xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

fbe703dc7ce7b89f04d8685a855de0529eaad5bb

10,409

py

Python

cms/tests/test_security.py

devyntk/django-cms

f889a30e94f268394ae9abf32c032239d0a9be55

[ "BSD-3-Clause" ]

5,659

2015-01-01T02:42:30.000Z

2020-10-07T02:38:29.000Z

cms/tests/test_security.py

devyntk/django-cms

f889a30e94f268394ae9abf32c032239d0a9be55

[ "BSD-3-Clause" ]

3,264

2015-01-02T10:11:48.000Z

2020-10-08T13:15:07.000Z

cms/tests/test_security.py

devyntk/django-cms

f889a30e94f268394ae9abf32c032239d0a9be55

[ "BSD-3-Clause" ]

2,132

2015-01-01T11:28:21.000Z

2020-10-06T09:09:11.000Z

from django.conf import settings from django.contrib.auth import get_user_model from django.http import QueryDict from cms.api import add_plugin, create_page from cms.models.pluginmodel import CMSPlugin from cms.test_utils.project.placeholderapp.models import Example1 from cms.test_utils.testcases import CMSTestCase class SecurityTests(CMSTestCase): """ Test security issues by trying some naive requests to add/alter/delete data. """ def get_data(self): page = create_page("page", "nav_playground.html", "en") placeholder = page.placeholders.get(slot='body') superuser = self.get_superuser() staff = self.get_staff_user_with_no_permissions() return page, placeholder, superuser, staff def test_add(self): """ Test adding a plugin to a *PAGE*. """ page, placeholder, superuser, staff = self.get_data() post_data = {} self.assertEqual(CMSPlugin.objects.count(), 0) # log the user out and post the plugin data to the cms add-plugin URL. self.client.logout() endpoint = self.get_add_plugin_uri( placeholder, 'TextPlugin', settings.LANGUAGES[0][0], ) response = self.client.post(endpoint, post_data) # since the user is not logged in, they should be prompted to log in. self.assertEqual(response.status_code, 302) querystring = QueryDict('', mutable=True) querystring['next'] = endpoint expected_url = '/{lang}/admin/login/?{next}'.format( lang=settings.LANGUAGES[0][0], next=querystring.urlencode(safe='/') ) self.assertRedirects(response, expected_url) self.assertEqual(CMSPlugin.objects.count(), 0) # now log a staff user without permissions in and do the same as above. self.client.login(username=getattr(staff, get_user_model().USERNAME_FIELD), password=getattr(staff, get_user_model().USERNAME_FIELD)) response = self.client.post(endpoint, post_data) # the user is logged in and the security check fails, so it should 403. self.assertEqual(response.status_code, 403) self.assertEqual(CMSPlugin.objects.count(), 0) def test_edit(self): """ Test editing a *PAGE* plugin """ page, placeholder, superuser, staff = self.get_data() # create the plugin using a superuser plugin = add_plugin(placeholder, 'TextPlugin', 'en', body='body') plugin_data = { 'plugin_id': plugin.pk, 'body': 'newbody', } self.assertEqual(plugin.body, 'body') # check the body is as expected. # log the user out, try to edit the plugin self.client.logout() endpoint = self.get_change_plugin_uri(plugin) response = self.client.post(endpoint, plugin_data) # since the user is not logged in, they should be prompted to log in. self.assertEqual(response.status_code, 302) querystring = QueryDict('', mutable=True) querystring['next'] = endpoint expected_url = '/{lang}/admin/login/?{next}'.format( lang=settings.LANGUAGES[0][0], next=querystring.urlencode(safe='/') ) self.assertRedirects(response, expected_url) plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') # now log a staff user without permissions in and do the same as above. self.client.login(username=getattr(staff, get_user_model().USERNAME_FIELD), password=getattr(staff, get_user_model().USERNAME_FIELD)) response = self.client.post(endpoint, plugin_data) # the user is logged in and the security check fails, so it should 403. self.assertEqual(response.status_code, 403) plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') def test_delete(self): """ Test deleting a *PAGE* plugin """ page, placeholder, superuser, staff = self.get_data() plugin = add_plugin(placeholder, 'TextPlugin', 'en', body='body') plugin_data = { 'plugin_id': plugin.pk, } plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') # log the user out, try to remove the plugin self.client.logout() endpoint = self.get_delete_plugin_uri(plugin) response = self.client.post(endpoint, plugin_data) # since the user is not logged in, they should be prompted to log in. self.assertEqual(response.status_code, 302) querystring = QueryDict('', mutable=True) querystring['next'] = endpoint expected_url = '/{lang}/admin/login/?{next}'.format( lang=settings.LANGUAGES[0][0], next=querystring.urlencode(safe='/') ) self.assertRedirects(response, expected_url) self.assertEqual(CMSPlugin.objects.count(), 1) plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') # now log a staff user without permissions in and do the same as above. self.client.login(username=getattr(staff, get_user_model().USERNAME_FIELD), password=getattr(staff, get_user_model().USERNAME_FIELD)) response = self.client.post(endpoint, plugin_data) # the user is logged in and the security check fails, so it should 403. self.assertEqual(response.status_code, 403) self.assertEqual(CMSPlugin.objects.count(), 1) plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') def test_add_ph(self): """ Test adding a *NON PAGE* plugin """ page, placeholder, superuser, staff = self.get_data() post_data = {} endpoint = self.get_add_plugin_uri(placeholder, 'TextPlugin', settings.LANGUAGES[0][0]) self.assertEqual(CMSPlugin.objects.count(), 0) # log the user out and try to add a plugin using PlaceholderAdmin self.client.logout() response = self.client.post(endpoint, post_data) # since the user is not logged in, they should be prompted to log in. self.assertEqual(response.status_code, 302) querystring = QueryDict('', mutable=True) querystring['next'] = endpoint expected_url = '/{lang}/admin/login/?{next}'.format( lang=settings.LANGUAGES[0][0], next=querystring.urlencode(safe='/') ) self.assertRedirects(response, expected_url) self.assertEqual(CMSPlugin.objects.count(), 0) # now log a staff user without permissions in and do the same as above. self.client.login(username=getattr(staff, get_user_model().USERNAME_FIELD), password=getattr(staff, get_user_model().USERNAME_FIELD)) response = self.client.post(endpoint, post_data) # the user is logged in and the security check fails, so it should 403. self.assertEqual(response.status_code, 403) self.assertEqual(CMSPlugin.objects.count(), 0) def test_edit_ph(self): """ Test editing a *NON PAGE* plugin """ page, placeholder, superuser, staff = self.get_data() plugin = add_plugin(placeholder, 'TextPlugin', 'en', body='body') endpoint = self.get_change_plugin_uri(plugin, container=Example1) plugin_data = { 'body': 'newbody', 'language': 'en', 'plugin_id': plugin.pk, } plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') # log the user out and try to edit a plugin using PlaceholderAdmin self.client.logout() response = self.client.post(endpoint, plugin_data) # since the user is not logged in, they should be prompted to log in. self.assertEqual(response.status_code, 302) querystring = QueryDict('', mutable=True) querystring['next'] = endpoint expected_url = '/{lang}/admin/login/?{next}'.format( lang=settings.LANGUAGES[0][0], next=querystring.urlencode(safe='/') ) self.assertRedirects(response, expected_url) plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') # now log a staff user without permissions in and do the same as above. self.client.login(username=getattr(staff, get_user_model().USERNAME_FIELD), password=getattr(staff, get_user_model().USERNAME_FIELD)) response = self.client.post(endpoint, plugin_data) # the user is logged in and the security check fails, so it should 403. self.assertEqual(response.status_code, 403) plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') def test_delete_ph(self): page, placeholder, superuser, staff = self.get_data() plugin = add_plugin(placeholder, 'TextPlugin', 'en', body='body') plugin_data = { 'plugin_id': plugin.pk, } plugin = self.reload(plugin) self.assertEqual(plugin.body, 'body') endpoint = self.get_delete_plugin_uri(plugin, container=Example1) # log the user out and try to remove a plugin using PlaceholderAdmin self.client.logout() response = self.client.post(endpoint, plugin_data) # since the user is not logged in, they should be prompted to log in. self.assertEqual(response.status_code, 302) querystring = QueryDict('', mutable=True) querystring['next'] = endpoint expected_url = '/{lang}/admin/login/?{next}'.format( lang=settings.LANGUAGES[0][0], next=querystring.urlencode(safe='/') ) self.assertRedirects(response, expected_url) self.assertEqual(CMSPlugin.objects.count(), 1) # now log a staff user without permissions in and do the same as above. self.client.login(username=getattr(staff, get_user_model().USERNAME_FIELD), password=getattr(staff, get_user_model().USERNAME_FIELD)) response = self.client.post(endpoint, plugin_data) # the user is logged in and the security check fails, so it should 403. self.assertEqual(response.status_code, 403) self.assertEqual(CMSPlugin.objects.count(), 1)

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7

2209f87e0d421461e944ecc57c3f2fae32d256af

7,250

py

Python

model-optimizer/unit_tests/extensions/front/onnx/quantize_linear_resolver_test.py

ledmonster/openvino

c1b1e2e7afc698ac82b32bb1f502ad2e90cd1419

[ "Apache-2.0" ]

null

model-optimizer/unit_tests/extensions/front/onnx/quantize_linear_resolver_test.py

ledmonster/openvino

c1b1e2e7afc698ac82b32bb1f502ad2e90cd1419

[ "Apache-2.0" ]

26

2021-01-18T16:21:41.000Z

2022-02-21T13:04:24.000Z

model-optimizer/unit_tests/extensions/front/onnx/quantize_linear_resolver_test.py

ngaloppo/openvino

7aad8827a585e2e08c5fd872bb17e40072718661

[ "Apache-2.0" ]

1

2021-08-18T14:29:37.000Z

# Copyright (C) 2018-2021 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import unittest import numpy as np from extensions.front.onnx.quantize_linear_resolver import QuantizeLinearResolver from mo.utils.ir_engine.compare_graphs import compare_graphs from unit_tests.utils.graph import build_graph nodes1_attributes = { 'input': {'kind': 'op', 'op': 'AnyOp'}, 'quantize': {'kind': 'op', 'op': 'QuantizeLinear'}, 'scale_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, 'zerop_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, 'out': {'kind': 'op', 'op': 'AnyOp'}, } nodes_ref_attributes = { 'input': {'kind': 'op', 'op': 'AnyOp'}, 'cast': {'kind': 'op', 'op': 'Cast', 'type': 'Convert'}, 'f_quantize': {'kind': 'op', 'op': 'FakeQuantize', 'type': 'FakeQuantize'}, 'mul1': {'kind': 'op', 'op': 'Mul', 'type': 'Multiply'}, 'mul2': {'kind': 'op', 'op': 'Mul', 'type': 'Multiply'}, 'scale_param_q': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, 'in_low': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, 'in_high': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, 'out_low': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, 'out_high': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, 'out': {'kind': 'op', 'op': 'AnyOp'}, } class TestQuantizeLinearResolver(unittest.TestCase): def test_quantize_uint8(self): graph = build_graph(nodes1_attributes, [('input', 'quantize'), ('scale_param_q', 'quantize'), ('zerop_param_q', 'quantize'), ('quantize', 'out'), ], {'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)}, 'zerop_param_q': {'shape': np.array([]), 'value': np.uint8(128)}, }, nodes_with_edges_only=True) graph_ref = build_graph(nodes_ref_attributes, [('input', 'f_quantize'), ('scale_param_q', 'mul1', {'out': 0}), ('in_low', 'mul1'), ('mul1', 'f_quantize'), ('scale_param_q', 'mul2', {'out': 0}), ('in_high', 'mul2'), ('mul2', 'f_quantize'), ('out_low', 'f_quantize'), ('out_high', 'f_quantize'), ('f_quantize', 'cast'), ('cast', 'out'), ], {'in_low': {'shape': np.array([]), 'value': -128}, 'in_high': {'shape': np.array([]), 'value': 127}, 'out_low': {'shape': np.array([]), 'value': 0}, 'out_high': {'shape': np.array([]), 'value': 255}, 'cast': {'dst_type': np.uint8} }, nodes_with_edges_only=True) graph.stage = 'front' QuantizeLinearResolver().find_and_replace_pattern(graph) (flag, resp) = compare_graphs(graph, graph_ref, 'out', check_op_attrs=True) self.assertTrue(flag, resp) def test_quantize_int8(self): graph = build_graph(nodes1_attributes, [('input', 'quantize'), ('scale_param_q', 'quantize'), ('zerop_param_q', 'quantize'), ('quantize', 'out'), ], {'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)}, 'zerop_param_q': {'shape': np.array([]), 'value': np.int8(0)}, }, nodes_with_edges_only=True) graph_ref = build_graph(nodes_ref_attributes, [('input', 'f_quantize'), ('scale_param_q', 'mul1', {'out': 0}), ('in_low', 'mul1'), ('mul1', 'f_quantize'), ('scale_param_q', 'mul2', {'out': 0}), ('in_high', 'mul2'), ('mul2', 'f_quantize'), ('out_low', 'f_quantize'), ('out_high', 'f_quantize'), ('f_quantize', 'cast'), ('cast', 'out'), ], {'in_low': {'shape': np.array([]), 'value': -128}, 'in_high': {'shape': np.array([]), 'value': 127}, 'out_low': {'shape': np.array([]), 'value': -128}, 'out_high': {'shape': np.array([]), 'value': 127}, 'cast': {'dst_type': np.int8} }, nodes_with_edges_only=True) graph.stage = 'front' QuantizeLinearResolver().find_and_replace_pattern(graph) (flag, resp) = compare_graphs(graph, graph_ref, 'out', check_op_attrs=True) self.assertTrue(flag, resp) def test_quantize_no_zerop(self): graph = build_graph(nodes1_attributes, [('input', 'quantize'), ('scale_param_q', 'quantize'), ('quantize', 'out'), ], {'scale_param_q': {'shape': np.array([]), 'value': np.float32(1.0 / 255)}, }, nodes_with_edges_only=True) graph_ref = build_graph(nodes_ref_attributes, [('input', 'f_quantize'), ('scale_param_q', 'mul1', {'out': 0}), ('in_low', 'mul1'), ('mul1', 'f_quantize'), ('scale_param_q', 'mul2', {'out': 0}), ('in_high', 'mul2'), ('mul2', 'f_quantize'), ('out_low', 'f_quantize'), ('out_high', 'f_quantize'), ('f_quantize', 'cast'), ('cast', 'out'), ], {'in_low': {'shape': np.array([]), 'value': 0}, 'in_high': {'shape': np.array([]), 'value': 255}, 'out_low': {'shape': np.array([]), 'value': 0}, 'out_high': {'shape': np.array([]), 'value': 255}, 'cast': {'dst_type': np.uint8} }, nodes_with_edges_only=True) graph.stage = 'front' QuantizeLinearResolver().find_and_replace_pattern(graph) (flag, resp) = compare_graphs(graph, graph_ref, 'out', check_op_attrs=True) self.assertTrue(flag, resp)

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1

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0

7

97e7f81531270b3e8c1e2b13604fd65beea47e55

186

py

Python

views/teams.py

Nicolasopf/Metrevs

5625e84332a527a35e70f0ac8f35facacbfc2277

[ "MIT" ]

1

2021-07-06T17:49:39.000Z

views/teams.py

Nicolasopf/Metrevs

5625e84332a527a35e70f0ac8f35facacbfc2277

[ "MIT" ]

null

views/teams.py

Nicolasopf/Metrevs

5625e84332a527a35e70f0ac8f35facacbfc2277

[ "MIT" ]

null

#!/usr/bin/python3 ''' View for /teams show teams data processed ''' from views import app_views @app_views.route('/teams') def show_teams(): ''' Show teams... ''' return 'hi'

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7

3f4180388e8f7b019a5a435bd41d3325344f562c

1,949

py

Python

test/test_meshio.py

keurfonluu/fteikpy

39e50b1772642b9c5cdda4d1b8171f6d5abaa5e2

[ "BSD-3-Clause" ]

15

2020-11-11T21:42:46.000Z

2022-03-20T10:21:47.000Z

test/test_meshio.py

keurfonluu/fteikpy

39e50b1772642b9c5cdda4d1b8171f6d5abaa5e2

[ "BSD-3-Clause" ]

10

2020-11-12T08:39:25.000Z

2022-03-30T10:56:51.000Z

test/test_meshio.py

keurfonluu/fteikpy

39e50b1772642b9c5cdda4d1b8171f6d5abaa5e2

[ "BSD-3-Clause" ]

5

2020-12-23T01:19:27.000Z

2022-02-24T14:16:56.000Z

import numpy from fteikpy import Eikonal2D, Eikonal3D, grid_to_meshio, ray_to_meshio def test_meshio_2d(): nz, nx = 8, 10 eik = Eikonal2D(numpy.ones((nz, nx)), (1.0, 1.0)) tt = eik.solve((float(nz // 2), float(nx // 2)), return_gradient=True) ray = tt.raytrace((0.0, 0.0), honor_grid=True) mesh = grid_to_meshio(eik, tt) mesh_ray = ray_to_meshio(ray) npts = (nz + 1) * (nx + 1) assert len(mesh.points) == npts assert sum(len(cell) for cell in mesh.cells) == nz * nx assert mesh.point_data["Traveltime"][npts // 2] == 0.0 assert numpy.allclose(mesh.point_data["Traveltime"].sum(), 378.23469225) for grad in mesh.point_data["Gradient"].T: assert grad[npts // 2] == 0.0 assert numpy.allclose(grad.sum(), 0.0) assert mesh.cell_data["Velocity"][0].sum() == nz * nx assert len(mesh_ray.points) == len(ray) assert len(mesh_ray.cells[0][1]) == len(ray) - 1 assert mesh_ray.cells[0][1].sum() == 64.0 def test_meshio_3d(): nz, nx, ny = 8, 10, 12 eik = Eikonal3D(numpy.ones((nz, nx, ny)), (1.0, 1.0, 1.0)) tt = eik.solve( (float(nz // 2), float(nx // 2), float(ny // 2)), return_gradient=True ) ray = tt.raytrace((0.0, 0.0, 0.0), honor_grid=True) mesh = grid_to_meshio(eik, tt) mesh_ray = ray_to_meshio(ray) npts = (nz + 1) * (nx + 1) * (ny + 1) assert len(mesh.points) == npts assert sum(len(cell) for cell in mesh.cells) == nz * nx * ny assert mesh.point_data["Traveltime"][npts // 2] == 0.0 assert numpy.allclose(mesh.point_data["Traveltime"].sum(), 6909.90160991) for grad in mesh.point_data["Gradient"].T: assert grad[npts // 2] == 0.0 assert numpy.allclose(grad.sum(), 0.0) assert mesh.cell_data["Velocity"][0].sum() == nz * nx * ny assert len(mesh_ray.points) == len(ray) assert len(mesh_ray.cells[0][1]) == len(ray) - 1 assert mesh_ray.cells[0][1].sum() == 169.0

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58d912b021566eccf850b54e82e71b68b8918cb8

14,141

py

Python

odoo-13.0/addons/sale_stock/tests/test_sale_stock_lead_time.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

12

2021-03-26T08:39:40.000Z

2022-03-16T02:20:10.000Z

odoo-13.0/addons/sale_stock/tests/test_sale_stock_lead_time.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

13

2020-12-20T16:00:21.000Z

2022-03-14T14:55:30.000Z

odoo-13.0/addons/sale_stock/tests/test_sale_stock_lead_time.py

VaibhavBhujade/Blockchain-ERP-interoperability

b5190a037fb6615386f7cbad024d51b0abd4ba03

[ "MIT" ]

17

2020-08-31T11:18:49.000Z

2022-02-09T05:57:31.000Z

# -*- coding: utf-8 -*- # Part of Odoo. See LICENSE file for full copyright and licensing details. from datetime import timedelta from odoo import fields from odoo.addons.stock.tests.common2 import TestStockCommon class TestSaleStockLeadTime(TestStockCommon): def setUp(self): super(TestSaleStockLeadTime, self).setUp() # Update the product_1 with type and Customer Lead Time self.product_1.write({'type': 'product', 'sale_delay': 5.0}) def test_00_product_company_level_delays(self): """ In order to check schedule date, set product's Customer Lead Time and company's Sales Safety Days.""" company = self.env.ref('base.main_company') # Update company with Sales Safety Days company.write({'security_lead': 3.00}) # Create sale order of product_1 order = self.env['sale.order'].create({ 'partner_id': self.partner_1.id, 'partner_invoice_id': self.partner_1.id, 'partner_shipping_id': self.partner_1.id, 'pricelist_id': self.env.ref('product.list0').id, 'picking_policy': 'direct', 'warehouse_id': self.warehouse_1.id, 'order_line': [(0, 0, {'name': self.product_1.name, 'product_id': self.product_1.id, 'product_uom_qty': 10, 'product_uom': self.uom_unit.id, 'customer_lead': self.product_1.sale_delay})]}) # Confirm our standard sale order order.action_confirm() # Check the picking crated or not self.assertTrue(order.picking_ids, "Picking should be created.") # Check schedule date of picking out_date = fields.Datetime.from_string(order.date_order) + timedelta(days=self.product_1.sale_delay) - timedelta(days=company.security_lead) min_date = fields.Datetime.from_string(order.picking_ids[0].scheduled_date) self.assertTrue(abs(min_date - out_date) <= timedelta(seconds=1), 'Schedule date of picking should be equal to: order date + Customer Lead Time - Sales Safety Days.') def test_01_product_route_level_delays(self): """ In order to check schedule dates, set product's Customer Lead Time and warehouse route's delay.""" # Update warehouse_1 with Outgoing Shippings pick + pack + ship self.warehouse_1.write({'delivery_steps': 'pick_pack_ship'}) # Set delay on pull rule for pull_rule in self.warehouse_1.delivery_route_id.rule_ids: pull_rule.write({'delay': 2}) # Create sale order of product_1 order = self.env['sale.order'].create({ 'partner_id': self.partner_1.id, 'partner_invoice_id': self.partner_1.id, 'partner_shipping_id': self.partner_1.id, 'pricelist_id': self.env.ref('product.list0').id, 'picking_policy': 'direct', 'warehouse_id': self.warehouse_1.id, 'order_line': [(0, 0, {'name': self.product_1.name, 'product_id': self.product_1.id, 'product_uom_qty': 5, 'product_uom': self.uom_unit.id, 'customer_lead': self.product_1.sale_delay})]}) # Confirm our standard sale order order.action_confirm() # Check the picking crated or not self.assertTrue(order.picking_ids, "Pickings should be created.") # Check schedule date of ship type picking out = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.out_type_id) out_min_date = fields.Datetime.from_string(out.scheduled_date) out_date = fields.Datetime.from_string(order.date_order) + timedelta(days=self.product_1.sale_delay) - timedelta(days=out.move_lines[0].rule_id.delay) self.assertTrue(abs(out_min_date - out_date) <= timedelta(seconds=1), 'Schedule date of ship type picking should be equal to: order date + Customer Lead Time - pull rule delay.') # Check schedule date of pack type picking pack = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.pack_type_id) pack_min_date = fields.Datetime.from_string(pack.scheduled_date) pack_date = out_date - timedelta(days=pack.move_lines[0].rule_id.delay) self.assertTrue(abs(pack_min_date - pack_date) <= timedelta(seconds=1), 'Schedule date of pack type picking should be equal to: Schedule date of ship type picking - pull rule delay.') # Check schedule date of pick type picking pick = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.pick_type_id) pick_min_date = fields.Datetime.from_string(pick.scheduled_date) pick_date = pack_date - timedelta(days=pick.move_lines[0].rule_id.delay) self.assertTrue(abs(pick_min_date - pick_date) <= timedelta(seconds=1), 'Schedule date of pick type picking should be equal to: Schedule date of pack type picking - pull rule delay.') def test_02_if_propagate_date(self): """ In order to check schedule dates, set product's Customer Lead Time and warehouse route's delay with propagate True in stock rules""" #Example : #-> set 'propagate_date' = True in stock rules #-> set propagate_date_minimum_delta = 5 days #-> Set Warehouse with Outgoing Shipments : pick + pack + ship #-> Set delay and propagate_date_minimum_delta on stock rules : 5 days and propagate_date = True #-> Set Customer Lead Time on product : 30 days #-> Create an SO and confirm it with confirmation Date : 12/18/2018 #-> Pickings : OUT -> Scheduled Date : 01/12/2019 # PACK -> Scheduled Date : 01/07/2019 # PICK -> Scheduled Date : 01/02/2019 #-> Now, change date of pick = +5 days #-> Scheduled Date should be changed: # OUT -> Scheduled Date : 01/17/2019 # PACK -> Scheduled Date : 01/12/2019 # PICK -> Scheduled Date : 01/07/2019 # set the propagate_date and # set propagate_date_minimum_delta = 5 in the stock rule # Update warehouse_1 with Outgoing Shippings pick + pack + ship self.warehouse_1.write({'delivery_steps': 'pick_pack_ship'}) # Set delay on pull rule self.warehouse_1.delivery_route_id.rule_ids.write({'delay': 5, 'propagate_date': True, 'propagate_date_minimum_delta': 5}) # Update the product_1 with type and Customer Lead Time self.product_1.write({'type': 'product', 'sale_delay': 30.0}) # Now, create sale order of product_1 with customer_lead set on product order = self.env['sale.order'].create({ 'partner_id': self.partner_1.id, 'partner_invoice_id': self.partner_1.id, 'partner_shipping_id': self.partner_1.id, 'pricelist_id': self.env.ref('product.list0').id, 'picking_policy': 'direct', 'warehouse_id': self.warehouse_1.id, 'order_line': [(0, 0, {'name': self.product_1.name, 'product_id': self.product_1.id, 'product_uom_qty': 5, 'product_uom': self.uom_unit.id, 'customer_lead': self.product_1.sale_delay})]}) # Confirm our standard sale order order.action_confirm() # Check the picking crated or not self.assertTrue(order.picking_ids, "Pickings should be created.") # Check schedule date of ship type picking out = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.out_type_id) out_min_date = out.scheduled_date out_date = order.date_order + timedelta(days=self.product_1.sale_delay) - timedelta(days=out.move_lines[0].rule_id.delay) self.assertTrue(abs(out_min_date - out_date) <= timedelta(seconds=1), 'Schedule date of ship type picking should be equal to: order date + Customer Lead Time - pull rule delay.') # Check schedule date of pack type picking pack = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.pack_type_id) pack_min_date = pack.scheduled_date pack_date = out_date - timedelta(days=pack.move_lines[0].rule_id.delay) self.assertTrue(abs(pack_min_date - pack_date) <= timedelta(seconds=1), 'Schedule date of pack type picking should be equal to: Schedule date of ship type picking - pull rule delay.') # Check schedule date of pick type picking pick = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.pick_type_id) pick_min_date = pick.scheduled_date pick_date = pack_date - timedelta(days=pick.move_lines[0].rule_id.delay) self.assertTrue(abs(pick_min_date - pick_date) <= timedelta(seconds=1), 'Schedule date of pick type picking should be equal to: Schedule date of pack type picking - pull rule delay.') # Now change the schedule date of pick # Note : pack and out has change scheduled_date automatically based on delay set on pick pick.write({'scheduled_date': pick_min_date + timedelta(days=5)}) # Now check scheduled_date of pack and out are changed or not based on propagate is true on rules? self.assertEquals(pack.scheduled_date, (pack_min_date + timedelta(days=5)), 'Schedule date of pack should be changed based on delay.') self.assertEquals(out.scheduled_date, (out_min_date + timedelta(days=5)), 'Schedule date of out should be changed based on delay.') def test_03_no_propagate_date(self): """ In order to check schedule dates, set product's Customer Lead Time and warehouse route's delay with propagate False in stock rule""" #Example : #-> Set Warehouse with Outgoing Shipments : pick + pack + ship #-> Set delay on stock rules : 5 days and propagate = False #-> Set Customer Lead Time on product : 30 days #-> Create an SO and confirm it with confirmation Date : 12/18/2018 #-> Pickings : OUT -> Scheduled Date : 01/12/2019 # PACK -> Scheduled Date : 01/07/2019 # PICK -> Scheduled Date : 01/02/2019 #-> Now, change date of pick = +5 days #-> Scheduled Date should be not changed: # OUT -> Scheduled Date : 01/12/2019 # PACK -> Scheduled Date : 01/07/2019 # PICK -> Scheduled Date : 01/07/2019 # Update warehouse_1 with Outgoing Shippings pick + pack + ship self.warehouse_1.write({'delivery_steps': 'pick_pack_ship'}) # Set delay on pull rule for pull_rule in self.warehouse_1.delivery_route_id.rule_ids: pull_rule.write({'delay': 5, 'propagate_date': False}) # Update the product_1 with type and Customer Lead Time self.product_1.write({'type': 'product', 'sale_delay': 30.0}) #Create sale order of product_1 order = self.env['sale.order'].create({ 'partner_id': self.partner_1.id, 'partner_invoice_id': self.partner_1.id, 'partner_shipping_id': self.partner_1.id, 'pricelist_id': self.env.ref('product.list0').id, 'picking_policy': 'direct', 'warehouse_id': self.warehouse_1.id, 'order_line': [(0, 0, {'name': self.product_1.name, 'product_id': self.product_1.id, 'product_uom_qty': 5, 'product_uom': self.uom_unit.id, 'customer_lead': self.product_1.sale_delay})]}) # Confirm our standard sale order order.action_confirm() # Check the picking crated or not self.assertTrue(order.picking_ids, "Pickings should be created.") # Check schedule date of ship type picking out = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.out_type_id) out_min_date = out.scheduled_date out_date = order.date_order + timedelta(days=self.product_1.sale_delay) - timedelta(days=out.move_lines[0].rule_id.delay) self.assertTrue(abs(out_min_date - out_date) <= timedelta(seconds=1), 'Schedule date of ship type picking should be equal to: order date + Customer Lead Time - pull rule delay.') # Check schedule date of pack type picking pack = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.pack_type_id) pack_min_date = pack.scheduled_date pack_date = out_date - timedelta(days=pack.move_lines[0].rule_id.delay) self.assertTrue(abs(pack_min_date - pack_date) <= timedelta(seconds=1), 'Schedule date of pack type picking should be equal to: Schedule date of ship type picking - pull rule delay.') # Check schedule date of pick type picking pick = order.picking_ids.filtered(lambda r: r.picking_type_id == self.warehouse_1.pick_type_id) pick_min_date = pick.scheduled_date pick_date = pack_date - timedelta(days=pick.move_lines[0].rule_id.delay) self.assertTrue(abs(pick_min_date - pick_date) <= timedelta(seconds=1), 'Schedule date of pick type picking should be equal to: Schedule date of pack type picking - pull rule delay.') # Now change the schedule date of pick pick.write({'scheduled_date': pick_min_date + timedelta(days=5)}) # Now check scheduled_date of pack and out are changed or not based on propagate is false on rules? self.assertEquals(pack.scheduled_date, pack_min_date, 'Schedule date of pack should not be changed.') self.assertEquals(out.scheduled_date, out_min_date, 'Schedule date of out should not be changed.')

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Python

boa3_test/tests/compiler_tests/test_native/test_cryptolib.py

OnBlockIO/neo3-boa

cb317292a67532a52ed26f2b0f0f7d0b10ac5f5f

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3_test/tests/compiler_tests/test_native/test_cryptolib.py

OnBlockIO/neo3-boa

cb317292a67532a52ed26f2b0f0f7d0b10ac5f5f

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3_test/tests/compiler_tests/test_native/test_cryptolib.py

OnBlockIO/neo3-boa

cb317292a67532a52ed26f2b0f0f7d0b10ac5f5f

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

import hashlib from boa3 import constants from boa3.boa3 import Boa3 from boa3.exception import CompilerError from boa3.model.builtin.interop.interop import Interop from boa3.model.type.type import Type from boa3.neo.vm.opcode.Opcode import Opcode from boa3.neo.vm.type.Integer import Integer from boa3.neo.vm.type.String import String from boa3.neo3.contracts.contracttypes import CallFlags from boa3.neo3.contracts.namedcurve import NamedCurve from boa3_test.tests.boa_test import BoaTest from boa3_test.tests.test_classes.testengine import TestEngine class TestCryptoLibClass(BoaTest): default_folder: str = 'test_sc/native_test/cryptolib' ecpoint_init = ( Opcode.CONVERT + Type.bytes.stack_item + Opcode.DUP + Opcode.ISNULL + Opcode.JMPIF + Integer(8).to_byte_array() + Opcode.DUP + Opcode.SIZE + Opcode.PUSHINT8 + Integer(33).to_byte_array(signed=True) + Opcode.JMPEQ + Integer(3).to_byte_array() + Opcode.THROW ) def test_ripemd160_str(self): path = self.get_contract_path('Ripemd160Str.py') engine = TestEngine() expected_result = hashlib.new('ripemd160', b'unit test') result = self.run_smart_contract(engine, path, 'Main', 'unit test') self.assertEqual(expected_result.digest(), result) expected_result = hashlib.new('ripemd160', b'') result = self.run_smart_contract(engine, path, 'Main', '') self.assertEqual(expected_result.digest(), result) def test_ripemd160_int(self): path = self.get_contract_path('Ripemd160Int.py') engine = TestEngine() expected_result = hashlib.new('ripemd160', Integer(10).to_byte_array()) result = self.run_smart_contract(engine, path, 'Main') self.assertEqual(expected_result.digest(), result) def test_ripemd160_bool(self): path = self.get_contract_path('Ripemd160Bool.py') engine = TestEngine() expected_result = hashlib.new('ripemd160', Integer(1).to_byte_array()) result = self.run_smart_contract(engine, path, 'Main') self.assertEqual(expected_result.digest(), result) def test_ripemd160_bytes(self): path = self.get_contract_path('Ripemd160Bytes.py') engine = TestEngine() expected_result = hashlib.new('ripemd160', b'unit test') result = self.run_smart_contract(engine, path, 'Main') self.assertEqual(expected_result.digest(), result) def test_ripemd160_too_many_parameters(self): path = self.get_contract_path('Ripemd160TooManyArguments.py') self.assertCompilerLogs(CompilerError.UnexpectedArgument, path) def test_ripemd160_too_few_parameters(self): path = self.get_contract_path('Ripemd160TooFewArguments.py') self.assertCompilerLogs(CompilerError.UnfilledArgument, path) def test_sha256_str(self): path = self.get_contract_path('Sha256Str.py') engine = TestEngine() expected_result = hashlib.sha256(b'unit test') result = self.run_smart_contract(engine, path, 'Main', 'unit test') self.assertEqual(expected_result.digest(), result) expected_result = hashlib.sha256(b'') result = self.run_smart_contract(engine, path, 'Main', '') self.assertEqual(expected_result.digest(), result) def test_sha256_int(self): path = self.get_contract_path('Sha256Int.py') engine = TestEngine() expected_result = hashlib.sha256(Integer(10).to_byte_array()) result = self.run_smart_contract(engine, path, 'Main') self.assertEqual(expected_result.digest(), result) def test_sha256_bool(self): path = self.get_contract_path('Sha256Bool.py') engine = TestEngine() expected_result = hashlib.sha256(Integer(1).to_byte_array()) result = self.run_smart_contract(engine, path, 'Main') self.assertEqual(expected_result.digest(), result) def test_sha256_bytes(self): path = self.get_contract_path('Sha256Bytes.py') engine = TestEngine() expected_result = hashlib.sha256(b'unit test') result = self.run_smart_contract(engine, path, 'Main') self.assertEqual(expected_result.digest(), result) def test_sha256_too_many_parameters(self): path = self.get_contract_path('Sha256TooManyArguments.py') self.assertCompilerLogs(CompilerError.UnexpectedArgument, path) def test_sha256_too_few_parameters(self): path = self.get_contract_path('Sha256TooFewArguments.py') self.assertCompilerLogs(CompilerError.UnfilledArgument, path) def test_verify_with_ecdsa(self): path = self.get_contract_path('VerifyWithECDsa.py') Boa3.compile(path) def test_verify_with_ecdsa_secp256r1_str(self): byte_input1 = b'0123456789ABCDEFGHIJKLMNOPQRSTUVW' byte_input2 = b'signature' string = b'unit test' named_curve = Integer(NamedCurve.SECP256R1).to_byte_array(signed=True, min_length=1) function_id = String(Interop.VerifyWithECDsa._sys_call).to_bytes() call_flag = Integer(CallFlags.ALL).to_byte_array(signed=True, min_length=1) expected_output = ( Opcode.PUSHDATA1 + Integer(len(named_curve)).to_byte_array(min_length=1) + named_curve + Opcode.CONVERT + Type.int.stack_item + Opcode.PUSHDATA1 + Integer(len(byte_input2)).to_byte_array(min_length=1) + byte_input2 + Opcode.PUSHDATA1 + Integer(len(byte_input1)).to_byte_array(min_length=1) + byte_input1 + self.ecpoint_init + Opcode.PUSHDATA1 + Integer(len(string)).to_byte_array(min_length=1) + string + Opcode.PUSH4 + Opcode.PACK + Opcode.PUSHDATA1 + Integer(len(call_flag)).to_byte_array(min_length=1) + call_flag + Opcode.PUSHDATA1 + Integer(len(function_id)).to_byte_array() + function_id + Opcode.PUSHDATA1 + Integer(len(constants.CRYPTO_SCRIPT)).to_byte_array() + constants.CRYPTO_SCRIPT + Opcode.SYSCALL + Interop.CallContract.interop_method_hash + Opcode.DROP + Opcode.RET ) path = self.get_contract_path('VerifyWithECDsaSecp256r1Str.py') output = Boa3.compile(path) self.assertEqual(expected_output, output) def test_verify_with_ecdsa_secp256r1_bool(self): byte_input1 = b'0123456789ABCDEFGHIJKLMNOPQRSTUVW' byte_input2 = b'signature' named_curve = Integer(NamedCurve.SECP256R1).to_byte_array(signed=True, min_length=1) function_id = String(Interop.VerifyWithECDsa._sys_call).to_bytes() call_flag = Integer(CallFlags.ALL).to_byte_array(signed=True, min_length=1) expected_output = ( Opcode.PUSHDATA1 + Integer(len(named_curve)).to_byte_array(min_length=1) + named_curve + Opcode.CONVERT + Type.int.stack_item + Opcode.PUSHDATA1 + Integer(len(byte_input2)).to_byte_array(min_length=1) + byte_input2 + Opcode.PUSHDATA1 + Integer(len(byte_input1)).to_byte_array(min_length=1) + byte_input1 + self.ecpoint_init + Opcode.PUSH0 + Opcode.PUSH4 + Opcode.PACK + Opcode.PUSHDATA1 + Integer(len(call_flag)).to_byte_array(min_length=1) + call_flag + Opcode.PUSHDATA1 + Integer(len(function_id)).to_byte_array() + function_id + Opcode.PUSHDATA1 + Integer(len(constants.CRYPTO_SCRIPT)).to_byte_array() + constants.CRYPTO_SCRIPT + Opcode.SYSCALL + Interop.CallContract.interop_method_hash + Opcode.DROP + Opcode.RET ) path = self.get_contract_path('VerifyWithECDsaSecp256r1Bool.py') output = Boa3.compile(path) self.assertEqual(expected_output, output) def test_verify_with_ecdsa_secp256r1_int(self): byte_input1 = b'0123456789ABCDEFGHIJKLMNOPQRSTUVW' byte_input2 = b'signature' named_curve = Integer(NamedCurve.SECP256R1).to_byte_array(signed=True, min_length=1) function_id = String(Interop.VerifyWithECDsa._sys_call).to_bytes() call_flag = Integer(CallFlags.ALL).to_byte_array(signed=True, min_length=1) expected_output = ( Opcode.PUSHDATA1 + Integer(len(named_curve)).to_byte_array(min_length=1) + named_curve + Opcode.CONVERT + Type.int.stack_item + Opcode.PUSHDATA1 + Integer(len(byte_input2)).to_byte_array(min_length=1) + byte_input2 + Opcode.PUSHDATA1 + Integer(len(byte_input1)).to_byte_array(min_length=1) + byte_input1 + self.ecpoint_init + Opcode.PUSH10 + Opcode.PUSH4 + Opcode.PACK + Opcode.PUSHDATA1 + Integer(len(call_flag)).to_byte_array(min_length=1) + call_flag + Opcode.PUSHDATA1 + Integer(len(function_id)).to_byte_array() + function_id + Opcode.PUSHDATA1 + Integer(len(constants.CRYPTO_SCRIPT)).to_byte_array() + constants.CRYPTO_SCRIPT + Opcode.SYSCALL + Interop.CallContract.interop_method_hash + Opcode.DROP + Opcode.RET ) path = self.get_contract_path('VerifyWithECDsaSecp256r1Int.py') output = Boa3.compile(path) self.assertEqual(expected_output, output) def test_verify_with_ecdsa_secp256r1_bytes(self): byte_input1 = b'0123456789ABCDEFGHIJKLMNOPQRSTUVW' byte_input2 = b'signature' string = b'unit test' named_curve = Integer(NamedCurve.SECP256R1).to_byte_array(signed=True, min_length=1) function_id = String(Interop.VerifyWithECDsa._sys_call).to_bytes() call_flag = Integer(CallFlags.ALL).to_byte_array(signed=True, min_length=1) expected_output = ( Opcode.PUSHDATA1 + Integer(len(named_curve)).to_byte_array(min_length=1) + named_curve + Opcode.CONVERT + Type.int.stack_item + Opcode.PUSHDATA1 + Integer(len(byte_input2)).to_byte_array(min_length=1) + byte_input2 + Opcode.PUSHDATA1 + Integer(len(byte_input1)).to_byte_array(min_length=1) + byte_input1 + self.ecpoint_init + Opcode.PUSHDATA1 + Integer(len(string)).to_byte_array(min_length=1) + string + Opcode.PUSH4 + Opcode.PACK + Opcode.PUSHDATA1 + Integer(len(call_flag)).to_byte_array(min_length=1) + call_flag + Opcode.PUSHDATA1 + Integer(len(function_id)).to_byte_array() + function_id + Opcode.PUSHDATA1 + Integer(len(constants.CRYPTO_SCRIPT)).to_byte_array() + constants.CRYPTO_SCRIPT + Opcode.SYSCALL + Interop.CallContract.interop_method_hash + Opcode.DROP + Opcode.RET ) path = self.get_contract_path('VerifyWithECDsaSecp256r1Bytes.py') output = Boa3.compile(path) self.assertEqual(expected_output, output) def test_verify_with_ecdsa_secp256r1_mismatched_type(self): path = self.get_contract_path('VerifyWithECDsaSecp256r1MismatchedType.py') self.assertCompilerLogs(CompilerError.MismatchedTypes, path) def test_verify_with_ecdsa_secp256k1_str(self): byte_input1 = b'0123456789ABCDEFGHIJKLMNOPQRSTUVW' byte_input2 = b'signature' string = b'unit test' named_curve = Integer(NamedCurve.SECP256K1).to_byte_array(signed=True, min_length=1) function_id = String(Interop.VerifyWithECDsa._sys_call).to_bytes() call_flag = Integer(CallFlags.ALL).to_byte_array(signed=True, min_length=1) expected_output = ( Opcode.PUSHDATA1 + Integer(len(named_curve)).to_byte_array(min_length=1) + named_curve + Opcode.CONVERT + Type.int.stack_item + Opcode.PUSHDATA1 + Integer(len(byte_input2)).to_byte_array(min_length=1) + byte_input2 + Opcode.PUSHDATA1 + Integer(len(byte_input1)).to_byte_array(min_length=1) + byte_input1 + self.ecpoint_init + Opcode.PUSHDATA1 + Integer(len(string)).to_byte_array(min_length=1) + string + Opcode.PUSH4 + Opcode.PACK + Opcode.PUSHDATA1 + Integer(len(call_flag)).to_byte_array() + call_flag + Opcode.PUSHDATA1 + Integer(len(function_id)).to_byte_array() + function_id + Opcode.PUSHDATA1 + Integer(len(constants.CRYPTO_SCRIPT)).to_byte_array() + constants.CRYPTO_SCRIPT + Opcode.SYSCALL + Interop.CallContract.interop_method_hash + Opcode.DROP + Opcode.RET ) path = self.get_contract_path('VerifyWithECDsaSecp256k1Str.py') output = Boa3.compile(path) self.assertEqual(expected_output, output) def test_verify_with_ecdsa_secp256k1_bool(self): byte_input1 = b'0123456789ABCDEFGHIJKLMNOPQRSTUVW' byte_input2 = b'signature' named_curve = Integer(NamedCurve.SECP256K1).to_byte_array(signed=True, min_length=1) function_id = String(Interop.VerifyWithECDsa._sys_call).to_bytes() call_flag = Integer(CallFlags.ALL).to_byte_array(signed=True, min_length=1) expected_output = ( Opcode.PUSHDATA1 + Integer(len(named_curve)).to_byte_array(min_length=1) + named_curve + Opcode.CONVERT + Type.int.stack_item + Opcode.PUSHDATA1 + Integer(len(byte_input2)).to_byte_array(min_length=1) + byte_input2 + Opcode.PUSHDATA1 + Integer(len(byte_input1)).to_byte_array(min_length=1) + byte_input1 + self.ecpoint_init + Opcode.PUSH0 + Opcode.PUSH4 + Opcode.PACK + Opcode.PUSHDATA1 + Integer(len(call_flag)).to_byte_array() + call_flag + Opcode.PUSHDATA1 + Integer(len(function_id)).to_byte_array() + function_id + Opcode.PUSHDATA1 + Integer(len(constants.CRYPTO_SCRIPT)).to_byte_array() + constants.CRYPTO_SCRIPT + Opcode.SYSCALL + Interop.CallContract.interop_method_hash + Opcode.DROP + Opcode.RET ) path = self.get_contract_path('VerifyWithECDsaSecp256k1Bool.py') output = Boa3.compile(path) self.assertEqual(expected_output, output) def test_verify_with_ecdsa_secp256k1_int(self): byte_input1 = b'0123456789ABCDEFGHIJKLMNOPQRSTUVW' byte_input2 = b'signature' named_curve = Integer(NamedCurve.SECP256K1).to_byte_array(signed=True, min_length=1) function_id = String(Interop.VerifyWithECDsa._sys_call).to_bytes() call_flag = Integer(CallFlags.ALL).to_byte_array(signed=True, min_length=1) expected_output = ( Opcode.PUSHDATA1 + Integer(len(named_curve)).to_byte_array(min_length=1) + named_curve + Opcode.CONVERT + Type.int.stack_item + Opcode.PUSHDATA1 + Integer(len(byte_input2)).to_byte_array(min_length=1) + byte_input2 + Opcode.PUSHDATA1 + Integer(len(byte_input1)).to_byte_array(min_length=1) + byte_input1 + self.ecpoint_init + Opcode.PUSH10 + Opcode.PUSH4 + Opcode.PACK + Opcode.PUSHDATA1 + Integer(len(call_flag)).to_byte_array() + call_flag + Opcode.PUSHDATA1 + Integer(len(function_id)).to_byte_array() + function_id + Opcode.PUSHDATA1 + Integer(len(constants.CRYPTO_SCRIPT)).to_byte_array() + constants.CRYPTO_SCRIPT + Opcode.SYSCALL + Interop.CallContract.interop_method_hash + Opcode.DROP + Opcode.RET ) path = self.get_contract_path('VerifyWithECDsaSecp256k1Int.py') output = Boa3.compile(path) self.assertEqual(expected_output, output) def test_verify_with_ecdsa_secp256k1_bytes(self): byte_input1 = b'0123456789ABCDEFGHIJKLMNOPQRSTUVW' byte_input2 = b'signature' string = b'unit test' named_curve = Integer(NamedCurve.SECP256K1).to_byte_array(signed=True, min_length=1) function_id = String(Interop.VerifyWithECDsa._sys_call).to_bytes() call_flag = Integer(CallFlags.ALL).to_byte_array(signed=True, min_length=1) expected_output = ( Opcode.PUSHDATA1 + Integer(len(named_curve)).to_byte_array(min_length=1) + named_curve + Opcode.CONVERT + Type.int.stack_item + Opcode.PUSHDATA1 + Integer(len(byte_input2)).to_byte_array(min_length=1) + byte_input2 + Opcode.PUSHDATA1 + Integer(len(byte_input1)).to_byte_array(min_length=1) + byte_input1 + self.ecpoint_init + Opcode.PUSHDATA1 + Integer(len(string)).to_byte_array(min_length=1) + string + Opcode.PUSH4 + Opcode.PACK + Opcode.PUSHDATA1 + Integer(len(call_flag)).to_byte_array() + call_flag + Opcode.PUSHDATA1 + Integer(len(function_id)).to_byte_array() + function_id + Opcode.PUSHDATA1 + Integer(len(constants.CRYPTO_SCRIPT)).to_byte_array() + constants.CRYPTO_SCRIPT + Opcode.SYSCALL + Interop.CallContract.interop_method_hash + Opcode.DROP + Opcode.RET ) path = self.get_contract_path('VerifyWithECDsaSecp256k1Bytes.py') output = Boa3.compile(path) self.assertEqual(expected_output, output) def test_verify_with_ecdsa_secp256k1_mismatched_type(self): path = self.get_contract_path('VerifyWithECDsaSecp256k1MismatchedType.py') self.assertCompilerLogs(CompilerError.MismatchedTypes, path)

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7

4512724738ffab84e8982d9c40bc09c650e381b5

7,519

py

Python

google/cloud/irm_v1alpha2/gapic/incident_service_client_config.py

renovate-bot/python-irm

9006000797ef55bcba14e8cb156e4430645a2c9e

[ "Apache-2.0" ]

2

2021-06-04T06:16:05.000Z

2021-10-07T21:29:26.000Z

google/cloud/irm_v1alpha2/gapic/incident_service_client_config.py

renovate-bot/python-irm

9006000797ef55bcba14e8cb156e4430645a2c9e

[ "Apache-2.0" ]

40

2019-07-16T10:04:48.000Z

2020-01-20T09:04:59.000Z

google/cloud/irm_v1alpha2/gapic/incident_service_client_config.py

renovate-bot/python-irm

9006000797ef55bcba14e8cb156e4430645a2c9e

[ "Apache-2.0" ]

4

2020-02-08T13:52:01.000Z

2020-11-03T11:02:29.000Z

config = { "interfaces": { "google.cloud.irm.v1alpha2.IncidentService": { "retry_codes": { "idempotent": ["DEADLINE_EXCEEDED", "UNAVAILABLE"], "non_idempotent": [], }, "retry_params": { "default": { "initial_retry_delay_millis": 100, "retry_delay_multiplier": 1.3, "max_retry_delay_millis": 60000, "initial_rpc_timeout_millis": 20000, "rpc_timeout_multiplier": 1.0, "max_rpc_timeout_millis": 20000, "total_timeout_millis": 600000, } }, "methods": { "DeleteArtifact": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "RequestIncidentRoleHandover": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ConfirmIncidentRoleHandover": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ForceIncidentRoleHandover": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "CreateIncident": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "GetIncident": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "SearchIncidents": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "UpdateIncident": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "SearchSimilarIncidents": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "CreateAnnotation": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ListAnnotations": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "CreateTag": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "DeleteTag": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ListTags": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "CreateSignal": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "SearchSignals": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "LookupSignal": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "GetSignal": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "UpdateSignal": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "EscalateIncident": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "CreateArtifact": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ListArtifacts": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "UpdateArtifact": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "SendShiftHandoff": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "CreateSubscription": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "UpdateSubscription": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ListSubscriptions": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "DeleteSubscription": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "CreateIncidentRoleAssignment": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "DeleteIncidentRoleAssignment": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, "ListIncidentRoleAssignments": { "timeout_millis": 60000, "retry_codes_name": "idempotent", "retry_params_name": "default", }, "CancelIncidentRoleHandover": { "timeout_millis": 60000, "retry_codes_name": "non_idempotent", "retry_params_name": "default", }, }, } } }

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452db20c733b0825950ef1c7a37f9b5e22b73b56

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py

Python

Prediction_Utils/Classification/__init__.py

xinyuwang1209/Prediction_Utils

a6ff6ec74c8fbdfe4013c760da361ad5b7447651

[ "MIT" ]

null

Prediction_Utils/Classification/__init__.py

xinyuwang1209/Prediction_Utils

a6ff6ec74c8fbdfe4013c760da361ad5b7447651

[ "MIT" ]

null

Prediction_Utils/Classification/__init__.py

xinyuwang1209/Prediction_Utils

a6ff6ec74c8fbdfe4013c760da361ad5b7447651

[ "MIT" ]

null

from ._use_lasso import * from ._use_svc import * from ._use_rf import *

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Python

models.py

jbcdnr/deit-collaborative-attention

07905829be28eac1277cbc0255796feeab589bfc

[ "Apache-2.0" ]

null

models.py

jbcdnr/deit-collaborative-attention

07905829be28eac1277cbc0255796feeab589bfc

[ "Apache-2.0" ]

null

models.py

jbcdnr/deit-collaborative-attention

07905829be28eac1277cbc0255796feeab589bfc

[ "Apache-2.0" ]

null

# Copyright (c) 2015-present, Facebook, Inc. # All rights reserved. from mmap import MAP_PRIVATE import torch import torch.nn as nn from functools import partial import pathlib from timm.models.vision_transformer import VisionTransformer, _cfg from timm.models.registry import register_model import collaborate_attention @register_model def deit_tiny_patch16_224(pretrained=False, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() if pretrained: checkpoint = torch.hub.load_state_dict_from_url( url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth", map_location="cpu", check_hash=True, ) model.load_state_dict(checkpoint["model"]) return model @register_model def deit_tiny_colab_patch16_224(pretrained=False, all_key_dim=None, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() if pretrained: checkpoint = torch.hub.load_state_dict_from_url( url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth", map_location="cpu", check_hash=True, ) model.load_state_dict(checkpoint["model"]) model.cuda() collaborate_attention.swap(model, all_key_dim) model.cpu() return model @register_model def deit_small_patch16_224(pretrained=False, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() if pretrained: checkpoint = torch.hub.load_state_dict_from_url( url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth", map_location="cpu", check_hash=True, ) model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base_patch16_224(pretrained=False, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() if pretrained: checkpoint = torch.hub.load_state_dict_from_url( url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth", map_location="cpu", check_hash=True, ) model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base_patch16_224_collab384(pretrained=False, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() collaborate_attention.swap(model, compressed_key_dim=384, reparametrize=False) return model @register_model def deit_base_patch16_224_collab256(pretrained=False, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() collaborate_attention.swap(model, compressed_key_dim=256, reparametrize=False) return model @register_model def deit_base3_patch16_224(pretrained=False, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, depth=3, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() assert not pretrained return model # ========== REDUCED KEY DIMENSION CONCATENATE ATTENTION MODELS ========== # @register_model def deit_base3_patch16_224_key384(pretrained=False, **kwargs): import timm.models.vision_transformer from collaborate_attention import FlexibleKeyDimensionAttention timm.models.vision_transformer.Attention = partial(FlexibleKeyDimensionAttention, all_key_dim=384) model = VisionTransformer( patch_size=16, embed_dim=768, depth=3, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() assert not pretrained return model @register_model def deit_base3_patch16_224_key192(pretrained=False, **kwargs): import timm.models.vision_transformer from collaborate_attention import FlexibleKeyDimensionAttention timm.models.vision_transformer.Attention = partial(FlexibleKeyDimensionAttention, all_key_dim=192) model = VisionTransformer( patch_size=16, embed_dim=768, depth=3, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() assert not pretrained return model @register_model def deit_base3_patch16_224_key96(pretrained=False, **kwargs): import timm.models.vision_transformer from collaborate_attention import FlexibleKeyDimensionAttention timm.models.vision_transformer.Attention = partial(FlexibleKeyDimensionAttention, all_key_dim=96) model = VisionTransformer( patch_size=16, embed_dim=768, depth=3, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) model.default_cfg = _cfg() assert not pretrained return model # ========== COLLABORATIVE ATTENTION MODELS ========== # # ========== BASE 3 LAYERS ========== # @register_model def deit_base3_patch16_224_collab384(pretrained=False, models_directory=None, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, depth=3, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) collaborate_attention.swap(model, compressed_key_dim=384, reparametrize=False) model.default_cfg = _cfg() if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base3_patch16_224_collab384.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base3_patch16_224_collab192(pretrained=False, models_directory=None, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, depth=3, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) collaborate_attention.swap(model, compressed_key_dim=192, reparametrize=False) model.default_cfg = _cfg() if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base3_patch16_224_collab192.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base3_patch16_224_collab96(pretrained=False, models_directory=None, **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, depth=3, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs, ) collaborate_attention.swap(model, compressed_key_dim=96, reparametrize=False) model.default_cfg = _cfg() if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base3_patch16_224_collab96.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model # ========== BASE ========== # @register_model def deit_base_patch16_224_collab64(pretrained=False, models_directory="./models", **kwargs): model = deit_base_patch16_224(pretrained=False) collaborate_attention.swap(model, compressed_key_dim=64, reparametrize=False) if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base_patch16_224_collab64.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base_patch16_224_collab128(pretrained=False, models_directory="./models", **kwargs): model = deit_base_patch16_224(pretrained=False) collaborate_attention.swap(model, compressed_key_dim=128, reparametrize=False) if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base_patch16_224_collab128.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base_patch16_224_collab256(pretrained=False, models_directory="./models", **kwargs): model = deit_base_patch16_224(pretrained=False) collaborate_attention.swap(model, compressed_key_dim=256, reparametrize=False) if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base_patch16_224_collab256.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base_patch16_224_collab384(pretrained=False, models_directory="./models", **kwargs): model = deit_base_patch16_224(pretrained=False) collaborate_attention.swap(model, compressed_key_dim=384, reparametrize=False) if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base_patch16_224_collab384.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base_patch16_224_collab512(pretrained=False, models_directory="./models", **kwargs): model = deit_base_patch16_224(pretrained=False) collaborate_attention.swap(model, compressed_key_dim=512, reparametrize=False) if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base_patch16_224_collab512.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model @register_model def deit_base_patch16_224_collab768(pretrained=False, models_directory="./models", **kwargs): model = deit_base_patch16_224(pretrained=False) collaborate_attention.swap(model, compressed_key_dim=768, reparametrize=False) if pretrained: checkpoint_path = pathlib.Path(models_directory) / "deit_base_patch16_224_collab768.pth" print(f"Load model from '{checkpoint_path}'") checkpoint = torch.load(checkpoint_path, map_location="cpu") model.load_state_dict(checkpoint["model"]) return model

