xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

1cc1456234d268ec5519fd7d40d1486a56173d80

23

py

Python

scholar/__init__.py

toni-heittola/pelican-btex

788ab934efcba4edf238237ad9fff8f489d685b7

[ "MIT" ]

5

2016-11-13T10:24:28.000Z

2019-08-05T05:03:50.000Z

scholar/__init__.py

toni-heittola/pelican-btex

788ab934efcba4edf238237ad9fff8f489d685b7

[ "MIT" ]

null

scholar/__init__.py

toni-heittola/pelican-btex

788ab934efcba4edf238237ad9fff8f489d685b7

[ "MIT" ]

null

from .scholar import *

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py

Python

set_up_grasp_models/tests/test_set_up_model.py

martamatos/set_up_grasp_models

0028f063c41104e3c0404956aa225e76aa6ac983

[ "MIT" ]

null

set_up_grasp_models/tests/test_set_up_model.py

martamatos/set_up_grasp_models

0028f063c41104e3c0404956aa225e76aa6ac983

[ "MIT" ]

5

2019-05-14T17:05:41.000Z

2019-05-29T13:17:11.000Z

set_up_grasp_models/tests/test_set_up_model.py

martamatos/set_up_grasp_models

0028f063c41104e3c0404956aa225e76aa6ac983

[ "MIT" ]

null

import os import unittest from unittest.mock import patch import pandas as pd import pickle from set_up_grasp_models.set_up_models.set_up_model import set_up_model class TestSetUpModel(unittest.TestCase): def setUp(self): this_dir, this_filename = os.path.split(__file__) self.test_folder = os.path.join(this_dir, 'test_files', 'test_set_up_models', 'set_up_model') self.file_in_stoic = os.path.join(self.test_folder, 'model_with_PPP_plaintext.txt') def test_set_up_model_empty_base(self): true_res = pd.read_excel(os.path.join(self.test_folder, 'true_res_model_v1.xlsx'), sheet_name=None) general_file = os.path.join(self.test_folder, '..', '..', '..', '..', '..', 'base_files', 'GRASP_general.xlsx') model_name = 'model_v1' file_out = os.path.join(self.test_folder, model_name + '.xlsx') set_up_model(model_name, self.file_in_stoic, general_file, file_out) res = pd.read_excel(os.path.join(self.test_folder, model_name + '.xlsx'), sheet_name=None) self.assertListEqual(list(true_res.keys()), list(res.keys())) for key in true_res: print(key) self.assertTrue(true_res[key].equals(res[key])) def test_set_up_model_empty_base_error(self): general_file = os.path.join(self.test_folder, 'GRASP_general_error.xlsx') model_name = 'model_v1' file_out = os.path.join(self.test_folder, 'model_v1.xlsx') with self.assertRaises(KeyError) as context: set_up_model(model_name, self.file_in_stoic, general_file, file_out) self.assertTrue( f'The base excel file {general_file} must contain a sheet named \'general\'' in context.exception) def test_set_up_model_not_empty_base(self): true_res = pd.read_excel(os.path.join(self.test_folder, 'true_res_model_v2.xlsx'), sheet_name=None) general_file = os.path.join(self.test_folder, 'model_v1_manual2_EX.xlsx') model_name = 'model_v2' file_out = os.path.join(self.test_folder, model_name + '.xlsx') set_up_model(model_name, self.file_in_stoic, general_file, file_out) res = pd.read_excel(os.path.join(self.test_folder, model_name + '.xlsx'), sheet_name=None) #with open(os.path.join(self.test_folder, 'true_res_model_v2.pkl'), 'wb') as handle: # pickle.dump(res, handle) self.assertListEqual(list(true_res.keys()), list(res.keys())) for key in true_res: print(key) #if key != 'measRates': self.assertTrue(true_res[key].equals(res[key])) def test_set_up_model_not_empty_base_equilibrator(self): with open(os.path.join(self.test_folder, 'true_res_model_v3.pkl'), 'rb') as f_in: true_res = pickle.load(f_in) general_file = os.path.join(self.test_folder, 'model_v1_manual2_EX.xlsx') model_name = 'model_v3' file_out = os.path.join(self.test_folder, model_name + '.xlsx') with patch('builtins.input', side_effect=['']): set_up_model(model_name, self.file_in_stoic, general_file, file_out, use_equilibrator=True) res = pd.read_excel(os.path.join(self.test_folder, model_name + '.xlsx'), sheet_name=None) #with open(os.path.join(self.test_folder, 'true_res_model_v3.pkl'), 'wb') as handle: # pickle.dump(res, handle) self.assertListEqual(list(true_res.keys()), list(res.keys())) for key in true_res: print(key) self.assertTrue(true_res[key].equals(res[key])) def test_set_up_model_not_empty_base_mets_file(self): with open(os.path.join(self.test_folder, 'true_res_model_v4.pkl'), 'rb') as f_in: true_res = pickle.load(f_in) general_file = os.path.join(self.test_folder, 'model_v1_manual2_EX.xlsx') file_in_mets_conc = os.path.join(self.test_folder, 'met_concs.xlsx') model_name = 'model_v4' file_out = os.path.join(self.test_folder, model_name + '.xlsx') with patch('builtins.input', side_effect=['']): set_up_model(model_name, self.file_in_stoic, general_file, file_out, file_in_mets_conc=file_in_mets_conc) res = pd.read_excel(os.path.join(self.test_folder, model_name + '.xlsx'), sheet_name=None) #with open(os.path.join(self.test_folder, 'true_res_model_v4.pkl'), 'wb') as handle: # pickle.dump(res, handle) self.assertListEqual(list(true_res.keys()), list(res.keys())) for key in true_res: print(key) self.assertTrue(true_res[key].equals(res[key])) def test_set_up_model_not_empty_base_rxns_file(self): true_res = pd.read_excel(os.path.join(self.test_folder, 'true_res_model_v5.xlsx'), sheet_name=None) general_file = os.path.join(self.test_folder, 'model_v1_manual2_EX2.xlsx') file_in_rxn_fluxes = os.path.join(self.test_folder, 'flux_file_rows.xlsx') model_name = 'model_v5' file_out = os.path.join(self.test_folder, model_name + '.xlsx') with patch('builtins.input', side_effect=['']): set_up_model(model_name, self.file_in_stoic, general_file, file_out, file_in_meas_fluxes=file_in_rxn_fluxes, fluxes_orient='rows') res = pd.read_excel(os.path.join(self.test_folder, model_name + '.xlsx'), sheet_name=None) self.assertListEqual(list(true_res.keys()), list(res.keys())) for key in true_res: print(key) self.assertTrue(true_res[key].equals(res[key]))

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1c771aff4232d23b8d12ee0f78dab7ef45dba319

20

py

Python

randomstate/prng/pcg32/__init__.py

bashtage/ng-numpy-randomstate

b397db9cb8688b291fc40071ab043009dfa05a85

[ "Apache-2.0", "BSD-3-Clause" ]

43

2016-02-11T03:38:16.000Z

2022-02-03T10:00:15.000Z

randomstate/prng/pcg32/__init__.py

bashtage/pcg-python

b397db9cb8688b291fc40071ab043009dfa05a85

[ "Apache-2.0", "BSD-3-Clause" ]

31

2015-12-26T19:47:36.000Z

2018-12-10T15:55:46.000Z

randomstate/prng/pcg32/__init__.py

bashtage/ng-numpy-randomstate

b397db9cb8688b291fc40071ab043009dfa05a85

[ "Apache-2.0", "BSD-3-Clause" ]

11

2016-04-28T02:00:38.000Z

2020-08-07T10:33:10.000Z

from .pcg32 import *

20

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98c06dadf0d0ae5abbfca513b2b063a57e779ce2

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py

Python

sdk/python/pulumi_azure/datafactory/__init__.py

kenny-wealth/pulumi-azure

e57e3a81f95bf622e7429c53f0bff93e33372aa1

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_azure/datafactory/__init__.py

kenny-wealth/pulumi-azure

e57e3a81f95bf622e7429c53f0bff93e33372aa1

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_azure/datafactory/__init__.py

kenny-wealth/pulumi-azure

e57e3a81f95bf622e7429c53f0bff93e33372aa1

[ "ECL-2.0", "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** # Export this package's modules as members: from .factory import * from .dataset_mysql import * from .dataset_postgresql import * from .dataset_sql_server_table import * from .integration_runtime_managed import * from .linked_service_data_lake_storage_gen2 import * from .linked_service_mysql import * from .linked_service_postgresql import * from .linked_service_sql_server import * from .pipeline import * from .get_factory import *

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6

c727ab7314b6a50cceaee5a0a5dd6fc60055d251

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py

Python

src/__init__.py

Yang-33/vjudge-atcoder-submitID

5b87594322a337e6acb25c84470d273427413445

[ "MIT" ]

null

src/__init__.py

Yang-33/vjudge-atcoder-submitID

5b87594322a337e6acb25c84470d273427413445

[ "MIT" ]

3

2018-02-07T16:35:27.000Z

2018-02-07T17:47:36.000Z

src/__init__.py

Yang-33/vjudge-atcoder-submitID

5b87594322a337e6acb25c84470d273427413445

[ "MIT" ]

null

from . import GetSubmitID from . import PrintIDs from . import GetFromfileURL from . import InputSTDIN from . import PrintColor

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c7b5957d881fb700bfd4cba0b8d26b1aa982e4b1

35,945

py

Python

python/test/ml_ops/cconv_python.py

leomariga/Open3D

d197339fcd29ad0803a182ef8953d89e563f94d7

[ "MIT" ]

1

2021-06-27T22:04:38.000Z

python/test/ml_ops/cconv_python.py

leomariga/Open3D

d197339fcd29ad0803a182ef8953d89e563f94d7

[ "MIT" ]

null

python/test/ml_ops/cconv_python.py

leomariga/Open3D

d197339fcd29ad0803a182ef8953d89e563f94d7

[ "MIT" ]

null

# ---------------------------------------------------------------------------- # - Open3D: www.open3d.org - # ---------------------------------------------------------------------------- # The MIT License (MIT) # # Copyright (c) 2018-2021 www.open3d.org # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. # ---------------------------------------------------------------------------- """This is a python implementation for the continuous convolutions meant for debugging and testing the C code. """ import numpy as np # interpolation LINEAR = 1 NEAREST_NEIGHBOR = 2 LINEAR_BORDER = 3 # coordinate mapping IDENTITY = 4 BALL_TO_CUBE_RADIAL = 5 BALL_TO_CUBE_VOLUME_PRESERVING = 6 # windows RECTANGLE = 7 TRAPEZOID = 8 POLY = 9 _convert_parameter_str_dict = { 'linear': LINEAR, 'linear_border': LINEAR_BORDER, 'nearest_neighbor': NEAREST_NEIGHBOR, 'identity': IDENTITY, 'ball_to_cube_radial': BALL_TO_CUBE_RADIAL, 'ball_to_cube_volume_preserving': BALL_TO_CUBE_VOLUME_PRESERVING, } def map_cube_to_cylinder(points, inverse=False): """maps a cube to a cylinder and vice versa The input and output range of the coordinates is [-1,1]. The cylinder axis is along z. points: numpy array with shape [n,3] inverse: If True apply the inverse transform: cylinder -> cube """ assert points.ndim == 2 assert points.shape[1] == 3 # yapf: disable result = np.empty_like(points) if inverse: for i, p in enumerate(points): x, y, z = p if np.allclose(p[0:2], np.zeros_like(p[0:2])): result[i] = (0,0,z) elif np.abs(y) <= x and x > 0: result[i] = (np.sqrt(x*x+y*y), 4/np.pi *np.sqrt(x*x+y*y)*np.arctan(y/x), z) elif np.abs(y) <= -x and x < 0: result[i] = (-np.sqrt(x*x+y*y), -4/np.pi *np.sqrt(x*x+y*y)*np.arctan(y/x), z) elif np.abs(x) <= y and y > 0: result[i] = (4/np.pi *np.sqrt(x*x+y*y)*np.arctan(x/y), np.sqrt(x*x+y*y), z) else: # elif np.abs(x) <= -y and y < 0: result[i] = (-4/np.pi *np.sqrt(x*x+y*y)*np.arctan(x/y), -np.sqrt(x*x+y*y), z) else: for i, p in enumerate(points): x, y, z = p if np.count_nonzero(p[0:2]) == 0: result[i] = (0,0,z) elif np.abs(y) <= np.abs(x): result[i] = (x*np.cos(y/x*np.pi/4), x*np.sin(y/x*np.pi/4), z) else: result[i] = (y*np.sin(x/y*np.pi/4), y*np.cos(x/y*np.pi/4), z) return result # yapf: enable def map_cylinder_to_sphere(points, inverse=False): """maps a cylinder to a sphere and vice versa. The input and output range of the coordinates is [-1,1]. The cylinder axis is along z. points: numpy array with shape [n,3] inverse: If True apply the inverse transform: sphere -> cylinder """ assert points.ndim == 2 assert points.shape[1] == 3 # yapf: disable result = np.empty_like(points) if inverse: for i, p in enumerate(points): x, y, z = p t = np.linalg.norm(p, ord=2) if np.allclose(p, np.zeros_like(p)): result[i] = 0,0,0 elif 5/4*z**2 > (x**2 + y**2): s, z = np.sqrt(3*t/(t+np.abs(z))), np.sign(z)*t result[i] = s*x, s*y, z else: # elif 5/4*z**2 <= (x**2 + y**2): s, z = t/np.sqrt(x*x+y*y), 3/2*z result[i] = s*x, s*y, z else: for i, p in enumerate(points): x, y, z = p if np.allclose(p, np.zeros_like(p)): result[i] = 0,0,0 elif z*z >= x*x + y*y: result[i] = ( x*np.sqrt(2/3-(x*x+y*y)/(9*z*z)), y*np.sqrt(2/3-(x*x+y*y)/(9*z*z)), z-(x*x+y*y)/(3*z) ) else: result[i] = ( x*np.sqrt(1-(4*z*z)/(9*(x*x+y*y))), y*np.sqrt(1-(4*z*z)/(9*(x*x+y*y))), 2*z/3 ) return result # yapf: enable def compute_filter_coordinates(pos, filter_xyz_size, inv_extents, offset, align_corners, mapping): """Computes the filter coordinates for a single point The input to this function are coordinates relative to the point where the convolution is evaluated. Coordinates are usually in the range [-extent/2,extent/2] with extent as the edge length of the bounding box of the filter shape. The output is a coordinate within the filter array, i.e. the range is [0, filter_size.xyz], if the point was inside the filter shape. The simplest filter shape is a cuboid (mapping=IDENTITY) and the transformation is simply [-extent/2,extent/2] -> [0, filter_size.xyz]. The other type of shape that is implemented is a sphere with mapping=BALL_TO_CUBE_RADIAL or mapping=BALL_TO_CUBE_VOLUME_PRESERVING. pos: A single 3D point. An array of shape [3] with x,y,z coordinates. filter_xyz_size: An array of shape [3], which defines the size of the filter array for the spatial dimensions. inv_extents: An array of shape [3], which defines the spatial extent of the filter. The values are the reciprocal of the spatial extent for x,y and z. offset: An array of shape [3]. An offset for shifting the center. Can be used to implement discrete filters with even filter size. align_corners: If True then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape. mapping: The mapping that is applied to the input coordinates. - BALL_TO_CUBE_RADIAL uses radial stretching to map a sphere to a cube. - BALL_TO_CUBE_VOLUME_PRESERVING is using a more expensive volume preserving mapping to map a sphere to a cube. - IDENTITY no mapping is applied to the coordinates. """ assert pos.ndim == 1 assert pos.shape[0] == 3 assert filter_xyz_size.ndim == 1 assert all(filter_xyz_size.shape) assert inv_extents.ndim == 1 assert inv_extents.shape[0] == 3 assert offset.ndim == 1 assert offset.shape[0] == 3 p = pos.copy() if mapping == BALL_TO_CUBE_RADIAL: p *= 2 * inv_extents # p is now a position in a sphere with radius 1 abs_max = np.max(np.abs(p)) if abs_max < 1e-8: p = np.zeros_like(p) else: # map to the unit cube with edge length 1 and range [-0.5,0.5] p *= 0.5 * np.sqrt(np.sum(p * p)) / abs_max elif mapping == BALL_TO_CUBE_VOLUME_PRESERVING: p *= 2 * inv_extents p = 0.5 * map_cube_to_cylinder(map_cylinder_to_sphere(p[np.newaxis, :], inverse=True), inverse=True)[0] elif mapping == IDENTITY: # map to the unit cube with edge length 1 and range [-0.5,0.5] p *= inv_extents else: raise ValueError("Unknown mapping") if align_corners: p += 0.5 p *= filter_xyz_size - 1 else: p *= filter_xyz_size p += offset # integer div p += filter_xyz_size // 2 if filter_xyz_size[0] % 2 == 0: p[0] -= 0.5 if filter_xyz_size[1] % 2 == 0: p[1] -= 0.5 if filter_xyz_size[2] % 2 == 0: p[2] -= 0.5 return p def window_function(pos, inv_extents, window, window_params): """Implements 3 types of window functions pos: A single 3D point. An array of shape [3] with x,y,z coordinates. inv_extents: An array of shape [3], which defines the spatial extent of the filter. The values are the reciprocal of the spatial extent for x,y and z. window: The window type. Allowed types are -RECTANGLE this just returns 1 everywhere. -TRAPEZOID /‾\ plateau with 1 at the center and decays linearly to 0 at the borders. -POLY The poly 6 window window_params: array with parameters for the windows. Only TRAPEZOID uses this to define the normalized distance from the center at which the linear decay starts. """ assert pos.ndim == 1 assert pos.shape[0] == 3 assert inv_extents.ndim == 1 assert inv_extents.shape[0] == 3 p = pos.copy() if window == RECTANGLE: return 1 elif window == TRAPEZOID: p *= 2 * inv_extents # p is now a position in a sphere with radius 1 d = np.linalg.norm(p, ord=2) d = np.clip(d, 0, 1) # the window parameter defines the distance at which the value decreases # linearly to 0 if d > window_params[0]: return (1 - d) / (1 - window_params[0]) else: return 1 elif window == POLY: p *= 2 * inv_extents # p is now a position in a sphere with radius 1 r_sqr = np.sum(p * p) return np.clip((1 - r_sqr)**3, 0, 1) else: raise ValueError("Unknown window type") def interpolate(xyz, xyz_size, interpolation): """ Computes interpolation weights and indices xyz: A single 3D point. xyz_size: An array of shape [3], which defines the size of the filter array for the spatial dimensions. interpolation: One of LINEAR, LINEAR_BORDER, NEAREST_NEIGHBOR. LINEAR is trilinear interpolation with coordinate clamping. LINEAR_BORDER uses a zero border if outside the range. NEAREST_NEIGHBOR uses the neares neighbor instead of interpolation. Returns a tuple with the interpolation weights and the indices """ # yapf: disable if interpolation == NEAREST_NEIGHBOR: pi = np.round(xyz).astype(np.int32) pi = np.clip(pi, np.zeros_like(pi), xyz_size-1) idx = pi[2]*xyz_size[0]*xyz_size[1] + pi[1]*xyz_size[0] + pi[0] return (1,), ((pi[2],pi[1],pi[0]),) elif interpolation == LINEAR_BORDER: pi0 = np.floor(xyz).astype(np.int32) pi1 = pi0+1 a = xyz[0]-pi0[0] b = xyz[1]-pi0[1] c = xyz[2]-pi0[2] w = ((1-a)*(1-b)*(1-c), (a)*(1-b)*(1-c), (1-a)*(b)*(1-c), (a)*(b)*(1-c), (1-a)*(1-b)*(c), (a)*(1-b)*(c), (1-a)*(b)*(c), (a)*(b)*(c)) idx=((pi0[2], pi0[1], pi0[0]), (pi0[2], pi0[1], pi1[0]), (pi0[2], pi1[1], pi0[0]), (pi0[2], pi1[1], pi1[0]), (pi1[2], pi0[1], pi0[0]), (pi1[2], pi0[1], pi1[0]), (pi1[2], pi1[1], pi0[0]), (pi1[2], pi1[1], pi1[0])) w_idx = [] for w_, idx_ in zip(w,idx): if np.any(np.array(idx_) < 0) or idx_[0] >= xyz_size[2] or idx_[1] >= xyz_size[1] or idx_[2] >= xyz_size[0]: w_idx.append((0.0, (0,0,0))) else: w_idx.append((w_,idx_)) w, idx = zip(*w_idx) return w, idx elif interpolation == LINEAR: pi0 = np.clip(xyz.astype(np.int32), np.zeros_like(xyz, dtype=np.int32), xyz_size-1) pi1 = np.clip(pi0+1, np.zeros_like(pi0), xyz_size-1) a = xyz[0]-pi0[0] b = xyz[1]-pi0[1] c = xyz[2]-pi0[2] a = np.clip(a, 0, 1) b = np.clip(b, 0, 1) c = np.clip(c, 0, 1) w = ((1-a)*(1-b)*(1-c), (a)*(1-b)*(1-c), (1-a)*(b)*(1-c), (a)*(b)*(1-c), (1-a)*(1-b)*(c), (a)*(1-b)*(c), (1-a)*(b)*(c), (a)*(b)*(c)) idx=((pi0[2], pi0[1], pi0[0]), (pi0[2], pi0[1], pi1[0]), (pi0[2], pi1[1], pi0[0]), (pi0[2], pi1[1], pi1[0]), (pi1[2], pi0[1], pi0[0]), (pi1[2], pi0[1], pi1[0]), (pi1[2], pi1[1], pi0[0]), (pi1[2], pi1[1], pi1[0])) return w, idx else: raise ValueError("Unknown interpolation mode") # yapf: enable def cconv(filter, out_positions, extent, offset, inp_positions, inp_features, inp_importance, neighbors_index, neighbors_importance, neighbors_row_splits, align_corners, coordinate_mapping, normalize, interpolation, **kwargs): """ Computes the output features of a continuous convolution. filter: 5D filter array with shape [depth,height,width,inp_ch, out_ch] out_positions: The positions of the output points. The shape is [num_out, 3]. extents: The spatial extents of the filter in coordinate units. This is a 2D array with shape [1,1] or [1,3] or [num_out,1] or [num_out,3] offset: A single 3D vector used in the filter coordinate computation. The shape is [3]. inp_positions: The positions of the input points. The shape is [num_inp, 3]. inp_features: The input features with shape [num_inp, in_channels]. inp_importance: Optional importance for each input point with shape [num_inp]. Set to np.array([]) to disable. neighbors_index: The array with lists of neighbors for each output point. The start and end of each sublist is defined by neighbors_row_splits. neighbors_importance: Optional importance for each entry in neighbors_index. Set to np.array([]) to disable. neighbors_row_splits: The prefix sum which defines the start and end of the sublists in neighbors_index. The size of the array is num_out + 1. align_corners: If true then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape. coordinate_mapping: The coordinate mapping function. One of IDENTITY, BALL_TO_CUBE_RADIAL, BALL_TO_CUBE_VOLUME_PRESERVING. normalize: If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance. interpolation: The interpolation mode. Either LINEAR or NEAREST_NEIGHBOR. """ assert filter.ndim == 5 assert all(filter.shape) assert filter.shape[3] == inp_features.shape[-1] assert out_positions.ndim == 2 assert extent.ndim == 2 assert extent.shape[0] == 1 or extent.shape[0] == out_positions.shape[0] assert extent.shape[1] in (1, 3) assert offset.ndim == 1 and offset.shape[0] == 3 assert inp_positions.ndim == 2 assert inp_positions.shape[0] == inp_features.shape[0] assert inp_features.ndim == 2 assert inp_importance.ndim == 1 assert (inp_importance.shape[0] == 0 or inp_importance.shape[0] == inp_positions.shape[0]) assert neighbors_importance.ndim == 1 assert (neighbors_importance.shape[0] == 0 or neighbors_importance.shape[0] == neighbors_index.shape[0]) assert neighbors_index.ndim == 1 assert neighbors_row_splits.ndim == 1 assert neighbors_row_splits.shape[0] == out_positions.shape[0] + 1 coordinate_mapping = _convert_parameter_str_dict[ coordinate_mapping] if isinstance(coordinate_mapping, str) else coordinate_mapping interpolation = _convert_parameter_str_dict[interpolation] if isinstance( interpolation, str) else interpolation dtype = inp_features.dtype num_out = out_positions.shape[0] num_inp = inp_positions.shape[0] in_channels = inp_features.shape[-1] out_channels = filter.shape[-1] inv_extent = 1 / np.broadcast_to(extent, out_positions.shape) if inp_importance.shape[0] == 0: inp_importance = np.ones([num_inp]) if neighbors_importance.shape[0] == 0: neighbors_importance = np.ones(neighbors_index.shape) filter_xyz_size = np.array(list(reversed(filter.shape[0:3]))) out_features = np.zeros((num_out, out_channels)) for out_idx, out_pos in enumerate(out_positions): neighbors_start = neighbors_row_splits[out_idx] neighbors_end = neighbors_row_splits[out_idx + 1] outfeat = out_features[out_idx:out_idx + 1] n_importance_sum = 0.0 for inp_idx, n_importance in zip( neighbors_index[neighbors_start:neighbors_end], neighbors_importance[neighbors_start:neighbors_end]): inp_pos = inp_positions[inp_idx] relative_pos = inp_pos - out_pos coords = compute_filter_coordinates(relative_pos, filter_xyz_size, inv_extent[out_idx], offset, align_corners, coordinate_mapping) interp_w, interp_idx = interpolate(coords, filter_xyz_size, interpolation=interpolation) n_importance_sum += n_importance infeat = inp_features[inp_idx:inp_idx + 1] * inp_importance[inp_idx] * n_importance filter_value = 0.0 for w, idx in zip(interp_w, interp_idx): filter_value += w * filter[idx] outfeat += infeat @ filter_value if normalize: if n_importance_sum != 0: outfeat /= n_importance_sum return out_features def cconv_backprop_filter(filter, out_positions, extent, offset, inp_positions, inp_features, inp_importance, neighbors_index, neighbors_importance, neighbors_row_splits, out_features_gradient, align_corners, coordinate_mapping, normalize, interpolation, **kwargs): """This implements the backprop to the filter weights for the cconv. out_features_gradient: An array with the gradient for the outputs of the cconv in the forward pass. See cconv for more info about the parameters. """ assert filter.ndim == 5 assert all(filter.shape) assert filter.shape[3] == inp_features.shape[-1] assert out_positions.ndim == 2 assert extent.ndim == 2 assert extent.shape[0] == 1 or extent.shape[0] == out_positions.shape[0] assert extent.shape[1] in (1, 3) assert offset.ndim == 1 and offset.shape[0] == 3 assert inp_positions.ndim == 2 assert inp_positions.shape[0] == inp_features.shape[0] assert inp_features.ndim == 2 assert inp_importance.ndim == 1 assert (inp_importance.shape[0] == 0 or inp_importance.shape[0] == inp_positions.shape[0]) assert neighbors_importance.ndim == 1 assert (neighbors_importance.shape[0] == 0 or neighbors_importance.shape[0] == neighbors_index.shape[0]) assert neighbors_index.ndim == 1 assert neighbors_row_splits.ndim == 1 assert neighbors_row_splits.shape[0] == out_positions.shape[0] + 1 coordinate_mapping = _convert_parameter_str_dict[ coordinate_mapping] if isinstance(coordinate_mapping, str) else coordinate_mapping interpolation = _convert_parameter_str_dict[interpolation] if isinstance( interpolation, str) else interpolation dtype = inp_features.dtype num_out = out_positions.shape[0] num_inp = inp_positions.shape[0] in_channels = inp_features.shape[-1] out_channels = filter.shape[-1] inv_extent = 1 / np.broadcast_to(extent, out_positions.shape) if inp_importance.shape[0] == 0: inp_importance = np.ones([num_inp]) if neighbors_importance.shape[0] == 0: neighbors_importance = np.ones(neighbors_index.shape) filter_xyz_size = np.array(list(reversed(filter.shape[0:3]))) filter_backprop = np.zeros_like(filter) for out_idx, out_pos in enumerate(out_positions): neighbors_start = neighbors_row_splits[out_idx] neighbors_end = neighbors_row_splits[out_idx + 1] n_importance_sum = 1.0 if normalize: n_importance_sum = 0.0 for inp_idx, n_importance in zip( neighbors_index[neighbors_start:neighbors_end], neighbors_importance[neighbors_start:neighbors_end]): inp_pos = inp_positions[inp_idx] relative_pos = inp_pos - out_pos n_importance_sum += n_importance normalizer = 1 / n_importance_sum if n_importance_sum != 0.0 else 1 outfeat_grad = normalizer * out_features_gradient[out_idx:out_idx + 1] for inp_idx, n_importance in zip( neighbors_index[neighbors_start:neighbors_end], neighbors_importance[neighbors_start:neighbors_end]): inp_pos = inp_positions[inp_idx] relative_pos = inp_pos - out_pos coords = compute_filter_coordinates(relative_pos, filter_xyz_size, inv_extent[out_idx], offset, align_corners, coordinate_mapping) interp_w, interp_idx = interpolate(coords, filter_xyz_size, interpolation=interpolation) infeat = inp_features[inp_idx:inp_idx + 1] * inp_importance[inp_idx] * n_importance for w, idx in zip(interp_w, interp_idx): filter_backprop[idx] += w * (infeat.T @ outfeat_grad) return filter_backprop def cconv_transpose(filter, out_positions, out_importance, extent, offset, inp_positions, inp_features, inp_neighbors_index, inp_neighbors_importance, inp_neighbors_row_splits, neighbors_index, neighbors_importance, neighbors_row_splits, align_corners, coordinate_mapping, normalize, interpolation, **kwargs): """Computes the output features of a transpose continuous convolution. This is also used for computing the backprop to the input features for the normal cconv. filter: 5D filter array with shape [depth,height,width,inp_ch, out_ch] out_positions: The positions of the output points. The shape is [num_out, 3]. inp_importance: Optional importance for each output point with shape [num_out]. Set to np.array([]) to disable. extents: The spatial extents of the filter in coordinate units. This is a 2D array with shape [1,1] or [1,3] or [num_inp,1] or [num_inp,3] offset: A single 3D vector used in the filter coordinate computation. The shape is [3]. inp_positions: The positions of the input points. The shape is [num_inp, 3]. inp_features: The input features with shape [num_inp, in_channels]. inp_neighbors_index: The array with lists of neighbors for each input point. The start and end of each sublist is defined by inp_neighbors_row_splits. inp_neighbors_importance: Optional importance for each entry in inp_neighbors_index. Set to np.array([]) to disable. inp_neighbors_row_splits: The prefix sum which defines the start and end of the sublists in inp_neighbors_index. The size of the array is num_inp + 1. neighbors_index: The array with lists of neighbors for each output point. The start and end of each sublist is defined by neighbors_row_splits. neighbors_importance: Optional importance for each entry in neighbors_index. Set to np.array([]) to disable. neighbors_row_splits: The prefix sum which defines the start and end of the sublists in neighbors_index. The size of the array is num_out + 1. align_corners: If true then the voxel centers of the outer voxels of the filter array are mapped to the boundary of the filter shape. If false then the boundary of the filter array is mapped to the boundary of the filter shape. coordinate_mapping: The coordinate mapping function. One of IDENTITY, BALL_TO_CUBE_RADIAL, BALL_TO_CUBE_VOLUME_PRESERVING. normalize: If true then the result is normalized either by the number of points (neighbors_importance is null) or by the sum of the respective values in neighbors_importance. interpolation: The interpolation mode. Either LINEAR or NEAREST_NEIGHBOR. """ assert filter.ndim == 5 assert all(filter.shape) assert filter.shape[3] == inp_features.shape[-1] assert out_positions.ndim == 2 assert out_importance.ndim == 1 assert (out_importance.shape[0] == 0 or out_importance.shape[0] == out_positions.shape[0]) assert extent.ndim == 2 assert extent.shape[0] == 1 or extent.shape[0] == inp_positions.shape[0] assert extent.shape[1] in (1, 3) assert offset.ndim == 1 and offset.shape[0] == 3 assert inp_positions.ndim == 2 assert inp_positions.shape[0] == inp_features.shape[0] assert inp_features.ndim == 2 assert inp_neighbors_index.ndim == 1 assert inp_neighbors_importance.ndim == 1 assert (inp_neighbors_importance.shape[0] == 0 or inp_neighbors_importance.shape[0] == inp_neighbors_index.shape[0]) assert inp_neighbors_row_splits.ndim == 1 assert inp_neighbors_row_splits.shape[0] == inp_positions.shape[0] + 1 assert neighbors_index.ndim == 1 assert neighbors_importance.ndim == 1 assert (neighbors_importance.shape[0] == 0 or neighbors_importance.shape[0] == neighbors_index.shape[0]) assert neighbors_row_splits.ndim == 1 assert neighbors_row_splits.shape[0] == out_positions.shape[0] + 1 assert neighbors_index.shape[0] == inp_neighbors_index.shape[0] coordinate_mapping = _convert_parameter_str_dict[ coordinate_mapping] if isinstance(coordinate_mapping, str) else coordinate_mapping interpolation = _convert_parameter_str_dict[interpolation] if isinstance( interpolation, str) else interpolation dtype = inp_features.dtype num_out = out_positions.shape[0] num_inp = inp_positions.shape[0] in_channels = inp_features.shape[-1] out_channels = filter.shape[ -1] # filter shape is [depth,height,width, in_ch, out_ch] inv_extent = 1 / np.broadcast_to(extent, inp_positions.shape) if out_importance.shape[0] == 0: out_importance = np.ones([num_out]) if neighbors_importance.shape[0] == 0: neighbors_importance = np.ones(neighbors_index.shape) if inp_neighbors_importance.shape[0] == 0: inp_neighbors_importance = np.ones(inp_neighbors_index.shape) if normalize: inp_n_importance_sums = np.zeros_like(inp_neighbors_row_splits[:-1], dtype=out_positions.dtype) for inp_idx, inp_pos in enumerate(inp_positions): inp_neighbors_start = inp_neighbors_row_splits[inp_idx] inp_neighbors_end = inp_neighbors_row_splits[inp_idx + 1] for out_idx, n_importance in zip( inp_neighbors_index[inp_neighbors_start:inp_neighbors_end], inp_neighbors_importance[ inp_neighbors_start:inp_neighbors_end]): inp_n_importance_sums[inp_idx] += n_importance filter_xyz_size = np.array(list(reversed(filter.shape[0:3]))) out_features = np.zeros((num_out, out_channels)) for out_idx, out_pos in enumerate(out_positions): neighbors_start = neighbors_row_splits[out_idx] neighbors_end = neighbors_row_splits[out_idx + 1] for inp_idx, n_importance in zip( neighbors_index[neighbors_start:neighbors_end], neighbors_importance[neighbors_start:neighbors_end]): inp_pos = inp_positions[inp_idx] normalizer = 1 if normalize: n_importance_sum = inp_n_importance_sums[inp_idx] if n_importance_sum != 0.0: normalizer = 1 / n_importance_sum relative_pos = out_pos - inp_pos coords = compute_filter_coordinates(relative_pos, filter_xyz_size, inv_extent[inp_idx], offset, align_corners, coordinate_mapping) infeat = normalizer * inp_features[inp_idx:inp_idx + 1] * n_importance interp_w, interp_idx = interpolate(coords, filter_xyz_size, interpolation=interpolation) filter_value = 0.0 for w, idx in zip(interp_w, interp_idx): filter_value += w * filter[idx] out_features[out_idx:out_idx + 1] += infeat @ filter_value out_features *= out_importance[:, np.newaxis] return out_features def cconv_transpose_backprop_filter( filter, out_positions, out_importance, extent, offset, inp_positions, inp_features, inp_neighbors_index, inp_neighbors_importance, inp_neighbors_row_splits, neighbors_index, neighbors_importance, neighbors_row_splits, out_features_gradient, align_corners, coordinate_mapping, normalize, interpolation, **kwargs): """This implements the backprop to the filter weights for the transpose cconv. out_features_gradient: An array with the gradient for the outputs of the cconv in the forward pass. See cconv_transpose for more info about the parameters. """ assert filter.ndim == 5 assert all(filter.shape) assert filter.shape[3] == inp_features.shape[-1] assert out_positions.ndim == 2 assert extent.ndim == 2 assert extent.shape[0] == 1 or extent.shape[0] == inp_positions.shape[0] assert extent.shape[1] in (1, 3) assert offset.ndim == 1 and offset.shape[0] == 3 assert inp_positions.ndim == 2 assert inp_positions.shape[0] == inp_features.shape[0] assert inp_features.ndim == 2 assert out_importance.ndim == 1 assert (out_importance.shape[0] == 0 or out_importance.shape[0] == out_positions.shape[0]) assert inp_neighbors_index.ndim == 1 assert inp_neighbors_importance.ndim == 1 assert (inp_neighbors_importance.shape[0] == 0 or inp_neighbors_importance.shape[0] == inp_neighbors_index.shape[0]) assert inp_neighbors_row_splits.ndim == 1 assert inp_neighbors_row_splits.shape[0] == inp_positions.shape[0] + 1 assert neighbors_index.ndim == 1 assert neighbors_importance.ndim == 1 assert (neighbors_importance.shape[0] == 0 or neighbors_importance.shape[0] == neighbors_index.shape[0]) assert neighbors_row_splits.ndim == 1 assert neighbors_row_splits.shape[0] == out_positions.shape[0] + 1 assert neighbors_index.shape[0] == inp_neighbors_index.shape[0] coordinate_mapping = _convert_parameter_str_dict[ coordinate_mapping] if isinstance(coordinate_mapping, str) else coordinate_mapping interpolation = _convert_parameter_str_dict[interpolation] if isinstance( interpolation, str) else interpolation dtype = inp_features.dtype num_out = out_positions.shape[0] num_inp = inp_positions.shape[0] in_channels = inp_features.shape[-1] out_channels = filter.shape[-1] inv_extent = 1 / np.broadcast_to(extent, inp_positions.shape) if out_importance.shape[0] == 0: out_importance = np.ones([num_out]) if neighbors_importance.shape[0] == 0: neighbors_importance = np.ones(neighbors_index.shape) if inp_neighbors_importance.shape[0] == 0: inp_neighbors_importance = np.ones(inp_neighbors_index.shape) if normalize: inp_n_importance_sums = np.zeros_like(inp_neighbors_row_splits[:-1], dtype=out_positions.dtype) for inp_idx, inp_pos in enumerate(inp_positions): inp_neighbors_start = inp_neighbors_row_splits[inp_idx] inp_neighbors_end = inp_neighbors_row_splits[inp_idx + 1] for out_idx, n_importance in zip( inp_neighbors_index[inp_neighbors_start:inp_neighbors_end], inp_neighbors_importance[ inp_neighbors_start:inp_neighbors_end]): inp_n_importance_sums[inp_idx] += n_importance filter_xyz_size = np.array(list(reversed(filter.shape[0:3]))) filter_backprop = np.zeros_like(filter) for out_idx, out_pos in enumerate(out_positions): neighbors_start = neighbors_row_splits[out_idx] neighbors_end = neighbors_row_splits[out_idx + 1] outfeat_grad = out_features_gradient[out_idx:out_idx + 1] * out_importance[out_idx] for inp_idx, n_importance in zip( neighbors_index[neighbors_start:neighbors_end], neighbors_importance[neighbors_start:neighbors_end]): inp_pos = inp_positions[inp_idx] normalizer = 1 if normalize: n_importance_sum = inp_n_importance_sums[inp_idx] if n_importance_sum != 0.0: normalizer = 1 / n_importance_sum relative_pos = out_pos - inp_pos coords = compute_filter_coordinates(relative_pos, filter_xyz_size, inv_extent[inp_idx], offset, align_corners, coordinate_mapping) interp_w, interp_idx = interpolate(coords, filter_xyz_size, interpolation=interpolation) infeat = normalizer * inp_features[inp_idx:inp_idx + 1] * n_importance for w, idx in zip(interp_w, interp_idx): filter_backprop[idx] += w * (outfeat_grad.T @ infeat).T return filter_backprop

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0.736358

0.729932

0.720677

0.715546

0

0.025597

0.296815

35,945

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121

40.206935

0.799414

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null

0

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1

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0

6

c7ceae327f92874a146e3e651be302b319d658fe

8,521

py

Python

map/get_shop.py

2218084076/hotpoor_autoclick_xhs

a52446ba691ac19e43410a465dc63f940c0e444d

[ "Apache-2.0" ]

1

2021-12-21T10:42:46.000Z

map/get_shop.py

2218084076/hotpoor_autoclick_xhs

a52446ba691ac19e43410a465dc63f940c0e444d

[ "Apache-2.0" ]

null

map/get_shop.py

2218084076/hotpoor_autoclick_xhs

a52446ba691ac19e43410a465dc63f940c0e444d

[ "Apache-2.0" ]

null

import pyautogui import time import pyperclip # 打开审查元素位置 921.6 # 2022/01/15 news_urls=[] for i in urls: news_urls.append(i) print(len(urls)) print(len(news_urls)) print(len(news_urls)) print(len(news_urls)) def pyautogui_action(action): if action["name"] in ["move_to_click"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') elif action["name"] in ["select_all_and_write"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') time.sleep(1) pyautogui.hotkey("ctrl", "a") write_content = action.get("content","") pyautogui.typewrite(write_content) pyautogui.press('enter') elif action["name"] in ["select_all_and_js_latest"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') pyautogui.hotkey("ctrl", "a") pyautogui.press('backspace') pyautogui.press('up') pyautogui.press('enter') elif action["name"] in ["select_all_and_copy"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') pyautogui.hotkey("ctrl", "a") pyautogui.hotkey("ctrl", "c") elif action["name"] in ["select_all_and_paste"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') pyautogui.hotkey("ctrl", "a") pyautogui.hotkey("ctrl", "v") elif action["name"] in ["select_item_and_close_tab"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') pyautogui.hotkey("ctrl", "w") elif action["name"] in ["select_all_and_copy_and_paste"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') write_content = action.get("content","") pyperclip.copy(write_content) pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') pyautogui.hotkey("ctrl", "v") pyautogui.press('enter') elif action["name"] in ["open_console"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') pyautogui.hotkey("f12") elif action["name"] in ["url_paste"]: pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') write_content = action.get("content","") pyperclip.copy(write_content) pyautogui.moveTo(x=action.get("x",None), y=action.get("y",None),duration=0, tween=pyautogui.linear) pyautogui.click(x=action.get("x",None), y=action.get("y",None),clicks=1, button='left') pyautogui.hotkey("ctrl", "l") pyautogui.hotkey("ctrl", "v") pyautogui.press('enter') print(action.get("action_name")) action_sleep = action.get("sleep",0) time.sleep(action_sleep) for u in urls: print(u) page={ "x":435, "y":69, "sleep":8, "name":"select_all_and_copy_and_paste", "content":'document.getElementsByClassName("pic")[0].getElementsByTagName("a")[0].click()', "action_name":"访问链接", } pyautogui_action(page) action_item_click_list = [ { "x": 1207, "y": 176, "sleep": 0.5, "name": "move_to_click", "content": "", "action_name": "清空console", }, { "x": 1376, "y": 997, "sleep": 0.5, "name": "select_all_and_copy_and_paste", "content": r''' result=[] result.push(document.getElementsByClassName("shop-name")[0].innerText) result.push(document.getElementsByClassName("brief-info")[0].getElementsByTagName("span")[0].getAttribute("class").split("mid-str")[1]) result.push(document.getElementsByClassName("brief-info")[0].getElementsByTagName("span")[1].innerText) try{ result.push(document.getElementsByClassName("brief-info")[0].getElementsByTagName("span")[2].innerText) }catch{ result.push("null") } try{ result.push(document.getElementsByClassName("tel")[0].innerText) }catch{ document.getElementsByClassName("phone")[0].getElementsByTagName("a")[0].click() result.push(document.getElementsByClassName("phone")[0].innerText) } result.push(document.getElementsByClassName("address")[0].innerText) result.push(document.getElementById("map").getElementsByTagName("img")[0].getAttribute("src").split(".png|")[1]) result_info = { "shop-name":result[0], "star":result[1]*0.1, "comment":result[2], "consume":result[3], "tel":result[4], "address":result[5], "coordinate":result[6], } dom=document.createElement("div") dom.id="wlb_cover" dom.style.position="fixed" dom.style.top="0px" dom.style.right="0px" dom.style.zIndex=9999999999999999999 dom.innerHTML="<textarea id=\"wlb_cover_textarea\" style=\"height:100px;width:200px;\">"+JSON.stringify(result_info)+"</textarea>" document.body.append(dom) ''', "action_name": "get店铺信息", }, { "x": 1026, "y": 149, "sleep": 0.5, "name": "select_all_and_copy", "content": "", "action_name": "copy" }, { "x": 431, "y": 20, "sleep": 1, "name": "move_to_click", "content": "", "action_name": "点击选项卡_pages", }, { "x": 533, "y": 209, "sleep": 1, "name": "select_all_and_paste", "content": "", "action_name": "提交", }, { "x": 416, "y": 283, "sleep": 1, "name": "move_to_click", "content": "", "action_name": "submit", }, { "x": 137, "y": 24, "sleep": 1, "name": "move_to_click", "content": "", "action_name": "切换pgy页面", }, ] for action_item_click in action_item_click_list: pyautogui_action(action_item_click) ''' result=[] result.push(document.getElementsByClassName("shop-name")[0].innerText) result.push(document.getElementsByClassName("brief-info")[0].getElementsByTagName("span")[0].getAttribute("class").split("mid-str")[1]) result.push(document.getElementsByClassName("brief-info")[0].getElementsByTagName("span")[1].innerText) try{ result.push(document.getElementsByClassName("brief-info")[0].getElementsByTagName("span")[2].innerText) }catch{ result.push("null") } try{ result.push(document.getElementsByClassName("tel")[0].innerText) }catch{ document.getElementsByClassName("phone")[0].getElementsByTagName("a")[0].click() result.push(document.getElementsByClassName("phone")[0].innerText) } result.push(document.getElementsByClassName("address")[0].innerText) result.push(document.getElementById("map").getElementsByTagName("img")[0].getAttribute("src").split(".png|")[1]) result_info = { "shop-name":result[0], "star":result[1]*0.1, "comment":result[2], "consume":result[3], "tel":result[4], "address":result[5], "coordinate":result[6], } dom=document.createElement("div") dom.id="wlb_cover" dom.style.position="fixed" dom.style.top="0px" dom.style.right="0px" dom.style.zIndex=9999999999999999999 dom.innerHTML="<textarea id=\"wlb_cover_textarea\" style=\"height:100px;width:200px;\">"+JSON.stringify(result_info)+"</textarea>" document.body.append(dom) '''

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1bfd2e704fa924fb676400a0a75d85d1015a7fca

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py

Python

bitwallet/__init__.py

lookfwd/bitwallet

b0f5efed7d8d896650c5e0d7318269abc20906b5

[ "MIT" ]

1

2018-01-25T15:04:34.000Z

bitwallet/__init__.py

lookfwd/bitwallet

b0f5efed7d8d896650c5e0d7318269abc20906b5

[ "MIT" ]

2

2018-02-19T02:27:13.000Z

2018-04-27T06:16:18.000Z

bitwallet/__init__.py

lookfwd/bitwallet

b0f5efed7d8d896650c5e0d7318269abc20906b5

[ "MIT" ]

null

from wallet import Wallet

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0

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4021f2d392e89b8d5032a922f86c0d373508d007

81

py

Python

app/modules/im/__init__.py

Eastwu5788/Heron

646eeaacea77e293c6eccc6dad82a04ece9294a3

[ "Apache-2.0" ]

7

2018-01-29T02:46:31.000Z

2018-03-25T11:15:10.000Z

app/modules/im/__init__.py

Eastwu5788/Heron

646eeaacea77e293c6eccc6dad82a04ece9294a3

[ "Apache-2.0" ]

4

2021-06-08T19:38:03.000Z

2022-03-11T23:18:46.000Z

app/modules/im/__init__.py

Eastwu5788/Heron

646eeaacea77e293c6eccc6dad82a04ece9294a3

[ "Apache-2.0" ]

1

2021-06-12T14:14:35.000Z

from flask import Blueprint im = Blueprint("im", __name__) from . import immsg

13.5

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405b4077c24814ac6e86f916af423926e95d3206

96

py

Python

pyball/models/conference/__init__.py

SebastianDang/PyBall

d1965aa01477b5ee0db9c0463ec584a7e3997395

[ "MIT" ]

74

2018-03-04T22:58:46.000Z

2021-07-06T12:28:50.000Z

pyball/models/conference/__init__.py

SebastianDang/PyBall

d1965aa01477b5ee0db9c0463ec584a7e3997395

[ "MIT" ]

18

2018-03-10T19:17:54.000Z

2020-01-04T15:42:47.000Z

pyball/models/conference/__init__.py

SebastianDang/PyBall

d1965aa01477b5ee0db9c0463ec584a7e3997395

[ "MIT" ]

13

2018-03-06T02:39:38.000Z

2020-01-17T04:38:53.000Z

from .conference import Conference from .conference import League from .conference import Sport

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6

40679a54b6b237ed1a7a0ae1cc73b7b8641c9ef7

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py

Python

__init__.py

dhruvagarwal/flask_restdemo

3292fc4cc03cb76297502bb2296784d6c81d1a42

[ "Apache-2.0" ]

null

__init__.py

dhruvagarwal/flask_restdemo

3292fc4cc03cb76297502bb2296784d6c81d1a42

[ "Apache-2.0" ]

null

__init__.py

dhruvagarwal/flask_restdemo

3292fc4cc03cb76297502bb2296784d6c81d1a42

[ "Apache-2.0" ]

null

from .flask_restdemo import CustomApi

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6

406f810443bf86957645845fb4d90fc6489a236f

3,632

py

Python

plot_error_correlations.py

danhagen/NonlinearControl

3bda71a058ec3b1a598df886e9485fd4d08982ba

[ "MIT" ]

null

plot_error_correlations.py

danhagen/NonlinearControl

3bda71a058ec3b1a598df886e9485fd4d08982ba

[ "MIT" ]

5

2018-08-01T17:19:38.000Z

2020-08-18T19:57:46.000Z

plot_error_correlations.py

danhagen/NonlinearControl

3bda71a058ec3b1a598df886e9485fd4d08982ba

[ "MIT" ]

1

2020-07-22T22:38:20.000Z

import pickle from pathlib import Path import numpy as np import matplotlib.pyplot as plt from scipy.stats import pearsonr from pendulum_eqns.physiology.muscle_params_BIC_TRI import * #### Fixed Initial Tendon Tension out=pickle.load( open( Path(r"C:\Users\hagen\Documents\Github\NonlinearControl\output_figures\integrator_backstepping_sinusoidal_activations_fixed_tensions\2020_05_23_112705\output.pkl"), "rb" ) ) Error = out["Error"] States = out["States"] lm1o = np.array([States[i,4,0] for i in range(States.shape[0])]) lm2o = np.array([States[i,5,0] for i in range(States.shape[0])]) MAE1 = np.array([np.mean(abs(Error[0][i,:])) for i in range(Error[0].shape[0])]) MAE2 = np.array([np.mean(abs(Error[1][i,:])) for i in range(Error[0].shape[0])]) fig1,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5)) plt.subplots_adjust(bottom=0.2) ax1.spines["top"].set_visible(False) ax1.spines['right'].set_visible(False) ax2.spines["top"].set_visible(False) ax2.spines['right'].set_visible(False) ax1.set_title("Muscle 1", fontsize=16) ax2.set_title("Muscle 2", fontsize=16) ax1.set_xlabel("Initial Normalized\nMuscle Fascicle Length",fontsize=14) ax1.set_ylabel("Percent Mean Absolute Error",fontsize=14) ax1.scatter(lm1o/lo1,100*(MAE1/lo1)) ax1.text( 0.5,0.9, f"PCC = {pearsonr(lm1o,MAE1)[0]:0.3f}", transform=ax1.transAxes, horizontalalignment='center', verticalalignment='center', color = "k", fontsize=14, bbox=dict( boxstyle='round,pad=0.5', edgecolor='k', facecolor='w' ) ) ax2.scatter(lm2o/lo2,100*(MAE2/lo2)) ax2.text( 0.5,0.9, f"PCC = {pearsonr(lm2o,MAE2)[0]:0.3f}", transform=ax2.transAxes, horizontalalignment='center', verticalalignment='center', color = "k", fontsize=14, bbox=dict( boxstyle='round,pad=0.5', edgecolor='k', facecolor='w' ) ) ax1.set_ylim([0,2]) ax2.set_ylim([0,2]) ### Fixed Initial Muscle Length out=pickle.load( open( Path(r"C:\Users\hagen\Documents\Github\NonlinearControl\output_figures\integrator_backstepping_sinusoidal_activations_fixed_muscle_lengths\2020_05_23_115050\output.pkl"), "rb" ) ) Error = out["Error"] States = out["States"] fT1o = np.array([States[i,2,0] for i in range(States.shape[0])]) fT2o = np.array([States[i,3,0] for i in range(States.shape[0])]) MAE1 = np.array([np.mean(abs(Error[0][i,:])) for i in range(Error[0].shape[0])]) MAE2 = np.array([np.mean(abs(Error[1][i,:])) for i in range(Error[0].shape[0])]) fig2,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5)) plt.subplots_adjust(bottom=0.2) ax1.spines["top"].set_visible(False) ax1.spines['right'].set_visible(False) ax2.spines["top"].set_visible(False) ax2.spines['right'].set_visible(False) ax1.set_title("Muscle 1", fontsize=16) ax2.set_title("Muscle 2", fontsize=16) ax1.set_xlabel("Initial Normalized\nTendon Force",fontsize=14) ax1.set_ylabel("Percent Mean Absolute Error",fontsize=14) ax1.scatter(fT1o/F_MAX1,100*(MAE1/lo1)) ax1.text( 0.5,0.9, f"PCC = {pearsonr(fT1o,MAE1)[0]:0.3f}", transform=ax1.transAxes, horizontalalignment='center', verticalalignment='center', color = "k", fontsize=14, bbox=dict( boxstyle='round,pad=0.5', edgecolor='k', facecolor='w' ) ) ax2.scatter(fT2o/F_MAX2,100*(MAE2/lo2)) ax2.text( 0.5,0.9, f"PCC = {pearsonr(fT2o,MAE2)[0]:0.3f}", transform=ax2.transAxes, horizontalalignment='center', verticalalignment='center', color = "k", fontsize=14, bbox=dict( boxstyle='round,pad=0.5', edgecolor='k', facecolor='w' ) ) ax1.set_ylim([0,2]) ax2.set_ylim([0,2]) plt.show()

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4.282953

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3,632

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0.059406

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null

0

1

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null

0

6

407cd497cd6c9bbc3422be6d9cc1a621c67d597c

103

py

Python

mag_report/main.py

Curtin-Open-Knowledge-Initiative/mag_coverage_report

a75dd1273c44895b5c857ebd498407aa95bd45e5

[ "Apache-2.0" ]

1

2021-09-07T06:42:40.000Z

main.py

bmkramer/what_do_we_lose_mag

c60ea99915caafd5209ef30d21ab456a89ff89a0

[ "Apache-2.0", "MIT" ]

2

2021-08-30T11:52:25.000Z

2021-09-02T12:11:05.000Z

mag_report/main.py

Curtin-Open-Knowledge-Initiative/mag_coverage_report

a75dd1273c44895b5c857ebd498407aa95bd45e5

[ "Apache-2.0" ]

3

2021-07-04T07:39:01.000Z

2021-08-24T15:24:29.000Z

import process from precipy.main import render_file render_file('config.json', [process], storages=[])

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1

0

1

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1

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6

40d41ef56a8243b1710293775b2395e29459ae28

1,603

py

Python

tensorflow/contrib/__init__.py

shishaochen/TensorFlow-0.8-Win

63221dfc4f1a1d064308e632ba12e6a54afe1fd8

[ "Apache-2.0" ]

1

2017-09-14T23:59:05.000Z

tensorflow/contrib/__init__.py

shishaochen/TensorFlow-0.8-Win

63221dfc4f1a1d064308e632ba12e6a54afe1fd8

[ "Apache-2.0" ]

1

2016-10-19T02:43:04.000Z

2016-10-31T14:53:06.000Z

tensorflow/contrib/__init__.py

shishaochen/TensorFlow-0.8-Win

63221dfc4f1a1d064308e632ba12e6a54afe1fd8

[ "Apache-2.0" ]

8

2016-10-23T00:50:02.000Z

2019-04-21T11:11:57.000Z

# Copyright 2015 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """contrib module containing volatile or experimental code.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # Add projects here, they will show up under tf.contrib. #from tensorflow.contrib import ctc #from tensorflow.contrib import distributions #from tensorflow.contrib import framework #from tensorflow.contrib import grid_rnn #from tensorflow.contrib import layers #from tensorflow.contrib import learn #from tensorflow.contrib import linear_optimizer #from tensorflow.contrib import lookup #from tensorflow.contrib import losses #from tensorflow.contrib import metrics #from tensorflow.contrib import quantization #from tensorflow.contrib import rnn #from tensorflow.contrib import skflow #from tensorflow.contrib import tensor_forest #from tensorflow.contrib import testing #from tensorflow.contrib import util #from tensorflow.contrib import copy_graph

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0

1

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6

90865e4d830451658c1eb422bb94edee870ce196

4,003

py

Python

python/test/test_bubble_sort.py

michaelreneer/Algorithms

3752d6ad542d798c54eedf78b2624d27296b00be

[ "MIT" ]

2

2019-01-08T04:35:44.000Z

2020-11-06T18:57:05.000Z

python/test/test_bubble_sort.py

michaelreneer/algorithms

3752d6ad542d798c54eedf78b2624d27296b00be

[ "MIT" ]

null

python/test/test_bubble_sort.py

michaelreneer/algorithms

3752d6ad542d798c54eedf78b2624d27296b00be

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import absolute_import import unittest import algorithms class TestBubbleSort(unittest.TestCase): def test_bubble_sort_with_one_item(self): iterable = [1] algorithms.bubble_sort(iterable) expected = [1] self.assertEqual(iterable, expected) def test_bubble_sort_with_two_items_1(self): iterable = [1, 2] algorithms.bubble_sort(iterable) expected = [1, 2] self.assertEqual(iterable, expected) def test_bubble_sort_with_two_items_2(self): iterable = [2, 1] algorithms.bubble_sort(iterable) expected = [1, 2] self.assertEqual(iterable, expected) def test_bubble_sort_with_three_items_1(self): iterable = [1, 2, 3] algorithms.bubble_sort(iterable) expected = [1, 2, 3] self.assertEqual(iterable, expected) def test_bubble_sort_with_three_items_2(self): iterable = [1, 3, 2] algorithms.bubble_sort(iterable) expected = [1, 2, 3] self.assertEqual(iterable, expected) def test_bubble_sort_with_three_items_3(self): iterable = [2, 1, 3] algorithms.bubble_sort(iterable) expected = [1, 2, 3] self.assertEqual(iterable, expected) def test_bubble_sort_with_three_items_4(self): iterable = [2, 3, 1] algorithms.bubble_sort(iterable) expected = [1, 2, 3] self.assertEqual(iterable, expected) def test_bubble_sort_with_three_items_5(self): iterable = [3, 1, 2] algorithms.bubble_sort(iterable) expected = [1, 2, 3] self.assertEqual(iterable, expected) def test_bubble_sort_with_three_items_6(self): iterable = [3, 2, 1] algorithms.bubble_sort(iterable) expected = [1, 2, 3] self.assertEqual(iterable, expected) def test_bubble_sort_with_ascending_items(self): iterable = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] algorithms.bubble_sort(iterable) expected = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] self.assertEqual(iterable, expected) def test_bubble_sort_with_descending_items(self): iterable = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1] algorithms.bubble_sort(iterable) expected = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] self.assertEqual(iterable, expected) def test_bubble_sort_with_equal_items(self): iterable = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] algorithms.bubble_sort(iterable) expected = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] self.assertEqual(iterable, expected) def test_bubble_sort_with_strings(self): iterable = ['a', 's', 'd', 'f'] algorithms.bubble_sort(iterable) expected = ['a', 'd', 'f', 's'] self.assertEqual(iterable, expected) def test_bubble_sort_with_no_items(self): iterable = [] algorithms.bubble_sort(iterable) expected = [] self.assertEqual(iterable, expected) def test_bubble_sort_with_none_iterable_raises_type_error(self): iterable = None with self.assertRaises(TypeError): algorithms.bubble_sort(iterable) def test_bubble_sort_is_stable_1(self): iterable = [[1], [1], [1], [1]] ids = [id(item) for item in iterable] algorithms.bubble_sort(iterable) expected = [id(item) for item in iterable] self.assertEqual(ids, expected) def test_bubble_sort_is_stable_2(self): iterable = [[1], [2], [3], [1]] ids = [id(item) for item in iterable if item[0] == 1] algorithms.bubble_sort(iterable) expected = [id(item) for item in iterable if item[0] == 1] self.assertEqual(ids, expected) def test_bubble_sort_is_stable_3(self): iterable = [[2], [3], [1], [1]] ids = [id(item) for item in iterable if item[0] == 1] algorithms.bubble_sort(iterable) expected = [id(item) for item in iterable if item[0] == 1] self.assertEqual(ids, expected) def test_bubble_sort_is_stable_4(self): iterable = [[3], [2], [3], [1]] ids = [id(item) for item in iterable if item[0] == 1] algorithms.bubble_sort(iterable) expected = [id(item) for item in iterable if item[0] == 1] self.assertEqual(ids, expected) if __name__ == '__main__': unittest.main()

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0.095922

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0.83068

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4,003

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null

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null

0

6

90dc8744bb7dba3256160f533976958cec988371

19

py

Python

systems/control/backend/__init__.py

stylekilla/syncmrt

816bb57d80d6595719b8b9d7f027f4f17d0a6c0a

[ "Apache-2.0" ]

null

systems/control/backend/__init__.py

stylekilla/syncmrt

816bb57d80d6595719b8b9d7f027f4f17d0a6c0a

[ "Apache-2.0" ]

25

2019-03-05T05:56:35.000Z

2019-07-24T13:11:57.000Z

systems/control/backend/__init__.py

stylekilla/syncmrt

816bb57d80d6595719b8b9d7f027f4f17d0a6c0a

[ "Apache-2.0" ]

1

2019-11-27T05:10:47.000Z

from . import epics

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1

0

6

90de378c74ebe60d74691b0f17e9511261e0c7ef

285

py

Python

Darlington/phase1/python Basic 2/day 28 solution/qtn2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

6

2020-05-23T19:53:25.000Z

2021-05-08T20:21:30.000Z

Darlington/phase1/python Basic 2/day 28 solution/qtn2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

