator def shortcut(f): f.shortcut = True return f def is_shortcut(x): return getattr(x, 'shortcut', False) def class_shortcut(*decorator_args, **defaults): def decorator(__target__): @functools.wraps(__target__) @shortcut @dispatch( **defaults ) def class_shortcut_wrapper(cls, *args, **kwargs): name = __target__.__name__ next_call_target = kwargs.pop('call_target', None) if ( isinstance(next_call_target, Namespace) and name == next_call_target.get('attribute', None) ): # Next call is to the same attribute name, but on the base class. initial_resolve = getattr(cls, name).__func__ # Loop until we find a super class implementation base_class_candidate = cls while getattr(base_class_candidate, name).__func__ == initial_resolve: base_class_candidate = base_class_candidate.__bases__[0] next_call_target_cls = base_class_candidate next_call_target_attribute = next_call_target.attribute # We need to retain the cls value for later use (as _final_cls). setdefaults_path(kwargs, _final_cls=cls) call_target_after_shortcut = Namespace( call_target__cls=next_call_target_cls, call_target__attribute=next_call_target_attribute, ) else: next_call_target_cls = kwargs.pop('_final_cls', cls) if next_call_target is None: # No call_target specified in the decorator, just use the cls (or _final_cls from earlier) call_target_after_shortcut = Namespace( call_target__cls=next_call_target_cls, ) else: # Merge decorator specified call_target with what final class we should have. call_target_after_shortcut = Namespace( call_target=next_call_target, call_target__cls=next_call_target_cls, ) result = __target__(cls, *args, call_target=call_target_after_shortcut, **kwargs) shortcut_stack = [name] + getattr(result, '__tri_declarative_shortcut_stack', []) try: result.__tri_declarative_shortcut_stack = shortcut_stack except AttributeError: pass return result class_shortcut_wrapper.__doc__ = __target__.__doc__ return class_shortcut_wrapper assert len(decorator_args) in (0, 1), "There are no (explicit) positional arguments to class_shortcut" # pragma: no mutate if len(decorator_args) == 1: return decorator(decorator_args[0]) return decorator def get_shortcuts_by_name(class_): return dict(get_members(class_, member_class=Shortcut, is_member=is_shortcut))

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null

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null

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0

98463fa3b6a220d27d47ba06579da54d0fd9e3d5

1,032

py

Python

ml-agents/mlagents/torch_utils/torch.py

Phong13/ml-agents

393808b50581e66085578b01d9d907b65a9240f0

[ "Apache-2.0" ]

null

ml-agents/mlagents/torch_utils/torch.py

Phong13/ml-agents

393808b50581e66085578b01d9d907b65a9240f0

[ "Apache-2.0" ]

null

ml-agents/mlagents/torch_utils/torch.py

Phong13/ml-agents

393808b50581e66085578b01d9d907b65a9240f0

[ "Apache-2.0" ]

null

import os # Detect availability of torch package here. # NOTE: this try/except is temporary until torch is required for ML-Agents. try: # This should be the only place that we import torch directly. # Everywhere else is caught by the banned-modules setting for flake8 import torch # noqa I201 torch.set_num_interop_threads(2) os.environ["KMP_BLOCKTIME"] = "0" # Known PyLint compatibility with PyTorch https://github.com/pytorch/pytorch/issues/701 # pylint: disable=E1101 if torch.cuda.is_available(): torch.set_default_tensor_type(torch.cuda.FloatTensor) device = torch.device("cuda") else: torch.set_default_tensor_type(torch.FloatTensor) device = torch.device("cpu") nn = torch.nn # pylint: disable=E1101 except ImportError: torch = None nn = None device = None def default_device(): return device def is_available(): """ Returns whether Torch is available in this Python environment """ return torch is not None

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null

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98480be6c6b339a9b16fc6aaedb013fff31d11b5

3,958

py

Python

tests/server/database.py

0x0mar/king-phisher

ff294c1a6ea3d59e238f0f4ed7ec668c5e8c2cfd

[ "BSD-3-Clause" ]

null

tests/server/database.py

0x0mar/king-phisher

ff294c1a6ea3d59e238f0f4ed7ec668c5e8c2cfd

[ "BSD-3-Clause" ]

null

tests/server/database.py

0x0mar/king-phisher

ff294c1a6ea3d59e238f0f4ed7ec668c5e8c2cfd

[ "BSD-3-Clause" ]

1

2018-12-18T00:44:14.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # tests/server/database.py # # 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 project 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 # OWNER 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. # import unittest from king_phisher import testing from king_phisher.server.database import manager as db_manager from king_phisher.server.database import models as db_models from king_phisher.utilities import random_string get_tables_with_column_id = db_models.get_tables_with_column_id class ServerDatabaseTests(testing.KingPhisherTestCase): def test_create_database(self): try: db_manager.init_database('sqlite://') except Exception as error: self.fail("failed to initialize the database (error: {0})".format(error.__class__.__name__)) def test_get_meta_data(self): try: db_manager.init_database('sqlite://') except Exception as error: self.fail("failed to initialize the database (error: {0})".format(error.__class__.__name__)) database_driver = db_manager.get_meta_data('database_driver') self.assertEqual(database_driver, 'sqlite') schema_version = db_manager.get_meta_data('schema_version') self.assertEqual(schema_version, db_models.SCHEMA_VERSION) def test_get_tables_id(self): tables = set([ 'alert_subscriptions', 'campaigns', 'credentials', 'deaddrop_connections', 'deaddrop_deployments', 'landing_pages', 'messages', 'meta_data', 'users', 'visits' ]) tables_with_id = get_tables_with_column_id('id') self.assertSetEqual(set(get_tables_with_column_id('id')), tables) def test_get_tables_campaign_id(self): tables = set([ 'alert_subscriptions', 'credentials', 'deaddrop_connections', 'deaddrop_deployments', 'landing_pages', 'messages', 'visits' ]) self.assertSetEqual(set(get_tables_with_column_id('campaign_id')), tables) def test_get_tables_message_id(self): tables = set([ 'credentials', 'visits' ]) self.assertSetEqual(set(get_tables_with_column_id('message_id')), tables) def test_set_meta_data(self): try: db_manager.init_database('sqlite://') except Exception as error: self.fail("failed to initialize the database (error: {0})".format(error.__class__.__name__)) # set a new value key = random_string(10) value = random_string(20) db_manager.set_meta_data(key, value) self.assertEqual(db_manager.get_meta_data(key), value) # update an existing value value = random_string(30) db_manager.set_meta_data(key, value) self.assertEqual(db_manager.get_meta_data(key), value) if __name__ == '__main__': unittest.main()

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0.179104

0

null

0

null

0

1

0

984a26f181b80332cd6014478a8eb8713af1bd02

860

py

Python

examples/plot_geodesics_s2.py

effigies/geomstats

0d6979a15cefcf98f7f92bade9d0e4abee3dde14

[ "MIT" ]

null

examples/plot_geodesics_s2.py

effigies/geomstats

0d6979a15cefcf98f7f92bade9d0e4abee3dde14

[ "MIT" ]

null

examples/plot_geodesics_s2.py

effigies/geomstats

0d6979a15cefcf98f7f92bade9d0e4abee3dde14

[ "MIT" ]

null

""" Plot a geodesic on the sphere S2 """ import matplotlib.pyplot as plt import numpy as np import geomstats.visualization as visualization from geomstats.hypersphere import Hypersphere SPHERE2 = Hypersphere(dimension=2) METRIC = SPHERE2.metric def main(): initial_point = [1., 0., 0.] initial_tangent_vec = SPHERE2.projection_to_tangent_space( vector=[1., 2., 0.8], base_point=initial_point) geodesic = METRIC.geodesic(initial_point=initial_point, initial_tangent_vec=initial_tangent_vec) n_steps = 10 t = np.linspace(0, 1, n_steps) points = geodesic(t) ax = plt.subplot(111, projection="3d", aspect="equal") visualization.plot(points, ax, space='S2') plt.show() if __name__ == "__main__": main()

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null

0

null

0

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0

984a405ea5778fbc9af3554ca90adfa1ba3e5686

3,666

py

Python

video_utils/collect_and_store.py

monocongo/video_utils

c1fd5d0041294f91b498f2878029723377fe6f66

[ "BSD-3-Clause" ]

null

video_utils/collect_and_store.py

monocongo/video_utils

c1fd5d0041294f91b498f2878029723377fe6f66

[ "BSD-3-Clause" ]

9

2019-05-14T21:52:09.000Z

2019-06-12T17:32:13.000Z

video_utils/collect_and_store.py

monocongo/video_save_boto3

c1fd5d0041294f91b498f2878029723377fe6f66

[ "BSD-3-Clause" ]

null

import argparse import datetime import os import boto3 import ffmpeg # ------------------------------------------------------------------------------ def collect_and_store(rtsp_url: str, start_seconds: int, duration_seconds: int, s3_bucket: str, s3_prefix: str=None) -> str: """ :param rtsp_url: :param start_seconds: :param duration_seconds: :param s3_bucket: :param s3_prefix: :return: S3 URL to MP4 clip file """ # build URL with start and end times # NOTE URL is for Uniview RTSP, add options for other camera types url = rtsp_url + f"/b{start_seconds}/replay/" # file where we'll write clip data temp_file = f"clip_b{start_seconds}_e{(start_seconds + duration_seconds)}.mp4" # create the equivalent of the ffmpeg command: # $ ffmpeg -i <rtsp_url> -vcodec copy -y -rtsp_transport tcp <output_mp4> stream = ffmpeg.input(url) stream = ffmpeg.output(stream, temp_file, **{"codec:v": "copy", "rtsp_transport": "tcp", "t": f"{(duration_seconds//3600):02}:{(duration_seconds%3600//60):02}:{(duration_seconds%60):02}", "y": None } ) ffmpeg.run(stream) # store the clip to the S3 bucket using the name s3_client = boto3.client("s3") s3_client.upload_file(temp_file, s3_bucket, s3_prefix + temp_file) os.remove(temp_file) # return the S3 URL for the created file return f"s3://{s3_bucket}/{s3_prefix}{temp_file}" # ------------------------------------------------------------------------------ if __name__ == "__main__": # USAGE # $ python collect_and_store.py --rtsp rtsp://user:pass1@71.85.125.110:554 \ # --s3_bucket scw.james.adams \ # --duration 30 --count 10 # construct the argument parser and parse the arguments args_parser = argparse.ArgumentParser() args_parser.add_argument("--rtsp", required=True, type=str, help="RTSP URL for video stream") args_parser.add_argument("--duration", required=True, type=int, help="duration of saved clips (in seconds)") args_parser.add_argument("--count", required=True, type=int, help="number of clips to save") args_parser.add_argument("--s3_bucket", required=True, type=str, help="Destination S3 bucket") args_parser.add_argument("--s3_prefix", type=str, help="Key prefix of the file that will be " "stored in the S3 bucket") args = vars(args_parser.parse_args()) # sanity check for some of the arguments if not args["rtsp"].lower().startswith("rtsp://"): raise ValueError("Invalid input URL -- only RTSP supported") start = int(datetime.datetime.now().strftime("%s")) end = start + args["duration"] number_of_files_to_collect = args["count"] while number_of_files_to_collect > 0: collect_and_store(args["rtsp"], start, args["duration"], args["s3_bucket"], args["s3_prefix"]) number_of_files_to_collect -= 1 start += args["duration"] end += args["duration"]

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null

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null

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1

0

984b7b0287b2a89ff337b5b57399afb859eeb8ee

332

py

Python

actions/update_field_value.py

gmenie-ak/stackstorm-jira

55c3cd92229e6cac15da270a324571aba83e5834

[ "Apache-2.0" ]

12

2017-11-18T04:34:56.000Z

2022-03-06T08:32:18.000Z

actions/update_field_value.py

gmenie-ak/stackstorm-jira

55c3cd92229e6cac15da270a324571aba83e5834

[ "Apache-2.0" ]

26

2016-12-22T01:53:40.000Z

2021-10-01T14:00:51.000Z

actions/update_field_value.py

gmenie-ak/stackstorm-jira

55c3cd92229e6cac15da270a324571aba83e5834

[ "Apache-2.0" ]

31

2017-03-06T20:16:12.000Z

2021-12-26T06:54:26.000Z

from lib.base import BaseJiraAction __all__ = [ 'UpdateFieldValue' ] class UpdateFieldValue(BaseJiraAction): def run(self, issue_key, field, value, notify): issue = self._client.issue(issue_key) issue.update(fields={field: value}, notify=notify) result = issue.fields.labels return result

23.714286

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null

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0

984d60f3c8bc904d220d941b1dd1c7fd3ce9eac2

6,524

py

Python

vespa/datasim/util_menu.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

null

vespa/datasim/util_menu.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

4

2021-04-17T13:58:31.000Z

2022-01-20T14:19:57.000Z

vespa/datasim/util_menu.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

3

2021-06-05T16:34:57.000Z

2022-01-19T16:13:22.000Z

# Python modules # 3rd party modules import wx # Our modules import vespa.common.menu as common_menu import vespa.common.util.config as util_common_config ######################################################################## # This is a collection of menu-related constants, functions and utilities. # The function that builds the menu bar lives here, as does the menu # definition. ######################################################################## class ViewIds(common_menu.IdContainer): """A container for the ids of all of the menu items to which we need explicit references. """ ZERO_LINE_SHOW = "replace me" ZERO_LINE_TOP = "replace me" ZERO_LINE_MIDDLE = "replace me" ZERO_LINE_BOTTOM = "replace me" XAXIS_SHOW = "replace me" XAXIS_PPM = "replace me" XAXIS_HERTZ = "replace me" DATA_TYPE_REAL = "replace me" DATA_TYPE_IMAGINARY = "replace me" DATA_TYPE_MAGNITUDE = "replace me" DATA_TYPE_SUMMED = "replace me" PLOT_VIEW_FINAL = "replace me" PLOT_VIEW_ALL = "replace me" EXPERIMENT_TO_TEXT = "replace me" VIEW_TO_PNG = "replace me" VIEW_TO_SVG = "replace me" VIEW_TO_PDF = "replace me" VIEW_TO_EPS = "replace me" # When main creates an instance of DatasimMenuBar(), it sets the variable # below to that instance. It's a convenience. It's the same as # wx.GetApp().GetTopWindow().GetMenuBar(), but much easier to type. bar = None class DatasimMenuBar(common_menu.VespaMenuBar): """A subclass of wx.MenuBar that adds some app-specific functions and constants. There should be only one instance of this class per invocation of the app. It's a singleton class. """ def __init__(self, main): common_menu.VespaMenuBar.__init__(self, main) ViewIds.init_ids() # _get_menu_data() is called just once, right here. datasim, view, help = _get_menu_data(main) # Build the top-level menus that are always present. datasim = common_menu.create_menu(main, "Datasim", datasim) view = common_menu.create_menu(main, "&View", view) help = common_menu.create_menu(main, "&Help", help) for menu in (datasim, view, help): self.Append(menu, menu.label) ViewIds.enumerate_booleans(self.view_menu) # ================ Module Internal Use Only ======================= def _get_menu_data(main): # Note that wx treats the ids wx.ID_EXIT and wx.ID_ABOUT specially by # moving them to their proper location on the Mac. wx will also change # the text of the ID_EXIT item to "Quit" as is standard under OS X. # Quit is also the standard under Gnome but unfortunately wx doesn't seem # to change Exit --> Quit there, so our menu looks a little funny under # Gnome. prior = ( ("N&ew Datasim from Experiment...\tCTRL+N", main.on_new), ("O&pen Datasim...\tCTRL+O", main.on_open), common_menu.SEPARATOR, ("S&ave\tCTRL+S", main.on_save_viff), ("S&ave As...", main.on_save_as_viff), common_menu.SEPARATOR, ("Close\tCTRL+W", main.on_close_datasim), common_menu.SEPARATOR, ("E&xport Spectrum", ( ("to VIFF Raw Data...", main.on_export_spectrum_viff), ("to Siemens *.rda...", main.on_export_spectrum_siemens_rda))), ("E&xport Monte Carlo", ( ("to VIFF Raw Data...", main.on_export_monte_carlo_viff), )), common_menu.SEPARATOR, ("&Exit", main.on_self_close)) view = ( ("Zero Line", ( ("Show", main.on_menu_view_option, wx.ITEM_CHECK, ViewIds.ZERO_LINE_SHOW), common_menu.SEPARATOR, ("Top", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.ZERO_LINE_TOP), ("Middle", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.ZERO_LINE_MIDDLE), ("Bottom", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.ZERO_LINE_BOTTOM))), ("X-Axis", ( ("Show", main.on_menu_view_option, wx.ITEM_CHECK, ViewIds.XAXIS_SHOW), common_menu.SEPARATOR, ("PPM", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.XAXIS_PPM), ("Hertz", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.XAXIS_HERTZ))), common_menu.SEPARATOR, ("Data Type", ( ("Real", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.DATA_TYPE_REAL), ("Imaginary", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.DATA_TYPE_IMAGINARY), ("Magnitude", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.DATA_TYPE_MAGNITUDE), common_menu.SEPARATOR, ("Summed", main.on_menu_view_option, wx.ITEM_CHECK, ViewIds.DATA_TYPE_SUMMED))), common_menu.SEPARATOR, ("Plot Views", ( ("Final Only", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.PLOT_VIEW_FINAL), ("All Three", main.on_menu_view_option, wx.ITEM_RADIO, ViewIds.PLOT_VIEW_ALL))), common_menu.SEPARATOR, ("Output Experiment Text", main.on_menu_view_output, wx.ITEM_NORMAL, ViewIds.EXPERIMENT_TO_TEXT), ("Output Plots", ( ("View to PNG", main.on_menu_view_output, wx.ITEM_NORMAL, ViewIds.VIEW_TO_PNG), ("View to SVG", main.on_menu_view_output, wx.ITEM_NORMAL, ViewIds.VIEW_TO_SVG), ("View to EPS", main.on_menu_view_output, wx.ITEM_NORMAL, ViewIds.VIEW_TO_EPS), ("View to PDF", main.on_menu_view_output, wx.ITEM_NORMAL, ViewIds.VIEW_TO_PDF) ))) help = ( # ("&User Manual", main.on_user_manual), ("&DataSim Online User Manual", main.on_datasim_online_user_manual), ("&Vespa Help Online", main.on_vespa_help_online), ("&About", main.on_about, wx.ITEM_NORMAL, wx.ID_ABOUT), ) if util_common_config.VespaConfig().show_wx_inspector: help = list(help) help.append(common_menu.SEPARATOR) help.append( ("Show Inspection Tool", main.on_show_inspection_tool) ) return (prior, view, help)

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984f29cb3a06a2a9327f4655badc0fb6832ecefe

5,198

py

Python

data.py

Simon-Swenson-8351/hs-bot

af2613ffa71c34e1d66c9f9d65249962d21a6b4a

[ "BSD-3-Clause" ]

null

data.py

Simon-Swenson-8351/hs-bot

af2613ffa71c34e1d66c9f9d65249962d21a6b4a

[ "BSD-3-Clause" ]

null

data.py

Simon-Swenson-8351/hs-bot

af2613ffa71c34e1d66c9f9d65249962d21a6b4a

[ "BSD-3-Clause" ]

null

import os import PIL.Image import numpy import cv2 import threading import datetime import config import game_region as game_region_module class BoundingBoxInfo(object): def __init__(self, name, source_coords): self.name = name self.source_coords = source_coords class BoundingBox(object): def __init__(self, bounding_box_info, game_region): self.name = bounding_box_info.name self.coords = game_region.transform_bounding_box(bounding_box_info.source_coords, game_region.SCALE_TYPE_BOUNDING_BOX) self.dimensions = [self.coords[2] - self.coords[0], self.coords[3] - self.coords[1]] class TemplateInfo(object): def __init__(self, name, input_image, threshold = 0): self.name = name self.input_image = input_image self.threshold = threshold @classmethod def create_and_fetch_positional_image(cls, name: str, resolution: list[int], threshold: float = 0): path = os.path.join( "res", "templates", str(resolution[0]) + "x" + str(resolution[1]), name + ".png") im = PIL.Image.open(path) return TemplateInfo(name, im, threshold) class Template(object): def __init__(self, name, template_image, search_region, threshold = 0): self.name = name self.image = template_image self.search_region = search_region self.threshold = threshold # A "positional image" is a screenshot with all irrelevant data deleted # (set to alpha). It simplifies things, as we now know where in the # game screen to look for the template. @classmethod def create_from_template_info(cls, template_info, game_region): im = template_info.input_image.copy() im_total_size = im.size for i in range(2): if im_total_size[i] != game_region.template_source_resolution[i]: raise Exception("Floating templates currently not supported. " "Input template image size must be equal to the source " "resolution.") old_bb = im.getbbox() im = im.crop(old_bb) old_size = im.size new_size = tuple(game_region.transform_size(old_size, game_region.SCALE_TYPE_TEMPLATE)) im = im.resize(new_size, PIL.Image.LANCZOS) im = im.convert("L") im = numpy.array(im) # converts to OpenCV format # the reason we use transform_size here is because, when we get a # comparison image, it will simply be a cropped image of the game # region, and transform_size does not add the screen-space offset, # whereas transform_coords does new_bb = game_region.transform_size([old_bb[0], old_bb[1]], game_region.SCALE_TYPE_TEMPLATE) new_bb += [new_bb[0] + new_size[0], new_bb[1] + new_size[1]] new_bb[0] -= 3 new_bb[1] -= 3 new_bb[2] += 3 new_bb[3] += 3 return Template(template_info.name, im, tuple(new_bb), template_info.threshold) def get_response(self, image): cropped = image[self.search_region[1] : self.search_region[3], self.search_region[0] : self.search_region[2]] r = cv2.minMaxLoc(cv2.matchTemplate(cropped, self.image, cv2.TM_CCOEFF_NORMED))[1] if config.debug: if self.name == "menu-main-modes" or \ self.name == "menu-modes-header" or \ self.name == "menu-modes-bg": print("template response: " + str(r)) #timestamp = datetime.datetime.now() #cv2.imwrite(str(timestamp) + "_screen.png", cropped) #cv2.imwrite(str(timestamp) + "_template.png", self.image) return r def threshold_reached(self, image): return self.get_response(image) > self.threshold def create_bounding_box_infos(): tmp_list = [] tmp_list.append(BoundingBoxInfo("play", [1342, 836, 1457, 942])) tmp_list.append(BoundingBoxInfo("end-turn", [1506, 475, 1615, 511])) tmp_list.append(BoundingBoxInfo("hero-power", [1090, 780, 1185, 871])) tmp_list.append(BoundingBoxInfo("menu-dead-zone", [1665, 47, 1892, 1010])) tmp_list.append(BoundingBoxInfo("concede", [880, 367, 1044, 399])) tmp_list.append(BoundingBoxInfo("options", [1858, 1042, 1896, 1070])) tmp_list.append(BoundingBoxInfo("bg-refresh", [1093, 166, 1166, 241])) result = {} for i in tmp_list: result[i.name] = i return result def create_template_infos(): tmp_list = [] tmp_list.append(TemplateInfo.create_and_fetch_positional_image("game-over-defeat", 0.78)) tmp_list.append(TemplateInfo.create_and_fetch_positional_image("game-over-victory", 0.78)) tmp_list.append(TemplateInfo.create_and_fetch_positional_image("your-turn-something-to-do", 0.88)) tmp_list.append(TemplateInfo.create_and_fetch_positional_image("play-standard", 0.88)) tmp_list.append(TemplateInfo.create_and_fetch_positional_image("turn-start", 0.8)) result = {} for i in tmp_list: result[i.name] = i return result mouse_lock = threading.Lock()

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0.293003

0.034848

0.048538

0.060983

0.220597

0.147169

0.119477

0

0.037592

0.242593

5,198

135

127

38.503704

0.778766

0.109465

0

0.177083

0

0.071893

0.005414

0

1

0.104167

false

0

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0.010417

0.291667

0.010417

0

null

0

null

0

1

0

9850c05df27bb10d9bb85ebb3ffbbd1b73871ffa

1,536

py

Python

ibt/docker_util.py

rcook/ibt

6ab5bda2067ef053874938a4ec445ea0e54b30f2

[ "MIT" ]

null

ibt/docker_util.py

rcook/ibt

6ab5bda2067ef053874938a4ec445ea0e54b30f2

[ "MIT" ]

13

2018-08-08T15:40:54.000Z

2021-06-22T21:00:36.000Z

ibt/docker_util.py

rcook/ibt

6ab5bda2067ef053874938a4ec445ea0e54b30f2

[ "MIT" ]

null

############################################################################### # # IBT: Isolated Build Tool # Copyright (C) 2016, Richard Cook. All rights reserved. # # Simple wrappers around Docker etc. for fully isolated build environments # ############################################################################### from __future__ import print_function import subprocess32 from ibt.container_util import check_process_in_container from ibt.util import call_process, check_process def docker_installed(): try: return call_process(["docker", "--version"], timeout=1) except (OSError, subprocess32.TimeoutExpired): return False def docker_image_exists(image_id): return call_process(["docker", "inspect", image_id]) def docker_image_build(image_id, context_dir): if docker_image_exists(image_id): print("Docker image {} already built".format(image_id)) else: print("Building Docker image {}".format(image_id)) check_process(["docker", "build", "-t", image_id, context_dir]) def docker_image_remove(image_id): if docker_image_exists(image_id): print("Destroying Docker image {}".format(image_id)) check_process(["docker", "rmi", image_id]) else: print("No Docker image {} to destroy".format(image_id)) def docker_run(ctx, project, args, container_run_path, alias_args=None): check_process_in_container( ctx, project, args, None, ["/bin/sh", container_run_path], alias_args )

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0.623698

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0.057269

0.072687

0.242291

0.160793

0.092511

0

0.007115

0.176432

1,536

47

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32.680851

0.710672

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0

0.125

0

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0

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false

0

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0.375

0.15625

0

null

0

null

0

1

0

98523176d76da547436cf4562259ecd15472ff25

3,241

py

Python

tests/systems/test_spins.py

sirmarcel/floq

a456957500c809a5bb4cdae2d7b1d4720c28f2c4

[ "MIT" ]

3

2020-10-31T00:30:42.000Z

2021-02-27T14:59:17.000Z

tests/systems/test_spins.py

sirmarcel/floq

a456957500c809a5bb4cdae2d7b1d4720c28f2c4

[ "MIT" ]

1

2019-06-06T12:56:46.000Z

2019-06-06T14:41:13.000Z

tests/systems/test_spins.py

sirmarcel/floq

a456957500c809a5bb4cdae2d7b1d4720c28f2c4

[ "MIT" ]

5

2017-07-25T11:22:56.000Z

2021-02-27T14:59:24.000Z

from tests.assertions import CustomAssertions import numpy as np import floq.systems.spins as spins def single_hf(controls, omega): a1 = controls[0] b1 = controls[1] a2 = controls[2] b2 = controls[3] return np.array([[[0, 0.25*(1j*a2 + b2)], [0.25*1j*(a2 + 1j*b2), 0]], [[0, 0.25*(1j*a1 + b1)], [0.25*1j*(a1 + 1j*b1), 0]], [[omega/2.0, 0], [0, -(omega/2.0)]], [[0, -0.25j*(a1 - 1j*b1)], [0.25*(-1j*a1 + b1), 0]], [[0, -0.25j*(a2 - 1j*b2)], [0.25*(-1j*a2 + b2), 0]]]) def dhf(): dhf_b1 = np.array([[[0., 0.], [0., 0.]], [[0., 0.25], [-0.25, 0.]], [[0., 0.], [0., 0.]], [[0., -0.25], [0.25, 0.]], [[0., 0.], [0., 0.]]]) dhf_a1 = np.array([[[0., 0.], [0., 0.]], [[0., 0. + 0.25j], [0. + 0.25j, 0.]], [[0., 0.], [0., 0.]], [[0., 0. - 0.25j], [0. - 0.25j, 0.]], [[0., 0.], [0., 0.]]]) dhf_b2 = np.array([[[0., 0.25], [-0.25, 0.]], [[0., 0.], [0., 0.]], [[0., 0.], [0., 0.]], [[0., 0.], [0., 0.]], [[0., -0.25], [0.25, 0.]]]) dhf_a2 = np.array([[[0., 0. + 0.25j], [0. + 0.25j, 0.]], [[0., 0.], [0., 0.]], [[0., 0.], [0., 0.]], [[0., 0.], [0., 0.]], [[0., 0. - 0.25j], [0. - 0.25j, 0.]]]) return np.array([dhf_a1, dhf_b1, dhf_a2, dhf_b2]) class TestSpinHf(CustomAssertions): def test_build_single_hf(self): controls = np.array([1.2, 2.3, 3.4, 5.4]) freq = 2.5 target = single_hf(controls, freq) result = spins.hf(2, freq, controls) self.assertArrayEqual(target, result) class TestSpindHf(CustomAssertions): def test_build_single_dhf(self): amp = 1.25 target = dhf() result = spins.dhf(2) self.assertArrayEqual(target, result) class TestRandomisedSpinEnsemble(CustomAssertions): def test_init(self): # Not a very sophisticated test -- at least verify that it's not broken rand = spins.RandomisedSpinEnsemble(10, 10, 1.0, 8.2, 0.25) self.assertIsInstance(rand.systems, list) class TestSpinEnsemble(CustomAssertions): def setUp(self): self.amps = np.array([1.2, 1.1, 0.7, 0.6]) self.freqs = np.array([0.8, 1.1, 0.9, 1.2]) self.ensemble = spins.SpinEnsemble(4, 2, 1.0, self.freqs, self.amps) self.controls = np.array([1.5, 1.3, 1.4, 1.1]) self.t = 3.0 def test_systems_works(self): self.assertIsInstance(self.ensemble.systems, list) def test_single_system_evolves_correctly(self): system = self.ensemble.systems[0] result = system.u(self.controls, self.t) single = spins.SpinSystem(2, self.amps[0], self.freqs[0], 1.0) target = single.u(self.controls, self.t) self.assertArrayEqual(result, target, decimals=10)

30.575472

79

0.444924

435

3,241

3.262069

0.181609

0.108527

0.122622

0.12685

0.326286

0.145877

0.10007

0.090204

0

0.125654

0.351743

3,241

105

80

30.866667

0.549738

0.02129

0

0.166667

0

0.138889

1

0.111111

false

0

0.041667

0

0.236111

0

null

0

null

0

1

0

9852f98b97ad9ca649e132fb523a2aba2132c424

877

py

Python

utils/localization/tests/test_helper/test_utils/test_utils.py

Open-Speech-EkStep/crowdsource-dataplatform

cf2a6c36717cb06f75de7b097959c59699564ad0

[ "MIT" ]

22

2021-03-23T05:36:51.000Z

2022-01-28T10:50:23.000Z

utils/localization/tests/test_helper/test_utils/test_utils.py

Open-Speech-EkStep/crowdsource-dataplatform

cf2a6c36717cb06f75de7b097959c59699564ad0

[ "MIT" ]

13

2021-05-27T07:18:58.000Z

2022-02-24T06:33:58.000Z

utils/localization/tests/test_helper/test_utils/test_utils.py

Open-Speech-EkStep/crowdsource-dataplatform

cf2a6c36717cb06f75de7b097959c59699564ad0

[ "MIT" ]

14

2021-03-22T22:47:17.000Z

2021-12-22T05:00:52.000Z

import json import pandas as pd from unittest import TestCase from pandas._testing import assert_frame_equal from helper.utils.utils import load_json_as_df, reformat_json class TestUtils(TestCase): def test_load_json_as_df(self): test_json_data = {'text1': 'translation1', 'text2': 'translation2'} expected_df = pd.DataFrame([['text1', 'translation1'], ['text2', 'translation2']], columns=['Key', 'value']) actual_df = load_json_as_df(test_json_data) assert_frame_equal(actual_df, expected_df) def test_reformat_json(self): df = pd.DataFrame([['a', 'b']], columns=['x', 'y']) json_file = df.to_json(orient='values') test_json_string = json.loads(json_file) actual_result = reformat_json(test_json_string) expected_result = {'a': 'b'} self.assertEqual(actual_result, expected_result)

35.08

116

0.692132

116

877

4.905172

0.387931

0.056239

0.052724

0.063269

0

0.011142

0.1813

877

24

117

36.541667

0.781337

0

0.100342

0

0.166667

1

0.111111

false

0

0.277778

0

0.444444

0

null

0

null

0

1

0

98537b856a259b47a9404a77c28aea3a4de091c5

1,954

py

Python

src/spider/guozhi/guozhi.py

objcat/test-python

97b3cb610a80b8e00b8a032ca94bb3fead4102fb

[ "MIT" ]

null

src/spider/guozhi/guozhi.py

objcat/test-python

97b3cb610a80b8e00b8a032ca94bb3fead4102fb

[ "MIT" ]

null

src/spider/guozhi/guozhi.py

objcat/test-python

97b3cb610a80b8e00b8a032ca94bb3fead4102fb

[ "MIT" ]

null

# description: guozhi # date: 2021/1/9 9:54 # author: objcat # version: 1.0 import requests from bs4 import BeautifulSoup from win32com.client import Dispatch import pathlib import os class Guozhi: FC_URL = "http://2018.emu618.net:6180/index.php?controller=site&action=pro_list&cat=23" SFC_URL = "http://2018.emu618.net:6180/index.php?controller=site&action=pro_list&cat=115" MD_URL = "http://2018.emu618.net:6180/index.php?controller=site&action=pro_list&cat=47" @classmethod def parse_url(cls): baseurl = "http://2018.emu618.net:6180" for page in range(79, 80): print(f"开始爬取第 {page} 页") res = requests.get(Guozhi.MD_URL + f"&page={page}") # 转换编码乱码的时候需要转换 res.encoding = "utf-8" main_page = BeautifulSoup(res.text, "html.parser") target = main_page.find("div", class_="games_list").find_all("a", class_="p_name") for a in target: child_res = requests.get(baseurl + a.get("href")) child_page = BeautifulSoup(child_res.text, "html.parser") child_a = child_page.find("div", class_="detail_down_adress_con_bottom_left_part2_con").find("a") child_download_btn_url = baseurl + child_a.get("href") download_res = requests.get(child_download_btn_url) download_page = BeautifulSoup(download_res.text, "html.parser") download_url = download_page.find("div", class_="download").find("a").get("href") print(download_url) cls.download_ftp(download_url) # exit() @staticmethod def download_ftp(download_url): # 调用迅雷下载 o = Dispatch("ThunderAgent.Agent64.1") # AddTask("下载地址", "另存为文件名", "保存目录", "任务注释", "引用地址", "开始模式", "只从原始地址下载", "从原始地址下载线程数") o.AddTask(download_url) o.CommitTasks() if __name__ == '__main__': Guozhi.parse_url()

36.867925

113

0.623337

252

1,954

4.611111

0.416667

0.047332

0.048193

0.05852

0.178141

0.160069

0

0.049865

0.240532

1,954

52

114

37.576923

0.733154

0.092119

0

0.085714

0.250425

0.037394

0

1

0.057143

false

0

0.142857

0

0.314286

0.057143

0

null

0

null

0

1

0

985612c5152d046b00d7b926e420d9322d0211d3

1,066

py

Python

setup.py

tzickel/forif

be976fdbb854df4e5366341733f5d7c9ecc11cc9

[ "Apache-2.0" ]

2

2018-04-01T19:33:27.000Z

2018-04-02T08:09:25.000Z

setup.py

tzickel/forif

be976fdbb854df4e5366341733f5d7c9ecc11cc9

[ "Apache-2.0" ]

null

setup.py

tzickel/forif

be976fdbb854df4e5366341733f5d7c9ecc11cc9

[ "Apache-2.0" ]

null

from setuptools import setup import codecs import os here = os.path.abspath(os.path.dirname(__file__)) def read(*parts): return codecs.open(os.path.join(here, *parts), 'r').read() long_description = read('README.md') setup( name='forif', version='0.0.1', py_modules=['forif'], url='https://github.com/tzickel/forif', license='WTFPLApache License 2.0', author='tzickel', author_email='', # tests_require=['nose'], description='A C-like condition assignment syntax in python', long_description=long_description, platforms='any', # test_suite='nose.collector', test_suite='tests.TestForIf', classifiers=[ 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 3', 'Development Status :: 4 - Beta', 'Environment :: Console', 'Intended Audience :: Developers', 'Operating System :: OS Independent', 'Topic :: Software Development :: Libraries :: Python Modules', ], # extras_require={ # 'testing': ['nose'], # } )

25.380952

71

0.62758

117

1,066

5.606838

0.65812

0.027439

0.07622

0

0.009581

0.216698

1,066

41

72

26

0.776048

0.101313

0

0.419389

0

1

0.034483

false

0

0.103448

0.034483

0.172414

0

null

0

null

0

1

0

9856e14fcd9d4be167f509ba97bef98318eeb15a

2,742

py

Python

Question_31_40/myanswers/myans_38.py

OverHall27/Gasyori100knock

341c528eb4c0789034898ee1f7d0a4b2f8b23eff

[ "MIT" ]

null

Question_31_40/myanswers/myans_38.py

OverHall27/Gasyori100knock

341c528eb4c0789034898ee1f7d0a4b2f8b23eff

[ "MIT" ]

null

Question_31_40/myanswers/myans_38.py

OverHall27/Gasyori100knock

341c528eb4c0789034898ee1f7d0a4b2f8b23eff

[ "MIT" ]

null

import cv2 import numpy as np def DCTConstant(u, v): if u == 0 and v == 0: return 1 / 2 elif u == 0 or v == 0: return 1 / np.sqrt(2) else: return 1 def DCT(img, block=8): Ver, Hor, Col = img.shape result = np.zeros((Ver, Hor, Col)).astype(np.float32) x = np.tile(np.arange(block), (block, 1)) y = np.arange(block).repeat(block).reshape([block, block]) for c in range(Col): for u in range(Hor): cu = u % block iu = u // block for v in range(Ver): cv = v % block iv = v // block result[v, u, c] = np.sum(img[iv*block:(iv+1)*block, iu*block:(iu+1)*block, c] * np.cos((2*x+1)*cu*np.pi/(2*block)) * np.cos((2*y+1)*cv*np.pi/(2*block))) result[v, u, c] *= DCTConstant(cu, cv) result *= (2/block) return result def InvDCT(dct, block=8, K=8): Ver, Hor, Col = img.shape result = np.zeros((Ver, Hor, Col)).astype(np.float32) u = np.tile(np.arange(K), (K, 1)) v = np.arange(K).repeat(K).reshape([K, K]) c_uv = np.zeros((K, K)) for x in range(K): for y in range(K): c_uv[y, x] = DCTConstant(y, x) for c in range(Col): for x in range(Hor): cx = x % block ix = x // block for y in range(Ver): cy = y % block iy = y // block result[y, x, c] = np.sum(dct[iy*block:iy*block+K, ix*block:ix*block+K, c] * np.cos((2*cx+1)*u*np.pi/(2*block)) * np.cos((2*cy+1)*v*np.pi/(2*block)) * c_uv) result *= (2/block) return result def Quantization(img, block=8): Ver, Hor, Col = img.shape N_Ver = Ver // block N_Hor = Hor // block Q = np.array(((16, 11, 10, 16, 24, 40, 51, 61), (12, 12, 14, 19, 26, 58, 60, 55), (14, 13, 16, 24, 40, 57, 69, 56), (14, 17, 22, 29, 51, 87, 80, 62), (18, 22, 37, 56, 68, 109, 103, 77), (24, 35, 55, 64, 81, 104, 113, 92), (49, 64, 78, 87, 103, 121, 120, 101), (72, 92, 95, 98, 112, 100, 103, 99)), dtype=np.float32) result = np.zeros((Ver, Hor, Col)).astype(np.float32) for c in range(Col): for x in range(N_Hor): for y in range(N_Ver): result[y*block:(y+1)*block, x*block:(x+1)*block, c] = np.round(img[y*block:(y+1)*block, x*block:(x+1)*block, c] / Q) * Q return result img = cv2.imread("../imori.jpg").astype(np.float32) K_ = 8 dct = DCT(img, block=8) dct = Quantization(dct, block=8) result = InvDCT(dct, block=8, K=K_) #result = np.clip(result, 0, 255).astype(np.uint8) cv2.imwrite("myans_38.jpg", result) cv2.imshow("result", result) cv2.waitKey(0) cv2.destroyAllWindows()

29.483871

171

0.515317

474

2,742

2.959916

0.253165

0.054882

0.038489

0.02851

0.319316

0.27655

0.225944

0.203136

0.178902

0.116892

0

0.104609

0.29577

2,742

92

172

29.804348

0.621958

0.01787

0

0.197183

0

0.011148

0

1

0.056338

false

0

0.028169

0

0.169014

0

null

0

null

0

1

0

98575fafd78ee322f3d70690d931128909d9c1dc

19,861

py

Python

prune.py

poppin-mice/ShiftAddNet

a17369a50da5bba6250fdeac7c065bd00f293f3c

[ "MIT" ]

55

2020-10-04T17:17:46.000Z

2022-03-31T02:56:51.000Z

prune.py

poppin-mice/ShiftAddNet

a17369a50da5bba6250fdeac7c065bd00f293f3c

[ "MIT" ]

8

2020-12-07T03:37:48.000Z

2021-07-21T09:26:45.000Z

prune.py

poppin-mice/ShiftAddNet

a17369a50da5bba6250fdeac7c065bd00f293f3c

[ "MIT" ]

14

2020-10-29T16:51:41.000Z

2021-11-16T01:36:43.000Z

import argparse import os, time import torch import shutil import numpy as np import torch.nn as nn import torchvision.transforms as transforms import torchvision.datasets as datasets import torch.nn.functional as F import torch.optim as optim from torch.autograd import Variable import models import torch.backends.cudnn as cudnn import deepshift from deepshift.convert import convert_to_shift, round_shift_weights, count_layer_type from models import adder as adder_slow from adder import adder as adder_fast import collections from collections import OrderedDict # Training settings parser = argparse.ArgumentParser(description='PyTorch Pruning') parser.add_argument('--dataset', type=str, default='cifar10', help='training dataset') parser.add_argument('--data_path', type=str, default=None, help='path to dataset') parser.add_argument('--batch_size', type=int, default=256, metavar='N', help='batch size for training') parser.add_argument('--test_batch_size', type=int, default=256, metavar='N', help='batch size for testing') parser.add_argument('--epochs', type=int, default=160, metavar='N', help='number of epochs to train') parser.add_argument('--start_epoch', type=int, default=0, metavar='N', help='restart point') parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed') parser.add_argument('--save', default='./logs', type=str, metavar='PATH', help='path to save prune model') parser.add_argument('--arch', default='resnet20', type=str, help='architecture to use') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') # multi-gpus parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') # shift hyper-parameters parser.add_argument('--shift_depth', type=int, default=0, help='how many layers to convert to shift') parser.add_argument('--shift_type', type=str, choices=['Q', 'PS'], help='shift type for representing weights') parser.add_argument('--rounding', default='deterministic', choices=['deterministic', 'stochastic']) parser.add_argument('--weight_bits', type=int, default=5, help='number of bits to represent the shift weights') parser.add_argument('--use-kernel', type=lambda x:bool(distutils.util.strtobool(x)), default=False, help='whether using custom shift kernel') # pruning ratio parser.add_argument('--percent', default=0.6, type=float, help='percentage of weight to prune') parser.add_argument('--prune_method', default='magnitude', choices=['random', 'magnitude']) parser.add_argument('--prune_layer', default='all', choices=['shift', 'add', 'all']) args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) if not os.path.exists(args.save): os.makedirs(args.save) cudnn.benchmark = True gpu = args.gpu_ids gpu_ids = args.gpu_ids.split(',') args.gpu_ids = [] for gpu_id in gpu_ids: id = int(gpu_id) args.gpu_ids.append(id) print(args.gpu_ids) if len(args.gpu_ids) > 0: torch.cuda.set_device(args.gpu_ids[0]) kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} if args.dataset == 'cifar10': train_loader = torch.utils.data.DataLoader( datasets.CIFAR10('./data.cifar10', train=True, download=True, transform=transforms.Compose([ transforms.Pad(4), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.CIFAR10('./data.cifar10', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == 'cifar100': train_loader = torch.utils.data.DataLoader( datasets.CIFAR100('./data.cifar100', train=True, download=True, transform=transforms.Compose([ transforms.Pad(4), transforms.RandomCrop(32), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.CIFAR100('./data.cifar100', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) elif args.dataset == 'mnist': trainset = datasets.MNIST('../MNIST', download=True, train=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] ) ) train_loader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=4) testset = datasets.MNIST('../MNIST', download=True, train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] ) ) test_loader = torch.utils.data.DataLoader(testset, batch_size=args.test_batch_size, shuffle=True, num_workers=4) else: # Data loading code traindir = os.path.join(args.data, 'train') valdir = os.path.join(args.data, 'val') normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) train_dataset = datasets.ImageFolder( traindir, transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ])) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=16, pin_memory=True) test_loader = torch.utils.data.DataLoader( datasets.ImageFolder(valdir, transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize, ])), batch_size=args.test_batch_size, shuffle=False, num_workers=16, pin_memory=True) if args.dataset == 'imagenet': model = models.__dict__[args.arch](num_classes=1000) elif args.dataset == 'cifar10': model = models.__dict__[args.arch](num_classes=10) elif args.dataset == 'cifar100': model = models.__dict__[args.arch](num_classes=100) elif args.dataset == 'mnist': model = models.__dict__[args.arch](num_classes=10) else: raise NotImplementedError('No such dataset!') if 'shift' in args.arch: # no pretrain model, _ = convert_to_shift(model, args.shift_depth, args.shift_type, convert_weights=False, use_kernel=args.use_kernel, rounding=args.rounding, weight_bits=args.weight_bits) if args.cuda: model.cuda() if len(args.gpu_ids) > 1: model = torch.nn.DataParallel(model, device_ids=args.gpu_ids) # save name # name model sub-directory "shift_all" if all layers are converted to shift layers conv2d_layers_count = count_layer_type(model, nn.Conv2d) #+ count_layer_type(model, unoptimized.UnoptimizedConv2d) linear_layers_count = count_layer_type(model, nn.Linear) #+ count_layer_type(model, unoptimized.UnoptimizedLinear) print(conv2d_layers_count) if (args.shift_depth > 0): if (args.shift_type == 'Q'): shift_label = "shift_q" else: shift_label = "shift_ps" else: shift_label = "shift" # if (conv2d_layers_count==0 and linear_layers_count==0): if conv2d_layers_count == 0: shift_label += "_all" else: shift_label += "_%s" % (args.shift_depth) if (args.shift_depth > 0): shift_label += "_wb_%s" % (args.weight_bits) args.save = os.path.join(args.save, shift_label) args.save = os.path.join(args.save, 'prune_'+str(args.prune_method)+'_'+str(args.prune_layer)+'_'+str(args.percent)) if not os.path.exists(args.save): os.makedirs(args.save) def save_checkpoint(state, is_best, epoch, filepath): filename = os.path.join(filepath, 'pruned.pth.tar') torch.save(state, filename) def accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0 / batch_size)) return res def test(model): model.eval() test_loss = 0 test_acc = 0 for data, target in test_loader: if args.cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data, volatile=True), Variable(target) output = model(data) test_loss += F.cross_entropy(output, target, size_average=False).item() # sum up batch loss prec1, prec5 = accuracy(output.data, target.data, topk=(1, 5)) test_acc += prec1.item() test_loss /= len(test_loader.dataset) return test_loss, np.round(test_acc / len(test_loader), 2) def change_name(state_dict): new_state_dict = OrderedDict() for key, value in state_dict.items(): if 'conv' in key and '.1.weight' in key: new_key = key.replace('weight', 'adder') elif 'downsample' in key and '.1.weight' in key: new_key = key.replace('weight', 'adder') else: new_key = key new_state_dict[new_key] = value return new_state_dict if args.resume: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) # args.start_epoch = checkpoint['epoch'] best_prec1 = checkpoint['best_prec1'] if 'add' in args.arch: checkpoint['state_dict'] = change_name(checkpoint['state_dict']) model.load_state_dict(checkpoint['state_dict']) print("=> loaded checkpoint '{}' (epoch {}) Prec1: {:f}" .format(args.resume, checkpoint['epoch'], best_prec1)) else: print("=> no checkpoint found at '{}'".format(args.resume)) else: save_checkpoint({'state_dict': model.state_dict()}, False, epoch='init', filepath=args.save) # round weights to ensure that the results are due to powers of 2 weights model = round_shift_weights(model) print('\nEvaluation only') test_loss0, test_acc0 = test(model) print('Before pruning: Test Loss: %.8f, Test Acc: %.2f' % (test_loss0, test_acc0)) def create_mask(shape, rate): mask = torch.cuda.FloatTensor(shape).uniform_() > rate return mask + 0 # ------------------------------------------------------------- if 'shift' in args.arch and args.prune_layer != 'add': print('prune for shift layer:') if args.shift_type == 'Q': shift_module = deepshift.modules_q.Conv2dShiftQ elif args.shift_type == 'PS': shift_module = deepshift.modules.Conv2dShift else: raise NotImplementedError # pruning if args.shift_type == 'Q': total = 0 for m in model.modules(): if isinstance(m, shift_module): total += m.weight.data.numel() shift_weights = torch.zeros(total) index = 0 for m in model.modules(): if isinstance(m, shift_module): size = m.weight.data.numel() shift_weights[index:(index+size)] = m.weight.data.view(-1).abs().clone() index += size y, i = torch.sort(shift_weights) thre_index = int(total * args.percent) thre = y[thre_index] - 1e-7 pruned = 0 print('Pruning threshold: {}'.format(thre)) zero_flag = False # ---------------------------------------------------------------- if args.prune_method == 'magnitude': for k, m in enumerate(model.modules()): if isinstance(m, shift_module): shift_copy = m.weight.data.abs().clone() # prune at boundary (weight == thre) _mask = torch.eq(shift_copy, thre+1e-7).float().cuda() _mask = _mask * torch.cuda.FloatTensor(shift_copy.shape).uniform_(-args.percent, 1-args.percent) shift_copy += _mask # --------------------------------- mask = shift_copy.gt(thre).float().cuda() pruned = pruned + mask.numel() - torch.sum(mask) m.weight.data = m.weight.data.mul_(mask) + 1 - mask # no shift if int(torch.sum(mask)) == 0: zero_flag = True print('layer index: {:d} \t total params: {:d} \t remaining params: {:d}'. format(k, mask.numel(), int(torch.sum(mask)))) elif args.prune_method == 'random': for k, m in enumerate(model.modules()): if isinstance(m, shift_module): shift_copy = m.weight.data.abs().clone() mask = create_mask(shift_copy.shape, args.percent) pruned = pruned + mask.numel() - torch.sum(mask) m.weight.data = m.weight.data.mul_(mask) + 1 - mask # no shift if int(torch.sum(mask)) == 0: zero_flag = True print('layer index: {:d} \t total params: {:d} \t remaining params: {:d}'. format(k, mask.numel(), int(torch.sum(mask)))) else: raise NotImplementedError # ---------------------------------------------------------------- elif args.shift_type == 'PS': total = 0 for m in model.modules(): if isinstance(m, shift_module): total += m.shift.data.numel() shift_weights = torch.zeros(total) index = 0 for m in model.modules(): if isinstance(m, shift_module): size = m.shift.data.numel() shift_weights[index:(index+size)] = m.shift.data.view(-1).abs().clone() index += size y, i = torch.sort(shift_weights) thre_index = int(total * args.percent) thre = y[thre_index] - 1e-7 pruned = 0 print('Pruning threshold: {}'.format(thre)) zero_flag = False # ---------------------------------------------------------------- if args.prune_method == 'magnitude': for k, m in enumerate(model.modules()): if isinstance(m, shift_module): shift_copy = m.shift.data.abs().clone() mask = shift_copy.gt(thre).float().cuda() pruned = pruned + mask.numel() - torch.sum(mask) m.shift.data.mul_(mask) m.sign.data.mul_(mask) if int(torch.sum(mask)) == 0: zero_flag = True print('layer index: {:d} \t total params: {:d} \t remaining params: {:d}'. format(k, mask.numel(), int(torch.sum(mask)))) elif args.prune_method == 'random': for k, m in enumerate(model.modules()): if isinstance(m, shift_module): shift_copy = m.shift.data.abs().clone() mask = create_mask(shift_copy.shape, args.percent) pruned = pruned + mask.numel() - torch.sum(mask) m.shift.data.mul_(mask) m.sign.data.mul_(mask) if int(torch.sum(mask)) == 0: zero_flag = True print('layer index: {:d} \t total params: {:d} \t remaining params: {:d}'. format(k, mask.numel(), int(torch.sum(mask)))) else: raise NotImplementedError # ---------------------------------------------------------------- print('Total conv params: {}, Pruned conv params: {}, Pruned ratio: {}'.format(total, pruned, float(pruned)/total)) if 'add' in args.arch and args.prune_layer != 'shift': print('prune for adder layer:') adder_module = adder_slow.adder2d adder_module = adder_fast.Adder2D total = 0 for m in model.modules(): if isinstance(m, adder_module): total += m.adder.data.numel() adder_weights = torch.zeros(total) index = 0 for m in model.modules(): if isinstance(m, adder_module): size = m.adder.data.numel() adder_weights[index:(index+size)] = m.adder.data.view(-1).abs().clone() index += size y, i = torch.sort(adder_weights) thre_index = int(total * args.percent) thre = y[thre_index] pruned = 0 print('Pruning threshold: {}'.format(thre)) zero_flag = False # ---------------------------------------------------------------- if args.prune_method == 'magnitude': for k, m in enumerate(model.modules()): if isinstance(m, adder_module): adder_copy = m.adder.data.abs().clone() mask = adder_copy.gt(thre).float().cuda() pruned = pruned + mask.numel() - torch.sum(mask) m.adder.data.mul_(mask) if int(torch.sum(mask)) == 0: zero_flag = True print('layer index: {:d} \t total params: {:d} \t remaining params: {:d}'. format(k, mask.numel(), int(torch.sum(mask)))) elif args.prune_method == 'random': for k, m in enumerate(model.modules()): if isinstance(m, shift_module): shift_copy = m.adder.data.abs().clone() mask = create_mask(shift_copy.shape, args.percent) pruned = pruned + mask.numel() - torch.sum(mask) m.adder.data.mul_(mask) if int(torch.sum(mask)) == 0: zero_flag = True print('layer index: {:d} \t total params: {:d} \t remaining params: {:d}'. format(k, mask.numel(), int(torch.sum(mask)))) else: raise NotImplementedError # ---------------------------------------------------------------- print('Total conv params: {}, Pruned conv params: {}, Pruned ratio: {}'.format(total, pruned, float(pruned)/total)) # ------------------------------------------------------------- print('\nTesting') test_loss1, test_acc1 = test(model) print('After Pruning: Test Loss: %.8f, Test Acc: %.2f' % (test_loss1, test_acc1)) save_checkpoint({ 'epoch': 0, 'state_dict': model.state_dict(), 'acc': test_acc1, 'best_acc': 0., }, False, epoch=0, filepath=args.save) with open(os.path.join(args.save, 'prune.txt'), 'w') as f: f.write('Before pruning: Test Loss: %.8f, Test Acc: %.2f\n' % (test_loss0, test_acc0)) f.write('Total conv params: {}, Pruned conv params: {}, Pruned ratio: {}\n'.format(total, pruned, float(pruned)/total)) f.write('After Pruning: Test Loss: %.8f, Test Acc: %.2f\n' % (test_loss1, test_acc1)) if zero_flag: f.write("There exists a layer with 0 parameters left.")