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7a0aea4fbfd72ec9a93f66e9e4c50718760b2334

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py

Python

schema_registry/serializers/__init__.py

abiodunjames/python-schema-registry-client

a8fb9e2aca0bf827b71b6ac31ac7cef76ff2059b

[ "MIT" ]

95

2019-05-20T06:59:06.000Z

2022-03-01T05:30:57.000Z

schema_registry/serializers/__init__.py

abiodunjames/python-schema-registry-client

a8fb9e2aca0bf827b71b6ac31ac7cef76ff2059b

[ "MIT" ]

94

2019-05-19T18:36:29.000Z

2022-03-30T18:54:52.000Z

schema_registry/serializers/__init__.py

abiodunjames/python-schema-registry-client

a8fb9e2aca0bf827b71b6ac31ac7cef76ff2059b

[ "MIT" ]

41

2019-05-20T06:59:33.000Z

2022-03-06T16:09:53.000Z

from schema_registry.serializers.message_serializer import AvroMessageSerializer # noqa from schema_registry.serializers.message_serializer import JsonMessageSerializer # noqa from schema_registry.serializers.message_serializer import MessageSerializer # noqa from schema_registry.serializers.message_serializer import AsyncAvroMessageSerializer # noqa from schema_registry.serializers.message_serializer import AsyncJsonMessageSerializer # noqa from schema_registry.serializers.message_serializer import AsyncMessageSerializer # noqa

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py

Python

utils/util.py

scut-bds/exampe_repo_from_scutbds

6528eeb25d6da53dd4f7eb6b92d534631794aa80

[ "Apache-2.0" ]

null

utils/util.py

scut-bds/exampe_repo_from_scutbds

6528eeb25d6da53dd4f7eb6b92d534631794aa80

[ "Apache-2.0" ]

null

utils/util.py

scut-bds/exampe_repo_from_scutbds

6528eeb25d6da53dd4f7eb6b92d534631794aa80

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2021 South China University of Technology and # Engineering Research Ceter of Minstry of Education on Human Body Perception. # # 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. # File: util.py # Used for dataset loading # Author: Chen Yirong <eeyirongchen@mail.scut.edu.cn> # Date: 2021.08.30 import os import re import math import json import torch import shutil import collections import pandas as pd from os.path import join from chardet import detect from itertools import chain from torch.utils.data import Dataset from torch.nn.utils.rnn import pad_sequence from torch.utils.data import DataLoader # reference: [CDial-GPT/od/](https://github.com/thu-coai/CDial-GPT) SPECIAL_TOKENS = ["[CLS]", "[SEP]", "[speaker1]", "[speaker2]"] MODEL_INPUTS = ["input_ids", "lm_labels", "token_type_ids"] # input_ids: [CLS] [speaker1] 妈 [speaker2] 你不要叫我妈 [speaker2] 我不是你妈 [SEP] # token_type_ids: [CLS] [DA1 ] Emo1] [DA2 ] [Emo2 ] [DA3 ] [Emo3 ] # positioning_embedding: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # input_ids: [CLS] [speaker1] 妈 [speaker2] 你不要叫我妈 [speaker2] 我不是你妈 [SEP] # token_type_ids: [CLS] [speaker1] [speaker1] [speaker2] [speaker1 ] [speaker2] [speaker2 ] [speaker2] # Emotion_embedding: [CLS] [Emo1 ] [Emo2 ] [Emo3 ] # DA_embedding: [CLS] [DA1 ] [DA2 ] [DA3 ] # positioning_embedding: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # dialogue_text data_dir: /home/MMMTD/dialogue_text # dialogue_video data_dir: /home/MMMTD/dialogue_video # dialogue_audio data_dir: /home/MMMTD/dialogue_audio DA_TOKENS = ["[greeting]","[question]","[answer]","[statement-opinion]","[statement-non-opinion]","[apology]", "[command]","[agreement]","[disagreement]","[acknowledge]","[appreciation]","[interjection]", "[conventional-closing]","[quotation]","[reject]","[irony]","[comfort]","[thanking]","[da-other]"] # 19 DA labels SENTIMENT_TOKENS = ["[neutral]","[positive]","[negative]"] EMOTION_TOKENS = ["[happy]","[grateful]","[relaxed]","[positive-other]","[anger]","[sadness]","[fear]", "[depress]","[disgust]","[astonished]","[worried]","[negative-other]","[neutral]"] # 13 emotion labels BASEEMOTION_TOKENS = ["[happy]"] DA_TO_TOKENS = {'greeting': '[greeting]', 'question': '[question]', 'answer': '[answer]', 'statement-opinion': '[statement-opinion]', 'statement-non-opinion': '[statement-non-opinion]', 'apology': '[apology]', 'command': '[command]', 'agreement': '[agreement]', 'disagreement': '[disagreement]', 'acknowledge': '[acknowledge]', 'appreciation': '[appreciation]', 'interjection': '[interjection]', 'conventional-closing': '[conventional-closing]', 'quotation': '[quotation]', 'reject': '[reject]', 'irony': '[irony]', 'comfort': '[comfort]','thanking':'[thanking]', 'other': '[da-other]'} SENTIMENT_TO_TOKENS = {'neutral': '[neutral]', 'positive': '[positive]', 'negative': '[negative]'} EMOTION_TO_TOKENS = {'happy': '[happy]', 'grateful': '[grateful]', 'relaxed': '[relaxed]', 'positive-other': '[positive-other]', 'anger': '[anger]', 'sadness': '[sadness]', 'fear': '[fear]', 'depress': '[depress]', 'disgust': '[disgust]', 'astonished': '[astonished]', 'worried': '[worried]', 'negative-other': '[negative-other]', 'neutral': '[neutral]'} BASEEMOTION_TO_TOKENS = {"happy":'[happy]'} AGEGROUP_TO_TOKENS = {"young":"young","middle-aged":"middle-aged","elderly":"elderly","teenager":"teenager","children":"children","unknown":"unknown"} # for BERT ERC and DAC DA_TO_ID = {'greeting': 0, 'question': 1, 'answer': 2, 'statement-opinion': 3, 'statement-non-opinion': 4, 'apology': 5, 'command': 6, 'agreement': 7, 'disagreement': 8, 'acknowledge': 9, 'appreciation': 10, 'interjection': 11, 'conventional-closing': 12, 'quotation': 13, 'reject': 14, 'irony': 15, 'comfort': 16,'thanking':17, 'other': 18} EMOTION_TO_ID = {'happy': 0, 'grateful': 1, 'relaxed': 2, 'positive-other': 3, 'anger': 4, 'sadness': 5, 'fear': 6, 'depress': 7, 'disgust': 8, 'astonished': 9, 'worried': 10, 'negative-other': 11, 'neutral': 12} GENDER_TO_ID = {'female': 0, 'unknown': 1, 'male': 2} BIGFIVE_TO_ID = {'low': 0, 'unknown': 1, 'high': 2} def get_data(args, tokenizer, data_path, logger): '''get_data Get .csv format dataset from data_path. ''' logger.info("Read dataset from %s", data_path) data = pd.read_csv(data_path, usecols=["Dialogue_ID","Utterance_ID","Speaker","Sentiment","Emotion","DA","Utterance","Gender","Age","Neuroticism","Extraversion","Openness","Agreeableness","Conscientiousness"], encoding="UTF-8-SIG") # 有待增加一列base情感，利用"Emotion"列转换 def createbaseemotion(emotion): new_emotion = emotion return new_emotion # data["BaseEmotion"] = [createbaseemotion(s) for s in data["Emotion"]] samples = data.iloc[0:30] logger.info("Start tokenizing and encoding the dataset") def tokenize(utterance): utterance = str(utterance) # 保证为str类型 # 对于问句添加问号 utterance = utterance.replace("吗", "吗？") utterance = utterance.replace("？？", "？") # 对于感叹句添加感叹号 utterance = utterance.replace("啊", "啊！") utterance = utterance.replace("吧", "吧！") utterance = utterance.replace("啦", "啦！") utterance = utterance.replace("呀", "呀！") utterance = utterance.replace("！！", "！") # 对于句子中间非问句，非感叹句添加逗号 utterance = utterance.replace(" ", "，") # 去除重复标点符号 utterance = utterance.split() # 去除全部空格 utt_list = list(utterance) # "季杨杨，好像我听凡凡说过" --> ['季', '杨', '杨', '，', '好', '像', '我', '听', '凡', '凡', '说', '过'] utterance = ' '.join(utt_list) # ['季', '杨', '杨', '，', '好', '像', '我', '听', '凡', '凡', '说', '过']--> “季杨杨，好像我听凡凡说过” # <class 'str'> return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(utterance)) data["Token"] = [tokenize(s) for s in data["Utterance"]] logger.info("Finished tokenizing and encoding the dataset") return data, samples def convert_EMOTION_TO_TOKENS(emotion_list,emotion_type): emotion_tokens_list = [] if emotion_type=="Sentiment": # "Sentiment" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[UNK]") else: emotion_tokens_list.append(SENTIMENT_TO_TOKENS[emo]) elif emotion_type=="BaseEmotion": # "BaseEmotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[UNK]") else: emotion_tokens_list.append(BASEEMOTION_TO_TOKENS[emo]) else: # "Emotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[UNK]") else: emotion_tokens_list.append(EMOTION_TO_TOKENS[emo]) return emotion_tokens_list def convert_DA_TO_TOKENS(da_list): da_tokens_list = [] for da in da_list: da_tokens_list.append(DA_TO_TOKENS[da]) return da_tokens_list def create_speaker(speaker_list): speaker1 = speaker_list[0] new_speaker_list = [] for speaker in speaker_list: if speaker==speaker1: new_speaker_list.append("[speaker1]") else: new_speaker_list.append("[speaker2]") return new_speaker_list def set_da_in_speaker(da_ids,input_ids,bos, eos, pad, speaker1, speaker2): special_token_ids_list = [bos, eos, speaker1, speaker2] new_da_ids = [] for i,da in enumerate(da_ids): if input_ids[i] in special_token_ids_list: new_da_ids.append(da_ids[i]) else: new_da_ids.append(pad) return new_da_ids def set_emotion_in_speaker(emotion_ids,input_ids,bos, eos, pad, speaker1, speaker2): special_token_ids_list = [bos, eos, speaker1, speaker2] new_emotion_ids = [] for i,emotion in enumerate(emotion_ids): if input_ids[i] in special_token_ids_list: new_emotion_ids.append(emotion_ids[i]) else: new_emotion_ids.append(pad) return new_emotion_ids class MMMTDDataset(Dataset): ''' word_tokens: [CLS] [speaker1] 您好 [speaker2] 您好 [speaker1] 再见 [SEP] emotion_list: [[neutral], [neutral], [neutral]] da_list: [[greeting], [greeting], [greeting]] input_ids： [ 0, 13086, 448, 53, 13087, 448, 53, 13086, 154, 124, 2] if with_emotion==True: token_type_ids: [ 0, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102] # "[neutral]": 13102 elif with_da==True: token_type_ids: [ 0, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088] # "[greeting]": 13088 else: token_type_ids: [ 0, 13086, 13086, 13086, 13087, 13087, 13087, 13086, 13086, 13086, 13086] labels： ''' def __init__(self, data, tokenizer, emotion_type="Sentiment", max_history=15, batch_first=True, lm_labels=True, with_emotion=False, with_da=False): self.data = data self.tokenizer = tokenizer self.emotion_type = emotion_type # "Sentiment" or "BaseEmotion" or "Emotion" self.da_size = 18 # Number of DA categories self.emotion_size = 3 # Number of emotion categories self.with_emotion=with_emotion # Whether use emotion to help generate dialogue self.with_da=with_da # # Whether use DA to help generate dialogue if self.emotion_type=="Sentiment": self.emotion_size = 3 elif self.emotion_type=="BaseEmotion": self.emotion_size = 7 else: # self.emotion_type==2 self.emotion_size = 15 self.max_history = max_history # Maximum number of dialogue sentences self.pad = tokenizer.pad_token_id self.batch_first = batch_first self.lm_labels = lm_labels self.keys = list(set(self.data['Dialogue_ID'])) self.len = len(self.keys) def __len__(self): return self.len def __getitem__(self, index): dialogue_id = self.keys[index] data_index = self.data[self.data['Dialogue_ID']==dialogue_id] if self.lm_labels: # for train and valid dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] emotion_list = self.convert_EMOTION_TO_TOKENS(data_index[self.emotion_type].tolist()[-2 * self.max_history:]) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.convert_DA_TO_TOKENS(data_index["DA"].tolist()[-2 * self.max_history:]) da_list = self.tokenizer.convert_tokens_to_ids(da_list) response = data_index["Token"].tolist()[-1] else: # for test dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] emotion_list = self.convert_EMOTION_TO_TOKENS(data_index[self.emotion_type].tolist()[-2 * self.max_history:]) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.convert_DA_TO_TOKENS(data_index["DA"].tolist()[-2 * self.max_history:]) da_list = self.tokenizer.convert_tokens_to_ids(da_list) response = [] return self.process(speaker_list, utterance_history, emotion_list, da_list, response) def create_speaker(self,speaker_list): speaker1 = speaker_list[0] new_speaker_list = [] for speaker in speaker_list: if speaker==speaker1: new_speaker_list.append("[speaker1]") else: new_speaker_list.append("[speaker2]") return new_speaker_list def convert_EMOTION_TO_TOKENS(self,emotion_list): emotion_tokens_list = [] if self.emotion_type=="Sentiment": # "Sentiment" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[UNK]") else: emotion_tokens_list.append(SENTIMENT_TO_TOKENS[emo]) elif self.emotion_type=="BaseEmotion": # "BaseEmotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[UNK]") else: emotion_tokens_list.append(BASEEMOTION_TO_TOKENS[emo]) else: # "Emotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[UNK]") else: emotion_tokens_list.append(EMOTION_TO_TOKENS[emo]) return emotion_tokens_list def convert_DA_TO_TOKENS(self,da_list): da_tokens_list = [] for da in da_list: da_tokens_list.append(DA_TO_TOKENS[da]) return da_tokens_list def process(self, speaker_list, history, emotion_list, da_list, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.with_da: instance["token_da_ids"] = [bos] + [da_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_emotion: instance["token_emotion_ids"] = [bos] + [emotion_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def testdata_process(self, speaker_list, history, emotion_list, da_list, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.with_da: instance["token_da_ids"] = [bos] + [da_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_emotion: instance["token_emotion_ids"] = [bos] + [emotion_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def collate(self, batch): input_ids = pad_sequence( [torch.tensor(instance["input_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) if self.with_emotion: token_type_ids = pad_sequence( [torch.tensor(instance["token_emotion_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) elif self.with_da: token_type_ids = pad_sequence( [torch.tensor(instance["token_da_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) else: token_type_ids = pad_sequence( [torch.tensor(instance["token_type_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) labels = pad_sequence( [torch.tensor(instance["lm_labels"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=-1) return input_ids, token_type_ids, labels class EDADIALDataset(Dataset): ''' word_tokens: [CLS] [speaker1] 您好 [speaker2] 您好 [speaker1] 再见 [SEP] emotion_list: [[neutral], [neutral], [neutral]] da_list: [[greeting], [greeting], [greeting]] input_ids： [ 0, 13086, 448, 53, 13087, 448, 53, 13086, 154, 124, 2] if with_emotion==True: emotion_ids:[ 0, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102] # "[neutral]": 13102 if with_da==True: da_ids: [ 0, 13103, 13103, 13103, 13103, 13103, 13103, 13103, 13103, 13103, 13103] # "[greeting]": 13103 token_type_ids: [ 0, 13103, 13086, 13086, 13087, 13087, 13087, 13086, 13086, 13086, 13086] labels： ''' def __init__(self, data, tokenizer, emotion_type="Sentiment", max_history=15, batch_first=True, lm_labels=True, with_emotion=False, with_da=False): self.data = data self.tokenizer = tokenizer self.emotion_type = emotion_type # "Sentiment" or "BaseEmotion" or "Emotion" self.da_size = 18 # Number of DA categories self.emotion_size = 3 # Number of emotion categories self.with_emotion=with_emotion # Whether use emotion to help generate dialogue self.with_da=with_da # # Whether use DA to help generate dialogue if self.emotion_type=="Sentiment": self.emotion_size = 3 elif self.emotion_type=="BaseEmotion": self.emotion_size = 7 else: # self.emotion_type==2 self.emotion_size = 15 self.max_history = max_history # Maximum number of dialogue sentences self.pad = tokenizer.pad_token_id self.batch_first = batch_first self.lm_labels = lm_labels self.keys = list(set(self.data['Dialogue_ID'])) self.len = len(self.keys) def __len__(self): return self.len def __getitem__(self, index): dialogue_id = self.keys[index] data_index = self.data[self.data['Dialogue_ID']==dialogue_id] if self.lm_labels: # for train and valid dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] emotion_list = self.convert_EMOTION_TO_TOKENS(data_index[self.emotion_type].tolist()[-2 * self.max_history:]) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.convert_DA_TO_TOKENS(data_index["DA"].tolist()[-2 * self.max_history:]) da_list = self.tokenizer.convert_tokens_to_ids(da_list) response = data_index["Token"].tolist()[-1] else: # for test dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] emotion_list = self.convert_EMOTION_TO_TOKENS(data_index[self.emotion_type].tolist()[-2 * self.max_history:]) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.convert_DA_TO_TOKENS(data_index["DA"].tolist()[-2 * self.max_history:]) da_list = self.tokenizer.convert_tokens_to_ids(da_list) response = [] return self.process(speaker_list, utterance_history, emotion_list, da_list, response) def create_speaker(self,speaker_list): speaker1 = speaker_list[0] new_speaker_list = [] for speaker in speaker_list: if speaker==speaker1: new_speaker_list.append("[speaker1]") else: new_speaker_list.append("[speaker2]") return new_speaker_list def convert_EMOTION_TO_TOKENS(self,emotion_list): emotion_tokens_list = [] if self.emotion_type=="Sentiment": # "Sentiment" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(SENTIMENT_TO_TOKENS[emo]) elif self.emotion_type=="BaseEmotion": # "BaseEmotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(BASEEMOTION_TO_TOKENS[emo]) else: # "Emotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(EMOTION_TO_TOKENS[emo]) return emotion_tokens_list def convert_DA_TO_TOKENS(self,da_list): da_tokens_list = [] for da in da_list: da_tokens_list.append(DA_TO_TOKENS[da]) return da_tokens_list def process(self, speaker_list, history, emotion_list, da_list, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.tokenizer.convert_tokens_to_ids(da_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_da: instance["da_ids"] = [bos] + [da_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_emotion: instance["emotion_ids"] = [bos] + [emotion_list[i] for i, s in enumerate(sequence[1:]) for _ in s] instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def testdata_process(self, speaker_list, history, emotion_list, da_list, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.tokenizer.convert_tokens_to_ids(da_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_da: instance["da_ids"] = [bos] + [da_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_emotion: instance["emotion_ids"] = [bos] + [emotion_list[i] for i, s in enumerate(sequence[1:]) for _ in s] instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def collate(self, batch): input_ids = pad_sequence( [torch.tensor(instance["input_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) if self.with_emotion: emotion_ids = pad_sequence( [torch.tensor(instance["emotion_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) else: emotion_ids = None if self.with_da: da_ids = pad_sequence( [torch.tensor(instance["da_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) else: da_ids = None token_type_ids = pad_sequence( [torch.tensor(instance["token_type_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) labels = pad_sequence( [torch.tensor(instance["lm_labels"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=-1) return input_ids, token_type_ids, emotion_ids, da_ids, labels class EDASDIALDataset(Dataset): ''' word_tokens: [CLS] [speaker1] 您好 [speaker2] 您好 [speaker1] 再见 [SEP] emotion_list: [[neutral], [neutral], [neutral]] da_list: [[greeting], [greeting], [greeting]] input_ids： [ 0, 13086, 448, 53, 13087, 448, 53, 13086, 154, 124, 2] if with_emotion==True: emotion_ids:[ 0, 13102, -1, -1, 13102, -1, -1, 13102, -1, -1, -1] # "[neutral]": 13102 if with_da==True: da_ids: [ 0, 13103, -1, -1, 13103, -1, -1, 13103, -1, -1, -1] # "[greeting]": 13103 token_type_ids: [ 0, 13103, 13086, 13086, 13087, 13087, 13087, 13086, 13086, 13086, 13086] labels： ''' def __init__(self, data, tokenizer, emotion_type="Sentiment", max_history=15, batch_first=True, lm_labels=True, with_emotion=False, with_da=False): self.data = data self.tokenizer = tokenizer self.emotion_type = emotion_type # "Sentiment" or "BaseEmotion" or "Emotion" self.da_size = 18 # Number of DA categories self.emotion_size = 3 # Number of emotion categories self.with_emotion=with_emotion # Whether use emotion to help generate dialogue self.with_da=with_da # # Whether use DA to help generate dialogue if self.emotion_type=="Sentiment": self.emotion_size = 3 elif self.emotion_type=="BaseEmotion": self.emotion_size = 7 else: # self.emotion_type==2 self.emotion_size = 15 self.max_history = max_history # Maximum number of dialogue sentences self.pad = tokenizer.pad_token_id self.batch_first = batch_first self.lm_labels = lm_labels self.keys = list(set(self.data['Dialogue_ID'])) self.len = len(self.keys) def __len__(self): return self.len def __getitem__(self, index): dialogue_id = self.keys[index] data_index = self.data[self.data['Dialogue_ID']==dialogue_id] if self.lm_labels: # for train and valid dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] emotion_list = self.convert_EMOTION_TO_TOKENS(data_index[self.emotion_type].tolist()[-2 * self.max_history:]) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.convert_DA_TO_TOKENS(data_index["DA"].tolist()[-2 * self.max_history:]) da_list = self.tokenizer.convert_tokens_to_ids(da_list) response = data_index["Token"].tolist()[-1] else: # for test dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] emotion_list = self.convert_EMOTION_TO_TOKENS(data_index[self.emotion_type].tolist()[-2 * self.max_history:]) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.convert_DA_TO_TOKENS(data_index["DA"].tolist()[-2 * self.max_history:]) da_list = self.tokenizer.convert_tokens_to_ids(da_list) response = [] return self.process(speaker_list, utterance_history, emotion_list, da_list, response) def create_speaker(self,speaker_list): speaker1 = speaker_list[0] new_speaker_list = [] for speaker in speaker_list: if speaker==speaker1: new_speaker_list.append("[speaker1]") else: new_speaker_list.append("[speaker2]") return new_speaker_list def convert_EMOTION_TO_TOKENS(self,emotion_list): emotion_tokens_list = [] if self.emotion_type=="Sentiment": # "Sentiment" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(SENTIMENT_TO_TOKENS[emo]) elif self.emotion_type=="BaseEmotion": # "BaseEmotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(BASEEMOTION_TO_TOKENS[emo]) else: # "Emotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(EMOTION_TO_TOKENS[emo]) return emotion_tokens_list def convert_DA_TO_TOKENS(self,da_list): da_tokens_list = [] for da in da_list: da_tokens_list.append(DA_TO_TOKENS[da]) return da_tokens_list def set_da_in_speaker(self,da_ids,input_ids,bos, eos, speaker1, speaker2): special_token_ids_list = [bos, eos, speaker1, speaker2] new_da_ids = [] for i,da in enumerate(da_ids): if input_ids[i] in special_token_ids_list: new_da_ids.append(da_ids[i]) else: new_da_ids.append(self.pad) return new_da_ids def set_emotion_in_speaker(self,emotion_ids,input_ids,bos, eos, speaker1, speaker2): special_token_ids_list = [bos, eos, speaker1, speaker2] new_emotion_ids = [] for i,emotion in enumerate(emotion_ids): if input_ids[i] in special_token_ids_list: new_emotion_ids.append(emotion_ids[i]) else: new_emotion_ids.append(self.pad) return new_emotion_ids def process(self, speaker_list, history, emotion_list, da_list, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.tokenizer.convert_tokens_to_ids(da_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_da: instance["da_ids"] = [bos] + [da_list[i] for i, s in enumerate(sequence[1:]) for _ in s] # only set the DA in [speaker1] or [speaker2] instance["da_ids"] = self.set_da_in_speaker(instance["da_ids"],instance["input_ids"],bos, eos, speaker1, speaker2) if self.with_emotion: instance["emotion_ids"] = [bos] + [emotion_list[i] for i, s in enumerate(sequence[1:]) for _ in s] # only set the emotion in [speaker1] or [speaker2] instance["emotion_ids"] = self.set_emotion_in_speaker(instance["emotion_ids"],instance["input_ids"],bos, eos, speaker1, speaker2) instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def testdata_process(self, speaker_list, history, emotion_list, da_list, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) da_list = self.tokenizer.convert_tokens_to_ids(da_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_da: instance["da_ids"] = [bos] + [da_list[i] for i, s in enumerate(sequence[1:]) for _ in s] # only set the DA in [speaker1] or [speaker2] instance["da_ids"] = self.set_da_in_speaker(instance["da_ids"],instance["input_ids"],bos, eos, speaker1, speaker2) if self.with_emotion: instance["emotion_ids"] = [bos] + [emotion_list[i] for i, s in enumerate(sequence[1:]) for _ in s] # only set the emotion in [speaker1] or [speaker2] instance["emotion_ids"] = self.set_emotion_in_speaker(instance["emotion_ids"],instance["input_ids"],bos, eos, speaker1, speaker2) instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def collate(self, batch): input_ids = pad_sequence( [torch.tensor(instance["input_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) if self.with_emotion: emotion_ids = pad_sequence( [torch.tensor(instance["emotion_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) else: emotion_ids = None if self.with_da: da_ids = pad_sequence( [torch.tensor(instance["da_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) else: da_ids = None token_type_ids = pad_sequence( [torch.tensor(instance["token_type_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) labels = pad_sequence( [torch.tensor(instance["lm_labels"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=-1) return input_ids, token_type_ids, emotion_ids, da_ids, labels def build_dataloaders(args, tokenizer, logger, load_test=False): if load_test==False: logger.info("Build train and validation dataloaders") train_data,train_samples = get_data(args,tokenizer, args.train_path, logger) # args.train_path="/home/MMMTD/dialogue_text/mmmtd_train_split.csv" valid_data,valid_samples = get_data(args,tokenizer, args.valid_path, logger) # args.valid_path="/home/MMMTD/dialogue_text/mmmtd_valid_split.csv" train_dataset = MMMTDDataset(data=train_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) valid_dataset = MMMTDDataset(data=valid_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None train_loader = DataLoader(train_dataset, sampler=train_sampler, collate_fn=train_dataset.collate, num_workers=args.num_workers, batch_size=args.train_batch_size, shuffle=(not args.distributed)) valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, collate_fn=valid_dataset.collate, num_workers=args.num_workers, batch_size=args.valid_batch_size, shuffle=False) return train_loader, valid_loader, train_sampler, valid_sampler else: logger.info("Build test dataloaders") test_data, test_samples = get_data(args, tokenizer, args.test_path, logger) # args.test_path="/home/MMMTD/dialogue_text/mmmtd_test_split.csv" test_dataset = MMMTDDataset(data=test_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) if args.distributed else None test_loader = DataLoader(test_dataset, sampler=test_sampler, collate_fn=test_dataset.collate, num_workers=args.num_workers, batch_size=args.test_batch_size, shuffle=False) return test_loader, test_sampler def build_edadial_dataloaders(args, tokenizer, logger, load_test=False): if load_test==False: logger.info("Build train and validation dataloaders") train_data,train_samples = get_data(args,tokenizer, args.train_path, logger) # args.train_path="/home/MMMTD/dialogue_text/mmmtd_train_split.csv" valid_data,valid_samples = get_data(args,tokenizer, args.valid_path, logger) # args.valid_path="/home/MMMTD/dialogue_text/mmmtd_valid_split.csv" train_dataset = EDADIALDataset(data=train_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) valid_dataset = EDADIALDataset(data=valid_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None train_loader = DataLoader(train_dataset, sampler=train_sampler, collate_fn=train_dataset.collate, num_workers=args.num_workers, batch_size=args.train_batch_size, shuffle=(not args.distributed)) valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, collate_fn=valid_dataset.collate, num_workers=args.num_workers, batch_size=args.valid_batch_size, shuffle=False) return train_loader, valid_loader, train_sampler, valid_sampler else: logger.info("Build test dataloaders") test_data, test_samples = get_data(args, tokenizer, args.test_path, logger) # args.test_path="/home/MMMTD/dialogue_text/mmmtd_test_split.csv" test_dataset = EDADIALDataset(data=test_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) if args.distributed else None test_loader = DataLoader(test_dataset, sampler=test_sampler, collate_fn=test_dataset.collate, num_workers=args.num_workers, batch_size=args.test_batch_size, shuffle=False) return test_loader, test_sampler def build_edasdial_dataloaders(args, tokenizer, logger, load_test=False): if load_test==False: logger.info("Build train and validation dataloaders") train_data,train_samples = get_data(args,tokenizer, args.train_path, logger) # args.train_path="/home/MMMTD/dialogue_text/mmmtd_train_split.csv" valid_data,valid_samples = get_data(args,tokenizer, args.valid_path, logger) # args.valid_path="/home/MMMTD/dialogue_text/mmmtd_valid_split.csv" train_dataset = EDASDIALDataset(data=train_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) valid_dataset = EDASDIALDataset(data=valid_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None train_loader = DataLoader(train_dataset, sampler=train_sampler, collate_fn=train_dataset.collate, num_workers=args.num_workers, batch_size=args.train_batch_size, shuffle=(not args.distributed)) valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, collate_fn=valid_dataset.collate, num_workers=args.num_workers, batch_size=args.valid_batch_size, shuffle=False) return train_loader, valid_loader, train_sampler, valid_sampler else: logger.info("Build test dataloaders") test_data, test_samples = get_data(args, tokenizer, args.test_path, logger) # args.test_path="/home/MMMTD/dialogue_text/mmmtd_test_split.csv" test_dataset = EDASDIALDataset(data=test_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) if args.distributed else None test_loader = DataLoader(test_dataset, sampler=test_sampler, collate_fn=test_dataset.collate, num_workers=args.num_workers, batch_size=args.test_batch_size, shuffle=False) return test_loader, test_sampler class REDIALDataset(Dataset): ''' word_tokens: [CLS] [speaker1] 您好 [speaker2] 您好 [speaker1] 再见 [SEP] emotion_list: [[neutral], [neutral], [neutral]] da_list: [[greeting], [greeting], [greeting]] input_ids： [ 0, 13086, 448, 53, 13087, 448, 53, 13086, 154, 124, 2] if with_emotion==True: token_type_ids: [ 0, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102] # "[neutral]": 13102 elif with_da==True: token_type_ids: [ 0, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088] # "[greeting]": 13088 else: token_type_ids: [ 0, 13086, 13086, 13086, 13087, 13087, 13087, 13086, 13086, 13086, 13086] labels： ''' def __init__(self, data, tokenizer, emotion_type="Sentiment", max_history=15, batch_first=True, lm_labels=True, with_emotion=False, with_da=False): self.data = data self.tokenizer = tokenizer self.emotion_type = emotion_type # "Sentiment" or "BaseEmotion" or "Emotion" self.with_emotion=with_emotion # Whether use emotion to help generate dialogue self.with_da=with_da # # Whether use DA to help generate dialogue self.max_history = max_history # Maximum number of dialogue sentences self.pad = tokenizer.pad_token_id self.batch_first = batch_first self.lm_labels = lm_labels self.keys = list(set(self.data['Dialogue_ID'])) self.len = len(self.keys) def __len__(self): return self.len def __getitem__(self, index): dialogue_id = self.keys[index] data_index = self.data[self.data['Dialogue_ID']==dialogue_id] if self.lm_labels: # for train and valid dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] current_speaker = speaker_list[-1] current_emotion_id = EMOTION_TO_ID[data_index[self.emotion_type].tolist()[-1]] response = data_index["Token"].tolist()[-1] else: # for test dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] current_speaker = speaker_list[-1] current_emotion_id = EMOTION_TO_ID[data_index[self.emotion_type].tolist()[-1]] response = [] return self.process(speaker_list, utterance_history, current_speaker, current_emotion_id, response) def create_speaker(self,speaker_list): speaker1 = speaker_list[0] new_speaker_list = [] for speaker in speaker_list: if speaker==speaker1: new_speaker_list.append("[speaker1]") else: new_speaker_list.append("[speaker2]") return new_speaker_list def process(self, speaker_list, history, current_speaker, current_emotion_id, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] instance["current_speaker_id"] = self.tokenizer.convert_tokens_to_ids(current_speaker) instance["current_emotion_id"] = current_emotion_id instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def testdata_process(self, speaker_list, history, current_speaker, current_emotion_id, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] instance["current_speaker_id"] = self.tokenizer.convert_tokens_to_ids(current_speaker) instance["current_emotion_id"] = current_emotion_id instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def collate(self, batch): input_ids = pad_sequence( [torch.tensor(instance["input_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) token_type_ids = pad_sequence( [torch.tensor(instance["token_type_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) current_speaker_id = torch.tensor( [torch.tensor(instance["current_speaker_id"], dtype=torch.long) for instance in batch], dtype=torch.long) current_emotion_id = torch.tensor( [torch.tensor(instance["current_emotion_id"], dtype=torch.long) for instance in batch], dtype=torch.long) labels = pad_sequence( [torch.tensor(instance["lm_labels"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=-1) return input_ids, token_type_ids, current_speaker_id, current_emotion_id, labels def build_redial_dataloaders(args, tokenizer, logger, load_test=False): if load_test==False: logger.info("Build train and validation dataloaders") train_data,train_samples = get_data(args,tokenizer, args.train_path, logger) # args.train_path="/home/MMMTD/dialogue_text/mmmtd_train_split.csv" valid_data,valid_samples = get_data(args,tokenizer, args.valid_path, logger) # args.valid_path="/home/MMMTD/dialogue_text/mmmtd_valid_split.csv" train_dataset = REDIALDataset(data=train_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) valid_dataset = REDIALDataset(data=valid_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None train_loader = DataLoader(train_dataset, sampler=train_sampler, collate_fn=train_dataset.collate, num_workers=args.num_workers, batch_size=args.train_batch_size, shuffle=(not args.distributed)) valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, collate_fn=valid_dataset.collate, num_workers=args.num_workers, batch_size=args.valid_batch_size, shuffle=False) return train_loader, valid_loader, train_sampler, valid_sampler else: logger.info("Build test dataloaders") test_data, test_samples = get_data(args, tokenizer, args.test_path, logger) # args.test_path="/home/MMMTD/dialogue_text/mmmtd_test_split.csv" test_dataset = REDIALDataset(data=test_data, tokenizer=tokenizer, emotion_type=args.emotion_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) if args.distributed else None test_loader = DataLoader(test_dataset, sampler=test_sampler, collate_fn=test_dataset.collate, num_workers=args.num_workers, batch_size=args.test_batch_size, shuffle=False) return test_loader, test_sampler class RDADIALDataset(Dataset): ''' word_tokens: [CLS] [speaker1] 您好 [speaker2] 您好 [speaker1] 再见 [SEP] emotion_list: [[neutral], [neutral], [neutral]] da_list: [[gRDAeting], [gRDAeting], [gRDAeting]] input_ids： [ 0, 13086, 448, 53, 13087, 448, 53, 13086, 154, 124, 2] if with_emotion==True: token_type_ids: [ 0, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102] # "[neutral]": 13102 elif with_da==True: token_type_ids: [ 0, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088] # "[gRDAeting]": 13088 else: token_type_ids: [ 0, 13086, 13086, 13086, 13087, 13087, 13087, 13086, 13086, 13086, 13086] labels： ''' def __init__(self, data, tokenizer, da_type="DA", # 读取DA数据 max_history=15, batch_first=True, lm_labels=True, with_emotion=False, with_da=False): self.data = data self.tokenizer = tokenizer self.da_type = da_type # "DA" DA数据的列名 self.with_emotion=with_emotion # Whether use emotion to help generate dialogue self.with_da=with_da # # Whether use DA to help generate dialogue self.max_history = max_history # Maximum number of dialogue sentences self.pad = tokenizer.pad_token_id self.batch_first = batch_first self.lm_labels = lm_labels self.keys = list(set(self.data['Dialogue_ID'])) self.len = len(self.keys) def __len__(self): return self.len def __getitem__(self, index): dialogue_id = self.keys[index] data_index = self.data[self.data['Dialogue_ID']==dialogue_id] if self.lm_labels: # for train and valid dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] current_speaker = speaker_list[-1] current_da_id = DA_TO_ID[data_index[self.da_type].tolist()[-1]] response = data_index["Token"].tolist()[-1] else: # for test dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] current_speaker = speaker_list[-1] current_da_id = DA_TO_ID[data_index[self.da_type].tolist()[-1]] response = [] return self.process(speaker_list, utterance_history, current_speaker, current_da_id, response) def create_speaker(self,speaker_list): speaker1 = speaker_list[0] new_speaker_list = [] for speaker in speaker_list: if speaker==speaker1: new_speaker_list.append("[speaker1]") else: new_speaker_list.append("[speaker2]") return new_speaker_list def process(self, speaker_list, history, current_speaker, current_da_id, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] instance["current_speaker_id"] = self.tokenizer.convert_tokens_to_ids(current_speaker) instance["current_da_id"] = current_da_id instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def testdata_process(self, speaker_list, history, current_speaker, current_da_id, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] instance["current_speaker_id"] = self.tokenizer.convert_tokens_to_ids(current_speaker) instance["current_da_id"] = current_da_id instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def collate(self, batch): input_ids = pad_sequence( [torch.tensor(instance["input_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) token_type_ids = pad_sequence( [torch.tensor(instance["token_type_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) current_speaker_id = torch.tensor( [torch.tensor(instance["current_speaker_id"], dtype=torch.long) for instance in batch], dtype=torch.long) current_da_id = torch.tensor( [torch.tensor(instance["current_da_id"], dtype=torch.long) for instance in batch], dtype=torch.long) labels = pad_sequence( [torch.tensor(instance["lm_labels"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=-1) return input_ids, token_type_ids, current_speaker_id, current_da_id, labels class CPEDDataset(Dataset): ''' word_tokens: [CLS] [speaker1] 您好 [speaker2] 您好 [speaker1] 再见 [SEP] emotion_list: [[neutral], [neutral], [neutral]] da_list: [[gRDAeting], [gRDAeting], [gRDAeting]] input_ids： [ 0, 13086, 448, 53, 13087, 448, 53, 13086, 154, 124, 2] if with_emotion==True: token_type_ids: [ 0, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102, 13102] # "[neutral]": 13102 elif with_da==True: token_type_ids: [ 0, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088, 13088] # "[gRDAeting]": 13088 else: token_type_ids: [ 0, 13086, 13086, 13086, 13087, 13087, 13087, 13086, 13086, 13086, 13086] labels： ''' def __init__(self, data, tokenizer, emotion_type="Sentiment", # 读取DA数据 da_type="DA", persona_type=["Gender","Neuroticism","Extraversion","Openness","Agreeableness","Conscientiousness"], max_history=15, batch_first=True, lm_labels=True, with_current_speaker=False, with_current_persona=False, with_current_emotion=False, with_current_da=False, with_emotion=False, with_da=False): self.data = data self.tokenizer = tokenizer self.emotion_type = emotion_type # 'Emotion' 情感标签列名 self.da_type = da_type # 'DA' DA标签列名 self.persona_type = persona_type self.with_current_speaker = with_current_speaker self.with_current_persona = with_current_persona self.with_current_emotion = with_current_emotion self.with_current_da = with_current_da self.with_emotion=with_emotion # Whether use emotion to help generate dialogue self.with_da=with_da # Whether use DA to help generate dialogue self.max_history = max_history # Maximum number of dialogue sentences self.pad = tokenizer.pad_token_id self.batch_first = batch_first self.lm_labels = lm_labels self.keys = list(set(self.data['Dialogue_ID'])) self.len = len(self.keys) def __len__(self): return self.len def __getitem__(self, index): dialogue_id = self.keys[index] data_index = self.data[self.data['Dialogue_ID']==dialogue_id] if self.lm_labels: # for train and valid dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] if self.with_emotion: emotion_list = self.convert_EMOTION_TO_TOKENS(data_index[self.emotion_type].tolist()[-2 * self.max_history:]) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) else: emotion_list = [] if self.with_da: da_list = self.convert_DA_TO_TOKENS(data_index["DA"].tolist()[-2 * self.max_history:]) da_list = self.tokenizer.convert_tokens_to_ids(da_list) else: da_list = [] current_speaker = speaker_list[-1] current_emotion_id = EMOTION_TO_ID[data_index[self.emotion_type].tolist()[-1]] current_da_id = DA_TO_ID[data_index[self.da_type].tolist()[-1]] if self.with_current_persona: current_gender_id = GENDER_TO_ID[data_index[self.persona_type[0]].tolist()[-1]] current_Neuroticism_id = BIGFIVE_TO_ID[data_index[self.persona_type[1]].tolist()[-1]] current_Extraversion_id = BIGFIVE_TO_ID[data_index[self.persona_type[2]].tolist()[-1]] current_Openness_id = BIGFIVE_TO_ID[data_index[self.persona_type[3]].tolist()[-1]] current_Agreeableness_id = BIGFIVE_TO_ID[data_index[self.persona_type[4]].tolist()[-1]] current_Conscientiousness_id = BIGFIVE_TO_ID[data_index[self.persona_type[5]].tolist()[-1]] current_persona_ids = [current_gender_id,current_Neuroticism_id,current_Extraversion_id,current_Openness_id, current_Agreeableness_id,current_Conscientiousness_id] else: current_persona_ids = [] response = data_index["Token"].tolist()[-1] else: # for test dataset speaker_list = self.create_speaker(data_index["Speaker"].tolist()[-2 * self.max_history:]) utterance_history = data_index["Token"].tolist()[-2 * self.max_history:-1] if self.with_emotion: emotion_list = self.convert_EMOTION_TO_TOKENS(data_index[self.emotion_type].tolist()[-2 * self.max_history:]) emotion_list = self.tokenizer.convert_tokens_to_ids(emotion_list) else: emotion_list = [] if self.with_da: da_list = self.convert_DA_TO_TOKENS(data_index["DA"].tolist()[-2 * self.max_history:]) da_list = self.tokenizer.convert_tokens_to_ids(da_list) else: da_list = [] current_speaker = speaker_list[-1] current_emotion_id = EMOTION_TO_ID[data_index[self.emotion_type].tolist()[-1]] current_da_id = DA_TO_ID[data_index[self.da_type].tolist()[-1]] if self.with_current_persona: current_gender_id = GENDER_TO_ID[data_index[self.persona_type[0]].tolist()[-1]] current_Neuroticism_id = BIGFIVE_TO_ID[data_index[self.persona_type[1]].tolist()[-1]] current_Extraversion_id = BIGFIVE_TO_ID[data_index[self.persona_type[2]].tolist()[-1]] current_Openness_id = BIGFIVE_TO_ID[data_index[self.persona_type[3]].tolist()[-1]] current_Agreeableness_id = BIGFIVE_TO_ID[data_index[self.persona_type[4]].tolist()[-1]] current_Conscientiousness_id = BIGFIVE_TO_ID[data_index[self.persona_type[5]].tolist()[-1]] current_persona_ids = [current_gender_id,current_Neuroticism_id,current_Extraversion_id,current_Openness_id, current_Agreeableness_id,current_Conscientiousness_id] else: current_persona_ids = [] response = [] return self.process(speaker_list, utterance_history, emotion_list, da_list, current_speaker, current_emotion_id, current_da_id, current_persona_ids, response) def create_speaker(self,speaker_list): speaker1 = speaker_list[0] new_speaker_list = [] for speaker in speaker_list: if speaker==speaker1: new_speaker_list.append("[speaker1]") else: new_speaker_list.append("[speaker2]") return new_speaker_list def convert_EMOTION_TO_TOKENS(self,emotion_list): emotion_tokens_list = [] if self.emotion_type=="Sentiment": # "Sentiment" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(SENTIMENT_TO_TOKENS[emo]) elif self.emotion_type=="BaseEmotion": # "BaseEmotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(BASEEMOTION_TO_TOKENS[emo]) else: # "Emotion" for emo in emotion_list: if emo not in SENTIMENT_TO_TOKENS: emotion_tokens_list.append("[neutral]") else: emotion_tokens_list.append(EMOTION_TO_TOKENS[emo]) return emotion_tokens_list def convert_DA_TO_TOKENS(self,da_list): da_tokens_list = [] for da in da_list: da_tokens_list.append(DA_TO_TOKENS[da]) return da_tokens_list def set_da_in_speaker(self,da_ids,input_ids,bos, eos, speaker1, speaker2): special_token_ids_list = [bos, eos, speaker1, speaker2] new_da_ids = [] for i,da in enumerate(da_ids): if input_ids[i] in special_token_ids_list: new_da_ids.append(da_ids[i]) else: new_da_ids.append(self.pad) return new_da_ids def set_emotion_in_speaker(self,emotion_ids,input_ids,bos, eos, speaker1, speaker2): special_token_ids_list = [bos, eos, speaker1, speaker2] new_emotion_ids = [] for i,emotion in enumerate(emotion_ids): if input_ids[i] in special_token_ids_list: new_emotion_ids.append(emotion_ids[i]) else: new_emotion_ids.append(self.pad) return new_emotion_ids def process(self, speaker_list, history, emotion_list, da_list, current_speaker, current_emotion_id, current_da_id, current_persona_ids, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_da: instance["da_ids"] = [bos] + [da_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_emotion: instance["emotion_ids"] = [bos] + [emotion_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_current_speaker: instance["current_speaker_id"] = self.tokenizer.convert_tokens_to_ids(current_speaker) if self.with_current_emotion: instance["current_emotion_id"] = current_emotion_id if self.with_current_da: instance["current_da_id"] = current_da_id if self.with_current_persona: instance["current_persona_ids"] = current_persona_ids instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def testdata_process(self, speaker_list, history, emotion_list, da_list, current_speaker, current_emotion_id, current_da_id, current_persona_ids, response, with_eos=True): bos, eos, speaker1, speaker2 = self.tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS) speaker_list = self.tokenizer.convert_tokens_to_ids(speaker_list) instance = {} sequence = [[bos]] + history + [response + ([eos] if with_eos else [])] sequence = [sequence[0]] + [[speaker_list[i]] + s for i, s in enumerate(sequence[1:])] instance["input_ids"] = list(chain(*sequence)) instance["token_type_ids"] = [bos] + [speaker_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_da: instance["da_ids"] = [bos] + [da_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_emotion: instance["emotion_ids"] = [bos] + [emotion_list[i] for i, s in enumerate(sequence[1:]) for _ in s] if self.with_current_speaker: instance["current_speaker_id"] = self.tokenizer.convert_tokens_to_ids(current_speaker) if self.with_current_emotion: instance["current_emotion_id"] = current_emotion_id if self.with_current_da: instance["current_da_id"] = current_da_id if self.with_current_persona: instance["current_persona_ids"] = current_persona_ids instance["lm_labels"] = [-1] * len(instance["input_ids"]) if self.lm_labels: instance["lm_labels"] = ([-1] * sum(len(s) for s in sequence[:-1])) + [-1] + sequence[-1][1:] return instance def collate(self, batch): input_ids = pad_sequence( [torch.tensor(instance["input_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) token_type_ids = pad_sequence( [torch.tensor(instance["token_type_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) if self.with_emotion: emotion_ids = pad_sequence( [torch.tensor(instance["emotion_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) else: emotion_ids = None if self.with_da: da_ids = pad_sequence( [torch.tensor(instance["da_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) else: da_ids = None if self.with_current_speaker: current_speaker_id = torch.tensor( [torch.tensor(instance["current_speaker_id"], dtype=torch.long) for instance in batch], dtype=torch.long) else: current_speaker_id = None if self.with_current_persona: current_persona_ids = pad_sequence( [torch.tensor(instance["current_persona_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=1) # padding_value=1 means unknown here else: current_persona_ids = None if self.with_current_emotion: current_emotion_id = torch.tensor( [torch.tensor(instance["current_emotion_id"], dtype=torch.long) for instance in batch], dtype=torch.long) else: current_emotion_id = None if self.with_current_da: current_da_id = torch.tensor( [torch.tensor(instance["current_da_id"], dtype=torch.long) for instance in batch], dtype=torch.long) else: current_da_id = None labels = pad_sequence( [torch.tensor(instance["lm_labels"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=-1) return input_ids, token_type_ids, emotion_ids, da_ids, current_speaker_id, current_persona_ids, current_emotion_id, current_da_id, labels def build_cped_dataloaders(args, tokenizer, logger, load_test=False): if load_test==False: logger.info("Build train and validation dataloaders") train_data,train_samples = get_data(args,tokenizer, args.train_path, logger) # args.train_path="/home/MMMTD/dialogue_text/mmmtd_train_split.csv" valid_data,valid_samples = get_data(args,tokenizer, args.valid_path, logger) # args.valid_path="/home/MMMTD/dialogue_text/mmmtd_valid_split.csv" train_dataset = CPEDDataset(data=train_data, tokenizer=tokenizer, emotion_type=args.emotion_type, da_type=args.da_type, persona_type=["Gender","Neuroticism","Extraversion","Openness","Agreeableness","Conscientiousness"], max_history=args.max_history, batch_first=True, lm_labels=True, with_current_speaker=args.with_current_speaker, with_current_persona=args.with_current_persona, with_current_emotion=args.with_current_emotion, with_current_da=args.with_current_da, with_emotion=args.with_emotion, with_da=args.with_da) valid_dataset = CPEDDataset(data=valid_data, tokenizer=tokenizer, emotion_type=args.emotion_type, da_type=args.da_type, persona_type=["Gender","Neuroticism","Extraversion","Openness","Agreeableness","Conscientiousness"], max_history=args.max_history, batch_first=True, lm_labels=True, with_current_speaker=args.with_current_speaker, with_current_persona=args.with_current_persona, with_current_emotion=args.with_current_emotion, with_current_da=args.with_current_da, with_emotion=args.with_emotion, with_da=args.with_da) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None train_loader = DataLoader(train_dataset, sampler=train_sampler, collate_fn=train_dataset.collate, num_workers=args.num_workers, batch_size=args.train_batch_size, shuffle=(not args.distributed)) valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, collate_fn=valid_dataset.collate, num_workers=args.num_workers, batch_size=args.valid_batch_size, shuffle=False) return train_loader, valid_loader, train_sampler, valid_sampler else: logger.info("Build test dataloaders") test_data, test_samples = get_data(args, tokenizer, args.test_path, logger) # args.test_path="/home/MMMTD/dialogue_text/mmmtd_test_split.csv" test_dataset = CPEDDataset(data=test_data, tokenizer=tokenizer, emotion_type=args.emotion_type, da_type=args.da_type, persona_type=["Gender","Neuroticism","Extraversion","Openness","Agreeableness","Conscientiousness"], max_history=args.max_history, batch_first=True, lm_labels=True, with_current_speaker=args.with_current_speaker, with_current_persona=args.with_current_persona, with_current_emotion=args.with_current_emotion, with_current_da=args.with_current_da, with_emotion=args.with_emotion, with_da=args.with_da) test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) if args.distributed else None test_loader = DataLoader(test_dataset, sampler=test_sampler, collate_fn=test_dataset.collate, num_workers=args.num_workers, batch_size=args.test_batch_size, shuffle=False) return test_loader, test_sampler def build_rdadial_dataloaders(args, tokenizer, logger, load_test=False): if load_test==False: logger.info("Build train and validation dataloaders") train_data,train_samples = get_data(args,tokenizer, args.train_path, logger) # args.train_path="/home/MMMTD/dialogue_text/mmmtd_train_split.csv" valid_data,valid_samples = get_data(args,tokenizer, args.valid_path, logger) # args.valid_path="/home/MMMTD/dialogue_text/mmmtd_valid_split.csv" train_dataset = RDADIALDataset(data=train_data, tokenizer=tokenizer, da_type=args.da_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) valid_dataset = RDADIALDataset(data=valid_data, tokenizer=tokenizer, da_type=args.da_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None train_loader = DataLoader(train_dataset, sampler=train_sampler, collate_fn=train_dataset.collate, num_workers=args.num_workers, batch_size=args.train_batch_size, shuffle=(not args.distributed)) valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, collate_fn=valid_dataset.collate, num_workers=args.num_workers, batch_size=args.valid_batch_size, shuffle=False) return train_loader, valid_loader, train_sampler, valid_sampler else: logger.info("Build test dataloaders") test_data, test_samples = get_data(args, tokenizer, args.test_path, logger) # args.test_path="/home/MMMTD/dialogue_text/mmmtd_test_split.csv" test_dataset = RDADIALDataset(data=test_data, tokenizer=tokenizer, da_type=args.da_type, max_history=args.max_history, batch_first=True, lm_labels=True, with_emotion=args.with_emotion, with_da=args.with_da) test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) if args.distributed else None test_loader = DataLoader(test_dataset, sampler=test_sampler, collate_fn=test_dataset.collate, num_workers=args.num_workers, batch_size=args.test_batch_size, shuffle=False) return test_loader, test_sampler class BERTEDADataset(Dataset): ''' load data for train BertEDARC ''' def __init__(self, data, tokenizer, batch_first=True, with_emotion=False, with_da=False): self.data = data self.tokenizer = tokenizer self.with_emotion=with_emotion # Whether use emotion to help generate dialogue self.with_da=with_da # # Whether use DA to help generate dialogue self.pad = tokenizer.pad_token_id self.batch_first = batch_first self.keys = list(set(self.data['Utterance_ID'])) self.len = len(self.keys) def __len__(self): return self.len def __getitem__(self, index): utterance_id = self.keys[index] data_index = self.data[self.data['Utterance_ID']==utterance_id] utterance = data_index["Token"].tolist()[0] emotion = EMOTION_TO_ID[data_index["Emotion"].tolist()[0]] da = DA_TO_ID[data_index["DA"].tolist()[0]] return self.process(utterance, emotion, da) def process(self, utterance, emotion, da): instance = {} instance["input_ids"] = utterance if self.with_emotion: instance["emotion_ids"] = emotion if self.with_da: instance["da_ids"] = da return instance def collate(self, batch): #input_ids = torch.tensor([torch.tensor(instance["input_ids"], dtype=torch.long) for instance in batch], dtype=torch.long) input_ids = pad_sequence( [torch.tensor(instance["input_ids"], dtype=torch.long) for instance in batch], batch_first=self.batch_first, padding_value=self.pad) if self.with_emotion: emotion_ids = torch.tensor([torch.tensor(instance["emotion_ids"], dtype=torch.long) for instance in batch],dtype=torch.long) else: emotion_ids = None if self.with_da: da_ids = torch.tensor([torch.tensor(instance["da_ids"], dtype=torch.long) for instance in batch],dtype=torch.long) else: da_ids = None return input_ids, emotion_ids, da_ids def build_berteda_dataloaders(args, tokenizer, logger): logger.info("Build train and validation dataloaders") train_data,train_samples = get_data(args,tokenizer, args.train_path, logger) # args.train_path="/home/MMMTD/dialogue_text/mmmtd_train_split.csv" valid_data,valid_samples = get_data(args,tokenizer, args.valid_path, logger) # args.valid_path="/home/MMMTD/dialogue_text/mmmtd_valid_split.csv" train_dataset = BERTEDADataset(data=train_data, tokenizer=tokenizer, batch_first=False, with_emotion=args.with_emotion, with_da=args.with_da) valid_dataset = BERTEDADataset(data=valid_data, tokenizer=tokenizer, batch_first=False, with_emotion=args.with_emotion, with_da=args.with_da) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) if args.distributed else None valid_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset) if args.distributed else None train_loader = DataLoader(train_dataset, sampler=train_sampler, collate_fn=train_dataset.collate, num_workers=args.num_workers, batch_size=args.train_batch_size, shuffle=(not args.distributed)) valid_loader = DataLoader(valid_dataset, sampler=valid_sampler, collate_fn=valid_dataset.collate, num_workers=args.num_workers, batch_size=args.valid_batch_size, shuffle=False) return train_loader, valid_loader, train_sampler, valid_sampler