8

2020-05-14T18:53:12.000Z

2020-07-03T00:06:20.000Z

Darlington/phase1/python Basic 2/day 28 solution/qtn2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

39

2020-05-10T20:55:02.000Z

2020-09-12T17:40:59.000Z

#program to compute the digit distance between two integers. def digit_distance_nums(n1, n2): return sum(map(int,str(abs(n1-n2)))) print(digit_distance_nums(123, 256)) print(digit_distance_nums(23, 56)) print(digit_distance_nums(1, 2)) print(digit_distance_nums(24232, 45645))

40.714286

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null

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0

1

0

null

0

1

0

1

0

6

29138bb1a037bad82fc3f688d71d83eb8fa96227

14,970

py

Python

src/model_sac.py

sesem738/Calamari

f3e16682901138c5ee8bb5e94b335bfaee36afad

[ "Unlicense" ]

null

src/model_sac.py

sesem738/Calamari

f3e16682901138c5ee8bb5e94b335bfaee36afad

[ "Unlicense" ]

null

src/model_sac.py

sesem738/Calamari

f3e16682901138c5ee8bb5e94b335bfaee36afad

[ "Unlicense" ]

null

######################################################################### # Implementation of Soft Actor Critic by Josias Moukpe # from the https://arxiv.org/abs/1812.05905 paper # and inspired by https://github.com/pranz24/pytorch-soft-actor-critic ########################################################################## # 12 channel : 3 lidar + 9 rgb import torch from torch import nn from torch._C import device import torch.nn.functional as F from torch.distributions import Normal LOG_SIG_MAX = 2 LOG_SIG_MIN = -20 epsilon = 1e-6 # Initialize Policy weights def weights_init_(m): '''initialize the po ''' if isinstance(m, nn.Linear): torch.nn.init.xavier_uniform_(m.weight, gain=1) torch.nn.init.constant_(m.bias, 0) class ValueNetwork(nn.Module): '''Soft Actor Critic Value Network''' def __init__(self, alpha, input_dim, output_dim = 1, name='ValueNet', checkpoint='checkpoints/sac'): super(ValueNetwork, self).__init__() c, h, w = input_dim self.name = name self.checkpoint_file = checkpoint if h != 256: raise ValueError(f"Expecting input height: 150, got: {h}") if w != 256: raise ValueError(f"Expecting input width: 150, got: {w}") c1 = 3; c2 = 3; c3 = 3# 3 channels lidar, 9 channels rgb camera # Feature extraction # 1 -> camera self.conv11 = nn.Conv2d(in_channels=c1, out_channels=64, kernel_size=8, stride=4) self.conv12 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv13 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) # 2 -> lidar self.conv21 = nn.Conv2d(in_channels=c2, out_channels=64, kernel_size=8, stride=4) self.conv22 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv23 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) # 3 -> birdeye self.conv31 = nn.Conv2d(in_channels=c3, out_channels=64, kernel_size=8, stride=4) self.conv32 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv33 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) out_size = 150528 #TODO: find out # Estimation self.dense1 = nn.Linear(out_size, 512) self.dense2 = nn.Linear(512,256) self.dense3 = nn.Linear(256,output_dim) # output_dim is 1 for SAC Value function self.optimizer = torch.optim.Adam(self.parameters(), lr=alpha) self.device = torch.device('cuda:0') self.to(device=self.device) #Optimal initialization of the weights self.apply(weights_init_) def forward(self, input): '''Forward pass to the Value Network to estimate the value''' # input is the state # input_1, _, input_2 = torch.tensor_split(input,(3,3), dim=1) # input_1 = F.relu(self.conv11(input_1)) # input_1 = F.relu(self.conv12(input_1)) # input_1 = F.relu(self.conv13(input_1)) # input_1 = torch.flatten(input_1,1) # input_2 = F.relu(self.conv21(input_2)) # input_2 = F.relu(self.conv22(input_2)) # input_2 = F.relu(self.conv23(input_2)) # input_2 = torch.flatten(input_2,1) # x = torch.cat((input_1, input_2),dim=-1) input_1 = input['camera'] input_2 = input['lidar'] input_3 = input['birdeye'] input_1 = F.relu(self.conv11(input_1)) input_1 = F.relu(self.conv12(input_1)) input_1 = F.relu(self.conv13(input_1)) input_1 = torch.flatten(input_1,1) input_2 = F.relu(self.conv21(input_2)) input_2 = F.relu(self.conv22(input_2)) input_2 = F.relu(self.conv23(input_2)) input_2 = torch.flatten(input_2,1) input_3 = F.relu(self.conv31(input_3)) input_3 = F.relu(self.conv32(input_3)) input_3 = F.relu(self.conv33(input_3)) input_3 = torch.flatten(input_3,1) x = torch.cat((input_1, input_2, input_3),dim=-1) x = F.relu(self.dense1(x)) x = F.relu(self.dense2(x)) x = self.dense3(x) return x def freeze_paramaters(self): """Freeze network parameters""" for p in self.parameters(): p.requires_grad = False # TODO: found out if it's affected by the update to SAC def save_checkpoint(self, epsilon, num): print('... saving checkpoint ...') path = self.checkpoint_file / (self.name+'_'+str(num)+'.chkpt') torch.save(dict(model=self.state_dict(), epsilon_decay=epsilon), path) def load_checkpoint(self, checkpoint_file): if not checkpoint_file.exists(): raise ValueError(f"{checkpoint_file} does not exist") print('... loading checkpoint ...') ckp = torch.load(checkpoint_file) exploration_rate = ckp.get('epsilon_decay') state_dict = ckp.get('model') self.load_state_dict(state_dict) return exploration_rate class QNetwork(nn.Module): '''Soft Actor Critic Q Network''' def __init__(self, alpha, input_dim, action_dim, output_dim =1, name='QNet', checkpoint='checkpoints/sac'): super(QNetwork, self).__init__() c, h, w = input_dim self.name = name self.checkpoint_file = checkpoint if h != 256: raise ValueError(f"Expecting input height: 150, got: {h}") if w != 256: raise ValueError(f"Expecting input width: 150, got: {w}") c1 = 3; c2 = 3; c3 = 3# 3 channels lidar, 9 channels rgb camera # Feature extraction # 1 -> camera self.conv11 = nn.Conv2d(in_channels=c1, out_channels=64, kernel_size=8, stride=4) self.conv12 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv13 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) # 2 -> lidar self.conv21 = nn.Conv2d(in_channels=c2, out_channels=64, kernel_size=8, stride=4) self.conv22 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv23 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) # 3 -> birdeye self.conv31 = nn.Conv2d(in_channels=c3, out_channels=64, kernel_size=8, stride=4) self.conv32 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv33 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) out_size = 150528 #TODO: find out # Estimation self.dense1 = nn.Linear(out_size, 512) self.dense2_2 = nn.Linear(512,256) self.dense3_2 = nn.Linear(256,output_dim) # output_dim =1 for self.optimizer = torch.optim.Adam(self.parameters(), lr=alpha) self.device = torch.device('cuda:0') self.to(device=self.device) # optimal initialization of weights self.apply(weights_init_) def forward(self, input, action): # input_1, _, input_2 = torch.tensor_split(input,(3,3), dim=1) # # going through the convolutions # input_1 = F.relu(self.conv11(input_1)) # input_1 = F.relu(self.conv12(input_1)) # input_1 = F.relu(self.conv13(input_1)) # input_1 = torch.flatten(input_1,1) # input_2 = F.relu(self.conv21(input_2)) # input_2 = F.relu(self.conv22(input_2)) # input_2 = F.relu(self.conv23(input_2)) # input_2 = torch.flatten(input_2,1) # # Concatenate conv outputs # state_input = torch.cat((input_1, input_2),dim=-1) input_1 = input['camera'] input_2 = input['lidar'] input_3 = input['birdeye'] input_1 = F.relu(self.conv11(input_1)) input_1 = F.relu(self.conv12(input_1)) input_1 = F.relu(self.conv13(input_1)) input_1 = torch.flatten(input_1,1) input_2 = F.relu(self.conv21(input_2)) input_2 = F.relu(self.conv22(input_2)) input_2 = F.relu(self.conv23(input_2)) input_2 = torch.flatten(input_2,1) input_3 = F.relu(self.conv31(input_3)) input_3 = F.relu(self.conv32(input_3)) input_3 = F.relu(self.conv33(input_3)) input_3 = torch.flatten(input_3,1) state_input = torch.cat((input_1, input_2, input_3),dim=-1) xu = torch.cat([state_input, action], 1) # Q1 fully connected forward x1 = F.relu(self.dense1_1(xu)) x1 = F.relu(self.dense2_1(x1)) x1 = self.dense3_1(x1) # Q2 fully connected forward x2 = F.relu(self.dense1_2(xu)) x2 = F.relu(self.dense2_2(x2)) x2 = self.dense3_2(x2) return x1, x2 def freeze_paramaters(self): """Freeze network parameters""" for p in self.parameters(): p.requires_grad = False #TODO: check if this still applies to the new Q network def save_checkpoint(self, epsilon, num): print('... saving checkpoint ...') path = self.checkpoint_file / (self.name+'_'+str(num)+'.chkpt') torch.save(dict(model=self.state_dict(), epsilon_decay=epsilon), path) def load_checkpoint(self, checkpoint_file): if not checkpoint_file.exists(): raise ValueError(f"{checkpoint_file} does not exist") print('... loading checkpoint ...') ckp = torch.load(checkpoint_file) exploration_rate = ckp.get('epsilon_decay') state_dict = ckp.get('model') self.load_state_dict(state_dict) return exploration_rate class PolicyNetwork(nn.Module): ''' Soft Actor Critic Gaussian Policy Network ''' def __init__(self, alpha, input_dim, action_dim, action_space=None, name='PolicyNet', checkpoint='checkpoints/sac'): super(PolicyNetwork, self).__init__() c, h, w = input_dim self.name = name self.checkpoint_file = checkpoint if h != 256: raise ValueError(f"Expecting input height: 150, got: {h}") if w != 256: raise ValueError(f"Expecting input width: 150, got: {w}") c1 = 3; c2 = 3; c3 = 3# 3 channels lidar, 9 channels rgb camera # Feature extraction # 1 -> camera self.conv11 = nn.Conv2d(in_channels=c1, out_channels=64, kernel_size=8, stride=4) self.conv12 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv13 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) # 2 -> lidar self.conv21 = nn.Conv2d(in_channels=c2, out_channels=64, kernel_size=8, stride=4) self.conv22 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv23 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) # 3 -> birdeye self.conv31 = nn.Conv2d(in_channels=c3, out_channels=64, kernel_size=8, stride=4) self.conv32 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2) self.conv33 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) out_size = 150528 #TODO: find out # Estimation self.dense1 = nn.Linear(out_size, 512) self.dense2 = nn.Linear(512,256) self.mean_dense = nn.Linear(256, action_dim) self.log_std_dense = nn.Linear(256, action_dim) # applying initial optimal weights self.apply(weights_init_) # action rescaling TODO: verify it applies to this case if action_space is None: self.action_scale = torch.tensor(1.) self.action_bias = torch.tensor(0.) else: self.action_scale = torch.FloatTensor( (action_space.high - action_space.low) / 2.) self.action_bias = torch.FloatTensor( (action_space.high + action_space.low) / 2.) self.optimizer = torch.optim.Adam(self.parameters(), lr=alpha) self.device = torch.device('cuda:0') self.to(device=self.device) def forward(self, input: dict): '''passing data through policy network''' # Extracting them features # input_1, _, input_2 = torch.tensor_split(input,(3,3), dim=1) input_1 = input['camera'] input_2 = input['lidar'] input_3 = input['birdeye'] input_1 = F.relu(self.conv11(input_1)) input_1 = F.relu(self.conv12(input_1)) input_1 = F.relu(self.conv13(input_1)) input_1 = torch.flatten(input_1,1) input_2 = F.relu(self.conv21(input_2)) input_2 = F.relu(self.conv22(input_2)) input_2 = F.relu(self.conv23(input_2)) input_2 = torch.flatten(input_2,1) input_3 = F.relu(self.conv31(input_3)) input_3 = F.relu(self.conv32(input_3)) input_3 = F.relu(self.conv33(input_3)) input_3 = torch.flatten(input_3,1) state_input = torch.cat((input_1, input_2, input_3),dim=-1) x = F.relu(self.dense1(state_input)) x = F.relu(self.dense2(x)) mean = self.mean_dense(x) log_std = self.log_std_dense(x) log_std = torch.clamp(log_std, min=LOG_SIG_MIN, max=LOG_SIG_MAX) return mean, log_std def sample(self, input): mean, log_std = self.forward(input) std = log_std.exp() normal = Normal(mean, std) x_t = normal.rsample() # for reparameterization trick (mean + std * N(0,1)) y_t = torch.tanh(x_t) action = y_t * self.action_scale + self.action_bias log_prob = normal.log_prob(x_t) # Enforcing Action Bound log_prob -= torch.log(self.action_scale * (1 - y_t.pow(2)) + epsilon) log_prob = log_prob.sum(1, keepdim=True) mean = torch.tanh(mean) * self.action_scale + self.action_bias return action, log_prob, mean def to(self, device): self.action_scale = self.action_scale.to(device) self.action_bias = self.action_bias.to(device) return super(PolicyNetwork, self).to(device) def freeze_paramaters(self): """Freeze network parameters""" for p in self.parameters(): p.requires_grad = False # TODO: check if it still applies to this case def save_checkpoint(self, epsilon, num): print('... saving checkpoint ...') path = self.checkpoint_file / (self.name+'_'+str(num)+'.chkpt') torch.save(dict(model=self.state_dict(), epsilon_decay=epsilon), path) def load_checkpoint(self, checkpoint_file): if not checkpoint_file.exists(): raise ValueError(f"{checkpoint_file} does not exist") print('... loading checkpoint ...') ckp = torch.load(checkpoint_file) exploration_rate = ckp.get('epsilon_decay') state_dict = ckp.get('model') self.load_state_dict(state_dict) return exploration_rate

38.286445

120

0.615965

2,115

14,970

4.170213

0.115366

0.034694

0.047959

0.055102

0.787415

0.770522

0.75102

0.74093

0.739569

0.720068

0

0.060339

0.250501

14,970

390

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0.02521

0

null

0

1

0

1

0

null

0

6

2933dd0a1327b51c1ed1223ead1915492611c715

216

py

Python

course/admin.py

LuoLuo0101/ChoiceCourse

93eba91cd39a524455edab52ad29dfd09ac000ba

[ "Apache-2.0" ]

null

course/admin.py

LuoLuo0101/ChoiceCourse

93eba91cd39a524455edab52ad29dfd09ac000ba

[ "Apache-2.0" ]

null

course/admin.py

LuoLuo0101/ChoiceCourse

93eba91cd39a524455edab52ad29dfd09ac000ba

[ "Apache-2.0" ]

null

from django.contrib import admin from course.models import Student, Teacher, Course, Enrollment admin.site.register(Student) admin.site.register(Teacher) admin.site.register(Course) admin.site.register(Enrollment)

24

62

0.824074

29

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6.137931

0.413793

0.202247

0.382022

0

0.078704

216

8

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0

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0

true

0

0.333333

0

0.333333

0

1

0

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1

0

1

0

null

0

1

0

1

0

6

293d69d7e1b5bebc5ff967901b2ddcaed49fa0db

42

py

Python

botovod/dbdrivers/__init__.py

OlegYurchik/botovod

20d7c97a4758ce280fcdc601e395b34c4f942a0f

[ "MIT" ]

7

2018-09-03T11:03:55.000Z

2020-07-11T16:13:56.000Z

botovod/dbdrivers/__init__.py

OlegYurchik/botovod

20d7c97a4758ce280fcdc601e395b34c4f942a0f

[ "MIT" ]

null

botovod/dbdrivers/__init__.py

OlegYurchik/botovod

20d7c97a4758ce280fcdc601e395b34c4f942a0f

[ "MIT" ]

2

2019-09-03T12:09:40.000Z

2020-06-05T18:09:52.000Z

from .dbdrivers import DBDriver, Follower

21

41

0.833333

5

42

7

1

0

0.119048

42

1

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pegasus/tools/clustering.py

slowkow/pegasus

9a840b4a485ad93e703b2087e21179b0329e0c41

[ "BSD-3-Clause" ]

null

pegasus/tools/clustering.py

slowkow/pegasus

9a840b4a485ad93e703b2087e21179b0329e0c41

[ "BSD-3-Clause" ]

null

pegasus/tools/clustering.py

slowkow/pegasus

9a840b4a485ad93e703b2087e21179b0329e0c41

[ "BSD-3-Clause" ]

null

import time import numpy as np import pandas as pd from pegasusio import MultimodalData from natsort import natsorted from sklearn.cluster import KMeans from typing import List, Optional from pegasus.tools import construct_graph import logging logger = logging.getLogger(__name__) from pegasusio import timer @timer(logger=logger) def louvain( data: MultimodalData, rep: str = "pca", resolution: int = 1.3, random_state: int = 0, class_label: str = "louvain_labels", ) -> None: """Cluster the cells using Louvain algorithm. [Blondel08]_ Parameters ---------- data: ``pegasusio.MultimodalData`` Annotated data matrix with rows for cells and columns for genes. rep: ``str``, optional, default: ``"pca"`` The embedding representation used for clustering. Keyword ``'X_' + rep`` must exist in ``data.obsm``. By default, use PCA coordinates. resolution: ``int``, optional, default: ``1.3`` Resolution factor. Higher resolution tends to find more clusters with smaller sizes. random_state: ``int``, optional, default: ``0`` Random seed for reproducing results. class_label: ``str``, optional, default: ``"louvain_labels"`` Key name for storing cluster labels in ``data.obs``. Returns ------- ``None`` Update ``data.obs``: * ``data.obs[class_label]``: Cluster labels of cells as categorical data. Examples -------- >>> pg.louvain(data) """ try: import louvain as louvain_module except ImportError: print("Need louvain! Try 'pip install louvain-github'.") rep_key = "W_" + rep if rep_key not in data.uns: raise ValueError("Cannot find affinity matrix. Please run neighbors first!") W = data.uns[rep_key] G = construct_graph(W) partition_type = louvain_module.RBConfigurationVertexPartition partition = partition_type(G, resolution_parameter=resolution, weights="weight") optimiser = louvain_module.Optimiser() optimiser.set_rng_seed(random_state) diff = optimiser.optimise_partition(partition) labels = np.array([str(x + 1) for x in partition.membership]) categories = natsorted(np.unique(labels)) data.obs[class_label] = pd.Categorical(values=labels, categories=categories) n_clusters = data.obs[class_label].cat.categories.size logger.info(f"Louvain clustering is done. Get {n_clusters} clusters.") @timer(logger=logger) def leiden( data: MultimodalData, rep: str = "pca", resolution: int = 1.3, n_iter: int = -1, random_state: int = 0, class_label: str = "leiden_labels", ) -> None: """Cluster the data using Leiden algorithm. [Traag19]_ Parameters ---------- data: ``pegasusio.MultimodalData`` Annotated data matrix with rows for cells and columns for genes. rep: ``str``, optional, default: ``"pca"`` The embedding representation used for clustering. Keyword ``'X_' + rep`` must exist in ``data.obsm``. By default, use PCA coordinates. resolution: ``int``, optional, default: ``1.3`` Resolution factor. Higher resolution tends to find more clusters. n_iter: ``int``, optional, default: ``-1`` Number of iterations that Leiden algorithm runs. If ``-1``, run the algorithm until reaching its optimal clustering. random_state: ``int``, optional, default: ``0`` Random seed for reproducing results. class_label: ``str``, optional, default: ``"leiden_labels"`` Key name for storing cluster labels in ``data.obs``. Returns ------- ``None`` Update ``data.obs``: * ``data.obs[class_label]``: Cluster labels of cells as categorical data. Examples -------- >>> pg.leiden(data) """ try: import leidenalg except ImportError: print("Need leidenalg! Try 'pip install leidenalg'.") rep_key = "W_" + rep if rep_key not in data.uns: raise ValueError("Cannot find affinity matrix. Please run neighbors first!") W = data.uns[rep_key] G = construct_graph(W) partition_type = leidenalg.RBConfigurationVertexPartition partition = leidenalg.find_partition( G, partition_type, seed=random_state, weights="weight", resolution_parameter=resolution, n_iterations=n_iter, ) labels = np.array([str(x + 1) for x in partition.membership]) categories = natsorted(np.unique(labels)) data.obs[class_label] = pd.Categorical(values=labels, categories=categories) n_clusters = data.obs[class_label].cat.categories.size logger.info(f"Leiden clustering is done. Get {n_clusters} clusters.") def partition_cells_by_kmeans( X: np.ndarray, n_clusters: int, n_clusters2: int, n_init: int, random_state: int, min_avg_cells_per_final_cluster: Optional[int] = 10, ) -> List[int]: n_clusters = min(n_clusters, max(X.shape[0] // min_avg_cells_per_final_cluster, 1)) if n_clusters == 1: return np.zeros(X.shape[0], dtype = np.int32) kmeans_params = { 'n_clusters': n_clusters, 'n_init': n_init, 'random_state': random_state, } km = KMeans(**kmeans_params) km.fit(X) coarse = km.labels_.copy() km.set_params(n_init=1) labels = coarse.copy() base_sum = 0 for i in range(n_clusters): idx = coarse == i nc = min(n_clusters2, max(idx.sum() // min_avg_cells_per_final_cluster, 1)) if nc == 1: labels[idx] = base_sum else: km.set_params(n_clusters=nc) km.fit(X[idx, :]) labels[idx] = base_sum + km.labels_ base_sum += nc return labels @timer(logger=logger) def spectral_louvain( data: MultimodalData, rep: str = "pca", resolution: float = 1.3, rep_kmeans: str = "diffmap", n_clusters: int = 30, n_clusters2: int = 50, n_init: int = 10, random_state: int = 0, class_label: str = "spectral_louvain_labels", ) -> None: """ Cluster the data using Spectral Louvain algorithm. [Li20]_ Parameters ---------- data: ``pegasusio.MultimodalData`` Annotated data matrix with rows for cells and columns for genes. rep: ``str``, optional, default: ``"pca"`` The embedding representation used for clustering. Keyword ``'X_' + rep`` must exist in ``data.obsm``. By default, use PCA coordinates. resolution: ``int``, optional, default: ``1.3`` Resolution factor. Higher resolution tends to find more clusters with smaller sizes. rep_kmeans: ``str``, optional, default: ``"diffmap"`` The embedding representation on which the KMeans runs. Keyword must exist in ``data.obsm``. By default, use Diffusion Map coordinates. If diffmap is not calculated, use PCA coordinates instead. n_clusters: ``int``, optional, default: ``30`` The number of first level clusters. n_clusters2: ``int``, optional, default: ``50`` The number of second level clusters. n_init: ``int``, optional, default: ``10`` Number of kmeans tries for the first level clustering. Default is set to be the same as scikit-learn Kmeans function. random_state: ``int``, optional, default: ``0`` Random seed for reproducing results. class_label: ``str``, optional, default: ``"spectral_louvain_labels"`` Key name for storing cluster labels in ``data.obs``. Returns ------- ``None`` Update ``data.obs``: * ``data.obs[class_label]``: Cluster labels for cells as categorical data. Examples -------- >>> pg.spectral_louvain(data) """ try: import louvain as louvain_module except ImportError: print("Need louvain! Try 'pip install louvain-github'.") if "X_" + rep_kmeans not in data.obsm.keys(): logger.warning( "{} is not calculated, switch to pca instead.".format(rep_kmeans) ) rep_kmeans = "pca" if "X_" + rep_kmeans not in data.obsm.keys(): raise ValueError("Please run {} first!".format(rep_kmeans)) if "W_" + rep not in data.uns: raise ValueError("Cannot find affinity matrix. Please run neighbors first!") labels = partition_cells_by_kmeans( data.obsm[rep_kmeans], n_clusters, n_clusters2, n_init, random_state, ) W = data.uns["W_" + rep] G = construct_graph(W) partition_type = louvain_module.RBConfigurationVertexPartition partition = partition_type( G, resolution_parameter=resolution, weights="weight", initial_membership=labels ) partition_agg = partition.aggregate_partition() optimiser = louvain_module.Optimiser() optimiser.set_rng_seed(random_state) diff = optimiser.optimise_partition(partition_agg) partition.from_coarse_partition(partition_agg) labels = np.array([str(x + 1) for x in partition.membership]) categories = natsorted(np.unique(labels)) data.obs[class_label] = pd.Categorical(values=labels, categories=categories) n_clusters = data.obs[class_label].cat.categories.size logger.info(f"Spectral Louvain clustering is done. Get {n_clusters} clusters.") @timer(logger=logger) def spectral_leiden( data: MultimodalData, rep: str = "pca", resolution: float = 1.3, rep_kmeans: str = "diffmap", n_clusters: int = 30, n_clusters2: int = 50, n_init: int = 10, random_state: int = 0, class_label: str = "spectral_leiden_labels", ) -> None: """Cluster the data using Spectral Leiden algorithm. [Li20]_ Parameters ---------- data: ``pegasusio.MultimodalData`` Annotated data matrix with rows for cells and columns for genes. rep: ``str``, optional, default: ``"pca"`` The embedding representation used for clustering. Keyword ``'X_' + rep`` must exist in ``data.obsm``. By default, use PCA coordinates. resolution: ``int``, optional, default: ``1.3`` Resolution factor. Higher resolution tends to find more clusters. rep_kmeans: ``str``, optional, default: ``"diffmap"`` The embedding representation on which the KMeans runs. Keyword must exist in ``data.obsm``. By default, use Diffusion Map coordinates. If diffmap is not calculated, use PCA coordinates instead. n_clusters: ``int``, optional, default: ``30`` The number of first level clusters. n_clusters2: ``int``, optional, default: ``50`` The number of second level clusters. n_init: ``int``, optional, default: ``10`` Number of kmeans tries for the first level clustering. Default is set to be the same as scikit-learn Kmeans function. random_state: ``int``, optional, default: ``0`` Random seed for reproducing results. class_label: ``str``, optional, default: ``"spectral_leiden_labels"`` Key name for storing cluster labels in ``data.obs``. Returns ------- ``None`` Update ``data.obs``: * ``data.obs[class_label]``: Cluster labels for cells as categorical data. Examples -------- >>> pg.spectral_leiden(data) """ try: import leidenalg except ImportError: print("Need leidenalg! Try 'pip install leidenalg'.") if "X_" + rep_kmeans not in data.obsm.keys(): logger.warning( "{} is not calculated, switch to pca instead.".format(rep_kmeans) ) rep_kmeans = "pca" if "X_" + rep_kmeans not in data.obsm.keys(): raise ValueError("Please run {} first!".format(rep_kmeans)) if "W_" + rep not in data.uns: raise ValueError("Cannot find affinity matrix. Please run neighbors first!") labels = partition_cells_by_kmeans( data.obsm[rep_kmeans], n_clusters, n_clusters2, n_init, random_state, ) W = data.uns["W_" + rep] G = construct_graph(W) partition_type = leidenalg.RBConfigurationVertexPartition partition = partition_type( G, resolution_parameter=resolution, weights="weight", initial_membership=labels ) partition_agg = partition.aggregate_partition() optimiser = leidenalg.Optimiser() optimiser.set_rng_seed(random_state) diff = optimiser.optimise_partition(partition_agg, -1) partition.from_coarse_partition(partition_agg) labels = np.array([str(x + 1) for x in partition.membership]) categories = natsorted(np.unique(labels)) data.obs[class_label] = pd.Categorical(values=labels, categories=categories) n_clusters = data.obs[class_label].cat.categories.size logger.info(f"Spectral Leiden clustering is done. Get {n_clusters} clusters.") def cluster( data: MultimodalData, algo: str = "louvain", rep: str = "pca", resolution: int = 1.3, random_state: int = 0, class_label: str = None, n_iter: int = -1, rep_kmeans: str = "diffmap", n_clusters: int = 30, n_clusters2: int = 50, n_init: int = 10, ) -> None: """Cluster the data using the chosen algorithm. Candidates are *louvain*, *leiden*, *spectral_louvain* and *spectral_leiden*. If data have < 1000 cells and there are clusters with sizes of 1, resolution is automatically reduced until no cluster of size 1 appears. Parameters ---------- data: ``pegasusio.MultimodalData`` Annotated data matrix with rows for cells and columns for genes. algo: ``str``, optional, default: ``"louvain"`` Which clustering algorithm to use. Choices are louvain, leiden, spectral_louvain, spectral_leiden rep: ``str``, optional, default: ``"pca"`` The embedding representation used for clustering. Keyword ``'X_' + rep`` must exist in ``data.obsm``. By default, use PCA coordinates. resolution: ``int``, optional, default: ``1.3`` Resolution factor. Higher resolution tends to find more clusters. random_state: ``int``, optional, default: ``0`` Random seed for reproducing results. class_label: ``str``, optional, default: None Key name for storing cluster labels in ``data.obs``. If None, use 'algo_labels'. n_iter: ``int``, optional, default: ``-1`` Number of iterations that Leiden algorithm runs. If ``-1``, run the algorithm until reaching its optimal clustering. rep_kmeans: ``str``, optional, default: ``"diffmap"`` The embedding representation on which the KMeans runs. Keyword must exist in ``data.obsm``. By default, use Diffusion Map coordinates. If diffmap is not calculated, use PCA coordinates instead. n_clusters: ``int``, optional, default: ``30`` The number of first level clusters. n_clusters2: ``int``, optional, default: ``50`` The number of second level clusters. n_init: ``int``, optional, default: ``10`` Number of kmeans tries for the first level clustering. Default is set to be the same as scikit-learn Kmeans function. Returns ------- ``None`` Update ``data.obs``: * ``data.obs[class_label]``: Cluster labels of cells as categorical data. Examples -------- >>> pg.cluster(data, algo = 'leiden') """ if algo not in {"louvain", "leiden", "spectral_louvain", "spectral_leiden"}: raise ValueError("Unknown clustering algorithm {}.".format(algo)) if class_label is None: class_label = algo + "_labels" kwargs = { "data": data, "rep": rep, "resolution": resolution, "random_state": random_state, "class_label": class_label, } if algo == "leiden": kwargs["n_iter"] = n_iter if algo in ["spectral_louvain", "spectral_leiden"]: kwargs.update( { "rep_kmeans": rep_kmeans, "n_clusters": n_clusters, "n_clusters2": n_clusters2, "n_init": n_init, } ) cluster_func = globals()[algo] cluster_func(**kwargs) # clustering if data.shape[0] < 100000 and data.obs[class_label].value_counts().min() == 1: new_resol = resolution while new_resol > 0.0: new_resol -= 0.1 kwargs["resolution"] = new_resol cluster_func(**kwargs) if data.obs[class_label].value_counts().min() > 1: break logger.warning( "Reduced resolution from {:.2f} to {:.2f} to avoid clusters of size 1.".format( resolution, new_resol ) )

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lib/exception.py

sumedhpb/testrunner

9ff887231c75571624abc31a3fb5248110e01203

[ "Apache-2.0" ]

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2015-02-06T02:47:57.000Z

2020-03-14T15:06:05.000Z

lib/exception.py

sumedhpb/testrunner

9ff887231c75571624abc31a3fb5248110e01203

[ "Apache-2.0" ]

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2019-02-27T19:29:11.000Z

2021-06-02T02:14:27.000Z

lib/exception.py

sumedhpb/testrunner

9ff887231c75571624abc31a3fb5248110e01203

[ "Apache-2.0" ]

108

2015-03-26T08:58:49.000Z

2022-03-21T05:21:39.000Z

class TimeoutException(Exception): def __init__(self, value): self.parameter = value def __str__(self): return repr(self.parameter)

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IOT/RaspberryPi/hellog/rasp_server/admin.py

syureu/Hellog2

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[ "Apache-2.0" ]

null

IOT/RaspberryPi/hellog/rasp_server/admin.py

syureu/Hellog2

f61524ffe6f2a3836d13085e9e29e2015bba9f87

[ "Apache-2.0" ]

null

IOT/RaspberryPi/hellog/rasp_server/admin.py

syureu/Hellog2

f61524ffe6f2a3836d13085e9e29e2015bba9f87

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import User, Record, Machine admin.site.register(User) admin.site.register(Record) admin.site.register(Machine)

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magi/wrappers/__init__.py

akbir/magi

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magi/wrappers/__init__.py

akbir/magi

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magi/wrappers/__init__.py

akbir/magi

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2022-01-24T14:38:53.000Z

"""Environment wrappers for dm_env.Environment.""" from magi.wrappers.filter import TakeKeyWrapper # noqa from magi.wrappers.frame_stack import FrameStackingWrapper # noqa

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Pytorch/denseForecastNet.py

khsibr/forecastNet

f3a3d8a7a675dfdd37365e9945c1d02548465c61

[ "MIT" ]

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Pytorch/denseForecastNet.py

khsibr/forecastNet

f3a3d8a7a675dfdd37365e9945c1d02548465c61

[ "MIT" ]

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2021-08-16T02:51:21.000Z

Pytorch/denseForecastNet.py

khsibr/forecastNet

f3a3d8a7a675dfdd37365e9945c1d02548465c61

[ "MIT" ]

23

2020-02-20T11:22:21.000Z

2022-03-26T07:46:58.000Z

""" ForecastNet with cells comprising densely connected layers. ForecastNetDenseModel provides the mixture density network outputs. ForecastNetDenseModel2 provides the linear outputs. Paper: "ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting" by Joel Janek Dabrowski, YiFan Zhang, and Ashfaqur Rahman Link to the paper: https://arxiv.org/abs/2002.04155 """ import torch import torch.nn as nn import torch.nn.functional as F class ForecastNetDenseModel(nn.Module): """ Class for the densely connected hidden cells version of the model """ def __init__(self, input_dim, hidden_dim, output_dim, in_seq_length, out_seq_length, device): """ Constructor :param input_dim: Dimension of the inputs :param hidden_dim: Number of hidden units :param output_dim: Dimension of the outputs :param in_seq_length: Length of the input sequence :param out_seq_length: Length of the output sequence """ super(ForecastNetDenseModel, self).__init__() self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = output_dim self.in_seq_length = in_seq_length self.out_seq_length = out_seq_length self.device = device # Input dimension of componed inputs and sequences input_dim_comb = input_dim * in_seq_length # Initialise layers hidden_layer1 = [nn.Linear(input_dim_comb, hidden_dim)] for i in range(out_seq_length - 1): hidden_layer1.append(nn.Linear(input_dim_comb + hidden_dim + output_dim, hidden_dim)) self.hidden_layer1 = nn.ModuleList(hidden_layer1) self.hidden_layer2 = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for i in range(out_seq_length)]) self.mu_layer = nn.ModuleList([nn.Linear(hidden_dim, output_dim) for i in range(out_seq_length)]) self.sigma_layer = nn.ModuleList([nn.Linear(hidden_dim, output_dim) for i in range(out_seq_length)]) def forward(self, input, target, is_training=False): """ Forward propagation of the dense ForecastNet model :param input: Input data in the form [input_seq_length, batch_size, input_dim] :param target: Target data in the form [output_seq_length, batch_size, output_dim] :param is_training: If true, use target data for training, else use the previous output. :return: outputs: Sampled forecast outputs in the form [decoder_seq_length, batch_size, input_dim] :return: mu: Outputs of the mean layer [decoder_seq_length, batch_size, input_dim] :return: sigma: Outputs of the standard deviation layer [decoder_seq_length, batch_size, input_dim] """ # Initialise outputs outputs = torch.zeros((self.out_seq_length, input.shape[0], self.output_dim)).to(self.device) mu = torch.zeros((self.out_seq_length, input.shape[0], self.output_dim)).to(self.device) sigma = torch.zeros((self.out_seq_length, input.shape[0], self.output_dim)).to(self.device) # First input next_cell_input = input for i in range(self.out_seq_length): # Propagate through cell out = F.relu(self.hidden_layer1[i](next_cell_input)) out = F.relu(self.hidden_layer2[i](out)) # Calculate the output mu_ = self.mu_layer[i](out) sigma_ = F.softplus(self.sigma_layer[i](out)) mu[i,:,:] = mu_ sigma[i,:,:] = sigma_ outputs[i,:,:] = torch.normal(mu_, sigma_).to(self.device) # Prepare the next input if is_training: next_cell_input = torch.cat((input, out, target[i, :, :]), dim=1) else: next_cell_input = torch.cat((input, out, outputs[i, :, :]), dim=1) return outputs, mu, sigma class ForecastNetDenseModel2(nn.Module): """ Class for the densely connected hidden cells version of the model """ def __init__(self, input_dim, hidden_dim, output_dim, in_seq_length, out_seq_length, device): """ Constructor :param input_dim: Dimension of the inputs :param hidden_dim: Number of hidden units :param output_dim: Dimension of the outputs :param in_seq_length: Length of the input sequence :param out_seq_length: Length of the output sequence """ super(ForecastNetDenseModel2, self).__init__() self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = output_dim self.in_seq_length = in_seq_length self.out_seq_length = out_seq_length self.device = device # Input dimension of componed inputs and sequences input_dim_comb = input_dim * in_seq_length # Initialise layers hidden_layer1 = [nn.Linear(input_dim_comb, hidden_dim)] for i in range(out_seq_length - 1): hidden_layer1.append(nn.Linear(input_dim_comb + hidden_dim + output_dim, hidden_dim)) self.hidden_layer1 = nn.ModuleList(hidden_layer1) self.hidden_layer2 = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for i in range(out_seq_length)]) self.output_layer = nn.ModuleList([nn.Linear(hidden_dim, output_dim) for i in range(out_seq_length)]) def forward(self, input, target, is_training=False): """ Forward propagation of the dense ForecastNet model :param input: Input data in the form [input_seq_length, batch_size, input_dim] :param target: Target data in the form [output_seq_length, batch_size, output_dim] :param is_training: If true, use target data for training, else use the previous output. :return: outputs: Forecast outputs in the form [decoder_seq_length, batch_size, input_dim] """ # Initialise outputs outputs = torch.zeros((self.out_seq_length, input.shape[0], self.output_dim)).to(self.device) # First input next_cell_input = input for i in range(self.out_seq_length): # Propagate through cell hidden = F.relu(self.hidden_layer1[i](next_cell_input)) hidden = F.relu(self.hidden_layer2[i](hidden)) # Calculate the output output = self.output_layer[i](hidden) outputs[i,:,:] = output # Prepare the next input if is_training: next_cell_input = torch.cat((input, hidden, target[i, :, :]), dim=1) else: next_cell_input = torch.cat((input, hidden, outputs[i, :, :]), dim=1) return outputs

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d3c47d0dd1bb5afb5fbbc8a8970bd181f216950a

7,773

py

Python

src/saltext/vmware/modules/ntp.py

jain-prerna/salt-ext-modules-vmware-old

89ea6dd77c6d5a35dc55c23adbdc361949a63057

[ "Apache-2.0" ]

null

src/saltext/vmware/modules/ntp.py

jain-prerna/salt-ext-modules-vmware-old

89ea6dd77c6d5a35dc55c23adbdc361949a63057

[ "Apache-2.0" ]

null

src/saltext/vmware/modules/ntp.py

jain-prerna/salt-ext-modules-vmware-old

89ea6dd77c6d5a35dc55c23adbdc361949a63057

[ "Apache-2.0" ]

null

# SPDX-License-Identifier: Apache-2.0 def set_ntp_config( host, username, password, ntp_servers, protocol=None, port=None, host_names=None, verify_ssl=True, ): """ Set NTP configuration for a given host of list of host_names. host The location of the host. username The username used to login to the host, such as ``root``. password The password used to login to the host. ntp_servers A list of servers that should be added to and configured for the specified host's NTP configuration. protocol Optionally set to alternate protocol if the host is not using the default protocol. Default protocol is ``https``. port Optionally set to alternate port if the host is not using the default port. Default port is ``443``. host_names List of ESXi host names. When the host, username, and password credentials are provided for a vCenter Server, the host_names argument is required to tell vCenter which hosts to configure ntp servers. If host_names is not provided, the NTP servers will be configured for the ``host`` location instead. This is useful for when service instance connection information is used for a single ESXi host. verify_ssl Verify the SSL certificate. Default: True CLI Example: .. code-block:: bash # Used for single ESXi host connection information salt '*' vsphere.ntp_configure my.esxi.host root bad-password '[192.174.1.100, 192.174.1.200]' # Used for connecting to a vCenter Server salt '*' vsphere.ntp_configure my.vcenter.location root bad-password '[192.174.1.100, 192.174.1.200]' \ host_names='[esxi-1.host.com, esxi-2.host.com]' """ service_instance = salt.utils.vmware.get_service_instance( host=host, username=username, password=password, protocol=protocol, port=port, verify_ssl=verify_ssl, ) if not isinstance(ntp_servers, list): raise CommandExecutionError("'ntp_servers' must be a list.") # Get NTP Config Object from ntp_servers ntp_config = vim.HostNtpConfig(server=ntp_servers) # Get DateTimeConfig object from ntp_config date_config = vim.HostDateTimeConfig(ntpConfig=ntp_config) host_names = _check_hosts(service_instance, host, host_names) ret = {} for host_name in host_names: host_ref = _get_host_ref(service_instance, host, host_name=host_name) date_time_manager = _get_date_time_mgr(host_ref) log.debug("Configuring NTP Servers '{}' for host '{}'.".format(ntp_servers, host_name)) try: date_time_manager.UpdateDateTimeConfig(config=date_config) except vim.fault.HostConfigFault as err: msg = "vsphere.ntp_configure_servers failed: {}".format(err) log.debug(msg) ret.update({host_name: {"Error": msg}}) continue ret.update({host_name: {"NTP Servers": ntp_config}}) return ret def update_host_datetime( host, username, password, protocol=None, port=None, host_names=None, verify_ssl=True ): """ Update the date/time on the given host or list of host_names. This function should be used with caution since network delays and execution delays can result in time skews. host The location of the host. username The username used to login to the host, such as ``root``. password The password used to login to the host. protocol Optionally set to alternate protocol if the host is not using the default protocol. Default protocol is ``https``. port Optionally set to alternate port if the host is not using the default port. Default port is ``443``. host_names List of ESXi host names. When the host, username, and password credentials are provided for a vCenter Server, the host_names argument is required to tell vCenter which hosts should update their date/time. If host_names is not provided, the date/time will be updated for the ``host`` location instead. This is useful for when service instance connection information is used for a single ESXi host. verify_ssl Verify the SSL certificate. Default: True CLI Example: .. code-block:: bash # Used for single ESXi host connection information salt '*' vsphere.update_date_time my.esxi.host root bad-password # Used for connecting to a vCenter Server salt '*' vsphere.update_date_time my.vcenter.location root bad-password \ host_names='[esxi-1.host.com, esxi-2.host.com]' """ service_instance = salt.utils.vmware.get_service_instance( host=host, username=username, password=password, protocol=protocol, port=port, verify_ssl=verify_ssl, ) host_names = _check_hosts(service_instance, host, host_names) ret = {} for host_name in host_names: host_ref = _get_host_ref(service_instance, host, host_name=host_name) date_time_manager = _get_date_time_mgr(host_ref) try: date_time_manager.UpdateDateTime(datetime.datetime.utcnow()) except vim.fault.HostConfigFault as err: msg = "'vsphere.update_date_time' failed for host {}: {}".format(host_name, err) log.debug(msg) ret.update({host_name: {"Error": msg}}) continue ret.update({host_name: {"Datetime Updated": True}}) return ret def get_ntp_config( host, username, password, protocol=None, port=None, host_names=None, verify_ssl=True ): """ Get the NTP configuration information for a given host or list of host_names. host The location of the host. username The username used to login to the host, such as ``root``. password The password used to login to the host. protocol Optionally set to alternate protocol if the host is not using the default protocol. Default protocol is ``https``. port Optionally set to alternate port if the host is not using the default port. Default port is ``443``. host_names List of ESXi host names. When the host, username, and password credentials are provided for a vCenter Server, the host_names argument is required to tell vCenter the hosts for which to get ntp configuration information. If host_names is not provided, the NTP configuration will be retrieved for the ``host`` location instead. This is useful for when service instance connection information is used for a single ESXi host. verify_ssl Verify the SSL certificate. Default: True CLI Example: .. code-block:: bash # Used for single ESXi host connection information salt '*' vsphere.get_ntp_config my.esxi.host root bad-password # Used for connecting to a vCenter Server salt '*' vsphere.get_ntp_config my.vcenter.location root bad-password \ host_names='[esxi-1.host.com, esxi-2.host.com]' """ service_instance = salt.utils.vmware.get_service_instance( host=host, username=username, password=password, protocol=protocol, port=port, verify_ssl=verify_ssl, ) host_names = _check_hosts(service_instance, host, host_names) ret = {} for host_name in host_names: host_ref = _get_host_ref(service_instance, host, host_name=host_name) ntp_config = host_ref.configManager.dateTimeSystem.dateTimeInfo.ntpConfig.server ret.update({host_name: ntp_config}) return ret

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6

d3f02b57b6a0fe2696df5aeff800a681cd29c4df

2,962

py

Python

terrascript/databricks/r.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/databricks/r.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/databricks/r.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/databricks/r.py # Automatically generated by tools/makecode.py () import warnings warnings.warn( "using the 'legacy layout' is deprecated", DeprecationWarning, stacklevel=2 ) import terrascript class databricks_aws_s3_mount(terrascript.Resource): pass class databricks_azure_adls_gen1_mount(terrascript.Resource): pass class databricks_azure_adls_gen2_mount(terrascript.Resource): pass class databricks_azure_blob_mount(terrascript.Resource): pass class databricks_cluster(terrascript.Resource): pass class databricks_cluster_policy(terrascript.Resource): pass class databricks_dbfs_file(terrascript.Resource): pass class databricks_directory(terrascript.Resource): pass class databricks_global_init_script(terrascript.Resource): pass class databricks_group(terrascript.Resource): pass class databricks_group_instance_profile(terrascript.Resource): pass class databricks_group_member(terrascript.Resource): pass class databricks_instance_pool(terrascript.Resource): pass class databricks_instance_profile(terrascript.Resource): pass class databricks_ip_access_list(terrascript.Resource): pass class databricks_job(terrascript.Resource): pass class databricks_mws_credentials(terrascript.Resource): pass class databricks_mws_customer_managed_keys(terrascript.Resource): pass class databricks_mws_log_delivery(terrascript.Resource): pass class databricks_mws_networks(terrascript.Resource): pass class databricks_mws_private_access_settings(terrascript.Resource): pass class databricks_mws_storage_configurations(terrascript.Resource): pass class databricks_mws_vpc_endpoint(terrascript.Resource): pass class databricks_mws_workspaces(terrascript.Resource): pass class databricks_notebook(terrascript.Resource): pass class databricks_obo_token(terrascript.Resource): pass class databricks_permissions(terrascript.Resource): pass class databricks_pipeline(terrascript.Resource): pass class databricks_secret(terrascript.Resource): pass class databricks_secret_acl(terrascript.Resource): pass class databricks_secret_scope(terrascript.Resource): pass class databricks_service_principal(terrascript.Resource): pass class databricks_sql_dashboard(terrascript.Resource): pass class databricks_sql_endpoint(terrascript.Resource): pass class databricks_sql_permissions(terrascript.Resource): pass class databricks_sql_query(terrascript.Resource): pass class databricks_sql_visualization(terrascript.Resource): pass class databricks_sql_widget(terrascript.Resource): pass class databricks_token(terrascript.Resource): pass class databricks_user(terrascript.Resource): pass class databricks_user_instance_profile(terrascript.Resource): pass class databricks_workspace_conf(terrascript.Resource): pass

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null

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6

3130608ee2b25f9f003e000c9bf2da1b03c611c6

68

py

Python

wand/apps/relations/__init__.py

phvalguima/kafka_charm_lib

0062a277c12f5650f2a18b17eae8529500fafafe

[ "Apache-2.0" ]

null

wand/apps/relations/__init__.py

phvalguima/kafka_charm_lib

0062a277c12f5650f2a18b17eae8529500fafafe

[ "Apache-2.0" ]

null

wand/apps/relations/__init__.py

phvalguima/kafka_charm_lib

0062a277c12f5650f2a18b17eae8529500fafafe

[ "Apache-2.0" ]

null

from .zookeeper import * # noqa from .kafka_connect import * # noqa

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31388a55f4ca3cec5c3cbec0c8964b824d486b7c

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py

Python

src/visualization/climate/amip.py

jejjohnson/2019_rbig_rs

00df5c623d55895e0b43a4130bb6c601fae84890

[ "MIT" ]

2

2020-05-15T17:31:39.000Z

2021-03-16T08:49:33.000Z

src/visualization/climate/amip.py

jejjohnson/rbig_eo

00df5c623d55895e0b43a4130bb6c601fae84890

[ "MIT" ]

null

src/visualization/climate/amip.py

jejjohnson/rbig_eo

00df5c623d55895e0b43a4130bb6c601fae84890

[ "MIT" ]

null

import seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np plt.style.use(["seaborn-talk", "ggplot"]) def plot_individual( df: pd.DataFrame, cmip_model: str, spatial_res: int, info: str = "h" ) -> None: # subset data df = df[df["cmip"] == cmip_model] df = df[df["spatial"] == spatial_res] fig, ax = plt.subplots(figsize=(10, 7)) if info == "h": pts1 = sns.lineplot(x="base_time", y="h_base", data=df, linewidth=5) pts2 = sns.lineplot(x="base_time", y="h_cmip", data=df, linewidth=5) ax.set_xticklabels(np.arange(1980, 2009, 1), fontsize=20) ax.set_ylabel("Entropy, H") elif info == "mi": pts1 = sns.lineplot(x="cmip_time", y="mi", data=df, linewidth=5) ax.set_xticklabels(np.arange(1980, 2009, 1), fontsize=20) ax.set_ylabel("Mutual Information, MI") else: raise ValueError("Unrecognized info measure:", info) plt.xticks(rotation="vertical") ax.set_xlabel("") plt.show() def plot_individual_all(df: pd.DataFrame, spatial_res: int, info: str = "h") -> None: # subset data df = df[df["spatial"] == spatial_res] fig, ax = plt.subplots(figsize=(10, 7)) if info == "h": pts1 = sns.lineplot( x="base_time", y="h_base", data=df, linestyle="--", color="black", linewidth=6, ) pts2 = sns.lineplot(x="base_time", y="h_cmip", data=df, hue="cmip", linewidth=5) ax.set_ylabel("Entropy, H") ax.set_xticklabels(np.arange(1980, 2009, 1), fontsize=20) elif info == "mi": pts2 = sns.lineplot(x="cmip_time", y="mi", data=df, hue="cmip", linewidth=5) ax.set_xticklabels(np.arange(1980, 2009, 1), fontsize=20) ax.set_ylabel("Mutual Information, MI") else: raise ValueError("Unrecognized info measure:", info) plt.xticks(rotation="vertical") ax.set_xlabel("") plt.show() def plot_diff(df: pd.DataFrame, spatial_res: int) -> None: # subset data df = df[df["spatial"] == spatial_res] fig, ax = plt.subplots(figsize=(10, 7)) df["h_abs_diff"] = abs(df["h_base"] - df["h_cmip"]) pts2 = sns.lineplot(x="base_time", y="h_abs_diff", hue="cmip", data=df, linewidth=5) ax.set_xlabel("Time", fontsize=20) ax.set_xticklabels(np.arange(1980, 2009, 1), fontsize=20) ax.set_ylabel("Absolute Difference, H", fontsize=20) plt.xticks(rotation="vertical") plt.show() def plot_individual_diff(df: pd.DataFrame, cmip_model: str, spatial_res: int) -> None: # subset data df = df[df["cmip"] == cmip_model] df = df[df["spatial"] == spatial_res] fig, ax = plt.subplots(figsize=(10, 7)) df["h_abs_diff"] = abs(df["h_base"] - df["h_cmip"]) pts2 = sns.lineplot(x="base_time", y="h_abs_diff", data=df, linewidth=5) plt.xticks(rotation="vertical") ax.set_xticklabels(np.arange(1980, 2009, 1), fontsize=20) plt.show()

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0.117647

0

null

0

1

0

null

0

6

314469c504e36285d4078f67f1447e280e0119cf

114

py

Python

app.py

FacundoLepere/test_heroku_cv2_python_3.9

5ca3ff8c5d4b661c3b55a92be2cbc770203a3c99

[ "MIT" ]

null

app.py

FacundoLepere/test_heroku_cv2_python_3.9

5ca3ff8c5d4b661c3b55a92be2cbc770203a3c99

[ "MIT" ]

null

app.py

FacundoLepere/test_heroku_cv2_python_3.9

5ca3ff8c5d4b661c3b55a92be2cbc770203a3c99

[ "MIT" ]

null

from flask import Flask import cv2 app = Flask(__name__) @app.route("/") def index(): return "Hello World!"

12.666667

25

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0.30137

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0.010753

0.184211

114

8

26

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0

0.114035

0

1

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0.166667

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1

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1

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6

316b053358082f4364ac5e355e4d5ff9687b3c18

22

py

Python

wamv_morse/src/wamv_sim/builder/sensors/__init__.py

mission-systems-pty-ltd/WamV-Morse-Sim

80007b4a02fa10f69d167526eb0add636305555f

[ "BSD-2-Clause" ]

2

2019-09-04T00:58:20.000Z

2020-08-17T05:20:16.000Z

wamv_morse/src/wamv_sim/builder/sensors/__init__.py

mission-systems-pty-ltd/WamV-Morse-Sim

80007b4a02fa10f69d167526eb0add636305555f

[ "BSD-2-Clause" ]

null

wamv_morse/src/wamv_sim/builder/sensors/__init__.py

mission-systems-pty-ltd/WamV-Morse-Sim

80007b4a02fa10f69d167526eb0add636305555f

[ "BSD-2-Clause" ]

1

2021-12-15T09:30:08.000Z

from .DVL import Dvl

7.333333

20

0.727273

4

22

4

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0

0.227273

22

2

21

11

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

317780c9aafe38ee41801a34e89682053d094689

40

py

Python

cluster_tools/meshes/__init__.py

constantinpape/cluster_tools

a7e88545b58f8315723bc47583916e1900a7892d

[ "MIT" ]

28

2018-12-09T22:11:52.000Z

2022-02-01T16:48:23.000Z

cluster_tools/meshes/__init__.py

constantinpape/cluster_tools

a7e88545b58f8315723bc47583916e1900a7892d

[ "MIT" ]

16

2019-01-27T10:59:33.000Z

2022-01-11T09:09:24.000Z

cluster_tools/meshes/__init__.py

constantinpape/cluster_tools

a7e88545b58f8315723bc47583916e1900a7892d

[ "MIT" ]

11

2018-12-09T22:11:56.000Z

2021-08-08T20:10:13.000Z

from .mesh_workflow import MeshWorkflow

20

39

0.875

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40

0.944444

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null

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1

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0

1

0

6

31845682767bcf2d8db4400e73c1f1c0990fa9ee

27

py

Python

axon_conabio/losses/__init__.py

mbsantiago/axon-conabio

0348edda1a49855912057b15b9516562a999ccee

[ "MIT" ]

null

axon_conabio/losses/__init__.py

mbsantiago/axon-conabio

0348edda1a49855912057b15b9516562a999ccee

[ "MIT" ]

null

axon_conabio/losses/__init__.py

mbsantiago/axon-conabio

0348edda1a49855912057b15b9516562a999ccee

[ "MIT" ]

null

from .baseloss import Loss

13.5

26

0.814815

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27

5.5

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1

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0.956522

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null

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1

0

1

0

6

31b3c6533e83926f1092e1f7937a46beb7abfa2e

125

py

Python

backend_rest/sales/admin.py

ezrankayamba/twiga_expodocs

39303f137f3761e7024e1e0e1a6449f4187e30e9

[ "MIT" ]

null

backend_rest/sales/admin.py

ezrankayamba/twiga_expodocs

39303f137f3761e7024e1e0e1a6449f4187e30e9

[ "MIT" ]

13

2020-02-21T13:58:18.000Z

2022-03-12T00:16:26.000Z

backend_rest/sales/admin.py

ezrankayamba/twiga_expodocs

39303f137f3761e7024e1e0e1a6449f4187e30e9

[ "MIT" ]

null

from django.contrib import admin from . import models admin.site.register(models.Sale) admin.site.register(models.SaleDoc)

17.857143

35

0.808

18

125

5.611111

0.555556

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0.336634

0.455446

0

0.096

125

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null

0

1

0

1

0

6

31c476dce6ecc6fc8eadb87d44d54635f2c42fae

5,987

py

Python

networks/resnet50.py

Times125/break_captcha

9ab16d1d7737630786039d07caec308bdb8a26d9

[ "MIT" ]

13

2019-08-14T09:36:03.000Z

2021-04-05T06:35:05.000Z

networks/resnet50.py

Times125/break_captcha

9ab16d1d7737630786039d07caec308bdb8a26d9

[ "MIT" ]

6

2019-09-26T09:45:12.000Z

2022-03-11T23:56:08.000Z

networks/resnet50.py

Times125/break_captcha

9ab16d1d7737630786039d07caec308bdb8a26d9

[ "MIT" ]

6

2019-08-16T14:46:04.000Z

2021-06-05T01:53:20.000Z

#! /usr/bin/env python # -*- coding: utf-8 -*- """ @Author: _defined @Time: 2019/8/21 14:20 @Description: This script defines some ResNet50 structures """ from tensorflow.python.keras import (backend, layers) def _identity_block(input_tensor, kernel_size, filters, stage, block): """The identity block is the block that has no conv layer at shortcut. # Arguments input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main file_list filters: list of integers, the filters of 3 conv layer at main file_list stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names # Returns Output tensor for the block. """ filters1, filters2, filters3 = filters if backend.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = layers.Conv2D(filters1, (1, 1), kernel_initializer='he_normal', name=conv_name_base + '2a')(input_tensor) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters2, kernel_size, padding='same', kernel_initializer='he_normal', name=conv_name_base + '2b')(x) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters3, (1, 1), kernel_initializer='he_normal', name=conv_name_base + '2c')(x) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) x = layers.add([x, input_tensor]) x = layers.Activation('relu')(x) return x def _conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)): """A block that has a conv layer at shortcut. # Arguments input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main file_list filters: list of integers, the filters of 3 conv layer at main file_list stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names strides: Strides for the first conv layer in the block. # Returns Output tensor for the block. Note that from stage 3, the first conv layer at main file_list is with strides=(2, 2) And the shortcut should have strides=(2, 2) as well """ filters1, filters2, filters3 = filters if backend.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = layers.Conv2D(filters1, (1, 1), strides=strides, kernel_initializer='he_normal', name=conv_name_base + '2a')(input_tensor) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters2, kernel_size, padding='same', kernel_initializer='he_normal', name=conv_name_base + '2b')(x) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x) x = layers.Activation('relu')(x) x = layers.Conv2D(filters3, (1, 1), kernel_initializer='he_normal', name=conv_name_base + '2c')(x) x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x) shortcut = layers.Conv2D(filters3, (1, 1), strides=strides, kernel_initializer='he_normal', name=conv_name_base + '1')(input_tensor) shortcut = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut) x = layers.add([x, shortcut]) x = layers.Activation('relu')(x) return x def ResNet50(input_tensor): """ ResNet50 :param input_tensor: :return: """ img_input = input_tensor if backend.image_data_format() == 'channels_last': bn_axis = 3 else: bn_axis = 1 x = layers.ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input) x = layers.Conv2D(64, (7, 7), strides=(2, 2), kernel_initializer='he_normal', name='conv1')(x) x = layers.BatchNormalization(axis=bn_axis, name='bn_conv1')(x) x = layers.Activation('relu')(x) x = layers.ZeroPadding2D(padding=(1, 1), name='pool1_pad')(x) x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x) x = _conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1)) x = _identity_block(x, 3, [64, 64, 256], stage=2, block='b') x = _identity_block(x, 3, [64, 64, 256], stage=2, block='c') x = _conv_block(x, 3, [128, 128, 512], stage=3, block='a') x = _identity_block(x, 3, [128, 128, 512], stage=3, block='b') x = _identity_block(x, 3, [128, 128, 512], stage=3, block='c') x = _identity_block(x, 3, [128, 128, 512], stage=3, block='d') x = _conv_block(x, 3, [256, 256, 1024], stage=4, block='a') x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='b') x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='c') x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='d') x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='e') x = _identity_block(x, 3, [256, 256, 1024], stage=4, block='f') x = _conv_block(x, 3, [512, 512, 2048], stage=5, block='a') x = _identity_block(x, 3, [512, 512, 2048], stage=5, block='b') x = _identity_block(x, 3, [512, 512, 2048], stage=5, block='c') return x

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1

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null

0

6

31c7f0fc1dff349aaa8fbf1d99639943f1e769b6

35

py

Python

sketch/sketch/__init__.py

mmathys/bagua

e17978690452318b65b317b283259f09c24d59bb

[ "MIT" ]

null

sketch/sketch/__init__.py

mmathys/bagua

e17978690452318b65b317b283259f09c24d59bb

[ "MIT" ]

null

sketch/sketch/__init__.py

mmathys/bagua

e17978690452318b65b317b283259f09c24d59bb

[ "MIT" ]

null

from .sketch import SketchAlgorithm

35

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4

35

7.75

1

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0.085714

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1

35

0.96875

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

9ec4fd463c55391a02817e7ec7d743c6653ed8e0

118

py

Python

dyntrack/plot/__init__.py

LouisFaure/dyntrack

1af9d9a1851900a8ae62f54e44b20e04df618f29

[ "BSD-3-Clause" ]

null

dyntrack/plot/__init__.py

LouisFaure/dyntrack

1af9d9a1851900a8ae62f54e44b20e04df618f29

[ "BSD-3-Clause" ]

null

dyntrack/plot/__init__.py

LouisFaure/dyntrack

1af9d9a1851900a8ae62f54e44b20e04df618f29

[ "BSD-3-Clause" ]

null

from .tracks import tracks from .vector_field import vector_field from .ftle import FTLE from .fit_ppt import fit_ppt

23.6

38

0.830508

20

118

4.7

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0.234043

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0.135593

118

4

39

29.5

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true

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null

0

1

0

1

0

1

0

6

9edae35377e2fa4c5edd6f49e00d8791402b29df

1,881

py

Python

ckanext/canada/tests/test_ati.py

HoussamBedja/ckanext-canada

9099223beb088c65262cab403be10774e29e06b8

[ "MIT" ]

31

2015-04-19T16:14:55.000Z

2021-08-20T13:18:44.000Z

ckanext/canada/tests/test_ati.py

HoussamBedja/ckanext-canada

9099223beb088c65262cab403be10774e29e06b8

[ "MIT" ]