44.431767

178

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19,861

4.629931

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9857800007ceff368c121bd9101455e6fb400d8d

6,200

py

Python

L5NeuronSimulation/raster_maker.py

JuliusvR/L5NeuronSimulation

1fc68c7367c439e1f9c9b73a15a95609858ec720

[ "MIT" ]

2

2020-11-12T15:12:31.000Z

2021-12-09T19:12:55.000Z

L5NeuronSimulation/raster_maker.py

JuliusvR/L5NeuronSimulation

1fc68c7367c439e1f9c9b73a15a95609858ec720

[ "MIT" ]

2

2021-05-20T21:36:12.000Z

2021-08-29T15:32:35.000Z

L5NeuronSimulation/raster_maker.py

JuliusvR/L5NeuronSimulation

1fc68c7367c439e1f9c9b73a15a95609858ec720

[ "MIT" ]

6

2021-03-03T22:14:39.000Z

2021-11-23T13:44:35.000Z

""" Contains the functions and class (SonataWriter) necessary for generating and saving the input spike rasters. """ import numpy as np import scipy.signal as ss import scipy import scipy.stats as st import matplotlib.pyplot as plt import h5py from bmtk.utils.reports.spike_trains import PoissonSpikeGenerator import pandas as pd from scipy.fft import fft import matplotlib import statsmodels.api as sm class SonataWriter: """Class used to dynamically writing spike rasters to an h5 file. Attributes ---------- file : h5py.File file object being worked on group : h5py.Group gropu where the datasets reside datasets : dict datasets that are saved to the file Methods ------- append_ds(vals, ds) appends the given values to the end of the given dataset append_repeat(ds, val, N) appends the given value N times to the end of the given dataset close() close the h5py file """ def __init__(self, f_name, groups, datasets, types): """ Parameters ---------- f_name : str name of file location groups : list list of group names (str) that are layered into the h5py file in the order given. datasets : list list of dataset names (str) types : list list of data types that corresponds to the datasets list """ self.file = h5py.File(f_name, 'w') self.group = self.file for group in groups: self.group = self.group.create_group(group) self.datasets = {} for i, ds in enumerate(datasets): self.datasets[ds] = self.group.create_dataset(ds, data=[], dtype=types[i], chunks=True, maxshape=(None,)) def append_ds(self, vals, ds): """appends the given values to the end of the given dataset Parameters ---------- vals : list list of values to be appended to the dataset ds : str key of the dataset to append to """ length = len(self.datasets[ds]) self.datasets[ds].resize((length + len(vals), )) self.datasets[ds][length:] = vals def append_repeat(self, ds, val, N): """appends the given value N times to the end of the given dataset Parameters ---------- ds : str key of the dataset to append to val : [type] value to be appended N times N : int number of vals to append to the dataset """ self.append_ds([val for i in range(N)], ds) def close(self): """Closes the h5py File """ self.file.close() def zscore(x): """z scores the given array""" return (x-np.mean(x))/np.std(x) def minmax(x): """min max normalizes the given array""" return (x - np.min(x))/(np.max(x)-np.min(x)) def moving_average(x, w): return np.convolve(x, np.ones(w), 'valid') / w def make_noise(num_traces=100,num_samples=4999): """Creates a noise trace used in generating spike rasters. Parameters ---------- num_traces : int, optional number of noise traces to create (first dimension), by default 100 num_samples : int, optional length of the trace (second dimension), by default 4999 Returns ------- np.array noise trace """ B = [0.049922035, -0.095993537, 0.050612699, -0.004408786] A = [1, -2.494956002, 2.017265875, -0.522189400] invfn = np.zeros((num_traces,num_samples)) for i in np.arange(0,num_traces): wn = np.random.normal(loc=1, scale=0.5,size=num_samples+2000) invfn[i,:] = minmax(ss.lfilter(B, A, wn)[2000:])+0.5 # Create '1/f' Noise return invfn def shift_exc_noise(ts, nid, seconds, time_shift=4): """Creates a shifted, min-max normalized average traces of the given spike raster. Parameters ---------- ts : list times (float) where spikes occur nid : int node id associated with each spike seconds : float length of the raster in seconds time_shift : int, optional how many ms to shift the average trace by, by default 4 Returns ------- [type] [description] """ h = np.histogram(ts,bins=np.arange(0,seconds*1000,1)) fr_prof = h[0]/(0.001*(np.max(nid)+1)) wrap = fr_prof[-4:] fr_prof[4:] = fr_prof[0:-4] fr_prof[0:4] = wrap fr_prof = minmax(fr_prof)+0.5 return fr_prof def make_save_spikes(writer, exp, dist, numUnits=100,rateProf=None,start_id=0,start_time=0): """Creates and saves spikes for the given nodes using the provided noise trace and a random mean firing rate generated with the given distribution. Parameters ---------- writer : SonataWriter how the spikes are saved exp : bool whether the value from dist should be fed to np.exp() dist : np.array() array of firing rates of shape (numUnits,) numUnits : int, optional number of nodes to generate spikes for, by default 100 rateProf : np.array(), optional noise trace for each unit must have numUnits rows, by default None start_id : int, optional node_id that the first unit/node should be associated with, by default 0 start_time : int, optional at what time the spikes should start being generated, by default 0 """ for i in np.arange(0,numUnits): try: r = rateProf[i,:] except: import pdb; pdb.set_trace() r[r<0] = 0#Can't have negative firing rates. rate_temp=[];simSpks_temp=[] #Multiplies the noise trace by the randomly generated firing rate. if exp: rate_temp = r*np.exp(dist[i]) else: rate_temp = r*dist[i] numbPoints = scipy.stats.poisson(rate_temp/1000).rvs()#Poisson number of points simSpks=np.where(numbPoints>0)[0] writer.append_repeat("node_ids", i + start_id, len(simSpks)) writer.append_ds(simSpks + start_time, "timestamps")

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null

0

null

0

1

0

98579a29b18e47304be5f1259c1a91c9aae8a65f

8,086

py

Python

Task.py

omercsp/taskrunner

f972c7879ce9f4e390524a332388ea8c478ea19b

[ "MIT" ]

5

2022-01-04T13:35:48.000Z

2022-03-20T11:34:32.000Z

Task.py

omercsp/taskrunner

f972c7879ce9f4e390524a332388ea8c478ea19b

[ "MIT" ]

null

Task.py

omercsp/taskrunner

f972c7879ce9f4e390524a332388ea8c478ea19b

[ "MIT" ]

null

from config import * from argparse import Namespace as Args from schemas import Schema import shlex import subprocess import signal class Task(object): def _check_empty_setting(self, s, title): if len(s) > 0: return raise TaskException("Expanded {} for task '{}' is empty".format(title, self.name)) def __init__(self, name: str, config: Config) -> None: super().__init__() info("Initializing task '{}'", name) task_descriptor = config.get_task_desc(name, True) self.name = name self.config = config self.short_desc = task_descriptor.get(Schema.Keys.Task.ShortDesc, None) self.long_desc = task_descriptor.get(Schema.Keys.Task.LongDesc, None) self.hidden = task_descriptor.get(Schema.Keys.Task.Hidden, False) self.stop_on_error = task_descriptor.get(Schema.Keys.Task.StopOnError, True) self.commands = task_descriptor.get(Schema.Keys.Task.Commands, []) self.cwd = task_descriptor.get(Schema.Keys.Task.Cwd, None) self.shell = task_descriptor.get(Schema.Keys.Task.Shell, False) self.shell_path = task_descriptor.get( Schema.Keys.Task.ShellPath, config.default_shell_path()) self.env = task_descriptor.get(Schema.Keys.Task.Env, None) self.c_image = task_descriptor.get(Schema.Keys.Task.CImage, None) self.c_volumes = task_descriptor.get(Schema.Keys.Task.CVolumes, []) self.c_interactive = task_descriptor.get(Schema.Keys.Task.CInteractive, False) self.c_tty = task_descriptor.get(Schema.Keys.Task.CTty, False) self.c_flags = task_descriptor.get(Schema.Keys.Task.CFlags, "") self.c_exec = task_descriptor.get(Schema.Keys.Task.CExec, False) self.c_rm = task_descriptor.get(Schema.Keys.Task.CRemove, True) self.c_tool = task_descriptor.get(Schema.Keys.Task.CTool, self.config.default_container_tool()) self.c_shell = task_descriptor.get(Schema.Keys.Task.CShell, False) self.c_shell_path = task_descriptor.get(Schema.Keys.Task.CShellPath, self.config.default_container_shell_path()) self.c_cwd = task_descriptor.get(Schema.Keys.Task.CCwd, None) self.c_env = task_descriptor.get(Schema.Keys.Task.CEnv, {}) self.c_sudo = task_descriptor.get(Schema.Keys.Task.CSudo, False) self.expanded = False def args_update(self, args: Args) -> None: if args.stop_on_error: self.stop_on_error = args.stop_on_error if args.command: self.commands = args.command if args.cwd: self.cwd = args.cwd if args.shell: self.shell = (args.shell == TASK_YES_TOKEN) if args.shell_path: self.shell_path = args.shell_path if args.env: self.env = {} for e in args.env: e_name, e_value = parse_assignment_str(e) self.env[e_name] = e_value if args.c_image: self.c_image = args.c_image if args.c_volume: self.c_volumes = args.c_volume if args.c_interactive: self.c_interactive = (args.c_interactive == TASK_YES_TOKEN) if args.c_tty: self.c_tty = (args.c_tty == TASK_YES_TOKEN) if args.c_flags: self.c_flags = args.c_flags if args.c_exec: self.c_exec = args.c_exec if args.c_rm: self.c_rm = (args.c_rm == TASK_YES_TOKEN) if args.c_tool: self.c_tool = args.container_tool if args.c_shell: self.c_shell = (args.c_shell == TASK_YES_TOKEN) if args.c_shell_path: self.c_shell_path = args.c_shell_path if args.c_cwd: self.c_cwd = args.c_cwd if args.c_env: self.c_env = {} for e in args.c_env: e_name, e_value = parse_assignment_str(e) self.c_env[e_name] = e_value def expand_args(self, expander: StringVarExpander) -> None: if self.expanded: warn("Task '{}' is already expanded", self.name) return info("Expanding task '{}'".format(self.name)) self.expanded = True if self.env is not None: self.env = {expander(k): expander(v) for k, v in self.env.items()} for k, v in self.env.items(): info("Environment variable will be set as '{}={}'", k, v) if self.cwd: self.cwd = expander(self.cwd) self.commands = [expander(c) for c in self.commands] if self.c_cwd: self.c_cwd = expander(self.c_cwd) if self.c_image: self.c_image = expander(self.c_image) self.c_volumes = [expander(v) for v in self.c_volumes] self.c_env = {expander(k): expander(v) for k, v in self.c_env.items()} def _simple_cmd_arr(self, cmd) -> list: info("Preparing simple command") if self.shell: return [cmd] try: return shlex.split(cmd) except ValueError as e: raise TaskException("Illegal command '{}' for task '{}' - {}".format(cmd, self.name, e)) def _container_cmd_arr(self, cmd) -> list: info("Preparing container command") cmd_array = ["sudo", self.c_tool] if self.c_sudo else [self.c_tool] cmd_array.append("exec" if self.c_exec else "run") if self.c_cwd: cmd_array += ["-w", self.c_cwd] if self.c_interactive: cmd_array.append("-i") if self.c_tty: cmd_array.append("-t") if not self.c_exec: if self.c_rm: cmd_array.append("--rm") for v in self.c_volumes: cmd_array += ["-v", v] for k, v in self.c_env.items(): cmd_array += ["-e", "{}={}".format(k, v)] info("Setting container environment variable will '{}={}'", k, v) cmd_array += self.c_flags.split() cmd_array.append(self.c_image) if self.c_shell and cmd is not None: cmd_array += [self.c_shell_path, "-c"] if cmd: cmd_array += [cmd] info("Command is {}", cmd_array) return cmd_array def _run_cmd(self, cmd: list, cmd_str: str) -> int: info("Running command (joined):") raw_msg(cmd_str) p = None try: p = subprocess.Popen(cmd, shell=self.shell, executable=self.shell_path, env=self.env, cwd=self.cwd) return p.wait() except (OSError, FileNotFoundError) as e: raise TaskException("Error occurred running command '{}' - {}".format(cmd_str, e)) except KeyboardInterrupt: if p: p.send_signal(signal.SIGINT) p.wait() raise TaskException("User interrupt") def run(self) -> int: if not self.expanded: raise TaskException("Task must be expanded before run") # Should never happen if self.cwd: info("Working directory will be set to '{}'", self.cwd) else: info("No working directory will be set") if len(self.commands) == 0: if self.c_image: info("Running container's default command") return self._run_cmd(self._container_cmd_arr(None), "<CONTAINER_DEFAULT>") print("No commands defined for task '{}'. Nothing to do.".format(self.name)) return 0 rc = 0 for cmd in self.commands: info("Command is '{}'", cmd) cmd_arr = self._container_cmd_arr(cmd) if self.c_image else self._simple_cmd_arr(cmd) cmd_rc = self._run_cmd(cmd_arr, cmd) if cmd_rc == 0: continue info("Command had failed") if self.stop_on_error: info("Stopping of first error") return cmd_rc if rc == 0: rc = cmd_rc return rc

40.029703

100

0.581499

1,080

8,086

4.155556

0.152778

0.06016

0.083333

0.112745

0.296569

0.260918

0.136586

0.055258

0.037433

0.030749

0

0.00107

0.306827

8,086

201

101

40.228856

0.799643

0.00235

0

0.066298

0

0.083323

0

1

0.044199

false

0

0.033149

0

0.138122

0.005525

0

null

0

null

0

1

0

985e8bfeca006e01603b2e2887b6636296128621

7,488

py

Python

CUT/experiments/tmux_launcher.py

Theomat/colorization-av-enseirb-2020

c54c2388ea39a62289fa2f1c51b4757bf55d3c4f

[ "Apache-2.0" ]

1,422

2020-07-31T00:31:19.000Z

2022-03-31T11:35:26.000Z

CUT/experiments/tmux_launcher.py

Theomat/colorization-av-enseirb-2020

c54c2388ea39a62289fa2f1c51b4757bf55d3c4f

[ "Apache-2.0" ]

123

2020-07-31T04:16:03.000Z

2022-03-21T14:02:20.000Z

CUT/experiments/tmux_launcher.py

Theomat/colorization-av-enseirb-2020

c54c2388ea39a62289fa2f1c51b4757bf55d3c4f

[ "Apache-2.0" ]

324

2020-07-31T00:40:11.000Z

2022-03-31T10:01:10.000Z

""" experiment launcher using tmux panes """ import os import math import GPUtil import re available_gpu_devices = None class Options(): def __init__(self, *args, **kwargs): self.args = [] self.kvs = {"gpu_ids": "0"} self.set(*args, **kwargs) def set(self, *args, **kwargs): for a in args: self.args.append(a) for k, v in kwargs.items(): self.kvs[k] = v return self def remove(self, *args): for a in args: if a in self.args: self.args.remove(a) if a in self.kvs: del self.kvs[a] return self def update(self, opt): self.args += opt.args self.kvs.update(opt.kvs) return self def __str__(self): final = " ".join(self.args) for k, v in self.kvs.items(): final += " --{} {}".format(k, v) return final def clone(self): opt = Options() opt.args = self.args.copy() opt.kvs = self.kvs.copy() return opt def grab_pattern(pattern, text): found = re.search(pattern, text) if found is not None: return found[1] else: None # http://code.activestate.com/recipes/252177-find-the-common-beginning-in-a-list-of-strings/ def findcommonstart(strlist): prefix_len = ([min([x[0] == elem for elem in x]) for x in zip(*strlist)] + [0]).index(0) prefix_len = max(1, prefix_len - 4) return strlist[0][:prefix_len] class TmuxLauncher(): def __init__(self): super().__init__() self.tmux_prepared = False def prepare_tmux_panes(self, num_experiments, dry=False): self.pane_per_window = 1 self.n_windows = int(math.ceil(num_experiments / self.pane_per_window)) print('preparing {} tmux panes'.format(num_experiments)) for w in range(self.n_windows): if dry: continue window_name = "experiments_{}".format(w) os.system("tmux new-window -n {}".format(window_name)) self.tmux_prepared = True def refine_command(self, command, which_epoch, continue_train, gpu_id=None): command = str(command) if "--gpu_ids" in command: gpu_ids = re.search(r'--gpu_ids ([\d,?]+)', command)[1] else: gpu_ids = "0" gpu_ids = gpu_ids.split(",") num_gpus = len(gpu_ids) global available_gpu_devices if available_gpu_devices is None and gpu_id is None: available_gpu_devices = [str(g) for g in GPUtil.getAvailable(limit=8, maxMemory=0.5)] if gpu_id is not None: available_gpu_devices = [i for i in str(gpu_id)] if len(available_gpu_devices) < num_gpus: raise ValueError("{} GPU(s) required for the command {} is not available".format(num_gpus, command)) active_devices = ",".join(available_gpu_devices[:num_gpus]) if which_epoch is not None: which_epoch = " --epoch %s " % which_epoch else: which_epoch = "" command = "CUDA_VISIBLE_DEVICES={} {} {}".format(active_devices, command, which_epoch) if continue_train: command += " --continue_train " # available_gpu_devices = [str(g) for g in GPUtil.getAvailable(limit=8, maxMemory=0.8)] available_gpu_devices = available_gpu_devices[num_gpus:] return command def send_command(self, exp_id, command, dry=False, continue_train=False): command = self.refine_command(command, None, continue_train=continue_train) pane_name = "experiments_{windowid}.{paneid}".format(windowid=exp_id // self.pane_per_window, paneid=exp_id % self.pane_per_window) if dry is False: os.system("tmux send-keys -t {} \"{}\" Enter".format(pane_name, command)) print("{}: {}".format(pane_name, command)) return pane_name def run_command(self, command, ids, which_epoch=None, continue_train=False, gpu_id=None): if type(command) is not list: command = [command] if ids is None: ids = list(range(len(command))) if type(ids) is not list: ids = [ids] for id in ids: this_command = command[id] refined_command = self.refine_command(this_command, which_epoch, continue_train=continue_train, gpu_id=gpu_id) print(refined_command) os.system(refined_command) def commands(self): return [] def launch(self, ids, test=False, dry=False, continue_train=False): commands = self.test_commands() if test else self.commands() if type(ids) is list: commands = [commands[i] for i in ids] if not self.tmux_prepared: self.prepare_tmux_panes(len(commands), dry) assert self.tmux_prepared for i, command in enumerate(commands): self.send_command(i, command, dry, continue_train=continue_train) def dry(self): self.launch(dry=True) def stop(self): num_experiments = len(self.commands()) self.pane_per_window = 4 self.n_windows = int(math.ceil(num_experiments / self.pane_per_window)) for w in range(self.n_windows): window_name = "experiments_{}".format(w) for i in range(self.pane_per_window): os.system("tmux send-keys -t {window}.{pane} C-c".format(window=window_name, pane=i)) def close(self): num_experiments = len(self.commands()) self.pane_per_window = 1 self.n_windows = int(math.ceil(num_experiments / self.pane_per_window)) for w in range(self.n_windows): window_name = "experiments_{}".format(w) os.system("tmux kill-window -t {}".format(window_name)) def print_names(self, ids, test=False): if test: cmds = self.test_commands() else: cmds = self.commands() if type(ids) is list: cmds = [cmds[i] for i in ids] for cmdid, cmd in enumerate(cmds): name = grab_pattern(r'--name ([^ ]+)', cmd) print(name) def create_comparison_html(self, expr_name, ids, subdir, title, phase): cmds = self.test_commands() if type(ids) is list: cmds = [cmds[i] for i in ids] no_easy_label = True dirs = [] labels = [] for cmdid, cmd in enumerate(cmds): name = grab_pattern(r'--name ([^ ]+)', cmd) which_epoch = grab_pattern(r'--epoch ([^ ]+)', cmd) if which_epoch is None: which_epoch = "latest" label = grab_pattern(r'--easy_label "([^"]+)"', cmd) if label is None: label = name else: no_easy_label = False labels.append(label) dir = "results/%s/%s_%s/%s/" % (name, phase, which_epoch, subdir) dirs.append(dir) commonprefix = findcommonstart(labels) if no_easy_label else "" labels = ['"' + label[len(commonprefix):] + '"' for label in labels] dirstr = ' '.join(dirs) labelstr = ' '.join(labels) command = "python ~/tools/html.py --web_dir_prefix results/comparison_ --name %s --dirs %s --labels %s --image_width 256" % (expr_name + '_' + title, dirstr, labelstr) print(command) os.system(command)

34.666667

175

0.581063

964

7,488

4.325726

0.182573

0.028777

0.045564

0.036691

0.259233

0.211271

0.190647

0.177698

0.165228

0

0.005323

0.297543

7,488

215

176

34.827907

0.787452

0.028579

0

0.197674

0

0.005814

0.079284

0.007433

0

0.005814

1

0.116279

false

0

0.023256

0.005814

0.209302

0.034884

0

null

0

null

0

1

0

985ff6ad544a34fd7123b7189fde0bbabfec1f58

1,557

py

Python

code/strats/punitiveDetective.py

yasirroni/PrisonersDilemmaTournament

ce3de71ff2ccb647aa00129473ff60f985e16e17

[ "MIT" ]

null

code/strats/punitiveDetective.py

yasirroni/PrisonersDilemmaTournament

ce3de71ff2ccb647aa00129473ff60f985e16e17

[ "MIT" ]

null

code/strats/punitiveDetective.py

yasirroni/PrisonersDilemmaTournament

ce3de71ff2ccb647aa00129473ff60f985e16e17

[ "MIT" ]

null

def strategy(history, memory): """ Orannis's punitive detective: Cooperate but when the other player defects, cooperate one more turn to see if they defect again. If they do, defect for 10 turns. Cooperate twice more and if they defect the second time, defect forever. memory is a tuple of (state, counter) where state is one of: "initial_cooperation" "first_punishment" "second_cooperation" "final_punishment" """ num_rounds = history.shape[1] if memory is None or memory[0] == "initial_cooperation": # If they defected twice in a row, transition to first punishment if num_rounds >= 2 and history[1, -1] == 0 and history[1, -2] == 0: return 0, ("first_punishment", 9) # Otherwise keep cooperating return 1, ("initial_cooperation", 0) elif memory[0] == "first_punishment": # Punish until the counter runs out if memory[1] > 0: return 0, ("first_punishment", memory[1] - 1) # Once done, transition to second cooperation else: return 1, ("second_cooperation", 0) elif memory[0] == "second_cooperation": # If they defected twice in a row, transition to final punishment if num_rounds >= 2 and history[1, -1] == 0 and history[1, -2] == 0: return 0, ("final_punishment", 0) # Otherwise keep cooperating return 1, ("second_cooperation", 0) elif memory[0] == "final_punishment": return 0, ("final_punishment", 0)

40.973684

80

0.619139

203

1,557

4.660099

0.344828

0.031712

0.046512

0.069767

0.457717

0.293869

0.217759

0

0.034081

0.283879

1,557

37

81

42.081081

0.81435

0.427103

0

0.352941

0

0.22515

0

1

0.058824

false

0

0.470588

0

null

0

null

0

1

0

9861a9c527d45b483c922148595e6eb6f8bdd786

4,743

py

Python

src/main/python/salento/reports/map_computation/get_state_call_values.py

khanhgithead/salento

b4933a44ce4a18639b207db0bd555785cee2cc94

[ "Apache-2.0" ]

4

2019-01-14T06:21:49.000Z

2019-12-10T08:55:23.000Z

src/main/python/salento/reports/map_computation/get_state_call_values.py

khanhgithead/salento

b4933a44ce4a18639b207db0bd555785cee2cc94

[ "Apache-2.0" ]

9

2017-12-03T18:27:07.000Z

2018-06-08T19:43:38.000Z

src/main/python/salento/reports/map_computation/get_state_call_values.py

khanhgithead/salento

b4933a44ce4a18639b207db0bd555785cee2cc94

[ "Apache-2.0" ]

3

2017-12-18T19:39:25.000Z

2019-01-17T11:00:05.000Z

""" # **************************************************************************** # # GOVERNMENT PURPOSE RIGHTS # # Contract Number: FA8750-15-2-0270 (Prime: William Marsh Rice University) # Contractor Name: GrammaTech, Inc. (Right Holder - subaward R18683) # Contractor Address: 531 Esty Street, Ithaca, NY 14850 # Expiration Date: 22 September 2023 # # The Government's rights to use, modify, reproduce, release, perform, # display, or disclose this software are restricted by DFARS 252.227-7014 # Rights in Noncommercial Computer Software and Noncommercial Computer Software # Documentation clause contained in the above identified contract. # No restrictions apply after the expiration date shown above. # Any reproduction of the software or portions thereof marked with this legend # must also reproduce the markings and any copyright. # # **************************************************************************** # **************************************************************************** # # (c) 2014-2018 GrammaTech, Inc. All rights reserved. # # **************************************************************************** """ from __future__ import print_function import argparse import json from salento.aggregators.base import Aggregator class RawProbAggregator(Aggregator): """ This is based on the simple sequence aggregator, here for each call the probability is retrieved. The schema of the output is below { "title" : "Schema File for representation of the probability values", "type" : "object", "properties" : { "type" : "object", "description" : "Each unit", "properties" : { "type" : "object", "description" : "Each Sequence", "properties" : { "type" : "object", "description" : "Each Call", "properties" : { "type" : "number", "description" : "raw probability values" } } } } } """ def __init__(self, data_file, model_dir): Aggregator.__init__(self, data_file, model_dir) def run(self): """ invoke the RNN to get the probability return combined call and state probability values """ result_data = {} # iterate over units for k, package in enumerate(self.packages()): result_data[str(k)] = {} spec = self.get_latent_specification(package) # iterate over sequence for j, sequence in enumerate(self.sequences(package)): events = self.events(sequence) seq_calls = "--".join(x['call'] for x in events) event_key = str(j) + '--' + seq_calls event_data = {} # iterate over calls for i, event in enumerate(events): call_key = (str(i) + '--' + event['call']) call_prob = float(self.distribution_next_call( spec, events[:i+1], call=self.call(event))) # next state probability dist = self.distribution_next_state(spec, events[:i+1], None) # use the probability summation rule on conditional # probability to get a unified probability value # Pr(Call, States) = Pr(State0| Call)Pr(Call) + # Pr(State1| Call)Pr(Call) + # Pr(State2| Call)Pr(Call) prob_value = 0 # get the individual states for key, value in dist.items(): if '#' in key: prob_value += call_prob*value event_data[call_key] = prob_value result_data[str(k)][event_key] = event_data return result_data if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data_file', type=str, required=True, help='input data file') parser.add_argument('--model_dir', type=str, required=True, help='directory to load model from') parser.add_argument('--result_file', type=str, default=None, help='write out result in json file') clargs = parser.parse_args() with RawProbAggregator(clargs.data_file, clargs.model_dir) as aggregator: result = aggregator.run() if clargs.result_file: with open(clargs.result_file, 'w') as fwrite: json.dump(result, fwrite) else: print(json.dumps(result))

41.243478

81

0.528779

474

4,743

5.164557

0.42827

0.01634

0.02451

0.03799

0.081291

0.019608

0

0.016713

0.318786

4,743

114

82

41.605263

0.740947

0.479865

0

0.056685

0

1

0.044444

false

0

0.088889

0

0.177778

0.044444

0

null

0

null

0

1

0

98659147aeb87e203d9832403d6ea41ee4d2ded3

17,679

py

Python

src/cascade/model/priors.py

adolgert/cascade

2084e07c9ee5e901dd407b817220de882c7246a3

[ "MIT" ]

null

src/cascade/model/priors.py

adolgert/cascade

2084e07c9ee5e901dd407b817220de882c7246a3

[ "MIT" ]

null

src/cascade/model/priors.py

adolgert/cascade

2084e07c9ee5e901dd407b817220de882c7246a3

[ "MIT" ]

null

from copy import copy from functools import total_ordering import numpy as np import scipy.stats as stats from cascade.core import getLoggers CODELOG, MATHLOG = getLoggers(__name__) # A description of how dismod interprets these distributions and their parameters can be found here: # https://bradbell.github.io/dismod_at/doc/prior_table.htm class PriorError(ValueError): """Wrong value passed into the priors.""" @total_ordering class _Prior: """The base for all Priors """ density = None def __init__(self, name=None): self.name = name def _parameters(self): raise NotImplementedError() def parameters(self): return dict(density=self.density, **self._parameters()) def assign(self, **kwargs): """Create a new distribution with modified parameters.""" modified = copy(self) if set(kwargs.keys()) - set(self.__dict__.keys()): missing = list(sorted(set(kwargs.keys()) - set(self.__dict__.keys()))) raise AttributeError(f"The prior doesn't have these attributes {missing}.") modified.__dict__.update(kwargs) return modified def __hash__(self): return hash((frozenset(self.parameters().items()), self.name)) def __eq__(self, other): if not isinstance(other, _Prior): return NotImplemented return self.name == other.name and self.parameters() == other.parameters() def __lt__(self, other): if not isinstance(other, _Prior): return NotImplemented self_dict = sorted([(k, v) for k, v in dict(name=self.name, **self.parameters()).items() if v is not None]) other_dict = sorted([(k, v) for k, v in dict(name=other.name, **other.parameters()).items() if v is not None]) return self_dict < other_dict def __repr__(self): return f"<{type(self).__name__} {self.parameters()}>" def _validate_bounds(lower, mean, upper): any_nones = lower is None or mean is None or upper is None any_invalid = any_nones or np.isnan(lower) or np.isnan(mean) or np.isnan(upper) if any_invalid: raise PriorError(f"Bounds contain invalid values: lower={lower} mean={mean} upper={upper}") if not lower <= mean <= upper: raise PriorError(f"Bounds are inconsistent: lower={lower} mean={mean} upper={upper}") def _validate_standard_deviation(standard_deviation): if standard_deviation is None or np.isnan(standard_deviation) or standard_deviation < 0: raise PriorError(f"Standard deviation must be positive: standard deviation={standard_deviation}") def _validate_nu(nu): if nu is None or np.isnan(nu) or nu <= 2: raise PriorError(f"Nu must be greater than 2: nu={nu}") class Uniform(_Prior): density = "uniform" def __init__(self, lower, upper, mean=None, eta=None, name=None): """ Args: lower (float): Lower bound upper (float): Upper bound mean (float): Doesn't make sense, but it's used to seed solver. eta (float): Used for logarithmic distributions. name (str): A name in case this is a pet prior. """ super().__init__(name=name) if mean is None: mean = (upper + lower) / 2 _validate_bounds(lower, mean, upper) self.lower = lower self.upper = upper self.mean = mean self.eta = eta def mle(self, draws): """Using draws, assign a new mean, guaranteed between lower and upper. Args: draws (np.ndarray): 1D array of floats. Returns: Uniform: A new distribution with the mean set to the mean of draws. """ return self.assign(mean=min(self.upper, max(self.lower, np.mean(draws)))) def rvs(self, size=1, random_state=None): """Sample from this distribution. Args: size (int): Number of random variates, default 1. random_state (numpy.random.RandomState): For repeatable draws. Returns: np.ndarray: Of size=size with floats. """ return stats.uniform.rvs(loc=self.lower, scale=self.upper - self.lower, size=size, random_state=random_state) def _parameters(self): return {"lower": self.lower, "upper": self.upper, "mean": self.mean, "eta": self.eta} class Constant(_Prior): density = "uniform" def __init__(self, mean, name=None): """ Args: mean (float): The const value. name (str): A name for this prior, e.g. Susan. """ super().__init__(name=name) self.mean = mean def mle(self, _=None): """Don't change the const value. It is unaffected by this call.""" return copy(self) def rvs(self, size=1, random_state=None): """Sample from this distribution. Args: size (int): Number of random variates, default 1. random_state (numpy.random.RandomState): For repeatable draws. Returns: np.ndarray: Of size=size with floats. """ return np.full((size,), self.mean, dtype=np.float) def _parameters(self): return {"lower": self.mean, "upper": self.mean, "mean": self.mean} class Gaussian(_Prior): r"""A Gaussian is .. math:: f(x) = \frac{1}{2\pi \sigma^2} e^{-(x-\mu)^2/(2\sigma^2)} where :math:`\sigma` is the variance and :math:`\mu` the mean. Args: mean (float): This is :math:`\mu`. standard_deviation (float): This is :math:`\sigma`. lower (float): lower limit. upper (float): upper limit. eta (float): Offset for calculating standard deviation. name (str): Name for this prior. """ density = "gaussian" def __init__(self, mean, standard_deviation, lower=float("-inf"), upper=float("inf"), eta=None, name=None): super().__init__(name=name) _validate_bounds(lower, mean, upper) _validate_standard_deviation(standard_deviation) self.lower = lower self.upper = upper self.mean = mean self.standard_deviation = standard_deviation self.eta = eta def mle(self, draws): """Assign new mean and stdev, with mean clamped between upper and lower. Args: draws (np.ndarray): A 1D array of floats. Returns: Gaussian: With mean and stdev set, where mean is between upper and lower, by force. Upper and lower are unchanged. """ # The mean and standard deviation for Dismod-AT match the location # and scale used by Scipy. mean, std = stats.norm.fit(draws) return self.assign( mean=min(self.upper, max(self.lower, mean)), standard_deviation=std ) def rvs(self, size=1, random_state=None): """Sample from this distribution. Args: size (int): Number of random variates, default 1. random_state (numpy.random.RandomState): For repeatable draws. Returns: np.ndarray: Of size=size with floats. """ vals = np.empty((0,), dtype=np.float) while vals.shape[0] < size: redraw_cnt = size - vals.shape[0] + 10 draws = stats.norm.rvs( loc=self.mean, scale=self.standard_deviation, size=redraw_cnt, random_state=random_state) draws = draws[(self.lower < draws) & (draws < self.upper)] vals = np.concatenate([vals, draws]) return vals[:size] def _parameters(self): return { "lower": self.lower, "upper": self.upper, "mean": self.mean, "std": self.standard_deviation, "eta": self.eta, } class Laplace(Gaussian): r""" This version of the Laplace distribution is parametrized by its variance instead of by scaling of the axis. Usually, the Laplace distribution is .. math:: f(x) = \frac{1}{2b}e^{-|x-\mu|/b} where :math:`\mu` is the mean and :math:`b` is the scale, but the variance is :math:`\sigma^2=2b^2`, so the Dismod-AT version looks like .. math:: f(x) = \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\sqrt{2}|x-\mu|/\sigma}. The standard deviation assigned is :math:`\sigma`. """ density = "laplace" def mle(self, draws): """Assign new mean and stdev, with mean clamped between upper and lower. Args: draws (np.ndarray): A 1D array of floats. Returns: Gaussian: With mean and stdev set, where mean is between upper and lower, by force. Upper and lower are unchanged. """ mean, scale = stats.laplace.fit(draws) return self.assign( mean=min(self.upper, max(self.lower, mean)), standard_deviation=scale * np.sqrt(2) # This is the adjustment. ) def rvs(self, size=1, random_state=None): """Sample from this distribution. Args: size (int): Number of random variates, default 1. random_state (numpy.random.RandomState): For repeatable draws. Returns: np.ndarray: Of size=size with floats. """ vals = np.empty((0,), dtype=np.float) while vals.shape[0] < size: redraw_cnt = size - vals.shape[0] + 10 draws = stats.laplace.rvs( loc=self.mean, scale=self.standard_deviation / np.sqrt(2), size=redraw_cnt, random_state=random_state) draws = draws[(self.lower < draws) & (draws < self.upper)] vals = np.concatenate([vals, draws]) return vals[:size] class StudentsT(_Prior): r""" This Students-t must have :math:`\nu>2`. Students-t distribution is usually .. math:: f(x,\nu) = \frac{\Gamma((\nu+1)/2)}{\sqrt{\pi\nu}\Gamma(\nu)}(1+x^2/\nu)^{-(\nu+1)/2} with mean 0 for :math:`\nu>1`. The variance is :math:`\nu/(\nu-2)` for :math:`\nu>2`. Dismod-AT rewrites this using :math:`\sigma^2=\nu/(\nu-2)` to get .. math:: f(x) = \frac{\Gamma((\nu+1)/2)}{\sqrt(\pi\nu)\Gamma(\nu/2)} \left(1 + (x-\mu)^2/(\sigma^2(\nu-2))\right)^{-(\nu+1)/2} """ density = "students" def __init__(self, mean, standard_deviation, nu, lower=float("-inf"), upper=float("inf"), eta=None, name=None): super().__init__(name=name) _validate_bounds(lower, mean, upper) _validate_standard_deviation(standard_deviation) _validate_nu(nu) self.lower = lower self.upper = upper self.mean = mean self.standard_deviation = standard_deviation self.nu = nu self.eta = eta def mle(self, draws): """Assign new mean and stdev, with mean clamped between upper and lower. Args: draws (np.ndarray): A 1D array of floats. Returns: Gaussian: With mean and stdev set, where mean is between upper and lower, by force. Upper and lower are unchanged. """ # This fixes the nu value. nu, mean, scale = stats.t.fit(draws, fix_df=self.nu) return self.assign( mean=min(self.upper, max(self.lower, mean)), standard_deviation=scale * np.sqrt(nu / (nu - 2)) ) def rvs(self, size=1, random_state=None): """Sample from this distribution. Args: size (int): Number of random variates, default 1. random_state (numpy.random.RandomState): For repeatable draws. Returns: np.ndarray: Of size=size with floats. """ vals = np.empty((0,), dtype=np.float) std_scale = np.sqrt(self.nu / (self.nu - 2)) while vals.shape[0] < size: redraw_cnt = size - vals.shape[0] + 10 draws = stats.t.rvs( loc=self.mean, scale=self.standard_deviation / std_scale, df=self.nu, size=redraw_cnt, random_state=random_state) draws = draws[(self.lower < draws) & (draws < self.upper)] vals = np.concatenate([vals, draws]) return vals[:size] def _parameters(self): return { "lower": self.lower, "upper": self.upper, "mean": self.mean, "std": self.standard_deviation, "nu": self.nu, "eta": self.eta, } class LogGaussian(_Prior): r""" Dismod-AT parametrizes the Log-Gaussian with the standard deviation as .. math:: f(x) = \frac{1}{\sqrt{2\pi\sigma^2}} e^{-\log((x-\mu)/\sigma)^2/2} """ density = "log_gaussian" def __init__(self, mean, standard_deviation, eta, lower=float("-inf"), upper=float("inf"), name=None): super().__init__(name=name) _validate_bounds(lower, mean, upper) _validate_standard_deviation(standard_deviation) self.lower = lower self.upper = upper self.mean = mean self.standard_deviation = standard_deviation self.eta = eta def mle(self, draws): """Assign new mean and stdev, with mean clamped between upper and lower. This does a fit using a normal distribution. Args: draws (np.ndarray): A 1D array of floats. Returns: Gaussian: With mean and stdev set, where mean is between upper and lower, by force. Upper and lower are unchanged. """ # The mean and standard deviation for Dismod-AT match the location # and scale used by Scipy. mean, std = stats.norm.fit(draws) return self.assign( mean=min(self.upper, max(self.lower, mean)), standard_deviation=std ) def rvs(self, size=1, random_state=None): """Sample from this distribution. Args: size (int): Number of random variates, default 1. random_state (numpy.random.RandomState): For repeatable draws. Returns: np.ndarray: Of size=size with floats. """ vals = np.empty((0,), dtype=np.float) while vals.shape[0] < size: redraw_cnt = size - vals.shape[0] + 10 draws = stats.lognorm.rvs( loc=self.mean, s=self.standard_deviation, scale=np.exp(self.mean), size=redraw_cnt, random_state=random_state) draws = draws[(self.lower < draws) & (draws < self.upper)] vals = np.concatenate([vals, draws]) return vals[:size] def _parameters(self): return { "lower": self.lower, "upper": self.upper, "mean": self.mean, "std": self.standard_deviation, "eta": self.eta, } class LogLaplace(LogGaussian): density = "log_laplace" class LogStudentsT(_Prior): density = "log_students" def __init__(self, mean, standard_deviation, nu, eta, lower=float("-inf"), upper=float("inf"), name=None): super().__init__(name=name) _validate_bounds(lower, mean, upper) _validate_standard_deviation(standard_deviation) _validate_nu(nu) self.lower = lower self.upper = upper self.mean = mean self.standard_deviation = standard_deviation self.nu = nu self.eta = eta def mle(self, draws): """Assign new mean and stdev, with mean clamped between upper and lower. This does a fit using a normal distribution. Args: draws (np.ndarray): A 1D array of floats. Returns: Gaussian: With mean and stdev set, where mean is between upper and lower, by force. Upper and lower are unchanged. """ # The mean and standard deviation for Dismod-AT match the location # and scale used by Scipy. mean, std = stats.norm.fit(draws) return self.assign( mean=min(self.upper, max(self.lower, mean)), standard_deviation=std ) def _parameters(self): return { "lower": self.lower, "upper": self.upper, "mean": self.mean, "std": self.standard_deviation, "nu": self.nu, "eta": self.eta, } # Useful predefined priors NO_PRIOR = Uniform(float("-inf"), float("inf"), 0, name="null_prior") ZERO = Uniform(0, 0, 0, name="constrain_to_zero") ZERO_TO_ONE = Uniform(0, 1, 0.1, name="uniform_zero_to_one") MINUS_ONE_TO_ONE = Uniform(-1, 1, 0, name="uniform_negative_one_to_one") DENSITY_ID_TO_PRIOR = { 0: Uniform, 1: Gaussian, 2: Laplace, 3: StudentsT, 4: LogGaussian, 5: LogLaplace, 6: LogStudentsT, } def prior_distribution(parameters): density, lower, upper, value, stdev, eta, nu = [ parameters[name] for name in [ "density", "lower", "upper", "mean", "std", "eta", "nu" ] ] if np.isclose(lower, upper): return Constant(value) elif density == "uniform": return Uniform(lower, upper, value, eta) elif density == "gaussian": return Gaussian(value, stdev, lower, upper, eta) elif density == "laplace": return Laplace(value, stdev, lower, upper, eta) elif density == "students": return StudentsT(value, stdev, nu, lower, upper, eta) elif density == "log_gaussian": return LogGaussian(value, stdev, eta, lower, upper) elif density == "log_laplace": return LogLaplace(value, stdev, eta, lower, upper) elif density == "log_students": return LogStudentsT(value, stdev, nu, eta, lower, upper) else: return None

32.085299

118

0.591606

2,253

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4.533511

0.115846

0.07989

0.019091

0.033288

0.615528

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0.578422

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0.351449

0

null

0

null

0

1

0

986599e9146af7fc917f4235236600f156665da2

2,581

py

Python

pubsubat/pubsub/schema.py

zerobased-co/pubsub.at

c53ee698d3d2beced0147a8aa9707f69c3ef46c1

[ "MIT" ]

null

pubsubat/pubsub/schema.py

zerobased-co/pubsub.at

c53ee698d3d2beced0147a8aa9707f69c3ef46c1

[ "MIT" ]

null

pubsubat/pubsub/schema.py

zerobased-co/pubsub.at

c53ee698d3d2beced0147a8aa9707f69c3ef46c1

[ "MIT" ]

null

from django.contrib.auth import get_user_model from graphene import relay from graphene_django.filter import DjangoFilterConnectionField from graphene_django.types import DjangoObjectType from .models import User, Category, Publisher, Subscription import graphene class UserType(DjangoObjectType): class Meta: model = User filter_fields = ['id', 'name', 'email'] interfaces = (relay.Node, ) class CategoryType(DjangoObjectType): class Meta: model = Category filter_fields = { 'id': ['exact'], 'name': ['exact', 'icontains', 'istartswith'], } interfaces = (relay.Node, ) class PublisherType(DjangoObjectType): class Meta: model = Publisher filter_fields = { 'id': ['exact'], 'name': ['exact', 'icontains', 'istartswith'], } interfaces = (relay.Node, ) class SubscriptionType(DjangoObjectType): class Meta: model = Subscription filter_fields = { 'id': ['exact'], 'is_active': ['exact'], 'user__id': ['exact'], 'user__username': ['exact'], 'publisher__name': ['exact'], } exclude_fields = ('start', 'end') interfaces = (relay.Node, ) class PubSubQuery(graphene.ObjectType): user = relay.Node.Field(UserType) category = relay.Node.Field(CategoryType) categories = DjangoFilterConnectionField(CategoryType) publisher = relay.Node.Field(PublisherType) publishers = DjangoFilterConnectionField(PublisherType) subscription = relay.Node.Field(SubscriptionType) subscriptions = DjangoFilterConnectionField(SubscriptionType) my_subscriptions = DjangoFilterConnectionField(SubscriptionType) def resolve_my_subscriptions(self, info): if not info.context.user.is_authenticated: return Subscription.objects.none() else: return Subscription.objects.filter(user=info.context.user) class CreateUser(graphene.Mutation): user = graphene.Field(UserType) class Arguments: username = graphene.String(required=True) password = graphene.String(required=True) email = graphene.String(required=True) def mutate(self, info, username, password, email): user = get_user_model()( username=username, email=email, ) user.set_password(password) user.save() return CreateUser(user=user) class PubSubMutation(graphene.ObjectType): create_user = CreateUser.Field()

28.362637

70

0.646649

235

2,581

7

0.297872

0.043769

0.06079

0.072948

0.087538

0

0.247578

2,581

90

71

28.677778

0.847065

0

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0

0.063541

0

1

0.028986

false

0.043478

0.086957

0

0.478261

0

null

0

null

0

1

0

98697fdbc551569cdb320c6bb06e86e48be7478f

389

py

Python

test.py

bpetterborg/vex_vacuum

f4a6dc69b4a10eebcf247d34d9155ec74c3f9f99

[ "MIT" ]

null

test.py

bpetterborg/vex_vacuum

f4a6dc69b4a10eebcf247d34d9155ec74c3f9f99

[ "MIT" ]

null

test.py

bpetterborg/vex_vacuum

f4a6dc69b4a10eebcf247d34d9155ec74c3f9f99

[ "MIT" ]

null

# file where i test everything import pi_vex_393, screen # fix this import time motor = pi_vex_393.Motor() screen = screen.Screen() while True: screen.clearScreen() screen.drawIP() screen.drawSystemLoad() screen.drawDebugLine(motor.currentStatus()) try: motor.spin('left', 50) motor.spin('right', 50) except KeyboardInterrupt: motor.stop('left') motor.stop('right')

16.208333

44

0.719794

51

389

5.411765

0.568627

0.036232

0.057971

0

0.030211

0.1491

389

24

45

16.208333

0.803625

0.095116

0

0.051429

0

1

0

false

0

0.133333

0

0.133333

0

null

0

null

0

1

0

986ac397932279d6a6baa2e1d1a81f4621e3adb3

2,193

py

Python

bin/watcher.py

kitaro-tn/lambda_deploy

7da5d604f214daf3d14ada5f3a69f324130c821b

[ "MIT" ]

null

bin/watcher.py

kitaro-tn/lambda_deploy

7da5d604f214daf3d14ada5f3a69f324130c821b

[ "MIT" ]

1

2021-06-01T23:00:26.000Z

bin/watcher.py

tanish-kr/lambda_deploy

7da5d604f214daf3d14ada5f3a69f324130c821b

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os import sys import time import re import logging import subprocess from watchdog.observers import Observer from watchdog.events import LoggingEventHandler from watchdog.events import FileSystemEventHandler class CIHandler(FileSystemEventHandler): def __init__(self, context): super(FileSystemEventHandler, self).__init__() self.context = context def on_created(self, event): test(event) def on_modified(self, event): test(event) def test(context): try: if not context.is_directory and re.compile(".py$").search(context.src_path): logging.info("Static code analysis with pep8 :%s", context.src_path) subprocess.call(["pep8", context.src_path]) prefix = "" if re.compile("^test_").search(context.src_path.split("/")[-1]) else "test_" test_file_name = prefix + context.src_path.split("/")[-1] test_file_path = os.path.join(current_path(), "tests", test_file_name) logging.info("Unit test :%s", test_file_path) if os.path.exists(test_file_path): subprocess.call(["python", "-m", "unittest", test_file_path]) else: logging.warn("No such file %s", test_file_path) except Exception as err: logging.exception("Error dosomething: %s", err) pass def current_path(): return os.path.abspath( os.path.join( os.path.dirname(__file__), '..' ) ) if __name__ == "__main__": logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s', datefmt='%Y-%m-%d%H:%M:%S') path = current_path() + "/lambda_deploy" test_path = current_path() + "/tests" event_handler = CIHandler(path) test_event_handler = CIHandler(test_path) observer = Observer() observer.schedule(event_handler, path, recursive=True) observer.schedule(test_event_handler, test_path, recursive=True) observer.start() try: while True: time.sleep(1) except KeyboardInterrupt: observer.stop() observer.join

30.887324

100

0.622891

256

2,193

5.109375

0.375

0.042813

0.053517

0.036697

0.062691

0

0.00367

0.254446

2,193

70

101

31.328571

0.79633

0.009576

0

0.070175

0

0.090323

0

1

0.087719

false

0.017544

0.157895

0.017544

0.280702

0

null

0

null

0

1

0

986b4eb3404a6056f028d5b2e0888b2f1504a9e7

1,009

py

Python

src/python/strelka/scanners/scan_bzip2.py

weslambert/strelka

a941020a9f363774017cb045c4ec173f144c13a0

[ "Apache-2.0" ]

513

2018-09-26T18:57:40.000Z

2022-03-31T18:13:53.000Z

src/python/strelka/scanners/scan_bzip2.py

weslambert/strelka

a941020a9f363774017cb045c4ec173f144c13a0

[ "Apache-2.0" ]

79

2018-09-28T01:05:25.000Z

2022-03-02T12:22:23.000Z

src/python/strelka/scanners/scan_bzip2.py

weslambert/strelka

a941020a9f363774017cb045c4ec173f144c13a0

[ "Apache-2.0" ]

88

2018-09-26T20:10:56.000Z

2022-03-28T02:06:22.000Z

import bz2 import io from strelka import strelka class ScanBzip2(strelka.Scanner): """Decompresses bzip2 files.""" def scan(self, data, file, options, expire_at): with io.BytesIO(data) as bzip2_io: with bz2.BZ2File(filename=bzip2_io) as bzip2_obj: try: decompressed = bzip2_obj.read() self.event['size'] = len(decompressed) extract_file = strelka.File( source=self.name, ) for c in strelka.chunk_string(decompressed): self.upload_to_coordinator( extract_file.pointer, c, expire_at, ) self.files.append(extract_file) except EOFError: self.flags.append('eof_error') except OSError: self.flags.append('os_error')

30.575758

64

0.474727

92

1,009

5.054348

0.554348

0.070968

0.064516

0

0.016216

0.44995

1,009

32

65

31.53125

0.821622

0.024777

0

0.021472

0

1

0.041667

false

0

0.125

0

0.208333

0

null

0

null

0

1

0

986e12c15ea58c908f90556561a79c323476a15d

2,985

py

Python

tgext/evolve/__init__.py

axant/tgext.evolve

9072a156f63c22445be385014541bb5c0ad065f2

[ "MIT" ]

null

tgext/evolve/__init__.py

axant/tgext.evolve

9072a156f63c22445be385014541bb5c0ad065f2

[ "MIT" ]

null

tgext/evolve/__init__.py

axant/tgext.evolve

9072a156f63c22445be385014541bb5c0ad065f2

[ "MIT" ]

null

from tg.configuration import milestones from tg.appwrappers.base import ApplicationWrapper from .evolver import Evolution from webob.exc import HTTPServiceUnavailable from threading import Thread __all__ = ['plugme', 'Evolution'] import logging log = logging.getLogger('tgext.evolve') def plugme(configurator, options=None): if options is None: options = {} evolutions = options.get('evolutions') if not evolutions: raise ValueError('"evolutions" option is required and must be a list of tgext.evolve.Evolution subclasses') log.info('Setting up tgext.evolve extension...') milestones.config_ready.register(_SetupExtension(configurator, evolutions)) # This is required to be compatible with the # tgext.pluggable interface return dict(appid='tgext.evolve') class _SetupExtension(object): def __init__(self, configurator, evolutions): self._configurator = configurator self._evolutions = evolutions def __call__(self): from tg import hooks hooks.register('configure_new_app', self.on_app_configured) self._configurator.register_wrapper(_MaintenanceApplicationWrapper) def on_app_configured(self, app): config = app.config enabled = config.get('tgext.evolve.enabled', 'True').lower() == 'true' log.info('tgext.evolve enabled: %s', enabled) if not enabled: return model = config['package'].model if config.get('use_sqlalchemy', False): log.info('Configuring tgext.evolve for SQLAlchemy') from .sqla_evolver import SQLAEvolver config['tgext.evolve._evolver'] = SQLAEvolver(model, self._evolutions) elif config.get('use_ming', False): log.info('Configuring tgext.evolve for Ming') from .ming_evolver import MingEvolver config['tgext.evolve._evolver'] = MingEvolver(model, self._evolutions) else: raise ValueError('tgext.evolve should be used with sqlalchemy or ming') evolver = config['tgext.evolve._evolver'] evolution_thread = Thread(target=lambda *args, **kwargs: evolver.evolve()) evolution_thread.daemon = True evolution_thread.start() class _MaintenanceApplicationWrapper(ApplicationWrapper): def __init__(self, handler, config): super(_MaintenanceApplicationWrapper, self).__init__(handler, config) self._should_check = True def __call__(self, controller, environ, context): if not self._should_check: return self.next_handler(controller, environ, context) if self._should_check: evolver = context.config.get('tgext.evolve._evolver', None) if evolver is None or not evolver.is_locked(): self._should_check = False return self.next_handler(controller, environ, context) return HTTPServiceUnavailable(detail='System is currently undergoing maintenance')

36.402439

115

0.688442

326

2,985

6.107362

0.343558

0.071823

0.036163

0.082371

0

0.220101

2,985

81

116

36.851852

0.855241

0.022781

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0.033898

0

0.178106

0.036376

0

1

0.101695

false

0

0.152542

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0.372881

0

null

0

null

0

1

0

986f4f99c75768f4e311bdc0d8bfb4d47dcf11f2

1,189

py

Python

simple linear regression/simpleLinearReg.py

Sammy-Barasa/Artificial-Inteligence-and-Machine-Learning

7668ec502f13fa0ec36bb909da1160c19106c4fc

[ "MIT" ]

null

simple linear regression/simpleLinearReg.py

Sammy-Barasa/Artificial-Inteligence-and-Machine-Learning

7668ec502f13fa0ec36bb909da1160c19106c4fc

[ "MIT" ]

null

simple linear regression/simpleLinearReg.py

Sammy-Barasa/Artificial-Inteligence-and-Machine-Learning

7668ec502f13fa0ec36bb909da1160c19106c4fc

[ "MIT" ]

null

#imports import matplotlib.pyplot as plt import pandas as pd from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split #Loading the data from a csv dataset=pd.read_csv("Salary_Data.csv") #Data pre processing for salary vs experience x=dataset.iloc[:,:-1].values print(x) y=dataset.iloc[:,1].values print(y) #spliting the data into Training set and Test set linear_regressor=LinearRegression() x_train,x_test,y_train,y_test=train_test_split(x,y,random_state=0,test_size=1/3) #Generating the model on training dataset linear_regressor.fit(x_train,y_train) #Evaluating model on testing dataset y_predicted=linear_regressor.predict(x_test) #Visualising the results #for training plt.scatter(x_train,y_train,color="red") plt.plot(x_train,linear_regressor.predict(x_train),color="blue") plt.title("Salary vs Experience(training set)") plt.xlabel("Years of Experience") plt.ylabel("Salary") plt.show() #for test plt.scatter(x_test,y_test,color="red") plt.plot(x_train,linear_regressor.predict(x_train),color="blue") plt.title("Salary vs Experience(Test set)") plt.xlabel("Years of Experience") plt.ylabel("Salary") plt.show()

23.78

80

0.787216

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1,189

4.664948

0.345361

0.046409

0.059669

0.076243

0.349171

0.298343

0

0.004647

0.095038

1,189

50

81

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0.836431

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0.32

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0.16

0.08

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null

0

null

0

1

0

986fb6556cd010b2a735198814dbb2a544ab1e34

2,235

py

Python

py/1041.robot-bounded-in-circle.py

ck2w/leetcode

2d411530b690a2e51b0ae518bf3efaad2edc1083

[ "MIT" ]

null

py/1041.robot-bounded-in-circle.py

ck2w/leetcode

2d411530b690a2e51b0ae518bf3efaad2edc1083

[ "MIT" ]

null

py/1041.robot-bounded-in-circle.py

ck2w/leetcode

2d411530b690a2e51b0ae518bf3efaad2edc1083

[ "MIT" ]

null

# # @lc app=leetcode id=1041 lang=python3 # # [1041] Robot Bounded In Circle # # https://leetcode.com/problems/robot-bounded-in-circle/description/ # # algorithms # Medium (54.22%) # Likes: 557 # Dislikes: 176 # Total Accepted: 39.9K # Total Submissions: 73.7K # Testcase Example: '"GGLLGG"' # # On an infinite plane, a robot initially stands at (0, 0) and faces north. # The robot can receive one of three instructions: # # # "G": go straight 1 unit; # "L": turn 90 degrees to the left; # "R": turn 90 degress to the right. # # # The robot performs the instructions given in order, and repeats them # forever. # # Return true if and only if there exists a circle in the plane such that the # robot never leaves the circle. # # # # Example 1: # # # Input: "GGLLGG" # Output: true # Explanation: # The robot moves from (0,0) to (0,2), turns 180 degrees, and then returns to # (0,0). # When repeating these instructions, the robot remains in the circle of radius # 2 centered at the origin. # # # Example 2: # # # Input: "GG" # Output: false # Explanation: # The robot moves north indefinitely. # # # Example 3: # # # Input: "GL" # Output: true # Explanation: # The robot moves from (0, 0) -> (0, 1) -> (-1, 1) -> (-1, 0) -> (0, 0) -> # ... # # # # # Note: # # # 1 <= instructions.length <= 100 # instructions[i] is in {'G', 'L', 'R'} # # # # @lc code=start class Solution: def isRobotBounded(self, instructions: str) -> bool: ''' It's a limit cycle trajectory if the robot is back to the center: x = y = 0 or if the robot doesn't face north: idx != 0. ''' directions = [[0, 1], [1, 0], [0, -1], [-1, 0]] # Initial position is in the center x = y = 0 # facing north idx = 0 for i in instructions: if i == "L": idx = (idx + 3) % 4 elif i == "R": idx = (idx + 1) % 4 else: x += directions[idx][0] y += directions[idx][1] # after one cycle: # robot returns into initial position # or robot doesn't face north return (x == 0 and y == 0) or idx != 0 # @lc code=end

21.699029

78

0.561074

314

2,235

3.993631

0.455414

0.057416

0.045455

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0.114833

0.063796

0

0.051216

0.301119

2,235

102

79

21.911765

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0.67472

0

0.003273

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0

0.214286

0

null

0

null

0

1

0

9871044a50f6b8ea1c144e417127b1e4c3c0e043

1,035

py

Python

Day10-19/11.py

bcongdon/advent_of_code_2017

ad9a9b028716c9387dddc3ef9ee34c3a70fea151

[ "MIT" ]

15

2017-12-05T16:01:39.000Z

2020-11-03T00:01:03.000Z

Day10-19/11.py

bcongdon/advent_of_code_2017

ad9a9b028716c9387dddc3ef9ee34c3a70fea151

[ "MIT" ]

null

Day10-19/11.py

bcongdon/advent_of_code_2017

ad9a9b028716c9387dddc3ef9ee34c3a70fea151

[ "MIT" ]

null

def manhatten_hex_dist(orig, dest): dist_x = dest[0] - orig[0] dist_y = dest[1] - orig[1] if dist_x > 0 == dist_y > 0: return abs(dist_x + dist_y) else: return max(abs(dist_x), abs(dist_y)) def next_loc(orig, direc): if direc == 'n': return (orig[0], orig[1]+1) elif direc == 'ne': return (orig[0]+1, orig[1]) elif direc == 'se': return (orig[0]+1, orig[1]-1) elif direc == 's': return (orig[0], orig[1]-1) elif direc == 'sw': return (orig[0]-1, orig[1]) elif direc == 'nw': return (orig[0]-1, orig[1]+1) def path_dist(path): loc = (0, 0) m = 0 for direc in path: loc = next_loc(loc, direc) m = max(m, manhatten_hex_dist((0, 0), loc)) return manhatten_hex_dist((0, 0), loc), m if __name__ == '__main__': with open('11.txt') as f: directions = f.read().split(',') part1, part2 = path_dist(directions) print("Part 1: {}".format(part1)) print("Part 2: {}".format(part2))

25.243902

51

0.535266

164

1,035

3.219512

0.280488

0.066288

0.125

0.090909

0.361742

0.344697

0.265152

0.19697

0

0.055034

0.280193

1,035

40

52

25.875

0.653691

0

0.043478

0

1

0.090909

false

0

0.363636

0.060606

0

null

0

null

0

1

0

987212e9935b27eb8454286fdd3a99fa1b5c3a93

3,809

py

Python

handwritten_ocr/pdf_to_images.py

testigos2022/ocr-forms

da4e4b70975d799754bdc470526295cdefc77d34

[ "MIT" ]

null

handwritten_ocr/pdf_to_images.py

testigos2022/ocr-forms

da4e4b70975d799754bdc470526295cdefc77d34

[ "MIT" ]

null

handwritten_ocr/pdf_to_images.py

testigos2022/ocr-forms

da4e4b70975d799754bdc470526295cdefc77d34

[ "MIT" ]

null

import os from dataclasses import dataclass, field from pathlib import Path from typing import Any, Callable, Union, Iterator, Iterable from PIL.PpmImagePlugin import PpmImageFile from misc_utils.cached_data import CachedData from misc_utils.dataclass_utils import _UNDEFINED, UNDEFINED from misc_utils.prefix_suffix import PrefixSuffix, BASE_PATHES from pdf2image import convert_from_path from tqdm import tqdm from misc_utils.buildable import Buildable @dataclass class CroppedImages(CachedData, Iterable[str]): pdf_file: Union[_UNDEFINED, str] = UNDEFINED x_window_scale: int = 1 y_window_scale: int = 3 x_step_size_fun: Callable[[PpmImageFile], int] = field( default=lambda page: round(page.width - 1) ) y_step_size_fun: Callable[[PpmImageFile], int] = field( default=lambda page: round(1.41 * page.width / 30) ) @property def name(self): return Path(self.pdf_file).name @property def output_dir(self): return self.prefix_cache_dir("cropped_images") def _build_cache(self): pages = convert_from_path(pdf_file, 200) for k, page in tqdm(enumerate(pages)): self._process_page(k, page) def generate_cropboxes(self, page, x_step, y_step): for x in range(0, page.width - x_step, x_step): for y in range(0, page.height - y_step, y_step): x1, y1 = ( x + x_step * self.x_window_scale, y + y_step * self.y_window_scale, ) yield x, y, x1, y1 def _process_page(self, k, page: PpmImageFile): x_step = self.x_step_size_fun(page) y_step = self.y_step_size_fun(page) page_dir = f"{self.output_dir}/{Path(pdf_file).name}-{k}" os.makedirs(page_dir, exist_ok=True) for b in tqdm(self.generate_cropboxes(page, x_step, y_step)): (x, y, x1, y1) = b cropped = page.crop(b) cropped.save(f"{page_dir}/cropped_{x}_{y}.jpg", "JPEG") page.save(f"{self.output_dir}/{Path(pdf_file).name}-{k}.jpg", "JPEG") def __iter__(self) -> Iterator[str]: for p in Path(self.output_dir).rglob("cropped*.jpg"): yield str(p) @dataclass class ImagesFromPdf(CachedData, Iterable[PrefixSuffix]): pdf_file: Union[_UNDEFINED, PrefixSuffix] = UNDEFINED cache_base: PrefixSuffix = field( default_factory=lambda: PrefixSuffix("cache_root", "pdf_page_images") ) @property def name(self): return Path(str(self.pdf_file)).name @property def output_dir(self): return self.prefix_cache_dir("data") def _build_cache(self): os.makedirs(self.output_dir, exist_ok=True) pages = convert_from_path(str(self.pdf_file), 200) for k, page in tqdm(enumerate(pages)): page.save( f"{self.output_dir}/{Path(str(self.pdf_file)).name}-{k}.jpg", "JPEG" ) def __iter__(self) -> Iterator[PrefixSuffix]: for p in Path(self.output_dir).rglob(f"*.jpg"): yield self.cache_dir.from_str_same_prefix(str(p)) if __name__ == "__main__": # pip install pdf2image data_path = os.environ["DATA_PATH"] BASE_PATHES["cache_root"] = f"{data_path}/cache" # pdf_file = f"{data_path}/esc_cong_2018/data/esc_cong_2018_archivos_divulgacion_AGE_XXX_2_01_004_XXX_XX_XX_X_1052_F_49.pdf" # pdf_file = f"{data_path}/esc_cong_2018_archivos_divulgacion_AGE_XXX_2_01_004_XXX_XX_XX_X_1052_F_49.pdf" pdf_file = f"{data_path}/e14_cong_2018__e14_divulgacion_01_001_001_CAM_E14_CAM_X_01_001_001_XX_01_005_X_XXX.pdf" CroppedImages( pdf_file=pdf_file, cache_base=PrefixSuffix("cache_root", "cropped_images"), x_window_scale=1, y_window_scale=3, ).build()

34.627273

128

0.667104

550

3,809

4.28

0.216364

0.041631

0.033135

0.019116

0.350042

0.333475

0.304588

0.290144

0.254885

0.234494

0

0.031419

0.222893

3,809

109

129

34.944954

0.763851

0.065109

0

0.164706

0

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0.077378

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1

0.117647

false

0

0.129412

0.047059

0.4

0

null

0

null

0

1

0

98725bcf75d90a74eac93b39fd6ebe4b9b3823b0

1,654

py

Python

pd_parallel/tools.py

xbanke/pandas-parallel

f3b5f0cba1b639551cc9a1e04af58f4015053c99

[ "MIT" ]

1

2019-05-11T22:11:46.000Z

pd_parallel/tools.py

xbanke/pandas-parallel

f3b5f0cba1b639551cc9a1e04af58f4015053c99

[ "MIT" ]

null

pd_parallel/tools.py

xbanke/pandas-parallel

f3b5f0cba1b639551cc9a1e04af58f4015053c99

[ "MIT" ]

null

#! /usr/bin/env python # -*- coding: utf-8 -*- """ @version: 0.1 @author: quantpy @file: tools.py @time: 2018/4/11 16:25 """ import os from functools import partial import numpy as np import pandas as pd from .apply_parallel import df_group_apply_parallel def get_grouper(df: pd.DataFrame, by=None, axis=0, level=None, section_size=None, section_count=os.cpu_count(), **kwargs): """regroup by df.groupby""" df_group = df.groupby(by=by, axis=axis, level=level, **kwargs) # n_groups = df_group.ngroups n_groups = df_group.count().shape[0] if section_size: section_count = int(np.ceil(n_groups / section_size)) # section_size = n_groups // section_count grouper = [pd.Series(i % section_count, index=d.index) for i, (k, d) in enumerate(df_group)] grouper = pd.concat(grouper) return grouper def double_groupby_apply_parallel(df: pd.DataFrame, func, *args, grouper_kws: dict, parallel_kws: dict = None, **kwargs): """""" grouper = get_grouper(df, **grouper_kws) df_group = df.groupby(grouper) _ = grouper_kws.pop('section_size', None) _ = grouper_kws.pop('section_count', None) f = partial(_f, func=func, args=args, grouper_kws=grouper_kws, kwargs=kwargs) ret = df_group_apply_parallel(df_group, f, **(parallel_kws or {})) # ret = df_group_apply_parallel(df_group, func, *args, **kwargs) ret.index = ret.index.droplevel(0) return ret def _f(df: pd.DataFrame, func, args, grouper_kws, kwargs): return df.groupby(**grouper_kws).apply(func, *args, **kwargs) pd.DataFrame.double_groupby_apply_parallel = double_groupby_apply_parallel

31.207547

121

0.689843

244

1,654

4.442623

0.315574

0.064576

0.03321

0.055351

0.112546

0

0.012445

0.174123

1,654

52

122

31.807692

0.781113

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0

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0

1

0.115385

false

0

0.192308

0.038462

0.423077

0

null

0

null

0

1

0

9873a808f133176d36f13d62415361d7e99b6f93

3,563

py

Python

general/lies-damnlies-stats/server/stats/tests.py

jeremyosborne/examples-python

5900b3a4f47d59de0a32d3257a8b90a44e80fdcd

[ "MIT" ]

null

general/lies-damnlies-stats/server/stats/tests.py

jeremyosborne/examples-python

5900b3a4f47d59de0a32d3257a8b90a44e80fdcd

[ "MIT" ]

null

general/lies-damnlies-stats/server/stats/tests.py

jeremyosborne/examples-python

5900b3a4f47d59de0a32d3257a8b90a44e80fdcd

[ "MIT" ]

null

""" Automated tests for the ldls application. """ import json from datetime import datetime from django.utils import unittest from django.db import models from jsonserializermixin import JSONSerializerMixin from jsonencoderdelegator import JSONEncoderDelegator class TestModel(models.Model, JSONSerializerMixin): """A sample test model. """ count = models.IntegerField() class TestRelatedModel(models.Model, JSONSerializerMixin): """A sample model related to the test model. """ owner = models.ForeignKey(TestModel) description = models.TextField() class TestDescribedModel(models.Model, JSONSerializerMixin): """A sample model related to the test model, but doesn't describe the relation. """ owner = models.ForeignKey(TestModel) description = models.TextField() def describe(self): """Testing out the whitelist for only the description. """ return { "description": "string", } class JSONSerializerMixinTest(unittest.TestCase): """Test the json serializer mixin, ensure that it returns a JSON friendly object. """ def setUp(self): self.t = TestModel.objects.create(count=42) self.d = TestDescribedModel.objects.create(owner=self.t, description=24) def tearDown(self): self.d.delete() self.t.delete() def test_sanity(self): self.assertEqual(self.t.tojsonobject(), {"count": 42, "id": 1}, "Serialized model matches JSON friendly object.") self.assertEqual(json.dumps(self.t.tojsonobject(), sort_keys=True), '{"count": 42, "id": 1}', "Serialized model behaves correctly in json.dumps.") def test_describe(self): self.assertEqual(self.d.tojsonobject(), {"description": "24"}, "White list correctly ignores the owner attribute.") class JSONEncoderDelegatorTest(unittest.TestCase): def setUp(self): self.testlist = [TestModel.objects.create(count=42), TestModel.objects.create(count=42),] self.relatedTestModel = TestRelatedModel.objects.create( owner=self.testlist[0], description="42" ) def tearDown(self): # Remove models with relations, first. self.relatedTestModel.delete() del self.relatedTestModel # Remove non related models. for t in self.testlist: t.delete() del self.testlist def test_sanity(self): # Expected iterators work as expected. testobject = [42, 42] json = JSONEncoderDelegator() output = json.encode(testobject) self.assertEqual(output, "[42, 42]", "Standard items serialized correctly.") def test_list(self): json = JSONEncoderDelegator() output = json.encode(self.testlist) self.assertEqual(output, '[{"count": 42, "id": 1}, {"count": 42, "id": 2}]', "jsonserializer in a list works as expected.") def test_related(self): self.assertEqual(self.relatedTestModel.tojsonobject(), {'owner': {'fk': 1}, 'id': 1, 'description': u'42'}, "Models return a simple object with related fk.")