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py

Python

pandasql/run_test.py

Cadair/conda-recipes

0227a81ed80e24eefffe091836dead93f8312ffe

[ "BSD-3-Clause" ]

302

2015-01-04T18:21:56.000Z

2021-11-16T12:14:37.000Z

pandasql/run_test.py

Cadair/conda-recipes

0227a81ed80e24eefffe091836dead93f8312ffe

[ "BSD-3-Clause" ]

393

2015-01-03T14:35:48.000Z

2019-12-09T15:09:07.000Z

pandasql/run_test.py

Cadair/conda-recipes

0227a81ed80e24eefffe091836dead93f8312ffe

[ "BSD-3-Clause" ]

325

2015-01-04T17:26:39.000Z

2021-11-04T16:25:54.000Z

import unittest import pandasql.tests.tests unittest.main(pandasql.tests.tests)

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py

Python

markup/views/__init__.py

vmun/SkyMed_Labeling

9c1e2268dcdde5a8450e6f70c747902f67980f15

[ "MIT" ]

null

markup/views/__init__.py

vmun/SkyMed_Labeling

9c1e2268dcdde5a8450e6f70c747902f67980f15

[ "MIT" ]

6

2021-03-19T03:58:53.000Z

2022-02-10T13:41:23.000Z

markup/views/__init__.py

vladek1934/SkyMed_Labeling

9c1e2268dcdde5a8450e6f70c747902f67980f15

[ "MIT" ]

null

from .markup_viewsets import * from .user_viewsets import * from .path_viewsets import * from .apiviews import * from .fbv import *

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py

Python

src/svr/tests/context.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

3

2019-05-04T12:30:00.000Z

2020-05-14T06:28:51.000Z

src/svr/tests/context.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

1

2019-05-05T01:30:37.000Z

2019-05-16T02:50:04.000Z

src/svr/tests/context.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

1

2020-03-27T07:12:40.000Z

import os import sys sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))) sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))

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py

Python

notebooks/eda_wordclouds.py

dominikmn/one-million-posts

a628e88874ca7134a7628d88de169e8520f8deba

[ "MIT" ]

null

notebooks/eda_wordclouds.py

dominikmn/one-million-posts

a628e88874ca7134a7628d88de169e8520f8deba

[ "MIT" ]

95

2021-03-26T14:37:37.000Z

2021-09-07T08:26:03.000Z

notebooks/eda_wordclouds.py

dominikmn/one-million-posts

a628e88874ca7134a7628d88de169e8520f8deba

[ "MIT" ]

2

2021-04-19T15:43:57.000Z

2021-04-19T15:57:47.000Z

# --- # jupyter: # jupytext: # formats: ipynb,py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.11.1 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # %% from utils import loading, nlp, cleaning, visualizing, feature_engineering import pandas as pd from nltk.corpus import stopwords stopwords=stopwords.words('german') from string import punctuation from collections import Counter import matplotlib.pyplot as plt # %% tags=[] df = loading.load_extended_posts() # %% df = feature_engineering.add_column_ann_round(df) # %% [markdown] # Defining function for top words for labels # %% def top_words_label(df, label, text, stop=False, stopwords=None, plot=True, return_list=True, all_plots=True): df_clean=df.dropna(subset=[label]) df_clean.loc[:,text]=cleaning.strip_punct(df_clean[text]) if stop: df_clean.loc[:,text]=nlp.strip_stopwords(df_clean[text], stopwords=stopwords) df_pos = df_clean[df_clean[label]==1] df_neg = df_clean[df_clean[label]==0] topwords_pos = feature_engineering.calculate_top_words(df_pos[text], relative=True) topwords_neg = feature_engineering.calculate_top_words(df_neg[text], relative=True) topwords_pos_rel = topwords_pos.subtract(topwords_neg, fill_value=0).sort_values(ascending=False) topwords_neg_rel = (-topwords_pos_rel).sort_values(ascending=False) if plot and all_plots: print(f'Order of plots:\nTop left: {label} = positive\nTop right: {label} = negative\nBottom left: {label} = positive, specific\nBottom right: {label} = negative, specific') plt.figure(figsize = (12, 12)) plt.subplot(2, 2, 1) visualizing.plot_wordcloud_freq(topwords_pos, colormap='BuGn') plt.subplot(2, 2, 2) visualizing.plot_wordcloud_freq(topwords_neg, colormap='RdPu') plt.subplot(2, 2, 3) visualizing.plot_wordcloud_freq(topwords_pos_rel,colormap='YlGn') plt.subplot(2, 2, 4) visualizing.plot_wordcloud_freq(topwords_neg_rel, colormap='OrRd') plt.show() elif plot and all_plots==False: plt.figure(figsize=(12,6)) plt.subplot(1, 2, 2) visualizing.plot_wordcloud_freq(topwords_neg_rel, colormap='binary') plt.subplot(1, 2, 1) visualizing.plot_wordcloud_freq(topwords_pos_rel,colormap='RdPu') plt.show() if return_list: return topwords_pos, topwords_neg, topwords_pos_rel, topwords_neg_rel # %% [markdown] # # Getting the top words in comments for every label # %% [markdown] # ## Arguments used # %% arg_pos, arg_neg, arg_pos_rel, arg_neg_rel = top_words_label(df, 'label_argumentsused', 'body', True, stopwords) # %% tags=[] print(f'top words for argument used positive:\n{arg_pos[:10]}') print(f'top words for argument used negative:\n{arg_neg[:10]}') print(f'top words for argument used positive specific:\n{arg_pos_rel[:10]}') print(f'top words for argument used negative specific:\n{arg_neg_rel[:10]}') # %% [markdown] # ## Discriminating # %% dis_pos, dis_neg, dis_pos_rel, dis_neg_rel = top_words_label(df, 'label_discriminating', 'body', True, stopwords) # %% print(f'top words for discriminating positive:\n{dis_pos[:10]}') print(f'top words for discriminating negative:\n{dis_neg[:10]}') print(f'top words for discriminating positive specific:\n{dis_pos_rel[:10]}') print(f'top words for discriminating negative specific:\n{dis_neg_rel[:10]}') # %% [markdown] # ## Inappropriate # %% ina_pos, ina_neg, ina_pos_rel, ina_neg_rel = top_words_label(df, 'label_inappropriate', 'body', True, stopwords) # %% print(f'top words for innapropriate positive:\n{ina_pos[:10]}') print(f'top words for innapropriate negative:\n{ina_neg[:10]}') print(f'top words for innapropriate positive specific:\n{ina_pos_rel[:10]}') print(f'top words for innapropriate negative specific:\n{ina_neg_rel[:10]}') # %% [markdown] # ## Off-Topic # %% ot_pos, ot_neg, ot_pos_rel, ot_neg_rel = top_words_label(df, 'label_offtopic', 'body', True, stopwords) # %% print(f'top words for Off-Topic positive:\n{ot_pos[:10]}') print(f'top words for Off-Topic negative:\n{ot_neg[:10]}') print(f'top words for Off-Topic positive specific:\n{ot_pos_rel[:10]}') print(f'top words for Off-Topic negative specific:\n{ot_neg_rel[:10]}') # %% [markdown] # ## Personal stories # %% ps_pos, ps_neg, ps_pos_rel, ps_neg_rel = top_words_label(df, 'label_personalstories', 'body', True, stopwords) # %% print(f'top words for Personal Stories positive:\n{ps_pos[:10]}') print(f'top words for Personal Stories negative:\n{ps_neg[:10]}') print(f'top words for Personal Stories positive specific:\n{ps_pos_rel[:10]}') print(f'top words for Personal Stories negative specific:\n{ps_neg_rel[:10]}') # %% [markdown] # ## Possibly Feedback # %% fb_pos, fb_neg, fb_pos_rel, fb_neg_rel = top_words_label(df, 'label_possiblyfeedback', 'body', True, stopwords) # %% print(f'top words for Possibly Feedback positive:\n{fb_pos[:10]}') print(f'top words for Possibly Feedback negative:\n{fb_neg[:10]}') print(f'top words for Possibly Feedback positive specific:\n{fb_pos_rel[:10]}') print(f'top words for Possibly Feedback negative specific:\n{fb_neg_rel[:10]}') # %% [markdown] # ## Sentiment # ### Negative # %% sng_pos, sng_neg, sng_pos_rel, sng_neg_rel = top_words_label(df, 'label_sentimentnegative', 'body', True, stopwords) # %% print(f'top words for Sentiment Negative positive:\n{sng_pos[:10]}') print(f'top words for Sentiment Negative negative:\n{sng_neg[:10]}') print(f'top words for Sentiment Negative positive specific:\n{sng_pos_rel[:10]}') print(f'top words for Sentiment Negative negative specific:\n{sng_neg_rel[:10]}') # %% [markdown] # ### Neutral # %% snt_pos, snt_neg, snt_pos_rel, snt_neg_rel = top_words_label(df, 'label_sentimentneutral', 'body', True, stopwords) # %% print(f'top words for Sentiment Neutral positive:\n{snt_pos[:10]}') print(f'top words for Sentiment Neutral negative:\n{snt_neg[:10]}') print(f'top words for Sentiment Neutral positive specific:\n{snt_pos_rel[:10]}') print(f'top words for Sentiment Neutral negative specific:\n{snt_neg_rel[:10]}') # %% [markdown] # ### Positive # %% spo_pos, spo_neg, spo_pos_rel, spo_neg_rel = top_words_label(df, 'label_sentimentpositive', 'body', True, stopwords) # %% print(f'top words for Sentiment Positive positive:\n{spo_pos[:10]}') print(f'top words for Sentiment Positive negative:\n{spo_neg[:10]}') print(f'top words for Sentiment Positive positive specific:\n{spo_pos_rel[:10]}') print(f'top words for Sentiment Positive negative specific:\n{spo_neg_rel[:10]}') # %% [markdown] # # Getting the top words in headline for every label # %% [markdown] # ## Arguments Used # %% arg_pos, arg_neg, arg_pos_rel, arg_neg_rel = top_words_label(df, 'label_argumentsused', 'headline', True, stopwords) # %% tags=[] print(f'top words for argument used positive:\n{arg_pos[:10]}') print(f'top words for argument used negative:\n{arg_neg[:10]}') print(f'top words for argument used positive specific:\n{arg_pos_rel[:10]}') print(f'top words for argument used negative specific:\n{arg_neg_rel[:10]}') # %% [markdown] # ## Discriminating # %% dis_pos, dis_neg, dis_pos_rel, dis_neg_rel = top_words_label(df, 'label_discriminating', 'headline', True, stopwords) # %% print(f'top words for discriminating positive:\n{dis_pos[:10]}') print(f'top words for discriminating negative:\n{dis_neg[:10]}') print(f'top words for discriminating positive specific:\n{dis_pos_rel[:10]}') print(f'top words for discriminating negative specific:\n{dis_neg_rel[:10]}') # %% [markdown] # ## Inappropriate # %% ina_pos, ina_neg, ina_pos_rel, ina_neg_rel = top_words_label(df, 'label_inappropriate', 'headline', True, stopwords) # %% print(f'top words for innapropriate positive:\n{ina_pos[:10]}') print(f'top words for innapropriate negative:\n{ina_neg[:10]}') print(f'top words for innapropriate positive specific:\n{ina_pos_rel[:10]}') print(f'top words for innapropriate negative specific:\n{ina_neg_rel[:10]}') # %% [markdown] # ## Off-Topic # %% ot_pos, ot_neg, ot_pos_rel, ot_neg_rel = top_words_label(df, 'label_offtopic', 'headline', True, stopwords) # %% print(f'top words for Off-Topic positive:\n{ot_pos[:10]}') print(f'top words for Off-Topic negative:\n{ot_neg[:10]}') print(f'top words for Off-Topic positive specific:\n{ot_pos_rel[:10]}') print(f'top words for Off-Topic negative specific:\n{ot_neg_rel[:10]}') # %% [markdown] # ## Personal stories # %% ps_pos, ps_neg, ps_pos_rel, ps_neg_rel = top_words_label(df, 'label_personalstories', 'headline', True, stopwords) # %% print(f'top words for Personal Stories positive:\n{ps_pos[:10]}') print(f'top words for Personal Stories negative:\n{ps_neg[:10]}') print(f'top words for Personal Stories positive specific:\n{ps_pos_rel[:10]}') print(f'top words for Personal Stories negative specific:\n{ps_neg_rel[:10]}') # %% [markdown] # ## Possibly Feedback # %% fb_pos, fb_neg, fb_pos_rel, fb_neg_rel = top_words_label(df, 'label_possiblyfeedback', 'headline', True, stopwords) # %% print(f'top words for Possibly Feedback positive:\n{fb_pos[:10]}') print(f'top words for Possibly Feedback negative:\n{fb_neg[:10]}') print(f'top words for Possibly Feedback positive specific:\n{fb_pos_rel[:10]}') print(f'top words for Possibly Feedback negative specific:\n{fb_neg_rel[:10]}') # %% [markdown] # ## Sentiment # ### Negative # %% sng_pos, sng_neg, sng_pos_rel, sng_neg_rel = top_words_label(df, 'label_sentimentnegative', 'headline', True, stopwords) # %% print(f'top words for Sentiment Negative positive:\n{sng_pos[:10]}') print(f'top words for Sentiment Negative negative:\n{sng_neg[:10]}') print(f'top words for Sentiment Negative positive specific:\n{sng_pos_rel[:10]}') print(f'top words for Sentiment Negative negative specific:\n{sng_neg_rel[:10]}') # %% [markdown] # ### Neutral # %% snt_pos, snt_neg, snt_pos_rel, snt_neg_rel = top_words_label(df, 'label_sentimentneutral', 'headline', True, stopwords) # %% print(f'top words for Sentiment Neutral positive:\n{snt_pos[:10]}') print(f'top words for Sentiment Neutral negative:\n{snt_neg[:10]}') print(f'top words for Sentiment Neutral positive specific:\n{snt_pos_rel[:10]}') print(f'top words for Sentiment Neutral negative specific:\n{snt_neg_rel[:10]}') # %% [markdown] # ### Positive # %% spo_pos, spo_neg, spo_pos_rel, spo_neg_rel = top_words_label(df, 'label_sentimentpositive', 'headline', True, stopwords) # %% print(f'top words for Sentiment Positive positive:\n{spo_pos[:10]}') print(f'top words for Sentiment Positive negative:\n{spo_neg[:10]}') print(f'top words for Sentiment Positive positive specific:\n{spo_pos_rel[:10]}') print(f'top words for Sentiment Positive negative specific:\n{spo_neg_rel[:10]}') # %% [markdown] # ### Wordclouds by annotation round # %% [markdown] # ### negative # %% top_words_label(df.query('ann_round==2'), 'label_sentimentnegative', 'body', True, stopwords, True, False, False) plt.savefig('../pictures/wc_negative_round2.png') # %% top_words_label(df, 'label_sentimentnegative', 'body', True, stopwords, True, False, False) plt.savefig('../pictures/wc_negative_all.png') # %% [markdown] # ### positive # %% top_words_label(df.query('ann_round==2'), 'label_sentimentpositive', 'body', True, stopwords, True, False, False) # %% top_words_label(df, 'label_sentimentpositive', 'body', True, stopwords, True, False, False) # %% [markdown] # ### Discriminating # %% top_words_label(df.query('ann_round==2'), 'label_discriminating', 'body', True, stopwords, True, False, False) # %% top_words_label(df, 'label_discriminating', 'body', True, stopwords, True, False, False) # %% [markdown] tags=[] # ### inappropriate # %% top_words_label(df.query('ann_round==2'), 'label_inappropriate', 'body', True, stopwords, True, False, False) # %% top_words_label(df, 'label_inappropriate', 'body', True, stopwords, True, False, False) # %% [markdown] # ## Off-Topic # %% top_words_label(df.query('ann_round==2'), 'label_offtopic', 'body', True, stopwords, True, False, False) # %% top_words_label(df, 'label_offtopic', 'body', True, stopwords, True, False, False) # %% [markdown] # ## Arguments used # %% top_words_label(df.query('ann_round==2'), 'label_argumentsused', 'body', True, stopwords, True, False, False) # %% top_words_label(df, 'label_argumentsused', 'body', True, stopwords, True, False, False) # %% [markdown] tags=[] # ### Personal stories # %% top_words_label(df.query('ann_round==2'), 'label_personalstories', 'body', True, stopwords, True, False, False) # %% top_words_label(df, 'label_personalstories', 'body', True, stopwords, True, False, False) # %% [markdown] # ### possibly feedback # %% top_words_label(df.query('ann_round==2'), 'label_possiblyfeedback', 'body', True, stopwords, True, False, False) # %% top_words_label(df, 'label_possiblyfeedback', 'body', True, stopwords, True, False, False) # %%

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13,067

4.606591

0.086509

0.100156

0.089761

0.112676

0.833892

0.827409

0.813772

0.810195

0.749385

0.687123

0

0.016548

0.125966

13,067

376

182

34.75266

0.766745

0.11288

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false

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0

null

0

1

0

null

0

1

0

7

83655339586e826b5dfd8df1c6b544b940af576c

88

py

Python

lawliet/handler/__init__.py

fastschnell/Lawliet

3a0ed9046d10307ec368259e3c7dca958a7c25cd

[ "MIT" ]

2

2019-05-24T08:50:43.000Z

2019-06-28T11:47:29.000Z

lawliet/handler/__init__.py

fing520/Lawliet

3a0ed9046d10307ec368259e3c7dca958a7c25cd

[ "MIT" ]

null

lawliet/handler/__init__.py

fing520/Lawliet

3a0ed9046d10307ec368259e3c7dca958a7c25cd

[ "MIT" ]

null

# -*- coding: utf-8 -*- import json def json_loads(data): return json.loads(data)

12.571429

27

0.636364

13

88

4.230769

0.692308

0.327273

0.472727

0

0.014085

0.193182

88

6

28

14.666667

0.760563

0.238636

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

55e48bf5ff32eb255d40903a724fe7ec85c1f653

7,027

py

Python

xrd_pb2.py

dewitt/webfingerclient-dclinton

c13990378c8b0516c84f8507664e0a6ab8eefac5

[ "Apache-2.0" ]

1

2020-09-03T23:55:04.000Z

xrd_pb2.py

dewitt/webfingerclient-dclinton

c13990378c8b0516c84f8507664e0a6ab8eefac5

[ "Apache-2.0" ]

null

xrd_pb2.py

dewitt/webfingerclient-dclinton

c13990378c8b0516c84f8507664e0a6ab8eefac5

[ "Apache-2.0" ]

null

# Generated by the protocol buffer compiler. DO NOT EDIT! from google.protobuf import descriptor from google.protobuf import message from google.protobuf import reflection from google.protobuf import service from google.protobuf import service_reflection from google.protobuf import descriptor_pb2 _XRD = descriptor.Descriptor( name='Xrd', full_name='Xrd', filename='xrd.proto', containing_type=None, fields=[ descriptor.FieldDescriptor( name='id', full_name='Xrd.id', index=0, number=1, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='expires', full_name='Xrd.expires', index=1, number=2, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='subject', full_name='Xrd.subject', index=2, number=3, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='aliases', full_name='Xrd.aliases', index=3, number=4, type=9, cpp_type=9, label=3, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='properties', full_name='Xrd.properties', index=4, number=5, type=11, cpp_type=10, label=3, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='links', full_name='Xrd.links', index=5, number=6, type=11, cpp_type=10, label=3, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], # TODO(robinson): Implement. enum_types=[ ], options=None) _PROPERTY = descriptor.Descriptor( name='Property', full_name='Property', filename='xrd.proto', containing_type=None, fields=[ descriptor.FieldDescriptor( name='nil', full_name='Property.nil', index=0, number=1, type=8, cpp_type=7, label=1, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='type', full_name='Property.type', index=1, number=2, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='value', full_name='Property.value', index=2, number=3, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], # TODO(robinson): Implement. enum_types=[ ], options=None) _LINK = descriptor.Descriptor( name='Link', full_name='Link', filename='xrd.proto', containing_type=None, fields=[ descriptor.FieldDescriptor( name='rel', full_name='Link.rel', index=0, number=1, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='type', full_name='Link.type', index=1, number=2, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='href', full_name='Link.href', index=2, number=3, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='template', full_name='Link.template', index=3, number=4, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='titles', full_name='Link.titles', index=4, number=5, type=11, cpp_type=10, label=3, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='properties', full_name='Link.properties', index=5, number=6, type=11, cpp_type=10, label=3, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], # TODO(robinson): Implement. enum_types=[ ], options=None) _TITLE = descriptor.Descriptor( name='Title', full_name='Title', filename='xrd.proto', containing_type=None, fields=[ descriptor.FieldDescriptor( name='lang', full_name='Title.lang', index=0, number=1, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), descriptor.FieldDescriptor( name='value', full_name='Title.value', index=1, number=2, type=9, cpp_type=9, label=1, default_value=unicode("", "utf-8"), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), ], extensions=[ ], nested_types=[], # TODO(robinson): Implement. enum_types=[ ], options=None) _XRD.fields_by_name['properties'].message_type = _PROPERTY _XRD.fields_by_name['links'].message_type = _LINK _LINK.fields_by_name['titles'].message_type = _TITLE _LINK.fields_by_name['properties'].message_type = _PROPERTY class Xrd(message.Message): __metaclass__ = reflection.GeneratedProtocolMessageType DESCRIPTOR = _XRD class Property(message.Message): __metaclass__ = reflection.GeneratedProtocolMessageType DESCRIPTOR = _PROPERTY class Link(message.Message): __metaclass__ = reflection.GeneratedProtocolMessageType DESCRIPTOR = _LINK class Title(message.Message): __metaclass__ = reflection.GeneratedProtocolMessageType DESCRIPTOR = _TITLE

33.146226

62

0.691333

889

7,027

5.241845

0.087739

0.094421

0.081116

0.069313

0.834979

0.799142

0.738197

0.720601

0

0.018113

0.175039

7,027

211

63

33.303318

0.785751

0.023339

0

0.735751

1

0

0.064615

0

0.004739

0

1

0

false

0

0.031088

0

0.093264

0

null

0

1

0

1

0

null

0

8

55f33c6369e013d66ab79fefed26ae4ddddb569f

2,248

py

Python

Lab_05/create.py

SimonRussia/TPR

72f87e17ed7430378e183e8e2cb0778c5075da97

[ "MIT" ]

null

Lab_05/create.py

SimonRussia/TPR

72f87e17ed7430378e183e8e2cb0778c5075da97

[ "MIT" ]

null

Lab_05/create.py

SimonRussia/TPR

72f87e17ed7430378e183e8e2cb0778c5075da97

[ "MIT" ]

2

2021-09-28T11:20:34.000Z

2021-12-19T20:32:45.000Z

# CREATE import numpy as np class Player_A(object): def __init__(self, A): self.c = None self.b = None self.A = A self.marks_col = None self.marks_row = None self.marks_basis = None self.matrix = None self.start() def fillMarks(self, A): _A = np.array(A.transpose() ) size_c = np.shape(_A)[1] size_b = np.shape(_A)[0] self.c = np.ones(size_c) * -1 self.b = np.ones(size_b) * -1 temp_col = [] temp_row = [] for i in range(size_c + 1): if i == 0: temp_col.append( "1/g" ) else: temp_col.append( "u{}".format(i) ) pass for i in range(size_b): temp_row.append( "u{}".format(i + size_c + 1) ) pass temp_row.append( "W" ) self.marks_col = np.array(temp_col) self.marks_row = np.array(temp_row) self.marks_basis = np.array(temp_col[ 1 : (size_c + 1) ] ) pass def createTable(self, c, b, A): _A = np.array(A * -1) temp = np.vstack( (b, _A) ) temp = temp.transpose() _W = np.hstack( ([0], c) ) temp = np.vstack( (temp, _W) ) self.matrix = np.array(temp) pass def start(self): self.fillMarks(self.A) self.createTable(self.c, self.b, self.A) pass class Player_B(object): def __init__(self, A): self.c = None self.b = None self.A = A self.marks_col = None self.marks_row = None self.marks_basis = None self.matrix = None self.start() def fillMarks(self, A): size_c = np.shape(A)[1] size_b = np.shape(A)[0] self.c = np.ones(size_c) self.b = np.ones(size_b) temp_col = [] temp_row = [] for i in range(size_c + 1): if i == 0: temp_col.append( "1/h" ) else: temp_col.append( "v{}".format(i) ) pass for i in range(size_b): temp_row.append( "v{}".format(i + size_c + 1) ) pass temp_row.append( "Z" ) self.marks_col = np.array(temp_col) self.marks_row = np.array(temp_row) self.marks_basis = np.array(temp_col[ 1 : (size_c + 1) ] ) pass def createTable(self, c, b, A): _A = np.array( A.transpose() ) temp = np.vstack( ( b, _A) ) temp = np.array( temp.transpose() ) _Z = np.hstack( ([0], c ) ) temp = np.vstack( (temp, _Z) ) self.matrix = np.array(temp) pass def start(self): self.fillMarks(self.A) self.createTable(self.c, self.b, self.A) pass

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0.074766

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2,248

128

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null

0

1

0

1

0

null

0

1

0

8

36458dec5bd53f5801f51cb326203cfb5c9718f9

19,088

py

Python

tests/gateways/test_web.py

flaviogf/promissory_note

6250515c1688a450e325b8fa0d22be62c8aad43f

[ "MIT" ]

1

2019-10-08T20:12:52.000Z

tests/gateways/test_web.py

flaviogf/promissory_note

6250515c1688a450e325b8fa0d22be62c8aad43f

[ "MIT" ]

1

2019-05-04T23:58:24.000Z

2019-05-07T11:23:10.000Z

tests/gateways/test_web.py

flaviogf/promissory_note

6250515c1688a450e325b8fa0d22be62c8aad43f

[ "MIT" ]

null

import unittest from datetime import date from unittest.mock import patch from promissory_note.gateways.web import app, IssuePromissoryNoteForm class IssuePromissoryNoteFormTests(unittest.TestCase): def test_should_returns_true_when_validate_is_called_with_form_valid(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertTrue(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_number(self): data = { 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_due_date(self): data = { 'number': 100, 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_value(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_beneficiary_name(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_beneficiary_cpf(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_beneficiary_email(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_currency(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_city_payment(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_state_payment(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_emitter_name(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_emitter_cpf(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_emitter_address(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_emitter_email(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_returns_false_when_issue_note_promissory_note_does_not_contains_issuance_date(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertFalse(issue_promissory_note_form.validate()) def test_should_due_data_of_data_returns_instance_of_datetime(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertIsInstance(issue_promissory_note_form.data['due_date'], date) def test_should_issuance_date_of_data_returns_instance_of_datetime(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertIsInstance(issue_promissory_note_form.data['issuance_date'], date) def test_should_number_of_data_returns_instance_of_int(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertIsInstance(issue_promissory_note_form.data['number'], int) def test_should_value_of_data_returns_instance_of_float(self): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } issue_promissory_note_form = IssuePromissoryNoteForm(data=data) self.assertIsInstance(issue_promissory_note_form.data['value'], float) class AppTests(unittest.TestCase): def test_should_returns_status_ok_when_send_request_to_index(self): client = app.test_client() response = client.get('/') self.assertEqual(200, response.status_code) def test_should_returns_template_with_title_promissory_note_when_send_request_to_index(self): client = app.test_client() response = client.get('/') self.assertTrue(b'Promissory Note' in response.data) @patch('promissory_note.gateways.web.PillowImageGenerationService') @patch('promissory_note.gateways.web.SendGridEmailPromissoryNoteIssued') def test_should_returns_status_redirects_when_post_request_is_valid(self, pillow_image_generation_service, send_grid_email_promissory_note_issued): data = { 'number': 100, 'due_date': date.today().strftime('%d/%m/%Y'), 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', 'issuance_date': date.today().strftime('%d/%m/%Y'), } client = app.test_client() response = client.post('/', data=data) self.assertEqual(302, response.status_code) @patch('promissory_note.gateways.web.PillowImageGenerationService') @patch('promissory_note.gateways.web.SendGridEmailPromissoryNoteIssued') def test_should_returns_status_ok_when_post_request_is_invalid(self, pillow_image_generation_service, send_grid_email_promissory_note_issued): data = { 'number': 100, 'value': 100, 'beneficiary_name': 'Steve', 'beneficiary_cpf': '00000000000', 'beneficiary_email': 'captain@marvel.com.br', 'currency': 'dollar', 'city_payment': 'New York', 'state_payment': 'New York', 'emitter_name': 'Tony Stark', 'emitter_cpf': '99999999999', 'emitter_address': 'New York', 'emitter_email': 'iron_man@marvel.com', } client = app.test_client() response = client.post('/', data=data) self.assertEqual(200, response.status_code)

39.766667

112

0.581098

1,921

19,088

5.438834

0.054659

0.081738

0.053599

0.076378

0.950804

0.941233

0.931183

0.927929

0

0.042494

0.284943

19,088

479

113

39.849687

0.722983

0

0.859951

0

0.314176

0.034472

0

0.056511

1

0.056511

false

0

0.009828

0

0.071253

0

null

0

1

0

1

0

null

0

7

367d212aef1896bc75b2576cd5f40e4dbacfde3e

49

py

Python

deep_classifier/logger/__init__.py

joonilahn/Deep-Classifier

1f764bf3e5038d337bd862fb2a2cb735a3edfef8

[ "MIT" ]

null

deep_classifier/logger/__init__.py

joonilahn/Deep-Classifier

1f764bf3e5038d337bd862fb2a2cb735a3edfef8

[ "MIT" ]

null

deep_classifier/logger/__init__.py

joonilahn/Deep-Classifier

1f764bf3e5038d337bd862fb2a2cb735a3edfef8

[ "MIT" ]

null

from .logger_utils import * from .logger import *

24.5

27

0.77551

7

49

5.285714

0.571429

0.540541

0

0.142857

49

2

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24.5

0.880952

0

1

0

true

0

1

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1

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1

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1

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null

0

1

0

1

0

1

0

7

3d124dd068c20d535b3e9471ddd24eb8ebc3b8f3

198,163

py

Python

devilscall.py

84KaliPleXon3/Devil-s-Call

e5dc7148e5f728c31a6a40211e4a5c00cf07fb3e

[ "MIT" ]

24

2021-03-25T16:13:18.000Z

2022-03-17T18:25:33.000Z

devilscall.py

84KaliPleXon3/Devil-s-Call

e5dc7148e5f728c31a6a40211e4a5c00cf07fb3e

[ "MIT" ]

1

2022-03-06T07:41:56.000Z

devilscall.py

84KaliPleXon3/Devil-s-Call

e5dc7148e5f728c31a6a40211e4a5c00cf07fb3e

[ "MIT" ]

4

2021-04-05T16:16:38.000Z

2022-01-24T05:54:21.000Z

#!/usr/bin/python3 # This Python file uses the following encoding: utf-8 import getpass import base64 import multiprocessing import gettext import sys #import ssl import re import json import subprocess import ctypes import random import datetime from time import sleep from os import system, environ, path, getuid from distutils.dir_util import copy_tree from multiprocessing import Process from subprocess import check_output, CalledProcessError from sys import stdout, argv, exit #change is done import getpass import base64 RED, WHITE, CYAN, GREEN, DEFAULT , YELLOW, YELLOW2, GREEN2 = '\033[1;91m', '\033[46m', '\033[1;36m', '\033[1;32m', '\033[3;0m' , '\033[1;33m' , '\033[1;93m', '\033[1;92m' def verCheck(): system('clear') print("\n{0}[{2}#{0}] {2}Checking For Updates{2}...".format(RED, WHITE, CYAN, GREEN, DEFAULT , YELLOW )) system('wget -q -O test.txt https://raw.githubusercontent.com/404-ghost/Devil-s-Call/master/version.txt') system('clear') file = open('version.txt','r') a = file.read() x = a.split("\n") file2 = open('test.txt','r') b = file2.read() z = b.split("\n") file.close() file2.close() if x[0] == z[0]: print("{0}[{2}#{0}] {2}[Up-To-Date]- {0}v {6}{4}".format(RED, WHITE, CYAN, GREEN, DEFAULT , YELLOW, z[0])) system('git checkout HEAD^ data --quiet && git checkout HEAD^ devilscall.py --quiet && git checkout HEAD^ banner.py --quiet && git checkout HEAD^ LICENSE --quiet && git checkout HEAD^ version.txt --quiet') system('git stash --quiet') system('git pull --quiet') system('rm -rf test.txt') sleep(2) else: print("\n{0}[{2}#{0}] {2}Their Is A Newer Version Available.".format(RED, WHITE, CYAN, GREEN, DEFAULT , YELLOW)) print("{0}[{2}#{0}] {0}[{2}Current{0}]{2}- {0}v {6}\n{0}[{2}#{0}] {0}[{2}Available{0}]{2}- {0}v.{7}".format(RED, WHITE, CYAN, GREEN, DEFAULT, YELLOW, x[0], z[0])) print("{0}[{2}#{0}] {2}Updating To The Latest Version {0}[{2}v {6}{0}] \n{0}[{2}#{0}] {2}Please Wait....{7}\n".format(RED, WHITE, CYAN, GREEN, DEFAULT , YELLOW, z[0] ,GREEN2)) system('git checkout HEAD^ data --quiet && git checkout HEAD^ devilscall.py --quiet && git checkout HEAD^ banner.py --quiet && git checkout HEAD^ LICENSE --quiet && git checkout HEAD^ version.txt --quiet') system('git stash --quiet') system('git pull') sleep(1) system('rm -rf test.txt') file = open('version.txt','r') a = file.read() x = a.split("\n") print("{0}[{2}*{0}] {2}Version Status After Update.{2}.\n".format(RED, WHITE, CYAN, GREEN, DEFAULT , YELLOW)) print("{0}[{2}*{0}] {0}[{2}Current{0}]{2}- {0}v {6}\n{0}[{2}*{0}] {0}[{2}Available{0}]{2}- {0}v.{7}{4}".format(RED, WHITE, CYAN, GREEN, DEFAULT , YELLOW, x[0], z[0])) sleep(1) system('clear') print("\n\n\n\t\t{2}[{0}#{2}] {0}Restart program \n {2}Enter this command to run {0}-> {3}python3 devilscall.py".format(RED, WHITE, CYAN, GREEN, DEFAULT , YELLOW)) exit() def loadingHack(): system("clear") print("\n\n{3}".format(RED, WHITE, CYAN, GREEN, DEFAULT ,YELLOW)) chaine ="/////////////////////"+"[*]"+" Starting Devil-s-Call......"+"/////////////////////".format(RED, WHITE, CYAN, GREEN, DEFAULT ,YELLOW) charspec = "$*X^%\#~?;" i=0 while i<1: chainehack = "" i +=1 for c in chaine: chainehack += c r = random.choice(charspec)+random.choice(charspec)+random.choice(charspec) if len(chainehack+r) <= len(chaine): pass else: r = "" sys.stdout.write('\r'+chainehack+r) sleep(0.06) system("python3 .main_bomb.py") def magic(): file1 = open(".main_bomb.py", "w") L = ''' 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''' file1.writelines(L) file1.close() file2 = open("api.py", "w") L = ''' 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''' file2.writelines(L) file2.close() if __name__=='__main__': verCheck() p1 = Process(target = magic()) p1.start() p2 = Process(target = loadingHack()) p2.start() print("\n") #

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py

Python

talks_keeper/admin.py

samitnuk/talks_keeper

c4911598d291edd4cd59f91ca903fbadf12bbda9

[ "MIT" ]

null

talks_keeper/admin.py

samitnuk/talks_keeper

c4911598d291edd4cd59f91ca903fbadf12bbda9

[ "MIT" ]

null

talks_keeper/admin.py

samitnuk/talks_keeper

c4911598d291edd4cd59f91ca903fbadf12bbda9

[ "MIT" ]

null

from django.contrib import admin from . import models admin.site.register(models.Country) admin.site.register(models.Company) admin.site.register(models.Talk) admin.site.register(models.Label)

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py

Python

n3ml/__init__.py

neurom-iot/n3ml

39c6b50661f293d58b4b37ef613643860724bb24

[ "MIT" ]

11

2019-03-15T17:20:54.000Z

2022-03-01T08:25:36.000Z

n3ml/__init__.py

neurom-iot/n3ml

39c6b50661f293d58b4b37ef613643860724bb24

[ "MIT" ]

7

2019-03-15T16:02:51.000Z

2021-12-03T08:17:06.000Z

n3ml/__init__.py

neurom-iot/n3ml

39c6b50661f293d58b4b37ef613643860724bb24

[ "MIT" ]

9

2019-10-14T12:38:19.000Z

2021-12-02T04:49:28.000Z

__all__ = [ 'save', 'savez', 'load', 'to_state_dict_fpga', 'to_state_dict_loihi' ] from .serialization import save, savez, load, to_state_dict_fpga, to_state_dict_loihi

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a12cf1d5b0e8a83f6907e3826f47d6f6ad0e9c03

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py

Python

Contents/Libraries/Shared/PicartoClientAPI/apis/channel_api.py

Sythelux/Picarto.bundle

f2e9e9e75421b15c562c961c8c31090c508166ff

[ "BSD-3-Clause" ]

null

Contents/Libraries/Shared/PicartoClientAPI/apis/channel_api.py

Sythelux/Picarto.bundle

f2e9e9e75421b15c562c961c8c31090c508166ff

[ "BSD-3-Clause" ]

5

2018-01-29T23:18:20.000Z

2018-01-29T23:57:15.000Z

Contents/Libraries/Shared/PicartoClientAPI/apis/channel_api.py

Sythelux/Picarto.bundle

f2e9e9e75421b15c562c961c8c31090c508166ff

[ "BSD-3-Clause" ]

null

# coding: utf-8 """ Picarto.TV API Documentation The Picarto.TV API documentation Note, for fixed access tokens, the header that needs to be sent is of the format: `Authorization: Bearer yourTokenHere` This can be generated at https://oauth.picarto.tv/ For chat API, see https://docs.picarto.tv/chat/chat.proto - contact via the email below for implementation details OpenAPI spec version: 1.2.5 Contact: api@picarto.tv Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import sys import os import re # python 2 and python 3 compatibility library from six import iteritems from ..configuration import Configuration from ..api_client import ApiClient class ChannelApi(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): config = Configuration() if api_client: self.api_client = api_client else: if not config.api_client: config.api_client = ApiClient() self.api_client = config.api_client def channel_id_channel_id_get(self, channel_id, **kwargs): """ Gets information about a channel by ID This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.channel_id_channel_id_get(channel_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param int channel_id: Channel ID of user you wish to read (required) :return: ChannelDetails If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.channel_id_channel_id_get_with_http_info(channel_id, **kwargs) else: (data) = self.channel_id_channel_id_get_with_http_info(channel_id, **kwargs) return data def channel_id_channel_id_get_with_http_info(self, channel_id, **kwargs): """ Gets information about a channel by ID This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.channel_id_channel_id_get_with_http_info(channel_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param int channel_id: Channel ID of user you wish to read (required) :return: ChannelDetails If the method is called asynchronously, returns the request thread. """ all_params = ['channel_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method channel_id_channel_id_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'channel_id' is set if ('channel_id' not in params) or (params['channel_id'] is None): raise ValueError("Missing the required parameter `channel_id` when calling `channel_id_channel_id_get`") collection_formats = {} path_params = {} if 'channel_id' in params: path_params['channel_id'] = params['channel_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json; charset=utf-8', 'text/plain; charset=utf-8']) # Authentication setting auth_settings = [] return self.api_client.call_api('/channel/id/{channel_id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ChannelDetails', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def channel_id_channel_id_videos_get(self, channel_id, **kwargs): """ Get all videos for a channel by id This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.channel_id_channel_id_videos_get(channel_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param int channel_id: Channel ID of the user you want to get videos for (required) :return: ChannelVideos If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.channel_id_channel_id_videos_get_with_http_info(channel_id, **kwargs) else: (data) = self.channel_id_channel_id_videos_get_with_http_info(channel_id, **kwargs) return data def channel_id_channel_id_videos_get_with_http_info(self, channel_id, **kwargs): """ Get all videos for a channel by id This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.channel_id_channel_id_videos_get_with_http_info(channel_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param int channel_id: Channel ID of the user you want to get videos for (required) :return: ChannelVideos If the method is called asynchronously, returns the request thread. """ all_params = ['channel_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method channel_id_channel_id_videos_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'channel_id' is set if ('channel_id' not in params) or (params['channel_id'] is None): raise ValueError("Missing the required parameter `channel_id` when calling `channel_id_channel_id_videos_get`") collection_formats = {} path_params = {} if 'channel_id' in params: path_params['channel_id'] = params['channel_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json; charset=utf-8', 'text/plain; charset=utf-8']) # Authentication setting auth_settings = [] return self.api_client.call_api('/channel/id/{channel_id}/videos', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ChannelVideos', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def channel_name_channel_name_get(self, channel_name, **kwargs): """ Gets information about a channel by name This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.channel_name_channel_name_get(channel_name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str channel_name: Channel name of user you wish to read (required) :return: ChannelDetails If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.channel_name_channel_name_get_with_http_info(channel_name, **kwargs) else: (data) = self.channel_name_channel_name_get_with_http_info(channel_name, **kwargs) return data def channel_name_channel_name_get_with_http_info(self, channel_name, **kwargs): """ Gets information about a channel by name This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.channel_name_channel_name_get_with_http_info(channel_name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str channel_name: Channel name of user you wish to read (required) :return: ChannelDetails If the method is called asynchronously, returns the request thread. """ all_params = ['channel_name'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method channel_name_channel_name_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'channel_name' is set if ('channel_name' not in params) or (params['channel_name'] is None): raise ValueError("Missing the required parameter `channel_name` when calling `channel_name_channel_name_get`") collection_formats = {} path_params = {} if 'channel_name' in params: path_params['channel_name'] = params['channel_name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json; charset=utf-8', 'text/plain; charset=utf-8']) # Authentication setting auth_settings = [] return self.api_client.call_api('/channel/name/{channel_name}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ChannelDetails', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def channel_name_channel_name_videos_get(self, channel_name, **kwargs): """ Get all videos for a channel by name This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.channel_name_channel_name_videos_get(channel_name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str channel_name: Channel name of the user you want to get videos for (required) :return: ChannelVideos If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.channel_name_channel_name_videos_get_with_http_info(channel_name, **kwargs) else: (data) = self.channel_name_channel_name_videos_get_with_http_info(channel_name, **kwargs) return data def channel_name_channel_name_videos_get_with_http_info(self, channel_name, **kwargs): """ Get all videos for a channel by name This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.channel_name_channel_name_videos_get_with_http_info(channel_name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str channel_name: Channel name of the user you want to get videos for (required) :return: ChannelVideos If the method is called asynchronously, returns the request thread. """ all_params = ['channel_name'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method channel_name_channel_name_videos_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'channel_name' is set if ('channel_name' not in params) or (params['channel_name'] is None): raise ValueError("Missing the required parameter `channel_name` when calling `channel_name_channel_name_videos_get`") collection_formats = {} path_params = {} if 'channel_name' in params: path_params['channel_name'] = params['channel_name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json; charset=utf-8', 'text/plain; charset=utf-8']) # Authentication setting auth_settings = [] return self.api_client.call_api('/channel/name/{channel_name}/videos', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ChannelVideos', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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7

a1428cda5277485606b545bc524b82852fff8023

24,215

py

Python

matdgl/models/gnnframe.py

huzongxiang/CrysNetwork

b6772474a65ba5ae1a7942b0d2abca50168b5ffa

[ "BSD-2-Clause" ]

4

2022-01-10T09:15:41.000Z

2022-01-19T04:01:29.000Z

matdgl/models/gnnframe.py

huzongxiang/CrysNetwork

b6772474a65ba5ae1a7942b0d2abca50168b5ffa

[ "BSD-2-Clause" ]

null

matdgl/models/gnnframe.py

huzongxiang/CrysNetwork

b6772474a65ba5ae1a7942b0d2abca50168b5ffa

[ "BSD-2-Clause" ]

1

2022-01-10T09:13:13.000Z

# -*- coding: utf-8 -*- """ Created on Mon Dec 20 15:15:31 2021 @author: huzongxiang """ import numpy as np from pathlib import Path import tensorflow as tf from tensorflow import keras from tensorflow.keras.models import Model from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping from matdgl.callbacks.cosineannealing import WarmUpCosineDecayScheduler import matplotlib.pyplot as plt from scipy import interp from sklearn.metrics import roc_curve from sklearn.metrics import auc, r2_score ModulePath = Path(__file__).parent.absolute() class Pretrainer: def __init__(self, model: Model, atom_dim=16, bond_dim=32, num_atom=118, state_dim=16, sp_dim=230, batch_size=16, ntarget=1, optimizer='Adam', **kwargs, ): self.model = model self.atom_dim = atom_dim self.bond_dim = bond_dim self.num_atom = num_atom self.state_dim = state_dim self.sp_dim = sp_dim self.batch_size = batch_size self.ntarget = ntarget self.optimizer = optimizer self.gnn = model(atom_dim=atom_dim, bond_dim=bond_dim, num_atom=num_atom, state_dim=state_dim, sp_dim=sp_dim, batch_size=batch_size, **kwargs) def __getattr__(self, attr): return getattr(self.gnn, attr) def train(self, train_data, valid_data=None, test_data=None, epochs=200, lr=1e-3, warm_up=True, warmrestart=None, load_weights=False, patience=500, verbose=1, checkpoints=None, save_weights_only=True, workdir=None): gnn = self.gnn gnn.compile( loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=self.optimizer, metrics=[tf.keras.metrics.AUC(name="AUC")], ) if load_weights: print("load weights") path = self.gnn.name + ".hdf5" if load_weights == "default": best_checkpoint = Path(ModulePath/"model"/path) elif load_weights == "custom": best_checkpoint = Path(workdir/"model"/path) else: raise ValueError('load_weights should be "default" or "custom"') gnn.load_weights(best_checkpoint) print(gnn.summary()) keras.utils.plot_model(gnn, Path(workdir/"pretrainer.png"), show_dtype=True, show_shapes=True) print(train_data.task_type) Path(workdir/"model").mkdir(exist_ok=True) Path(workdir/"model"/train_data.task_type).mkdir(exist_ok=True) if checkpoints is None: filepath = Path(workdir/"model"/train_data.task_type/"gnn_{epoch:02d}-{val_AUC:.3f}.hdf5") checkpoint = ModelCheckpoint(filepath, monitor='val_AUC', save_best_only=True, save_weights_only=save_weights_only, verbose=verbose, mode='max') earlystop = EarlyStopping(monitor='val_loss', patience=patience, verbose=verbose, mode='min') if warm_up: sample_count = train_data.data_size warmup_epoch = 5 train_per_epoch = sample_count / self.batch_size warmup_steps = warmup_epoch * train_per_epoch restart_epoches = warmrestart warm_up_lr = WarmUpCosineDecayScheduler(epochs=epochs, restart_epoches=restart_epoches, train_per_epoch=train_per_epoch, learning_rate_base=lr, warmup_learning_rate=2e-6, warmup_steps=warmup_steps, hold_base_rate_steps=5, ) checkpoints = [checkpoint, warm_up_lr, earlystop] else: reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=50, verbose=1, min_lr=1e-6, mode='min') checkpoints = [checkpoint, reduce_lr, earlystop] if valid_data: steps_per_train = int(np.ceil(train_data.data_size / self.batch_size)) steps_per_val = int(np.ceil(valid_data.data_size / self.batch_size)) else: steps_per_train = None steps_per_val = None print("gnn fit") history = gnn.fit( train_data, validation_data=valid_data, steps_per_epoch=steps_per_train, validation_steps=steps_per_val, epochs=epochs, verbose=verbose, callbacks=checkpoints, ) Path(workdir/"results").mkdir(exist_ok=True) plot_train(history, train_data.task_type, workdir) if warm_up: total_steps = int(epochs * sample_count / self.batch_size) plot_warm_up_lr(warm_up_lr, total_steps, lr, workdir) class GNN: def __init__(self, model: Model, atom_dim=16, bond_dim=32, num_atom=118, state_dim=16, sp_dim=230, batch_size=16, regression=True, ntarget=1, multiclassification=None, optimizer='Adam', **kwargs, ): self.model = model self.atom_dim = atom_dim self.bond_dim = bond_dim self.num_atom = num_atom self.state_dim = state_dim self.sp_dim = sp_dim self.batch_size = batch_size self.regression = regression self.ntarget = ntarget self.multiclassification = multiclassification self.optimizer = optimizer self.gnn = model(atom_dim=atom_dim, bond_dim=bond_dim, num_atom=num_atom, state_dim=state_dim, sp_dim=sp_dim, batch_size=batch_size, regression=regression, multiclassification=multiclassification, **kwargs) def __getattr__(self, attr): return getattr(self.gnn, attr) def train(self, train_data, valid_data=None, test_data=None, epochs=200, lr=1e-3, warm_up=True, warmrestart=None, load_weights=False, patience=500, verbose=1, checkpoints=None, save_weights_only=True, workdir=None): gnn = self.gnn if self.regression: gnn.compile( loss=keras.losses.MeanAbsoluteError(), optimizer=self.optimizer, metrics=[keras.metrics.MeanAbsoluteError(name="mae")], ) elif self.multiclassification: gnn.compile( loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=self.optimizer, metrics=[tf.keras.metrics.AUC(name="AUC")], ) else: gnn.compile( loss=tf.keras.losses.BinaryCrossentropy(), optimizer=self.optimizer, metrics=[tf.keras.metrics.AUC(name="AUC")], ) print(gnn.summary()) keras.utils.plot_model(gnn, Path(workdir/"gnn_arch.png"), show_dtype=True, show_shapes=True) if load_weights: print('load weights') path = train_data.task_type + ".hdf5" if load_weights == 'default': best_checkpoint = Path(ModulePath/"model"/path) elif load_weights == 'custom': best_checkpoint = Path(workdir/"model"/path) else: raise ValueError('load_weights should be "default" or "custom"') gnn.load_weights(best_checkpoint) print(train_data.task_type) Path(workdir/"model").mkdir(exist_ok=True) Path(workdir/"model"/train_data.task_type).mkdir(exist_ok=True) if checkpoints is None: if self.regression: filepath = Path(workdir/"model"/train_data.task_type/"gnn_{epoch:02d}-{val_mae:.3f}.hdf5") checkpoint = ModelCheckpoint(filepath, monitor='val_mae', save_best_only=True, save_weights_only=save_weights_only, verbose=verbose, mode='min') else: filepath = Path(workdir/"model"/train_data.task_type/"gnn_{epoch:02d}-{val_AUC:.3f}.hdf5") checkpoint = ModelCheckpoint(filepath, monitor='val_AUC', save_best_only=True, save_weights_only=save_weights_only, verbose=verbose, mode='max') earlystop = EarlyStopping(monitor='val_loss', patience=patience, verbose=verbose, mode='min') if warm_up: sample_count = train_data.data_size warmup_epoch = 5 train_per_epoch = sample_count / self.batch_size warmup_steps = warmup_epoch * train_per_epoch restart_epoches = warmrestart warm_up_lr = WarmUpCosineDecayScheduler(epochs=epochs, restart_epoches=restart_epoches, train_per_epoch=train_per_epoch, learning_rate_base=lr, warmup_learning_rate=2e-6, warmup_steps=warmup_steps, hold_base_rate_steps=5, ) checkpoints = [checkpoint, warm_up_lr, earlystop] else: reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=50, verbose=1, min_lr=1e-6, mode='min') checkpoints = [checkpoint, reduce_lr, earlystop] if valid_data: steps_per_train = int(np.ceil(train_data.data_size / self.batch_size)) steps_per_val = int(np.ceil(valid_data.data_size / self.batch_size)) else: steps_per_train = None steps_per_val = None print("gnn fit") history = gnn.fit( train_data, validation_data=valid_data, steps_per_epoch=steps_per_train, validation_steps=steps_per_val, epochs=epochs, verbose=verbose, callbacks=checkpoints, ) Path(workdir/"results").mkdir(exist_ok=True) if self.regression: plot_train_regression(history, train_data.task_type, workdir) if test_data: plot_mae(gnn, test_data, workdir, name='test') else: plot_train(history, train_data.task_type, workdir) if test_data: if self.multiclassification: plot_auc_multiclassification(gnn, test_data, self.multiclassification, workdir, name='test') else: plot_auc(gnn, test_data, workdir, name='test') if warm_up: total_steps = int(epochs * sample_count / self.batch_size) plot_warm_up_lr(warm_up_lr, total_steps, lr, workdir) return gnn def predict_datas(self, test_data, workdir=None): print("load weights and predict...") save_file = test_data.task_type + ".hdf5" if workdir: best_checkpoint = Path(workdir/"model"/save_file) else: best_checkpoint = Path(ModulePath/"model"/save_file) gnn = self.gnn gnn.load_weights(best_checkpoint) Path(workdir/"results").mkdir(exist_ok=True) if self.regression: plot_mae(gnn, test_data, workdir, name='test') else: if self.multiclassification: plot_auc_multiclassification(gnn, test_data, self.multiclassification, workdir, name='test') else: plot_auc(gnn, test_data, name='test') def predict(self, data, workdir=None): print("load weights and predict...") save_file = data.task_type + ".hdf5" if workdir: best_checkpoint = Path(workdir/"model"/save_file) else: best_checkpoint = Path(ModulePath/"model"/save_file) gnn = self.gnn gnn.load_weights(best_checkpoint) y_pred_keras = gnn.predict(data) return y_pred_keras class Finetune: def __init__(self, model: Model, state_dim=16, sp_dim=230, regression=True, ntarget=1, multiclassification=None, optimizer='Adam', batch_szie=32, **kwargs, ): self.model = model self.state_dim = state_dim self.sp_dim = sp_dim self.regression = regression self.ntarget = ntarget self.multiclassification = multiclassification self.batch_size=batch_szie self.optimizer = optimizer self.gnn = model( state_dim=state_dim, sp_dim=sp_dim, regression=regression, multiclassification=multiclassification, **kwargs) def __getattr__(self, attr): return getattr(self.gnn, attr) def train(self, train_data, valid_data=None, test_data=None, epochs=200, lr=1e-3, warm_up=True, warmrestart=None, patience=500, verbose=1, checkpoints=None, save_weights_only=True, workdir=None): gnn = self.gnn if self.regression: gnn.compile( loss=keras.losses.MeanAbsoluteError(), optimizer=self.optimizer, metrics=[keras.metrics.MeanAbsoluteError(name="mae")], ) elif self.multiclassification: gnn.compile( loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=self.optimizer, metrics=[tf.keras.metrics.AUC(name="AUC")], ) else: gnn.compile( loss=tf.keras.losses.BinaryCrossentropy(), optimizer=self.optimizer, metrics=[tf.keras.metrics.AUC(name="AUC")], ) print(gnn.summary()) keras.utils.plot_model(gnn, Path(workdir/"finetune.png"), show_dtype=True, show_shapes=True) print(train_data.task_type) Path(workdir/"model").mkdir(exist_ok=True) Path(workdir/"model"/train_data.task_type).mkdir(exist_ok=True) if checkpoints is None: if self.regression: filepath = Path(workdir/"model"/train_data.task_type/"gnn_{epoch:02d}-{val_mae:.3f}.hdf5") checkpoint = ModelCheckpoint(filepath, monitor='val_mae', save_best_only=True, save_weights_only=save_weights_only, verbose=verbose, mode='min') else: filepath = Path(workdir/"model"/train_data.task_type/"gnn_{epoch:02d}-{val_AUC:.3f}.hdf5") checkpoint = ModelCheckpoint(filepath, monitor='val_AUC', save_best_only=True, save_weights_only=save_weights_only, verbose=verbose, mode='max') earlystop = EarlyStopping(monitor='val_loss', patience=patience, verbose=verbose, mode='min') if warm_up: sample_count = train_data.data_size warmup_epoch = 5 train_per_epoch = sample_count / self.batch_size warmup_steps = warmup_epoch * train_per_epoch restart_epoches = warmrestart warm_up_lr = WarmUpCosineDecayScheduler(epochs=epochs, restart_epoches=restart_epoches, train_per_epoch=train_per_epoch, learning_rate_base=lr, warmup_learning_rate=2e-6, warmup_steps=warmup_steps, hold_base_rate_steps=5, ) checkpoints = [checkpoint, warm_up_lr, earlystop] else: reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=50, verbose=1, min_lr=1e-6, mode='min') checkpoints = [checkpoint, reduce_lr, earlystop] if valid_data: steps_per_train = int(np.ceil(train_data.data_size / self.batch_size)) steps_per_val = int(np.ceil(valid_data.data_size / self.batch_size)) else: steps_per_train = None steps_per_val = None print("gnn fit") history = gnn.fit( train_data, validation_data=valid_data, steps_per_epoch=steps_per_train, validation_steps=steps_per_val, epochs=epochs, verbose=verbose, callbacks=checkpoints, ) Path(workdir/"results").mkdir(exist_ok=True) if self.regression: plot_train_regression(history, train_data.task_type, workdir) if test_data: plot_mae(gnn, test_data, workdir, name='test') else: plot_train(history, train_data.task_type, workdir) if test_data: if self.multiclassification: plot_auc_multiclassification(gnn, test_data, self.multiclassification, workdir, name='test') else: plot_auc(gnn, test_data, workdir, name='test') if warm_up: total_steps = int(epochs * sample_count / self.batch_size) plot_warm_up_lr(warm_up_lr, total_steps, lr, workdir) return gnn def predict_datas(self, test_data, workdir=None): print("load weights and predict...") save_file = test_data.task_type + ".hdf5" if workdir: best_checkpoint = Path(workdir/"model"/save_file) else: best_checkpoint = Path(ModulePath/"model"/save_file) gnn = self.gnn gnn.load_weights(best_checkpoint) Path(workdir/"results").mkdir(exist_ok=True) if self.regression: plot_mae(gnn, test_data, workdir, name='test') else: if self.multiclassification: plot_auc_multiclassification(gnn, test_data, self.multiclassification, workdir, name='test') else: plot_auc(gnn, test_data, name='test') def predict(self, data, workdir=None): print("load weights and predict...") save_file = data.task_type + ".hdf5" if workdir: best_checkpoint = Path(workdir/"model"/save_file) else: best_checkpoint = Path(ModulePath/"model"/save_file) gnn = self.gnn gnn.load_weights(best_checkpoint) y_pred_keras = gnn.predict(data) return y_pred_keras def plot_train(history, name, path): print("plot curve of training") plt.figure(figsize=(10, 12)) plt.subplot(2,1,1) plt.plot(history.history["AUC"], label="train AUC") plt.plot(history.history["val_AUC"], label="valid AUC") plt.xlabel("Epochs", fontsize=16) plt.ylabel("AUC", fontsize=16) plt.legend(fontsize=16) plt.subplot(2,1,2) plt.plot(history.history["loss"], label="train loss") plt.plot(history.history["val_loss"], label="valid loss") plt.xlabel("Epochs", fontsize=16) plt.ylabel("loss", fontsize=16) plt.legend(fontsize=16) save_path = name + "_train.png" plt.savefig(path/"results"/save_path) def plot_train_regression(history, name, path): print("plot curve of training") plt.figure(figsize=(10, 12)) plt.subplot(2,1,1) plt.plot(history.history["mae"], label="train mae") plt.plot(history.history["val_mae"], label="valid mae") plt.xlabel("Epochs", fontsize=16) plt.ylabel("mae", fontsize=16) plt.legend(fontsize=16) plt.subplot(2,1,2) plt.plot(history.history["loss"], label="train loss") plt.plot(history.history["val_loss"], label="valid loss") plt.xlabel("Epochs", fontsize=16) plt.ylabel("loss", fontsize=16) plt.legend(fontsize=16) save_path = name + "_train.png" plt.savefig(path/"results"/save_path) def plot_auc(gnn, test_data, path, name="test"): print("predict") name = test_data.task_type + '_' + name y_pred_keras = gnn.predict(test_data).ravel() fpr_keras, tpr_keras, _ = roc_curve(test_data.labels, y_pred_keras) auc_keras = auc(fpr_keras, tpr_keras) print("auc on test data: ", auc_keras) plt.figure(figsize=(10, 6)) plt.plot([0, 1], [0, 1], 'k--') plt.plot(fpr_keras, tpr_keras, label="Keras (area = {:.3f})".format(auc_keras)) plt.xlabel("False positive rate") plt.ylabel("True positive rate") plt.title("ROC curve test") plt.legend(loc="best") save_path = name + "_predict" + ".png" plt.savefig(Path(path/"results"/save_path)) def plot_auc_multiclassification(gnn, datas, n_classes, path, name="test"): print("predict") name = datas.task_type + '_' + name y_pred_keras = gnn.predict(datas) fpr = dict() tpr = dict() roc_auc = dict() for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(datas.labels[:, i], y_pred_keras[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area fpr["micro"], tpr["micro"], _ = roc_curve(np.array(datas.labels)[:, i], y_pred_keras[:, i]) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) # Compute macro-average ROC curve and ROC area # First aggregate all false positive rates all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) # Then interpolate all ROC curves at this points mean_tpr = np.zeros_like(all_fpr) for i in range(n_classes): mean_tpr += interp(all_fpr, fpr[i], tpr[i]) # Finally average it and compute AUC mean_tpr /= n_classes fpr["macro"] = all_fpr tpr["macro"] = mean_tpr roc_auc["macro"] = auc(fpr["macro"], tpr["macro"]) # Plot all ROC curves plt.figure(figsize=(10, 6)) plt.plot(fpr["micro"], tpr["micro"], label='micro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc["micro"]), color='deeppink', linestyle=':', linewidth=4) plt.plot(fpr["macro"], tpr["macro"], label='macro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc["macro"]), color='navy', linestyle=':', linewidth=4) for i in range(n_classes): print("auc on ", name, " datas: class", i, " auc: ", roc_auc[i]) plt.plot(fpr[i], tpr[i], lw=2, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=2) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") plt.title("Some extension of Receiver operating characteristic to multi-class") plt.legend(loc="lower right") save_path = name + "_predict" + ".png" plt.savefig(Path(path/"results"/save_path)) def plot_mae(gnn, test_data, path, name="test"): print('predict') name = test_data.task_type + '_' + name y_pred_keras = gnn.predict(test_data).ravel() r2 = r2_score(test_data.labels, y_pred_keras) axis_min = np.mean(test_data.labels) - np.std(test_data.labels) axis_max = np.mean(test_data.labels) + np.std(test_data.labels) print("r2 score: ", r2) plt.figure(figsize=(10, 6)) plt.scatter(test_data.labels, y_pred_keras, color="orange") plt.plot([-2, 6], [-2, 6], 'r--') plt.xlim(axis_min, axis_max) plt.ylim(axis_min, axis_max) plt.xlabel("experimetal", fontsize=16) plt.ylabel("pred", fontsize=16) plt.title('predicted') save_path = name + "_predict" + ".png" plt.savefig(Path(path/"results"/save_path)) def plot_warm_up_lr(warm_up_lr, total_steps, lr, path): plt.plot(warm_up_lr.learning_rates) plt.xlabel("Step", fontsize=20) plt.ylabel("lr", fontsize=20) plt.axis([0, total_steps, 0, lr*1.1]) # plt.xticks(np.arange(0, epochs, 1)) plt.grid() plt.title("Cosine decay with warmup", fontsize=20) plt.savefig(Path(path/"results"/"cosine_decay.png"))