214

2015-01-20T20:43:26.000Z

2022-03-29T20:36:01.000Z

ckanext/canada/tests/test_ati.py

HoussamBedja/ckanext-canada

9099223beb088c65262cab403be10774e29e06b8

[ "MIT" ]

46

2015-02-18T17:11:06.000Z

2022-01-17T17:05:09.000Z

# -*- coding: UTF-8 -*- from nose.tools import assert_equal, assert_raises from ckanapi import LocalCKAN, ValidationError from ckan.tests.helpers import FunctionalTestBase from ckan.tests.factories import Organization from ckanext.recombinant.tables import get_chromo class TestAti(FunctionalTestBase): def setup(self): super(TestAti, self).setup() org = Organization() lc = LocalCKAN() lc.action.recombinant_create(dataset_type='ati', owner_org=org['name']) rval = lc.action.recombinant_show(dataset_type='ati', owner_org=org['name']) self.resource_id = rval['resources'][0]['id'] def test_example(self): lc = LocalCKAN() record = get_chromo('ati')['examples']['record'] lc.action.datastore_upsert( resource_id=self.resource_id, records=[record]) def test_blank(self): lc = LocalCKAN() assert_raises(ValidationError, lc.action.datastore_upsert, resource_id=self.resource_id, records=[{}]) class TestAtiNil(FunctionalTestBase): def setup(self): super(TestAtiNil, self).setup() org = Organization() lc = LocalCKAN() lc.action.recombinant_create(dataset_type='ati', owner_org=org['name']) rval = lc.action.recombinant_show(dataset_type='ati', owner_org=org['name']) self.resource_id = rval['resources'][1]['id'] def test_example(self): lc = LocalCKAN() record = get_chromo('ati-nil')['examples']['record'] lc.action.datastore_upsert( resource_id=self.resource_id, records=[record]) def test_blank(self): lc = LocalCKAN() assert_raises(ValidationError, lc.action.datastore_upsert, resource_id=self.resource_id, records=[{}])

33.589286

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1,881

5.504808

0.269231

0.087336

0.073362

0.066376

0.765066

0.70393

0

0.002123

0.248804

1,881

55

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1

0.133333

false

0

0.111111

0

0.288889

0

null

0

1

0

1

0

null

0

6

9eeb47506cef88ee1225580c675e375cc7ce6674

2,039

py

Python

contacts/swagger_params.py

sauravpanda/Django-CRM

c6b8cde02c9cf3d3f30f4e05b825f77d00734e87

[ "MIT" ]

null

contacts/swagger_params.py

sauravpanda/Django-CRM

c6b8cde02c9cf3d3f30f4e05b825f77d00734e87

[ "MIT" ]

null

contacts/swagger_params.py

sauravpanda/Django-CRM

c6b8cde02c9cf3d3f30f4e05b825f77d00734e87

[ "MIT" ]

null

from drf_yasg import openapi company_params_in_header = openapi.Parameter( "company", openapi.IN_HEADER, required=True, type=openapi.TYPE_STRING ) contact_list_get_params = [ company_params_in_header, openapi.Parameter( "name", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "city", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "assigned_to", openapi.IN_QUERY, type=openapi.TYPE_STRING ), ] contact_detail_get_params = [company_params_in_header] contact_delete_get_params = [company_params_in_header] contact_create_post_params = [ company_params_in_header, openapi.Parameter( "first_name", openapi.IN_QUERY, required=True, type=openapi.TYPE_STRING ), openapi.Parameter( "last_name", openapi.IN_QUERY, required=True, type=openapi.TYPE_STRING ), openapi.Parameter( "phone", openapi.IN_QUERY, required=True, type=openapi.TYPE_STRING ), openapi.Parameter( "email", openapi.IN_QUERY, required=True, type=openapi.TYPE_STRING ), openapi.Parameter( "teams", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "assigned_to", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "address_line", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "street", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "city", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "state", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "postcode", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "country", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "description", openapi.IN_QUERY, type=openapi.TYPE_STRING ), openapi.Parameter( "contact_attachment", openapi.IN_QUERY, type=openapi.TYPE_FILE ), ]

29.128571

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0.20316

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0.872837

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0.785553

0.665162

0.461249

0

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2,039

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false

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0

0.015873

0

null

1

0

null

0

6

7318ce959245f1bda9db8492c73d803c32479af5

32

py

Python

nwb_conversion_tools/behavior/__init__.py

wuffi/nwb-conversion-tools

39cfb95b714155b26a17fdda9ed7d801eefd14ea

[ "BSD-3-Clause" ]

null

nwb_conversion_tools/behavior/__init__.py

wuffi/nwb-conversion-tools

39cfb95b714155b26a17fdda9ed7d801eefd14ea

[ "BSD-3-Clause" ]

null

nwb_conversion_tools/behavior/__init__.py

wuffi/nwb-conversion-tools

39cfb95b714155b26a17fdda9ed7d801eefd14ea

[ "BSD-3-Clause" ]

null

from .bpod.bpod import Bpod2NWB

16

31

0.8125

5

32

5.2

0.8

0

0.035714

0.125

32

1

32

0.892857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

731952eb4c9e4b7b003bb7dbe20a19de2236b5a8

10,515

py

Python

huaweicloud-sdk-swr/huaweicloudsdkswr/v2/__init__.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

64

2020-06-12T07:05:07.000Z

2022-03-30T03:32:50.000Z

huaweicloud-sdk-swr/huaweicloudsdkswr/v2/__init__.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

11

2020-07-06T07:56:54.000Z

2022-01-11T11:14:40.000Z

huaweicloud-sdk-swr/huaweicloudsdkswr/v2/__init__.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

24

2020-06-08T11:42:13.000Z

2022-03-04T06:44:08.000Z

# coding: utf-8 from __future__ import absolute_import # import SwrClient from huaweicloudsdkswr.v2.swr_client import SwrClient from huaweicloudsdkswr.v2.swr_async_client import SwrAsyncClient # import models into sdk package from huaweicloudsdkswr.v2.model.auth_info import AuthInfo from huaweicloudsdkswr.v2.model.create_image_sync_repo_request import CreateImageSyncRepoRequest from huaweicloudsdkswr.v2.model.create_image_sync_repo_request_body import CreateImageSyncRepoRequestBody from huaweicloudsdkswr.v2.model.create_image_sync_repo_response import CreateImageSyncRepoResponse from huaweicloudsdkswr.v2.model.create_manual_image_sync_repo_request import CreateManualImageSyncRepoRequest from huaweicloudsdkswr.v2.model.create_manual_image_sync_repo_request_body import CreateManualImageSyncRepoRequestBody from huaweicloudsdkswr.v2.model.create_manual_image_sync_repo_response import CreateManualImageSyncRepoResponse from huaweicloudsdkswr.v2.model.create_namespace_auth_request import CreateNamespaceAuthRequest from huaweicloudsdkswr.v2.model.create_namespace_auth_response import CreateNamespaceAuthResponse from huaweicloudsdkswr.v2.model.create_namespace_request import CreateNamespaceRequest from huaweicloudsdkswr.v2.model.create_namespace_request_body import CreateNamespaceRequestBody from huaweicloudsdkswr.v2.model.create_namespace_response import CreateNamespaceResponse from huaweicloudsdkswr.v2.model.create_repo_domains_request import CreateRepoDomainsRequest from huaweicloudsdkswr.v2.model.create_repo_domains_request_body import CreateRepoDomainsRequestBody from huaweicloudsdkswr.v2.model.create_repo_domains_response import CreateRepoDomainsResponse from huaweicloudsdkswr.v2.model.create_repo_request import CreateRepoRequest from huaweicloudsdkswr.v2.model.create_repo_request_body import CreateRepoRequestBody from huaweicloudsdkswr.v2.model.create_repo_response import CreateRepoResponse from huaweicloudsdkswr.v2.model.create_retention_request import CreateRetentionRequest from huaweicloudsdkswr.v2.model.create_retention_request_body import CreateRetentionRequestBody from huaweicloudsdkswr.v2.model.create_retention_response import CreateRetentionResponse from huaweicloudsdkswr.v2.model.create_secret_request import CreateSecretRequest from huaweicloudsdkswr.v2.model.create_secret_response import CreateSecretResponse from huaweicloudsdkswr.v2.model.create_trigger_request import CreateTriggerRequest from huaweicloudsdkswr.v2.model.create_trigger_request_body import CreateTriggerRequestBody from huaweicloudsdkswr.v2.model.create_trigger_response import CreateTriggerResponse from huaweicloudsdkswr.v2.model.create_user_repository_auth_request import CreateUserRepositoryAuthRequest from huaweicloudsdkswr.v2.model.create_user_repository_auth_response import CreateUserRepositoryAuthResponse from huaweicloudsdkswr.v2.model.delete_image_sync_repo_request import DeleteImageSyncRepoRequest from huaweicloudsdkswr.v2.model.delete_image_sync_repo_request_body import DeleteImageSyncRepoRequestBody from huaweicloudsdkswr.v2.model.delete_image_sync_repo_response import DeleteImageSyncRepoResponse from huaweicloudsdkswr.v2.model.delete_namespace_auth_request import DeleteNamespaceAuthRequest from huaweicloudsdkswr.v2.model.delete_namespace_auth_response import DeleteNamespaceAuthResponse from huaweicloudsdkswr.v2.model.delete_namespaces_request import DeleteNamespacesRequest from huaweicloudsdkswr.v2.model.delete_namespaces_response import DeleteNamespacesResponse from huaweicloudsdkswr.v2.model.delete_repo_domains_request import DeleteRepoDomainsRequest from huaweicloudsdkswr.v2.model.delete_repo_domains_response import DeleteRepoDomainsResponse from huaweicloudsdkswr.v2.model.delete_repo_request import DeleteRepoRequest from huaweicloudsdkswr.v2.model.delete_repo_response import DeleteRepoResponse from huaweicloudsdkswr.v2.model.delete_repo_tag_request import DeleteRepoTagRequest from huaweicloudsdkswr.v2.model.delete_repo_tag_response import DeleteRepoTagResponse from huaweicloudsdkswr.v2.model.delete_retention_request import DeleteRetentionRequest from huaweicloudsdkswr.v2.model.delete_retention_response import DeleteRetentionResponse from huaweicloudsdkswr.v2.model.delete_trigger_request import DeleteTriggerRequest from huaweicloudsdkswr.v2.model.delete_trigger_response import DeleteTriggerResponse from huaweicloudsdkswr.v2.model.delete_user_repository_auth_request import DeleteUserRepositoryAuthRequest from huaweicloudsdkswr.v2.model.delete_user_repository_auth_response import DeleteUserRepositoryAuthResponse from huaweicloudsdkswr.v2.model.link import Link from huaweicloudsdkswr.v2.model.list_api_versions_request import ListApiVersionsRequest from huaweicloudsdkswr.v2.model.list_api_versions_response import ListApiVersionsResponse from huaweicloudsdkswr.v2.model.list_image_auto_sync_repos_details_request import ListImageAutoSyncReposDetailsRequest from huaweicloudsdkswr.v2.model.list_image_auto_sync_repos_details_response import ListImageAutoSyncReposDetailsResponse from huaweicloudsdkswr.v2.model.list_namespaces_request import ListNamespacesRequest from huaweicloudsdkswr.v2.model.list_namespaces_response import ListNamespacesResponse from huaweicloudsdkswr.v2.model.list_repo_domains_request import ListRepoDomainsRequest from huaweicloudsdkswr.v2.model.list_repo_domains_response import ListRepoDomainsResponse from huaweicloudsdkswr.v2.model.list_repos_details_request import ListReposDetailsRequest from huaweicloudsdkswr.v2.model.list_repos_details_response import ListReposDetailsResponse from huaweicloudsdkswr.v2.model.list_repository_tags_request import ListRepositoryTagsRequest from huaweicloudsdkswr.v2.model.list_repository_tags_response import ListRepositoryTagsResponse from huaweicloudsdkswr.v2.model.list_retention_histories_request import ListRetentionHistoriesRequest from huaweicloudsdkswr.v2.model.list_retention_histories_response import ListRetentionHistoriesResponse from huaweicloudsdkswr.v2.model.list_retentions_request import ListRetentionsRequest from huaweicloudsdkswr.v2.model.list_retentions_response import ListRetentionsResponse from huaweicloudsdkswr.v2.model.list_shared_repos_details_request import ListSharedReposDetailsRequest from huaweicloudsdkswr.v2.model.list_shared_repos_details_response import ListSharedReposDetailsResponse from huaweicloudsdkswr.v2.model.list_triggers_details_request import ListTriggersDetailsRequest from huaweicloudsdkswr.v2.model.list_triggers_details_response import ListTriggersDetailsResponse from huaweicloudsdkswr.v2.model.retention import Retention from huaweicloudsdkswr.v2.model.retention_log import RetentionLog from huaweicloudsdkswr.v2.model.rule import Rule from huaweicloudsdkswr.v2.model.show_access_domain_request import ShowAccessDomainRequest from huaweicloudsdkswr.v2.model.show_access_domain_response import ShowAccessDomainResponse from huaweicloudsdkswr.v2.model.show_api_version_request import ShowApiVersionRequest from huaweicloudsdkswr.v2.model.show_api_version_response import ShowApiVersionResponse from huaweicloudsdkswr.v2.model.show_namespace import ShowNamespace from huaweicloudsdkswr.v2.model.show_namespace_auth_request import ShowNamespaceAuthRequest from huaweicloudsdkswr.v2.model.show_namespace_auth_response import ShowNamespaceAuthResponse from huaweicloudsdkswr.v2.model.show_namespace_request import ShowNamespaceRequest from huaweicloudsdkswr.v2.model.show_namespace_response import ShowNamespaceResponse from huaweicloudsdkswr.v2.model.show_repo_domains_response import ShowRepoDomainsResponse from huaweicloudsdkswr.v2.model.show_repos_resp import ShowReposResp from huaweicloudsdkswr.v2.model.show_repos_tag_resp import ShowReposTagResp from huaweicloudsdkswr.v2.model.show_repository_request import ShowRepositoryRequest from huaweicloudsdkswr.v2.model.show_repository_response import ShowRepositoryResponse from huaweicloudsdkswr.v2.model.show_retention_request import ShowRetentionRequest from huaweicloudsdkswr.v2.model.show_retention_response import ShowRetentionResponse from huaweicloudsdkswr.v2.model.show_sync_job_request import ShowSyncJobRequest from huaweicloudsdkswr.v2.model.show_sync_job_response import ShowSyncJobResponse from huaweicloudsdkswr.v2.model.show_trigger_request import ShowTriggerRequest from huaweicloudsdkswr.v2.model.show_trigger_response import ShowTriggerResponse from huaweicloudsdkswr.v2.model.show_user_repository_auth_request import ShowUserRepositoryAuthRequest from huaweicloudsdkswr.v2.model.show_user_repository_auth_response import ShowUserRepositoryAuthResponse from huaweicloudsdkswr.v2.model.sync_job import SyncJob from huaweicloudsdkswr.v2.model.sync_repo import SyncRepo from huaweicloudsdkswr.v2.model.tag_selector import TagSelector from huaweicloudsdkswr.v2.model.trigger import Trigger from huaweicloudsdkswr.v2.model.trigger_histories import TriggerHistories from huaweicloudsdkswr.v2.model.update_namespace_auth_request import UpdateNamespaceAuthRequest from huaweicloudsdkswr.v2.model.update_namespace_auth_response import UpdateNamespaceAuthResponse from huaweicloudsdkswr.v2.model.update_repo_domains_request import UpdateRepoDomainsRequest from huaweicloudsdkswr.v2.model.update_repo_domains_request_body import UpdateRepoDomainsRequestBody from huaweicloudsdkswr.v2.model.update_repo_domains_response import UpdateRepoDomainsResponse from huaweicloudsdkswr.v2.model.update_repo_request import UpdateRepoRequest from huaweicloudsdkswr.v2.model.update_repo_request_body import UpdateRepoRequestBody from huaweicloudsdkswr.v2.model.update_repo_response import UpdateRepoResponse from huaweicloudsdkswr.v2.model.update_retention_request import UpdateRetentionRequest from huaweicloudsdkswr.v2.model.update_retention_request_body import UpdateRetentionRequestBody from huaweicloudsdkswr.v2.model.update_retention_response import UpdateRetentionResponse from huaweicloudsdkswr.v2.model.update_trigger_request import UpdateTriggerRequest from huaweicloudsdkswr.v2.model.update_trigger_request_body import UpdateTriggerRequestBody from huaweicloudsdkswr.v2.model.update_trigger_response import UpdateTriggerResponse from huaweicloudsdkswr.v2.model.update_user_repository_auth_request import UpdateUserRepositoryAuthRequest from huaweicloudsdkswr.v2.model.update_user_repository_auth_response import UpdateUserRepositoryAuthResponse from huaweicloudsdkswr.v2.model.user_auth import UserAuth from huaweicloudsdkswr.v2.model.version_detail import VersionDetail

83.452381

120

0.919353

1,156

10,515

8.08391

0.150519

0.265169

0.290423

0.347566

0.563617

0.517817

0.365864

0.140931

0.053826

0.02504

0

0.011872

0.046695

10,515

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1

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6

73245a03b60bd8e4cd7d07da6f291701e6b06381

286

py

Python

roomexplorer/__init__.py

alaflaquiere/room-explorer

0fb1e4f9a65f18a4f7a20d3c22bf5f584ab7ed90

[ "MIT" ]

null

roomexplorer/__init__.py

alaflaquiere/room-explorer

0fb1e4f9a65f18a4f7a20d3c22bf5f584ab7ed90

[ "MIT" ]

null

roomexplorer/__init__.py

alaflaquiere/room-explorer

0fb1e4f9a65f18a4f7a20d3c22bf5f584ab7ed90

[ "MIT" ]

null

from roomexplorer.renderer.bullet import bullet_tools from roomexplorer.renderer.bullet.camera import Camera, CameraResolution from roomexplorer.Agent import MobileArm from roomexplorer.RoomEnvironment import create_environment from roomexplorer.dataset_builder import generate_dataset

47.666667

72

0.895105

33

286

7.636364

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0.31746

0.190476

0.238095

0

0.073427

286

5

73

57.2

0.950943

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1

0

null

0

1

0

1

0

6

7326f746ad5cab74373a3c7f78d1aea02408c8e9

2,189

py

Python

MNIST-master/tf_test_congress.py

FisherEat/python_mashine_projects

9cf2e644e7f0814fe048488278c994e5fd40d629

[ "MIT" ]

null

MNIST-master/tf_test_congress.py

FisherEat/python_mashine_projects

9cf2e644e7f0814fe048488278c994e5fd40d629

[ "MIT" ]

null

MNIST-master/tf_test_congress.py

FisherEat/python_mashine_projects

9cf2e644e7f0814fe048488278c994e5fd40d629

[ "MIT" ]

1

2019-09-18T02:30:11.000Z

''' 本案例是tensorflow 回归模型,采用tensorflow自带的梯度下降法生成回归曲线 ''' # import tensorflow as tf # import numpy as np # import matplotlib.pyplot as plt # # # Prepare train data # train_X = np.linspace(-1, 1, 100) # train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.33 + 10 # # # Define the model # X = tf.placeholder("float") # Y = tf.placeholder("float") # w = tf.Variable(0.0, name="weight") # b = tf.Variable(0.0, name="bias") # loss = tf.square(Y - X*w - b) # train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss) # # # Create session to run # with tf.Session() as sess: # sess.run(tf.initialize_all_variables()) # epoch = 1 # for i in range(10): # for(x, y) in zip(train_X, train_Y): # _, w_value, b_value = sess.run([train_op, w, b], feed_dict={X: x, Y: y}) # print("Epoch: {}, w: {}, b: {}".format(epoch, w_value, b_value)) # epoch += 1 # # # # draw # plt.plot(train_X, train_Y, "+") # plt.plot(train_X, train_Y.dot(w_value) + b_value) # plt.show() ''' 本案例是tensorflow 回归模型,采用tensorflow自带的梯度下降法生成回归曲线 ''' import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # Prepare train data train_X = np.linspace(-1, 1, 100) # np.array([0.23, 0.34, 0.67, 0.89, 0.90, 0.97]) # np.linspace(-1, 1, 100) train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.33 + 10 # np.array([0.25, 0.33, 0.65, 0.80, 0.89, 0.99]) # 2 * train_X + np.random.randn(*train_X.shape) * 0.33 + 10 # Define the model X = tf.placeholder("float") Y = tf.placeholder("float") w = tf.Variable(0.0, name="weight") b = tf.Variable(0.0, name="bias") loss = tf.square(Y - X*w - b) train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss) # Create session to run with tf.Session() as sess: sess.run(tf.initialize_all_variables()) epoch = 1 for i in range(100): for (x, y) in zip(train_X, train_Y): _, w_value, b_value = sess.run([train_op, w, b], feed_dict={X: x, Y: y}) print("Epoch: op: {}, {}, w: {}, b: {}".format(epoch, _, w_value, b_value)) epoch += 1 # draw plt.plot(train_X, train_Y, "+") plt.plot(train_X, train_X.dot(w_value)+b_value) plt.show()

27.3625

86

0.620831

366

2,189

3.584699

0.215847

0.068598

0.050305

0.054878

0.955793

0.95503

0.921494

0

0.052273

0.19598

2,189

80

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0.540429

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0.057906

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0.045455

0

null

0

1

0

null

0

6

733ba9569b401c728fac0d23c1f928d491d9e98d

34

py

Python

hello_world.py

eddycalgary/profiles-rest-api2

d7416a36a274853550aea3a950893d86e3020781

[ "MIT" ]

null

hello_world.py

eddycalgary/profiles-rest-api2

d7416a36a274853550aea3a950893d86e3020781

[ "MIT" ]

7

2019-12-05T00:38:30.000Z

2022-02-10T10:33:27.000Z

hello_world.py

eddycalgary/profiles-rest-api2

d7416a36a274853550aea3a950893d86e3020781

[ "MIT" ]

null

print("Hello Edgar how are you??")

34

0.705882

6

34

4

1

0

0.117647

34

1

34

0.8

0

0.714286

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

736168ff4711b8b7b91358bc866093d5d0d8069b

79

py

Python

modules/2.79/bpy/types/LocRotScale.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/LocRotScale.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/LocRotScale.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

class LocRotScale: def generate(self, context, ks, data): pass

9.875

42

0.607595

9

79

5.333333

1

0

0.303797

79

7

43

11.285714

0.872727

0

1

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1

0.333333

false

0.333333

0

0.666667

0

1

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null

0

1

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1

0

null

0

1

0

1

0

1

0

6

7363b1bbf50890b8c03707f0764f65035ae45dfe

16,755

py

Python

src/python/turicreate/visualization/show.py

crouchingtigre/turicreate

38c18e019be5b0c202790bedd6113d13d5d59796

[ "BSD-3-Clause" ]

1

2019-06-21T21:40:10.000Z

src/python/turicreate/visualization/show.py

crouchingtigre/turicreate

38c18e019be5b0c202790bedd6113d13d5d59796

[ "BSD-3-Clause" ]

null

src/python/turicreate/visualization/show.py

crouchingtigre/turicreate

38c18e019be5b0c202790bedd6113d13d5d59796

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Copyright © 2017 Apple Inc. All rights reserved. # # Use of this source code is governed by a BSD-3-clause license that can # be found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause from __future__ import print_function as _ from __future__ import division as _ from __future__ import absolute_import as _ from ._plot import Plot, LABEL_DEFAULT import turicreate as tc def _get_title(title): if title == "": title = " " if title is None: title = "" return title def plot(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT): """ Plots the data in `x` on the X axis and the data in `y` on the Y axis in a 2d visualization, and shows the resulting visualization. Uses the following heuristic to choose the visualization: * If `x` and `y` are both numeric (SArray of int or float), and they contain fewer than or equal to 5,000 values, show a scatter plot. * If `x` and `y` are both numeric (SArray of int or float), and they contain more than 5,000 values, show a heat map. * If `x` is numeric and `y` is an SArray of string, show a box and whisker plot for the distribution of numeric values for each categorical (string) value. * If `x` and `y` are both SArrays of string, show a categorical heat map. This show method supports SArrays of dtypes: int, float, str. Notes ----- - The plot will be returned as a Plot object, which can then be shown, saved, etc. and will display automatically in a Jupyter Notebook. Parameters ---------- x : SArray The data to plot on the X axis of a 2d visualization. y : SArray The data to plot on the Y axis of a 2d visualization. Must be the same length as `x`. xlabel : str (optional) The text label for the X axis. Defaults to "X". ylabel : str (optional) The text label for the Y axis. Defaults to "Y". title : str (optional) The title of the plot. Defaults to LABEL_DEFAULT. If the value is LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value is None, the title will be omitted. Otherwise, the string passed in as the title will be used as the plot title. Examples -------- Show a categorical heat map of pets and their feelings. >>> x = turicreate.SArray(['dog', 'cat', 'dog', 'dog', 'cat']) >>> y = turicreate.SArray(['happy', 'grumpy', 'grumpy', 'happy', 'grumpy']) >>> turicreate.show(x, y) Show a scatter plot of the function y = 2x, for x from 0 through 9, labeling the axes and plot title with custom strings. >>> x = turicreate.SArray(range(10)) >>> y = x * 2 >>> turicreate.show(x, y, ... xlabel="Custom X label", ... ylabel="Custom Y label", ... title="Custom title") """ title = _get_title(title) plt_ref = tc.extensions.plot(x, y, xlabel, ylabel, title) return Plot(plt_ref) def show(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT): """ Plots the data in `x` on the X axis and the data in `y` on the Y axis in a 2d visualization, and shows the resulting visualization. Uses the following heuristic to choose the visualization: * If `x` and `y` are both numeric (SArray of int or float), and they contain fewer than or equal to 5,000 values, show a scatter plot. * If `x` and `y` are both numeric (SArray of int or float), and they contain more than 5,000 values, show a heat map. * If `x` is numeric and `y` is an SArray of string, show a box and whisker plot for the distribution of numeric values for each categorical (string) value. * If `x` and `y` are both SArrays of string, show a categorical heat map. This show method supports SArrays of dtypes: int, float, str. Notes ----- - The plot will render either inline in a Jupyter Notebook, or in a native GUI window, depending on the value provided in `turicreate.visualization.set_target` (defaults to 'auto'). Parameters ---------- x : SArray The data to plot on the X axis of a 2d visualization. y : SArray The data to plot on the Y axis of a 2d visualization. Must be the same length as `x`. xlabel : str (optional) The text label for the X axis. Defaults to "X". ylabel : str (optional) The text label for the Y axis. Defaults to "Y". title : str (optional) The title of the plot. Defaults to LABEL_DEFAULT. If the value is LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value is None, the title will be omitted. Otherwise, the string passed in as the title will be used as the plot title. Examples -------- Show a categorical heat map of pets and their feelings. >>> x = turicreate.SArray(['dog', 'cat', 'dog', 'dog', 'cat']) >>> y = turicreate.SArray(['happy', 'grumpy', 'grumpy', 'happy', 'grumpy']) >>> turicreate.show(x, y) Show a scatter plot of the function y = 2x, for x from 0 through 9, labeling the axes and plot title with custom strings. >>> x = turicreate.SArray(range(10)) >>> y = x * 2 >>> turicreate.show(x, y, ... xlabel="Custom X label", ... ylabel="Custom Y label", ... title="Custom title") """ plot(x, y, xlabel, ylabel, title).show() def scatter(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT): """ Plots the data in `x` on the X axis and the data in `y` on the Y axis in a 2d scatter plot, and returns the resulting Plot object. The function supports SArrays of dtypes: int, float. Parameters ---------- x : SArray The data to plot on the X axis of the scatter plot. Must be numeric (int/float). y : SArray The data to plot on the Y axis of the scatter plot. Must be the same length as `x`. Must be numeric (int/float). xlabel : str (optional) The text label for the X axis. Defaults to "X". ylabel : str (optional) The text label for the Y axis. Defaults to "Y". title : str (optional) The title of the plot. Defaults to LABEL_DEFAULT. If the value is LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value is None, the title will be omitted. Otherwise, the string passed in as the title will be used as the plot title. Returns ------- out : Plot A :class: Plot object that is the scatter plot. Examples -------- Make a scatter plot. >>> x = turicreate.SArray([1,2,3,4,5]) >>> y = x * 2 >>> scplt = turicreate.visualization.scatter(x, y) """ if (not isinstance(x, tc.data_structures.sarray.SArray) or not isinstance(y, tc.data_structures.sarray.SArray) or x.dtype not in [int, float] or y.dtype not in [int, float]): raise ValueError("turicreate.visualization.scatter supports " + "SArrays of dtypes: int, float") # legit input title = _get_title(title) plt_ref = tc.extensions.plot_scatter(x, y, xlabel, ylabel,title) return Plot(plt_ref) def categorical_heatmap(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT): """ Plots the data in `x` on the X axis and the data in `y` on the Y axis in a 2d categorical heatmap, and returns the resulting Plot object. The function supports SArrays of dtypes str. Parameters ---------- x : SArray The data to plot on the X axis of the categorical heatmap. Must be string SArray y : SArray The data to plot on the Y axis of the categorical heatmap. Must be string SArray and must be the same length as `x`. xlabel : str (optional) The text label for the X axis. Defaults to "X". ylabel : str (optional) The text label for the Y axis. Defaults to "Y". title : str (optional) The title of the plot. Defaults to LABEL_DEFAULT. If the value is LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value is None, the title will be omitted. Otherwise, the string passed in as the title will be used as the plot title. Returns ------- out : Plot A :class: Plot object that is the categorical heatmap. Examples -------- Make a categorical heatmap. >>> x = turicreate.SArray(['1','2','3','4','5']) >>> y = turicreate.SArray(['a','b','c','d','e']) >>> catheat = turicreate.visualization.categorical_heatmap(x, y) """ if (not isinstance(x, tc.data_structures.sarray.SArray) or not isinstance(y, tc.data_structures.sarray.SArray) or x.dtype != str or y.dtype != str): raise ValueError("turicreate.visualization.categorical_heatmap supports " + "SArrays of dtype: str") # legit input title = _get_title(title) plt_ref = tc.extensions.plot_categorical_heatmap(x, y, xlabel, ylabel, title) return Plot(plt_ref) def heatmap(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT): """ Plots the data in `x` on the X axis and the data in `y` on the Y axis in a 2d heatmap, and returns the resulting Plot object. The function supports SArrays of dtypes int, float. Parameters ---------- x : SArray The data to plot on the X axis of the heatmap. Must be numeric (int/float). y : SArray The data to plot on the Y axis of the heatmap. Must be numeric (int/float) and must be the same length as `x`. xlabel : str (optional) The text label for the X axis. Defaults to "X". ylabel : str (optional) The text label for the Y axis. Defaults to "Y". title : str (optional) The title of the plot. Defaults to LABEL_DEFAULT. If the value is LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value is None, the title will be omitted. Otherwise, the string passed in as the title will be used as the plot title. Returns ------- out : Plot A :class: Plot object that is the heatmap. Examples -------- Make a heatmap. >>> x = turicreate.SArray([1,2,3,4,5]) >>> y = x * 2 >>> heat = turicreate.visualization.heatmap(x, y) """ if (not isinstance(x, tc.data_structures.sarray.SArray) or not isinstance(y, tc.data_structures.sarray.SArray) or x.dtype not in [int, float] or y.dtype not in [int, float]): raise ValueError("turicreate.visualization.heatmap supports " + "SArrays of dtype: int, float") title = _get_title(title) plt_ref = tc.extensions.plot_heatmap(x, y, xlabel, ylabel, title) return Plot(plt_ref) def box_plot(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT): """ Plots the data in `x` on the X axis and the data in `y` on the Y axis in a 2d box and whiskers plot, and returns the resulting Plot object. The function x as SArray of dtype str and y as SArray of dtype: int, float. Parameters ---------- x : SArray The data to plot on the X axis of the box and whiskers plot. Must be an SArray with dtype string. y : SArray The data to plot on the Y axis of the box and whiskers plot. Must be numeric (int/float) and must be the same length as `x`. xlabel : str (optional) The text label for the X axis. Defaults to "X". ylabel : str (optional) The text label for the Y axis. Defaults to "Y". title : str (optional) The title of the plot. Defaults to LABEL_DEFAULT. If the value is LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value is None, the title will be omitted. Otherwise, the string passed in as the title will be used as the plot title. Returns ------- out : Plot A :class: Plot object that is the box and whiskers plot. Examples -------- Make a box and whiskers plot. >>> bp = turicreate.visualization.box_plot(tc.SArray(['a','b','c','a','a']),tc.SArray([4.0,3.25,2.1,2.0,1.0])) """ if (not isinstance(x, tc.data_structures.sarray.SArray) or not isinstance(y, tc.data_structures.sarray.SArray) or x.dtype != str or y.dtype not in [int, float]): raise ValueError("turicreate.visualization.box_plot supports " + "x as SArray of dtype str and y as SArray of dtype: int, float." + "\nExample: turicreate.visualization.box_plot(tc.SArray(['a','b','c','a','a']),tc.SArray([4.0,3.25,2.1,2.0,1.0]))") title = _get_title(title) plt_ref = tc.extensions.plot_boxes_and_whiskers(x, y, xlabel, ylabel, title) return Plot(plt_ref) def columnwise_summary(sf): """ Plots a columnwise summary of the sframe provided as input, and returns the resulting Plot object. The function supports SFrames. Parameters ---------- sf : SFrame The data to get a columnwise summary for. Returns ------- out : Plot A :class: Plot object that is the columnwise summary plot. Examples -------- Make a columnwise summary of an SFrame. >>> x = turicreate.SArray([1,2,3,4,5]) >>> s = turicreate.SArray(['a','b','c','a','a']) >>> sf_test = turicreate.SFrame([x,x,x,x,s,s,s,x,s,x,s,s,s,x,x]) >>> colsum = turicreate.visualization.columnwise_summary(sf_test) """ if not isinstance(sf, tc.data_structures.sframe.SFrame): raise ValueError("turicreate.visualization.columnwise_summary " + "supports SFrame") plt_ref = tc.extensions.plot_columnwise_summary(sf) return Plot(plt_ref) def histogram(sa, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT): """ Plots a histogram of the sarray provided as input, and returns the resulting Plot object. The function supports numeric SArrays with dtypes int or float. Parameters ---------- sa : SArray The data to get a histogram for. Must be numeric (int/float). xlabel : str (optional) The text label for the X axis. Defaults to "Values". ylabel : str (optional) The text label for the Y axis. Defaults to "Count". title : str (optional) The title of the plot. Defaults to LABEL_DEFAULT. If the value is LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value is None, the title will be omitted. Otherwise, the string passed in as the title will be used as the plot title. Returns ------- out : Plot A :class: Plot object that is the histogram. Examples -------- Make a histogram of an SArray. >>> x = turicreate.SArray([1,2,3,4,5,1,1,1,1,2,2,3,2,3,1,1,1,4]) >>> hist = turicreate.visualization.histogram(x) """ if (not isinstance(sa, tc.data_structures.sarray.SArray) or sa.dtype not in [int, float]): raise ValueError("turicreate.visualization.histogram supports " + "SArrays of dtypes: int, float") title = _get_title(title) plt_ref = tc.extensions.plot_histogram(sa, xlabel, ylabel, title) return Plot(plt_ref) def item_frequency(sa, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT): """ Plots an item frequency of the sarray provided as input, and returns the resulting Plot object. The function supports SArrays with dtype str. Parameters ---------- sa : SArray The data to get an item frequency for. Must have dtype str xlabel : str (optional) The text label for the X axis. Defaults to "Values". ylabel : str (optional) The text label for the Y axis. Defaults to "Count". title : str (optional) The title of the plot. Defaults to LABEL_DEFAULT. If the value is LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value is None, the title will be omitted. Otherwise, the string passed in as the title will be used as the plot title. Returns ------- out : Plot A :class: Plot object that is the item frequency plot. Examples -------- Make an item frequency of an SArray. >>> x = turicreate.SArray(['a','ab','acd','ab','a','a','a','ab','cd']) >>> ifplt = turicreate.visualization.item_frequency(x) """ if (not isinstance(sa, tc.data_structures.sarray.SArray) or sa.dtype != str): raise ValueError("turicreate.visualization.item_frequency supports " + "SArrays of dtype str") title = _get_title(title) plt_ref = tc.extensions.plot_item_frequency(sa, xlabel, ylabel, title) return Plot(plt_ref)

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py

Python

tests/integrations/sqlalchemy/__init__.py

annu-ps31/sentry-python

3966b4a9744bfcb8c53dcca1b615bbadf4935aec

[ "BSD-2-Clause" ]

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2018-06-19T00:51:01.000Z

2022-03-31T06:37:16.000Z

tests/integrations/sqlalchemy/__init__.py

annu-ps31/sentry-python

3966b4a9744bfcb8c53dcca1b615bbadf4935aec

[ "BSD-2-Clause" ]

1,020

2018-07-16T12:50:36.000Z

2022-03-31T20:42:49.000Z

tests/integrations/sqlalchemy/__init__.py

annu-ps31/sentry-python

3966b4a9744bfcb8c53dcca1b615bbadf4935aec

[ "BSD-2-Clause" ]

340

2018-07-16T12:47:27.000Z

2022-03-22T10:13:21.000Z

import pytest pytest.importorskip("sqlalchemy")

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py

Python

tests/test_resource_manager.py

nghazali/KubeCapWatch

5b99dedcee45e6e53c2361891e399b9247acfcf1

[ "Apache-2.0" ]

null

tests/test_resource_manager.py

nghazali/KubeCapWatch

5b99dedcee45e6e53c2361891e399b9247acfcf1

[ "Apache-2.0" ]

1

2021-08-17T23:13:37.000Z

2021-08-18T06:39:04.000Z

tests/test_resource_manager.py

nghazali/KubeCapWatch

5b99dedcee45e6e53c2361891e399b9247acfcf1

[ "Apache-2.0" ]

null

import pytest def test_default(): assert True == True

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py

Python

particle/__init__.py

abelsonlive/particle

0058474e97f76c80deca7bb9ddb2a1248297eb3a

[ "MIT" ]

3

2015-01-05T23:13:18.000Z

2015-08-20T16:23:06.000Z

particle/__init__.py

abelsonlive/particle

0058474e97f76c80deca7bb9ddb2a1248297eb3a

[ "MIT" ]

null

particle/__init__.py

abelsonlive/particle

0058474e97f76c80deca7bb9ddb2a1248297eb3a

[ "MIT" ]

null

from particle.app import Particle from particle.common import db from particle.web import api from particle.cli import cli

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py

Python

EDX_refit_under_dev.py

tkcroat/EDX

f447dfbc0b7cde248b70b43772eb177185232960

[ "MIT" ]

null

EDX_refit_under_dev.py

tkcroat/EDX

f447dfbc0b7cde248b70b43772eb177185232960

[ "MIT" ]

null

EDX_refit_under_dev.py

tkcroat/EDX

f447dfbc0b7cde248b70b43772eb177185232960

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Thu Nov 2 13:55:04 2017 @author: tkc """ import matplotlib.pyplot as plt from matplotlib.widgets import LassoSelector from matplotlib.widgets import Lasso from matplotlib import path

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py

Python

model/subsystems.py

JBHilton/covid-19-in-households-public

42fb31a3c581a3d5b6b6959078a5f6bf4f25212e

[ "Apache-2.0" ]

4

2020-04-17T13:19:43.000Z

2021-12-02T19:56:27.000Z

model/subsystems.py

JBHilton/covid-19-in-households-public

42fb31a3c581a3d5b6b6959078a5f6bf4f25212e

[ "Apache-2.0" ]

6

2020-06-16T17:06:52.000Z

2021-02-08T18:32:39.000Z

model/subsystems.py

JBHilton/covid-19-in-households-public

42fb31a3c581a3d5b6b6959078a5f6bf4f25212e

[ "Apache-2.0" ]

3

2020-05-12T12:09:48.000Z

2021-06-07T09:16:09.000Z

'''This script defines all the subsystems for specific compartmental structures. First, some functions for doing standard transition events are defined, then these are used to create some functions for implementing common comprtmental structures. These are then stored in a dictionary, which always needs to go at the end of this script.''' from copy import copy, deepcopy from numpy import ( arange, around, array, atleast_2d, concatenate, cumprod, diag, ix_, ones, prod, sum, where, zeros) from numpy import int64 as my_int from scipy.sparse import csc_matrix as sparse def state_recursor( states, no_compartments, age_class, b_size, n_blocks, con_reps, c, x, depth, k): if depth < no_compartments-1: for x_i in arange(c + 1 - x.sum()): x[0, depth] = x_i x[0, depth+1:] = zeros( (1, no_compartments-depth-1), dtype=my_int) states, k = state_recursor( states, no_compartments, age_class, b_size, n_blocks, con_reps, c, x, depth+1, k) else: x[0, -1] = c - sum(x[0, :depth]) for block in arange(n_blocks): repeat_range = arange( block * b_size + k * con_reps, block * b_size + (k + 1) * con_reps) states[repeat_range, no_compartments*age_class:no_compartments*(age_class+1)] = \ ones( (con_reps, 1), dtype=my_int) \ * array( x, ndmin=2, dtype=my_int) k += 1 return states, k return states, k def build_states_recursively( total_size, no_compartments, classes_present, block_size, num_blocks, consecutive_repeats, composition): states = zeros( (total_size, no_compartments*len(classes_present)), dtype=my_int) for age_class in range(len(classes_present)): k = 0 states, k = state_recursor( states, no_compartments, age_class, block_size[age_class], num_blocks[age_class], consecutive_repeats[age_class], composition[classes_present[age_class]], zeros([1, no_compartments], dtype=my_int), 0, k) return states, k def build_state_matrix(household_spec): # Number of times you repeat states for each configuration consecutive_repeats = concatenate(( ones(1, dtype=my_int), cumprod(household_spec.system_sizes[:-1]))) block_size = consecutive_repeats * household_spec.system_sizes num_blocks = household_spec.total_size // block_size states, k = build_states_recursively( household_spec.total_size, household_spec.no_compartments, household_spec.class_indexes, block_size, num_blocks, consecutive_repeats, household_spec.composition) # Now construct a sparse vector which tells you which row a state appears # from in the state array # This loop tells us how many values each column of the state array can # take state_sizes = concatenate([ (household_spec.composition[i] + 1) * ones(household_spec.no_compartments, dtype=my_int) for i in household_spec.class_indexes]).ravel() # This vector stores the number of combinations you can get of all # subsequent elements in the state array, i.e. reverse_prod(i) tells you # how many arrangements you can get in states(:,i+1:end) reverse_prod = array([0, *cumprod(state_sizes[:0:-1])])[::-1] # We can then define index_vector look up the location of a state by # weighting its elements using reverse_prod - this gives a unique mapping # from the set of states to the integers. Because lots of combinations # don't actually appear in the states array, we use a sparse array which # will be much bigger than we actually require rows = [ states[k, :].dot(reverse_prod) + states[k, -1] for k in range(household_spec.total_size)] if min(rows) < 0: print( 'Negative row indices found, proportional total', sum(array(rows) < 0), '/', len(rows), '=', sum(array(rows) < 0) / len(rows)) index_vector = sparse(( arange(household_spec.total_size), (rows, [0]*household_spec.total_size))) return states, reverse_prod, index_vector, rows def inf_events(from_compartment, to_compartment, inf_compartment_list, inf_scales, r_home, density_expo, no_compartments, composition, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int, inf_event_row, inf_event_col, inf_event_class): # This function adds infection events to a within-household transition # matrix, allowing for multiple infectious classes # Total number of compartments contributing to within-household infection: no_inf_compartments = len(inf_compartment_list) for i in range(len(class_idx)): from_present = where(states[:, no_compartments*i+from_compartment] > 0)[0] inf_to = zeros(len(from_present), dtype=my_int) inf_rate = zeros(len(from_present)) for k in range(len(from_present)): old_state = copy(states[from_present[k], :]) old_infs = zeros(len(class_idx)) for ic in range(no_inf_compartments): old_infs += \ (old_state[inf_compartment_list[ic] + no_compartments * arange(len(class_idx))] / (composition[class_idx]**density_expo)) * inf_scales[ic] inf_rate[k] = old_state[no_compartments*i] * ( r_home[i, :].dot( old_infs )) new_state = old_state.copy() new_state[no_compartments*i + from_compartment] -= 1 new_state[no_compartments*i + to_compartment] += 1 inf_to[k] = index_vector[ new_state.dot(reverse_prod) + new_state[-1], 0] Q_int += sparse( (inf_rate, (from_present, inf_to)), shape=matrix_shape,) inf_event_row = concatenate((inf_event_row, from_present)) inf_event_col = concatenate((inf_event_col, inf_to)) inf_event_class = concatenate( (inf_event_class, class_idx[i]*ones((len(from_present))))) return Q_int, inf_event_row, inf_event_col, inf_event_class ''' The next function is used to set up the infection events in the SEPIRQ model, where some members may be temporarily absent, changing the current size of the household ''' def size_adj_inf_events(from_compartment, to_compartment, inf_compartment_list, inf_scales, r_home, iso_adjusted_comp, density_expo, no_compartments, composition, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int, inf_event_row, inf_event_col, inf_event_class): # Total number of compartments contributing to within-household infection no_inf_compartments = len(inf_compartment_list) for i in range(len(class_idx)): from_present = \ where(states[:, no_compartments*i+from_compartment] > 0)[0] inf_to = zeros(len(from_present), dtype=my_int) inf_rate = zeros(len(from_present)) for k in range(len(from_present)): old_state = copy(states[from_present[k], :]) old_infs = zeros(len(class_idx)) for ic in range(no_inf_compartments): old_infs += (old_state[inf_compartment_list[ic] + \ no_compartments * arange(len(class_idx))] / (iso_adjusted_comp[k]**density_expo)) * inf_scales[ic] inf_rate[k] = old_state[no_compartments*i] * ( r_home[i, :].dot( old_infs )) new_state = old_state.copy() new_state[no_compartments*i + from_compartment] -= 1 new_state[no_compartments*i + to_compartment] += 1 inf_to[k] = index_vector[ new_state.dot(reverse_prod) + new_state[-1], 0] Q_int += sparse( (inf_rate, (from_present, inf_to)), shape=matrix_shape,) inf_event_row = concatenate((inf_event_row, from_present)) inf_event_col = concatenate((inf_event_col, inf_to)) inf_event_class = concatenate( (inf_event_class, class_idx[i]*ones((len(from_present))))) return Q_int, inf_event_row, inf_event_col, inf_event_class def progression_events(from_compartment, to_compartment, pc_rate, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int): # This function adds a single set of progression events to a # within-household transition matrix for i in range(len(class_idx)): from_present = \ where(states[:, no_compartments*i+from_compartment] > 0)[0] prog_to = zeros(len(from_present), dtype=my_int) prog_rate = zeros(len(from_present)) for k in range(len(from_present)): old_state = copy(states[from_present[k], :]) prog_rate[k] = \ pc_rate * old_state[no_compartments*i+from_compartment] new_state = copy(old_state) new_state[no_compartments*i+from_compartment] -= 1 new_state[no_compartments*i+to_compartment] += 1 prog_to[k] = index_vector[ new_state.dot(reverse_prod) + new_state[-1], 0] Q_int += sparse( (prog_rate, (from_present, prog_to)), shape=matrix_shape,) return Q_int def stratified_progression_events(from_compartment, to_compartment, pc_rate_by_class, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int): ''' This function adds a single set of progression events to a within-household transition matrix, with progression rates stratified by class.''' for i in range(len(class_idx)): from_present = \ where(states[:, no_compartments*i+from_compartment] > 0)[0] prog_to = zeros(len(from_present), dtype=my_int) prog_rate = zeros(len(from_present)) for k in range(len(from_present)): old_state = copy(states[from_present[k], :]) prog_rate[k] = pc_rate_by_class[i] * \ old_state[no_compartments*i+from_compartment] new_state = copy(old_state) new_state[no_compartments*i+from_compartment] -= 1 new_state[no_compartments*i+to_compartment] += 1 prog_to[k] = index_vector[ new_state.dot(reverse_prod) + new_state[-1], 0] Q_int += sparse( (prog_rate, (from_present, prog_to)), shape=matrix_shape) return Q_int def isolation_events(from_compartment, to_compartment, iso_rate_by_class, class_is_isolating, iso_method, adult_bd, no_adults, children_present, adults_isolating, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int): ''' This function adds a single set of isolation events to a within-household transition matrix, with isolation rates stratified by class.''' for i in range(len(class_idx)): # The following if statement checks whether class i is meant to isolate # and whether any of the vulnerable classes are present if (class_is_isolating[class_idx[i],class_idx]).any(): # If isolating internally, i is a child class, or there are no # children around, anyone can isolate if iso_method=='int' or (i<adult_bd) or not children_present: iso_permitted = \ where((states[:,no_compartments*i+from_compartment] > 0) * \ (states[:, no_compartments*i+to_compartment] == 0))[0] # If children are present adults_isolating must stay below # no_adults-1 so the children still have a guardian else: iso_permitted = \ where( (states[:, no_compartments*i+from_compartment] > 0) * \ (adults_isolating<no_adults-1))[0] iso_present = \ where(states[:, no_compartments*i+to_compartment] > 0)[0] iso_to = zeros(len(iso_permitted), dtype=my_int) iso_rate = zeros(len(iso_permitted)) for k in range(len(iso_permitted)): old_state = copy(states[iso_permitted[k], :]) iso_rate[k] = iso_rate_by_class[i] * \ old_state[no_compartments*i+from_compartment] new_state = copy(old_state) new_state[no_compartments*i+from_compartment] -= 1 new_state[no_compartments*i+to_compartment] += 1 iso_to[k] = index_vector[ new_state.dot(reverse_prod) + new_state[-1], 0] Q_int += sparse( (iso_rate, (iso_permitted, iso_to)), shape=matrix_shape) return Q_int def _sir_subsystem(self, household_spec): '''This function processes a composition to create subsystems i.e. matrices and vectors describing all possible epdiemiological states for a given household composition Assuming frequency-dependent homogeneous within-household mixing composition[i] is the number of individuals in age-class i inside the household''' no_compartments = household_spec.no_compartments s_comp, i_comp, r_comp = range(no_compartments) composition = household_spec.composition matrix_shape = household_spec.matrix_shape sus = self.model_input.sus K_home = self.model_input.k_home gamma = self.model_input.gamma density_expo = self.model_input.density_expo # Set of individuals actually present here class_idx = household_spec.class_indexes K_home = K_home[ix_(class_idx, class_idx)] sus = sus[class_idx] r_home = atleast_2d(diag(sus).dot(K_home)) states, \ reverse_prod, \ index_vector, \ rows = build_state_matrix(household_spec) Q_int = sparse(household_spec.matrix_shape,) inf_event_row = array([], dtype=my_int) inf_event_col = array([], dtype=my_int) inf_event_class = array([], dtype=my_int) Q_int, inf_event_row, inf_event_col, inf_event_class = inf_events(s_comp, i_comp, [i_comp], [1], r_home, density_expo, no_compartments, composition, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int, inf_event_row, inf_event_col, inf_event_class) Q_int = progression_events(i_comp, r_comp, gamma, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) S = Q_int.sum(axis=1).getA().squeeze() Q_int += sparse(( -S, ( arange(household_spec.total_size), arange(household_spec.total_size) ))) return tuple(( Q_int, states, array(inf_event_row, dtype=my_int, ndmin=1), array(inf_event_col, dtype=my_int, ndmin=1), array(inf_event_class, dtype=my_int, ndmin=1), reverse_prod, index_vector)) def _seir_subsystem(self, household_spec): '''This function processes a composition to create subsystems i.e. matrices and vectors describing all possible epdiemiological states for a given household composition Assuming frequency-dependent homogeneous within-household mixing composition[i] is the number of individuals in age-class i inside the household''' no_compartments = household_spec.no_compartments s_comp, e_comp, i_comp, r_comp = range(no_compartments) composition = household_spec.composition matrix_shape = household_spec.matrix_shape sus = self.model_input.sus K_home = self.model_input.k_home alpha = self.model_input.alpha gamma = self.model_input.gamma density_expo = self.model_input.density_expo # Set of individuals actually present here class_idx = household_spec.class_indexes K_home = K_home[ix_(class_idx, class_idx)] sus = sus[class_idx] r_home = atleast_2d(diag(sus).dot(K_home)) states, \ reverse_prod, \ index_vector, \ rows = build_state_matrix(household_spec) Q_int = sparse(household_spec.matrix_shape,) inf_event_row = array([], dtype=my_int) inf_event_col = array([], dtype=my_int) inf_event_class = array([], dtype=my_int) Q_int, inf_event_row, inf_event_col, inf_event_class = inf_events(s_comp, i_comp, [i_comp], [1], r_home, density_expo, no_compartments, composition, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int, inf_event_row, inf_event_col, inf_event_class) Q_int = progression_events(e_comp, i_comp, gamma, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = progression_events(i_comp, r_comp, gamma, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) S = Q_int.sum(axis=1).getA().squeeze() Q_int += sparse(( -S, ( arange(household_spec.total_size), arange(household_spec.total_size) ))) return tuple(( Q_int, states, array(inf_event_row, dtype=my_int, ndmin=1), array(inf_event_col, dtype=my_int, ndmin=1), array(inf_event_class, dtype=my_int, ndmin=1), reverse_prod, index_vector)) def _sepir_subsystem(self, household_spec): '''This function processes a composition to create subsystems i.e. matrices and vectors describing all possible epdiemiological states for a given household composition Assuming frequency-dependent homogeneous within-household mixing composition[i] is the number of individuals in age-class i inside the household''' no_compartments = household_spec.no_compartments s_comp, e_comp, p_comp, i_comp, r_comp = range(no_compartments) composition = household_spec.composition matrix_shape = household_spec.matrix_shape sus = self.model_input.sus K_home = self.model_input.k_home inf_scales = copy(self.model_input.inf_scales) alpha_1 = self.model_input.alpha_1 alpha_2 = self.model_input.alpha_2 gamma = self.model_input.gamma density_expo = self.model_input.density_expo # Set of individuals actually present here class_idx = household_spec.class_indexes K_home = K_home[ix_(class_idx, class_idx)] sus = sus[class_idx] r_home = atleast_2d(diag(sus).dot(K_home)) for i in range(len(inf_scales)): inf_scales[i] = inf_scales[i][class_idx] states, \ reverse_prod, \ index_vector, \ rows = build_state_matrix(household_spec) Q_int = sparse(household_spec.matrix_shape,) inf_event_row = array([], dtype=my_int) inf_event_col = array([], dtype=my_int) inf_event_class = array([], dtype=my_int) Q_int, inf_event_row, inf_event_col, inf_event_class = inf_events(s_comp, e_comp, [p_comp, i_comp], inf_scales, r_home, density_expo, no_compartments, composition, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int, inf_event_row, inf_event_col, inf_event_class) Q_int = progression_events(e_comp, p_comp, alpha_1, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = progression_events(p_comp, i_comp, alpha_2, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = progression_events(i_comp, r_comp, gamma, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) S = Q_int.sum(axis=1).getA().squeeze() Q_int += sparse(( -S, ( arange(household_spec.total_size), arange(household_spec.total_size) ))) return tuple(( Q_int, states, array(inf_event_row, dtype=my_int, ndmin=1), array(inf_event_col, dtype=my_int, ndmin=1), array(inf_event_class, dtype=my_int, ndmin=1), reverse_prod, index_vector)) def _sepirq_subsystem(self, household_spec): '''This function processes a composition to create subsystems i.e. matrices and vectors describing all possible epdiemiological states for a given household composition Assuming frequency-dependent homogeneous within-household mixing composition[i] is the number of individuals in age-class i inside the household''' no_compartments = household_spec.no_compartments s_comp, e_comp, p_comp, i_comp, r_comp, q_comp = range(no_compartments) composition = household_spec.composition matrix_shape = household_spec.matrix_shape sus = self.model_input.sus K_home = self.model_input.k_home inf_scales = copy(self.model_input.inf_scales) alpha_1 = self.model_input.alpha_1 alpha_2 = self.model_input.alpha_2 gamma = self.model_input.gamma iso_rates = deepcopy(self.model_input.iso_rates) discharge_rate = self.model_input.discharge_rate density_expo = self.model_input.density_expo class_is_isolating = self.model_input.class_is_isolating iso_method = self.model_input.iso_method adult_bd = self.model_input.adult_bd no_adults = sum(composition[adult_bd:]) children_present = sum(composition[:adult_bd])>0 # Set of individuals actually present here class_idx = household_spec.class_indexes K_home = K_home[ix_(class_idx, class_idx)] sus = sus[class_idx] r_home = atleast_2d(diag(sus).dot(K_home)) for i in range(len(inf_scales)): inf_scales[i] = inf_scales[i][class_idx] states, \ reverse_prod, \ index_vector, \ rows = build_state_matrix(household_spec) iso_pos = q_comp + no_compartments * arange(len(class_idx)) if iso_method == "ext": # This is number of people of each age class present in the household # given some may isolate iso_adjusted_comp = composition[class_idx] - states[:,iso_pos] # Replace zeros with ones - we only ever use this as a denominator # whose numerator will be zero anyway if it should be zero iso_adjusted_comp[iso_adjusted_comp==0] = 1 if (iso_adjusted_comp<1).any(): pdb.set_trace() else: iso_adjusted_comp = \ composition[class_idx] - zeros(states[:,iso_pos].shape) # Number of adults isolating by state adults_isolating = \ states[:,no_compartments*adult_bd+q_comp::no_compartments].sum(axis=1) Q_int = sparse(household_spec.matrix_shape,) inf_event_row = array([], dtype=my_int) inf_event_col = array([], dtype=my_int) inf_event_class = array([], dtype=my_int) if iso_method == 'int': inf_comps = [p_comp, i_comp, q_comp] else: inf_comps = [p_comp, i_comp] if iso_method == 'ext': for cmp in range(len(iso_rates)): iso_rates[cmp] = \ self.model_input.ad_prob * iso_rates[cmp][class_idx] else: for cmp in range(len(iso_rates)): iso_rates[cmp] = iso_rates[cmp][class_idx] Q_int, inf_event_row, inf_event_col, inf_event_class = \ size_adj_inf_events(s_comp, e_comp, inf_comps, inf_scales, r_home, iso_adjusted_comp, density_expo, no_compartments, composition, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int, inf_event_row, inf_event_col, inf_event_class) Q_int = progression_events(e_comp, p_comp, alpha_1, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = progression_events(p_comp, i_comp, alpha_2, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = progression_events(i_comp, r_comp, gamma, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = isolation_events(e_comp, q_comp, iso_rates[e_comp], class_is_isolating, iso_method, adult_bd, no_adults, children_present, adults_isolating, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = isolation_events(p_comp, q_comp, iso_rates[p_comp], class_is_isolating, iso_method, adult_bd, no_adults, children_present, adults_isolating, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = isolation_events(i_comp, q_comp, iso_rates[i_comp], class_is_isolating, iso_method, adult_bd, no_adults, children_present, adults_isolating, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = progression_events(q_comp, r_comp, discharge_rate, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) S = Q_int.sum(axis=1).getA().squeeze() Q_int += sparse(( -S, ( arange(household_spec.total_size), arange(household_spec.total_size) ))) return tuple(( Q_int, states, array(inf_event_row, dtype=my_int, ndmin=1), array(inf_event_col, dtype=my_int, ndmin=1), array(inf_event_class, dtype=my_int, ndmin=1), reverse_prod, index_vector)) def _sedur_subsystem(self, household_spec): '''This function processes a composition to create subsystems i.e. matrices and vectors describing all possible epdiemiological states for a given household composition Assuming frequency-dependent homogeneous within-household mixing composition[i] is the number of individuals in age-class i inside the household''' no_compartments = household_spec.no_compartments s_comp, e_comp, d_comp, u_comp, r_comp = range(no_compartments) composition = household_spec.composition matrix_shape = household_spec.matrix_shape sus = self.model_input.sus det = self.model_input.det inf_scales = copy(self.model_input.inf_scales) K_home = self.model_input.k_home alpha = self.model_input.alpha gamma = self.model_input.gamma density_expo = self.model_input.density_expo # Set of individuals actually present here class_idx = household_spec.class_indexes K_home = K_home[ix_(class_idx, class_idx)] sus = sus[class_idx] det = det[class_idx] r_home = atleast_2d(diag(sus).dot(K_home)) for i in range(len(inf_scales)): inf_scales[i] = inf_scales[i][class_idx] states, \ reverse_prod, \ index_vector, \ rows = build_state_matrix(household_spec) Q_int = sparse(household_spec.matrix_shape,) inf_event_row = array([], dtype=my_int) inf_event_col = array([], dtype=my_int) inf_event_class = array([], dtype=my_int) Q_int, inf_event_row, inf_event_col, inf_event_class = inf_events(s_comp, e_comp, [d_comp,u_comp], inf_scales, r_home, density_expo, no_compartments, composition, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int, inf_event_row, inf_event_col, inf_event_class) Q_int = stratified_progression_events(e_comp, d_comp, alpha*det, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = stratified_progression_events(e_comp, u_comp, alpha*(1-det), no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = progression_events(d_comp, r_comp, gamma, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) Q_int = progression_events(u_comp, r_comp, gamma, no_compartments, states, index_vector, reverse_prod, class_idx, matrix_shape, Q_int) S = Q_int.sum(axis=1).getA().squeeze() Q_int += sparse(( -S, ( arange(household_spec.total_size), arange(household_spec.total_size) ))) return tuple(( Q_int, states, array(inf_event_row, dtype=my_int, ndmin=1), array(inf_event_col, dtype=my_int, ndmin=1), array(inf_event_class, dtype=my_int, ndmin=1), reverse_prod, index_vector)) ''' Entries in the subsystem key are in the following order: [list of compartments, number of compartments, list of compartments which contribute to infection, compartment corresponding to new infections]. ''' subsystem_key = { 'SIR' : [_sir_subsystem, 3, [1], 1], 'SEIR' : [_seir_subsystem, 4, [2], 1], 'SEPIR' : [_sepir_subsystem,5, [2,3], 1], 'SEPIRQ' : [_sepirq_subsystem,6, [2,3,5], 1], 'SEDUR' : [_sedur_subsystem,5, [2,3], 1], }

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6

c3442d547ed16220ae290209724033dee4258e18

22

py

Python

fos/shader/__init__.py

fos/fos

8d33bf0cd60292ad5164973b5285122acbc03b86

[ "BSD-3-Clause" ]

5

2015-08-08T22:04:49.000Z

2020-05-29T10:30:09.000Z

fos/shader/__init__.py

fos/fos

8d33bf0cd60292ad5164973b5285122acbc03b86

[ "BSD-3-Clause" ]