33.613208

80

0.579848

345

3,563

5.971014

0.310145

0.023786

0.017476

0.045146

0.242718

0.171845

0.115534

0.061165

0

0.01522

0.31771

3,563

105

81

33.933333

0.832168

0.121527

0

0.208955

0

0.132597

0

0.089552

1

0.149254

false

0

0.089552

0

0.402985

0

null

0

null

0

1

0

98750fb5aa7191d7cee8eb6e8dd2026b54b8928a

344

py

Python

test/test_pettingzoo.py

PettingZoo-Team/hanabi-learning-environment

2c91462cbdd8df94e6e63f24aec746343b48b664

[ "Apache-2.0" ]

8

2020-05-29T04:21:29.000Z

2020-06-05T20:24:39.000Z

test/test_pettingzoo.py

PettingZoo-Team/hanabi-learning-environment

2c91462cbdd8df94e6e63f24aec746343b48b664

[ "Apache-2.0" ]

null

test/test_pettingzoo.py

PettingZoo-Team/hanabi-learning-environment

2c91462cbdd8df94e6e63f24aec746343b48b664

[ "Apache-2.0" ]

4

2020-05-29T04:31:28.000Z

2020-06-05T20:24:43.000Z

import pytest from pettingzoo.classic import hanabi_v4 def test_pettingzoo(): env = hanabi_v4.env() env.reset() for agent in env.agent_iter(): observation, reward, done, info = env.last() if done: return else: action = env.action_space(agent).sample() env.step(action)

21.5

53

0.59593

42

344

4.761905

0.642857

0.08

0

0.008368

0.305233

344

15

54

22.933333

0.828452

0

1

0.083333

false

0

0.166667

0

0.333333

0

null

0

null

0

1

0

98802c9f1ac3cac5655a6b81c0649a8621ae583b

38,401

py

Python

bucky/model/main.py

ragram88/bucky

7840e3821af0124bedf4d806a1af34b8ebb20647

[ "MIT" ]

1

2021-08-11T20:31:35.000Z

bucky/model/main.py

ragram88/bucky

7840e3821af0124bedf4d806a1af34b8ebb20647

[ "MIT" ]

null

bucky/model/main.py

ragram88/bucky

7840e3821af0124bedf4d806a1af34b8ebb20647

[ "MIT" ]

null

"""The main module handling the simulation""" import copy import datetime import logging import os import pickle import queue import random import sys import threading import warnings from functools import lru_cache from pprint import pformat # TODO set some defaults for width/etc with partial? import numpy as np import pandas as pd import pyarrow as pa import pyarrow.parquet as pap import tqdm from ..numerical_libs import enable_cupy, reimport_numerical_libs, xp, xp_ivp from ..util.distributions import approx_mPERT_sample, truncnorm from ..util.util import TqdmLoggingHandler, _banner from .arg_parser_model import parser from .estimation import estimate_Rt from .exceptions import SimulationException from .graph import buckyGraphData from .mc_instance import buckyMCInstance from .npi import get_npi_params from .parameters import buckyParams from .rhs import RHS_func from .state import buckyState # supress pandas warning caused by pyarrow warnings.simplefilter(action="ignore", category=FutureWarning) # TODO we do alot of allowing div by 0 and then checking for nans later, we should probably refactor that warnings.simplefilter(action="ignore", category=RuntimeWarning) @lru_cache(maxsize=None) def get_runid(): # TODO move to util and rename to timeid or something """Gets a UUID based of the current datatime and caches it""" dt_now = datetime.datetime.now() return str(dt_now).replace(" ", "__").replace(":", "_").split(".")[0] def frac_last_n_vals(arr, n, axis=0, offset=0): # TODO assumes come from end of array currently, move to util """Return the last n values along an axis of an array; if n is a float, include the fractional amount of the int(n)-1 element""" int_slice_ind = ( [slice(None)] * (axis) + [slice(-int(n + offset), -int(xp.ceil(offset)) or None)] + [slice(None)] * (arr.ndim - axis - 1) ) ret = arr[int_slice_ind] # handle fractional element before the standard slice if (n + offset) % 1: frac_slice_ind = ( [slice(None)] * (axis) + [slice(-int(n + offset + 1), -int(n + offset))] + [slice(None)] * (arr.ndim - axis - 1) ) ret = xp.concatenate((((n + offset) % 1) * arr[frac_slice_ind], ret), axis=axis) # handle fractional element after the standard slice if offset % 1: frac_slice_ind = ( [slice(None)] * (axis) + [slice(-int(offset + 1), -int(offset) or None)] + [slice(None)] * (arr.ndim - axis - 1) ) ret = xp.concatenate((ret, (1.0 - (offset % 1)) * arr[frac_slice_ind]), axis=axis) return ret class buckyModelCovid: """Class that handles one full simulation (both time integration and managing MC states)""" def __init__( self, debug=False, sparse_aij=False, t_max=None, graph_file=None, par_file=None, npi_file=None, disable_npi=False, reject_runs=False, ): """Initialize the class, do some bookkeeping and read in the input graph""" self.debug = debug self.sparse = sparse_aij # we can default to none and autodetect # w/ override (maybe when #adm2 > 5k and some sparsity critera?) # Integrator params self.t_max = t_max self.run_id = get_runid() logging.info(f"Run ID: {self.run_id}") self.npi_file = npi_file self.disable_npi = disable_npi self.reject_runs = reject_runs self.output_dates = None # COVID/model params from par file self.bucky_params = buckyParams(par_file) self.consts = self.bucky_params.consts self.dists = self.bucky_params.dists self.g_data = self.load_graph(graph_file) def update_params(self, update_dict): self.bucky_params.update_params(update_dict) self.consts = self.bucky_params.consts self.dists = self.bucky_params.dists def load_graph(self, graph_file): """Load the graph data and calculate all the variables that are static across MC runs""" # TODO refactor to just have this return g_data logging.info("loading graph") with open(graph_file, "rb") as f: G = pickle.load(f) # nosec # Load data from input graph # TODO we should go through an replace lots of math using self.g_data.* with function IN buckyGraphData g_data = buckyGraphData(G, self.sparse) # Make contact mats sym and normalized self.contact_mats = G.graph["contact_mats"] if self.debug: logging.debug(f"graph contact mats: {G.graph['contact_mats'].keys()}") for mat in self.contact_mats: c_mat = xp.array(self.contact_mats[mat]) c_mat = (c_mat + c_mat.T) / 2.0 self.contact_mats[mat] = c_mat # remove all_locations so we can sum over the them ourselves if "all_locations" in self.contact_mats: del self.contact_mats["all_locations"] # Remove unknown contact mats valid_contact_mats = ["home", "work", "other_locations", "school"] self.contact_mats = {k: v for k, v in self.contact_mats.items() if k in valid_contact_mats} self.Cij = xp.vstack([self.contact_mats[k][None, ...] for k in sorted(self.contact_mats)]) # Get stratified population (and total) self.Nij = g_data.Nij self.Nj = g_data.Nj self.n_age_grps = self.Nij.shape[0] # TODO factor out self.init_date = datetime.date.fromisoformat(G.graph["start_date"]) self.base_mc_instance = buckyMCInstance(self.init_date, self.t_max, self.Nij, self.Cij) # fill in npi_params either from file or as ones self.npi_params = get_npi_params(g_data, self.init_date, self.t_max, self.npi_file, self.disable_npi) if self.npi_params["npi_active"]: self.base_mc_instance.add_npi(self.npi_params) self.adm0_cfr_reported = None self.adm1_cfr_reported = None self.adm2_cfr_reported = None # If HHS hospitalization data is on the graph, use it to rescale initial H counts and CHR # self.rescale_chr = "hhs_data" in G.graph if self.consts.rescale_chr: self.adm1_current_hosp = xp.zeros((g_data.max_adm1 + 1,), dtype=float) # TODO move hosp data to the graph nodes and handle it with graph.py the way cases/deaths are hhs_data = G.graph["hhs_data"].reset_index() hhs_data["date"] = pd.to_datetime(hhs_data["date"]) hhs_data = ( hhs_data.set_index("date") .sort_index() .groupby("adm1") .rolling(7) .mean() .drop(columns="adm1") .reset_index() ) hhs_curr_data = hhs_data.loc[hhs_data.date == pd.Timestamp(self.init_date)] hhs_curr_data = hhs_curr_data.set_index("adm1").sort_index() tot_hosps = ( hhs_curr_data.total_adult_patients_hospitalized_confirmed_covid + hhs_curr_data.total_pediatric_patients_hospitalized_confirmed_covid ) self.adm1_current_hosp[tot_hosps.index.to_numpy()] = tot_hosps.to_numpy() if self.debug: logging.debug("Current hospitalizations: " + pformat(self.adm1_current_hosp)) # Estimate the recent CFR during the period covered by the historical data cfr_delay = 25 # 14 # TODO This should come from CDC and Nij n_cfr = 14 last_cases = ( g_data.rolling_cum_cases[-cfr_delay - n_cfr : -cfr_delay] - g_data.rolling_cum_cases[-cfr_delay - n_cfr - 1] ) last_deaths = g_data.rolling_cum_deaths[-n_cfr:] - g_data.rolling_cum_deaths[-n_cfr - 1] adm1_cases = g_data.sum_adm1(last_cases.T) adm1_deaths = g_data.sum_adm1(last_deaths.T) negative_mask = (adm1_deaths < 0.0) | (adm1_cases < 0.0) adm1_cfr = adm1_deaths / adm1_cases adm1_cfr[negative_mask] = xp.nan # take mean over n days self.adm1_current_cfr = xp.nanmedian(adm1_cfr, axis=1) # Estimate recent CHR if self.consts.rescale_chr: chr_delay = 20 # TODO This should come from I_TO_H_TIME and Nij as a float (it's ~5.8) n_chr = 7 tmp = hhs_data.loc[hhs_data.date > pd.Timestamp(self.init_date - datetime.timedelta(days=n_chr))] tmp = tmp.loc[tmp.date <= pd.Timestamp(self.init_date)] tmp = tmp.set_index(["adm1", "date"]).sort_index() tmp = ( tmp.previous_day_admission_adult_covid_confirmed + tmp.previous_day_admission_pediatric_covid_confirmed ) cum_hosps = xp.zeros((adm1_cfr.shape[0], n_chr)) tmp = tmp.unstack() tmp_data = tmp.T.cumsum().to_numpy() tmp_ind = tmp.index.to_numpy() cum_hosps[tmp_ind] = tmp_data.T last_cases = ( g_data.rolling_cum_cases[-chr_delay - n_chr : -chr_delay] - g_data.rolling_cum_cases[-chr_delay - n_chr - 1] ) adm1_cases = g_data.sum_adm1(last_cases.T) adm1_hosps = cum_hosps # g_data.sum_adm1(last_hosps.T) adm1_chr = adm1_hosps / adm1_cases # take mean over n days self.adm1_current_chr = xp.mean(adm1_chr, axis=1) # self.adm1_current_chr = self.calc_lagged_rate(g_data.adm1_cum_case_hist, cum_hosps.T, chr_delay, n_chr) if self.debug: logging.debug("Current CFR: " + pformat(self.adm1_current_cfr)) return g_data def reset(self, seed=None, params=None): """Reset the state of the model and generate new inital data from a new random seed""" # TODO we should refactor reset of the compartments to be real pop numbers then /Nij at the end if seed is not None: random.seed(int(seed)) np.random.seed(seed) xp.random.seed(seed) # reroll model params if we're doing that kind of thing self.g_data.Aij.perturb(self.consts.reroll_variance) self.params = self.bucky_params.generate_params() if params is not None: self.params = copy.deepcopy(params) if self.debug: logging.debug("params: " + pformat(self.params, width=120)) for k in self.params: if type(self.params[k]).__module__ == np.__name__: self.params[k] = xp.asarray(self.params[k]) # TODO consolidate all the broadcast_to calls self.params.H = xp.broadcast_to(self.params.H[:, None], self.Nij.shape) self.params.F = xp.broadcast_to(self.params.F[:, None], self.Nij.shape) if self.consts.rescale_chr: # TODO this needs to be cleaned up BAD adm1_Ni = self.g_data.adm1_Nij adm1_N = self.g_data.adm1_Nj # estimate adm2 expected CFR weighted by local age demo tmp = self.params.F[:, 0][..., None] * self.g_data.adm1_Nij / self.g_data.adm1_Nj adm1_F = xp.sum(tmp, axis=0) # get ratio of actual CFR to expected CFR adm1_F_fac = self.adm1_current_cfr / adm1_F adm0_F_fac = xp.nanmean(adm1_N * adm1_F_fac) / xp.sum(adm1_N) adm1_F_fac[xp.isnan(adm1_F_fac)] = adm0_F_fac F_RR_fac = truncnorm(1.0, self.dists.F_RR_var, size=adm1_F_fac.size, a_min=1e-6) if self.debug: logging.debug("adm1 cfr rescaling factor: " + pformat(adm1_F_fac)) self.params.F = self.params.F * F_RR_fac[self.g_data.adm1_id] * adm1_F_fac[self.g_data.adm1_id] self.params.F = xp.clip(self.params.F, a_min=1.0e-10, a_max=1.0) adm1_Hi = self.g_data.sum_adm1((self.params.H * self.Nij).T).T adm1_Hi = adm1_Hi / adm1_Ni adm1_H = xp.nanmean(adm1_Hi, axis=0) adm1_H_fac = self.adm1_current_chr / adm1_H adm0_H_fac = xp.nanmean(adm1_N * adm1_H_fac) / xp.sum(adm1_N) adm1_H_fac[xp.isnan(adm1_H_fac)] = adm0_H_fac H_RR_fac = truncnorm(1.0, self.dists.H_RR_var, size=adm1_H_fac.size, a_min=1e-6) adm1_H_fac = adm1_H_fac * H_RR_fac # adm1_H_fac = xp.clip(adm1_H_fac, a_min=0.1, a_max=10.0) # prevent extreme values if self.debug: logging.debug("adm1 chr rescaling factor: " + pformat(adm1_H_fac)) self.params.H = self.params.H * adm1_H_fac[self.g_data.adm1_id] self.params.H = xp.clip(self.params.H, a_min=self.params.F, a_max=1.0) # crr_days_needed = max( #TODO this depends on all the Td params, and D_REPORT_TIME... case_reporting = self.estimate_reporting( self.g_data, self.params, cfr=self.params.F, # case_lag=14, days_back=25, min_deaths=self.consts.case_reporting_min_deaths, ) self.case_reporting = approx_mPERT_sample( # TODO these facs should go in param file mu=xp.clip(case_reporting, a_min=0.05, a_max=0.95), a=xp.clip(0.7 * case_reporting, a_min=0.01, a_max=0.9), b=xp.clip(1.3 * case_reporting, a_min=0.1, a_max=1.0), gamma=50.0, ) mean_case_reporting = xp.nanmean(self.case_reporting[-self.consts.case_reporting_N_historical_days :], axis=0) self.params["CASE_REPORT"] = mean_case_reporting self.params["THETA"] = xp.broadcast_to( self.params["THETA"][:, None], self.Nij.shape ) # TODO move all the broadcast_to's to one place, they're all over reset() self.params["GAMMA_H"] = xp.broadcast_to(self.params["GAMMA_H"][:, None], self.Nij.shape) self.params["F_eff"] = xp.clip(self.params["F"] / self.params["H"], 0.0, 1.0) # state building init state vector (self.y) yy = buckyState(self.consts, self.Nij) if self.debug: logging.debug("case init") Ti = self.params.Ti current_I = xp.sum(frac_last_n_vals(self.g_data.rolling_inc_cases, Ti, axis=0), axis=0) current_I[xp.isnan(current_I)] = 0.0 current_I[current_I < 0.0] = 0.0 current_I *= 1.0 / (self.params["CASE_REPORT"]) # Roll some random factors for the init compartment values R_fac = approx_mPERT_sample(**(self.dists.R_fac_dist)) E_fac = approx_mPERT_sample(**(self.dists.E_fac_dist)) H_fac = approx_mPERT_sample(**(self.dists.H_fac_dist)) age_dist_fac = self.Nij / xp.sum(self.Nij, axis=0, keepdims=True) I_init = E_fac * current_I[None, :] * age_dist_fac / self.Nij # / self.n_age_grps D_init = self.g_data.cum_death_hist[-1][None, :] * age_dist_fac / self.Nij # / self.n_age_grps recovered_init = (self.g_data.cum_case_hist[-1] / self.params["SYM_FRAC"]) * R_fac R_init = ( (recovered_init) * age_dist_fac / self.Nij - D_init - I_init / self.params["SYM_FRAC"] ) # Rh is factored in later Rt = estimate_Rt(self.g_data, self.params, 7, self.case_reporting) Rt_fac = approx_mPERT_sample(**(self.dists.Rt_dist)) Rt = Rt * Rt_fac self.params["R0"] = Rt self.params["BETA"] = Rt * self.params["GAMMA"] / self.g_data.Aij.diag exp_frac = ( E_fac * xp.ones(I_init.shape[-1]) * (self.params.R0) * self.params.GAMMA / self.params.SIGMA / (1.0 - R_init) / self.params["SYM_FRAC"] ) yy.I = (1.0 - self.params.H) * I_init / yy.Im yy.Ic = self.params.H * I_init / yy.Im # TODO this is an estimate, we should rescale it to the real data if we have it rh_fac = 1.0 # .4 yy.Rh = self.params.H * I_init / yy.Rhn if self.consts.rescale_chr: adm1_hosp = xp.zeros((self.g_data.max_adm1 + 1,), dtype=float) xp.scatter_add(adm1_hosp, self.g_data.adm1_id, xp.sum(yy.Rh * self.Nij, axis=(0, 1))) adm2_hosp_frac = (self.adm1_current_hosp / adm1_hosp)[self.g_data.adm1_id] adm0_hosp_frac = xp.nansum(self.adm1_current_hosp) / xp.nansum(adm1_hosp) adm2_hosp_frac[xp.isnan(adm2_hosp_frac) | (adm2_hosp_frac == 0.0)] = adm0_hosp_frac adm2_hosp_frac = xp.sqrt(adm2_hosp_frac * adm0_hosp_frac) scaling_F = F_RR_fac[self.g_data.adm1_id] * self.consts.F_scaling / H_fac scaling_H = adm2_hosp_frac * H_fac self.params["F"] = xp.clip(self.params["F"] * scaling_F, 0.0, 1.0) self.params["H"] = xp.clip(self.params["H"] * scaling_H, self.params["F"], 1.0) / 1.2 self.params["F_eff"] = xp.clip(self.params["F"] / self.params["H"], 0.0, 1.0) # TODO rename F_eff to HFR adm2_chr_delay = xp.sum(self.params["I_TO_H_TIME"][:, None] * self.g_data.Nij / self.g_data.Nj, axis=0) adm2_chr_delay_int = adm2_chr_delay.astype(int) # TODO temp, this should be a distribution of floats adm2_chr_delay_mod = adm2_chr_delay % 1 inc_case_h_delay = (1.0 - adm2_chr_delay_mod) * xp.take_along_axis( self.g_data.rolling_inc_cases, -adm2_chr_delay_int[None, :], axis=0 )[0] + adm2_chr_delay_mod * xp.take_along_axis( self.g_data.rolling_inc_cases, -adm2_chr_delay_int[None, :] - 1, axis=0 )[ 0 ] inc_case_h_delay[(inc_case_h_delay > 0.0) & (inc_case_h_delay < 1.0)] = 1.0 inc_case_h_delay[inc_case_h_delay < 0.0] = 0.0 adm2_chr = xp.sum(self.params["H"] * self.g_data.Nij / self.g_data.Nj, axis=0) tmp = xp.sum(self.params.H * I_init / yy.Im * self.g_data.Nij, axis=0) / 3.0 # 1/3 is mean sigma tmp2 = inc_case_h_delay * adm2_chr # * 3.0 # 3 == mean sigma, these should be read from base_params ic_fac = tmp2 / tmp ic_fac[~xp.isfinite(ic_fac)] = xp.nanmean(ic_fac[xp.isfinite(ic_fac)]) yy.I = (1.0 - self.params.H) * I_init / yy.Im yy.Ic = ic_fac * self.params.H * I_init / yy.Im yy.Rh = ( rh_fac * self.params.H * I_init / yy.Rhn # * 1.15 # fit to runs, we should be able to calculate this somehow... ) R_init -= xp.sum(yy.Rh, axis=0) yy.Ia = self.params.ASYM_FRAC / self.params.SYM_FRAC * I_init / yy.Im yy.E = exp_frac[None, :] * I_init / yy.En # this should be calcable from Rt and the time before symp yy.R = xp.clip(R_init, a_min=0.0, a_max=None) yy.D = D_init # TMP mask = xp.sum(yy.N, axis=0) > 1.0 yy.state[:, mask] /= xp.sum(yy.N, axis=0)[mask] yy.init_S() # init the bin we're using to track incident cases # (it's filled with cumulatives until we diff it later) # TODO should this come from the rolling hist? yy.incC = xp.clip(self.g_data.cum_case_hist[-1][None, :], a_min=0.0, a_max=None) * age_dist_fac / self.Nij self.y = yy # Sanity check state vector self.y.validate_state() if self.debug: logging.debug("done reset()") # return y # @staticmethod need to move the caching out b/c its in the self namespace def estimate_reporting(self, g_data, params, cfr, days_back=14, case_lag=None, min_deaths=100.0): """Estimate the case reporting rate based off observed vs. expected CFR""" if case_lag is None: adm0_cfr_by_age = xp.sum(cfr * g_data.Nij, axis=1) / xp.sum(g_data.Nj, axis=0) adm0_cfr_total = xp.sum( xp.sum(cfr * g_data.Nij, axis=1) / xp.sum(g_data.Nj, axis=0), axis=0, ) case_lag = xp.sum(params["D_REPORT_TIME"] * adm0_cfr_by_age / adm0_cfr_total, axis=0) case_lag_int = int(case_lag) recent_cum_cases = g_data.rolling_cum_cases - g_data.rolling_cum_cases[0] recent_cum_deaths = g_data.rolling_cum_deaths - g_data.rolling_cum_deaths[0] case_lag_frac = case_lag % 1 # TODO replace with util function for the indexing cases_lagged = frac_last_n_vals(recent_cum_cases, days_back + case_lag_frac, offset=case_lag_int) if case_lag_frac: cases_lagged = cases_lagged[0] + cases_lagged[1:] # adm0 adm0_cfr_param = xp.sum(xp.sum(cfr * g_data.Nij, axis=1) / xp.sum(g_data.Nj, axis=0), axis=0) if self.adm0_cfr_reported is None: self.adm0_cfr_reported = xp.sum(recent_cum_deaths[-days_back:], axis=1) / xp.sum(cases_lagged, axis=1) adm0_case_report = adm0_cfr_param / self.adm0_cfr_reported if self.debug: logging.debug("Adm0 case reporting rate: " + pformat(adm0_case_report)) if xp.any(~xp.isfinite(adm0_case_report)): if self.debug: logging.debug("adm0 case report not finite") logging.debug(adm0_cfr_param) logging.debug(self.adm0_cfr_reported) raise SimulationException case_report = xp.repeat(adm0_case_report[:, None], cases_lagged.shape[-1], axis=1) # adm1 adm1_cfr_param = xp.zeros((g_data.max_adm1 + 1,), dtype=float) adm1_totpop = g_data.adm1_Nj # xp.zeros((self.g_data.max_adm1 + 1,), dtype=float) tmp_adm1_cfr = xp.sum(cfr * g_data.Nij, axis=0) xp.scatter_add(adm1_cfr_param, g_data.adm1_id, tmp_adm1_cfr) # xp.scatter_add(adm1_totpop, self.g_data.adm1_id, self.Nj) adm1_cfr_param /= adm1_totpop # adm1_cfr_reported is const, only calc it once and cache it if self.adm1_cfr_reported is None: self.adm1_deaths_reported = xp.zeros((g_data.max_adm1 + 1, days_back), dtype=float) adm1_lagged_cases = xp.zeros((g_data.max_adm1 + 1, days_back), dtype=float) xp.scatter_add( self.adm1_deaths_reported, g_data.adm1_id, recent_cum_deaths[-days_back:].T, ) xp.scatter_add(adm1_lagged_cases, g_data.adm1_id, cases_lagged.T) self.adm1_cfr_reported = self.adm1_deaths_reported / adm1_lagged_cases adm1_case_report = (adm1_cfr_param[:, None] / self.adm1_cfr_reported)[g_data.adm1_id].T valid_mask = (self.adm1_deaths_reported > min_deaths)[g_data.adm1_id].T & xp.isfinite(adm1_case_report) case_report[valid_mask] = adm1_case_report[valid_mask] # adm2 adm2_cfr_param = xp.sum(cfr * (g_data.Nij / g_data.Nj), axis=0) if self.adm2_cfr_reported is None: self.adm2_cfr_reported = recent_cum_deaths[-days_back:] / cases_lagged adm2_case_report = adm2_cfr_param / self.adm2_cfr_reported valid_adm2_cr = xp.isfinite(adm2_case_report) & (recent_cum_deaths[-days_back:] > min_deaths) case_report[valid_adm2_cr] = adm2_case_report[valid_adm2_cr] return case_report def run_once(self, seed=None): """Perform one complete run of the simulation""" # rename to integrate or something? it also resets... # reset everything logging.debug("Resetting state") self.reset(seed=seed) logging.debug("Done reset") self.base_mc_instance.epi_params = self.params self.base_mc_instance.state = self.y self.base_mc_instance.Aij = self.g_data.Aij.A self.base_mc_instance.rhs = RHS_func self.base_mc_instance.dy = self.y.zeros_like() # TODO this logic needs to go somewhere else (its rescaling beta to account for S/N term) # TODO R0 need to be changed before reset()... S_eff = self.base_mc_instance.S_eff(0, self.base_mc_instance.state) adm2_S_eff = xp.sum(S_eff * self.g_data.Nij / self.g_data.Nj, axis=0) adm2_beta_scale = xp.clip(1.0 / (adm2_S_eff + 1e-10), a_min=1.0, a_max=5.0) self.base_mc_instance.epi_params["R0"] = self.base_mc_instance.epi_params["R0"] * adm2_beta_scale self.base_mc_instance.epi_params["BETA"] = self.base_mc_instance.epi_params["BETA"] * adm2_beta_scale adm2_E_tot = xp.sum(self.y.E * self.g_data.Nij / self.g_data.Nj, axis=(0, 1)) adm2_new_E_tot = adm2_beta_scale * adm2_E_tot S_dist = S_eff / (xp.sum(S_eff, axis=0) + 1e-10) new_E = xp.tile( (S_dist * adm2_new_E_tot / self.g_data.Nij * self.g_data.Nj / self.params.consts["En"])[None, ...], (xp.to_cpu(self.params.consts["En"]), 1, 1), ) new_S = self.y.S - xp.sum(new_E - self.y.E, axis=0) self.base_mc_instance.state.E = new_E self.base_mc_instance.state.S = new_S # do integration logging.debug("Starting integration") sol = xp_ivp.solve_ivp( # self.RHS_func, # y0=self.y.state.ravel(), # args=( # #self.g_data.Aij.A, # self.base_mc_instance, # #self.base_mc_instance.state, # ), **self.base_mc_instance.integrator_args ) logging.debug("Done integration") return sol def run_multiple(self, n_mc, base_seed=42, out_columns=None): """Perform multiple monte carlos and return their postprocessed results""" seed_seq = np.random.SeedSequence(base_seed) success = 0 ret = [] pbar = tqdm.tqdm(total=n_mc, desc="Performing Monte Carlos", dynamic_ncols=True) while success < n_mc: mc_seed = seed_seq.spawn(1)[0].generate_state(1)[0] # inc spawn key then grab next seed pbar.set_postfix_str( "seed=" + str(mc_seed), refresh=True, ) try: with xp.optimize_kernels(): sol = self.run_once(seed=mc_seed) df_data = self.postprocess_run(sol, mc_seed, out_columns) ret.append(df_data) success += 1 pbar.update(1) except SimulationException: pass pbar.close() return ret # TODO Move this to a class thats like run_parser or something (that caches all the info it needs like Nij, and manages the write thread/queue) # Also give it methods like to_dlpack, to_pytorch, etc def save_run(self, sol, base_filename, seed, output_queue): """Postprocess and write to disk the output of run_once""" df_data = self.postprocess_run(sol, seed) # flatten the shape for c in df_data: df_data[c] = df_data[c].ravel() # push the data off to the write thread data_folder = os.path.join(base_filename, "data") output_queue.put((data_folder, df_data)) metadata_folder = os.path.join(base_filename, "metadata") if not os.path.exists(metadata_folder): os.mkdir(metadata_folder) # write dates uniq_dates = pd.Series(self.output_dates) pd.DataFrame({"date": uniq_dates}).to_csv(os.path.join(metadata_folder, "dates.csv"), index=False) # write out adm mapping adm_map = pd.DataFrame( { "adm2": xp.to_cpu(self.g_data.adm2_id), "adm1": xp.to_cpu(self.g_data.adm1_id), "adm0": self.g_data.adm0_name, } ) adm_map.to_csv(os.path.join(metadata_folder, "adm_mapping.csv"), index=False) # TODO write params out (to yaml?) in another subfolder # TODO we should output the per monte carlo param rolls, this got lost when we switched from hdf5 def postprocess_run(self, sol, seed, columns=None): """Process the output of a run (sol, returned by the integrator) into the requested output vars""" if columns is None: columns = [ "adm2_id", "date", "rid", "total_population", "current_hospitalizations", "active_asymptomatic_cases", "cumulative_deaths", "daily_hospitalizations", "daily_cases", "daily_reported_cases", "daily_deaths", "cumulative_cases", "cumulative_reported_cases", "current_icu_usage", "current_vent_usage", "case_reporting_rate", "R_eff", ] columns = set(columns) df_data = {} out = buckyState(self.consts, self.Nij) y = sol.y.reshape(self.y.state_shape + (sol.y.shape[-1],)) # rescale by population out.state = self.Nij[None, ..., None] * y # collapse age groups out.state = xp.sum(out.state, axis=1) # population_conserved = (xp.diff(xp.around(xp.sum(out.N, axis=(0, 1)), 1)) == 0.0).all() # if not population_conserved: # pass # TODO we're getting small fp errors here # # print(xp.sum(xp.diff(xp.around(xp.sum(out[:incH], axis=(0, 1)), 1)))) # # logging.error("Population not conserved!") # # print(xp.sum(xp.sum(y[:incH],axis=0)-1.)) # # raise SimulationException if "adm2_id" in columns: adm2_ids = np.broadcast_to(self.g_data.adm2_id[:, None], out.state.shape[1:]) df_data["adm2_id"] = adm2_ids if "date" in columns: if self.output_dates is None: t_output = xp.to_cpu(sol.t) dates = [str(self.init_date + datetime.timedelta(days=np.round(t))) for t in t_output] self.output_dates = dates df_data["date"] = np.broadcast_to(np.arange(len(self.output_dates)), out.state.shape[1:]) if "rid" in columns: df_data["rid"] = np.broadcast_to(seed, out.state.shape[1:]) if "current_icu_usage" in columns or "current_vent_usage" in columns: icu = self.Nij[..., None] * self.params["ICU_FRAC"][:, None, None] * xp.sum(y[out.indices["Rh"]], axis=0) if "current_icu_usage" in columns: df_data["current_icu_usage"] = xp.sum(icu, axis=0) if "current_vent_usage" in columns: vent = self.params.ICU_VENT_FRAC[:, None, None] * icu df_data["current_vent_usage"] = xp.sum(vent, axis=0) if "daily_deaths" in columns: daily_deaths = xp.gradient(out.D, axis=-1, edge_order=2) df_data["daily_deaths"] = daily_deaths if self.reject_runs: init_inc_death_mean = xp.mean(xp.sum(daily_deaths[:, 1:4], axis=0)) hist_inc_death_mean = xp.mean(xp.sum(self.g_data.inc_death_hist[-7:], axis=-1)) inc_death_rejection_fac = 2.0 # TODO These should come from the cli arg -r if (init_inc_death_mean > inc_death_rejection_fac * hist_inc_death_mean) or ( inc_death_rejection_fac * init_inc_death_mean < hist_inc_death_mean ): logging.info("Inconsistent inc deaths, rejecting run") raise SimulationException if "daily_cases" in columns or "daily_reported_cases" in columns: daily_reported_cases = xp.gradient(out.incC, axis=-1, edge_order=2) if self.reject_runs: init_inc_case_mean = xp.mean(xp.sum(daily_reported_cases[:, 1:4], axis=0)) hist_inc_case_mean = xp.mean(xp.sum(self.g_data.inc_case_hist[-7:], axis=-1)) inc_case_rejection_fac = 1.5 # TODO These should come from the cli arg -r if (init_inc_case_mean > inc_case_rejection_fac * hist_inc_case_mean) or ( inc_case_rejection_fac * init_inc_case_mean < hist_inc_case_mean ): logging.info("Inconsistent inc cases, rejecting run") raise SimulationException if "daily_reported_cases" in columns: df_data["daily_reported_cases"] = daily_reported_cases if "daily_cases" in columns: daily_cases_total = daily_reported_cases / self.params.CASE_REPORT[:, None] df_data["daily_cases"] = daily_cases_total if "cumulative_reported_cases" in columns: cum_cases_reported = out.incC df_data["cumulative_reported_cases"] = cum_cases_reported if "cumulative_cases" in columns: cum_cases_total = out.incC / self.params.CASE_REPORT[:, None] df_data["cumulative_cases"] = cum_cases_total if "daily_hospitalizations" in columns: out.incH[:, 0] = out.incH[:, 1] daily_hosp = xp.gradient(out.incH, axis=-1, edge_order=2) df_data["daily_hospitalizations"] = daily_hosp if "total_population" in columns: N = xp.broadcast_to(self.g_data.Nj[..., None], out.state.shape[1:]) df_data["total_population"] = N if "current_hospitalizations" in columns: hosps = xp.sum(out.Rh, axis=0) # why not just using .H? df_data["current_hospitalizations"] = hosps if "cumulative_deaths" in columns: cum_deaths = out.D df_data["cumulative_deaths"] = cum_deaths if "active_asymptomatic_cases" in columns: asym_I = xp.sum(out.Ia, axis=0) df_data["active_asymptomatic_cases"] = asym_I if "case_reporting_rate" in columns: crr = xp.broadcast_to(self.params.CASE_REPORT[:, None], adm2_ids.shape) df_data["case_reporting_rate"] = crr if "R_eff" in columns: r_eff = self.npi_params["r0_reduct"].T * np.broadcast_to( (self.params.R0 * self.g_data.Aij.diag)[:, None], adm2_ids.shape ) df_data["R_eff"] = r_eff # Collapse the gamma-distributed compartments and move everything to cpu negative_values = False for k in df_data: # if df_data[k].ndim == 2: # df_data[k] = xp.sum(df_data[k], axis=0) if k != "date" and xp.any(xp.around(df_data[k], 2) < 0.0): logging.info("Negative values present in " + k) negative_values = True if negative_values and self.reject_runs: logging.info("Rejecting run b/c of negative values in output") raise SimulationException return df_data def main(args=None): """Main method for a complete simulation called with a set of CLI args""" if args is None: args = sys.argv[1:] args = parser.parse_args(args=args) if args.gpu: logging.info("Using GPU backend") enable_cupy(optimize=args.opt) reimport_numerical_libs("model.main.main") warnings.simplefilter(action="ignore", category=xp.ExperimentalWarning) if not os.path.exists(args.output_dir): os.mkdir(args.output_dir) loglevel = 30 - 10 * min(args.verbosity, 2) runid = get_runid() # Setup output folder TODO change over to pathlib output_folder = os.path.join(args.output_dir, runid) if not os.path.exists(output_folder): os.mkdir(output_folder) # fh = logging.FileHandler(output_folder + "/stdout") # fh.setLevel(logging.DEBUG) logging.basicConfig( level=loglevel, format="%(asctime)s - %(levelname)s - %(filename)s:%(funcName)s:%(lineno)d - %(message)s", handlers=[TqdmLoggingHandler()], ) debug_mode = loglevel < 20 # TODO we should output the logs to output_dir too... _banner() # TODO move the write_thread stuff to a util (postprocess uses something similar) to_write = queue.Queue(maxsize=100) def writer(): """Write thread loop that pulls from an async queue""" # Call to_write.get() until it returns None stream = xp.cuda.Stream(non_blocking=True) if args.gpu else None pinned_mem = {} for base_fname, df_data in iter(to_write.get, None): for k, v in df_data.items(): if k not in pinned_mem: pinned_mem[k] = xp.empty_like_pinned(v) xp.to_cpu(v, stream=stream, out=pinned_mem[k]) if stream is not None: stream.synchronize() pa_data = {k: pa.array(v) for k, v in pinned_mem.items()} table = pa.table(pa_data) pap.write_to_dataset(table, base_fname, partition_cols=["date"]) write_thread = threading.Thread(target=writer, daemon=True) write_thread.start() logging.info(f"command line args: {args}") env = buckyModelCovid( debug=debug_mode, sparse_aij=(not args.dense), t_max=args.days, graph_file=args.graph_file, par_file=args.par_file, npi_file=args.npi_file, disable_npi=args.disable_npi, reject_runs=args.reject_runs, ) seed_seq = np.random.SeedSequence(args.seed) total_start = datetime.datetime.now() success = 0 n_runs = 0 pbar = tqdm.tqdm(total=args.n_mc, desc="Performing Monte Carlos", dynamic_ncols=True) try: while success < args.n_mc: mc_seed = seed_seq.spawn(1)[0].generate_state(1)[0] # inc spawn key then grab next seed pbar.set_postfix_str( "seed=" + str(mc_seed) + ", rej%=" # TODO disable rej% if not -r + str(np.around(float(n_runs - success) / (n_runs + 0.00001) * 100, 1)), refresh=True, ) try: n_runs += 1 with xp.optimize_kernels(): sol = env.run_once(seed=mc_seed) env.save_run(sol, output_folder, mc_seed, output_queue=to_write) success += 1 pbar.update(1) except SimulationException: pass except (KeyboardInterrupt, SystemExit): logging.warning("Caught SIGINT, cleaning up") to_write.put(None) write_thread.join() finally: to_write.put(None) write_thread.join() pbar.close() logging.info(f"Total runtime: {datetime.datetime.now() - total_start}") if __name__ == "__main__": main()

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0.161536

0.113569

0.093168

0.069184

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41.968306

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0.156715

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0.00156

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null

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0

988147fa4143728a5e12261688a08199610fecd9

10,557

py

Python

bootini_star/views.py

rseichter/bootini-star

a80258f01a05e4df38748b8cb47dfadabd42c20d

[ "MIT" ]

null

bootini_star/views.py

rseichter/bootini-star

a80258f01a05e4df38748b8cb47dfadabd42c20d

[ "MIT" ]

null

bootini_star/views.py

rseichter/bootini-star

a80258f01a05e4df38748b8cb47dfadabd42c20d

[ "MIT" ]

null

""" Application views/routes are based on Flask's MethodView. All primary views are combined into a Flask blueprint. """ __author__ = 'Ralph Seichter' import datetime from operator import attrgetter import flask_login from flask import Blueprint, flash, redirect, url_for from flask.templating import render_template from flask.views import MethodView, View from flask_login.utils import current_user from pymongo.errors import OperationFailure import swagger_client from bootini_star import esi from bootini_star.esi import Cache, EveGroup, EveType from bootini_star.forms import AdminForm from swagger_client.rest import ApiException from .extensions import app_config, log from .models import User, create_unique_index from .sso import EveSso ADMIN_REQUIRED = 'Admin privileges are required.' eveCache = esi.IdNameCache() class RenderTemplate(View): def __init__(self, template): self.template = template def dispatch_request(self): return render_template(self.template, config=app_config) def flash_form_errors(form): for field, errors in form.errors.items(): for error in errors: flash(error, 'danger') class Dashboard(MethodView): methods = ['GET'] @flask_login.login_required def get(self): auth_url, auth_state = EveSso().auth_url_state() cu: User = current_user characters = sorted(cu.characters, key=attrgetter('name')) if cu.characters else None return render_template( 'dashboard.html', characters=characters, auth_url=auth_url, auth_state=auth_state, config=app_config ) class Character(MethodView): methods = ['GET'] @flask_login.login_required def get(self, character_id): api = swagger_client.CharacterApi() try: return render_template( 'character.html', character=esi.get_character(api, character_id) ) except ApiException as e: return api_fail(e) def refresh_token(api, character: Character): es = EveSso(character.token) rt = es.refresh_token() if rt.token_changed: log.debug(f'Updating token for character {character.eve_id}') character.token = rt.token character.modified_at = datetime.datetime.utcnow() if current_user.update() != 1: log.error( f'Error updating token for character {character.eve_id}') client = api.api_client client.set_default_header('User-Agent', app_config['USER_AGENT']) client.configuration.access_token = rt.token['access_token'] return api def mail_api(current_character): return refresh_token(swagger_client.MailApi(), current_character) def skills_api(current_character): return refresh_token(swagger_client.SkillsApi(), current_character) def api_fail(api_exception): flash('EVE Swagger Interface call failed: ' + api_exception.reason + '.', 'danger') return redirect(url_for('.index')) class MailList(MethodView): methods = ['GET'] @flask_login.login_required def get(self, character_id, label=None): cc = current_user.get_character(character_id) if cc: # pragma: no cover (Needs live character) api = mail_api(cc) kwargs = {'labels': [label]} if isinstance(label, int) else {} try: labels = esi.get_mail_labels(api, character_id) mails = esi.get_mails(api, character_id, **kwargs) mail_ids = {m._from for m in mails} eveCache.eve_characters(mail_ids) except ApiException as e: return api_fail(e) sl = sorted(labels.labels, key=attrgetter('label_id')) sm = sorted(mails, key=attrgetter('timestamp'), reverse=True) return render_template('maillist.html', eveCache=eveCache, character_id=character_id, labels=sl, maillist=sm) else: flash('Please select one of your characters.', 'warning') return redirect(url_for('.dashboard')) class Mail(MethodView): methods = ['GET'] @flask_login.login_required def get(self, character_id: int, mail_id: int): cc = current_user.get_character(character_id) if cc: api = mail_api(cc) try: rv = api.get_characters_character_id_mail_mail_id( character_id, mail_id) return render_template('mail.html', character_id=character_id, mail_id=mail_id, eveCache=eveCache, mail=rv) except ApiException as e: return api_fail(e) else: flash('Please select one of your characters.', 'warning') return redirect(url_for('.dashboard')) class MarkMailRead(MethodView): methods = ['GET'] @flask_login.login_required def get(self, character_id: int, mail_id: int, read: int): cc = current_user.get_character(character_id) if cc: # pragma: no cover (Needs live character) api = mail_api(cc) try: status = 'read' if read else 'unread' esi.mark_mail_read(api, character_id, mail_id, read) flash(f'Mail has been marked as {status}.', 'success') except ApiException as e: return api_fail(e) return redirect(url_for('.maillist', character_id=character_id)) else: flash('Please select one of your characters.', 'warning') return redirect(url_for('.dashboard')) class RemoveMail(MethodView): methods = ['GET'] @flask_login.login_required def get(self, character_id: int, mail_id: int): cc = current_user.get_character(character_id) if cc: # pragma: no cover (Needs live character) api = mail_api(cc) try: api.delete_characters_character_id_mail_mail_id( character_id, mail_id) flash('Mail has been deleted.', 'success') except ApiException as e: return api_fail(e) return redirect(url_for('.maillist', character_id=character_id)) else: flash('Please select one of your characters.', 'warning') return redirect(url_for('.dashboard')) class RemoveCharacter(MethodView): methods = ['GET'] @flask_login.login_required def get(self, character_id): try: log.debug(f'Remove character {character_id}') current_user.remove_character(character_id) if current_user.update() == 1: flash(f'Character {character_id} was removed.', 'success') else: log.warning(f'Character {character_id} could not be removed') flash(f'Character {character_id} could not be removed.', 'danger') except OperationFailure as e: log.error(f'Error removing character: {e}') flash(f'Character {character_id} could not be removed.', 'danger') return redirect(url_for('.dashboard')) class Skills(MethodView): methods = ['GET'] @flask_login.login_required def get(self, character_id): cc = current_user.get_character(character_id) if cc: # pragma: no cover (Needs live character) api = skills_api(cc) try: rv = api.get_characters_character_id_skillqueue(character_id) return render_template('skillqueue.html', eveCache=eveCache, character_id=character_id, skillq=sorted(rv, key=attrgetter( 'queue_position'))) except ApiException as e: return api_fail(e) else: flash('Please select one of your characters.', 'warning') return redirect(url_for('.dashboard')) class Skill(MethodView): methods = ['GET'] @staticmethod def get(skill_id): skill = eveCache.eve_type(skill_id) return render_template('skill.html', skill=skill) class Admin(MethodView): methods = ['GET', 'POST'] @flask_login.fresh_login_required def get(self): if current_user.is_admin: return render_template('quickform.html', form=AdminForm()) flash(ADMIN_REQUIRED, 'warning') return redirect(url_for('.login')) @flask_login.fresh_login_required def post(self): if current_user.is_admin: create_unique_index(Cache().collection, 'eve_id') create_unique_index(EveGroup().collection, 'eve_id') create_unique_index(EveType().collection, 'eve_id') create_unique_index(User().collection, 'email') flash('Created database indexes.', 'success') return redirect(url_for('.dashboard')) flash(ADMIN_REQUIRED, 'warning') return redirect(url_for('.login')) blueprint = Blueprint('bs', __name__) blueprint.add_url_rule( '/', view_func=RenderTemplate.as_view('index', template='index.html')) blueprint.add_url_rule('/admin', view_func=Admin.as_view('admin')) blueprint.add_url_rule('/dashboard', view_func=Dashboard.as_view('dashboard')) blueprint.add_url_rule('/dashboard/rm/<int:character_id>', view_func=RemoveCharacter.as_view('rmcharacter')) blueprint.add_url_rule('/character/<int:character_id>', view_func=Character.as_view('character')) blueprint.add_url_rule( '/mail/<int:character_id>/<int:mail_id>', view_func=Mail.as_view('mail')) blueprint.add_url_rule('/mail/rm/<int:character_id>/<int:mail_id>', view_func=RemoveMail.as_view('rmmail')) blueprint.add_url_rule('/mail/rd/<int:character_id>/<int:mail_id>/<int:read>', view_func=MarkMailRead.as_view('mailread')) blueprint.add_url_rule('/maillist/<int:character_id>/<int:label>', view_func=MailList.as_view('maillabel')) blueprint.add_url_rule('/maillist/<int:character_id>', view_func=MailList.as_view('maillist')) blueprint.add_url_rule('/skill/<int:skill_id>', view_func=Skill.as_view('skill')) blueprint.add_url_rule('/skillqueue/<int:character_id>', view_func=Skills.as_view('skillqueue'))

36.278351

79

0.62783

1,236

10,557

5.131877

0.165049

0.079773

0.028693

0.037837

0.486836

0.426139

0.387829

0.357402

0.301908

0.277787

0

0.000259

0.268353

10,557

290

80

36.403448

0.820948

0.02586

0

0.387234

0

0.150297

0.030274

0

1

0.076596

false

0

0.068085

0.012766

0.357447

0.059574

0

null

0

null

0

1

0

9881570c2307209bb16898489ef2c768f90b46b1

514

py

Python

3_corpus_chatterbot.py

OtacilioMaia/Chatbots-Desenvolvimento-Orientado-a-Conversas

aadb7384d64549749508d594138e07221c200f68

[ "MIT" ]

null

3_corpus_chatterbot.py

OtacilioMaia/Chatbots-Desenvolvimento-Orientado-a-Conversas

aadb7384d64549749508d594138e07221c200f68

[ "MIT" ]

null

3_corpus_chatterbot.py

OtacilioMaia/Chatbots-Desenvolvimento-Orientado-a-Conversas

aadb7384d64549749508d594138e07221c200f68

[ "MIT" ]

null

__author__ = "Otacilio Maia" __version__ = "1.0.0" from chatterbot import ChatBot from chatterbot.trainers import ChatterBotCorpusTrainer def main(): chatbot = ChatBot('Chat AI') trainer = ChatterBotCorpusTrainer(chatbot) trainer.train("chatterbot.corpus.portuguese") user_text = "" while(user_text != "sair"): user_text = input("Voce: ") response = chatbot.get_response(user_text).text.encode('utf-8') print(response.decode()) if __name__ == "__main__": main()

25.7

71

0.684825

57

514

5.807018

0.614035

0.096677

0

0.009569

0.18677

514

20

72

25.7

0.782297

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1

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false

0

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0.2

0.066667

0

null

0

null

0

1

0

9883506bbef48623a9d156fc156d445016a20d6a

5,103

py

Python

examples/test_wf/test_convergence.py

yakutovicha/yambo-aiida

5c722961ebabe5ea14fbcb20a866c6fb99398a7d

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

null

examples/test_wf/test_convergence.py

yakutovicha/yambo-aiida

5c722961ebabe5ea14fbcb20a866c6fb99398a7d

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

null

examples/test_wf/test_convergence.py

yakutovicha/yambo-aiida

5c722961ebabe5ea14fbcb20a866c6fb99398a7d

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

null

from aiida.backends.utils import load_dbenv, is_dbenv_loaded if not is_dbenv_loaded(): load_dbenv() from aiida_yambo.workflows.yamboconvergence import YamboConvergenceWorkflow try: from aiida.orm.data.base import Float, Str, NumericType, BaseType, List from aiida.work.run import run, submit except ImportError: from aiida.workflows2.db_types import Float, Str, NumericType, SimpleData, Bool from aiida.workflows2.db_types import SimpleData as BaseType from aiida.orm.data.simple import SimpleData as SimpleData_ from aiida.workflows2.run import run from aiida.orm.utils import DataFactory ParameterData = DataFactory("parameter") yambo_parameters = {'ppa': True, 'gw0': True, 'HF_and_locXC': True, 'em1d': True, 'DIP_Threads': 0 , 'BndsRnXp': (1,16), 'NGsBlkXp': 1, 'NGsBlkXp_units': 'RL', 'PPAPntXp': 20, 'PPAPntXp_units': 'eV', 'GbndRnge': (1,16), 'GDamping': 0.1, 'GDamping_units': 'eV', 'dScStep': 0.1, 'dScStep_units': 'eV', 'DysSolver': "n", 'QPkrange': [(1,1,16,18)], } calculation_set_p2y ={'resources': {"num_machines": 1,"num_mpiprocs_per_machine": 1}, 'max_wallclock_seconds': 60*29, 'max_memory_kb': 1*80*1000000 ,"queue_name":"s3par8cv3" ,#'custom_scheduler_commands': u"#PBS -A Pra14_3622" , 'environment_variables': {"omp_num_threads": "1" } } calculation_set_yambo ={'resources': {"num_machines": 1,"num_mpiprocs_per_machine": 16}, 'max_wallclock_seconds': 6*60*60, 'max_memory_kb': 1*80*1000000 , "queue_name":"s3par8cv3" ,#'custom_scheduler_commands': u"#PBS -A Pra14_3622" , 'environment_variables': {"omp_num_threads": "0" } } settings_pw = ParameterData(dict= {'cmdline':['-npool', '2' , '-ndiag', '8', '-ntg', '2' ]}) settings_p2y = ParameterData(dict={"ADDITIONAL_RETRIEVE_LIST":[ 'r-*','o-*','l-*','l_*','LOG/l-*_CPU_1','aiida/ndb.QP','aiida/ndb.HF_and_locXC'], 'INITIALISE':True}) settings_yambo = ParameterData(dict={"ADDITIONAL_RETRIEVE_LIST":[ 'r-*','o-*','l-*','l_*','LOG/l-*_CPU_1','aiida/ndb.QP','aiida/ndb.HF_and_locXC'], 'INITIALISE':False }) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description='GW QP calculation.') parser.add_argument('--precode', type=str, dest='precode', required=True, help='The p2y codename to use') parser.add_argument('--yambocode', type=str, dest='yambocode', required=True, help='The yambo codename to use') parser.add_argument('--pwcode', type=str, dest='pwcode', required=True, help='The pw codename to use') parser.add_argument('--pseudo', type=str, dest='pseudo', required=True, help='The pesudo to use') parser.add_argument('--structure', type=int, dest='structure', required=True, help='The structure to use') parser.add_argument('--parent', type=int, dest='parent', required=False, help='The parent to use') parser.add_argument('--parent_nscf', type=int, dest='parent_nscf', required=False, help='The parent nscf to use') args = parser.parse_args() structure = load_node(int(args.structure)) parentcalc = parent_folder_ = parentnscfcalc = parent_nscf_folder_ = False if args.parent: parentcalc = load_node(int(args.parent)) parent_folder_ = parentcalc.out.remote_folder parentnscfcalc = load_node(int(args.parent_nscf)) parent_nscf_folder_ = parentnscfcalc.out.remote_folder convergence_parameters = {'variable_to_converge': 'kpoints', 'conv_tol':0.1, 'start_value': .9 , 'step':.1 , 'max_value': 0.017 } p2y_result =run(YamboConvergenceWorkflow, pwcode= Str( args.pwcode), precode= Str( args.precode), yambocode=Str(args.yambocode), calculation_set= ParameterData(dict=calculation_set_yambo), settings = settings_yambo, convergence_parameters = ParameterData(dict=convergence_parameters), #parent_scf_folder = parent_folder_, #parent_nscf_folder = parent_nscf_folder_, parameters = ParameterData(dict=yambo_parameters), structure = structure , pseudo = Str(args.pseudo), ) print ("Workflow launched: ", p2y_result)

50.524752

130

0.562806

529

5,103

5.189036

0.31758

0.029508

0.043352

0.030601

0.293625

0.251366

0.174863

0.144262

0

0.027659

0.312757

5,103

100

131

51.03

0.755061

0.035469

0

0.02439

0

0.209156

0.045575

0

1

0

false

0

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0

0.134146

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0

null

0

null

0

1

0

9883b6e8d4d307c1b52e2f3a77df6fc639e8d98d

4,936

py

Python

lungSegmentation/evalutate_performance.py

slowy07/medical-BCDU

dab1ddcacbe093b78e6830d52db2a4e6fabc3d52

[ "MIT" ]

null

lungSegmentation/evalutate_performance.py

slowy07/medical-BCDU

dab1ddcacbe093b78e6830d52db2a4e6fabc3d52

[ "MIT" ]

null

lungSegmentation/evalutate_performance.py

slowy07/medical-BCDU

dab1ddcacbe093b78e6830d52db2a4e6fabc3d52

[ "MIT" ]

null

import os os.environ["CUDA_VISIBLE_DEVICES"] = "1" import models as M import numpy as np import scipy import matplotlib.pyplot as plt from sklearn.metrics import roc_curve from sklearn.metrics import roc_auc_score from sklearn.metrics import confusion_matrix from sklearn.metrics import precision_recall_curve from sklearn.metrics import jaccard_similarity_score from sklearn.metrics import f1_score from scipy.ndimage.morphology import binary_erosion #load data folder = './processed_data/' te_data = np.load(folder+'data_test.npy') FOV = np.load(folder+'FOV_te.npy') te_mask = np.load(folder+'mask_test.npy') te_data = np.expand_dims(te_data, axis=3) print('Dataset loaded') #te_data2 = dataset_normalized(te_data) te_data2 = te_data /255. model = M.BCDU_net_D3(input_size = (512,512,1)) model.summary() model.load_weights('weight_lung') predictions = model.predict(te_data2, batch_size=2, verbose=1) # Post-processing predictions = np.squeeze(predictions) predictions = np.where(predictions>0.5, 1, 0) Estimated_lung = np.where((FOV - predictions)>0.5, 1, 0) # Performance checking y_scores = Estimated_lung.reshape(Estimated_lung.shape[0]*Estimated_lung.shape[1]*Estimated_lung.shape[2], 1) print(y_scores.shape) y_true = te_mask.reshape(te_mask.shape[0]*te_mask.shape[1]*te_mask.shape[2], 1) y_scores = np.where(y_scores>0.5, 1, 0) y_true = np.where(y_true>0.5, 1, 0) output_folder = 'output/' if not os.path.exists(output_folder): os.makedirs(output_folder) #Area under the ROC curve fpr, tpr, thresholds = roc_curve((y_true), y_scores) AUC_ROC = roc_auc_score(y_true, y_scores) print ("\nArea under the ROC curve: " +str(AUC_ROC)) roc_curve =plt.figure() plt.plot(fpr,tpr,'-',label='Area Under the Curve (AUC = %0.4f)' % AUC_ROC) plt.title('ROC curve') plt.xlabel("FPR (False Positive Rate)") plt.ylabel("TPR (True Positive Rate)") plt.legend(loc="lower right") plt.savefig(output_folder+"ROC.png") #Precision-recall curve precision, recall, thresholds = precision_recall_curve(y_true, y_scores) precision = np.fliplr([precision])[0] recall = np.fliplr([recall])[0] AUC_prec_rec = np.trapz(precision,recall) print ("\nArea under Precision-Recall curve: " +str(AUC_prec_rec)) prec_rec_curve = plt.figure() plt.plot(recall,precision,'-',label='Area Under the Curve (AUC = %0.4f)' % AUC_prec_rec) plt.title('Precision - Recall curve') plt.xlabel("Recall") plt.ylabel("Precision") plt.legend(loc="lower right") plt.savefig(output_folder+"Precision_recall.png") #Confusion matrix threshold_confusion = 0.5 print ("\nConfusion matrix: Custom threshold (for positive) of " +str(threshold_confusion)) y_pred = np.empty((y_scores.shape[0])) for i in range(y_scores.shape[0]): if y_scores[i]>=threshold_confusion: y_pred[i]=1 else: y_pred[i]=0 confusion = confusion_matrix(y_true, y_pred) print (confusion) accuracy = 0 if float(np.sum(confusion))!=0: accuracy = float(confusion[0,0]+confusion[1,1])/float(np.sum(confusion)) print ("Global Accuracy: " +str(accuracy)) specificity = 0 if float(confusion[0,0]+confusion[0,1])!=0: specificity = float(confusion[0,0])/float(confusion[0,0]+confusion[0,1]) print ("Specificity: " +str(specificity)) sensitivity = 0 if float(confusion[1,1]+confusion[1,0])!=0: sensitivity = float(confusion[1,1])/float(confusion[1,1]+confusion[1,0]) print ("Sensitivity: " +str(sensitivity)) precision = 0 if float(confusion[1,1]+confusion[0,1])!=0: precision = float(confusion[1,1])/float(confusion[1,1]+confusion[0,1]) print ("Precision: " +str(precision)) #Jaccard similarity index jaccard_index = jaccard_similarity_score(y_true, y_pred, normalize=True) print ("\nJaccard similarity score: " +str(jaccard_index)) #F1 score F1_score = f1_score(y_true, y_pred, labels=None, average='binary', sample_weight=None) print ("\nF1 score (F-measure): " +str(F1_score)) #Save the results file_perf = open(output_folder+'performances.txt', 'w') file_perf.write("Area under the ROC curve: "+str(AUC_ROC) + "\nArea under Precision-Recall curve: " +str(AUC_prec_rec) + "\nJaccard similarity score: " +str(jaccard_index) + "\nF1 score (F-measure): " +str(F1_score) +"\n\nConfusion matrix:" +str(confusion) +"\nACCURACY: " +str(accuracy) +"\nSENSITIVITY: " +str(sensitivity) +"\nSPECIFICITY: " +str(specificity) +"\nPRECISION: " +str(precision) ) file_perf.close() # Sample results fig,ax = plt.subplots(5, 3, figsize=[15,15]) all_ind = [1, 100, 200, 253, 193] # random samples all_ind = np.array(all_ind) for idx in range(5): ax[idx, 0].imshow(np.uint8(np.squeeze(te_data[all_ind[idx]]))) ax[idx, 1].imshow(np.squeeze(te_mask[all_ind[idx]]), cmap='gray') ax[idx, 2].imshow(np.squeeze(Estimated_lung[all_ind[idx]]), cmap='gray') plt.savefig('sample_results.png')

36.562963

109

0.709887

755

4,936

4.480795

0.235762

0.020692

0.022761

0.042566

0.289388

0.181496

0.15903

0.093999

0

0.030075

0.137763

4,936

135

110

36.562963

0.764803

0.045583

0

0.018519

0

0.167872

0

1

0

false

0

0.111111

0

0.111111

0

null

0

null

0

1

0

988492255fff6a05146e48a47f6f7316997bd490

600

py

Python

A_source_code/carbon/code/mocsy_module.py

vanHoek-dgnm/CARBON-DISC

3ecd5f4efba5e032d43679ee977064d6b25154a9

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

null

A_source_code/carbon/code/mocsy_module.py

vanHoek-dgnm/CARBON-DISC

3ecd5f4efba5e032d43679ee977064d6b25154a9

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

null

A_source_code/carbon/code/mocsy_module.py

vanHoek-dgnm/CARBON-DISC

3ecd5f4efba5e032d43679ee977064d6b25154a9

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

null

# ****************************************************** ## Copyright 2019, PBL Netherlands Environmental Assessment Agency and Utrecht University. ## Reuse permitted under Gnu Public License, GPL v3. # ****************************************************** ''' imports mocsy module ''' try: import mocsy except ModuleNotFoundError: import os import sys root = os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..') if "/" in root: # working in linux OS path = os.path.join(root,"libs","mocsy","linux") if (os.path.exists(path)): sys.path.insert(3, path)

31.578947

90

0.55

66

600

4.939394

0.621212

0.110429

0.06135

0

0.011765

0.15

600

18

91

33.333333

0.627451

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0

0.063973

0

1

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false

0

0.3

0

0.3

0

null

0

null

0

1

0

98862016069a5935ad27d5ce8118f56358c9629c

1,548

py

Python

1.5-one-away.py

jmartenstein/interview-questions

0e2dca36b02f82e7a30453ef7ae2165f04e50710

[ "MIT" ]

null

1.5-one-away.py

jmartenstein/interview-questions

0e2dca36b02f82e7a30453ef7ae2165f04e50710

[ "MIT" ]

null

1.5-one-away.py

jmartenstein/interview-questions

0e2dca36b02f82e7a30453ef7ae2165f04e50710

[ "MIT" ]

null

import unittest class OneAwayTest(unittest.TestCase): def test_oneaway_missing_letter1(self): actual = one_away("pale", "ple") self.assertTrue(actual) def test_oneaway_missing_letter2(self): actual = one_away("p", "") self.assertTrue(actual) def test_oneaway_same_letters(self): actual = one_away("justin", "justin") self.assertTrue(actual) def test_oneaway_missing_letter3(self): actual = one_away("jstin", "jsti") self.assertTrue(actual) def test_oneaway_changed_letter1(self): actual = one_away("pale", "bake") self.assertFalse(actual) def test_oneaway_changed_letters1(self): actual = one_away("pale", "bale") self.assertTrue(actual) def one_away(string1, string2): diff_count = 0 i = 0 j = 0 # for now, we break out separate case for if the string lengths are # different or not if len(string1) == len(string2): while (i < len(string1)) and (diff_count <= 1): if string1[i] != string2[i]: diff_count += 1 i += 1 else: if len(string1) < len(string2): string_short = string1 string_long = string2 else: string_long = string1 string_short = string2 while(i < len(string_long)) and (diff_count <= 1): if j >= len(string_short): diff_count += 1 else: if string_long[i] != string_short[j]: diff_count += 1 else: j += 1 i += 1 if diff_count > 1: return False else: return True if __name__ == '__main__': unittest.main()

21.802817

70

0.631783

206

1,548

4.514563

0.31068

0.052688

0.090323

0.109677

0.360215

0.221505

0.088172

0

0.025884

0.251292

1,548

70

71

22.114286

0.776531

0.053618

0

0.294118

0

0.036252

0

0.117647

1

0.137255

false

0

0.019608

0

0.215686

0

null

0

null

0

1

0

9886841ad97481579a6dce1caae1343591e52f5d

9,593

py

Python

Pressure_Converter/pressure_converter_script.py

Affanmir/Awesome-Python-Scripts

bba0512e1c580d605205744ece878da13f2c7661

[ "MIT" ]

1,026

2018-10-02T18:51:12.000Z

2022-03-31T13:45:14.000Z

Pressure_Converter/pressure_converter_script.py

Affanmir/Awesome-Python-Scripts

bba0512e1c580d605205744ece878da13f2c7661

[ "MIT" ]

164

2018-10-02T18:37:40.000Z

2021-11-18T13:29:54.000Z

Pressure_Converter/pressure_converter_script.py

Affanmir/Awesome-Python-Scripts

bba0512e1c580d605205744ece878da13f2c7661

[ "MIT" ]