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a16fed8fa689c69d100d2f8191f42bd284a6932a

16,626

py

Python

cnuchatbot/chatbotapp/cnudata/cafeteria/cafeteria.py

ChanhyukPark-Tech/CnuChatBot

c204b7f037a7dc8a2ca7f398a84aae68df245327

[ "MIT" ]

null

cnuchatbot/chatbotapp/cnudata/cafeteria/cafeteria.py

ChanhyukPark-Tech/CnuChatBot

c204b7f037a7dc8a2ca7f398a84aae68df245327

[ "MIT" ]

null

cnuchatbot/chatbotapp/cnudata/cafeteria/cafeteria.py

ChanhyukPark-Tech/CnuChatBot

c204b7f037a7dc8a2ca7f398a84aae68df245327

[ "MIT" ]

null

from chatbotapp.cnudata.cafeteria.studenthall1_info import * from chatbotapp.cnudata.cafeteria.food_court_time import * from chatbotapp.cnudata.cafeteria.dorm_info import * from chatbotapp.cnudata.cafeteria.new_studenthall2_info import * from chatbotapp.common.variables.cafeteria import * from chatbotapp.common.functions import * from GrabzIt import GrabzItImageOptions from GrabzIt import GrabzItClient from datetime import datetime import requests import schedule def get_entire_cafeteria_info(): response_text = "충남대학교 학식 정보" answer = insert_text(response_text) answer = insert_multiple_reply(answer, cafeteriaNormalReplies) return answer def get_studenthall1_answer(): answer = insert_image(studenthall1Image_BASE_URL, "img") answer = insert_multiple_reply(answer, cafeteriaNormalReplies) return answer def get_variousCafeteria_info(): text = "보고 싶은 식당을 선택해주세요" answer = insert_text(text) answer = insert_multiple_reply(answer, [["학생", "학생"], ["교직원", "교직원"]]) return answer # http://3.38.250.164/cnuchatbot/media/savedImage/123.png/ def get_variousCafeteria_answer(person): # default 교직원 personCategory = "CCS01.10" if person == "학생": personCategory = "CCS01.20" answer = make_card( mealCategoryKoreans[0], "", "http://3.38.250.164/cnuchatbot/media/savedImage/{}-{}.png".format( personCategory, mealCategory[0] ), ) for index in range(1, 5): insert_card( answer, mealCategoryKoreans[index], "", "http://3.38.250.164/cnuchatbot/media/savedImage/{}-{}.png".format( personCategory, mealCategory[index] ), ) return answer def get_variousCafeteria_images(): # jsp 사진 보내주는 로직 grabzIt = GrabzItClient.GrabzItClient( "N2Y5Yjg1ZTY5NGIzNDE5ZmIzYmM4OGQ0MGQwMDk1N2Y=", "Qz9LP1lkPz8GPz8QTDk/TGU/Pz8/PyQ/IC4xNT8EVD8=", ) options = GrabzItImageOptions.GrabzItImageOptions() options.browserHeight = -1 options.format = "png" options.width = -1 options.height = -1 for person in personCategory: for meal in mealCategory: data = {"cafe_div_cd": person, "food_div_cd": meal, "langType": "1"} r = requests.post( variousCafeteria_BASE_URL, data=data, headers={"Content-Type": "application/x-www-form-urlencoded"}, ) grabzIt.HTMLToImage(r.text, options) # Then call the Save or SaveTo method grabzIt.SaveTo( "cnuchatbot/media/savedImage/{0}-{1}.png".format(person, meal) ) return insert_text("sad") # 매주 월요일에 동작 schedule.every().monday.do(get_variousCafeteria_images) def get_studenthall23_answer(name): answer = get_studenthall23_answer_info(name) return answer def get_entire_time(): answer = entire_time() return answer def day_of_week_dorm(the_day_of_week_number): if Weekday.MONDAY.value == the_day_of_week_number: answer = day_of_week("MONDAY") if Weekday.TUESDAY.value == the_day_of_week_number: answer = day_of_week("TUESDAY") if Weekday.WEDNESDAY.value == the_day_of_week_number: answer = day_of_week("WEDNESDAY") if Weekday.THURSDAY.value == the_day_of_week_number: answer = day_of_week("THURSDAY") if Weekday.FRIDAY.value == the_day_of_week_number: answer = day_of_week("FRIDAY") if Weekday.SATURDAY.value == the_day_of_week_number: answer = day_of_week("SATURDAY") if Weekday.SUNDAY.value == the_day_of_week_number: answer = day_of_week("SUNDAY") return answer # def get_monday_breakfast_menu(): # text = monday_dorm_menu("breakfast") # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "월요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer # # def get_monday_lunch_menu(): # text = monday_dorm_menu("lunch") # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "월요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer # # def get_monday_dinner_menu(): # text = monday_dorm_menu("dinner") # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "월요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer def get_entire_menu(when, the_day_of_week_number): # if Weekday.MONDAY.value == the_day_of_week_number: # reply = make_reply("다른시간보기", "월요일기숙사식당") # if Weekday.TUESDAY.value == the_day_of_week_number: # reply = make_reply("다른시간보기", "화요일기숙사식당") # if Weekday.WEDNESDAY.value == the_day_of_week_number: # reply = make_reply("다른시간보기", "수요일기숙사식당") # if Weekday.THURSDAY.value == the_day_of_week_number: # reply = make_reply("다른시간보기", "목요일기숙사식당") # if Weekday.FRIDAY.value == the_day_of_week_number: # reply = make_reply("다른시간보기", "금요일기숙사식당") # if Weekday.SATURDAY.value == the_day_of_week_number: # reply = make_reply("다른시간보기", "토요일기숙사식당") # if Weekday.SUNDAY.value == the_day_of_week_number: # reply = make_reply("다른시간보기", "일요일기숙사식당") # text = dorm_menu(when, the_day_of_week_number) 원래 이거였는데 , 3가지 다 한꺼번에 나오도록 text = dorm_menu("breakfast", the_day_of_week_number) text += "\n" text += dorm_menu("lunch", the_day_of_week_number) text += "\n" text += dorm_menu("dinner", the_day_of_week_number) answer = insert_text(text) # answer = insert_replies(answer,reply) reply = make_reply("다른식당보기", "학식") answer = insert_replies(answer, reply) reply = make_reply("다른요일보기", "기숙사식당") answer = insert_replies(answer, reply) return answer # print(get_entire_menu("breakfast",1)) # # def get_monday_menu(when): # text = monday_dorm_menu(when) # answer = insert_text(text) # reply = make_reply("다른시간보기", "월요일기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # # # return answer # # def get_tuesday_menu(when): # text = tuesday_dorm_menu(when) # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "화요일기숙사식당") # answer = insert_replies(answer, reply) # return answer # # def get_tuesday_breakfast_menu(): # # text = tuesday_dorm_menu("breakfast") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "화요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_tuesday_lunch_menu(): # # text = tuesday_dorm_menu("lunch") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "화요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_tuesday_dinner_menu(): # # text = tuesday_dorm_menu("dinner") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "화요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # def get_wednesday_menu(when): # text = wednesday_dorm_menu(when) # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "수요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer # # # def get_wednesday_breakfast_menu(): # # text = wednesday_dorm_menu("breakfast") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "수요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_wednesday_lunch_menu(): # # text = wednesday_dorm_menu("lunch") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "수요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_wednesday_dinner_menu(): # # text = wednesday_dorm_menu("dinner") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "수요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # def get_thursday_menu(when): # text = thursday_dorm_menu(when) # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "목요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer # # # def get_thursday_breakfast_menu(): # # text = thursday_dorm_menu("breakfast") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "목요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_thursday_lunch_menu(): # # text = thursday_dorm_menu("lunch") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "목요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_thursday_dinner_menu(): # # text = thursday_dorm_menu("dinner") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "목요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # def get_friday_menu(when): # text = friday_dorm_menu(when) # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "금요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer # # def get_friday_breakfast_menu(): # # text = friday_dorm_menu("breakfast") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "금요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_friday_lunch_menu(): # # text = friday_dorm_menu("lunch") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "금요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_friday_dinner_menu(): # # text = friday_dorm_menu("dinner") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "금요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # def get_saturday_menu(when): # text = saturday_dorm_menu(when) # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "토요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer # # # def get_saturday_breakfast_menu(): # # text = saturday_dorm_menu("breakfast") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "토요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_saturday_lunch_menu(): # # text = saturday_dorm_menu("lunch") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "토요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_saturday_dinner_menu(): # # text = saturday_dorm_menu("dinner") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "토요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # # # def get_sunday_breakfast_menu(): # # text = sunday_dorm_menu("breakfast") # # answer = insert_text(text) # # reply = make_reply("다른식당보기", "학식") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른요일보기", "기숙사식당") # # answer = insert_replies(answer, reply) # # reply = make_reply("다른시간보기", "일요일기숙사식당") # # answer = insert_replies(answer, reply) # # # # return answer # # def get_sunday_menu(when): # text = sunday_dorm_menu(when) # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "일요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer # def get_sunday_lunch_menu(): # text = sunday_dorm_menu("lunch") # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "일요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer # # def get_sunday_dinner_menu(): # text = sunday_dorm_menu("dinner") # answer = insert_text(text) # reply = make_reply("다른식당보기", "학식") # answer = insert_replies(answer, reply) # reply = make_reply("다른요일보기", "기숙사식당") # answer = insert_replies(answer, reply) # reply = make_reply("다른시간보기", "일요일기숙사식당") # answer = insert_replies(answer, reply) # # return answer #

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a1759a2c25ae32cd79edc55f45269fc1da552b9b

38,184

py

Python

software/scripts/SCurveNP.py

slaclab/atlas-chess2

2135a79e1b43bb404abc50aeabe50e577242aa45

[ "BSD-3-Clause-LBNL" ]

1

2017-10-24T19:04:40.000Z

software/scripts/SCurveNP.py

slaclab/atlas-chess2

2135a79e1b43bb404abc50aeabe50e577242aa45

[ "BSD-3-Clause-LBNL" ]

null

software/scripts/SCurveNP.py

slaclab/atlas-chess2

2135a79e1b43bb404abc50aeabe50e577242aa45

[ "BSD-3-Clause-LBNL" ]

3

2017-10-24T19:04:22.000Z

2020-12-13T00:13:32.000Z

####import ROOT as R import numpy as np import matplotlib #.pyplot as plt import sys import time import re import logging # Generating log file def logfile(logfilename): logger=logging.getLogger() LOG_FILE=logfilename LOG_FORMAT="%(asctime)s : %(funcName)s: %(message)s" logging.basicConfig(filename=LOG_FILE,level=logging.DEBUG, format=LOG_FORMAT) return logger #transfer the data def load_chess2_data(filename): for i in [2]: file_data=open(sys.argv[1],'r') for line in file_data.readlines(): if ('Shape' in line): shape_hist=re.findall('\d+',line) # print(len(shape_hist)) break data_1d=np.loadtxt(sys.argv[1]) hists=data_1d.reshape(int(shape_hist[0]),int(shape_hist[1]),int(shape_hist[2]),int(shape_hist[3])) return hists def get_thresholds(filename): file_data=open(sys.argv[1],'r') line_count=0 start=False for line in file_data.readlines(): line_count+=1 if ('thresholds (raw)' in line): thresholds=re.findall('\d+',line) start_line=line_count start=True if (start): if (line_count>start_line): if (not (']' in line)): thresholds1=re.findall('\d+',line) thresholds.extend(thresholds1) else: thresholds1=re.findall('\d+',line) thresholds.extend(thresholds1) break return thresholds def makeSCurve(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root"): nColumns = 32 nRows = 128 allHists = [] logging.info("Using makeCurve......") ####R.TH1.AddDirectory(R.kFALSE) # thresholdCuts = [0x7ce] # system.root.readConfig("chess2_config.yml") --- should be in the driver script #####tf = R.TFile(histFileName, "recreate") # Turn on one pixel at a time print("Disable all pixels") system.feb.Chess2Ctrl0.writeAllPixels(enable=0,chargeInj=0) #chargeInj should be 1 in this line and following 2 lines system.feb.Chess2Ctrl1.writeAllPixels(enable=0,chargeInj=0) system.feb.Chess2Ctrl2.writeAllPixels(enable=0,chargeInj=0) pixels = pixels if (pixels!=None) else [ (row,col) for row in range(nRows) for col in range(nColumns) ] for (row,col) in pixels: print("Pixel: (%i,%i)"%(row,col)) system.feb.Chess2Ctrl0.writePixel(enable=1, chargeInj=1, col=col, row=row, trimI= 15) #chargeInj should be 0 in these 3 lines system.feb.Chess2Ctrl1.writePixel(enable=1, chargeInj=1, col=col, row=row, trimI= 15) system.feb.Chess2Ctrl2.writePixel(enable=1, chargeInj=1, col=col, row=row, trimI= 15) ####hists_row = [ R.TH1F("row_%i_%i_%i"%(i_asic,row,col),"",128,0,128) for i_asic in range(3) ] ####hists_col = [ R.TH1F("col_%i_%i_%i"%(i_asic,row,col),"",32,0,32) for i_asic in range(3) ] hists_row = [[], [], []] hists_col = [[], [], []] for threshold in thresholdCuts: ####hists = [ R.TH1F("deltaT_%i_%i_%i_%s"%(i_asic,row,col,hex(threshold)),"",100,0,1000) for i_asic in range(3) ] # deltaT in ns print("Thresholds (system.feb.dac.dacBLRRaw): ", hex(threshold)) hists = [[], [], []] # system.feb.dac.dacPIXTHRaw.set(threshold) #system.feb.dac.dacBLRaw.set(threshold+608) system.feb.dac.dacBLRRaw.set(threshold) #system.feb.dac.dacBLRaw.set(threshold) # this delay seems to be very important to enable the comparitor inside the asic to settle. (smaller values tend to make this # tests to report wrong times time.sleep(2.0) system.readAll() for cnt in range(nCounts): #time.sleep(0.1) # start charge injection system.feb.memReg.chargInjStartEventReg.set(0) time.sleep(0.1) #system.feb.chargeInj.calPulseVar.set(1) system.readAll() if system.feb.chargeInj.hitDetValid0._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow0._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol0._rawGet()) ####hists_row[0].Fill(row_det) ####hists_col[0].Fill(col_det) hists_row[0].append(row_det) hists_col[0].append(col_det) #if (row == row_det) and (col == col_det): ####hists[0].Fill(float(system.feb.chargeInj.hitDetTime0._rawGet())) hists[0].append(float(system.feb.chargeInj.hitDetTime0._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime0: ", float(system.feb.chargeInj.hitDetTime0._rawGet())) else: hists[0].append(-1.0) if system.feb.chargeInj.hitDetValid1._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow1._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol1._rawGet()) ####hists_row[1].Fill(row_det) ####hists_col[1].Fill(col_det) hists_row[1].append(row_det) hists_col[1].append(col_det) #if (row == row_det) and (col == col_det): ####hists[1].Fill(float(system.feb.chargeInj.hitDetTime1._rawGet())) hists[1].append(float(system.feb.chargeInj.hitDetTime1._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime1: ", float(system.feb.chargeInj.hitDetTime1._rawGet())) else: hists[1].append(-1.0) if system.feb.chargeInj.hitDetValid2._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow2._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol2._rawGet()) ####hists_row[2].Fill(row_det) ####hists_col[2].Fill(col_det) hists_row[2].append(row_det) hists_col[2].append(col_det) #if (row == row_det) and (col == col_det): ####hists[2].Fill(float(system.feb.chargeInj.hitDetTime2._rawGet())) hists[2].append(float(system.feb.chargeInj.hitDetTime2._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime2: ", float(system.feb.chargeInj.hitDetTime2._rawGet())) else: hists[2].append(-1.0) allHists.append(hists) ####[ hist.Write() for hist in hists ] # for i in range(3): # fig = matplotlib.figure() # ax = fig.add_subplot(1, 1, 1) # n, bins, patches = ax.hist(hists[i], bins=100, range=(0, 1000)) # ax.set_xlabel('Delta T in ns') # ax.set_ylabel('Frequency') # fig.savefig("plotDir/deltaT_%i_%i_%i_%s"%(i,row,col,hex(threshold))) # fig.clf() ####[ print("... ASIC%i %f"%(i_h,hist.GetEntries())) for (i_h,hist) in enumerate(hists) ] # [ print("... ASIC%i %f"%(i_h,len(hist))) for (i_h,hist) in enumerate(hists) ] ####[ hist.Write() for hist in hists_row ] ####[ hist.Write() for hist in hists_col ] # for i in range(3): # fig = matplotlib.figure() # ax1 = fig.add_subplot(2, 1, 1) # ax2 = fig.add_subplot(2, 1, 2) # n, bins, patches = ax1.hist(hists_row[i], bins=128, range=(0, 128)) # ax1.set_xlabel('Row') # ax1.set_ylabel('Frequency') # n, bins, patches = ax2.hist(hists_col[i], bins=32, range=(0,32)) # ax2.set_xlabel('Column') # ax2.set_ylabel('Frequency') # fig.savefig("plotDir/asic_row_col_%i_%i_%i.png"%(i,row,col)) # fig.clf() # system.feb.Chess2Ctrl0.writePixel(enable=0, chargeInj=0, col=col, row=row) # system.feb.Chess2Ctrl1.writePixel(enable=0, chargeInj=0, col=col, row=row) # system.feb.Chess2Ctrl2.writePixel(enable=0, chargeInj=0, col=col, row=row) return allHists # tf.Close() """ The following test enables to test a set of pixels for all trim values. The makeCalibCurveLoop function is called to implement the inner loops for the set of pixels and for the thresholdCuts""" def makeCalibCurve(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root"): allHists = [] pixEnableLogic = 1 chargeInjLogic = 0 logging.info("Using makeCalibCurve......") print("Disable all pixels") system.feb.Chess2Ctrl0.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) system.feb.Chess2Ctrl1.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) system.feb.Chess2Ctrl2.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) #for trim in range(0,16,2): for trim in range(7,8): # pixEnableLogic = 1 # chargeInjLogic = 1 # print("Trim, pixEnableLogic, chargeInjLogic: (%i,%i, %i)"%(trim, pixEnableLogic, chargeInjLogic)) # hists = makeCalibCurveLoop(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnableLogic, chargeInjLogic = chargeInjLogic, pixTrimI = trim) # allHists.append(hists) pixEnableLogic = 1 chargeInjLogic = 0 print("Trim, pixEnableLogic, chargeInjLogic: (%i,%i,%i)"%(trim, pixEnableLogic, chargeInjLogic)) hists = makeCalibCurveLoop(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnableLogic, chargeInjLogic = chargeInjLogic, pixTrimI = trim) allHists.append(hists) # pixEnableLogic = 0 # chargeInjLogic = 1 # print("Trim, pixEnableLogic, chargeInjLogic: (%i,%i, %i)"%(trim, pixEnableLogic, chargeInjLogic)) # hists = makeCalibCurveLoop(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnableLogic, chargeInjLogic = chargeInjLogic, pixTrimI = trim) # allHists.append(hists) # pixEnableLogic = 0 # chargeInjLogic = 0 # print("Trim, pixEnableLogic, chargeInjLogic: (%i,%i, %i)"%(trim, pixEnableLogic, chargeInjLogic)) # hists = makeCalibCurveLoop(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnableLogic, chargeInjLogic = chargeInjLogic, pixTrimI = trim) # allHists.append(hists) return allHists """ The following test specifies a single pixel memory configuration (pixEnableLogic, chargeInjLogic and trim). The makeCalibCurveLoop function is called to implement the inner loops for the set of pixels and for the thresholdCuts""" def makeCalibCurve2(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root"): allHists = [] logging.info("Using makeCalibCurve2......") pixEnableLogic = 1 chargeInjLogic = 0 trim = 15 print("Disable all pixels") system.feb.Chess2Ctrl0.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) system.feb.Chess2Ctrl1.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) system.feb.Chess2Ctrl2.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) print("Trim, pixEnableLogic, chargeInjLogic: (%i,%i,%i)"%(trim, pixEnableLogic, chargeInjLogic)) hists = makeCalibCurveLoop(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnableLogic, chargeInjLogic = chargeInjLogic, pixTrimI = trim) allHists.append(hists) def makeCalibCurve3(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root"): allHists = [] pixEnable = 1 chargeInj = 0 # 0 - enable / 1 - disabled trim = 7 system.feb.chargeInj.pulseWidthRaw.set(0x7fff) print("Disable all pixels") system.feb.Chess2Ctrl0.writeAllPixels(enable= 0,chargeInj= 1) system.feb.Chess2Ctrl1.writeAllPixels(enable= 0,chargeInj= 1) system.feb.Chess2Ctrl2.writeAllPixels(enable= 0,chargeInj= 1) print("Trim, pixEnable, chargeInj: (%i,%i,%i)"%(trim, pixEnable, chargeInj)) hists = makeCalibCurveLoopTH(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnable, chargeInjLogic = chargeInj, pixTrimI = trim) allHists.append(hists) return allHists def configAsicsPreampTest(system = []): system.feb.Chess2Ctrl0.VNatt.set(0x1e) system.feb.Chess2Ctrl0.VNres.set(0x1) system.feb.Chess2Ctrl0.VPLoadatt.set(0x1c) system.feb.Chess2Ctrl0.VPLoadres.set(0x2) system.feb.Chess2Ctrl0.VNSFatt.set(0x1f) system.feb.Chess2Ctrl0.VNSFres.set(0x3) system.feb.Chess2Ctrl1.VNatt.set(0x1e) system.feb.Chess2Ctrl1.VNres.set(0x1) system.feb.Chess2Ctrl1.VPLoadatt.set(0x1c) system.feb.Chess2Ctrl1.VPLoadres.set(0x2) system.feb.Chess2Ctrl1.VNSFatt.set(0x1f) system.feb.Chess2Ctrl1.VNSFres.set(0x3) system.feb.Chess2Ctrl2.VNatt.set(0x1e) system.feb.Chess2Ctrl2.VNres.set(0x1) system.feb.Chess2Ctrl2.VPLoadatt.set(0x1c) system.feb.Chess2Ctrl2.VPLoadres.set(0x2) system.feb.Chess2Ctrl2.VNSFatt.set(0x1f) system.feb.Chess2Ctrl2.VNSFres.set(0x3) def configAsicsPreampTestRestoreDefaultValues(system = []): system.feb.Chess2Ctrl0.VNatt.set(0x1F) system.feb.Chess2Ctrl0.VNres.set(0x0) system.feb.Chess2Ctrl0.VPLoadatt.set(0x1e) system.feb.Chess2Ctrl0.VPLoadres.set(0x1) system.feb.Chess2Ctrl0.VNSFatt.set(0x1b) system.feb.Chess2Ctrl0.VNSFres.set(0x0) system.feb.Chess2Ctrl1.VNatt.set(0x1F) system.feb.Chess2Ctrl1.VNres.set(0x0) system.feb.Chess2Ctrl1.VPLoadatt.set(0x1e) system.feb.Chess2Ctrl1.VPLoadres.set(0x1) system.feb.Chess2Ctrl1.VNSFatt.set(0x1b) system.feb.Chess2Ctrl1.VNSFres.set(0x0) system.feb.Chess2Ctrl2.VNatt.set(0x1F) system.feb.Chess2Ctrl2.VNres.set(0x0) system.feb.Chess2Ctrl2.VPLoadatt.set(0x1e) system.feb.Chess2Ctrl2.VPLoadres.set(0x1) system.feb.Chess2Ctrl2.VNSFatt.set(0x1b) system.feb.Chess2Ctrl2.VNSFres.set(0x0) def makeCalibCurve4(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root", deltaBLToBLR = 608, chargeInjectionEnbled = 0): allHists = [] logging.info("Using makeCalibCurve4......") #ASIC specific configuration selected depending on the test being run configAsicsPreampTest(system = system) #configAsicsPreampTestRestoreDefaultValues(system = system) pixEnable = 1 chargeInj1 = not chargeInjectionEnbled # 0 - enable / 1 - disabled trim = 7 system.feb.chargeInj.pulseWidthRaw.set(0x7fff) system.feb.chargeInj.calPulseInh.set(chargeInj1) print("Disable all pixels") system.feb.Chess2Ctrl0.writeAllPixels(enable= 0,chargeInj= 1) system.feb.Chess2Ctrl1.writeAllPixels(enable= 0,chargeInj= 1) system.feb.Chess2Ctrl2.writeAllPixels(enable= 0,chargeInj= 1) print("Trim, pixEnable, chargeInj: (%i,%i,%i)"%(trim, pixEnable, chargeInj1)) hists = makeCalibCurveLoopBLx(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnable, chargeInjLogic = chargeInj1, pixTrimI = trim, deltaBLToBLR = deltaBLToBLR) allHists.append(hists) return allHists def makeCalibCurveLoop(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root", pixEnableLogic = 1, chargeInjLogic = 0, pixTrimI = 0): nColumns = 32 nRows = 128 allHists = [] logging.info("Using makeCalibCurveLoop......") # Turn on one pixel at a time # print("Disable all pixels") # system.feb.Chess2Ctrl0.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) # system.feb.Chess2Ctrl1.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) # system.feb.Chess2Ctrl2.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) pixels = pixels if (pixels!=None) else [ (row,col) for row in range(nRows) for col in range(nColumns) ] for (row,col) in pixels: print("Pixel: (%i,%i)"%(row,col)) system.feb.Chess2Ctrl0.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) system.feb.Chess2Ctrl1.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) system.feb.Chess2Ctrl2.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) ####hists_row = [ R.TH1F("row_%i_%i_%i"%(i_asic,row,col),"",128,0,128) for i_asic in range(3) ] ####hists_col = [ R.TH1F("col_%i_%i_%i"%(i_asic,row,col),"",32,0,32) for i_asic in range(3) ] hists_row = [[], [], []] hists_col = [[], [], []] for threshold in thresholdCuts: ####hists = [ R.TH1F("deltaT_%i_%i_%i_%s"%(i_asic,row,col,hex(threshold)),"",100,0,1000) for i_asic in range(3) ] # deltaT in ns hists = [[], [], []] # system.feb.dac.dacPIXTHRaw.set(threshold) #system.feb.dac.dacBLRaw.set(threshold+608) #print("Thresholds (system.feb.dac.dacBLRRaw): ", hex(threshold)) #system.feb.dac.dacBLRRaw.set(threshold) print("Thresholds (system.feb.dac.dacBLRaw): ", hex(threshold)) system.feb.dac.dacBLRaw.set(threshold) # this delay seems to be very important to enable the comparitor inside the asic to settle. (smaller values tend to make this # tests to report wrong times time.sleep(2.0) system.readAll() for cnt in range(nCounts): #time.sleep(0.1) # start charge injection #system.feb.memReg.chargInjStartEventReg.set(0) system.feb.chargeInj.calPulseVar.set(1) time.sleep(0.1) system.readAll() if system.feb.chargeInj.hitDetValid0._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow0._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol0._rawGet()) ####hists_row[0].Fill(row_det) ####hists_col[0].Fill(col_det) hists_row[0].append(row_det) hists_col[0].append(col_det) #if (row == row_det) and (col == col_det): ####hists[0].Fill(float(system.feb.chargeInj.hitDetTime0._rawGet())) hists[0].append(float(system.feb.chargeInj.hitDetTime0._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime0: ", float(system.feb.chargeInj.hitDetTime0._rawGet())) else: hists[0].append(-1.0) if system.feb.chargeInj.hitDetValid1._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow1._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol1._rawGet()) ####hists_row[1].Fill(row_det) ####hists_col[1].Fill(col_det) hists_row[1].append(row_det) hists_col[1].append(col_det) #if (row == row_det) and (col == col_det): ####hists[1].Fill(float(system.feb.chargeInj.hitDetTime1._rawGet())) hists[1].append(float(system.feb.chargeInj.hitDetTime1._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime1: ", float(system.feb.chargeInj.hitDetTime1._rawGet())) else: hists[1].append(-1.0) if system.feb.chargeInj.hitDetValid2._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow2._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol2._rawGet()) ####hists_row[2].Fill(row_det) ####hists_col[2].Fill(col_det) hists_row[2].append(row_det) hists_col[2].append(col_det) #if (row == row_det) and (col == col_det): ####hists[2].Fill(float(system.feb.chargeInj.hitDetTime2._rawGet())) hists[2].append(float(system.feb.chargeInj.hitDetTime2._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime2: ", float(system.feb.chargeInj.hitDetTime2._rawGet())) else: hists[2].append(-1.0) allHists.append(hists) return allHists def makeCalibCurveLoopBLx(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root", pixEnableLogic = 1, chargeInjLogic = 0, pixTrimI = 0, deltaBLToBLR = 608): nColumns = 32 nRows = 128 allHists = [] logging.info(" Using makeCalibCurveLoopBLx......") # Turn on one pixel at a time # print("Disable all pixels") # system.feb.Chess2Ctrl0.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) # system.feb.Chess2Ctrl1.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) # system.feb.Chess2Ctrl2.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) pixels = pixels if (pixels!=None) else [ (row,col) for row in range(nRows) for col in range(nColumns) ] for (row,col) in pixels: print("Pixel: (%i,%i)"%(row,col)) system.feb.Chess2Ctrl0.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) system.feb.Chess2Ctrl1.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) system.feb.Chess2Ctrl2.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) ####hists_row = [ R.TH1F("row_%i_%i_%i"%(i_asic,row,col),"",128,0,128) for i_asic in range(3) ] ####hists_col = [ R.TH1F("col_%i_%i_%i"%(i_asic,row,col),"",32,0,32) for i_asic in range(3) ] hists_row = [[], [], []] hists_col = [[], [], []] for threshold in thresholdCuts: BLRValue = threshold + deltaBLToBLR ####hists = [ R.TH1F("deltaT_%i_%i_%i_%s"%(i_asic,row,col,hex(threshold)),"",100,0,1000) for i_asic in range(3) ] # deltaT in ns hists = [[], [], []] #print("Thresholds (system.feb.dac.dacPIXTHRaw): ", hex(threshold)) #system.feb.dac.dacPIXTHRaw.set(threshold) system.feb.dac.dacBLRRaw.set(BLRValue) print("Thresholds (system.feb.dac.dacBLRRaw): ", hex(BLRValue)) system.feb.dac.dacBLRaw.set(threshold) print("Thresholds (system.feb.dac.dacBLRaw): ", hex(threshold)) # system.feb.dac.dacBLRaw.set(threshold) # this delay seems to be very important to enable the comparitor inside the asic to settle. (smaller values tend to make this # tests to report wrong times time.sleep(1.0) system.readAll() for cnt in range(nCounts): #time.sleep(0.1) # start charge injection #system.feb.memReg.chargInjStartEventReg.set(0) system.feb.chargeInj.calPulseVar.set(1) time.sleep(0.05) system.readAll() if system.feb.chargeInj.hitDetValid0._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow0._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol0._rawGet()) ####hists_row[0].Fill(row_det) ####hists_col[0].Fill(col_det) hists_row[0].append(row_det) hists_col[0].append(col_det) #if (row == row_det) and (col == col_det): ####hists[0].Fill(float(system.feb.chargeInj.hitDetTime0._rawGet())) #hists[0].append(float(system.feb.chargeInj.hitDetTimeRaw0._rawGet())) hists[0].append(float(system.feb.chargeInj.hitDetTime0._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime0: ", float(system.feb.chargeInj.hitDetTime0._rawGet())) else: hists[0].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime0: ", float(-1)) if system.feb.chargeInj.hitDetValid1._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow1._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol1._rawGet()) ####hists_row[1].Fill(row_det) ####hists_col[1].Fill(col_det) hists_row[1].append(row_det) hists_col[1].append(col_det) #if (row == row_det) and (col == col_det): ####hists[1].Fill(float(system.feb.chargeInj.hitDetTime1._rawGet())) #hists[1].append(float(system.feb.chargeInj.hitDetTimeRaw1._rawGet())) hists[1].append(float(system.feb.chargeInj.hitDetTime1._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime1: ", float(system.feb.chargeInj.hitDetTime1._rawGet())) else: hists[1].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime1: ", float(-1)) if system.feb.chargeInj.hitDetValid2._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow2._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol2._rawGet()) ####hists_row[2].Fill(row_det) ####hists_col[2].Fill(col_det) hists_row[2].append(row_det) hists_col[2].append(col_det) #if (row == row_det) and (col == col_det): ####hists[2].Fill(float(system.feb.chargeInj.hitDetTime2._rawGet())) #hists[2].append(float(system.feb.chargeInj.hitDetTimeRaw2._rawGet())) hists[2].append(float(system.feb.chargeInj.hitDetTime2._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime2: ", float(system.feb.chargeInj.hitDetTime2._rawGet())) else: hists[2].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime2: ", float(-1)) allHists.append(hists) return allHists def makeCalibCurveLoopTH(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root", pixEnableLogic = 1, chargeInjLogic = 0, pixTrimI = 0): nColumns = 32 nRows = 128 allHists = [] logging.info(" Using makeCalibCurveLoopTH......") # Turn on one pixel at a time # print("Disable all pixels") # system.feb.Chess2Ctrl0.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) # system.feb.Chess2Ctrl1.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) # system.feb.Chess2Ctrl2.writeAllPixels(enable= not pixEnableLogic,chargeInj= not chargeInjLogic) pixels = pixels if (pixels!=None) else [ (row,col) for row in range(nRows) for col in range(nColumns) ] for (row,col) in pixels: print("Pixel: (%i,%i)"%(row,col)) system.feb.Chess2Ctrl0.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) system.feb.Chess2Ctrl1.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) system.feb.Chess2Ctrl2.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) ####hists_row = [ R.TH1F("row_%i_%i_%i"%(i_asic,row,col),"",128,0,128) for i_asic in range(3) ] ####hists_col = [ R.TH1F("col_%i_%i_%i"%(i_asic,row,col),"",32,0,32) for i_asic in range(3) ] hists_row = [[], [], []] hists_col = [[], [], []] for threshold in thresholdCuts: ####hists = [ R.TH1F("deltaT_%i_%i_%i_%s"%(i_asic,row,col,hex(threshold)),"",100,0,1000) for i_asic in range(3) ] # deltaT in ns hists = [[], [], []] print("Thresholds (system.feb.dac.dacPIXTHRaw): ", hex(threshold)) system.feb.dac.dacPIXTHRaw.set(threshold) #system.feb.dac.dacBLRaw.set(threshold+608) #print("Thresholds (system.feb.dac.dacBLRRaw): ", hex(threshold)) #system.feb.dac.dacBLRRaw.set(threshold) # print("Thresholds (system.feb.dac.dacBLRaw): ", hex(threshold)) # system.feb.dac.dacBLRaw.set(threshold) # this delay seems to be very important to enable the comparitor inside the asic to settle. (smaller values tend to make this # tests to report wrong times time.sleep(2.0) system.readAll() for cnt in range(nCounts): #time.sleep(0.1) # start charge injection #system.feb.memReg.chargInjStartEventReg.set(0) system.feb.chargeInj.calPulseVar.set(1) time.sleep(0.1) system.readAll() if system.feb.chargeInj.hitDetValid0._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow0._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol0._rawGet()) ####hists_row[0].Fill(row_det) ####hists_col[0].Fill(col_det) hists_row[0].append(row_det) hists_col[0].append(col_det) #if (row == row_det) and (col == col_det): ####hists[0].Fill(float(system.feb.chargeInj.hitDetTime0._rawGet())) hists[0].append(float(system.feb.chargeInj.hitDetTime0._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime0: ", float(system.feb.chargeInj.hitDetTime0._rawGet())) else: hists[0].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime0: ", float(-1)) if system.feb.chargeInj.hitDetValid1._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow1._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol1._rawGet()) ####hists_row[1].Fill(row_det) ####hists_col[1].Fill(col_det) hists_row[1].append(row_det) hists_col[1].append(col_det) #if (row == row_det) and (col == col_det): ####hists[1].Fill(float(system.feb.chargeInj.hitDetTime1._rawGet())) hists[1].append(float(system.feb.chargeInj.hitDetTime1._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime1: ", float(system.feb.chargeInj.hitDetTime1._rawGet())) else: hists[1].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime1: ", float(-1)) if system.feb.chargeInj.hitDetValid2._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow2._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol2._rawGet()) ####hists_row[2].Fill(row_det) ####hists_col[2].Fill(col_det) hists_row[2].append(row_det) hists_col[2].append(col_det) #if (row == row_det) and (col == col_det): ####hists[2].Fill(float(system.feb.chargeInj.hitDetTime2._rawGet())) hists[2].append(float(system.feb.chargeInj.hitDetTime2._rawGet())) print("row_det: ",row_det, "col_det", col_det, "system.feb.chargeInj.hitDetTime2: ", float(system.feb.chargeInj.hitDetTime2._rawGet())) else: hists[2].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime2: ", float(-1)) allHists.append(hists) return allHists def swingTHvsBL(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root"): allHists = [] logging.info("Using swingTHvsBL......") pixEnable = 1 chargeInj = 1 trim = 15 # system.feb.dac.dacPIXTHRaw.set(0x9ce) system.feb.dac.dacBLRRaw.set(0x5c2) system.feb.dac.dacBLRaw.set(0x5c2) # system.feb.memReg.initValueReg.set(0x0) system.feb.memReg.endValueReg.set(0xfff) system.feb.memReg.delayValueReg.set(0x5) print("Disable all pixels") system.feb.Chess2Ctrl0.writeAllPixels(enable= 0,chargeInj= 1) system.feb.Chess2Ctrl1.writeAllPixels(enable= 0,chargeInj= 1) system.feb.Chess2Ctrl2.writeAllPixels(enable= 0,chargeInj= 1) print("Trim, pixEnable, chargeInj: (%i,%i,%i)"%(trim, pixEnable, chargeInj)) hists = SwingThLoopBLx(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnable, chargeInjLogic = chargeInj, pixTrimI = trim, vs = 'BL') allHists.append(hists) return allHists def swingTHvsBLR(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root"): allHists = [] pixEnable = 1 chargeInj = 1 trim = 15 # system.feb.dac.dacPIXTHRaw.set(0x9ce) system.feb.dac.dacBLRRaw.set(0x5c2) system.feb.dac.dacBLRaw.set(0x5c2) # system.feb.memReg.initValueReg.set(0x0) system.feb.memReg.endValueReg.set(0xfff) system.feb.memReg.delayValueReg.set(0x5) print("Disable all pixels") system.feb.Chess2Ctrl0.writeAllPixels(enable= 0,chargeInj= 1) system.feb.Chess2Ctrl1.writeAllPixels(enable= 0,chargeInj= 1) system.feb.Chess2Ctrl2.writeAllPixels(enable= 0,chargeInj= 1) print("Trim, pixEnable, chargeInj: (%i,%i,%i)"%(trim, pixEnable, chargeInj)) hists = SwingThLoopBLx(system,nCounts,thresholdCuts,pixels,histFileName, pixEnableLogic = pixEnable, chargeInjLogic = chargeInj, pixTrimI = trim, vs = 'BLR') allHists.append(hists) return allHists def SwingThLoopBLx(system,nCounts,thresholdCuts,pixels=None,histFileName="scurve.root", pixEnableLogic = 1, chargeInjLogic = 0, pixTrimI = 0, vs = 'BL'): nColumns = 32 nRows = 128 allHists = [] logging.info("Using SwingThLoopBLx......") pixels = pixels if (pixels!=None) else [ (row,col) for row in range(nRows) for col in range(nColumns) ] for (row,col) in pixels: print("Pixel: (%i,%i)"%(row,col)) system.feb.Chess2Ctrl0.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) system.feb.Chess2Ctrl1.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) system.feb.Chess2Ctrl2.writePixel(enable=pixEnableLogic, chargeInj=chargeInjLogic, col=col, row=row, trimI= pixTrimI) hists_row = [[], [], []] hists_col = [[], [], []] for threshold in thresholdCuts: ####hists = [ R.TH1F("deltaT_%i_%i_%i_%s"%(i_asic,row,col,hex(threshold)),"",100,0,1000) for i_asic in range(3) ] # deltaT in ns hists = [[], [], []] #print("Thresholds (system.feb.dac.dacPIXTHRaw): ", hex(threshold)) #system.feb.dac.dacPIXTHRaw.set(threshold) #system.feb.dac.dacBLRaw.set(threshold+608) if (vs == 'BL'): system.feb.dac.dacBLRaw.set(threshold) print("Thresholds (system.feb.dac.dacBLRaw): ", hex(threshold), ':system.feb.dac.dacBL:', system.feb.dac.dacBL._rawGet()) else: print("Thresholds (system.feb.dac.dacBLRRaw): ", hex(threshold), ':system.feb.dac.dacBLR:', system.feb.dac.dacBLR._rawGet()) system.feb.dac.dacBLRRaw.set(threshold) # this delay seems to be very important to enable the comparitor inside the asic to settle. (smaller values tend to make this # tests to report wrong times time.sleep(2.0) system.readAll() for cnt in range(nCounts): #time.sleep(0.1) # start charge injection system.feb.memReg.chargInjStartEventReg.set(0) #system.feb.chargeInj.calPulseVar.set(1) time.sleep(0.1) system.readAll() if system.feb.chargeInj.hitDetValid0._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow0._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol0._rawGet()) ####hists_row[0].Fill(row_det) ####hists_col[0].Fill(col_det) hists_row[0].append(row_det) hists_col[0].append(col_det) #if (row == row_det) and (col == col_det): ####hists[0].Fill(float(system.feb.chargeInj.hitDetTime0._rawGet())) hists[0].append(float(system.feb.chargeInj.hitDetTime0._rawGet())) print("row_det: ",row_det, ":col_det:", col_det, ":system.feb.chargeInj.hitDetTime0: ", float(system.feb.chargeInj.hitDetTime0._rawGet())) else: hists[0].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime0: ", float(-1)) if system.feb.chargeInj.hitDetValid1._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow1._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol1._rawGet()) ####hists_row[1].Fill(row_det) ####hists_col[1].Fill(col_det) hists_row[1].append(row_det) hists_col[1].append(col_det) #if (row == row_det) and (col == col_det): ####hists[1].Fill(float(system.feb.chargeInj.hitDetTime1._rawGet())) hists[1].append(float(system.feb.chargeInj.hitDetTime1._rawGet())) print("row_det: ",row_det, ":col_det:", col_det, ":system.feb.chargeInj.hitDetTime1: ", float(system.feb.chargeInj.hitDetTime1._rawGet())) else: hists[1].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime1: ", float(-1)) if system.feb.chargeInj.hitDetValid2._rawGet(): row_det = int(system.feb.chargeInj.hitDetRow2._rawGet()) col_det = int(system.feb.chargeInj.hitDetCol2._rawGet()) ####hists_row[2].Fill(row_det) ####hists_col[2].Fill(col_det) hists_row[2].append(row_det) hists_col[2].append(col_det) #if (row == row_det) and (col == col_det): ####hists[2].Fill(float(system.feb.chargeInj.hitDetTime2._rawGet())) hists[2].append(float(system.feb.chargeInj.hitDetTime2._rawGet())) print("row_det: ",row_det, ":col_det:", col_det, ":system.feb.chargeInj.hitDetTime2: ", float(system.feb.chargeInj.hitDetTime2._rawGet())) else: hists[2].append(-1.0) print("row_det: ",-1, ":col_det:", -1, ":system.feb.chargeInj.hitDetTime2: ", float(-1)) allHists.append(hists) return allHists

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a1b84bf348a720ce474d4f69b46a722fd4b0f805

752

py

Python

venv/lib/python3.8/site-packages/tensorflow/_api/v2/__internal__/nest/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

1

2021-05-24T10:08:51.000Z

venv/lib/python3.8/site-packages/tensorflow/_api/v2/compat/v2/__internal__/nest/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

null

venv/lib/python3.8/site-packages/tensorflow/_api/v2/compat/v2/__internal__/nest/__init__.py

JIANG-CX/data_labeling

8d2470bbb537dfc09ed2f7027ed8ee7de6447248

[ "MIT" ]

null

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Public API for tf.__internal__.nest namespace. """ from __future__ import print_function as _print_function import sys as _sys from tensorflow.python.util.nest import _sequence_like as sequence_like from tensorflow.python.util.nest import flatten_up_to from tensorflow.python.util.nest import get_traverse_shallow_structure from tensorflow.python.util.nest import is_attrs from tensorflow.python.util.nest import is_mapping from tensorflow.python.util.nest import list_to_tuple from tensorflow.python.util.nest import map_structure_up_to from tensorflow.python.util.nest import yield_flat_paths del _print_function

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a1eaf1c850d95b5db71acf1ec9fd3f8b2b755e0a

41,201

py

Python

html2vec/base/vectortypes/unit_tests/test_termslist2tfavgspeed.py

dpritsos/html2vectors

be5629d6dc2665891472c5795c191286f0de31e7

[ "MIT" ]