1

2018-04-25T12:59:56.000Z

2018-04-25T13:26:47.000Z

fos/shader/__init__.py

fos/fos

8d33bf0cd60292ad5164973b5285122acbc03b86

[ "BSD-3-Clause" ]

null

from .lib import *

4.4

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64,001

py

Python

core/test/test_signal_processing.py

ajmal017/amp

8de7e3b88be87605ec3bad03c139ac64eb460e5c

[ "BSD-3-Clause" ]

null

core/test/test_signal_processing.py

ajmal017/amp

8de7e3b88be87605ec3bad03c139ac64eb460e5c

[ "BSD-3-Clause" ]

null

core/test/test_signal_processing.py

ajmal017/amp

8de7e3b88be87605ec3bad03c139ac64eb460e5c

[ "BSD-3-Clause" ]

null

import collections import logging import os import pprint from typing import Any, List, Optional, Tuple, Union import numpy as np import pandas as pd import pytest import core.artificial_signal_generators as cartif import core.signal_processing as csigna import helpers.git as git import helpers.printing as hprint import helpers.unit_test as hut _LOG = logging.getLogger(__name__) class Test__compute_lagged_cumsum(hut.TestCase): def test1(self) -> None: input_df = self._get_df() output_df = csigna._compute_lagged_cumsum(input_df, 3) self.check_string( f"{hprint.frame('input')}\n" f"{hut.convert_df_to_string(input_df, index=True)}\n" f"{hprint.frame('output')}\n" f"{hut.convert_df_to_string(output_df, index=True)}" ) def test2(self) -> None: input_df = self._get_df() input_df.columns = ["x", "y1", "y2"] output_df = csigna._compute_lagged_cumsum(input_df, 3, ["y1", "y2"]) self.check_string( f"{hprint.frame('input')}\n" f"{hut.convert_df_to_string(input_df, index=True)}\n" f"{hprint.frame('output')}\n" f"{hut.convert_df_to_string(output_df, index=True)}" ) def test_lag_1(self) -> None: input_df = self._get_df() input_df.columns = ["x", "y1", "y2"] output_df = csigna._compute_lagged_cumsum(input_df, 1, ["y1", "y2"]) self.check_string( f"{hprint.frame('input')}\n" f"{hut.convert_df_to_string(input_df, index=True)}\n" f"{hprint.frame('output')}\n" f"{hut.convert_df_to_string(output_df, index=True)}" ) @staticmethod def _get_df() -> pd.DataFrame: df = pd.DataFrame([list(range(10))] * 3).T df[1] = df[0] + 1 df[2] = df[0] + 2 df.index = pd.date_range(start="2010-01-01", periods=10) df.rename(columns=lambda x: f"col_{x}", inplace=True) return df class Test_correlate_with_lagged_cumsum(hut.TestCase): def test1(self) -> None: input_df = self._get_arma_df() output_df = csigna.correlate_with_lagged_cumsum( input_df, 3, y_vars=["y1", "y2"] ) self.check_string( f"{hprint.frame('input')}\n" f"{hut.convert_df_to_string(input_df, index=True)}\n" f"{hprint.frame('output')}\n" f"{hut.convert_df_to_string(output_df, index=True)}" ) def test2(self) -> None: input_df = self._get_arma_df() output_df = csigna.correlate_with_lagged_cumsum( input_df, 3, y_vars=["y1"], x_vars=["x"] ) self.check_string( f"{hprint.frame('input')}\n" f"{hut.convert_df_to_string(input_df, index=True)}\n" f"{hprint.frame('output')}\n" f"{hut.convert_df_to_string(output_df, index=True)}" ) @staticmethod def _get_arma_df(seed: int = 0) -> pd.DataFrame: arma_process = cartif.ArmaProcess([], []) date_range = {"start": "2010-01-01", "periods": 40, "freq": "M"} srs1 = arma_process.generate_sample( date_range_kwargs=date_range, scale=0.1, seed=seed ).rename("x") srs2 = arma_process.generate_sample( date_range_kwargs=date_range, scale=0.1, seed=seed + 1 ).rename("y1") srs3 = arma_process.generate_sample( date_range_kwargs=date_range, scale=0.1, seed=seed + 2 ).rename("y2") return pd.concat([srs1, srs2, srs3], axis=1) class Test_accumulate(hut.TestCase): def test1(self) -> None: srs = pd.Series( range(0, 20), index=pd.date_range("2010-01-01", periods=20) ) actual = csigna.accumulate(srs, num_steps=1) expected = srs.astype(float) pd.testing.assert_series_equal(actual, expected) def test2(self) -> None: idx = pd.date_range("2010-01-01", periods=10) srs = pd.Series([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], index=idx) actual = csigna.accumulate(srs, num_steps=2) expected = pd.Series([np.nan, 1, 3, 5, 7, 9, 11, 13, 15, 17], index=idx) pd.testing.assert_series_equal(actual, expected) def test3(self) -> None: idx = pd.date_range("2010-01-01", periods=10) srs = pd.Series([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], index=idx) actual = csigna.accumulate(srs, num_steps=3) expected = pd.Series( [np.nan, np.nan, 3, 6, 9, 12, 15, 18, 21, 24], index=idx ) pd.testing.assert_series_equal(actual, expected) def test4(self) -> None: srs = pd.Series( np.random.randn(100), index=pd.date_range("2010-01-01", periods=100) ) output = pd.concat([srs, csigna.accumulate(srs, num_steps=5)], axis=1) output.columns = ["series", "series_accumulated"] self.check_string(hut.convert_df_to_string(output, index=True)) def test_long_step1(self) -> None: idx = pd.date_range("2010-01-01", periods=3) srs = pd.Series([1, 2, 3], index=idx) actual = csigna.accumulate(srs, num_steps=5) expected = pd.Series([np.nan, np.nan, np.nan], index=idx) pd.testing.assert_series_equal(actual, expected) def test_nans1(self) -> None: idx = pd.date_range("2010-01-01", periods=10) srs = pd.Series([0, 1, np.nan, 2, 3, 4, np.nan, 5, 6, 7], index=idx) actual = csigna.accumulate(srs, num_steps=3) expected = pd.Series( [ np.nan, np.nan, np.nan, np.nan, np.nan, 9, np.nan, np.nan, np.nan, 18, ], index=idx, ) pd.testing.assert_series_equal(actual, expected) def test_nans2(self) -> None: idx = pd.date_range("2010-01-01", periods=6) srs = pd.Series([np.nan, np.nan, np.nan, 2, 3, 4], index=idx) actual = csigna.accumulate(srs, num_steps=3) expected = pd.Series( [np.nan, np.nan, np.nan, np.nan, np.nan, 9], index=idx ) pd.testing.assert_series_equal(actual, expected) def test_nans3(self) -> None: idx = pd.date_range("2010-01-01", periods=6) srs = pd.Series([np.nan, np.nan, np.nan, 2, 3, 4], index=idx) actual = csigna.accumulate(srs, num_steps=2) expected = pd.Series([np.nan, np.nan, np.nan, np.nan, 5, 7], index=idx) pd.testing.assert_series_equal(actual, expected) class Test_get_symmetric_equisized_bins(hut.TestCase): def test_zero_in_bin_interior_false(self) -> None: input_ = pd.Series([-1, 3]) expected = np.array([-3, -2, -1, 0, 1, 2, 3]) actual = csigna.get_symmetric_equisized_bins(input_, 1) np.testing.assert_array_equal(actual, expected) def test_zero_in_bin_interior_true(self) -> None: input_ = pd.Series([-1, 3]) expected = np.array([-3.5, -2.5, -1.5, -0.5, 0.5, 1.5, 2.5, 3.5]) actual = csigna.get_symmetric_equisized_bins(input_, 1, True) np.testing.assert_array_equal(actual, expected) def test_infs(self) -> None: data = pd.Series([-1, np.inf, -np.inf, 3]) expected = np.array([-4, -2, 0, 2, 4]) actual = csigna.get_symmetric_equisized_bins(data, 2) np.testing.assert_array_equal(actual, expected) class Test_compute_rolling_zscore1(hut.TestCase): def test_default_values1(self) -> None: """ Test with default parameters on a heaviside series. """ heaviside = cartif.get_heaviside(-10, 252, 1, 1).rename("input") actual = csigna.compute_rolling_zscore(heaviside, tau=40).rename("output") output_df = pd.concat([heaviside, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_default_values2(self) -> None: """ Test for tau with default parameters on a heaviside series. """ heaviside = cartif.get_heaviside(-10, 252, 1, 1).rename("input") actual = csigna.compute_rolling_zscore(heaviside, tau=20).rename("output") output_df = pd.concat([heaviside, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_arma_clean1(self) -> None: """ Test on a clean arma series. """ series = self._get_arma_series(seed=1) actual = csigna.compute_rolling_zscore(series, tau=20).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_arma_nan1(self) -> None: """ Test on an arma series with leading NaNs. """ series = self._get_arma_series(seed=1) series[:5] = np.nan actual = csigna.compute_rolling_zscore(series, tau=20).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_arma_nan2(self) -> None: """ Test on an arma series with interspersed NaNs. """ series = self._get_arma_series(seed=1) series[5:10] = np.nan actual = csigna.compute_rolling_zscore(series, tau=20).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_arma_zero1(self) -> None: """ Test on an arma series with leading zeros. """ series = self._get_arma_series(seed=1) series[:5] = 0 actual = csigna.compute_rolling_zscore(series, tau=20).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_arma_zero2(self) -> None: """ Test on an arma series with interspersed zeros. """ series = self._get_arma_series(seed=1) series[5:10] = 0 actual = csigna.compute_rolling_zscore(series, tau=20).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_arma_atol1(self) -> None: """ Test on an arma series with all-zeros period and `atol>0`. """ series = self._get_arma_series(seed=1) series[10:25] = 0 actual = csigna.compute_rolling_zscore(series, tau=2, atol=0.01).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_arma_inf1(self) -> None: """ Test on an arma series with leading infs. """ series = self._get_arma_series(seed=1) series[:5] = np.inf actual = csigna.compute_rolling_zscore(series, tau=20).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_arma_inf2(self) -> None: """ Test on an arma series with interspersed infs. """ series = self._get_arma_series(seed=1) series[5:10] = np.inf actual = csigna.compute_rolling_zscore(series, tau=20).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay1_arma_clean1(self) -> None: """ Test on a clean arma series when `delay=1`. """ series = self._get_arma_series(seed=1) actual = csigna.compute_rolling_zscore(series, tau=20, delay=1).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay1_arma_nan1(self) -> None: """ Test on an arma series with leading NaNs when `delay=1`. """ series = self._get_arma_series(seed=1) series[:5] = np.nan actual = csigna.compute_rolling_zscore(series, tau=20, delay=1).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay1_arma_nan2(self) -> None: """ Test on an arma series with interspersed NaNs when `delay=1`. """ series = self._get_arma_series(seed=1) series[5:10] = np.nan actual = csigna.compute_rolling_zscore(series, tau=20, delay=1).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay1_arma_zero1(self) -> None: """ Test on an arma series with leading zeros when `delay=1`. """ series = self._get_arma_series(seed=1) series[:5] = 0 actual = csigna.compute_rolling_zscore(series, tau=20, delay=1).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay1_arma_zero2(self) -> None: """ Test on an arma series with interspersed zeros when `delay=1`. """ series = self._get_arma_series(seed=1) series[5:10] = 0 actual = csigna.compute_rolling_zscore(series, tau=20, delay=1).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay1_arma_atol1(self) -> None: """ Test on an arma series with all-zeros period, `delay=1` and `atol>0`. """ series = self._get_arma_series(seed=1) series[10:25] = 0 actual = csigna.compute_rolling_zscore( series, tau=2, delay=1, atol=0.01 ).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay1_arma_inf1(self) -> None: """ Test on an arma series with leading infs when `delay=1`. """ series = self._get_arma_series(seed=1) series[:5] = np.inf actual = csigna.compute_rolling_zscore(series, tau=20, delay=1).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay1_arma_inf2(self) -> None: """ Test on an arma series with interspersed infs when `delay=1`. """ series = self._get_arma_series(seed=1) series[5:10] = np.inf actual = csigna.compute_rolling_zscore(series, tau=20, delay=1).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay2_arma_clean1(self) -> None: """ Test on a clean arma series when `delay=2`. """ series = self._get_arma_series(seed=1) actual = csigna.compute_rolling_zscore(series, tau=20, delay=2).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay2_arma_nan1(self) -> None: """ Test on an arma series with leading NaNs when `delay=2`. """ series = self._get_arma_series(seed=1) series[:5] = np.nan actual = csigna.compute_rolling_zscore(series, tau=20, delay=2).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay2_arma_nan2(self) -> None: """ Test on an arma series with interspersed NaNs when `delay=2`. """ series = self._get_arma_series(seed=1) series[5:10] = np.nan actual = csigna.compute_rolling_zscore(series, tau=20, delay=2).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay2_arma_zero1(self) -> None: """ Test on an arma series with leading zeros when `delay=2`. """ series = self._get_arma_series(seed=1) series[:5] = 0 actual = csigna.compute_rolling_zscore(series, tau=20, delay=2).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay2_arma_zero2(self) -> None: """ Test on an arma series with interspersed zeros when `delay=2`. """ series = self._get_arma_series(seed=1) series[5:10] = 0 actual = csigna.compute_rolling_zscore(series, tau=20, delay=2).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay2_arma_atol1(self) -> None: """ Test on an arma series with all-zeros period, `delay=2` and `atol>0`. """ series = self._get_arma_series(seed=1) series[10:25] = 0 actual = csigna.compute_rolling_zscore( series, tau=2, delay=2, atol=0.01 ).rename("output") output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay2_arma_inf1(self) -> None: """ Test on an arma series with leading infs when `delay=2`. """ series = self._get_arma_series(seed=1) series[:5] = np.inf actual = csigna.compute_rolling_zscore(series, tau=20, delay=2).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) def test_delay2_arma_inf2(self) -> None: """ Test on an arma series with interspersed infs when `delay=2`. """ series = self._get_arma_series(seed=1) series[5:10] = np.inf actual = csigna.compute_rolling_zscore(series, tau=20, delay=2).rename( "output" ) output_df = pd.concat([series, actual], axis=1) output_df_string = hut.convert_df_to_string(output_df, index=True) self.check_string(output_df_string) @staticmethod def _get_arma_series(seed: int) -> pd.Series: arma_process = cartif.ArmaProcess([1], [1]) date_range = {"start": "1/1/2010", "periods": 40, "freq": "M"} series = arma_process.generate_sample( date_range_kwargs=date_range, scale=0.1, seed=seed ).rename("input") return series class Test_process_outliers1(hut.TestCase): def test_winsorize1(self) -> None: srs = self._get_data1() mode = "winsorize" lower_quantile = 0.01 # Check. self._helper(srs, mode, lower_quantile) def test_set_to_nan1(self) -> None: srs = self._get_data1() mode = "set_to_nan" lower_quantile = 0.01 # Check. self._helper(srs, mode, lower_quantile) def test_set_to_zero1(self) -> None: srs = self._get_data1() mode = "set_to_zero" lower_quantile = 0.01 # Check. self._helper(srs, mode, lower_quantile) def test_winsorize2(self) -> None: srs = self._get_data2() mode = "winsorize" lower_quantile = 0.2 # Check. self._helper(srs, mode, lower_quantile, num_df_rows=len(srs)) def test_set_to_nan2(self) -> None: srs = self._get_data2() mode = "set_to_nan" lower_quantile = 0.2 # Check. self._helper(srs, mode, lower_quantile, num_df_rows=len(srs)) def test_set_to_zero2(self) -> None: srs = self._get_data2() mode = "set_to_zero" lower_quantile = 0.2 upper_quantile = 0.5 # Check. self._helper( srs, mode, lower_quantile, num_df_rows=len(srs), upper_quantile=upper_quantile, ) def _helper( self, srs: pd.Series, mode: str, lower_quantile: float, num_df_rows: int = 10, window: int = 100, min_periods: Optional[int] = 2, **kwargs: Any, ) -> None: info: collections.OrderedDict = collections.OrderedDict() srs_out = csigna.process_outliers( srs, mode, lower_quantile, window=window, min_periods=min_periods, info=info, **kwargs, ) txt = [] txt.append("# info") txt.append(pprint.pformat(info)) txt.append("# srs_out") txt.append(str(srs_out.head(num_df_rows))) self.check_string("\n".join(txt)) @staticmethod def _get_data1() -> pd.Series: np.random.seed(100) n = 100000 data = np.random.normal(loc=0.0, scale=1.0, size=n) return pd.Series(data) @staticmethod def _get_data2() -> pd.Series: return pd.Series(range(1, 10)) class Test_compute_smooth_derivative1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 min_periods = 20 scaling = 2 order = 2 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_smooth_derivative( signal, tau, min_periods, scaling, order ) self.check_string(actual.to_string()) class Test_compute_smooth_moving_average1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 min_periods = 20 min_depth = 1 max_depth = 5 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_smooth_moving_average( signal, tau, min_periods, min_depth, max_depth ) self.check_string(actual.to_string()) class Test_extract_smooth_moving_average_weights(hut.TestCase): def test1(self) -> None: df = pd.DataFrame(index=range(0, 20)) weights = csigna.extract_smooth_moving_average_weights( df, tau=1.4, index_location=15, ) actual = hut.convert_df_to_string( weights.round(5), index=True, decimals=5 ) self.check_string(actual) def test2(self) -> None: df = pd.DataFrame(index=range(0, 20)) weights = csigna.extract_smooth_moving_average_weights( df, tau=16, index_location=15, ) actual = hut.convert_df_to_string( weights.round(5), index=True, decimals=5 ) self.check_string(actual) def test3(self) -> None: df = pd.DataFrame(index=range(0, 20)) weights = csigna.extract_smooth_moving_average_weights( df, tau=16, min_depth=2, max_depth=2, index_location=15, ) actual = hut.convert_df_to_string( weights.round(5), index=True, decimals=5 ) self.check_string(actual) def test4(self) -> None: df = pd.DataFrame( index=pd.date_range(start="2001-01-04", end="2001-01-31", freq="B") ) weights = csigna.extract_smooth_moving_average_weights( df, tau=16, index_location="2001-01-24", ) actual = hut.convert_df_to_string( weights.round(5), index=True, decimals=5 ) self.check_string(actual) def test5(self) -> None: df = pd.DataFrame( index=pd.date_range(start="2001-01-04", end="2001-01-31", freq="B") ) weights = csigna.extract_smooth_moving_average_weights( df, tau=252, index_location="2001-01-24", ) actual = hut.convert_df_to_string( weights.round(5), index=True, decimals=5 ) self.check_string(actual) def test6(self) -> None: df = pd.DataFrame( index=pd.date_range(start="2001-01-04", end="2001-01-31", freq="B") ) weights = csigna.extract_smooth_moving_average_weights( df, tau=252, ) actual = hut.convert_df_to_string( weights.round(5), index=True, decimals=5 ) self.check_string(actual) class Test_digitize1(hut.TestCase): def test1(self) -> None: np.random.seed(42) bins = [0, 0.2, 0.4] right = False n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.digitize(signal, bins, right) self.check_string(actual.to_string()) def test_heaviside1(self) -> None: heaviside = cartif.get_heaviside(-10, 20, 1, 1) bins = [0, 0.2, 0.4] right = False actual = csigna.digitize(heaviside, bins, right) self.check_string(actual.to_string()) class Test_compute_rolling_moment1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_rolling_moment( signal, tau, min_periods, min_depth, max_depth, p_moment ) self.check_string(actual.to_string()) class Test_compute_rolling_norm1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_rolling_norm( signal, tau, min_periods, min_depth, max_depth, p_moment ) self.check_string(actual.to_string()) class Test_compute_rolling_var1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_rolling_var( signal, tau, min_periods, min_depth, max_depth, p_moment ) self.check_string(actual.to_string()) class Test_compute_rolling_std1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_rolling_std( signal, tau, min_periods, min_depth, max_depth, p_moment ) self.check_string(actual.to_string()) class Test_compute_rolling_demean1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 min_periods = 20 min_depth = 1 max_depth = 5 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_rolling_demean( signal, tau, min_periods, min_depth, max_depth ) self.check_string(actual.to_string()) class Test_compute_rolling_skew1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau_z = 40 tau_s = 20 min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_rolling_skew( signal, tau_z, tau_s, min_periods, min_depth, max_depth, p_moment ) self.check_string(actual.to_string()) class Test_compute_rolling_kurtosis1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau_z = 40 tau_s = 20 min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_rolling_kurtosis( signal, tau_z, tau_s, min_periods, min_depth, max_depth, p_moment ) self.check_string(actual.to_string()) class Test_compute_rolling_sharpe_ratio1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 signal = pd.Series(np.random.randn(n)) actual = csigna.compute_rolling_sharpe_ratio( signal, tau, min_periods, min_depth, max_depth, p_moment ) self.check_string(actual.to_string()) class Test_compute_rolling_corr1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 demean = True min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 df = pd.DataFrame(np.random.randn(n, 2)) signal1 = df[0] signal2 = df[1] actual = csigna.compute_rolling_corr( signal1, signal2, tau, demean, min_periods, min_depth, max_depth, p_moment, ) self.check_string(actual.to_string()) class Test_compute_rolling_zcorr1(hut.TestCase): def test1(self) -> None: np.random.seed(42) tau = 40 demean = True min_periods = 20 min_depth = 1 max_depth = 5 p_moment = 2 n = 1000 df = pd.DataFrame(np.random.randn(n, 2)) signal1 = df[0] signal2 = df[1] actual = csigna.compute_rolling_zcorr( signal1, signal2, tau, demean, min_periods, min_depth, max_depth, p_moment, ) self.check_string(actual.to_string()) class Test_compute_ipca(hut.TestCase): def test1(self) -> None: """ Test for a clean input. """ df = self._get_df(seed=1) num_pc = 3 tau = 16 lambda_df, unit_eigenvec_dfs = csigna.compute_ipca(df, num_pc, tau) unit_eigenvec_dfs_txt = "\n".join( [f"{i}:\n{df.to_string()}" for i, df in enumerate(unit_eigenvec_dfs)] ) txt = ( f"lambda_df:\n{lambda_df.to_string()}\n, " f"unit_eigenvecs_dfs:\n{unit_eigenvec_dfs_txt}" ) self.check_string(txt) def test2(self) -> None: """ Test for an input with leading NaNs in only a subset of cols. """ df = self._get_df(seed=1) df.iloc[0:3, :-3] = np.nan num_pc = 3 tau = 16 lambda_df, unit_eigenvec_dfs = csigna.compute_ipca(df, num_pc, tau) unit_eigenvec_dfs_txt = "\n".join( [f"{i}:\n{df.to_string()}" for i, df in enumerate(unit_eigenvec_dfs)] ) txt = ( f"lambda_df:\n{lambda_df.to_string()}\n, " f"unit_eigenvecs_dfs:\n{unit_eigenvec_dfs_txt}" ) self.check_string(txt) def test3(self) -> None: """ Test for an input with interspersed NaNs. """ df = self._get_df(seed=1) df.iloc[5:8, 3:5] = np.nan df.iloc[2:4, 8:] = np.nan num_pc = 3 tau = 16 lambda_df, unit_eigenvec_dfs = csigna.compute_ipca(df, num_pc, tau) unit_eigenvec_dfs_txt = "\n".join( [f"{i}:\n{df.to_string()}" for i, df in enumerate(unit_eigenvec_dfs)] ) txt = ( f"lambda_df:\n{lambda_df.to_string()}\n, " f"unit_eigenvecs_dfs:\n{unit_eigenvec_dfs_txt}" ) self.check_string(txt) def test4(self) -> None: """ Test for an input with a full-NaN row among the 3 first rows. The eigenvalue estimates aren't in sorted order but should be. TODO(*): Fix problem with not sorted eigenvalue estimates. """ df = self._get_df(seed=1) df.iloc[1:2, :] = np.nan num_pc = 3 tau = 16 lambda_df, unit_eigenvec_dfs = csigna.compute_ipca(df, num_pc, tau) unit_eigenvec_dfs_txt = "\n".join( [f"{i}:\n{df.to_string()}" for i, df in enumerate(unit_eigenvec_dfs)] ) txt = ( f"lambda_df:\n{lambda_df.to_string()}\n, " f"unit_eigenvecs_dfs:\n{unit_eigenvec_dfs_txt}" ) self.check_string(txt) def test5(self) -> None: """ Test for an input with 5 leading NaNs in all cols. """ df = self._get_df(seed=1) df.iloc[:5, :] = np.nan num_pc = 3 tau = 16 lambda_df, unit_eigenvec_dfs = csigna.compute_ipca(df, num_pc, tau) unit_eigenvec_dfs_txt = "\n".join( [f"{i}:\n{df.to_string()}" for i, df in enumerate(unit_eigenvec_dfs)] ) txt = ( f"lambda_df:\n{lambda_df.to_string()}\n, " f"unit_eigenvecs_dfs:\n{unit_eigenvec_dfs_txt}" ) self.check_string(txt) def test6(self) -> None: """ Test for interspersed all-NaNs rows. """ df = self._get_df(seed=1) df.iloc[0:1, :] = np.nan df.iloc[2:3, :] = np.nan num_pc = 3 tau = 16 lambda_df, unit_eigenvec_dfs = csigna.compute_ipca(df, num_pc, tau) unit_eigenvec_dfs_txt = "\n".join( [f"{i}:\n{df.to_string()}" for i, df in enumerate(unit_eigenvec_dfs)] ) txt = ( f"lambda_df:\n{lambda_df.to_string()}\n, " f"unit_eigenvecs_dfs:\n{unit_eigenvec_dfs_txt}" ) self.check_string(txt) @staticmethod def _get_df(seed: int) -> pd.DataFrame: """ Generate a dataframe via `cartif.MultivariateNormalProcess()`. """ mn_process = cartif.MultivariateNormalProcess() mn_process.set_cov_from_inv_wishart_draw(dim=10, seed=seed) df = mn_process.generate_sample( {"start": "2000-01-01", "periods": 40, "freq": "B"}, seed=seed ) return df class Test__compute_ipca_step(hut.TestCase): def test1(self) -> None: """ Test for clean input series. """ mn_process = cartif.MultivariateNormalProcess() mn_process.set_cov_from_inv_wishart_draw(dim=10, seed=1) df = mn_process.generate_sample( {"start": "2000-01-01", "periods": 10, "freq": "B"}, seed=1 ) u = df.iloc[1] v = df.iloc[2] alpha = 0.5 u_next, v_next = csigna._compute_ipca_step(u, v, alpha) txt = self._get_output_txt(u, v, u_next, v_next) self.check_string(txt) def test2(self) -> None: """ Test for input series with all zeros. """ mn_process = cartif.MultivariateNormalProcess() mn_process.set_cov_from_inv_wishart_draw(dim=10, seed=1) df = mn_process.generate_sample( {"start": "2000-01-01", "periods": 10, "freq": "B"}, seed=1 ) u = df.iloc[1] v = df.iloc[2] u[:] = 0 v[:] = 0 alpha = 0.5 u_next, v_next = csigna._compute_ipca_step(u, v, alpha) txt = self._get_output_txt(u, v, u_next, v_next) self.check_string(txt) def test3(self) -> None: """ Test that u == u_next for the case when np.linalg.norm(v)=0. """ mn_process = cartif.MultivariateNormalProcess() mn_process.set_cov_from_inv_wishart_draw(dim=10, seed=1) df = mn_process.generate_sample( {"start": "2000-01-01", "periods": 10, "freq": "B"}, seed=1 ) u = df.iloc[1] v = df.iloc[2] v[:] = 0 alpha = 0.5 u_next, v_next = csigna._compute_ipca_step(u, v, alpha) txt = self._get_output_txt(u, v, u_next, v_next) self.check_string(txt) def test4(self) -> None: """ Test for input series with all NaNs. Output is not intended. TODO(Dan): implement a way to deal with NaNs in the input. """ mn_process = cartif.MultivariateNormalProcess() mn_process.set_cov_from_inv_wishart_draw(dim=10, seed=1) df = mn_process.generate_sample( {"start": "2000-01-01", "periods": 10, "freq": "B"}, seed=1 ) u = df.iloc[1] v = df.iloc[2] u[:] = np.nan v[:] = np.nan alpha = 0.5 u_next, v_next = csigna._compute_ipca_step(u, v, alpha) txt = self._get_output_txt(u, v, u_next, v_next) self.check_string(txt) def test5(self) -> None: """ Test for input series with some NaNs. Output is not intended. """ mn_process = cartif.MultivariateNormalProcess() mn_process.set_cov_from_inv_wishart_draw(dim=10, seed=1) df = mn_process.generate_sample( {"start": "2000-01-01", "periods": 10, "freq": "B"}, seed=1 ) u = df.iloc[1] v = df.iloc[2] u[3:6] = np.nan v[5:8] = np.nan alpha = 0.5 u_next, v_next = csigna._compute_ipca_step(u, v, alpha) txt = self._get_output_txt(u, v, u_next, v_next) self.check_string(txt) @staticmethod def _get_output_txt( u: pd.Series, v: pd.Series, u_next: pd.Series, v_next: pd.Series ) -> str: """ Create string output for tests results. """ u_string = hut.convert_df_to_string(u, index=True) v_string = hut.convert_df_to_string(v, index=True) u_next_string = hut.convert_df_to_string(u_next, index=True) v_next_string = hut.convert_df_to_string(v_next, index=True) txt = ( f"u:\n{u_string}\n" f"v:\n{v_string}\n" f"u_next:\n{u_next_string}\n" f"v_next:\n{v_next_string}" ) return txt @pytest.mark.slow class Test_gallery_signal_processing1(hut.TestCase): def test_notebook1(self) -> None: file_name = os.path.join( git.get_amp_abs_path(), "core/notebooks/gallery_signal_processing.ipynb", ) scratch_dir = self.get_scratch_space() hut.run_notebook(file_name, scratch_dir) class TestProcessNonfinite1(hut.TestCase): def test1(self) -> None: series = self._get_messy_series(1) actual = csigna.process_nonfinite(series) actual_string = hut.convert_df_to_string(actual, index=True) self.check_string(actual_string) def test2(self) -> None: series = self._get_messy_series(1) actual = csigna.process_nonfinite(series, remove_nan=False) actual_string = hut.convert_df_to_string(actual, index=True) self.check_string(actual_string) def test3(self) -> None: series = self._get_messy_series(1) actual = csigna.process_nonfinite(series, remove_inf=False) actual_string = hut.convert_df_to_string(actual, index=True) self.check_string(actual_string) @staticmethod def _get_messy_series(seed: int) -> pd.Series: arparams = np.array([0.75, -0.25]) maparams = np.array([0.65, 0.35]) arma_process = cartif.ArmaProcess(arparams, maparams) date_range = {"start": "1/1/2010", "periods": 40, "freq": "M"} series = arma_process.generate_sample( date_range_kwargs=date_range, seed=seed ) series[:5] = 0 series[-5:] = np.nan series[10:13] = np.inf series[13:16] = -np.inf return series class Test_compute_rolling_annualized_sharpe_ratio(hut.TestCase): def test1(self) -> None: ar_params: List[float] = [] ma_params: List[float] = [] arma_process = cartif.ArmaProcess(ar_params, ma_params) realization = arma_process.generate_sample( {"start": "2000-01-01", "periods": 40, "freq": "B"}, scale=1, burnin=5, ) rolling_sr = csigna.compute_rolling_annualized_sharpe_ratio( realization, tau=16 ) self.check_string(hut.convert_df_to_string(rolling_sr, index=True)) class Test_get_swt(hut.TestCase): def test_clean1(self) -> None: """ Test for default values. """ series = self._get_series(seed=1, periods=40) actual = csigna.get_swt(series, wavelet="haar") output_str = self._get_tuple_output_txt(actual) self.check_string(output_str) def test_timing_mode1(self) -> None: """ Test for timing_mode="knowledge_time". """ series = self._get_series(seed=1) actual = csigna.get_swt( series, wavelet="haar", timing_mode="knowledge_time" ) output_str = self._get_tuple_output_txt(actual) self.check_string(output_str) def test_timing_mode2(self) -> None: """ Test for timing_mode="zero_phase". """ series = self._get_series(seed=1) actual = csigna.get_swt(series, wavelet="haar", timing_mode="zero_phase") output_str = self._get_tuple_output_txt(actual) self.check_string(output_str) def test_timing_mode3(self) -> None: """ Test for timing_mode="raw". """ series = self._get_series(seed=1) actual = csigna.get_swt(series, wavelet="haar", timing_mode="raw") output_str = self._get_tuple_output_txt(actual) self.check_string(output_str) def test_output_mode1(self) -> None: """ Test for output_mode="tuple". """ series = self._get_series(seed=1) actual = csigna.get_swt(series, wavelet="haar", output_mode="tuple") output_str = self._get_tuple_output_txt(actual) self.check_string(output_str) def test_output_mode2(self) -> None: """ Test for output_mode="smooth". """ series = self._get_series(seed=1) actual = csigna.get_swt(series, wavelet="haar", output_mode="smooth") actual_str = hut.convert_df_to_string(actual, index=True) output_str = f"smooth_df:\n{actual_str}\n" self.check_string(output_str) def test_output_mode3(self) -> None: """ Test for output_mode="detail". """ series = self._get_series(seed=1) actual = csigna.get_swt(series, wavelet="haar", output_mode="detail") actual_str = hut.convert_df_to_string(actual, index=True) output_str = f"detail_df:\n{actual_str}\n" self.check_string(output_str) @staticmethod def _get_series(seed: int, periods: int = 20) -> pd.Series: arma_process = cartif.ArmaProcess([0], [0]) date_range = {"start": "1/1/2010", "periods": periods, "freq": "M"} series = arma_process.generate_sample( date_range_kwargs=date_range, scale=0.1, seed=seed ) return series @staticmethod def _get_tuple_output_txt( output: Union[pd.DataFrame, Tuple[pd.DataFrame, pd.DataFrame]] ) -> str: """ Create string output for a tuple type return. """ smooth_df_string = hut.convert_df_to_string(output[0], index=True) detail_df_string = hut.convert_df_to_string(output[1], index=True) output_str = ( f"smooth_df:\n{smooth_df_string}\n" f"\ndetail_df\n{detail_df_string}\n" ) return output_str class Test_compute_swt_var(hut.TestCase): def test1(self) -> None: srs = self._get_data(seed=0) swt_var = csigna.compute_swt_var(srs, depth=6) actual = swt_var.count().values[0] np.testing.assert_equal(actual, 1179) def test2(self) -> None: srs = self._get_data(seed=0) swt_var = csigna.compute_swt_var(srs, depth=6) actual = swt_var.sum() np.testing.assert_allclose(actual, [1102.66], atol=0.01) def test3(self) -> None: srs = self._get_data(seed=0) swt_var = csigna.compute_swt_var(srs, depth=6, axis=1) actual = swt_var.sum() np.testing.assert_allclose(actual, [1102.66], atol=0.01) def _get_data(self, seed: int) -> pd.Series: process = cartif.ArmaProcess([], []) realization = process.generate_sample( {"start": "2000-01-01", "end": "2005-01-01", "freq": "B"}, seed=seed ) return realization class Test_resample_srs(hut.TestCase): # TODO(gp): Replace `check_string()` with `assert_equal()` to tests that benefit # from seeing / freezing the results, using a command like: # ``` # > invoke find_check_string_output -c Test_resample_srs -m test_day_to_year1 # ``` # Converting days to other units. def test_day_to_year1(self) -> None: """ Test freq="D", unit="Y". """ series = self._get_series(seed=1, periods=9, freq="D") rule = "Y" actual_default = ( csigna.resample(series, rule=rule) .sum() .rename(f"Output in freq='{rule}'") ) actual_closed_left = ( csigna.resample(series, rule=rule, closed="left") .sum() .rename(f"Output in freq='{rule}'") ) act = self._get_output_txt(series, actual_default, actual_closed_left) exp = r""" Input: Input in freq='D' 2014-12-26 0.162435 2014-12-27 0.263693 2014-12-28 0.149701 2014-12-29 -0.010413 2014-12-30 -0.031170 2014-12-31 -0.174783 2015-01-01 -0.230455 2015-01-02 -0.132095 2015-01-03 -0.176312 Output with default arguments: Output in freq='Y' 2014-12-31 0.359463 2015-12-31 -0.538862 Output with closed='left': Output in freq='Y' 2014-12-31 0.534246 2015-12-31 -0.713644 """.lstrip().rstrip() self.assert_equal(act, exp, fuzzy_match=True) def test_day_to_month1(self) -> None: """ Test freq="D", unit="M". """ series = self._get_series(seed=1, periods=9, freq="D") actual_default = ( csigna.resample(series, rule="M").sum().rename("Output in freq='M'") ) actual_closed_left = ( csigna.resample(series, rule="M", closed="left") .sum() .rename("Output in freq='M'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) def test_day_to_week1(self) -> None: """ Test freq="D", unit="W". """ series = self._get_series(seed=1, periods=9, freq="D") actual_default = ( csigna.resample(series, rule="W").sum().rename("Output in freq='W'") ) actual_closed_left = ( csigna.resample(series, rule="W", closed="left") .sum() .rename("Output in freq='W'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) def test_day_to_business_day1(self) -> None: """ Test freq="D", unit="B". """ series = self._get_series(seed=1, periods=9, freq="D") actual_default = ( csigna.resample(series, rule="B").sum().rename("Output in freq='B'") ) actual_closed_left = ( csigna.resample(series, rule="B", closed="left") .sum() .rename("Output in freq='B'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) # Equal frequency resampling. def test_only_day1(self) -> None: """ Test freq="D", unit="D". """ series = self._get_series(seed=1, periods=9, freq="D") actual_default = ( csigna.resample(series, rule="D").sum().rename("Output in freq='D'") ) actual_closed_left = ( csigna.resample(series, rule="D", closed="left") .sum() .rename("Output in freq='D'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) def test_only_minute1(self) -> None: """ Test freq="T", unit="T". """ series = self._get_series(seed=1, periods=9, freq="T") actual_default = ( csigna.resample(series, rule="T").sum().rename("Output in freq='T'") ) actual_closed_left = ( csigna.resample(series, rule="T", closed="left") .sum() .rename("Output in freq='T'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) def test_only_business_day1(self) -> None: """ Test freq="B", unit="B". """ series = self._get_series(seed=1, periods=9, freq="B") actual_default = ( csigna.resample(series, rule="B").sum().rename("Output in freq='B'") ) actual_closed_left = ( csigna.resample(series, rule="B", closed="left") .sum() .rename("Output in freq='B'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) # Upsampling. def test_upsample_month_to_day1(self) -> None: """ Test freq="M", unit="D". """ series = self._get_series(seed=1, periods=3, freq="M") actual_default = ( csigna.resample(series, rule="D").sum().rename("Output in freq='D'") ) actual_closed_left = ( csigna.resample(series, rule="D", closed="left") .sum() .rename("Output in freq='D'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) def test_upsample_business_day_to_day1(self) -> None: """ Test freq="B", unit="D". """ series = self._get_series(seed=1, periods=9, freq="B") actual_default = ( csigna.resample(series, rule="D").sum().rename("Output in freq='D'") ) actual_closed_left = ( csigna.resample(series, rule="D", closed="left") .sum() .rename("Output in freq='D'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) # Resampling freq-less series. def test_no_freq_day_to_business_day1(self) -> None: """ Test for an input without `freq`. """ series = self._get_series(seed=1, periods=9, freq="D").rename( "Input with no freq" ) # Remove some observations in order to make `freq` None. series = series.drop(series.index[3:7]) actual_default = ( csigna.resample(series, rule="B").sum().rename("Output in freq='B'") ) actual_closed_left = ( csigna.resample(series, rule="B", closed="left") .sum() .rename("Output in freq='B'") ) txt = self._get_output_txt(series, actual_default, actual_closed_left) self.check_string(txt) @staticmethod def _get_series(seed: int, periods: int, freq: str) -> pd.Series: """ Periods include: 26/12/2014 - Friday, workday, 5th DoW 27/12/2014 - Saturday, weekend, 6th DoW 28/12/2014 - Sunday, weekend, 7th DoW 29/12/2014 - Monday, workday, 1th DoW 30/12/2014 - Tuesday, workday, 2th DoW 31/12/2014 - Wednesday, workday, 3th DoW 01/12/2014 - Thursday, workday, 4th DoW 02/12/2014 - Friday, workday, 5th DoW 03/12/2014 - Saturday, weekend, 6th DoW """ arma_process = cartif.ArmaProcess([1], [1]) date_range = {"start": "2014-12-26", "periods": periods, "freq": freq} series = arma_process.generate_sample( date_range_kwargs=date_range, scale=0.1, seed=seed ).rename(f"Input in freq='{freq}'") return series @staticmethod def _get_output_txt( input_data: pd.Series, output_default: pd.Series, output_closed_left: pd.Series, ) -> str: """ Create string output for tests results. """ input_string = hut.convert_df_to_string(input_data, index=True) output_default_string = hut.convert_df_to_string( output_default, index=True ) output_closed_left_string = hut.convert_df_to_string( output_closed_left, index=True ) txt = ( f"Input:\n{input_string}\n\n" f"Output with default arguments:\n{output_default_string}\n\n" f"Output with closed='left':\n{output_closed_left_string}\n" ) return txt class Test_resample_df(hut.TestCase): # Converting days to other units. def test_day_to_year1(self) -> None: """ Test freq="D", unit="Y". """ df = self._get_df(seed=1, periods=9, freq="D") actual_default = csigna.resample(df, rule="Y").sum() actual_default.columns = [ "1st output in freq='Y'", "2nd output in freq='Y'", ] actual_closed_left = csigna.resample(df, rule="Y", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='Y'", "2nd output in freq='Y'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) def test_day_to_month1(self) -> None: """ Test freq="D", unit="M". """ df = self._get_df(seed=1, periods=9, freq="D") actual_default = csigna.resample(df, rule="M").sum() actual_default.columns = [ "1st output in freq='M'", "2nd output in freq='M'", ] actual_closed_left = csigna.resample(df, rule="M", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='M'", "2nd output in freq='M'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) def test_day_to_week1(self) -> None: """ Test freq="D", unit="W". """ df = self._get_df(seed=1, periods=9, freq="D") actual_default = csigna.resample(df, rule="W").sum() actual_default.columns = [ "1st output in freq='W'", "2nd output in freq='W'", ] actual_closed_left = csigna.resample(df, rule="W", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='W'", "2nd output in freq='W'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) def test_day_to_business_day1(self) -> None: """ Test freq="D", unit="B". """ df = self._get_df(seed=1, periods=9, freq="D") actual_default = csigna.resample(df, rule="B").sum() actual_default.columns = [ "1st output in freq='B'", "2nd output in freq='B'", ] actual_closed_left = csigna.resample(df, rule="B", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='B'", "2nd output in freq='B'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) # Equal frequency resampling. def test_only_day1(self) -> None: """ Test freq="D", unit="D". """ df = self._get_df(seed=1, periods=9, freq="D") actual_default = csigna.resample(df, rule="D").sum() actual_default.columns = [ "1st output in freq='D'", "2nd output in freq='D'", ] actual_closed_left = csigna.resample(df, rule="D", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='D'", "2nd output in freq='D'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) def test_only_minute1(self) -> None: """ Test freq="T", unit="T". """ df = self._get_df(seed=1, periods=9, freq="T") actual_default = csigna.resample(df, rule="T").sum() actual_default.columns = [ "1st output in freq='T'", "2nd output in freq='T'", ] actual_closed_left = csigna.resample(df, rule="T", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='T'", "2nd output in freq='T'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) def test_only_business_day1(self) -> None: """ Test freq="B", unit="B". """ df = self._get_df(seed=1, periods=9, freq="B") actual_default = csigna.resample(df, rule="B").sum() actual_default.columns = [ "1st output in freq='B'", "2nd output in freq='B'", ] actual_closed_left = csigna.resample(df, rule="B", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='B'", "2nd output in freq='B'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) # Upsampling. def test_upsample_month_to_day1(self) -> None: """ Test freq="M", unit="D". """ df = self._get_df(seed=1, periods=3, freq="M") actual_default = csigna.resample(df, rule="D").sum() actual_default.columns = [ "1st output in freq='D'", "2nd output in freq='D'", ] actual_closed_left = csigna.resample(df, rule="D", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='D'", "2nd output in freq='D'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) def test_upsample_business_day_to_day1(self) -> None: """ Test freq="B", unit="D". """ df = self._get_df(seed=1, periods=9, freq="B") actual_default = csigna.resample(df, rule="D").sum() actual_default.columns = [ "1st output in freq='D'", "2nd output in freq='D'", ] actual_closed_left = csigna.resample(df, rule="D", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='D'", "2nd output in freq='D'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) # Resampling freq-less series. def test_no_freq_day_to_business_day1(self) -> None: """ Test for an input without `freq`. """ df = self._get_df(seed=1, periods=9, freq="D") df.columns = ["1st input with no freq", "2nd input with no freq"] # Remove some observations in order to make `freq` None. df = df.drop(df.index[3:7]) actual_default = csigna.resample(df, rule="B").sum() actual_default.columns = [ "1st output in freq='B'", "2nd output in freq='B'", ] actual_closed_left = csigna.resample(df, rule="B", closed="left").sum() actual_closed_left.columns = [ "1st output in freq='B'", "2nd output in freq='B'", ] txt = self._get_output_txt(df, actual_default, actual_closed_left) self.check_string(txt) @staticmethod def _get_df(seed: int, periods: int, freq: str) -> pd.DataFrame: """ Periods include: 26/12/2014 - Friday, workday, 5th DoW 27/12/2014 - Saturday, weekend, 6th DoW 28/12/2014 - Sunday, weekend, 7th DoW 29/12/2014 - Monday, workday, 1th DoW 30/12/2014 - Tuesday, workday, 2th DoW 31/12/2014 - Wednesday, workday, 3th DoW 01/12/2014 - Thursday, workday, 4th DoW 02/12/2014 - Friday, workday, 5th DoW 03/12/2014 - Saturday, weekend, 6th DoW """ arma_process = cartif.ArmaProcess([1], [1]) date_range = {"start": "2014-12-26", "periods": periods, "freq": freq} srs_1 = arma_process.generate_sample( date_range_kwargs=date_range, scale=0.1, seed=seed ).rename(f"1st input in freq='{freq}'") srs_2 = arma_process.generate_sample( date_range_kwargs=date_range, scale=0.1, seed=seed + 1 ).rename(f"2nd input in freq='{freq}'") df = pd.DataFrame([srs_1, srs_2]).T return df @staticmethod def _get_output_txt( input_data: pd.DataFrame, output_default: pd.DataFrame, output_closed_left: pd.DataFrame, ) -> str: """ Create string output for tests results. """ input_string = hut.convert_df_to_string(input_data, index=True) output_default_string = hut.convert_df_to_string( output_default, index=True ) output_closed_left_string = hut.convert_df_to_string( output_closed_left, index=True ) txt = ( f"Input:\n{input_string}\n\n" f"Output with default arguments:\n{output_default_string}\n\n" f"Output with closed='left':\n{output_closed_left_string}\n" ) return txt class Test_calculate_inverse(hut.TestCase): def test1(self) -> None: df = pd.DataFrame([[1, 2], [3, 4]]) inverse_df = hut.convert_df_to_string( csigna.calculate_inverse(df), index=True ) self.check_string(inverse_df) class Test_calculate_presudoinverse(hut.TestCase): def test1(self) -> None: df = pd.DataFrame([[1, 2], [3, 4], [5, 6]]) inverse_df = hut.convert_df_to_string( csigna.calculate_pseudoinverse(df), index=True ) self.check_string(inverse_df)

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c3746be33782173c6e26ae613070a38808df811d

236

py

Python

iotbx/dsn6/__init__.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

iotbx/dsn6/__init__.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

iotbx/dsn6/__init__.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

[ "BSD-3-Clause-LBNL" ]

null

# TODO TESTS from __future__ import division import cctbx.array_family.flex # import dependency import boost.python ext = boost.python.import_ext("iotbx_dsn6_map_ext") from iotbx_dsn6_map_ext import * import iotbx_dsn6_map_ext as ext

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36,657

py

Python

tessellate/utils/getRing.py

scientificomputing/tessellate

2166863476faf7542530d0d6e2a28da3ab3f909c

[ "Apache-2.0" ]

null

tessellate/utils/getRing.py

scientificomputing/tessellate

2166863476faf7542530d0d6e2a28da3ab3f909c

[ "Apache-2.0" ]

null

tessellate/utils/getRing.py

scientificomputing/tessellate

2166863476faf7542530d0d6e2a28da3ab3f909c

[ "Apache-2.0" ]

2

2017-12-08T22:13:43.000Z

2019-10-14T10:12:50.000Z

import logging logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) class dd_dict(dict): # the dd is for "deferred delete" _deletes = None def __delitem__(self, key): if key not in self: raise KeyError(str(key)) dict.__delitem__(self, key) if self._deletes is None else self._deletes.add(key) def __enter__(self): self._deletes = set() def __exit__(self, type, value, tb): for key in self._deletes: try: dict.__delitem__(self, key) except KeyError: pass self._deletes = None #| common class ordering of ring systems for 5,6 rings common5rings = [("C2'", "C3'", "C4'", "O4'", "C1'"), ("C2R", "C3R", "C4R", "O4R", "C1R")] common6rings = [("C3'", "C4'", "C5'", "O5'", "C1'", "C2'"), ("C3A", "C4A", "C5A", "O5A", "C1A", "C2A"), ("C3", "C4", "C5", "O5", "C1", "C2")] commonrings = common5rings + common6rings def getcommonring(ring): """ Common atom naming systems for rings, this routine is used to automagically reorder guesses for rings s.t. that they provide the expected results... """ thiscycle = {} if len(ring) == 5: import re for atom in ring: # note this is for a five ring... if (atom.startswith('C1') or atom.endswith('C1')) and ('1' in re.findall(r'\d+',atom)): # make sure not matching C13 thiscycle['C1'] = atom elif (atom.startswith('C2') or atom.endswith('C2')) and ('2' in re.findall(r'\d+',atom)): # make sure not matching C23 thiscycle['C2'] = atom elif (atom.startswith('C3') or atom.endswith('C3')) and ('3' in re.findall(r'\d+',atom)): # make sure not matching C33 thiscycle['C3'] = atom elif (atom.startswith('C4') or atom.endswith('C4')) and ('4' in re.findall(r'\d+',atom)): # make sure not matching C44 thiscycle['C4'] = atom elif atom.startswith('O') or atom.endswith('O'): thiscycle['O4'] = atom else: # there is an atom that is not common. So cannot apply this ordering trick. return None try: return (thiscycle['C2'], thiscycle['C3'], thiscycle['C4'], thiscycle['O4'], thiscycle['C1']) except: return None elif len(ring) == 6: import re for atom in ring: # note this is for a six ring... if (atom.startswith('C1') or atom.endswith('C1')) and ('1' in re.findall(r'\d+',atom)): # make sure not matching C13 thiscycle['C1'] = atom elif (atom.startswith('C2') or atom.endswith('C2')) and ('2' in re.findall(r'\d+',atom)): thiscycle['C2'] = atom elif (atom.startswith('C3') or atom.endswith('C3')) and ('3' in re.findall(r'\d+',atom)): thiscycle['C3'] = atom elif (atom.startswith('C4') or atom.endswith('C4')) and ('4' in re.findall(r'\d+',atom)): thiscycle['C4'] = atom elif (atom.startswith('C5') or atom.endswith('C5')) and ('5' in re.findall(r'\d+',atom)): thiscycle['C5'] = atom elif atom.startswith('O') or atom.endswith('O'): thiscycle['O5'] = atom else: # what about C10 O9 C14 C13 C12 C11 - should reverse to C11 C12 C13 C14 O9 C10 # if 10>9 and 10<11 and 11 < 12 then reverse # import copy # allatoms = " ".join(ring) # intsinatoms=re.findall(r'\d+',allatoms) # logger.debug("ORDERING %s %s", ring, intsinatoms) # if int(intsinatoms[0])> int(intsinatoms[1]) and int(intsinatoms[0])<int(intsinatoms[5]) and int(intsinatoms[5])< int(intsinatoms[4]): # localcopyofring=copy.deepcopy(ring) # #popped=localcopyofring.pop() # #localcopyofring.insert(0,popped) # localcopyofring.reverse() # logger.debug("REVERSE %s", localcopyofring) # return localcopyofring #. get O into position 3 (0,1,2,3) #. Get O to position 3, then check numbering 4<0 #!. this depends on atom numbering. If C1 is numbered higher than C4 then the non-contextualised conformer will seem incorrect indices = [] for i, elem in enumerate(ring): if 'O' in elem: indices.append(i) if len(indices) == 1: opos=indices[0] if opos==3: return ring pos=opos while pos!=3: popped=ring.pop() ring.insert(0,popped) indices=[] for i, elem in enumerate(ring): if 'O' in elem: indices.append(i) pos=indices[0] logger.debug("POPPEDTOPOS4 %s", ring) try: if int(re.findall(r'\d+',ring[4])[0]) < int(re.findall(r'\d+',ring[0])[0]) : logger.debug("RETURN4 %s", ring) return ring else: popped=ring.pop() ring.insert(0,popped) logger.debug("POPPEDAGAIN %s", ring) return ring except: return None else: return None try: return ( thiscycle['C3'], thiscycle['C4'], thiscycle['C5'], thiscycle['O5'], thiscycle['C1'], thiscycle['C2']) except: return None else: return None def getring(startatom, atomset): """getRing(startatom, atomset, lookup, oatoms)->atoms, bonds starting at startatom do a bfs traversal through the atoms in atomset and return the smallest ring found returns (), () on failure note: atoms and bonds are not returned in traversal order""" path = {} bpaths = {} for atomID in atomset.keys(): # initially the paths are empty path[atomID] = [] bpaths[atomID] = [] #... insert from Figueras paper # The BFS algorithm. We wish to find the smallest ring in the molecule that includes startatom # we assign paths , path[i] to each node i. The path conrinas the nodes in the path from the starting node to node i # initialise all the paths to null # assign values to paths in the starting node #for subnodes in atomset[startatom]: # path[subnodes]=[startatom,subnodes] # now check if subnodes subnodes is empty # but actually will start at starting node but do all nodes... q = [] # Initialize the queue with nodes attached to rootNode # and initialise these paths for subnodes in atomset[startatom]: q.append([startatom, subnodes]) path[subnodes] = [startatom, subnodes] logger.debug("getRING q nodes %s",q) logger.debug("getRING path nodes %s",path) # loop while the queue size is greater than zero (it exists) while q: root, node = q.pop() for subnodes in atomset[node]: logger.debug("getRING subnodes %s in set %s, root is %s", subnodes, atomset[node], root) if subnodes != root: # node shouldn't be start atom but check... # check if path is empty or not if not path[subnodes]: # if empty assign path as root path + subnodes path[subnodes] = path[node] + [subnodes] logger.debug("getRING had no paths now path %s from node %s paths %s and itself %s", path[subnodes], node, path[node],[subnodes]) q.append([node, subnodes]) else: # possible ring closure # compute the intersection of path[root], path[subnodes], it must be a singleton i.e. one element intersection = set(path[node]) & set(path[subnodes]) logger.debug("getRING intersection %s", intersection) #print "INTERSECTION ", set(path[node])&set(path[subnodes]), len(intersection) if len(intersection) == int(1): logger.debug("getRING use intersection %s is size %i", intersection, len(intersection)) #union union = set(path[node]) | set(path[subnodes]) logger.debug("getRING union %s", union) avail = sorted(list(union)) # sort here to prevent dupl logger.debug("getRING avail union %s", avail) chosen = [] if len(union) < 5 or len(union)> 400: # it is pointless considering return () while avail: if not chosen: chosen.append(avail[0]) del avail[0] else: lastadded = chosen[-1] for children in atomset[lastadded]: if children not in chosen and children in avail: # child must not be used and must be part of the atoms in the ring chosen.append(children) del avail[avail.index(children)] break return chosen else: # ignore path logger.debug("getRING pass on intersection %s is size %i", intersection, len(intersection)) pass else: logger.debug("subnode=root %s %s", subnodes, root) logger.debug("returning nothing") return () # for subnodes in atomSet[startAtom]: def min_degree(edges): """getRing(edges) loop through all edges and calculate the min degree of the current nodes in the graph returns an int min_degree This is this literally the lowest available degree. A graph with 1,2 and 3 degree nodes has a mindegree of 1""" min_atom, min_degree = None, int(10000) # loop over edges for atom in edges: if len(edges[atom]) < min_degree: min_atom = atom min_degree = len(edges[atom]) return min_atom, min_degree def create_graph_and_find_rings_suite(atomlist, mineuclid=1.1, maxeuclid=2.0): """ find possible rings """ try: # import getRing import tessellate.utils.getRing as getRing except Exception as e: print("Error - Cannot import module ", e) exit(1) SSSR=[] SSSR1=getRing.create_graph_and_find_rings_d3069(atomlist,mineuclid,maxeuclid) SSSR2=getRing.create_graph_and_find_rings_6abb(atomlist,mineuclid,maxeuclid) SSSR.extend(SSSR1) for itm in SSSR2: if itm not in SSSR: SSSR.extend([itm]) return SSSR def make_unique(original_list): unique_list = [] map(lambda x: unique_list.append(x) if (x not in unique_list) else False, original_list) return unique_list def create_graph_and_find_rings_d3069(atomlist, mineuclid=1.1, maxeuclid=2.0): """ :type atomlist: list :type mineuclid: float :type maxeuclid: float :rtype : list """ try: #import getRing import tessellate.utils.getRing as getRing import itertools import numpy as np except Exception as e: print("Error - Cannot import module ", e) exit(1) SSSR = [] # keep track of all the rings edges = {} for a, b in itertools.combinations(atomlist, 2): # work out euclidean distance and choose to call this an edge if mineuclid<dist<maxeuclid dist = np.linalg.norm(a[1] - b[1]) if maxeuclid > dist > mineuclid: try: edges[a[0]].append(b[0]) except: edges[a[0]] = [b[0]] try: edges[b[0]].append(a[0]) except: edges[b[0]] = [a[0]] # debug print out the edges for atom in edges: logger.debug('Atom edges info %s %s %s', atom, edges[atom], len(edges[atom])) # Now recursively remove all terminal nodes i.e.with only one edge alledges = dict(edges) deletededges = {} #logger.debug("allededges dict %s",alledges) #logger.debug("edges %s",edges) if edges != []: while edges: for atom in dict(edges).keys(): N2nodes = [] N3nodes = [] logger.debug("all edge keys %s",edges.keys()) logger.debug("Current edge %s",atom) if atom in deletededges.keys(): logger.critical("revisiting a deleted edge") exit(1) if int(len(edges[atom])) == int(0): # trim degree zero nodes logger.debug("Zero edge node deleted %s",atom) deletededges[atom]=edges[atom] del edges[atom] # cannot delete dictionary while iterating use a while elif int(len(edges[atom])) == int(1): # trim degree one nodes # pop it from other atoms logger.debug("One edge node %s with %i edges %s removed from parent node edges list %s",atom, len(edges[atom]),edges[atom],edges[edges[atom][0]]) edges[edges[atom][0]].remove(atom) edges[atom] = [] deletededges[atom]=edges[atom] del edges[atom] # cannot delete dictionary while iterating use a while elif int(len(edges[atom])) == int(2): # find nodes of degree 2 and add to N2 nodes logger.debug("Two edge node %s with %i edges",atom, len(edges[atom])) N2nodes.append(atom) elif int(len(edges[atom])) == int(3): # find nodes of degree 2 and add to N3 nodes logger.debug("Three edge node %s with %i edges",atom, len(edges[atom])) N3nodes.append(atom) if getRing.min_degree(edges)[1] == int(2) and N2nodes: # the minimum degree of the entire graph logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) for N2 in N2nodes: ring = getRing.getring(N2, alledges) # give this ring the entire graph logger.debug("ring from getring %s", ring) if len(ring) > 0: if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) pass else: SSSR.append(ring) else: pass try: # try isolate and eliminate one N2 node logger.debug("isolate N2 node %s with %i edges and break one bond ", N2, len(edges[N2]) ) bondtobreak=edges[N2].pop() logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) edges[bondtobreak].remove(N2) except: logger.debug("N2 not eliminated") pass elif getRing.min_degree(edges)[1] == int(3) and N3nodes: # the minimum degree of the entire graph logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) logger.debug("N3 d3069") for N3 in N3nodes: ring = getRing.getring(N3, alledges) # give this ring the entire graph logger.debug("ring from getring %s", ring) if len(ring) > 0: if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) pass else: SSSR.append(ring) else: pass try: # try isolate and eliminate one N3 node if len(edges[N3])>1: logger.debug("isolate N3 node %s with %i edges and break one bond ", N3, len(edges[N3]) ) bondtobreak=edges[N3].pop() logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) edges[bondtobreak].remove(N3) except Exception as e: logger.error(e) raise e # if edges != []: # while edges: # #currentkeyset=list(edges.keys()) # for atom in list(edges.keys()): # N2nodes = [] # N3nodes = [] # logger.debug("all edge keys %s",edges.keys()) # logger.debug("deleted edge keys %s",deletededges.keys()) # logger.debug("Current edge %s",atom) # if atom in deletededges.keys(): # logger.critical("revisiting a deleted edge") # exit(1) # if int(len(edges[atom])) == int(0): # trim degree zero nodes # logger.debug("Zero edge node deleted %s",atom) # deletededges[atom]=edges[atom] # del edges[atom] # cannot delete dictionary while iterating use a while # continue # elif int(len(edges[atom])) == int(1): # trim degree one nodes # # pop it from other atoms # logger.debug("One edge node %s with %i edges %s removed from parent node edges list %s",atom, len(edges[atom]),edges[atom],edges[edges[atom][0]]) # edges[edges[atom][0]].remove(atom) # edges[atom] = [] # deletededges[atom]=edges[atom] # del edges[atom] # cannot delete dictionary while iterating use a while # continue # elif int(len(edges[atom])) == int(2): # find nodes of degree 2 and add to N2 nodes # logger.debug("Two edge node %s with %i edges",atom, len(edges[atom])) # N2nodes.append(atom) # elif int(len(edges[atom])) == int(3): # find nodes of degree 2 and add to N3 nodes # logger.debug("Three edge node %s with %i edges",atom, len(edges[atom])) # N3nodes.append(atom) # # if getRing.min_degree(edges)[1] == int(2) and N2nodes: # the minimum degree of the entire graph # logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) # for N2 in N2nodes: # ring = getRing.getring(N2, alledges) # give this ring the entire graph # logger.debug("ring from getring %s", ring) # if len(ring) > 0: # if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing # logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) # pass # else: # SSSR.append(ring) # else: # pass # try: # try isolate and eliminate one N2 node # logger.debug("isolate N2 node %s with %i edges and break one bond ", N2, len(edges[N2]) ) # bondtobreak=edges[N2].pop() # logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) # edges[bondtobreak].remove(N2) # except: # logger.debug("N2 not eliminated") # pass # # elif getRing.min_degree(edges)[1] == int(3) and N3nodes: # the minimum degree of the entire graph # logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) # logger.debug("N3 d3069") # for N3 in N3nodes: # ring = getRing.getring(N3, alledges) # give this ring the entire graph # logger.debug("ring from getring %s", ring) # if len(ring) > 0: # if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing # logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) # pass # else: # SSSR.append(ring) # else: # pass # try: # try isolate and eliminate one N3 node # logger.debug("isolate N3 node %s with %i edges and break one bond ", N3, len(edges[N3]) ) # bondtobreak=edges[N3].pop() # logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) # edges[bondtobreak].remove(N3) # except Exception as e: # logger.error(e) # print(e) if SSSR: logger.debug("SSSR %s", SSSR) return SSSR def create_graph_and_find_rings_old(atomlist, mineuclid=1.1, maxeuclid=2.0): """ :type atomlist: list :type mineuclid: float :type maxeuclid: float :rtype : list """ try: # import getRing import tessellate.utils.getRing as getRing import itertools import numpy as np except Exception as e: print("Error - Cannot import module ", e) exit(1) SSSR = [] # keep track of all the rings edges = {} for a, b in itertools.combinations(atomlist, 2): # work out euclidean distance and choose to call this an edge if mineuclid<dist<maxeuclid dist = np.linalg.norm(a[1] - b[1]) if maxeuclid > dist > mineuclid: try: edges[a[0]].append(b[0]) except: edges[a[0]] = [b[0]] try: edges[b[0]].append(a[0]) except: edges[b[0]] = [a[0]] for atom in edges: logger.debug('Atom edges info %s %s %s', atom, edges[atom], len(edges[atom])) # Now recursively remove all terminal nodes i.e.with only one edge alledges = dict(edges) if edges != []: while edges: for atom in dict(edges).keys(): N2nodes = [] logger.debug("all edge keys %s",edges.keys()) logger.debug("Current edge %s",atom) if int(len(edges[atom])) == int(0): # trim degree zero nodes logger.debug("Zero edge node deleted %s",atom) del edges[atom] # cannot delete dictionary while iterating use a while elif int(len(edges[atom])) == int(1): # trim degree one nodes # pop it from other atoms logger.debug("One edge node %s with %i edges %s removed from parent node edges list %s",atom, len(edges[atom]),edges[atom],edges[edges[atom][0]]) edges[edges[atom][0]].remove(atom) edges[atom] = [] del edges[atom] # cannot delete dictionary while iterating use a while elif int(len(edges[atom])) == int(2): # find nodes of degree 2 and add to N2 nodes logger.debug("Two edge node %s with %i edges",atom, len(edges[atom])) N2nodes.append(atom) if getRing.min_degree(edges)[1] == int(2): # the minimum degree of the entire graph logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) for N2 in N2nodes: ring = getRing.getring(N2, alledges) # give this ring the entire graph logger.debug("ring from getring %s", ring) if len(ring) > 0: if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) pass else: SSSR.append(ring) else: pass try: # try isolate and eliminate one N2 node logger.debug("isolate N2 node %s with %i edges and break one bond ", N2, len(edges[N2]) ) bondtobreak=edges[N2].pop() logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) edges[bondtobreak].remove(N2) except: logger.debug("Couldn't eliminate N2") pass elif getRing.min_degree(edges)[1] == int(3): # the minimum degree of the entire graph logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) ring = getRing.getring(atom, alledges) logger.debug("ring from getring %s", ring) if len(ring) > 0: if ring in SSSR: pass else: SSSR.append(ring) else: pass try: # select an optimum edge for elimination. trial each edge in alledges logger.error("FUTURETODO - N3 check edges not yet implemented only applicable to cages etc." ) exit(1) except: pass if SSSR: logger.debug("SSSR %s", SSSR) return SSSR def create_graph_and_find_rings_6abb(atomlist, mineuclid=1.1, maxeuclid=2.0): """ :type atomlist: list :type mineuclid: float :type maxeuclid: float :rtype : list """ try: # import getRing import tessellate.utils.getRing as getRing import itertools import numpy as np except Exception as e: print("Error - Cannot import module ", e) exit(1) SSSR = [] # keep track of all the rings edges = {} for a, b in itertools.combinations(atomlist, 2): # work out euclidean distance and choose to call this an edge if mineuclid<dist<maxeuclid dist = np.linalg.norm(a[1] - b[1]) if maxeuclid > dist > mineuclid: #edges.append([a,b]) # this works but is difficult to remove edges later try: edges[a[0]].append(b[0]) except: edges[a[0]] = [b[0]] try: edges[b[0]].append(a[0]) except: edges[b[0]] = [a[0]] for atom in edges: logger.debug('Atom edges info %s %s %s', atom, edges[atom], len(edges[atom])) # Now recursively remove all terminal nodes i.e.with only one edge alledges = dict(edges) deletededges = {} if edges != []: while edges: N2nodes = [] N3nodes = [] for atom in dict(edges).keys(): logger.debug("all edge keys %s",edges.keys()) logger.debug("Current edge %s",atom) if atom in deletededges.keys(): logger.critical("revisiting a deleted edge") exit(1) if int(len(edges[atom])) == int(0): # trim degree zero nodes logger.debug("Zero edge node deleted %s",atom) deletededges[atom]=edges[atom] del edges[atom] # cannot delete dictionary while iterating use a while elif int(len(edges[atom])) == int(1): # trim degree one nodes # pop it from other atoms logger.debug("One edge node %s with %i edges %s removed from parent node edges list %s",atom, len(edges[atom]),edges[atom],edges[edges[atom][0]]) edges[edges[atom][0]].remove(atom) edges[atom] = [] deletededges[atom]=edges[atom] #logger.critical("Deleting atom %s %s",atom, deletededges) del edges[atom] # cannot delete dictionary while iterating use a while elif int(len(edges[atom])) == int(2): # find nodes of degree 2 and add to N2 nodes logger.debug("Two edge node %s with %i edges",atom, len(edges[atom])) N2nodes.append(atom) elif int(len(edges[atom])) == int(3): # find nodes of degree 2 and add to N3 nodes logger.debug("Three edge node %s with %i edges",atom, len(edges[atom])) N3nodes.append(atom) if getRing.min_degree(edges)[1] == int(2) and N2nodes: # the minimum degree of the entire graph logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) for N2 in N2nodes: ring = getRing.getring(N2, alledges) # give this ring the entire graph logger.debug("ring from getring %s", ring) if len(ring) > 0: if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) else: SSSR.append(ring) logger.debug("isolate N2 node %s with %i edges and break one bond ", N2nodes[0], len(edges[N2nodes[0]]) ) bondtobreak=edges[N2nodes[0]].pop(0) logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) edges[bondtobreak].remove(N2nodes[0]) elif getRing.min_degree(edges)[1] == int(3) and N3nodes: # the minimum degree of the entire graph logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) logger.debug("N3 6abb") for N3 in N3nodes: ring = getRing.getring(N3, alledges) # give this ring the entire graph logger.debug("ring from getring %s", ring) if len(ring) > 0: if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) pass else: SSSR.append(ring) else: pass try: # try isolate and eliminate one N3 node if len(edges[N3])>1: logger.debug("isolate N3 node %s with %i edges and break one bond ", N3, len(edges[N3]) ) bondtobreak=edges[N3].pop() logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) edges[bondtobreak].remove(N3) except Exception as e: logger.error(e) raise e # if edges != []: # while edges: # N2nodes = [] # N3nodes = [] # #for atom in dict(edges).keys(): # for atom in list(edges.keys()): # logger.debug("all edge keys %s",edges.keys()) # logger.debug("deleted edge keys %s",deletededges.keys()) # logger.debug("Current edge %s",atom) # if atom in deletededges.keys(): # logger.critical("revisiting a deleted edge") # exit(1) # # if int(len(edges[atom])) == int(0): # trim degree zero nodes # logger.debug("Zero edge node deleted %s",atom) # deletededges[atom]=edges[atom] # del edges[atom] # cannot delete dictionary while iterating use a while # elif int(len(edges[atom])) == int(1): # trim degree one nodes # # pop it from other atoms # logger.debug("One edge node %s with %i edges %s removed from parent node edges list %s",atom, len(edges[atom]),edges[atom],edges[edges[atom][0]]) # edges[edges[atom][0]].remove(atom) # edges[atom] = [] # deletededges[atom]=edges[atom] # #logger.critical("Deleting atom %s %s",atom, deletededges) # del edges[atom] # cannot delete dictionary while iterating use a while # elif int(len(edges[atom])) == int(2): # find nodes of degree 2 and add to N2 nodes # logger.debug("Two edge node %s with %i edges",atom, len(edges[atom])) # N2nodes.append(atom) # elif int(len(edges[atom])) == int(3): # find nodes of degree 2 and add to N3 nodes # logger.debug("Three edge node %s with %i edges",atom, len(edges[atom])) # N3nodes.append(atom) # # if getRing.min_degree(edges)[1] == int(2) and N2nodes: # the minimum degree of the entire graph # logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) # for N2 in N2nodes: # ring = getRing.getring(N2, alledges) # give this ring the entire graph # logger.debug("ring from getring %s", ring) # if len(ring) > 0: # if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing # logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) # else: # SSSR.append(ring) # logger.debug("isolate N2 node %s with %i edges and break one bond ", N2nodes[0], len(edges[N2nodes[0]]) ) # bondtobreak=edges[N2nodes[0]].pop(0) # logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) # edges[bondtobreak].remove(N2nodes[0]) # # elif getRing.min_degree(edges)[1] == int(3) and N3nodes: # the minimum degree of the entire graph # logger.debug("current min degree %i", getRing.min_degree(edges)[1] ) # logger.debug("N3 6abb") # for N3 in N3nodes: # ring = getRing.getring(N3, alledges) # give this ring the entire graph # logger.debug("ring from getring %s", ring) # if len(ring) > 0: # if ring in SSSR: # if exists then ignore, not sorting as a unique ring is being offered by getRing # logger.debug("ring already in SSSR %s %i %s", ring, len(ring),type(ring)) # pass # else: # SSSR.append(ring) # else: # pass # try: # try isolate and eliminate one N3 node # logger.debug("isolate N3 node %s with %i edges and break one bond ", N3, len(edges[N3]) ) # bondtobreak=edges[N3].pop() # logger.debug("break %s with current edges %s ", bondtobreak, edges[bondtobreak] ) # edges[bondtobreak].remove(N3) # except Exception as e: # logger.error(e) # print(e) if SSSR: logger.debug("SSSR %s", SSSR) return SSSR