521

2018-10-02T18:15:40.000Z

2022-03-26T12:10:15.000Z

from typing import Union def atmospeheres_to_bars(atm: float, unit: str) -> Union[float, str]: """ This function converts atm to bar Wikipedia reference: https://en.wikipedia.org/wiki/Standard_atmosphere_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Bar_(unit) >>> atmospeheres_to_bars(2.5, "atm") 2.533125 >>> atmospeheres_to_bars("12", "atm") 12.158999999999999 >>> atmospeheres_to_bars(0, "atm") 0.0 >>> atmospeheres_to_bars(35, "mmHg") 'Invalid unit' >>> atmospeheres_to_bars("atmospheres", "atm") Traceback (most recent call last): ... ValueError: could not convert string to float: 'atmospheres' """ if unit == "atm": bar = float(atm) * 1.01325 return bar else: return "Invalid unit" def bars_to_atmospheres(bar: float, unit: str) -> Union[float, str]: """ This function converts bar to atm Wikipedia reference: https://en.wikipedia.org/wiki/Standard_atmosphere_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Bar_(unit) >>> bars_to_atmospheres(36, "bar") 35.529237601776465 >>> bars_to_atmospheres("57.6", "bar") 56.84678016284234 >>> bars_to_atmospheres(0, "bar") 0.0 >>> bars_to_atmospheres(35, "Pa") 'Invalid unit' >>> bars_to_atmospheres("barrs", "bar") Traceback (most recent call last): ... ValueError: could not convert string to float: 'barrs' """ if unit == "bar": atm = float(bar) / 1.01325 return atm else: return "Invalid unit" def atmospheres_to_milimeter_mercury(atm: float, unit: str) -> Union[float, str]: """ This function converts atm to mmHg Wikipedia reference: https://en.wikipedia.org/wiki/Standard_atmosphere_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre_of_mercury >>> atmospheres_to_milimeter_mercury(2, "atm") 1520.0 >>> atmospheres_to_milimeter_mercury("6.9", "atm") 5244.0 >>> atmospheres_to_milimeter_mercury(0, "atm") 0.0 >>> atmospheres_to_milimeter_mercury(35, "torr") 'Invalid unit' >>> atmospheres_to_milimeter_mercury("atmos", "atm") Traceback (most recent call last): ... ValueError: could not convert string to float: 'atmos' """ if unit == "atm": mm_hg = float(atm) * 760 return mm_hg else: return "Invalid unit" def milimeter_mercury_to_atmospheres(mm_hg: float, unit: str) -> Union[float, str]: """ This function converts mmHg to atm Wikipedia reference: https://en.wikipedia.org/wiki/Standard_atmosphere_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre_of_mercury >>> milimeter_mercury_to_atmospheres(23506.92, "mmHg") 30.93015789473684 >>> milimeter_mercury_to_atmospheres("304000", "mmHg") 400.0 >>> milimeter_mercury_to_atmospheres(0, "mmHg") 0.0 >>> milimeter_mercury_to_atmospheres(35, "bar") 'Invalid unit' >>> milimeter_mercury_to_atmospheres("merc", "mmHg") Traceback (most recent call last): ... ValueError: could not convert string to float: 'merc' """ if unit == "mmHg": atm = float(mm_hg) / 760 return atm else: return "Invalid unit" def atmospheres_to_pascals(atm: float, unit: str) -> Union[float, str]: """ This function converts atm to Pa Wikipedia reference: https://en.wikipedia.org/wiki/Standard_atmosphere_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Pascal_(unit) >>> atmospheres_to_pascals(5.4, "atm") 547155.0 >>> atmospheres_to_pascals("7.098", "atm") 719204.85 >>> atmospheres_to_pascals(0, "atm") 0.0 >>> atmospheres_to_pascals(35, "Pa") 'Invalid unit' >>> atmospheres_to_pascals("ats", "atm") Traceback (most recent call last): ... ValueError: could not convert string to float: 'ats' """ if unit == "atm": pa = float(atm) * 101325 return pa else: return "Invalid unit" def pascals_to_atmospheres(pa: float, unit: str) -> Union[float, str]: """ This function converts Pa to atm Wikipedia reference: https://en.wikipedia.org/wiki/Standard_atmosphere_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Pascal_(unit) >>> pascals_to_atmospheres(202650, "Pa") 2.0 >>> pascals_to_atmospheres("1013250", "Pa") 10.0 >>> pascals_to_atmospheres(0, "Pa") 0.0 >>> pascals_to_atmospheres(35, "mmhg") 'Invalid unit' >>> pascals_to_atmospheres("Pas", "Pa") Traceback (most recent call last): ... ValueError: could not convert string to float: 'Pas' """ if unit == "Pa": atm = float(pa) / 101325 return atm else: return "Invalid unit" def bars_to_milimeter_mercury(bar: float, unit: str) -> Union[float, str]: """ This function converts bar to mmHg Wikipedia reference: https://en.wikipedia.org/wiki/Bar_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre_of_mercury >>> bars_to_milimeter_mercury(3.75, "bar") 2812.725 >>> bars_to_milimeter_mercury("0.82", "bar") 615.0491999999999 >>> bars_to_milimeter_mercury(0, "bar") 0.0 >>> bars_to_milimeter_mercury(3, "atm") 'Invalid unit' >>> bars_to_milimeter_mercury("brs", "bar") Traceback (most recent call last): ... ValueError: could not convert string to float: 'brs' """ if unit == "bar": mm_hg = float(bar) * round(760 / 1.01325, 2) return mm_hg else: return "Invalid unit" def milimeter_mercury_to_bars(mm_hg: float, unit: str) -> Union[float, str]: """ This function converts mmHg to bar Wikipedia reference: https://en.wikipedia.org/wiki/Bar_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre_of_mercury >>> milimeter_mercury_to_bars(4970.5, "mmHg") 6.626803189078208 >>> milimeter_mercury_to_bars("378", "mmHg") 0.503959683225342 >>> milimeter_mercury_to_bars(0, "mmHg") 0.0 >>> milimeter_mercury_to_bars(3, "bar") 'Invalid unit' >>> milimeter_mercury_to_bars("brs", "mmHg") Traceback (most recent call last): ... ValueError: could not convert string to float: 'brs' """ if unit == "mmHg": bar = float(mm_hg) / round(760 / 1.01325, 2) return bar else: return "Invalid unit" def bars_to_pascals(bar: float, unit: str) -> Union[float, str]: """ This function converts bar to Pa Wikipedia reference: https://en.wikipedia.org/wiki/Bar_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Pascal_(unit) >>> bars_to_pascals(0.653, "bar") 65300.0 >>> bars_to_pascals("1.2", "bar") 120000.0 >>> bars_to_pascals(0, "bar") 0.0 >>> bars_to_pascals(3.1, "Pa") 'Invalid unit' >>> bars_to_pascals("bP", "bar") Traceback (most recent call last): ... ValueError: could not convert string to float: 'bP' """ if unit == "bar": pa = float(bar) * 100000 return pa else: return "Invalid unit" def pascals_to_bars(pa: float, unit: str) -> Union[float, str]: """ This function converts Pa to bar Wikipedia reference: https://en.wikipedia.org/wiki/Bar_(unit) Wikipedia reference: https://en.wikipedia.org/wiki/Pascal_(unit) >>> pascals_to_bars(45000, "Pa") 0.45 >>> pascals_to_bars("1200000", "Pa") 12.0 >>> pascals_to_bars(0, "Pa") 0.0 >>> pascals_to_bars(3.1, "mmHg") 'Invalid unit' >>> pascals_to_bars("pass", "Pa") Traceback (most recent call last): ... ValueError: could not convert string to float: 'pass' """ if unit == "Pa": bar = float(pa) / 100000 return bar else: return "Invalid unit" def milimeter_mercury_to_pascals(mm_hg: float, unit: str) -> Union[float, str]: """ This function converts mmHg to Pa Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre_of_mercury Wikipedia reference: https://en.wikipedia.org/wiki/Pascal_(unit) >>> milimeter_mercury_to_pascals(25, "mmHg") 3333.0 >>> milimeter_mercury_to_pascals("652", "mmHg") 86924.64 >>> milimeter_mercury_to_pascals(0, "mmHg") 0.0 >>> milimeter_mercury_to_pascals(342.1, "bar") 'Invalid unit' >>> milimeter_mercury_to_pascals("mercurium", "mmHg") Traceback (most recent call last): ... ValueError: could not convert string to float: 'mercurium' """ if unit == "mmHg": pa = float(mm_hg) * round(101325 / 760, 2) return pa else: return "Invalid unit" def pascals_to_milimeter_mercury(pa: float, unit: str) -> Union[float, str]: """ This function converts Pa to mmHg Wikipedia reference: https://en.wikipedia.org/wiki/Millimetre_of_mercury Wikipedia reference: https://en.wikipedia.org/wiki/Pascal_(unit) >>> pascals_to_milimeter_mercury(153000, "Pa") 1147.6147614761476 >>> pascals_to_milimeter_mercury("97650.8", "Pa") 732.4542454245425 >>> pascals_to_milimeter_mercury(0, "Pa") 0.0 >>> pascals_to_milimeter_mercury(342.1, "mmhg") 'Invalid unit' >>> pascals_to_milimeter_mercury("merc", "Pa") Traceback (most recent call last): ... ValueError: could not convert string to float: 'merc' """ if unit == "Pa": mm_hg = float(pa) / round(101325 / 760, 2) return mm_hg else: return "Invalid unit" if __name__ == "__main__": import doctest doctest.testmod()

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null

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null

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9886f6ef61ce8671578994a8e3555d6131699f26

444

py

Python

jp.atcoder/abc129/abc129_c/10071155.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

1

2022-02-09T03:06:25.000Z

jp.atcoder/abc129/abc129_c/10071155.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

1

2022-02-05T22:53:18.000Z

2022-02-09T01:29:30.000Z

jp.atcoder/abc129/abc129_c/10071155.py

kagemeka/atcoder-submissions

91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e

[ "MIT" ]

null

import sys MOD = 10 ** 9 + 7 n, m, *a = map(int, sys.stdin.read().split()) broken = set(a) def main(): res = [None] * (n + 1) res[0] = 1 res[1] = 0 if 1 in broken else 1 for i in range(2, n+1): if i in broken: res[i] = 0 else: res[i] = res[i-2] + res[i-1] res[i] %= MOD return res[n] if __name__ == '__main__': ans = main() print(ans)

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null

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9886fd5ef77fff993bad45edb77225659ddd988c

6,569

py

Python

venv/lib/python3.6/site-packages/ansible_collections/cisco/nxos/plugins/modules/nxos_banner.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

1

2020-01-22T13:11:23.000Z

venv/lib/python3.6/site-packages/ansible_collections/cisco/nxos/plugins/modules/nxos_banner.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

12

2020-02-21T07:24:52.000Z

2020-04-14T09:54:32.000Z

venv/lib/python3.6/site-packages/ansible_collections/cisco/nxos/plugins/modules/nxos_banner.py

usegalaxy-no/usegalaxy

75dad095769fe918eb39677f2c887e681a747f3a

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function __metaclass__ = type # (c) 2017, Ansible by Red Hat, inc # # This file is part of Ansible by Red Hat # # Ansible is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Ansible is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Ansible. If not, see <http://www.gnu.org/licenses/>. # DOCUMENTATION = """ module: nxos_banner author: Trishna Guha (@trishnaguha) short_description: Manage multiline banners on Cisco NXOS devices description: - This will configure both exec and motd banners on remote devices running Cisco NXOS. It allows playbooks to add or remove banner text from the active running configuration. notes: - Since responses from the device are always read with surrounding whitespaces stripped, tasks that configure banners with preceeding or trailing whitespaces will not be idempotent. - Limited Support for Cisco MDS version_added: 1.0.0 options: banner: description: - Specifies which banner that should be configured on the remote device. required: true choices: - exec - motd type: str text: description: - The banner text that should be present in the remote device running configuration. This argument accepts a multiline string, with no empty lines. Requires I(state=present). type: str state: description: - Specifies whether or not the configuration is present in the current devices active running configuration. default: present choices: - present - absent type: str extends_documentation_fragment: - cisco.nxos.nxos """ EXAMPLES = """ - name: configure the exec banner cisco.nxos.nxos_banner: banner: exec text: | this is my exec banner that contains a multiline string state: present - name: remove the motd banner cisco.nxos.nxos_banner: banner: motd state: absent - name: Configure banner from file cisco.nxos.nxos_banner: banner: motd text: "{{ lookup('file', './config_partial/raw_banner.cfg') }}" state: present """ RETURN = """ commands: description: The list of configuration mode commands to send to the device returned: always type: list sample: - banner exec - this is my exec banner - that contains a multiline - string """ from ansible.module_utils.basic import AnsibleModule from ansible.module_utils._text import to_text from ansible_collections.cisco.nxos.plugins.module_utils.network.nxos.nxos import ( load_config, run_commands, ) from ansible_collections.cisco.nxos.plugins.module_utils.network.nxos.nxos import ( nxos_argument_spec, ) import re def execute_show_command(module, command): format = "text" cmds = [{"command": command, "output": format}] output = run_commands(module, cmds) return output def map_obj_to_commands(want, have, module): commands = list() state = module.params["state"] platform_regex = "Nexus.*Switch" if state == "absent": if have.get("text") and not ( (have.get("text") == "User Access Verification") or re.match(platform_regex, have.get("text")) ): commands.append("no banner %s" % module.params["banner"]) elif state == "present" and want.get("text") != have.get("text"): banner_cmd = "banner %s @\n%s\n@" % ( module.params["banner"], want["text"], ) commands.append(banner_cmd) return commands def map_config_to_obj(module): command = "show banner %s" % module.params["banner"] output = execute_show_command(module, command)[0] if "Invalid command" in output: module.fail_json( msg="banner: %s may not be supported on this platform. Possible values are : exec | motd" % module.params["banner"] ) if isinstance(output, dict): output = list(output.values()) if output != []: output = output[0] else: output = "" if isinstance(output, dict): output = list(output.values()) if output != []: output = output[0] else: output = "" else: output = output.rstrip() obj = {"banner": module.params["banner"], "state": "absent"} if output: obj["text"] = output obj["state"] = "present" return obj def map_params_to_obj(module): text = module.params["text"] return { "banner": module.params["banner"], "text": to_text(text) if text else None, "state": module.params["state"], } def main(): """ main entry point for module execution """ argument_spec = dict( banner=dict(required=True, choices=["exec", "motd"]), text=dict(), state=dict(default="present", choices=["present", "absent"]), ) argument_spec.update(nxos_argument_spec) required_if = [("state", "present", ("text",))] module = AnsibleModule( argument_spec=argument_spec, required_if=required_if, supports_check_mode=True, ) warnings = list() result = {"changed": False} if warnings: result["warnings"] = warnings want = map_params_to_obj(module) have = map_config_to_obj(module) commands = map_obj_to_commands(want, have, module) result["commands"] = commands if commands: if not module.check_mode: msgs = load_config(module, commands, True) if msgs: for item in msgs: if item: if isinstance(item, dict): err_str = item["clierror"] else: err_str = item if ( "more than 40 lines" in err_str or "buffer overflowed" in err_str ): load_config(module, commands) result["changed"] = True module.exit_json(**result) if __name__ == "__main__": main()

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0.005682

0

null

0

null

0

1

0

98877c694a0a259f7bba574f818a2c84334902b2

8,555

py

Python

guv/greenthread.py

timgates42/guv

d7bac2ca6a73cc2059969af08223b82f3e187922

[ "MIT" ]

120

2015-01-05T15:15:26.000Z

2020-07-28T11:25:10.000Z

guv/greenthread.py

timgates42/guv

d7bac2ca6a73cc2059969af08223b82f3e187922

[ "MIT" ]

22

2015-01-12T21:52:32.000Z

2017-01-22T18:18:20.000Z

guv/greenthread.py

timgates42/guv

d7bac2ca6a73cc2059969af08223b82f3e187922

[ "MIT" ]

13

2015-01-18T11:42:34.000Z

2021-07-15T10:59:22.000Z

from collections import deque import sys import greenlet from . import event, hubs from .support import reraise __all__ = ['sleep', 'spawn', 'spawn_n', 'kill', 'spawn_after', 'GreenThread'] def sleep(seconds=0): """Yield control to the hub until at least `seconds` have elapsed :param float seconds: time to sleep for """ hub = hubs.get_hub() current = greenlet.getcurrent() assert hub is not current, 'do not call blocking functions from the hub' timer = hub.schedule_call_global(seconds, current.switch) try: hub.switch() finally: timer.cancel() def spawn_n(func, *args, **kwargs): """Spawn a greenlet Execution control returns immediately to the caller; the created greenlet is scheduled to be run at the start of the next event loop iteration, after other scheduled greenlets, but before greenlets waiting for I/O events. This is faster than :func:`spawn`, but it is not possible to retrieve the return value of the greenlet, or whether it raised any exceptions. It is fastest if there are no keyword arguments. If an exception is raised in the function, a stack trace is printed; the print can be disabled by calling :func:`guv.debug.hub_exceptions` with False. :return: greenlet object :rtype: greenlet.greenlet """ hub = hubs.get_hub() g = greenlet.greenlet(func, parent=hub) hub.schedule_call_now(g.switch, *args, **kwargs) return g def spawn(func, *args, **kwargs): """Spawn a GreenThread Execution control returns immediately to the caller; the created GreenThread is scheduled to be run at the start of the next event loop iteration, after other scheduled greenlets, but before greenlets waiting for I/O events. :return: GreenThread object which can be used to retrieve the return value of the function :rtype: GreenThread """ hub = hubs.get_hub() g = GreenThread(hub) hub.schedule_call_now(g.switch, func, *args, **kwargs) return g def spawn_after(seconds, func, *args, **kwargs): """Spawn a GreenThread after `seconds` have elapsed Execution control returns immediately to the caller. To cancel the spawn and prevent *func* from being called, call :meth:`GreenThread.cancel` on the returned GreenThread. This will not abort the function if it's already started running, which is generally the desired behavior. If terminating *func* regardless of whether it's started or not is the desired behavior, call :meth:`GreenThread.kill`. :return: GreenThread object which can be used to retrieve the return value of the function :rtype: GreenThread """ hub = hubs.get_hub() g = GreenThread(hub) hub.schedule_call_global(seconds, g.switch, func, *args, **kwargs) return g def _spawn_n(seconds, func, args, kwargs): hub = hubs.get_hub() g = greenlet.greenlet(func, parent=hub) t = hub.schedule_call_global(seconds, g.switch, *args, **kwargs) return t, g class GreenThread(greenlet.greenlet): """The GreenThread class is a type of Greenlet which has the additional property of being able to retrieve the return value of the main function. Do not construct GreenThread objects directly; call :func:`spawn` to get one. """ def __init__(self, parent): """ :param parent: parent greenlet :type parent: greenlet.greenlet """ greenlet.greenlet.__init__(self, self.main, parent) self._exit_event = event.Event() self._resolving_links = False def wait(self): """Return the result of the main function of this GreenThread If the result is a normal return value, :meth:`wait` returns it. If it raised an exception, :meth:`wait` will raise the same exception (though the stack trace will unavoidably contain some frames from within the GreenThread module). """ return self._exit_event.wait() def link(self, func, *curried_args, **curried_kwargs): """Set up a function to be called with the results of the GreenThread The function must have the following signature:: func(gt, [curried args/kwargs]) When the GreenThread finishes its run, it calls *func* with itself and with the `curried arguments <http://en.wikipedia.org/wiki/Currying>`_ supplied at link-time. If the function wants to retrieve the result of the GreenThread, it should call wait() on its first argument. Note that *func* is called within execution context of the GreenThread, so it is possible to interfere with other linked functions by doing things like switching explicitly to another GreenThread. """ self._exit_funcs = getattr(self, '_exit_funcs', deque()) self._exit_funcs.append((func, curried_args, curried_kwargs)) if self._exit_event.ready(): self._resolve_links() def unlink(self, func, *curried_args, **curried_kwargs): """Remove linked function set by :meth:`link` Remove successfully return True, otherwise False """ if not getattr(self, '_exit_funcs', None): return False try: self._exit_funcs.remove((func, curried_args, curried_kwargs)) return True except ValueError: return False def main(self, function, *args, **kwargs): try: result = function(*args, **kwargs) except: self._exit_event.send_exception(*sys.exc_info()) self._resolve_links() raise else: self._exit_event.send(result) self._resolve_links() def _resolve_links(self): # ca and ckw are the curried function arguments if self._resolving_links: return self._resolving_links = True try: exit_funcs = getattr(self, '_exit_funcs', deque()) while exit_funcs: f, ca, ckw = exit_funcs.popleft() f(self, *ca, **ckw) finally: self._resolving_links = False def kill(self, *throw_args): """Kill the GreenThread using :func:`kill` After being killed all calls to :meth:`wait` will raise `throw_args` (which default to :class:`greenlet.GreenletExit`). """ return kill(self, *throw_args) def cancel(self, *throw_args): """Kill the GreenThread using :func:`kill`, but only if it hasn't already started running After being canceled, all calls to :meth:`wait` will raise `throw_args` (which default to :class:`greenlet.GreenletExit`). """ return cancel(self, *throw_args) def cancel(g, *throw_args): """Cancel the target greenlet/GreenThread if it hasn't already started This is like :func:`kill`, but only has an effect if the target greenlet/GreenThread has not yet started. """ if not g: kill(g, *throw_args) def kill(g, *throw_args): """Terminate the target greenlet/GreenThread by raising an exception into it Whatever that GreenThread might be doing, be it waiting for I/O or another primitive, it sees an exception right away. By default, this exception is GreenletExit, but a specific exception may be specified. `throw_args` should be the same as the arguments to raise; either an exception instance or an exc_info tuple. Calling :func:`kill` causes the calling greenlet to cooperatively yield. :param g: target greenlet/GreenThread to kill :type g: greenlet.greenlet or GreenThread """ if g.dead: return hub = hubs.get_hub() if not g: # greenlet hasn't started yet and therefore throw won't work on its own; semantically we # want it to be as though the main method never got called def just_raise(*a, **kw): if throw_args: reraise(throw_args[0], throw_args[1], throw_args[2]) else: raise greenlet.GreenletExit() g.run = just_raise if isinstance(g, GreenThread): # it's a GreenThread object, so we want to call its main method to take advantage of # the notification try: g.main(just_raise, (), {}) except: pass current = greenlet.getcurrent() if current is not hub: # arrange to wake the caller back up immediately hub.schedule_call_now(current.switch) g.throw(*throw_args)

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0

1

0

988be3dce863409b6b062d305bd83405f34d1ed8

460

py

Python

While_loop/while_loop.py

SaicharanKandukuri/snippets-python-from-scrach

b0823fde3cf1a88bf43d97bdc542de7e32c76dac

[ "MIT" ]

1

2021-05-29T03:09:24.000Z

While_loop/while_loop.py

SaicharanKandukuri/snippets-python-from-scrach

b0823fde3cf1a88bf43d97bdc542de7e32c76dac

[ "MIT" ]

null

While_loop/while_loop.py

SaicharanKandukuri/snippets-python-from-scrach

b0823fde3cf1a88bf43d97bdc542de7e32c76dac

[ "MIT" ]

null

name = None x=0 while not name: x=x+1 if x<3: name = input("Enter your name: ") if x==3: name = input("Man enter your name : ") if x==4: name = input("Why are spamming mate: ") if x==5: name = input("For God sake: ") if x==6: name = input("Bro!........") if x==7: print("Iam out mate!") exit(0) print("Hello "+ name) if x>=5: print("Nice to hear you name :)")

20.909091

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0.465217

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460

3.014085

0.478873

0.098131

0.074766

0.257009

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0.033898

0.358696

460

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0

0.15

0

null

0

null

0

1

0

988edc13ccae6c0801420ecb6d188c9b4a82c75b

1,052

py

Python

Lecture_notes/数据提取与验证码的识别（下）/code/多线程爬虫.py

littleturings/2021PythonWebCrawler

a9089a912affce4369cf50df3c22c55eb4ebf2d5

[ "MIT" ]

1

2021-02-03T08:28:16.000Z

Lecture_notes/数据提取与验证码的识别（下）/code/多线程爬虫.py

littleturings/2021PythonWebCrawler

a9089a912affce4369cf50df3c22c55eb4ebf2d5

[ "MIT" ]

null

Lecture_notes/数据提取与验证码的识别（下）/code/多线程爬虫.py

littleturings/2021PythonWebCrawler

a9089a912affce4369cf50df3c22c55eb4ebf2d5

[ "MIT" ]

null

from threading import Thread import requests from lxml import etree from fake_useragent import UserAgent from queue import Queue class Spider(Thread): def __init__(self,url_queue): Thread.__init__(self) self.url_queue = url_queue def run(self): while not self.url_queue.empty(): url = self.url_queue.get() print(url) header = {"User-Agent": UserAgent().chrome} resp = requests.get(url, headers=header) e = etree.HTML(resp.text) contents = [div.xpath('string(.)').strip() for div in e.xpath("//div[@class='content']")] with open('duanzi.txt', 'a', encoding='utf-8') as f: for content in contents: f.write(content + "\n") if __name__ == "__main__": base_url = "https://www.qiushibaike.com/text/page/{}/" url_queue =Queue() for num in range(1,6): url_queue.put(base_url.format(num)) for num in range(3): spider = Spider(url_queue) spider.start()

30.941176

101

0.586502

136

1,052

4.338235

0.5

0.108475

0.081356

0.044068

0

0.005298

0.282319

1,052

34

102

30.941176

0.776159

0

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0.021842

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1

0.071429

false

0

0.178571

0

0.285714

0.035714

0

null

0

null

0

1

0

988feb164822c678128261338e61810b6c4748b8

486

py

Python

__isVowel.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

__isVowel.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

__isVowel.py

simdevex/01.Basics

cf4f372384e66f4b26e4887d2f5d815a1f8e929c

[ "MIT" ]

null

''' A Python program to test whether a passed letter is a vowel ornot. ''' def isVowel (inputStr): vowelTuple = ('a', 'e', 'i', 'o', 'u') for vowel in vowelTuple: if inputStr == vowel: return True return False def main (): myStr = input ("Enter a letter") isStrVowel = isVowel(myStr) if isStrVowel: print ("You input a vowel") else: print ("You input a consonant") main()

20.25

66

0.524691

57

486

4.473684

0.596491

0.047059

0.101961

0.109804

0

0.364198

486

24

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20.25

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0.285714

0.142857

0

null

0

null

0

1

0

9890311c2c70976d1163fe80f05202d37c39505b

5,821

py

Python

scripts/cscap/harvest_soil_bd.py

isudatateam/datateam

eb8e1dad6c05cb1b236689862fe87c56b25ea6fc

[ "MIT" ]

5

2017-05-20T04:51:55.000Z

2022-03-07T18:55:27.000Z

scripts/cscap/harvest_soil_bd.py

isudatateam/datateam

eb8e1dad6c05cb1b236689862fe87c56b25ea6fc

[ "MIT" ]

275

2017-03-09T20:31:30.000Z

2022-03-30T22:43:47.000Z

scripts/cscap/harvest_soil_bd.py

isudatateam/datateam

eb8e1dad6c05cb1b236689862fe87c56b25ea6fc

[ "MIT" ]

3

2020-06-01T15:03:06.000Z

2021-02-01T13:46:58.000Z

"""Scrape out the Soil Bulk Density and Texture data from Google Drive""" from __future__ import print_function import sys import psycopg2 import pyiem.cscap_utils as util YEAR = sys.argv[1] config = util.get_config() pgconn = psycopg2.connect( database="sustainablecorn", host=config["database"]["host"] ) pcursor = pgconn.cursor() # Get me a client, stat spr_client = util.get_spreadsheet_client(config) drive_client = util.get_driveclient(config) res = ( drive_client.files() .list( q=("title contains '%s'") % (("Soil Bulk Density and " "Water Retention Data"),) ) .execute() ) DOMAIN = [ "SOIL1", "SOIL2", "SOIL29", "SOIL30", "SOIL31", "SOIL32", "SOIL8", "SOIL33", "SOIL34", "SOIL35", "SOIL39", "SOIL41", "SOIL42", ] # Load up current data, incase we need to do some deleting current = {} pcursor.execute( """ SELECT uniqueid, plotid, varname, depth, subsample from soil_data WHERE year = %s and varname in %s """, (YEAR, tuple(DOMAIN)), ) for row in pcursor: key = "|".join([str(s) for s in row]) current[key] = True for item in res["items"]: if item["mimeType"] != "application/vnd.google-apps.spreadsheet": continue try: # print("Processing %s %s" % (item['title'], item['id'])) spreadsheet = util.Spreadsheet(spr_client, item["id"]) except Exception as exp: print("harvest_soil_bd FAIL: %s\n%s" % (exp, item["title"])) continue siteid = item["title"].split()[0] spreadsheet.get_worksheets() worksheet = spreadsheet.worksheets.get(YEAR) if worksheet is None: # print 'Missing Soil BD+WR %s sheet for %s' % (YEAR, siteid) continue worksheet.get_cell_feed() if siteid == "DPAC": pass elif ( worksheet.get_cell_value(1, 1) != "plotid" or worksheet.get_cell_value(1, 2) != "depth" or worksheet.get_cell_value(1, 3) != "subsample" ): print( ("FATAL site: %s(%s) bd & wr has bad header 1:%s 2:%s 3:%s") % ( siteid, YEAR, worksheet.get_cell_value(1, 1), worksheet.get_cell_value(1, 2), worksheet.get_cell_value(1, 3), ) ) continue for row in range(3, worksheet.rows + 1): plotid = worksheet.get_cell_value(row, 1) if siteid == "DPAC": depth = worksheet.get_cell_value(row, 3) # Combine the location value into the subsample subsample = "%s%s" % ( worksheet.get_cell_value(row, 2), worksheet.get_cell_value(row, 4), ) else: depth = worksheet.get_cell_value(row, 2) subsample = worksheet.get_cell_value(row, 3) if depth.find(" to ") == -1: print( ("harvest_soil_bd found invalid depth: %s %s %s") % (depth, siteid, YEAR) ) continue if plotid is None or depth is None: continue for col in range(4, worksheet.cols + 1): if worksheet.get_cell_value(1, col) is None: # print(("harvest_soil_bd %s(%s) row: %s col: %s is null" # ) % (siteid, YEAR, row, col)) continue varname = worksheet.get_cell_value(1, col).strip().split()[0] if varname[:4] != "SOIL": # print 'Invalid varname: %s site: %s year: %s' % ( # worksheet.get_cell_value(1,col).strip(), # siteid, YEAR) continue inval = worksheet.get_cell_value(row, col) val = util.cleanvalue(inval) if inval is not None and val is None: print( ( "harvest_soil_bd found None. site: %s year: %s " " row: %s col: %s varname: %s" ) % (siteid, YEAR, row, col, varname) ) try: pcursor.execute( """ INSERT into soil_data(uniqueid, plotid, varname, year, depth, value, subsample) values (%s, %s, %s, %s, %s, %s, %s) """, (siteid, plotid, varname, YEAR, depth, val, subsample), ) except Exception as exp: print("HARVEST_SOIL_BD TRACEBACK") print(exp) print( ("%s %s %s %s %s %s") % (siteid, plotid, varname, depth, val, subsample) ) sys.exit() key = "%s|%s|%s|%s|%s" % ( siteid, plotid, varname, depth, subsample, ) if key in current: del current[key] for key in current: (siteid, plotid, varname, depth, subsample) = key.split("|") if varname in DOMAIN: print( ("harvest_soil_bd rm %s %s %s %s %s %s") % (YEAR, siteid, plotid, varname, repr(depth), repr(subsample)) ) d1 = "depth is null" if depth == "None" else "depth = '%s'" % (depth,) d2 = ( "subsample is null" if subsample == "None" else "subsample = '%s'" % (subsample,) ) pcursor.execute( """DELETE from soil_data where uniqueid = %s and plotid = %s and varname = %s and year = %s and """ + d1 + """ and """ + d2, (siteid, plotid, varname, YEAR), ) pcursor.close() pgconn.commit() pgconn.close()

30.798942

78

0.49373

646

5,821

4.349845

0.252322

0.018505

0.096797

0.119573

0.254093

0.196441

0.078292

0.05694

0.029893

0

0.017208

0.381034

5,821

188

79

30.962766

0.762698

0.093455

0

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0

0.006452

0.133044

0.00807

0

1

0

false

0.006452

0.025806

0

0.025806

0.058065

0

null

0

null

0

1

0

9891dd87fdf4f97e52addf857b05cd50067c4326

3,335

py

Python

app/training.py

m-triple-m/project_junky

e55d3eeae6e97f002ab3087245e9dbeb4ed3eafd

[ "MIT" ]

null

app/training.py

m-triple-m/project_junky

e55d3eeae6e97f002ab3087245e9dbeb4ed3eafd

[ "MIT" ]

null

app/training.py

m-triple-m/project_junky

e55d3eeae6e97f002ab3087245e9dbeb4ed3eafd

[ "MIT" ]

null

import pandas as pd import numpy as np import pickle import sklearn.ensemble as ske from sklearn import tree, linear_model from sklearn.model_selection import train_test_split from sklearn.feature_selection import SelectFromModel from sklearn.externals import joblib from sklearn.naive_bayes import GaussianNB from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os def AI_Trainer(dataset, classifier_file, feature_file): data = pd.read_csv(dataset, sep='|') X = data.drop(['Name', 'md5', 'legitimate'], axis=1).values y = data['legitimate'].values print('Researching important feature based on %i total features\n' % X.shape[1]) # Feature selection using Trees Classifier fsel = ske.ExtraTreesClassifier().fit(X, y) model = SelectFromModel(fsel, prefit=True) X_new = model.transform(X) nb_features = X_new.shape[1] X_new, y = shuffle(X_new, y, random_state=0) X_train, X_test, y_train, y_test = train_test_split(X_new, y ,test_size=0.2) features = [] print('%i features identified as important:' % nb_features) indices = np.argsort(fsel.feature_importances_)[::-1][:nb_features] for f in range(nb_features): print("%d. feature %s (%f)" % (f + 1, data.columns[2+indices[f]], fsel.feature_importances_[indices[f]])) # XXX : take care of the feature order for f in sorted(np.argsort(fsel.feature_importances_)[::-1][:nb_features]): features.append(data.columns[2+f]) #Algorithm comparison algorithms = { #"DecisionTree": tree.DecisionTreeClassifier(max_depth=10), "RandomForest": ske.RandomForestClassifier(n_estimators=100,criterion='entropy',max_features="auto"), #"GradientBoosting": ske.GradientBoostingClassifier(n_estimators=100,max_features="log2"), #"AdaBoost": ske.AdaBoostClassifier(n_estimators=100), #"GNB": GaussianNB() } results = {} print("\nNow testing algorithms") for algo in algorithms: clf = algorithms[algo] clf.fit(X_train, y_train) score = clf.score(X_test, y_test) print("%s : %f %%" % (algo, score*100)) results[algo] = score winner = "RandomForest" #max(results, key=results.get) print('\nWinner algorithm is %s with a %f %% success' % (results[winner], results[winner]*100)) # Save the algorithm and the feature list for later predictions print('Saving algorithm and feature list in classifier directory...') joblib.dump(algorithms[winner], classifier_file) open(feature_file, 'wb').write(pickle.dumps(features)) print('Saved') # Identify false and true positive rates clf = algorithms[winner] res = clf.predict(X_test) mt = confusion_matrix(y_test, res) print("False positive rate : %f %%" % ((mt[0][1] / float(sum(mt[0])))*100)) print('False negative rate : %f %%' % ( (mt[1][0] / float(sum(mt[1]))*100))) def train(): dataset=os.path.join(os.path.dirname(os.path.realpath(__file__)), 'dataset.csv') classifier=os.path.join(os.path.dirname(os.path.realpath(__file__)), 'classifier/classifier.pkl') features=os.path.join(os.path.dirname(os.path.realpath(__file__)), 'classifier/feature.pkl') AI_Trainer(dataset, classifier, features) if __name__ == "__main__": train()

38.333333

113

0.686657

445

3,335

4.986517

0.359551

0.024335

0.00676

0.016224

0.102298

0.064443

0

0.015323

0.178111

3,335

87

114

38.333333

0.794236

0.133733

0

0.153526

0.016325

0

1

0.033898

false

0

0.288136

0

0.322034

0.169492

0

null

0

null

0

1

0

9894780acddb9d5e169d21af5bce8cf904e87b23

5,466

py

Python

examples/Forecasting/models/forecast.py

vophihungvn/h1st

d421995bb0b8de6a5a76788261efef5b26bc7c12

[ "Apache-2.0" ]

1

2021-12-31T08:51:11.000Z

examples/Forecasting/models/forecast.py

vophihungvn/h1st

d421995bb0b8de6a5a76788261efef5b26bc7c12

[ "Apache-2.0" ]

null

examples/Forecasting/models/forecast.py

vophihungvn/h1st

d421995bb0b8de6a5a76788261efef5b26bc7c12

[ "Apache-2.0" ]

null

import h1st as h1 import pandas as pd import os import sklearn import sklearn.metrics import subprocess import pathlib from sklearn.ensemble import RandomForestRegressor from sklearn.pipeline import Pipeline from sklearn.compose import make_column_transformer from sklearn.preprocessing import OneHotEncoder from Forecasting import config class ForecastModel(h1.MLModel): def __init__(self): super().__init__() self.base_model = None self.feature_cols = ['Open', 'Promo', 'StateHoliday', 'SchoolHoliday', 'DayOfWeek', 'DayOfMonth', 'Month', 'StoreType', 'Assortment', 'CompetitionDistance', 'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2', 'Promo2SinceWeek', 'Promo2SinceYear'] self.data_dir = config.FORECAST_DATA_PATH def load_data(self): # needs to have kaggle tools, and user credentials, and agreed to competition rules etc. pathlib.Path(self.data_dir).mkdir(parents=True, exist_ok=True) if not os.path.isfile(os.path.join(self.data_dir, "train.csv")): print("Using `kaggle` command to download data from rossmann-store-sales competition.") print("You'll need https://pypi.org/project/kaggle/ tool and agrees to the terms of the competition at https://www.kaggle.com/c/rossmann-store-sales/") subprocess.run("kaggle competitions download -c rossmann-store-sales -p {data}/".format(data=self.data_dir), shell=True, check=True) subprocess.run("cd {data}; unzip rossmann-store-sales.zip".format(data=self.data_dir), shell=True, check=True) df = pd.read_csv(os.path.join(self.data_dir, "train.csv"), low_memory=False) store_info = pd.read_csv(os.path.join(self.data_dir, "store.csv")) df = df.merge(store_info, on="Store") return df def explore(self): df = self.load_data() import seaborn print(df.count()) # count NA seaborn.distplot(df.Sales) # Sales distribution def prep(self, loaded_data): """ Prepare data for modelling :param loaded_data: data return from load_data method :returns: dictionary contains train data and validation data """ df = loaded_data df.fillna(0, inplace=True) # safe to fill, see countNA table below: # Store 1017209 # DayOfWeek 1017209 # Date 1017209 # Sales 1017209 # Customers 1017209 # Open 1017209 # Promo 1017209 # StateHoliday 1017209 # SchoolHoliday 1017209 # StoreType 1017209 # Assortment 1017209 # CompetitionDistance 1014567 # CompetitionOpenSinceMonth 693861 # CompetitionOpenSinceYear 693861 # Promo2 1017209 # Promo2SinceWeek 509178 # Promo2SinceYear 509178 # PromoInterval 509178 # dtype: int64 df["Date"] = pd.to_datetime(df.Date) df["DayOfWeek"] = df.Date.dt.dayofweek df["DayOfMonth"] = df.Date.dt.day df["Month"] = df.Date.dt.month train_df = df[df["Date"] < "2015-06-01"] val_df = df[df["Date"] >= "2015-06-01"] print(len(train_df), len(val_df)) # sales only should get 949194 68015 # after dropNA on storeinfo: 302061 22265 return { 'train_df': train_df, 'val_df': val_df, 'len_train_val': (len(train_df), len(val_df)) } def train(self, prepared_data): train_df = prepared_data['train_df'][self.feature_cols] sales = prepared_data['train_df']["Sales"] transformer = make_column_transformer( (OneHotEncoder(handle_unknown="ignore"), ['StateHoliday', "StoreType", "Assortment"]), remainder="passthrough") transformer.fit(train_df[self.feature_cols]) model = Pipeline([('transform', transformer), ('model', RandomForestRegressor(max_depth=10, n_estimators=200))]) model.fit(train_df, sales) self.base_model = model def evaluate(self, prepared_data): val_df = prepared_data['val_df'] y_pred = self.base_model.predict(val_df[self.feature_cols]) y_true = val_df['Sales'] self.metrics = {'mae': sklearn.metrics.mean_absolute_error(y_true, y_pred), } def predict(self, input_data): # repeat this because input_data might not be "prepared" e.g. come from another test file store_info = pd.read_csv(os.path.join(self.data_dir, "store.csv")) input_data = input_data.merge(store_info, on="Store") input_data.fillna(0, inplace=True) input_data["Date"] = pd.to_datetime(input_data.Date) input_data["DayOfWeek"] = input_data.Date.dt.dayofweek input_data["DayOfMonth"] = input_data.Date.dt.day input_data["Month"] = input_data.Date.dt.month input_data = input_data[self.feature_cols] result = self.base_model.predict(input_data) return result

41.725191

163

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0

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0.049383

0

null

0

null

0

1

0

989688c2724f16a6ea2d06900ed3f29271555bc8

13,063

py

Python

ceviche/fdtd.py

kwadwo00/ceviche

a1ee155304c4679b262e4fdf8c8a28bc4d060ec8

[ "MIT" ]

111

2019-11-14T13:55:15.000Z

2022-03-29T12:19:01.000Z

ceviche/fdtd.py

kwadwo00/ceviche

a1ee155304c4679b262e4fdf8c8a28bc4d060ec8

[ "MIT" ]

13

2019-11-22T05:49:07.000Z

2022-03-20T17:02:59.000Z

ceviche/fdtd.py

kwadwo00/ceviche

a1ee155304c4679b262e4fdf8c8a28bc4d060ec8

[ "MIT" ]

42

2019-11-13T19:29:06.000Z

2022-03-19T11:58:09.000Z

import numpy as np import autograd.numpy as npa from copy import copy, deepcopy from .constants import * from .utils import reshape_to_ND, grid_center_to_xyz, grid_xyz_to_center from .derivatives import curl_E, curl_H class fdtd(): def __init__(self, eps_r, dL, npml): """ Makes an FDTD object eps_r: the relative permittivity (array > 1) if eps_r.shape = 3, it holds a single permittivity if eps_r.shape = 4, the last index is the batch index (running several simulations at once) dL: the grid size(s) (float/int or list of 3 floats/ints for dx, dy, dz) npml: the number of PML grids in each dimension (list of 3 ints) """ # set the grid shape eps_r = reshape_to_ND(eps_r, N=3) self.Nx, self.Ny, self.Nz = self.grid_shape = eps_r.shape # set the attributes self.dL = dL self.npml = npml self.eps_r = eps_r def __repr__(self): return "FDTD(eps_r.shape={}, dL={}, NPML={})".format(self.grid_shape, self.dL, self.npml) def __str__(self): return "FDTD object:\n\tdomain size = {}\n\tdL = {}\n\tNPML = {}".format(self.grid_shape, self.dL, self.npml) @property def dL(self): """ Returns the grid size """ return self.__dL @dL.setter def dL(self, new_dL): """ Resets the time step when dL is set. """ self.__dL = new_dL self._set_time_step() @property def npml(self): """ Returns the pml grid size list """ return self.__npml @npml.setter def npml(self, new_npml): """ Defines some attributes when npml is set. """ self.__npml = new_npml self._compute_sigmas() @property def eps_r(self): """ Returns the relative permittivity grid """ return self.__eps_r @eps_r.setter def eps_r(self, new_eps): """ Defines some attributes when eps_r is set. """ self.__eps_r = new_eps self.eps_xx, self.eps_yy, self.eps_zz = grid_center_to_xyz(self.__eps_r) self.eps_arr = self.__eps_r.flatten() self.N = self.eps_arr.size self.grid_shape = self.Nx, self.Ny, self.Nz = self.__eps_r.shape self._compute_update_parameters() self.initialize_fields() def forward(self, Jx=None, Jy=None, Jz=None): """ one time step of FDTD """ self.t_index += 1 # get curls of E CEx = curl_E(0, self.Ex, self.Ey, self.Ez, self.dL) CEy = curl_E(1, self.Ex, self.Ey, self.Ez, self.dL) CEz = curl_E(2, self.Ex, self.Ey, self.Ez, self.dL) # update the curl E integrals self.ICEx = self.ICEx + CEx self.ICEy = self.ICEy + CEy self.ICEz = self.ICEz + CEz # update the H field integrals self.IHx = self.IHx + self.Hx self.IHy = self.IHy + self.Hy self.IHz = self.IHz + self.Hz # update the H fields self.Hx = self.mHx1 * self.Hx + self.mHx2 * CEx + self.mHx3 * self.ICEx + self.mHx4 * self.IHx self.Hy = self.mHy1 * self.Hy + self.mHy2 * CEy + self.mHy3 * self.ICEy + self.mHy4 * self.IHy self.Hz = self.mHz1 * self.Hz + self.mHz2 * CEz + self.mHz3 * self.ICEz + self.mHz4 * self.IHz # update fields dict self.fields['Hx'] = self.Hx self.fields['Hy'] = self.Hy self.fields['Hz'] = self.Hz # get curls of H CHx = curl_H(0, self.Hx, self.Hy, self.Hz, self.dL) CHy = curl_H(1, self.Hx, self.Hy, self.Hz, self.dL) CHz = curl_H(2, self.Hx, self.Hy, self.Hz, self.dL) # update the curl E integrals self.ICHx = self.ICHx + CHx self.ICHy = self.ICHy + CHy self.ICHz = self.ICHz + CHz # update the D field integrals self.IDx = self.IDx + self.Dx self.IDy = self.IDy + self.Dy self.IDz = self.IDz + self.Dz # update the D fields self.Dx = self.mDx1 * self.Dx + self.mDx2 * CHx + self.mDx3 * self.ICHx + self.mDx4 * self.IDx self.Dy = self.mDy1 * self.Dy + self.mDy2 * CHy + self.mDy3 * self.ICHy + self.mDy4 * self.IDy self.Dz = self.mDz1 * self.Dz + self.mDz2 * CHz + self.mDz3 * self.ICHz + self.mDz4 * self.IDz # add sources to the electric fields self.Dx += 0 if Jx is None else Jx self.Dy += 0 if Jy is None else Jy self.Dz += 0 if Jz is None else Jz # update field dict self.fields['Dx'] = self.Dx self.fields['Dy'] = self.Dy self.fields['Dz'] = self.Dz # update the E fields self.Ex = self.mEx1 * self.Dx self.Ey = self.mEy1 * self.Dy self.Ez = self.mEz1 * self.Dz # update field dict self.fields['Ex'] = self.Ex self.fields['Ey'] = self.Ey self.fields['Ez'] = self.Ez return self.fields def initialize_fields(self): """ Initializes: - the H, D, and E fields for updating - the integration terms needed to deal with PML - the curls of the fields """ self.t_index = 0 # magnetic fields self.Hx = npa.zeros(self.grid_shape) self.Hy = npa.zeros(self.grid_shape) self.Hz = npa.zeros(self.grid_shape) # E field curl integrals self.ICEx = npa.zeros(self.grid_shape) self.ICEy = npa.zeros(self.grid_shape) self.ICEz = npa.zeros(self.grid_shape) # H field integrals self.IHx = npa.zeros(self.grid_shape) self.IHy = npa.zeros(self.grid_shape) self.IHz = npa.zeros(self.grid_shape) # E field curls self.CEx = npa.zeros(self.grid_shape) self.CEy = npa.zeros(self.grid_shape) self.CEz = npa.zeros(self.grid_shape) # H field curl integrals self.ICHx = npa.zeros(self.grid_shape) self.ICHy = npa.zeros(self.grid_shape) self.ICHz = npa.zeros(self.grid_shape) # D field integrals self.IDx = npa.zeros(self.grid_shape) self.IDy = npa.zeros(self.grid_shape) self.IDz = npa.zeros(self.grid_shape) # H field curls self.CHx = npa.zeros(self.grid_shape) self.CHy = npa.zeros(self.grid_shape) self.CHz = npa.zeros(self.grid_shape) # electric displacement fields self.Dx = npa.zeros(self.grid_shape) self.Dy = npa.zeros(self.grid_shape) self.Dz = npa.zeros(self.grid_shape) # electric fields self.Ex = npa.zeros(self.grid_shape) self.Ey = npa.zeros(self.grid_shape) self.Ez = npa.zeros(self.grid_shape) # field dictionary to return layer self.fields = {'Ex': npa.zeros(self.grid_shape), 'Ey': npa.zeros(self.grid_shape), 'Ez': npa.zeros(self.grid_shape), 'Dx': npa.zeros(self.grid_shape), 'Dy': npa.zeros(self.grid_shape), 'Dz': npa.zeros(self.grid_shape), 'Hx': npa.zeros(self.grid_shape), 'Hy': npa.zeros(self.grid_shape), 'Hz': npa.zeros(self.grid_shape) } def _set_time_step(self, stability_factor=0.5): """ Set the time step based on the generalized Courant stability condition Delta T < 1 / C_0 / sqrt(1 / dx^2 + 1/dy^2 + 1/dz^2) dt = courant_condition * stability_factor, so stability factor should be < 1 """ dL_sum = 3 / self.dL ** 2 dL_avg = 1 / npa.sqrt(dL_sum) courant_stability = dL_avg / C_0 self.dt = courant_stability * stability_factor def _compute_sigmas(self): """ Computes sigma tensors for PML """ # initialize sigma matrices on the full 2X grid grid_shape_2X = (2 * self.Nx, 2 * self.Ny, 2 * self.Nz) sigx2 = np.zeros(grid_shape_2X) sigy2 = np.zeros(grid_shape_2X) sigz2 = np.zeros(grid_shape_2X) # sigma vector in the X direction for nx in range(2 * self.npml[0]): nx1 = 2 * self.npml[0] - nx + 1 nx2 = 2 * self.Nx - 2 * self.npml[0] + nx sigx2[nx1, :, :] = (0.5 * EPSILON_0 / self.dt) * (nx / 2 / self.npml[0])**3 sigx2[nx2, :, :] = (0.5 * EPSILON_0 / self.dt) * (nx / 2 / self.npml[0])**3 # sigma arrays in the Y direction for ny in range(2 * self.npml[1]): ny1 = 2 * self.npml[1] - ny + 1 ny2 = 2 * self.Ny - 2 * self.npml[1] + ny sigy2[:, ny1, :] = (0.5 * EPSILON_0 / self.dt) * (ny / 2 / self.npml[1])**3 sigy2[:, ny2, :] = (0.5 * EPSILON_0 / self.dt) * (ny / 2 / self.npml[1])**3 # sigma arrays in the Z direction for nz in range(2 * self.npml[2]): nz1 = 2 * self.npml[2] - nz + 1 nz2 = 2 * self.Nz - 2 * self.npml[2] + nz sigz2[:, :, nz1] = (0.5 * EPSILON_0 / self.dt) * (nz / 2 / self.npml[2])**3 sigz2[:, :, nz2] = (0.5 * EPSILON_0 / self.dt) * (nz / 2 / self.npml[2])**3 # # PML tensors for H field self.sigHx = sigx2[1::2, ::2, ::2] self.sigHy = sigy2[ ::2, 1::2, ::2] self.sigHz = sigz2[ ::2, ::2, 1::2] # # PML tensors for D field self.sigDx = sigx2[ ::2, 1::2, 1::2] self.sigDy = sigy2[1::2, ::2, 1::2] self.sigDz = sigz2[1::2, 1::2, ::2] def _compute_update_parameters(self, mu_r=1.0): """ Computes update coefficients based on values computed earlier. For more details, see http://emlab.utep.edu/ee5390fdtd/Lecture%2014%20--%203D%20Update%20Equations%20with%20PML.pdf NOTE: relative permeability set = 1 for now """ # H field update coefficients self.mHx0 = (1 / self.dt + (self.sigHy + self.sigHz) / 2 / EPSILON_0 + self.sigHy * self.sigHz * self.dt / 4 / EPSILON_0**2) self.mHy0 = (1 / self.dt + (self.sigHx + self.sigHz) / 2 / EPSILON_0 + self.sigHx * self.sigHz * self.dt / 4 / EPSILON_0**2) self.mHz0 = (1 / self.dt + (self.sigHx + self.sigHy) / 2 / EPSILON_0 + self.sigHx * self.sigHy * self.dt / 4 / EPSILON_0**2) self.mHx1 = (1 / self.mHx0 * (1/self.dt - (self.sigHy + self.sigHz) / 2 / EPSILON_0 - self.sigHy * self.sigHz * self.dt / 4 / EPSILON_0**2)) self.mHy1 = (1 / self.mHy0 * (1/self.dt - (self.sigHx + self.sigHz) / 2 / EPSILON_0 - self.sigHx * self.sigHz * self.dt / 4 / EPSILON_0**2)) self.mHz1 = (1 / self.mHz0 * (1/self.dt - (self.sigHx + self.sigHy) / 2 / EPSILON_0 - self.sigHx * self.sigHy * self.dt / 4 / EPSILON_0**2)) self.mHx2 = (-1 / self.mHx0 * C_0 / mu_r) self.mHy2 = (-1 / self.mHy0 * C_0 / mu_r) self.mHz2 = (-1 / self.mHz0 * C_0 / mu_r) self.mHx3 = (-1 / self.mHx0 * C_0 * self.dt * self.sigHx / EPSILON_0 / mu_r) self.mHy3 = (-1 / self.mHy0 * C_0 * self.dt * self.sigHy / EPSILON_0 / mu_r) self.mHz3 = (-1 / self.mHz0 * C_0 * self.dt * self.sigHz / EPSILON_0 / mu_r) self.mHx4 = (-1 / self.mHx0 * self.dt * self.sigHy * self.sigHz / EPSILON_0**2) self.mHy4 = (-1 / self.mHy0 * self.dt * self.sigHx * self.sigHz / EPSILON_0**2) self.mHz4 = (-1 / self.mHz0 * self.dt * self.sigHx * self.sigHy / EPSILON_0**2) # D field update coefficients self.mDx0 = (1 / self.dt + (self.sigDy + self.sigDz) / 2 / EPSILON_0 + self.sigDy * self.sigDz * self.dt / 4 / EPSILON_0**2) self.mDy0 = (1 / self.dt + (self.sigDx + self.sigDz) / 2 / EPSILON_0 + self.sigDx * self.sigDz * self.dt / 4 / EPSILON_0**2) self.mDz0 = (1 / self.dt + (self.sigDx + self.sigDy) / 2 / EPSILON_0 + self.sigDx * self.sigDy * self.dt / 4 / EPSILON_0**2) self.mDx1 = (1 / self.mDx0 * (1/self.dt - (self.sigDy + self.sigDz) / 2 / EPSILON_0 - self.sigDy * self.sigDz * self.dt / 4 / EPSILON_0**2)) self.mDy1 = (1 / self.mDy0 * (1/self.dt - (self.sigDx + self.sigDz) / 2 / EPSILON_0 - self.sigDx * self.sigDz * self.dt / 4 / EPSILON_0**2)) self.mDz1 = (1 / self.mDz0 * (1/self.dt - (self.sigDx + self.sigDy) / 2 / EPSILON_0 - self.sigDx * self.sigDy * self.dt / 4 / EPSILON_0**2)) self.mDx2 = (1 / self.mDx0 * C_0) self.mDy2 = (1 / self.mDy0 * C_0) self.mDz2 = (1 / self.mDz0 * C_0) self.mDx3 = (1 / self.mDx0 * C_0 * self.dt * self.sigDx / EPSILON_0) self.mDy3 = (1 / self.mDy0 * C_0 * self.dt * self.sigDy / EPSILON_0) self.mDz3 = (1 / self.mDz0 * C_0 * self.dt * self.sigDz / EPSILON_0) self.mDx4 = (-1 / self.mDx0 * self.dt * self.sigDy * self.sigDz / EPSILON_0**2) self.mDy4 = (-1 / self.mDy0 * self.dt * self.sigDx * self.sigDz / EPSILON_0**2) self.mDz4 = (-1 / self.mDz0 * self.dt * self.sigDx * self.sigDy / EPSILON_0**2) # D -> E update coefficients self.mEx1 = (1 / self.eps_xx) self.mEy1 = (1 / self.eps_yy) self.mEz1 = (1 / self.eps_zz)

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9897920e4b31c1105fd21257030e059f22b924ba

5,289

py

Python

examples/probabilistic_keypoint_estimation/processors.py

niqbal996/paz

f27205907367415d5b21f90e1a1d1d1ce598e889

[ "MIT" ]

300

2020-10-29T08:02:05.000Z

2022-03-30T21:47:32.000Z

examples/probabilistic_keypoint_estimation/processors.py

albertofernandezvillan/paz

9fbd50b993f37e1e807297a29c6044c09967c9cc

[ "MIT" ]

30

2020-10-29T12:40:32.000Z

2022-03-31T14:06:35.000Z

examples/probabilistic_keypoint_estimation/processors.py

albertofernandezvillan/paz

9fbd50b993f37e1e807297a29c6044c09967c9cc

[ "MIT" ]

62

2020-10-29T12:34:13.000Z

2022-03-29T05:21:45.000Z

from paz.backend.image import draw_circle from paz.backend.image.draw import GREEN from paz.backend.image import resize_image from paz import processors as pr from paz.abstract import Processor import numpy as np from paz.backend.image import lincolor import seaborn as sns from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure class PartitionKeypoints(Processor): """Partitions keypoints from shape ''[num_keypoints, 2]'' into a list of the form ''[(2, 1), (2, 1), ....]'' and length equal to the number of of_keypoints. """ def __init__(self): super(PartitionKeypoints, self).__init__() def call(self, keypoints): keypoints = np.vsplit(keypoints, len(keypoints)) keypoints = [np.squeeze(keypoint) for keypoint in keypoints] return (*keypoints, ) class ToNumpyArray(Processor): def __init__(self): super(ToNumpyArray, self).__init__() def call(self, predictions): return np.array(predictions) class PredictDistributions(Processor): def __init__(self, model, preprocess=None): super(PredictDistributions, self).__init__() self.model = model self.preprocess = preprocess def call(self, x): if self.preprocess is not None: x = self.preprocess(x) distributions = self.model(x) return distributions class ComputeMeans(Processor): def __init__(self): super(ComputeMeans, self).__init__() def call(self, distributions): keypoints = np.zeros((len(distributions), 2)) for arg, distribution in enumerate(distributions): keypoints[arg] = distribution.mean() return keypoints class ToProbabilityGrid(Processor): def __init__(self, grid): self.grid = grid def call(self, distribution): probability = distribution.prob(self.grid).numpy()[::-1, :] return probability def build_figure(): figure = Figure() canvas = FigureCanvas(figure) axis = figure.gca() axis.axis('off') figure.tight_layout(pad=0) axis.margins(0) # figure.canvas.draw() return figure, axis, canvas def to_pixels(figure): figure.canvas.draw() image = np.frombuffer(figure.canvas.tostring_rgb(), dtype=np.uint8) image = image.reshape(figure.canvas.get_width_height()[::-1] + (3,)) return image def interpolate_probability(probability, shape): normalization_constant = np.max(probability) probability = probability / normalization_constant probability = probability * 255.0 probability = probability.astype('uint8') probability = resize_image(probability, shape) probability = probability / 255.0 probability = probability * normalization_constant return probability class DrawProbabilities(Processor): def __init__(self, num_keypoints, normalized=True): self.colors = lincolor(num_keypoints, normalized=normalized) self.figure, self.axis, self.canvas = build_figure() # self._figure, self._axis, self._canvas = build_figure() def call(self, image, probabilities): for color, probability in zip(self.colors, probabilities): cmap = sns.light_palette(color, input='hsl', as_cmap=True) probability = interpolate_probability(probability, image.shape[:2]) self.axis.contour(probability, cmap=cmap, levels=np.arange(1, 50, 3)) self.axis.imshow(image) contour = to_pixels(self.figure) # contour = resize_image(contour, (image.shape[:2])) # self._axis.imshow(image) # self._axis.imshow(contour) # new_image = to_pixels(self._figure) return contour class PredictMeanDistribution(Processor): def __init__(self, model, preprocess=None): super(PredictMeanDistribution, self).__init__() print('Building graph...') self.num_keypoints = len(model.output_shape) # self.model = tf.function(model.mean) self.model = model self.preprocess = preprocess def call(self, x): if self.preprocess is not None: x = self.preprocess(x) distributions = self.model(x) keypoints = np.zeros((self.num_keypoints, 2)) for arg, distribution in enumerate(distributions): keypoints[arg] = distribution.mean() return keypoints def draw_circles(image, points, color=GREEN, radius=3): for point in points: draw_circle(image, point, color, radius) return image if __name__ == '__main__': from facial_keypoints import FacialKeypoints from paz.backend.image import show_image from paz.abstract import SequentialProcessor data_manager = FacialKeypoints('dataset/', 'train') datasets = data_manager.load_data() augment_keypoints = SequentialProcessor() augment_keypoints.add(pr.RandomKeypointRotation()) augment_keypoints.add(pr.RandomKeypointTranslation()) for arg in range(100): original_image = datasets[0]['image'].copy() kp = datasets[0]['keypoints'].copy() original_image, kp = augment_keypoints(original_image, kp) original_image = draw_circles(original_image, kp.astype('int')) show_image(original_image.astype('uint8'))

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null

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989dc6b7e06440eaa1baf455b551c9b102c10503

2,993

py

Python

scripts/blueprint_graph_queries.py

azdolinski/apstra-api-python

2380bcdbd5be31e552d9259592249b13fa432286

[ "Apache-2.0" ]

1

2022-03-23T22:16:15.000Z

scripts/blueprint_graph_queries.py

azdolinski/apstra-api-python

2380bcdbd5be31e552d9259592249b13fa432286

[ "Apache-2.0" ]

4

2022-03-26T15:12:50.000Z

2022-03-31T07:31:53.000Z

scripts/blueprint_graph_queries.py

azdolinski/apstra-api-python

2380bcdbd5be31e552d9259592249b13fa432286

[ "Apache-2.0" ]

2

2022-03-26T00:04:42.000Z

2022-03-26T14:23:20.000Z

# Copyright 2020-present, Apstra, Inc. All rights reserved. # # This source code is licensed under End User License Agreement found in the # LICENSE file at http://www.apstra.com/eula from aos.client import AosClient from scripts.utils import deserialize_fixture import urllib3 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) # You will need to update the connection details below with your # specific AOS instance AOS_IP = "<aos-IP>" AOS_PORT = 443 AOS_USER = "admin" AOS_PW = "aos-aos" # Login aos = AosClient(protocol="https", host=AOS_IP, port=AOS_PORT) aos.auth.login(AOS_USER, AOS_PW) # Find Blueprint by Name bp_name = "apstra-pod1" bp = aos.blueprint.get_id_by_name(label=bp_name) # QE Queries # Return all fabric switches (nodes). Notice the use of "is_in" with role # to filter the query. switch_query = ( "match(node('system', name='switches', " "role=is_in(['spine', 'leaf', 'superspine'])))" ) resp = aos.blueprint.qe_query(bp.id, query=switch_query) # aos.blueprint.get_all_tor_nodes() uses two queries to return all top of # rack (ToR) nodes and their properties. It calls two methods to do this. # # aos.blueprint.get_bp_system_leaf_nodes() # Return all nodes of type system with a role of 'leaf'. leaf_query = "match(node('system', name='leaf', role='leaf'))" resp = aos.blueprint.qe_query(bp.id, query=leaf_query) # aos.blueprint.get_bp_system_redundancy_group() returns the # redundancy_group details a given system is a member of system_id = 'foo' rg_query = ( "match(node('redundancy_group', name='rg')" ".out('composed_of_systems')" ".node('system', role='leaf'," f" id='{system_id}'))" ) resp = aos.blueprint.qe_query(bp.id, query=rg_query) # Query the Blueprint for all fabric links between leafs and spines link_query = ( "match(node('system', role='leaf', name='system')" ".out('hosted_interfaces')" ".node('interface', name='iface').out('link')" ".node('link', role='spine_leaf'))" ), resp = aos.blueprint.qe_query(bp.id, query=link_query) # Query the Blueprint for all links in the fabric belonging to a specific # routing-zone (VRF). We are using routing-zone 'blue' in this example. link_query = ( "match(node('system', role='spine', deploy_mode='deploy')" ".out('hosted_interfaces')" ".node('interface', name='leaf_intf')" ".out('link')" ".node('link', role='spine_leaf')" ".in_('link')" ".node('interface')" ".in_('hosted_interfaces')" ".node('system', role='leaf')," "node(name='leaf_intf')" ".in_('member_interfaces')" ".node('sz_instance')" ".in_('instantiated_by')" ".node('security_zone', vrf_name='blue')" ) resp = aos.blueprint.qe_query(bp.id, query=link_query) # QL Queries

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null

0

null

0

1

0

98a08f9c2482c0d99eb42cf5f396b2658fb698d8

490

py

Python

grade.py

pooja160699/fullstackprograms

60db5db8e49ba99a106188c6771a8163be3d18be

[ "bzip2-1.0.6" ]

null

grade.py

pooja160699/fullstackprograms

60db5db8e49ba99a106188c6771a8163be3d18be

[ "bzip2-1.0.6" ]

null

grade.py

pooja160699/fullstackprograms

60db5db8e49ba99a106188c6771a8163be3d18be

[ "bzip2-1.0.6" ]

null

#grade def grade(a): total=0 for i in a: total=total+i print(total) per=float(total/5) print(per) if per>80: print("a") elif per>70: print("b") elif per>60: print("c") elif per>50: print("d") elif per>40: print("e") else: print("fail") print("enter marks of 5 subjects") a=[] for i in range(0,5): inp=input() a.append(inp) print(a) grade(a)

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null

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98a54a0f98ef2d7111fbff0beed3ba90e0411134

1,233

py

Python

tasks/config.py

laruja/f-show

0ef15d25d230ffeb20e70fae5b4769daa9a10828

[ "MIT" ]