7

2017-08-31T22:45:46.000Z

2022-01-14T21:08:34.000Z

html2vec/base/vectortypes/unit_tests/test_termslist2tfavgspeed.py

dpritsos/html2vec

be5629d6dc2665891472c5795c191286f0de31e7

[ "MIT" ]

null

html2vec/base/vectortypes/unit_tests/test_termslist2tfavgspeed.py

dpritsos/html2vec

be5629d6dc2665891472c5795c191286f0de31e7

[ "MIT" ]

null

# # Unit Test for html2vect.base.vectortypes.string2tf # # Author: Dimitiros Pritsos # # License: BSD Style # # Last update: Please refer to the GIT tracking # import sys sys.path.append('../../../../') import unittest import numpy as np import scipy.sparse as ssp from html2vect.base.termstypes.cngrams import String2CNGramsList from html2vect.base.termstypes.wngrams import String2WNGramsList from html2vect.base.vectortypes.termslist2tfavgspeed import trms2tfspd_dict class Test_BaseString2TF(unittest.TestCase): def setUp(self): self.s2ngl_c3grams = String2CNGramsList(n=3) self.s2ngl_words = String2WNGramsList(n=1) self.s2ngl_w3grams = String2WNGramsList(n=3) self.txt_sample = "This is a unit test for html2tfd.charngrams.BaseString2TF class for html2vectors package/module" self.txt_sample = "This This This aaa is a unit test for html2tfd.charngrams.BaseString2TF class aaa for html2vectors package/module" #I could have the list of Char-3-grams and Word-3-grams, and/or Word-unigrams. #However the above string will be conveted first to the proper terms list which is the functions' input. #Terms-Indexs Vocabulary self.c3grams_tid_vocab = { ' a ' : 0, ' cl' : 1, ' fo' : 2, ' ht' : 3, ' is' : 4, ' pa' : 5,\ ' te' : 6, ' un' : 7, '.Ba' : 8, '.ch' : 9, '/mo' : 10, '2TF' : 11,\ '2tf' : 12, '2ve' : 13, 'Bas' : 14, 'F c' : 15, 'Str' : 16, 'TF ' : 17,\ 'Thi' : 18, 'a u' : 19, 'ack' : 20, 'age' : 21, 'ams' : 22, 'arn' : 23,\ 'ase' : 24, 'ass' : 25, 'cha' : 26, 'cka' : 27, 'cla' : 28, 'cto' : 29,\ 'd.c' : 30, 'dul' : 31, 'e/m' : 32, 'eSt' : 33, 'ect' : 34, 'est' : 35,\ 'fd.' : 36, 'for' : 37, 'g2T' : 38, 'ge/' : 39, 'gra' : 40, 'har' : 41,\ 'his' : 42, 'htm' : 43, 'ing' : 44, 'is ' : 45, 'it ' : 46, 'kag' : 47,\ 'l2t' : 48, 'l2v' : 49, 'las' : 50, 'ml2' : 51, 'mod' : 52, 'ms.' : 53,\ 'ng2' : 54, 'ngr' : 55, 'nit' : 56, 'odu' : 57, 'or ' : 58, 'ors' : 59,\ 'pac' : 60, 'r h' : 61, 'ram' : 62, 'rin' : 63, 'rng' : 64, 'rs ' : 65,\ 's a' : 66, 's f' : 67, 's i' : 68, 's p' : 69, 's.B' : 70, 'seS' : 71,\ 'ss ' : 72, 'st ' : 73, 't f' : 74, 't t' : 75, 'tes' : 76, 'tfd' : 77,\ 'tml' : 78, 'tor' : 79, 'tri' : 80, 'ule' : 81, 'uni' : 82, 'vec' : 83\ } self.c3grams_tid_vocab_small = { ' a ' : 0, ' cl' : 1, ' fo' : 2, ' ht' : 3, ' is' : 4, ' pa' : 5,\ ' te' : 6, ' un' : 7, '.Ba' : 8, '.ch' : 9, '/mo' : 10, '2TF' : 11,\ '2tf' : 12, '2ve' : 13, 'Bas' : 14, 'F c' : 15, 'Str' : 16, 'TF ' : 17,\ 'Thi' : 18, 'a u' : 19, 'ack' : 20, 'age' : 21, 'ams' : 22, 'arn' : 23,\ 'ase' : 24, 'ass' : 25, 'cha' : 26, 'cka' : 27, 'cla' : 28, 'cto' : 29,\ 'd.c' : 30, 'dul' : 31, 'e/m' : 32, 'eSt' : 33, 'ect' : 34, 'est' : 35,\ 'fd.' : 36, 'for' : 37, 'g2T' : 38, 'ge/' : 39, 'gra' : 40, 'har' : 41,\ 'his' : 42, 'htm' : 43, 'ing' : 44, 'is ' : 45, 'it ' : 46, 'kag' : 47,\ 'l2t' : 48, 'l2v' : 49, 'las' : 50, 'ml2' : 51, 'mod' : 52, 'ms.' : 53,\ 'ng2' : 54, 'ngr' : 55, 'nit' : 56, 'odu' : 57, 'or ' : 58, 'ors' : 59,\ 'pac' : 60,\ } self.c3grams_tid_vocab_large = { ' a ' : 0, ' cl' : 1, ' fo' : 2, ' ht' : 3, ' is' : 4, ' pa' : 5,\ ' te' : 6, ' un' : 7, '.Ba' : 8, '.ch' : 9, '/mo' : 10, '2TF' : 11,\ '2tf' : 12, '2ve' : 13, 'Bas' : 14, 'F c' : 15, 'Str' : 16, 'TF ' : 17,\ 'Thi' : 18, 'a u' : 19, 'ack' : 20, 'age' : 21, 'ams' : 22, 'arn' : 23,\ 'ase' : 24, 'ass' : 25, 'cha' : 26, 'cka' : 27, 'cla' : 28, 'cto' : 29,\ 'd.c' : 30, 'dul' : 31, 'e/m' : 32, 'eSt' : 33, 'ect' : 34, 'est' : 35,\ 'fd.' : 36, 'for' : 37, 'g2T' : 38, 'ge/' : 39, 'gra' : 40, 'har' : 41,\ 'his' : 42, 'htm' : 43, 'ing' : 44, 'is ' : 45, 'it ' : 46, 'kag' : 47,\ 'l2t' : 48, 'l2v' : 49, 'las' : 50, 'ml2' : 51, 'mod' : 52, 'ms.' : 53,\ 'ng2' : 54, 'ngr' : 55, 'nit' : 56, 'odu' : 57, 'or ' : 58, 'ors' : 59,\ 'pac' : 60, 'r h' : 61, 'ram' : 62, 'rin' : 63, 'rng' : 64, 'rs ' : 65,\ 's a' : 66, 's f' : 67, 's i' : 68, 's p' : 69, 's.B' : 70, 'seS' : 71,\ 'ss ' : 72, 'st ' : 73, 't f' : 74, 't t' : 75, 'tes' : 76, 'tfd' : 77,\ 'tml' : 78, 'tor' : 79, 'tri' : 80, 'ule' : 81, 'uni' : 82, 'vec' : 83,\ 'aaa' : 84, 'bbb' : 85, 'ccc' : 86, 'ddd' : 87, 'eee' : 88, 'fff' : 89,\ 'ggg' : 90, 'hhh' : 91, 'iii' : 92, 'jjj' : 93, 'kkk' : 94, 'lll' : 95\ } self.words_tid_vocab = { 'a': 1, 'for': 2, 'This': 3, 'is': 4, 'html2vectors': 5, 'test': 6,\ 'package/module': 7, 'html2tfd.charngrams.BaseString2TF': 8, 'class': 9, 'unit': 10\ } self.words_tid_vocab_small = { 'a': 1, 'for': 2, 'This': 3, 'is': 4, 'html2vectors': 5, 'test': 6,\ 'package/module': 7\ } self.words_tid_vocab_large = { 'a': 1, 'for': 2, 'This': 3, 'is': 4, 'html2vectors': 5, 'test': 6,\ 'package/module': 7, 'html2tfd.charngrams.BaseString2TF': 8, 'class': 9,\ 'unit': 10, 'aaaword': 11, 'bbbword': 12, 'cccword': 13, 'dddword': 14\ } self.w3grams_tid_vocab = { 'This is a' : 1, 'unit test for' : 2, 'html2tfd.charngrams.BaseString2TF class for' : 3,\ 'is a unit' : 4, 'test for html2tfd.charngrams.BaseString2TF' : 5, 'class for html2vectors' : 6,\ 'a unit test' : 7, 'for html2tfd.charngrams.BaseString2TF class' : 8, 'for html2vectors package/module' : 9\ } self.w3grams_tid_vocab_small = { 'This is a' : 1, 'unit test for' : 2, 'html2tfd.charngrams.BaseString2TF class for' : 3,\ 'is a unit' : 4, 'test for html2tfd.charngrams.BaseString2TF' : 5\ } self.w3grams_tid_vocab_large = { 'This is a' : 1, 'unit test for' : 2, 'html2tfd.charngrams.BaseString2TF class for' : 3,\ 'is a unit' : 4, 'test for html2tfd.charngrams.BaseString2TF' : 5, 'class for html2vectors' : 6,\ 'a unit test' : 7, 'for html2tfd.charngrams.BaseString2TF class' : 8, 'for html2vectors package/module' : 9,\ 'aa bb cc' : 10, 'ee ff gg' : 11, 'hh ii jj' : 12\ } #Terms-Frequencies (python) Dictionaries self.expected_c3grams_tf_dict = { 's i': 1, 't t': 1, 'ase': 1, 's a': 1, 'htm': 2, 'ram': 1, 'rs ': 1, 'TF ': 1, 's f': 1,\ '.ch': 1, 't f': 1, ' un': 1, '2tf': 1, 'l2t': 1, 'l2v': 1, 's p': 1, 'eSt': 1, 'tes': 1,\ 'ge/': 1, 'ams': 1, 'or ': 2, 'cha': 1, 'est': 1, 'st ': 1, 'Str': 1, 'for': 2, 'tor': 1,\ ' is': 1, 'ing': 1, 'cla': 1, 'e/m': 1, 'fd.': 1, 'ml2': 2, 'pac': 1, 'arn': 1, 'ngr': 1,\ 'r h': 2, '2TF': 1, 'har': 1, 'is ': 2, 'tml': 2, 'F c': 1, 'ass': 1, 'tri': 1, 'g2T': 1,\ 'his': 1, 'kag': 1, 'Bas': 1, '2ve': 1, 'tfd': 1, 'gra': 1, 'rng': 1, 'ors': 1, 'it ': 1,\ 'odu': 1, 'mod': 1, ' pa': 1, 'ect': 1, 'ule': 1, 'Thi': 1, 's.B': 1, ' te': 1, '.Ba': 1,\ 'nit': 1, 'las': 1, ' a ': 1, 'rin': 1, 'seS': 1, 'cka': 1, ' cl': 1, 'd.c': 1, 'dul': 1,\ 'ack': 1, 'age': 1, ' ht': 2, 'ms.': 1, '/mo': 1, 'ng2': 1, 'ss ': 1, 'uni': 1, 'cto': 1,\ 'vec': 1, ' fo': 2, 'a u': 1 } self.expected_c3grams_tf_dict_smallVocab = { '2TF': 1, 'ase': 1, 'htm': 2, '/mo': 1, 'TF ': 1, '.ch': 1, ' un': 1, '2tf': 1, 'l2t': 1,\ 'l2v': 1, 'eSt': 1, 'ing': 1, 'ge/': 1, 'ams': 1, 'or ': 2, 'cha': 1, 'est': 1, 'Str': 1,\ 'for': 2, ' is': 1, 'cla': 1, 'e/m': 1, 'fd.': 1, 'ml2': 2, 'pac': 1, 'arn': 1, 'ngr': 1,\ 'gra': 1, 'har': 1, 'is ': 2, 'F c': 1, 'ass': 1, 'g2T': 1, 'his': 1, 'kag': 1, 'Bas': 1,\ '2ve': 1, 'ors': 1, 'it ': 1, 'odu': 1, 'mod': 1, ' pa': 1, 'ect': 1, 'Thi': 1, 'dul': 1,\ ' te': 1, '.Ba': 1, 'nit': 1, 'las': 1, ' a ': 1, 'cka': 1, ' cl': 1, 'd.c': 1, 'ack': 1,\ 'age': 1, ' ht': 2, 'ms.': 1, 'ng2': 1, 'cto': 1, ' fo': 2, 'a u': 1 } self.expected_words_tf_dict = { 'a': 1, 'for': 2, 'This': 1, 'is': 1, 'html2vectors': 1, 'test': 1,\ 'package/module': 1, 'html2tfd.charngrams.BaseString2TF': 1, 'class': 1, 'unit': 1 } self.expected_words_tf_dict_smallVocab = { 'a': 1, 'for': 2, 'This': 1, 'is': 1, 'html2vectors': 1, 'test': 1,\ 'package/module': 1\ } self.expected_w3grams_tf_dict = { 'This is a' : 1, 'unit test for' : 1, 'html2tfd.charngrams.BaseString2TF class for' : 1,\ 'is a unit' : 1, 'test for html2tfd.charngrams.BaseString2TF' : 1, 'class for html2vectors' : 1,\ 'a unit test' : 1, 'for html2tfd.charngrams.BaseString2TF class' : 1, 'for html2vectors package/module' : 1\ } self.expected_w3grams_tf_dict_smallVocab = { 'This is a' : 1, 'unit test for' : 1, 'html2tfd.charngrams.BaseString2TF class for' : 1,\ 'is a unit' : 1, 'test for html2tfd.charngrams.BaseString2TF' : 1\ } #Terms-Frequencies numpy.arrays self.expected_c3grams_tf_arr = np.array( [ (' a ', 1.0), (' cl', 1.0), (' fo', 2.0), (' ht', 2.0), (' is', 1.0),\ (' pa', 1.0), (' te', 1.0), (' un', 1.0), ('.Ba', 1.0), ('.ch', 1.0),\ ('/mo', 1.0), ('2TF', 1.0), ('2tf', 1.0), ('2ve', 1.0), ('Bas', 1.0),\ ('F c', 1.0), ('Str', 1.0), ('TF ', 1.0), ('Thi', 1.0), ('a u', 1.0),\ ('ack', 1.0), ('age', 1.0), ('ams', 1.0), ('arn', 1.0), ('ase', 1.0),\ ('ass', 1.0), ('cha', 1.0), ('cka', 1.0), ('cla', 1.0), ('cto', 1.0),\ ('d.c', 1.0), ('dul', 1.0), ('e/m', 1.0), ('eSt', 1.0), ('ect', 1.0),\ ('est', 1.0), ('fd.', 1.0), ('for', 2.0), ('g2T', 1.0), ('ge/', 1.0),\ ('gra', 1.0), ('har', 1.0), ('his', 1.0), ('htm', 2.0), ('ing', 1.0),\ ('is ', 2.0), ('it ', 1.0), ('kag', 1.0), ('l2t', 1.0), ('l2v', 1.0),\ ('las', 1.0), ('ml2', 2.0), ('mod', 1.0), ('ms.', 1.0), ('ng2', 1.0),\ ('ngr', 1.0), ('nit', 1.0), ('odu', 1.0), ('or ', 2.0), ('ors', 1.0),\ ('pac', 1.0), ('r h', 2.0), ('ram', 1.0), ('rin', 1.0), ('rng', 1.0),\ ('rs ', 1.0), ('s a', 1.0), ('s f', 1.0), ('s i', 1.0), ('s p', 1.0),\ ('s.B', 1.0), ('seS', 1.0), ('ss ', 1.0), ('st ', 1.0), ('t f', 1.0),\ ('t t', 1.0), ('tes', 1.0), ('tfd', 1.0), ('tml', 2.0), ('tor', 1.0),\ ('tri', 1.0), ('ule', 1.0), ('uni', 1), ('vec', 1)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) self.expected_c3grams_tf_arr_Vocab = np.array( [ ('s i', 1.0), ('t t', 1.0), ('ase', 1.0), ('s a', 1.0), ('htm', 2.0),\ ('ram', 1.0), ('rs ', 1.0), ('TF ', 1.0), ('s f', 1.0), ('.ch', 1.0),\ ('t f', 1.0), (' un', 1.0), ('2tf', 1.0), ('l2t', 1.0), ('l2v', 1.0),\ ('s p', 1.0), ('eSt', 1.0), ('tes', 1.0), ('ge/', 1.0), ('ams', 1.0),\ ('or ', 2.0), ('cha', 1.0), ('est', 1.0), ('st ', 1.0), ('Str', 1.0),\ ('for', 2.0), ('tor', 1.0), (' is', 1.0), ('ing', 1.0), ('cla', 1.0),\ ('e/m', 1.0), ('fd.', 1.0), ('ml2', 2.0), ('pac', 1.0), ('arn', 1.0),\ ('ngr', 1.0), ('r h', 2.0), ('2TF', 1.0), ('har', 1.0), ('is ', 2.0),\ ('tml', 2.0), ('F c', 1.0), ('ass', 1.0), ('tri', 1.0), ('g2T', 1.0),\ ('his', 1.0), ('kag', 1.0), ('Bas', 1.0), ('2ve', 1.0), ('tfd', 1.0),\ ('gra', 1.0), ('rng', 1.0), ('ors', 1.0), ('it ', 1.0), ('odu', 1.0),\ ('mod', 1.0), (' pa', 1.0), ('ect', 1.0), ('ule', 1.0), ('Thi', 1.0),\ ('s.B', 1.0), (' te', 1.0), ('.Ba', 1.0), ('nit', 1.0), ('las', 1.0),\ (' a ', 1.0), ('rin', 1.0), ('seS', 1.0), ('cka', 1.0), (' cl', 1.0),\ ('d.c', 1.0), ('dul', 1.0), ('ack', 1.0), ('age', 1.0), (' ht', 2.0),\ ('ms.', 1.0), ('/mo', 1.0), ('ng2', 1.0), ('ss ', 1.0), ('uni', 1.0),\ ('cto', 1.0), ('vec', 1.0), (' fo', 2.0), ('a u', 1.0)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) self.expected_c3grams_tf_arr_smallVocab = np.array( [ ('2TF', 1.0), ('ase', 1.0), ('htm', 2.0), ('/mo', 1.0), ('TF ', 1.0),\ ('.ch', 1.0), (' un', 1.0), ('2tf', 1.0), ('l2t', 1.0), ('l2v', 1.0),\ ('eSt', 1.0), ('ing', 1.0), ('ge/', 1.0), ('ams', 1.0), ('or ', 2.0),\ ('cha', 1.0), ('est', 1.0), ('Str', 1.0), ('for', 2.0), (' is', 1.0),\ ('cla', 1.0), ('e/m', 1.0), ('fd.', 1.0), ('ml2', 2.0), ('pac', 1.0),\ ('arn', 1.0), ('ngr', 1.0), ('gra', 1.0), ('har', 1.0), ('is ', 2.0),\ ('F c', 1.0), ('ass', 1.0), ('g2T', 1.0), ('his', 1.0), ('kag', 1.0),\ ('Bas', 1.0), ('2ve', 1.0), ('ors', 1.0), ('it ', 1.0), ('odu', 1.0),\ ('mod', 1.0), (' pa', 1.0), ('ect', 1.0), ('Thi', 1.0), ('dul', 1.0),\ (' te', 1.0), ('.Ba', 1.0), ('nit', 1.0), ('las', 1.0), (' a ', 1.0),\ ('cka', 1.0), (' cl', 1.0), ('d.c', 1.0), ('ack', 1.0), ('age', 1.0),\ (' ht', 2.0), ('ms.', 1.0), ('ng2', 1.0), ('cto', 1.0), (' fo', 2.0),\ ('a u', 1.0)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) self.expected_words_tf_arr = np.array( [ ('This', 1.0), ('a', 1.0), ('class', 1.0), ('for', 2.0),\ ('html2tfd.charngrams.BaseString2TF', 1.0), ('html2vectors', 1.0),\ ('is', 1.0), ('package/module', 1.0), ('test', 1.0), ('unit', 1.0)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) self.expected_words_tf_arr_Vocab = np.array( [ ('a', 1.0), ('for', 2.0), ('This', 1.0), ('is', 1.0), ('html2vectors', 1.0),\ ('test', 1.0), ('package/module', 1.0), ('html2tfd.charngrams.BaseString2TF', 1.0),\ ('class', 1.0), ('unit', 1.0)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) self.expected_words_tf_arr_smallVocab = np.array( [ ('a', 1.0), ('for', 2.0), ('This', 1.0), ('is', 1.0), ('html2vectors', 1.0),\ ('test', 1.0), ('package/module', 1.0)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) self.expected_w3grams_tf_arr = np.array( [ ('This is a', 1.0), ('a unit test', 1.0), ('class for html2vectors', 1.0),\ ('for html2tfd.charngrams.BaseString2TF class', 1.0), ('for html2vectors package/module', 1.0),\ ('html2tfd.charngrams.BaseString2TF class for', 1.0), ('is a unit', 1.0),\ ('test for html2tfd.charngrams.BaseString2TF', 1.0), ('unit test for', 1.0)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) self.expected_w3grams_tf_arr_Vocab = np.array( [ ('a unit test', 1.0), ('html2tfd.charngrams.BaseString2TF class for', 1.0),\ ('test for html2tfd.charngrams.BaseString2TF', 1.0), ('class for html2vectors', 1.0),\ ('for html2tfd.charngrams.BaseString2TF class', 1.0), ('is a unit', 1.0),\ ('for html2vectors package/module', 1.0), ('This is a', 1.0), ('unit test for', 1.0)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) self.expected_w3grams_tf_arr_smallVocab = np.array( [ ('is a unit', 1.0), ('test for html2tfd.charngrams.BaseString2TF', 1.0), ('unit test for', 1.0),\ ('This is a', 1.0), ('html2tfd.charngrams.BaseString2TF class for', 1.0)\ ],\ dtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])\ ) #Frequencies scipy.sparse matrices self.expected_c3grams_f_sparse_NoVocab = ssp.csr_matrix( ([ 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0\ ],\ (np.zeros(84), np.arange(84)) ),\ dtype=np.float32\ ) self.expected_c3grams_f_sparse_largeVocab = ssp.csr_matrix( ([ 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\ ],\ (np.zeros(96), np.arange(96)) ),\ dtype=np.float32\ ) self.expected_c3grams_f_sparse_smallVocab = ssp.csr_matrix( ([ 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 1.0,\ ],\ (np.zeros(61), np.arange(61)) ),\ dtype=np.float32\ ) self.expected_words_f_sparse_Vocab = ssp.csr_matrix( ([ 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],\ (np.zeros(11), np.arange(11))\ ), dtype=np.float32\ ) self.expected_words_f_sparse_smallVocab = ssp.csr_matrix( ([ 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],\ (np.zeros(8), np.arange(8))\ ), dtype=np.float32\ ) self.expected_words_f_sparse_largeVocab = ssp.csr_matrix( ([ 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 ],\ (np.zeros(15), np.arange(15))\ ), dtype=np.float32\ ) self.expected_w3grams_f_sparse_Vocab = ssp.csr_matrix( ([ 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],\ (np.zeros(10), np.arange(10))\ ), dtype=np.float32\ ) self.expected_w3grams_f_sparse_smallVocab = ssp.csr_matrix( ([ 0.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],\ (np.zeros(6), np.arange(6))\ ), dtype=np.float32\ ) self.expected_w3grams_f_sparse_largeVocab = ssp.csr_matrix( ([ 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0 ],\ (np.zeros(13), np.arange(13))\ ), dtype=np.float32\ ) #Frequencies numpy.arrays matrices self.expected_c3grams_f_narray_Vocab = np.array( ([ 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0\ ]),\ dtype=np.float32\ ) self.expected_c3grams_f_narray_largeVocab = np.array( ([ 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\ ]),\ dtype=np.float32\ ) self.expected_c3grams_f_narray_smallVocab = np.array( ([ 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,\ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0,\ 1.0,\ ]),\ dtype=np.float32\ ) self.expected_words_f_narray_Vocab = np.array( [ 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],\ dtype=np.float32\ ) self.expected_words_f_narray_smallVocab = np.array( [ 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],\ dtype=np.float32\ ) self.expected_words_f_narray_largeVocab = np.array( [ 0.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 ],\ dtype=np.float32\ ) self.expected_w3grams_f_narray_Vocab = np.array( [ 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],\ dtype=np.float32\ ) self.expected_w3grams_f_narray_smallVocab = np.array( [ 0.0, 1.0, 1.0, 1.0, 1.0, 1.0 ],\ dtype=np.float32\ ) self.expected_w3grams_f_narray_largeVocab = np.array( [ 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0 ],\ dtype=np.float32\ ) """trms2tf_dict()""" def test_trms2tf_dict_c3grams_NoVocab(self): cngrams_tf_dict = trms2tfspd_dict( self.s2ngl_c3grams.terms_lst( self.txt_sample ), vocabulary=None ) print cngrams_tf_dict self.assertEqual(cngrams_tf_dict, self.expected_c3grams_tf_dict) """ def test_trms2tf_dict_c3grams_Vocab(self): cngrams_tf_dict = trms2tf_dict( self.s2ngl_c3grams.terms_lst( self.txt_sample ), vocabulary=self.c3grams_tid_vocab ) self.assertEqual(cngrams_tf_dict, self.expected_c3grams_tf_dict) def test_trms2tf_dict_c3grams_smallVocab(self): cngrams_tf_dict = trms2tf_dict( self.s2ngl_c3grams.terms_lst( self.txt_sample ), vocabulary=self.c3grams_tid_vocab_small ) self.assertEqual(cngrams_tf_dict, self.expected_c3grams_tf_dict_smallVocab) def test_trms2tf_dict_c3grams_largeVocab(self): cngrams_tf_dict = trms2tf_dict( self.s2ngl_c3grams.terms_lst( self.txt_sample ), vocabulary=self.c3grams_tid_vocab_large ) self.assertEqual(cngrams_tf_dict, self.expected_c3grams_tf_dict) def test_trms2tf_dict_words_NoVocab(self): words_tf_dict = trms2tf_dict( self.s2ngl_words.terms_lst( self.txt_sample ), vocabulary=None ) #Output excpected to be the same as in case of an input Vocabulary having same size (in terms) to the input terms-list. self.assertEqual(words_tf_dict, self.expected_words_tf_dict) def test_trms2tf_dict_words_Vocab(self): cngrams_tf_dict = trms2tf_dict( self.s2ngl_words.terms_lst( self.txt_sample ), vocabulary=self.words_tid_vocab ) self.assertEqual(cngrams_tf_dict, self.expected_words_tf_dict) def test_trms2tf_dict_words_smallVocab(self): words_tf_dict = trms2tf_dict( self.s2ngl_words.terms_lst( self.txt_sample ), vocabulary=self.words_tid_vocab_small ) #Output excpected to be the smaller that the _Vocabe case since input Vocabulary has smaller size (in terms) than terms-list. self.assertEqual(words_tf_dict, self.expected_words_tf_dict_smallVocab) def test_trms2tf_dict_words_largeVocab(self): words_tf_dict = trms2tf_dict( self.s2ngl_words.terms_lst( self.txt_sample ), vocabulary=self.words_tid_vocab_large ) #Output excpected to be the same as in case of an input Vocabulary having same size (in terms) to the input terms-list. self.assertEqual(words_tf_dict, self.expected_words_tf_dict) def test_trms2tf_dict_w3grams_NoVocab(self): wngrams_tf_dict = trms2tf_dict( self.s2ngl_w3grams.terms_lst( self.txt_sample ), vocabulary=None ) #Output excpected to be the same as in case of an input Vocabulary having same size (in terms) to the input terms-list. #Because, the input Vocabulary happess to have the same terms-order to the one returned by default in the case of 'Vocabulary = None' self.assertEqual(wngrams_tf_dict, self.expected_w3grams_tf_dict) def test_trms2tf_dict_w3grams_Vocab(self): wngrams_tf_dict = trms2tf_dict( self.s2ngl_w3grams.terms_lst( self.txt_sample ), vocabulary=self.w3grams_tid_vocab ) self.assertEqual(wngrams_tf_dict, self.expected_w3grams_tf_dict) def test_trms2tf_dict_w3grams_smallVocab(self): wngrams_tf_dict = trms2tf_dict( self.s2ngl_w3grams.terms_lst( self.txt_sample ), vocabulary=self.w3grams_tid_vocab_small ) self.assertEqual(wngrams_tf_dict, self.expected_w3grams_tf_dict_smallVocab) def test_trms2tf_dict_w3grams_largeVocab(self): wngrams_tf_dict = trms2tf_dict( self.s2ngl_w3grams.terms_lst( self.txt_sample ), vocabulary=self.w3grams_tid_vocab_large ) #Output excpected to be the same as in case of an input Vocabulary having same size (in terms) to the input terms-list. self.assertEqual(wngrams_tf_dict, self.expected_w3grams_tf_dict) #trms2tf_narray() def test_trms2tf_narray_c3grams_NoVocab(self): cngrams_tf_arr = trms2tf_narray( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ vocabulary=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) self.assertTrue( np.all(cngrams_tf_arr == self.expected_c3grams_tf_arr) ) def test_trms2tf_narray_c3grams_Vocab(self): cngrams_tf_arr = trms2tf_narray( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ vocabulary=self.c3grams_tid_vocab,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) #Output excpected to be the same as in case of an input Vocabulary is None. #However, the order of terms will follow the one of input Vocabulary. self.assertTrue( np.all(cngrams_tf_arr == self.expected_c3grams_tf_arr_Vocab) ) def test_trms2tf_narray_c3grams_smallVocab(self): cngrams_tf_arr = trms2tf_narray( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ vocabulary=self.c3grams_tid_vocab_small,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) #Output excpected to be smaller than input terms Vocabulary having same size (in terms) to the input terms-list. self.assertTrue( np.all(cngrams_tf_arr == self.expected_c3grams_tf_arr_smallVocab) ) def test_trms2tf_narray_c3grams_largeVocab(self): cngrams_tf_arr = trms2tf_narray( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ vocabulary=self.c3grams_tid_vocab_large,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) #Output excpected to be the same as in case a Vocabulary is given, having the same terms as the ones into the terms-list. #That is, the extra terms into the vocabulary will not be included into the output recored-array. self.assertTrue( np.all(cngrams_tf_arr == self.expected_c3grams_tf_arr_Vocab) ) def test_trms2tf_narray_words_NoVocab(self): words_tf_arr = trms2tf_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) self.assertTrue( np.all(words_tf_arr == self.expected_words_tf_arr) ) def test_trms2tf_narray_words_Vocab(self): words_tf_arr = trms2tf_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ vocabulary=self.words_tid_vocab,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) self.assertTrue( np.all(words_tf_arr == self.expected_words_tf_arr_Vocab) ) def test_trms2tf_narray_words_smallVocab(self): words_tf_arr = trms2tf_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ vocabulary=self.words_tid_vocab_small,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) self.assertTrue( np.all(words_tf_arr == self.expected_words_tf_arr_smallVocab) ) def test_trms2tf_narray_words_largeVocab(self): words_tf_arr = trms2tf_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ vocabulary=self.words_tid_vocab_large,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) #Output excpected to be the same as in case a Vocabulary is given, having the same terms as the ones into the terms-list. #That is, the extra terms into the vocabulary will not be included into the output recored-array. self.assertTrue( np.all(words_tf_arr == self.expected_words_tf_arr_Vocab) ) def test_trms2tf_narray_w3grams_NoVocab(self): w3grams_tf_arr = trms2tf_narray( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) self.assertTrue( np.all(w3grams_tf_arr == self.expected_w3grams_tf_arr) ) def test_trms2tf_narray_w3grams_Vocab(self): w3grams_tf_arr = trms2tf_narray( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ vocabulary=self.w3grams_tid_vocab,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) self.assertTrue( np.all(w3grams_tf_arr == self.expected_w3grams_tf_arr_Vocab) ) def test_trms2tf_narray_w3grams_smallVocab(self): w3grams_tf_arr = trms2tf_narray( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ vocabulary=self.w3grams_tid_vocab_small,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) self.assertTrue( np.all(w3grams_tf_arr == self.expected_w3grams_tf_arr_smallVocab) ) def test_trms2tf_narray_words_largeVocab(self): words_tf_arr = trms2tf_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ vocabulary=self.words_tid_vocab_large,\ norm_func=None, ndtype=np.dtype([('terms', 'S128'), ('freq', 'float32')])) #Output excpected to be the same as in case a Vocabulary is given, having the same terms as the ones into the terms-list. #That is, the extra terms into the vocabulary will not be included into the output recored-array. self.assertTrue( np.all(words_tf_arr == self.expected_words_tf_arr_Vocab) ) #trms2f_sparse() def test_trms2f_sparse_c3grams_NoVocab(self): with self.assertRaises(ValueError): cngrams_f_sparse = trms2f_sparse( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=None, norm_func=None, ndtype=np.float32 ) def test_trms2f_sparse_c3grams_Vocab(self): cngrams_f_sparse = trms2f_sparse( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.c3grams_tid_vocab, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(cngrams_f_sparse.toarray() == self.expected_c3grams_f_sparse_NoVocab.toarray()) ) def test_trms2f_sparse_c3grams_smallVocab(self): cngrams_f_sparse = trms2f_sparse( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.c3grams_tid_vocab_small, norm_func=None, ndtype=np.float32 ) #Output excpected to be smaller in size than the input terms-list but same in size to the input Vocabulary. self.assertTrue( np.all(cngrams_f_sparse.toarray() == self.expected_c3grams_f_sparse_smallVocab.toarray()) ) def test_trms2f_sparse_c3grams_largeVocab(self): cngrams_f_sparse = trms2f_sparse( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.c3grams_tid_vocab_large, norm_func=None, ndtype=np.float32 ) #Output excpected to be larger in size than the input terms-list but same in size to the input Vocabulary. #terms-poisition of terms not inlcuded in terms-list being setted to zero. self.assertTrue( np.all(cngrams_f_sparse.toarray() == self.expected_c3grams_f_sparse_largeVocab.toarray()) ) def test_trms2f_sparse_words_NoVocab(self): with self.assertRaises(ValueError): words_f_sparse = trms2f_sparse( self.s2ngl_words.terms_lst( self.txt_sample ),\ tid_vocabulary=None, norm_func=None, ndtype=np.float32 ) def test_trms2f_sparse_words_Vocab(self): words_f_sparse = trms2f_sparse( self.s2ngl_words.terms_lst( self.txt_sample ),\ tid_vocabulary=self.words_tid_vocab, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(words_f_sparse.toarray() == self.expected_words_f_sparse_Vocab.toarray()) ) def test_trms2f_sparse_words_smallVocab(self): words_f_sparse = trms2f_sparse( self.s2ngl_words.terms_lst( self.txt_sample ),\ tid_vocabulary=self.words_tid_vocab_small, norm_func=None, ndtype=np.float32 ) #Output excpected to be smaller in size than the input terms-list but same in size to the input Vocabulary. self.assertTrue( np.all(words_f_sparse.toarray() == self.expected_words_f_sparse_smallVocab.toarray()) ) def test_trms2f_sparse_words_largeVocab(self): words_f_sparse = trms2f_sparse( self.s2ngl_words.terms_lst( self.txt_sample ),\ tid_vocabulary=self.words_tid_vocab_large, norm_func=None, ndtype=np.float32 ) #Output excpected to be larger in size than the input terms-list but same in size to the input Vocabulary. #terms-poisition of terms not inlcuded in terms-list being setted to zero. self.assertTrue( np.all(words_f_sparse.toarray() == self.expected_words_f_sparse_largeVocab.toarray()) ) def test_trms2f_sparse_w3grams_NoVocab(self): with self.assertRaises(ValueError): w3grams_f_sparse = trms2f_sparse( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=None, norm_func=None, ndtype=np.float32 ) def test_trms2f_sparse_w3grams_Vocab(self): w3grams_f_sparse = trms2f_sparse( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.w3grams_tid_vocab, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(w3grams_f_sparse.toarray() == self.expected_w3grams_f_sparse_Vocab.toarray()) ) def test_trms2f_sparse_w3grams_smallVocab(self): w3grams_f_sparse = trms2f_sparse( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.w3grams_tid_vocab_small, norm_func=None, ndtype=np.float32 ) #Output excpected to be smaller in size than the input terms-list but same in size to the input Vocabulary. self.assertTrue( np.all(w3grams_f_sparse.toarray() == self.expected_w3grams_f_sparse_smallVocab.toarray()) ) def test_trms2f_sparse_w3grams_largeVocab(self): w3grams_f_sparse = trms2f_sparse( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.w3grams_tid_vocab_large, norm_func=None, ndtype=np.float32 ) #Output excpected to be larger in size than the input terms-list but same in size to the input Vocabulary. #terms-poisition of terms not inlcuded in terms-list being setted to zero. self.assertTrue( np.all(w3grams_f_sparse.toarray() == self.expected_w3grams_f_sparse_largeVocab.toarray()) ) #trms2f_narray" def test_trms2f_narray_c3grams_NoVocab(self): with self.assertRaises(ValueError): cngrams_f_narray = trms2f_narray( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=None, norm_func=None, ndtype=np.float32 ) def test_trms2f_narray_c3grams_Vocab(self): cngrams_f_narray = trms2f_narray( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.c3grams_tid_vocab, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(cngrams_f_narray == self.expected_c3grams_f_narray_Vocab) ) def test_trms2f_narray_c3grams_smallVocab(self): cngrams_f_narray = trms2f_narray( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.c3grams_tid_vocab_small, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(cngrams_f_narray == self.expected_c3grams_f_narray_smallVocab) ) def test_trms2f_narray_c3grams_largeVocab(self): cngrams_f_narray = trms2f_narray( self.s2ngl_c3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.c3grams_tid_vocab_large, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(cngrams_f_narray == self.expected_c3grams_f_narray_largeVocab) ) def test_trms2f_narray_words_NoVocab(self): with self.assertRaises(ValueError): words_f_narray = trms2f_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ tid_vocabulary=None, norm_func=None, ndtype=np.float32 ) def test_trms2f_narray_words_Vocab(self): words_f_narray = trms2f_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ tid_vocabulary=self.words_tid_vocab, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(words_f_narray == self.expected_words_f_narray_Vocab) ) def test_trms2f_narray_words_smallVocab(self): words_f_narray = trms2f_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ tid_vocabulary=self.words_tid_vocab_small, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(words_f_narray == self.expected_words_f_narray_smallVocab) ) def test_trms2f_narray_words_largeVocab(self): words_f_narray = trms2f_narray( self.s2ngl_words.terms_lst( self.txt_sample ),\ tid_vocabulary=self.words_tid_vocab_large, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(words_f_narray == self.expected_words_f_narray_largeVocab) ) def test_trms2f_narray_w3grams_NoVocab(self): with self.assertRaises(ValueError): w3grams_f_narray = trms2f_narray( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=None, norm_func=None, ndtype=np.float32 ) def test_trms2f_narray_w3grams_Vocab(self): w3grams_f_narray = trms2f_narray( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.w3grams_tid_vocab, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(w3grams_f_narray == self.expected_w3grams_f_narray_Vocab) ) def test_trms2f_narray_w3grams_smallVocab(self): w3grams_f_narray = trms2f_narray( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.w3grams_tid_vocab_small, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(w3grams_f_narray == self.expected_w3grams_f_narray_smallVocab) ) def test_trms2f_narray_w3grams_largeVocab(self): w3grams_f_narray = trms2f_narray( self.s2ngl_w3grams.terms_lst( self.txt_sample ),\ tid_vocabulary=self.w3grams_tid_vocab_large, norm_func=None, ndtype=np.float32 ) self.assertTrue( np.all(w3grams_f_narray == self.expected_w3grams_f_narray_largeVocab) ) """ suite = unittest.TestSuite() suite.addTest( unittest.TestLoader().loadTestsFromTestCase(Test_BaseString2TF) ) unittest.TextTestRunner(verbosity=2).run(suite)

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tests/test_implementations/test_memory_sqlalchemy/api_async_test/test_post_redirect_get_api.py

aebrahim/FastAPIQuickCRUD

5c4d1bea2203c30eb21557f18bf9016b55fffa60

[ "MIT" ]

123

2021-08-17T01:54:12.000Z

2022-03-29T20:41:56.000Z

tests/test_implementations/test_memory_sqlalchemy/api_async_test/test_post_redirect_get_api.py

aebrahim/FastAPIQuickCRUD

5c4d1bea2203c30eb21557f18bf9016b55fffa60

[ "MIT" ]

10

2021-12-28T21:34:20.000Z

2022-03-16T13:31:24.000Z

tests/test_implementations/test_memory_sqlalchemy/api_async_test/test_post_redirect_get_api.py

aebrahim/FastAPIQuickCRUD

5c4d1bea2203c30eb21557f18bf9016b55fffa60

[ "MIT" ]

10

2021-08-17T07:37:36.000Z

2022-03-31T13:16:55.000Z

import json import uuid from datetime import date, timedelta, datetime, timezone from http import HTTPStatus from starlette.testclient import TestClient from src.fastapi_quickcrud import CrudMethods from src.fastapi_quickcrud import crud_router_builder from src.fastapi_quickcrud import sqlalchemy_to_pydantic from tests.test_implementations.test_memory_sqlalchemy.api_test import app, UntitledTable256 UntitledTable256Model = sqlalchemy_to_pydantic(UntitledTable256, crud_methods=[ CrudMethods.POST_REDIRECT_GET ], exclude_columns=['bytea_value', 'xml_value', 'box_valaue']) test_post_and_redirect_get = crud_router_builder(db_model=UntitledTable256, crud_models=UntitledTable256Model, prefix="/test_post_direct_get", tags=["test"], async_mode=True ) UntitledTable256Model = sqlalchemy_to_pydantic(UntitledTable256, crud_methods=[ CrudMethods.FIND_ONE ], exclude_columns=['bytea_value', 'xml_value', 'box_valaue']) test_get_data = crud_router_builder(db_model=UntitledTable256, crud_models=UntitledTable256Model, prefix="/test_post_direct_get", tags=["test"], async_mode=True ) UntitledTable256Model = sqlalchemy_to_pydantic(UntitledTable256, crud_methods=[ CrudMethods.POST_REDIRECT_GET ], exclude_columns=['bytea_value', 'xml_value', 'box_valaue']) test_post_and_redirect_get_without_get = crud_router_builder(db_model=UntitledTable256, crud_models=UntitledTable256Model, prefix="/test_post_direct_get_without_get", tags=["test"], async_mode=True ) [app.include_router(i) for i in [test_post_and_redirect_get, test_get_data, test_post_and_redirect_get_without_get]] client = TestClient(app) primary_key_name = UntitledTable256.primary_key_of_table unique_fields = UntitledTable256.unique_fields # Post Redirect Get API Test def test_create_one_but_no_follow_redirect(): headers = { 'accept': '*/*', 'Content-Type': 'application/json', } data = '{ "bool_value": true, "char_value": "string", "date_value": "2021-07-24", "float4_value": 0, "float8_value": 0, "int2_value": 0, "int4_value": 0, "int8_value": 0, "interval_value": 0, "json_value": {}, "jsonb_value": {}, "numeric_value": 0, "text_value": "string", "timestamp_value": "2021-07-24T02:54:53.285Z", "timestamptz_value": "2021-07-24T02:54:53.285Z", "uuid_value": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "varchar_value": "string", "array_value": [ 0 ], "array_str__value": [ "string" ] }' response = client.post('/test_post_direct_get', headers=headers, data=data, allow_redirects=False) assert response.status_code == HTTPStatus.SEE_OTHER def test_create_one_with_redirect(): headers = { 'accept': '*/*', 'Content-Type': 'application/json', } change = {} bool_value_change = False char_value_change = "test" date_value_change = str(date.today() - timedelta(days=1)) float8_value_change = 0.1 int2_value_change = 100 int8_value_change = 100 interval_value_change = float(5400) json_value_change = {"hello": "world"} jsonb_value_change = {"hello": "world"} numeric_value_change = 19.0 text_value_change = 'hello world' time_value_change = '18:18:18' timestamp_value_change = str(datetime.utcnow().isoformat()) timestamptz_value_change = str(datetime.utcnow().replace(tzinfo=timezone.utc).isoformat()) timetz_value_change = '18:18:18+00:00' uuid_value_change = str(uuid.uuid4()) varchar_value_change = 'hello world' array_value_change = [1, 2, 3, 4] array_str__value_change = ['1', '2', '3', '4'] change['bool_value'] = bool_value_change change['char_value'] = char_value_change change['date_value'] = date_value_change change['float8_value'] = float8_value_change change['int2_value'] = int2_value_change change['int8_value'] = int8_value_change change['float4_value'] = 0.4 change['int4_value'] = 4 change['interval_value'] = interval_value_change change['json_value'] = json_value_change change['jsonb_value'] = jsonb_value_change change['numeric_value'] = numeric_value_change change['text_value'] = text_value_change change['time_value'] = time_value_change change['timestamp_value'] = timestamp_value_change change['timestamptz_value'] = timestamptz_value_change change['timetz_value'] = timetz_value_change change['uuid_value'] = uuid_value_change change['varchar_value'] = varchar_value_change change['array_value'] = array_value_change change['array_str__value'] = array_str__value_change data_ = json.dumps(change) response = client.post('/test_post_direct_get', headers=headers, data=data_, allow_redirects=True) assert response.status_code == HTTPStatus.OK response_data = response.json() assert primary_key_name in response_data return response_data def test_create_but_conflict(): data = test_create_one_with_redirect() headers = { 'accept': '*/*', 'Content-Type': 'application/json', } response = client.post('/test_post_direct_get', headers=headers, data=json.dumps(data), allow_redirects=True) assert response.status_code == HTTPStatus.CONFLICT def test_create_but_not_found_get_api(): change = {} bool_value_change = False char_value_change = "test" date_value_change = str(date.today() - timedelta(days=1)) float8_value_change = 0.1 int2_value_change = 100 int8_value_change = 100 interval_value_change = float(5400) json_value_change = {"hello": "world"} jsonb_value_change = {"hello": "world"} numeric_value_change = 19 text_value_change = 'hello world' time_value_change = '18:18:18' timestamp_value_change = str(datetime.utcnow().isoformat()) timestamptz_value_change = str(datetime.utcnow().replace(tzinfo=timezone.utc).isoformat()) timetz_value_change = '18:18:18+00:00' uuid_value_change = str(uuid.uuid4()) varchar_value_change = 'hello world' array_value_change = [1, 2, 3, 4] array_str__value_change = ['1', '2', '3', '4'] change['bool_value'] = bool_value_change change['char_value'] = char_value_change change['date_value'] = date_value_change change['float8_value'] = float8_value_change change['int2_value'] = int2_value_change change['int8_value'] = int8_value_change change['float4_value'] = 0.4 change['int4_value'] = 4 change['interval_value'] = interval_value_change change['json_value'] = json_value_change change['jsonb_value'] = jsonb_value_change change['numeric_value'] = numeric_value_change change['text_value'] = text_value_change change['time_value'] = time_value_change change['timestamp_value'] = timestamp_value_change change['timestamptz_value'] = timestamptz_value_change change['timetz_value'] = timetz_value_change change['uuid_value'] = uuid_value_change change['varchar_value'] = varchar_value_change change['array_value'] = array_value_change change['array_str__value'] = array_str__value_change data = json.dumps(change) headers = { 'accept': '*/*', 'Content-Type': 'application/json', } response = client.post('/test_post_direct_get_without_get', headers=headers, data=data, allow_redirects=True) assert response.status_code == HTTPStatus.NOT_FOUND

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b821f79b42df0a7d7e510fc1784ba397079fd985

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Python

train_with_your_data/scripts/cpmg/automated_metabolite_quantification/data_generators.py

ciceklab/targeted_brain_tumor_margin_assessment

2cf729019dfc1785992208a69c353a659c9b6448

[ "MIT" ]

1

2021-12-11T20:06:39.000Z

train_with_your_data/scripts/cpmg/automated_metabolite_quantification/data_generators.py

ciceklab/targeted_brain_tumor_margin_assessment

2cf729019dfc1785992208a69c353a659c9b6448

[ "MIT" ]

null

train_with_your_data/scripts/cpmg/automated_metabolite_quantification/data_generators.py

ciceklab/targeted_brain_tumor_margin_assessment

2cf729019dfc1785992208a69c353a659c9b6448

[ "MIT" ]

2

2021-12-15T18:17:17.000Z

2021-12-16T12:08:30.000Z

import pdb import pickle import pandas as pd import os import numpy as np import sys sys.path.insert(1,"../") sys.path.insert(1,"../../") sys.path.insert(1,"../../../") from config_u import base project_base_path = base current_path = "scripts/cpmg/automated_metabolite_quantification/" sys.path.insert(1, os.path.join(project_base_path, current_path)) from data_utils import split_to_kfold, spectrum2ppm, spectrum_peak_unit_quantification ''' Custom data generator functions for fold generation with no patient and sample overlap''' # Option #1.A: only valid PC and fully quantified samples (train, vald and test) def cpmg_generator_1A(k, fold_dct, statistics, spectra, ppm_spectra, quant, class_labels): cur_iter = 0 while cur_iter < k: test_fold_idx = fold_dct[str(cur_iter)] test_fold = {} test_fold["spectra"] = spectra[test_fold_idx,:] test_fold["quant"] = quant[test_fold_idx,:] test_fold["ppm_spectra"] = ppm_spectra[test_fold_idx,:] test_fold["class_labels"] = class_labels[test_fold_idx,:] test_fold["stats"] = statistics.iloc[test_fold_idx,:].reset_index(drop=True) vald_fold_idx = fold_dct[str((cur_iter+1) % k)] vald_fold = {} vald_fold["spectra"] = spectra[vald_fold_idx,:] vald_fold["quant"] = quant[vald_fold_idx,:] vald_fold["ppm_spectra"] = ppm_spectra[vald_fold_idx,:] vald_fold["class_labels"] = class_labels[vald_fold_idx,:] vald_fold["stats"] = statistics.iloc[vald_fold_idx,:].reset_index(drop=True) train_fold_indices = list(range(k)) train_fold_indices.remove(cur_iter) train_fold_indices.remove((cur_iter+1) % k) train_fold_idx = [] + fold_dct[str(train_fold_indices[0])] + fold_dct[str(train_fold_indices[1])] + fold_dct[str(train_fold_indices[2])] train_fold = {} train_fold["spectra"] = spectra[train_fold_idx,:] train_fold["quant"] = quant[train_fold_idx,:] train_fold["ppm_spectra"] = ppm_spectra[train_fold_idx,:] train_fold["class_labels"] = class_labels[train_fold_idx,:] train_fold["stats"] = statistics.iloc[train_fold_idx,:].reset_index(drop=True) all_data = {} all_data["spectra"] = spectra all_data["quant"] = quant all_data["ppm_spectra"] = ppm_spectra all_data["class_labels"] = class_labels all_data["stats"] = statistics yield (train_fold_idx, vald_fold_idx, test_fold_idx, train_fold, vald_fold, test_fold, all_data) cur_iter += 1 # Option #1.B: only valid PC and fully quantified samples (train and test) def cpmg_generator_1B(k, fold_dct, statistics, spectra, ppm_spectra, quant, class_labels): cur_iter = 0 while cur_iter < k: test_fold_idx = fold_dct[str(cur_iter)] test_fold = {} test_fold["spectra"] = spectra[test_fold_idx,:] test_fold["quant"] = quant[test_fold_idx,:] test_fold["ppm_spectra"] = ppm_spectra[test_fold_idx,:] test_fold["class_labels"] = class_labels[test_fold_idx,:] test_fold["stats"] = statistics.iloc[test_fold_idx,:].reset_index(drop=True) train_fold_indices = list(range(k)) train_fold_indices.remove(cur_iter) train_fold_idx = [] + fold_dct[str(train_fold_indices[0])] + fold_dct[str(train_fold_indices[1])] + fold_dct[str(train_fold_indices[2])] + fold_dct[str(train_fold_indices[3])] train_fold = {} train_fold["spectra"] = spectra[train_fold_idx,:] train_fold["quant"] = quant[train_fold_idx,:] train_fold["ppm_spectra"] = ppm_spectra[train_fold_idx,:] train_fold["class_labels"] = class_labels[train_fold_idx,:] train_fold["stats"] = statistics.iloc[train_fold_idx,:].reset_index(drop=True) all_data = {} all_data["spectra"] = spectra all_data["quant"] = quant all_data["ppm_spectra"] = ppm_spectra all_data["class_labels"] = class_labels all_data["stats"] = statistics yield (train_fold_idx, test_fold_idx, train_fold, test_fold, all_data) cur_iter += 1 # Option #2.A: valid PC and fully quantified samples form test folds # but invalid samples are injected to the training dataset by hand (train, vald and test) def cpmg_generator_2A(k, fold_dct, valid_statistics, valid_spectra, valid_ppm_spectra, valid_quant, valid_class_labels,\ invalid_statistics, invalid_spectra, invalid_ppm_spectra, invalid_quant): cur_iter = 0 while cur_iter < k: test_fold_idx = fold_dct[str(cur_iter)] test_fold = {} test_fold["spectra"] = spectra[test_fold_idx,:] test_fold["quant"] = quant[test_fold_idx,:] test_fold["ppm_spectra"] = ppm_spectra[test_fold_idx,:] test_fold["class_labels"] = class_labels[test_fold_idx,:] test_fold["stats"] = statistics.iloc[test_fold_idx,:].reset_index(drop=True) vald_fold_idx = fold_dct[str((cur_iter+1) % k)] vald_fold = {} vald_fold["spectra"] = spectra[vald_fold_idx,:] vald_fold["quant"] = quant[vald_fold_idx,:] vald_fold["ppm_spectra"] = ppm_spectra[vald_fold_idx,:] vald_fold["class_labels"] = class_labels[vald_fold_idx,:] vald_fold["stats"] = statistics.iloc[vald_fold_idx,:].reset_index(drop=True) invalid_sample_cnt = invalid_spectra.shape[0] train_fold_indices = list(range(k)) train_fold_indices.remove(cur_iter) train_fold_indices.remove((cur_iter+1) % k) train_fold_idx = [] + fold_dct[str(train_fold_indices[0])] + fold_dct[str(train_fold_indices[1])] + fold_dct[str(train_fold_indices[2])] train_fold = {} train_fold["spectra"] = np.concat((spectra[train_fold_idx,:], invalid_spectra[:,:]), axis=0) train_fold["quant"] = np.concat((quant[train_fold_idx,:], invalid_quant[:,:]), axis=0) train_fold["ppm_spectra"] = np.concat((ppm_spectra[train_fold_idx,:], invalid_ppm_spectra[:,:]), axis=0) train_fold["class_labels"] = np.concat((class_labels[train_fold_idx,:], np.array([-1]*invalid_sample_cnt).reshape((-1,1))), axis=1) train_fold["stats"] = pd.concat([statistics.iloc[train_fold_idx,:], invalid_statistics]).reset_index(drop=True) all_data = {} all_data["spectra"] = spectra all_data["quant"] = quant all_data["ppm_spectra"] = ppm_spectra all_data["class_labels"] = class_labels all_data["stats"] = statistics yield (train_fold_idx, vald_fold_idx, test_fold_idx, train_fold, vald_fold, test_fold, all_data) cur_iter += 1 # Option #2.B: valid PC and fully quantified samples form test folds # but invalid samples are injected to the training dataset by hand (train, vald and test) def cpmg_generator_2B(k, fold_dct, valid_statistics, valid_spectra, valid_ppm_spectra, valid_quant, valid_class_labels,\ invalid_statistics, invalid_spectra, invalid_ppm_spectra, invalid_quant): cur_iter = 0 while cur_iter < k: test_fold_idx = fold_dct[str(cur_iter)] test_fold = {} test_fold["spectra"] = spectra[test_fold_idx,:] test_fold["quant"] = quant[test_fold_idx,:] test_fold["ppm_spectra"] = ppm_spectra[test_fold_idx,:] test_fold["class_labels"] = class_labels[test_fold_idx,:] test_fold["stats"] = statistics.iloc[test_fold_idx,:].reset_index(drop=True) invalid_sample_cnt = invalid_spectra.shape[0] train_fold_indices = list(range(k)) train_fold_indices.remove(cur_iter) train_fold_idx = [] + fold_dct[str(train_fold_indices[0])] + fold_dct[str(train_fold_indices[1])] + fold_dct[str(train_fold_indices[2])] + fold_dct[str(train_fold_indices[3])] train_fold = {} train_fold["spectra"] = np.concat((spectra[train_fold_idx,:], invalid_spectra[:,:]), axis=0) train_fold["quant"] = np.concat((quant[train_fold_idx,:], invalid_quant[:,:]), axis=0) train_fold["ppm_spectra"] = np.concat((ppm_spectra[train_fold_idx,:], invalid_ppm_spectra[:,:]), axis=0) train_fold["class_labels"] = np.concat((class_labels[train_fold_idx,:], np.array([-1]*invalid_sample_cnt).reshape((-1,1))), axis=1) train_fold["stats"] = pd.concat([statistics.iloc[train_fold_idx,:], invalid_statistics]).reset_index(drop=True) all_data = {} all_data["spectra"] = spectra all_data["quant"] = quant all_data["ppm_spectra"] = ppm_spectra all_data["class_labels"] = class_labels all_data["stats"] = statistics yield (train_fold_idx, test_fold_idx, train_fold, test_fold, all_data) cur_iter += 1 # Option #3.A: only valid PC samples form test folds (train, vald and test) def cpmg_generator_3A(k, fold_dct, statistics, spectra, ppm_spectra, quant, quant_availability, class_labels): cur_iter = 0 while cur_iter < k: test_fold_idx = fold_dct[str(cur_iter)] test_fold = {} test_fold["spectra"] = spectra[test_fold_idx,:] test_fold["quant"] = quant[test_fold_idx,:] test_fold["quant_availability"] = quant_availability[test_fold_idx,:] test_fold["ppm_spectra"] = ppm_spectra[test_fold_idx,:] test_fold["class_labels"] = class_labels[test_fold_idx,:] test_fold["stats"] = statistics.iloc[test_fold_idx,:].reset_index(drop=True) vald_fold_idx = fold_dct[str((cur_iter+1) % k)] vald_fold = {} vald_fold["spectra"] = spectra[vald_fold_idx,:] vald_fold["quant"] = quant[vald_fold_idx,:] vald_fold["quant_availability"] = quant_availability[vald_fold_idx,:] vald_fold["ppm_spectra"] = ppm_spectra[vald_fold_idx,:] vald_fold["class_labels"] = class_labels[vald_fold_idx,:] vald_fold["stats"] = statistics.iloc[vald_fold_idx,:].reset_index(drop=True) train_fold_indices = list(range(k)) train_fold_indices.remove(cur_iter) train_fold_indices.remove((cur_iter+1) % k) train_fold_idx = [] + fold_dct[str(train_fold_indices[0])] + fold_dct[str(train_fold_indices[1])] + fold_dct[str(train_fold_indices[2])] train_fold = {} train_fold["spectra"] = spectra[train_fold_idx,:] train_fold["quant"] = quant[train_fold_idx,:] train_fold["quant_availability"] = quant_availability[train_fold_idx,:] train_fold["ppm_spectra"] = ppm_spectra[train_fold_idx,:] train_fold["class_labels"] = class_labels[train_fold_idx,:] train_fold["stats"] = statistics.iloc[train_fold_idx,:].reset_index(drop=True) all_data = {} all_data["spectra"] = spectra all_data["quant"] = quant all_data["quant_availability"] = quant_availability all_data["ppm_spectra"] = ppm_spectra all_data["class_labels"] = class_labels all_data["stats"] = statistics yield (train_fold_idx, vald_fold_idx, test_fold_idx, train_fold, vald_fold, test_fold, all_data) cur_iter += 1 # Option #3.B: only valid PC samples form test folds (train and test) def cpmg_generator_3B(k, fold_dct, statistics, spectra, ppm_spectra, quant, quant_availability, class_labels): cur_iter = 0 while cur_iter < k: test_fold_idx = fold_dct[str(cur_iter)] test_fold = {} test_fold["spectra"] = spectra[test_fold_idx,:] test_fold["quant"] = quant[test_fold_idx,:] test_fold["quant_availability"] = quant_availability[test_fold_idx,:] test_fold["ppm_spectra"] = ppm_spectra[test_fold_idx,:] test_fold["class_labels"] = class_labels[test_fold_idx,:] test_fold["stats"] = statistics.iloc[test_fold_idx,:].reset_index(drop=True) train_fold_indices = list(range(k)) train_fold_indices.remove(cur_iter) train_fold_idx = [] + fold_dct[str(train_fold_indices[0])] + fold_dct[str(train_fold_indices[1])] + fold_dct[str(train_fold_indices[2])] + fold_dct[str(train_fold_indices[3])] train_fold = {} train_fold["spectra"] = spectra[train_fold_idx,:] train_fold["quant"] = quant[train_fold_idx,:] train_fold["quant_availability"] = quant_availability[train_fold_idx,:] train_fold["ppm_spectra"] = ppm_spectra[train_fold_idx,:] train_fold["class_labels"] = class_labels[train_fold_idx,:] train_fold["stats"] = statistics.iloc[train_fold_idx,:].reset_index(drop=True) all_data = {} all_data["spectra"] = spectra all_data["quant"] = quant all_data["quant_availability"] = quant_availability all_data["ppm_spectra"] = ppm_spectra all_data["class_labels"] = class_labels all_data["stats"] = statistics yield (train_fold_idx, test_fold_idx, train_fold, test_fold, all_data) cur_iter += 1 # Option #4.A: only valid PC samples form test folds # but invalid pc samples are injected to the training dataset def cpmg_generator_4A(k, fold_dct, valid_statistics, valid_spectra, valid_ppm_spectra, valid_quant, valid_quant_availability, valid_class_labels,\ invalid_statistics, invalid_spectra, invalid_ppm_spectra, invalid_quant, invalid_quant_availability): cur_iter = 0 while cur_iter < k: test_fold_idx = fold_dct[str(cur_iter)] test_fold = {} test_fold["spectra"] = valid_spectra[test_fold_idx,:] test_fold["quant"] = valid_quant[test_fold_idx,:] test_fold["quant_availability"] = valid_quant_availability[test_fold_idx,:] test_fold["ppm_spectra"] = valid_ppm_spectra[test_fold_idx,:] test_fold["class_labels"] = valid_class_labels[test_fold_idx,:] test_fold["stats"] = valid_statistics.iloc[test_fold_idx,:].reset_index(drop=True) vald_fold_idx = fold_dct[str((cur_iter+1) % k)] vald_fold = {} vald_fold["spectra"] = valid_spectra[vald_fold_idx,:] vald_fold["quant"] = valid_quant[vald_fold_idx,:] vald_fold["quant_availability"] = valid_quant_availability[vald_fold_idx,:] vald_fold["ppm_spectra"] = valid_ppm_spectra[vald_fold_idx,:] vald_fold["class_labels"] = valid_class_labels[vald_fold_idx,:] vald_fold["stats"] = valid_statistics.iloc[vald_fold_idx,:].reset_index(drop=True) invalid_sample_cnt = invalid_spectra.shape[0] train_fold_indices = list(range(k)) train_fold_indices.remove(cur_iter) train_fold_indices.remove((cur_iter+1) % k) train_fold_idx = [] + fold_dct[str(train_fold_indices[0])] + fold_dct[str(train_fold_indices[1])] + fold_dct[str(train_fold_indices[2])] train_fold = {} train_fold["spectra"] = np.concat((valid_spectra[train_fold_idx,:],invalid_spectra[:,:]), axis=1) train_fold["quant"] = np.concat((valid_quant[train_fold_idx,:],invalid_quant[:,:]), axis=1) train_fold["quant_availability"] = np.concat((valid_quant_availability[train_fold_idx,:],invalid_quant_availability[:,:]), axis=1) train_fold["ppm_spectra"] = np.concat((valid_ppm_spectra[train_fold_idx,:],invalid_ppm_spectra[:,:]), axis=1) train_fold["class_labels"] = np.concat((valid_class_labels[train_fold_idx,:],np.array([-1]*invalid_sample_cnt).reshape((-1,1))), axis=1) train_fold["stats"] = pd.concat([valid_statistics.iloc[train_fold_idx,:].reset_index(drop=True),invalid_statistics]) all_data = {} all_data["spectra"] = valid_spectra all_data["quant"] = valid_quant all_data["quant_availability"] = valid_quant_availability all_data["ppm_spectra"] = valid_ppm_spectra all_data["class_labels"] = valid_class_labels all_data["stats"] = valid_statistics yield (train_fold_idx, vald_fold_idx, test_fold_idx, train_fold, vald_fold, test_fold, all_data) cur_iter += 1 # Option #4.B: only valid PC samples form test folds # but invalid pc samples are injected to the training dataset def cpmg_generator_4B(k, fold_dct, valid_statistics, valid_spectra, valid_ppm_spectra, valid_quant, valid_quant_availability, valid_class_labels,\ invalid_statistics, invalid_spectra, invalid_ppm_spectra, invalid_quant, invalid_quant_availability): cur_iter = 0 while cur_iter < k: test_fold_idx = fold_dct[str(cur_iter)] test_fold = {} test_fold["spectra"] = valid_spectra[test_fold_idx,:] test_fold["quant"] = valid_quant[test_fold_idx,:] test_fold["quant_availability"] = valid_quant_availability[test_fold_idx,:] test_fold["ppm_spectra"] = valid_ppm_spectra[test_fold_idx,:] test_fold["class_labels"] = valid_class_labels[test_fold_idx,:] test_fold["stats"] = valid_statistics.iloc[test_fold_idx,:].reset_index(drop=True) invalid_sample_cnt = invalid_spectra.shape[0] train_fold_indices = list(range(k)) train_fold_indices.remove(cur_iter) train_fold_idx = [] + fold_dct[str(train_fold_indices[0])] + fold_dct[str(train_fold_indices[1])] + fold_dct[str(train_fold_indices[2])] + fold_dct[str(train_fold_indices[3])] train_fold = {} train_fold["spectra"] = np.concat((valid_spectra[train_fold_idx,:],invalid_spectra[:,:]), axis=1) train_fold["quant"] = np.concat((valid_quant[train_fold_idx,:],invalid_quant[:,:]), axis=1) train_fold["quant_availability"] = np.concat((valid_quant_availability[train_fold_idx,:],invalid_quant_availability[:,:]), axis=1) train_fold["ppm_spectra"] = np.concat((valid_ppm_spectra[train_fold_idx,:],invalid_ppm_spectra[:,:]), axis=1) train_fold["class_labels"] = np.concat((valid_class_labels[train_fold_idx,:],np.array([-1]*invalid_sample_cnt).reshape((-1,1))), axis=1) train_fold["stats"] = pd.concat([valid_statistics.iloc[train_fold_idx,:].reset_index(drop=True),invalid_statistics]) all_data = {} all_data["spectra"] = valid_spectra all_data["quant"] = valid_quant all_data["quant_availability"] = valid_quant_availability all_data["ppm_spectra"] = valid_ppm_spectra all_data["class_labels"] = valid_class_labels all_data["stats"] = valid_statistics yield (train_fold_idx, test_fold_idx, train_fold, test_fold, all_data) cur_iter += 1