51.629577

166

0.505961

4,280

36,657

4.310748

0.089486

0.064986

0.024715

0.012466

0.761626

0.7471

0.736369

0.726883

0.711545

0.70206

0

0.021041

0.389339

36,657

709

167

51.702398

0.803172

0.403197

0

0.736486

0

0.107359

0

1

0.024775

false

0.036036

0.038288

0

0.112613

0.009009

0

null

0

1

0

1

0

null

0

6

6f2abe8cd021cfc3a2b9a1151c68a7b8fe051a62

113

py

Python

python/map/map.py

hulkcoder/studycoder

93e05ff6c8778ac5698284c2b0862bab7e58b696

[ "MIT" ]

null

python/map/map.py

hulkcoder/studycoder

93e05ff6c8778ac5698284c2b0862bab7e58b696

[ "MIT" ]

9

2020-02-25T22:02:21.000Z

2022-03-30T23:06:06.000Z

python/map/map.py

bodhileafy/studycoder

07b526b8cc040ed3b3baf17ae0d93eac83a48d7a

[ "MIT" ]

null

print("map") a = "[" x = range(30000,32767,1) for i in range(30000,32768,1): a = a + str(i) + "," print a + "]"

18.833333

30

0.530973

21

113

2.857143

0.619048

0.333333

0

0.244444

0.20354

113

6

31

18.833333

0.422222

0

0.052632

0

null

0

null

0.333333

1

0

null

1

0

1

0

1

0

1

0

null

0

1

0

6

6f344cd75700d42f0d8fa6ffba402679bdf47ebc

38,490

py

Python

tests/backend/builders/test_sdist.py

ashemedai/hatch

9ec00d5e027c992efbc16dd777b1f6926368b6bf

[ "MIT" ]

null

tests/backend/builders/test_sdist.py

ashemedai/hatch

9ec00d5e027c992efbc16dd777b1f6926368b6bf

[ "MIT" ]

null

tests/backend/builders/test_sdist.py

ashemedai/hatch

9ec00d5e027c992efbc16dd777b1f6926368b6bf

[ "MIT" ]

null

import os import tarfile import pytest from hatchling.builders.plugin.interface import BuilderInterface from hatchling.builders.sdist import SdistBuilder from hatchling.builders.utils import get_reproducible_timestamp from hatchling.metadata.utils import get_core_metadata_constructors def test_class(): assert issubclass(SdistBuilder, BuilderInterface) def test_default_versions(isolation): builder = SdistBuilder(str(isolation)) assert builder.get_default_versions() == ['standard'] class TestSupportLegacy: def test_default(self, isolation): builder = SdistBuilder(str(isolation)) assert builder.support_legacy is builder.support_legacy is False def test_target(self, isolation): config = {'tool': {'hatch': {'build': {'targets': {'sdist': {'support-legacy': True}}}}}} builder = SdistBuilder(str(isolation), config=config) assert builder.support_legacy is builder.support_legacy is True class TestCoreMetadataConstructor: def test_default(self, isolation): builder = SdistBuilder(str(isolation)) assert builder.core_metadata_constructor is builder.core_metadata_constructor assert builder.core_metadata_constructor is get_core_metadata_constructors()['2.1'] def test_not_string(self, isolation): config = {'tool': {'hatch': {'build': {'targets': {'sdist': {'core-metadata-version': 42}}}}}} builder = SdistBuilder(str(isolation), config=config) with pytest.raises( TypeError, match='Field `tool.hatch.build.targets.sdist.core-metadata-version` must be a string' ): _ = builder.core_metadata_constructor def test_unknown(self, isolation): config = {'tool': {'hatch': {'build': {'targets': {'sdist': {'core-metadata-version': '9000'}}}}}} builder = SdistBuilder(str(isolation), config=config) with pytest.raises( ValueError, match=( f'Unknown metadata version `9000` for field `tool.hatch.build.targets.sdist.core-metadata-version`. ' f'Available: {", ".join(sorted(get_core_metadata_constructors()))}' ), ): _ = builder.core_metadata_constructor class TestConstructSetupPyFile: def test_default(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0'}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file([]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', ) """ ) def test_packages(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0'}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_description(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'description': 'foo'}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', description='foo', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_readme(self, helpers, isolation): config = { 'project': { 'name': 'my__app', 'version': '0.1.0', 'readme': {'content-type': 'text/markdown', 'text': 'test content\n'}, } } builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', long_description='test content\\n', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_authors_name(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'authors': [{'name': 'foo'}]}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', author='foo', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_authors_email(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'authors': [{'email': 'foo@domain'}]}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', author_email='foo@domain', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_authors_name_and_email(self, helpers, isolation): config = { 'project': {'name': 'my__app', 'version': '0.1.0', 'authors': [{'email': 'bar@domain', 'name': 'foo'}]} } builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', author_email='foo <bar@domain>', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_authors_multiple(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'authors': [{'name': 'foo'}, {'name': 'bar'}]}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', author='foo, bar', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_maintainers_name(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'maintainers': [{'name': 'foo'}]}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', maintainer='foo', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_maintainers_email(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'maintainers': [{'email': 'foo@domain'}]}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', maintainer_email='foo@domain', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_maintainers_name_and_email(self, helpers, isolation): config = { 'project': {'name': 'my__app', 'version': '0.1.0', 'maintainers': [{'email': 'bar@domain', 'name': 'foo'}]} } builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', maintainer_email='foo <bar@domain>', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_maintainers_multiple(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'maintainers': [{'name': 'foo'}, {'name': 'bar'}]}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', maintainer='foo, bar', packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_classifiers(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'classifiers': ['foo', 'bar']}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', classifiers=[ 'bar', 'foo', ], packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_dependencies(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'dependencies': ['foo==1', 'bar==5']}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', install_requires=[ 'bar==5', 'foo==1', ], packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_optional_dependencies(self, helpers, isolation): config = { 'project': { 'name': 'my__app', 'version': '0.1.0', 'optional-dependencies': { 'feature2': ['foo==1; python_version < "3"', 'bar==5'], 'feature1': ['foo==1', 'bar==5; python_version < "3"'], }, } } builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', extras_require={ 'feature1': [ 'bar==5; python_version < "3"', 'foo==1', ], 'feature2': [ 'bar==5', 'foo==1; python_version < "3"', ], }, packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_scripts(self, helpers, isolation): config = {'project': {'name': 'my__app', 'version': '0.1.0', 'scripts': {'foo': 'pkg:bar', 'bar': 'pkg:foo'}}} builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', entry_points={ 'console_scripts': [ 'bar = pkg:foo', 'foo = pkg:bar', ], }, packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_gui_scripts(self, helpers, isolation): config = { 'project': {'name': 'my__app', 'version': '0.1.0', 'gui-scripts': {'foo': 'pkg:bar', 'bar': 'pkg:foo'}} } builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', entry_points={ 'gui_scripts': [ 'bar = pkg:foo', 'foo = pkg:bar', ], }, packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_entry_points(self, helpers, isolation): config = { 'project': { 'name': 'my__app', 'version': '0.1.0', 'entry-points': { 'foo': {'bar': 'pkg:foo', 'foo': 'pkg:bar'}, 'bar': {'foo': 'pkg:bar', 'bar': 'pkg:foo'}, }, } } builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', entry_points={ 'bar': [ 'bar = pkg:foo', 'foo = pkg:bar', ], 'foo': [ 'bar = pkg:foo', 'foo = pkg:bar', ], }, packages=[ 'my_app', 'my_app.pkg', ], ) """ ) def test_all(self, helpers, isolation): config = { 'project': { 'name': 'my__app', 'version': '0.1.0', 'description': 'foo', 'readme': {'content-type': 'text/markdown', 'text': 'test content\n'}, 'authors': [{'email': 'bar@domain', 'name': 'foo'}], 'maintainers': [{'email': 'bar@domain', 'name': 'foo'}], 'classifiers': ['foo', 'bar'], 'dependencies': ['foo==1', 'bar==5'], 'optional-dependencies': { 'feature2': ['foo==1; python_version < "3"', 'bar==5'], 'feature1': ['foo==1', 'bar==5; python_version < "3"'], 'feature3': [], }, 'scripts': {'foo': 'pkg:bar', 'bar': 'pkg:foo'}, 'gui-scripts': {'foo': 'pkg:bar', 'bar': 'pkg:foo'}, 'entry-points': { 'foo': {'bar': 'pkg:foo', 'foo': 'pkg:bar'}, 'bar': {'foo': 'pkg:bar', 'bar': 'pkg:foo'}, }, } } builder = SdistBuilder(str(isolation), config=config) assert builder.construct_setup_py_file(['my_app', os.path.join('my_app', 'pkg')]) == helpers.dedent( """ # -*- coding: utf-8 -*- from setuptools import setup setup( name='my-app', version='0.1.0', description='foo', long_description='test content\\n', author_email='foo <bar@domain>', maintainer_email='foo <bar@domain>', classifiers=[ 'bar', 'foo', ], install_requires=[ 'bar==5', 'foo==1', ], extras_require={ 'feature1': [ 'bar==5; python_version < "3"', 'foo==1', ], 'feature2': [ 'bar==5', 'foo==1; python_version < "3"', ], }, entry_points={ 'console_scripts': [ 'bar = pkg:foo', 'foo = pkg:bar', ], 'gui_scripts': [ 'bar = pkg:foo', 'foo = pkg:bar', ], 'bar': [ 'bar = pkg:foo', 'foo = pkg:bar', ], 'foo': [ 'bar = pkg:foo', 'foo = pkg:bar', ], }, packages=[ 'my_app', 'my_app.pkg', ], ) """ ) class TestBuildStandard: def test_default(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version']}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': {'targets': {'sdist': {'versions': ['standard']}}}, }, }, } builder = SdistBuilder(str(project_path), config=config) build_path = project_path / 'dist' with project_path.as_cwd(): artifacts = list(builder.build()) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_default', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) stat = os.stat(str(extraction_directory / builder.project_id / 'PKG-INFO')) assert stat.st_mtime == get_reproducible_timestamp() def test_default_no_reproducible(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version']}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': {'targets': {'sdist': {'versions': ['standard'], 'reproducible': False}}}, }, }, } builder = SdistBuilder(str(project_path), config=config) build_path = project_path / 'dist' build_path.mkdir() with project_path.as_cwd(): artifacts = list(builder.build(str(build_path))) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_default', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) stat = os.stat(str(extraction_directory / builder.project_id / 'PKG-INFO')) assert stat.st_mtime != get_reproducible_timestamp() def test_default_support_legacy(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version']}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': {'targets': {'sdist': {'versions': ['standard'], 'support-legacy': True}}}, }, }, } builder = SdistBuilder(str(project_path), config=config) build_path = project_path / 'dist' build_path.mkdir() with project_path.as_cwd(): artifacts = list(builder.build(str(build_path))) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_default_support_legacy', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) def test_default_build_script_artifacts(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' vcs_ignore_file = project_path / '.gitignore' vcs_ignore_file.write_text('*.pyc\n*.so\n*.h\n') build_script = project_path / 'build.py' build_script.write_text( helpers.dedent( """ import pathlib from hatchling.builders.hooks.plugin.interface import BuildHookInterface class CustomHook(BuildHookInterface): def initialize(self, version, build_data): pathlib.Path('my_app', 'lib.so').touch() pathlib.Path('my_app', 'lib.h').touch() """ ) ) config = { 'project': {'name': 'my__app', 'dynamic': ['version']}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': { 'targets': {'sdist': {'versions': ['standard']}}, 'artifacts': ['my_app/lib.so'], 'hooks': {'custom': {'path': 'build.py'}}, }, }, }, } builder = SdistBuilder(str(project_path), config=config) build_path = project_path / 'dist' build_path.mkdir() with project_path.as_cwd(): artifacts = list(builder.build(str(build_path))) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_default_build_script_artifacts', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) def test_include_project_file(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version'], 'readme': 'README.md'}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': { 'targets': {'sdist': {'versions': ['standard'], 'include': ['my_app/', 'pyproject.toml']}} }, }, }, } builder = SdistBuilder(str(project_path), config=config) build_path = project_path / 'dist' with project_path.as_cwd(): artifacts = list(builder.build()) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_include', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) stat = os.stat(str(extraction_directory / builder.project_id / 'PKG-INFO')) assert stat.st_mtime == get_reproducible_timestamp() def test_project_file_always_included(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version'], 'readme': 'README.md'}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': { 'targets': { 'sdist': {'versions': ['standard'], 'include': ['my_app/'], 'exclude': ['pyproject.toml']}, }, }, }, }, } builder = SdistBuilder(str(project_path), config=config) # Ensure that only the root project file is forcibly included (project_path / 'my_app' / 'pyproject.toml').touch() build_path = project_path / 'dist' with project_path.as_cwd(): artifacts = list(builder.build()) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_include', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) stat = os.stat(str(extraction_directory / builder.project_id / 'PKG-INFO')) assert stat.st_mtime == get_reproducible_timestamp() def test_include_readme(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version'], 'readme': 'README.md'}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': {'targets': {'sdist': {'versions': ['standard'], 'include': ['my_app/', 'README.md']}}}, }, }, } builder = SdistBuilder(str(project_path), config=config) build_path = project_path / 'dist' with project_path.as_cwd(): artifacts = list(builder.build()) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_include', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) stat = os.stat(str(extraction_directory / builder.project_id / 'PKG-INFO')) assert stat.st_mtime == get_reproducible_timestamp() def test_readme_always_included(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version'], 'readme': 'README.md'}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': { 'targets': { 'sdist': {'versions': ['standard'], 'include': ['my_app/'], 'exclude': ['README.md']}, }, }, }, }, } builder = SdistBuilder(str(project_path), config=config) # Ensure that only the desired readme is forcibly included (project_path / 'my_app' / 'README.md').touch() build_path = project_path / 'dist' with project_path.as_cwd(): artifacts = list(builder.build()) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_include', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) stat = os.stat(str(extraction_directory / builder.project_id / 'PKG-INFO')) assert stat.st_mtime == get_reproducible_timestamp() def test_include_license_files(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version'], 'readme': 'README.md'}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': {'targets': {'sdist': {'versions': ['standard'], 'include': ['my_app/', 'LICENSE.txt']}}}, }, }, } builder = SdistBuilder(str(project_path), config=config) build_path = project_path / 'dist' with project_path.as_cwd(): artifacts = list(builder.build()) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_include', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) stat = os.stat(str(extraction_directory / builder.project_id / 'PKG-INFO')) assert stat.st_mtime == get_reproducible_timestamp() def test_license_files_always_included(self, hatch, helpers, temp_dir): project_name = 'My App' with temp_dir.as_cwd(): result = hatch('new', project_name) assert result.exit_code == 0, result.output project_path = temp_dir / 'my-app' config = { 'project': {'name': 'my__app', 'dynamic': ['version'], 'readme': 'README.md'}, 'tool': { 'hatch': { 'version': {'path': 'my_app/__about__.py'}, 'build': { 'targets': { 'sdist': {'versions': ['standard'], 'include': ['my_app/'], 'exclude': ['LICENSE.txt']}, }, }, }, }, } builder = SdistBuilder(str(project_path), config=config) # Ensure that only the desired readme is forcibly included (project_path / 'my_app' / 'LICENSE.txt').touch() build_path = project_path / 'dist' with project_path.as_cwd(): artifacts = list(builder.build()) assert len(artifacts) == 1 expected_artifact = artifacts[0] build_artifacts = list(build_path.iterdir()) assert len(build_artifacts) == 1 assert expected_artifact == str(build_artifacts[0]) assert expected_artifact == str(build_path / f'{builder.project_id}.tar.gz') extraction_directory = temp_dir / '_archive' extraction_directory.mkdir() with tarfile.open(str(expected_artifact), 'r:gz') as tar_archive: tar_archive.extractall(str(extraction_directory)) expected_files = helpers.get_template_files( 'sdist.standard_include', project_name, relative_root=builder.project_id ) helpers.assert_files(extraction_directory, expected_files, check_contents=True) stat = os.stat(str(extraction_directory / builder.project_id / 'PKG-INFO')) assert stat.st_mtime == get_reproducible_timestamp()

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6f345ebde49099357c68eb4601fc1103022f1c56

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py

Python

fetchmesh/bgp/__init__.py

SmartMonitoringSchemes/fetchmesh

139a68a380786cca5caba33f16f4ff482477d66a

[ "MIT" ]

null

fetchmesh/bgp/__init__.py

SmartMonitoringSchemes/fetchmesh

139a68a380786cca5caba33f16f4ff482477d66a

[ "MIT" ]

5

2021-08-01T18:11:07.000Z

2022-02-01T18:42:10.000Z

fetchmesh/bgp/__init__.py

SmartMonitoringSchemes/fetchmesh

139a68a380786cca5caba33f16f4ff482477d66a

[ "MIT" ]

null

from .asnames import * from .asndb import * from .collectors import *

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6f3eb94eeb4e87edff113004c29917ae86989ffe

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py

Python

django_soc_lite/HTML_Escape.py

threatequation/django-soc-lite

3df27d06ed63e5a382f5ab4e387c51f611b48f81

[ "0BSD" ]

null

django_soc_lite/HTML_Escape.py

threatequation/django-soc-lite

3df27d06ed63e5a382f5ab4e387c51f611b48f81

[ "0BSD" ]

null

django_soc_lite/HTML_Escape.py

threatequation/django-soc-lite

3df27d06ed63e5a382f5ab4e387c51f611b48f81

[ "0BSD" ]

null

"""custom escaping methods""" def XSSEncode(maliciouscode): """custom xss containg input escaper""" html_code = ( ('"', '"'), ('%22', '"'), ("'", '''), ('%27', '''), ('/', '/'), ('%2f', '/'), ('%2F', '/'), ('<', '<'), ('%3C', '<'), ('%3c', '<'), ('>', '>'), ('%3E', '>'), ('%3e', '>'), (';', '&end;'), ('%3B', '&end;'), ('%3b', '&end;'), ('&', '&'), ('%26', '&'), ) for code in html_code: maliciouscode = maliciouscode.replace(code[0], code[1]) import re maliciouscode = re.sub(' +', ' ', maliciouscode) return maliciouscode def CommandEscape(maliciouscode): """custom command input escaper""" html_code = ( ('"', '"'), ('%22', '"'), ("'", '''), ('%27', '''), ('/', '/'), ('%2f', '/'), ('%2F', '/'), ('<', '<'), ('%3C', '<'), ('%3c', '<'), ('>', '>'), ('%3E', '>'), ('%3e', '>'), (';', '&end;'), ('%3B', '&end;'), ('%3b', '&end;'), ('&', '&'), ('%26', '&'), ) for code in html_code: maliciouscode = maliciouscode.replace(code[0], code[1]) import re maliciouscode = re.sub(' +', ' ', maliciouscode) return maliciouscode def UrlEncode(url): html_code = ( (':', '-'), ('//', '--'), ('/', '-'), ('.', '-'), ) for code in html_code: url = url.replace(code[0], code[1]) return url

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d2a2d7edaa3c299d94cf6a706ef02621335a07e2

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py

Python

pyspark_utilities/spark_udfs/__init__.py

zaksamalik/pyspark-utilities

da9f843bdf03658d58d1dbc8d5f3067bd6702494

[ "MIT" ]

9

2020-03-10T10:31:06.000Z

2021-12-03T03:43:00.000Z

pyspark_utilities/spark_udfs/__init__.py

zaksamalik/pyspark-utilities

da9f843bdf03658d58d1dbc8d5f3067bd6702494

[ "MIT" ]

null

pyspark_utilities/spark_udfs/__init__.py

zaksamalik/pyspark-utilities

da9f843bdf03658d58d1dbc8d5f3067bd6702494

[ "MIT" ]

2

2020-11-14T15:13:43.000Z

2021-12-22T11:33:03.000Z

from .spark_udfs import SparkUDFs

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960be7cf0370b3714d9693080ef55b70bf5c63c9

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py

Python

migrations/versions/2018_12_03_fix_migrations.py

AlexKouzy/ethnicity-facts-and-figures-publisher

18ab2495a8633f585e18e607c7f75daa564a053d

[ "MIT" ]

1

2021-10-06T13:48:36.000Z

migrations/versions/2018_12_03_fix_migrations.py

AlexKouzy/ethnicity-facts-and-figures-publisher

18ab2495a8633f585e18e607c7f75daa564a053d

[ "MIT" ]

116

2018-11-02T17:20:47.000Z

2022-02-09T11:06:22.000Z

migrations/versions/2018_12_03_fix_migrations.py

racedisparityaudit/rd_cms

a12f0e3f5461cc41eed0077ed02e11efafc5dd76

[ "MIT" ]

2

2018-11-09T16:47:35.000Z

2020-04-09T13:06:48.000Z

"""fix migrations by incorporating outstanding changes Revision ID: 2018_12_03_fix_migrations Revises: 2018_11_28_drop_contact_details Create Date: 2018-12-03 10:51:52.822365 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = "2018_12_03_fix_migrations" down_revision = "2018_11_28_drop_contact_details" branch_labels = None depends_on = None def upgrade(): op.create_foreign_key( "data_source_frequency_of_release_id_fkey", "data_source", "frequency_of_release", ["frequency_of_release_id"], ["id"], ) op.alter_column("dimension_chart", "classification_id", existing_type=sa.VARCHAR(length=255), nullable=False) op.alter_column("dimension_table", "classification_id", existing_type=sa.VARCHAR(length=255), nullable=False) op.alter_column( "ethnicity_in_classification", "classification_id", existing_type=sa.VARCHAR(length=255), nullable=False ) op.alter_column("ethnicity_in_classification", "ethnicity_id", existing_type=sa.INTEGER(), nullable=False) op.alter_column( "parent_ethnicity_in_classification", "classification_id", existing_type=sa.VARCHAR(length=255), nullable=False ) op.alter_column("parent_ethnicity_in_classification", "ethnicity_id", existing_type=sa.INTEGER(), nullable=False) def downgrade(): op.alter_column("parent_ethnicity_in_classification", "ethnicity_id", existing_type=sa.INTEGER(), nullable=True) op.alter_column( "parent_ethnicity_in_classification", "classification_id", existing_type=sa.VARCHAR(length=255), nullable=True ) op.alter_column("ethnicity_in_classification", "ethnicity_id", existing_type=sa.INTEGER(), nullable=True) op.alter_column( "ethnicity_in_classification", "classification_id", existing_type=sa.VARCHAR(length=255), nullable=True ) op.alter_column("dimension_table", "classification_id", existing_type=sa.VARCHAR(length=255), nullable=True) op.alter_column("dimension_chart", "classification_id", existing_type=sa.VARCHAR(length=255), nullable=True) op.drop_constraint("data_source_frequency_of_release_id_fkey", "data_source", type_="foreignkey")

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82511270d5450862f4f276842012c9b0221f3782

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py

Python

venv/lib/python3.8/site-packages/rope/refactor/occurrences.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/rope/refactor/occurrences.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/rope/refactor/occurrences.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/af/90/f7/f89d139c04c31ed7354f13cdac5ec00282c4d4306d56ec430f55165137

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6

82c0a2e9e7007de4b7524c813fd6111e126c677f

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py

Python

spidermon/python/__init__.py

heylouiz/spidermon

3ae2c46d1cf5b46efb578798b881264be3e68394

[ "BSD-3-Clause" ]

2

2019-10-03T16:47:11.000Z

2022-02-22T11:56:02.000Z

spidermon/python/__init__.py

heylouiz/spidermon

3ae2c46d1cf5b46efb578798b881264be3e68394

[ "BSD-3-Clause" ]

23

2019-05-30T20:27:38.000Z

2019-08-20T07:23:09.000Z

spidermon/python/__init__.py

heylouiz/spidermon

3ae2c46d1cf5b46efb578798b881264be3e68394

[ "BSD-3-Clause" ]

1

2022-03-24T03:01:19.000Z

from __future__ import absolute_import from .interpreter import Interpreter from . import factory from . import schemas

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py

Python

tests/test_imports.py

team23/django_textformat

e195f56a5d12f6ceb70855da1164251fc43d1c9d

[ "BSD-3-Clause" ]

2

2016-03-22T16:59:26.000Z

2016-07-15T09:39:31.000Z

tests/test_imports.py

team23/django_textformat

e195f56a5d12f6ceb70855da1164251fc43d1c9d

[ "BSD-3-Clause" ]

2

2016-03-22T16:56:50.000Z

2016-04-05T08:18:50.000Z

tests/test_imports.py

team23/django_textformat

e195f56a5d12f6ceb70855da1164251fc43d1c9d

[ "BSD-3-Clause" ]

null

def test_imports(): import django_textformat # noqa from django_textformat import TextFormatField # noqa assert TextFormatField is not None def test_has_version(): import django_textformat assert django_textformat.__version__.count('.') >= 2

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81e164c90a2050c8f16a6941c46f1a1bab4387eb

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py

Python

ogusa/tests/test_SS.py

rickecon/TaxFuncIntegr

715cc76e3305c00dd64d79521c504bb388c6d87d

[ "CC0-1.0" ]

null

ogusa/tests/test_SS.py

rickecon/TaxFuncIntegr

715cc76e3305c00dd64d79521c504bb388c6d87d

[ "CC0-1.0" ]

1

2018-07-02T18:24:17.000Z

ogusa/tests/test_SS.py

rickecon/TaxFuncIntegr

715cc76e3305c00dd64d79521c504bb388c6d87d

[ "CC0-1.0" ]

6

2016-09-18T01:39:54.000Z

2020-09-02T12:54:55.000Z

from __future__ import print_function import pytest import json import pickle import numpy as np import os import multiprocessing from multiprocessing import Process from dask.distributed import Client from ogusa import SS, utils # Define parameters to use for multiprocessing # client = Client(processes=False) # # num_workers = int(os.cpu_count()) # not in os on Python 2.7? # num_workers = multiprocessing.cpu_count() CUR_PATH = os.path.abspath(os.path.dirname(__file__)) def test_SS_fsolve(): # Test SS.SS_fsolve function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/SS_fsolve_inputs.pkl')) guesses, params = input_tuple params = params + (None, 1) (bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params, small_open_params, client, num_workers) = params income_tax_params = ('DEP',) + income_tax_params params = (bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params, small_open_params, client, num_workers) test_list = SS.SS_fsolve(guesses, params) expected_list = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/SS_fsolve_outputs.pkl')) print('outputs = ', np.absolute(np.array(test_list) - np.array(expected_list)).max()) assert(np.allclose(np.array(test_list), np.array(expected_list))) def test_SS_fsolve_reform(): # Test SS.SS_fsolve_reform function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/SS_fsolve_reform_inputs.pkl')) guesses, params = input_tuple params = params + (None, 1) (bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params, factor, small_open_params, client, num_workers) = params income_tax_params = ('DEP',) + income_tax_params params = (bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params, factor, small_open_params, client, num_workers) test_list = SS.SS_fsolve_reform(guesses, params) expected_list = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/SS_fsolve_reform_outputs.pkl')) assert(np.allclose(np.array(test_list), np.array(expected_list))) def test_SS_fsolve_reform_baselinespend(): # Test SS.SS_fsolve_reform_baselinespend function. Provide inputs # to function and ensure that output returned matches what it has # been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/SS_fsolve_reform_baselinespend_inputs.pkl')) guesses, params = input_tuple params = params + (None, 1) (bssmat, nssmat, T_Hss, chi_params, ss_params, income_tax_params, iterative_params, factor, small_open_params, client, num_workers) = params income_tax_params = ('DEP',) + income_tax_params params = (bssmat, nssmat, T_Hss, chi_params, ss_params, income_tax_params, iterative_params, factor, small_open_params, client, num_workers) test_list = SS.SS_fsolve_reform_baselinespend(guesses, params) expected_list = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/SS_fsolve_reform_baselinespend_outputs.pkl')) assert(np.allclose(np.array(test_list), np.array(expected_list))) def test_SS_solver(): # Test SS.SS_solver function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/SS_solver_inputs.pkl')) (b_guess_init, n_guess_init, rss, T_Hss, factor_ss, Yss, params, baseline, fsolve_flag, baseline_spending) = input_tuple (bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params, small_open_params) = params income_tax_params = ('DEP',) + income_tax_params params = (bssmat, nssmat, chi_params, ss_params, income_tax_params, iterative_params, small_open_params) test_dict = SS.SS_solver( b_guess_init, n_guess_init, rss, T_Hss, factor_ss, Yss, params, baseline, fsolve_flag, baseline_spending) expected_dict = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/SS_solver_outputs.pkl')) for k, v in expected_dict.items(): assert(np.allclose(test_dict[k], v)) def test_inner_loop(): # Test SS.inner_loop function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/inner_loop_inputs.pkl')) (outer_loop_vars, params, baseline, baseline_spending) = input_tuple ss_params, income_tax_params, chi_params, small_open_params = params income_tax_params = ('DEP',) + income_tax_params params = (ss_params, income_tax_params, chi_params, small_open_params) test_tuple = SS.inner_loop( outer_loop_vars, params, baseline, baseline_spending) expected_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/inner_loop_outputs.pkl')) for i, v in enumerate(expected_tuple): assert(np.allclose(test_tuple[i], v)) def test_euler_equation_solver(): # Test SS.inner_loop function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/euler_eqn_solver_inputs.pkl')) (guesses, params) = input_tuple (r, w, T_H, factor, j, J, S, beta, sigma, ltilde, g_y, g_n_ss, tau_payroll, retire, mean_income_data, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, j, chi_b, chi_n, tau_bq, rho, lambdas, omega_SS, e, analytical_mtrs, etr_params, mtrx_params, mtry_params) = params tax_func_type = 'DEP' params = (r, w, T_H, factor, j, J, S, beta, sigma, ltilde, g_y, g_n_ss, tau_payroll, retire, mean_income_data, h_wealth, p_wealth, m_wealth, b_ellipse, upsilon, j, chi_b, chi_n, tau_bq, rho, lambdas, omega_SS, e, tax_func_type, analytical_mtrs, etr_params, mtrx_params, mtry_params) test_list = SS.euler_equation_solver(guesses, params) expected_list = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/euler_eqn_solver_outputs.pkl')) assert(np.allclose(np.array(test_list), np.array(expected_list))) def test_create_steady_state_parameters(): # Test that SS parameters creates same objects with same inputs. input_dict = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/create_params_inputs.pkl')) input_dict['tax_func_type'] = 'DEP' test_tuple = SS.create_steady_state_parameters(**input_dict) expected_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data/create_params_outputs.pkl')) (income_tax_params, ss_params, iterative_params, chi_params, small_open_params) = expected_tuple income_tax_params = ('DEP', ) + income_tax_params expected_tuple = (income_tax_params, ss_params, iterative_params, chi_params, small_open_params) for i, v in enumerate(expected_tuple): for i2, v2 in enumerate(v): try: assert(all(test_tuple[i][i2] == v2)) except ValueError: assert((test_tuple[i][i2] == v2).all()) except TypeError: assert(test_tuple[i][i2] == v2) @pytest.mark.parametrize('input_path,expected_path', [('run_SS_open_unbal_inputs.pkl', 'run_SS_open_unbal_outputs.pkl'), ('run_SS_closed_balanced_inputs.pkl', 'run_SS_closed_balanced_outputs.pkl')], ids=['Open, Unbalanced', 'Closed Balanced']) def test_run_SS(input_path, expected_path): # Test SS.run_SS function. Provide inputs to function and # ensure that output returned matches what it has been before. input_tuple = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data', input_path)) (income_tax_params, ss_params, iterative_params, chi_params, small_open_params, baseline, baseline_spending, baseline_dir) =\ input_tuple income_tax_params = ('DEP',) + income_tax_params test_dict = SS.run_SS( income_tax_params, ss_params, iterative_params, chi_params, small_open_params, baseline, baseline_spending, baseline_dir) expected_dict = utils.safe_read_pickle( os.path.join(CUR_PATH, 'test_io_data', expected_path)) for k, v in expected_dict.items(): assert(np.allclose(test_dict[k], v))

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0

null

0

1

0

null

0

6

c4e4bb583fc0e23cd20d9b5f6fa6cf0aa2858e4c

8,658

py

Python

Dragon/python/dragon/vm/caffe/layers/loss.py

awesome-archive/Dragon

b35f9320909d07d138c2f6b345a4c24911f7c521

[ "BSD-2-Clause" ]

null

Dragon/python/dragon/vm/caffe/layers/loss.py

awesome-archive/Dragon

b35f9320909d07d138c2f6b345a4c24911f7c521

[ "BSD-2-Clause" ]

null

Dragon/python/dragon/vm/caffe/layers/loss.py

awesome-archive/Dragon

b35f9320909d07d138c2f6b345a4c24911f7c521

[ "BSD-2-Clause" ]

null

# ------------------------------------------------------------ # Copyright (c) 2017-present, SeetaTech, Co.,Ltd. # # Licensed under the BSD 2-Clause License. # You should have received a copy of the BSD 2-Clause License # along with the software. If not, See, # # <https://opensource.org/licenses/BSD-2-Clause> # # ------------------------------------------------------------ """The Implementation of the data layers.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import dragon from ..layer import Layer class SoftmaxWithLossLayer(Layer): """The implementation of ``SoftmaxWithLossLayer``. Parameters ---------- axis : int The axis of softmax. Refer `SoftmaxParameter.axis`_. ignore_label : int The label id to ignore. Refer `LossParameter.ignore_label`_. normalization : NormalizationMode The normalization. Refer `LossParameter.normalization`_. normalize : boolean Whether to normalize. Refer `LossParameter.normalize`_. """ def __init__(self, LayerParameter): super(SoftmaxWithLossLayer, self).__init__(LayerParameter) param = LayerParameter.loss_param softmax_param = LayerParameter.softmax_param norm_mode = {0: 'FULL', 1: 'VALID', 2: 'BATCH_SIZE', 3: 'NONE', 4: 'UNIT'} normalization = 'VALID' if param.HasField('normalize'): if not param.normalize: normalization = 'BATCH_SIZE' else: normalization = norm_mode[param.normalization] self.arguments = { 'axis': softmax_param.axis, 'normalization': normalization, 'ignore_labels': [param.ignore_label] if param.HasField('ignore_label') else [], } def LayerSetup(self, bottom): loss = dragon.ops.SparseSoftmaxCrossEntropy(bottom, **self.arguments) if self._loss_weight is not None: loss *= self._loss_weight return loss class SigmoidCrossEntropyLossLayer(Layer): """The implementation of ``SigmoidCrossEntropyLossLayer``. Parameters ---------- normalization : NormalizationMode The normalization. Refer `LossParameter.normalization`_. normalize : boolean Whether to normalize. Refer `LossParameter.normalize`_. """ def __init__(self, LayerParameter): super(SigmoidCrossEntropyLossLayer, self).__init__(LayerParameter) param = LayerParameter.loss_param norm_mode = {0: 'FULL', 1: 'VALID', 2: 'BATCH_SIZE', 3: 'NONE', 4: 'UNIT'} normalization = 'VALID' if param.HasField('normalize'): if not param.normalize: normalization = 'BATCH_SIZE' else: normalization = norm_mode[param.normalization] self.arguments = {'normalization': normalization} def LayerSetup(self, bottom): loss = dragon.ops.SigmoidCrossEntropy(bottom, **self.arguments) if self._loss_weight is not None: loss *= self._loss_weight return loss class L2LossLayer(Layer): """The implementation of ``L2LossLayer``. Parameters ---------- normalization : NormalizationMode The normalization. Refer `LossParameter.normalization`_. normalize : boolean Whether to normalize. Refer `LossParameter.normalize`_. """ def __init__(self, LayerParameter): super(L2LossLayer, self).__init__(LayerParameter) param = LayerParameter.loss_param norm_mode = {0: 'FULL', 1: 'BATCH_SIZE', 2: 'BATCH_SIZE', 3: 'NONE'} normalization = 'BATCH_SIZE' if param.HasField('normalize'): if param.normalize: normalization = 'FULL' else: normalization = norm_mode[param.normalization] self.arguments = {'normalization': normalization} def LayerSetup(self, bottom): loss = dragon.ops.L2Loss(bottom, **self.arguments) if self._loss_weight is not None: loss *= self._loss_weight return loss class SmoothL1LossLayer(Layer): """The implementation of ``SmoothL1LossLayer``. Parameters ---------- sigma : float The sigma. Refer `SmoothL1LossParameter.sigma`_. normalization : NormalizationMode The normalization. Refer `LossParameter.normalization`_. normalize : boolean Whether to normalize. Refer `LossParameter.normalize`_. """ def __init__(self, LayerParameter): super(SmoothL1LossLayer, self).__init__(LayerParameter) param = LayerParameter.loss_param smooth_l1_param = LayerParameter.smooth_l1_loss_param norm_mode = {0: 'FULL', 1: 'BATCH_SIZE', 2: 'BATCH_SIZE', 3: 'NONE'} normalization = 'BATCH_SIZE' if param.HasField('normalize'): if param.normalize: normalization = 'FULL' else: normalization = norm_mode[param.normalization] sigma2 = smooth_l1_param.sigma * smooth_l1_param.sigma self.arguments = { 'beta': float(1. / sigma2), 'normalization': normalization, } def LayerSetup(self, bottom): loss = dragon.ops.SmoothL1Loss(bottom, **self.arguments) if self._loss_weight is not None: loss *= self._loss_weight return loss class SigmoidWithFocalLossLayer(Layer): """The implementation of ``SigmoidWithFocalLossLayer``. Parameters ---------- axis : int The axis of softmax. Refer `SoftmaxParameter.axis`_. alpha : float The scale on the rare class. Refer `FocalLossParameter.alpha`_. gamma : float The exponential decay. Refer `FocalLossParameter.gamma`_. neg_id : int The negative id. Refer `FocalLossParameter.neg_id`_. normalization : NormalizationMode The normalization. Refer `LossParameter.normalization`_. normalize : boolean Whether to normalize. Refer `LossParameter.normalize`_. """ def __init__(self, LayerParameter): super(SigmoidWithFocalLossLayer, self).__init__(LayerParameter) param = LayerParameter.loss_param softmax_param = LayerParameter.softmax_param focal_loss_param = LayerParameter.focal_loss_param norm_mode = {0: 'FULL', 1: 'VALID', 2: 'BATCH_SIZE', 3: 'NONE', 4: 'UNIT'} normalization = 'VALID' if param.HasField('normalize'): if not param.normalize: normalization = 'BATCH_SIZE' else: normalization = norm_mode[param.normalization] self.arguments = { 'axis': softmax_param.axis, 'normalization': normalization, 'alpha': float(focal_loss_param.alpha), 'gamma': float(focal_loss_param.gamma), 'neg_id': focal_loss_param.neg_id, } def LayerSetup(self, bottom): loss = dragon.ops.SigmoidFocalLoss(bottom, **self.arguments) if self._loss_weight is not None: loss *= self._loss_weight return loss class SoftmaxWithFocalLossLayer(Layer): """The implementation of ``SoftmaxWithFocalLossLayer``. Parameters ---------- axis : int The axis of softmax. Refer `SoftmaxParameter.axis`_. alpha : float The scale on the rare class. Refer `FocalLossParameter.alpha`_. gamma : float The exponential decay. Refer `FocalLossParameter.gamma`_. neg_id : int The negative id. Refer `FocalLossParameter.neg_id`_. normalization : NormalizationMode The normalization. Refer `LossParameter.normalization`_. normalize : boolean Whether to normalize. Refer `LossParameter.normalize`_. """ def __init__(self, LayerParameter): super(SoftmaxWithFocalLossLayer, self).__init__(LayerParameter) param = LayerParameter.loss_param softmax_param = LayerParameter.softmax_param focal_loss_param = LayerParameter.focal_loss_param norm_mode = {0: 'FULL', 1: 'VALID', 2: 'BATCH_SIZE', 3: 'NONE', 4: 'UNIT'} normalization = 'VALID' if param.HasField('normalize'): if not param.normalize: normalization = 'BATCH_SIZE' else: normalization = norm_mode[param.normalization] self.arguments = { 'axis': softmax_param.axis, 'normalization': normalization, 'ignore_labels': [param.ignore_label] if param.HasField('ignore_label') else [], 'alpha': float(focal_loss_param.alpha), 'gamma': float(focal_loss_param.gamma), 'neg_id': focal_loss_param.neg_id, } def LayerSetup(self, bottom): loss = dragon.ops.SoftmaxFocalLoss(bottom, **self.arguments) if self._loss_weight is not None: loss *= self._loss_weight return loss

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c4fd718e3889f57aa812056c2008c07b14cb2431

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py

Python

loci/__init__.py

t-brandt/acorns-adi

6645fae7878a1801beeda0c6604b01e61f37ca15

[ "BSD-2-Clause" ]

1

2016-10-30T16:29:51.000Z

loci/__init__.py

t-brandt/acorns-adi

6645fae7878a1801beeda0c6604b01e61f37ca15

[ "BSD-2-Clause" ]

null

loci/__init__.py

t-brandt/acorns-adi

6645fae7878a1801beeda0c6604b01e61f37ca15

[ "BSD-2-Clause" ]

null

from loci import loci

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6

f20513ffe1da16c904bfe793fb24c8e8230fa4c1

4,191

py

Python

pyEX/alternative/alternative.py

cjwang/pyEX

1b5f40f80110afaa4809ea48fac067033c7bdf89

[ "Apache-2.0" ]

1

2020-10-11T07:05:49.000Z

pyEX/alternative/alternative.py

cjwang/pyEX

1b5f40f80110afaa4809ea48fac067033c7bdf89

[ "Apache-2.0" ]

null

pyEX/alternative/alternative.py

cjwang/pyEX

1b5f40f80110afaa4809ea48fac067033c7bdf89

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import pandas as pd from ..common import _expire, _getJson, _raiseIfNotStr, _strOrDate, _reindex, _toDatetime def crypto(token='', version='', filter=''): '''This will return an array of quotes for all Cryptocurrencies supported by the IEX API. Each element is a standard quote object with four additional keys. https://iexcloud.io/docs/api/#crypto Args: token (string); Access token version (string); API version filter (string); filters: https://iexcloud.io/docs/api/#filter-results Returns: dict: result ''' return _getJson('stock/market/crypto/', token, version, filter) def cryptoDF(token='', version='', filter=''): '''This will return an array of quotes for all Cryptocurrencies supported by the IEX API. Each element is a standard quote object with four additional keys. https://iexcloud.io/docs/api/#crypto Args: token (string); Access token version (string); API version filter (string); filters: https://iexcloud.io/docs/api/#filter-results Returns: DataFrame: result ''' df = pd.DataFrame(crypto(token, version, filter)) _toDatetime(df) _reindex(df, 'symbol') return df def sentiment(symbol, type='daily', date=None, token='', version='', filter=''): '''This endpoint provides social sentiment data from StockTwits. Data can be viewed as a daily value, or by minute for a given date. https://iexcloud.io/docs/api/#social-sentiment Continuous Args: symbol (string); Ticker to request type (string); 'daily' or 'minute' date (string); date in YYYYMMDD or datetime token (string); Access token version (string); API version filter (string); filters: https://iexcloud.io/docs/api/#filter-results Returns: dict: result ''' _raiseIfNotStr(symbol) if date: date = _strOrDate(date) return _getJson('stock/{symbol}/sentiment/{type}/{date}'.format(symbol=symbol, type=type, date=date), token, version, filter) return _getJson('stock/{symbol}/sentiment/{type}/'.format(symbol=symbol, type=type), token, version, filter) def sentimentDF(symbol, type='daily', date=None, token='', version='', filter=''): '''This endpoint provides social sentiment data from StockTwits. Data can be viewed as a daily value, or by minute for a given date. https://iexcloud.io/docs/api/#social-sentiment Continuous Args: symbol (string); Ticker to request type (string); 'daily' or 'minute' date (string); date in YYYYMMDD or datetime token (string); Access token version (string); API version filter (string); filters: https://iexcloud.io/docs/api/#filter-results Returns: DataFrame: result ''' ret = sentiment(symbol, type, date, token, version, filter) if type == 'daily': ret = [ret] df = pd.DataFrame(ret) _toDatetime(df) return df @_expire(hour=1) def ceoCompensation(symbol, token='', version='', filter=''): '''This endpoint provides CEO compensation for a company by symbol. https://iexcloud.io/docs/api/#ceo-compensation 1am daily Args: symbol (string); Ticker to request token (string); Access token version (string); API version filter (string); filters: https://iexcloud.io/docs/api/#filter-results Returns: dict: result ''' _raiseIfNotStr(symbol) return _getJson('stock/{symbol}/ceo-compensation'.format(symbol=symbol), token, version, filter) def ceoCompensationDF(symbol, token='', version='', filter=''): '''This endpoint provides CEO compensation for a company by symbol. https://iexcloud.io/docs/api/#ceo-compensation 1am daily Args: symbol (string); Ticker to request token (string); Access token version (string); API version filter (string); filters: https://iexcloud.io/docs/api/#filter-results Returns: DataFrame: result ''' ret = ceoCompensation(symbol, token, version, filter) df = pd.io.json.json_normalize(ret) _toDatetime(df) return df

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null

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6

48006e8af8d6fa7af96f9554b33b8860ec73194e

29

py

Python

src/compas_maya/com/maya/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

src/compas_maya/com/maya/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

src/compas_maya/com/maya/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

from .sock import MayaSocket

14.5

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29

6

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1

29

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1

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1

0

6

4809412885ea70f8e146265c90bcc8e20db9fa31

398

py

Python

autofit/plot/__init__.py

caoxiaoyue/PyAutoFit

819cd2acc8d4069497a161c3bb6048128e44d828

[ "MIT" ]

39

2019-01-24T10:45:23.000Z

2022-03-18T09:37:59.000Z

autofit/plot/__init__.py

caoxiaoyue/PyAutoFit

819cd2acc8d4069497a161c3bb6048128e44d828

[ "MIT" ]

260

2018-11-27T12:56:33.000Z

2022-03-31T16:08:59.000Z

autofit/plot/__init__.py

caoxiaoyue/PyAutoFit

819cd2acc8d4069497a161c3bb6048128e44d828

[ "MIT" ]

13

2018-11-30T16:49:05.000Z

2022-01-21T17:39:29.000Z

from autofit.plot.samples_plotters import SamplesPlotter from autofit.non_linear.nest.dynesty.plotter import DynestyPlotter from autofit.non_linear.nest.ultranest.plotter import UltraNestPlotter from autofit.non_linear.mcmc.emcee.plotter import EmceePlotter from autofit.non_linear.mcmc.zeus.plotter import ZeusPlotter from autofit.non_linear.optimize.pyswarms.plotter import PySwarmsPlotter

56.857143

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0

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1

0

6

48094a27594f15064f8bb42cc12e7efe63e18edf

165

py

Python

continuum/tasks/__init__.py

psychicmario/continuum

22f60d3fc71553f1334cffa7e88a1727cdf2413c

[ "MIT" ]

null

continuum/tasks/__init__.py

psychicmario/continuum

22f60d3fc71553f1334cffa7e88a1727cdf2413c

[ "MIT" ]

null

continuum/tasks/__init__.py

psychicmario/continuum

22f60d3fc71553f1334cffa7e88a1727cdf2413c

[ "MIT" ]

null

# pylint: disable=C0401 # flake8: noqa from continuum.tasks.task_set import TaskSet from continuum.tasks.utils import split_train_val, concat __all__ = ["TaskSet"]

23.571429

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1

0

6

4809c422bf1321bdb476d8bf295e84a6d8af7d38

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py

Python

example/boost_lin_tree_1d.py

wgurecky/pCRTree

3b7bf8596793b32bb8162db5df7a765cf774e2f1

[ "BSD-3-Clause" ]

1

2020-06-07T04:36:06.000Z

example/boost_lin_tree_1d.py

wgurecky/pCRTree

3b7bf8596793b32bb8162db5df7a765cf774e2f1

[ "BSD-3-Clause" ]

6

2017-07-21T07:03:05.000Z

2020-09-24T18:38:36.000Z

example/boost_lin_tree_1d.py

wgurecky/pCRTree

3b7bf8596793b32bb8162db5df7a765cf774e2f1

[ "BSD-3-Clause" ]

null

#!/usr/bin/python3 from boosting.gbm import GBRTmodel from matplotlib import gridspec import numpy as np MPL = False try: from pylab import cm import matplotlib.pyplot as plt MPL = True except: pass def example_boosed_lin_reg_lin(f, plot_name='1d_boosted_regression_lin_lin_ex.png'): n_samples_per_edit = 1 X = np.atleast_2d(np.linspace(0, 10.0, 80).repeat(n_samples_per_edit)).T X = X.astype(np.float32) y = f(X).ravel() std_dev = 4.2 noise = np.random.normal(0, std_dev, size=y.shape) y += noise y = y.astype(np.float32) # Mesh the input space for evaluations of the real function, the prediction and # its MSE xx = np.atleast_2d(np.linspace(0, 10, 1000)).T xx = xx.astype(np.float32) # fit to median gbt = GBRTmodel(max_depth=1, learning_rate=0.03, subsample=0.8, loss="se", tree_method="lin", minSplitPts=10, minDataLeaf=20) gbt.train(X, y, n_estimators=400) y_median = gbt.predict(xx) if MPL: # Plot the function, the prediction and the 90% confidence interval based on # the MSE fig = plt.figure() plt.plot(X, y, 'b.', markersize=2, label=u'Observations', alpha=0.3) plt.plot(xx, y_median, 'r-', label=r'$\hat \mu$') plt.plot(xx, f(xx), 'g', label=u'$f(x) = 0.25x+10$') plt.xlabel('$x$') plt.ylabel('$f(x)$') # plt.ylim(-10, 20) plt.legend(loc='upper left') plt.savefig('lin_' + plot_name + '.png', dpi=120) plt.close() # fit to median gbt = GBRTmodel(max_depth=1, learning_rate=0.03, subsample=0.8, loss="se", tree_method="cart", minSplitPts=4) gbt.train(X, y, n_estimators=400) y_median = gbt.predict(xx) if MPL: # Plot the function, the prediction and the 90% confidence interval based on # the MSE fig = plt.figure() plt.plot(X, y, 'b.', markersize=2, label=u'Observations', alpha=0.3) plt.plot(xx, y_median, 'r-', label=r'$\hat \mu$') plt.plot(xx, f(xx), 'g', label=u'$f(x) = 0.25x+10$') plt.xlabel('$x$') plt.ylabel('$f(x)$') # plt.ylim(-10, 20) plt.legend(loc='upper left') plt.savefig('const_' + plot_name + '.png', dpi=120) plt.close() def example_boosed_lin_reg_sin(): def f(x): heavyside = np.heaviside(x - 5.0, 1.0) * 12. return x * np.sin(x) + heavyside + 10. n_samples_per_edit = 1 X = np.atleast_2d(np.linspace(0, 10.0, 220).repeat(n_samples_per_edit)).T X = X.astype(np.float32) y = f(X).ravel() # std_dev = 1.5 + 1.0 * np.random.random(y.shape) std_dev = 2.0 noise = np.random.normal(0, std_dev, size=y.shape) y += noise y = y.astype(np.float32) # Mesh the input space for evaluations of the real function, the prediction and # its MSE xx = np.atleast_2d(np.linspace(0, 10, 1000)).T xx = xx.astype(np.float32) # fit to median gbt = GBRTmodel(max_depth=2, learning_rate=0.01, subsample=0.5, loss="se", tree_method="lin", minSplitPts=20, minDataLeaf=25) gbt.train(X, y, n_estimators=380) y_median = gbt.predict(xx) if MPL: # Plot the function, the prediction and the 90% confidence interval based on # the MSE fig = plt.figure() plt.plot(X, y, 'b.', markersize=2, label=u'Observations', alpha=0.3) plt.plot(xx, y_median, 'r-', label=r'$\hat q_{0.50}$') plt.plot(xx, f(xx), 'g', label=u'$f(x) = x\,\sin(x) + 12 H(x-5)$') plt.xlabel('$x$') plt.ylabel('$f(x)$') # plt.ylim(-10, 20) plt.legend(loc='upper left') plt.savefig('1d_boosted_regression_lin_sin_ex.png', dpi=120) plt.close() if __name__ == "__main__": def f(x): #y = np.zeros(len(x)) #y[np.asarray(x < 5).flatten()] = x[x < 5] * 0.25 + 10. #y[np.asarray(x >= 5).flatten()] = x[x >= 5] * -0.25 + 12. y = x ** 2.0 return y example_boosed_lin_reg_lin(f, 'pos_quadratic') def f(x): #y = np.zeros(len(x)) #y[np.asarray(x < 5).flatten()] = x[x < 5] * 0.25 + 10. #y[np.asarray(x >= 5).flatten()] = x[x >= 5] * -0.25 + 12. y = -x ** 2.0 return y example_boosed_lin_reg_lin(f, 'neg_quadratic') example_boosed_lin_reg_sin()