4

2021-07-27T09:14:44.000Z

2021-09-08T02:14:31.000Z

tasks/config.py

laruja/f-show

0ef15d25d230ffeb20e70fae5b4769daa9a10828

[ "MIT" ]

null

tasks/config.py

laruja/f-show

0ef15d25d230ffeb20e70fae5b4769daa9a10828

[ "MIT" ]

null

import util # 日志目录 global logfileDir logfileDir = './log' # 数据库配置 global db db = { 'host': '127.0.0.1', 'port': 27017 } global daliy daliy = 'daliy' global daliyxxx daliyxxx = 'daliy'+util.yesterday().strftime('%Y%m%d') # daliy20210622 global history history = 'histest' global fund fund = 'fund' # 基金类型字典 global fundType1 global fundType2 global head1 global head2 fundType1 = {'股票型': '6020-6010', '混合型': '6020-6040', '债券型': '6020-6030', 'QDII型': '6020-6050'} fundType2 = {'货币型': '6020-6020', '短期理财债券型': '6020-6060'} # 基金代码分级代码基金简称份额净值累计净值基金资产净值估值日期备注 head1 = ['code', 'subcode', 'shortName', 'shareNetValue', 'totalNetValue', 'zcNetValue', 'valuationDate'] # 基金代码分级代码基金简称每万份基金已实现收益 7日年化收益率百分比基金份额净值基金累计净值基金资产净值估值日期备注 head2 = ['code', 'subcode', 'shortName', 'gainPer', 'yearSevenDayYieldRatePercent', 'shareNetValue', 'totalNetValue', 'zcNetValue', 'valuationDate'] # db中基金类型定义 global fundTypeDB fundTypeDB = [ {'code': '6020-6010', 'name': '股票型'}, {'code': '6020-6040', 'name': '混合型'}, {'code': '6020-6030', 'name': '债券型'}, {'code': '6020-6050', 'name': 'QDII型'}, {'code': '6020-6020', 'name': '货币型'}, {'code': '6020-6060', 'name': '短期理财债券型'}, ]

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null

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null

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1

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98a5c9401ffa5bdac95b62746713c3eae81af041

6,172

py

Python

mindquantum/algorithm/nisq/chem/unitary_cc.py

Takishima/mindquantum

e90dfe474b759023d7ae18281b9a87cb8d223d04

[ "Apache-2.0" ]

null

mindquantum/algorithm/nisq/chem/unitary_cc.py

Takishima/mindquantum

e90dfe474b759023d7ae18281b9a87cb8d223d04

[ "Apache-2.0" ]

null

mindquantum/algorithm/nisq/chem/unitary_cc.py

Takishima/mindquantum

e90dfe474b759023d7ae18281b9a87cb8d223d04

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Unitary coupled-cluster ansatz.""" from mindquantum.core.circuit import Circuit from mindquantum.core.circuit.utils import add_prefix from mindquantum.core.operators import TimeEvolution from .._ansatz import Ansatz from .transform import Transform from .uccsd0 import uccsd0_singlet_generator def _check_int_list(input_list, name): if not isinstance(input_list, list): raise ValueError( "The input {} should be a list, \ but get {}.".format( str(name), type(input_list) ) ) for i in input_list: if not isinstance(i, int): raise ValueError( "The indices of {} should be integer, \ but get {}.".format( str(name), type(i) ) ) class UCCAnsatz(Ansatz): r""" The unitary coupled-cluster ansatz for molecular simulations. .. math:: U(\vec{\theta}) = \prod_{j=1}^{N(N\ge1)}{\prod_{i=0}^{N_{j}}{\exp{(\theta_{i}\hat{\tau}_{i})}}} where :math:`\hat{\tau}` are anti-Hermitian operators. Note: Currently, the circuit is construncted using JW transformation. In addition, the reference state wave function (Hartree-Fock) will NOT be included. Args: n_qubits(int): Number of qubits (spin-orbitals). Default: None. n_electrons(int): Number of electrons (occupied spin-orbitals). Default: None. occ_orb(list): Indices of manually assigned occupied spatial orbitals, for ansatz construction only. Default: None. vir_orb(list): Indices of manually assigned virtual spatial orbitals, for ansatz construction only. Default: None. generalized(bool): Whether to use generalized excitations which do not distinguish occupied or virtual orbitals (UCCGSD). Default: False. trotter_step(int): The order of Trotterization step. Default: 1. Examples: >>> from mindquantum.algorithm.nisq.chem import UCCAnsatz >>> ucc = UCCAnsatz(12, 4, occ_orb=[1], ... vir_orb=[2, 3], ... generalized=True, ... trotter_step=2) >>> circuit = ucc.circuit.remove_barrier() >>> len(circuit) 3624 >>> params_list = ucc.circuit.params_name >>> len(params_list) 48 >>> circuit[-10:] q5: ──●────RX(7π/2)───────H───────●────────────────────────────●───────H────── │ │ │ q7: ──X───────H────────RX(π/2)────X────RZ(-0.5*t_1_d0_d_17)────X────RX(7π/2)── """ def __init__(self, n_qubits=None, n_electrons=None, occ_orb=None, vir_orb=None, generalized=False, trotter_step=1): """Initialize a UCCAnsatz object.""" if n_qubits is not None and not isinstance(n_qubits, int): raise ValueError( "The number of qubits should be integer, \ but get {}.".format( type(n_qubits) ) ) if n_electrons is not None and not isinstance(n_electrons, int): raise ValueError( "The number of electrons should be integer, \ but get {}.".format( type(n_electrons) ) ) if isinstance(n_electrons, int) and n_electrons > n_qubits: raise ValueError( "The number of electrons must be smaller than \ the number of qubits (spin-orbitals) in the ansatz!" ) if occ_orb is not None: _check_int_list(occ_orb, "occupied orbitals") if vir_orb is not None: _check_int_list(vir_orb, "virtual orbitals") if not isinstance(generalized, bool): raise ValueError( "The parameter generalized should be bool, \ but get {}.".format( type(generalized) ) ) if not isinstance(trotter_step, int) or trotter_step < 1: raise ValueError("Trotter step must be a positive integer!") super().__init__("Unitary CC", n_qubits, n_qubits, n_electrons, occ_orb, vir_orb, generalized, trotter_step) def _implement(self, n_qubits, n_electrons, occ_orb=None, vir_orb=None, generalized=False, trotter_step=1): """Implement the UCC ansatz using uccsd0.""" ansatz_circuit = Circuit() for trotter_idx in range(trotter_step): uccsd0_fermion_op = uccsd0_singlet_generator(n_qubits, n_electrons, True, occ_orb, vir_orb, generalized) uccsd0_circuit = TimeEvolution(Transform(uccsd0_fermion_op).jordan_wigner().imag, 1).circuit # Modify parameter names uccsd0_circuit_modified = add_prefix(uccsd0_circuit, "t_" + str(trotter_idx)) ansatz_circuit += uccsd0_circuit_modified n_qubits_circuit = 0 if list(ansatz_circuit): n_qubits_circuit = ansatz_circuit.n_qubits # If the ansatz's n_qubits is not set by user, use n_qubits_circuit. if self.n_qubits is None: self.n_qubits = n_qubits_circuit if self.n_qubits < n_qubits_circuit: raise ValueError( "The number of qubits in the ansatz circuit {} is larger than \ the input n_qubits {}! Please check input parameters such as occ_orb, etc.".format( n_qubits_circuit, n_qubits ) ) self._circuit = ansatz_circuit

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null

0

null

0

1

0

98a5de6ff37a8157fd5acdb6ec1f751faeddd147

6,506

py

Python

Tax-Calculator-3.0.0/taxcalc/policy.py

grantseiter/Tax-Benefits-Of-Parenthood

5350e832e8b877b46c2a3cab070fc8262b914a52

[ "MIT" ]

null

Tax-Calculator-3.0.0/taxcalc/policy.py

grantseiter/Tax-Benefits-Of-Parenthood

5350e832e8b877b46c2a3cab070fc8262b914a52

[ "MIT" ]

null

Tax-Calculator-3.0.0/taxcalc/policy.py

grantseiter/Tax-Benefits-Of-Parenthood

5350e832e8b877b46c2a3cab070fc8262b914a52

[ "MIT" ]

null

""" Tax-Calculator federal tax policy Policy class. """ # CODING-STYLE CHECKS: # pycodestyle policy.py # pylint --disable=locally-disabled policy.py import os import json import numpy as np from taxcalc.parameters import Parameters from taxcalc.growfactors import GrowFactors class Policy(Parameters): """ Policy is a subclass of the abstract Parameters class, and therefore, inherits its methods (none of which are shown here). Constructor for the federal tax policy class. Parameters ---------- gfactors: GrowFactors class instance containing price inflation rates and wage growth rates Raises ------ ValueError: if gfactors is not a GrowFactors class instance or None. Returns ------- class instance: Policy """ DEFAULTS_FILE_NAME = 'policy_current_law.json' DEFAULTS_FILE_PATH = os.path.abspath(os.path.dirname(__file__)) JSON_START_YEAR = 2013 # remains the same unless earlier data added LAST_KNOWN_YEAR = 2019 # last year for which indexed param vals are known # should increase LAST_KNOWN_YEAR by one every calendar year LAST_BUDGET_YEAR = 2030 # last extrapolation year # should increase LAST_BUDGET_YEAR by one every calendar year DEFAULT_NUM_YEARS = LAST_BUDGET_YEAR - JSON_START_YEAR + 1 # NOTE: the following three data structures use internal parameter names: # (1) specify which Policy parameters have been removed or renamed REMOVED_PARAMS = { # following five parameters removed in PR 2223 merged on 2019-02-06 'DependentCredit_Child_c': 'is a removed parameter name', 'DependentCredit_Nonchild_c': 'is a removed parameter name', 'DependentCredit_before_CTC': 'is a removed parameter name', 'FilerCredit_c': 'is a removed parameter name', 'ALD_InvInc_ec_base_RyanBrady': 'is a removed parameter name', # TODO: following parameter renamed in PR 2292 merged on 2019-04-15 "cpi_offset": ( "was renamed parameter_indexing_CPI_offset. " "See documentation for change in usage." ), "CPI_offset": ( "was renamed parameter_indexing_CPI_offset. " "See documentation for change in usage." ), # TODO: following parameters renamed in PR 2345 merged on 2019-06-24 'PT_excl_rt': 'was renamed PT_qbid_rt in release 2.4.0', 'PT_excl_wagelim_thd': 'was renamed PT_qbid_taxinc_thd in release 2.4.0', 'PT_excl_wagelim_prt': 'was renamed PT_qbid_taxinc_gap in release 2.4.0', 'PT_excl_wagelim_rt': 'was renamed PT_qbid_w2_wages_rt in release 2.4.0', 'CTC_c_under5_bonus': 'was renamed CTC_c_under6_bonus.', 'ACTC_rt_bonus_under5family': 'was renamed ACTC_rt_bonus_under6family.', 'CTC_new_c_under5_bonus': 'was renamed CTC_new_c_under6_bonus.' } # (2) specify which Policy parameters have been redefined recently REDEFINED_PARAMS = {} # (3) specify which Policy parameters are wage (rather than price) indexed WAGE_INDEXED_PARAMS = ['SS_Earnings_c', 'SS_Earnings_thd'] def __init__(self, gfactors=None, only_reading_defaults=False, **kwargs): # put JSON contents of DEFAULTS_FILE_NAME into self._vals dictionary super().__init__() # handle gfactors argument if gfactors is None: self._gfactors = GrowFactors() elif isinstance(gfactors, GrowFactors): self._gfactors = gfactors else: raise ValueError('gfactors is not None or a GrowFactors instance') # read default parameters and initialize syr = Policy.JSON_START_YEAR lyr = Policy.LAST_BUDGET_YEAR nyrs = Policy.DEFAULT_NUM_YEARS self._inflation_rates = None self._wage_growth_rates = None self.initialize(syr, nyrs, Policy.LAST_KNOWN_YEAR, Policy.REMOVED_PARAMS, Policy.REDEFINED_PARAMS, Policy.WAGE_INDEXED_PARAMS, **kwargs) @staticmethod def read_json_reform(obj): """ Return a reform dictionary suitable for use with implement_reform method generated from the specified JSON object, which can be None or a string containing a local filename, a URL beginning with 'http' pointing to a valid JSON file hosted online, or a valid JSON text. """ return Parameters._read_json_revision(obj, 'policy') def implement_reform(self, reform, print_warnings=True, raise_errors=True): """ Implement reform using Tax-Calculator syled reforms/adjustments. Users may also use the adjust method with ParamTools styled reforms. """ # need to do conversion: return self._update(reform, print_warnings, raise_errors) @staticmethod def parameter_list(): """ Returns list of parameter names in the policy_current_law.json file. """ path = os.path.join( Policy.DEFAULTS_FILE_PATH, Policy.DEFAULTS_FILE_NAME ) with open(path) as f: defaults = json.loads(f.read()) # pylint: disable=protected-access return [k for k in defaults if k != "schema"] def set_rates(self): """Initialize taxcalc indexing data.""" cpi_vals = [ vo["value"] for vo in self._data["parameter_indexing_CPI_offset"]["value"] ] # extend parameter_indexing_CPI_offset values through budget window # if they have not been extended already. cpi_vals = cpi_vals + cpi_vals[-1:] * ( self.end_year - self.start_year + 1 - len(cpi_vals) ) cpi_offset = { (self.start_year + ix): val for ix, val in enumerate(cpi_vals) } self._gfactors = GrowFactors() self._inflation_rates = [ np.round(rate + cpi_offset[self.start_year + ix], 4) for ix, rate in enumerate( self._gfactors.price_inflation_rates( self.start_year, self.end_year ) ) ] self._wage_growth_rates = self._gfactors.wage_growth_rates( self.start_year, self.end_year )

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null

0

null

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98a60c53249410258e27e16f17c6bf4773ac5726

2,550

py

Python

tests/test_ops/test_merge_cells.py

BIGWangYuDong/mmcv

c46deb0576edaff5cd5a7d384c617478c7a73a70

[ "Apache-2.0" ]

1

2022-03-18T02:41:11.000Z

tests/test_ops/test_merge_cells.py

BIGWangYuDong/mmcv

c46deb0576edaff5cd5a7d384c617478c7a73a70

[ "Apache-2.0" ]

null

tests/test_ops/test_merge_cells.py

BIGWangYuDong/mmcv

c46deb0576edaff5cd5a7d384c617478c7a73a70

[ "Apache-2.0" ]

null

# Copyright (c) OpenMMLab. All rights reserved. """ CommandLine: pytest tests/test_merge_cells.py """ import torch import torch.nn.functional as F from mmcv.ops.merge_cells import (BaseMergeCell, ConcatCell, GlobalPoolingCell, SumCell) def test_sum_cell(): inputs_x = torch.randn([2, 256, 32, 32]) inputs_y = torch.randn([2, 256, 16, 16]) sum_cell = SumCell(256, 256) output = sum_cell(inputs_x, inputs_y, out_size=inputs_x.shape[-2:]) assert output.size() == inputs_x.size() output = sum_cell(inputs_x, inputs_y, out_size=inputs_y.shape[-2:]) assert output.size() == inputs_y.size() output = sum_cell(inputs_x, inputs_y) assert output.size() == inputs_x.size() def test_concat_cell(): inputs_x = torch.randn([2, 256, 32, 32]) inputs_y = torch.randn([2, 256, 16, 16]) concat_cell = ConcatCell(256, 256) output = concat_cell(inputs_x, inputs_y, out_size=inputs_x.shape[-2:]) assert output.size() == inputs_x.size() output = concat_cell(inputs_x, inputs_y, out_size=inputs_y.shape[-2:]) assert output.size() == inputs_y.size() output = concat_cell(inputs_x, inputs_y) assert output.size() == inputs_x.size() def test_global_pool_cell(): inputs_x = torch.randn([2, 256, 32, 32]) inputs_y = torch.randn([2, 256, 32, 32]) gp_cell = GlobalPoolingCell(with_out_conv=False) gp_cell_out = gp_cell(inputs_x, inputs_y, out_size=inputs_x.shape[-2:]) assert (gp_cell_out.size() == inputs_x.size()) gp_cell = GlobalPoolingCell(256, 256) gp_cell_out = gp_cell(inputs_x, inputs_y, out_size=inputs_x.shape[-2:]) assert (gp_cell_out.size() == inputs_x.size()) def test_resize_methods(): inputs_x = torch.randn([2, 256, 128, 128]) target_resize_sizes = [(128, 128), (256, 256)] resize_methods_list = ['nearest', 'bilinear'] for method in resize_methods_list: merge_cell = BaseMergeCell(upsample_mode=method) for target_size in target_resize_sizes: merge_cell_out = merge_cell._resize(inputs_x, target_size) gt_out = F.interpolate(inputs_x, size=target_size, mode=method) assert merge_cell_out.equal(gt_out) target_size = (64, 64) # resize to a smaller size merge_cell = BaseMergeCell() merge_cell_out = merge_cell._resize(inputs_x, target_size) kernel_size = inputs_x.shape[-1] // target_size[-1] gt_out = F.max_pool2d( inputs_x, kernel_size=kernel_size, stride=kernel_size) assert (merge_cell_out == gt_out).all()

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null

0

null

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1

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98a754ea36c6f75e3a944d4f376a736140a55916

6,337

py

Python

bot.py

der-test/3dp_helpbot

db379d7738231d0b1879f1ef8a70f7fdfc6b2d47

[ "MIT" ]

null

bot.py

der-test/3dp_helpbot

db379d7738231d0b1879f1ef8a70f7fdfc6b2d47

[ "MIT" ]

null

bot.py

der-test/3dp_helpbot

db379d7738231d0b1879f1ef8a70f7fdfc6b2d47

[ "MIT" ]

null

""" /r/3DPrinting Help Bot V2 Tries to help users by replying to them with helpful links :author: Tim Estermann, @der-test :original author: Connor Henley, @thatging3rkid """ import os import sys import time import pickle import praw import praw.models import datetime import traceback import subprocess import config version = "" try: version = subprocess.check_output(["git", "describe", "--tags"]).strip().decode("utf-8") except: version = "unknown" DEBUG_INFO = "\n\n*****\n I am a bot | /r/3DPrinting Help Bot by " \ "[/u/vurt_feather](https://reddit.com/user/vurt_feather) | version " + version + \ " | [GitHub](https://github.com/der-test/3dp-helpbot)" \ " | [How to summon](https://github.com/der-test/3dp_helpbot#reddit-usage)" KEYWORDS = ["3d modeling program", "cad program", "cad software", "looking for modeling program", "3d modeling software", "software for designing 3d models", "software to make 3d models", "software for 3d modeling", "3d modelling program", "3d editing software", "3d editing program"] """ Generates the responses when a user asks for help with different keywords after the mention """ def resp_modeling(): return "[Here](https://reddit.com/r/3Dprinting/wiki/index#wiki_what_should_i_do_to_start_modelling_" + \ "things_to_print.3F) is the wiki entry of CAD/3D modeling software.\n\n[Here](https://reddit.com/r/" + \ "3Dprinting/comments/bm6wq2/so_you_want_to_learn_x_program/) is a guide containing resources to" + \ " learn most CAD/3D modeling software." + DEBUG_INFO def resp_slicers(): return "[Here](https://www.reddit.com/r/3Dprinting/wiki/slicers)" + \ " is the wiki entry for slicer software." + DEBUG_INFO def resp_trouble(): return "[Here](https://www.reddit.com/r/3Dprinting/wiki/troubleshootingandcalibration)" + \ " is the wiki entry for general troubleshooting & calibration help." + DEBUG_INFO def resp_services(): return "[Here](http://www.reddit.com/r/3DPrinting/wiki/Services)" + \ " is the wiki entry for print, design and model-host services." + DEBUG_INFO def resp_general(): return "[Here](https://reddit.com/r/3Dprinting/wiki/index)" + \ " is the general wiki entry." + DEBUG_INFO # A class isn't necessary, but globals in Python are weird class Bot: def __init__(self): # Login # Config located in config.py, copy config.txt or cp config.txt config.py self.__bot = praw.Reddit(username=config.username, password=config.password, client_id=config.client_id, client_secret=config.client_secret, user_agent="3dp_helpbot " + version) print("Logged in...") # Initialize data try: df = open("data.dat", "rb") self.__viewed = pickle.load(df) df.close() except: self.__viewed = [] # Run the bot i = 0 while True: try: self.__run() if len(self.__viewed) > 200: for i in range(0, 15): self.__viewed.remove(0) # Write the viewed ids to disk every 5 iterations if i == 5: i = 0 df = open("data.dat", "wb") pickle.dump(self.__viewed, df) df.close() else: i += 1 except Exception: traceback.print_exc() pass def __run(self): """ Checks for new posts and replies """ # Commented out checking new posts # Get new posts #for post in self.__bot.subreddit('3dprinting').new(limit = 20): # Only check the post once #if post.id not in self.__viewed: #self.__viewed.append(post.id) # See if a post needs a reply #for word in KEYWORDS: #if word in post.title.lower() or word in post.selftext.lower(): #post.reply(resp_modeling()) #break #pass # Read the inbox for mentions and replies read = [] for item in self.__bot.inbox.unread(limit = 25): if isinstance(item, praw.models.Comment): # Mark as read read.append(item) # Check the contents of the inbox item # if "/u/3dp_helpbot modeling" in item.body.lower(): # Bot has been summoned with keyword modeling, give out info item.reply(resp_modeling()) elif "/u/3dp_helpbot slicers" in item.body.lower(): # Bot has been summoned with keyword slicers, give out info item.reply(resp_slicers()) elif "/u/3dp_helpbot trouble" in item.body.lower(): # Bot has been summoned with keyword trouble, give out info item.reply(resp_trouble()) elif "/u/3dp_helpbot services" in item.body.lower(): # Bot has been summoned with keyword trouble, give out info item.reply(resp_services()) elif "/u/3dp_helpbot" in item.body.lower(): # Bot has been summoned, give out info item.reply(resp_general()) elif "good bot" == item.body.lower().strip(): # Someone called the bot a good bot! item.reply("Thanks!" + DEBUG_INFO) elif "bad bot" == item.body.lower().strip(): # Oh no, the bot did something bad. Feedback is welcome! item.reply("I'm sorry to hear that. You can leave feedback [here](https://reddit.com/r/3dp_helpbot)." + DEBUG_INFO) self.__bot.inbox.mark_read(read) time.sleep(4) # Conform to Reddit's API; reduce spam and processing load pass def main(): print("Starting /u/3dp_helpbot...") Bot() # logging commmenteed out during development # os.chdir("/home/user/3dp-helpbot") # sys.stdout = open("logs/log-" + datetime.datetime.now().strftime("%m-%d-%Y_%X") + ".txt", "w+") # sys.stderr = sys.stdout main()

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Python

pytorch_nlu/pytorch_sequencelabeling/slLayer.py

dumpmemory/Pytorch-NLU

864fb9acc7751fc51abd3d05d24b5a9a7eab7110

[ "Apache-2.0" ]

115

2021-08-29T04:28:40.000Z

2022-03-29T22:57:48.000Z

pytorch_nlu/pytorch_sequencelabeling/slLayer.py

dumpmemory/Pytorch-NLU

864fb9acc7751fc51abd3d05d24b5a9a7eab7110

[ "Apache-2.0" ]

2

2022-01-14T01:52:07.000Z

2022-03-04T11:40:10.000Z

pytorch_nlu/pytorch_sequencelabeling/slLayer.py

dumpmemory/Pytorch-NLU

864fb9acc7751fc51abd3d05d24b5a9a7eab7110

[ "Apache-2.0" ]

18

2021-09-23T06:41:10.000Z

2022-03-22T04:37:05.000Z

# !/usr/bin/python # -*- coding: utf-8 -*- # @time : 2021/7/24 21:45 # @author : Mo # @function: Layer and Loss from torch import nn import torch import numpy as np __all__ = ["PriorMultiLabelSoftMarginLoss", "LabelSmoothingCrossEntropyV1", "LabelSmoothingCrossEntropy", "MultiLabelCircleLoss", "FocalLoss", "DiceLossV1", "DiceLoss", "SpanFCLayer", "FCLayer", "Mish", "CRF", "GridPointer", ] class PriorMultiLabelSoftMarginLoss(nn.Module): def __init__(self, prior=None, num_labels=None, reduction="mean", eps=1e-9, tau=1.0): """PriorCrossEntropy categorical-crossentropy-with-prior urls: [通过互信息思想来缓解类别不平衡问题](https://spaces.ac.cn/archives/7615) args: prior: List<float>, prior of label, 先验知识. eg. [0.6, 0.2, 0.1, 0.1] num_labels: int, num of labels, 类别数. eg. 10 reduction: str, Specifies the reduction to apply to the output, 输出形式. eg.``'none'`` | ``'mean'`` | ``'sum'``. ``'none'`` eps: float, Minimum of maths, 极小值. eg. 1e-9 tau: float, weight of prior in loss, 先验知识的权重, eg. ``1.0`` returns: Tensor of loss. examples: >>> loss = PriorCrossEntropy(prior)(logits, label) """ super(PriorMultiLabelSoftMarginLoss, self).__init__() self.loss_mlsm = torch.nn.MultiLabelSoftMarginLoss(reduction=reduction) if not prior: prior = np.array([1/num_labels for _ in range(num_labels)]) # 如果不存在就设置为num if type(prior) ==list: prior = np.array(prior) self.log_prior = torch.tensor(np.log(prior + eps)).unsqueeze(0) self.eps = eps self.tau = tau def forward(self, logits, labels): # 使用与输入label相同的device logits = logits + self.tau * self.log_prior.to(labels.device) loss = self.loss_mlsm(logits, labels) return loss class LabelSmoothingCrossEntropyV1(nn.Module): def __init__(self, eps=0.1, reduction="mean", ignore_index=-100): """【ERROR，直接smooth输入logits效果不好，原因未知】LabelSmoothingCrossEntropy, no-softmax-input eps==0-1, 通过控制ce权重、新增后置项来处理来平滑 urls: [pytorch | labelSmooth](https://zhuanlan.zhihu.com/p/265704145) args: ignore_index: (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. Default: -100 reduction: str, Specifies the reduction to apply to the output, 输出形式. eg.``'none'`` | ``'mean'`` | ``'sum'``. ``'none'`` eps: float, Minimum of maths, 极小值. eg. 0.1 returns: Tensor of loss. examples: >>> loss = LabelSmoothingCrossEntropyV1()(logits, label) """ super(LabelSmoothingCrossEntropyV1, self).__init__() self.ignore_index = ignore_index self.reduction = reduction self.eps = eps def forward(self, logits, labels): # logits --- logistic unit V = max(logits.size()[-1] - 1, 1) logits_smooth = (1 - self.eps) * logits + self.eps / V logits_smooth_logsigmoid = torch.nn.functional.logsigmoid(logits_smooth) loss = -(labels * logits_smooth_logsigmoid + (1 - labels) * logits_smooth_logsigmoid) loss = loss.sum(dim=1) # / logits.size(1) # only return N loss values if "mean" == self.reduction: loss = loss.mean() elif "sum" == self.reduction: loss = loss.sum() else: _ return loss class LabelSmoothingCrossEntropy(nn.Module): def __init__(self, eps=0.1, reduction="mean", ignore_index=-100): """LabelSmoothingCrossEntropy, no-softmax-input 对logits进行smoothing, 即log_softmax后进行操作 args: ignore_index: (int, optional): Specifies a target value that is ignored and does not contribute to the input gradient. Default: -100 reduction: str, Specifies the reduction to apply to the output, 输出形式. eg.``'none'`` | ``'mean'`` | ``'sum'``. ``'none'`` eps: float, Minimum of maths, 极小值. eg. 0.1 returns: Tensor of loss. examples: >>> loss = LabelSmoothingCrossEntropyV1()(logits, label) """ super(LabelSmoothingCrossEntropy, self).__init__() self.ignore_index = ignore_index self.reduction = reduction self.eps = eps def forward(self, logits, labels): V = max(logits.size()[-1] - 1, 1) loss = (1 - self.eps) * (-(labels * torch.nn.functional.logsigmoid(logits) + (1 - labels) * torch.nn.functional.logsigmoid(-logits))) + self.eps / V loss = loss.sum(dim=1) / logits.size(1) # only return N loss values if "mean" == self.reduction: loss = loss.mean() elif "sum" == self.reduction: loss = loss.sum() else: _ return loss class MultiLabelCircleLoss(nn.Module): def __init__(self, reduction="mean", inf=1e12): """CircleLoss of MultiLabel, 多个目标类的多标签分类场景，希望“每个目标类得分都不小于每个非目标类的得分” 多标签分类的交叉熵(softmax+crossentropy推广, N选K问题), LSE函数的梯度恰好是softmax函数让同类相似度与非同类相似度之间拉开一定的margin。 - 使同类相似度比最大的非同类相似度更大。 - 使最小的同类相似度比最大的非同类相似度更大。 - 所有同类相似度都比所有非同类相似度更大。 urls: [将“softmax+交叉熵”推广到多标签分类问题](https://spaces.ac.cn/archives/7359) args: reduction: str, Specifies the reduction to apply to the output, 输出形式. eg.``'none'`` | ``'mean'`` | ``'sum'``. ``'none'`` inf: float, Minimum of maths, 无穷大. eg. 1e12 returns: Tensor of loss. examples: >>> label, logits = [[1, 1, 1, 1], [0, 0, 0, 1]], [[0, 1, 1, 0], [1, 0, 0, 1],] >>> label, logits = torch.tensor(label).float(), torch.tensor(logits).float() >>> loss = MultiLabelCircleLoss()(logits, label) """ super(MultiLabelCircleLoss, self).__init__() self.reduction = reduction self.inf = inf # 无穷大 def forward(self, logits, labels): logits = (1 - 2 * labels) * logits # <3, 4> logits_neg = logits - labels * self.inf # <3, 4> logits_pos = logits - (1 - labels) * self.inf # <3, 4> zeros = torch.zeros_like(logits[..., :1]) # <3, 1> logits_neg = torch.cat([logits_neg, zeros], dim=-1) # <3, 5> logits_pos = torch.cat([logits_pos, zeros], dim=-1) # <3, 5> neg_loss = torch.logsumexp(logits_neg, dim=-1) # <3, > pos_loss = torch.logsumexp(logits_pos, dim=-1) # <3, > loss = neg_loss + pos_loss if "mean" == self.reduction: loss = loss.mean() else: loss = loss.sum() return loss class FocalLoss(nn.Module): def __init__(self, alpha=0.5, gamma=2, reduction="mean"): """FocalLoss 聚焦损失, 不确定的情况下alpha==0.5效果可能会好一点 Usage is same as nn.BCEWithLogits: >>> loss = criteria(logits, lbs) """ super(FocalLoss, self).__init__() self.crit = nn.BCEWithLogitsLoss(reduction="none") self.reduction = reduction self.alpha = alpha self.gamma = gamma def forward(self, logits, labels): probs = torch.sigmoid(logits) coeff = torch.abs(labels - probs).pow(self.gamma).neg() log_0_probs = torch.where(logits >= 0, -logits + nn.functional.softplus(logits, -1, 50), -nn.functional.softplus(logits, 1, 50)) log_1_probs = torch.where(logits >= 0, nn.functional.softplus(logits, -1, 50), logits - nn.functional.softplus(logits, 1, 50)) loss = labels * self.alpha * log_1_probs + (1. - labels) * (1. - self.alpha) * log_0_probs loss = loss * coeff if self.reduction == "mean": loss = loss.mean() if self.reduction == "sum": loss = loss.sum() return loss class DiceLossV1(nn.Module): def __init__(self, reduction="mean", epsilon=1e-9): """【ERROR, 不收敛-原因未知】Dice-Loss, 切块损失, 用于不均衡数据, 但是收敛困难 paper: Dice Loss for Data-imbalanced NLP Tasks url: https://arxiv.org/pdf/1911.02855.pdf args: reduction: str, Specifies the reduction to apply to the output, 输出形式. eg.``'none'`` | ``'mean'`` | ``'sum'``. ``'none'`` epsilon: float, Minimum of maths, 无穷小. eg. 1e-9 returns: Tensor of loss. examples: >>> label, logits = [[1, 1, 1, 1], [0, 0, 0, 1]], [[0, 1, 1, 0], [1, 0, 0, 1],] >>> label, logits = torch.tensor(label).float(), torch.tensor(logits).float() >>> loss = DiceLoss()(logits, label) """ super(DiceLossV1, self).__init__() self.reduction = reduction self.epsilon = epsilon def forward(self, logits, labels): prob = torch.sigmoid(logits) # <2, 4> # logits: [N, C], index: [N, ] index = labels.unsqueeze(1).view(prob.size(0), -1) # <2, 4> prob = torch.gather(prob, dim=1, index=index) dsc_i = 1 - ((1 - prob) * prob + self.epsilon) / ((1 - prob) * prob + 1 + self.epsilon) if "mean" == self.reduction: loss = dsc_i.mean() else: loss = dsc_i.sum() return loss class DiceLoss(nn.Module): def __init__(self, epsilon=1e-9): """Dice-Loss, 切块损失, 用于不均衡数据, 但是收敛困难, 不太稳定 paper: Dice Loss for Data-imbalanced NLP Tasks url: https://arxiv.org/pdf/1911.02855.pdf args: reduction: str, Specifies the reduction to apply to the output, 输出形式. eg.``'none'`` | ``'mean'`` | ``'sum'``. ``'none'`` epsilon: float, Minimum of maths, 无穷小. eg. 1e-9 returns: Tensor of loss. examples: >>> label, logits = [[1, 1, 1, 1], [0, 0, 0, 1]], [[0, 1, 1, 0], [1, 0, 0, 1],] >>> label, logits = torch.tensor(label).long(), torch.tensor(logits).float() >>> loss = DiceLoss()(logits, label) """ super(DiceLoss, self).__init__() self.epsilon = epsilon def forward(self, logits, labels): # 利用预测值与标签相乘当作交集 predict = torch.sigmoid(logits) intersect = predict * labels + self.epsilon unionset = predict + labels + self.epsilon loss = 1. - 2 * intersect.sum() / unionset.sum() return loss class SpanFCLayer(nn.Module): def __init__(self, input_dim, output_dim, dropout_rate=0.1, is_active=True, is_dropout=True, active_type="mish"): """SpanFCLayer Span-FC-Layer, mostly last output of span of model, 新增LayerNorm(条件层标准化) args: input_dim: input dimension, 输入维度, eg. 768 output_dim: output dimension, 输出维度, eg. 32 dropout_rate: dropout rate, 随机失活, eg. 0.1 is_dropout: use dropout or not, 是否使用随机失活dropout, eg. True is_active: use activation or not, 是否使用激活函数如tanh, eg. True active_type: type of activate function, 激活函数类型, eg. "tanh", "relu", "mish" Returns: Tensor of batch. """ super(SpanFCLayer, self).__init__() self.linear_0 = nn.Linear(input_dim, input_dim) self.linear_1 = nn.Linear(input_dim, output_dim) self.layer_norm = nn.LayerNorm(input_dim) self.dropout = nn.Dropout(dropout_rate) # probability of an element to be zeroed self.is_dropout = is_dropout self.active_type = active_type self.is_active = is_active self.softmax = nn.Softmax(1) self.sigmoid = nn.Sigmoid() self.relu = nn.ReLU(inplace=True) # inplace是否覆盖, 为了节省内存 self.tanh = nn.Tanh() self.gelu = nn.GELU() def forward(self, x): if self.is_dropout: x = self.dropout(x) x = self.linear_0(x) if self.is_active: if self.active_type.upper() == "MISH": x = x * torch.tanh(nn.functional.softplus(x)) elif self.active_type.upper() == "SWISH": x = x * torch.sigmoid(x) elif self.active_type.upper() == "TANH": x = self.tanh(x) elif self.active_type.upper() == "GELU": x = self.gelu(x) elif self.active_type.upper() == "RELU": x = self.relu(x) else: x = self.relu(x) x = self.layer_norm(x) x = self.linear_1(x) if self.is_active: if self.active_type.upper() == "MISH": x = x * torch.tanh(nn.functional.softplus(x)) elif self.active_type.upper() == "SWISH": x = x * torch.sigmoid(x) elif self.active_type.upper() == "TANH": x = self.tanh(x) elif self.active_type.upper() == "GELU": x = self.gelu(x) elif self.active_type.upper() == "RELU": x = self.relu(x) else: x = self.relu(x) return x class FCLayer(nn.Module): def __init__(self, input_dim, output_dim, dropout_rate=0.1, is_active=True, is_dropout=True, active_type="mish"): """ FC-Layer, mostly last output of model args: input_dim: input dimension, 输入维度, eg. 768 output_dim: output dimension, 输出维度, eg. 32 dropout_rate: dropout rate, 随机失活, eg. 0.1 is_dropout: use dropout or not, 是否使用随机失活dropout, eg. True is_active: use activation or not, 是否使用激活函数如tanh, eg. True active_type: type of activate function, 激活函数类型, eg. "tanh", "relu" Returns: Tensor of batch. """ super(FCLayer, self).__init__() self.linear = nn.Linear(input_dim, output_dim) self.dropout = nn.Dropout(dropout_rate) # probability of an element to be zeroed self.is_dropout = is_dropout self.active_type = active_type self.is_active = is_active self.softmax = nn.Softmax(1) self.sigmoid = nn.Sigmoid() self.relu = nn.ReLU(inplace=True) self.tanh = nn.Tanh() self.gelu = nn.GELU() def forward(self, x): if self.is_dropout: x = self.dropout(x) x = self.linear(x) if self.is_active: if self.active_type.upper() == "MISH": x = x * torch.tanh(nn.functional.softplus(x)) elif self.active_type.upper() == "SWISH": x = x * torch.sigmoid(x) elif self.active_type.upper() == "TANH": x = self.tanh(x) elif self.active_type.upper() == "GELU": x = self.gelu(x) elif self.active_type.upper() == "RELU": x = self.relu(x) else: x = self.relu(x) return x class Swish(nn.Module): def __init__(self): """ Swish函数可以看做是介于线性函数与ReLU函数之间的平滑函数.(sigmoid和Relu的拼凑) Searching for Activation Functions Applies the swish function element-wise: f(x)=x⋅sigmoid(βx) paper: https://arxiv.org/abs/1710.05941(2017) """ super(Swish, self).__init__() def forward(self, x): return x * torch.sigmoid(x) class Mish(nn.Module): def __init__(self): """ Mish函数可以看做是介于线性函数与ReLU函数之间的平滑函数.(tanh和Relu的拼凑) Script provides functional interface for Mish activation function. Applies the mish function element-wise: mish(x) = x * tanh(softplus(x)) = x * tanh(ln(1 + exp(x))) See additional documentation for mish class. """ super(Mish).__init__() def forword(self, x): x = x * torch.tanh(nn.functional.softplus(x)) return x class CRF(nn.Module): """Conditional random field. This module implements a conditional random field [LMP01]_. The forward computation of this class computes the log likelihood of the given sequence of tags and emission score tensor. This class also has `~CRF.decode` method which finds the best tag sequence given an emission score tensor using `Viterbi algorithm`_. Args: num_tags: Number of tags. batch_first: Whether the first dimension corresponds to the size of a minibatch. Attributes: start_transitions (`~torch.nn.Parameter`): Start transition score tensor of size ``(num_tags,)``. end_transitions (`~torch.nn.Parameter`): End transition score tensor of size ``(num_tags,)``. transitions (`~torch.nn.Parameter`): Transition score tensor of size ``(num_tags, num_tags)``. .. [LMP01] Lafferty, J., McCallum, A., Pereira, F. (2001). "Conditional random fields: Probabilistic models for segmenting and labeling sequence data". *Proc. 18th International Conf. on Machine Learning*. Morgan Kaufmann. pp. 282–289. .. _Viterbi algorithm: https://en.wikipedia.org/wiki/Viterbi_algorithm """ def __init__(self, num_tags: int, batch_first: bool = False) -> None: if num_tags <= 0: raise ValueError('invalid number of tags: {}'.format(num_tags)) super().__init__() self.num_tags = num_tags self.batch_first = batch_first self.start_transitions = nn.Parameter(torch.empty(num_tags)) self.end_transitions = nn.Parameter(torch.empty(num_tags)) self.transitions = nn.Parameter(torch.empty(num_tags, num_tags)) self.reset_parameters() def reset_parameters(self) -> None: """Initialize the transition parameters. The parameters will be initialized randomly from a uniform distribution between -0.1 and 0.1. """ nn.init.uniform_(self.start_transitions, -0.1, 0.1) nn.init.uniform_(self.end_transitions, -0.1, 0.1) nn.init.uniform_(self.transitions, -0.1, 0.1) def __repr__(self) -> str: return '{}(num_tags={})'.format(self.__class__.__name__, self.num_tags) def forward(self, emissions: torch.Tensor, tags: torch.LongTensor, mask = None, reduction = 'mean'): """Compute the conditional log likelihood of a sequence of tags given emission scores. Args: emissions (`~torch.Tensor`): Emission score tensor of size ``(seq_length, batch_size, num_tags)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length, num_tags)`` otherwise. tags (`~torch.LongTensor`): Sequence of tags tensor of size ``(seq_length, batch_size)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length)`` otherwise. mask (`~torch.ByteTensor`): Mask tensor of size ``(seq_length, batch_size)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length)`` otherwise. reduction: Specifies the reduction to apply to the output: ``none|sum|mean|token_mean``. ``none``: no reduction will be applied. ``sum``: the output will be summed over batches. ``mean``: the output will be averaged over batches. ``token_mean``: the output will be averaged over tokens. Returns: `~torch.Tensor`: The log likelihood. This will have size ``(batch_size,)`` if reduction is ``none``, ``()`` otherwise. """ if reduction not in ('none', 'sum', 'mean', 'token_mean'): raise ValueError('invalid reduction: {}'.format(reduction)) if mask is None: mask = torch.ones_like(tags, dtype=torch.uint8, device=tags.device) if mask.dtype != torch.uint8: mask = mask.byte() self._validate(emissions, tags=tags, mask=mask) if self.batch_first: emissions = emissions.transpose(0, 1) tags = tags.transpose(0, 1) mask = mask.transpose(0, 1) # shape: (batch_size,) numerator = self._compute_score(emissions, tags, mask) # shape: (batch_size,) denominator = self._compute_normalizer(emissions, mask) # shape: (batch_size,) llh = numerator - denominator if reduction == 'none': return llh if reduction == 'sum': return llh.sum() if reduction == 'mean': return llh.mean() return llh.sum() / mask.float().sum() def decode(self, emissions: torch.Tensor, mask = None, nbest = None, pad_tag = None): """Find the most likely tag sequence using Viterbi algorithm. Args: emissions (`~torch.Tensor`): Emission score tensor of size ``(seq_length, batch_size, num_tags)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length, num_tags)`` otherwise. mask (`~torch.ByteTensor`): Mask tensor of size ``(seq_length, batch_size)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length)`` otherwise. nbest (`int`): Number of most probable paths for each sequence pad_tag (`int`): Tag at padded positions. Often input varies in length and the length will be padded to the maximum length in the batch. Tags at the padded positions will be assigned with a padding tag, i.e. `pad_tag` Returns: A PyTorch tensor of the best tag sequence for each batch of shape (nbest, batch_size, seq_length) """ if nbest is None: nbest = 1 if mask is None: mask = torch.ones(emissions.shape[:2], dtype=torch.uint8, device=emissions.device) if mask.dtype != torch.uint8: mask = mask.byte() self._validate(emissions, mask=mask) if self.batch_first: emissions = emissions.transpose(0, 1) mask = mask.transpose(0, 1) if nbest == 1: return self._viterbi_decode(emissions, mask, pad_tag).unsqueeze(0) return self._viterbi_decode_nbest(emissions, mask, nbest, pad_tag) def _validate(self, emissions: torch.Tensor, tags = None, mask = None): if emissions.dim() != 3: raise ValueError('emissions must have dimension of 3, got {}'.format(emissions.dim())) if emissions.size(2) != self.num_tags: raise ValueError( 'expected last dimension of emissions is {}, '.format(self.num_tags) + 'got {}'.format(emissions.size(2))) if tags is not None: if emissions.shape[:2] != tags.shape: raise ValueError( 'the first two dimensions of emissions and tags must match, ' 'got {} and {}'.format(tuple(emissions.shape[:2]), tuple(tags.shape))) if mask is not None: if emissions.shape[:2] != mask.shape: raise ValueError( 'the first two dimensions of emissions and mask must match, ' 'got {} and {}'.format(tuple(emissions.shape[:2]), tuple(mask.shape))) no_empty_seq = not self.batch_first and mask[0].all() no_empty_seq_bf = self.batch_first and mask[:, 0].all() if not no_empty_seq and not no_empty_seq_bf: raise ValueError('mask of the first timestep must all be on') def _compute_score(self, emissions: torch.Tensor, tags: torch.LongTensor, mask: torch.ByteTensor): # emissions: (seq_length, batch_size, num_tags) # tags: (seq_length, batch_size) # mask: (seq_length, batch_size) seq_length, batch_size = tags.shape mask = mask.float() # Start transition score and first emission # shape: (batch_size,) score = self.start_transitions[tags[0]] score += emissions[0, torch.arange(batch_size), tags[0]] for i in range(1, seq_length): # Transition score to next tag, only added if next timestep is valid (mask == 1) # shape: (batch_size,) score += self.transitions[tags[i - 1], tags[i]] * mask[i] # Emission score for next tag, only added if next timestep is valid (mask == 1) # shape: (batch_size,) score += emissions[i, torch.arange(batch_size), tags[i]] * mask[i] # End transition score # shape: (batch_size,) seq_ends = mask.long().sum(dim=0) - 1 # shape: (batch_size,) last_tags = tags[seq_ends, torch.arange(batch_size)] # shape: (batch_size,) score += self.end_transitions[last_tags] return score def _compute_normalizer(self, emissions: torch.Tensor, mask: torch.ByteTensor): # emissions: (seq_length, batch_size, num_tags) # mask: (seq_length, batch_size) seq_length = emissions.size(0) # Start transition score and first emission; score has size of # (batch_size, num_tags) where for each batch, the j-th column stores # the score that the first timestep has tag j # shape: (batch_size, num_tags) score = self.start_transitions + emissions[0] for i in range(1, seq_length): # Broadcast score for every possible next tag # shape: (batch_size, num_tags, 1) broadcast_score = score.unsqueeze(2) # Broadcast emission score for every possible current tag # shape: (batch_size, 1, num_tags) broadcast_emissions = emissions[i].unsqueeze(1) # Compute the score tensor of size (batch_size, num_tags, num_tags) where # for each sample, entry at row i and column j stores the sum of scores of all # possible tag sequences so far that end with transitioning from tag i to tag j # and emitting # shape: (batch_size, num_tags, num_tags) next_score = broadcast_score + self.transitions + broadcast_emissions # Sum over all possible current tags, but we're in score space, so a sum # becomes a log-sum-exp: for each sample, entry i stores the sum of scores of # all possible tag sequences so far, that end in tag i # shape: (batch_size, num_tags) next_score = torch.logsumexp(next_score, dim=1) # Set score to the next score if this timestep is valid (mask == 1) # shape: (batch_size, num_tags) score = torch.where(mask[i].unsqueeze(1), next_score, score) # End transition score # shape: (batch_size, num_tags) score += self.end_transitions # Sum (log-sum-exp) over all possible tags # shape: (batch_size,) return torch.logsumexp(score, dim=1) def _viterbi_decode(self, emissions: torch.FloatTensor, mask: torch.ByteTensor, pad_tag = None): # emissions: (seq_length, batch_size, num_tags) # mask: (seq_length, batch_size) # return: (batch_size, seq_length) if pad_tag is None: pad_tag = 0 device = emissions.device seq_length, batch_size = mask.shape # Start transition and first emission # shape: (batch_size, num_tags) score = self.start_transitions + emissions[0] history_idx = torch.zeros((seq_length, batch_size, self.num_tags), dtype=torch.long, device=device) oor_idx = torch.zeros((batch_size, self.num_tags), dtype=torch.long, device=device) oor_tag = torch.full((seq_length, batch_size), pad_tag, dtype=torch.long, device=device) # - score is a tensor of size (batch_size, num_tags) where for every batch, # value at column j stores the score of the best tag sequence so far that ends # with tag j # - history_idx saves where the best tags candidate transitioned from; this is used # when we trace back the best tag sequence # - oor_idx saves the best tags candidate transitioned from at the positions # where mask is 0, i.e. out of range (oor) # Viterbi algorithm recursive case: we compute the score of the best tag sequence # for every possible next tag for i in range(1, seq_length): # Broadcast viterbi score for every possible next tag # shape: (batch_size, num_tags, 1) broadcast_score = score.unsqueeze(2) # Broadcast emission score for every possible current tag # shape: (batch_size, 1, num_tags) broadcast_emission = emissions[i].unsqueeze(1) # Compute the score tensor of size (batch_size, num_tags, num_tags) where # for each sample, entry at row i and column j stores the score of the best # tag sequence so far that ends with transitioning from tag i to tag j and emitting # shape: (batch_size, num_tags, num_tags) next_score = broadcast_score + self.transitions + broadcast_emission # Find the maximum score over all possible current tag # shape: (batch_size, num_tags) next_score, indices = next_score.max(dim=1) # Set score to the next score if this timestep is valid (mask == 1) # and save the index that produces the next score # shape: (batch_size, num_tags) score = torch.where(mask[i].unsqueeze(-1), next_score, score) indices = torch.where(mask[i].unsqueeze(-1), indices, oor_idx) history_idx[i - 1] = indices # End transition score # shape: (batch_size, num_tags) end_score = score + self.end_transitions _, end_tag = end_score.max(dim=1) # shape: (batch_size,) seq_ends = mask.long().sum(dim=0) - 1 # insert the best tag at each sequence end (last position with mask == 1) history_idx = history_idx.transpose(1, 0).contiguous() history_idx.scatter_(1, seq_ends.view(-1, 1, 1).expand(-1, 1, self.num_tags), end_tag.view(-1, 1, 1).expand(-1, 1, self.num_tags)) history_idx = history_idx.transpose(1, 0).contiguous() # The most probable path for each sequence best_tags_arr = torch.zeros((seq_length, batch_size), dtype=torch.long, device=device) best_tags = torch.zeros(batch_size, 1, dtype=torch.long, device=device) for idx in range(seq_length - 1, -1, -1): best_tags = torch.gather(history_idx[idx], 1, best_tags) best_tags_arr[idx] = best_tags.data.view(batch_size) return torch.where(mask, best_tags_arr, oor_tag).transpose(0, 1) def _viterbi_decode_nbest(self, emissions: torch.FloatTensor, mask: torch.ByteTensor, nbest: int, pad_tag = None): # emissions: (seq_length, batch_size, num_tags) # mask: (seq_length, batch_size) # return: (nbest, batch_size, seq_length) if pad_tag is None: pad_tag = 0 device = emissions.device seq_length, batch_size = mask.shape # Start transition and first emission # shape: (batch_size, num_tags) score = self.start_transitions + emissions[0] history_idx = torch.zeros((seq_length, batch_size, self.num_tags, nbest), dtype=torch.long, device=device) oor_idx = torch.zeros((batch_size, self.num_tags, nbest), dtype=torch.long, device=device) oor_tag = torch.full((seq_length, batch_size, nbest), pad_tag, dtype=torch.long, device=device) # + score is a tensor of size (batch_size, num_tags) where for every batch, # value at column j stores the score of the best tag sequence so far that ends # with tag j # + history_idx saves where the best tags candidate transitioned from; this is used # when we trace back the best tag sequence # - oor_idx saves the best tags candidate transitioned from at the positions # where mask is 0, i.e. out of range (oor) # Viterbi algorithm recursive case: we compute the score of the best tag sequence # for every possible next tag for i in range(1, seq_length): if i == 1: broadcast_score = score.unsqueeze(-1) broadcast_emission = emissions[i].unsqueeze(1) # shape: (batch_size, num_tags, num_tags) next_score = broadcast_score + self.transitions + broadcast_emission else: broadcast_score = score.unsqueeze(-1) broadcast_emission = emissions[i].unsqueeze(1).unsqueeze(2) # shape: (batch_size, num_tags, nbest, num_tags) next_score = broadcast_score + self.transitions.unsqueeze(1) + broadcast_emission # Find the top `nbest` maximum score over all possible current tag # shape: (batch_size, nbest, num_tags) next_score, indices = next_score.view(batch_size, -1, self.num_tags).topk(nbest, dim=1) if i == 1: score = score.unsqueeze(-1).expand(-1, -1, nbest) indices = indices * nbest # convert to shape: (batch_size, num_tags, nbest) next_score = next_score.transpose(2, 1) indices = indices.transpose(2, 1) # Set score to the next score if this timestep is valid (mask == 1) # and save the index that produces the next score # shape: (batch_size, num_tags, nbest) score = torch.where(mask[i].unsqueeze(-1).unsqueeze(-1), next_score, score) indices = torch.where(mask[i].unsqueeze(-1).unsqueeze(-1), indices, oor_idx) history_idx[i - 1] = indices # End transition score shape: (batch_size, num_tags, nbest) end_score = score + self.end_transitions.unsqueeze(-1) _, end_tag = end_score.view(batch_size, -1).topk(nbest, dim=1) # shape: (batch_size,) seq_ends = mask.long().sum(dim=0) - 1 # insert the best tag at each sequence end (last position with mask == 1) history_idx = history_idx.transpose(1, 0).contiguous() history_idx.scatter_(1, seq_ends.view(-1, 1, 1, 1).expand(-1, 1, self.num_tags, nbest), end_tag.view(-1, 1, 1, nbest).expand(-1, 1, self.num_tags, nbest)) history_idx = history_idx.transpose(1, 0).contiguous() # The most probable path for each sequence best_tags_arr = torch.zeros((seq_length, batch_size, nbest), dtype=torch.long, device=device) best_tags = torch.arange(nbest, dtype=torch.long, device=device) \ .view(1, -1).expand(batch_size, -1) for idx in range(seq_length - 1, -1, -1): best_tags = torch.gather(history_idx[idx].view(batch_size, -1), 1, best_tags) best_tags_arr[idx] = best_tags.data.view(batch_size, -1) // nbest return torch.where(mask.unsqueeze(-1), best_tags_arr, oor_tag).permute(2, 1, 0) class GridPointer(nn.Module): def __init__(self, head_nums, head_size, is_RoPE=True): """GridPointer, 分类-网格(全局)指针模块将序列的每个(start, end)作为整体来进行判断代码来源: 网址url: [GlobalPointer：用统一的方式处理嵌套和非嵌套NER](https://kexue.fm/archives/8373) ptorch版gaohongkui: https://github.com/gaohongkui/GlobalPointer_pytorch """ super(GridPointer, self).__init__() self.head_nums = head_nums self.head_size = head_size self.is_RoPE = is_RoPE def forward(self, x, attention_mask, token_type_ids): batch_size = x.size(0) max_len = x.size(1) outputs = torch.split(x, self.head_size * 2, dim=-1) # <batch, len, label, head*2> outputs = torch.stack(outputs, dim=-2) qw, kw = outputs[..., :self.head_size], outputs[..., self.head_size:] # <batch, len, label, head> if self.is_RoPE: def SinusoidalPositionEmbedding(output_size, batch_size, max_len, device): """embedding of Sinusoidal-Position """ position_ids = torch.arange(0, max_len, dtype=torch.float).unsqueeze(-1) indices = torch.arange(0, output_size // 2, dtype=torch.float) indices = torch.pow(10000, -2 * indices / output_size) embeddings = position_ids * indices embeddings = torch.stack([torch.sin(embeddings), torch.cos(embeddings)], dim=-1) embeddings = embeddings.repeat((batch_size, *([1] * len(embeddings.shape)))) embeddings = torch.reshape(embeddings, (batch_size, max_len, output_size)) embeddings = embeddings.to(device) return embeddings pos_emb = SinusoidalPositionEmbedding(self.head_size, batch_size, max_len, device=x.device) # <batch, len, head> cos_pos = pos_emb[..., None, 1::2].repeat_interleave(2, dim=-1) # <batch, len, 1, head> sin_pos = pos_emb[..., None, ::2].repeat_interleave(2, dim=-1) # <batch, len, 1, head> qw2 = torch.stack([-qw[..., 1::2], qw[..., ::2]], -1) qw2 = qw2.reshape(qw.shape) qw = qw * cos_pos + qw2 * sin_pos kw2 = torch.stack([-kw[..., 1::2], kw[..., ::2]], -1) kw2 = kw2.reshape(kw.shape) kw = kw * cos_pos + kw2 * sin_pos logits = torch.einsum("bmhd, bnhd->bhmn", qw, kw) # <batch, label, len, len> pad_mask = attention_mask.unsqueeze(1).unsqueeze(1).expand(batch_size, self.head_nums, max_len, max_len) logits = logits*pad_mask - (1-pad_mask)*1e12 # 排除下三角 mask = torch.tril(torch.ones_like(logits), diagonal=-1) logits = (logits - mask * 1e12) logits = logits / self.head_size**0.5 # scale return logits

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98a9ddb77716f5ad2a2d263d27421845bd0aff98

1,528

py

Python

models/lossfuns.py

zekunhao1995/DualSDF

177a102b315949bfa59a6ae1c47de52ddbea6eaa

[ "MIT" ]

107

2020-04-07T01:15:14.000Z

2022-03-17T09:32:46.000Z

models/lossfuns.py

zekunhao1995/DualSDF

177a102b315949bfa59a6ae1c47de52ddbea6eaa

[ "MIT" ]

6

2020-05-16T00:41:28.000Z

2021-04-27T16:04:21.000Z

models/lossfuns.py

zekunhao1995/DualSDF

177a102b315949bfa59a6ae1c47de52ddbea6eaa

[ "MIT" ]

17

2020-04-14T10:50:24.000Z

2022-01-20T09:43:08.000Z

import numpy as np # PyTorch import torch import torch.nn as nn import torch.nn.functional as F # Original DeepSDF loss def clamped_l1(pred_dist, gt_dist, trunc=0.1): pred_dist_trunc = torch.clamp(pred_dist, -trunc, trunc) gt_dist_trunc = torch.clamp(gt_dist, -trunc, trunc) loss = torch.abs(pred_dist_trunc - gt_dist_trunc) return loss # [B N] def clamped_l1_correct(pred_dist, gt_dist, trunc=0.1): pred_dist_lower = torch.clamp(pred_dist, None, trunc) pred_dist_upper = torch.clamp(pred_dist, -trunc, None) pos_trunced_mask = (gt_dist >= trunc) neg_trunced_mask = (gt_dist <= -trunc) valid_mask = ~(pos_trunced_mask|neg_trunced_mask) loss_valid = torch.sum(torch.abs(pred_dist - gt_dist) * valid_mask.float(), dim=-1) loss_lower = torch.sum((trunc - pred_dist_lower) * pos_trunced_mask.float(), dim=-1) loss_upper = torch.sum((pred_dist_upper + trunc) * neg_trunced_mask.float(), dim=-1) loss = (loss_lower + loss_upper + loss_valid) / pred_dist.size(1) return loss # L2 loss on the outside, encourage inside to < 0.0 def onesided_l2(pred_dist, gt_dist): valid_mask = (gt_dist >= 0.0).float() num_valid = torch.sum(valid_mask, dim=-1) num_inside = valid_mask[0].numel() - num_valid loss_valid = torch.sum((gt_dist-pred_dist)**2 * valid_mask, dim=-1) / (num_valid+1e-8) loss_inside = torch.sum(torch.clamp(pred_dist, 0.0, None) * (1.0-valid_mask), dim=-1) / (num_inside+1e-8) loss = loss_valid + loss_inside return loss

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null

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null

0

1

0

98ab4c8c271cda0de6cc40da611003519c199dfe

5,184

py

Python

rmot/generate_stim/run.py

DrugowitschLab/motion-structure-used-in-perception

d4f0115e154d5e529094383963c8cdaa1386720b

[ "MIT" ]

4

2020-04-03T09:34:29.000Z

2020-10-22T20:36:40.000Z

rmot/generate_stim/run.py

DrugowitschLab/motion-structure-used-in-perception

d4f0115e154d5e529094383963c8cdaa1386720b

[ "MIT" ]

null

rmot/generate_stim/run.py

DrugowitschLab/motion-structure-used-in-perception

d4f0115e154d5e529094383963c8cdaa1386720b

[ "MIT" ]

1

2020-09-17T09:48:27.000Z

import numpy as np import pylab as pl from time import time import logging from motionstruct.functions import init_logging, asciiL, recursive_dict_update from motionstruct.classes import PhiWorld import scipy.io as sio import os # Help string and argument parsing from argparse import ArgumentParser, RawTextHelpFormatter parser = ArgumentParser(formatter_class=RawTextHelpFormatter, description="Stimulus generator for rotational MOT", epilog="If using ipython3, indicate end of ipython arg parser via '--':\n $ ipython3 run.py -- <args>") parser.add_argument(dest="cfgfile", metavar="filename", default="config.py", nargs='?', type=str, help="config.py file holding dictionary 'cfg' (same directory, default: config.py)") parser.add_argument("-u", "--update", dest="updatefile", metavar="filename", default=[], nargs='*', type=str, help="file holding dictionary 'cfg' for recursively updating the main cfg (same directory, default: None)") args = parser.parse_args() # Import config from specified config file cfgmodulename = args.cfgfile.split(".py")[0] cmd = "from " + cfgmodulename + " import cfg" exec(cmd) msg = [] for udf in args.updatefile: updatemodulename = udf.split(".py")[0] cmd = "from " + updatemodulename + " import cfg as ucfg" exec(cmd) _,msg = recursive_dict_update(cfg, ucfg, msg=msg, s="[%s] cfg." % updatemodulename) # Dryrun? DRYRUN = cfg["global"]["DRYRUN"] # # # # # # # # # # # # # # # # # # # # # # # # # M A I N R O U T I N E # # # # # # # # # # # # # # # # # # # # # # # # # # Create the output directory outdir = cfg["global"]["outdir"] if not DRYRUN: import os if outdir[-1] != "/": outdir += "/" if not os.path.exists(outdir): os.makedirs(outdir) # Create the logger logger = init_logging(cfg, outdir) logger.info("Stimulus generator started.") logger.info("Loading config from file: %s.py" % cfgmodulename) if len(args.updatefile) > 0: logger.info("Updating config from files: %s" % str(args.updatefile)) logger.debug("Number of entry updates: %d. Details follow." % len(msg)) for m in msg: logger.debug("Updated key: %s" % m) logger.info("DSL: '%s'" % cfg["global"]["dsl"]) if not DRYRUN: logger.debug("Output directory: %s" % outdir) # copy the config file to outdir from shutil import copyfile logger.debug("Copy file '%s.py' to '%s'." % (cfgmodulename, outdir)) copyfile("%s.py" % cfgmodulename, outdir+"config.py") if len(args.updatefile) > 0: for i,udf in enumerate(args.updatefile): logger.debug("Copy file '%s' to '%s'." % (udf, outdir)) copyfile(udf, outdir+"uconfig_%d.py" % (i+1)) import os if os.path.isfile("speed_and_seed.py"): logger.debug("Copy file 'speed_and_seed.py' to '%s'." % (outdir,)) copyfile("speed_and_seed.py", outdir+"speed_and_seed.py") L = cfg["world"]["L"] N, M = L.shape T = cfg["global"]["T"] targets = cfg["global"]["targets"] logger.info("The data's motion structure matrix L looks as follows:\n" + asciiL(L, indent=5)) # Generate World (can be reused in repetitions) kwargs = cfg["world"] wld = PhiWorld(**kwargs) # Generate Observation Generator (can be reused in repetitions) kwargs = cfg["observe"] obscls = kwargs.pop("cls") obs = obscls(**kwargs) # Data storage archive = dict( wld_t = [], # world (ground truth) simulation times wld_S = [], # world (ground truth) states obs_t = [], # observation (visible data) time points obs_X = [], # observation (visible data) values ) # Take start time t_start = time() reps = cfg["global"]["reps"] # HERE COMES THE OUTER MAIN LOOP logger.info("Enter simulation main loop.") for rep in range(reps): logger.info("*** Trial %d of %d ***" % (rep+1, reps)) # Draw the data using the Observation Generator which calls World Generator obs.run_sim_and_generate_observations(T, wld) # Store data logger.debug("Store data to archive.") archive["wld_t"].append(wld.get_times()) archive["wld_S"].append(wld.S) archive["obs_t"].append(obs.get_times()) archive["obs_X"].append(obs.X) # Write matlab file if not DRYRUN: logger.debug("Write matlab file.") fname = cfg["global"]["f_outfname"](rep+1) # make index matlab friendly if not os.path.exists(os.path.dirname(fname)): os.makedirs(os.path.dirname(fname)) mdict = {'X':obs.X, 'T':obs.get_times(), 'targets':[t+1 for t in targets], 'dsl' : cfg["global"]["dsl"]} sio.savemat( fname, mdict ) t_end = time() logger.info("Stimulus generation main loop completed. Main loop runtime: %5.3fs." % (t_end - t_start)) if not DRYRUN: fname = outdir + "simdata.npz" logger.info("Save results to file '%s'." % fname) np.savez_compressed(fname, **archive) logger.info("Generation completed successfully.") # TEST WITH #fig = figure(figsize=(16,9)); ax = fig.add_axes((0,0,1,1), aspect='auto', xlim=(0,1920), ylim=(0,1080)); tn=0 #while tn < obs.X.shape[0]: ax.plot(obs.X[tn,:,0], obs.X[tn,:,1], 'o'); tn+=1