53.037143

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0.917632

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0.761988

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62977ec9e8e6bc7ad876840aae510ac2f62bd01e

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py

Python

GeneratorScripts/CADGeneratorModel4.py

ankitpatnala/RhinoScriptsForSlots

bde3974799b74fda9da7fd928a6b6848db99a94f

[ "MIT" ]

null

GeneratorScripts/CADGeneratorModel4.py

ankitpatnala/RhinoScriptsForSlots

bde3974799b74fda9da7fd928a6b6848db99a94f

[ "MIT" ]

null

GeneratorScripts/CADGeneratorModel4.py

ankitpatnala/RhinoScriptsForSlots

bde3974799b74fda9da7fd928a6b6848db99a94f

[ "MIT" ]

null

import Rhino import scriptcontext import rhinoscriptsyntax as rs import System.Guid import random import json import sys import os import math def SquareDistanceBetweenTwoPoints(point1,point2): difference = Rhino.Geometry.Point3d((point2.X-point1.X), (point2.Y-point1.Y), (point2.Z-point1.Z)) return difference.X*difference.X + difference.Y*difference.Y +difference.Z*difference.Z def AddBrepBox(point1, point2): box = Rhino.Geometry.BoundingBox(point1, point2) brep = box.ToBrep() rc = Rhino.Commands.Result.Failure return brep def AddCylinder(centerPoint,orientation,radius,height): plane = Rhino.Geometry.Plane(centerPoint, orientation) circle = Rhino.Geometry.Circle(plane, radius) cylinder = Rhino.Geometry.Cylinder(circle, height) brep = cylinder.ToBrep(True, True) return brep def GenerateSlotsForSteppedModel(): point1 = Rhino.Geometry.Point3d(10 + random.random()*5, 10 + random.random()*5, 10 + random.random()*5) point2 = Rhino.Geometry.Point3d(point1.X + 10 + random.random()*5, point1.Y, point1.Z + 10 + random.random()) brepBox1 = AddBrepBox(Rhino.Geometry.Point3d(0,0,0),point1) brepBox2 = AddBrepBox(Rhino.Geometry.Point3d(point1.X,0,0), Rhino.Geometry.Point3d(point2)) brepArray = [brepBox1,brepBox2] tolerance = scriptcontext.doc.ModelAbsoluteTolerance orientationIdx = random.randint(0,5) straight = bool(random.randint(0,1)) leftMargin = 3 filled = False height = 2 + random.random()*2 randomEdge = random.randint(0,3) filletRadius = 2+ 2*random.random() if(randomEdge == 0): filletBrepBox = AddBrepBox(Rhino.Geometry.Point3d(0,0,0),Rhino.Geometry.Point3d(filletRadius,filletRadius,point1.Z)) filletCylinder = AddCylinder(Rhino.Geometry.Point3d(filletRadius,filletRadius,0),Rhino.Geometry.Vector3d(0,0,1),filletRadius,point1.Z) brepFillet = Rhino.Geometry.Brep.CreateBooleanDifference(filletBrepBox,filletCylinder,tolerance)[0] elif(randomEdge == 1): filletBrepBox = AddBrepBox(Rhino.Geometry.Point3d(point2.X - filletRadius,0,0),Rhino.Geometry.Point3d(point2.X,filletRadius,point2.Z)) filletCylinder = AddCylinder(Rhino.Geometry.Point3d(point2.X - filletRadius,filletRadius,0),Rhino.Geometry.Vector3d(0,0,1),filletRadius,point2.Z) brepFillet = Rhino.Geometry.Brep.CreateBooleanDifference(filletBrepBox,filletCylinder,tolerance)[0] elif(randomEdge == 2): filletBrepBox = AddBrepBox(Rhino.Geometry.Point3d(point2.X - filletRadius,point2.Y - filletRadius ,0),Rhino.Geometry.Point3d(point2.X,point2.Y,point2.Z)) filletCylinder = AddCylinder(Rhino.Geometry.Point3d(point2.X - filletRadius,point2.Y - filletRadius ,0),Rhino.Geometry.Vector3d(0,0,1),filletRadius,point2.Z) brepFillet = Rhino.Geometry.Brep.CreateBooleanDifference(filletBrepBox,filletCylinder,tolerance)[0] else: filletBrepBox = AddBrepBox(Rhino.Geometry.Point3d(0,point1.Y - filletRadius ,0),Rhino.Geometry.Point3d(filletRadius,point1.Y,point1.Z)) filletCylinder = AddCylinder(Rhino.Geometry.Point3d(filletRadius,point1.Y - filletRadius ,0),Rhino.Geometry.Vector3d(0,0,1),filletRadius,point1.Z) brepFillet = Rhino.Geometry.Brep.CreateBooleanDifference(filletBrepBox,filletCylinder,tolerance)[0] if(orientationIdx == 0): lowerBlock = bool(random.randint(0,1)) if(straight): xAxis = bool(random.randint(0,1)) if(lowerBlock): leftPointX = leftMargin + random.random()*(point1.X-leftMargin) leftPointY = leftMargin + random.random()*(point1.Y- leftMargin) if(xAxis): rightPointX = leftPointX + random.random()*(point1.X-leftPointX-3) rightPointY = leftPointY width = 2.5 + random.random()*(min(leftPointY,point2.Y-leftPointY)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX,leftPointY-width/2,point1.Z-height) upperDiagonal = Rhino.Geometry.Point3d(rightPointX, rightPointY + width/2,point1.Z) else: rightPointX = leftPointX rightPointY = leftPointY + random.random()*(point1.Y - leftPointY) width = 2.5 + random.random()*(min(leftPointX,point2.X-leftPointX)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX-width/2,leftPointY,point1.Z-height) upperDiagonal = Rhino.Geometry.Point3d(rightPointX + width/2,rightPointY,point1.Z) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 else: leftPointX = point1.X + leftMargin + random.random()*(point2.X - point1.X - leftMargin) leftPointY = leftMargin + random.random()*(point2.Y- leftMargin) if(xAxis): rightPointX = leftPointX + random.random()*(point2.X-leftPointX-3) rightPointY = leftPointY width = 2.5 + random.random()*(min(leftPointY,point2.Y-leftPointY)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX,leftPointY-width/2,point2.Z-height) upperDiagonal = Rhino.Geometry.Point3d(rightPointX, rightPointY + width/2,point2.Z) else: rightPointX = leftPointX rightPointY = leftPointY + random.random()*(point2.Y - leftPointY-3) width = 2.5 + random.random()*(min(leftPointX,point2.X-leftPointX)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX-width/2,leftPointY,point2.Z-height) upperDiagonal = Rhino.Geometry.Point3d(rightPointX + width/2,rightPointY,point2.Z) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,leftPointY,0), Rhino.Geometry.Point3d(rightPointX,rightPointY,0)) else: if(lowerBlock): leftPointX = leftMargin + random.random()*(point1.X-leftMargin) leftPointY = leftMargin + random.random()*(point1.Y-leftMargin) rightPointX = leftPointX + random.random()*(point1.X - leftPointX-3) rightPointY = leftPointY + random.random()*(point1.Y - leftPointY-3) width = 2.5 + random.random()*(min(leftPointY,point1.Y-rightPointY)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,leftPointY,0), Rhino.Geometry.Point3d(rightPointX,rightPointY,0)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(-1*(rightPointX-leftPointX),(rightPointY-leftPointY),0) points = [Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2, leftPointY - perpendicularVector.Y*width/2,point1.Z - height), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2, leftPointY + perpendicularVector.Y*width/2,point1.Z - height), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2, rightPointY + perpendicularVector.Y*width/2,point1.Z - height), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2, rightPointY - perpendicularVector.Y*width/2,point1.Z - height), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2, rightPointY - perpendicularVector.Y*width/2,point1.Z), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2, rightPointY + perpendicularVector.Y*width/2,point1.Z), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2, leftPointY + perpendicularVector.Y*width/2,point1.Z), Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2, leftPointY - perpendicularVector.Y*width/2,point1.Z)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: leftPointX = leftMargin + point1.X + random.random()*(point2.X - leftMargin - point1.X) leftPointY = leftMargin + random.random()*(point2.Y - leftMargin) rightPointX = leftPointX + random.random()*(point2.X - leftPointX-3) rightPointY = leftPointY + random.random()*(point2.Y - leftPointY -3) width = 2.5 + random.random()*(min(leftPointY, point1.Y - leftPointY)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,leftPointY,0), Rhino.Geometry.Point3d(rightPointX,rightPointY,0)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(-1*(rightPointX-leftPointX),(rightPointY-leftPointY),0) points = [Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2, leftPointY - perpendicularVector.Y*width/2,point2.Z - height), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2, leftPointY + perpendicularVector.Y*width/2,point2.Z - height), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2, rightPointY + perpendicularVector.Y*width/2,point2.Z - height), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2, rightPointY - perpendicularVector.Y*width/2,point2.Z - height), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2, rightPointY - perpendicularVector.Y*width/2,point2.Z), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2, rightPointY + perpendicularVector.Y*width/2,point2.Z), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2, leftPointY + perpendicularVector.Y*width/2,point2.Z), Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2, leftPointY - perpendicularVector.Y*width/2,point2.Z)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepArrayToCheck = [brepBox2,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume elif (orientationIdx == 1): leftPointX = leftMargin + random.random()*(point2.X-leftMargin) leftPointY = leftMargin + random.random()*(point2.Y-leftMargin) if(straight): xAxis = bool(random.randint(0,1)) if(xAxis): rightPointX = leftPointX + random.random()*(point2.X - leftPointX-3) rightPointY = leftPointY width = 2.5 + random.random()*(min(leftPointY,point2.Y - leftPointY)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX,leftPointY - width/2,0) upperDiagonal = Rhino.Geometry.Point3d(rightPointX,rightPointY + width/2,height) else: rightPointX = leftPointX rightPointY = leftPointY + random.random()*(point2.Y - leftPointY -3) width = 2.5 + random.random()*(min(leftPointX,point2.X - leftPointX)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX - width/2,leftPointY,0) upperDiagonal = Rhino.Geometry.Point3d(rightPointX + width/2,rightPointY, height) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,leftPointY,0), Rhino.Geometry.Point3d(rightPointX,rightPointY,0)) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepTotalVolume = brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: rightPointX = leftPointX + random.random()*(point2.X-leftPointX) rightPointY = leftPointY + random.random()*(point2.Y -leftPointY) width = 2.5 + random.random()*(min(leftPointY, point2.Y -leftPointY, rightPointY, point2.Y -rightPointY)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,leftPointY,0), Rhino.Geometry.Point3d(rightPointX,rightPointY,0)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(-1*(rightPointX-leftPointX),(rightPointY-leftPointY),0) points = [Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2, leftPointY - perpendicularVector.Y*width/2,height), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2, leftPointY + perpendicularVector.Y*width/2,height), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2, rightPointY + perpendicularVector.Y*width/2,height), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2, rightPointY - perpendicularVector.Y*width/2,height), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2, rightPointY - perpendicularVector.Y*width/2,0), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2, rightPointY + perpendicularVector.Y*width/2,0), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2, leftPointY + perpendicularVector.Y*width/2,0), Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2, leftPointY - perpendicularVector.Y*width/2,0)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArray,tolerance) brepTotalVolume = Rhino.Geometry.Brep.CreateBooleanUnion([brepUnion[0],brepToSubtract],tolerance)[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume if(brepTotalVolume == brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume and brepUnion is not None): brepTotalVolume = brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 elif(orientationIdx == 2): lowerBlock = bool(random.randint(0,1)) if(straight): yAxis = bool(random.randint(0,1)) if(lowerBlock): leftPointY = leftMargin + random.random()*(point1.Y - leftMargin) leftPointZ = leftMargin + random.random()*(point1.Z - leftMargin) if(yAxis): rightPointY = leftPointY + random.random()*(point1.Y-leftPointY-3) rightPointZ = leftPointZ width = 2.5 + random.random()*(min(leftPointZ- point1.Z,point2.Z-rightPointZ)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(point1.X,leftPointY,leftPointZ-width/2) upperDiagonal = Rhino.Geometry.Point3d(point1.X + height, rightPointY, rightPointZ + width/2) else: rightPointY = leftPointY rightPointZ = leftPointZ + random.random()*(point2.Z - leftPointZ) width = 2.5 + random.random()*(min(leftPointY,point1.Y-leftPointY)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(point1.X,leftPointY - width/2,leftPointZ) upperDiagonal = Rhino.Geometry.Point3d(point1.X + height,rightPointY + width/2,rightPointZ) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 else: leftPointY = leftMargin + random.random()*(point1.Y -leftMargin) leftPointZ = leftMargin + point1.Z + random.random()*(point2.Z -point1.Z - leftMargin) if(yAxis): rightPointY = leftPointY + random.random()*(point2.Y-leftPointY-3) rightPointZ = leftPointZ width = 2.5 + random.random()*(min(leftPointZ - point1.Z ,point2.Z - rightPointZ )-2.5) bottomDiagonal = Rhino.Geometry.Point3d(point1.X,leftPointY,leftPointZ-width/2) upperDiagonal = Rhino.Geometry.Point3d(point1.X + height, rightPointY ,rightPointZ + width/2) else: rightPointY = leftPointY rightPointZ = leftPointZ + random.random()*(point2.Z - leftPointZ-3) width = 2.5 + random.random()*(min(leftPointY,point2.Y-leftPointY)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(0,leftPointY - width/2,leftPointZ) upperDiagonal = Rhino.Geometry.Point3d(height,rightPointY + width/2,rightPointZ) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(0,leftPointY,leftPointZ), Rhino.Geometry.Point3d(0,rightPointY,rightPointZ)) else: if(lowerBlock): leftPointY = leftMargin + random.random()*(point1.Y-leftMargin) leftPointZ = leftMargin + random.random()*(point1.Z-leftMargin) rightPointY = leftPointY + random.random()*(point1.Y - leftPointY - 3) rightPointZ = leftPointZ + random.random()*(point1.Z - leftPointZ - 3) width = 2.5 + random.random()*(min(leftPointY,point1.Y-rightPointY)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(0,leftPointY,leftPointZ), Rhino.Geometry.Point3d(0,rightPointY,rightPointZ)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(0,-1*(rightPointZ-leftPointZ),(rightPointY-leftPointY)) points = [Rhino.Geometry.Point3d(0, leftPointY - perpendicularVector.Y*width/2,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(0, leftPointY + perpendicularVector.Y*width/2,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(0, rightPointY + perpendicularVector.Y*width/2,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(0, rightPointY - perpendicularVector.Y*width/2,rightPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(height, leftPointY - perpendicularVector.Y*width/2,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(height, leftPointY + perpendicularVector.Y*width/2,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(height, rightPointY + perpendicularVector.Y*width/2,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(height, rightPointY - perpendicularVector.Y*width/2,rightPointZ - perpendicularVector.Z*width/2)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: leftPointY = leftMargin + random.random()*(point1.Y - leftMargin) leftPointZ = leftMargin + point1.Z + random.random()*(point2.Z - leftMargin - point1.Z) rightPointY = leftPointY + random.random()*(point2.Y - leftPointY -3) rightPointZ = leftPointZ + random.random()*(point2.Z - leftPointZ -3) width = 2.5 + random.random()*(min(leftPointY, point1.Y - leftPointY)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(0,leftPointY,leftPointZ), Rhino.Geometry.Point3d(0,rightPointY,rightPointZ)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(0,-1*(rightPointZ-leftPointZ),(rightPointY-leftPointY)) points = [Rhino.Geometry.Point3d(point1.X, leftPointY - perpendicularVector.Y*width/2,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point1.X, leftPointY + perpendicularVector.Y*width/2,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point1.X, rightPointY + perpendicularVector.Y*width/2,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point1.X, rightPointY - perpendicularVector.Y*width/2,rightPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point1.X + height, leftPointY - perpendicularVector.Y*width/2,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point1.X + height, leftPointY + perpendicularVector.Y*width/2,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point1.X + height, rightPointY + perpendicularVector.Y*width/2,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point1.X + height, rightPointY - perpendicularVector.Y*width/2,rightPointZ - perpendicularVector.Z*width/2)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepArrayToCheck = [brepBox2,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume elif (orientationIdx == 3): leftPointY = leftMargin + random.random()*(point2.Y-leftMargin) leftPointZ = leftMargin + random.random()*(point2.Z-leftMargin) if(straight): yAxis = bool(random.randint(0,1)) if(yAxis): rightPointY = leftPointY + random.random()*(point2.Y - leftPointY-3) rightPointZ = leftPointZ width = 2.5 + random.random()*(min(leftPointZ,point2.Z - leftPointZ)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(point2.X -height,leftPointY,leftPointZ - width/2) upperDiagonal = Rhino.Geometry.Point3d(point2.X,rightPointY ,rightPointZ + width/2) else: rightPointY = leftPointY rightPointZ = leftPointZ + random.random()*(point2.Z - leftPointZ -3) width = 2.5 + random.random()*(min(leftPointY,point2.Y - leftPointY)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(point2.X - height,leftPointY - width/2,leftPointZ) upperDiagonal = Rhino.Geometry.Point3d(point2.X,rightPointY + width/2, rightPointZ) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(0,leftPointY,leftPointZ), Rhino.Geometry.Point3d(0,rightPointY,rightPointZ)) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepTotalVolume = brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: rightPointY = leftPointY + random.random()*(point2.Y -leftPointY - 3) rightPointZ = leftPointZ + random.random()*(point2.Z -leftPointZ - 3) width = 2.5 + random.random()*(min(leftPointY, point2.Y -leftPointY, leftPointZ, point2.Z -rightPointZ)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(0,leftPointY,leftPointZ), Rhino.Geometry.Point3d(0,rightPointY,rightPointZ)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(0,-1*(rightPointZ-leftPointZ),(rightPointY-leftPointY)) points = [Rhino.Geometry.Point3d(point2.X - height, leftPointY - perpendicularVector.Y*width/2,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point2.X - height, leftPointY + perpendicularVector.Y*width/2,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point2.X - height, rightPointY + perpendicularVector.Y*width/2,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point2.X - height, rightPointY - perpendicularVector.Y*width/2,rightPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point2.X, leftPointY - perpendicularVector.Y*width/2,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point2.X, leftPointY + perpendicularVector.Y*width/2,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point2.X, rightPointY + perpendicularVector.Y*width/2,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(point2.X, rightPointY - perpendicularVector.Y*width/2,rightPointZ - perpendicularVector.Z*width/2)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArray,tolerance) brepTotalVolume = Rhino.Geometry.Brep.CreateBooleanUnion([brepUnion[0],brepToSubtract],tolerance)[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume if(brepTotalVolume == brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume): brepTotalVolume = brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 elif(orientationIdx == 4): lowerBlock = True#bool(random.randint(0,1)) if(straight): xAxis = bool(random.randint(0,1)) if(lowerBlock): leftPointX = leftMargin + random.random()*(point2.X - leftMargin) leftPointZ = leftMargin + random.random()*(point1.Z - leftMargin) if(xAxis): rightPointX = leftPointX + random.random()*(point2.X-leftPointX-3) rightPointZ = leftPointZ width = 2.5 + random.random()*(min(leftPointZ,point1.Z-rightPointZ)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX,0,leftPointZ-width/2) upperDiagonal = Rhino.Geometry.Point3d(rightPointX,height,rightPointZ + width/2) else: rightPointX = leftPointX rightPointZ = leftPointZ + random.random()*(point1.Z - leftPointZ - 3) width = 2.5 + random.random()*(min(leftPointX,point2.X-leftPointX)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX - width/2,0,leftPointZ) upperDiagonal = Rhino.Geometry.Point3d(rightPointX + width/2,height,rightPointZ) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 else: leftPointX = leftMargin + point1.X + random.random()*(point2.X - point1.X -leftMargin) leftPointZ = leftMargin + random.random()*(point2.Z - leftMargin) if(xAxis): rightPointX = leftPointX + random.random()*(point2.X-leftPointX-3) rightPointZ = leftPointZ width = 2.5 + random.random()*(min(leftPointZ, point2.Z - rightPointZ )-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX,0,leftPointZ-width/2) upperDiagonal = Rhino.Geometry.Point3d(rightPointX , height, rightPointZ + width/2) else: rightPointX = leftPointX rightPointZ = leftPointZ + random.random()*(point2.Z - leftPointZ-3) width = 2.5 + random.random()*(min(leftPointX-point1.X,point2.X-rightPointX)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX - width/2,0,leftPointZ) upperDiagonal = Rhino.Geometry.Point3d(rightPointX + width/2,height,rightPointZ) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,0,leftPointZ), Rhino.Geometry.Point3d(rightPointX,0,rightPointZ)) else: if(lowerBlock): leftPointX = leftMargin + random.random()*(point2.X-leftMargin) leftPointZ = leftMargin + random.random()*(point1.Z-leftMargin) rightPointX = leftPointX + random.random()*(point2.X - leftPointX - 3) rightPointZ = leftPointZ + random.random()*(point1.Z - leftPointZ - 3) width = 2.5 + random.random()*(min(leftPointX,point2.X-rightPointX)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,0,leftPointZ), Rhino.Geometry.Point3d(rightPointZ,0,rightPointZ)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(-1*(rightPointZ-leftPointZ),0,(rightPointX-leftPointX)) points = [Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2,0,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2,0 ,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2,0,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2,0,rightPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2,height,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2,height ,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2,height,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2,height,rightPointZ - perpendicularVector.Z*width/2)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepArrayToCheck = [brepBox1,brepBox2,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 else: leftPointX = leftMargin + point1.X + random.random()*(point2.X - point1.X - leftMargin) leftPointZ = leftMargin + random.random()*(point2.Z - leftMargin) rightPointX = leftPointX + random.random()*(point2.X - leftPointX -3) rightPointZ = leftPointZ + random.random()*(point2.Z - leftPointZ -3) width = 2.5 + random.random()*(min(leftPointX - point1.X, point2.X - rightPointX)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,0,leftPointZ), Rhino.Geometry.Point3d(rightPointX,0,rightPointZ)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(-1*(rightPointZ-leftPointZ),0,(rightPointX-leftPointX)) points = [Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2,0,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2,0 ,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2,0,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2,0,rightPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2,height,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2,height ,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2,height,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2,height,rightPointZ - perpendicularVector.Z*width/2)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepArrayToCheck = [brepBox2,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: lowerBlock = bool(random.randint(0,1)) if(straight): xAxis = bool(random.randint(0,1)) if(lowerBlock): leftPointX = leftMargin + random.random()*(point2.X - leftMargin) leftPointZ = leftMargin + random.random()*(point1.Z - leftMargin) if(xAxis): rightPointX = leftPointX + random.random()*(point2.X-leftPointX-3) rightPointZ = leftPointZ width = 2.5 + random.random()*(min(leftPointZ,point1.Z-rightPointZ)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX,0,leftPointZ-width/2) upperDiagonal = Rhino.Geometry.Point3d(rightPointX,height,rightPointZ + width/2) else: rightPointX = leftPointX rightPointZ = leftPointZ + random.random()*(point1.Z - leftPointZ - 3) width = 2.5 + random.random()*(min(leftPointX,point2.X-leftPointX)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX - width/2,0,leftPointZ) upperDiagonal = Rhino.Geometry.Point3d(rightPointX + width/2,height,rightPointZ) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 else: leftPointX = leftMargin + point1.X + random.random()*(point2.X - point1.X -leftMargin) leftPointZ = leftMargin + random.random()*(point2.Z - leftMargin) if(xAxis): rightPointX = leftPointX + random.random()*(point2.X-leftPointX-3) rightPointZ = leftPointZ width = 2.5 + random.random()*(min(leftPointZ, point2.Z - rightPointZ )-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX,0,leftPointZ-width/2) upperDiagonal = Rhino.Geometry.Point3d(rightPointX , height, rightPointZ + width/2) else: rightPointX = leftPointX rightPointZ = leftPointZ + random.random()*(point2.Z - leftPointZ-3) width = 2.5 + random.random()*(min(leftPointX-point1.X,point2.X-rightPointX)-2.5) bottomDiagonal = Rhino.Geometry.Point3d(leftPointX - width/2,0,leftPointZ) upperDiagonal = Rhino.Geometry.Point3d(rightPointX + width/2,height,rightPointZ) brepToSubtract = AddBrepBox(bottomDiagonal,upperDiagonal) brepArrayToCheck = [brepBox1,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,0,leftPointZ), Rhino.Geometry.Point3d(rightPointX,0,rightPointZ)) else: if(lowerBlock): leftPointX = leftMargin + random.random()*(point2.X-leftMargin) leftPointZ = leftMargin + random.random()*(point1.Z-leftMargin) rightPointX = leftPointX + random.random()*(point2.X - leftPointX - 3) rightPointZ = leftPointZ + random.random()*(point1.Z - leftPointZ - 3) width = 2.5 + random.random()*(min(leftPointX,point2.X-rightPointX)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,0,leftPointZ), Rhino.Geometry.Point3d(rightPointZ,0,rightPointZ)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(-1*(rightPointZ-leftPointZ),0,(rightPointX-leftPointX)) points = [Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2,0,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2,0 ,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2,0,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2,0,rightPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2,height,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2,height ,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2,height,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2,height,rightPointZ - perpendicularVector.Z*width/2)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepArrayToCheck = [brepBox1,brepBox2,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) if(brepUnion is not None): brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume else: brepTotalVolume = 100000 else: leftPointX = leftMargin + point1.X + random.random()*(point2.X - point1.X - leftMargin) leftPointZ = leftMargin + random.random()*(point2.Z - leftMargin) rightPointX = leftPointX + random.random()*(point2.X - leftPointX -3) rightPointZ = leftPointZ + random.random()*(point2.Z - leftPointZ -3) width = 2.5 + random.random()*(min(leftPointX - point1.X, point2.X - rightPointX)-2.5) sqrDist = SquareDistanceBetweenTwoPoints( Rhino.Geometry.Point3d(leftPointX,0,leftPointZ), Rhino.Geometry.Point3d(rightPointX,0,rightPointZ)) perpendicularVector = (1/math.sqrt(sqrDist))*Rhino.Geometry.Vector3d(-1*(rightPointZ-leftPointZ),0,(rightPointX-leftPointX)) points = [Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2,0,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2,0 ,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2,0,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2,0,rightPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX - perpendicularVector.X*width/2,height,leftPointZ - perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(leftPointX + perpendicularVector.X*width/2,height ,leftPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX + perpendicularVector.X*width/2,height,rightPointZ + perpendicularVector.Z*width/2), Rhino.Geometry.Point3d(rightPointX - perpendicularVector.X*width/2,height,rightPointZ - perpendicularVector.Z*width/2)] plane = Rhino.Geometry.Plane(points[0],points[3],points[4]) brepToSubtract = Rhino.Geometry.Box(plane,points).ToBrep() brepArrayToCheck = [brepBox2,brepToSubtract] brepUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArrayToCheck,tolerance) brepTotalVolume = brepUnion[0].GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume brepSubtractArray = [brepToSubtract] check,curves,points = Rhino.Geometry.Intersect.Intersection.BrepBrep(brepToSubtract,brepFillet,tolerance) brepBoxUnion = Rhino.Geometry.Brep.CreateBooleanUnion(brepArray,tolerance) brepAddArray = [brepBoxUnion[0]] if(len(curves)== 0): brepSubtractArray.append(brepFillet) brepModel = Rhino.Geometry.Brep.CreateBooleanDifference(brepAddArray,brepSubtractArray,tolerance) totalVolume = brepBox1.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume + brepBox2.GetBoundingBox(Rhino.Geometry.Vector3d(0,0,1)).Volume if ( brepModel is not None and width > 4 and sqrDist > 16 and brepTotalVolume <= totalVolume): for brep in brepModel: if(scriptcontext.doc.Objects.AddBrep(brep) != System.Guid.Empty ): scriptcontext.doc.Views.Redraw() #rs.Command("_-SaveAs"+" F:\ModuleWorks\RhinoNoHoleIGS\\" + str(modelIdx) + ".igs" + " enter" + " enter",True) objs = rs.AllObjects(select = True) #rs.Command("_Delete ") filled = True if(filled): return 1 else: return 0 if( __name__ == "__main__" ): i = 0 while (i < 20): i = i + GenerateSlotsForSteppedModel()

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null

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7

c525f873d9cb4c5a8a235a7aeead5a11e7a6d4de

174

py

Python

.idea/fileTemplates/Blueprint Init.py

marcoprado17/flask-bone

772d25bdf6c6e41701da1ef2e2a67bae7ae21757

[ "MIT" ]

null

.idea/fileTemplates/Blueprint Init.py

marcoprado17/flask-bone

772d25bdf6c6e41701da1ef2e2a67bae7ae21757

[ "MIT" ]

null

.idea/fileTemplates/Blueprint Init.py

marcoprado17/flask-bone

772d25bdf6c6e41701da1ef2e2a67bae7ae21757

[ "MIT" ]

null

#parse("header.py") from flask import Blueprint ${BLUEPRINT_NAME}_blueprint = Blueprint("${BLUEPRINT_NAME}", __name__, static_folder="static", template_folder="templates")

29

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null

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7

c52a3c803e228cb6d1b29b676b706c92848fc034

14,538

py

Python

Cartwheel/cartwheel-3d/Python/Data/Characters/BipV3/Controllers/Walking.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

Cartwheel/cartwheel-3d/Python/Data/Characters/BipV3/Controllers/Walking.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

Cartwheel/cartwheel-3d/Python/Data/Characters/BipV3/Controllers/Walking.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

from App.Proxys import * data = IKVMCController( name = '', controlParamsList = [ ControlParams( joint = 'root', kp = 1000.0, kd = 200.0, tauMax = 200.0, scale = ( 1.0, 1.0, 1.0 ) ), ControlParams( joint = 'pelvis_lowerback', kp = 75.0, kd = 17.0, tauMax = 100.0, scale = ( 1.0, 1.0, 1.0 ) ), ControlParams( joint = 'lowerback_torso', kp = 75.0, kd = 17.0, tauMax = 100.0, scale = ( 1.0, 1.0, 1.0 ) ), ControlParams( joint = 'torso_head', kp = 10.0, kd = 3.0, tauMax = 200.0, scale = ( 1.0, 0.2, 1.0 ) ), ControlParams( joint = 'lShoulder', kp = 15.0, kd = 5.0, tauMax = 200.0, scale = ( 0.5, 1.0, 1.0 ) ), ControlParams( joint = 'rShoulder', kp = 15.0, kd = 5.0, tauMax = 200.0, scale = ( 0.3, 1.0, 1.0 ) ), ControlParams( joint = 'lElbow', kp = 5.0, kd = 1.0, tauMax = 200.0, scale = ( 0.2, 1.0, 1.0 ) ), ControlParams( joint = 'rElbow', kp = 5.0, kd = 1.0, tauMax = 200.0, scale = ( 0.2, 1.0, 1.0 ) ), ControlParams( joint = 'lHip', kp = 300.0, kd = 35.0, tauMax = 200.0, scale = ( 1.0, 1.0, 1.0 ) ), ControlParams( joint = 'rHip', kp = 300.0, kd = 35.0, tauMax = 200.0, scale = ( 1.0, 1.0, 1.0 ) ), ControlParams( joint = 'lKnee', kp = 300.0, kd = 35.0, tauMax = 1000.0, scale = ( 1.0, 1.0, 1.0 ) ), ControlParams( joint = 'rKnee', kp = 300.0, kd = 35.0, tauMax = 1000.0, scale = ( 1.0, 1.0, 1.0 ) ), ControlParams( joint = 'lAnkle', kp = 50.0, kd = 15.0, tauMax = 100.0, scale = ( 1.0, 0.2, 0.2 ) ), ControlParams( joint = 'rAnkle', kp = 50.0, kd = 15.0, tauMax = 100.0, scale = ( 1.0, 0.2, 0.2 ) ), ControlParams( joint = 'lToeJoint', kp = 2.0, kd = 0.2, tauMax = 100.0, scale = ( 1.0, 1.0, 1.0 ) ), ControlParams( joint = 'rToeJoint', kp = 2.0, kd = 0.2, tauMax = 100.0, scale = ( 1.0, 1.0, 1.0 ) ) ], states = [ SimBiConState( name = 'State 0', nextStateIndex = 0, duration = 0.6, externalForces = [ ], trajectories = [ Trajectory( joint = 'root', strength = [ ], components = [ TrajectoryComponent( rotationAxis = ( 0.0, 1.0, 0.0 ), reverseOnStance = 'RIGHT', baseTrajectory = [ ( 1.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 0.0, 0.0, 1.0 ), reverseOnStance = 'RIGHT', baseTrajectory = [ ( 1.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'SWING_Hip', strength = [ ( 0.2, 0.2 ), ( 0.4, 1.0 ) ], components = [ ] ), Trajectory( joint = 'SWING_Knee', strength = [ ( 0.2, 0.2 ), ( 0.4, 1.0 ) ], components = [ TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'STANCE_Knee', strength = [ ], components = [ TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.003344, 0.204846 ), ( 0.959866, 0.070153 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'SWING_Ankle', strength = [ ( 0.2, 0.2 ), ( 0.4, 1.0 ) ], referenceFrame = 'CHARACTER_RELATIVE', components = [ TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.3 ), ( 0.3, 0.3 ), ( 0.4, 0.0 ), ( 1.0, -0.3 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ( -0.5, 2.0 ), ( -0.1, 1.0 ), ( 0.0, 0.0 ), ( 0.1, 1.0 ), ( 0.5, 2.5 ), ( 1.0, 6.0 ), ( 1.1, 7.0 ), ( 1.5, 3.0 ) ] ), TrajectoryComponent( rotationAxis = ( 0.0, 0.0, 1.0 ), baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'STANCE_Ankle', strength = [ ( 0.3, 1.0 ) ], referenceFrame = 'CHARACTER_RELATIVE', components = [ TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, -0.1 ), ( 0.3, 0.0 ), ( 0.8, 0.0 ), ( 1.0, 0.2 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ( -0.1, 0.5 ), ( 0.0, 0.0 ), ( 0.2, 0.2 ), ( 0.5, 0.2 ), ( 1.0, 2.5 ) ] ), TrajectoryComponent( rotationAxis = ( 0.0, 0.0, 1.0 ), reverseOnStance = 'LEFT', baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'SWING_Shoulder', strength = [ ], components = [ TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.2 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 0.0, 0.0, 1.0 ), reverseOnStance = 'LEFT', baseTrajectory = [ ( 0.0, -1.57 ), ( 0.752508, -1.473995 ), ( 0.979933, -1.308908 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), feedback = LinearBalanceFeedback( axis = ( 0.0, 0.0, 1.0 ), cv = 0.1, vLimits = ( -0.6, 0.6 ) ), baseTrajectory = [ ( 0.0, 0.143195 ), ( 0.558653, 0.193845 ), ( 0.813333, 0.16319 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'STANCE_Shoulder', strength = [ ], components = [ TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 0.0, 0.0, 1.0 ), reverseOnStance = 'LEFT', baseTrajectory = [ ( 0.0, 1.57 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), feedback = LinearBalanceFeedback( axis = ( 0.0, 0.0, 1.0 ), cv = -0.1, vLimits = ( -0.6, 0.6 ) ), baseTrajectory = [ ( 0.0, -0.2 ), ( 0.842809, -0.176382 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'STANCE_Elbow', strength = [ ], components = [ TrajectoryComponent( rotationAxis = ( 0.0, 1.0, 0.0 ), reverseOnStance = 'LEFT', baseTrajectory = [ ( 0.0, 0.1 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'SWING_Elbow', strength = [ ], components = [ TrajectoryComponent( rotationAxis = ( 0.0, 1.0, 0.0 ), reverseOnStance = 'LEFT', baseTrajectory = [ ( 0.006689, -0.1 ), ( 0.568562, -0.2 ), ( 0.989967, -0.1 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'pelvis_lowerback', strength = [ ], referenceFrame = 'CHARACTER_RELATIVE', components = [ TrajectoryComponent( rotationAxis = ( 0.0, 1.0, 0.0 ), reverseOnStance = 'RIGHT', baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 0.0, 0.0, 1.0 ), reverseOnStance = 'RIGHT', baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.0 ), ( 0.5, 0.0 ), ( 0.8, 0.15 ), ( 1.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ( -0.75, -0.5 ), ( 0.0, 0.0 ), ( 0.8, 1.0 ) ] ) ] ), Trajectory( joint = 'lowerback_torso', strength = [ ], referenceFrame = 'CHARACTER_RELATIVE', components = [ TrajectoryComponent( rotationAxis = ( 0.0, 1.0, 0.0 ), reverseOnStance = 'RIGHT', baseTrajectory = [ ( 0.0, 0.0 ), ( 0.508361, -0.2 ), ( 1.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ( -0.75, -0.5 ), ( 0.0, 0.1 ), ( 0.5, 0.5 ), ( 1.0, 1.0 ) ] ), TrajectoryComponent( rotationAxis = ( 0.0, 0.0, 1.0 ), reverseOnStance = 'RIGHT', baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.0 ), ( 0.3, 0.0 ), ( 0.75, 0.2 ), ( 1.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ( -0.75, -0.5 ), ( 0.0, 0.0 ), ( 0.8, 1.0 ) ] ) ] ), Trajectory( joint = 'torso_head', strength = [ ], referenceFrame = 'CHARACTER_RELATIVE', components = [ TrajectoryComponent( rotationAxis = ( 0.0, 1.0, 0.0 ), reverseOnStance = 'RIGHT', baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 0.0, 0.0, 1.0 ), reverseOnStance = 'RIGHT', baseTrajectory = [ ( 1.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ), TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'SWING_ToeJoint', strength = [ ( 0.3, 0.1 ), ( 0.5, 0.1 ), ( 0.6, 1.0 ) ], components = [ TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ), Trajectory( joint = 'STANCE_ToeJoint', strength = [ ], components = [ TrajectoryComponent( rotationAxis = ( 1.0, 0.0, 0.0 ), baseTrajectory = [ ( 0.0, 0.0 ) ], dScaledTrajectory = [ ], vScaledTrajectory = [ ] ) ] ) ] ) ] )

51.736655

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0.091954

0.084261

0.077504

0.051669

0.870827

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14,538

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null

0

8

c53e3a14cc972235ec4de18b8690e2b3b1f1100b

491

py

Python

redlist/logo.py

Ramenseller/RedList

e7fb3d351af350c18421370a3423af42e857a450

[ "MIT" ]

2

2018-05-03T04:47:56.000Z

2018-05-17T03:54:48.000Z

redlist/logo.py

Ramenseller/B4_OS18

e7fb3d351af350c18421370a3423af42e857a450

[ "MIT" ]

37

2018-05-16T14:18:46.000Z

2018-05-29T15:01:33.000Z

redlist/logo.py

Ramenseller/RedList

e7fb3d351af350c18421370a3423af42e857a450

[ "MIT" ]

5

2018-05-03T04:35:05.000Z

2018-05-14T11:47:42.000Z

# -*- coding: utf-8 -*- from colorama import Fore def print_logo(): """ ___ __ __ _ __ / _ \___ ___/ / / / (_)__ / /_ / , _/ -_) _ / / /__/ (_-</ __/ /_/|_|\__/\_,_/ /____/_/___/\__/ by Team Avengers MIT LICENSE VERSION """ print(Fore.RED + " ___ __ __ _ __ \n / _ \___ ___/ / / / (_)__ / /_\n / , _/ -_) _ / / /__/ (_-</ __/\n/_/|_|\__/\_,_/ /____/_/___/\__/ \n") print("by Team Avengers\nMIT LICENSE\nVERSION 0.0.8\n\n" + Fore.RESET)

30.6875

161

0.476578

37

491

3.864865

0.594595

0.055944

0.195804

0

0.011364

0.283096

491

16

162

30.6875

0.394886

0.386965

0

0.25

0.664311

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true

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0.25

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0.5

0.75

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null

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1

0

null

0

1

0

1

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7

c5477f45e6dfa2badd5339c1d180bcbf0140f2f9

5,102

py

Python

Packs/CommonScripts/Scripts/RemoveKeyFromList/RemoveKeyFromList_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

799

2016-08-02T06:43:14.000Z

2022-03-31T11:10:11.000Z

Packs/CommonScripts/Scripts/RemoveKeyFromList/RemoveKeyFromList_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

9,317

2016-08-07T19:00:51.000Z

2022-03-31T21:56:04.000Z

Packs/CommonScripts/Scripts/RemoveKeyFromList/RemoveKeyFromList_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

1,297

2016-08-04T13:59:00.000Z

2022-03-31T23:43:06.000Z

from RemoveKeyFromList import remove_key_from_list_command import demistomock as demisto # noqa # pylint: disable=unused-wildcard-import from typing import List, Dict, Any import json MOCK_LIST_NAME = "TestList" MOCK_KEY_NAME = "TestKey" def test_remove_nonexisting_key_in_nonempty_list(mocker): """ Given: - a nonempty list with some value - a key that doesn't exist in the list When - trying to remove a key that doesn't exist in the list Then - a message saying the key was not found is returned """ MOCKED_START_LIST: Dict = { "AnotherKey": "SomeValue" } def executeCommand(name: str, args: Dict[str, Any]) -> List[Dict[str, Any]]: if name == 'getList': return [{"Contents": json.dumps(MOCKED_START_LIST)}] elif name == 'setList': return [{"Contents": f"Done: list {name} was updated"}] raise ValueError(f"Error: Unknown command or command/argument pair: {name} {args!r}") mocked_ec = mocker.patch.object(demisto, 'executeCommand', side_effect=executeCommand) result = remove_key_from_list_command({ 'listName': MOCK_LIST_NAME, 'keyName': MOCK_KEY_NAME, }) assert result.readable_output == f'Key {MOCK_KEY_NAME} not found in list {MOCK_LIST_NAME}, cannot remove.' assert len(mocked_ec.call_args_list) == 1 def test_remove_nonexisting_key_in_empty_list(mocker): """ Given: - an empty list - a key that doesn't exist in the list When - trying to remove a key Then - a message saying the key was not found is returned """ MOCKED_START_LIST: Dict = {} def executeCommand(name: str, args: Dict[str, Any]) -> List[Dict[str, Any]]: if name == 'getList': return [{"Contents": json.dumps(MOCKED_START_LIST)}] elif name == 'setList': return [{"Contents": f"Done: list {name} was updated"}] raise ValueError(f"Error: Unknown command or command/argument pair: {name} {args!r}") mocked_ec = mocker.patch.object(demisto, 'executeCommand', side_effect=executeCommand) result = remove_key_from_list_command({ 'listName': MOCK_LIST_NAME, 'keyName': MOCK_KEY_NAME, }) assert result.readable_output == f'Key {MOCK_KEY_NAME} not found in list {MOCK_LIST_NAME}, cannot remove.' assert len(mocked_ec.call_args_list) == 1 def test_remove_existing_key(mocker): """ Given: - a nonempty list with 2 values - a key that exists in the list When - trying to remove a key exists exist in the list Then - requested key is removed from list - list is left with only one item """ MOCKED_START_LIST: Dict = { MOCK_KEY_NAME: "Value", "AnotherKey": "AnotherValue" } MOCKED_END_LIST: Dict = { "AnotherKey": "AnotherValue" } def executeCommand(name: str, args: Dict[str, Any]) -> List[Dict[str, Any]]: if name == 'getList': return [{"Contents": json.dumps(MOCKED_START_LIST)}] elif name == 'setList': return [{"Contents": f"Done: list {name} was updated"}] raise ValueError(f"Error: Unknown command or command/argument pair: {name} {args!r}") mocked_ec = mocker.patch.object(demisto, 'executeCommand', side_effect=executeCommand) result = remove_key_from_list_command({ 'listName': MOCK_LIST_NAME, 'keyName': MOCK_KEY_NAME, }) assert result.readable_output == f'Successfully removed key {MOCK_KEY_NAME} from list {MOCK_LIST_NAME}.' assert len(mocked_ec.call_args_list) == 2 assert mocked_ec.call_args_list[1][0][0] == 'setList' assert json.loads(mocked_ec.call_args_list[1][0][1]['listData']) == MOCKED_END_LIST def test_remove_existing_last_key(mocker): """ Given: - a nonempty list with 1 value - a key that exists in the list (the only one that exists) When - trying to remove the last key of the list Then - requested key is removed from list - list is empty """ MOCKED_START_LIST: Dict = { MOCK_KEY_NAME: "Value" } MOCKED_END_LIST: Dict = {} def executeCommand(name: str, args: Dict[str, Any]) -> List[Dict[str, Any]]: if name == 'getList': return [{"Contents": json.dumps(MOCKED_START_LIST)}] elif name == 'setList': return [{"Contents": f"Done: list {name} was updated"}] raise ValueError(f"Error: Unknown command or command/argument pair: {name} {args!r}") mocked_ec = mocker.patch.object(demisto, 'executeCommand', side_effect=executeCommand) result = remove_key_from_list_command({ 'listName': MOCK_LIST_NAME, 'keyName': MOCK_KEY_NAME, }) assert result.readable_output == f'Successfully removed key {MOCK_KEY_NAME} from list {MOCK_LIST_NAME}.' assert len(mocked_ec.call_args_list) == 2 assert mocked_ec.call_args_list[1][0][0] == 'setList' assert json.loads(mocked_ec.call_args_list[1][0][1]['listData']) == MOCKED_END_LIST

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3d97754d8779ba8c30df67581f86cfacfb093add

2,713

py

Python

tests/collide/test_collision_tube.py

Psychotropos/panda3d

ffe4f387ae9dd6299e6002be95037a44aa5b2a27

[ "PHP-3.01", "PHP-3.0" ]

null

tests/collide/test_collision_tube.py

Psychotropos/panda3d

ffe4f387ae9dd6299e6002be95037a44aa5b2a27

[ "PHP-3.01", "PHP-3.0" ]

null

tests/collide/test_collision_tube.py

Psychotropos/panda3d

ffe4f387ae9dd6299e6002be95037a44aa5b2a27

[ "PHP-3.01", "PHP-3.0" ]

null

from panda3d import core def test_collision_tube_alias(): assert hasattr(core, 'CollisionCapsule') assert hasattr(core, 'CollisionTube') assert core.CollisionTube is core.CollisionCapsule def test_collision_tube_write_old(): buffer = core.DatagramBuffer() writer = core.BamWriter(buffer) assert writer.get_file_major_ver() == 6 writer.set_file_minor_ver(43) capsule = core.CollisionCapsule((0, 0, -1), (0, 0, 1), 0.5) writer.init() writer.write_object(capsule) writer.flush() data = buffer.data assert b'CollisionTube' in data assert b'CollisionCapsule' not in data def test_collision_tube_write_new(): buffer = core.DatagramBuffer() writer = core.BamWriter(buffer) assert writer.get_file_major_ver() == 6 writer.set_file_minor_ver(44) capsule = core.CollisionCapsule((0, 0, -1), (0, 0, 1), 0.5) writer.init() writer.write_object(capsule) writer.flush() data = buffer.data assert b'CollisionTube' not in data assert b'CollisionCapsule' in data def test_collision_tube_read_old(): # Make sure we can read an older file that contains CollisionTube. buffer = core.DatagramBuffer(b'\x06\x00\x00\x00\x06\x00+\x00\x01\x00\xd6\x00\x00\x00\x00j\x01\r\x00CollisionTube\x01h\x01\x0e\x00CollisionSolid\x01B\x00\x11\x00CopyOnWriteObject\x01A\x00!\x00CachedTypedWritableReferenceCount\x01=\x00\x1b\x00TypedWritableReferenceCount\x02<\x00\r\x00TypedWritable\x01\x03\x00\x0b\x00TypedObject\x00\x07\x00\x0e\x00ReferenceCount\x00\x01\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80\xbf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?\x00\x00\x00?\x01\x00\x00\x00\x01') reader = core.BamReader(buffer) reader.init() assert reader.file_version == (6, 43) capsule = reader.read_object() reader.resolve() assert isinstance(capsule, core.CollisionCapsule) def test_collision_tube_read_new(): # Make sure we can read a newer file that contains CollisionCapsule. buffer = core.DatagramBuffer(b'\x06\x00\x00\x00\x06\x00,\x00\x01\x00\xd9\x00\x00\x00\x00j\x01\x10\x00CollisionCapsule\x01h\x01\x0e\x00CollisionSolid\x01B\x00\x11\x00CopyOnWriteObject\x01A\x00!\x00CachedTypedWritableReferenceCount\x01=\x00\x1b\x00TypedWritableReferenceCount\x02<\x00\r\x00TypedWritable\x01\x03\x00\x0b\x00TypedObject\x00\x07\x00\x0e\x00ReferenceCount\x00\x01\x00\x15\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80\xbf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?\x00\x00\x00?\x01\x00\x00\x00\x01') reader = core.BamReader(buffer) reader.init() assert reader.file_version == (6, 44) capsule = reader.read_object() reader.resolve() assert isinstance(capsule, core.CollisionCapsule)