36.267717

133

0.546678

704

4,606

3.455966

0.21875

0.009042

0.036991

0.039046

0.815043

0.787505

0.751336

0.730785

0

0.057478

0.297438

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0

0.156627

0

null

0

1

0

null

0

6

485061818a0eb4e60d6caa58c3947b002619beaa

24

py

Python

ppca/__init__.py

sinziana91/pca-magic

db813177028e7453f4bceec42cb9383809559dc0

[ "Apache-2.0" ]

218

2015-02-22T21:22:49.000Z

2022-03-11T14:00:58.000Z

ppca/__init__.py

asinga1982/pca-magic

1e94f983c61e41b31e353465810fc0d8d46a8c98

[ "Apache-2.0" ]

11

2016-07-09T23:49:45.000Z

2022-03-08T09:19:18.000Z

ppca/__init__.py

asinga1982/pca-magic

1e94f983c61e41b31e353465810fc0d8d46a8c98

[ "Apache-2.0" ]

45

2015-02-23T16:06:01.000Z

2022-01-12T15:58:08.000Z

from ._ppca import PPCA

12

23

0.791667

4

24

4.5

0.75

0

0.166667

24

1

24

0.9

0

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true

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null

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1

0

1

0

1

0

6

6f83e73965f15f8699279a754045bb9013357ccb

3,267

py

Python

plaso/parsers/__init__.py

rick-slin/plaso

8685bd1689cbbaea78ebf723881a842eeaf94c35

[ "Apache-2.0" ]

null

plaso/parsers/__init__.py

rick-slin/plaso

8685bd1689cbbaea78ebf723881a842eeaf94c35

[ "Apache-2.0" ]

null

plaso/parsers/__init__.py

rick-slin/plaso

8685bd1689cbbaea78ebf723881a842eeaf94c35

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """This file imports Python modules that register parsers.""" from plaso.parsers import asl from plaso.parsers import android_app_usage from plaso.parsers import apache_access from plaso.parsers import apt_history from plaso.parsers import aws_elb_access from plaso.parsers import bash_history from plaso.parsers import bencode_parser from plaso.parsers import bsm from plaso.parsers import chrome_cache from plaso.parsers import chrome_preferences from plaso.parsers import cups_ipp from plaso.parsers import custom_destinations from plaso.parsers import czip from plaso.parsers import dpkg from plaso.parsers import esedb from plaso.parsers import filestat from plaso.parsers import firefox_cache from plaso.parsers import fish_history from plaso.parsers import fseventsd from plaso.parsers import gdrive_synclog from plaso.parsers import google_logging from plaso.parsers import iis from plaso.parsers import ios_lockdownd from plaso.parsers import ios_logd from plaso.parsers import ios_mobile_installation_log from plaso.parsers import java_idx from plaso.parsers import jsonl_parser from plaso.parsers import locate from plaso.parsers import mac_appfirewall from plaso.parsers import mac_keychain from plaso.parsers import mac_securityd from plaso.parsers import mac_wifi from plaso.parsers import mactime from plaso.parsers import mcafeeav from plaso.parsers import msiecf from plaso.parsers import networkminer from plaso.parsers import ntfs from plaso.parsers import olecf from plaso.parsers import opera from plaso.parsers import pe from plaso.parsers import plist from plaso.parsers import popcontest from plaso.parsers import pls_recall from plaso.parsers import recycler from plaso.parsers import safari_cookies from plaso.parsers import santa from plaso.parsers import sccm from plaso.parsers import selinux from plaso.parsers import setupapi from plaso.parsers import skydrivelog from plaso.parsers import sophos_av from plaso.parsers import spotlight_storedb from plaso.parsers import sqlite from plaso.parsers import symantec from plaso.parsers import systemd_journal from plaso.parsers import syslog from plaso.parsers import trendmicroav from plaso.parsers import utmp from plaso.parsers import utmpx from plaso.parsers import vsftpd from plaso.parsers import winevt from plaso.parsers import winevtx from plaso.parsers import winfirewall from plaso.parsers import winjob from plaso.parsers import winlnk from plaso.parsers import winprefetch from plaso.parsers import winreg_parser from plaso.parsers import winrestore from plaso.parsers import xchatlog from plaso.parsers import xchatscrollback from plaso.parsers import zsh_extended_history # Register plugins. from plaso.parsers import bencode_plugins from plaso.parsers import czip_plugins from plaso.parsers import esedb_plugins from plaso.parsers import jsonl_plugins from plaso.parsers import olecf_plugins from plaso.parsers import plist_plugins from plaso.parsers import sqlite_plugins from plaso.parsers import syslog_plugins from plaso.parsers import winreg_plugins # These modules do not register parsers themselves, but contain super classes # used by parsers in other modules. # from plaso.parsers import dsv_parser # from plaso.parsers import text_parser

35.901099

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0.854913

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3,267

5.652263

0.251029

0.268657

0.477612

0.656716

0.383327

0

0.000345

0.112642

3,267

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true

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0

null

0

1

0

1

0

1

0

6

6f8f7ed8337271bf7ac7ab965547f3e0fecc552d

107

py

Python

core/primitive/string.py

ponyatov/metaLpy

96149313e8083536ade1c331825242f6996f05b3

[ "MIT" ]

null

core/primitive/string.py

ponyatov/metaLpy

96149313e8083536ade1c331825242f6996f05b3

[ "MIT" ]

null

core/primitive/string.py

ponyatov/metaLpy

96149313e8083536ade1c331825242f6996f05b3

[ "MIT" ]

null

## @file from .primitive import * ## text string ## @ingroup primitive class String(Primitive): pass

11.888889

24

0.682243

12

107

6.083333

0.75

0

0.196262

107

8

25

13.375

0.848837

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0

1

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true

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0.666667

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1

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null

0

1

0

null

0

1

0

1

0

6

6fceb6b5ab16f3001ea7837c90e6cb2481f3d4e2

113,303

py

Python

train_functions.py

vassilis-karavias/fNRI-mastersigma

d3f4fecf9d28a9bc6e6150994824ca7674006ed3

[ "MIT" ]

null

train_functions.py

vassilis-karavias/fNRI-mastersigma

d3f4fecf9d28a9bc6e6150994824ca7674006ed3

[ "MIT" ]

null

train_functions.py

vassilis-karavias/fNRI-mastersigma

d3f4fecf9d28a9bc6e6150994824ca7674006ed3

[ "MIT" ]

null

''' This code is based on https://github.com/ekwebb/fNRI which in turn is based on https://github.com/ethanfetaya/NRI (MIT licence) ''' import argparse import csv import datetime import os import pickle import torch.optim as optim from torch.optim import lr_scheduler import time from modules_sigma import * from utils import * import math class Model(object): def __init__(self, args, edge_types, log_prior, encoder_file, decoder_file, current_encoder_file, current_decoder_file, log, loss_data, csv_writer, perm_writer, encoder, decoder, optimizer, scheduler): # initialises states super(Model, self).__init__() self.edge_types = edge_types self.log_prior = log_prior self.encoder_file = encoder_file self.decoder_file = decoder_file self.current_encoder_file = current_encoder_file self.current_decoder_file = current_decoder_file self.log = log self.loss_data = loss_data self.csv_writer = csv_writer self.perm_writer = perm_writer self.encoder = encoder self.decoder = decoder self.optimizer = optimizer self.scheduler = scheduler if args.NRI: self.train_loader, self.valid_loader, self.test_loader, self.loc_max, self.loc_min, self.vel_max, \ self.vel_min = load_data_NRI(args.batch_size, args.sim_folder, shuffle=True, data_folder=args.data_folder) else: self.train_loader, self.valid_loader, self.test_loader, self.loc_max, self.loc_min, self.vel_max,\ self.vel_min = load_data_fNRI(args.batch_size, args.sim_folder, shuffle=True, data_folder=args.data_folder) self.datatensor = torch.FloatTensor([]) if args.cuda: self.datatensor = self.datatensor.cuda() self.datatensor = Variable(self.datatensor) for batch_idx, (data, relations) in enumerate(self.train_loader): if args.cuda: data, relations = data.cuda(), relations.cuda() data, relations = Variable(data), Variable(relations) self.datatensor = torch.cat((self.datatensor, data), dim=0) # get the prior for sigma self.sigma_target = getsigma_target(self.datatensor, phys_error_folder=args.phys_folder, comp_error_folder=args.comp_folder, data_folder=args.data_folder, sim_folder=args.sim_folder) self.sigma_target = self.sigma_target.unsqueeze(dim=0) self.sigma_target = self.sigma_target.unsqueeze(dim=1) if args.cuda: self.sigma_target = self.sigma_target.cuda() # Generate off-diagonal interaction graph self.off_diag = np.ones([args.num_atoms, args.num_atoms]) - np.eye(args.num_atoms) self.rel_rec = np.array(encode_onehot(np.where(self.off_diag)[1]), dtype=np.float32) self.rel_send = np.array(encode_onehot(np.where(self.off_diag)[0]), dtype=np.float32) self.rel_rec = torch.FloatTensor(self.rel_rec) self.rel_send = torch.FloatTensor(self.rel_send) # initialise parameters needed for convexification self.alpha = 1 self.preds_prev = torch.zeros((args.num_atoms, args.timesteps-1, 4)) self.sigma_prev = torch.zeros((args.num_atoms, args.timesteps-1, 4)) if args.cuda: self.preds_prev, self.sigma_prev = self.preds_prev.cuda(), self.sigma_prev.cuda() self.preds_prev, self.sigma_prev = Variable(self.preds_prev), Variable(self.sigma_prev) if args.cuda: self.rel_rec = self.rel_rec.cuda() self.rel_send = self.rel_send.cuda() self.rel_rec = Variable(self.rel_rec) self.rel_send = Variable(self.rel_send) def close_log(self, save_folder, log_csv, perm_csv): # close the log files if self.log is not None: print(save_folder) self.log.close() log_csv.close() perm_csv.close() self.loss_data.close() def loss_fixed(self, data_decoder, edges, sigma, args, pred_steps=1, use_onepred=False): # calculate the loss for the fixed variance case. Also returns the output of the NN. target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, False, False, args.temp_softplus, pred_steps) else: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, False, False, args.temp_softplus, args.prediction_steps) # calculate loss function loss_nll = nll_gaussian(output, target, args.var) loss_nll_var = nll_gaussian_var(output, target, args.var) return loss_nll, loss_nll_var, output def loss_anisotropic(self, data_decoder, edges, sigma, args, pred_steps=1, use_onepred=False): # calculate the loss for the anisotropic case. Also returns the output of the NN target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, pred_steps) else: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, args.prediction_steps) # calculate loss function loss_nll, loss_1, loss_2 = nll_gaussian_multivariatesigma_efficient(output, target, sigma, accel, vel) loss_nll_var = nll_gaussian_var_multivariatesigma_efficient(output, target, sigma, accel, vel) return loss_nll, loss_nll_var, output def loss_KL(self, data_decoder, edges, sigma, args, pred_steps=1, use_onepred=False): # calculate the loss for the anisotropic case with a KL divergence to account for prior. Also returns the output of the NN target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # recast target sigma to correct shape. sigma_target_1 = tile(self.sigma_target, 0, sigma.size(0)) sigma_target_1 = tile(sigma_target_1, 1, sigma.size(1)) # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, pred_steps) else: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, args.prediction_steps) # calculate the loss function loss_nll, loss_1, loss_2 = nll_gaussian_multivariatesigma_efficient(output, target, sigma, accel, vel) loss_nll_var = nll_gaussian_var_multivariatesigma_efficient(output, target, sigma, accel, vel) loss_kl_decoder = KL_output_multivariate(output, sigma, target, sigma_target_1) return loss_nll + args.beta * loss_kl_decoder, loss_nll_var, output def loss_normalinversewishart(self, data_decoder, edges, sigma, args, batch_idx, settouse, pred_steps=1, use_onepred=False): # calculate the loss for the anisotropic Normal-Inverse-Wishart distribution. Also returns the output of the NN. target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, pred_steps) else: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, args.prediction_steps) # get the appropriate prior. if settouse.lower() == 'train': prior_pos = self.prior_pos_tensor_train[batch_idx] prior_vel = self.prior_vel_tensor_train[batch_idx] elif settouse.lower() == 'validation': prior_pos = self.prior_pos_tensor_valid[batch_idx] prior_vel = self.prior_vel_tensor_valid[batch_idx] elif settouse.lower() == 'test': prior_pos = self.prior_pos_tensor_test[batch_idx] prior_vel = self.prior_vel_tensor_test[batch_idx] else: print("The set to use parameter must be one of 'train', 'validation' or 'test'.") # calculate the loss loss_nll, loss_nll_var = nll_Normal_Inverse_WishartLoss(output, sigma, accel, vel, prior_pos, prior_vel) return loss_nll, loss_nll_var, output def loss_kalmanfilter(self, data_decoder, edges, sigma, args, pred_steps=1, use_onepred=False): # calculate the loss for the anisotropic Normal distribution with a Kalman filter envelope. Also returns the output of the NN. # Note that this follows the suggestion given in: Multivariate Uncertainty in Deep Learning, # Rebecca L. Russell and Christopher Reale, # 2019, arXiv:1910.14215 [cs.LG] target = data_decoder[:, :, 1:, :] # recast target sigma to correct shape sigma_target_1 = tile(self.sigma_target, 0, sigma.size(0)) sigma_target_1 = tile(sigma_target_1, 1, sigma.size(1)) # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, pred_steps) else: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, args.prediction_steps) # calculates the output after passing through a kalman filter kalmanfiler = KalmanFilter(sigma_target_1[:output.size(0), :, 0, :]) output, covmat = kalmanfiler.kalman_filter_steps(target, output, sigma) # loss here is the MSE. loss_nll = F.mse_loss(output, target) loss_nll_var = nll_gaussian_var(output, target, args.var) return loss_nll, loss_nll_var, output def loss_isotropic(self, data_decoder, edges, sigma, args, epoch, pred_steps=1, use_onepred=False): # calculates the loss for the isotropic model. Also returns the output of the NN target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma_1, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, False, args.temp_softplus, pred_steps) else: output, sigma_1, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, False, args.temp_softplus, args.prediction_steps) # in case of isotropic we need to recast sigma to the same shape as output as it is required in the gaussian loss calculation sigma_1 = tile(sigma_1, 3, list(output.size())[3]) # calculates loss function loss_nll, loss_1, loss_2 = nll_gaussian_variablesigma(output, target, sigma_1, epoch, args.temp_sigmoid, args.epochs) loss_nll_var = nll_gaussian_var__variablesigma(output, target, sigma_1, epoch, args.temp_sigmoid, args.epochs) return loss_nll, loss_nll_var, output def loss_lorentzian(self, data_decoder, edges, sigma, args, pred_steps=1, use_onepred=False): # calculates the loss for the Lorentzian model. Also returns the output of the NN target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma_1, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, False, args.temp_softplus, pred_steps) else: output, sigma_1, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, False, args.temp_softplus, args.prediction_steps) # in case of isotropic we need to recast sigma to the same shape as output as it is required in the gaussian loss calculation sigma_1 = tile(sigma_1, 3, list(output.size())[3]) # calculates loss function loss_nll = nll_lorentzian(output, target, sigma_1) loss_nll_var = nll_lorentzian_var(output, target, sigma_1) return loss_nll, loss_nll_var, output def loss_semi_isotropic(self, data_decoder, edges, sigma, args, epoch, pred_steps=1, use_onepred=False): # calculates the loss for the semi-isotropic Gaussian model. Also returns the output of the NN target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, False, args.temp_softplus, pred_steps) else: output, sigma, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, False, args.temp_softplus, args.prediction_steps) # calculates loss function loss_nll, loss_1, loss_2 = nll_gaussian_variablesigma_semiisotropic(output, target, sigma, epoch, args.temp_sigmoid, args.epochs) loss_nll_var = nll_gaussian_var__variablesigma_semiisotropic(output, target, sigma, epoch, args.temp_sigmoid, args.epochs) return loss_nll, loss_nll_var, output def loss_anisotropic_withconvex(self, data_decoder, edges, sigma, args, vvec, sigmavec, pred_steps=1, use_onepred= False): # calculates the loss for the anisotropic Gaussian model with convexification. Also returns the output of the NN # The algorithm was designed by Edoardo Calvello target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # ensures alpha is not too small if (abs(self.alpha) > 1e-16): self.alpha = 1e-16 # forward() in decoder called here - carries out decoding step if use_onepred: output, sigma_1, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, pred_steps) else: output, sigma_1, accel, vel = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, args.prediction_steps) sigma_prev = tile(self.sigma_prev.unsqueeze(0), 0, target.size(0)) preds_prev = tile(self.preds_prev.unsqueeze(0), 0, target.size(0)) # calculates the loss loss_nll, loss_1, loss_2 = nll_gaussian_multivariatesigma_convexified(output, target, sigma_1, accel, vel, sigma_prev, preds_prev, vvec, sigmavec, self.alpha) loss_nll_var = nll_gaussian_multivariatesigma_var_convexified(output, target, sigma_1, accel, vel, sigma_prev, preds_prev, vvec, sigmavec, self.alpha) # update step 3 of algorithm by Edoardo Calvello vvec_new = preds_prev + (output-preds_prev) /self.alpha sigmavec_new = sigma_prev + (sigma_1-sigma_prev) / self.alpha self.alpha = (np.sqrt(pow(self.alpha,4) + 4 * pow(self.alpha, 2)) - pow(self.alpha, 2)) / 2 return loss_nll, loss_nll_var, output, vvec_new, sigmavec_new def fixed_var_plot(self, args, acc_blocks_batch, target, output_plot): import matplotlib.pyplot as plt import matplotlib.patches as patches from trajectory_plot import draw_lines # plots trajectories over timesteps - Plot the trajectories of the output of the NN and the target for i in range(args.batch_size): fig = plt.figure(figsize=(7, 7)) ax = fig.add_subplot(111) # ax = fig.add_axes([0, 0, 1, 1]) ax.xaxis.set_visible(True) ax.yaxis.set_visible(True) xmin_t, ymin_t, xmax_t, ymax_t = draw_lines(target, i, linestyle=':', alpha=0.6) xmin_o, ymin_o, xmax_o, ymax_o = draw_lines(output_plot.detach().cpu().numpy(), i, linestyle='-') # ax.set_xlim([min(xmin_t, xmin_o), max(xmax_t, xmax_o)]) # ax.set_ylim([min(ymin_t, ymin_o), max(ymax_t, ymax_o)])# ax.set_xlim([-1,1]) ax.set_ylim([-1,1]) rect = patches.Rectangle((-1,-1),2,2, edgecolor='r', facecolor='none') ax.add_patch(rect) # ax.set_xticks(np.linspace(math.ceil(min(xmin_t, xmin_o)*10)/10, math.floor(max(xmax_t, xmax_o)*10)/10,2)) # ax.set_yticks(np.linspace(math.ceil(min(ymin_t, ymin_o)*10)/10, math.floor(max(ymax_t, ymax_o)*10)/10,2)) block_names = ['layer ' + str(j) for j in range(len(args.edge_types_list))] # block_names = [ 'springs', 'charges' ] acc_text = [block_names[j] + ' acc: {:02.0f}%'.format(100 * acc_blocks_batch[i, j]) for j in range(acc_blocks_batch.shape[1])] acc_text = ', '.join(acc_text) plt.text(0.5, 0.95, acc_text, horizontalalignment='center', transform=ax.transAxes) ax.xaxis.set_visible(True) ax.yaxis.set_visible(True) plt.xlabel('x') plt.ylabel('y') plt.savefig(os.path.join(args.load_folder,str(i)+'_pred_and_true.png'), dpi=300) plt.show() def isotropic_plot(self, args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y): # plots the graphs for the isotropic model. returns the z-score values parallel and perpendicular to the velocity. import matplotlib.pyplot as plt # for plotting output_plot, sigma_plot, accel_plot, vel_plot = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, False, args.temp_softplus, 49) if args.loss_type.lower() == 'isotropic' or args.loss_type.lower() == 'lorentzian': # put sigma_plot in correct form for isotropic case sigma_plot = tile(sigma_plot, 3, list(output_plot.size())[3]) else: # semi-isotropic need to select the position coords and velocity coords and convert from # [batch, particle, time , 1] -> [batch, particle, time, 2 (3 for 3D)] then reconcatinate to # put sigma_plot in correct form indices_pos = torch.LongTensor([0]) indices_vel = torch.LongTensor([1]) if args.cuda: indices_pos = indices_pos.cuda() sigma_plot_pos = torch.index_select(sigma_plot, 3, indices_pos) sigma_plot_vel = torch.index_select(sigma_plot, 3, indices_vel) sigma_plot_pos = tile(sigma_plot_pos, 3, 2) sigma_plot_vel = tile(sigma_plot_vel, 3, 2) sigma_plot = torch.cat((sigma_plot_pos, sigma_plot_vel), 3) if args.NRI: acc_batch, perm, acc_blocks_batch = edge_accuracy_perm_NRI_batch(logits, relations, args.edge_types_list) else: acc_batch, perm, acc_blocks_batch = edge_accuracy_perm_fNRI_batch(logits_split, relations, args.edge_types_list) # plot the mean value of sigma over timestep: timestep = np.arange(0.1, 0.1 * (args.timesteps - 1), 0.1) sigma_mean = sigma_plot.mean(dim=0) sigma_mean = sigma_mean.mean(dim=0) sigma_mean = sigma_mean.mean(dim=1) sigma_mean = sigma_mean.detach().cpu().numpy() fig = plt.figure() plt.plot(timestep, sigma_mean, label='raw data') plt.ylabel('Sigma Value') plt.xlabel('Time along Trajectory') plt.show() from trajectory_plot import draw_lines_sigma from matplotlib.patches import Ellipse, Rectangle # plotting graphs for isotropic/anisotropic case - here we plot the values of sigma using # Ellipses which are same colour as plots (see draw_lines_sigma in trajectory_plot) for i in range(args.batch_size): fig = plt.figure(figsize=(7, 7)) ax = fig.add_subplot(111) # ax = fig.add_axes([0, 0, 1, 1]) ax.xaxis.set_visible(True) ax.yaxis.set_visible(True) xmin_t, ymin_t, xmax_t, ymax_t = -1, -1, 1, 1 xmin_o, ymin_o, xmax_o, ymax_o = -0.5, -0.5, 0.5, 0.5 xmin_t, ymin_t, xmax_t, ymax_t = draw_lines_sigma(target, i, sigma_plot.detach().cpu().numpy(), ax, linestyle=':', alpha=0.6) xmin_o, ymin_o, xmax_o, ymax_o = draw_lines_sigma(output_plot.detach().cpu().numpy(), i, sigma_plot.detach().cpu().numpy(), ax, linestyle='-', plot_ellipses=True) # ax.set_xlim([min(xmin_t, xmin_o), max(xmax_t, xmax_o)]) # ax.set_ylim([min(ymin_t, ymin_o), max(ymax_t, ymax_o)]) ax.set_xlim([-1,1]) ax.set_ylim([-1,1]) rect = Rectangle((-1, -1), 2, 2, edgecolor='r', facecolor='none') ax.add_patch(rect) block_names = ['layer ' + str(j) for j in range(len(args.edge_types_list))] # block_names = [ 'springs', 'charges' ] acc_text = [block_names[j] + ' acc: {:02.0f}%'.format(100 * acc_blocks_batch[i, j]) for j in range(acc_blocks_batch.shape[1])] acc_text = ', '.join(acc_text) plt.text(0.5, 0.95, acc_text, horizontalalignment='center', transform=ax.transAxes) ax.xaxis.set_visible(True) ax.yaxis.set_visible(True) plt.xlabel('x') plt.ylabel('y') # plt.savefig(os.path.join(args.load_folder,str(i)+'_pred_and_true.png'), dpi=300) plt.show() # z-score calcualtion if (torch.min(sigma_plot) < pow(10, -7)): accuracy = np.full((sigma_plot.size(0), sigma_plot.size(1), sigma_plot.size(2), sigma_plot.size(3)), pow(10, -7), dtype=np.float32) accuracy = torch.from_numpy(accuracy) if args.cuda: accuracy = accuracy.cuda() output_plot = torch.max(output_plot, accuracy) # z = (y-yhat)/sigma zscore = (output_plot - target) / (sigma_plot) # select out velocity coords to get direction parallel and perpendicular to velocity- need this # to find z-score valuesalong these directions indices = torch.LongTensor([2, 3]) if args.cuda: indices = indices.cuda() velocities = torch.index_select(output_plot, 3, indices) # abs(v) velnorm = velocities.norm(p=2, dim=3, keepdim=True) # vhat = v/abs(v) normalisedvel = velocities.div(velnorm.expand_as(velocities)) accelnorm = accel_plot.norm(p=2, dim=3, keepdim=True) normalisedaccel = accel_plot.div(accelnorm.expand_as(accel_plot)) # get perpendicular components to the accelerations and velocities accelperp, velperp # note in 2D perpendicular vector is just rotation by pi/2 about origin (x,y) -> (-y,x) rotationmatrix = np.zeros( (velocities.size(0), velocities.size(1), velocities.size(2), 2, 2), dtype=np.float32) for i in range(len(rotationmatrix)): for j in range(len(rotationmatrix[i])): for l in range(len(rotationmatrix[i][j])): rotationmatrix[i][j][l][0][1] = np.float32(-1) rotationmatrix[i][j][l][1][0] = np.float32(1) rotationmatrix = torch.from_numpy(rotationmatrix) if args.cuda: rotationmatrix = rotationmatrix.cuda() velperp = torch.matmul(rotationmatrix, normalisedvel.unsqueeze(4)) velperp = velperp.squeeze() accelperp = torch.matmul(rotationmatrix, normalisedaccel.unsqueeze(4)) accelperp = accelperp.squeeze() indices = torch.LongTensor([0, 1]) if args.cuda: indices = indices.cuda() # zscore along the position axes zscore = torch.index_select(zscore, 3, indices) # zscore parallel to velocity zscore_x = torch.matmul(zscore.unsqueeze(3), normalisedvel.unsqueeze(4)) # zscore perp to velocity zscore_y = torch.matmul(zscore.unsqueeze(3), velperp.unsqueeze(4)) zscorelist_x.append(zscore_x) zscorelist_y.append(zscore_y) return zscorelist_x, zscorelist_y def anisotropic_plot(self, args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y): # plots the graphs for the anisotropic model. returns the z-score values parallel and perpendicular to the velocity. import matplotlib.pyplot as plt from scipy.optimize import curve_fit output_plot, sigma_plot, accel_plot, vel_plot = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, True, True, args.temp_softplus, 49) # plot the MSE over time to investigate chaos theory. loc = target[:, :, :, 0:2].detach().numpy() loc_new = output_plot[:, :, :, 0:2].detach().numpy() mse_loc = ((loc_new - loc) ** 2).mean(axis=3) / args.num_atoms mse_loc = mse_loc.mean(axis=1) mse_loc = mse_loc.mean(axis=0) deltaT = 0.1 T = np.arange(0, deltaT * (len(mse_loc) - 1 / 2), 0.1) optimised_params_x, pcov = curve_fit(exp, T, mse_loc, p0=[1, 5, -1], maxfev=1000000) fig = plt.figure() plt.plot(T, np.log(exp(T, *optimised_params_x)), label='fit') plt.plot(T, np.log(mse_loc), label='raw data') plt.ylabel('log(Mean Square Error)') plt.xlabel('Time along Trajectory') plt.legend(loc='best') plt.show() # plot the mean value of sigma over timestep: timestep = np.arange(0.1, 0.1 * (args.timesteps - 1), 0.1) sigma_mean = sigma_plot.mean(dim=0) sigma_mean = sigma_mean.mean(dim=0) sigma_mean = sigma_mean.mean(dim=1) sigma_mean = sigma_mean.detach().cpu().numpy() optimised_params_x, pcov = curve_fit(exp, timestep, sigma_mean, p0=[1, 5, -1], maxfev=1000000) fig = plt.figure() plt.plot(T, exp(T, *optimised_params_x), label='fit') plt.plot(timestep, sigma_mean, label='raw data') plt.ylabel('Sigma Value') plt.xlabel('Time along Trajectory') plt.legend(loc='best') plt.show() fig = plt.figure() plt.plot(T, np.log(exp(T, *optimised_params_x)), label='fit') plt.plot(timestep, np.log(sigma_mean), label='raw data') plt.ylabel('log(Sigma Value)') plt.xlabel('Time along Trajectory') plt.legend(loc='best') plt.show() if args.NRI: acc_batch, perm, acc_blocks_batch = edge_accuracy_perm_NRI_batch(logits, relations, args.edge_types_list) else: acc_batch, perm, acc_blocks_batch = edge_accuracy_perm_fNRI_batch(logits_split, relations, args.edge_types_list) # plot trajectories and sigma's - use ellipses to plot anisotropy - see draw_lines_anisotropic from trajectory_plot import draw_lines_anisotropic, draw_lines_sigma_animation from matplotlib.patches import Rectangle for i in range(args.batch_size): # draw_lines_sigma_animation(target, output_plot, i, sigma_plot, vel_plot) fig = plt.figure(figsize=(7, 7)) ax = fig.add_subplot(111) # ax = fig.add_axes([0, 0, 1, 1]) ax.xaxis.set_visible(True) ax.yaxis.set_visible(True) xmin_t, ymin_t, xmax_t, ymax_t = draw_lines_anisotropic(target, i, sigma_plot.detach().cpu().numpy(), vel_plot, ax, linestyle=':', alpha=0.6) xmin_o, ymin_o, xmax_o, ymax_o = draw_lines_anisotropic( output_plot.detach().cpu().numpy(), i, sigma_plot.detach().cpu().numpy(), vel_plot, ax, linestyle='-', plot_ellipses=True) # ax.set_xlim([min(xmin_t, xmin_o), max(xmax_t, xmax_o)]) # ax.set_ylim([min(ymin_t, ymin_o), max(ymax_t, ymax_o)]) ax.set_xlim([-1,1]) ax.set_ylim([-1,1]) rect = Rectangle((-1, -1), 2, 2, edgecolor='r', facecolor='none') ax.add_patch(rect) block_names = ['layer ' + str(j) for j in range(len(args.edge_types_list))] # block_names = [ 'springs', 'charges' ] acc_text = [block_names[j] + ' acc: {:02.0f}%'.format(100 * acc_blocks_batch[i, j]) for j in range(acc_blocks_batch.shape[1])] acc_text = ', '.join(acc_text) plt.text(0.5, 0.95, acc_text, horizontalalignment='center', transform=ax.transAxes) ax.xaxis.set_visible(True) ax.yaxis.set_visible(True) plt.xlabel('x') plt.ylabel('y') # plt.savefig(os.path.join(args.load_folder,str(i)+'_pred_and_true.png'), dpi=300) plt.show() # for z score # make sure we aren't dividing by 0 if (torch.min(sigma_plot) < pow(10, -7)): accuracy = np.full((sigma_plot.size(0), sigma_plot.size(1), sigma_plot.size(2), sigma_plot.size(3)), pow(10, -7), dtype=np.float32) accuracy = torch.from_numpy(accuracy) if args.cuda: accuracy = accuracy.cuda() output_plot = torch.max(output_plot, accuracy) zscore = (output_plot - target) / (sigma_plot) # select out velocity coords to get direction parallel and perpendicular to velocity- need this # to find z-score values along these directions velnorm = vel_plot.norm(p=2, dim=3, keepdim=True) normalisedvel = vel_plot.div(velnorm.expand_as(vel_plot)) accelnorm = accel_plot.norm(p=2, dim=3, keepdim=True) normalisedaccel = accel_plot.div(accelnorm.expand_as(accel_plot)) # get perpendicular components to the accelerations and velocities accelperp, velperp # note in 2D perpendicular vector is just rotation by pi/2 about origin (x,y) -> (-y,x) rotationmatrix = np.zeros( (normalisedvel.size(0), normalisedvel.size(1), normalisedvel.size(2), 2, 2), dtype=np.float32) for i in range(len(rotationmatrix)): for j in range(len(rotationmatrix[i])): for l in range(len(rotationmatrix[i][j])): rotationmatrix[i][j][l][0][1] = np.float32(-1) rotationmatrix[i][j][l][1][0] = np.float32(1) rotationmatrix = torch.from_numpy(rotationmatrix) if args.cuda: rotationmatrix = rotationmatrix.cuda() velperp = torch.matmul(rotationmatrix, normalisedvel.unsqueeze(4)) velperp = velperp.squeeze() accelperp = torch.matmul(rotationmatrix, normalisedaccel.unsqueeze(4)) accelperp = accelperp.squeeze() indices = torch.LongTensor([0, 1]) if args.cuda: indices = indices.cuda() zscore = torch.index_select(zscore, 3, indices) zscore_x = torch.matmul(zscore.unsqueeze(3), normalisedvel.unsqueeze(4)) zscore_y = torch.matmul(zscore.unsqueeze(3), velperp.unsqueeze(4)) zscorelist_x.append(zscore_x) zscorelist_y.append(zscore_y) return zscorelist_x, zscorelist_y def zscore_plot(self, zscorelist_x, zscorelist_y): # plots the z-score graphs import matplotlib.pyplot as plt from scipy.optimize import curve_fit from scipy.stats import chisquare # average over all timesteps zscorelistintx = np.empty((0)) zscorelistinty = np.empty((0)) for i in range(len(zscorelist_x)): zscorelistintx = np.append(zscorelistintx, zscorelist_x[i].numpy()) zscorelistinty = np.append(zscorelistinty, zscorelist_y[i].numpy()) bins = np.arange(-4, 4.1, 0.1) # get histogram distribution for terms parallel to the velocity histdatax, bin_edges, patches = plt.hist(zscorelistintx, bins, density=True) # take the histdata point to be at the centre of the bin_edges: # Gaussian fit- we expect a good model to give mean = 0 and sigma = 1 # for fit to full graph. NOTE USE histdatax instead of histdatax_new xcoords = np.empty(len(bin_edges) - 1) for i in range(len(bin_edges) - 1): xcoords[i] = (bin_edges[i] + bin_edges[i + 1]) / 2 numberofpoints = len(xcoords) # for fit to all points except central peak. NOTE USE histdatax_new instead of histdatax # xcoords = np.empty(0) # histdatax_new = np.empty(0) # xcoords_small = np.empty(0) # histdatax_small = np.empty(0) # for i in range(len(bin_edges) - 1): # if (abs(bin_edges[i])>0.5): # xcoords = np.append(xcoords, [(bin_edges[i] + bin_edges[i + 1])/ 2]) # histdatax_new = np.append(histdatax_new, [histdatax[i]]) # else: # xcoords_small = np.append(xcoords_small, [(bin_edges[i] + bin_edges[i + 1])/ 2]) # histdatax_small = np.append(histdatax_small, [histdatax[i]]) # numberofpoints = len(xcoords) # mean is 1/N SUM(xy) mean_gaussian_x = np.sum(xcoords * histdatax) / numberofpoints # var = 1/N SUM(y*(x-mean) ** 2) sigma_x = np.sqrt(np.sum(histdatax * (xcoords - mean_gaussian_x) ** 2) / numberofpoints) # Fit to Gaussian optimised_params_x, pcov = curve_fit(gaussian, xcoords, histdatax, p0=[1, mean_gaussian_x, sigma_x]) plt.plot(xcoords, gaussian(xcoords, *optimised_params_x), label='fit') # fit ro Lorentzian optimised_params_lor_x, pcov = curve_fit(lorentzian, xcoords, histdatax, p0=[1, mean_gaussian_x, sigma_x]) plt.plot(xcoords, lorentzian(xcoords, *optimised_params_lor_x), 'k') plt.xlabel("z-score") plt.ylabel("frequency") plt.text(60, .025, r'$\mu=100,\ \sigma=15$') plt.xlim(-4, 4) plt.savefig('zscorepartimestep.png') plt.show() area_under_gauss, p_1 = chisquare(f_obs = histdatax, f_exp = gaussian(xcoords, *optimised_params_x)) area_under_lor, p_2 = chisquare(f_obs= histdatax, f_exp = lorentzian(xcoords, *optimised_params_lor_x)) # area_under_gauss = (((gaussian(xcoords, *optimised_params_x) - histdatax) ** 2)/ (gaussian(xcoords, *optimised_params_x))).mean(axis=None) # area_under_lor = (((lorentzian(xcoords, *optimised_params_lor_x) - histdatax) ** 2)/ (lorentzian(xcoords, *optimised_params_lor_x))).mean(axis= None) # get histogram distribution for terms perpendicular to velocity histdatay, bin_edges, patches = plt.hist(zscorelistinty, bins, density=True) # take the histdata point to be at the centre of the bin_edges: # Gaussian fit- we expect a good model to give mean = 0 and sigma = 1 ## for fit to full graph. NOTE USE histdatay instead of histdatay_new ycoords = np.empty(len(bin_edges) - 1) for i in range(len(bin_edges) - 1): ycoords[i] = (bin_edges[i] + bin_edges[i + 1]) / 2 numberofpoints = len(ycoords) # for fit to all points except central peak. NOTE USE histdatay_new instead of histdatay # ycoords = np.empty(0) # histdatay_new = np.empty(0) # ycoords_small = np.empty(0) # histdatay_small = np.empty(0) # for i in range(len(bin_edges) - 1): # if (abs(bin_edges[i]) > 0.5): # ycoords = np.append(ycoords,[(bin_edges[i] + bin_edges[i + 1]) / 2]) # histdatay_new = np.append(histdatay_new,[histdatay[i]]) # else: # ycoords_small = np.append(ycoords_small, [(bin_edges[i] + bin_edges[i + 1]) / 2]) # histdatay_small = np.append(histdatay_small, [histdatay[i]]) # numberofpoints = len(ycoords) # mean is 1/N SUM(xy) mean_gaussian_y = np.sum(ycoords * histdatay) / numberofpoints # var = 1/N SUM(y*(x-mean) ** 2) sigma_y = np.sqrt(np.sum(histdatay * (ycoords - mean_gaussian_y) ** 2) / numberofpoints) # Fit to Gaussian # optimised_params_y, pcov = curve_fit(gaussian, ycoords, histdatay, p0=[1, mean_gaussian_y, sigma_y]) # plt.plot(ycoords, gaussian(ycoords, *optimised_params_y), label='fit') # Fit to Lorentzian # optimised_params_lor_y, pcov = curve_fit(lorentzian, ycoords, histdatay, # p0=[1, mean_gaussian_y, sigma_y]) # plt.plot(ycoords, lorentzian(ycoords, *optimised_params_lor_y), 'k') plt.xlabel("z-score") plt.ylabel("frequency") plt.text(60, .025, r'$\mu=100,\ \sigma=15$') # plt.title('Timestep = ' + str(j)) plt.xlim(-4, 4) plt.savefig('zscoreorth.png') plt.show() print("Gaussian Fit parallel to vel with mean: " + str( optimised_params_x[1]) + " and std: " + str(optimised_params_x[2])) print( "Lorentzian Fit parallel to vel with mean: " + str(optimised_params_lor_x[1]) + " and std: " + str( optimised_params_lor_x[2])) print("Gaussian Fit parallel to vel area between curves: " + str(area_under_gauss) + ". p value: " + str(p_1)) print("Lorentzian Fit parallel to vel area between curves: " + str(area_under_lor) + ". p value: " + str(p_2)) # print( # "Gaussian Fit perpendicular to vel with mean: " + str(optimised_params_y[1]) + " and std: " + str( # optimised_params_y[2])) # print("Lorentzian Fit perpendicular to vel with mean: " + str( # optimised_params_lor_y[1]) + " and std: " + str( # optimised_params_lor_y[2])) # each timestep z-score uncomment for this plot. # for j in range(1,len(zscorelist_x[0][0,0])-1): # zscorelistintx = np.empty((0)) # zscorelistinty = np.empty((0)) # for i in range(len(zscorelist_x)): # zscorelistintx = np.append(zscorelistintx, zscorelist_x[i][:, :, j, :].numpy()) # zscorelistinty = np.append(zscorelistinty, zscorelist_y[i][:, :, j, :].numpy()) # bins = np.arange(-4, 4.1, 0.1) # # get histogram distribution # histdatax, bin_edges, patches = plt.hist(zscorelistintx, bins, density = True) # take the histdata point to be at the centre of the bin_edges: # Gaussian fit- we expect a good model to give mean = 0 and sigma = 1 # xcoords = np.empty(len(bin_edges) - 1) # for i in range(len(bin_edges) - 1): # xcoords[i] = (bin_edges[i] + bin_edges[i+1]) /2 # numberofpoints = len(xcoords) # # mean is 1/N SUM(xy) # mean_gaussian_x = np.sum(xcoords * histdatax) / numberofpoints # # var = 1/N SUM(y*(x-mean) ** 2) # sigma_x = np.sqrt(np.sum(histdatax * (xcoords - mean_gaussian_x) ** 2) / numberofpoints) # optimised_params_x, pcov = curve_fit(gaussian, xcoords, histdatax, p0 = [1, mean_gaussian_x, sigma_x]) # plt.plot(xcoords, gaussian(xcoords, *optimised_params_x), label = 'fit') # optimised_params_lor_x, pcov = curve_fit(lorentzian, xcoords, histdatax, p0=[1, mean_gaussian_x, sigma_x]) # plt.plot(xcoords, lorentzian(xcoords, *optimised_params_lor_x), 'k') # plt.xlabel("z-score") # plt.ylabel("frequency") # plt.text(60, .025, r'$\mu=100,\ \sigma=15$') # plt.title('Timestep = ' + str(j)) # plt.xlim(-4, 4) # plt.savefig('zscorepartimestep' + str(j) + '.png') # plt.show() # # get histogram distribution # histdatay, bin_edges, patches = plt.hist(zscorelistinty, bins, density=True) # # # take the histdata point to be at the centre of the bin_edges: # # Gaussian fit- we expect a good model to give mean = 0 and sigma = 1 # ycoords = np.empty(len(bin_edges) - 1) # for i in range(len(bin_edges) - 1): # ycoords[i] = (bin_edges[i] + bin_edges[i + 1]) / 2 # numberofpoints = len(ycoords) # # mean is 1/N SUM(xy) # mean_gaussian_y = np.sum(ycoords * histdatay) / numberofpoints # # var = 1/N SUM(y*(x-mean) ** 2) # sigma_y = np.sqrt(np.sum(histdatay * (ycoords - mean_gaussian_y) ** 2) / numberofpoints) # optimised_params_y, pcov = curve_fit(gaussian, ycoords, histdatay, p0=[1, mean_gaussian_y, sigma_y]) # plt.plot(ycoords, gaussian(ycoords, *optimised_params_y), label='fit') # optimised_params_lor_y, pcov = curve_fit(lorentzian, ycoords, histdatay, p0=[1, mean_gaussian_y, sigma_y]) # plt.plot(ycoords, lorentzian(ycoords, *optimised_params_lor_y), 'k') # plt.xlabel("z-score") # plt.ylabel("frequency") # plt.text(60, .025, r'$\mu=100,\ \sigma=15$') # plt.title('Timestep = ' + str(j)) # plt.xlim(-4, 4) # plt.savefig('zscoreorthtimestep' + str(j)+ '.png') # plt.show() # # print('Timestep = ' + str(j) + ". Gaussian Fit parallel to vel with mean: " + str(optimised_params_x[1]) + " and std: " + str(optimised_params_x[2])) # # print("Lorentzian Fit parallel to vel with mean: " + str(optimised_params_lor_x[1]) + " and std: " + str(optimised_params_lor_x[2])) # print( # "Gaussian Fit perpendicular to vel with mean: " + str(optimised_params_y[1]) + " and std: " + str(optimised_params_y[2])) # # print("Lorentzian Fit perpendicular to vel with mean: " + str(optimised_params_lor_y[1]) + " and std: " + str( # # optimised_params_lor_y[2])) class Trainer(Model): def __init__(self, args, edge_types, log_prior, encoder_file, decoder_file, current_encoder_file, current_decoder_file, log, loss_data, csv_writer, perm_writer, encoder, decoder, optimizer, scheduler): super(Trainer, self).__init__(args, edge_types, log_prior, encoder_file, decoder_file, current_encoder_file, current_decoder_file, log, loss_data, csv_writer, perm_writer, encoder, decoder, optimizer, scheduler) # gets the prior for the normal inverse wishart distribution if args.loss_type.lower() == 'norminvwishart': # prior for training data t = time.time() self.prior_pos_tensor_train = np.empty(0) self.prior_vel_tensor_train = np.empty(0) for batch_idx, (data, relations) in enumerate(self.train_loader): if args.cuda: data, relations = data.cuda(), relations.cuda() data, relations = Variable(data), Variable(relations) data = data.clone() relations = relations.clone() indices_pos = torch.LongTensor([0, 1]) indices_vel = torch.LongTensor([2, 3]) if args.cuda: indices_pos, indices_vel = indices_pos.cuda(), indices_vel.cuda() data_pos = torch.index_select(data, 3, indices_pos) data_vel = torch.index_select(data, 3, indices_vel) sigma_pos = torch.index_select(self.sigma_target, 3, indices_pos) sigma_vel = torch.index_select(self.sigma_target, 3, indices_vel) prior_pos = getpriordist(data_pos, sigma_pos, 4) prior_vel = getpriordist(data_vel, sigma_vel, 4) self.prior_pos_tensor_train = np.concatenate((self.prior_pos_tensor_train, [prior_pos])) self.prior_vel_tensor_train = np.concatenate((self.prior_vel_tensor_train, [prior_vel])) print('train time: {:.1f}s'.format(time.time() - t)) t = time.time() # prior for validation data self.prior_pos_tensor_valid = np.empty(0) self.prior_vel_tensor_valid = np.empty(0) for batch_idx, (data, relations) in enumerate(self.valid_loader): if args.cuda: data, relations = data.cuda(), relations.cuda() data, relations = Variable(data), Variable(relations) data = data.clone() relations = relations.clone() indices_pos = torch.LongTensor([0, 1]) indices_vel = torch.LongTensor([2, 3]) if args.cuda: indices_pos, indices_vel = indices_pos.cuda(), indices_vel.cuda() data_pos = torch.index_select(data, 3, indices_pos) data_vel = torch.index_select(data, 3, indices_vel) sigma_pos = torch.index_select(self.sigma_target, 3, indices_pos) sigma_vel = torch.index_select(self.sigma_target, 3, indices_vel) prior_pos = getpriordist(data_pos, sigma_pos, 4) prior_vel = getpriordist(data_vel, sigma_vel, 4) self.prior_pos_tensor_valid = np.concatenate((self.prior_pos_tensor_valid, [prior_pos])) self.prior_vel_tensor_valid = np.concatenate((self.prior_vel_tensor_valid, [prior_vel])) print('validation time: {:.1f}s'.format(time.time() - t)) def train(self, epoch, best_val_loss, args): t = time.time() # train set nll_train = [] nll_var_train = [] mse_train = [] kl_train = [] kl_list_train = [] kl_var_list_train = [] acc_train = [] acc_var_train = [] perm_train = [] acc_var_blocks_train = [] acc_blocks_train = [] KLb_train = [] KLb_blocks_train = [] # array of loss components loss_1_array = [] loss_2_array = [] # gets an array of the sigma tensor per run through of the batch sigmadecoderoutput = [] self.encoder.train() self.decoder.train() self.scheduler.step() if not args.plot: for batch_idx, (data, relations) in enumerate(self.train_loader): # relations are the ground truth interactions graphs # tottime = time.time() if args.cuda: data, relations = data.cuda(), relations.cuda() data, relations = Variable(data), Variable(relations) if args.dont_split_data: data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data[:, :, :args.timesteps, :].contiguous() elif args.split_enc_only: data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data else: # assert (data.size(2) - args.timesteps) >= args.timesteps data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data[:, :, -args.timesteps:, :].contiguous() # stores the values of the uncertainty. This will be an array of size [batchsize, no. of particles, time,no. of axes (isotropic = 1, anisotropic = 4] # initialise sigma to an array large negative numbers, under softplus function this will make them small positive numbers sigma = initsigma(len(data_decoder), len(data_decoder[0][0]), args.anisotropic, args.num_atoms, inversesoftplus(pow(args.var,1/2), args.temp_softplus)) if args.cuda: sigma = sigma.cuda() if args.loss_type.lower() == 'semi_isotropic'.lower(): sigma = tile(sigma, 3, 2) sigma = Variable(sigma) self.optimizer.zero_grad() logits = self.encoder(data_encoder, self.rel_rec, self.rel_send) if args.NRI: # dim of logits, edges and prob are [batchsize, N^2-N, edgetypes] where N = no. of particles edges = gumbel_softmax(logits, tau=args.temp, hard=args.hard) prob = my_softmax(logits, -1) loss_kl = kl_categorical_uniform(prob, args.num_atoms, self.edge_types) loss_kl_split = [loss_kl] loss_kl_var_split = [kl_categorical_uniform_var(prob, args.num_atoms, self.edge_types)] KLb_train.append(0) KLb_blocks_train.append([0]) if args.no_edge_acc: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = 0, np.array([0]), np.zeros(len(args.edge_types_list)), 0, np.zeros(len(args.edge_types_list)) else: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = edge_accuracy_perm_NRI(logits, relations, args.edge_types_list) else: # dim of logits, edges and prob are [batchsize, N^2-N, sum(edge_types_list)] where N = no. of particles logits_split = torch.split(logits, args.edge_types_list, dim=-1) edges_split = tuple([gumbel_softmax(logits_i, tau=args.temp, hard=args.hard) for logits_i in logits_split]) edges = torch.cat(edges_split, dim=-1) prob_split = [my_softmax(logits_i, -1) for logits_i in logits_split] if args.prior: loss_kl_split = [kl_categorical(prob_split[type_idx], self.log_prior[type_idx], args.num_atoms) for type_idx in range(len(args.edge_types_list))] loss_kl = sum(loss_kl_split) else: loss_kl_split = [kl_categorical_uniform(prob_split[type_idx], args.num_atoms, args.edge_types_list[type_idx]) for type_idx in range(len(args.edge_types_list))] loss_kl = sum(loss_kl_split) loss_kl_var_split = [kl_categorical_uniform_var(prob_split[type_idx], args.num_atoms, args.edge_types_list[type_idx]) for type_idx in range(len(args.edge_types_list))] if args.no_edge_acc: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = 0, np.array([0]), np.zeros(len(args.edge_types_list)), 0, np.zeros(len(args.edge_types_list)) else: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = edge_accuracy_perm_fNRI(logits_split, relations, args.edge_types_list, args.skip_first) KLb_blocks = KL_between_blocks(prob_split, args.num_atoms) KLb_train.append(sum(KLb_blocks).data.item()) KLb_blocks_train.append([KL.data.item() for KL in KLb_blocks]) # fixed variance train loss calculation target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] if args.loss_type.lower() == 'fixed_var'.lower(): loss_nll, loss_nll_var, output = self.loss_fixed(data_decoder, edges, sigma, args) # variable variance train loss calculation elif args.loss_type.lower() == 'anisotropic': loss_nll, loss_nll_var, output = self.loss_anisotropic(data_decoder, edges, sigma, args) elif args.loss_type.upper() == 'KL': loss_nll, loss_nll_var, output = self.loss_KL(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'norminvwishart': loss_nll, loss_nll_var, output = self.loss_normalinversewishart(data_decoder, edges, sigma, args, batch_idx, 'train') elif args.loss_type.lower() == 'kalmanfilter': loss_nll, loss_nll_var, output = self.loss_kalmanfilter(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'isotropic': loss_nll, loss_nll_var, output = self.loss_isotropic(data_decoder, edges, sigma, args, epoch) elif args.loss_type.lower() == 'lorentzian': loss_nll, loss_nll_var, output = self.loss_lorentzian(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'semi_isotropic'.lower(): loss_nll, loss_nll_var, output = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch) elif args.loss_type.lower() == 'ani_convex'.lower(): target = data_decoder[:, :, 1:, :] if epoch == 0: vvec = target.clone() sigma_vec = sigma[:,:,1:,:].clone() loss_nll, loss_nll_var, output, vvec, sigma_vec = self.loss_anisotropic_withconvex(data_decoder, edges, sigma, args, vvec, sigma_vec) if args.mse_loss: loss = F.mse_loss(output, target) else: loss = loss_nll if not math.isclose(args.beta, 0, rel_tol=1e-6): loss += args.beta * loss_kl perm_train.append(perm) acc_train.append(acc_perm) acc_blocks_train.append(acc_blocks) acc_var_train.append(acc_var) acc_var_blocks_train.append(acc_var_blocks) loss.backward() self.optimizer.step() mse_train.append(F.mse_loss(output, target).data.item()) nll_train.append(loss_nll.data.item()) kl_train.append(loss_kl.data.item()) kl_list_train.append([kl.data.item() for kl in loss_kl_split]) nll_var_train.append(loss_nll_var.data.item()) kl_var_list_train.append([kl_var.data.item() for kl_var in loss_kl_var_split]) # validation set nll_val = [] nll_var_val = [] mse_val = [] kl_val = [] kl_list_val = [] kl_var_list_val = [] acc_val = [] acc_var_val = [] acc_blocks_val = [] acc_var_blocks_val = [] perm_val = [] KLb_val = [] KLb_blocks_val = [] # KL between blocks list nll_M_val = [] nll_M_var_val = [] self.encoder.eval() self.decoder.eval() for batch_idx, (data, relations) in enumerate(self.valid_loader): with torch.no_grad(): if args.cuda: data, relations = data.cuda(), relations.cuda() if args.dont_split_data: data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data[:, :, :args.timesteps, :].contiguous() elif args.split_enc_only: data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data else: assert (data.size(2) - args.timesteps) >= args.timesteps data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data[:, :, -args.timesteps:, :].contiguous() # stores the values of the uncertainty. This will be an array of size [batchsize, no. of particles, time,no. of axes (isotropic = 1, anisotropic = 4)] # initialise sigma to an array of large negative numbers which become small positive numbers when passed through softplus sigma = initsigma(len(data_decoder), len(data_decoder[0][0]), args.anisotropic, args.num_atoms, inversesoftplus(pow(args.var,1/2), args.temp_softplus)) if args.cuda: sigma = sigma.cuda() if args.loss_type.lower() == 'semi_isotropic'.lower(): sigma = tile(sigma, 3, 2) sigma = Variable(sigma) # dim of logits, edges and prob are [batchsize, N^2-N, sum(edge_types_list)] where N = no. of particles logits = self.encoder(data_encoder, self.rel_rec, self.rel_send) if args.NRI: # dim of logits, edges and prob are [batchsize, N^2-N, edgetypes] where N = no. of particles edges = gumbel_softmax(logits, tau=args.temp, hard=args.hard) # uses concrete distribution (for hard=False) to sample edge types prob = my_softmax(logits, -1) # my_softmax returns the softmax over the edgetype dim loss_kl = kl_categorical_uniform(prob, args.num_atoms, self.edge_types) loss_kl_split = [loss_kl] loss_kl_var_split = [kl_categorical_uniform_var(prob, args.num_atoms, self.edge_types)] KLb_val.append(0) KLb_blocks_val.append([0]) if args.no_edge_acc: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = 0, np.array([0]), np.zeros(len(args.edge_types_list)), 0, np.zeros(len(args.edge_types_list)) else: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = edge_accuracy_perm_NRI(logits, relations, args.edge_types_list) else: # dim of logits, edges and prob are [batchsize, N^2-N, sum(edge_types_list)] where N = no. of particles logits_split = torch.split(logits, args.edge_types_list, dim=-1) edges_split = tuple([gumbel_softmax(logits_i, tau=args.temp, hard=args.hard) for logits_i in logits_split]) edges = torch.cat(edges_split, dim=-1) prob_split = [my_softmax(logits_i, -1) for logits_i in logits_split] if args.prior: loss_kl_split = [kl_categorical(prob_split[type_idx], self.log_prior[type_idx], args.num_atoms) for type_idx in range(len(args.edge_types_list))] loss_kl = sum(loss_kl_split) else: loss_kl_split = [kl_categorical_uniform(prob_split[type_idx], args.num_atoms, args.edge_types_list[type_idx]) for type_idx in range(len(args.edge_types_list))] loss_kl = sum(loss_kl_split) loss_kl_var_split = [kl_categorical_uniform_var(prob_split[type_idx], args.num_atoms, args.edge_types_list[type_idx]) for type_idx in range(len(args.edge_types_list))] if args.no_edge_acc: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = 0, np.array([0]), np.zeros(len(args.edge_types_list)), 0, np.zeros(len(args.edge_types_list)) else: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = edge_accuracy_perm_fNRI(logits_split, relations, args.edge_types_list, args.skip_first) KLb_blocks = KL_between_blocks(prob_split, args.num_atoms) KLb_val.append(sum(KLb_blocks).data.item()) KLb_blocks_val.append([KL.data.item() for KL in KLb_blocks]) target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] # validation loss calculation if args.loss_type.lower() == 'fixed_var'.lower(): loss_nll, loss_nll_var, output = self.loss_fixed(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_fixed(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'isotropic': loss_nll, loss_nll_var, output = self.loss_isotropic(data_decoder, edges, sigma, args, epoch, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_isotropic(data_decoder, edges, sigma, args, epoch) elif args.loss_type.lower() == 'lorentzian': loss_nll, loss_nll_var, output = self.loss_lorentzian(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_lorentzian(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'semi_isotropic'.lower(): loss_nll, loss_nll_var, output = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch) elif args.loss_type.lower() == 'anisotropic': loss_nll, loss_nll_var, output = self.loss_anisotropic(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_anisotropic(data_decoder, edges, sigma, args) elif args.loss_type.upper() == 'KL': loss_nll, loss_nll_var, output = self.loss_KL(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_KL(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'norminvwishart': loss_nll, loss_nll_var, output = self.loss_normalinversewishart(data_decoder, edges, sigma, args, batch_idx, 'validation', use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_normalinversewishart(data_decoder, edges, sigma, args, batch_idx, 'validation') elif args.loss_type.lower() == 'kalmanfilter': loss_nll, loss_nll_var, output = self.loss_kalmanfilter(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_kalmanfilter(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'ani_convex'.lower(): target = data_decoder[:, :, 1:, :] if epoch == 0: vvec_ver = target.clone() sigma_vec_ver = sigma[:,:,1:,:].clone() loss_nll, loss_nll_var, output, vvec_ver_n, sigma_vec_ver_n = self.loss_anisotropic_withconvex(data_decoder, edges, sigma, args, vvec_ver, sigma_vec_ver, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M, vvec_ver, sigma_vec_ver = self.loss_anisotropic_withconvex(data_decoder, edges, sigma, args, vvec_ver, sigma_vec_ver) perm_val.append(perm) acc_val.append(acc_perm) acc_blocks_val.append(acc_blocks) acc_var_val.append(acc_var) acc_var_blocks_val.append(acc_var_blocks) mse_val.append(F.mse_loss(output_M, target).data.item()) nll_val.append(loss_nll.data.item()) nll_var_val.append(loss_nll_var.data.item()) kl_val.append(loss_kl.data.item()) kl_list_val.append([kl_loss.data.item() for kl_loss in loss_kl_split]) kl_var_list_val.append([kl_var.data.item() for kl_var in loss_kl_var_split]) nll_M_val.append(loss_nll_M.data.item()) nll_M_var_val.append(loss_nll_M_var.data.item()) print('Epoch: {:03d}'.format(epoch), 'perm_val: ' + str(np.around(np.mean(np.array(perm_val), axis=0), 4)), 'time: {:.1f}s'.format(time.time() - t)) print('nll_trn: {:.2f}'.format(np.mean(nll_train)), 'kl_trn: {:.5f}'.format(np.mean(kl_train)), 'mse_trn: {:.10f}'.format(np.mean(mse_train)), 'acc_trn: {:.5f}'.format(np.mean(acc_train)), 'KLb_trn: {:.5f}'.format(np.mean(KLb_train)) ) print('acc_b_trn: ' + str(np.around(np.mean(np.array(acc_blocks_train), axis=0), 4)), 'kl_trn: ' + str(np.around(np.mean(np.array(kl_list_train), axis=0), 4)) ) print('nll_val: {:.2f}'.format(np.mean(nll_M_val)), 'kl_val: {:.5f}'.format(np.mean(kl_val)), 'mse_val: {:.10f}'.format(np.mean(mse_val)), 'acc_val: {:.5f}'.format(np.mean(acc_val)), 'KLb_val: {:.5f}'.format(np.mean(KLb_val)) ) print('acc_b_val: ' + str(np.around(np.mean(np.array(acc_blocks_val), axis=0), 4)), 'kl_val: ' + str(np.around(np.mean(np.array(kl_list_val), axis=0), 4)) ) print('Epoch: {:04d}'.format(epoch), 'perm_val: ' + str(np.around(np.mean(np.array(perm_val), axis=0), 4)), 'time: {:.4f}s'.format(time.time() - t), file=self.log) print('nll_trn: {:.5f}'.format(np.mean(nll_train)), 'kl_trn: {:.5f}'.format(np.mean(kl_train)), 'mse_trn: {:.10f}'.format(np.mean(mse_train)), 'acc_trn: {:.5f}'.format(np.mean(acc_train)), 'KLb_trn: {:.5f}'.format(np.mean(KLb_train)), 'acc_b_trn: ' + str(np.around(np.mean(np.array(acc_blocks_train), axis=0), 4)), 'kl_trn: ' + str(np.around(np.mean(np.array(kl_list_train), axis=0), 4)), file=self.log) print('nll_val: {:.5f}'.format(np.mean(nll_M_val)), 'kl_val: {:.5f}'.format(np.mean(kl_val)), 'mse_val: {:.10f}'.format(np.mean(mse_val)), 'acc_val: {:.5f}'.format(np.mean(acc_val)), 'KLb_val: {:.5f}'.format(np.mean(KLb_val)), 'acc_b_val: ' + str(np.around(np.mean(np.array(acc_blocks_val), axis=0), 4)), 'kl_val: ' + str(np.around(np.mean(np.array(kl_list_val), axis=0), 4)), file=self.log) if epoch == 0: labels = ['epoch', 'nll trn', 'kl trn', 'mse train', 'KLb trn', 'acc trn'] labels += ['b' + str(i) + ' acc trn' for i in range(len(args.edge_types_list))] + ['nll var trn'] labels += ['b' + str(i) + ' kl trn' for i in range(len(kl_list_train[0]))] labels += ['b' + str(i) + ' kl var trn' for i in range(len(kl_list_train[0]))] labels += ['acc var trn'] + ['b' + str(i) + ' acc var trn' for i in range(len(args.edge_types_list))] labels += ['nll val', 'nll_M_val', 'kl val', 'mse val', 'KLb val', 'acc val'] labels += ['b' + str(i) + ' acc val' for i in range(len(args.edge_types_list))] labels += ['nll var val', 'nll_M var val'] labels += ['b' + str(i) + ' kl val' for i in range(len(kl_list_val[0]))] labels += ['b' + str(i) + ' kl var val' for i in range(len(kl_list_val[0]))] labels += ['acc var val'] + ['b' + str(i) + ' acc var val' for i in range(len(args.edge_types_list))] self.csv_writer.writerow(labels) labels = ['trn ' + str(i) for i in range(len(perm_train[0]))] labels += ['val ' + str(i) for i in range(len(perm_val[0]))] self.perm_writer.writerow(labels) self.csv_writer.writerow([epoch, np.mean(nll_train), np.mean(kl_train), np.mean(mse_train), np.mean(KLb_train), np.mean(acc_train)] + list(np.mean(np.array(acc_blocks_train), axis=0)) + [np.mean(nll_var_train)] + list(np.mean(np.array(kl_list_train), axis=0)) + list(np.mean(np.array(kl_var_list_train), axis=0)) + # list(np.mean(np.array(KLb_blocks_train),axis=0)) + [np.mean(acc_var_train)] + list(np.mean(np.array(acc_var_blocks_train), axis=0)) + [np.mean(nll_val), np.mean(nll_M_val), np.mean(kl_val), np.mean(mse_val), np.mean(KLb_val), np.mean(acc_val)] + list(np.mean(np.array(acc_blocks_val), axis=0)) + [np.mean(nll_var_val), np.mean(nll_M_var_val)] + list(np.mean(np.array(kl_list_val), axis=0)) + list(np.mean(np.array(kl_var_list_val), axis=0)) + # list(np.mean(np.array(KLb_blocks_val),axis=0)) [np.mean(acc_var_val)] + list(np.mean(np.array(acc_var_blocks_val), axis=0)) ) self.perm_writer.writerow(list(np.mean(np.array(perm_train), axis=0)) + list(np.mean(np.array(perm_val), axis=0)) ) self.log.flush() # save condn if args.save_folder and np.mean(nll_M_val) < best_val_loss: torch.save(self.encoder.state_dict(), self.encoder_file) torch.save(self.decoder.state_dict(), self.decoder_file) print('Best model so far, saving...') # save model in different folder even if not the best model. This is a temporary fix for BUS errors- not ideal but # the best we can do.. if args.save_folder: torch.save(self.encoder.state_dict(), self.current_encoder_file) torch.save(self.decoder.state_dict(), self.current_decoder_file) return np.mean(acc_val), np.mean(nll_M_val), np.around(np.mean(np.array(acc_blocks_val), axis=0), 4) def train_plot(self, epoch, args): t = time.time() # validation set nll_val = [] nll_var_val = [] mse_val = [] kl_val = [] kl_list_val = [] kl_var_list_val = [] acc_val = [] acc_var_val = [] acc_blocks_val = [] acc_var_blocks_val = [] perm_val = [] KLb_val = [] KLb_blocks_val = [] # KL between blocks list nll_M_val = [] nll_M_var_val = [] # for z-score analysis zscorelist_x = [] zscorelist_y = [] self.encoder.eval() self.decoder.eval() for batch_idx, (data, relations) in enumerate(self.valid_loader): with torch.no_grad(): if args.cuda: data, relations = data.cuda(), relations.cuda() if args.dont_split_data: data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data[:, :, :args.timesteps, :].contiguous() elif args.split_enc_only: data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data else: assert (data.size(2) - args.timesteps) >= args.timesteps data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data[:, :, -args.timesteps:, :].contiguous() # stores the values of the uncertainty. This will be an array of size [batchsize, no. of particles, time,no. of axes (isotropic = 1, anisotropic = 4)] # initialise sigma to an array of large negative numbers which become small positive numbers when passed through softplus sigma = initsigma(len(data_decoder), len(data_decoder[0][0]), args.anisotropic, args.num_atoms, inversesoftplus(pow(args.var, 1 / 2), args.temp_softplus)) if args.cuda: sigma = sigma.cuda() if args.loss_type.lower() == 'semi_isotropic'.lower(): sigma = tile(sigma, 3, 2) sigma = Variable(sigma) # dim of logits, edges and prob are [batchsize, N^2-N, sum(edge_types_list)] where N = no. of particles logits = self.encoder(data_encoder, self.rel_rec, self.rel_send) if args.NRI: # dim of logits, edges and prob are [batchsize, N^2-N, edgetypes] where N = no. of particles edges = gumbel_softmax(logits, tau=args.temp, hard=args.hard) # uses concrete distribution (for hard=False) to sample edge types prob = my_softmax(logits, -1) # my_softmax returns the softmax over the edgetype dim loss_kl = kl_categorical_uniform(prob, args.num_atoms, self.edge_types) loss_kl_split = [loss_kl] loss_kl_var_split = [kl_categorical_uniform_var(prob, args.num_atoms, self.edge_types)] KLb_val.append(0) KLb_blocks_val.append([0]) if args.no_edge_acc: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = 0, np.array([0]), np.zeros( len(args.edge_types_list)), 0, np.zeros(len(args.edge_types_list)) else: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = edge_accuracy_perm_NRI(logits, relations, args.edge_types_list) else: # dim of logits, edges and prob are [batchsize, N^2-N, sum(edge_types_list)] where N = no. of particles logits_split = torch.split(logits, args.edge_types_list, dim=-1) edges_split = tuple([gumbel_softmax(logits_i, tau=args.temp, hard=args.hard) for logits_i in logits_split]) edges = torch.cat(edges_split, dim=-1) prob_split = [my_softmax(logits_i, -1) for logits_i in logits_split] if args.prior: loss_kl_split = [kl_categorical(prob_split[type_idx], self.log_prior[type_idx], args.num_atoms) for type_idx in range(len(args.edge_types_list))] loss_kl = sum(loss_kl_split) else: loss_kl_split = [kl_categorical_uniform(prob_split[type_idx], args.num_atoms, args.edge_types_list[type_idx]) for type_idx in range(len(args.edge_types_list))] loss_kl = sum(loss_kl_split) loss_kl_var_split = [kl_categorical_uniform_var(prob_split[type_idx], args.num_atoms, args.edge_types_list[type_idx]) for type_idx in range(len(args.edge_types_list))] if args.no_edge_acc: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = 0, np.array([0]), np.zeros( len(args.edge_types_list)), 0, np.zeros(len(args.edge_types_list)) else: acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = edge_accuracy_perm_fNRI(logits_split, relations, args.edge_types_list, args.skip_first) KLb_blocks = KL_between_blocks(prob_split, args.num_atoms) KLb_val.append(sum(KLb_blocks).data.item()) KLb_blocks_val.append([KL.data.item() for KL in KLb_blocks]) # plotting for fixed variance models if args.loss_type.lower() == 'fixed_var'.lower(): target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] if args.plot: # for plotting output_plot, sigma_plot, accel_plot, vel_plot = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, False, False, args.temp_softplus, 49) if args.NRI: acc_batch, perm, acc_blocks_batch = edge_accuracy_perm_NRI_batch(logits, relations, args.edge_types_list) else: acc_batch, perm, acc_blocks_batch = edge_accuracy_perm_fNRI_batch(logits_split, relations, args.edge_types_list) self.fixed_var_plot(args, acc_blocks_batch, target, output_plot) loss_nll, loss_nll_var, output = self.loss_fixed(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_fixed(data_decoder, edges, sigma, args) # plotting for non-fixed variance models elif args.loss_type.lower() == 'isotropic': target = data_decoder[:, :, 1:, :] zscorelist_x, zscorelist_y = self.isotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_isotropic(data_decoder, edges, sigma, args, epoch, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_isotropic(data_decoder, edges, sigma, args, epoch) elif args.loss_type.lower() == 'lorentzian': target = data_decoder[:, :, 1:, :] zscorelist_x, zscorelist_y = self.isotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_lorentzian(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_lorentzian(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'semi_isotropic'.lower(): target = data_decoder[:, :, 1:, :] zscorelist_x, zscorelist_y = self.isotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch) elif args.loss_type.lower() == 'anisotropic'.lower(): target = data_decoder[:, :, 1:, :] zscorelist_x, zscorelist_y = self.anisotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_anisotropic(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_anisotropic(data_decoder, edges, sigma, args) elif args.loss_type.upper() == 'KL': target = data_decoder[:, :, 1:, :] zscorelist_x, zscorelist_y = self.anisotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_KL(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_KL(data_decoder, edges, sigma) elif args.loss_type.lower() == 'norminvwishart': target = data_decoder[:, :, 1:, :] zscorelist_x, zscorelist_y = self.anisotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_normalinversewishart(data_decoder, edges, sigma, args, batch_idx, 'validation', use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_normalinversewishart(data_decoder, edges, sigma, args, batch_idx, 'validation') elif args.loss_type.lower() == 'kalmanfilter': target = data_decoder[:, :, 1:, :] zscorelist_x, zscorelist_y = self.anisotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_kalmanfilter(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_kalmanfilter(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'ani_convex'.lower(): target = data_decoder[:, :, 1:, :] if epoch == 0: vvec_ver = target.clone() sigma_vec_ver = sigma.clone() loss_nll, loss_nll_var, output, vvec_ver_n, sigma_vec_ver_n = self.loss_anisotropic_withconvex(data_decoder, edges, sigma, args, vvec_ver, sigma_vec_ver, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M, vvec_ver, sigma_vec_ver = self.loss_anisotropic_withconvex(data_decoder, edges, sigma, args, vvec_ver, sigma_vec_ver) perm_val.append(perm) acc_val.append(acc_perm) acc_blocks_val.append(acc_blocks) acc_var_val.append(acc_var) acc_var_blocks_val.append(acc_var_blocks) mse_val.append(F.mse_loss(output_M, target).data.item()) nll_val.append(loss_nll.data.item()) nll_var_val.append(loss_nll_var.data.item()) kl_val.append(loss_kl.data.item()) kl_list_val.append([kl_loss.data.item() for kl_loss in loss_kl_split]) kl_var_list_val.append([kl_var.data.item() for kl_var in loss_kl_var_split]) nll_M_val.append(loss_nll_M.data.item()) nll_M_var_val.append(loss_nll_M_var.data.item()) # deal with z-score here - plot zscores and fit gaussian/lorentzian to the histogram plot if not args.loss_type.lower() == 'fixed_var'.lower(): self.zscore_plot(zscorelist_x, zscorelist_y) print('Epoch: {:03d}'.format(epoch), 'perm_val: ' + str(np.around(np.mean(np.array(perm_val), axis=0), 4)), 'time: {:.1f}s'.format(time.time() - t)) print('nll_val: {:.2f}'.format(np.mean(nll_M_val)), 'kl_val: {:.5f}'.format(np.mean(kl_val)), 'mse_val: {:.10f}'.format(np.mean(mse_val)), 'acc_val: {:.5f}'.format(np.mean(acc_val)), 'KLb_val: {:.5f}'.format(np.mean(KLb_val)) ) print('acc_b_val: ' + str(np.around(np.mean(np.array(acc_blocks_val), axis=0), 4)), 'kl_val: ' + str(np.around(np.mean(np.array(kl_list_val), axis=0), 4)) ) print('Epoch: {:04d}'.format(epoch), 'perm_val: ' + str(np.around(np.mean(np.array(perm_val), axis=0), 4)), 'time: {:.4f}s'.format(time.time() - t), file=self.log) print('nll_val: {:.5f}'.format(np.mean(nll_M_val)), 'kl_val: {:.5f}'.format(np.mean(kl_val)), 'mse_val: {:.10f}'.format(np.mean(mse_val)), 'acc_val: {:.5f}'.format(np.mean(acc_val)), 'KLb_val: {:.5f}'.format(np.mean(KLb_val)), 'acc_b_val: ' + str(np.around(np.mean(np.array(acc_blocks_val), axis=0), 4)), 'kl_val: ' + str(np.around(np.mean(np.array(kl_list_val), axis=0), 4)), file=self.log) class Tester(Model): def __init__(self, args, edge_types, log_prior, encoder_file, decoder_file, current_encoder_file, current_decoder_file, log, loss_data, csv_writer, perm_writer, encoder, decoder, optimizer, scheduler): super(Tester, self).__init__(args, edge_types, log_prior, encoder_file, decoder_file, current_encoder_file, current_decoder_file, log, loss_data, csv_writer, perm_writer, encoder, decoder, optimizer, scheduler) # gets the prior for the normal inverse wishart distribution on test data if args.loss_type.lower() == 'norminvwishart': # prior for test data self.prior_pos_tensor_test = np.empty(0) self.prior_vel_tensor_test = np.empty(0) t = time.time() for batch_idx, (data, relations) in enumerate(self.test_loader): if args.cuda: data, relations = data.cuda(), relations.cuda() data, relations = Variable(data), Variable(relations) data = data.clone() relations = relations.clone() indices_pos = torch.LongTensor([0, 1]) indices_vel = torch.LongTensor([2, 3]) if args.cuda: indices_pos, indices_vel = indices_pos.cuda(), indices_vel.cuda() data_pos = torch.index_select(data, 3, indices_pos) data_vel = torch.index_select(data, 3, indices_vel) sigma_pos = torch.index_select(self.sigma_target, 3, indices_pos) sigma_vel = torch.index_select(self.sigma_target, 3, indices_vel) prior_pos = getpriordist(data_pos, sigma_pos, 4) prior_vel = getpriordist(data_vel, sigma_vel, 4) self.prior_pos_tensor_test = np.concatenate((self.prior_pos_tensor_test, [prior_pos])) self.prior_vel_tensor_test = np.concatenate((self.prior_vel_tensor_test, [prior_vel])) print('test time: {:.1f}s'.format(time.time() - t)) def test(self, args): # test set t = time.time() nll_test = [] nll_var_test = [] mse_1_test = [] mse_10_test = [] mse_20_test = [] kl_test = [] kl_list_test = [] kl_var_list_test = [] acc_test = [] acc_var_test = [] acc_blocks_test = [] acc_var_blocks_test = [] perm_test = [] KLb_test = [] KLb_blocks_test = [] # KL between blocks list nll_M_test = [] nll_M_var_test = [] # for zscore analysis zscorelist_x = [] zscorelist_y = [] self.encoder.eval() self.decoder.eval() if not args.cuda: self.encoder.load_state_dict(torch.load(self.encoder_file, map_location='cpu')) self.decoder.load_state_dict(torch.load(self.decoder_file, map_location='cpu')) else: self.encoder.load_state_dict(torch.load(self.encoder_file)) self.decoder.load_state_dict(torch.load(self.decoder_file)) for batch_idx, (data, relations) in enumerate(self.test_loader): with torch.no_grad(): if args.cuda: data, relations = data.cuda(), relations.cuda() assert (data.size(2) - args.timesteps) >= args.timesteps data_encoder = data[:, :, :args.timesteps, :].contiguous() data_decoder = data[:, :, -args.timesteps:, :].contiguous() # stores the values of the uncertainty. This will be an array of size [batchsize, no. of particles, time,no. of axes (isotropic = 1, anisotropic = 2)] # initialise sigma to an array of large negative numbers which become small positive numbers when passted through softplus function. sigma = initsigma(len(data_decoder), len(data_decoder[0][0]), args.anisotropic, args.num_atoms, inversesoftplus(pow(args.var, 1 / 2), args.temp_softplus)) if args.cuda: sigma = sigma.cuda() if args.loss_type.lower() == 'semi_isotropic'.lower(): sigma = tile(sigma, 3, 2) sigma = Variable(sigma) # dim of logits, edges and prob are [batchsize, N^2-N, sum(edge_types_list)] where N = no. of particles logits = self.encoder(data_encoder, self.rel_rec, self.rel_send) if args.NRI: edges = gumbel_softmax(logits, tau=args.temp, hard=args.hard) prob = my_softmax(logits, -1) loss_kl = kl_categorical_uniform(prob, args.num_atoms, self.edge_types) loss_kl_split = [loss_kl] loss_kl_var_split = [kl_categorical_uniform_var(prob, args.num_atoms, self.edge_types)] KLb_test.append(0) KLb_blocks_test.append([0]) acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = edge_accuracy_perm_NRI(logits, relations, args.edge_types_list) else: logits_split = torch.split(logits, args.edge_types_list, dim=-1) edges_split = tuple( [gumbel_softmax(logits_i, tau=args.temp, hard=args.hard) for logits_i in logits_split]) edges = torch.cat(edges_split, dim=-1) prob_split = [my_softmax(logits_i, -1) for logits_i in logits_split] if args.prior: loss_kl_split = [kl_categorical(prob_split[type_idx], self.log_prior[type_idx], args.num_atoms) for type_idx in range(len(args.edge_types_list))] loss_kl = sum(loss_kl_split) else: loss_kl_split = [kl_categorical_uniform(prob_split[type_idx], args.num_atoms, args.edge_types_list[type_idx]) for type_idx in range(len(args.edge_types_list))] loss_kl = sum(loss_kl_split) loss_kl_var_split = [kl_categorical_uniform_var(prob_split[type_idx], args.num_atoms, args.edge_types_list[type_idx]) for type_idx in range(len(args.edge_types_list))] acc_perm, perm, acc_blocks, acc_var, acc_var_blocks = edge_accuracy_perm_fNRI(logits_split, relations, args.edge_types_list, args.skip_first) KLb_blocks = KL_between_blocks(prob_split, args.num_atoms) KLb_test.append(sum(KLb_blocks).data.item()) KLb_blocks_test.append([KL.data.item() for KL in KLb_blocks]) epoch = 0 # plotting fixed variance models if args.loss_type.lower() == 'fixed_var'.lower(): target = data_decoder[:, :, 1:, :] # dimensions are [batch, particle, time, state] if args.plot: # for plotting output_plot, sigma_plot, accel_plot, vel_plot = self.decoder(data_decoder, edges, self.rel_rec, self.rel_send, sigma, False, False, args.temp_softplus, 49) if args.NRI: acc_batch, perm, acc_blocks_batch = edge_accuracy_perm_NRI_batch(logits, relations, args.edge_types_list) else: acc_batch, perm, acc_blocks_batch = edge_accuracy_perm_fNRI_batch(logits_split, relations, args.edge_types_list) self.fixed_var_plot(args, acc_blocks_batch, target, output_plot) loss_nll, loss_nll_var, output = self.loss_fixed(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_10, loss_nll_var_10, output_10 = self.loss_fixed(data_decoder, edges, sigma, args, pred_steps=10, use_onepred=True) loss_nll_20, loss_nll_var_20, output_20 = self.loss_fixed(data_decoder, edges, sigma, args, pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_fixed(data_decoder, edges, sigma, args) # plotting varying variance models. NOTE: THE MSE for 1, 10 and 20 trajectories is also calculated. elif args.loss_type.lower() == 'isotropic': target = data_decoder[:, :, 1:, :] if args.plot: zscorelist_x, zscorelist_y = self.isotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_isotropic(data_decoder, edges, sigma, args, epoch, use_onepred=True) loss_nll_10, loss_nll_var_10, output_10 = self.loss_isotropic(data_decoder, edges, sigma, args, epoch, pred_steps=10, use_onepred=True) loss_nll_20, loss_nll_var_20, output_20 = self.loss_isotropic(data_decoder, edges, sigma, args, epoch, pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_isotropic(data_decoder, edges, sigma, args, epoch) elif args.loss_type.lower() == 'lorentzian': target = data_decoder[:, :, 1:, :] if args.plot: zscorelist_x, zscorelist_y = self.isotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_lorentzian(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_10, loss_nll_var_10, output_10 = self.loss_lorentzian(data_decoder, edges, sigma, args, pred_steps=10, use_onepred=True) loss_nll_20, loss_nll_var_20, output_20 = self.loss_lorentzian(data_decoder, edges, sigma, args, pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_lorentzian(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'semi_isotropic'.lower(): target = data_decoder[:, :, 1:, :] if args.plot: zscorelist_x, zscorelist_y = self.isotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch, use_onepred=True) loss_nll_10, loss_nll_var_10, output_10 = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch, pred_steps=10, use_onepred=True) loss_nll_20, loss_nll_var_20, output_20 = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch, pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_semi_isotropic(data_decoder, edges, sigma, args, epoch) elif args.loss_type.lower() == 'anisotropic'.lower(): target = data_decoder[:, :, 1:, :] if args.plot: zscorelist_x, zscorelist_y = self.anisotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_anisotropic(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_10, loss_nll_var_10, output_10 = self.loss_anisotropic(data_decoder, edges, sigma, args, pred_steps=10, use_onepred=True) loss_nll_20, loss_nll_var_20, output_20 = self.loss_anisotropic(data_decoder, edges, sigma, args, pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_anisotropic(data_decoder, edges, sigma, args) elif args.loss_type.upper() == 'KL': target = data_decoder[:, :, 1:, :] if args.plot: zscorelist_x, zscorelist_y = self.anisotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_KL(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_10, loss_nll_var_10, output_10 = self.loss_KL(data_decoder, edges, sigma, args, pred_steps=10, use_onepred=True) loss_nll_20, loss_nll_var_20, output_20 = self.loss_KL(data_decoder, edges, sigma,args, pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_KL(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'norminvwishart': target = data_decoder[:, :, 1:, :] if args.plot: zscorelist_x, zscorelist_y = self.anisotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_normalinversewishart(data_decoder, edges, sigma, args, batch_idx, 'test', use_onepred=True) loss_nll_10, loss_nll_var_10, output_10 = self.loss_normalinversewishart(data_decoder, edges, sigma, args, batch_idx, 'test', pred_steps=10, use_onepred=True) loss_nll_20, loss_nll_var_20, output_20 = self.loss_normalinversewishart(data_decoder, edges, sigma,args, batch_idx, 'test', pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_normalinversewishart(data_decoder, edges, sigma, args, batch_idx, 'test') elif args.loss_type.lower() == 'kalmanfilter': target = data_decoder[:, :, 1:, :] if args.plot: zscorelist_x, zscorelist_y = self.anisotropic_plot(args, data_decoder, edges, sigma, logits, logits_split, relations, target, zscorelist_x, zscorelist_y) loss_nll, loss_nll_var, output = self.loss_kalmanfilter(data_decoder, edges, sigma, args, use_onepred=True) loss_nll_10, loss_nll_var_10, output_10 = self.loss_kalmanfilter(data_decoder, edges, sigma, args, pred_steps=10 ,use_onepred=True) loss_nll_20, loss_nll_var_20, output_20 = self.loss_kalmanfilter(data_decoder, edges, sigma, args, pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M = self.loss_kalmanfilter(data_decoder, edges, sigma, args) elif args.loss_type.lower() == 'ani_convex'.lower(): target = data_decoder[:, :, 1:, :] if epoch == 0: vvec_ver = target.clone() sigma_vec_ver = sigma.clone() loss_nll, loss_nll_var, output, vvec_ver_n, sigma_vec_ver_n = self.loss_anisotropic_withconvex( data_decoder, edges, sigma, args, vvec_ver, sigma_vec_ver, use_onepred=True) loss_nll_10, loss_nll_var_10, output_10, vvec_ver_n, sigma_vec_ver_n = self.loss_anisotropic_withconvex( data_decoder, edges, sigma, args, vvec_ver, sigma_vec_ver, pred_steps=10, use_onepred=True) loss_nll_20, loss_nll_var_20, output_20, vvec_ver_n, sigma_vec_ver_n = self.loss_anisotropic_withconvex( data_decoder, edges, sigma, args, vvec_ver, sigma_vec_ver, pred_steps=20, use_onepred=True) loss_nll_M, loss_nll_M_var, output_M, vvec_ver, sigma_vec_ver = self.loss_anisotropic_withconvex( data_decoder, edges, sigma, args, vvec_ver, sigma_vec_ver) perm_test.append(perm) acc_test.append(acc_perm) acc_blocks_test.append(acc_blocks) acc_var_test.append(acc_var) acc_var_blocks_test.append(acc_var_blocks) mse_1_test.append(F.mse_loss(output, target).data.item()) mse_10_test.append(F.mse_loss(output_10, target).data.item()) mse_20_test.append(F.mse_loss(output_20, target).data.item()) nll_test.append(loss_nll.data.item()) kl_test.append(loss_kl.data.item()) kl_list_test.append([kl_loss.data.item() for kl_loss in loss_kl_split]) nll_var_test.append(loss_nll_var.data.item()) kl_var_list_test.append([kl_var.data.item() for kl_var in loss_kl_var_split]) nll_M_test.append(loss_nll_M.data.item()) nll_M_var_test.append(loss_nll_M_var.data.item()) # deal with z-score here - plot zscores and fit gaussian/lorentzian to the histogram plot if not args.loss_type.lower() == 'fixed_var'.lower(): if args.plot: self.zscore_plot(zscorelist_x, zscorelist_y) print('--------------------------------') print('------------Testing-------------') print('--------------------------------') print('nll_test: {:.2f}'.format(np.mean(nll_test)), 'nll_M_test: {:.2f}'.format(np.mean(nll_M_test)), 'kl_test: {:.5f}'.format(np.mean(kl_test)), 'mse_1_test: {:.10f}'.format(np.mean(mse_1_test)), 'mse_10_test: {:.10f}'.format(np.mean(mse_10_test)), 'mse_20_test: {:.10f}'.format(np.mean(mse_20_test)), 'acc_test: {:.5f}'.format(np.mean(acc_test)), 'acc_var_test: {:.5f}'.format(np.mean(acc_var_test)), 'KLb_test: {:.5f}'.format(np.mean(KLb_test)), 'time: {:.1f}s'.format(time.time() - t)) print('acc_b_test: ' + str(np.around(np.mean(np.array(acc_blocks_test), axis=0), 4)), 'acc_var_b: ' + str(np.around(np.mean(np.array(acc_var_blocks_test), axis=0), 4)), 'kl_test: ' + str(np.around(np.mean(np.array(kl_list_test), axis=0), 4)) ) if args.save_folder: print('--------------------------------', file=self.log) print('------------Testing-------------', file=self.log) print('--------------------------------', file=self.log) print('nll_test: {:.2f}'.format(np.mean(nll_test)), 'nll_M_test: {:.2f}'.format(np.mean(nll_M_test)), 'kl_test: {:.5f}'.format(np.mean(kl_test)), 'mse_1_test: {:.10f}'.format(np.mean(mse_1_test)), 'mse_10_test: {:.10f}'.format(np.mean(mse_10_test)), 'mse_20_test: {:.10f}'.format(np.mean(mse_20_test)), 'acc_test: {:.5f}'.format(np.mean(acc_test)), 'acc_var_test: {:.5f}'.format(np.mean(acc_var_test)), 'KLb_test: {:.5f}'.format(np.mean(KLb_test)), 'time: {:.1f}s'.format(time.time() - t), file=self.log) print('acc_b_test: ' + str(np.around(np.mean(np.array(acc_blocks_test), axis=0), 4)), 'acc_var_b_test: ' + str(np.around(np.mean(np.array(acc_var_blocks_test), axis=0), 4)), 'kl_test: ' + str(np.around(np.mean(np.array(kl_list_test), axis=0), 4)), file=self.log) self.log.flush()