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null

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null

0

1

0

98acf47b5de02fab44c2cbbf5152e7f879ad95fc

598

py

Python

Interview_Questions/Tencent_20190310/3_card.py

sintocos/CS_Roaming

c85d2c95337a00079160f7f5a8a1b0e31eb5fcb3

[ "BSD-3-Clause" ]

null

Interview_Questions/Tencent_20190310/3_card.py

sintocos/CS_Roaming

c85d2c95337a00079160f7f5a8a1b0e31eb5fcb3

[ "BSD-3-Clause" ]

null

Interview_Questions/Tencent_20190310/3_card.py

sintocos/CS_Roaming

c85d2c95337a00079160f7f5a8a1b0e31eb5fcb3

[ "BSD-3-Clause" ]

null

# -*- coding: UTF-8 -*- # description: 小Q有一叠纸牌,一共有n张,从上往下依次编号为1到n。现在小Q要对这叠纸牌反复做以下操作: # 把当前位于顶端的牌扔掉，然后把新的顶端的牌放到新叠牌的底部。 # 小Q会一直操作到只剩下一张牌为止。小Q想知道每次丢掉的牌的编号。 # example: input:7 (输入为一行，只有一个数字n, 1<=n<=1e6) # # output:1 3 5 7 4 2 6 (输出n个用空格间隔的整数，表示每次丢掉的牌的编号) """ @param string line 一个测试用例 @return string 处理后的结果 """ def solution(line): cards = [str(x + 1) for x in range(int(line))] res = [] while len(cards) > 2: res += [cards.pop(0)] cards.append(cards.pop(0)) return " ".join(res + cards) print(solution(8))

22.148148

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null

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0

98ae3f1be8211f99c7d8b22fd98d93670a9d293c

10,626

py

Python

scripts/gis_append.py

mwb249/connect-dispatch

c6f17c088bce639daaa9fd1ab04f3edd15dc1887

[ "MIT" ]

null

scripts/gis_append.py

mwb249/connect-dispatch

c6f17c088bce639daaa9fd1ab04f3edd15dc1887

[ "MIT" ]

null

scripts/gis_append.py

mwb249/connect-dispatch

c6f17c088bce639daaa9fd1ab04f3edd15dc1887

[ "MIT" ]

null

""" Connect|DISPATCH: Connecting Computer-Aided Dispatch (CAD) Systems to ArcGIS. The gis_append script is activated when incident_push.p is modified in the watch directory. """ from connectdispatch import fire_ops import logging import os import yaml import pickle import csv import mgrs from datetime import datetime from arcgis.gis import GIS from arcgis.geocoding import Geocoder, geocode from arcgis.geometry import filters, Point from pyproj import Proj, transform from copy import deepcopy # Logging logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO")) # Directories cwd = os.getcwd() watch_dir = cwd + '/watch' config_dir = cwd + '/config' # Open config file with open(config_dir + '/config.yml', 'r') as yamlfile: cfg = yaml.load(yamlfile) # Open incident push file file_incident_push = open(watch_dir + '/incident_push/incident_push.p', 'rb') incident_push = pickle.load(file_incident_push) # Open agency codes table file_agency_codes = csv.DictReader(open(config_dir + '/agency_codes.csv')) agency_codes = {rows['agency_code']: rows['city_desc'] for rows in file_agency_codes} # Define projections mi_south = Proj(init='epsg:3593', preserve_units=True) # NAD 1983 StatePlane Michigan South, FIPS 2113 IntlFeet web_mercator = Proj(init='epsg:3857') # WGS 1984 Web Mercator Auxiliary Sphere wgs84 = Proj(init='epsg:4326') # WGS84 for i in incident_push: for agency in cfg['agencies'].keys(): if i['agency_code'] == cfg['agencies'][agency]['agency_code']: # Assign variable to web GIS ago_portal = cfg['agencies'][agency]['ago_portal'] ago_user = cfg['agencies'][agency]['ago_user'] ago_pass = cfg['agencies'][agency]['ago_pass'] gis = GIS(ago_portal, ago_user, ago_pass) # Assign variable to feature layers fl_fireincidents = gis.content.get(cfg['agencies'][agency]['flc_fireincidents']).layers[0] fl_serviceareas = gis.content.get(cfg['agencies'][agency]['flc_serviceareas']).layers[0] fl_firedistricts = gis.content.get(cfg['agencies'][agency]['flc_firedistricts']).layers[0] fl_boxalarmareas = gis.content.get(cfg['agencies'][agency]['flc_boxalarmareas']).layers[0] fl_taxparcels = gis.content.get(cfg['agencies'][agency]['flc_taxparcels']).layers[0] # If incident is mutual-aid, find agency_code mutaid_agency_code = '' if i['inc_type_code'] == 'MUTAID': if i['city_desc'] == agency_codes['city_desc']: mutaid_agency_code = agency_codes['agency_code'] else: mutaid_agency_code = i['agency_code'] # Query feature layer and create search extent dictionary if i['inc_type_code'] == 'MUTAID': where_statement = i['agency_code'] + " LIKE '" + mutaid_agency_code + "'" else: where_statement = i['agency_code'] + " LIKE '" + i['agency_code'] + "'" f_servicearea = fl_serviceareas.query(where=where_statement).features[0] sa_xmax = f_servicearea.attributes['xmax_fips2113_ftintl'] sa_xmin = f_servicearea.attributes['xmin_fips2113_ftintl'] sa_ymax = f_servicearea.attributes['ymax_fips2113_ftintl'] sa_ymin = f_servicearea.attributes['ymin_fips2113_ftintl'] search_area = {'xmax': sa_xmax, 'xmin': sa_xmin, 'ymax': sa_ymax, 'ymin': sa_ymin} # Determine geocoder, default is the 'World Geocoder for ArcGIS' if cfg['agencies'][agency]['use_oc_geocoder']: geocoder = Geocoder(cfg['geocoders']['oc_geocoder']) else: geocoder = None # Geocode address geocode_result = geocode(i['address'], search_extent=search_area, geocoder=geocoder) if not geocode_result: # If geocode fails use the agency default location geocode_result = geocode(cfg['agencies'][agency]['default_location'], geocoder=geocoder) geocode_success = 'N' else: geocode_success = 'Y' pass # Transform coordinates x, y = transform(mi_south, web_mercator, geocode_result[0]['location']['x'], geocode_result[0]['location']['y']) long, lat = transform(mi_south, wgs84, geocode_result[0]['location']['x'], geocode_result[0]['location']['y']) # Round Lat/Long lat = round(lat, 6) long = round(long, 6) # Convert Lat/Long to USNG m = mgrs.MGRS() usng_raw = m.toMGRS(lat, long) u = str(usng_raw.decode('utf-8')) usng = u[0:3] + ' ' + u[3:5] + ' ' + u[5:10] + ' ' + u[10:15] # Construct point feature geocode_xy = Point({'x': x, 'y': y}) # Feature layer query to find box alarm areas fset_boxalarmareas = fl_boxalarmareas.query(geometry_filter=filters.intersects(geocode_xy)) # Assign box alarm variables boxalarm_fire = None boxalarm_medical = None boxalarm_wildland = None # Loop to populate Box Alarm Variables for boxalarmarea in fset_boxalarmareas: if boxalarmarea.attributes['BoxAlarmType'] == 'FIRE': boxalarm_fire = boxalarmarea.attributes['BoxAlarmNumber'] elif boxalarmarea.attributes['BoxAlarmType'] == 'MEDICAL': boxalarm_medical = boxalarmarea.attributes['BoxAlarmNumber'] elif boxalarmarea.attributes['BoxAlarmType'] == 'WILDLAND': boxalarm_wildland = boxalarmarea.attributes['BoxAlarmNumber'] # Determine agency district fset_firedistricts = fl_firedistricts.query(geometry_filter=filters.intersects(geocode_xy), return_geometry=False) agency_district = None if fset_firedistricts: agency_district = fset_firedistricts.features[0].attributes['primarystation'] else: pass # Determine shift on duty at time of call pattern_start = datetime.strptime(cfg['agencies'][agency]['shift_start_date'], '%m-%d-%Y') shift_start = cfg['agencies'][agency]['shift_start_time'] agency_shift = fire_ops.kelly_shift(i['datetime_call'], pattern_start, shift_start) # Query tax parcel layer to get structure data fset_taxparcels = fl_taxparcels.query(where="SITEADDRESS LIKE '" + i['address'] + "'", return_geometry=False, result_record_count=1) parcel_id = None map_index = None structure_desc = None structure_livingarea = None structure_numbbeds = None structure_assessvalue = None structure_taxvalue = None if fset_taxparcels and geocode_success == 'Y': parcel_id = fset_taxparcels.features[0].attributes['PIN'] map_index = fset_taxparcels.features[0].attributes['PIN'][2:4] structure_desc = fset_taxparcels.features[0].attributes['STRUCTURE_DESC'] structure_livingarea = fset_taxparcels.features[0].attributes['LIVING_AREA_SQFT'] structure_numbbeds = fset_taxparcels.features[0].attributes['NUM_BEDS'] structure_assessvalue = fset_taxparcels.features[0].attributes['ASSESSEDVALUE'] structure_taxvalue = fset_taxparcels.features[0].attributes['TAXABLEVALUE'] else: pass # Create new feature based on template fset_fireincidents = fl_fireincidents.query(result_record_count=1) f = deepcopy(fset_fireincidents.features[0]) # Assign geometry & attributes to new feature f.geometry = geocode_xy f.attributes['incident_number'] = i['incident_number'] f.attributes['incident_type_code'] = i['incident_type_code'] f.attributes['incident_type_desc'] = i['incident_type_desc'] f.attributes['incident_temp_url'] = i['incident_temp_url'] f.attributes['agency_code'] = i['agency_code'] f.attributes['agency_district'] = agency_district f.attributes['agency_shift'] = agency_shift f.attributes['parcel_id'] = parcel_id f.attributes['address'] = i['address'] f.attributes['location'] = i['location'] f.attributes['apt_number'] = i['apt_number'] f.attributes['city_code'] = i['city_code'] f.attributes['city_desc'] = i['city_desc'] f.attributes['state'] = ['state'] f.attributes['map_index'] = map_index f.attributes['latitude'] = lat f.attributes['longitude'] = long f.attributes['usng'] = usng f.attributes['low_street'] = i['low_street'] f.attributes['high_street'] = i['high_street'] f.attributes['geocode_success'] = geocode_success f.attributes['datetime_call'] = i['datetime_call'] f.attributes['datetime_dispatched'] = None f.attributes['datetime_enroute'] = None f.attributes['datetime_arrival'] = None f.attributes['datetime_clear'] = None f.attributes['units_assigned'] = i['incident_units'] f.attributes['chief_complaint'] = i['chief_complaint'] f.attributes['proqa_code'] = i['proqa_code'] f.attributes['proqa_suffix_code'] = i['proqa_code_suf'] f.attributes['proqa_desc'] = i['proqa_desc'] f.attributes['proqa_suffix_desc'] = i['proqa_desc_suf'] f.attributes['boxalarm_fire'] = boxalarm_fire f.attributes['boxalarm_medical'] = boxalarm_medical f.attributes['boxalarm_wildland'] = boxalarm_wildland f.attributes['structure_desc'] = structure_desc f.attributes['structure_livingarea'] = structure_livingarea f.attributes['structure_numbbeds'] = structure_numbbeds f.attributes['structure_assessvalue'] = structure_assessvalue f.attributes['structure_taxvalue'] = structure_taxvalue # Create empty list for new GIS features feature_list = [f] # Add features to feature layer fl_fireincidents.edit_features(adds=feature_list)

48.081448

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10,626

5.348936

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0.070008

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0.025617

0.147335

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0.078759

0.014638

0

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0.272257

10,626

220

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0

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false

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0.080745

0

null

0

null

0

1

0

98b2b467fe732e9f25c6541b5a2df8198a18a396

1,959

py

Python

api_reflector/api.py

backwardspy/api-reflector

5feacf7d7d549418b31acbf9407602a7659e431d

[ "MIT" ]

4

2021-09-24T13:58:37.000Z

2022-03-01T22:19:41.000Z

api_reflector/api.py

backwardspy/api-reflector

5feacf7d7d549418b31acbf9407602a7659e431d

[ "MIT" ]

10

2021-09-21T16:28:06.000Z

2022-03-25T16:48:36.000Z

api_reflector/api.py

backwardspy/api-reflector

5feacf7d7d549418b31acbf9407602a7659e431d

[ "MIT" ]

2

2021-09-13T13:00:33.000Z

2022-02-24T14:55:46.000Z

""" Provides the top level flask application configuration. """ import sentry_sdk from flask import Flask from flask_dance.contrib.azure import make_azure_blueprint from sentry_sdk.integrations.flask import FlaskIntegration from sentry_sdk.integrations.sqlalchemy import SqlalchemyIntegration from werkzeug.middleware.proxy_fix import ProxyFix from api_reflector import db from api_reflector.admin import admin from api_reflector.migrations import run_migrations from api_reflector.reporting import get_logger from api_reflector.views import api from settings import settings log = get_logger(__name__) def create_app() -> Flask: """ Creates a flask application and registers the api blueprint. """ if settings.sentry_dsn: log.debug("Initialising Sentry SDK.") sentry_sdk.init( # pylint: disable=abstract-class-instantiated dsn=settings.sentry_dsn, integrations=[FlaskIntegration(), SqlalchemyIntegration()], ) log.debug("Initializing app.") app = Flask(__name__) app.wsgi_app = ProxyFix( # type: ignore app.wsgi_app, x_proto=int(settings.use_x_forwarded_proto), x_host=int(settings.use_x_forwarded_host), ) app.config.update( SECRET_KEY=settings.secret_key, SQLALCHEMY_DATABASE_URI=settings.postgres_dsn, SQLALCHEMY_TRACK_MODIFICATIONS=False, FLASK_ADMIN_SWATCH="darkly", ) if settings.azure_auth_enabled: azure_blueprint = make_azure_blueprint( client_id=settings.azure_client_id, client_secret=settings.azure_client_secret, tenant=settings.azure_tenant, redirect_url="/", ) app.register_blueprint(azure_blueprint) db.sqla.init_app(app) admin.init_app(app) app.register_blueprint(api) log.debug("Migrating database.") run_migrations.main() log.debug("App initialisation complete.") return app

28.391304

71

0.721797

234

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5.764957

0.388889

0.033358

0.059303

0.037064

0.035582

0

0.200613

1,959

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28.808824

0.86143

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0

1

0.020833

false

0

0.25

0

0.291667

0.083333

0

null

0

null

0

1

0

98b51bf927ecb8711b46f44c5fe353e2b5cb0870

16,590

py

Python

app_common/apptools/io/deserializer.py

KBIbiopharma/app_common

bd913e24741fb070aad058a0f90cbb2c64d8b106

[ "MIT" ]

2

2020-02-12T17:51:13.000Z

2021-05-03T05:36:15.000Z

app_common/apptools/io/deserializer.py

KBIbiopharma/app_common

bd913e24741fb070aad058a0f90cbb2c64d8b106

[ "MIT" ]

30

2020-02-04T21:38:58.000Z

2021-05-25T20:55:01.000Z

app_common/apptools/io/deserializer.py

KBIbiopharma/app_common

bd913e24741fb070aad058a0f90cbb2c64d8b106

[ "MIT" ]

null

""" Module containing the deserializer api function and supporting classes. The deserializers are versioned and the ones in this module correspond to the latest protocol. Old versions of deserializers that were updated are stored in the legacy_deserializers dict. """ import logging from six import string_types from traits.api import HasStrictTraits, Type from .serialization_utils import sanitize_class_name logger = logging.getLogger(__name__) class NoDeserializerFoundError(ValueError): pass class deSerializer(HasStrictTraits): """ Base class for all other deserialization related classes. Contains the generic dynamic methods for deserializing some data back into a class instance. """ #: Default version of the deserializer protocol_version = 0 #: Class attr with all ndarrays mapped to their id in the serial data. array_collection = {} #: Dict mapping ids (set at serialization) to instances already #: deserialized, in case the same object needs to be pointed to. instance_collection = {} #: Class level attribute to lookup available deserializers (same protocole) deserializer_map = {} #: Map between (object type, old_protocole) and deserializers classes legacy_deserializers = {} #: Whether at least 1 legacy deserializer has been used legacy_file = False @classmethod def set_array_collection(cls, array_collection): # Modify the dict in place to make sure all subclasses share the same # container: cls.array_collection.clear() cls.array_collection.update(array_collection) @classmethod def instance_lookup(cls, obj_id): """ Lookup if an object with provided id has been deserialized already. Parameters ---------- obj_id : any immutable type Id, assigned at serialization, of the object to deserialize. This id is expected to be found as one of the keys of the instance_collection class attribute. Returns ------- The instance of the object if the id has been encountered, and stored in the instance_collection class attribute. Returns None otherwise. """ obj = None if cls.instance_collection is None: cls.instance_collection = {} if obj_id in cls.instance_collection: obj = cls.instance_collection[obj_id] return obj def deserialize(self, serial_data): """ Actual deserialization of the serialized data. Parameters ---------- serial_data : dictionary serial_data is a dictionary with the following items. 'class_metadata' : Information about the class to be instantiated 'data' : Contains arguments required for instantiating the class Returns ------- A new class instance for the serial_data. """ deserializer = self.select_deserializer(serial_data) obj = deserializer.build_object(serial_data) return obj def select_deserializer(self, serial_data): """ Returns the deserializer class appropriate for the provided serial data. Raises a ValueError if none is found. Parameters ---------- serial_data : dictionary or basic type serial_data either a basic object that can be instanciated automatically or a dictionary with the following items: 'class_metadata' : Information about the class to be instantiated 'data' : Contains arguments required for instantiating the class """ if isinstance(serial_data, dict) and 'class_metadata' in serial_data: klass = sanitize_class_name(serial_data['class_metadata']['type']) written_version = serial_data["class_metadata"]["version"] else: # Basic type: no metadata, they are stored as themselves written_version = None klass = sanitize_class_name(serial_data.__class__.__name__) try: default_deserializer = self.deserializer_map[klass+'DeSerializer'] except (KeyError, AttributeError) as e: msg = "No active deserializer was found for class {}. Now " \ "searching in the legacy deserializers... (Error was {}.)" logger.warning(msg.format(klass, e)) default_deserializer = None use_default = False current_version = None else: current_version = default_deserializer.protocol_version use_default = (written_version is None or current_version == written_version) if use_default: deserializer = default_deserializer elif current_version is not None and current_version < written_version: msg = ("Trying to load a {} version {} but the most recent " "deserializer available is version {}. This file was " "created with a newer version of the software. It will need" "to be updated to be able to load this file.") msg = msg.format(klass, written_version, current_version) logger.exception(msg) raise NoDeserializerFoundError(msg) else: # Search for a deserializer support the version 'written_version' old_deserializer_dict = self.legacy_deserializers.get(klass, None) if old_deserializer_dict is None: msg = "Unable to find a legacy deserializer for a {}.".format( klass) logger.exception(msg) raise NoDeserializerFoundError(msg) deSerializer.legacy_file = True deserializer = old_deserializer_dict.get(written_version, None) if deserializer is None: versions = sorted(old_deserializer_dict.keys()) msg = "Unable to find a deserializer for a {} version {}. " \ "Available versions are {}." msg = msg.format(klass, written_version, versions) logger.exception(msg) raise NoDeserializerFoundError(msg) return deserializer() def build_object(self, serial_data): """ Recreate class objects from the serialized data. Deserialize all arguments to the class constructor for the target data type, and build the instance. Parameters ---------- serial_data : dictionary serial_data is a dictionary with the following items. 'class_metadata' : Information about the class to be instantiated 'data' : Contains arguments required for instantiating the class Returns ------ A class instance for the serial_data, whether from the instance_lookup or a newly created one if the id has never been encountered. """ data = serial_data.pop('data', None) metadata = serial_data.pop('class_metadata', None) obj_id = metadata['id'] constructor_data = {'metadata': metadata} metadata_name = None if data is not None: data = self.deserialize(data) constructor_data['args'] = data if serial_data: instance_data = self.deserialize(serial_data) if instance_data.get('metadata'): metadata_name = instance_data['metadata'].get('name') constructor_data['kwargs'] = instance_data if obj_id is None: instance = self.get_instance(constructor_data) else: # For objects that were saved with a unique "id", lookup # if that object has already been built instance = self.instance_lookup(obj_id) if instance is None: instance = self.get_instance(constructor_data) self.instance_collection[obj_id] = instance # FIXME: Instance metadata name is overwritten with the instance.name # in the ChromatographyData __init__ so reassign name here. Better way # to do it? if metadata_name: instance.metadata['name'] = metadata_name return instance class simpleObjDeSerializer(deSerializer): klass = Type def get_instance(self, constructor_data): instance = self.klass(**constructor_data['kwargs']) return instance class dataElementDeSerializer(simpleObjDeSerializer): def _klass_default(self): from app_common.model_tools.data_element import DataElement return DataElement class basicTypeDeSerializer(deSerializer): def build_object(self, serial_data): # Convert unicode to string ... required for existing code if isinstance(serial_data, string_types): serial_data = str(serial_data) instance = type(serial_data)(serial_data) return instance class boolDeSerializer(basicTypeDeSerializer): pass class floatDeSerializer(basicTypeDeSerializer): pass class float64DeSerializer(basicTypeDeSerializer): """ Deserialization for numpy.float64 """ def build_object(self, serial_data): instance = float(serial_data) return instance class timestampDeSerializer(basicTypeDeSerializer): """ Deserialization for pandas.tslib.Timestamp. """ def build_object(self, serial_data): from pandas import Timestamp instance = Timestamp(serial_data['data']) return instance class dateDeSerializer(basicTypeDeSerializer): """ Deserialization for datetime.date. """ def build_object(self, serial_data): from datetime import date instance = date(*serial_data['data']) return instance class intDeSerializer(basicTypeDeSerializer): pass class longDeSerializer(basicTypeDeSerializer): pass class strDeSerializer(basicTypeDeSerializer): pass class unicodeDeSerializer(basicTypeDeSerializer): pass class noneTypeDeSerializer(basicTypeDeSerializer): def build_object(self, serial_data): return serial_data class dictDeSerializer(deSerializer): def build_object(self, serial_data): deserialized_dict = {} if 'class_metadata' in serial_data and \ 'dict' in serial_data['class_metadata']['type']: serial_data.pop('class_metadata', None) for key, val in serial_data.items(): deserialized_dict.update({key: self.deserialize(val)}) return deserialized_dict class seriesDeSerializer(deSerializer): def get_instance(self, constructor_data): from pandas.core.series import Series instance = Series(constructor_data['args'], index=constructor_data['kwargs']['index']) return instance class dataFrameDeSerializer(deSerializer): protocol_version = 1 def build_object(self, serial_data): filename = serial_data['class_metadata']['filename'] df_id = serial_data['class_metadata']['id'] return self.array_collection[(filename, df_id)] class traitDictObjectDeSerializer(deSerializer): def build_object(self, serial_data): # TraitsDict object is deserialized as a regular dict because the # constructors of HasTraits objects expect dictionaries not # TraitDictObjects deserialized_dict = {} serial_data.pop('class_metadata', None) for key, val in serial_data.items(): deserialized_dict.update({key: self.deserialize(val)}) return deserialized_dict class listDeSerializer(deSerializer): def build_object(self, serial_data): _list = [] for item in serial_data: _list.append(self.deserialize(item)) return _list class setDeSerializer(deSerializer): def build_object(self, serial_data): _set = set() for item in serial_data["data"]: _set.add(self.deserialize(item)) return _set class ndarrayDeSerializer(deSerializer): protocol_version = 1 def build_object(self, serial_data): filename = serial_data['class_metadata']['filename'] arr_uuid = serial_data['class_metadata']['id'] return self.array_collection[(filename, arr_uuid)] class smartUnitDeSerializer(deSerializer): def get_instance(self, constructor_data): from scimath.units.smart_unit import SmartUnit instance = SmartUnit(*constructor_data['args']) return instance class traitListObjectDeSerializer(deSerializer): def build_object(self, serial_data): _list = [] for item in serial_data['data']: deserializer = self.select_deserializer(item) _list.append(deserializer.build_object(item)) return _list class tupleDeSerializer(deSerializer): def build_object(self, serial_data): elements = [] for item in serial_data['data']: deserializer = self.select_deserializer(item) elements.append(deserializer.build_object(item)) return tuple(elements) class unitDeSerializer(deSerializer): def get_instance(self, constructor_data): import scimath.units.unit instance = getattr(scimath.units.unit, constructor_data['metadata']['type'])( *constructor_data['args']) # Set the unit.label because the Unit Class assigns # the label attribute equal to None when the Unit Class is constructed instance.label = constructor_data['kwargs']['label'] return instance class unitScalarDeSerializer(deSerializer): def get_instance(self, constructor_data): import scimath.units.unit_scalar instance = getattr(scimath.units.unit_scalar, constructor_data['metadata']['type'])( constructor_data['args'], **constructor_data['kwargs']) return instance class unitArrayDeSerializer(unitScalarDeSerializer): """ Version 1 of the unitArray deserializer. """ protocol_version = 1 def build_object(self, serial_data): from scimath.units.unit_array import UnitArray filename = serial_data['class_metadata']['filename'] array_id = serial_data['class_metadata']['id'] data = self.array_collection.get((filename, array_id), None) units = self.deserialize(serial_data["units"]) return UnitArray(data, units=units) class uUIDDeSerializer(deSerializer): def get_instance(self, constructor_data): from uuid import UUID instance = UUID(constructor_data['args']) return instance def deserialize(serial_data, array_collection=None, klass=None, additional_deserializers=None): """ Functional entry point to deserialize any serial data. Note that this function resets the instance_collection class attribute, and should therefore not be called more than once for each file loading. Parameters ---------- serial_data : dict All non-array data to rebuild the object. array_collection : dict Dictionary mapping all numpy arrays stored to an id in the serial data. klass : deSerializer [OPTIONAL] Implementation of the deserialization class. Can be passed for example to set the legacy_deserializers dict. additional_deserializers : dict Map between object class names and corresponding deserializer object to use to recreate the instance. Returns ------- any, bool Object being deserialized and whether or not legacy deserializers were needed. """ if klass is None: klass = deSerializer if array_collection is None: array_collection = {} if additional_deserializers is None: additional_deserializers = {} klass.instance_collection.clear() klass.legacy_file = False deserializer_map = {} # Additional serializers added afterwards, to allow projects to override # the way to serialize basic types: app_common_content = {key: val for key, val in globals().items() if key.endswith("DeSerializer")} deserializer_map.update(app_common_content) deserializer_map.update(additional_deserializers) deserializer = klass() deserializer.deserializer_map.update(deserializer_map) deserializer.set_array_collection(array_collection) obj = deserializer.deserialize(serial_data['data']) return obj, klass.legacy_file

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79

0.662929

1,810

16,590

5.91326

0.190608

0.064468

0.022237

0.025227

0.317481

0.279174

0.20555

0.161263

0.139307

0

0.000819

0.264436

16,590

482

80

34.419087

0.87626

0.267752

0

0.326923

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0.07387

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0.103846

false

0.026923

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0.003846

0.403846

0

null

0

null

0

1

0

98b56a5b7675ee876b08c64bf15a49e2ad1f944e

1,162

py

Python

index.py

garlfin/RPC-Client-Server

d13dc9988e5582ce8ccb396a5aa1db21fc1041c5

[ "MIT" ]

null

index.py

garlfin/RPC-Client-Server

d13dc9988e5582ce8ccb396a5aa1db21fc1041c5

[ "MIT" ]

null

index.py

garlfin/RPC-Client-Server

d13dc9988e5582ce8ccb396a5aa1db21fc1041c5

[ "MIT" ]

null

import res.server.server as server import res.client as client import threading import os import res.server.keyboardlistener address = ("localhost", 8000) dataPath = os.getcwd()+"/res/database/clients/db.json" def startServer(): Server = server.RpcServer(dataPath) Server.initialize(address) KeyboardListener = res.server.keyboardlistener.KeyboardListener(Server) threading.Thread(target=KeyboardListener.status, args=[KeyboardListener.DoIListen.listen]).start() Server.register([Server.retrieveStats, Server.close]) def StartClient(client_name): try: client_thread = client.RPCClient(client_name) client_thread.listen(client.getDomainFromAddress(address, "RPC")) client_thread.receiveStats() except ConnectionRefusedError: print(f"[{str(client_name)}] Machine refused connection.") currentWorkingThreads = [threading.Thread(target=startServer), threading.Thread(target=StartClient, args=["0442246"]), threading.Thread(target=StartClient, args=["5561111"])] for thread in currentWorkingThreads: thread.start() for thread in currentWorkingThreads: thread.join()

33.2

118

0.744406

121

1,162

7.099174

0.429752

0.069849

0.097788

0.074505

0.172293

0

0.01809

0.143718

1,162

34

119

34.176471

0.845226

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1

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0

0.192308

0

0.269231

0.038462

0

null

0

null

0

1

0

98b9e22db3260d4dd56031107290c4fa484ebf3e

5,016

py

Python

tools/uavcan_ip_interface.py

greck2908/robot-software

2e1e8177148a089e8883967375dde7f8ed3d878b

[ "MIT" ]

40

2016-10-04T19:59:22.000Z

2020-12-25T18:11:35.000Z

tools/uavcan_ip_interface.py

greck2908/robot-software

2e1e8177148a089e8883967375dde7f8ed3d878b

[ "MIT" ]

209

2016-09-21T21:54:28.000Z

2022-01-26T07:42:37.000Z

tools/uavcan_ip_interface.py

greck2908/robot-software

2e1e8177148a089e8883967375dde7f8ed3d878b

[ "MIT" ]

21

2016-11-07T14:40:16.000Z

2021-11-02T09:53:37.000Z

#!/usr/bin/env python3 """ UAVCAN to TUN network adapter. """ import argparse import os import struct import sys import fcntl import uavcan import subprocess import time import logging from queue import Queue, Empty import threading DSDL_DIR = os.path.join(os.path.dirname(__file__), "../uavcan_data_types/cvra") def parse_args(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--interface", "-i", help="CAN Interface to use (e.g. can0 or /dev/ttyUSB0", required=True, ) parser.add_argument( "--ip-address", "-a", default="10.0.0.1/24", help="IP address of this interface (default 10.0.0.1/24)", ) parser.add_argument( "--packets-per-second", type=int, default=1000, help="Max number of packet per second to transmit (protects the CAN bus).", ) parser.add_argument("--dsdl", help="Path to DSDL directory", default=DSDL_DIR) parser.add_argument( "--verbose", "-v", action="store_true", help="Enable debug output." ) return parser.parse_args() def open_tun_interface(ip_addr): if sys.platform == "linux": fd = os.open("/dev/net/tun", os.O_RDWR) # Values obtained with a test C program IFF_TAP = 0x2 IFF_NO_PI = 4096 TUNSETIFF = 0x400454CA # See man netdevice for struct ifreq val = struct.pack("16sh15x", "uavcan0".encode(), IFF_TAP | IFF_NO_PI) fcntl.ioctl(fd, TUNSETIFF, val) subprocess.check_call("ip link set dev uavcan0 up".split()) subprocess.check_call("ip addr add dev uavcan0 {}".format(ip_addr).split()) return fd elif sys.platform == "darwin": # macOS tap = "tap0" fd = os.open("/dev/" + tap, os.O_RDWR) subprocess.call("ifconfig {} {}".format(tap, ip_addr).split()) return fd else: raise RuntimeError("supports mac and linux only") class RateLimiter: """Simple rate limiter. See https://stackoverflow.com/questions/667508/whats-a-good-rate-limiting-algorithm """ def __init__(self, max_rate): self.max_rate = max_rate self.quota = max_rate self.last_time = time.time() def check(self) -> bool: """Checks if we are allowed to proceed based on max rate.""" t = time.time() dt, self.last_time = t - self.last_time, t self.quota += self.max_rate * dt self.quota = min(self.quota, self.max_rate) # If we don't have quota left, forbid the transaction if self.quota <= 1.0: return False # If we still have quota, take one from it and allow the transaction self.quota -= 1.0 return True def rx_thread(tun_fd, queue, max_packet_per_second): limiter = RateLimiter(max_packet_per_second) while True: packet = os.read(tun_fd, 1500) if limiter.check(): queue.put(packet) else: logging.debug("Dropped packet") def node_thread(tun_fd, node, can_to_tap, tap_to_can): def msg_callback(event): msg = event.message can_to_tap.put(msg.data) node.add_handler(uavcan.thirdparty.cvra.uwb_beacon.DataPacket, msg_callback) while True: # A timeout of 0 means only process frames that are immediately # available try: node.spin(timeout=0) except uavcan.transport.TransferError: logging.warning("uavcan exception, ignoring...") pass try: packet = tap_to_can.get(block=False) except Empty: continue # Checks that the packet fits in a UWB frame assert len(packet) < 1024 # Finally send it over CAN msg = uavcan.thirdparty.cvra.uwb_beacon.DataPacket() msg.dst_addr = 0xFFFF # broadcast msg.data = list(packet) node.broadcast(msg) def tx_thread(tun_fd, queue): while True: packet = queue.get() os.write(tun_fd, bytes(packet)) def main(): args = parse_args() level = logging.INFO if args.verbose: level = logging.DEBUG logging.basicConfig(level=level) if os.getuid() != 0: logging.error("must run as root.") sys.exit(1) uavcan.load_dsdl(args.dsdl) tun_fd = open_tun_interface(args.ip_address) node = uavcan.make_node(args.interface, node_id=42) tap_to_can = Queue() can_to_tap = Queue() logging.info("waiting for packets, press 3x Ctrl-C to stop...") rx_thd = threading.Thread( target=rx_thread, args=(tun_fd, tap_to_can, args.packets_per_second) ) tx_thd = threading.Thread(target=tx_thread, args=(tun_fd, can_to_tap)) node_thd = threading.Thread( target=node_thread, args=(tun_fd, node, can_to_tap, tap_to_can) ) rx_thd.start() tx_thd.start() node_thd.start() node_thd.join() rx_thd.join() tx_thd.join() if __name__ == "__main__": main()

26.125

87

0.621212

681

5,016

4.397944

0.372981

0.015025

0.028381

0.02404

0.100167

0.05409

0.044741

0.016694

0

0.018443

0.264952

5,016

191

88

26.26178

0.79387

0.111842

0

0.099237

0

0.129018

0.005659

0

0.004301

0

0.007634

1

0.068702

false

0.007634

0.083969

0

0.198473

0

null

0

null

0

1

0

98ba753b081a3ea187c35cbd90efdcf17178d65c

840

py

Python

gpx_builder.py

jvicu2001/GGeoTrace

7796daaa505586cf7b867e321aa528f5dd64a8c6

[ "MIT" ]

null

gpx_builder.py

jvicu2001/GGeoTrace

7796daaa505586cf7b867e321aa528f5dd64a8c6

[ "MIT" ]

null

gpx_builder.py

jvicu2001/GGeoTrace

7796daaa505586cf7b867e321aa528f5dd64a8c6

[ "MIT" ]

null

from xml.etree import ElementTree def gpx_builder(data): gpx = ElementTree.Element('gpx') tree = ElementTree.ElementTree(gpx) gpx.set('version', '1.0') gpxname = ElementTree.SubElement(gpx, 'name') gpxname.text = 'PLACEHOLDER' rte = ElementTree.SubElement(gpx, 'rte') for routepoint in range(len(data)): rtept = ElementTree.SubElement(rte, 'rtept') current_jump = data[routepoint] rtept.set('lat', current_jump['latitude']) rtept.set('lon', current_jump['longitude']) rtept_name = ElementTree.SubElement(rtept, 'name') rtept_name.text = 'Jump n° {}'.format(routepoint) rtept_desc = ElementTree.SubElement(rtept, 'desc') rtept_desc.text = '{}\nip: {}\nTime: PLACEHOLDER'.format(data[routepoint]['ptr'], data[routepoint]['ip']) return tree

33.6

113

0.653571

97

840

5.587629

0.43299

0.193727

0.088561

0

0.002954

0.194048

840

24

114

35

0.79616

0

0.132143

0

1

0.055556

false

0

0.055556

0

0.166667

0

null

0

null

0

1

0

7f268c8fd92f2096679b4eb0bada219d945e28f0

1,428

py

Python

python/recognize.py

qcumba0/voicekit-examples

43eaf1800674ac82d7211dfea1ca0fb85affd6d1

[ "Apache-2.0" ]

null

python/recognize.py

qcumba0/voicekit-examples

43eaf1800674ac82d7211dfea1ca0fb85affd6d1

[ "Apache-2.0" ]

null

python/recognize.py

qcumba0/voicekit-examples

43eaf1800674ac82d7211dfea1ca0fb85affd6d1

[ "Apache-2.0" ]

null

#! /usr/bin/env python3 from tinkoff.cloud.stt.v1 import stt_pb2_grpc from auth import authorization_metadata from audio import audio_open_read from common import build_recognition_request, make_channel, print_recognition_response, BaseRecognitionParser from google.protobuf.json_format import MessageToDict from tinkoff.cloud.stt.v1 import stt_pb2 def main(): args = BaseRecognitionParser().parse_args() total = '' if args.encoding == stt_pb2.RAW_OPUS: raise ValueError("RAW_OPUS encoding is not supported by this script") with audio_open_read(args.audio_file, args.encoding, args.rate, args.num_channels, args.chunk_size, args.pyaudio_max_seconds) as reader: stub = stt_pb2_grpc.SpeechToTextStub(make_channel(args)) metadata = authorization_metadata(args.api_key, args.secret_key, "tinkoff.cloud.stt") response = stub.Recognize(build_recognition_request(args, reader), metadata=metadata) if not isinstance(response, dict): # https://developers.google.com/protocol-buffers/docs/proto3#json response = MessageToDict(response, including_default_value_fields=True, preserving_proto_field_name=True) for result in response["results"]: for alternative in result["alternatives"]: total = total + alternative["transcript"] print(total) if __name__ == "__main__": main()

42

117

0.72619

176

1,428

5.636364

0.522727

0.024194

0.045363

0.038306

0.066532

0

0.006891

0.186975

1,428

33

118

43.272727

0.847545

0.059524

0

0.076923

0

1

0.041667

false

0

0.25

0

0.291667

0.083333

0

null

0

null

0

1

0

7f281eff9319b1d52f1e904c4e69206d883b8adc

3,872

py

Python

coral/reglib.py

kdberends/coral

637b1a4756f09841b71da7520754fc15d7fac72c

[ "MIT" ]

null

coral/reglib.py

kdberends/coral

637b1a4756f09841b71da7520754fc15d7fac72c

[ "MIT" ]

1

2019-04-15T09:52:59.000Z

coral/reglib.py

kdberends/coral

637b1a4756f09841b71da7520754fc15d7fac72c

[ "MIT" ]

1

2019-08-17T13:36:02.000Z

""" Regression library """ # ============================================================================= # Imports # ============================================================================= import theano import numpy as np import pymc3 as pm import random from coral.statsfunc import get_empirical_cdf, empirical_ppf import matplotlib.pyplot as plt # ============================================================================= # Regression functions # ============================================================================= def general_linear(predictor_sample, response_sample, predictor, params): """ General linear model of the form y=ax + b + e """ X = predictor_sample y = response_sample X_new = predictor with pm.Model(): sigma = pm.HalfCauchy('sigma', beta=10, testval=1.) intercept = pm.Normal('Intercept', 0, sd=20) slope_coeff = pm.Normal('Slope', 0, sd=20) # Define likelihood likelihood = pm.Normal('y', mu=intercept + slope_coeff * X, sd=sigma, observed=y) # Draw samples using NUTS sampler trace = pm.sample(draws=params.draws, chains=params.chains, cores=params.cores, tune=params.burn_in) # After burn-in MCMC should sample from the poster predictive a = trace.get_values('Intercept', burn=params.burn_in, combine=True) # trace['Intercept'][params.burn_in:] b = trace.get_values('Slope', burn=params.burn_in, combine=True) # trace['Slope'][params.burn_in:] sigma = trace.get_values('sigma', burn=params.burn_in, combine=True) # trace['sigma'][params.burn_in:] a, b, sigma = map(np.array, [a, b, sigma]) response_modelled = list() for i in range(len(a)): response_modelled.append(a[i] + b[i] * X_new + np.random.normal(loc=0, scale=sigma[i], size=len(X_new))) # summarize trace for 95, 89, 80, 50, 20 and 10 % ci prob_x = np.array([2.5, 5, 10, 25, 40, 45, 55, 60, 75, 90, 95, 97.5])/100 def sumtrace(data): p, val = get_empirical_cdf(data) return {'p':prob_x.tolist(), 'val': np.interp(prob_x, p, val).tolist()} trace_summary = {'intercept':a[::10].tolist(), 'slope':b[::10].tolist(), 'sigma':sigma[::10].tolist()} return np.array(response_modelled), trace_summary def gaussian_process(predictor_sample, response_sample, predictor, params): """ Args: predictor_sample : subsample of predictor response_sample : subsample of response predictor: full sample of predictor params: ParameterContainer object Returns: modelled response, inference trace (if mcmc) inference estimates (if map) """ X = predictor_sample[:, None] y = response_sample X_new = predictor[:, None] with pm.Model() as model: # length scale factor L = pm.Gamma("L", alpha=2, beta=1) # Covariance scale factor eta = pm.HalfCauchy("eta", beta=5) # todo: set by parameters? kernel = 'radialbasis' if kernel == 'matern': # Matern kernel cov = eta**2 * pm.gp.cov.Matern52(1, L) elif kernel == 'radialbasis': # Radial basis kernel cov = eta**2 * pm.gp.cov.ExpQuad(1, L) gp = pm.gp.Marginal(cov_func=cov) sigma = pm.HalfCauchy("sigma", beta=15) y_ = gp.marginal_likelihood("y", X=X, y=y, noise=sigma) if params.inference == 'map': mp = pm.find_MAP() elif params.inference == 'mcmc': mp = pm.sample(10000) with model: y_pred = gp.conditional("y_pred", X_new, pred_noise=True) response_modelled = pm.sample_ppc([mp], vars=[y_pred], samples=params.ppc_draws) return response_modelled['y_pred'], mp

33.669565

112

0.557593

473

3,872

4.448203

0.340381

0.022814

0.039924

0.022814

0.157795

0.13308

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null

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26,367

py

Python

pysit/optimization/optimization.py

zfang-slim/PysitForPython3

dc60537b26018e28d92b7a956a2cf96775f0bdf9

[ "BSD-3-Clause" ]

null

pysit/optimization/optimization.py

zfang-slim/PysitForPython3

dc60537b26018e28d92b7a956a2cf96775f0bdf9

[ "BSD-3-Clause" ]

null

pysit/optimization/optimization.py

zfang-slim/PysitForPython3

dc60537b26018e28d92b7a956a2cf96775f0bdf9

[ "BSD-3-Clause" ]

1

2020-06-13T07:13:07.000Z

import sys import time import copy import numpy as np import scipy.io as sio __all__=['OptimizationBase'] __docformat__ = "restructuredtext en" class OptimizationBase(object): """ Base class for descent-like optimization routines. These are stateful algorithms. The current step, as well as the step index are stored so that (in the future) further steps can be taken without repeating computational effort. The basic structure of a pysit descent algorithm is focused on three computational phases: computation of the residual, the gradient of the objective function, and selection of a step direction based on this information. The rest of this class specifies the layout of these methods, which will be useful in nearly all descent algorithms. A subclass, GradientDescent, will implement basic versions of these routines. Other algorithms should inherit from there to prevent excess code rewriting. A separate function method is provided for each of the three basic phases to allow for overriding of the behavior of each. The residual and gradient computation are least likely to be changed, but the step selection may be changed frequently. For example, a gradient descent algorithm might implement an adjustment that performs a line search along the gradient. An implementation of Newton's method would simply override the adjust method to solve the Hessian equation. Algorithms like CG or BFGS can also be implemented in this manner. Attributes ---------- solver : pysit wave solver object A wave solver that inherits from pysit.solvers.WaveSolverBase ivnersion_methods : class A class containing all of the methods required to compute the inversion steps. verbose : bool Verbosity flag. xi : solver.WaveSolverParameters Current state of the unknowns. i : float Current iteration index. <blank>_history : list of tuples History of property (e.g., step length) with entries like the tuple (i, step_length_i) for index i. <blank>_frequency : int Iteration frequency at which to store a particular property. """ def __init__(self, objective): """Constructor for the BasicDescentAlgorithm class. Parameters ---------- solver : pysit wave solver object A wave solver that inherits from pysit.solvers.WaveSolverBase inversion_methods : class A class containing all of the methods required to compute the inversion steps. Notes ----- * InversionMethodsType is a data type that takes a wave solver object as a construction argument. The collection of inversion methods will depend on the solver. * InversionMethodsType must have member functions that implement the basic wave imaging procedures, e.g., forward modeling, adjoint modeling, demigration, etc. """ self.objective_function = objective self.solver = objective.solver self.verbose = False self.use_parallel = objective.use_parallel() self.max_linesearch_iterations = 10 self.logfile = sys.stdout self.proj_op = None self.write = False def reset(self, append_mode, value_frequency=0, gradient_frequency=0, gradient_length_frequency=0, step_frequency=0, step_length_frequency=0, residual_length_frequency=0, objective_frequency=0, run_time_frequency=0, alpha_frequency=1, *args, **kwargs): """Resets the state of the optimization algorithm. Parameters ---------- value_frequency : int Iteration frequency that the value of the solution should be stored. gradient_frequency : int Iteration frequency that the gradient vector should be stored. step_frequency : int Iteration frequency that the step vector and step length should be stored. objective_frequency : int Iteration frequency that the value of the objective function should be stored. """ # if we are not appending reset things # if things have not been set yet, reset things if not append_mode or not hasattr(self, 'iteration'): self.base_model = self.solver.ModelParameters(self.solver.mesh) self.iteration = 0 # Reset the history lists self.init_history("value", value_frequency) self.init_history("gradient", gradient_frequency) self.init_history("gradient_length", gradient_length_frequency) self.init_history("step", step_frequency) self.init_history("step_length", step_length_frequency) self.init_history("residual_length", residual_length_frequency) # Requires self.residual_norm to be implemented by residual class if L2 is not appropriate self.init_history("objective", objective_frequency) self.init_history("run_time", run_time_frequency) # All methods line search somehow self.init_history("alpha", alpha_frequency) def init_history(self, arg, freq): """Initializes a history variable. Creates or overwrites an object attribute named arg_history and arg_frequency dynamically. This allows for storing properties of some descent algorithms that may not be relevant or exist in others. Parameters ---------- arg : string String prefix for naming the history and frequency variables. freq : int Frequency for storing the associated arg. """ setattr(self, arg + "_history", {}) setattr(self, arg + "_frequency", freq) def query_store_history(self, arg, i): f = getattr(self, arg + "_frequency") # Only store the history if this index matches the frequency. return f and (not np.mod(i,f)) def store_history(self, arg, i, val, force=False): """Stores a data point for a history variable. To prevent repeated checks to see if a current iteration requires history storage, this function both checks to see if data should be stored and actually stores it. Parameters ---------- arg : string String prefix for naming the history and frequency variables. i : int Index of the current iteration. val : arbitrary Value to be stored. force : boolean Force storage anyway. """ # only processor 0 should store anything if self.use_parallel and (self.objective_function.parallel_wrap_shot.rank != 0): return f = getattr(self, arg + "_frequency") # Only store the history if this index matches the frequency. if f and (force or not np.mod(i,f)): loc = getattr(self, arg + "_history") # if not loc.has_key(i): # loc[i] = [] # # Always make a copy of things that are stored # loc[i].append(copy.deepcopy(val)) if i not in loc: loc[i] = None # Always make a copy of things that are stored loc[i] = copy.deepcopy(val) def retrieve_history(self, arg): """Convenience routine for extracting a given history. Parameters ---------- arg : string String prefix for naming the history and frequency variables. Returns ------- iters, data : list of int, list of type(data) If the history has been stored. None, None Otherwise """ f = getattr(self, arg + "_frequency") # Only store the history if this index matches the frequency. if f: hist = getattr(self, arg + "_history") return list(zip(*sorted(hist.items()))) else: return None, None def _print(self, *args): # only processor 0 should store anything if self.use_parallel and (self.objective_function.parallel_wrap_shot.rank != 0): return if self.verbose: print(*args, file=self.logfile, flush=True) # #### Actual optimization stuff below... def initialize(self, initial_value, **kwargs): """Handle any optimization loop initialization and verify any preconditions. Parameters ---------- initial_value : solver.ModelData Starting guess. """ # Generally, there will be an initial value, but just in case... self.base_model = copy.deepcopy(initial_value) self.solver.model_parameters = self.base_model def set_linesearch_configuration(self, geom_fac=0.5, geom_fac_up=0.7, Wolfe_c1=0.1, # 1e-4 Wolfe_c2=0.9, Wolfe_fac_up=1.5, goldstein_c=1e-4, fp_comp=1e-6): """Set up configurations for linesearch Parameters: geom_fac: factor to reduce the search step size geom_fac_up: factor to increase the search step size goldstein_c: the c parameter for the goldstein condition Wolfe_c1: the c1 parameter for the Wolfe condition Wolfe_c2: the c2 parameter for the Wolfe condition Wolfe_fac_up: the factor to increase the search step size for the Wolfe condition fp_comp: reasonable floating point cutoff """ setattr(self, "geom_fac", geom_fac) setattr(self, "geom_fac_up", geom_fac_up) setattr(self, "goldstein_c", goldstein_c) setattr(self, "Wolfe_c1", Wolfe_c1) setattr(self, "Wolfe_c2", Wolfe_c2) setattr(self, "Wolfe_fac_up", Wolfe_fac_up) setattr(self, "fp_comp", fp_comp) def __call__(self, shots, initial_value, iteration_parameters, line_search='backtrack', tolerance=1e-9, verbose=False, append=False, status_configuration={}, linesearch_configuration={}, write=False, history_iter=0, **kwargs): """The main function for executing a number of steps of the descent algorith. Most things can be done without directly overriding this function. Parameters ---------- shots : list of pysit.Shot List of Shots for which to compute on. initial_value : solver.WaveParameters Initial guess for the iteration. iteration_parameters : int, iterable Loop iteration parameters, like number of steps or frequency sets. <blank>_frequency : int, optional kwarg Frequency with which to store histories. Detailed in reset method. verbose : bool Verbosity flag. linesearch_configuration : dictionary Possible parameters for linesearch, for more details, please check the introduction of the function set_linesearch_configuration """ self.reset(append, **status_configuration) self.set_linesearch_configuration(**linesearch_configuration) self.tolerance = tolerance self.verbose=verbose self.write = write self.history_iter = history_iter self.line_search = line_search if type(line_search) is str: self.ls_method = line_search self.ls_config = None else: #assume line_search is tuple('method', config1, config2, ...) self.ls_method = line_search[0] self.ls_config = line_search[1:] self.initialize(initial_value, **kwargs) # valid ieration parameters: # int, e.g., iteration_parameters=4 # iterable(int), e.g., iteration_parameters=[50,50,50] will run the loop 3 times with 50 iterations each # iterable( list(int, arguments)), e.g., iteration_parameters=[(50,[1,2,3,4,5]), (50,[6,7,8,9])] will run the loop twice, 50 times each, for the frequencies listed in arguments if np.iterable(iteration_parameters): for ip in iteration_parameters: if type(ip) in [tuple, list]: steps, arguments = ip elif type(iteration_parameters) is int: steps = ip arguments = {} else: raise ValueError('Invalid iteration parameter {0} detected.'.format(ip)) # Call the inner loop self.inner_loop(shots, steps, objective_arguments=arguments, **kwargs) else: if type(iteration_parameters) is int: # Call the inner loop steps=iteration_parameters self.inner_loop(shots, steps, **kwargs) else: raise ValueError('Singular iteration parameters of type {0} are not permitted at this time.'.format(type(iteration_parameters))) #Floats as a convergence epsilon may happen, but nothing runs to convergence. # Return the current state at the end of the run return self.base_model def inner_loop(self, shots, steps, objective_arguments={}, **kwargs): """Inner loop the optimization iteration This is a separate method so that the workings of the inner loop can be overridden without duplicating the wrapper code in the call function. Parameters ---------- shots : list of pysit.Shot List of Shots for which to compute the residual. steps : int Number of iterations to run. """ stop = False iteration = 0 while not stop: # for step in range(steps): # Zeroth step is always the initial condition. tt = time.time() i = self.iteration self.store_history('value', i, self.base_model) self._print('Iteration {0}'.format(i)) self.solver.model_parameters = self.base_model # extra data to try to extract from gradient call aux_info = {'objective_value': (True, None), 'residual_norm': (True, None)} # pass information for the solver type objective_arguments.update(kwargs) # Compute the gradient gradient = self.objective_function.compute_gradient(shots, self.base_model, aux_info=aux_info, **objective_arguments) objective_value = aux_info['objective_value'][1] # tmp_data_write = {'data': self.base_model.data} # fname = 'x_' + str(i) + '_2.mat' # sio.savemat(fname, tmp_data_write) # Process and store meta data about the gradient self.store_history('gradient', i, gradient) gradient_norm = gradient.norm() self._print(' gradnorm {0}'.format(gradient_norm)) self.store_history('gradient_length', i, gradient_norm) if aux_info['objective_value'][1] is not None: self.store_history('objective', i, aux_info['objective_value'][1]) self._print(' objective {0}'.format(aux_info['objective_value'][1])) if aux_info['residual_norm'][1] is not None: self.store_history('residual_length', i, aux_info['residual_norm'][1]) self._print(' residual {0}'.format(aux_info['residual_norm'][1])) # Compute step modifier step = self._select_step(shots, objective_value, gradient, i, objective_arguments, **kwargs) # Process and store meta data about the step step_len = step.norm() self.store_history('step_length', i, step_len) self.store_history('step', i, step) if self.write is True: if self.use_parallel and (self.objective_function.parallel_wrap_shot.rank != 0): [] else: if i == 0: tmp_data_write = {'data': self.base_model.data} fname = 'x_' + str(i+self.history_iter) + '.mat' sio.savemat(fname, tmp_data_write) if self.use_parallel is True: self.objective_function.parallel_wrap_shot.comm.Barrier() # Apply new step self.base_model += step if self.write is True: if self.use_parallel and (self.objective_function.parallel_wrap_shot.rank != 0): [] else: tmp_data_write = {'data': self.base_model.data} fname = 'x_' + str(i+1+self.history_iter) + '.mat' sio.savemat(fname, tmp_data_write) if self.use_parallel is True: self.objective_function.parallel_wrap_shot.comm.Barrier() ttt = time.time()-tt self.store_history('run_time', i, ttt) self.iteration += 1 self._print(' run time {0}s'.format(ttt)) if (iteration >= steps) or (objective_value < self.tolerance): stop = True else: iteration += 1 def _select_step(self, shots, current_objective_value, gradient, iteration, objective_arguments, **kwargs): raise NotImplementedError("_select_step must be implemented by a subclass.") def select_alpha(self, shots, gradient, direction, objective_arguments, **kwargs): """Resets the state of the optimization algorithm. Parameters ---------- shots : list of pysit.Shot List of Shots for which to compute on. gradient : Solver.ModelData The gradient in model space. direction : Solver.ModelData The search direction in model space. method : {'constant', 'linear', 'quadratic', 'linesearch'}, optional The technique used to select alpha. alpha : float, optional The returned value for 'constant'. Returns ------- alpha : float Line search parameter. """ if self.ls_method == 'constant': return self._constant_line_search() elif self.ls_method == 'linear': return self._linear_line_search(shots, gradient, direction, objective_arguments, **kwargs) elif self.ls_method == 'backtrack': return self._backtrack_line_search(shots, gradient, direction, objective_arguments, **kwargs) elif self.ls_method == 'Wolfe': return self._Wolfe_line_search(shots, gradient, direction, objective_arguments, **kwargs) else: raise ValueError('Alpha selection method {0} invalid'.format(self.ls_method)) def _constant_line_search(self): alpha = self.ls_config[0] return alpha def _linear_line_search(self, shots, gradient, direction, objective_arguments, **kwargs): raise NotImplementedError('Linear selection of alpha is an objective function dependent operation.') # # \int{gradient*s}dx = -\int{gradient^2} = -\int{s^2} # d_norm = -1*np.linalg.norm(direction) * np.prod(self.solver.mesh.deltas) # # # # The commented out bit is probably the correct way to do things, # # but it does not generalize between time and frequency due to # # differences in the way the data are stored (eg, array, dict of # # arrays, etc). Also, the "linear" test is ## res = map(lambda x: self.objective_function.modeling_tools.linear_forward_model(x, self.base_model, direction, return_parameters=['pseudodata'], **kwargs), shots) ## pds = [np.linalg.norm(r['pseudodata'])**2 for r in res] ## denominator = np.sum(pds) * self.solver.dt # # res = self.objective_function.apply_hessian(shots, self.base_model, direction, hessian_mode='approximate', **objective_arguments) ## res = self.objective_function.apply_hessian(shots, direction, hessian_mode='full', **objective_arguments) # denominator = np.dot(direction.T, res).squeeze() * np.prod(self.solver.mesh.deltas) # # numerator = d_norm**2 # # return numerator / denominator def _backtrack_line_search(self, shots, gradient, direction, objective_arguments, current_objective_value=None, alpha0_kwargs={}, **kwargs): geom_fac = self.geom_fac geom_fac_up = self.geom_fac_up goldstein_c = self.goldstein_c #1e-4 fp_comp = 1e-6 if current_objective_value is None: fk = self.objective_function.evaluate(shots, self.base_model, **objective_arguments) else: fk = current_objective_value myalpha0_kwargs = dict() myalpha0_kwargs.update(alpha0_kwargs) myalpha0_kwargs.update({'upscale_factor' : geom_fac_up}) alpha = self._compute_alpha0(current_objective_value, gradient, **myalpha0_kwargs) stop = False itercnt = 1 self._print(" Starting: ".format(itercnt), alpha, fk) Alphas = [] Objs = [] while not stop: # Cut the initial alpha until it is as large as can be and still satisfy the valid conditions for an updated model. valid=False alpha *= 2 cnt = 0 while not valid: alpha/=2 tdir = alpha*direction model = self.base_model + tdir # if self.proj_op is not None: # model = self.proj_op(model) cnt +=1 valid = model.validate() self.solver.model_parameters = model fkp1 = self.objective_function.evaluate(shots, model, **objective_arguments) Alphas.append(alpha) Objs.append(fkp1) cmpval = fk + alpha * goldstein_c * gradient.inner_product(tdir) self._print(" Pass {0}: a:{1}; {2} ?<= {3}".format(itercnt, alpha, fkp1, cmpval)) if (fkp1 <= cmpval) or ((abs(fkp1-cmpval)/abs(fkp1)) <= fp_comp): # reasonable floating point cutoff stop = True elif itercnt > self.max_linesearch_iterations: stop = True alpha_idx = np.argmin(Objs) alpha = Alphas[alpha_idx] self._print('Too many passes ({0}), attempting to use current alpha ({1}).'.format(alpha_idx, alpha)) else: itercnt += 1 alpha = alpha * geom_fac self.prev_alpha = alpha return alpha def _Wolfe_line_search(self, shots, gradient, direction, objective_arguments, current_objective_value=None, alpha0_kwargs={}, **kwargs): geom_fac = self.geom_fac geom_fac_up = self.geom_fac_up c1 = self.Wolfe_c1 #1e-4 c2 = self.Wolfe_c2 Wolfe_fac_up = self.Wolfe_fac_up fp_comp = self.fp_comp if current_objective_value is None: fk = self.objective_function.evaluate(shots, self.base_model, **objective_arguments) else: fk = current_objective_value myalpha0_kwargs = dict() myalpha0_kwargs.update(alpha0_kwargs) myalpha0_kwargs.update({'upscale_factor' : geom_fac_up}) alpha = self._compute_alpha0(current_objective_value, gradient, **myalpha0_kwargs) stop = False itercnt = 1 self._print(" Starting: ".format(itercnt), alpha, fk) aux_info = {'objective_value': (True, None), 'residual_norm': (True, None)} Alphas = [] Objs = [] while not stop: # Cut the initial alpha until it is as large as can be and still satisfy the valid conditions for an updated model. valid=False alpha *= 2 cnt = 0 while not valid: alpha/=2 tdir = alpha*direction model = self.base_model + tdir # if self.proj_op is not None: # model = self.proj_op(model) cnt +=1 valid = model.validate() self.solver.model_parameters = model gradient_kp1 = self.objective_function.compute_gradient(shots, model, aux_info=aux_info, **objective_arguments) fkp1 = aux_info['objective_value'][1] Alphas.append(alpha) Objs.append(fkp1) cmpval = fk + alpha * c1 * gradient.inner_product(tdir) cmpval2 = c2 * gradient.inner_product(tdir) f2kp1 = gradient_kp1.inner_product(tdir) self._print(" Pass {0}: a:{1}; {2} ?<= {3}; |{4}| ?<= |{5}|".format(itercnt, alpha, fkp1, cmpval, f2kp1, cmpval2)) if (fkp1 <= cmpval) or ((abs(fkp1-cmpval)/abs(fkp1)) <= fp_comp): # reasonable floating point cutoff if (abs(f2kp1) <= abs(cmpval2)) or ((abs(f2kp1-cmpval2)/abs(cmpval2)) <= fp_comp): stop = True else: alpha_org = alpha alpha *= Wolfe_fac_up itercnt += 1 else: itercnt += 1 alpha_org = alpha alpha = alpha * geom_fac if itercnt > self.max_linesearch_iterations: stop = True alpha_idx = np.argmin(Objs) alpha = Alphas[alpha_idx] self._print('Too many passes ({0}), attempting to use current alpha ({1}).'.format(alpha_idx, alpha)) self.prev_alpha = alpha return alpha