42.390625

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0.743826

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2,713

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1

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false

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0

null

0

1

0

null

0

8

3df65b8c8e6ce73839bc2aea7f1cb7702a7198e0

57,417

py

Python

alfworld/agents/agent/text_dqn_agent.py

zhaozj89/alfworld_meta_dqn

4ad3ee6e57a6b808d4d90d48f00f14e4e8ec593d

[ "MIT" ]

null

alfworld/agents/agent/text_dqn_agent.py

zhaozj89/alfworld_meta_dqn

4ad3ee6e57a6b808d4d90d48f00f14e4e8ec593d

[ "MIT" ]

null

alfworld/agents/agent/text_dqn_agent.py

zhaozj89/alfworld_meta_dqn

4ad3ee6e57a6b808d4d90d48f00f14e4e8ec593d

[ "MIT" ]

null

import copy import operator import logging from queue import PriorityQueue import numpy as np import torch import torch.nn.functional as F logging.getLogger("transformers.tokenization_utils").setLevel(logging.ERROR) from alfworld.agents.agent import BaseAgent from alfworld.agents.modules.generic import to_np, to_pt, _words_to_ids, pad_sequences, preproc, max_len, ez_gather_dim_1, LinearSchedule, BeamSearchNode from alfworld.agents.modules.layers import NegativeLogLoss, masked_mean, compute_mask, GetGenerationQValue class TextDQNAgent(BaseAgent): ''' TextAgent trained with DQN (Reinforcement Learning) ''' def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) assert self.training_method == "dqn" def choose_random_action(self, action_rank, action_unpadded=None): """ Select an action randomly. """ batch_size = action_rank.size(0) action_space_size = action_rank.size(1) if action_unpadded is None: indices = np.random.choice(action_space_size, batch_size) else: indices = [] for j in range(batch_size): indices.append(np.random.choice(len(action_unpadded[j]))) indices = np.array(indices) return indices def choose_maxQ_action(self, action_rank, action_mask=None): """ Generate an action by maximum q values. """ action_rank = action_rank - torch.min(action_rank, -1, keepdim=True)[0] + 1e-2 # minus the min value, so that all values are non-negative if action_mask is not None: assert action_mask.size() == action_rank.size(), (action_mask.size().shape, action_rank.size()) action_rank = action_rank * action_mask action_indices = torch.argmax(action_rank, -1) # batch return to_np(action_indices) # choosing from list of admissible commands def admissible_commands_act_greedy(self, observation_strings, task_desc_strings, action_candidate_list, previous_dynamics): with torch.no_grad(): h_obs, obs_mask = self.encode(observation_strings, use_model="online") h_td, td_mask = self.encode(task_desc_strings, use_model="online") action_scores, action_masks, current_dynamics = self.action_scoring(action_candidate_list, h_obs, obs_mask, h_td, td_mask, previous_dynamics, use_model="online") action_indices_maxq = self.choose_maxQ_action(action_scores, action_masks) chosen_indices = action_indices_maxq chosen_indices = chosen_indices.astype(int) chosen_actions = [item[idx] for item, idx in zip(action_candidate_list, chosen_indices)] return chosen_actions, chosen_indices, current_dynamics def admissible_commands_act_random(self, observation_strings, task_desc_strings, action_candidate_list, previous_dynamics): with torch.no_grad(): h_obs, obs_mask = self.encode(observation_strings, use_model="online") h_td, td_mask = self.encode(task_desc_strings, use_model="online") action_scores, action_masks, current_dynamics = self.action_scoring(action_candidate_list, h_obs, obs_mask, h_td, td_mask, previous_dynamics, use_model="online") action_indices_random = self.choose_random_action(action_scores, action_candidate_list) chosen_indices = action_indices_random chosen_indices = chosen_indices.astype(int) chosen_actions = [item[idx] for item, idx in zip(action_candidate_list, chosen_indices)] return chosen_actions, chosen_indices, current_dynamics def admissible_commands_act(self, observation_strings, task_desc_strings, action_candidate_list, previous_dynamics, random=False): with torch.no_grad(): if self.mode == "eval": return self.admissible_commands_act_greedy(observation_strings, task_desc_strings, action_candidate_list, previous_dynamics) if random: return self.admissible_commands_act_random(observation_strings, task_desc_strings, action_candidate_list, previous_dynamics) batch_size = len(observation_strings) h_obs, obs_mask = self.encode(observation_strings, use_model="online") h_td, td_mask = self.encode(task_desc_strings, use_model="online") action_scores, action_masks, current_dynamics = self.action_scoring(action_candidate_list, h_obs, obs_mask, h_td, td_mask, previous_dynamics, use_model="online") action_indices_maxq = self.choose_maxQ_action(action_scores, action_masks) action_indices_random = self.choose_random_action(action_scores, action_candidate_list) # random number for epsilon greedy rand_num = np.random.uniform(low=0.0, high=1.0, size=(batch_size,)) less_than_epsilon = (rand_num < self.epsilon).astype("float32") # batch greater_than_epsilon = 1.0 - less_than_epsilon chosen_indices = less_than_epsilon * action_indices_random + greater_than_epsilon * action_indices_maxq chosen_indices = chosen_indices.astype(int) chosen_actions = [item[idx] for item, idx in zip(action_candidate_list, chosen_indices)] return chosen_actions, chosen_indices, current_dynamics # choosing from output of beam search (without re-compute some intermediate representations) def beam_search_choice_act_greedy(self, observation_strings, task_desc_strings, previous_dynamics): with torch.no_grad(): action_candidate_list, current_dynamics, obs_mask, aggregated_obs_representation = self.command_generation_by_beam_search(observation_strings, task_desc_strings, previous_dynamics) action_scores, action_masks = self.beam_search_candidate_scoring(action_candidate_list, aggregated_obs_representation, obs_mask, current_dynamics, use_model="online") action_indices_maxq = self.choose_maxQ_action(action_scores, action_masks) chosen_indices = action_indices_maxq chosen_indices = chosen_indices.astype(int) chosen_actions = [item[idx] for item, idx in zip(action_candidate_list, chosen_indices)] return chosen_actions, chosen_indices, current_dynamics, action_candidate_list def beam_search_choice_act_random(self, observation_strings, task_desc_strings, previous_dynamics): with torch.no_grad(): action_candidate_list, current_dynamics, obs_mask, aggregated_obs_representation = self.command_generation_by_beam_search(observation_strings, task_desc_strings, previous_dynamics) action_scores, _ = self.beam_search_candidate_scoring(action_candidate_list, aggregated_obs_representation, obs_mask, current_dynamics, use_model="online") action_indices_random = self.choose_random_action(action_scores, action_candidate_list) chosen_indices = action_indices_random chosen_indices = chosen_indices.astype(int) chosen_actions = [item[idx] for item, idx in zip(action_candidate_list, chosen_indices)] return chosen_actions, chosen_indices, current_dynamics, action_candidate_list def beam_search_choice_act(self, observation_strings, task_desc_strings, previous_dynamics, random=False): with torch.no_grad(): if self.mode == "eval": return self.beam_search_choice_act_greedy(observation_strings, task_desc_strings, previous_dynamics) if random: return self.beam_search_choice_act_random(observation_strings, task_desc_strings, previous_dynamics) batch_size = len(observation_strings) action_candidate_list, current_dynamics, obs_mask, aggregated_obs_representation = self.command_generation_by_beam_search(observation_strings, task_desc_strings, previous_dynamics) action_scores, action_masks = self.beam_search_candidate_scoring(action_candidate_list, aggregated_obs_representation, obs_mask, current_dynamics, use_model="online") action_indices_maxq = self.choose_maxQ_action(action_scores, action_masks) action_indices_random = self.choose_random_action(action_scores, action_candidate_list) # random number for epsilon greedy rand_num = np.random.uniform(low=0.0, high=1.0, size=(batch_size,)) less_than_epsilon = (rand_num < self.epsilon).astype("float32") # batch greater_than_epsilon = 1.0 - less_than_epsilon chosen_indices = less_than_epsilon * action_indices_random + greater_than_epsilon * action_indices_maxq chosen_indices = chosen_indices.astype(int) chosen_actions = [item[idx] for item, idx in zip(action_candidate_list, chosen_indices)] return chosen_actions, chosen_indices, current_dynamics, action_candidate_list # generating token by token def command_generation_by_beam_search(self, observation_strings, task_desc_strings, previous_dynamics): with torch.no_grad(): batch_size = len(observation_strings) beam_width = self.beam_width generate_top_k = self.generate_top_k chosen_actions = [] input_obs = self.get_word_input(observation_strings) h_obs, obs_mask = self.encode(observation_strings, use_model="online") h_td, td_mask = self.encode(task_desc_strings, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid if self.recurrent: averaged_representation = self.online_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid current_dynamics = self.online_net.rnncell(averaged_representation, previous_dynamics) if previous_dynamics is not None else self.online_net.rnncell(averaged_representation) else: current_dynamics = None for b in range(batch_size): # starts from CLS tokens __input_target_list = [self.word2id["[CLS]"]] __input_obs = input_obs[b: b + 1] # 1 x obs_len __obs_mask = obs_mask[b: b + 1] # 1 x obs_len __aggregated_obs_representation = aggregated_obs_representation[b: b + 1] # 1 x obs_len x hid if current_dynamics is not None: __current_dynamics = current_dynamics[b: b + 1] # 1 x hid else: __current_dynamics = None ended_nodes = [] # starting node - previous node, input target, logp, length node = BeamSearchNode(None, __input_target_list, 0, 1) nodes_queue = PriorityQueue() # start the queue nodes_queue.put((node.val, node)) queue_size = 1 while(True): # give up when decoding takes too long if queue_size > 2000: break # fetch the best node score, n = nodes_queue.get() __input_target_list = n.input_target if (n.input_target[-1] == self.word2id["[SEP]"] or n.length >= self.max_target_length) and n.previous_node != None: ended_nodes.append((score, n)) # if we reached maximum # of sentences required if len(ended_nodes) >= generate_top_k: break else: continue input_target = pad_sequences([__input_target_list], dtype='int32') input_target = to_pt(input_target, self.use_cuda) target_mask = compute_mask(input_target) # decode for one step using decoder pred = self.online_net.decode(input_target, target_mask, __aggregated_obs_representation, __obs_mask, __current_dynamics, __input_obs) # 1 x target_length x vocab pred = pred[0][-1].cpu() gt_zero = torch.gt(pred, 0.0).float() # vocab epsilon = torch.le(pred, 0.0).float() * 1e-8 # vocab log_pred = torch.log(pred + epsilon) * gt_zero # vocab top_beam_width_log_probs, top_beam_width_indicies = torch.topk(log_pred, beam_width) next_nodes = [] for new_k in range(beam_width): pos = top_beam_width_indicies[new_k] log_p = top_beam_width_log_probs[new_k].item() node = BeamSearchNode(n, __input_target_list + [pos], n.log_prob + log_p, n.length + 1) next_nodes.append((node.val, node)) # put them into queue for i in range(len(next_nodes)): score, nn = next_nodes[i] nodes_queue.put((score, nn)) # increase qsize queue_size += len(next_nodes) - 1 # choose n best paths if len(ended_nodes) == 0: ended_nodes = [nodes_queue.get() for _ in range(generate_top_k)] utterances = [] for score, n in sorted(ended_nodes, key=operator.itemgetter(0)): utte = n.input_target utte_string = self.tokenizer.decode(utte) utterances.append(utte_string) utterances = [item.replace("[CLS]", "").replace("[SEP]", "").strip() for item in utterances] utterances = [item.replace(" in / on ", " in/on " ) for item in utterances] chosen_actions.append(utterances) return chosen_actions, current_dynamics, obs_mask, aggregated_obs_representation def command_generation_act_greedy(self, observation_strings, task_desc_strings, previous_dynamics): with torch.no_grad(): batch_size = len(observation_strings) input_obs = self.get_word_input(observation_strings) h_obs, obs_mask = self.encode(observation_strings, use_model="online") h_td, td_mask = self.encode(task_desc_strings, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid if self.recurrent: averaged_representation = self.online_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid current_dynamics = self.online_net.rnncell(averaged_representation, previous_dynamics) if previous_dynamics is not None else self.online_net.rnncell(averaged_representation) else: current_dynamics = None # greedy generation input_target_list = [[self.word2id["[CLS]"]] for i in range(batch_size)] eos = np.zeros(batch_size) for _ in range(self.max_target_length): input_target = copy.deepcopy(input_target_list) input_target = pad_sequences(input_target, maxlen=max_len(input_target)).astype('int32') input_target = to_pt(input_target, self.use_cuda) target_mask = compute_mask(input_target) # mask of ground truth should be the same pred = self.online_net.decode(input_target, target_mask, aggregated_obs_representation, obs_mask, current_dynamics, input_obs) # batch x target_length x vocab # pointer softmax pred = to_np(pred[:, -1]) # batch x vocab pred = np.argmax(pred, -1) # batch for b in range(batch_size): new_stuff = [pred[b]] if eos[b] == 0 else [] input_target_list[b] = input_target_list[b] + new_stuff if pred[b] == self.word2id["[SEP]"]: eos[b] = 1 if np.sum(eos) == batch_size: break chosen_actions = [self.tokenizer.decode(item) for item in input_target_list] chosen_actions = [item.replace("[CLS]", "").replace("[SEP]", "").strip() for item in chosen_actions] chosen_actions = [item.replace(" in / on ", " in/on " ) for item in chosen_actions] chosen_indices = [item[1:] for item in input_target_list] for i in range(len(chosen_indices)): if chosen_indices[i][-1] == self.word2id["[SEP]"]: chosen_indices[i] = chosen_indices[i][:-1] return chosen_actions, chosen_indices, current_dynamics def command_generation_act_random(self, observation_strings, task_desc_strings, previous_dynamics): with torch.no_grad(): batch_size = len(observation_strings) beam_width = self.beam_width generate_top_k = self.generate_top_k chosen_actions, chosen_indices = [], [] input_obs = self.get_word_input(observation_strings) h_obs, obs_mask = self.encode(observation_strings, use_model="online") h_td, td_mask = self.encode(task_desc_strings, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid if self.recurrent: averaged_representation = self.online_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid current_dynamics = self.online_net.rnncell(averaged_representation, previous_dynamics) if previous_dynamics is not None else self.online_net.rnncell(averaged_representation) else: current_dynamics = None for b in range(batch_size): # starts from CLS tokens __input_target_list = [self.word2id["[CLS]"]] __input_obs = input_obs[b: b + 1] # 1 x obs_len __obs_mask = obs_mask[b: b + 1] # 1 x obs_len __aggregated_obs_representation = aggregated_obs_representation[b: b + 1] # 1 x obs_len x hid if current_dynamics is not None: __current_dynamics = current_dynamics[b: b + 1] # 1 x hid else: __current_dynamics = None ended_nodes = [] # starting node - previous node, input target, logp, length node = BeamSearchNode(None, __input_target_list, 0, 1) nodes_queue = PriorityQueue() # start the queue nodes_queue.put((node.val, node)) queue_size = 1 while(True): # give up when decoding takes too long if queue_size > 2000: break # fetch the best node score, n = nodes_queue.get() __input_target_list = n.input_target if (n.input_target[-1] == self.word2id["[SEP]"] or n.length >= self.max_target_length) and n.previous_node != None: ended_nodes.append((score, n)) # if we reached maximum # of sentences required if len(ended_nodes) >= generate_top_k: break else: continue input_target = pad_sequences([__input_target_list], dtype='int32') input_target = to_pt(input_target, self.use_cuda) target_mask = compute_mask(input_target) # decode for one step using decoder pred = self.online_net.decode(input_target, target_mask, __aggregated_obs_representation, __obs_mask, __current_dynamics, __input_obs) # 1 x target_length x vocab pred = pred[0][-1].cpu() gt_zero = torch.gt(pred, 0.0).float() # vocab epsilon = torch.le(pred, 0.0).float() * 1e-8 # vocab log_pred = torch.log(pred + epsilon) * gt_zero # vocab top_beam_width_log_probs, top_beam_width_indicies = torch.topk(log_pred, beam_width) next_nodes = [] for new_k in range(beam_width): pos = top_beam_width_indicies[new_k] log_p = top_beam_width_log_probs[new_k].item() node = BeamSearchNode(n, __input_target_list + [pos], n.log_prob + log_p, n.length + 1) next_nodes.append((node.val, node)) # put them into queue for i in range(len(next_nodes)): score, nn = next_nodes[i] nodes_queue.put((score, nn)) # increase qsize queue_size += len(next_nodes) - 1 # choose n best paths if len(ended_nodes) == 0: ended_nodes = [nodes_queue.get() for _ in range(generate_top_k)] indicies, utterances = [], [] for score, n in sorted(ended_nodes, key=operator.itemgetter(0)): utte = n.input_target utte_string = self.tokenizer.decode(utte) utterances.append(utte_string) indicies.append(utte) utterances = [item.replace("[CLS]", "").replace("[SEP]", "").strip() for item in utterances] utterances = [item.replace(" in / on ", " in/on " ) for item in utterances] indicies = [item[1:] for item in indicies] for i in range(len(indicies)): if indicies[i][-1] == self.word2id["[SEP]"]: indicies[i] = indicies[i][:-1] # sample one from all generated beams rand_idx = np.random.choice(len(indicies)) chosen_actions.append(utterances[rand_idx]) chosen_indices.append(indicies[rand_idx]) return chosen_actions, chosen_indices, current_dynamics def command_generation_act(self, observation_strings, task_desc_strings, previous_dynamics, random=False): with torch.no_grad(): if self.mode == "eval": return self.command_generation_act_greedy(observation_strings, task_desc_strings, previous_dynamics) if random: return self.command_generation_act_random(observation_strings, task_desc_strings, previous_dynamics) batch_size = len(observation_strings) greedy_actions, greedy_indices, greedy_current_dynamics = self.command_generation_act_greedy(observation_strings, task_desc_strings, previous_dynamics) # random number for epsilon greedy chosen_actions, chosen_indices, current_dynamics = [], [], [] rand_num = np.random.uniform(low=0.0, high=1.0, size=(batch_size,)) for b in range(batch_size): if rand_num[b] < self.epsilon: # random random_actions, random_indices, random_current_dynamics = self.command_generation_act_random(observation_strings[b: b + 1], task_desc_strings[b: b + 1], None if previous_dynamics is None else previous_dynamics[b: b + 1]) chosen_actions.append(random_actions[0]) chosen_indices.append(random_indices[0]) if self.recurrent: current_dynamics.append(random_current_dynamics[0]) else: # greedy chosen_actions.append(greedy_actions[b]) chosen_indices.append(greedy_indices[b]) if self.recurrent: current_dynamics.append(greedy_current_dynamics[b]) current_dynamics = torch.stack(current_dynamics, 0) if self.recurrent else None # batch x hidden return chosen_actions, chosen_indices, current_dynamics # update: admissible commands def get_dqn_loss_admissible_commands(self): """ Update neural model in agent. In this example we follow algorithm of updating model in dqn with replay memory. """ if len(self.dqn_memory) < self.replay_batch_size: return None, None data = self.dqn_memory.get_batch(self.replay_batch_size, multi_step=self.multi_step) if data is None: return None, None obs_list, task_list, candidate_list, action_indices, rewards, next_obs_list, next_candidate_list, actual_ns = data if self.use_cuda: rewards = rewards.cuda() h_obs, obs_mask = self.encode(obs_list, use_model="online") h_td, td_mask = self.encode(task_list, use_model="online") action_scores, _, _ = self.action_scoring(candidate_list, h_obs, obs_mask, h_td, td_mask, None, use_model="online") # ps_a action_indices = to_pt(action_indices, enable_cuda=self.use_cuda, type='long').unsqueeze(-1) q_value = ez_gather_dim_1(action_scores, action_indices).squeeze(1) # batch with torch.no_grad(): if self.noisy_net: self.target_net.reset_noise() # Sample new target net noise # pns Probabilities p(s_t+n, ·; θonline) h_obs, obs_mask = self.encode(next_obs_list, use_model="online") next_action_scores, next_action_masks, _ = self.action_scoring(next_candidate_list, h_obs, obs_mask, h_td.detach(), td_mask.detach(), None, use_model="online") # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] next_action_indices = self.choose_maxQ_action(next_action_scores, next_action_masks) # batch next_action_indices = to_pt(next_action_indices, enable_cuda=self.use_cuda, type='long').unsqueeze(-1) # pns # Probabilities p(s_t+n, ·; θtarget) h_obs, obs_mask = self.encode(next_obs_list, use_model="target") h_td_t, td_mask_t = self.encode(task_list, use_model="target") next_action_scores, _, _ = self.action_scoring(next_candidate_list, h_obs, obs_mask, h_td_t, td_mask_t, None, use_model="target") # pns_a # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) next_q_value = ez_gather_dim_1(next_action_scores, next_action_indices).squeeze(1) # batch discount = to_pt((np.ones_like(actual_ns) * self.discount_gamma_game_reward) ** actual_ns, self.use_cuda, type="float") rewards = rewards + next_q_value * discount # batch loss = F.smooth_l1_loss(q_value, rewards) return loss, q_value def get_drqn_loss_admissible_commands(self): """ Update neural model in agent. In this example we follow algorithm of updating model in dqn with replay memory. """ if len(self.dqn_memory) < self.replay_batch_size: return None, None data, contains_first_step = self.dqn_memory.get_batch_of_sequences(self.replay_batch_size, sample_history_length=self.rl_replay_sample_history_length) if data is None: return None, None seq_obs, task, seq_candidates, seq_chosen_indices, seq_reward, seq_next_obs, seq_next_candidates = data loss_list, q_value_list = [], [] entropy_list = [] prev_dynamics = None h_td, td_mask = self.encode(task, use_model="online") with torch.no_grad(): h_td_t, td_mask_t = self.encode(task, use_model="target") for step_no in range(self.rl_replay_sample_history_length): obs, candidates, chosen_indices, reward, next_obs, next_candidates = seq_obs[step_no], seq_candidates[step_no], seq_chosen_indices[step_no], seq_reward[step_no], seq_next_obs[step_no], seq_next_candidates[step_no] if self.use_cuda: reward = reward.cuda() h_obs, obs_mask = self.encode(obs, use_model="online") action_scores, action_masks, current_dynamics = self.action_scoring(candidates, h_obs, obs_mask, h_td, td_mask, prev_dynamics, use_model="online") # ps_a chosen_indices = to_pt(chosen_indices, enable_cuda=self.use_cuda, type='long').unsqueeze(-1) q_value = ez_gather_dim_1(action_scores, chosen_indices).squeeze(1) # batch prev_dynamics = current_dynamics if (not contains_first_step) and step_no < self.rl_replay_sample_update_from: q_value = q_value.detach() prev_dynamics = prev_dynamics.detach() continue action_probabilities = (action_scores + (action_masks-1)*999999).softmax(dim=-1) + 0.000001 logp_pi = torch.log(action_probabilities) # logp_pi = torch.where(torch.isinf(logp_pi),torch.full_like(logp_pi,-999999),logp_pi) entropy = torch.mean(action_probabilities*logp_pi*action_masks, dim=-1) entropy_list.append(entropy) with torch.no_grad(): if self.noisy_net: self.target_net.reset_noise() # Sample new target net noise # pns Probabilities p(s_t+n, ·; θonline) h_obs, obs_mask = self.encode(next_obs, use_model="online") next_action_scores, next_action_masks, _ = self.action_scoring(next_candidates, h_obs, obs_mask, h_td, td_mask, prev_dynamics, use_model="online") # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] next_action_indices = self.choose_maxQ_action(next_action_scores, next_action_masks) # batch next_action_indices = to_pt(next_action_indices, enable_cuda=self.use_cuda, type='long').unsqueeze(-1) # pns # Probabilities p(s_t+n, ·; θtarget) h_obs, obs_mask = self.encode(next_obs, use_model="target") next_action_scores, _, _ = self.action_scoring(next_candidates, h_obs, obs_mask, h_td_t, td_mask_t, prev_dynamics, use_model="target") # pns_a # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) next_q_value = ez_gather_dim_1(next_action_scores, next_action_indices).squeeze(1) # batch reward = reward + next_q_value * self.discount_gamma_game_reward # batch loss = F.smooth_l1_loss(q_value, reward) # 1 loss_list.append(loss) q_value_list.append(q_value) loss = torch.stack(loss_list).mean() q_value = torch.stack(q_value_list).mean() entropy_loss = torch.stack(entropy_list).mean() return loss-0.5*entropy_loss, q_value def update_dqn_admissible_commands(self): # update neural model by replaying snapshots in replay memory if self.recurrent: dqn_loss, q_value = self.get_drqn_loss_admissible_commands() else: dqn_loss, q_value = self.get_dqn_loss_admissible_commands() if dqn_loss is None: return None, None # param_with_grad = [param for param in self.online_net.parameters() if param.requires_grad] # grad_params = torch.autograd.grad(dqn_loss, param_with_grad, create_graph=True, retain_graph=True, allow_unused=True) # grad_norm = 0 # for grad in grad_params: # if grad is not None: # grad_norm += grad.pow(2).sum() # # grad_norm = grad_norm.sqrt() # loss = dqn_loss - grad_norm # loss = loss.cuda() # Backpropagate self.online_net.zero_grad() self.optimizer.zero_grad() dqn_loss.backward() # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs. torch.nn.utils.clip_grad_norm_(self.online_net.parameters(), self.clip_grad_norm) self.optimizer.step() # apply gradients return to_np(torch.mean(dqn_loss)), to_np(torch.mean(q_value)) # update: beam search choice def get_dqn_loss_beam_search_choice(self): """ Update neural model in agent. In this example we follow algorithm of updating model in dqn with replay memory. """ if len(self.dqn_memory) < self.replay_batch_size: return None, None data = self.dqn_memory.get_batch(self.replay_batch_size, multi_step=self.multi_step) if data is None: return None, None obs_list, task_list, candidate_list, action_indices, rewards, next_obs_list, next_candidate_list, actual_ns = data if self.use_cuda: rewards = rewards.cuda() with torch.no_grad(): h_obs, obs_mask = self.encode(obs_list, use_model="online") h_td, td_mask = self.encode(task_list, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid action_scores, _ = self.beam_search_candidate_scoring(candidate_list, aggregated_obs_representation, obs_mask, None, use_model="online") # ps_a action_indices = to_pt(action_indices, enable_cuda=self.use_cuda, type='long').unsqueeze(-1) q_value = ez_gather_dim_1(action_scores, action_indices).squeeze(1) # batch with torch.no_grad(): if self.noisy_net: self.target_net.reset_noise() # Sample new target net noise # pns Probabilities p(s_t+n, ·; θonline) h_obs, obs_mask = self.encode(next_obs_list, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid next_action_scores, next_action_masks = self.beam_search_candidate_scoring(next_candidate_list, aggregated_obs_representation, obs_mask, None, use_model="online") # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] next_action_indices = self.choose_maxQ_action(next_action_scores, next_action_masks) # batch next_action_indices = to_pt(next_action_indices, enable_cuda=self.use_cuda, type='long').unsqueeze(-1) # pns # Probabilities p(s_t+n, ·; θtarget) h_obs, obs_mask = self.encode(next_obs_list, use_model="target") h_td_t, td_mask_t = self.encode(task_list, use_model="target") aggregated_obs_representation = self.target_net.aggretate_information(h_obs, obs_mask, h_td_t, td_mask_t) # batch x obs_length x hid next_action_scores, _ = self.beam_search_candidate_scoring(next_candidate_list, aggregated_obs_representation, obs_mask, None, use_model="target") # pns_a # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) next_q_value = ez_gather_dim_1(next_action_scores, next_action_indices).squeeze(1) # batch discount = to_pt((np.ones_like(actual_ns) * self.discount_gamma_game_reward) ** actual_ns, self.use_cuda, type="float") rewards = rewards + next_q_value * discount # batch loss = F.smooth_l1_loss(q_value, rewards) return loss, q_value def get_drqn_loss_beam_search_choice(self): """ Update neural model in agent. In this example we follow algorithm of updating model in dqn with replay memory. """ if len(self.dqn_memory) < self.replay_batch_size: return None, None data, contains_first_step = self.dqn_memory.get_batch_of_sequences(self.replay_batch_size, sample_history_length=self.rl_replay_sample_history_length) if data is None: return None, None seq_obs, task, seq_candidates, seq_chosen_indices, seq_reward, seq_next_obs, seq_next_candidates = data loss_list, q_value_list = [], [] prev_dynamics = None with torch.no_grad(): h_td, td_mask = self.encode(task, use_model="online") h_td_t, td_mask_t = self.encode(task, use_model="target") for step_no in range(self.rl_replay_sample_history_length): obs, candidates, chosen_indices, reward, next_obs, next_candidates = seq_obs[step_no], seq_candidates[step_no], seq_chosen_indices[step_no], seq_reward[step_no], seq_next_obs[step_no], seq_next_candidates[step_no] if self.use_cuda: reward = reward.cuda() with torch.no_grad(): h_obs, obs_mask = self.encode(obs, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid averaged_representation = self.online_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid current_dynamics = self.online_net.rnncell(averaged_representation, prev_dynamics) if prev_dynamics is not None else self.online_net.rnncell(averaged_representation) action_scores, _ = self.beam_search_candidate_scoring(candidates, aggregated_obs_representation, obs_mask, current_dynamics, use_model="online") # ps_a chosen_indices = to_pt(chosen_indices, enable_cuda=self.use_cuda, type='long').unsqueeze(-1) q_value = ez_gather_dim_1(action_scores, chosen_indices).squeeze(1) # batch prev_dynamics = current_dynamics if (not contains_first_step) and step_no < self.rl_replay_sample_update_from: q_value = q_value.detach() prev_dynamics = prev_dynamics.detach() continue with torch.no_grad(): if self.noisy_net: self.target_net.reset_noise() # Sample new target net noise # pns Probabilities p(s_t+n, ·; θonline) h_obs, obs_mask = self.encode(next_obs, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid averaged_representation = self.online_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid next_dynamics = self.online_net.rnncell(averaged_representation, current_dynamics) if current_dynamics is not None else self.online_net.rnncell(averaged_representation) next_action_scores, next_action_masks = self.beam_search_candidate_scoring(next_candidates, aggregated_obs_representation, obs_mask, next_dynamics, use_model="online") # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] next_action_indices = self.choose_maxQ_action(next_action_scores, next_action_masks) # batch next_action_indices = to_pt(next_action_indices, enable_cuda=self.use_cuda, type='long').unsqueeze(-1) # pns # Probabilities p(s_t+n, ·; θtarget) h_obs, obs_mask = self.encode(next_obs, use_model="target") aggregated_obs_representation = self.target_net.aggretate_information(h_obs, obs_mask, h_td_t, td_mask_t) # batch x obs_length x hid averaged_representation = self.target_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid next_dynamics = self.target_net.rnncell(averaged_representation, current_dynamics) if current_dynamics is not None else self.target_net.rnncell(averaged_representation) next_action_scores, _ = self.beam_search_candidate_scoring(next_candidates, aggregated_obs_representation, obs_mask, next_dynamics, use_model="target") # pns_a # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget) next_q_value = ez_gather_dim_1(next_action_scores, next_action_indices).squeeze(1) # batch reward = reward + next_q_value * self.discount_gamma_game_reward # batch loss = F.smooth_l1_loss(q_value, reward) # 1 loss_list.append(loss) q_value_list.append(q_value) loss = torch.stack(loss_list).mean() q_value = torch.stack(q_value_list).mean() return loss, q_value def update_dqn_beam_search_choice(self): # update neural model by replaying snapshots in replay memory if self.recurrent: dqn_loss, q_value = self.get_drqn_loss_beam_search_choice() else: dqn_loss, q_value = self.get_dqn_loss_beam_search_choice() if dqn_loss is None: return None, None # Backpropagate self.online_net.zero_grad() self.optimizer.zero_grad() dqn_loss.backward() # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs. torch.nn.utils.clip_grad_norm_(self.online_net.parameters(), self.clip_grad_norm) self.optimizer.step() # apply gradients return to_np(torch.mean(dqn_loss)), to_np(torch.mean(q_value)) # update: command generation def get_dqn_loss_command_generation(self): """ Update neural model in agent. In this example we follow algorithm of updating model in dqn with replay memory. """ if len(self.dqn_memory) < self.replay_batch_size: return None, None data = self.dqn_memory.get_batch(self.replay_batch_size, multi_step=self.multi_step) if data is None: return None, None observation_strings, task_desc_strings, _, action_indices, rewards, next_observation_strings, _, actual_ns = data batch_size = len(observation_strings) if self.use_cuda: rewards = rewards.cuda() input_target = [[self.word2id["[CLS]"]] + item for item in action_indices] ground_truth = [item + [self.word2id["[SEP]"]] for item in action_indices] input_target = self.get_word_input_from_ids(input_target) ground_truth = self.get_word_input_from_ids(ground_truth) input_obs = self.get_word_input(observation_strings) next_input_obs = self.get_word_input(next_observation_strings) h_obs, obs_mask = self.encode(observation_strings, use_model="online") h_td, td_mask = self.encode(task_desc_strings, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid target_mask = compute_mask(input_target) # mask of ground truth should be the same pred = self.online_net.decode(input_target, target_mask, aggregated_obs_representation, obs_mask, None, input_obs) # batch x target_length x vocab q_value = GetGenerationQValue(pred * target_mask.unsqueeze(-1), ground_truth, target_mask) with torch.no_grad(): if self.noisy_net: self.target_net.reset_noise() # Sample new target net noise # pns Probabilities p(s_t+n, ·; θonline) next_h_obs, next_obs_mask = self.encode(next_observation_strings, use_model="online") next_aggregated_obs_representation = self.online_net.aggretate_information(next_h_obs, next_obs_mask, h_td, td_mask) # batch x obs_length x hid # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] # greedy generation input_target_list = [[self.word2id["[CLS]"]] for i in range(batch_size)] eos = np.zeros(batch_size) for _ in range(self.max_target_length): input_target = copy.deepcopy(input_target_list) input_target = pad_sequences(input_target, maxlen=max_len(input_target)).astype('int32') input_target = to_pt(input_target, self.use_cuda) target_mask = compute_mask(input_target) # mask of ground truth should be the same pred = self.online_net.decode(input_target, target_mask, next_aggregated_obs_representation, next_obs_mask, None, next_input_obs) # batch x target_length x vocab # pointer softmax pred = to_np(pred[:, -1]) # batch x vocab pred = np.argmax(pred, -1) # batch for b in range(batch_size): new_stuff = [pred[b]] if eos[b] == 0 else [] input_target_list[b] = input_target_list[b] + new_stuff if pred[b] == self.word2id["[SEP]"]: eos[b] = 1 if np.sum(eos) == batch_size: break chosen_indices = [item[1:] for item in input_target_list] for i in range(len(chosen_indices)): if chosen_indices[i][-1] == self.word2id["[SEP]"]: chosen_indices[i] = chosen_indices[i][:-1] # pns # Probabilities p(s_t+n, ·; θtarget) next_input_target = [[self.word2id["[CLS]"]] + item for item in chosen_indices] next_ground_truth = [item + [self.word2id["[SEP]"]] for item in chosen_indices] next_input_target = self.get_word_input_from_ids(next_input_target) next_ground_truth = self.get_word_input_from_ids(next_ground_truth) next_h_obs, next_obs_mask = self.encode(next_observation_strings, use_model="target") next_h_td, next_td_mask = self.encode(task_desc_strings, use_model="target") next_aggregated_obs_representation = self.target_net.aggretate_information(next_h_obs, next_obs_mask, next_h_td, next_td_mask) # batch x obs_length x hid next_target_mask = compute_mask(next_input_target) # mask of ground truth should be the same next_pred = self.target_net.decode(next_input_target, next_target_mask, next_aggregated_obs_representation, next_obs_mask, None, next_input_obs) # batch x target_length x vocab next_q_value = GetGenerationQValue(next_pred * next_target_mask.unsqueeze(-1), next_ground_truth, next_target_mask) # batch discount = to_pt((np.ones_like(actual_ns) * self.discount_gamma_game_reward) ** actual_ns, self.use_cuda, type="float") rewards = rewards + next_q_value * discount # batch loss = F.smooth_l1_loss(q_value, rewards) return loss, q_value def get_drqn_loss_command_generation(self): if len(self.dqn_memory) < self.replay_batch_size: return None, None data, contains_first_step = self.dqn_memory.get_batch_of_sequences(self.replay_batch_size, sample_history_length=self.rl_replay_sample_history_length) if data is None: return None, None seq_obs, task_desc_strings, _, seq_chosen_indices, seq_reward, seq_next_obs, _ = data batch_size = len(seq_obs[0]) loss_list, q_value_list = [], [] previous_dynamics = None h_td, td_mask = self.encode(task_desc_strings, use_model="online") with torch.no_grad(): h_td_t, td_mask_t = self.encode(task_desc_strings, use_model="target") for step_no in range(self.rl_replay_sample_history_length): observation_strings, action_indices, reward, next_observation_strings = seq_obs[step_no], seq_chosen_indices[step_no], seq_reward[step_no], seq_next_obs[step_no] if self.use_cuda: reward = reward.cuda() input_target = [[self.word2id["[CLS]"]] + item for item in action_indices] ground_truth = [item + [self.word2id["[SEP]"]] for item in action_indices] input_target = self.get_word_input_from_ids(input_target) ground_truth = self.get_word_input_from_ids(ground_truth) input_obs = self.get_word_input(observation_strings) next_input_obs = self.get_word_input(next_observation_strings) h_obs, obs_mask = self.encode(observation_strings, use_model="online") aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid averaged_representation = self.online_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid current_dynamics = self.online_net.rnncell(averaged_representation, previous_dynamics) if previous_dynamics is not None else self.online_net.rnncell(averaged_representation) target_mask = compute_mask(input_target) # mask of ground truth should be the same pred = self.online_net.decode(input_target, target_mask, aggregated_obs_representation, obs_mask, current_dynamics, input_obs) # batch x target_length x vocab q_value = GetGenerationQValue(pred * target_mask.unsqueeze(-1), ground_truth, target_mask) previous_dynamics = current_dynamics if (not contains_first_step) and step_no < self.rl_replay_sample_update_from: q_value = q_value.detach() previous_dynamics = previous_dynamics.detach() continue with torch.no_grad(): if self.noisy_net: self.target_net.reset_noise() # Sample new target net noise # pns Probabilities p(s_t+n, ·; θonline) next_h_obs, next_obs_mask = self.encode(next_observation_strings, use_model="online") next_aggregated_obs_representation = self.online_net.aggretate_information(next_h_obs, next_obs_mask, h_td, td_mask) # batch x obs_length x hid next_averaged_representation = self.online_net.masked_mean(next_aggregated_obs_representation, next_obs_mask) # batch x hid next_dynamics = self.online_net.rnncell(averaged_representation, current_dynamics) if current_dynamics is not None else self.online_net.rnncell(next_averaged_representation) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))] # greedy generation input_target_list = [[self.word2id["[CLS]"]] for i in range(batch_size)] eos = np.zeros(batch_size) for _ in range(self.max_target_length): input_target = copy.deepcopy(input_target_list) input_target = pad_sequences(input_target, maxlen=max_len(input_target)).astype('int32') input_target = to_pt(input_target, self.use_cuda) target_mask = compute_mask(input_target) # mask of ground truth should be the same pred = self.online_net.decode(input_target, target_mask, next_aggregated_obs_representation, next_obs_mask, next_dynamics, next_input_obs) # batch x target_length x vocab # pointer softmax pred = to_np(pred[:, -1]) # batch x vocab pred = np.argmax(pred, -1) # batch for b in range(batch_size): new_stuff = [pred[b]] if eos[b] == 0 else [] input_target_list[b] = input_target_list[b] + new_stuff if pred[b] == self.word2id["[SEP]"]: eos[b] = 1 if np.sum(eos) == batch_size: break chosen_indices = [item[1:] for item in input_target_list] for i in range(len(chosen_indices)): if chosen_indices[i][-1] == self.word2id["[SEP]"]: chosen_indices[i] = chosen_indices[i][:-1] # pns # Probabilities p(s_t+n, ·; θtarget) next_input_target = [[self.word2id["[CLS]"]] + item for item in chosen_indices] next_ground_truth = [item + [self.word2id["[SEP]"]] for item in chosen_indices] next_input_target = self.get_word_input_from_ids(next_input_target) next_ground_truth = self.get_word_input_from_ids(next_ground_truth) next_h_obs, next_obs_mask = self.encode(next_observation_strings, use_model="target") next_aggregated_obs_representation = self.target_net.aggretate_information(next_h_obs, next_obs_mask, h_td_t, td_mask_t) # batch x obs_length x hid next_averaged_representation = self.target_net.masked_mean(next_aggregated_obs_representation, next_obs_mask) # batch x hid next_dynamics = self.target_net.rnncell(averaged_representation, current_dynamics) if current_dynamics is not None else self.target_net.rnncell(next_averaged_representation) next_target_mask = compute_mask(next_input_target) # mask of ground truth should be the same next_pred = self.target_net.decode(next_input_target, next_target_mask, next_aggregated_obs_representation, next_obs_mask, next_dynamics, next_input_obs) # batch x target_length x vocab next_q_value = GetGenerationQValue(next_pred * next_target_mask.unsqueeze(-1), next_ground_truth, next_target_mask) # batch reward = reward + next_q_value * self.discount_gamma_game_reward # batch loss = F.smooth_l1_loss(q_value, reward) # 1 loss_list.append(loss) q_value_list.append(q_value) loss = torch.stack(loss_list).mean() q_value = torch.stack(q_value_list).mean() return loss, q_value def update_dqn_command_generation(self): # update neural model by replaying snapshots in replay memory if self.recurrent: dqn_loss, q_value = self.get_drqn_loss_command_generation() else: dqn_loss, q_value = self.get_dqn_loss_command_generation() if dqn_loss is None: return None, None # Backpropagate self.online_net.zero_grad() self.optimizer.zero_grad() dqn_loss.backward() # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs. torch.nn.utils.clip_grad_norm_(self.online_net.parameters(), self.clip_grad_norm) self.optimizer.step() # apply gradients return to_np(torch.mean(dqn_loss)), to_np(torch.mean(q_value)) def update_dqn(self): if self.action_space == "generation": return self.update_dqn_command_generation() elif self.action_space == "beam_search_choice": return self.update_dqn_beam_search_choice() elif self.action_space in ["admissible", "exhaustive"]: return self.update_dqn_admissible_commands() else: raise NotImplementedError()

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Python

tests/test_command.py

bp-flugsimulator/client

ad2fa25d28f6d05a5ddb8bb3ed1b844a13eddda4

[ "MIT" ]

null

tests/test_command.py

bp-flugsimulator/client

ad2fa25d28f6d05a5ddb8bb3ed1b844a13eddda4

[ "MIT" ]

6

2018-01-22T21:19:29.000Z

2018-03-31T11:45:53.000Z

tests/test_command.py

bp-flugsimulator/client

ad2fa25d28f6d05a5ddb8bb3ed1b844a13eddda4

[ "MIT" ]

null

""" Unit tests for the module client.command. """ #pylint: disable=C0111, C0103 import unittest import asyncio import os import sys import random import string import websockets import shutil from os import remove, getcwd from os.path import join, isfile from uuid import uuid4 from utils import Rpc, Status from .testcases import EventLoopTestCase, FileSystemTestCase import client.command import client.shorthand from client.logger import LOGGER class TestCommands(EventLoopTestCase): def test_execution_nonexisting_directory(self): path = os.path.join(os.getcwd(), 'appplications', 'tee.py') if os.name == 'nt': return_value = '1' else: return_value = '127' self.assertEqual( return_value, self.loop.run_until_complete( client.command.execute( random.choice(string.digits), uuid4().hex, path, [])), ) def test_execution_wrong_path_object(self): self.assertRaises( ValueError, self.loop.run_until_complete, client.command.execute( random.choice(string.digits), uuid4().hex, "calcs.exe", "this is a arguments list"), ) def test_execution_wrong_prog_object(self): self.assertRaises( ValueError, self.loop.run_until_complete, client.command.execute( random.choice(string.digits), uuid4().hex, ["calcs.exe"], []), ) def test_execution_wrong_arguments_elements(self): self.assertRaises( ValueError, self.loop.run_until_complete, client.command.execute( random.choice(string.digits), uuid4().hex, "calcs.exe", [1, 2, 34]), ) def test_execution_echo_shell(self): if os.name == 'nt': prog = "C:\\Windows\\System32\\cmd.exe" args = ["/c", "ECHO %date%"] else: prog = "/bin/sh" args = ["-c", "echo $(date)"] self.assertEqual( '0', self.loop.run_until_complete( client.command.execute( random.choice(string.digits), uuid4().hex, prog, args)), ) def test_online(self): result = self.loop.run_until_complete(client.command.online()) self.assertIsNone(result) def test_execution_directory(self): path = join(getcwd(), 'applications') if os.name == 'nt': prog = join(path, 'folder with spaces', 'echo with spaces.bat') else: prog = join(path, 'folder with spaces', 'echo with spaces.sh') self.assertEqual('0', self.loop.run_until_complete( client.command.execute( random.choice(string.digits), uuid4().hex, prog, []))) self.assertTrue(isfile(join(path, 'folder with spaces', 'test.txt'))) remove(join(path, 'folder with spaces', 'test.txt')) def test_cancel_execution_with_terminate(self): if os.name is 'nt': prog = "C:\\Windows\\System32\\cmd.exe" args = ["/c", "notepad.exe"] return_code = '15' else: prog = "/bin/bash" args = ['-c', '"sleep 100"'] return_code = '143' # TODO why not -15 ??? @asyncio.coroutine def create_and_cancel_task(): task = self.loop.create_task( client.command.execute( random.choice(string.digits), uuid4().hex, prog, args)) yield from asyncio.sleep(0.5) task.cancel() print("canceled task") result = yield from task return result res = self.loop.run_until_complete(create_and_cancel_task()) self.assertEqual(return_code, res) def test_cancel_execution_with_kill(self): prog = sys.executable args = [join(getcwd(), 'applications', 'kill_me.py')] if os.name is 'nt': return_code = '15' else: return_code = '137' # TODO why not -9 ??? @asyncio.coroutine def create_and_cancel_task(): task = self.loop.create_task( client.command.execute( random.choice(string.digits), uuid4().hex, prog, args)) yield from asyncio.sleep(0.5) task.cancel() print("canceled task") result = yield from task return result res = self.loop.run_until_complete(create_and_cancel_task()) self.assertEqual(return_code, res) def test_get_log(self): uuid = uuid4().hex message = ''.join([ random.choice(string.ascii_letters + string.digits) for n in range(32) ]) self.assertEqual('0', self.loop.run_until_complete( client.command.execute( random.choice(string.digits), uuid, 'echo', [message]))) res = self.loop.run_until_complete(client.command.get_log(uuid)) if os.name == 'nt': self.assertIn( 'echo ' + message + ' \r\n ' + message + '\r\n', res['log'], ) else: self.assertIn( message + '\n', res['log'], ) self.assertEqual( uuid, res['uuid'], ) def test_get_log_unknown_uuid(self): self.assertRaises(KeyError, self.loop.run_until_complete, client.command.get_log('abcdefg')) def test_websocket_logging(self): if os.name is 'nt': prog = 'cmd' def sleep_hack(seconds): return 'ping 8.8.8.8 -n ' + seconds + ' >nul' args = [ '/c', sleep_hack('3') + '& echo 0&' + sleep_hack('1') + ' & echo 1' ] expected_log = b'0\r\n1\r\n' else: prog = '/bin/bash' args = ['-c', '"sleep 3; echo 0; sleep 1; echo 1"'] expected_log = b'0\n1\n' uuid = uuid4().hex @asyncio.coroutine def enable_logging(): yield from asyncio.sleep(1) yield from client.command.enable_logging(uuid) @asyncio.coroutine def start_execution(): yield from client.command.execute( random.choice(string.digits), uuid, prog, args) @asyncio.coroutine def start_server(): finished = asyncio.Future() @asyncio.coroutine def websocket_handler(websocket, path): self.assertEqual('/logs', path) # receive log from file json = yield from websocket.recv() log = Status.from_json(json).payload['log'].encode() # ack yield from websocket.send('') #receive dynamic log while True: json = yield from websocket.recv() # ack msg = Status.from_json(json).payload['log'].encode() log += msg if msg == b'': break else: yield from websocket.send('') self.assertIn(expected_log, log) print('finished server') finished.set_result(None) server_handle = yield from websockets.serve( websocket_handler, host='127.0.0.1', port=8750) yield from finished server_handle.close() yield from server_handle.wait_closed() @asyncio.coroutine def wait_for_all(): tasks = { start_server(), start_execution(), enable_logging(), } yield from asyncio.wait(tasks, return_when=asyncio.ALL_COMPLETED) yield from client.command.disable_logging(uuid) LOGGER.url = 'ws://localhost:8750/logs' self.loop.run_until_complete(wait_for_all()) def test_websocket_logging_early_disable(self): if os.name is 'nt': prog = 'cmd' def sleep_hack(seconds): return 'ping 8.8.8.8 -n ' + seconds + ' >nul' args = [ '/c', sleep_hack('3') + '& echo 0&' + sleep_hack('3') + ' & echo 1' ] expected_log = b'0\r\n' else: prog = '/bin/bash' args = ['-c', '"sleep 3; echo 0; sleep 3; echo 1"'] expected_log = b'0\n' uuid = uuid4().hex @asyncio.coroutine def enable_logging(): yield from asyncio.sleep(1) yield from client.command.enable_logging(uuid) @asyncio.coroutine def disable_logging(): yield from asyncio.sleep(4) yield from client.command.disable_logging(uuid) @asyncio.coroutine def start_execution(): yield from client.command.execute( random.choice(string.digits), uuid, prog, args) @asyncio.coroutine def start_server(): finished = asyncio.Future() @asyncio.coroutine def websocket_handler(websocket, path): self.assertEqual('/logs', path) # receive log from file json = yield from websocket.recv() log = Status.from_json(json).payload['log'].encode() # ack yield from websocket.send('') #receive dynamic log while True: try: json = yield from websocket.recv() # ack yield from websocket.send('') msg = Status.from_json(json).payload['log'].encode() if msg == b'': break log += msg except websockets.exceptions.ConnectionClosed: break self.assertIn(expected_log, log) print('finished server') finished.set_result(None) server_handle = yield from websockets.serve( websocket_handler, host='127.0.0.1', port=8750) yield from finished server_handle.close() yield from server_handle.wait_closed() @asyncio.coroutine def wait_for_all(): tasks = { start_server(), start_execution(), enable_logging(), disable_logging(), } yield from asyncio.wait(tasks, return_when=asyncio.ALL_COMPLETED) LOGGER.url = 'ws://localhost:8750/logs' self.loop.run_until_complete(wait_for_all()) def test_chain_command_none(self): result = self.loop.run_until_complete( client.command.chain_execution(commands=[{ 'method': None, 'uuid': None, 'arguments': [], }])) self.assertEqual(result, []) def test_chain_command_success(self): if os.name == 'nt': prog = "C:\\Windows\\System32\\cmd.exe" args = ["/c", "ECHO %date%"] else: prog = "/bin/sh" args = ["-c", "echo $(date)"] result = self.loop.run_until_complete( client.command.chain_execution(commands=[{ 'method': 'execute', 'uuid': 'thisisunique', 'arguments': { 'pid': random.choice(string.digits), 'own_uuid': uuid4().hex, 'path': prog, 'arguments': args }, }])) response = Status( Status.ID_OK, { 'method': 'execute', 'result': '0', }, 'thisisunique', ) self.assertEqual(Status(**result[0]), response) def test_chain_command_one_failed(self): if os.name == 'nt': prog = "C:\\Windows\\System32\\cmd.exe" args = ["/c", "ECHO %date%"] else: prog = "/bin/sh" args = ["-c", "echo $(date)"] result = self.loop.run_until_complete( client.command.chain_execution(commands=[{ 'method': 'execute', 'uuid': 0, 'arguments': { 'pid': 1, 'own_uuid': 0, 'path': prog, }, }, { 'method': 'execute', 'uuid': 1, 'arguments': { 'pid': 1, 'own_uuid': 1, 'path': prog, 'arguments': args }, }])) response1 = Status( Status.ID_ERR, { 'method': 'execute', 'result': "execute() missing 1 required positional argument: 'arguments'", }, 'uniqueidforfirst', ) response2 = Status( Status.ID_ERR, { 'method': 'execute', 'result': 'Could not execute because earlier command was not successful.', }, 'uniqueidforsecond', ) self.assertEqual(Status(**result[0]), response1) self.assertEqual(Status(**result[1]), response2) class FileCommandFilesTests(FileSystemTestCase): @classmethod def setUpClass(cls): super().setUpClass() cls.backup_ending = "_BACK" def test_filesystem_move_destination_exists(self): (source, _, _) = self.provideFile("test.abc") (destination, _, _) = self.provideFile("test.abc.link") backup = destination + self.backup_ending self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) self.loop.run_until_complete( client.command.filesystem_move( source, "file", destination, "file", self.backup_ending, )) self.assertFilesArePresent(destination, backup, source) def test_filesystem_move_destination_not_exists(self): (source, _, _) = self.provideFile("test.abc") destination = self.joinPath("test.abc.link") backup = destination + self.backup_ending self.assertFilesArePresent(source) self.assertFilesAreNotPresent(backup, destination) self.loop.run_until_complete( client.command.filesystem_move( source, "file", destination, "file", self.backup_ending, )) self.assertFilesArePresent(destination, source) self.assertFilesAreNotPresent(backup) def test_filesystem_move_source_not_exists(self): source = self.joinPath("test.abc") (destination, _, _) = self.provideFile("test.abc.link") self.assertFilesArePresent(destination) self.assertFilesAreNotPresent(source) self.assertRaises( FileNotFoundError, self.loop.run_until_complete, client.command.filesystem_move( source, "file", destination, "file", self.backup_ending, ), ) self.assertFilesArePresent(destination) self.assertFilesAreNotPresent(source) def test_filesystem_move_backup_exists(self): (source, _, _) = self.provideFile("test.abc") (destination, _, _) = self.provideFile("test.abc.link") (backup, _, _) = self.provideFile("test.abc.link" + self.backup_ending) self.assertFilesArePresent(source, destination, backup) self.assertRaises( FileExistsError, self.loop.run_until_complete, client.command.filesystem_move( source, "file", destination, "file", self.backup_ending, ), ) self.assertFilesArePresent(source, destination, backup) def test_filesystem_move_destination_folder_success(self): (source, _, _) = self.provideFile("test.abc") destination_path = self.provideDirectory("this_is_my_folder") destination = self.joinPath("this_is_my_folder/test.abc") backup = destination + self.backup_ending self.assertFilesArePresent(source) self.assertFilesAreNotPresent(backup, destination) self.assertDirsArePresent(destination_path) self.loop.run_until_complete( client.command.filesystem_move( source, "file", destination_path, "dir", self.backup_ending, )) self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) self.assertDirsArePresent(destination_path) def test_filesystem_move_destination_folder_destination_exist(self): (source, _, _) = self.provideFile("test.abc") destination_path = self.provideDirectory("this_is_my_folder") (destination, _, _) = self.provideFile("this_is_my_folder/test.abc") backup = destination + self.backup_ending self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) self.assertDirsArePresent(destination_path) self.loop.run_until_complete( client.command.filesystem_move( source, "file", destination_path, "dir", self.backup_ending, )) self.assertFilesArePresent(source, destination, backup) self.assertDirsArePresent(destination_path) def test_filesystem_move_destination_folder_backup_exist(self): (source, _, _) = self.provideFile("test.abc") destination_path = self.provideDirectory("this_is_my_folder") (destination, _, _) = self.provideFile("this_is_my_folder/test.abc") (backup, _, _) = self.provideFile( "this_is_my_folder/test.abc" + self.backup_ending) self.assertFilesArePresent(source, destination, backup) self.assertDirsArePresent(destination_path) self.assertRaises( FileExistsError, self.loop.run_until_complete, client.command.filesystem_move( source, "file", destination_path, "dir", self.backup_ending, ), ) self.assertFilesArePresent(source, destination, backup) self.assertDirsArePresent(destination_path) def test_filesystem_restore_no_backup(self): (source, _, hash_source) = self.provideFile("test.abc") destination = self.joinPath("test.abc.link") backup = destination + self.backup_ending self.assertFilesArePresent(source) self.assertFilesAreNotPresent(destination, backup) self.loop.run_until_complete( client.command.filesystem_move( source, "file", destination, "file", self.backup_ending, )) self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) self.loop.run_until_complete( client.command.filesystem_restore( source, "file", destination, "file", self.backup_ending, hash_source, )) self.assertFilesArePresent(source) self.assertFilesAreNotPresent(destination, backup) def test_filesystem_restore_no_backup_destination_dir(self): (source, _, hash_source) = self.provideFile("test.abc") destination_path = self.provideDirectory("this_is_my_folder") (destination, _, _) = self.provideFile("this_is_my_folder/test.abc") backup = self.joinPath( "this_is_my_folder/test.abc" + self.backup_ending) self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) self.assertDirsArePresent(destination_path) self.loop.run_until_complete( client.command.filesystem_move( source, "file", destination_path, "dir", self.backup_ending, )) self.assertFilesArePresent(source, destination, backup) self.assertDirsArePresent(destination_path) self.loop.run_until_complete( client.command.filesystem_restore( source, "file", destination_path, "dir", self.backup_ending, hash_source, )) self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) self.assertDirsArePresent(destination_path) def test_filesystem_restore_with_backup(self): (source, data_source, hash_source) = self.provideFile("test.abc") destination_path = self.provideDirectory("this_is_my_folder") ( destination, data_destination, _, ) = self.provideFile("this_is_my_folder/test.abc") backup = self.joinPath( "this_is_my_folder/test.abc" + self.backup_ending) self.assertFilesArePresent(source, destination) self.assertDirsArePresent(destination_path) self.loop.run_until_complete( client.command.filesystem_move( source, "file", destination_path, "dir", self.backup_ending, )) self.assertFilesArePresent(source, destination, backup) with open(destination, 'r') as clone: self.assertEqual(clone.read(), data_source) self.assertDirsArePresent(destination_path) self.loop.run_until_complete( client.command.filesystem_restore( source, "file", destination_path, "dir", self.backup_ending, hash_source, )) self.assertFilesArePresent(source, destination) with open(destination, 'r') as clone: self.assertEqual(clone.read(), data_destination) self.assertDirsArePresent(destination_path) def test_filesystem_restore_no_destination_with_backup(self): (source, _, hash_source) = self.provideFile("test.abc") destination = self.joinPath("test.abc.link") ( backup, data_backup, _, ) = self.provideFile("test.abc.link" + self.backup_ending) self.assertFilesArePresent(source, backup) self.assertFilesAreNotPresent(destination) self.loop.run_until_complete( client.command.filesystem_restore( source, "file", destination, "file", self.backup_ending, hash_source, )) self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) with open(destination, 'r') as clone: self.assertEqual(clone.read(), data_backup) def test_filesystem_restore_no_destination(self): (source, _, hash_source) = self.provideFile("test.abc") destination = self.joinPath("test.abc.link") backup = destination + self.backup_ending self.assertFilesArePresent(source) self.assertFilesAreNotPresent(destination, backup) self.loop.run_until_complete( client.command.filesystem_restore( source, "file", destination, "file", self.backup_ending, hash_source, )) self.assertFilesArePresent(source) self.assertFilesAreNotPresent(destination, backup) """ def test_filesystem_restore_replaced(self): (source, _, hash_source) = self.provideFile("test.abc") (destination, _, _) = self.provideFile("test.abc.link") backup = destination + self.backup_ending self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) self.assertRaisesRegex( ValueError, "file .* was changed while it was replaced", self.loop.run_until_complete, client.command.filesystem_restore( source, "file", destination, "file", self.backup_ending, hash_source, ), ) self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) """ def test_filesystem_restore_modified(self): (source, _, hash_source) = self.provideFile("test.abc") destination = self.joinPath("test.abc.link") backup = destination + self.backup_ending self.assertFilesArePresent(source) self.assertFilesAreNotPresent(backup, destination) self.loop.run_until_complete( client.command.filesystem_move( source, "file", destination, "file", self.backup_ending, )) self.assertFilesArePresent(source, destination) self.assertFilesAreNotPresent(backup) with open(destination, 'w+') as clone: clone.write("test") self.loop.run_until_complete( client.command.filesystem_restore( source, "file", destination, "file", self.backup_ending, hash_source, )) self.assertFilesArePresent(source) self.assertFilesAreNotPresent(backup, destination) class FileCommandDirsTests(FileSystemTestCase): @classmethod def setUpClass(cls): super().setUpClass() cls.backup_ending = "_BACK" def test_filesystem_move_destination_exists(self): (source, _, _) = self.provideFilledDirectory("test.abc") (destination, _, _) = self.provideFilledDirectory("test.abc.link") backup = destination + self.backup_ending self.assertDirsArePresent(source, destination) self.assertDirsAreNotPresent(backup) self.loop.run_until_complete( client.command.filesystem_move( source, "dir", destination, "file", self.backup_ending, )) self.assertDirsArePresent(destination, backup, source) def test_filesystem_move_destination_not_exists(self): (source, _, _) = self.provideFilledDirectory("test.abc") destination = self.joinPath("test.abc.link") backup = destination + self.backup_ending self.assertDirsArePresent(source) self.assertDirsAreNotPresent(backup, destination) self.loop.run_until_complete( client.command.filesystem_move( source, "dir", destination, "file", self.backup_ending, )) self.assertDirsArePresent(destination, source) self.assertDirsAreNotPresent(backup) def test_filesystem_move_source_not_exists(self): source = self.joinPath("test.abc") (destination, _, _) = self.provideFilledDirectory("test.abc.link") self.assertDirsArePresent(destination) self.assertDirsAreNotPresent(source) self.assertRaises( FileNotFoundError, self.loop.run_until_complete, client.command.filesystem_move( source, "dir", destination, "file", self.backup_ending, ), ) self.assertDirsArePresent(destination) self.assertDirsAreNotPresent(source) def test_filesystem_move_backup_exists(self): (source, _, _) = self.provideFilledDirectory("test.abc") (destination, _, _) = self.provideFilledDirectory("test.abc.link") (backup, _, _) = self.provideFilledDirectory("test.abc.link" + self.backup_ending) self.assertDirsArePresent(source, destination, backup) self.assertRaises( FileExistsError, self.loop.run_until_complete, client.command.filesystem_move( source, "dir", destination, "file", self.backup_ending, ), ) self.assertDirsArePresent(source, destination, backup) def test_filesystem_move_destination_folder_success(self): (source, _, _) = self.provideFilledDirectory("test.abc") destination_path = self.provideDirectory("this_is_my_folder") destination = self.joinPath("this_is_my_folder/test.abc") backup = destination + self.backup_ending self.assertDirsAreNotPresent(backup, destination) self.assertDirsArePresent(destination_path, source) self.loop.run_until_complete( client.command.filesystem_move( source, "dir", destination_path, "dir", self.backup_ending, )) self.assertDirsAreNotPresent(backup) self.assertDirsArePresent(destination_path, source, destination) def test_filesystem_move_destination_folder_destination_exist(self): (source, _, _) = self.provideFilledDirectory("test.abc") destination_path = self.provideDirectory("this_is_my_folder") (destination, _, _) = self.provideFilledDirectory("this_is_my_folder/test.abc") backup = destination + self.backup_ending self.assertDirsAreNotPresent(backup) self.assertDirsArePresent(destination_path, source, destination) self.loop.run_until_complete( client.command.filesystem_move( source, "dir", destination_path, "dir", self.backup_ending, )) self.assertDirsArePresent(source, destination, backup, destination_path) def test_filesystem_move_destination_folder_backup_exist(self): (source, _, _) = self.provideFilledDirectory("test.abc") destination_path = self.provideDirectory("this_is_my_folder") (destination, _, _) = self.provideFilledDirectory("this_is_my_folder/test.abc") (backup, _, _) = self.provideFilledDirectory( "this_is_my_folder/test.abc" + self.backup_ending) self.assertDirsArePresent(source, destination, backup, destination_path) self.assertRaises( FileExistsError, self.loop.run_until_complete, client.command.filesystem_move( source, "dir", destination_path, "dir", self.backup_ending, ), ) self.assertDirsArePresent(source, destination, backup, destination_path) def test_filesystem_restore_no_backup(self): (source, _, hash_source) = self.provideFilledDirectory("test.abc") destination = self.joinPath("test.abc.link") backup = destination + self.backup_ending self.assertDirsArePresent(source) self.assertDirsAreNotPresent(destination, backup) self.loop.run_until_complete( client.command.filesystem_move( source, "dir", destination, "file", self.backup_ending, )) self.assertDirsArePresent(source, destination) self.assertDirsAreNotPresent(backup) self.loop.run_until_complete( client.command.filesystem_restore( source, "dir", destination, "file", self.backup_ending, hash_source, )) self.assertDirsArePresent(source) self.assertDirsAreNotPresent(destination, backup) def test_filesystem_restore_no_backup_destination_dir(self): (source, _, hash_source) = self.provideFilledDirectory("test.abc") destination_path = self.provideDirectory("this_is_my_folder") (destination, _, _) = self.provideFilledDirectory("this_is_my_folder/test.abc") backup = self.joinPath( "this_is_my_folder/test.abc" + self.backup_ending) self.assertDirsArePresent(source, destination, destination_path) self.assertDirsAreNotPresent(backup) self.loop.run_until_complete( client.command.filesystem_move( source, "dir", destination_path, "dir", self.backup_ending, )) self.assertDirsArePresent(source, destination, backup, destination_path) self.loop.run_until_complete( client.command.filesystem_restore( source, "dir", destination_path, "dir", self.backup_ending, hash_source, )) self.assertDirsArePresent(source, destination, destination_path) self.assertDirsAreNotPresent(backup) def test_filesystem_restore_with_backup(self): (source, _, hash_source) = self.provideFilledDirectory("test.abc") destination_path = self.provideDirectory("this_is_my_folder") ( destination, files_destination, _, ) = self.provideFilledDirectory("this_is_my_folder/test.abc") backup = self.joinPath( "this_is_my_folder/test.abc" + self.backup_ending) self.assertDirsArePresent(source, destination, destination_path) self.loop.run_until_complete( client.command.filesystem_move( source, "dir", destination_path, "dir", self.backup_ending, )) self.assertDirsArePresent(source, destination, backup) self.assertDirsArePresent(destination_path) self.assertDirsEqual(destination, source) self.loop.run_until_complete( client.command.filesystem_restore( source, "dir", destination_path, "dir", self.backup_ending, hash_source, )) self.assertDirsArePresent(source, destination, destination_path) self.assertDirEqual(destination, list(map( lambda f: f[2], files_destination, ))) def test_filesystem_restore_no_destination_with_backup(self): (source, _, hash_source) = self.provideFilledDirectory("test.abc") destination = self.joinPath("test.abc.link") ( backup, files_backup, _, ) = self.provideFilledDirectory("test.abc.link" + self.backup_ending) self.assertDirsArePresent(source, backup) self.assertDirsAreNotPresent(destination) self.loop.run_until_complete( client.command.filesystem_restore( source, "dir", destination, "file", self.backup_ending, hash_source, )) self.assertDirsArePresent(source, destination) self.assertDirsAreNotPresent(backup) self.assertDirEqual(destination, list( map(lambda f: f[2], files_backup))) def test_filesystem_restore_no_destination(self): (source, _, hash_source) = self.provideFilledDirectory("test.abc") destination = self.joinPath("test.abc.link") backup = destination + self.backup_ending self.assertDirsArePresent(source) self.assertDirsAreNotPresent(destination, backup) self.loop.run_until_complete( client.command.filesystem_restore( source, "dir", destination, "file", self.backup_ending, hash_source, )) self.assertDirsArePresent(source) self.assertDirsAreNotPresent(destination, backup) """ def test_filesystem_restore_replaced(self): (source, _, hash_source) = self.provideFilledDirectory("test.abc") (destination, _, _) = self.provideFilledDirectory("test.abc.link") backup = destination + self.backup_ending self.assertDirsArePresent(source, destination) self.assertDirsAreNotPresent(backup) self.assertRaisesRegex( ValueError, "directory .* was changed while it was replaced", self.loop.run_until_complete, client.command.filesystem_restore( source, "dir", destination, "file", self.backup_ending, hash_source, ), ) self.assertDirsArePresent(source, destination) self.assertDirsAreNotPresent(backup) """ def test_filesystem_restore_modified(self): (source, _, hash_source) = self.provideFilledDirectory("test.abc") destination = self.joinPath("test.abc.link") backup = destination + self.backup_ending self.assertDirsArePresent(source) self.assertDirsAreNotPresent(backup, destination) self.loop.run_until_complete( client.command.filesystem_move( source, "dir", destination, "file", self.backup_ending, )) self.assertDirsArePresent(source, destination) self.assertDirsAreNotPresent(backup) # create a new file in source and destination new_file_name = "12345678901234567890123456" (new_file, _, _) = self.provideFile( os.path.join(destination, new_file_name)) shutil.copy2(new_file, self.joinPath(os.path.join(source, new_file_name))) self.assertDirsEqual(source, destination) self.loop.run_until_complete( client.command.filesystem_restore( source, "dir", destination, "file", self.backup_ending, hash_source, )) self.assertDirsArePresent(source) self.assertDirsAreNotPresent(backup, destination) class FileCommandGenericTests(FileSystemTestCase): @classmethod def setUpClass(cls): super().setUpClass() cls.backup_ending = "_BACK" def test_hash_file_not_found(self): self.provideDirectory("test") self.assertRaisesRegex( ValueError, "The given path .* is not a file", client.shorthand.hash_file, self.joinPath("test"), ) def test_hash_dir_not_found(self): self.provideFile("test") self.assertRaisesRegex( ValueError, "The given path .* is not a directory", client.shorthand.hash_directory, self.joinPath("test"), ) def test_filesystem_move_source_not_exists_wrong_type_dir(self): (source, _, _) = self.provideFile("test.abc") destination = self.joinPath("test.abc.link") self.assertFilesArePresent(source) self.assertFilesAreNotPresent(destination) self.assertRaisesRegex( ValueError, "source path .* is not a directory", self.loop.run_until_complete, client.command.filesystem_move( source, "dir", destination, "file", self.backup_ending, ), ) self.assertFilesArePresent(source) self.assertFilesAreNotPresent(destination) def test_filesystem_move_source_not_exists_wrong_type_file(self): source = self.provideDirectory("test.abc") destination = self.joinPath("test.abc.link") self.assertDirsArePresent(source) self.assertFilesAreNotPresent(destination) self.assertRaisesRegex( ValueError, "source path .* is not a file", self.loop.run_until_complete, client.command.filesystem_move( source, "file", destination, "file", self.backup_ending, ), ) self.assertDirsArePresent(source) self.assertFilesAreNotPresent(destination) def test_filesystem_wrong_source_type_object(self): self.assertRaisesRegex( ValueError, "source_type", self.loop.run_until_complete, client.command.filesystem_restore("file.txt", "none", "ende", "file", "string", "hash"), ) def test_filesystem_wrong_destination_type_object(self): self.assertRaisesRegex( ValueError, "destination_type", self.loop.run_until_complete, client.command.filesystem_restore("file.txt", "file", "ende", "none", "string", "hash"), )