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35,282

py

Python

pybind/slxos/v17r_2_00/mpls_state/transit_traffic_statistics/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

null

pybind/slxos/v17r_2_00/mpls_state/transit_traffic_statistics/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

null

pybind/slxos/v17r_2_00/mpls_state/transit_traffic_statistics/__init__.py

extremenetworks/pybind

44c467e71b2b425be63867aba6e6fa28b2cfe7fb

[ "Apache-2.0" ]

1

2021-11-05T22:15:42.000Z

from operator import attrgetter import pyangbind.lib.xpathhelper as xpathhelper from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType from pyangbind.lib.base import PybindBase from decimal import Decimal from bitarray import bitarray import __builtin__ class transit_traffic_statistics(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module brocade-mpls-operational - based on the path /mpls-state/transit-traffic-statistics. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: Transit Traffic Statistics """ __slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__number_of_packets','__number_of_packets_since_clear','__number_of_bytes','__number_of_bytes_since_clear','__packets_per_second','__bytes_per_second','__averaging_interval_seconds','__in_label','__protocol','__statistics_valid',) _yang_name = 'transit-traffic-statistics' _rest_name = 'transit-traffic-statistics' _pybind_generated_by = 'container' def __init__(self, *args, **kwargs): path_helper_ = kwargs.pop("path_helper", None) if path_helper_ is False: self._path_helper = False elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper): self._path_helper = path_helper_ elif hasattr(self, "_parent"): path_helper_ = getattr(self._parent, "_path_helper", False) self._path_helper = path_helper_ else: self._path_helper = False extmethods = kwargs.pop("extmethods", None) if extmethods is False: self._extmethods = False elif extmethods is not None and isinstance(extmethods, dict): self._extmethods = extmethods elif hasattr(self, "_parent"): extmethods = getattr(self._parent, "_extmethods", None) self._extmethods = extmethods else: self._extmethods = False self.__in_label = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="in-label", rest_name="in-label", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False) self.__protocol = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'mpls-protocol-none': {'value': 0}, u'mpls-protocol-ldp': {'value': 1}, u'mpls-protocol-rsvp': {'value': 2}},), is_leaf=True, yang_name="protocol", rest_name="protocol", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='mpls-protocol', is_config=False) self.__number_of_packets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-packets", rest_name="number-of-packets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) self.__packets_per_second = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="packets-per-second", rest_name="packets-per-second", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) self.__number_of_bytes = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-bytes", rest_name="number-of-bytes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) self.__number_of_bytes_since_clear = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-bytes-since-clear", rest_name="number-of-bytes-since-clear", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) self.__averaging_interval_seconds = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="averaging-interval-seconds", rest_name="averaging-interval-seconds", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False) self.__bytes_per_second = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="bytes-per-second", rest_name="bytes-per-second", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) self.__number_of_packets_since_clear = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-packets-since-clear", rest_name="number-of-packets-since-clear", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) self.__statistics_valid = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="statistics-valid", rest_name="statistics-valid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path()+[self._yang_name] else: return [u'mpls-state', u'transit-traffic-statistics'] def _rest_path(self): if hasattr(self, "_parent"): if self._rest_name: return self._parent._rest_path()+[self._rest_name] else: return self._parent._rest_path() else: return [u'mpls-state', u'transit-traffic-statistics'] def _get_number_of_packets(self): """ Getter method for number_of_packets, mapped from YANG variable /mpls_state/transit_traffic_statistics/number_of_packets (uint64) YANG Description: Total number of packets """ return self.__number_of_packets def _set_number_of_packets(self, v, load=False): """ Setter method for number_of_packets, mapped from YANG variable /mpls_state/transit_traffic_statistics/number_of_packets (uint64) If this variable is read-only (config: false) in the source YANG file, then _set_number_of_packets is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_number_of_packets() directly. YANG Description: Total number of packets """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-packets", rest_name="number-of-packets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """number_of_packets must be of a type compatible with uint64""", 'defined-type': "uint64", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-packets", rest_name="number-of-packets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False)""", }) self.__number_of_packets = t if hasattr(self, '_set'): self._set() def _unset_number_of_packets(self): self.__number_of_packets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-packets", rest_name="number-of-packets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) def _get_number_of_packets_since_clear(self): """ Getter method for number_of_packets_since_clear, mapped from YANG variable /mpls_state/transit_traffic_statistics/number_of_packets_since_clear (uint64) YANG Description: Total number of packets since lst clear """ return self.__number_of_packets_since_clear def _set_number_of_packets_since_clear(self, v, load=False): """ Setter method for number_of_packets_since_clear, mapped from YANG variable /mpls_state/transit_traffic_statistics/number_of_packets_since_clear (uint64) If this variable is read-only (config: false) in the source YANG file, then _set_number_of_packets_since_clear is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_number_of_packets_since_clear() directly. YANG Description: Total number of packets since lst clear """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-packets-since-clear", rest_name="number-of-packets-since-clear", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """number_of_packets_since_clear must be of a type compatible with uint64""", 'defined-type': "uint64", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-packets-since-clear", rest_name="number-of-packets-since-clear", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False)""", }) self.__number_of_packets_since_clear = t if hasattr(self, '_set'): self._set() def _unset_number_of_packets_since_clear(self): self.__number_of_packets_since_clear = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-packets-since-clear", rest_name="number-of-packets-since-clear", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) def _get_number_of_bytes(self): """ Getter method for number_of_bytes, mapped from YANG variable /mpls_state/transit_traffic_statistics/number_of_bytes (uint64) YANG Description: Total number of bytes """ return self.__number_of_bytes def _set_number_of_bytes(self, v, load=False): """ Setter method for number_of_bytes, mapped from YANG variable /mpls_state/transit_traffic_statistics/number_of_bytes (uint64) If this variable is read-only (config: false) in the source YANG file, then _set_number_of_bytes is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_number_of_bytes() directly. YANG Description: Total number of bytes """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-bytes", rest_name="number-of-bytes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """number_of_bytes must be of a type compatible with uint64""", 'defined-type': "uint64", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-bytes", rest_name="number-of-bytes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False)""", }) self.__number_of_bytes = t if hasattr(self, '_set'): self._set() def _unset_number_of_bytes(self): self.__number_of_bytes = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-bytes", rest_name="number-of-bytes", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) def _get_number_of_bytes_since_clear(self): """ Getter method for number_of_bytes_since_clear, mapped from YANG variable /mpls_state/transit_traffic_statistics/number_of_bytes_since_clear (uint64) YANG Description: Total number of bytes since last clear """ return self.__number_of_bytes_since_clear def _set_number_of_bytes_since_clear(self, v, load=False): """ Setter method for number_of_bytes_since_clear, mapped from YANG variable /mpls_state/transit_traffic_statistics/number_of_bytes_since_clear (uint64) If this variable is read-only (config: false) in the source YANG file, then _set_number_of_bytes_since_clear is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_number_of_bytes_since_clear() directly. YANG Description: Total number of bytes since last clear """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-bytes-since-clear", rest_name="number-of-bytes-since-clear", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """number_of_bytes_since_clear must be of a type compatible with uint64""", 'defined-type': "uint64", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-bytes-since-clear", rest_name="number-of-bytes-since-clear", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False)""", }) self.__number_of_bytes_since_clear = t if hasattr(self, '_set'): self._set() def _unset_number_of_bytes_since_clear(self): self.__number_of_bytes_since_clear = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="number-of-bytes-since-clear", rest_name="number-of-bytes-since-clear", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) def _get_packets_per_second(self): """ Getter method for packets_per_second, mapped from YANG variable /mpls_state/transit_traffic_statistics/packets_per_second (uint64) YANG Description: Packets per second """ return self.__packets_per_second def _set_packets_per_second(self, v, load=False): """ Setter method for packets_per_second, mapped from YANG variable /mpls_state/transit_traffic_statistics/packets_per_second (uint64) If this variable is read-only (config: false) in the source YANG file, then _set_packets_per_second is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_packets_per_second() directly. YANG Description: Packets per second """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="packets-per-second", rest_name="packets-per-second", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """packets_per_second must be of a type compatible with uint64""", 'defined-type': "uint64", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="packets-per-second", rest_name="packets-per-second", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False)""", }) self.__packets_per_second = t if hasattr(self, '_set'): self._set() def _unset_packets_per_second(self): self.__packets_per_second = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="packets-per-second", rest_name="packets-per-second", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) def _get_bytes_per_second(self): """ Getter method for bytes_per_second, mapped from YANG variable /mpls_state/transit_traffic_statistics/bytes_per_second (uint64) YANG Description: Bytes per second """ return self.__bytes_per_second def _set_bytes_per_second(self, v, load=False): """ Setter method for bytes_per_second, mapped from YANG variable /mpls_state/transit_traffic_statistics/bytes_per_second (uint64) If this variable is read-only (config: false) in the source YANG file, then _set_bytes_per_second is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_bytes_per_second() directly. YANG Description: Bytes per second """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="bytes-per-second", rest_name="bytes-per-second", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """bytes_per_second must be of a type compatible with uint64""", 'defined-type': "uint64", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="bytes-per-second", rest_name="bytes-per-second", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False)""", }) self.__bytes_per_second = t if hasattr(self, '_set'): self._set() def _unset_bytes_per_second(self): self.__bytes_per_second = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="bytes-per-second", rest_name="bytes-per-second", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint64', is_config=False) def _get_averaging_interval_seconds(self): """ Getter method for averaging_interval_seconds, mapped from YANG variable /mpls_state/transit_traffic_statistics/averaging_interval_seconds (uint32) YANG Description: Averaging Interval """ return self.__averaging_interval_seconds def _set_averaging_interval_seconds(self, v, load=False): """ Setter method for averaging_interval_seconds, mapped from YANG variable /mpls_state/transit_traffic_statistics/averaging_interval_seconds (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_averaging_interval_seconds is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_averaging_interval_seconds() directly. YANG Description: Averaging Interval """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="averaging-interval-seconds", rest_name="averaging-interval-seconds", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """averaging_interval_seconds must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="averaging-interval-seconds", rest_name="averaging-interval-seconds", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""", }) self.__averaging_interval_seconds = t if hasattr(self, '_set'): self._set() def _unset_averaging_interval_seconds(self): self.__averaging_interval_seconds = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="averaging-interval-seconds", rest_name="averaging-interval-seconds", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False) def _get_in_label(self): """ Getter method for in_label, mapped from YANG variable /mpls_state/transit_traffic_statistics/in_label (uint32) YANG Description: In Label """ return self.__in_label def _set_in_label(self, v, load=False): """ Setter method for in_label, mapped from YANG variable /mpls_state/transit_traffic_statistics/in_label (uint32) If this variable is read-only (config: false) in the source YANG file, then _set_in_label is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_in_label() directly. YANG Description: In Label """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError("Cannot set keys directly when" + " within an instantiated list") if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="in-label", rest_name="in-label", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """in_label must be of a type compatible with uint32""", 'defined-type': "uint32", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="in-label", rest_name="in-label", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False)""", }) self.__in_label = t if hasattr(self, '_set'): self._set() def _unset_in_label(self): self.__in_label = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="in-label", rest_name="in-label", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='uint32', is_config=False) def _get_protocol(self): """ Getter method for protocol, mapped from YANG variable /mpls_state/transit_traffic_statistics/protocol (mpls-protocol) YANG Description: MPLS protocol """ return self.__protocol def _set_protocol(self, v, load=False): """ Setter method for protocol, mapped from YANG variable /mpls_state/transit_traffic_statistics/protocol (mpls-protocol) If this variable is read-only (config: false) in the source YANG file, then _set_protocol is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_protocol() directly. YANG Description: MPLS protocol """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'mpls-protocol-none': {'value': 0}, u'mpls-protocol-ldp': {'value': 1}, u'mpls-protocol-rsvp': {'value': 2}},), is_leaf=True, yang_name="protocol", rest_name="protocol", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='mpls-protocol', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """protocol must be of a type compatible with mpls-protocol""", 'defined-type': "brocade-mpls-operational:mpls-protocol", 'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'mpls-protocol-none': {'value': 0}, u'mpls-protocol-ldp': {'value': 1}, u'mpls-protocol-rsvp': {'value': 2}},), is_leaf=True, yang_name="protocol", rest_name="protocol", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='mpls-protocol', is_config=False)""", }) self.__protocol = t if hasattr(self, '_set'): self._set() def _unset_protocol(self): self.__protocol = YANGDynClass(base=RestrictedClassType(base_type=unicode, restriction_type="dict_key", restriction_arg={u'mpls-protocol-none': {'value': 0}, u'mpls-protocol-ldp': {'value': 1}, u'mpls-protocol-rsvp': {'value': 2}},), is_leaf=True, yang_name="protocol", rest_name="protocol", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='mpls-protocol', is_config=False) def _get_statistics_valid(self): """ Getter method for statistics_valid, mapped from YANG variable /mpls_state/transit_traffic_statistics/statistics_valid (boolean) YANG Description: Statistics are valid """ return self.__statistics_valid def _set_statistics_valid(self, v, load=False): """ Setter method for statistics_valid, mapped from YANG variable /mpls_state/transit_traffic_statistics/statistics_valid (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_statistics_valid is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_statistics_valid() directly. YANG Description: Statistics are valid """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="statistics-valid", rest_name="statistics-valid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False) except (TypeError, ValueError): raise ValueError({ 'error-string': """statistics_valid must be of a type compatible with boolean""", 'defined-type': "boolean", 'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="statistics-valid", rest_name="statistics-valid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False)""", }) self.__statistics_valid = t if hasattr(self, '_set'): self._set() def _unset_statistics_valid(self): self.__statistics_valid = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="statistics-valid", rest_name="statistics-valid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-mpls-operational', defining_module='brocade-mpls-operational', yang_type='boolean', is_config=False) number_of_packets = __builtin__.property(_get_number_of_packets) number_of_packets_since_clear = __builtin__.property(_get_number_of_packets_since_clear) number_of_bytes = __builtin__.property(_get_number_of_bytes) number_of_bytes_since_clear = __builtin__.property(_get_number_of_bytes_since_clear) packets_per_second = __builtin__.property(_get_packets_per_second) bytes_per_second = __builtin__.property(_get_bytes_per_second) averaging_interval_seconds = __builtin__.property(_get_averaging_interval_seconds) in_label = __builtin__.property(_get_in_label) protocol = __builtin__.property(_get_protocol) statistics_valid = __builtin__.property(_get_statistics_valid) _pyangbind_elements = {'number_of_packets': number_of_packets, 'number_of_packets_since_clear': number_of_packets_since_clear, 'number_of_bytes': number_of_bytes, 'number_of_bytes_since_clear': number_of_bytes_since_clear, 'packets_per_second': packets_per_second, 'bytes_per_second': bytes_per_second, 'averaging_interval_seconds': averaging_interval_seconds, 'in_label': in_label, 'protocol': protocol, 'statistics_valid': statistics_valid, }

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0.045708

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0.12743

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0

null

0

1

0

null

0

6

6ffc4da15e0025892e43e4f3c07dd18b2566c3a5

43

py

Python

quel/sort.py

eppingere/hackcmu18-backend

696c050c4ce5acdf49aeaeeaded730a33443f5bd

[ "MIT" ]

2

2018-09-22T00:18:06.000Z

2018-09-23T04:49:29.000Z

quel/sort.py

eppingere/hackcmu18-backend

696c050c4ce5acdf49aeaeeaded730a33443f5bd

[ "MIT" ]

null

quel/sort.py

eppingere/hackcmu18-backend

696c050c4ce5acdf49aeaeeaded730a33443f5bd

[ "MIT" ]

null

def sort_a_list(xs): return sorted(xs)

14.333333

21

0.697674

8

43

3.5

0.875

0

0.186047

43

2

22

21.5

0.8

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

d264700d4866f36751930a2bbd7fb7604dcb757f

44

py

Python

Network Automation/Router/__init__.py

kuhakuu04/Network_Automation

f3eb99943e569f3311233f437ea17cd1862e3dc9

[ "Apache-2.0" ]

null

Network Automation/Router/__init__.py

kuhakuu04/Network_Automation

f3eb99943e569f3311233f437ea17cd1862e3dc9

[ "Apache-2.0" ]

null

Network Automation/Router/__init__.py

kuhakuu04/Network_Automation

f3eb99943e569f3311233f437ea17cd1862e3dc9

[ "Apache-2.0" ]

null

from .Mikrotik import * from .Cisco import *

22

23

0.75

6

44

5.5

0.666667

0

0.159091

44

2

24

22

0.891892

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

d2694b9724a3b46e8859d0a0478f55a5f9278566

5,968

py

Python

tests/test_cmd_execution.py

maximium/ffmpy

40737b1ce2251914e8fb6b67e1b0ded274997a4f

[ "MIT" ]

null

tests/test_cmd_execution.py

maximium/ffmpy

40737b1ce2251914e8fb6b67e1b0ded274997a4f

[ "MIT" ]

null

tests/test_cmd_execution.py

maximium/ffmpy

40737b1ce2251914e8fb6b67e1b0ded274997a4f

[ "MIT" ]

null

import os import subprocess import threading import time import pytest from ffmpy import FFExecutableNotFoundError, FFmpeg, FFRuntimeError def test_invalid_executable_path(): ff = FFmpeg(executable="/tmp/foo/bar/ffmpeg") with pytest.raises(FFExecutableNotFoundError) as exc_info: ff.run() assert str(exc_info.value) == "Executable '/tmp/foo/bar/ffmpeg' not found" def test_no_redirection(): global_options = "--stdin none --stdout oneline --stderr multiline --exit-code 0" ff = FFmpeg(global_options=global_options) stdout, stderr = ff.run() assert stdout is None assert stderr is None def test_redirect_to_devnull(): global_options = "--stdin none --stdout oneline --stderr multiline --exit-code 0" ff = FFmpeg(global_options=global_options) devnull = open(os.devnull, "wb") stdout, stderr = ff.run(stdout=devnull, stderr=devnull) assert stdout is None assert stderr is None def test_redirect_to_pipe(): global_options = "--stdin none --stdout oneline --stderr multiline --exit-code 0" ff = FFmpeg(global_options=global_options) stdout, stderr = ff.run(stdout=subprocess.PIPE, stderr=subprocess.PIPE) assert stdout == b"This is printed to stdout" assert stderr == b"These are\nmultiple lines\nprinted to stderr" def test_input(): global_options = "--stdin pipe --stdout oneline --stderr multiline --exit-code 0" ff = FFmpeg(global_options=global_options) stdout, stderr = ff.run( input_data=b"my input data", stdout=subprocess.PIPE, stderr=subprocess.PIPE ) assert stdout == b"my input data\nThis is printed to stdout" assert stderr == b"These are\nmultiple lines\nprinted to stderr" def test_non_zero_exitcode(): global_options = "--stdin none --stdout multiline --stderr multiline --exit-code 42" ff = FFmpeg(global_options=global_options) with pytest.raises(FFRuntimeError) as exc_info: ff.run(stdout=subprocess.PIPE, stderr=subprocess.PIPE) assert exc_info.value.cmd == ( "ffmpeg --stdin none --stdout multiline --stderr multiline --exit-code 42" ) assert exc_info.value.exit_code == 42 assert exc_info.value.stdout == b"These are\nmultiple lines\nprinted to stdout" assert exc_info.value.stderr == b"These are\nmultiple lines\nprinted to stderr" assert str(exc_info.value) == ( "`ffmpeg --stdin none --stdout multiline --stderr multiline --exit-code 42` " "exited with status 42\n\n" "STDOUT:\n" "These are\n" "multiple lines\n" "printed to stdout\n\n" "STDERR:\n" "These are\n" "multiple lines\n" "printed to stderr" ) def test_non_zero_exitcode_no_stderr(): global_options = "--stdin none --stdout multiline --stderr none --exit-code 42" ff = FFmpeg(global_options=global_options) with pytest.raises(FFRuntimeError) as exc_info: ff.run(stdout=subprocess.PIPE, stderr=subprocess.PIPE) assert exc_info.value.cmd == ( "ffmpeg --stdin none --stdout multiline --stderr none --exit-code 42" ) assert exc_info.value.exit_code == 42 assert exc_info.value.stdout == b"These are\nmultiple lines\nprinted to stdout" assert exc_info.value.stderr == b"" assert str(exc_info.value) == ( "`ffmpeg --stdin none --stdout multiline --stderr none --exit-code 42` " "exited with status 42\n\n" "STDOUT:\n" "These are\n" "multiple lines\n" "printed to stdout\n\n" "STDERR:\n" ) def test_non_zero_exitcode_no_stdout(): global_options = "--stdin none --stdout none --stderr multiline --exit-code 42" ff = FFmpeg(global_options=global_options) with pytest.raises(FFRuntimeError) as exc_info: ff.run(stdout=subprocess.PIPE, stderr=subprocess.PIPE) assert exc_info.value.cmd == ( "ffmpeg --stdin none --stdout none --stderr multiline --exit-code 42" ) assert exc_info.value.exit_code == 42 assert exc_info.value.stdout == b"" assert exc_info.value.stderr == b"These are\nmultiple lines\nprinted to stderr" assert str(exc_info.value) == ( "`ffmpeg --stdin none --stdout none --stderr multiline --exit-code 42` " "exited with status 42\n\n" "STDOUT:\n" "\n\n" "STDERR:\n" "These are\n" "multiple lines\n" "printed to stderr" ) def test_non_zero_exitcode_no_stdout_and_stderr(): global_options = "--stdin none --stdout none --stderr none --exit-code 42" ff = FFmpeg(global_options=global_options) with pytest.raises(FFRuntimeError) as exc_info: ff.run(stdout=subprocess.PIPE, stderr=subprocess.PIPE) assert exc_info.value.cmd == ( "ffmpeg --stdin none --stdout none --stderr none --exit-code 42" ) assert exc_info.value.exit_code == 42 assert exc_info.value.stdout == b"" assert exc_info.value.stderr == b"" assert str(exc_info.value) == ( "`ffmpeg --stdin none --stdout none --stderr none --exit-code 42` " "exited with status 42\n\n" "STDOUT:\n" "\n\n" "STDERR:\n" ) def test_raise_exception_with_stdout_stderr_none(): global_options = "--stdin none --stdout none --stderr none --exit-code 42" ff = FFmpeg(global_options=global_options) with pytest.raises(FFRuntimeError) as exc_info: ff.run() assert str(exc_info.value) == ( "`ffmpeg --stdin none --stdout none --stderr none --exit-code 42` " "exited with status 42\n\n" "STDOUT:\n" "\n\n" "STDERR:\n" ) def test_terminate_process(): global_options = "--long-run" ff = FFmpeg(global_options=global_options) thread_1 = threading.Thread(target=ff.run) thread_1.start() while not ff.process: time.sleep(0.05) print(ff.process.returncode) ff.process.terminate() thread_1.join() assert ff.process.returncode == -15

33.52809

88

0.665214

804

5,968

4.80597

0.110697

0.100932

0.068323

0.074534

0.851449

0.832557

0.820652

0.79736

0.752588

0

0.012372

0.214477

5,968

177

89

33.717514

0.81186

0

0.594406

0

0.332105

0.003519

0

0.216783

1

0.076923

false

0

0.041958

0

0.118881

0.090909

0

null

0

1

0

null

0

6

d27bf89e6938087054172df20a8c99c634e7a89d

13,396

py

Python

koapy/cli/utils/grpc_options.py

webclinic017/koapy

0cdbfac6a10c70e83df800a3a4362872b8792aba

[ "MIT" ]

null

koapy/cli/utils/grpc_options.py

webclinic017/koapy

0cdbfac6a10c70e83df800a3a4362872b8792aba

[ "MIT" ]

null

koapy/cli/utils/grpc_options.py

webclinic017/koapy

0cdbfac6a10c70e83df800a3a4362872b8792aba

[ "MIT" ]

null

import click from koapy.cli.extensions.functools import update_wrapper_with_click_params from koapy.cli.extensions.parser import ClickArgumentParser from koapy.cli.utils.verbose_option import full_verbose_option from koapy.config import config # grpc configs for default values grpc_config = config.get("koapy.backend.kiwoom_open_api_plus.grpc") default_server_bind_address = ( grpc_config.get("server.bind_address") or grpc_config.get("server.host") or grpc_config.get("host") ) default_server_host = default_server_bind_address default_server_port = grpc_config.get("server.port") or grpc_config.get("port") default_client_host = grpc_config.get("client.host") or grpc_config.get("host") default_client_port = grpc_config.get("client.port") or grpc_config.get("port") default_common_host = grpc_config.get("host") or grpc_config.get("client.host") default_common_port = ( grpc_config.get("port") or grpc_config.get("server.port") or grpc_config.get("client.port") ) # server specific options server_bind_address_option = click.option( "--bind-address", "--host", metavar="ADDRESS", help="Host address of gRPC server to bind.", default=default_server_bind_address, show_default=True, ) server_port_option = click.option( "--port", metavar="PORT", type=int, help="Port number of gRPC server to bind.", default=default_server_port, show_default=True, ) server_key_file_option = click.option( "--key-file", type=click.Path(), help="PEM encoded private key file for server SSL/TLS.", ) server_cert_file_option = click.option( "--cert-file", type=click.Path(), help="PEM encoded certificate chain file for server SSL/TLS.", ) server_root_certs_file_option = click.option( "--root-certs-file", type=click.Path(), help=""" PEM encoded client root certificates file for client authentication. Assumes --require-client-auth flag is set if this option is given, unless --no-require-client-auth flag is set explicitly. """, ) server_require_client_auth_option = click.option( "--require-client-auth", help="Require clients to be authenticated, root certificates are required.", is_flag=True, ) server_no_require_client_auth_option = click.option( "--no-require-client-auth", help="Force not to require clients to be authenticated, even if root certificates are given.", is_flag=True, ) server_options = [ server_bind_address_option, server_port_option, server_key_file_option, server_cert_file_option, server_root_certs_file_option, server_require_client_auth_option, server_no_require_client_auth_option, ] # client specific options client_host_option = click.option( "--host", metavar="ADDRESS", help="Host address of gRPC server to connect.", default=default_client_host, show_default=True, ) client_port_option = click.option( "--port", metavar="PORT", type=int, help="Port number of gRPC server to connect.", default=default_client_port, show_default=True, ) client_enable_ssl_option = click.option( "--enable-ssl", help=""" Enable SSL/TLS for gRPC connection. If --root-certs-file option is not given, will retrieve them from a default location chosen by gRPC runtime. """, is_flag=True, ) client_root_certs_file_option = click.option( "--root-certs-file", type=click.Path(), help="PEM encoded root certificates file for SSL/TLS.", ) client_key_file_option = click.option( "--key-file", type=click.Path(), help="PEM encoded private key file for client authentication.", ) client_cert_file_option = click.option( "--cert-file", type=click.Path(), help="PEM encoded certificate chain file for client authentication.", ) client_options = [ client_host_option, client_port_option, client_enable_ssl_option, client_root_certs_file_option, client_key_file_option, client_cert_file_option, ] # server and client options (resolving option conflicts) server_and_client_bind_address_option = click.option( "--bind-address", "--server-host", metavar="ADDRESS", help="Host address of gRPC server to bind.", default=default_server_bind_address, show_default=True, ) server_and_client_host_option = click.option( "--host", "--client-host", metavar="ADDRESS", help="Host address of gRPC server to connect.", default=default_client_host, show_default=True, ) server_and_client_port_option = click.option( "--port", metavar="PORT", type=int, help="Port number of gRPC server to bind and connect.", default=default_common_port, show_default=True, ) server_and_client_enable_ssl_option = click.option( "--enable-ssl", help=""" Enable SSL/TLS for gRPC connection. If --client-root-certs-file option is not given, will retrieve them from a default location chosen by gRPC runtime. """, is_flag=True, show_default=True, ) server_and_client_server_key_file_option = click.option( "--server-key-file", type=click.Path(), help="PEM encoded private key file for server SSL/TLS.", ) server_and_client_server_cert_file_option = click.option( "--server-cert-file", type=click.Path(), help="PEM encoded certificate chain file for server SSL/TLS.", ) server_and_client_server_root_certs_file_option = click.option( "--server-root-certs-file", type=click.Path(), help=""" PEM encoded client root certificates file for client authentication. Assumes --require-client-auth flag is set if this option is given, unless --no-require-client-auth flag is set explicitly. """, ) server_and_client_require_client_auth_option = click.option( "--require-client-auth", help="Require clients to be authenticated, root certificates are required.", is_flag=True, ) server_and_client_no_require_client_auth_option = click.option( "--no-require-client-auth", help="Foce not to require clients to be authenticated, even if root certificates are given.", is_flag=True, ) server_and_client_client_root_certs_file_option = click.option( "--client-root-certs-file", type=click.Path(), help="PEM encoded root certificates file for SSL/TLS.", ) server_and_client_client_key_file_option = click.option( "--client-key-file", type=click.Path(), help="PEM encoded private key file for client authentication.", ) server_and_client_client_cert_file_option = click.option( "--client-cert-file", type=click.Path(), help="PEM encoded certificate chain file for client authentication.", ) server_and_client_options = [ server_and_client_bind_address_option, server_and_client_host_option, server_and_client_port_option, server_and_client_enable_ssl_option, server_and_client_server_key_file_option, server_and_client_server_cert_file_option, server_and_client_server_root_certs_file_option, server_and_client_require_client_auth_option, server_and_client_no_require_client_auth_option, server_and_client_client_root_certs_file_option, server_and_client_client_key_file_option, server_and_client_client_cert_file_option, ] def grpc_server_options(): def decorator(f): @click.pass_context @server_bind_address_option @server_port_option @server_key_file_option @server_cert_file_option @server_root_certs_file_option @server_require_client_auth_option @server_no_require_client_auth_option def new_func(ctx: click.Context, *args, **kwargs): key_file = kwargs.get("key_file") cert_file = kwargs.get("cert_file") root_certs_file = kwargs.get("root_certs_file") require_client_auth = kwargs.get("require_client_auth") no_require_client_auth = kwargs.pop("no_require_client_auth") # both --key-file and --cert-file should be given if bool(key_file) != bool(cert_file): ctx.fail("both --key-file and --cert-file should be given.") # assume --require-client-auth flag is set if --root-certs-file is given if root_certs_file is not None: require_client_auth = True kwargs["require_client_auth"] = require_client_auth # value of --require-client-auth flag should be false if --no-require-client-auth flag is set if no_require_client_auth: require_client_auth = False kwargs["require_client_auth"] = require_client_auth # --require-client-auth flag is set but no --root-certs-file was given if require_client_auth and root_certs_file is None: ctx.fail( "--require-client-auth flag is set but no --root-certs-file was given." ) return ctx.invoke(f, *args, **kwargs) return update_wrapper_with_click_params(new_func, f) return decorator def grpc_client_options(): def decorator(f): @click.pass_context @client_host_option @client_port_option @client_enable_ssl_option @client_root_certs_file_option @client_key_file_option @client_cert_file_option def new_func(ctx: click.Context, *args, **kwargs): enable_ssl = kwargs.get("enable_ssl") root_certs_file = kwargs.get("root_certs_file") key_file = kwargs.get("key_file") cert_file = kwargs.get("cert_file") # assume --enable-ssl flag is set if --root-certs-file is given if root_certs_file is not None: enable_ssl = True kwargs["enable_ssl"] = enable_ssl # both --key-file and --cert-file should be given if bool(key_file) != bool(cert_file): ctx.fail("both --key-file and --cert-file should be given.") return ctx.invoke(f, *args, **kwargs) return update_wrapper_with_click_params(new_func, f) return decorator def grpc_server_and_client_options(): def decorator(f): @click.pass_context @server_and_client_bind_address_option @server_and_client_host_option @server_and_client_port_option @server_and_client_enable_ssl_option @server_and_client_server_key_file_option @server_and_client_server_cert_file_option @server_and_client_server_root_certs_file_option @server_and_client_require_client_auth_option @server_and_client_no_require_client_auth_option @server_and_client_client_root_certs_file_option @server_and_client_client_key_file_option @server_and_client_client_cert_file_option def new_func(ctx: click.Context, *args, **kwargs): # server related options and logics key_file = kwargs.get("server_key_file") cert_file = kwargs.get("server_cert_file") root_certs_file = kwargs.get("server_root_certs_file") require_client_auth = kwargs.get("require_client_auth") no_require_client_auth = kwargs.pop("no_require_client_auth") # both --key-file and --cert-file should be given if bool(key_file) != bool(cert_file): ctx.fail( "both --server-key-file and --server-cert-file should be given." ) # assume --require-client-auth flag is set if --root-certs-file is given if root_certs_file is not None: require_client_auth = True kwargs["require_client_auth"] = require_client_auth # value of --require-client-auth flag should be false if --no-require-client-auth flag is set if no_require_client_auth: require_client_auth = False kwargs["require_client_auth"] = require_client_auth # --require-client-auth flag is set but no --root-certs-file was given if require_client_auth and root_certs_file is None: ctx.fail( "--require-client-auth flag is set but no --server-root-certs-file was given." ) # client related options and logics enable_ssl = kwargs.get("enable_ssl") root_certs_file = kwargs.get("client_root_certs_file") key_file = kwargs.get("client_key_file") cert_file = kwargs.get("client_cert_file") # assume --enable-ssl flag is set if --root-certs-file is given if root_certs_file is not None: enable_ssl = True kwargs["enable_ssl"] = enable_ssl # both --key-file and --cert-file should be given if bool(key_file) != bool(cert_file): ctx.fail( "both --client-key-file and --client-cert-file should be given." ) return ctx.invoke(f, *args, **kwargs) return update_wrapper_with_click_params(new_func, f) return decorator server_argument_parser = ClickArgumentParser(server_options + [full_verbose_option()]) client_argument_parser = ClickArgumentParser(client_options + [full_verbose_option()]) server_and_client_argument_parser = ClickArgumentParser( server_and_client_options + [full_verbose_option()] )

34.794805

105

0.689609

1,790

13,396

4.83743

0.069274

0.081072

0.106017

0.058205

0.892366

0.857258

0.828502

0.781499

0.751011

0.734727

0

0.217154

13,396

384

106

34.885417

0.825689

0.073305

0

0.479751

0

0.003115

0.2572

0.035902

0

1

0.028037

false

0.009346

0.015576

0

0.071651

0

null

0

1

0

null

0

6

967d05cbf3f4da6ee46031647ef77ffc13ff3b67

74

py

Python

src/dataset/__init__.py

pigmon/SqueezeDet_Win

beb88d5a5652d2b3088aa2f670e6680043d13ac3

[ "BSD-2-Clause" ]

2

2017-05-25T01:26:41.000Z

2019-08-16T13:38:57.000Z

src/dataset/__init__.py

pigmon/SqueezeDet_Win

beb88d5a5652d2b3088aa2f670e6680043d13ac3

[ "BSD-2-Clause" ]

null

src/dataset/__init__.py

pigmon/SqueezeDet_Win

beb88d5a5652d2b3088aa2f670e6680043d13ac3

[ "BSD-2-Clause" ]

1

2017-05-25T01:26:50.000Z

from dataset.kitti import kitti from dataset.pascal_voc import pascal_voc

24.666667

41

0.864865

12

74

5.166667

0.5

0.354839

0

0.108108

74

2

42

37

0.939394

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

96b95b21313c44901627bb3bae017294e2625190

717

py

Python

proxySTAR_V3/certbot/venv.1509389747.bak/lib/python2.7/site-packages/pylint/test/input/func_disable_linebased.py

mami-project/lurk

98c293251e9b1e9c9a4b02789486c5ddaf46ba3c

[ "Apache-2.0" ]

2

2017-07-05T09:57:33.000Z

2017-11-14T23:05:53.000Z

Libraries/Python/pylint/v1.4.4/pylint/test/input/func_disable_linebased.py

davidbrownell/Common_Environment

4015872aeac8d5da30a6aa7940e1035a6aa6a75d

[ "BSL-1.0" ]

1

2019-01-17T14:26:22.000Z

2019-01-17T22:56:26.000Z

Libraries/Python/pylint/v1.4.4/pylint/test/input/func_disable_linebased.py

davidbrownell/Common_Environment

4015872aeac8d5da30a6aa7940e1035a6aa6a75d

[ "BSL-1.0" ]

1

2017-08-31T14:33:03.000Z

# This is a very very very very very very very very very very very very very very very very very very very very very long line. # pylint: disable=line-too-long, print-statement """Make sure enable/disable pragmas work for messages that are applied to lines and not syntax nodes. A disable pragma for a message that applies to nodes is applied to the whole block if it comes before the first statement (excluding the docstring). For line-based messages, this behavior needs to be altered to really only apply to the enclosed lines. """ # pylint: enable=line-too-long __revision__ = '1' print('This is a very long line which the linter will warn about, now that line-too-long has been enabled again.')