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null

0

null

0

1

0

7f289f79fa5cf6d1164d782245286d6f16f90388

7,091

py

Python

dashboard.py

fschw/dashoard

6583d8bdfd1f14d40607df121832c5ceb6cbaa81

[ "Apache-2.0" ]

null

dashboard.py

fschw/dashoard

6583d8bdfd1f14d40607df121832c5ceb6cbaa81

[ "Apache-2.0" ]

null

dashboard.py

fschw/dashoard

6583d8bdfd1f14d40607df121832c5ceb6cbaa81

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding:utf-8 -*- import os import sys import logging import ccs811LIBRARY logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.DEBUG, datefmt='%Y-%m-%d %H:%M:%S') picdir = os.path.join(os.path.join(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'e-Paper'),'RaspberryPi_JetsonNano'),'python'),'pic') logging.info("Add pic dir: "+ picdir) #inintialize mockups on dev env, and real libs on rasp if os.path.exists('/sys/bus/platform/drivers/gpiomem-bcm2835'): logging.info("Start in productive mode...") libdir = os.path.join(os.path.join(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'e-Paper'),'RaspberryPi_JetsonNano'),'python'),'lib') if os.path.exists(libdir): sys.path.append(libdir) logging.info("Add lib dir: "+ libdir) libdir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'Adafruit_Python_DHT') if os.path.exists(libdir): sys.path.append(libdir) logging.info("Add lib dir: "+ libdir) import Adafruit_DHT from waveshare_epd import epd4in2 else: logging.info("Start in mockup mode...") sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'mockups')) import Adafruit_DHT_mock from waveshare_epd import epd4in2_mock import time from PIL import Image, ImageDraw, ImageFont import subprocess import traceback import requests from flask import Flask from flask import request import threading access_token = "" app = Flask(__name__) @app.route("/") def receive_code(): logging.info("HTTP req") code = request.args.get('code', '') if code is not "": print("Code received:" + code) url = "https://iam.viessmann.com/idp/v2/token" header = {"Content-Type": "application/x-www-form-urlencoded"} data = "grant_type=authorization_code&client_id=9ceff2a5f57d345a580142626e3b4a7f&redirect_uri=http://192.168.178.201:4200/&code_verifier=2e21faa1-db2c-4d0b-a10f-575fd372bc8c-575fd372bc8c&code="+code response = requests.post(url=url, headers=header, data=data) if response.ok: global access_token access_token = response.json()['access_token'] logging.info("New access token: " + access_token) return "Authorisation OK" else: return "Authorisation NOK" return "No code received" if __name__ == "__main__": args = {'host': '0.0.0.0', 'port' : 4200} threading.Thread(target=app.run, kwargs=args).start() font24 = ImageFont.truetype(os.path.join(picdir, 'Font.ttc'), 24) sensor = ccs811LIBRARY.CCS811() def setup(mode=1): print('Starting CCS811 Read') sensor.configure_ccs811() sensor.set_drive_mode(mode) if sensor.check_for_error(): sensor.print_error() raise ValueError('Error at setDriveMode.') result = sensor.get_base_line() sys.stdout.write("baseline for this sensor: 0x") if result < 0x100: sys.stdout.write('0') if result < 0x10: sys.stdout.write('0') sys.stdout.write(str(result) + "\n") ''' try: res = subprocess.run("sudo pigpiod", shell=True, check=True, text=True) logging.info(res.stdout) setup(1) res = subprocess.run("sudo killall pigpiod", shell=True, check=True, text=True) logging.info(res.stdout) except IOError as e: logging.info(e) ''' try: epd = epd4in2.EPD() logging.info("Init and Clear display") epd.init() epd.Clear() loop = True cnt = 1 while loop: image = Image.new('1', (epd.width, epd.height), 255) draw = ImageDraw.Draw(image) logging.info("Updating for Iteration " + str(cnt)) cnt = cnt + 1 #read outside temp '''logging.info("Read outside temp...") logging.info("Token:" + access_token) header = {"Authorization": "Bearer " + access_token} req1 = "https://api.viessmann.com/iot/v1/equipment/installations/952499/gateways/7637415022052208/devices/0/features/heating.sensors.temperature.outside" logging.info("reading temperature.outside") response = requests.get(url=req1, headers=header) outsideTemp = "" if response.status_code == 200: outsideTemp = response.json()["data"]["properties"]["value"]["value"] logging.info('Outside temp: {:.1f}°'.format(outsideTemp)) draw.text((10, 0), 'Außen: {:.1f}°'.format(outsideTemp), font=font24, fill=0) # read humidity and inside temp logging.info("Read inside temperature and humidity...") insideHumidity, insideTemp = Adafruit_DHT.read_retry(Adafruit_DHT.AM2302, 4) if insideHumidity is not None and insideTemp is not None: logging.info( 'Inside temp: {:.1f}°'.format(insideTemp)) logging.info( 'Rel. Humidity: {:.1f}%'.format(insideHumidity)) draw.text((10, 50), 'Innen: {:.1f}°'.format(insideTemp), font = font24, fill = 0) draw.text((10, 100), 'Rel: {:.1f}%'.format(insideHumidity), font = font24, fill = 0) else: logging.info( "Could not read from Inside temp/Humidity") ''' #res = subprocess.run("sudo pigpiod", shell=True, check=True, text=True) #logging.info(res.stdout) ''' logging.info("Read CO2 and TVOC...") if sensor.data_available(): sensor.read_logorithm_results() logging.info( "CO2: {0:.1f} TVOC: {1:.1f}".format(sensor.CO2, sensor.tVOC)) draw.text((10, 150), "CO2: {0:.1f} TVOC: {1:.1f}".format(sensor.CO2, sensor.tVOC), font = font24, fill = 0) elif sensor.check_for_error(): logging.info( "Could not read from CO2/TVOC Sensor") ''' #res = subprocess.run("sudo killall pigpiod", shell=True, check=True, text=True) #logging.info(res.stdout) logging.info("Adding visuals to image...") upper = 80 lower = 299 left = 70 right = 330 #house frame draw.line((left, lower, right, lower), fill = 0, width = 3) draw.line((left, lower, left, upper), fill = 0, width = 3) draw.line((left, upper, right, upper), fill = 0, width = 3) draw.line((right, upper, right, lower), fill = 0, width = 3) #roof mid = 200 y = mid*upper/(mid-left) draw.line((mid, 1, 0, y), fill = 0, width = 3) draw.line((mid, 1, 400, y), fill = 0, width = 3) '''draw.line((70, 50, 20, 100), fill = 0) draw.rectangle((20, 50, 70, 100), outline = 0) draw.line((165, 50, 165, 100), fill = 0) draw.line((140, 75, 190, 75), fill = 0) draw.arc((140, 50, 190, 100), 0, 360, fill = 0) draw.rectangle((80, 50, 130, 100), fill = 0)''' #draw.chord((200, 50, 250, 100), 0, 360, fill = 0) epd.display(epd.getbuffer(image)) time.sleep(30) except IOError as e: logging.info(e) except KeyboardInterrupt: logging.info("ctrl + c:") epd.Clear() epd.sleep() epd4in2.epdconfig.module_exit() exit()

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206

0.626287

940

7,091

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0.311702

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0.280165

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0.227377

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0.05788

0.217882

7,091

187

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37.919786

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0.052038

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0.028302

0

null

0

null

0

1

0

7f28a7ab38e9b7eef62b3b54becbde2c0fcfdf3c

7,025

py

Python

polyply/src/polyply_parser.py

jan-stevens/polyply_1.0

17578a0ea546584164722129f0d718a5c9533a1a

[ "Apache-2.0" ]

34

2020-07-23T14:50:22.000Z

2022-03-17T02:03:41.000Z

polyply/src/polyply_parser.py

jan-stevens/polyply_1.0

17578a0ea546584164722129f0d718a5c9533a1a

[ "Apache-2.0" ]

136

2020-06-12T15:06:18.000Z

2022-03-31T11:31:09.000Z

polyply/src/polyply_parser.py

jan-stevens/polyply_1.0

17578a0ea546584164722129f0d718a5c9533a1a

[ "Apache-2.0" ]

7

2020-07-30T10:53:47.000Z

2022-03-11T19:27:57.000Z

# Copyright 2020 University of Groningen # # 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 collections import Counter, defaultdict import numpy as np import networkx as nx import vermouth import vermouth.gmx from vermouth.parser_utils import SectionLineParser from vermouth.gmx.itp_read import ITPDirector class PolyplyParser(ITPDirector): ''' Parser for polyply input format. ''' def __init__(self, force_field): super().__init__(force_field) self.citations = set() @SectionLineParser.section_parser('moleculetype', 'citation') def _parse_citation(self, line, lineno=0): cite_keys = line.split() self.current_block.citations.update(cite_keys) @SectionLineParser.section_parser('citations') def _pase_ff_citations(self, line, lineno=0): # parses force-field wide citations cite_keys = line.split() self.citations.update(cite_keys) # overwritten to allow for dangling bonds def _treat_block_interaction_atoms(self, atoms, context, section): all_references = [] for atom in atoms: reference = atom[0] if reference.isdigit(): if int(reference) < 1: msg = ('In section {} is a negative atom reference, which is not allowed.') raise IOError(msg.format(section.name)) # The indices in the file are 1-based reference = int(reference) - 1 atom[0] = reference else: msg = ('Atom names in blocks cannot be prefixed with + or -. ' 'The name "{}", used in section "{}" of the block "{}" ' 'is not valid in a block.') raise IOError(msg.format(reference, section, context.name)) all_references.append(reference) return all_references def treat_link_multiple(self): """ Iterates over a :class:`vermouth.force_field.ForceField` and adds version tags for all interactions within a :class:`vermouth.molecule.Link` that are applied to the same atoms. """ for link in self.force_field.links: for key in link.interactions: terms = link.interactions[key] count_terms = Counter(tuple(term.atoms) for term in terms) for term in terms: tag = count_terms[tuple(term.atoms)] if tag >= 1: term.meta.update({"version":tag}) count_terms[tuple(term.atoms)] = tag -1 def _treat_link_atoms(self, block, link, inter_type): # we need to convert the atom index to an atom-name n_atoms = len(block.nodes) # the uncommented statement does not work because node and # atom name are couple for blocks, which is debatably useful #atom_names = list(nx.get_node_attributes(block, 'atomname')) atom_names = [block.nodes[node]["atomname"] for node in block.nodes] for inter_type in link.interactions: for interaction in link.interactions[inter_type]: new_atoms = [] for atom in interaction.atoms: prefix = "" while atom/n_atoms >= 1: atom = atom - n_atoms prefix = prefix + "+" new_name = prefix + atom_names[atom] new_atoms.append(new_name) attrs = block.nodes[atom] link.add_node(new_name, **attrs) order = prefix.count("+") nx.set_node_attributes(link, {new_name:order}, "order") interaction.atoms[:] = new_atoms return new_atoms def _split_links_and_blocks(self, block): # Make sure to add the atomtype resdidue number etc to # the proper nodes. n_atoms = len(block.nodes) res_name = block.name prev_atoms = [] links = [] for key in block.interactions: block_interactions = [] for interaction in block.interactions[key]: if any(isinstance(atom, str) for atom in interaction.atoms): return if np.sum(np.array(interaction.atoms) > n_atoms - 1) > 0: if interaction.atoms != prev_atoms: prev_atoms[:] = interaction.atoms new_link = vermouth.molecule.Link() new_link.interactions = defaultdict(list) new_link.citations = block.citations new_link.name = res_name links.append(new_link) links[-1].interactions[key].append(interaction) else: block_interactions.append(interaction) block.interactions[key] = block_interactions for link in links: self._treat_link_atoms(block, link, key) self.force_field.links.append(link) def _make_edges(self): for block in self.force_field.blocks.values(): inter_types = list(block.interactions.keys()) for inter_type in inter_types: block.make_edges_from_interaction_type(type_=inter_type) for link in self.force_field.links: inter_types = list(link.interactions.keys()) for inter_type in inter_types: link.make_edges_from_interaction_type(type_=inter_type) # overwrites the finalize method to deal with dangling bonds # and to deal with multiple interactions in the way needed # for polyply to work def finalize(self, lineno=0): if self.current_meta is not None: raise IOError("Your #ifdef/#ifndef section is orderd incorrectly." "There is no #endif for the last pragma..") prev_section = self.section self.section = [] self.finalize_section(prev_section, prev_section) self.macros = {} self.section = None for block in self.force_field.blocks.values(): block.citations.update(self.citations) if len(block.nodes) > 0: n_atoms = len(block.nodes) self._split_links_and_blocks(block) self.treat_link_multiple() self._make_edges() def read_polyply(lines, force_field): director = PolyplyParser(force_field) return list(director.parse(iter(lines)))

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95

0.601423

831

7,025

4.932611

0.277978

0.026836

0.020493

0.015614

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0.057087

0

0.004791

0.316584

7,025

178

96

39.466292

0.84899

0.186904

0

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0

0.059826

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1

0.084746

false

0

0.059322

0

0.186441

0

null

0

null

0

1

0

7f28c2e82480941f199ea350974100507d9d1569

8,138

py

Python

timeflux/nodes/query.py

jonathanjfshaw/timeflux

a17100bce754bd6d85d2c9c65a95bfcc0e7fe219

[ "MIT" ]

null

timeflux/nodes/query.py

jonathanjfshaw/timeflux

a17100bce754bd6d85d2c9c65a95bfcc0e7fe219

[ "MIT" ]

null

timeflux/nodes/query.py

jonathanjfshaw/timeflux

a17100bce754bd6d85d2c9c65a95bfcc0e7fe219

[ "MIT" ]

null

import numpy as np from timeflux.core.exceptions import WorkerInterrupt from timeflux.core.node import Node class SelectRange(Node): """Select a subset of the given data along vertical (index) or horizontal (columns) axis. Attributes: i (Port): default data input, expects DataFrame with eventually MultiIndex. o (Port): default output, provides DataFrame with eventually MultiIndex. Args: ranges (dict): Dict with keys are level names and values are selection ranges. axis (int): If 0, the level concerns row index, if 1, columns index (`0` or `1`). Default: `0`. inclusive (bool) : Whether the boundaries are strict or included. Default: `False`. Example: In this example, we have an input DataFrame with multi level columns and we want to select data with index from level of name `second` in range `[1,1.5]`. We set: * ``ranges`` = `{"second": [1, 1.5]}` * ``axis`` = `1` * ``inclusive`` = `True` If the data received on port ``i`` is: :: first A ... B second 1.3 1.6 1.9 1.3 1.6 1.9 2017-12-31 23:59:59.998745401 0.185133 0.541901 0.806561 ... 0.732225 0.806561 0.658783 2018-01-01 00:00:00.104507143 0.692277 0.849196 0.987668 ... 0.489425 0.221209 0.987668 2018-01-01 00:00:00.202319939 0.944059 0.039427 0.567945 ... 0.925248 0.180575 0.567945 The data provided on port ``o`` will be: :: first A B second 1.3 1.3 2017-12-31 23:59:59.998745401 0.185133 0.732225 2018-01-01 00:00:00.104507143 0.692277 0.489425 2018-01-01 00:00:00.202319939 0.944059 0.925248 """ def __init__(self, ranges, axis=0, inclusive=False): self._ranges = ranges # list of ranges per level self._inclusive = inclusive # include boundaries. self._axis = axis def update(self): if not self.i.ready(): return self.o.meta = self.i.meta if self._axis == 1: self.i.data = self.i.data.T mask = self._mask() self.o.data = self.i.data[np.logical_and.reduce(mask)] if self._axis == 1: self.o.data = self.o.data.T def _mask(self): if self._inclusive: mask = [(self.i.data.index.get_level_values(l) >= r[0]) & (self.i.data.index.get_level_values(l) <= r[1]) for l, r in (self._ranges).items() if r is not None] else: mask = [(self.i.data.index.get_level_values(l) > r[0]) & (self.i.data.index.get_level_values(l) < r[1]) for l, r in (self._ranges).items() if r is not None] return mask class XsQuery(Node): """Returns a cross-section (row(s) or column(s)) from the data. Attributes: i (Port): default input, expects DataFrame with eventually MultiIndex. o (Port): default output, provides DataFrame with eventually MultiIndex. Args: key (str|tuple): Some label contained in the index, or partially in a MultiIndex index. axis (int): Axis to retrieve cross-section on (`0` or `1`). Default: `0`. level (str|int|tuple) : In case of a key partially contained in a MultiIndex, indicates which levels are used. Levels can be referred by label or position. drop_level (bool) : If False, returns DataFrame with same level. Default: `False`. Example: In this example, we have an input DataFrame with multi level columns and we want to select cross section between `B` from level of name `first` and `1` from level of name `second`. We set: * ``key`` = `("B", 1)` * ``axis`` = `1` * ``level`` = `["first", "second"]` * ``drop_level`` = `False` If the data received on port ``i`` is: :: first A ... B second 1 2 ... 1 2 2017-12-31 23:59:59.998745401 0.185133 0.541901 ... 0.297349 0.806561 2018-01-01 00:00:00.104507143 0.692277 0.849196 ... 0.844549 0.221209 2018-01-01 00:00:00.202319939 0.944059 0.039427 ... 0.120567 0.180575 The data provided on port ``o`` will be: :: first B second 1 2018-01-01 00:00:00.300986584 0.297349 2018-01-01 00:00:00.396560186 0.844549 2018-01-01 00:00:00.496559945 0.120567 References: See the documentation of `pandas.DataFrame.xs <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.xs.html>`_ . """ def __init__(self, key, **kwargs): """ Args: key (str|tuple): Some label contained in the index, or partially in a MultiIndex index. kwargs: Keyword arguments to call pandas xs method: axis, level, drop_level """ self._key = key self._kwargs = kwargs self._ready = False def update(self): if not self.i.ready(): return self.o.meta = self.i.meta if not self._ready: try: self._query() self._ready = True except KeyError as e: raise WorkerInterrupt(e) else: self._query() def _query(self): self.o.data = self.i.data.xs(key=self._key, **self._kwargs) class LocQuery(Node): """Slices DataFrame on group of rows and columns by label(s) Attributes: i (Port): default data input, expects DataFrame. o (Port): default output, provides DataFrame. Args: key (str|list|tuple): Label selection specification. axis (int): Axis to query the label from (`0` or `1`). Default: `1`. Example: In this example, we have an input DataFrame with 5 columns `[A, B, C, D, E]` and we want to select columns A and E. We set: * ``key`` = `["A", "E"]` * ``axis`` = `1` If the data received on port ``i`` is: :: A B ... E F 2017-12-31 23:59:59.998745401 0.185133 0.541901 ... 0.806561 0.658783 2018-01-01 00:00:00.104507143 0.692277 0.849196 ... 0.221209 0.987668 2018-01-01 00:00:00.202319939 0.944059 0.039427 ... 0.180575 0.567945 The data provided on port ``o`` will be: :: A E 2017-12-31 23:59:59.998745401 0.185133 0.806561 2018-01-01 00:00:00.104507143 0.692277 0.221209 2018-01-01 00:00:00.202319939 0.944059 0.180575 References: See the documentation of `pandas.DataFrame.loc <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.loc.html>`_ . """ def __init__(self, key, axis=1): self._axis = axis if not isinstance(key, (list, tuple)): self._key = [key] else: self._key = key self._ready = False def update(self): if not self.i.ready(): return self.o = self.i if not self.i.ready(): return self.o.meta = self.i.meta if not self._ready: try: self._query() self._ready = True except KeyError as e: raise WorkerInterrupt(e) else: self.o.data = self.i.data.loc[:, self._key] def _query(self): if self._axis == 0: self.o.data = self.i.data.loc[self._key, :] else: # self._axis == 1: self.o.data = self.i.data.loc[:, self._key]

35.229437

188

0.52986

1,087

8,138

3.912604

0.185833

0.024453

0.030567

0.628968

0.597461

0.568775

0.536798

0.51352

0.485775

0

0.166347

0.359548

8,138

230

189

35.382609

0.649655

0.635291

0

0.613333

0

1

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false

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0.266667

0

null

0

null

0

1

0

7f2a45cc54e29cb7e353983ef17c8b140cf637fe

2,205

py

Python

tools/report-converter/codechecker_report_converter/analyzers/tslint/analyzer_result.py

hyker/codechecker

8ddb9bd1aa037d4499a2be8d35e9c1d470163baf

[ "Apache-2.0" ]

1

2021-04-08T16:51:45.000Z

tools/report-converter/codechecker_report_converter/analyzers/tslint/analyzer_result.py

hyker/codechecker

8ddb9bd1aa037d4499a2be8d35e9c1d470163baf

[ "Apache-2.0" ]

1

2021-11-30T10:43:49.000Z

tools/report-converter/codechecker_report_converter/analyzers/tslint/analyzer_result.py

hyker/codechecker

8ddb9bd1aa037d4499a2be8d35e9c1d470163baf

[ "Apache-2.0" ]

null

# ------------------------------------------------------------------------- # # Part of the CodeChecker project, under the Apache License v2.0 with # LLVM Exceptions. See LICENSE for license information. # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception # # ------------------------------------------------------------------------- import logging import os import json from typing import Dict, List from codechecker_report_converter.report import File, get_or_create_file, \ Report from ..analyzer_result import AnalyzerResultBase LOG = logging.getLogger('report-converter') class AnalyzerResult(AnalyzerResultBase): """ Transform analyzer result of the TSLint analyzer. """ TOOL_NAME = 'tslint' NAME = 'TSLint' URL = 'https://palantir.github.io/tslint' def get_reports(self, result_file_path: str) -> List[Report]: """ Parse the given analyzer result. """ reports: List[Report] = [] if not os.path.exists(result_file_path): LOG.error("Report file does not exist: %s", result_file_path) return reports try: with open(result_file_path, 'r', encoding="utf-8", errors="ignore") as report_f: bugs = json.load(report_f) except (IOError, json.decoder.JSONDecodeError): LOG.error("Failed to parse the given analyzer result '%s'. Please " "give a valid json file generated by TSLint.", result_file_path) return reports file_cache: Dict[str, File] = {} for bug in bugs: file_path = os.path.join( os.path.dirname(result_file_path), bug.get('name')) if not os.path.exists(file_path): LOG.warning("Source file does not exists: %s", file_path) continue end_pos = bug['startPosition'] line = int(end_pos['line'] + 1) col = int(end_pos['character'] + 1) reports.append(Report( get_or_create_file(os.path.abspath(file_path), file_cache), line, col, bug['failure'], bug['ruleName'] )) return reports

32.426471

79

0.565079

249

2,205

4.86747

0.437751

0.066007

0.069307

0.024752

0.117162

0

0.004367

0.273016

2,205

67

80

32.910448

0.751716

0.187755

0

0.073171

0

0.156321

0

1

0.02439

false

0

0.146341

0

0.341463

0

null

0

null

0

1

0

7f2b5175127736ab4e0d2ce18a1b4d34dfabbaf8

5,040

py

Python

FetchPatents.py

tymrail/PatentFPO

530f73148a8249cdc146ca2c3cedef1995df998e

[ "MIT" ]

null

FetchPatents.py

tymrail/PatentFPO

530f73148a8249cdc146ca2c3cedef1995df998e

[ "MIT" ]

null

FetchPatents.py

tymrail/PatentFPO

530f73148a8249cdc146ca2c3cedef1995df998e

[ "MIT" ]

1

2020-05-21T11:42:02.000Z

import requests from bs4 import BeautifulSoup import sqlite3 import re from OperateDatabase import * import socket import random import time socket.setdefaulttimeout(1000) base_url = 'http://www.freepatentsonline.com' additional_url = ['/result.html?p=', '&sort=relevance&srch=top&query_txt=AN%2F%22', '%22&patents=on'] cx = sqlite3.connect('patents.db') company_list = ['nintendo'] utils = {'Title': 'title', 'Inventors': 'inventor', 'Application Number': 'app_num', 'Abstract': 'abstract', 'Publication Date': 'pub_date', 'Filing Date': 'fil_date', 'Assignee': 'assignee', } user_agents = [ 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36 OPR/26.0.1656.60' 'Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/2.0.0 Opera 9.50' 'Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; en) Opera 9.50' 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0' 'Mozilla/5.0 (X11; U; Linux x86_64; zh-CN; rv:1.9.2.10) Gecko/20100922 Ubuntu/10.10 (maverick) Firefox/3.6.10' 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534.57.2 (KHTML, like Gecko) Version/5.1.7 Safari/534.57.2' 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36' 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11' 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/534.16 (KHTML, like Gecko) Chrome/10.0.648.133 Safari/534.16' 'Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50' 'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-us) AppleWebKit/534.50 (KHTML, like Gecko) Version/5.1 Safari/534.50' 'Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0' 'Mozilla/4.0 (compatible; MSIE 8.0; Windows NT 6.0; Trident/4.0)' 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36' ] def make_up(page, assignee_name): return base_url + additional_url[0] \ + str(page) + additional_url[1] \ + assignee_name + additional_url[2] def lets_rock(companies): for ic in companies: try: company_url = make_up(1, ic) r = requests.get(company_url, headers={'User-Agent': random.choice(user_agents)}) soup = BeautifulSoup(r.text, 'lxml') page_count = int(str(soup.find_all(text=re.compile('Matches'))[0]).strip().split(' ')[-1]) print(page_count) print('let\'s rock') for i in range(1, 5): print('fetching page' + str(i)) fetch_page(make_up(i, ic)) except: with open('error_report.txt', 'w+') as f: f.write('fail fetching company ' + ic + '\n') else: time.sleep(random.randint(0, 3) / 10) def fetch_page(company_url): r = requests.get(company_url, headers={'User-Agent': random.choice(user_agents)}) soup = BeautifulSoup(r.text, 'lxml') text = soup.find_all('tr', 'rowalt') for it in text: url = str(it.select('a')[0]['href']).strip() doc_number = str(it.select('td')[1].text).strip() try: fetch_detail(base_url + url, doc_number) except: with open('error_report.txt', 'w+') as f: f.write('fail fetching ' + url + '\n') else: time.sleep(random.randint(0, 3) / 10) def fetch_detail(detail_url, doc_number): # print(detail_url) # print(doc_number) print('fetching patent: ' + str(doc_number)) r = requests.get(detail_url, headers={'User-Agent': random.choice(user_agents)}) soup = BeautifulSoup(r.text, 'lxml') text = soup.find_all('div', 'disp_doc2') # print(text[0].find('div', 'disp_elm_title').text) # print(str(text[0].find('div', 'disp_elm_text').text).strip()) data_dict = {} for it in text: title = it.find('div', 'disp_elm_title') t = it.find('div', 'disp_elm_text') if title is not None and t is not None: title_text = str(title.text).strip().replace(':', '') t_text = str(t.text) \ .strip() \ .replace('\t', '') \ .replace(' ', '') if title_text in utils: # print(utils[title_text]) # print(t_text) data_dict[utils[title_text]] = t_text # print(str(title.text).strip().replace(':', '')) # print(str(t.text).strip()) # print('\n') data_dict['app_num'] = doc_number insert_data(data_dict) if __name__ == '__main__': # fetch_detail('http://www.freepatentsonline.com/y2017/0346746.html', 'D1234567') lets_rock(company_list)

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0.040816

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null

0

null

0

1

0

7f2bf725dc4bfa7321dd8446ecf83336132ad3f8

3,005

py

Python

ib_trading_calendars/status.py

alexanu/ib-trading-calendars

5a92770d106542968e856aa54ae48d48b306d7f3

[ "Apache-2.0" ]

9

2019-02-04T19:42:12.000Z

2021-08-04T18:36:43.000Z

ib_trading_calendars/status.py

alexanu/ib-trading-calendars

5a92770d106542968e856aa54ae48d48b306d7f3

[ "Apache-2.0" ]

1

2020-03-12T17:32:38.000Z

ib_trading_calendars/status.py

alexanu/ib-trading-calendars

5a92770d106542968e856aa54ae48d48b306d7f3

[ "Apache-2.0" ]

8

2019-02-04T21:08:38.000Z

2021-08-04T18:36:45.000Z

#!/usr/bin/env python # Copyright 2019 QuantRocket LLC - All Rights Reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import pandas as pd import json from ib_trading_calendars.calendar_utils import ib_calendar_factories from trading_calendars.calendar_utils import _default_calendar_factories def get_exchange_status(exchange, dt): """ Returns the exchange status at the specified datetime. """ try: calendar_cls = ib_calendar_factories[exchange] except KeyError: calendar_cls = _default_calendar_factories[exchange] asof_datetime = pd.Timestamp(dt, tz=calendar_cls.tz) start = asof_datetime - pd.Timedelta(days=30) start = pd.Timestamp(start.date(), tz="UTC") end = asof_datetime + pd.Timedelta(days=30) end = pd.Timestamp(end.date(), tz="UTC") calendar = calendar_cls(start=start, end=end) is_open = calendar.is_open_on_minute(asof_datetime) # Note: The `trading_calendars` package sets exchange open times 1 minute # later than the actual open. For example, the exchange hours for NYSE # are 9:31-16:00 in `trading_calendars`, even though NYSE actually opens # at 9:30. This behavior reflects the needs of zipline. To deal with # this, we consider the exchange open if it is open this minute, or next # minute. if not is_open: is_open = calendar.is_open_on_minute(asof_datetime + pd.Timedelta(minutes=1)) if is_open: # Rewind open 1 minute since = calendar.previous_open(asof_datetime) - pd.Timedelta(minutes=1) until = calendar.next_close(asof_datetime) else: since = calendar.previous_close(asof_datetime) # Rewind open 1 minute until = calendar.next_open(asof_datetime) - pd.Timedelta(minutes=1) since = since.tz_convert(asof_datetime.tz.zone).strftime("%Y-%m-%dT%H:%M:%S") until = until.tz_convert(asof_datetime.tz.zone).strftime("%Y-%m-%dT%H:%M:%S") return dict( status="open" if is_open else "closed", since=since, until=until) def main(): parser = argparse.ArgumentParser( description="check the status of an exchange at the specified time") parser.add_argument( "exchange", help="the IB exchange code") parser.add_argument( "dt", help="the ISO format datetime to check") args = parser.parse_args() args = vars(args) status = get_exchange_status(args["exchange"], args["dt"]) print(json.dumps(status))

36.646341

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null

0

null

0

1

0

7f2c6071c31a2ed0bd4232d7ae1073c3257e303e

8,520

py

Python

src/data_preparation/data_prep.py

christianhilscher/dynasim

881cfd3bd9d4b9291d289d703ec7da4a617a479a

[ "MIT" ]

null

src/data_preparation/data_prep.py

christianhilscher/dynasim

881cfd3bd9d4b9291d289d703ec7da4a617a479a

[ "MIT" ]

2

2020-08-06T10:01:59.000Z

2021-05-17T12:14:44.000Z

src/data_preparation/data_prep.py

christianhilscher/dynasim

881cfd3bd9d4b9291d289d703ec7da4a617a479a

[ "MIT" ]

2

2020-08-19T06:52:09.000Z

2021-12-10T08:57:54.000Z

import numpy as np import pandas as pd def SOEP_to_df_old(dataf): """ This function takes the SOEP data as a dataframe and returns the the harmonized data such that the rest of the code can work with it. It also renames the columns etc """ dataf = dataf.copy() # Checking whether some adjustments have already been made if "emplstatus" in dataf.columns.tolist(): dataf = dataf.drop(['emplstatus', 'married_h'], axis = 1) print('Attention, this data is not the original SOEP data but already preprocessed.') else: dataf = dataf dataf = dataf.rename(columns={'syear': 'year', 'phrf': 'personweight', 'pglabgro': 'gross_earnings', 'hhrf': 'hhweight', 'hgheat': 'heizkosten', 'kaltmiete': 'bruttokaltmiete', 'kind': 'child', 'pgpsbil': 'education', 'whours_actual': 'hours' }) dataf['orighid'] = dataf['hid'] # For now motherpid is 0 as a placeholder and maximum age is set to 99 dataf['motherpid'] = 0 dataf['age_max'] = 99 dataf = _numeric_eduation(dataf) dataf = _numeric_employment_status(dataf) dataf = _numeric_laborforce(dataf) dataf = _numeric_working(dataf) dataf = _numeric_hours(dataf) dataf = _numeric_migration(dataf) dataf = make_hh_vars(dataf) return dataf def _numeric_eduation(dataf): dataf = dataf.copy() dataf.loc[:, "educ"] = 0 dataf.loc[(dataf['education'] == "[1] Hauptschulabschluss"), "educ"] = 0 dataf.loc[(dataf['education'] == "[2] Realschulabschluss"), "educ"] = 1 dataf.loc[(dataf['education'] == "[3] Fachhochschulreife"), "educ"] = 2 dataf.loc[(dataf['education'] == "[4] Abitur"), "educ"] = 3 dataf.loc[(dataf['education'] == "[5] Anderer Abschluss"), "educ"] = 4 dataf.loc[(dataf['education'] == "[6] Ohne Abschluss verlassen"), "educ"] = 5 dataf.loc[(dataf['education'] == "[7] Noch kein Abschluss"), "educ"] = 6 dataf.drop("education", axis = 1, inplace = True) dataf.rename(columns={'educ': 'education'}, inplace=True) return dataf def _numeric_employment_status(dataf): dataf = dataf.copy() dataf.loc[:, "emp"] = 0 dataf.loc[(dataf['employment_status'] == "Teilzeit"), "emp"] = 2 dataf.loc[(dataf['employment_status'] == "Vollzeit"), "emp"] = 3 dataf.loc[(dataf['employment_status'] == "Bildung"), "emp"] = 0 dataf.loc[(dataf['employment_status'] == "Nicht erwerbstaetig"), "emp"] = 0 dataf.loc[(dataf['employment_status'] == "Rente"), "emp"] = 1 dataf.drop("employment_status", axis = 1, inplace = True) dataf.rename(columns={'emp': 'employment_status'}, inplace=True) dataf['fulltime'] = 0 dataf.loc[dataf['employment_status'] == 3, 'fulltime'] = 1 return dataf def _numeric_laborforce(dataf): dataf = dataf.copy() dataf.loc[:,'lfs'] = 0 dataf.loc[dataf['pglfs'] == '[11] Working', 'lfs'] = 1 dataf.loc[dataf['pglfs'] == "[12] Working but NW past 7 days" , 'lfs'] = 1 dataf.drop("pglfs", axis = 1, inplace = True) return dataf def _numeric_working(dataf): dataf = dataf.copy() dataf.loc[:,'working'] = 0 dataf.loc[dataf['employment_status'] == 2, 'working'] = 1 dataf.loc[dataf['employment_status'] == 3, 'working'] = 1 return dataf def _numeric_migration(dataf): dataf = dataf.copy() dataf['migration'] = 0 dataf.loc[dataf['migback'] == 0, 'migration'] = 1 dataf.loc[dataf['migback'] == "[1] kein Migrationshintergrund", 'migration'] = 0 dataf.drop('migback', axis=1, inplace=True) dataf.rename(columns={'migration': 'migback'}, inplace=True) return dataf def _numeric_hours(dataf): dataf = dataf.copy() condition = [type(typ)==str for typ in dataf['hours']] dataf.loc[condition, 'hours'] = np.nan dataf['hours'] = dataf['hours'].astype(np.float64) dataf.loc[(dataf["hours"].isna()) & (dataf["employment_status"] == 0) & (dataf["lfs"]==0), "hours"] = 0 dataf.loc[(dataf["hours"].isna()) & (dataf["employment_status"] == 1) & (dataf["lfs"]==0), "hours"] = 0 return dataf # Making household wide variables def make_hh_vars(dataf): dataf = dataf.copy() dataf = _get_multiindex(dataf) dataf = _hh_income(dataf) dataf = _hh_age_youngest(dataf) dataf = _hh_fraction_working(dataf) # dataf = _hh_children(dataf) # dataf = indicate_births(dataf) dataf = _indicate_birth(dataf) dataf.reset_index(inplace=True, drop=True) return dataf def _get_tupleindices(dataf): years = dataf['year'].tolist() hids = dataf['hid'].tolist() return list(zip(years, hids)) def _get_multiindex(dataf): dataf = dataf.copy() index_list = _get_tupleindices(dataf) mindex = pd.MultiIndex.from_tuples(index_list, names=['year' , 'hid']) dataf_out = dataf.set_index(mindex) dataf_out = dataf_out.sort_index(level=1) return dataf_out def _hh_income(dataf): dataf = dataf.copy() earnings = dataf.groupby(level=['year', 'hid'])['gross_earnings'].sum() dataf['hh_income'] = earnings return dataf def _hh_size(dataf): dataf = dataf.copy() size = dataf.groupby(level=['year', 'hid'])['gross_earnings'].size() dataf['n_people'] = size return dataf def _hh_children(dataf): dataf = dataf.copy() children = dataf.groupby(level=['year', 'hid'])['child'].sum() dataf['n_children'] = children return dataf def _hh_fraction_working(dataf): dataf = dataf.copy() dataf = _hh_size(dataf) dataf = _hh_children(dataf) total = dataf.groupby(level=['year', 'hid'])['working'].sum() dataf['total_working'] = total dataf['n_adults'] = dataf['n_people'] - dataf['n_children'] dataf['hh_frac_working'] = dataf['total_working']/dataf['n_adults'] dataf.loc[dataf['n_adults']==0, 'hh_frac_working'] = 0 # Children could also be working, but bound it at 1 dataf.loc[dataf["hh_frac_working"]>1, "hh_frac_working"] = 1 dataf.drop(['total_working', 'n_adults'], axis=1, inplace=True) return dataf def _hh_age_youngest(dataf): dataf = dataf.copy() smallest_age = dataf.groupby(level=['year', 'hid'])['age'].min() dataf['hh_youngest_age'] = smallest_age return dataf def _make_motherpid(dataf): dataf = dataf.copy() # Mothers in cildbearing age interv = np.arange(18, 50) mother_cond = (dataf["female"]==1) & (dataf["age"].isin(interv)) child_cond = dataf["child"]==1 baby_df = dataf[mother_cond | child_cond] rest_df = dataf[(~mother_cond) & (~child_cond)] baby_hh = baby_df.groupby("pid")["hid"].median() baby_df = pd.merge(baby_df, baby_hh, on="pid", suffixes=("_current", "")) baby_df.drop("hid_current", axis=1, inplace=True) mother_pids = baby_df[baby_df["child"]==0].groupby("hid")["pid"].min() merged = pd.merge(baby_df, mother_pids, on="hid", suffixes=("", "mother_pid")) merged.loc[merged["child"]==1, "motherpid"] = merged.loc[merged["child"]==1, "pidmother_pid"] merged.drop("pidmother_pid",axis=1, inplace=True) df_out = pd.concat([rest_df, merged]) return df_out def indicate_births(dataf): dataf = dataf.copy() df_motherpids = _make_motherpid(dataf) tmp = df_motherpids.loc[df_motherpids["motherpid"]!=0, :].groupby("pid")[["year", "age", "motherpid"]].min() tmp["child_birthyear"] = tmp["year"] - tmp["age"] tmp.reset_index(inplace=True, drop=True) mother_birth_list = list(zip(tmp["motherpid"], tmp["child_birthyear"])) df_motherpids["mother_pid"] = list(zip(df_motherpids["pid"], df_motherpids["year"])) df_motherpids.loc[df_motherpids["mother_pid"].isin(mother_birth_list), "birth"] = 1 df_motherpids.drop("mother_pid", axis=1, inplace=True) return df_motherpids def _indicate_birth(dataf): """ Indictaes whether a mother has had a baby in that particular year """ dataf = dataf.copy() minage = dataf.groupby(level=['year', 'hid'])['age'].min() dataf["minage"] = minage dataf["birth"] = 0 dataf.loc[(dataf["minage"]==0)&(dataf["female"]==1)&(dataf["child"]==0), "birth"] = 1 dataf.drop("minage", axis=1, inplace=True) return dataf

32.643678

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1,067

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0.098406

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0.194844

0.096635

0.030703

0

0.013176

0.21608

8,520

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170

32.643678

0.747567

0.061737

0

0.179191

0

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false

0

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0

0.219653

0.00578

0

null

0

null

0

1

0

7f2cd64976be46ed25ddd2ae11ac7035e1a5d54f

1,404

py

Python

Tracker_LoRa/test_sdb_sof/main.py

pbaron2/tracker-lora

e713ae090748c4416c4c689b1c2a2dcc7cc44a11

[ "MIT" ]

null

Tracker_LoRa/test_sdb_sof/main.py

pbaron2/tracker-lora

e713ae090748c4416c4c689b1c2a2dcc7cc44a11

[ "MIT" ]

null

Tracker_LoRa/test_sdb_sof/main.py

pbaron2/tracker-lora

e713ae090748c4416c4c689b1c2a2dcc7cc44a11

[ "MIT" ]

null

from network import LoRa import socket import time modes=[LoRa.LORA] coding=[LoRa.CODING_4_5,LoRa.CODING_4_6,LoRa.CODING_4_7,LoRa.CODING_4_8] pub=[True,False] for k in modes: for i in range(0,16): for j in range(0,6): for l in coding: for p in pub: #LoRa # Europe = LoRa.EU868 #print("TEST1") lora = LoRa(mode=k, region=LoRa.EU868) #print("TEST2") lora.init(frequency=885000000, mode=k, tx_power=14, sf=7+int(j),preamble=int(i),coding_rate=l,public=p) #print("TEST3") s = socket.socket(socket.AF_LORA, socket.SOCK_RAW) #print("TEST4") s.setblocking(False) #print("LoRa initialise !") flag= True compteur = 0 print('Debut Test : ', 'sf =', 7+int(j), ', mode =', k ,', preamble = ', i, ', coding_rate =', l) while flag: data = s.recv(128) if data != b'': print (data) time.sleep(0.1) compteur+=1 if compteur >= 10: flag=False s.close() print('Fin Test\n')

28.653061

123

0.421652

156

1,404

3.711538

0.435897

0.086356

0.075993

0.02418

0

0.05984

0.464387

1,404

48

124

29.25

0.710106

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0

0.048799

0

1

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false

0

0.107143

0

0.107143

0

null

0

null

0

1

0

7f2e724ea35fff6427fd9fe2bf26c971384b0524

1,268

py

Python

setup.py

lnknguyen/niimpy

2aebd1d59b0b7562103128d5eaff90f20091f6fd

[ "MIT" ]

null

setup.py

lnknguyen/niimpy

2aebd1d59b0b7562103128d5eaff90f20091f6fd

[ "MIT" ]

null

setup.py

lnknguyen/niimpy

2aebd1d59b0b7562103128d5eaff90f20091f6fd

[ "MIT" ]

null

#!/usr/bin/env python from setuptools import setup, find_packages from os.path import join, dirname with open("README.md", "r") as fh: long_description = fh.read() version_ns = { } exec(open('niimpy/_version.py').read(), version_ns) version = version_ns['__version__'] del version_ns requirementstxt = join(dirname(__file__), "requirements.txt") requirements = [ line.strip() for line in open(requirementstxt, "r") if line.strip() ] setup(name='niimpy', version=version, description='Behavorial data analysis', long_description=long_description, long_description_content_type='text/markdown', author='Richard Darst', author_email='rkd@zgib.net', url='https://github.com/digitraceslab/niimpy', #packages=['niimpy', 'niimpy.preprocessing', 'niimpy.reading'], packages=find_packages(where='.'), package_data={'niimpy': ['sampledata/*.sqlite3', 'sampledata/*.csv']}, include_package_data=True, python_requires=">=3.6", install_requires=requirements, classifiers=[ "Programming Language :: Python :: 3", "Development Status :: 3 - Alpha", "Operating System :: OS Independent", "Topic :: Scientific/Engineering :: Information Analysis", ], )

32.512821

86

0.67429

141

1,268

5.87234

0.602837

0.072464

0.031401

0.072464

0

0.004798

0.178233

1,268

38

87

33.368421

0.789827

0.064669

0

0.309122

0.018581

0

1

0

false

0

0.066667

0

0.066667

0

null

0

null

0

1

0

7f3143b48ee084919d22e8532883574dc131cbba

4,374

py

Python

robovat/io/hdf5_utils.py

leobxpan/robovat

0d360c34c677cf018c4daab0b8e758943ae1d2c1

[ "MIT" ]

62

2020-04-08T11:26:24.000Z

2021-09-06T02:45:53.000Z

robovat/io/hdf5_utils.py

leobxpan/robovat

0d360c34c677cf018c4daab0b8e758943ae1d2c1

[ "MIT" ]

7

2020-04-12T13:10:10.000Z

2022-03-12T00:15:03.000Z

robovat/io/hdf5_utils.py

leobxpan/robovat

0d360c34c677cf018c4daab0b8e758943ae1d2c1

[ "MIT" ]

17

2020-04-12T17:37:01.000Z

2021-09-07T01:51:46.000Z

"""File IO utilities using HDF5. The data element saved in HDF5 should be a dictionary. The types of each value should be in HDF5_DATA_TYPES. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import h5py import numpy as np import uuid import traceback def write_data_to_hdf5(f, data, compress_size_thresh=100): """Wrte data to HDF5 group. Args: f: The HDF5 group to write the data to. data: The data to be writtent, which can be a group, a dict or a list. compress_size_thresh: Data larger or equal to this size will be compressed by the gzip format. """ for key, value in data.items(): if isinstance(value, dict): group = f.create_group(key) write_data_to_hdf5(group, value) elif isinstance(value, list): group_list = f.create_group(key + '[]') for i, value_i in enumerate(value): assert isinstance(value_i, (dict, np.ndarray)), ( 'List \'%s\' has type %s value %s, which is forbidden.' ' Lists should only have dict or numpy.ndarray values.' % (key, type(value_i), value_i)) group = group_list.create_group(str(i)) write_data_to_hdf5(group, value_i) else: try: if value is None: f[key] = 'None' else: value = np.array(value) if np.prod(value.shape) >= compress_size_thresh: f.create_dataset( key, data=value, compression="gzip", compression_opts=9) else: f.create_dataset(key, data=value) except Exception: traceback.print_exc() raise ValueError('Unsupported data \'%s\' of type %s.' % (key, type(value))) def read_data_from_hdf5(f): """Read data from HDF5 group. Args: f: The HDF5 group to read the data from. Returns: The data read from the group. """ data = dict() for key, value in f.items(): if isinstance(value, h5py._hl.group.Group): if key[-2:] != '[]': # Read dictionary. data[key] = read_data_from_hdf5(value) else: # Read list. list_var = [None] * len(value) for ind, element in value.items(): list_var[int(ind)] = read_data_from_hdf5(element) data[key[:-2]] = list_var else: # Read numpy array or scalar. value = value.value if value == 'None': data[key] = None else: value = np.array(value) if value.shape == (): data[key] = np.asscalar(value) else: data[key] = value # assert isinstance(value, h5py._hl.dataset.Dataset), ( # 'Item \'%s\' has type %s.' % (key, type(value))) # data[key] = np.array(value.value) return data class HDF5Writer(object): """A class to dump pickle to file. """ def __init__(self, filename): """Initialize. Args: filename: The filename of the HDF5 file. """ self._file = h5py.File(filename, 'w') def write(self, data): """Write data to a pickle file. Args: data: An element of the data. """ assert isinstance(data, dict) name = str(uuid.uuid4()) group = self._file.create_group(name) write_data_to_hdf5(group, data) def close(self): """Close the HDF5 file. """ self._file.close() def read(filename): """Read data from an HDF5 file. Args: filename: The path to the HDf5 file. Yields: data: An element of the data. """ with h5py.File(filename, 'r') as f: for name, group in f.items(): try: data = read_data_from_hdf5(group) yield data except Exception: raise ValueError('Errors in reading data from the HDF5 file.')

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4,374

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0.80379

0.238455

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0.192308

0.025641

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null

0

null

0

1

0

7f33ed65f4ac4089a0724c48004c99fbc453b64f

344

py

Python

tests/python/test_sparse_basics.py

ericjang/taichi

0dec0bac6d9c16b7b62ba528f9e0d4268d1d05d2

[ "MIT" ]

null

tests/python/test_sparse_basics.py

ericjang/taichi

0dec0bac6d9c16b7b62ba528f9e0d4268d1d05d2

[ "MIT" ]

null

tests/python/test_sparse_basics.py

ericjang/taichi

0dec0bac6d9c16b7b62ba528f9e0d4268d1d05d2

[ "MIT" ]

null

import taichi as ti @ti.program_test def test_while(): x = ti.var(ti.f32) s = ti.var(ti.i32) n = 128 @ti.layout def place(): ti.root.dense(ti.i, n).bitmasked().dense(ti.i, n).place(x) ti.root.place(s) @ti.kernel def func(): for i in x: ti.atomic_add(s[None], 1) x[0] = 1 func() assert s[None] == 128

14.333333

62

0.572674

65

344

2.984615

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0.046392

0.072165

0.092784

0

0.05

0.244186

344

23

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0.176471

false

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0

0.235294

0

null

0

null

0

1

0

7f34018b23497a72c128d5bbb73251d18c61b077

2,752

py

Python

idea.py

szandala/autoencoder

3e9e41fa3f0f69a9e12acfa661e0de05bf190aeb

[ "MIT" ]

null

idea.py

szandala/autoencoder

3e9e41fa3f0f69a9e12acfa661e0de05bf190aeb

[ "MIT" ]

null

idea.py

szandala/autoencoder

3e9e41fa3f0f69a9e12acfa661e0de05bf190aeb

[ "MIT" ]

null

from keras.models import Sequential from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D from sklearn.neighbors import NearestNeighbors from keras.datasets import cifar10 import numpy as np from keras import backend as K ################################################################ # input image dimensions img_rows, img_cols = 32, 32 # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = cifar10.load_data() if K.image_data_format() == 'channels_first': x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 3) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 3) input_shape = (img_rows, img_cols, 3) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') ################################################################ x = np.array([image.flatten() for image in x_train]) print(x[0]) # import sys # sys.exit(0) model = Sequential() # hiddens model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) model.add(Conv2D(64, kernel_size=(3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(48, activation='relu')) # output model.add(Dense(img_rows * img_cols * 3, activation='linear')) # Compile model model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mean_squared_error']) # Fit the model model.fit(x_train, x, epochs=3, batch_size=10) # remove last layer model.pop() # prepare map of points points_values = model.predict(x_train) print(points_values[0]) # https://stackabuse.com/k-nearest-neighbors-algorithm-in-python-and-scikit-learn/ knn = NearestNeighbors() knn.fit(points_values) for image in [120, 300, 700, 1000]: fitting = knn.kneighbors([points_values[image]], n_neighbors=5, return_distance=False)[0] for fit in fitting: print("ID: {}, class: {}".format(fit, y_train[fit])) # ID: 120, class: [2] # ID: 827, class: [2] # ID: 7509, class: [0] # ID: 30612, class: [2] # ID: 49719, class: [4] # ID: 300, class: [2] # ID: 31691, class: [2] # ID: 2573, class: [4] # ID: 18888, class: [2] # ID: 40966, class: [2] # ID: 700, class: [0] # ID: 35599, class: [0] # ID: 33571, class: [8] # ID: 36547, class: [8] # ID: 48158, class: [8] # ID: 1000, class: [9] # ID: 20544, class: [7] # ID: 24979, class: [1] # ID: 40359, class: [0] # ID: 17782, class: [7]

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0.65189

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2,752

4.051643

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0.05562

0.04635

0.06489

0.178447

0.161066

0.151796

0.117034

0.048667

0

0.075777

0.146439

2,752

96

94

28.666667

0.659004

0.24782

0

0.074346

0

1

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false

0

0.133333

0

0.133333

0

null

0

null

0

1

0

7f343eb3682ac8f3b2f2549023501deaa06faf0e

2,291

py

Python

setup.py

copernicusmarine/cmemsapi

b2b2f8e9c80d989fe1aa1374d8174a30c819847e

[ "MIT" ]

null

setup.py

copernicusmarine/cmemsapi

b2b2f8e9c80d989fe1aa1374d8174a30c819847e

[ "MIT" ]

null

setup.py

copernicusmarine/cmemsapi

b2b2f8e9c80d989fe1aa1374d8174a30c819847e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (C) 2020 E.U. Copernicus Marine Service Information import sys from pathlib import Path # noqa E402 from setuptools import find_packages, setup assert sys.version_info >= (3, 6, 0), "cmtb requires Python 3.6+" CURRENT_DIR = Path(__file__).parent sys.path.insert(0, str(CURRENT_DIR)) #def get_long_description() -> str: # return ( # (CURRENT_DIR / "README.md").read_text(encoding="utf8") # + "\n\n" # + (CURRENT_DIR / "CHANGES.md").read_text(encoding="utf8") #) #with open('HISTORY.rst') as history_file: # history = history_file.read() #with open('README.md') as readme_file: # README = readme_file.read() #REQUIREMENTS = [line.strip() for line in open('requirements_prod.txt')] REQUIREMENTS = ["dask fire funcy ipython jedi<0.18.0 lxml motuclient==1.8.4 netCDF4<=1.5.4 pandas requests scipy toolz xarray ".split(' ')] SETUP_REQUIREMENTS = [] TEST_REQUIREMENTS = [] setup( author="E.U. Copernicus Marine Service Information", author_email='servicedesk.cmems@mercator-ocean.eu', python_requires='>=3.6', classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Natural Language :: English', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Scientific/Engineering', ], description="A package to help generating reliable data requests" " about earth observation and marine related information " "from Copernicus Marine Database.", install_requires=REQUIREMENTS, license="MIT", long_description='long description', long_description_content_type="text/markdown", include_package_data=True, keywords='cmemsapi', name='cmemsapi', packages=find_packages(include=['cmemsapi', 'cmemsapi.*']), setup_requires=SETUP_REQUIREMENTS, test_suite='tests', tests_require=TEST_REQUIREMENTS, url='https://github.com/copernicusmarine/cmemsapi', version='0.1.17', zip_safe=False, entry_points={'console_scripts':['cmust=cmemsapi.cmemsapi:cli']}, )

33.202899

139

0.67787

278

2,291

5.446043

0.568345

0.005284

0.049538

0.051519

0.076618

0.047556

0

0.021299

0.180271

2,291

68

140

33.691176

0.784878

0.226102

0

0.023256

0.473265

0.047782

0

0.023256

1

0

false

0

0.069767

0

0.069767

0

null

0

null

0

1

0

7f34aa81b3070681377031f89c73e262ce096a91

1,520

py

Python

application/services/tag_service.py

Praveen-0208/developer-cheatsheet

0fe494c2b01e320891c4048172569eacd15c73a4

[ "Apache-2.0" ]

null

application/services/tag_service.py

Praveen-0208/developer-cheatsheet

0fe494c2b01e320891c4048172569eacd15c73a4

[ "Apache-2.0" ]

null

application/services/tag_service.py

Praveen-0208/developer-cheatsheet

0fe494c2b01e320891c4048172569eacd15c73a4

[ "Apache-2.0" ]

null

from application.models.Tag import Tag from application.app import db from flask import jsonify import traceback class TagService: def create_tag(self, params): try: tag_object = Tag(tag_name = params["tag_name"]) db.session.add(tag_object) db.session.commit() return {"Message": "Tag created successfully"}, 200 except Exception as ex: return {"Message": "Something went wrong", "exception": traceback.format_exc()}, 500 def get_all_tags(self): try: tags = Tag.query.filter_by(is_deleted= False).all() response = [{"id": tag.id, "tag_name": tag.tag_name, "is_deleted": tag.is_deleted, "created_date": tag.created_date, "updated_date": tag.updated_date } for tag in tags] return jsonify(response), 200 except Exception as ex: return {"Message": "Something went wrong", "exception": traceback.format_exc()}, 500 def update_tag(self, id, params): try: tag = Tag.query.filter_by(id=id, is_deleted=False).first() setattr(tag, "tag_name", params["tag_name"]) db.session.commit() return {"Message": "Tag updated successfully"}, 200 except Exception as ex: return {"Message": "Something went wrong", "exception": traceback.format_exc()}, 500 def delete_tag(self, id): try: tag = Tag.query.filter_by(id=id, is_deleted=False).first() setattr(tag, "is_deleted", True) db.session.commit() return {"Message": "Tag deleted successfully"}, 200 except Exception as ex: return {"Message": "Something went wrong", "exception": traceback.format_exc()}, 500

38

171

0.709868

215

1,520

4.87907

0.265116

0.086749

0.068637

0.076263

0.602479

0.522402

0.464252

0

0.018547

0.148684

1,520

40

172

38

0.792117

0

0.472222

0

0.207101

0

1

0.111111

false

0

0.111111

0

0.472222

0

null

0

null

0

1

0

7f38e58a4f42e68862f6e882856a02eb76e3e7d3

479

py

Python

fopp/Chapter 12. Functions/num_test.py

H2u-Hwng/EVC

c650fe7356a333011514cf9025dfd97bf71b1de3

[ "MIT" ]

null

fopp/Chapter 12. Functions/num_test.py

H2u-Hwng/EVC

c650fe7356a333011514cf9025dfd97bf71b1de3

[ "MIT" ]

null

fopp/Chapter 12. Functions/num_test.py

H2u-Hwng/EVC

c650fe7356a333011514cf9025dfd97bf71b1de3

[ "MIT" ]

null

# Check number and return result def num_test(num): # Check number if num > 10: return 'Greater than 10.' elif num < 10: return 'Less than 10.' else: return 'Equal to 10.' # Define main function def main(): # Prompt user for a number number = float(input('Enter a number: ')) # Check number check_number = num_test(number) # Display result print(check_number) # Call main function main()

17.740741

45

0.586639

62

479

4.467742

0.483871

0.198556

0.079422

0

0.03096

0.325679

479

26

46

18.423077

0.826625

0.283925

0

0.170149

0

1

0.166667

false

0

0.416667

0.083333

0

null

0

null

0

1

0

7f3cda38afd2ff0962b2ae79ebdaa829c5bed6a1

556

py

Python

example/nn-andor.py

Sean-Mabli/aiinpy

827e4f85861436c0332046fa8aa84e24153513d6

[ "MIT" ]

2

2021-04-19T21:49:34.000Z

2021-05-17T21:03:08.000Z

example/nn-andor.py

Sean-Mabli/aiinpy

827e4f85861436c0332046fa8aa84e24153513d6

[ "MIT" ]

null

example/nn-andor.py

Sean-Mabli/aiinpy

827e4f85861436c0332046fa8aa84e24153513d6

[ "MIT" ]

null

import aiinpy as ai import numpy as np # Create Dataset inTrainData = np.random.choice(([0, 1]), (2, 100)) outTrainData = np.zeros((2, 100)) for i in range(100): outTrainData[:, i] = [1, 0] if sum(inTrainData[:, i]) == 1 else [0, 1] # NN model model = ai.model(2, 2, [ ai.nn(outshape=16, activation=ai.relu(), learningrate=0.1), ai.nn(outshape=16, activation=ai.relu(), learningrate=0.1), ai.nn(outshape=2, activation=ai.sigmoid(), learningrate=0.1) ]) model.train((inTrainData, outTrainData), 100) print(model.test((inTrainData, outTrainData)))

30.888889

72

0.681655

87

556

4.356322

0.402299

0.026385

0.094987

0.073879

0.263852

0

0.070103

0.127698

556

18

73

30.888889

0.71134

0.041367

0

0.153846

0

1

0

false

0

0.153846

0

0.153846

0.076923

0

null

0

null

0

1

0

7f3e415b02824042a222510b85f1f06332620f82

10,212

py

Python

evaluation.py

twankim/svdped

afe67b5c4636bca03a6d68b7a9cf89485a4dabec

[ "Apache-2.0" ]

1

2020-01-02T03:01:28.000Z

evaluation.py

twankim/svdped

afe67b5c4636bca03a6d68b7a9cf89485a4dabec

[ "Apache-2.0" ]

null

evaluation.py

twankim/svdped

afe67b5c4636bca03a6d68b7a9cf89485a4dabec

[ "Apache-2.0" ]

null

# Copyright 2017 Tensorflow. All Rights Reserved. # Modifications copyright 2018 UT Austin/Saharsh Oza & Taewan Kim # We follow the object detection API of Tensorflow # # 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 absolute_import from __future__ import division from __future__ import print_function import os import sys import csv import numpy as np import tensorflow as tf import _init_paths from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util from object_detection.utils import metrics from object_detection.utils import object_detection_evaluation as obj_eval from object_detection.core import standard_fields from skvideo.io import FFmpegWriter from skimage.io import imread tf.app.flags.DEFINE_string('gt_dir', '', 'Location of root directory for the ' 'ground truth data. Folder structure is assumed to be:' '<gt_dir>/cstopp_train.tfrecord,' '<gt_dir>/cstopp_test.tfrecord' '<gt_dir>/cstopp_val.tfrecord') tf.app.flags.DEFINE_string('det_dir', '', 'Location of root directory for the ' 'inference data. Folder structure is assumed to be:' '<det_dir>/cstopp_train.tfrecord,' '<det_dir>/cstopp_test.tfrecord' '<det_dir>/cstopp_val.tfrecord') tf.app.flags.DEFINE_string('output_dir', '', 'Path to which metrics' 'will be written.') tf.app.flags.DEFINE_string('split', 'train', 'Data split when record file is being read from gt_dir and det_dir ex: train, test, val') tf.app.flags.DEFINE_string( 'label_map_path', 'configs/cstopp_label_map.pbtxt', 'file path for the labels') tf.app.flags.DEFINE_integer( 'num_class', 1, 'Number of Classes to consider from 1 in the label map') tf.app.flags.DEFINE_boolean( 'is_vout', False, 'Generate a video with bounding boxes') FLAGS = tf.app.flags.FLAGS gt_feature = { 'image/object/bbox/ymin': tf.VarLenFeature(tf.float32), 'image/object/bbox/xmin': tf.VarLenFeature(tf.float32), 'image/object/bbox/ymax': tf.VarLenFeature(tf.float32), 'image/object/bbox/xmax': tf.VarLenFeature(tf.float32), 'image/object/class/label': tf.VarLenFeature(tf.int64), 'image/filename': tf.FixedLenFeature([], tf.string), 'image/object/difficult': tf.VarLenFeature(tf.int64), } det_feature = { 'image/object/bbox/ymin': tf.VarLenFeature(tf.float32), 'image/object/bbox/xmin': tf.VarLenFeature(tf.float32), 'image/object/bbox/ymax': tf.VarLenFeature(tf.float32), 'image/object/bbox/xmax': tf.VarLenFeature(tf.float32), 'image/object/class/label': tf.VarLenFeature(tf.int64), 'image/object/score': tf.VarLenFeature(tf.float32), 'image/filename': tf.FixedLenFeature([], tf.string), } class Reader: def __init__(self, record_path, split, is_infer=False): data_path = [] if is_infer: data_path.append(os.path.join(record_path, 'cstopp_inference_{}.tfrecord'.format(split))) else: data_path.append(os.path.join(record_path, 'cstopp_{}.tfrecord'.format(split))) self.read_graph = tf.Graph() with self.read_graph.as_default(): # old_graph_def = tf.GraphDef() self.filename_queue = tf.train.string_input_producer(data_path) self.reader = tf.TFRecordReader() self.num_records = 0 for f in data_path: self.num_records += sum(1 for _ in tf.python_io.tf_record_iterator(f)) # tf.import_graph_def(old_graph_def, name='') self.sess = tf.Session(graph=self.read_graph) def get_field(self, field, decode=False): if not decode: if type(self.features[field])==tf.SparseTensor: return tf.sparse_tensor_to_dense(self.features[field]) else: return self.features[field] else: return tf.image.decode_png(self.features[field]) def get_fields(self, feature_dict): # Modify graph to add these ops with self.read_graph.as_default(): list_fields = feature_dict.keys() # old_graph_def = tf.GraphDef() # Read next record from queue _, serialized_example = self.reader.read(self.filename_queue) self.features = tf.parse_single_example( serialized_example, features=feature_dict) # Get required fields from record fields_out = [self.get_field(f) for f in list_fields] # Close queue coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=self.sess, coord=coord) # Import updated graph in current read_graph # tf.import_graph_def(old_graph_def, name='') eval_out = self.sess.run(fields_out) out_dict = dict(zip(list_fields, eval_out)) return out_dict def get_bbox(box_list): ymin_eval = box_list['image/object/bbox/ymin'] xmin_eval = box_list['image/object/bbox/xmin'] ymax_eval = box_list['image/object/bbox/ymax'] xmax_eval = box_list['image/object/bbox/xmax'] return np.vstack((ymin_eval,xmin_eval,ymax_eval,xmax_eval)).T def write_metrics(metrics, output_path): """Write metrics to the output directory. Args: metrics: A dictionary containing metric names and values. output_dir: Directory to write metrics to. """ tf.logging.info('Writing metrics.') with open(output_path, 'w') as csvfile: metrics_writer = csv.writer(csvfile, delimiter=',') for metric_name, metric_value in metrics.items(): metrics_writer.writerow([metric_name, str(metric_value)]) def evaluate(gt_dir=FLAGS.gt_dir, det_dir=FLAGS.det_dir, output_dir=FLAGS.output_dir, split='train', label_map_path=None, is_vout=False, num_class=1, fps_out=5): gt_reader = Reader(gt_dir, split) num_records = gt_reader.num_records det_reader = Reader(det_dir, split, is_infer=True) label_map = label_map_util.load_labelmap(label_map_path) categories = label_map_util.convert_label_map_to_categories( label_map, max_num_classes=num_class, use_display_name=True) evaluator = obj_eval.ObjectDetectionEvaluator(categories) output_path = os.path.join(output_dir, 'cstopp_{}_eval.csv'.format(split)) if is_vout: category_index = label_map_util.create_category_index(categories) list_valid_ids = [int(cat_dict['id']) for cat_dict in categories] vwriter = FFmpegWriter(os.path.join(output_dir,split+'_det_gt.mp4'), inputdict={'-r':str(fps_out)}, outputdict={'-r':str(fps_out)}) for image_num in range(0, num_records): print('Evaluating {}/{}'.format(image_num+1,num_records)) gt_fields = gt_reader.get_fields(gt_feature) gt_bbox = get_bbox(gt_fields) gt_classes = gt_fields['image/object/class/label'].astype(np.int32) gt_diff = gt_fields['image/object/difficult'] det_fields = det_reader.get_fields(det_feature) det_bbox = get_bbox(det_fields) det_scores = det_fields['image/object/score'] det_classes = det_fields['image/object/class/label'].astype(np.int32) filename = gt_fields['image/filename'] ground_dict = { standard_fields.InputDataFields.groundtruth_boxes: gt_bbox, standard_fields.InputDataFields.groundtruth_classes: gt_classes, standard_fields.InputDataFields.groundtruth_difficult: gt_diff} det_dict = { standard_fields.DetectionResultFields.detection_boxes: det_bbox, standard_fields.DetectionResultFields.detection_scores: det_scores, standard_fields.DetectionResultFields.detection_classes: det_classes} if is_vout: image = imread(filename) # Visualization of the results of a detection. image_labeled = np.copy(image) vis_util.visualize_boxes_and_labels_on_image_array( image_labeled, gt_bbox, gt_classes, None, category_index, max_boxes_to_draw=None, min_score_thresh=0, use_normalized_coordinates=True, line_thickness=2) idx_consider = [cid in list_valid_ids for cid in det_classes] vis_util.visualize_boxes_and_labels_on_image_array( image_labeled, det_bbox[idx_consider,:], det_classes[idx_consider], det_scores[idx_consider], category_index, max_boxes_to_draw=None, min_score_thresh=0, use_normalized_coordinates=True, line_thickness=2) vwriter.writeFrame(image_labeled) evaluator.add_single_ground_truth_image_info(filename, ground_dict) evaluator.add_single_detected_image_info(filename, det_dict) eval_result = evaluator.evaluate() print(eval_result) write_metrics(eval_result, output_path) if is_vout: vwriter.close() if __name__ == '__main__': evaluate( gt_dir=FLAGS.gt_dir, det_dir=FLAGS.det_dir, output_dir=FLAGS.output_dir, split=FLAGS.split, label_map_path=FLAGS.label_map_path, is_vout=FLAGS.is_vout, num_class=FLAGS.num_class)

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0

null

0

null

0

1

0

7f416087cddccd8f2f43da2aae4ed7a5d932a824

2,246

py

Python

Tweet_Parser.py

berkehanozen/481project

e1f8f98871ad47bfc7ac2363824b86eca3ef80e3

[ "MIT" ]

1

2019-04-03T20:04:10.000Z

Tweet_Parser.py

berkehanozen/481project

e1f8f98871ad47bfc7ac2363824b86eca3ef80e3

[ "MIT" ]

8

2019-03-17T20:03:15.000Z

2019-04-26T23:36:50.000Z

Tweet_Parser.py

berkehanozen/481project

e1f8f98871ad47bfc7ac2363824b86eca3ef80e3

[ "MIT" ]

null

import tweepy CONSUMER_KEY = 'EQxFiVurRxZRKZzH4tJ2PtsX8' CONSUMER_SECRET = '59WSgueMvx7VUbuYywwC6rqwdlLUkc0yufiTnITvgmPJAN9RzE' ACCESS_TOKEN = "349586645-4e7WmYpjvzKUmsKh3C9pNyv0QzlbVB80nlvR4q02" ACCESS_TOKEN_SECRET = "qblYvvigttmP5elDFMsIJacO7gOknN794ubMThlyV0pfj" auth = tweepy.OAuthHandler('EQxFiVurRxZRKZzH4tJ2PtsX8', '59WSgueMvx7VUbuYywwC6rqwdlLUkc0yufiTnITvgmPJAN9RzE') auth.set_access_token("349586645-4e7WmYpjvzKUmsKh3C9pNyv0QzlbVB80nlvR4q02", "qblYvvigttmP5elDFMsIJacO7gOknN794ubMThlyV0pfj") api = tweepy.API(auth) class ParseTweets(object): @staticmethod def getTweets(userId): user=api.get_user(userId) if user.protected: print("User is protected") return "" timeline=api.user_timeline(screen_name=userId,count=5,tweet_mode="extended") tweetTexts=[] followerCount=user.followers_count followingCount=user.friends_count tweetCount=user.statuses_count for tweet in timeline: #taking tweet texts if 'retweeted_status' in tweet._json: #getting full text for retweets tweetTexts.append(tweet._json['retweeted_status']['full_text']) else: tweetTexts.append(tweet.full_text) #getting full text for self tweets tweetDates=[[tweet.created_at]for tweet in timeline]#taking tweet dates imageUrls=[] for tweet in timeline: if 'media' in tweet.entities: for media in tweet.extended_entities['media']: imageUrls.append(media['media_url']) else: imageUrls.append("") dates=[] for t in tweetDates: for i in t: date=str(i).split(" ")[0].split("-") dates.append(date[2]+"."+date[1]+"."+date[0]) profileImage=user.profile_image_url informations=[] informations.append(tweetTexts) informations.append(dates) informations.append(tweetCount) informations.append(followingCount) informations.append(followerCount) informations.append(imageUrls) informations.append(profileImage) # for info in informations: # print(info) return informations

43.192308

124

0.670971

209

2,246

7.090909

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0.036437

0.039136

0

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51

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0.104167

0.020833

0

null

0

null

0

1

0

7f482aee1ac3fa8273f5cc77f82ac708531977d2

3,115

py

Python

src/main/python/apache/thermos/cli/commands/run.py

jeremyvdw/aurora

fa9d83a7ef3a96c522884089a471bbb0bef74c48

[ "Apache-2.0" ]

479

2015-03-27T22:59:49.000Z

2022-03-09T08:40:49.000Z

src/main/python/apache/thermos/cli/commands/run.py

jeremyvdw/aurora

fa9d83a7ef3a96c522884089a471bbb0bef74c48

[ "Apache-2.0" ]

69

2015-05-26T20:06:29.000Z

2020-01-13T19:18:59.000Z

src/main/python/apache/thermos/cli/commands/run.py

jeremyvdw/aurora

fa9d83a7ef3a96c522884089a471bbb0bef74c48

[ "Apache-2.0" ]

226

2015-03-27T20:02:59.000Z

2022-03-09T08:40:53.000Z

# # 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 getpass from twitter.common import app from apache.thermos.cli.common import get_task_from_options, really_run from apache.thermos.common.options import add_binding_to, add_port_to @app.command @app.command_option("--user", metavar="USER", default=getpass.getuser(), dest='user', help="run as this user. if not $USER, must have setuid privilege.") @app.command_option("--enable_chroot", dest="chroot", default=False, action='store_true', help="chroot tasks to the sandbox before executing them, requires " "root privileges.") @app.command_option("--task", metavar="TASKNAME", default=None, dest='task', help="The thermos task within the config that should be run. Only required if " "there are multiple tasks exported from the thermos configuration.") @app.command_option("--task_id", metavar="STRING", default=None, dest='task_id', help="The id to which this task should be bound, synthesized from the task " "name if none provided.") @app.command_option("--json", default=False, action='store_true', dest='json', help="Read the source file in json format.") @app.command_option("--sandbox", metavar="PATH", default="/var/lib/thermos/sandbox", dest='sandbox', help="The sandbox in which to run the task.") @app.command_option("-P", "--port", type="string", nargs=1, action="callback", callback=add_port_to('prebound_ports'), dest="prebound_ports", default=[], metavar="NAME:PORT", help="bind named PORT to NAME.") @app.command_option("-E", "--environment", type="string", nargs=1, action="callback", callback=add_binding_to('bindings'), default=[], dest="bindings", metavar="NAME=VALUE", help="bind the configuration environment variable NAME to VALUE.") @app.command_option("--daemon", default=False, action='store_true', dest='daemon', help="fork and daemonize the thermos runner.") def run(args, options): """Run a thermos task. Usage: thermos run [options] config """ thermos_task = get_task_from_options(args, options) really_run(thermos_task, options.root, options.sandbox, task_id=options.task_id, user=options.user, prebound_ports=options.prebound_ports, chroot=options.chroot, daemon=options.daemon)

47.923077

100

0.660353

402

3,115

5.007463

0.380597

0.049677

0.071535

0.034277

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0.071535

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3,115

64

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48.671875

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0.05

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0

0.15

0.025

0

null

0

null

0

1

0

7f48681294588a03fc4aad0737c8a3c34dabc142

280

py

Python

gallery/random_color.py

Commander07/Enviroment

bdf05dca42a14a5f061c8dda64c3ec6b085e4db3

[ "MIT" ]

null

gallery/random_color.py

Commander07/Enviroment

bdf05dca42a14a5f061c8dda64c3ec6b085e4db3

[ "MIT" ]

null

gallery/random_color.py

Commander07/Enviroment

bdf05dca42a14a5f061c8dda64c3ec6b085e4db3

[ "MIT" ]

null

import random import time from enviroment import Enviroment ENV = Enviroment("") def random_color(): return random.randint(0, 256) while True: ENV.console.print( "[color({color})]COLOR SUPPORT[/color({color})]".format(color=random_color())) time.sleep(1)

17.5

86

0.685714

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280

5.277778

0.555556

0.157895

0

0.021459

0.167857

280

15

87

18.666667

0.793991

0

0.164286

0.160714

0

1

0.1

false

0

0.3

0.1

0.5

0.1

0

null

0

null

0

1

0

7f494b88262e2106d77b9c0a20561febf55c8994

5,933

py

Python

radiation/python/encoding.py

dfridovi/exploration

5e66115178988bd264a920041dfeab6d3539caec

[ "BSD-3-Clause" ]

5

2018-07-08T08:32:49.000Z

2022-03-13T10:17:09.000Z

radiation/python/encoding.py

dfridovi/exploration

5e66115178988bd264a920041dfeab6d3539caec

[ "BSD-3-Clause" ]

5

2016-11-30T02:52:58.000Z

2018-05-24T04:46:49.000Z

radiation/python/encoding.py

dfridovi/exploration

5e66115178988bd264a920041dfeab6d3539caec

[ "BSD-3-Clause" ]

2

2016-12-01T04:06:40.000Z

2019-06-19T16:32:28.000Z

""" Copyright (c) 2015, The Regents of the University of California (Regents). All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. 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. 3. Neither the name of the copyright holder 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. Please contact the author(s) of this library if you have any questions. Authors: David Fridovich-Keil ( dfk@eecs.berkeley.edu ) """ ########################################################################### # # Encoders and decoders for measurements, trajectories, and maps. # ########################################################################### from grid_pose_2d import GridPose2D from grid_map_2d import GridMap2D from source_2d import Source2D from sensor_2d import Sensor2D import numpy as np import math # Encode a list of (dx, dy, da) tuples in an integer from 0 to # (len(delta_xs) * len(delta_ys) * len(delta_as))**len(delta_sequence) - 1. def EncodeTrajectory(delta_xs, delta_ys, delta_as, delta_sequence): base = len(delta_xs) * len(delta_ys) * len(delta_as) trajectory_id = 0 for ii, delta in enumerate(delta_sequence): x_id = delta_xs.index(delta[0]) y_id = delta_ys.index(delta[1]) a_id = delta_as.index(delta[2]) delta_id = x_id + y_id*len(delta_xs) + a_id*len(delta_xs)*len(delta_ys) trajectory_id += delta_id * base**ii return trajectory_id # Decode a trajectory id into a list of GridPose2Ds. def DecodeTrajectory(delta_xs, delta_ys, delta_as, trajectory_id, initial_pose, num_steps): base = len(delta_xs) * len(delta_ys) * len(delta_as) trajectory = [] current_pose = initial_pose while trajectory_id > 0: remainder = trajectory_id % base # Convert remainder to delta tuple (dx, dy, da). x_id = remainder % len(delta_xs) y_id = ((remainder - x_id) / len(delta_xs)) % len(delta_ys) a_id = ((remainder - x_id - y_id*len(delta_xs)) / (len(delta_xs)*len(delta_ys))) dx = delta_xs[x_id] dy = delta_ys[y_id] da = delta_as[a_id] # Append to 'trajectory'. next_pose = GridPose2D.Copy(current_pose) assert next_pose.MoveBy(dx, dy, da) trajectory.append(next_pose) # Update 'trajectory_id'. trajectory_id = (trajectory_id - remainder) / base # Reset 'current_pose'. current_pose = next_pose # If not the right length, that means that the last remainders were 0. # Update 'trajectory' accordingly. while len(trajectory) < num_steps: next_pose = GridPose2D.Copy(current_pose) assert next_pose.MoveBy(delta_xs[0], delta_ys[0], delta_as[0]) trajectory.append(next_pose) current_pose = next_pose return trajectory # Encode a list of measurements in an integer. def EncodeMeasurements(max_measurement, measurement_sequence): base = max_measurement + 1 measurement_id = 0 for ii, measurement in enumerate(measurement_sequence): measurement_id += measurement * base**ii return measurement_id # Decode a measurement id into a list of measurements. def DecodeMeasurements(max_measurement, measurement_id, num_measurements): base = max_measurement + 1 measurements = [] while measurement_id > 0: remainder = measurement_id % base measurements.append(remainder) # Update 'measurement_id'. measurement_id = (measurement_id - remainder) / base # If not enough measurements, the rest must have been zero. while len(measurements) < num_measurements: measurements.append(0) return measurements # Encode a map (list of sources) as an integer. def EncodeMap(num_rows, num_cols, sources): base = num_rows * num_cols map_id = 0 for ii, source in enumerate(sources): source_id = int(source.x_) + int(source.y_) * num_rows map_id += source_id * base**ii return map_id # Decode a map id into a list of sources. def DecodeMap(num_rows, num_cols, map_id, num_sources): base = num_rows * num_cols sources = [] while map_id > 0: remainder = map_id % base # Unpack remainder into (x, y) coordinates of a source. source = Source2D(float(remainder % num_rows) + 0.5, float(remainder // num_rows) + 0.5) sources.append(source) # Update 'map_id'. map_id = (map_id - remainder) / base # If not enough sources, the remainders must have been zero. while len(sources) < num_sources: sources.append(Source2D(0.5, 0.5)) return sources

35.106509

79

0.68178

819

5,933

4.781441

0.279609

0.038815

0.022983

0.023238

0.222676

0.18335

0.111338

0.091931

0.08427

0

0.00995

0.220799

5,933

168

80

35.315476

0.837119

0.432328

0

0.157895

0

0.026316

1

0.078947

false

0

0.078947

0

0.236842

0

null

0

null

0

1

0

7f49f6fa3ef035698bb0c0bf44b54064ea2d62dc

504

py

Python

Cogs/game.py

k-rite/mirai-kuriyama-discord.py

2974e2a544fe2d12491eae83e3ff16f5b776294c

[ "Apache-2.0" ]

1

2021-12-02T00:22:08.000Z

Cogs/game.py

k-rite/mirai-kuriyama-discord.py

2974e2a544fe2d12491eae83e3ff16f5b776294c

[ "Apache-2.0" ]

null

Cogs/game.py

k-rite/mirai-kuriyama-discord.py

2974e2a544fe2d12491eae83e3ff16f5b776294c

[ "Apache-2.0" ]

null

import discord from discord.ext import commands from discord import Game class game(commands.Cog): def __init__(self, Bot): self.bot = Bot #jus a comfy seperate cog to change game status, will add versions setup nd other backend codes here @commands.Cog.listener() async def on_ready(self): await self.bot.change_presence(activity=discord.Activity(type=discord.ActivityType.watching, name="KRITE DYING VOL 79")) print('Game status is changed') def setup(bot): bot.add_cog(game(bot))

33.6

124

0.757937

78

504

4.807692

0.589744

0.056

0

0.004651

0.146825

504

15

125

33.6

0.867442

0.196429

0

0.098765

0

1

0.166667

false

0

0.25

0

0.5

0.083333

0

null

0

null

0

1

0

7f4caadc8cb6f4e82da5982e34a205614c81d65e

1,475

py

Python

myapp/Entry.py

abars/illustbook

3e790a688c19205b7384cc5815ca76c23b88f09a

[ "MIT" ]

3

2016-06-16T20:11:45.000Z

2022-01-27T04:23:09.000Z

myapp/Entry.py

abars/illustbook

3e790a688c19205b7384cc5815ca76c23b88f09a

[ "MIT" ]

1

2017-10-23T00:23:13.000Z

myapp/Entry.py

abars/illustbook

3e790a688c19205b7384cc5815ca76c23b88f09a

[ "MIT" ]

null

#!-*- coding:utf-8 -*- #!/usr/bin/env python #--------------------------------------------------- #コメント構造体 #copyright 2010-2012 ABARS all rights reserved. #--------------------------------------------------- from google.appengine.ext import db from google.appengine.api import users from myapp.Bbs import Bbs from myapp.MesThread import MesThread from myapp.ThreadImage import ThreadImage from myapp.CachedDbModel import CachedDbModel class Entry(CachedDbModel): bbs_key = db.ReferenceProperty(Bbs) thread_key = db.ReferenceProperty(MesThread) editor = db.StringProperty() mail = db.StringProperty() homepage_addr = db.StringProperty() content = db.TextProperty() image = db.BlobProperty() thumbnail = db.BlobProperty() del_flag = db.IntegerProperty() res_list=db.ListProperty(item_type=db.Key) create_date = db.DateTimeProperty() date = db.DateTimeProperty(auto_now=False) illust_reply = db.IntegerProperty() illust_reply_image = db.StringProperty() #Deleted illust_reply_image_key = db.ReferenceProperty(ThreadImage) last_update_editor = db.StringProperty() #Deleted(old:for response update to comment cache) user_id= db.StringProperty() #Submitter hidden_flag = db.IntegerProperty() #HiddenComment violate_terms = db.IntegerProperty() remote_addr = db.StringProperty() remote_host = db.StringProperty() comment_no = db.IntegerProperty() search_index_version= db.IntegerProperty() adult = db.IntegerProperty() sand = db.StringProperty()

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1

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null

0

null

0

1

0

7f53b321819c0bfc40c5ab85b02bbe5ae5205ad3

5,386

py

Python

src/kegnet/utils/tucker.py

videoturingtest/KegNet

fe5d1eb5ab5453be70c4be473fd3da71afe4b06c

[ "Apache-2.0" ]

42

2019-10-01T02:14:34.000Z

2022-03-07T06:57:58.000Z

src/kegnet/utils/tucker.py

videoturingtest/KegNet

fe5d1eb5ab5453be70c4be473fd3da71afe4b06c

[ "Apache-2.0" ]

5

2019-12-18T16:44:52.000Z

2021-10-02T15:45:37.000Z

src/kegnet/utils/tucker.py

videoturingtest/KegNet

fe5d1eb5ab5453be70c4be473fd3da71afe4b06c

[ "Apache-2.0" ]

13

2019-10-04T02:55:51.000Z

2021-08-13T09:10:17.000Z

""" Knowledge Extraction with No Observable Data (NeurIPS 2019) Authors: - Jaemin Yoo (jaeminyoo@snu.ac.kr), Seoul National University - Minyong Cho (chominyong@gmail.com), Seoul National University - Taebum Kim (k.taebum@snu.ac.kr), Seoul National University - U Kang (ukang@snu.ac.kr), Seoul National University This software may be used only for research evaluation purposes. For other purposes (e.g., commercial), please contact the authors. """ import tensorly as tl import torch from tensorly import decomposition as decomp from tensorly import tucker_tensor as tucker from torch import nn from kegnet.utils import vbmf tl.set_backend('pytorch') class DecomposedConv2d(nn.Module): """ Decomposed (or compressed) convolutional layer. """ @staticmethod def choose_ranks(weight, ranks): """ Choose the target ranks. """ out_channels, in_channels, _, _ = weight.shape if ranks == 'evbmf': unfold_0 = tl.base.unfold(weight, 0) unfold_1 = tl.base.unfold(weight, 1) _, diag_0, _, _ = vbmf.EVBMF(unfold_0) _, diag_1, _, _ = vbmf.EVBMF(unfold_1) out_rank = diag_0.shape[0] in_rank = diag_1.shape[1] elif isinstance(ranks, float): out_rank = int(out_channels * ranks) in_rank = int(in_channels * ranks) elif isinstance(ranks, tuple): in_rank, out_rank = ranks else: raise ValueError(ranks) return out_rank, in_rank def __init__(self, layer, ranks='evbmf', init=True): """ Class initializer. """ super(DecomposedConv2d, self).__init__() device = layer.weight.device weight = layer.weight.data out_channels, in_channels, _, _ = weight.shape out_rank, in_rank = self.choose_ranks(weight, ranks) self.in_channel_layer = nn.Conv2d( in_channels=in_channels, out_channels=in_rank, kernel_size=1, stride=1, padding=0, dilation=layer.dilation, bias=False).to(device) self.core_layer = nn.Conv2d( in_channels=in_rank, out_channels=out_rank, kernel_size=layer.kernel_size, stride=layer.stride, padding=layer.padding, dilation=layer.dilation, bias=False).to(device) self.out_channel_layer = nn.Conv2d( in_channels=out_rank, out_channels=out_channels, kernel_size=1, stride=1, padding=0, dilation=layer.dilation, bias=layer.bias is not None).to(device) if init: core, factors = decomp.partial_tucker( weight, modes=[0, 1], ranks=(out_rank, in_rank), init='svd') (out_channel_factor, in_channel_factor) = factors if self.out_channel_layer.bias is not None: self.out_channel_layer.bias.data = layer.bias.data transposed = torch.transpose(in_channel_factor, 1, 0) self.in_channel_layer.weight.data = \ transposed.unsqueeze(-1).unsqueeze(-1) self.out_channel_layer.weight.data = \ out_channel_factor.unsqueeze(-1).unsqueeze(-1) self.core_layer.weight.data = core def forward(self, x): """ Forward propagation. """ x = self.in_channel_layer(x) x = self.core_layer(x) x = self.out_channel_layer(x) return x def recover(self): """ Recover the original shape. """ core = self.core_layer.weight.data out_factor = self.out_channel_layer.weight.data.squeeze() in_factor = self.in_channel_layer.weight.data.squeeze() in_factor = torch.transpose(in_factor, 1, 0) return tucker.tucker_to_tensor(core, [out_factor, in_factor]) class DecomposedLinear(nn.Module): """ Decomposed (or compressed) linear layer. """ def __init__(self, layer, ranks, init=True): """ Class initializer. """ super(DecomposedLinear, self).__init__() device = layer.weight.device weight = layer.weight.data out_dim, in_dim = weight.shape out_rank, in_rank = ranks self.in_layer = nn.Linear( in_features=in_dim, out_features=in_rank, bias=False).to(device) self.core_layer = nn.Linear( in_features=in_rank, out_features=out_rank, bias=False).to(device) self.out_layer = nn.Linear( in_features=out_rank, out_features=out_dim, bias=layer.bias is not None).to(device) if init: core, factors = decomp.tucker(weight, ranks=ranks, init='svd') out_factor, in_factor = factors if self.out_layer.bias is not None: self.out_layer.bias.data = layer.bias.data self.in_layer.weight.data = torch.transpose(in_factor, 1, 0) self.out_layer.weight.data = out_factor self.core_layer.weight.data = core def forward(self, x): """ Forward propagation. """ x = self.in_layer(x) x = self.core_layer(x) x = self.out_layer(x) return x

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null

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null

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7f56a46d74e7ecd5b432e74c3c2222b210440061

21,615

py

Python

source/tpJointOrient/jointorient.py

tpoveda/tpJointOrient

deeebf847403c2938518c75a2b1a22d61e46f74a

[ "MIT" ]

9

2017-08-21T01:04:22.000Z

2020-08-28T02:54:54.000Z

source/tpJointOrient/jointorient.py

tpoveda/tpJointOrient

deeebf847403c2938518c75a2b1a22d61e46f74a

[ "MIT" ]

1

2019-11-06T18:02:41.000Z

source/tpJointOrient/jointorient.py

tpoveda/tpJointOrient

deeebf847403c2938518c75a2b1a22d61e46f74a

[ "MIT" ]

3

2018-04-03T00:14:50.000Z

2020-11-04T00:33:45.000Z

from functools import partial from Qt.QtCore import * from Qt.QtWidgets import * import tpDccLib as tp import tpMayaLib as maya from tpQtLib.core import window from tpQtLib.widgets import splitters from tpMayaLib.core import decorators class JointOrient(window.MainWindow, object): def __init__(self): super(JointOrient, self).__init__( name='JointOrientWindow', title='Joint Orient', size=(350, 700), fixed_size=False, auto_run=True, frame_less=True, use_style=True ) def ui(self): super(JointOrient, self).ui() ### Auto Orient Joint Widget ### joint_ori_widget = QWidget() joint_ori_widget.setLayout(QVBoxLayout()) joint_ori_widget.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Fixed) joint_ori_widget.layout().setContentsMargins(0, 0, 0, 0) joint_ori_widget.layout().setSpacing(2) self.main_layout.addWidget(joint_ori_widget) joint_ori_splitter = splitters.Splitter('JOINT ORIENT') joint_ori_widget.layout().addWidget(joint_ori_splitter) aim_axis_layout = QHBoxLayout() aim_axis_layout.setContentsMargins(5, 5, 5, 5) aim_axis_layout.setSpacing(2) # Aim Axis aim_axis_box = QGroupBox() aim_axis_box.setLayout(aim_axis_layout) aim_axis_box.setTitle('Aim Axis') joint_ori_widget.layout().addWidget(aim_axis_box) self.aim_x_radio = QRadioButton('X') self.aim_y_radio = QRadioButton('Y') self.aim_z_radio = QRadioButton('Z') self.aim_rev_cbx = QCheckBox('Reverse') self.aim_x_radio.setChecked(True) aim_axis_layout.addWidget(self.aim_x_radio) aim_axis_layout.addWidget(self.aim_y_radio) aim_axis_layout.addWidget(self.aim_z_radio) aim_axis_layout.addWidget(self.aim_rev_cbx) # Up Axis up_axis_layout = QHBoxLayout() up_axis_layout.setContentsMargins(5, 5, 5, 5) up_axis_layout.setSpacing(2) up_axis_box = QGroupBox() up_axis_box.setLayout(up_axis_layout) up_axis_box.setTitle('Up Axis') joint_ori_widget.layout().addWidget(up_axis_box) self.up_x_radio = QRadioButton('X') self.up_y_radio = QRadioButton('Y') self.upZRadio = QRadioButton('Z') self.upRevCbx = QCheckBox('Reverse') self.up_y_radio.setChecked(True) up_axis_layout.addWidget(self.up_x_radio) up_axis_layout.addWidget(self.up_y_radio) up_axis_layout.addWidget(self.upZRadio) up_axis_layout.addWidget(self.upRevCbx) # Up World Axis up_world_axis_layout = QHBoxLayout() up_world_axis_layout.setContentsMargins(5, 5, 5, 5) up_world_axis_layout.setSpacing(5) up_world_axis_box = QGroupBox() up_world_axis_box.setLayout(up_world_axis_layout) up_world_axis_box.setTitle('Up World Axis') joint_ori_widget.layout().addWidget(up_world_axis_box) self.up_world_x_spin = QDoubleSpinBox() self.up_world_y_spin = QDoubleSpinBox() self.up_world_z_spin = QDoubleSpinBox() self.up_world_x_spin.setDecimals(3) self.up_world_y_spin.setDecimals(3) self.up_world_z_spin.setDecimals(3) self.up_world_x_spin.setRange(-360, 360) self.up_world_y_spin.setRange(-360, 360) self.up_world_z_spin.setRange(-360, 360) self.up_world_x_spin.setLocale(QLocale.English) self.up_world_y_spin.setLocale(QLocale.English) self.up_world_z_spin.setLocale(QLocale.English) self.up_world_x_spin.setValue(1.0) up_world_x = QPushButton('X') up_world_y = QPushButton('Y') up_world_z = QPushButton('Z') up_world_x.setMaximumWidth(20) up_world_y.setMaximumWidth(20) up_world_z.setMaximumWidth(20) up_world_axis_layout.addWidget(self.up_world_x_spin) up_world_axis_layout.addWidget(self.up_world_y_spin) up_world_axis_layout.addWidget(self.up_world_z_spin) up_world_axis_layout.addWidget(up_world_x) up_world_axis_layout.addWidget(up_world_y) up_world_axis_layout.addWidget(up_world_z) joint_ori_widget.layout().addLayout(splitters.SplitterLayout()) joint_orient_btn_layout = QHBoxLayout() joint_orient_btn_layout.setAlignment(Qt.AlignCenter) joint_ori_widget.layout().addLayout(joint_orient_btn_layout) spacer_item = QSpacerItem(2, 2, QSizePolicy.Minimum, QSizePolicy.Minimum) joint_orient_btn_layout.addSpacerItem(spacer_item) joint_orient_btn = QPushButton('Apply') self.joint_orient_cbx = QCheckBox('Hierarchy') joint_orient_btn.setMaximumWidth(80) self.joint_orient_cbx.setChecked(True) joint_orient_btn_layout.addWidget(joint_orient_btn) joint_orient_btn_layout.addWidget(self.joint_orient_cbx) spacer_item = QSpacerItem(2, 2, QSizePolicy.Fixed) self.main_layout.addSpacerItem(spacer_item) ### Manual Orient Joint Widget ### manual_joint_ori_widget = QWidget() manual_joint_ori_widget.setLayout(QVBoxLayout()) manual_joint_ori_widget.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Fixed) manual_joint_ori_widget.layout().setContentsMargins(5, 5, 5, 5) manual_joint_ori_widget.layout().setSpacing(10) self.main_layout.addWidget(manual_joint_ori_widget) manual_joint_ori_splitter = splitters.Splitter('MANUAL JOINT ORIENT') manual_joint_ori_widget.layout().addWidget(manual_joint_ori_splitter) manual_joint_ori_layout = QHBoxLayout() manual_joint_ori_widget.layout().addLayout(manual_joint_ori_layout) manual_joint_ori_lbl = QLabel(' X Y Z ') self.manual_joint_ori_x_spin = QDoubleSpinBox() self.manual_joint_ori_y_spin = QDoubleSpinBox() self.manual_joint_ori_z_spin = QDoubleSpinBox() self.manual_joint_ori_x_spin.setDecimals(3) self.manual_joint_ori_y_spin.setDecimals(3) self.manual_joint_ori_z_spin.setDecimals(3) self.manual_joint_ori_x_spin.setRange(-360, 360) self.manual_joint_ori_y_spin.setRange(-360, 360) self.manual_joint_ori_z_spin.setRange(-360, 360) self.manual_joint_ori_x_spin.setLocale(QLocale.English) self.manual_joint_ori_y_spin.setLocale(QLocale.English) self.manual_joint_ori_z_spin.setLocale(QLocale.English) manualJointOriResetBtn = QPushButton('Reset') manual_joint_ori_layout.addWidget(manual_joint_ori_lbl) manual_joint_ori_layout.addWidget(self.manual_joint_ori_x_spin) manual_joint_ori_layout.addWidget(self.manual_joint_ori_y_spin) manual_joint_ori_layout.addWidget(self.manual_joint_ori_z_spin) manual_joint_ori_layout.addWidget(manualJointOriResetBtn) manual_joint_splitter_layout = QVBoxLayout() manual_joint_ori_widget.layout().addLayout(manual_joint_splitter_layout) degree_layout = QHBoxLayout() degree_layout.setContentsMargins(5, 5, 5, 5) degree_layout.setSpacing(2) degree_box = QGroupBox() degree_box.setLayout(degree_layout) degree_box.setStyleSheet("border:0px;") manual_joint_splitter_layout.layout().addWidget(degree_box) self.degree1_radio = QRadioButton('1') self.degree5_radio = QRadioButton('5') self.degree10_radio = QRadioButton('10') self.degree20_radio = QRadioButton('20') self.degree45_radio = QRadioButton('45') self.degree90_radio = QRadioButton('90') self.degree90_radio.setChecked(True) self._set_value_change(90) degree_layout.addWidget(self.degree1_radio) degree_layout.addWidget(self.degree5_radio) degree_layout.addWidget(self.degree10_radio) degree_layout.addWidget(self.degree20_radio) degree_layout.addWidget(self.degree45_radio) degree_layout.addWidget(self.degree90_radio) manual_joint_splitter_layout.addLayout(splitters.SplitterLayout()) manual_joint_ori_buttons_layout = QHBoxLayout() manual_joint_ori_buttons_layout.setContentsMargins(2, 2, 2, 2) manual_joint_ori_buttons_layout.setSpacing(5) manual_joint_ori_widget.layout().addLayout(manual_joint_ori_buttons_layout) manual_joint_ori_add_btn = QPushButton('Add ( + ) ') manual_joint_ori_subtract_btn = QPushButton('Subract ( - ) ') manual_joint_ori_buttons_layout.addWidget(manual_joint_ori_add_btn) manual_joint_ori_buttons_layout.addWidget(manual_joint_ori_subtract_btn) manual_joint_ori_set_btn_layout = QVBoxLayout() manual_joint_ori_set_btn_layout.setAlignment(Qt.AlignCenter) manual_joint_ori_set_btn_layout.setContentsMargins(2, 2, 2, 2) manual_joint_ori_set_btn_layout.setSpacing(5) manual_joint_ori_widget.layout().addLayout(manual_joint_ori_set_btn_layout) manual_joint_ori_set_btn = QPushButton('Set') manual_joint_ori_set_btn.setMaximumWidth(100) self.manual_joint_ori_set_cbx = QCheckBox('Affect children') manual_joint_ori_set_btn_layout.addWidget(manual_joint_ori_set_btn) manual_joint_ori_set_btn_layout.addWidget(self.manual_joint_ori_set_cbx) set_rot_axis_widget = QWidget() set_rot_axis_widget.setLayout(QVBoxLayout()) set_rot_axis_widget.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Fixed) set_rot_axis_widget.layout().setContentsMargins(5, 5, 5, 5) set_rot_axis_widget.layout().setSpacing(10) self.main_layout.addWidget(set_rot_axis_widget) set_rot_axis_splitter = splitters.Splitter('SET ROTATION AXIS') set_rot_axis_widget.layout().addWidget(set_rot_axis_splitter) set_rot_axis_layout = QVBoxLayout() set_rot_axis_widget.layout().addLayout(set_rot_axis_layout) set_rot_top_layout = QHBoxLayout() set_rot_top_layout.setSpacing(5) set_rot_axis_layout.addLayout(set_rot_top_layout) self.set_rot_axis_box = QComboBox() set_rot_top_layout.addWidget(self.set_rot_axis_box) for rotAxis in ['xyz', 'yzx', 'zxy', 'xzy', 'yxz', 'zyx']: self.set_rot_axis_box.addItem(rotAxis) set_rot_axis_common_btn = QPushButton(' <') set_rot_axis_common_btn.setMaximumWidth(45) set_rot_axis_common_btn.setStyleSheet("QPushButton::menu-indicator{image:url(none.jpg);}") self.set_rot_axis_common_btn_menu = QMenu(self) self._set_common_rotation_axis() set_rot_axis_common_btn.setMenu(self.set_rot_axis_common_btn_menu) set_rot_top_layout.addWidget(set_rot_axis_common_btn) set_rot_axis_btn_layout = QHBoxLayout() set_rot_axis_btn_layout.setAlignment(Qt.AlignCenter) set_rot_axis_layout.addLayout(set_rot_axis_btn_layout) set_rot_axis_btn = QPushButton('Set') set_rot_axis_btn.setMaximumWidth(100) set_rot_axis_btn_layout.addWidget(set_rot_axis_btn) set_rot_axis_splitter_layout = QVBoxLayout() set_rot_axis_widget.layout().addLayout(set_rot_axis_splitter_layout) set_rot_axis_splitter_layout.addLayout(splitters.SplitterLayout()) spacer_item = QSpacerItem(2, 2, QSizePolicy.Fixed) self.main_layout.addSpacerItem(spacer_item) layout_lra_buttons = QHBoxLayout() self.main_layout.addLayout(layout_lra_buttons) display_lra_btn = QPushButton('Display LRA') hide_lra_btn = QPushButton('Hide LRA') layout_lra_buttons.addWidget(display_lra_btn) layout_lra_buttons.addWidget(hide_lra_btn) select_hierarchy_btn = QPushButton('Select Hierarchy') self.main_layout.addWidget(select_hierarchy_btn) # ==== SIGNALS ==== # up_world_x.clicked.connect(partial(self._reset_axis, 'x')) up_world_y.clicked.connect(partial(self._reset_axis, 'y')) up_world_z.clicked.connect(partial(self._reset_axis, 'z')) joint_orient_btn.clicked.connect(self.orient_joints) manualJointOriResetBtn.clicked.connect(self._reset_manual_orient) manual_joint_ori_add_btn.clicked.connect(partial(self.manual_orient_joints, 'add')) manual_joint_ori_subtract_btn.clicked.connect(partial(self.manual_orient_joints, 'subtract')) manual_joint_ori_set_btn.clicked.connect(self.set_manual_orient_joints) self.degree1_radio.clicked.connect(partial(self._set_value_change, 0)) self.degree5_radio.clicked.connect(partial(self._set_value_change, 5)) self.degree10_radio.clicked.connect(partial(self._set_value_change, 10)) self.degree20_radio.clicked.connect(partial(self._set_value_change, 20)) self.degree45_radio.clicked.connect(partial(self._set_value_change, 45)) self.degree90_radio.clicked.connect(partial(self._set_value_change, 90)) set_rot_axis_btn.clicked.connect(self.set_rot_axis) display_lra_btn.clicked.connect(partial(self.set_lra, True)) hide_lra_btn.clicked.connect(partial(self.set_lra, False)) select_hierarchy_btn.clicked.connect(self.select_hierarchy) def _reset_axis(self, axis): for spin in [self.up_world_x_spin, self.up_world_y_spin, self.up_world_z_spin]: spin.setValue(0.0) if axis == 'x': self.up_world_x_spin.setValue(1.0) elif axis == 'y': self.up_world_y_spin.setValue(1.0) elif axis == 'z': self.up_world_z_spin.setValue(1.0) def _reset_manual_orient(self): for spin in [self.manual_joint_ori_x_spin, self.manual_joint_ori_y_spin, self.manual_joint_ori_z_spin]: spin.setValue(0.0) def _set_value_change(self, value): for spin in [self.manual_joint_ori_x_spin, self.manual_joint_ori_y_spin, self.manual_joint_ori_z_spin]: spin.setSingleStep(value) def _set_common_rotation_axis(self): self.set_rot_axis_common_btn_menu.addAction('Wrist (YXZ)', partial(self._set_common_rot_order, 'yxz')) self.set_rot_axis_common_btn_menu.addAction('Finger (XYZ)', partial(self._set_common_rot_order, 'xyz')) self.set_rot_axis_common_btn_menu.addAction('Spine (ZYX)', partial(self._set_common_rot_order, 'zyx')) self.set_rot_axis_common_btn_menu.addAction('Hips (ZYX)', partial(self._set_common_rot_order, 'zyx')) self.set_rot_axis_common_btn_menu.addAction('Root (ZYX)', partial(self._set_common_rot_order, 'zyx')) self.set_rot_axis_common_btn_menu.addAction('Upper Leg (ZYX)', partial(self._set_common_rot_order, 'zyx')) self.set_rot_axis_common_btn_menu.addAction('Knee (YXZ)', partial(self._set_common_rot_order, 'yxz')) self.set_rot_axis_common_btn_menu.addAction('Ankle (XZY)', partial(self._set_common_rot_order, 'xzy')) def _set_common_rot_order(self, rot_axis): rot_order = self._get_rot_order(rot_axis) self.set_rot_axis_box.setCurrentIndex(rot_order) @staticmethod def _get_rot_order(rot_axis): rot_order = {} for i, order in enumerate(['xyz', 'yzx', 'zxy', 'xzy', 'yxz', 'zyx']): rot_order[order] = i rot_order[order.upper()] = i return rot_order[rot_axis] @decorators.undo_chunk def orient_joints(self): reset_joints = [] # Get up and aim axis aim_axis = [0, 0, 0] up_axis = [0, 0, 0] for i, aim_radio in enumerate([self.aim_x_radio, self.aim_y_radio, self.aim_z_radio]): if aim_radio.isChecked(): aim_axis_num = i for i, up_radio in enumerate([self.up_x_radio, self.up_y_radio, self.upZRadio]): if up_radio.isChecked(): up_axup_axis_nums_num = i if aim_axis_num == up_axup_axis_nums_num: tp.logger.warning('tpJointOrient: aim and up axis are the same, maybe orientation wont work correctly!') aim_axis_reverse = 1.0 if self.aim_rev_cbx.isChecked(): aim_axis_reverse = -1.0 up_axis_reverse = 1.0 if self.upRevCbx.isChecked(): up_axis_reverse = -1.0 aim_axis[aim_axis_num] = aim_axis_reverse up_axis[up_axup_axis_nums_num] = up_axis_reverse world_up_axis = [self.up_world_x_spin.value(), self.up_world_y_spin.value(), self.up_world_z_spin.value()] # Get selected joints if self.joint_orient_cbx.isChecked(): maya.cmds.select(hierarchy=True) joints = maya.cmds.ls(selection=True, type='joint') # ======================================================================= # Loop all selected joints ... for jnt in reversed(joints): # Get child node childs = maya.cmds.listRelatives(jnt, children=True, type=['transform', 'joint']) # If the joints has direct childs, unparent that childs and store names if childs: if len(childs) > 0: childs = maya.cmds.parent(childs, world=True) # Get parent of this joints for later use parent = '' parents = maya.cmds.listRelatives(jnt, parent=True) if parents: parent = parents[0] # Aim to the child aim_target = '' if childs: for child in childs: if maya.cmds.nodeType(child) == 'joint': aim_target = child break # print '//DEBUG: JNT=' + jnt + " Parent=" + parent + " AimTarget=" + aim_target + "//\n" if aim_target != '': # Apply an aim constraint from the joint to its child (target) maya.cmds.delete(maya.cmds.aimConstraint(aim_target, jnt, aim=aim_axis, upVector=up_axis, worldUpVector=world_up_axis, worldUpType='vector', weight=1.0)) # Clear joint axis maya.cmds.joint(jnt, edit=True, zeroScaleOrient=True) maya.cmds.makeIdentity(jnt, apply=True) elif parent != '': reset_joints.append(jnt) # Reparent child if childs: if len(childs) > 0: maya.cmds.parent(childs, jnt) for jnt in reset_joints: # If there is no target, the joint will take its parent orientation for axis in ['x', 'y', 'z']: maya.cmds.setAttr(jnt + '.jointOrient' + axis.upper(), maya.cmds.getAttr(jnt + '.r' + axis)) maya.cmds.setAttr(jnt + '.r' + axis, 0) @decorators.undo_chunk def manual_orient_joints(self, type): if type == 'add': tweak = 1.0 else: tweak = -1.0 tweak_rot = [self.manual_joint_ori_x_spin.value() * tweak, self.manual_joint_ori_y_spin.value() * tweak, self.manual_joint_ori_z_spin.value() * tweak] joints = maya.cmds.ls(selection=True, type='joint') for jnt in joints: # Adjust the rotation axis maya.cmds.xform(jnt, rotateAxis=[tweak_rot[0], tweak_rot[1], tweak_rot[2]], relative=True, objectSpace=True) # Clear joint axis maya.cmds.joint(jnt, edit=True, zeroScaleOrient=True) maya.cmds.makeIdentity(jnt, apply=True) maya.cmds.select(joints, replace=True) @decorators.undo_chunk def set_manual_orient_joints(self): tweak_rot = [self.manual_joint_ori_x_spin.value(), self.manual_joint_ori_y_spin.value(), self.manual_joint_ori_z_spin.value()] joints = maya.cmds.ls(selection=True, type='joint') for jnt in joints: if not self.manual_joint_ori_set_cbx.isChecked(): childs = maya.cmds.listRelatives(jnt, children=True, type=['transform', 'joint']) if childs: if len(childs) > 0: for child in childs: maya.cmds.parent(child, world=True) # Set the rotation axis for i, axis in enumerate(['x', 'y', 'z']): maya.cmds.setAttr(jnt + '.jointOrient' + axis.upper(), tweak_rot[i]) # Clear joint axis maya.cmds.joint(jnt, edit=True, zeroScaleOrient=True) maya.cmds.makeIdentity(jnt, apply=True) if childs: for child in childs: maya.cmds.parent(child, jnt) maya.cmds.select(joints, replace=True) @decorators.undo_chunk def set_rot_axis(self): sel = maya.cmds.ls(selection=True, type=['joint', 'transform']) for obj in sel: rot_order = self._get_rot_order(self.set_rot_axis_box.currentText()) maya.cmds.setAttr(obj + '.rotateOrder', rot_order) @staticmethod @decorators.undo_chunk def set_lra(state): sel = maya.cmds.ls(selection=True) for obj in sel: if maya.cmds.attributeQuery('displayLocalAxis', node=obj, exists=True): maya.cmds.setAttr(obj + '.displayLocalAxis', state) @staticmethod def select_hierarchy(): """ Method that selects the hierachy of the selected nodes """ sel = maya.cmds.ls(selection=True) for obj in sel: maya.cmds.select(obj, hi=True, add=True) def run(): win = JointOrient() win.show() return win

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null

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null

0

1

0

7f58bef75ba0ec877afcf1b719c8b7a1c29e9cfb

2,584

py

Python

run.py

tobias-fyi/print-fiction

b0befb9906fea85bf1a553fb4bc4d229b8ed957b

[ "MIT" ]

null

run.py

tobias-fyi/print-fiction

b0befb9906fea85bf1a553fb4bc4d229b8ed957b

[ "MIT" ]

null

run.py

tobias-fyi/print-fiction

b0befb9906fea85bf1a553fb4bc4d229b8ed957b

[ "MIT" ]

null

import dash import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output from app import app, server from pages import index # ====== Navigation ====== # # nav = html.Ul( # [ # html.Li(html.A("print(fiction)", href="/", className="nav-link")), # html.Li(html.A("Introduction", href="/#introduction", className="nav-link")), # html.Li(html.A("Predict", href="/#predict", className="nav-link")), # ], # className="nav", # ) # ====== Footer ====== # customFooter = dbc.Container( html.Div( html.Div( [ html.H2("Tobias Reaper"), html.Ul( [ html.Li( html.A( html.I(className="fas fa-globe mr-1"), href="https://tobias.fyi/", ) ), html.Li( html.A( html.I(className="fab fa-github-square mr-1"), href="https://github.com/tobias-fyi/print-fiction/", ) ), html.Li( html.A( html.I(className="fab fa-linkedin mr-1"), href="https://www.linkedin.com/in/tobias-reaper/", ) ), html.Li( html.A( html.I(className="fab fa-twitter-square mr-1"), href="https://twitter.com/tobiasfyi/", ) ), ], className="icons", ), ], className="container medium", ), id="footer", ), fluid=True, ) # ====== URL Routing ====== # # https://dash.plot.ly/urls # app.layout = html.Div( [ dcc.Location(id="url", refresh=False), # nav, dbc.Container(id="page-content", fluid=True), html.Hr(), customFooter, ] ) @app.callback(Output("page-content", "children"), [Input("url", "pathname")]) def display_page(pathname): if pathname == "/": return index.layout else: return dcc.Markdown("## Page not found") if __name__ == "__main__": app.run_server(debug=True)

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0.015625

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null

0

null

0

1

0

7f5eb990fc9bad4f6475bb538b62266b6b5e7f41

7,978

py

Python

legacy/examples/distribute_graphsage/model.py

zbmain/PGL

dbded6a1543248b0a33c05eb476ddc513401a774

[ "Apache-2.0" ]

1,389

2019-06-11T03:29:20.000Z

2022-03-29T18:25:43.000Z

legacy/examples/distribute_graphsage/model.py

zbmain/PGL

dbded6a1543248b0a33c05eb476ddc513401a774

[ "Apache-2.0" ]

232

2019-06-21T06:52:10.000Z

2022-03-29T08:20:31.000Z

legacy/examples/distribute_graphsage/model.py

zbmain/PGL

dbded6a1543248b0a33c05eb476ddc513401a774

[ "Apache-2.0" ]

229

2019-06-20T12:13:58.000Z

2022-03-25T12:04:48.000Z

# Copyright (c) 2019 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. """ graphsage model. """ from __future__ import division from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import math import pgl import numpy as np import paddle import paddle.fluid.layers as L import paddle.fluid as F import paddle.fluid as fluid def copy_send(src_feat, dst_feat, edge_feat): return src_feat["h"] def mean_recv(feat): return fluid.layers.sequence_pool(feat, pool_type="average") def sum_recv(feat): return fluid.layers.sequence_pool(feat, pool_type="sum") def max_recv(feat): return fluid.layers.sequence_pool(feat, pool_type="max") def lstm_recv(feat): hidden_dim = 128 forward, _ = fluid.layers.dynamic_lstm( input=feat, size=hidden_dim * 4, use_peepholes=False) output = fluid.layers.sequence_last_step(forward) return output def graphsage_mean(gw, feature, hidden_size, act, name): msg = gw.send(copy_send, nfeat_list=[("h", feature)]) neigh_feature = gw.recv(msg, mean_recv) self_feature = feature self_feature = fluid.layers.fc(self_feature, hidden_size, act=act, name=name + '_l') neigh_feature = fluid.layers.fc(neigh_feature, hidden_size, act=act, name=name + '_r') output = fluid.layers.concat([self_feature, neigh_feature], axis=1) output = fluid.layers.l2_normalize(output, axis=1) return output def graphsage_meanpool(gw, feature, hidden_size, act, name, inner_hidden_size=512): neigh_feature = fluid.layers.fc(feature, inner_hidden_size, act="relu") msg = gw.send(copy_send, nfeat_list=[("h", neigh_feature)]) neigh_feature = gw.recv(msg, mean_recv) neigh_feature = fluid.layers.fc(neigh_feature, hidden_size, act=act, name=name + '_r') self_feature = feature self_feature = fluid.layers.fc(self_feature, hidden_size, act=act, name=name + '_l') output = fluid.layers.concat([self_feature, neigh_feature], axis=1) output = fluid.layers.l2_normalize(output, axis=1) return output def graphsage_maxpool(gw, feature, hidden_size, act, name, inner_hidden_size=512): neigh_feature = fluid.layers.fc(feature, inner_hidden_size, act="relu") msg = gw.send(copy_send, nfeat_list=[("h", neigh_feature)]) neigh_feature = gw.recv(msg, max_recv) neigh_feature = fluid.layers.fc(neigh_feature, hidden_size, act=act, name=name + '_r') self_feature = feature self_feature = fluid.layers.fc(self_feature, hidden_size, act=act, name=name + '_l') output = fluid.layers.concat([self_feature, neigh_feature], axis=1) output = fluid.layers.l2_normalize(output, axis=1) return output def graphsage_lstm(gw, feature, hidden_size, act, name): inner_hidden_size = 128 neigh_feature = fluid.layers.fc(feature, inner_hidden_size, act="relu") hidden_dim = 128 forward_proj = fluid.layers.fc(input=neigh_feature, size=hidden_dim * 4, bias_attr=False, name="lstm_proj") msg = gw.send(copy_send, nfeat_list=[("h", forward_proj)]) neigh_feature = gw.recv(msg, lstm_recv) neigh_feature = fluid.layers.fc(neigh_feature, hidden_size, act=act, name=name + '_r') self_feature = feature self_feature = fluid.layers.fc(self_feature, hidden_size, act=act, name=name + '_l') output = fluid.layers.concat([self_feature, neigh_feature], axis=1) output = fluid.layers.l2_normalize(output, axis=1) return output def build_graph_model(graph_wrapper, num_class, k_hop, graphsage_type, hidden_size): node_index = fluid.layers.data( "node_index", shape=[None], dtype="int64", append_batch_size=False) node_label = fluid.layers.data( "node_label", shape=[None, 1], dtype="int64", append_batch_size=False) #feature = fluid.layers.gather(feature, graph_wrapper.node_feat['feats']) feature = graph_wrapper.node_feat['feats'] feature.stop_gradient = True for i in range(k_hop): if graphsage_type == 'graphsage_mean': feature = graphsage_mean( graph_wrapper, feature, hidden_size, act="relu", name="graphsage_mean_%s" % i) elif graphsage_type == 'graphsage_meanpool': feature = graphsage_meanpool( graph_wrapper, feature, hidden_size, act="relu", name="graphsage_meanpool_%s" % i) elif graphsage_type == 'graphsage_maxpool': feature = graphsage_maxpool( graph_wrapper, feature, hidden_size, act="relu", name="graphsage_maxpool_%s" % i) elif graphsage_type == 'graphsage_lstm': feature = graphsage_lstm( graph_wrapper, feature, hidden_size, act="relu", name="graphsage_maxpool_%s" % i) else: raise ValueError("graphsage type %s is not" " implemented" % graphsage_type) feature = fluid.layers.gather(feature, node_index) logits = fluid.layers.fc(feature, num_class, act=None, name='classification_layer') proba = fluid.layers.softmax(logits) loss = fluid.layers.softmax_with_cross_entropy( logits=logits, label=node_label) loss = fluid.layers.mean(loss) acc = fluid.layers.accuracy(input=proba, label=node_label, k=1) return loss, acc class GraphsageModel(object): def __init__(self, args): self.args = args def forward(self): args = self.args graph_wrapper = pgl.graph_wrapper.GraphWrapper( "sub_graph", node_feat=[('feats', [None, 602], np.dtype('float32'))]) loss, acc = build_graph_model( graph_wrapper, num_class=args.num_class, hidden_size=args.hidden_size, graphsage_type=args.graphsage_type, k_hop=len(args.samples)) loss.persistable = True self.graph_wrapper = graph_wrapper self.loss = loss self.acc = acc return loss

35.145374

81

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888

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4.82545

0.217342

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null

0

null

0

1

0

7f5f8e7da675ccdf94c2845ab889407163f48cad

16,914

py

Python

ensemble_detectors/src/Algorithm_1_matchfilter/spectral_lib/spectral/algorithms/perceptron.py

satish1901/Methane-detection-from-hyperspectral-imagery

741dee02e76931f572cf3e06af8faabe871e8e4a

[ "MIT" ]

27

2020-06-11T21:59:54.000Z

2022-03-22T03:10:50.000Z

ensemble_detectors/src/Algorithm_1_matchfilter/spectral_lib/spectral/algorithms/perceptron.py

N-NSH/Methane-detection-from-hyperspectral-imagery

741dee02e76931f572cf3e06af8faabe871e8e4a

[ "MIT" ]

7

2020-09-25T22:41:18.000Z

2022-02-09T23:41:04.000Z

ensemble_detectors/src/Algorithm_1_matchfilter/spectral_lib/spectral/algorithms/perceptron.py

N-NSH/Methane-detection-from-hyperspectral-imagery

741dee02e76931f572cf3e06af8faabe871e8e4a

[ "MIT" ]

4

2021-01-18T15:57:13.000Z

2022-03-12T20:51:27.000Z

######################################################################### # # perceptron.py - This file is part of the Spectral Python (SPy) package. # # Copyright (C) 2001-2014 Thomas Boggs # # Spectral Python is free software; you can redistribute it and/ # or modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # Spectral Python is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this software; if not, write to # # Free Software Foundation, Inc. # 59 Temple Place, Suite 330 # Boston, MA 02111-1307 # USA # ######################################################################### # # Send comments to: # Thomas Boggs, tboggs@users.sourceforge.net # ''' Classes and functions for classification with neural networks. ''' from __future__ import division, print_function, unicode_literals import numpy as np import sys class PerceptronLayer: '''A multilayer perceptron layer with sigmoid activation function.''' def __init__(self, shape, k=1.0, weights=None): ''' Arguments: `shape` (2-tuple of int): Should have the form (`num_inputs`, `num_neurons`), where `num_inputs` does not include an input for the bias weights. `k` (float): Sigmoid shape parameter. `weights` (ndarray): Initial weights for the layer. Note that if provided, this argument must have shape (`num_neurons`, `num_inputs` + 1). If not provided, initial weights will be randomized. ''' self.k = k self.shape = (shape[1], shape[0] + 1) if weights: if weights.shape != self.shape: raise Exception('Shape of weight matrix does not ' \ 'match Perceptron layer shape.') self.weights = np.array(weights, dtype=np.float64) else: self.randomize_weights() self.dW = np.zeros_like(self.weights) self.dW_buf = np.zeros_like(self.dW) self.x = np.ones(self.shape[1], float) def randomize_weights(self): '''Randomizes the layer weight matrix. The bias weight will be in the range [0, 1). The remaining weights will correspond to a vector with unit length and uniform random orienation. ''' import math self.weights = 1. - 2. * np.random.rand(*self.shape) for row in self.weights: row[1:] /= math.sqrt(np.sum(row[1:]**2)) row[0] = -0.5 * np.random.rand() - 0.5 * np.sum(row[1:]) def input(self, x, clip=0.0): '''Sets layer input and computes output. Arguments: `x` (sequence): Layer input, not including bias input. `clip` (float >= 0): Optional clipping value to limit sigmoid output. The sigmoid function has output in the range (0, 1). If the `clip` argument is set to `a` then all neuron outputs for the layer will be constrained to the range [a, 1 - a]. This can improve perceptron learning rate in some situations. Return value: The ndarray of output values is returned and is also set in the `y` attribute of the layer. For classifying samples, call `classify` instead. ''' self.x[1:] = x self.z = np.dot(self.weights, self.x) if clip > 0.: self.y = np.clip(self.g(self.z), clip, 1. - clip) else: self.y = self.g(self.z) return self.y def g(self, a): '''Neuron activation function (logistic sigmoid)''' return 1. / (1. + np.exp(- self.k * a)) def dy_da(self): '''Derivative of activation function at current activation level.''' return self.k * (self.y * (1.0 - self.y)) class Perceptron: ''' A Multi-Layer Perceptron network with backpropagation learning.''' def __init__(self, layers, k=1.0): ''' Creates the Perceptron network. Arguments: layers (sequence of integers): A list specifying the network structure. `layers`[0] is the number of inputs. `layers`[-1] is the number of perceptron outputs. `layers`[1: -1] are the numbers of units in the hidden layers. `k` (float): Sigmoid shape parameter. ''' if type(layers) != list or len(layers) < 2: raise Exception('ERROR: Perceptron argument must be list of 2 or ' 'more integers.') self.shape = layers[:] self.layers = [PerceptronLayer((layers[i - 1], layers[i]), k) for i in range(1, len(layers))] self.accuracy = 0 self.error = 0 # To prevent overflow when scaling inputs self.min_input_diff = 1.e-8 # If True, previous iteration weights are preserved after interrupting # training (with CTRL-C) self.cache_weights = True def input(self, x, clip=0.0): '''Sets Perceptron input, activates neurons and sets & returns output. Arguments: `x` (sequence): Inputs to input layer. Should not include a bias input. `clip` (float >= 0): Optional clipping value to limit sigmoid output. The sigmoid function has output in the range (0, 1). If the `clip` argument is set to `a` then all neuron outputs for the layer will be constrained to the range [a, 1 - a]. This can improve perceptron learning rate in some situations. For classifying samples, call `classify` instead of `input`. ''' self.x = x[:] x = self._scale * (x - self._offset) for layer in self.layers: x = layer.input(x, clip) self.y = np.array(x) return x def classify(self, x): '''Classifies the given sample. This has the same result as calling input and rounding the result. ''' return [int(round(xx)) for xx in self.input(x)] def train(self, X, Y, max_iterations=10000, accuracy=100.0, rate=0.3, momentum=0., batch=1, clip=0.0, on_iteration=None, stdout=sys.stdout): ''' Trains the Perceptron to classify the given samples. Arguments: `X`: The sequence of observations to be learned. Each element of `X` must have a length corresponding to the input layer of the network. Values in `X` are not required to be scaled. `Y`: Truth values corresponding to elements of `X`. `Y` must contain as many elements as `X` and each element of `Y` must contain a number of elements corresponding to the output layer of the network. All values in `Y` should be in the range [0, 1] and for training a classifier, values in `Y` are typically *only* 0 or 1 (i.e., no intermediate values). `max_iterations` (int): Maximum number of iterations through the data to perform. Training will end sooner if the specified accuracy is reached in fewer iterations. `accuracy` (float): The percent training accuracy at which to terminate training, if the maximum number of iterations are not reached first. This value can be set greater than 100 to force a specified number of training iterations to be performed (e.g., to continue reducing the error term after 100% classification accuracy has been achieved. `rate` (float): The perceptron learning rate (typically in the range (0, 1]). `momentum` (float): The perceptron learning momentum term, which specifies the fraction of the previous update value that should be added to the current update term. The value should be in the range [0, 1). `batch` (positive integer): Specifies how many samples should be evaluated before an update is made to the perceptron weights. A value of 0 indicates batch updates should be performed (evaluate all training inputs prior to updating). Otherwise, updates will be aggregated for every `batch` inputs (i.e., `batch` == 1 is stochastic learning). `clip` (float >= 0): Optional clipping value to limit sigmoid output during training. The sigmoid function has output in the range (0, 1). If the `clip` argument is set to `a` then all neuron outputs for the layer will be constrained to the range [a, 1 - a]. This can improve perceptron learning rate in some situations. After training the perceptron with a clipping value, `train` can be called again with clipping set to 0 to continue reducing the training error. `on_iteration` (callable): A callable object that accepts the perceptron as input and returns bool. If this argument is set, the object will be called at the end of each training iteration with the perceptron as its argument. If the callable returns True, training will terminate. `stdout`: An object with a `write` method that can be set to redirect training status messages somewhere other than stdout. To suppress output, set `stats` to None. ''' import itertools import os if stdout is None: stdout = open(os.devnull, 'w') try: self._set_scaling(X) for layer in self.layers: layer.dW_old = np.zeros_like(layer.dW) for iteration in range(max_iterations): self._reset_corrections() self.error = 0 num_samples = 0 num_correct = 0 num_summed = 0 for (x, t) in zip(X, Y): num_samples += 1 num_summed += 1 num_correct += np.all(np.round(self.input(x, clip)) == t) delta = np.array(t) - self.y self.error += 0.5 * sum(delta**2) # Determine incremental weight adjustments self._update_dWs(t) if batch > 0 and num_summed == batch: self._adjust_weights(rate, momentum, num_summed, stdout) num_summed = 0 # In case a partial batch is remaining if batch > 0 and num_summed > 0: self._adjust_weights(rate, momentum, num_summed, stdout) num_summed = 0 self.accuracy = 100. * num_correct / num_samples if on_iteration and on_iteration(self): return True stdout.write('Iter % 5d: Accuracy = %.2f%% E = %f\n' % (iteration, self.accuracy, self.error)) if self.accuracy >= accuracy: stdout.write('Network trained to %.1f%% sample accuracy ' 'in %d iterations.\n' % (self.accuracy, iteration + 1)) return True # If doing full batch learning (batch == 0) if num_summed > 0: self._adjust_weights(rate, momentum, num_summed, stdout) num_summed = 0 except KeyboardInterrupt: stdout.write("KeyboardInterrupt: Terminating training.\n") self._reset_corrections() return False stdout.write('Terminating network training after %d iterations.\n' % (iteration + 1)) return False def _update_dWs(self, t): '''Update weight adjustment values for the current sample.''' # Output layer: # dE/dy = t - y # dz/dW = x layerK = self.layers[-1] layerK.delta = layerK.dy_da() * (t - self.y) layerK.dW += np.outer(layerK.delta, layerK.x) # Hidden layers for i in range(len(self.layers) - 2, -1, -1): (layerJ, layerK) = self.layers[i: i + 2] b = np.dot(layerK.delta, layerK.weights[:, 1:]) layerJ.delta = layerJ.dy_da() * b layerJ.dW += np.outer(layerJ.delta, layerJ.x) def _adjust_weights(self, rate, momentum, num_summed, stdout): '''Applies aggregated weight adjustments to the perceptron weights.''' if self.cache_weights: weights = [np.array(layer.weights) for layer in self.layers] try: if momentum > 0: for layer in self.layers: layer.dW *= (float(rate) / num_summed) layer.dW += momentum * layer.dW_old layer.weights += layer.dW (layer.dW_old, layer.dW) = (layer.dW, layer.dW_old) else: for layer in self.layers: layer.dW *= (float(rate) / num_summed) layer.weights += layer.dW except KeyboardInterrupt: if self.cache_weights: stdout.write('Interrupt during weight adjustment. Restoring ' \ 'previous weights.\n') for i in range(len(weights)): self.layers[i].weights = weights[i] else: stdout.write('Interrupt during weight adjustment. Weight ' \ 'cacheing was disabled so current weights may' \ 'be corrupt.\n') raise finally: self._reset_corrections() def _reset_corrections(self): for layer in self.layers: layer.dW.fill(0) def _set_scaling(self, X): '''Sets translation/scaling of inputs to map X to the range [0, 1].''' mins = maxes = None for x in X: if mins is None: mins = x maxes = x else: mins = np.min([mins, x], axis=0) maxes = np.max([maxes, x], axis = 0) self._offset = mins r = maxes - mins self._scale = 1. / np.where(r < self.min_input_diff, 1, r) # Sample data xor_data = [ [[0, 0], [0]], [[0, 1], [1]], [[1, 0], [1]], [[1, 1], [0]], ] xor_data2 = [ [[0, 0], [0, 1]], [[0, 1], [1, 0]], [[1, 0], [1, 0]], [[1, 1], [0, 1]], ] and_data = [ [[0, 0], [0]], [[0, 1], [0]], [[1, 0], [0]], [[1, 1], [1]], ] def test_case(XY, shape, *args, **kwargs): (X, Y) = list(zip(*XY)) p = Perceptron(shape) trained = p.train(X, Y, *args, **kwargs) return (trained, p) def test_xor(*args, **kwargs): XY = xor_data shape = [2, 2, 1] return test_case(XY, shape, *args, **kwargs) def test_xor222(*args, **kwargs): XY = xor_data2 shape = [2, 2, 2] return test_case(XY, shape, *args, **kwargs) def test_xor231(*args, **kwargs): XY = xor_data shape = [2, 3, 1] return test_case(XY, shape, *args, **kwargs) def test_and(*args, **kwargs): XY = and_data shape = [2, 1] return test_case(XY, shape, *args, **kwargs) if __name__ == '__main__': tests = [('AND (2x1)', test_and), ('XOR (2x2x1)', test_xor), ('XOR (2x2x2)', test_xor222), ('XOR (2x3x1)', test_xor231)] results = [test[1](5000)[0] for test in tests] nr = [(p[0][0], p[1]) for p in zip(tests, results)] print() print('Training results for 5000 iterations') print('------------------------------------') for (name, result) in nr: s = [ 'FAILED', 'PASSED'][result] print('{0:<20}: {1}'.format(name, s)) if False in results: print('\nNote: XOR convergence for these small network sizes is') print('dependent on initial weights, which are randomized. Try') print('running the test again.')

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