47.8

128

0.760112

123

717

4.398374

0.495935

0.295749

0.421442

0.532348

0.155268

0

0.001712

0.185495

717

14

129

51.214286

0.924658

0.772664

0

0.5

0.757143

0

1

0

false

0

0.5

0

null

1

0

1

0

null

0

1

0

6

73d8c55f08cb7d2869165c3df287bf23c2c67303

97

py

Python

model/__init__.py

GauravSarkar/BERT-CRF

649c4f5fce7b887f3db9a9303e938d90c4b87677

[ "Apache-2.0" ]

null

model/__init__.py

GauravSarkar/BERT-CRF

649c4f5fce7b887f3db9a9303e938d90c4b87677

[ "Apache-2.0" ]

null

model/__init__.py

GauravSarkar/BERT-CRF

649c4f5fce7b887f3db9a9303e938d90c4b87677

[ "Apache-2.0" ]

null

from .modeling_jointbert import JointBERT from .modeling_jointdistilbert import JointDistilBERT

24.25

53

0.886598

10

97

8.4

0.5

0.285714

0

0.092784

97

3

54

32.333333

0.954545

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

fb7a2bb57fa2e2cf642c9d3d082afb6820613ddb

5,207

py

Python

Project Euler/013.py

terror/solutions

bbe5d30b21d6194666c6c09ecb43f777e12925fa

[ "CC0-1.0" ]

2

2021-04-05T14:26:37.000Z

2021-06-10T04:22:01.000Z

Project Euler/013.py

terror/solutions

bbe5d30b21d6194666c6c09ecb43f777e12925fa

[ "CC0-1.0" ]

null

Project Euler/013.py

terror/solutions

bbe5d30b21d6194666c6c09ecb43f777e12925fa

[ "CC0-1.0" ]

null

nums = """ 37107287533902102798797998220837590246510135740250 46376937677490009712648124896970078050417018260538 74324986199524741059474233309513058123726617309629 91942213363574161572522430563301811072406154908250 23067588207539346171171980310421047513778063246676 89261670696623633820136378418383684178734361726757 28112879812849979408065481931592621691275889832738 44274228917432520321923589422876796487670272189318 47451445736001306439091167216856844588711603153276 70386486105843025439939619828917593665686757934951 62176457141856560629502157223196586755079324193331 64906352462741904929101432445813822663347944758178 92575867718337217661963751590579239728245598838407 58203565325359399008402633568948830189458628227828 80181199384826282014278194139940567587151170094390 35398664372827112653829987240784473053190104293586 86515506006295864861532075273371959191420517255829 71693888707715466499115593487603532921714970056938 54370070576826684624621495650076471787294438377604 53282654108756828443191190634694037855217779295145 36123272525000296071075082563815656710885258350721 45876576172410976447339110607218265236877223636045 17423706905851860660448207621209813287860733969412 81142660418086830619328460811191061556940512689692 51934325451728388641918047049293215058642563049483 62467221648435076201727918039944693004732956340691 15732444386908125794514089057706229429197107928209 55037687525678773091862540744969844508330393682126 18336384825330154686196124348767681297534375946515 80386287592878490201521685554828717201219257766954 78182833757993103614740356856449095527097864797581 16726320100436897842553539920931837441497806860984 48403098129077791799088218795327364475675590848030 87086987551392711854517078544161852424320693150332 59959406895756536782107074926966537676326235447210 69793950679652694742597709739166693763042633987085 41052684708299085211399427365734116182760315001271 65378607361501080857009149939512557028198746004375 35829035317434717326932123578154982629742552737307 94953759765105305946966067683156574377167401875275 88902802571733229619176668713819931811048770190271 25267680276078003013678680992525463401061632866526 36270218540497705585629946580636237993140746255962 24074486908231174977792365466257246923322810917141 91430288197103288597806669760892938638285025333403 34413065578016127815921815005561868836468420090470 23053081172816430487623791969842487255036638784583 11487696932154902810424020138335124462181441773470 63783299490636259666498587618221225225512486764533 67720186971698544312419572409913959008952310058822 95548255300263520781532296796249481641953868218774 76085327132285723110424803456124867697064507995236 37774242535411291684276865538926205024910326572967 23701913275725675285653248258265463092207058596522 29798860272258331913126375147341994889534765745501 18495701454879288984856827726077713721403798879715 38298203783031473527721580348144513491373226651381 34829543829199918180278916522431027392251122869539 40957953066405232632538044100059654939159879593635 29746152185502371307642255121183693803580388584903 41698116222072977186158236678424689157993532961922 62467957194401269043877107275048102390895523597457 23189706772547915061505504953922979530901129967519 86188088225875314529584099251203829009407770775672 11306739708304724483816533873502340845647058077308 82959174767140363198008187129011875491310547126581 97623331044818386269515456334926366572897563400500 42846280183517070527831839425882145521227251250327 55121603546981200581762165212827652751691296897789 32238195734329339946437501907836945765883352399886 75506164965184775180738168837861091527357929701337 62177842752192623401942399639168044983993173312731 32924185707147349566916674687634660915035914677504 99518671430235219628894890102423325116913619626622 73267460800591547471830798392868535206946944540724 76841822524674417161514036427982273348055556214818 97142617910342598647204516893989422179826088076852 87783646182799346313767754307809363333018982642090 10848802521674670883215120185883543223812876952786 71329612474782464538636993009049310363619763878039 62184073572399794223406235393808339651327408011116 66627891981488087797941876876144230030984490851411 60661826293682836764744779239180335110989069790714 85786944089552990653640447425576083659976645795096 66024396409905389607120198219976047599490197230297 64913982680032973156037120041377903785566085089252 16730939319872750275468906903707539413042652315011 94809377245048795150954100921645863754710598436791 78639167021187492431995700641917969777599028300699 15368713711936614952811305876380278410754449733078 40789923115535562561142322423255033685442488917353 44889911501440648020369068063960672322193204149535 41503128880339536053299340368006977710650566631954 81234880673210146739058568557934581403627822703280 82616570773948327592232845941706525094512325230608 22918802058777319719839450180888072429661980811197 77158542502016545090413245809786882778948721859617 72107838435069186155435662884062257473692284509516 20849603980134001723930671666823555245252804609722 53503534226472524250874054075591789781264330331690 """ print("".join(list(str(sum([int(n.rstrip()) for n in nums.split("\n") if n != ""])))[:10]))

50.067308

91

0.970616

118

5,207

42.830508

0.966102

0

0.98194

0.021702

5,207

103

92

50.553398

0.010208

0

0.980027

0.960246

0

1

0

1

0

false

0

0.009709

0

1

null

0

1

0

1

0

1

null

1

0

6

fba2407ed479ddc386811b480a3326ca31ae7f97

43

py

Python

toeicbert/__main__.py

graykode/BERT-TOEIC

da97af28e91e843025c8cfeabddd99ed1c0dbcc8

[ "MIT" ]

108

2019-04-29T18:27:07.000Z

2021-12-11T13:19:01.000Z

toeicbert/__main__.py

graykode/BERT-TOEIC

da97af28e91e843025c8cfeabddd99ed1c0dbcc8

[ "MIT" ]

5

2019-05-09T20:18:33.000Z

2020-06-15T13:40:12.000Z

toeicbert/__main__.py

graykode/BERT-TOEIC

da97af28e91e843025c8cfeabddd99ed1c0dbcc8

[ "MIT" ]

23

2019-04-30T01:34:39.000Z

2021-11-06T19:07:06.000Z

from . import bert_toeic bert_toeic.main()

14.333333

24

0.790698

7

43

4.571429

0.714286

0.5625

0

0.116279

43

3

25

14.333333

0.842105

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

83731a18f1190819c33c6a80b62f1ed098cc50be

45

py

Python

src/ui/handlers/__init__.py

Rabbithy/Fyks

8a2e8fac75b445ae8a608dc873a732c6d66a0f6b

[ "MIT" ]

1

2020-06-11T03:39:40.000Z

src/ui/handlers/__init__.py

Rabbithy/Fyks

8a2e8fac75b445ae8a608dc873a732c6d66a0f6b

[ "MIT" ]

6

2020-10-19T23:08:27.000Z

2020-11-24T12:03:59.000Z

src/ui/handlers/__init__.py

Rabbithy/Fyks

8a2e8fac75b445ae8a608dc873a732c6d66a0f6b

[ "MIT" ]

null

from .mousehandler import CustomMouseHandler

22.5

44

0.888889

4

45

10

1

0

0.088889

45

1

45

0.97561

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

83b0a1de629ae27565f8477ee80bb6b31529cc16

49,653

py

Python

preprocessing_image_datasets.py

neu-spiral/DeepSpectralRanking

dbdf320478002e247cb3293d34e9f56e9e1e9a21

[ "MIT" ]

1

2021-06-27T05:02:14.000Z

preprocessing_image_datasets.py

neu-spiral/DeepSpectralRanking

dbdf320478002e247cb3293d34e9f56e9e1e9a21

[ "MIT" ]

null

preprocessing_image_datasets.py

neu-spiral/DeepSpectralRanking

dbdf320478002e247cb3293d34e9f56e9e1e9a21

[ "MIT" ]

1

2021-03-16T19:12:51.000Z

import pandas as pd import numpy as np import pickle import argparse from utils import * from PIL import Image from os.path import exists from keras.preprocessing.image import load_img, img_to_array from keras.layers import Input from keras.models import Model from googlenet_functional import * from scipy.sparse import save_npz from scipy.misc import imresize from itertools import combinations import codecs input_shape = (3,224,224) def create_partitions(rankings_all, comparisons_all, n_fold): ''' :param n_fold: number of cross validation folds :param rankings_all: [(i_1,i_2, ...), (i_1,i_2, ...), ...] :param comparisons_all: +1/-1 :return: rankings_train (n_fold x d), rankings_test (n_fold x len(rankings_all/n_fold)) ''' d_all = len(rankings_all) d_fold = int(d_all / (n_fold + 1)) # last fold is the holdout/test set # partition observations into train, validation, and test np.random.seed(1) # stock indices to a matrix of (n_fold, indices) shuffled_ind = np.reshape(np.random.permutation(d_fold * (n_fold + 1)), ((n_fold + 1), d_fold)) rankings_train = [] rankings_val = [] comparisons_train = [] comparisons_val = [] # create train and validation sets for test_fold in range(n_fold): train_ind = shuffled_ind[[fold for fold in range(n_fold) if (fold != test_fold)]] train_ind = train_ind.flatten() # get training rankings rankings_train.append(rankings_all[train_ind]) # get training comparisons comparisons_train.append(comparisons_all[train_ind]) # get validation rankings rankings_val.append(rankings_all[shuffled_ind[test_fold]]) # get validation comparisons comparisons_val.append(comparisons_all[shuffled_ind[test_fold]]) # get test rankings rankings_test = rankings_all[shuffled_ind[n_fold]] # dims for train and validation: (n_fold, d_train, number of ranked items at a time (A_l)) return rankings_train, rankings_val, rankings_test, comparisons_train, comparisons_val def create_partitions_wrt_sample(rankings_all, comparisons_all, n, n_fold): ''' :param n: number of samples :param n_fold: number of cross validation folds :param rankings_all: [(i_1,i_2, ...), (i_1,i_2, ...), ...] :param comparisons_all: +1/-1 partition rankings by the samples participating in train or test. no rankings across :return: rankings_train (n_fold x d), rankings_test (n_fold x len(rankings_all/n_fold)) ''' samp_fold = int(n / (n_fold + 1)) # partition observations into train, validation, and test np.random.seed(1) # stock indices to a matrix of (n_fold, indices) shuffled_samp = np.reshape(np.random.permutation(samp_fold * (n_fold + 1)), ((n_fold + 1), samp_fold)) rankings_train = [] rankings_val = [] comparisons_train = [] comparisons_val = [] train_samp_folds = [] for test_fold in range(n_fold): train_samp = shuffled_samp[[fold for fold in range(n_fold) if (fold != test_fold)]] train_samp = train_samp.flatten() val_samp = shuffled_samp[test_fold] # get training rankings associated with only training samples rankings_train_fold = [] comparisons_train_fold = [] for i, rank in enumerate(rankings_all): if np.all(np.isin(rank, train_samp)): rankings_train_fold.append(rank) comparisons_train_fold.append(comparisons_all[i]) rankings_train.append(rankings_train_fold) comparisons_train.append(comparisons_train_fold) train_samp.sort() train_samp_folds.append(train_samp) # get validation rankings associated with only validation samples rankings_val_fold = [] comparisons_val_fold = [] for i, rank in enumerate(rankings_all): if np.all(np.isin(rank, val_samp)): rankings_val_fold.append(rank) comparisons_val_fold.append(comparisons_all[i]) rankings_val.append(rankings_val_fold) comparisons_val.append(comparisons_val_fold) # get rankings associated with only test samples test_samp = shuffled_samp[n_fold] rankings_test = [] for rank in rankings_all: if np.all(np.isin(rank, test_samp)): rankings_test.append(rank) # dims for train and validation: (n_fold, d_train, number of ranked items at a time (A_l)) return train_samp_folds, rankings_train, rankings_val, rankings_test, comparisons_train, comparisons_val def partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, imgs_lst, ranking_length_for_siamese=2): n = X.shape[0] rankings_all = np.array(rankings_all) comparisons_all = np.array(comparisons_all) if partition == 'per_samp': train_samp_folds, rankings_train, rankings_val, rankings_test, comparisons_train, comparisons_val = \ create_partitions_wrt_sample(rankings_all, comparisons_all, n, n_fold) else: rankings_train, rankings_val, rankings_test, comparisons_train, comparisons_val = \ create_partitions(rankings_all, comparisons_all, n_fold) train_samp_folds = [list(range(n)) for _ in range(n_fold)] ##########################################################################save for test_fold in range(n_fold): print(test_fold, 'folds have been saved') current_rankings = np.array(rankings_train[test_fold]) np.save('../data/' + dir + 'data/' + str(test_fold) + '_train_samp', train_samp_folds[test_fold]) np.save('../data/' + dir + 'data/' + str(test_fold) + '_features', X) np.save('../data/' + dir + 'data/' + str(test_fold) + '_imgs_lst', imgs_lst) np.save('../data/' + dir + 'data/' + str(test_fold) + '_train', current_rankings) np.save('../data/' + dir + 'data/' + str(test_fold) + '_val', np.array(rankings_val[test_fold])) ### save comp_imgs_lst_pair and comp_labels for siamese training current_comparisons = np.array(comparisons_train[test_fold]) comp_imgs_lst_pair_left = imgs_lst[current_comparisons[:, 0]] comp_imgs_lst_pair_right = imgs_lst[current_comparisons[:, 1]] np.save('../data/' + dir + 'data/' + str(test_fold) + '_comp_train_imgs_lst_left', comp_imgs_lst_pair_left) np.save('../data/' + dir + 'data/' + str(test_fold) + '_comp_train_imgs_lst_right', comp_imgs_lst_pair_right) np.save('../data/' + dir + 'data/' + str(test_fold) + '_comp_train_labels', current_comparisons[:, 2]) ### save rank_imgs_lst and true_order_labels for siamese training rank_imgs_lst = [] # ranking length x (number of rankings, number of features) true_order_labels = np.tile(list(range(ranking_length_for_siamese)), (len(current_rankings), 1)) # (number of rankings, ranking length). images are appended wrt ranking order for ranking_pos in range(ranking_length_for_siamese): rank_imgs_lst.append(imgs_lst[current_rankings[:, ranking_pos]]) np.save('../data/' + dir + 'data/' + str(test_fold) + '_rank_imgs_lst', rank_imgs_lst) np.save('../data/' + dir + 'data/' + str(test_fold) + '_true_order_labels', true_order_labels) # Compute initial parameters and save mat_Pij = est_Pij(n, current_rankings) save_npz('../data/' + dir + 'data/' + str(test_fold) + '_mat_Pij', mat_Pij) (beta_init, b_init, time_beta_b_init), (exp_beta_init, time_exp_beta_init), (u_init, time_u_init) = \ init_params(X, current_rankings, mat_Pij) all_init_params = [(beta_init, b_init, time_beta_b_init), (exp_beta_init, time_exp_beta_init), (u_init, time_u_init)] with open('../data/' + dir + 'data/' + str(test_fold) + '_init_params.pickle', "wb") as pickle_out: pickle.dump(all_init_params, pickle_out) pickle_out.close() # save test set np.save('../data/' + dir + 'data/rankings_test', np.array(rankings_test)) def save_gifgif_happy_data(n_fold, dir='gifgif_happy_', partition='per_obs', n_img = 50): ''' n: number of items p: feature dimension X: n*p, feature matrix :param n_fold: number of cross validation folds :param dir: current directory to read features and labels ''' # First pass over the data to transform GIFGIF HAPPINESS IDs to consecutive integers. image_ids = set([]) with open('../data/' + dir + 'data/' + 'gifgif-dataset-20150121-v1.csv') as f: next(f) # First line is header. for line in f: emotion, left, right, choice = line.strip().split(",") if len(left) > 0 and len(right) > 0 and (emotion == 'happiness' or emotion == 'sadness') and \ exists('../data/' + dir + 'data/images/' + left + '.gif') and \ exists('../data/' + dir + 'data/images/' + right + '.gif'): image_ids.add(left) image_ids.add(right) # take n_gif images if len(image_ids) >= n_img: image_ids = list(image_ids)[:n_img] break # create googlenet feature extractor model input1 = Input(shape=input_shape) input2 = Input(shape=input_shape) feature1, _ = create_googlenet(input1, input2) base_net = Model(input1, feature1) feature_model = Model(inputs=base_net.input, outputs=base_net.get_layer('feature_extractor').get_output_at(0)) feature_model.load_weights(GOOGLENET_INIT_WEIGHTS_PATH, by_name=True) feature_model.compile(loss='mean_squared_error', optimizer='sgd') # load images and googlenet features X_imagenet = np.zeros((n_img, 1024), dtype=float) # Extract image matrix, (n, 3, 224, 224) int_to_idx = dict(enumerate(image_ids)) idx_to_int = dict((v, k) for k, v in int_to_idx.items()) imgs_lst = np.zeros((0, 3, 224, 224)) for image_id, i in idx_to_int.items(): # load image_mtx = img_to_array(load_img('../data/' + dir + 'data/images/' + image_id + '.gif')).astype(np.uint8) # resize image_mtx = np.reshape(imresize(image_mtx, input_shape[1:]), input_shape) # standardize image_mtx = (image_mtx - np.mean(image_mtx)) / np.std(image_mtx) image_mtx = image_mtx[np.newaxis, :, :, :] # concatenate imgs_lst = np.concatenate((imgs_lst, image_mtx), axis=0) # take googlenet features X_imagenet[i, :] = np.squeeze(feature_model.predict(image_mtx)) # take rankings (ordered lists) and comparisons (+1/-1) of images in image_ids rankings_all = [] comparisons_all = [] with open('../data/' + dir + 'data/' + 'gifgif-dataset-20150121-v1.csv') as f: next(f) # First line is header. for line in f: emotion, left, right, choice = line.strip().split(",") if left in image_ids and right in image_ids: if emotion == 'happiness': # left is happier # Map ids to integers. left = idx_to_int[left] right = idx_to_int[right] if choice == "left": # Left image won the happiness comparison. rankings_all.append((left, right)) # Append to comparisons comparisons_all.append((left, right, +1)) elif choice == "right": # Right image won the happiness comparison. rankings_all.append((right, left)) # Append to comparisons comparisons_all.append((left, right, -1)) elif emotion == 'sadness': # right is happier # Map ids to integers. left = idx_to_int[left] right = idx_to_int[right] if choice == "right": # Left image won the sadness comparison. rankings_all.append((left, right)) # Append to comparisons comparisons_all.append((left, right, +1)) elif choice == "left": # Right image won the sadness comparison. rankings_all.append((right, left)) # Append to comparisons comparisons_all.append((left, right, -1)) partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X_imagenet, imgs_lst) def save_fac_data(n_fold, dir='fac_', partition='per_obs', n_img = 50): ''' n: number of items p: feature dimension X: n*p, feature matrix :param n_fold: number of cross validation folds :param dir: current directory to read features and labels ''' comp_label_file = "/pairwise_comparison.pkl" with open('../data/' + dir + 'data/' + comp_label_file, 'rb') as f: comp_label_matrix = pickle.load(f) image_ids = set([]) # get all unique images in category for row in comp_label_matrix: # category, f1, f2, workerID, passDup, imgId, ans if row['category'] == 0: left = row['f1'] + '/' + row['imgId'] + '.jpg' right = row['f2'] + '/' + row['imgId'] + '.jpg' if exists('../data/' + dir + 'data/' + left) and exists('../data/' + dir + 'data/' + right): image_ids.add(left) image_ids.add(right) # take n_img images if len(image_ids) >= n_img: image_ids = list(image_ids)[:n_img] break # create googlenet feature extractor model input1 = Input(shape=input_shape) input2 = Input(shape=input_shape) feature1, _ = create_googlenet(input1, input2) base_net = Model(input1, feature1) feature_model = Model(inputs=base_net.input, outputs=base_net.get_layer('feature_extractor').get_output_at(0)) feature_model.load_weights(GOOGLENET_INIT_WEIGHTS_PATH, by_name=True) feature_model.compile(loss='mean_squared_error', optimizer='sgd') # load images and googlenet features X_imagenet = np.zeros((n_img, 1024), dtype=float) # Extract image matrix, (n, 3, 224, 224) int_to_idx = dict(enumerate(image_ids)) idx_to_int = dict((v, k) for k, v in int_to_idx.items()) imgs_lst = np.zeros((0, 3, 224, 224)) for image_id, i in idx_to_int.items(): # load image_mtx = img_to_array(load_img('../data/' + dir + 'data/' + image_id)).astype(np.uint8) # resize image_mtx = np.reshape(imresize(image_mtx, input_shape[1:]), input_shape) # standardize image_mtx = (image_mtx - np.mean(image_mtx)) / np.std(image_mtx) image_mtx = image_mtx[np.newaxis, :, :, :] # concatenate imgs_lst = np.concatenate((imgs_lst, image_mtx), axis=0) # take googlenet features X_imagenet[i, :] = np.squeeze(feature_model.predict(image_mtx)) # take rankings (ordered lists) and comparisons (+1/-1) of images in image_ids rankings_all = [] comparisons_all = [] for row in comp_label_matrix: # category, f1, f2, workerID, passDup, imgId, ans if row['category'] == 0: left = row['f1'] + '/' + row['imgId'] + '.jpg' right = row['f2'] + '/' + row['imgId'] + '.jpg' choice = row['ans'] if left in image_ids and right in image_ids: # Map ids to integers. left = idx_to_int[left] right = idx_to_int[right] if choice == "left": # Left image won the comparison. rankings_all.append((left, right)) # Append to comparisons comparisons_all.append((left, right, +1)) elif choice == "right": # Right image won the comparison. rankings_all.append((right, left)) # Append to comparisons comparisons_all.append((left, right, -1)) partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X_imagenet, imgs_lst) def save_rop_data(n_fold, dir='rop_', partition='per_obs', manual_feature=False): ''' n: number of items p: feature dimension X: n*p, feature matrix :param n_fold: number of cross validation folds :param dir: current directory to read features and labels ''' n_img = 100 # load all comparisons with open('../data/' + dir + 'data/' + 'Partitions.p', 'rb') as f: u = pickle._Unpickler(f) u.encoding = 'latin1' label_cmp = u.load()['cmpData'] # (expert,pair_index,label) df = pd.read_excel('../data/' + dir + 'data/' + '100Features.xlsx') image_ids = df.as_matrix()[:n_img, 0] image_ids = np.array([name[:-4] for name in image_ids]) # correct extension X = df.as_matrix()[:n_img, 1:144].astype('float') # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std # create googlenet feature extractor model input1 = Input(shape=input_shape) input2 = Input(shape=input_shape) feature1, _ = create_googlenet(input1, input2) base_net = Model(input1, feature1) feature_model = Model(inputs=base_net.input, outputs=base_net.get_layer('feature_extractor').get_output_at(0)) feature_model.load_weights(GOOGLENET_INIT_WEIGHTS_PATH, by_name=True) feature_model.compile(loss='mean_squared_error', optimizer='sgd') # load images and googlenet features X_imagenet = np.zeros((n_img, 1024), dtype=float) # Extract image matrix, (n, 3, 224, 224) int_to_idx = dict(enumerate(image_ids)) idx_to_int = dict((v, k) for k, v in int_to_idx.items()) imgs_lst = np.zeros((0, 3, 224, 224)) for image_id, i in idx_to_int.items(): # load image_mtx = img_to_array(load_img('../data/' + dir + 'data/images/' + image_id + '.png')).astype(np.uint8) # resize image_mtx = np.reshape(imresize(image_mtx, input_shape[1:]), input_shape) # standardize image_mtx = (image_mtx - np.mean(image_mtx)) / np.std(image_mtx) image_mtx = image_mtx[np.newaxis, :, :, :] # concatenate imgs_lst = np.concatenate((imgs_lst, image_mtx), axis=0) # take googlenet features X_imagenet[i, :] = np.squeeze(feature_model.predict(image_mtx)) # take rankings (ordered lists) and comparisons (+1/-1) of images in image_ids M_per_expert = len(label_cmp[0]) # Number of comparisons per expert rankings_all = [] comparisons_all = [] for expert in range(5): for pair_ind in range(M_per_expert): item1 = np.where(image_ids == label_cmp[expert][pair_ind][0])[0] item2 = np.where(image_ids == label_cmp[expert][pair_ind][1])[0] if item1 != np.empty((1,)) and item2 != np.empty((1,)): item1 = np.asscalar(item1) item2 = np.asscalar(item2) if label_cmp[expert][pair_ind][2] == 1: rankings_all.append((item1, item2)) # Append to comparisons comparisons_all.append((item1, item2, +1)) else: rankings_all.append((item2, item1)) # Append to comparisons comparisons_all.append((item1, item2, -1)) if manual_feature: partition_and_save(n_fold, dir + "manual_", partition, rankings_all, comparisons_all, X, X) else: partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X_imagenet, imgs_lst) def save_candy_data(n_fold, dir='candy_', partition='per_obs', flip_noise_prob=0.0, ranking_length_for_siamese=2): ''' n: number of items p: feature dimension X: n*p, feature matrix :param n_fold: number of cross validation folds :param dir: current directory to read features and labels ''' X = [] # 85x11 win_percent = [] # open file in read mode csv_reader = codecs.open('../data/' + dir + 'data/candy-data.csv', 'r', 'utf8') # Iterate over each row in the csv using reader object for row in csv_reader: row = row.rstrip().split(",") # row variable is a list that represents a row in csv, first column is row names X.append(row[1:-1]) win_percent.append(row[-1]) # first row is column names X = np.array(X[1:]).astype("float") win_percent = np.array(win_percent[1:]) full_ranking_indices = np.flip(np.argsort(win_percent)) X = X[full_ranking_indices] # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std n = X.shape[0] # generate comparisons and rankings w.r.t. win percent all_multiway_rankings = list(combinations(range(n), ranking_length_for_siamese)) rankings_all = [] comparisons_all = [] for ranking in all_multiway_rankings: #temp_ranking = np.array(temp_ranking) #ranking = list(temp_ranking[np.flip(np.argsort(win_percent[temp_ranking]))]) # flip ranking and comparison to add noise eps = np.random.uniform() if eps > flip_noise_prob: # correct one item1 = ranking[0] item2 = ranking[1] rankings_all.append(ranking) else: item1 = ranking[-1] item2 = ranking[0] if ranking_length_for_siamese > 2: rankings_all.append((item1, item2) + tuple(ranking[1:-1])) else: rankings_all.append((item1, item2)) # choose which way to compare if np.random.uniform() > 0.5: comparisons_all.append((item1, item2, +1)) else: comparisons_all.append((item2, item1, -1)) # features and images are not separate for numerical datasets partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, X, ranking_length_for_siamese) def save_living_cost_data(n_fold, dir='living_cost_', partition='per_obs', flip_noise_prob=0.0, ranking_length_for_siamese=2, n_countries=50): ''' n: number of items p: feature dimension X: n*p, feature matrix :param n_fold: number of cross validation folds :param dir: current directory to read features and labels ''' X = [] # 216x6 # open file in read mode csv_reader = codecs.open('../data/' + dir + 'data/movehubcostofliving.csv', 'r', 'utf8') # Iterate over each row in the csv using reader object for row in csv_reader: row = row.rstrip().split(",") # row variable is a list that represents a row in csv, first column is row names X.append(row[1:]) # first row is column names X = np.array(X[1:]).astype("float") X = X[:n_countries] # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std # generate comparisons and rankings, ranking order w.r.t. indices all_multiway_rankings = list(combinations(range(n_countries), ranking_length_for_siamese)) rankings_all = [] comparisons_all = [] for ranking in all_multiway_rankings: # flip ranking and comparison to add noise eps = np.random.uniform() if eps > flip_noise_prob: # correct one item1 = ranking[0] item2 = ranking[1] rankings_all.append(ranking) else: item1 = ranking[-1] item2 = ranking[0] if ranking_length_for_siamese > 2: rankings_all.append((item1, item2) + tuple(ranking[1:-1])) else: rankings_all.append((item1, item2)) # choose which way to compare if np.random.uniform() > 0.5: comparisons_all.append((item1, item2, +1)) else: comparisons_all.append((item2, item1, -1)) # features and images are not separate for numerical datasets partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, X, ranking_length_for_siamese) def save_living_quality_data(n_fold, dir='living_quality_', partition='per_obs', flip_noise_prob=0.0, ranking_length_for_siamese=2, n_countries=50): ''' n: number of items p: feature dimension X: n*p, feature matrix :param n_fold: number of cross validation folds :param dir: current directory to read features and labels ''' X = [] # 216x6 # open file in read mode csv_reader = codecs.open('../data/' + dir + 'data/movehubqualityoflife.csv', 'r', 'utf8') # Iterate over each row in the csv using reader object for row in csv_reader: row = row.rstrip().split(",") # row variable is a list that represents a row in csv, first column is row names X.append(row[1:]) # first row is column names X = np.array(X[1:]).astype("float") X = X[:n_countries] # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std # generate comparisons and rankings, ranking order w.r.t. indices all_multiway_rankings = list(combinations(range(n_countries), ranking_length_for_siamese)) rankings_all = [] comparisons_all = [] for ranking in all_multiway_rankings: # flip ranking and comparison to add noise eps = np.random.uniform() if eps > flip_noise_prob: # correct one item1 = ranking[0] item2 = ranking[1] rankings_all.append(ranking) else: item1 = ranking[-1] item2 = ranking[0] if ranking_length_for_siamese > 2: rankings_all.append((item1, item2) + tuple(ranking[1:-1])) else: rankings_all.append((item1, item2)) # choose which way to compare if np.random.uniform() > 0.5: comparisons_all.append((item1, item2, +1)) else: comparisons_all.append((item2, item1, -1)) # features and images are not separate for numerical datasets partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, X, ranking_length_for_siamese) def save_imdb_data(n_fold, dir='imdb_', partition='per_obs', n_movies = 50, flip_noise_prob=0.0): X = [] # n_moviesx36 ratings = [] row_ind = 0 # open file in read mode csv_reader = codecs.open('../data/' + dir + 'data/imdb.csv', 'r', 'utf8') # Iterate over each row in the csv using reader object for row in csv_reader: # first row is column names if row_ind > 0 and len(X) < n_movies: row = row.rstrip().split(",") # check for invalid rows try: cur_rating = float(row[5]) feature1 = float(row[7]) feature2 = float(row[8]) feature3 = [float(elm) for elm in row[10:]] res = True except: res = False if res: ratings.append(cur_rating) features = [] features.append(feature1) features.append(feature2) features.extend(feature3) X.append(features) row_ind += 1 # first row is column names X = np.array(X).astype("float") # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std # generate comparisons w.r.t. ratings in decreasing order unique_ratings = np.flip(np.unique(ratings)) movie_indices_grouped = [] rankings_all = [] comparisons_all = [] for rating in unique_ratings: movie_indices_grouped.append([i for i in range(len(ratings)) if ratings[i] == rating]) for i, movies1 in enumerate(movie_indices_grouped[:-1]): for movies2 in movie_indices_grouped[i + 1:]: for temp_item1 in movies1: for temp_item2 in movies2: # flip ranking and comparison to add noise eps = np.random.uniform() if eps > flip_noise_prob: # correct one item1 = temp_item1 item2 = temp_item2 else: item1 = temp_item2 item2 = temp_item1 rankings_all.append((item1, item2)) if np.random.uniform() > 0.5: comparisons_all.append((item1, item2, +1)) else: comparisons_all.append((item2, item1, -1)) # features and images are not separate for numerical datasets partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, X) def save_imdb_4way_data(n_fold, dir='imdb_multiway_', partition='per_obs', n_movies=50, flip_noise_prob=0.0): X = [] # n_moviesx36 ratings = [] row_ind = 0 # open file in read mode csv_reader = codecs.open('../data/' + dir + 'data/imdb.csv', 'r', 'utf8') # Iterate over each row in the csv using reader object for row in csv_reader: # first row is column names if row_ind > 0 and len(X) < n_movies: row = row.rstrip().split(",") # check for invalid rows try: cur_rating = float(row[5]) feature1 = float(row[7]) feature2 = float(row[8]) feature3 = [float(elm) for elm in row[10:]] res = True except: res = False if res: ratings.append(cur_rating) features = [] features.append(feature1) features.append(feature2) features.extend(feature3) X.append(features) row_ind += 1 # first row is column names X = np.array(X).astype("float") # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std # generate comparisons w.r.t. ratings in decreasing order unique_ratings = np.flip(np.unique(ratings)) movie_indices_grouped = [] rankings_all = [] comparisons_all = [] for rating in unique_ratings: movie_indices_grouped.append([i for i in range(len(ratings)) if ratings[i] == rating]) for first in np.arange(0, len(movie_indices_grouped) - 3): for second in np.arange(first + 1, len(movie_indices_grouped) - 2): for third in np.arange(second + 1, len(movie_indices_grouped) - 1): for forth in np.arange(third + 1, len(movie_indices_grouped)): movies1 = movie_indices_grouped[first] movies2 = movie_indices_grouped[second] movies3 = movie_indices_grouped[third] movies4 = movie_indices_grouped[forth] print([first, second, third, forth]) for temp_item1 in movies1: for temp_item2 in movies2: for temp_item3 in movies3: for temp_item4 in movies4: # flip ranking and comparison to add noise eps = np.random.uniform() ranking = [temp_item1, temp_item2, temp_item3, temp_item4] print(ranking) if eps > flip_noise_prob: # correct one item1 = ranking[0] item2 = ranking[1] rankings_all.append(ranking) else: item1 = ranking[-1] item2 = ranking[0] #rankings_all.append((item1, item2) + tuple(np.random.permutation(ranking[1:-1]))) rankings_all.append((item1, item2) + tuple(ranking[1:-1])) # choose which way to compare if np.random.uniform() > 0.5: comparisons_all.append((item1, item2, +1)) else: comparisons_all.append((item2, item1, -1)) # features and images are not separate for numerical datasets partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, X, 4) def save_iclr_3way_data(n_fold, dir='iclr_multiway_', partition='per_obs', n_docs=100, flip_noise_prob=0.0): """ Crawled data is here: https://github.com/shaohua0116/ICLR2020-OpenReviewData Features are extracted by pre-trained BERT model from transformers import BertTokenizer, BertModel import torch tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertModel.from_pretrained('bert-base-uncased', return_dict=True) ratings = [] embeddings = [] for i, m in enumerate(meta_list): if len(m.rating) > 0: rating = np.mean(m.rating) / (np.std(m.rating) + 1e-4) inputs = tokenizer(m.abstract, return_tensors="pt") outputs = model(**inputs) # 768 dimensions per document embedding = outputs.last_hidden_state[-1, -1].data.numpy() # take the last element of the sequence and batch print("Paper count", i) print("Average rating", rating) ratings.append(rating) embeddings.append(embedding) """ X = np.load("../data/iclr_3way_noisy_data/iclr_2020_embeddings.npy").astype("float")[:n_docs] # n_abstracts x 768 ratings = np.load("../data/iclr_3way_noisy_data/iclr_2020_ratings.npy")[:n_docs] # n_abstracts x 1 # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std # generate comparisons w.r.t. ratings in decreasing order unique_ratings = np.flip(np.unique(ratings)) movie_indices_grouped = [] rankings_all = [] comparisons_all = [] for rating in unique_ratings: movie_indices_grouped.append([i for i in range(len(ratings)) if ratings[i] == rating]) for first in np.arange(0, len(movie_indices_grouped) - 2): for second in np.arange(first + 1, len(movie_indices_grouped) - 1): for third in np.arange(second + 1, len(movie_indices_grouped)): movies1 = movie_indices_grouped[first] movies2 = movie_indices_grouped[second] movies3 = movie_indices_grouped[third] print([first, second, third]) for temp_item1 in movies1: for temp_item2 in movies2: for temp_item3 in movies3: # flip ranking and comparison to add noise eps = np.random.uniform() ranking = [temp_item1, temp_item2, temp_item3] print(ranking) if eps > flip_noise_prob: # correct one item1 = ranking[0] item2 = ranking[1] rankings_all.append(ranking) else: item1 = ranking[-1] item2 = ranking[0] #rankings_all.append((item1, item2) + tuple(np.random.permutation(ranking[1:-1]))) rankings_all.append((item1, item2) + tuple(ranking[1:-1])) # choose which way to compare if np.random.uniform() > 0.5: comparisons_all.append((item1, item2, +1)) else: comparisons_all.append((item2, item1, -1)) # features and images are not separate for numerical datasets partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, X, 3) def save_iclr_4way_data(n_fold, dir='iclr_multiway_', partition='per_obs', n_docs=100, flip_noise_prob=0.0): """ Crawled data is here: https://github.com/shaohua0116/ICLR2020-OpenReviewData Features are extracted by pre-trained BERT model """ X = np.load("../data/iclr_3way_noisy_data/iclr_2020_embeddings.npy").astype("float")[:n_docs] # n_abstracts x 768 ratings = np.load("../data/iclr_3way_noisy_data/iclr_2020_ratings.npy")[:n_docs] # n_abstracts x 1 # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std # generate comparisons w.r.t. ratings in decreasing order unique_ratings = np.flip(np.unique(ratings)) movie_indices_grouped = [] rankings_all = [] comparisons_all = [] for rating in unique_ratings: movie_indices_grouped.append([i for i in range(len(ratings)) if ratings[i] == rating]) for first in np.arange(0, len(movie_indices_grouped) - 3): for second in np.arange(first + 1, len(movie_indices_grouped) - 2): for third in np.arange(second + 1, len(movie_indices_grouped) - 1): for forth in np.arange(third + 1, len(movie_indices_grouped)): movies1 = movie_indices_grouped[first] movies2 = movie_indices_grouped[second] movies3 = movie_indices_grouped[third] movies4 = movie_indices_grouped[forth] print([first, second, third, forth]) for temp_item1 in movies1: for temp_item2 in movies2: for temp_item3 in movies3: for temp_item4 in movies4: # flip ranking and comparison to add noise eps = np.random.uniform() ranking = [temp_item1, temp_item2, temp_item3, temp_item4] print(ranking) if eps > flip_noise_prob: # correct one item1 = ranking[0] item2 = ranking[1] rankings_all.append(ranking) else: item1 = ranking[-1] item2 = ranking[0] # rankings_all.append((item1, item2) + tuple(np.random.permutation(ranking[1:-1]))) rankings_all.append((item1, item2) + tuple(ranking[1:-1])) # choose which way to compare if np.random.uniform() > 0.5: comparisons_all.append((item1, item2, +1)) else: comparisons_all.append((item2, item1, -1)) # features and images are not separate for numerical datasets partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, X, 4) def save_iclr_5way_data(n_fold, dir='iclr_multiway_', partition='per_obs', n_docs=100, flip_noise_prob=0.0): """ Crawled data is here: https://github.com/shaohua0116/ICLR2020-OpenReviewData Features are extracted by pre-trained BERT model """ X = np.load("../data/iclr_3way_noisy_data/iclr_2020_embeddings.npy").astype("float")[:n_docs] # n_abstracts x 768 ratings = np.load("../data/iclr_3way_noisy_data/iclr_2020_ratings.npy")[:n_docs] # n_abstracts x 1 # standardize X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) + rtol X = (X - X_mean) / X_std # generate comparisons w.r.t. ratings in decreasing order unique_ratings = np.flip(np.unique(ratings)) movie_indices_grouped = [] rankings_all = [] comparisons_all = [] for rating in unique_ratings: movie_indices_grouped.append([i for i in range(len(ratings)) if ratings[i] == rating]) for first in np.arange(0, len(movie_indices_grouped) - 4): for second in np.arange(first + 1, len(movie_indices_grouped) - 3): for third in np.arange(second + 1, len(movie_indices_grouped) - 2): for forth in np.arange(third + 1, len(movie_indices_grouped) - 1): for fifth in np.arange(forth + 1, len(movie_indices_grouped)): movies1 = movie_indices_grouped[first] movies2 = movie_indices_grouped[second] movies3 = movie_indices_grouped[third] movies4 = movie_indices_grouped[forth] movies5 = movie_indices_grouped[fifth] print([first, second, third, forth, fifth]) for temp_item1 in movies1: for temp_item2 in movies2: for temp_item3 in movies3: for temp_item4 in movies4: for temp_item5 in movies5: # flip ranking and comparison to add noise eps = np.random.uniform() ranking = [temp_item1, temp_item2, temp_item3, temp_item4, temp_item5] print(ranking) if eps > flip_noise_prob: # correct one item1 = ranking[0] item2 = ranking[1] rankings_all.append(ranking) else: item1 = ranking[-1] item2 = ranking[0] rankings_all.append((item1, item2) + tuple(ranking[1:-1])) # choose which way to compare if np.random.uniform() > 0.5: comparisons_all.append((item1, item2, +1)) else: comparisons_all.append((item2, item1, -1)) # features and images are not separate for numerical datasets partition_and_save(n_fold, dir, partition, rankings_all, comparisons_all, X, X, 5) if __name__ == "__main__": n_fold = 5 parser = argparse.ArgumentParser(description='prep', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('dir', type=str) args = parser.parse_args() dir = args.dir flip_noise_prob = 0.1 if dir == 'rop_': save_rop_data(n_fold, dir='rop_', manual_feature=False) if dir == 'rop_manual_': save_rop_data(n_fold, dir='rop_', manual_feature=True) elif dir == 'fac_': save_fac_data(n_fold, dir=dir) elif dir == 'gifgif_happy_': save_gifgif_happy_data(n_fold, dir=dir) elif dir == 'living_cost_noisy_': save_living_cost_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob) elif dir == 'living_cost_3way_noisy_': save_living_cost_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=3) elif dir == 'living_cost_4way_noisy_': save_living_cost_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=4) elif dir == 'living_cost_5way_noisy_': save_living_cost_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=5) elif dir == 'living_cost_6way_noisy_': save_living_cost_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=6) elif dir == 'living_quality_noisy_': save_living_quality_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob) elif dir == 'living_quality_3way_noisy_': save_living_quality_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=3) elif dir == 'living_quality_4way_noisy_': save_living_quality_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=4) elif dir == 'living_quality_5way_noisy_': save_living_quality_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=5) elif dir == 'living_quality_6way_noisy_': save_living_quality_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=6) elif dir == 'imdb_noisy_': save_imdb_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob) elif dir == 'imdb_4way_noisy_': save_imdb_4way_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob) elif dir == 'iclr_3way_noisy_': save_iclr_3way_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob) elif dir == 'iclr_4way_noisy_': save_iclr_4way_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob) elif dir == 'iclr_5way_noisy_': save_iclr_5way_data(n_fold, dir=dir, flip_noise_prob=flip_noise_prob) elif dir == 'rop_par_': save_rop_data(n_fold, dir='rop_par_', partition='per_samp', manual_feature=False) elif dir == 'rop_par_manual_': save_rop_data(n_fold, dir='rop_par_', partition='per_samp', manual_feature=True) elif dir == 'gifgif_happy_par_': save_gifgif_happy_data(n_fold, dir=dir, partition='per_samp') elif dir == 'fac_par_': save_fac_data(n_fold, dir=dir, partition='per_samp') elif dir == 'living_cost_noisy_par_': save_living_cost_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob) elif dir == 'living_cost_3way_noisy_par_': save_living_cost_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=3) elif dir == 'living_cost_4way_noisy_par_': save_living_cost_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=4) elif dir == 'living_cost_5way_noisy_par_': save_living_cost_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=5) elif dir == 'living_cost_6way_noisy_par_': save_living_cost_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=6) elif dir == 'living_quality_noisy_par_': save_living_quality_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob) elif dir == 'living_quality_3way_noisy_par_': save_living_quality_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=3) elif dir == 'living_quality_4way_noisy_par_': save_living_quality_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=4) elif dir == 'living_quality_5way_noisy_par_': save_living_quality_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=5) elif dir == 'living_quality_6way_noisy_par_': save_living_quality_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob, ranking_length_for_siamese=6) elif dir == 'imdb_noisy_par_': save_imdb_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob) elif dir == 'imdb_4way_noisy_par_': save_imdb_4way_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob) elif dir == 'iclr_3way_noisy_par_': save_iclr_3way_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob) elif dir == 'iclr_4way_noisy_par_': save_iclr_4way_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob) elif dir == 'iclr_5way_noisy_par_': save_iclr_5way_data(n_fold, dir=dir, partition='per_samp', flip_noise_prob=flip_noise_prob)

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83e72d18dbaf89deb20c7a6afca1160f6e1ba4f4

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py

Python

huaweicloud-sdk-hss/huaweicloudsdkhss/v1/__init__.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

64

2020-06-12T07:05:07.000Z

2022-03-30T03:32:50.000Z

huaweicloud-sdk-hss/huaweicloudsdkhss/v1/__init__.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

11

2020-07-06T07:56:54.000Z

2022-01-11T11:14:40.000Z

huaweicloud-sdk-hss/huaweicloudsdkhss/v1/__init__.py

huaweicloud/huaweicloud-sdk-python-v3

7a6270390fcbf192b3882bf763e7016e6026ef78

[ "Apache-2.0" ]

24

2020-06-08T11:42:13.000Z

2022-03-04T06:44:08.000Z

# coding: utf-8 from __future__ import absolute_import # import HssClient from huaweicloudsdkhss.v1.hss_client import HssClient from huaweicloudsdkhss.v1.hss_async_client import HssAsyncClient # import models into sdk package from huaweicloudsdkhss.v1.model.event import Event from huaweicloudsdkhss.v1.model.host import Host from huaweicloudsdkhss.v1.model.list_events_request import ListEventsRequest from huaweicloudsdkhss.v1.model.list_events_response import ListEventsResponse from huaweicloudsdkhss.v1.model.list_hosts_request import ListHostsRequest from huaweicloudsdkhss.v1.model.list_hosts_response import ListHostsResponse

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1

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1

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1

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6

83ff378862e436cd81a9cb109dc624c080d2dba5

240

py

Python

simpleflow/signal.py

nstott/simpleflow

483602deb745a09b59ad6e24052dd5096c54fad2

[ "MIT" ]

null

simpleflow/signal.py

nstott/simpleflow

483602deb745a09b59ad6e24052dd5096c54fad2

[ "MIT" ]

null

simpleflow/signal.py

nstott/simpleflow

483602deb745a09b59ad6e24052dd5096c54fad2

[ "MIT" ]

null

from .base import Submittable class WaitForSignal(Submittable): """ Mark the executor must wait on a signal. """ def __init__(self, signal_name): self.signal_name = signal_name def execute(self): pass

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6

f7d9dc4a91a0901900f652e46c96b3ad07877a12

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py

Python

wntr/utils/__init__.py

yejustme/WNTR

4228853c84217392b57e99c486e878ddf7959bbd

[ "BSD-3-Clause" ]

null

wntr/utils/__init__.py

yejustme/WNTR

4228853c84217392b57e99c486e878ddf7959bbd

[ "BSD-3-Clause" ]

null

wntr/utils/__init__.py

yejustme/WNTR

4228853c84217392b57e99c486e878ddf7959bbd

[ "BSD-3-Clause" ]

null

""" The wntr.utils package contains helper functions. """ from wntr.utils import logger

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py

Python

db_model/post-comment.py

UtkarshR8j/evolv-challenge

81469c2eab27db140e2c7a369885b7e3b1584b77

[ "MIT" ]

null

db_model/post-comment.py

UtkarshR8j/evolv-challenge

81469c2eab27db140e2c7a369885b7e3b1584b77

[ "MIT" ]

null

db_model/post-comment.py

UtkarshR8j/evolv-challenge

81469c2eab27db140e2c7a369885b7e3b1584b77

[ "MIT" ]

null

from . import crud_db

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6

791f0980ae88ddd85f910b412e3f7940e8eced9e

164

py

Python

src/Entity.py

andreuesteras/sim-port

a7d8bbe04abff890f10353a83e40e16d6db64415

[ "Apache-1.1" ]

null

src/Entity.py

andreuesteras/sim-port

a7d8bbe04abff890f10353a83e40e16d6db64415

[ "Apache-1.1" ]

null

src/Entity.py

andreuesteras/sim-port

a7d8bbe04abff890f10353a83e40e16d6db64415

[ "Apache-1.1" ]

null

class Entity: def __init__(self, operationType): self.operationType = operationType def getOperationType(self): return self.operationType

20.5

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792a963a36ad0c981a1dc7d6232e2053616230af

117

py

Python

linkcheckerjs/checker/__init__.py

LeResKP/linkcheckerjs

64a0f9da47781f324bd647546a273160ff4516fa

[ "MIT" ]

null

linkcheckerjs/checker/__init__.py

LeResKP/linkcheckerjs

64a0f9da47781f324bd647546a273160ff4516fa

[ "MIT" ]

null

linkcheckerjs/checker/__init__.py

LeResKP/linkcheckerjs

64a0f9da47781f324bd647546a273160ff4516fa

[ "MIT" ]

null

from .phantomjs import phantomjs_checker from .requestspy import requests_checker from .utils import standardize_url

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f74741c069ee1b6a4ae1b7ff6d986f2ce19bf3a7

4,568

py

Python

level3/prepare_the_bunnies_escape/solution.py

lcsm29/goog-foobar

6ea44879d9d9f3483fa320d92d6c25b14565c899

[ "MIT" ]

null

level3/prepare_the_bunnies_escape/solution.py

lcsm29/goog-foobar

6ea44879d9d9f3483fa320d92d6c25b14565c899

[ "MIT" ]

null

level3/prepare_the_bunnies_escape/solution.py

lcsm29/goog-foobar

6ea44879d9d9f3483fa320d92d6c25b14565c899

[ "MIT" ]

null

def count_step(m, w, h): m = [[i for i in l] for l in m] next_pos = [(0, 0)] while next_pos: x, y = next_pos.pop(0) for i, j in ((-1, 0), (1, 0), (0, -1), (0, 1)): x_, y_ = x + i, y + j if 0 <= x_ < w and 0 <= y_ < h: if not m[y_][x_]: m[y_][x_] = m[y][x] + 1 next_pos.append((x_, y_)) step = m[-1][-1] return step + 1 if step else float('inf') def solution(m): w, h = len(m[0]), len(m) shortest_possible = w + h - 1 if count_step(m, w, h) == shortest_possible: return shortest_possible shortest = float('inf') for x, y in [(x, y) for x in range(w) for y in range(h) if m[y][x]]: tmp = [[i for i in l] for l in m] tmp[y][x] = 0 result = count_step(tmp, w, h) shortest = min(shortest, result) if result == shortest_possible: break return shortest if __name__ == '__main__': from time import perf_counter_ns basic_tests = ( ([ [0, 1, 1, 0], [0, 0, 0, 1], [1, 1, 0, 0], [1, 1, 1, 0]], 7), ([ [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]], 11) ) additional_tests = ( ([ [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]], 11), ([ [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]], 21), ([ [0, 0, 0, 1, 1, 0], [0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 0], [0, 1, 0, 0, 1, 0], [1, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 0]], 13), ([ [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]], float('inf')), ([ [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0]], 19), ([ [0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1], [0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1], [1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1], [0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1], [0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1], [0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1], [0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1], [0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1], [1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1], [0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0]], 53), ) results = {} num_iters = 1 for func in [func for func in dir() if func.startswith('solution')]: results[func] = [] print(f'\n{func}() (Number of Iterations {num_iters:,})') for test in basic_tests + additional_tests: matrix, expected = test start = perf_counter_ns() for i in range(num_iters): result = globals()[func](matrix) end = perf_counter_ns() results[func].append(end - start) print(f'{func}("{matrix}") returned {result} ' f'({"correct" if result == expected else f"expected: {expected}"})' f' in {end - start:,} nanoseconds.')

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6

f78a52d66eb4d8744e7efa1d98de0a71abdadb72

5,242

py

Python

model.py

NarendraPatwardhan/gym_venv

9c7456cc64d416556f1d1d8eca7a72df0821cf00

[ "MIT" ]

null

model.py

NarendraPatwardhan/gym_venv

9c7456cc64d416556f1d1d8eca7a72df0821cf00

[ "MIT" ]

null

model.py

NarendraPatwardhan/gym_venv

9c7456cc64d416556f1d1d8eca7a72df0821cf00

[ "MIT" ]

null

import numpy as np import mxnet as mx import matplotlib.pyplot as plt #----------------------------------------------------------------------------- class StateModel(mx.gluon.Block): def __init__(self,config): super(StateModel, self).__init__() self.config = config x = mx.nd.array(self.config['S0A']) y = mx.nd.array(self.config['S1']) self.dataset = mx.gluon.data.dataset.ArrayDataset(x,y) self.dataloader = mx.gluon.data.DataLoader(self.dataset,batch_size=self.config['batch_size']) with self.name_scope(): self.state_transition = mx.gluon.nn.Sequential('state_transition_') with self.state_transition.name_scope(): self.state_transition.add(mx.gluon.nn.Dense(10, activation='relu')) self.state_transition.add(mx.gluon.nn.Dense(20, activation='relu')) self.state_transition.add(mx.gluon.nn.Dense(10, activation='relu')) self.state_transition.add(mx.gluon.nn.Dense(self.config['S1'].shape[1])) def forward(self, x): return self.state_transition(x) def fit(self): self.collect_params().initialize(mx.init.Xavier(), ctx=mx.cpu()) criterion = mx.gluon.loss.HuberLoss() optimizer = mx.gluon.Trainer(self.collect_params(), 'adam',{'learning_rate': self.config['learning_rate'],'wd': self.config['weight_decay']}) errors = [] for epoch in range(self.config['max_epochs']): running_loss = 0.0 n_total = 0.0 for data in self.dataloader: x, y = data with mx.autograd.record(): output = self.forward(x) loss = criterion(output, y) loss.backward() optimizer.step(self.config['batch_size']) running_loss += mx.nd.sum(loss).asscalar() n_total += x.shape[0] errors.append(running_loss / n_total) if epoch%self.config['verbosity']==0: print('epoch [{}/{}], loss:{:.4f}' .format(epoch + 1, self.config['max_epochs'], running_loss / n_total)) fig,ax = plt.subplots() ax.plot(range(len(errors)),np.array(errors)) ax.set_title('State Modelling') ax.set_ylabel('Huber Loss') ax.set_xlabel('Epoch') fig.savefig('state_modelling') #----------------------------------------------------------------------------- class RewardModel(mx.gluon.Block): def __init__(self,config): super(RewardModel, self).__init__() self.config = config x = mx.nd.array(self.config['S0AS1']) y = mx.nd.array(self.config['R']) self.dataset = mx.gluon.data.dataset.ArrayDataset(x,y) self.dataloader = mx.gluon.data.DataLoader(self.dataset,batch_size=self.config['batch_size']) with self.name_scope(): self.reward_function = mx.gluon.nn.Sequential('reward_function_') with self.reward_function.name_scope(): self.reward_function.add(mx.gluon.nn.Dense(10, activation='relu')) self.reward_function.add(mx.gluon.nn.Dense(20, activation='relu')) self.reward_function.add(mx.gluon.nn.Dense(10, activation='relu')) self.reward_function.add(mx.gluon.nn.Dense(1)) def forward(self, x): return self.reward_function(x) def fit(self): self.collect_params().initialize(mx.init.Xavier(), ctx=mx.cpu()) criterion = mx.gluon.loss.HuberLoss() optimizer = mx.gluon.Trainer(self.collect_params(), 'adam',{'learning_rate': self.config['learning_rate'],'wd': self.config['weight_decay']}) errors = [] for epoch in range(self.config['max_epochs']): running_loss = 0.0 n_total = 0.0 for data in self.dataloader: x, y = data with mx.autograd.record(): output = self.forward(x) loss = criterion(output, y) loss.backward() optimizer.step(self.config['batch_size']) running_loss += mx.nd.sum(loss).asscalar() n_total += x.shape[0] errors.append(running_loss / n_total) if epoch%self.config['verbosity']==0: print('epoch [{}/{}], loss:{:.4f}' .format(epoch + 1, self.config['max_epochs'], running_loss / n_total)) fig,ax = plt.subplots() ax.plot(range(len(errors)),np.array(errors)) ax.set_title('Reward Modelling') ax.set_ylabel('Huber Loss') ax.set_xlabel('Epoch') fig.savefig('reward_modelling') #----------------------------------------------------------------------------- if __name__ == '__main__': x = np.random.randn(100,4) xt = np.random.randn(100,4) y = x[:,:3] yt = xt[:,:3] random_config = { 'max_epochs': 5000, 'batch_size': 64, 'learning_rate': 1e-3, 'weight_decay': 1e-5, 'verbosity': 25, 'S0A': x, 'S1': y } random_sm = StateModel(random_config) random_sm.fit() yp = random_sm(mx.nd.array(xt)) print(abs(yp.asnumpy() - yt).sum())

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6

f79e8c283908eeb9288ef98cbffba38f1005acfc

34

py

Python

pga/basemodel/__init__.py

pgallen90/basemodel

ab453c43b121b393055929c6c0218b50c6c73fa2

[ "MIT" ]

2

2022-01-26T04:02:29.000Z

2022-02-05T23:29:02.000Z

pga/basemodel/__init__.py

pgallen90/basemodel

ab453c43b121b393055929c6c0218b50c6c73fa2

[ "MIT" ]

null

pga/basemodel/__init__.py

pgallen90/basemodel

ab453c43b121b393055929c6c0218b50c6c73fa2

[ "MIT" ]

null

from .mixin import BaseModelMixin

17

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1

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0

1

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1

0

1

0

6

e398027bfc38424c31b972070d2ac945cd629998

218

py

Python

bots.sample/serial-entrepreneur/__init__.py

0xdc/botfriend

6157a873c4158ccfdda4bf021059bddf14217654

[ "MIT" ]

39

2017-06-19T16:12:34.000Z

2022-03-02T10:06:29.000Z

bots.sample/serial-entrepreneur/__init__.py

0xdc/botfriend

6157a873c4158ccfdda4bf021059bddf14217654

[ "MIT" ]

null

bots.sample/serial-entrepreneur/__init__.py

0xdc/botfriend

6157a873c4158ccfdda4bf021059bddf14217654

[ "MIT" ]

5

2018-08-27T19:49:56.000Z

2020-10-22T02:31:04.000Z

from .entrepreneur import Announcements from botfriend.bot import TextGeneratorBot class EntrepreneurBot(TextGeneratorBot): def generate_text(self): return Announcements().choice() Bot = EntrepreneurBot

21.8

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6

e3b0bc290719636377964132df80d1f15f8509de

37

py

Python

kora/s/losses.py

wannaphong/kora

8a9034097d07b14094e077769c02a0b4857d179b

[ "MIT" ]

91

2020-05-26T05:54:51.000Z

2022-03-09T07:33:44.000Z

kora/s/losses.py

wannaphong/kora

8a9034097d07b14094e077769c02a0b4857d179b

[ "MIT" ]

12

2020-10-03T10:09:11.000Z

2021-03-06T23:12:21.000Z

kora/s/losses.py

wannaphong/kora

8a9034097d07b14094e077769c02a0b4857d179b

[ "MIT" ]

16

2020-07-07T18:39:29.000Z

2021-03-06T03:46:49.000Z

from tensorflow.keras.losses import *

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0

6

541a413a249a493df5734542608918483e11faf3

30

py

Python

cursesqr/tools/__init__.py

Ruunyox/CursesQR

cd3be10eaae77f20eca765fc77c946a284712ad8

[ "MIT" ]

5

2020-11-01T17:19:25.000Z

2020-11-05T20:28:21.000Z

cursesqr/tools/__init__.py

Ruunyox/CursesQR

cd3be10eaae77f20eca765fc77c946a284712ad8

[ "MIT" ]

null

cursesqr/tools/__init__.py

Ruunyox/CursesQR

cd3be10eaae77f20eca765fc77c946a284712ad8

[ "MIT" ]

null

from .cursesqr_tools import *

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