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9ad0acaed9bc39aa3a73fbb1c2c0cddfbff94b0a

1,879

py

Python

src/func/solve_matrix.py

wakky927/Computational-Engineering-B

3720d96668a32dc73f38ed0bc8afe4705452de9e

[ "MIT" ]

1

2021-05-03T09:11:35.000Z

src/func/solve_matrix.py

wakky927/Computational-Engineering-B

3720d96668a32dc73f38ed0bc8afe4705452de9e

[ "MIT" ]

null

src/func/solve_matrix.py

wakky927/Computational-Engineering-B

3720d96668a32dc73f38ed0bc8afe4705452de9e

[ "MIT" ]

null

import numpy as np from numba import jit @jit def SOR1(md, p, ap, ae, aw, bb, m, p_exact, relax_factor): eps = 1e-15 # convergence criterion error1, error2, error3 = 0, 0, 0 # initialize parameters p_old = np.zeros(md + 1) ''' SOR algorithm ''' iter_n = 1 while True: error1 = 0 # error reset error2 = 0 error3 = 0 for i in range(1, m + 1): # step increase p_old[i] = p[i] for i in range(1, m + 1): p_diff = -p_old[i] + (bb[i] - ae[i] * p_old[i + 1] - aw[i] * p[i - 1]) / ap[i] p[i] = p_old[i] + p_diff * relax_factor error1 = max(error1, abs(p[i] - p_old[i])) error2 = max(error2, abs(p[i] - p_exact[i])) error3 = max(error3, abs(p_diff)) if error1 < eps: break elif iter_n > 5000: break else: iter_n += 1 return iter_n, error1, error2, error3 @jit def SOR3(md, p, ap, ae, aw, bb, m, p_exact, relax_factor): eps = 1e-15 # convergence criterion error1, error2, error3 = 0, 0, 0 # initialize parameters p_old = np.zeros(md + 1) ''' SOR algorithm ''' iter_n = 1 while True: error1 = 0 # error reset error2 = 0 error3 = 0 for i in range(1, m + 1): # step increase p_old[i] = p[i] for i in range(1, m + 1): p_diff = -p_old[i] + (bb[i] - ae[i] * p_old[i + 1] - aw[i] * p[i - 1]) / ap[i] p[i] = p_old[i] + p_diff * relax_factor error1 = max(error1, abs(p[i] - p_old[i])) error2 = max(error2, abs(p[i] - p_exact[i])) error3 = max(error3, abs(p_diff)) if error3 < eps: break elif iter_n > 5000: break else: iter_n += 1 return iter_n, error1, error2, error3

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9ad3dade1dad31c3a996e1bafc837051bc282c45

9,914

py

Python

django/electric_power_sale/migrations/0009_auto_20220312_0221.py

zcjwin/hasura-django-auth

fd052bb05f051ee7fdaecf9433d5f6d7db580ca9

[ "MIT" ]

null

django/electric_power_sale/migrations/0009_auto_20220312_0221.py

zcjwin/hasura-django-auth

fd052bb05f051ee7fdaecf9433d5f6d7db580ca9

[ "MIT" ]

1

2022-03-21T03:04:31.000Z

django/electric_power_sale/migrations/0009_auto_20220312_0221.py

zcjwin/hasura-django-auth

fd052bb05f051ee7fdaecf9433d5f6d7db580ca9

[ "MIT" ]

null

# Generated by Django 3.2.12 on 2022-03-12 02:21 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import electric_power_sale.models class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('electric_power_sale', '0008_contract_is_active'), ] operations = [ migrations.AddField( model_name='agent', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='agent', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='contract', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='contract', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='contractline', name='created_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='录入时间'), ), migrations.AddField( model_name='contractline', name='created_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='录入人'), ), migrations.AddField( model_name='contractline', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='contractline', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='customer', name='created_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='录入时间'), ), migrations.AddField( model_name='customer', name='created_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='录入人'), ), migrations.AddField( model_name='customer', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='customer', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthadjust', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthadjust', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthagentbill', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthagentbill', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthagentbillline', name='created_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='录入时间'), ), migrations.AddField( model_name='mthagentbillline', name='created_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='录入人'), ), migrations.AddField( model_name='mthagentbillline', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthagentbillline', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthcustomerbill', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthcustomerbill', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthcustomerbillline', name='created_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='录入时间'), ), migrations.AddField( model_name='mthcustomerbillline', name='created_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='录入人'), ), migrations.AddField( model_name='mthcustomerbillline', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthcustomerbillline', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthdiffcustomerbill', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthdiffcustomerbill', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthdiffcustomerbillline', name='created_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='录入时间'), ), migrations.AddField( model_name='mthdiffcustomerbillline', name='created_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='录入人'), ), migrations.AddField( model_name='mthdiffcustomerbillline', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthdiffcustomerbillline', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthdraftcustomerbill', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthdraftcustomerbill', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), migrations.AddField( model_name='mthdraftcustomerbillline', name='created_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='录入时间'), ), migrations.AddField( model_name='mthdraftcustomerbillline', name='created_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='录入人'), ), migrations.AddField( model_name='mthdraftcustomerbillline', name='updated_at', field=models.DateTimeField(default=electric_power_sale.models.default_cur_datetime, verbose_name='更新时间'), ), migrations.AddField( model_name='mthdraftcustomerbillline', name='updated_by', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL, verbose_name='更新人'), ), ]

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0.166748

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0.234618

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null

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0

1

0

null

0

9

b143f41933b36ec80a58296066fc4e91d9ae7f86

7,611

py

Python

examples/detect_Dat.py

datwwe/dronecontrol-MAVSDK

85d68de9b085832801de5acc77a6eb875613290c

[ "BSD-3-Clause" ]

null

examples/detect_Dat.py

datwwe/dronecontrol-MAVSDK

85d68de9b085832801de5acc77a6eb875613290c

[ "BSD-3-Clause" ]

null

examples/detect_Dat.py

datwwe/dronecontrol-MAVSDK

85d68de9b085832801de5acc77a6eb875613290c

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 import asyncio from mavsdk import System import io import time import picamera from PIL import Image from detect_img import detect_img from yolo import YOLO import math import json from mavsdk import (OffboardError, PositionNedYaw) with open('/home/pi/Downloads/MAVSDK-Python/examples/connect.json','r') as f: aa = json.load(f) def image_generator(camera): while(True): stream = io.BytesIO() camera.capture(stream, format='jpeg') stream.seek(0) image = Image.open(stream) yield image def calculate_distance(result, helipad_edge, image_width, image_height): image_center_x = int(image_width/2) iamge_center_y = int(image_height/2) helipad = result['helipad'] if helipad is not None: delta_x = helipad['center_x'] - image_center_x delta_y = -(helipad['center_y'] - iamge_center_y) size = max(helipad['width'], helipad['height']) ratio = helipad_edge / size actual_delta_x = delta_x * ratio actual_delta_y = delta_y * ratio angle =math.degrees(math.atan(delta_y/delta_x)) return { 'actual_delta_x':actual_delta_x, 'actual_delta_y':actual_delta_y, 'angle': angle } return None async def print_flight_mode(yolo, helipad_edge, image_width, image_height ): drone = System() await drone.connect(system_address=aa['address']) print("Waiting for drone to connect...") async for state in drone.core.connection_state(): if state.is_connected: print(f"Drone discovered with UUID: {state.uuid}") break async for flight_mode in drone.telemetry.flight_mode(): print("FlightMode:{}|||||||||".format(flight_mode)) if str(flight_mode) == "HOLD": await asyncio.sleep(10) break print("-- Setting initial setpoint") await drone.offboard.set_position_ned(PositionNedYaw(0.0, 0.0, 0.0, 0.0)) print("-- Starting offboard") try: await drone.offboard.start() except OffboardError as error: print(f"Starting offboard mode failed with error code: {error._result.result}") print("-- Disarming") await drone.action.disarm() return with picamera.PiCamera() as camera: camera.resolution = (640, 480) camera.start_preview() count =0 for image in image_generator(camera): print('image' + str(count) +'.jpg') image.save('./video/test' + str(count)+'.jpg','jpeg') # image = Image.open('./video/outputframe2.jpg') count+=1 result = detect_img(yolo,image) result = calculate_distance(result, helipad_edge, image_width, image_height) if result is not None: print(result) print("-- Go 0m North, 10m East, 0m Down within local coordinate system, turn to face South") await drone.offboard.set_position_ned(PositionNedYaw(result['actual_delta_x'], result['actual_delta_y'], 0.0, 0.0)) await asyncio.sleep(10) print("-- Stopping offboard") try: await drone.offboard.stop() except OffboardError as error: print(f"Stopping offboard mode failed with error code: {error._result.result}") await drone.action.land() await asyncio.sleep(5) break print(result) # if count ==3: # break if __name__ == "__main__": yolo = YOLO() HELIPAD_EDGE = 0.562 IMAGE_WIDTH = 640 IMAGE_HEGIHT = 480 loop = asyncio.get_event_loop() loop.run_until_complete(print_flight_mode(yolo, HELIPAD_EDGE, IMAGE_WIDTH,IMAGE_HEGIHT)) #!/usr/bin/env python3 import asyncio from mavsdk import System import io import time import picamera from PIL import Image from detect_img import detect_img from yolo import YOLO import math import json from mavsdk import (OffboardError, PositionNedYaw) with open('/home/pi/Downloads/MAVSDK-Python/examples/connect.json','r') as f: aa = json.load(f) def image_generator(camera): while(True): stream = io.BytesIO() camera.capture(stream, format='jpeg') stream.seek(0) image = Image.open(stream) yield image def calculate_distance(result, helipad_edge, image_width, image_height): image_center_x = int(image_width/2) iamge_center_y = int(image_height/2) helipad = result['helipad'] if helipad is not None: delta_x = helipad['center_x'] - image_center_x delta_y = -(helipad['center_y'] - iamge_center_y) size = max(helipad['width'], helipad['height']) ratio = helipad_edge / size actual_delta_x = delta_x * ratio actual_delta_y = delta_y * ratio angle =math.degrees(math.atan(delta_y/delta_x)) return { 'actual_delta_x':actual_delta_x, 'actual_delta_y':actual_delta_y, 'angle': angle } return None async def print_flight_mode(yolo, helipad_edge, image_width, image_height ): drone = System() await drone.connect(system_address=aa['address']) print("Waiting for drone to connect...") async for state in drone.core.connection_state(): if state.is_connected: print(f"Drone discovered with UUID: {state.uuid}") break async for flight_mode in drone.telemetry.flight_mode(): print("FlightMode:{}|||||||||".format(flight_mode)) if str(flight_mode) == "HOLD": await asyncio.sleep(10) break print("-- Setting initial setpoint") await drone.offboard.set_position_ned(PositionNedYaw(0.0, 0.0, 0.0, 0.0)) print("-- Starting offboard") try: await drone.offboard.start() except OffboardError as error: print(f"Starting offboard mode failed with error code: {error._result.result}") print("-- Disarming") await drone.action.disarm() return with picamera.PiCamera() as camera: camera.resolution = (640, 480) camera.start_preview() count =0 for image in image_generator(camera): print('image' + str(count) +'.jpg') image.save('./video/test' + str(count)+'.jpg','jpeg') # image = Image.open('./video/outputframe2.jpg') count+=1 result = detect_img(yolo,image) result = calculate_distance(result, helipad_edge, image_width, image_height) if result is not None: print(result) print("-- Go 0m North, 10m East, 0m Down within local coordinate system, turn to face South") await drone.offboard.set_position_ned(PositionNedYaw(result['actual_delta_x'], result['actual_delta_y'], 0.0, 0.0)) await asyncio.sleep(10) print("-- Stopping offboard") try: await drone.offboard.stop() except OffboardError as error: print(f"Stopping offboard mode failed with error code: {error._result.result}") await drone.action.land() await asyncio.sleep(5) break print(result) # if count ==3: # break if __name__ == "__main__": yolo = YOLO() HELIPAD_EDGE = 0.562 IMAGE_WIDTH = 640 IMAGE_HEGIHT = 480 loop = asyncio.get_event_loop() loop.run_until_complete(print_flight_mode(yolo, HELIPAD_EDGE, IMAGE_WIDTH,IMAGE_HEGIHT))

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7

b15718493a2e2753707b6f903fb7161858346b35

20,986

py

Python

zc_common/remote_resource/test_mixins.py

ZeroCater/common

28569037df93fc17336d7fdafe1318401b426331

[ "MIT" ]

2

2016-02-26T19:49:24.000Z

2017-04-04T18:36:43.000Z

zc_common/remote_resource/test_mixins.py

ZeroCater/common

28569037df93fc17336d7fdafe1318401b426331

[ "MIT" ]

68

2016-03-24T00:10:22.000Z

2021-03-19T21:49:22.000Z

zc_common/remote_resource/test_mixins.py

ZeroCater/common

28569037df93fc17336d7fdafe1318401b426331

[ "MIT" ]

5

2016-04-29T19:02:17.000Z

2020-01-29T03:03:31.000Z

import ujson import datetime import dateutil.parser from inflection import camelize from rest_framework import status from rest_framework.reverse import reverse from .tests import USER, STAFF class ResourceCreateTestCase(object): def test_create__valid_data_incorrect_header(self): url = reverse(self.resource_view_name) response = self.client_post_auth(url, user_role=self.USER_ROLE, data=self.json_request, content_type='application/json') self.failure_response_structure_test(response, status.HTTP_415_UNSUPPORTED_MEDIA_TYPE) def test_create__empty_request(self): url = reverse(self.resource_view_name) response = self.client_post_auth(url, user_role=self.USER_ROLE, data={}, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_400_BAD_REQUEST) def test_create__missing_required_parameters(self): if not self.required_params: return True url = reverse(self.resource_view_name) self.create_json_format['data']['attributes'] = {'random_key': 'This is a random value'} missing_parameters_request = ujson.dumps(self.create_json_format) response = self.client_post_auth(url, user_role=self.USER_ROLE, data=missing_parameters_request, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_400_BAD_REQUEST) def test_create__without_relationships(self): if not self.relationship_keys: return True url = reverse(self.resource_view_name) self.create_json_format['data']['relationships'] = {} no_relationships_request = ujson.dumps(self.create_json_format) response = self.client_post_auth(url, user_role=self.USER_ROLE, data=no_relationships_request, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_400_BAD_REQUEST) def test_create__with_malformed_relationship_data(self): if not self.relationship_keys: return True url = reverse(self.resource_view_name) key = camelize(self.relationship_keys[0], False) self.create_json_format['data']['relationships'][key]['data'] = { # No type provided 'id': '1234'} empty_relationships_request = ujson.dumps(self.create_json_format) response = self.client_post_auth(url, user_role=self.USER_ROLE, data=empty_relationships_request, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_400_BAD_REQUEST) def test_create__post_non_json_data_correctly_errors(self): url = reverse(self.resource_view_name) response = self.client_post_auth(url, user_role=self.USER_ROLE, data='invalid_JSON_obj', content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_400_BAD_REQUEST) def test_create__unauthorized(self): url = reverse(self.resource_view_name) response = self.client_post_auth(url, data=self.json_request, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_401_UNAUTHORIZED) class ResourceCreateWithoutPermissionTestCase(object): def test_create__valid_data_incorrect_header(self): url = reverse(self.resource_view_name) response = self.client_post_auth(url, user_role=self.USER_ROLE, data=self.json_request, content_type='application/json') self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_create__empty_request(self): url = reverse(self.resource_view_name) response = self.client_post_auth(url, user_role=self.USER_ROLE, data={}, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_create__missing_required_parameters(self): if not self.required_params: return True url = reverse(self.resource_view_name) self.create_json_format['data']['attributes'] = {'random_key': 'This is a random value'} missing_parameters_request = ujson.dumps(self.create_json_format) response = self.client_post_auth(url, user_role=self.USER_ROLE, data=missing_parameters_request, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_create__without_relationships(self): if not self.relationship_keys: return True url = reverse(self.resource_view_name) self.create_json_format['data']['relationships'] = {} no_relationships_request = ujson.dumps(self.create_json_format) response = self.client_post_auth(url, user_role=self.USER_ROLE, data=no_relationships_request, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_create__with_malformed_relationship_data(self): if not self.relationship_keys: return True url = reverse(self.resource_view_name) key = camelize(self.relationship_keys[0], False) self.create_json_format['data']['relationships'][key]['data'] = { # No type provided 'id': '1234'} empty_relationships_request = ujson.dumps(self.create_json_format) response = self.client_post_auth(url, user_role=self.USER_ROLE, data=empty_relationships_request, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_create__post_non_json_data_correctly_errors(self): url = reverse(self.resource_view_name) response = self.client_post_auth(url, user_role=self.USER_ROLE, data='invalid_JSON_obj', content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_create__unauthorized(self): url = reverse(self.resource_view_name) response = self.client_post_auth(url, data=self.json_request, content_type='application/vnd.api+json') self.failure_response_structure_test(response, status.HTTP_401_UNAUTHORIZED) class ResourceUpdateTestCase(object): def get_patch_response(self, request_data): user_id = getattr(getattr(self.resource, 'user', None), 'id', None) return self.client_patch_auth( reverse(self.resource_view_name, args=(self.resource.id,)), user_role=self.USER_ROLE, user_id=user_id, company_permissions=getattr(self, 'company_permissions', {}), data=ujson.dumps(request_data), content_type='application/vnd.api+json' ) def test_update__resource(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.get_patch_response(update_data) self.success_response_structure_test(response, status.HTTP_200_OK) setattr(self.resource, attribute_name, new_attribute_value) self.validate_instance_in_response(response, self.resource, self.attributes, relationship_keys=self.relationship_keys) db_obj = self.resource_class.objects.get(id=self.resource.id) db_value = getattr(db_obj, attribute_name) if isinstance(db_value, datetime.datetime): new_attribute_value = dateutil.parser.parse(new_attribute_value) elif isinstance(db_value, datetime.date): new_attribute_value = new_attribute_value.isoformat() elif isinstance(db_value, datetime.time): new_attribute_value = new_attribute_value.isoformat() self.assertEqual(db_value, new_attribute_value) def test_update__incorrect_type(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['type'] = 'RandomType' update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.get_patch_response(update_data) self.failure_response_structure_test(response, status.HTTP_409_CONFLICT) def test_update__multiple_fields(self): if len(self.new_attribute_values) < 2: return True attribute_name_1 = self.attributes[0] new_attribute_value_1 = self.new_attribute_values[0] attribute_name_2 = self.attributes[1] new_attribute_value_2 = self.new_attribute_values[1] update_data = self.patch_request_stub update_data['data']['attributes'] = { attribute_name_1: new_attribute_value_1, attribute_name_2: new_attribute_value_2 } response = self.get_patch_response(update_data) setattr(self.resource, attribute_name_1, new_attribute_value_1) setattr(self.resource, attribute_name_2, new_attribute_value_2) self.success_response_structure_test(response, status.HTTP_200_OK) self.validate_instance_in_response(response, self.resource, self.attributes, relationship_keys=self.relationship_keys) db_obj = self.resource_class.objects.get(id=self.resource.id) self.assertEqual(getattr(db_obj, attribute_name_1), new_attribute_value_1) self.assertEqual(getattr(db_obj, attribute_name_2), new_attribute_value_2) def test_update__missing_item_404(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['id'] = 12457 update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.client_patch_auth( reverse(self.resource_view_name, args=(12457,)), user_role=self.USER_ROLE, data=ujson.dumps(update_data), content_type='application/vnd.api+json' ) self.failure_response_structure_test(response, status.HTTP_404_NOT_FOUND) def test_update__unauthorized(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.client_patch_auth( reverse(self.resource_view_name, args=(self.resource.id,)), data=ujson.dumps(update_data), content_type='application/vnd.api+json' ) self.failure_response_structure_test(response, status.HTTP_401_UNAUTHORIZED) class ResourceUpdateLimitedPermissionTestCase(object): def get_patch_response(self, request_data): user_id = getattr(getattr(self.resource, 'user', None), 'id', None) return self.client_patch_auth( reverse(self.resource_view_name, args=(self.resource.id,)), user_role=self.USER_ROLE, user_id=user_id, company_permissions=getattr(self, 'company_permissions', {}), data=ujson.dumps(request_data), content_type='application/vnd.api+json' ) def test_update__resource(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.get_patch_response(update_data) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_update__incorrect_type(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['type'] = 'RandomType' update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.get_patch_response(update_data) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_update__multiple_fields(self): if len(self.new_attribute_values) < 2: return True attribute_name_1 = self.attributes[0] new_attribute_value_1 = self.new_attribute_values[0] attribute_name_2 = self.attributes[1] new_attribute_value_2 = self.new_attribute_values[1] update_data = self.patch_request_stub update_data['data']['attributes'] = { attribute_name_1: new_attribute_value_1, attribute_name_2: new_attribute_value_2 } response = self.get_patch_response(update_data) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_update__missing_item_404(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['id'] = 12457 update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.client_patch_auth( reverse(self.resource_view_name, args=(12457,)), user_role=self.USER_ROLE, data=ujson.dumps(update_data), content_type='application/vnd.api+json' ) self.failure_response_structure_test(response, status.HTTP_404_NOT_FOUND) def test_update__unauthorized(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.client_patch_auth( reverse(self.resource_view_name, args=(self.resource.id,)), data=ujson.dumps(update_data), content_type='application/vnd.api+json' ) self.failure_response_structure_test(response, status.HTTP_401_UNAUTHORIZED) class ResourceUpdateWithoutPermissionTestCase(ResourceUpdateLimitedPermissionTestCase): # Overriding method, when no permission we return a 403 instead of a 404 def test_update__missing_item_404(self): attribute_name = self.attributes[0] new_attribute_value = self.new_attribute_values[0] update_data = self.patch_request_stub update_data['data']['id'] = 12457 update_data['data']['attributes'] = {attribute_name: new_attribute_value} response = self.client_patch_auth( reverse(self.resource_view_name, args=(12457,)), user_role=self.USER_ROLE, data=ujson.dumps(update_data), content_type='application/vnd.api+json' ) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) class ResourceFlaggedDeleteTestCase(object): def test_resource(self): url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth( url, user_role=self.USER_ROLE, company_permissions=getattr(self, 'company_permissions', {})) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) # Verify that the object still exists and that the deleted_at field has been set deleted_resource = self.resource_class.objects.filter(id=self.resource.id) self.assertEqual(deleted_resource.count(), 1) self.assertIsNotNone(deleted_resource[0].deleted_at) def test_delete__404_for_nonexistant_resource(self): url = reverse(self.resource_view_name, args=(9999,)) response = self.client_delete_auth(url, user_role=self.USER_ROLE) self.failure_response_structure_test(response, status.HTTP_404_NOT_FOUND) def test_delete__unauthorized(self): url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth(url) self.failure_response_structure_test(response, status.HTTP_401_UNAUTHORIZED) class ResourceDeleteTestCase(object): def test_delete_resource__by_user(self): if not hasattr(self.resource, 'user'): return True resource = self.resource_class.objects.filter(id=self.resource.id) self.assertEqual(resource.count(), 1) url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth( url, user_role=self.USER_ROLE, user_id=self.resource.user.id, company_permissions=getattr(self, 'company_permissions', {})) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) deleted_resource = self.resource_class.objects.filter(id=self.resource.id) self.assertEqual(deleted_resource.count(), 0) def test_delete_resource__by_staff(self): resource = self.resource_class.objects.filter(id=self.resource.id) self.assertEqual(resource.count(), 1) url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth( url, user_role=STAFF, company_permissions=getattr(self, 'company_permissions', {})) self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT) deleted_resource = self.resource_class.objects.filter(id=self.resource.id) self.assertEqual(deleted_resource.count(), 0) def test_delete__404_for_nonexistant_resource(self): url = reverse(self.resource_view_name, args=(9999,)) response = self.client_delete_auth(url, user_role=STAFF) self.failure_response_structure_test(response, status.HTTP_404_NOT_FOUND) def test_delete__unauthorized(self): url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth(url) self.failure_response_structure_test(response, status.HTTP_401_UNAUTHORIZED) def test_delete__forbidden(self): url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth(url, user_role=USER) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) class ResourceNoDeleteTestCase(object): def test_delete_resource__by_user(self): if not hasattr(self.resource, 'user'): return True resource = self.resource_class.objects.filter(id=self.resource.id) self.assertEqual(resource.count(), 1) url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth(url, user_role=self.USER_ROLE, user_id=self.resource.user.id) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_delete_resource__by_staff(self): url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth(url, user_role=self.USER_ROLE) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_delete__for_nonexistant_resource(self): url = reverse(self.resource_view_name, args=(9999,)) response = self.client_delete_auth(url, user_role=self.USER_ROLE) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) def test_delete__unauthorized(self): url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth(url) self.failure_response_structure_test(response, status.HTTP_401_UNAUTHORIZED) def test_delete__forbidden(self): url = reverse(self.resource_view_name, args=(self.resource.id,)) response = self.client_delete_auth(url, user_role=self.USER_ROLE) self.failure_response_structure_test(response, status.HTTP_403_FORBIDDEN) class ResourceDeleteWithoutPermissionTestCase(ResourceNoDeleteTestCase): pass class ResourceDeleteLimitedTestCase(ResourceNoDeleteTestCase): def test_delete__for_nonexistant_resource(self): url = reverse(self.resource_view_name, args=(9999,)) response = self.client_delete_auth(url, user_role=self.USER_ROLE) self.failure_response_structure_test(response, status.HTTP_404_NOT_FOUND)

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b180d1f8b2ee3a8671147b7174fd61649ac75c2e

31,365

py

Python

skyportal/facility_apis/lco.py

bparazin/skyportal

c160610ca0cc28eef9f36c2d11cc15bd9bcbfe56

[ "BSD-3-Clause" ]

52

2018-11-02T00:53:21.000Z

2022-03-08T16:03:52.000Z

skyportal/facility_apis/lco.py

bparazin/skyportal

c160610ca0cc28eef9f36c2d11cc15bd9bcbfe56

[ "BSD-3-Clause" ]

1,944

2017-04-27T18:51:20.000Z

2022-03-31T20:17:44.000Z

skyportal/facility_apis/lco.py

bparazin/skyportal

c160610ca0cc28eef9f36c2d11cc15bd9bcbfe56

[ "BSD-3-Clause" ]

63

2017-05-13T01:40:47.000Z

2022-03-12T11:32:11.000Z

import json import requests from datetime import datetime, timedelta from . import FollowUpAPI from baselayer.app.env import load_env from ..utils import http env, cfg = load_env() requestpath = f"{cfg['app.lco_protocol']}://{cfg['app.lco_host']}:{cfg['app.lco_port']}/api/requestgroups/" class SINISTRORequest: """A JSON structure for LCO 1m SINISTRO requests.""" def __init__(self, request): """Initialize SINISTRO request. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. """ self.requestgroup = self._build_payload(request) def _build_payload(self, request): """Payload json for LCO 1m SINISTRO queue requests. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. Returns ---------- payload: json payload for requests. """ # Constraints used for scheduling this observation constraints = { 'max_airmass': request.payload["maximum_airmass"], 'min_lunar_distance': 30, } # The target of the observation target = { 'name': request.obj.id, 'type': 'ICRS', 'ra': request.obj.ra, 'dec': request.obj.dec, 'epoch': 2000, } exp_time = request.payload["exposure_time"] exp_count = int(request.payload["exposure_counts"]) configurations = [] for filt in request.payload['observation_choices']: configurations.append( { 'type': 'EXPOSE', 'instrument_type': '1M0-SCICAM-SINISTRO', 'constraints': constraints, 'target': target, 'acquisition_config': {}, 'guiding_config': {}, 'instrument_configs': [ { 'exposure_time': exp_time, 'exposure_count': exp_count, 'optical_elements': {'filter': '%s' % filt}, } ], } ) tstart = request.payload["start_date"] + ' 00:00:00' tend = request.payload["end_date"] + ' 00:00:00' windows = [{'start': tstart, 'end': tend}] # The telescope class that should be used for this observation location = {'telescope_class': '1m0'} altdata = request.allocation.altdata # The full RequestGroup, with additional meta-data requestgroup = { 'name': '%s' % (request.obj.id), # The title 'proposal': altdata["PROPOSAL_ID"], 'ipp_value': request.payload["priority"], 'operator': 'SINGLE', 'observation_type': 'NORMAL', 'requests': [ { 'configurations': configurations, 'windows': windows, 'location': location, } ], } return requestgroup class SPECTRALRequest: """A JSON structure for LCO 2m SPECTRAL requests.""" def __init__(self, request): """Initialize SPECTRAL request. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. """ self.requestgroup = self._build_payload(request) def _build_payload(self, request): """Payload json for LCO 2m SPECTRAL queue requests. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. Returns ---------- payload: json payload for requests. """ if request.obj.dec > 17: raise ValueError('Spectral only available in South.') # Constraints used for scheduling this observation constraints = { 'max_airmass': request.payload["maximum_airmass"], 'min_lunar_distance': request.payload["minimum_lunar_distance"], } # The target of the observation target = { 'name': request.obj.id, 'type': 'ICRS', 'ra': request.obj.ra, 'dec': request.obj.dec, 'epoch': 2000, } exp_time = request.payload["exposure_time"] exp_count = int(request.payload["exposure_counts"]) configurations = [] for filt in request.payload['observation_choices']: configurations.append( { 'type': 'EXPOSE', 'instrument_type': '2M0-SCICAM-SPECTRAL', 'constraints': constraints, 'target': target, 'acquisition_config': {}, 'guiding_config': {}, 'instrument_configs': [ { 'exposure_time': exp_time, 'exposure_count': exp_count, 'optical_elements': {'filter': '%s' % filt}, } ], } ) tstart = request.payload["start_date"] + ' 00:00:00' tend = request.payload["end_date"] + ' 00:00:00' windows = [{'start': tstart, 'end': tend}] # The telescope class that should be used for this observation location = {'telescope_class': '2m0'} altdata = request.allocation.altdata # The full RequestGroup, with additional meta-data requestgroup = { 'name': '%s' % (request.obj.id), # The title 'proposal': altdata["PROPOSAL_ID"], 'ipp_value': request.payload["priority"], 'operator': 'SINGLE', 'observation_type': 'NORMAL', 'requests': [ { 'configurations': configurations, 'windows': windows, 'location': location, } ], } return requestgroup class MUSCATRequest: """An XML structure for LCO 2m MUSCAT requests.""" def __init__(self, request): """Initialize MUSCAT request. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. """ self.requestgroup = self._build_payload(request) def _build_payload(self, request): """Payload json for LCO 2m MUSCAT queue requests. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. Returns ---------- payload: json payload for requests. """ # Constraints used for scheduling this observation constraints = { 'max_airmass': request.payload["maximum_airmass"], 'min_lunar_distance': request.payload["minimum_lunar_distance"], } # The target of the observation target = { 'name': request.obj.id, 'type': 'ICRS', 'ra': request.obj.ra, 'dec': request.obj.dec, 'epoch': 2000, } exp_time = request.payload["exposure_time"] exp_count = int(request.payload["exposure_counts"]) configurations = [ { 'type': 'EXPOSE', 'instrument_type': '2M0-SCICAM-MUSCAT', 'target': target, 'constraints': constraints, 'acquisition_config': {}, 'guiding_config': {}, 'instrument_configs': [ { 'exposure_time': exp_time, 'exposure_count': exp_count, 'optical_elements': { 'diffuser_g_position': 'out', 'diffuser_r_position': 'out', 'diffuser_i_position': 'out', 'diffuser_z_position': 'out', }, 'extra_params': { 'exposure_mode': 'SYNCHRONOUS', 'exposure_time_g': exp_time, 'exposure_time_r': exp_time, 'exposure_time_i': exp_time, 'exposure_time_z': exp_time, }, } ], } ] tstart = request.payload["start_date"] + ' 00:00:00' tend = request.payload["end_date"] + ' 00:00:00' windows = [{'start': tstart, 'end': tend}] # The telescope class that should be used for this observation location = {'telescope_class': '2m0'} altdata = request.allocation.altdata # The full RequestGroup, with additional meta-data requestgroup = { 'name': '%s' % (request.obj.id), # The title 'proposal': altdata["PROPOSAL_ID"], 'ipp_value': request.payload["priority"], 'operator': 'SINGLE', 'observation_type': 'NORMAL', 'requests': [ { 'configurations': configurations, 'windows': windows, 'location': location, } ], } return requestgroup class FLOYDSRequest: """An XML structure for LCO 2m FLOYDS requests.""" def __init__(self, request): """Initialize FLOYDS request. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. """ self.requestgroup = self._build_payload(request) def _build_payload(self, request): """Payload header for LCO 2m FLOYDS queue requests. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. Returns ---------- payload: json payload for requests. """ # Constraints used for scheduling this observation constraints = { 'max_airmass': request.payload["maximum_airmass"], 'min_lunar_distance': request.payload["minimum_lunar_distance"], } # The target of the observation target = { 'name': request.obj.id, 'type': 'ICRS', 'ra': request.obj.ra, 'dec': request.obj.dec, 'epoch': 2000, } # The telescope class that should be used for this observation location = {'telescope_class': '2m0'} exp_time = request.payload["exposure_time"] exp_count = int(request.payload["exposure_counts"]) configurations = [ { 'type': 'LAMP_FLAT', 'instrument_type': '2M0-FLOYDS-SCICAM', 'constraints': constraints, 'target': target, 'acquisition_config': {}, 'guiding_config': {'mode': 'OFF', 'optional': False}, 'instrument_configs': [ { 'exposure_time': 50, 'exposure_count': 1, 'rotator_mode': 'VFLOAT', 'optical_elements': {'slit': 'slit_1.6as'}, } ], }, { 'type': 'ARC', 'instrument_type': '2M0-FLOYDS-SCICAM', 'constraints': constraints, 'target': target, 'acquisition_config': {}, 'guiding_config': {'mode': 'OFF', 'optional': False}, 'instrument_configs': [ { 'exposure_time': 60, 'exposure_count': 1, 'rotator_mode': 'VFLOAT', 'optical_elements': {'slit': 'slit_1.6as'}, } ], }, { 'type': 'SPECTRUM', 'instrument_type': '2M0-FLOYDS-SCICAM', 'constraints': constraints, 'target': target, 'acquisition_config': {'mode': 'WCS'}, 'guiding_config': {'mode': 'ON', 'optional': False}, 'instrument_configs': [ { 'exposure_time': exp_time, 'exposure_count': exp_count, 'rotator_mode': 'VFLOAT', 'optical_elements': {'slit': 'slit_1.6as'}, } ], }, { 'type': 'ARC', 'instrument_type': '2M0-FLOYDS-SCICAM', 'constraints': constraints, 'target': target, 'acquisition_config': {}, 'guiding_config': {'mode': 'OFF', 'optional': False}, 'instrument_configs': [ { 'exposure_time': 60, 'exposure_count': 1, 'rotator_mode': 'VFLOAT', 'optical_elements': {'slit': 'slit_1.6as'}, } ], }, { 'type': 'LAMP_FLAT', 'instrument_type': '2M0-FLOYDS-SCICAM', 'constraints': constraints, 'target': target, 'acquisition_config': {}, 'guiding_config': {'mode': 'OFF', 'optional': False}, 'instrument_configs': [ { 'exposure_time': 50, 'exposure_count': 1, 'rotator_mode': 'VFLOAT', 'optical_elements': {'slit': 'slit_1.6as'}, } ], }, ] tstart = request.payload["start_date"] + ' 00:00:00' tend = request.payload["end_date"] + ' 00:00:00' windows = [{'start': tstart, 'end': tend}] altdata = request.allocation.altdata # The full RequestGroup, with additional meta-data requestgroup = { 'name': '%s' % (request.obj.id), # The title 'proposal': altdata["PROPOSAL_ID"], 'ipp_value': request.payload["priority"], 'operator': 'SINGLE', 'observation_type': 'NORMAL', 'requests': [ { 'configurations': configurations, 'windows': windows, 'location': location, } ], } return requestgroup class LCOAPI(FollowUpAPI): """An interface to LCO operations.""" @staticmethod def delete(request): """Delete a follow-up request from LCO queue (all instruments). Parameters ---------- request: skyportal.models.FollowupRequest The request to delete from the queue and the SkyPortal database. """ from ..models import DBSession, FollowupRequest, FacilityTransaction req = ( DBSession() .query(FollowupRequest) .filter(FollowupRequest.id == request.id) .one() ) altdata = request.allocation.altdata if not altdata: raise ValueError('Missing allocation information.') content = req.transactions[0].response["content"] content = json.loads(content) uid = content["id"] r = requests.post( f"{requestpath}{uid}/cancel/", headers={"Authorization": f'Token {altdata["API_TOKEN"]}'}, ) r.raise_for_status() request.status = "deleted" transaction = FacilityTransaction( request=http.serialize_requests_request(r.request), response=http.serialize_requests_response(r), followup_request=request, initiator_id=request.last_modified_by_id, ) DBSession().add(transaction) @staticmethod def update(request): """Update a follow-up request from LCO queue (all instruments). Parameters ---------- request: skyportal.models.FollowupRequest The request to update from the queue and the SkyPortal database. """ from ..models import DBSession, FollowupRequest, FacilityTransaction req = ( DBSession() .query(FollowupRequest) .filter(FollowupRequest.id == request.id) .one() ) # this happens for failed submissions # just go ahead and delete if len(req.transactions) == 0: DBSession().query(FollowupRequest).filter( FollowupRequest.id == request.id ).delete() DBSession().commit() return altdata = request.allocation.altdata if not altdata: raise ValueError('Missing allocation information.') content = req.transactions[0].response["content"] content = json.loads(content) uid = content["id"] r = requests.get( f"{requestpath}{uid}/", headers={"Authorization": f'Token {altdata["API_TOKEN"]}'}, ) r.raise_for_status() content = req.transactions[0].response["content"] content = json.loads(content) if content["state"] == "COMPLETED": request.status = "complete" transaction = FacilityTransaction( request=http.serialize_requests_request(r.request), response=http.serialize_requests_response(r), followup_request=request, initiator_id=request.last_modified_by_id, ) DBSession().add(transaction) class SINISTROAPI(LCOAPI): """An interface to LCO SINISTRO operations.""" # subclasses *must* implement the method below @staticmethod def submit(request): """Submit a follow-up request to LCO's SINISTRO. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. """ from ..models import FacilityTransaction, DBSession altdata = request.allocation.altdata if not altdata: raise ValueError('Missing allocation information.') lcoreq = SINISTRORequest(request) requestgroup = lcoreq.requestgroup r = requests.post( requestpath, headers={"Authorization": f'Token {altdata["API_TOKEN"]}'}, json=requestgroup, # Make sure you use json! ) r.raise_for_status() if r.status_code == 201: request.status = 'submitted' else: request.status = f'rejected: {r.content}' transaction = FacilityTransaction( request=http.serialize_requests_request(r.request), response=http.serialize_requests_response(r), followup_request=request, initiator_id=request.last_modified_by_id, ) DBSession().add(transaction) form_json_schema = { "type": "object", "properties": { "observation_choices": { "type": "array", "title": "Desired Observations", "items": {"type": "string", "enum": ["gp", "rp", "ip", "zs", "Y"]}, "uniqueItems": True, "minItems": 1, }, "exposure_time": { "title": "Exposure Time [s]", "type": "number", "default": 300.0, }, "exposure_counts": { "title": "Exposure Counts", "type": "number", "default": 1, }, "start_date": { "type": "string", "format": "date", "default": datetime.utcnow().date().isoformat(), "title": "Start Date (UT)", }, "end_date": { "type": "string", "format": "date", "title": "End Date (UT)", "default": (datetime.utcnow().date() + timedelta(days=7)).isoformat(), }, "maximum_airmass": { "title": "Maximum Airmass (1-3)", "type": "number", "default": 2.0, "minimum": 1, "maximum": 3, }, "minimum_lunar_distance": { "title": "Maximum Seeing [arcsec] (0-180)", "type": "number", "default": 30.0, "minimum": 0, "maximum": 180, }, "priority": { "title": "IPP (0-2)", "type": "number", "default": 1.0, "minimum": 0, "maximum": 2, }, }, "required": [ "start_date", "end_date", "maximum_airmass", "minimum_lunar_distance", "priority", ], } ui_json_schema = {"observation_choices": {"ui:widget": "checkboxes"}} class SPECTRALAPI(LCOAPI): """An interface to LCO SPECTRAL operations.""" # subclasses *must* implement the method below @staticmethod def submit(request): """Submit a follow-up request to LCO's SPECTRAL. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. """ from ..models import FacilityTransaction, DBSession altdata = request.allocation.altdata if not altdata: raise ValueError('Missing allocation information.') lcoreq = SPECTRALRequest(request) requestgroup = lcoreq.requestgroup r = requests.post( requestpath, headers={"Authorization": f'Token {altdata["API_TOKEN"]}'}, json=requestgroup, # Make sure you use json! ) r.raise_for_status() if r.status_code == 201: request.status = 'submitted' else: request.status = f'rejected: {r.content}' transaction = FacilityTransaction( request=http.serialize_requests_request(r.request), response=http.serialize_requests_response(r), followup_request=request, initiator_id=request.last_modified_by_id, ) DBSession().add(transaction) form_json_schema = { "type": "object", "properties": { "observation_choices": { "type": "array", "title": "Desired Observations", "items": {"type": "string", "enum": ["gp", "rp", "ip", "zs", "Y"]}, "uniqueItems": True, "minItems": 1, }, "exposure_time": { "title": "Exposure Time [s]", "type": "number", "default": 300.0, }, "exposure_counts": { "title": "Exposure Counts", "type": "number", "default": 1, }, "start_date": { "type": "string", "format": "date", "default": datetime.utcnow().date().isoformat(), "title": "Start Date (UT)", }, "end_date": { "type": "string", "format": "date", "title": "End Date (UT)", "default": (datetime.utcnow().date() + timedelta(days=7)).isoformat(), }, "maximum_airmass": { "title": "Maximum Airmass (1-3)", "type": "number", "default": 2.0, "minimum": 1, "maximum": 3, }, "minimum_lunar_distance": { "title": "Maximum Seeing [arcsec] (0-180)", "type": "number", "default": 30.0, "minimum": 0, "maximum": 180, }, "priority": { "title": "IPP (0-2)", "type": "number", "default": 1.0, "minimum": 0, "maximum": 2, }, }, "required": [ "start_date", "end_date", "maximum_airmass", "minimum_lunar_distance", "priority", ], } ui_json_schema = {"observation_choices": {"ui:widget": "checkboxes"}} class MUSCATAPI(LCOAPI): """An interface to LCO MUSCAT operations.""" # subclasses *must* implement the method below @staticmethod def submit(request): """Submit a follow-up request to LCO's MUSCAT. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. """ from ..models import FacilityTransaction, DBSession altdata = request.allocation.altdata if not altdata: raise ValueError('Missing allocation information.') lcoreq = MUSCATRequest(request) requestgroup = lcoreq.requestgroup r = requests.post( requestpath, headers={"Authorization": f'Token {altdata["API_TOKEN"]}'}, json=requestgroup, # Make sure you use json! ) r.raise_for_status() if r.status_code == 201: request.status = 'submitted' else: request.status = f'rejected: {r.content}' transaction = FacilityTransaction( request=http.serialize_requests_request(r.request), response=http.serialize_requests_response(r), followup_request=request, initiator_id=request.last_modified_by_id, ) DBSession().add(transaction) form_json_schema = { "type": "object", "properties": { "exposure_time": { "title": "Exposure Time [s]", "type": "number", "default": 300.0, }, "exposure_counts": { "title": "Exposure Counts", "type": "number", "default": 1, }, "start_date": { "type": "string", "format": "date", "default": datetime.utcnow().date().isoformat(), "title": "Start Date (UT)", }, "end_date": { "type": "string", "format": "date", "title": "End Date (UT)", "default": (datetime.utcnow().date() + timedelta(days=7)).isoformat(), }, "maximum_airmass": { "title": "Maximum Airmass (1-3)", "type": "number", "default": 2.0, "minimum": 1, "maximum": 3, }, "minimum_lunar_distance": { "title": "Maximum Seeing [arcsec] (0-180)", "type": "number", "default": 30.0, "minimum": 0, "maximum": 180, }, "priority": { "title": "IPP (0-2)", "type": "number", "default": 1.0, "minimum": 0, "maximum": 2, }, }, "required": [ "start_date", "end_date", "maximum_airmass", "minimum_lunar_distance", "priority", ], } ui_json_schema = {} class FLOYDSAPI(LCOAPI): """An interface to LCO FLOYDS operations.""" # subclasses *must* implement the method below @staticmethod def submit(request): """Submit a follow-up request to LCO's FLOYDS. Parameters ---------- request: skyportal.models.FollowupRequest The request to add to the queue and the SkyPortal database. """ from ..models import FacilityTransaction, DBSession altdata = request.allocation.altdata if not altdata: raise ValueError('Missing allocation information.') lcoreq = FLOYDSRequest(request) requestgroup = lcoreq.requestgroup r = requests.post( requestpath, headers={"Authorization": f'Token {altdata["API_TOKEN"]}'}, json=requestgroup, # Make sure you use json! ) r.raise_for_status() if r.status_code == 201: request.status = 'submitted' else: request.status = f'rejected: {r.content}' transaction = FacilityTransaction( request=http.serialize_requests_request(r.request), response=http.serialize_requests_response(r), followup_request=request, initiator_id=request.last_modified_by_id, ) DBSession().add(transaction) form_json_schema = { "type": "object", "properties": { "exposure_time": { "title": "Exposure Time [s]", "type": "number", "default": 300.0, }, "exposure_counts": { "title": "Exposure Counts", "type": "number", "default": 1, }, "start_date": { "type": "string", "format": "date", "default": datetime.utcnow().date().isoformat(), "title": "Start Date (UT)", }, "end_date": { "type": "string", "format": "date", "title": "End Date (UT)", "default": (datetime.utcnow().date() + timedelta(days=7)).isoformat(), }, "maximum_airmass": { "title": "Maximum Airmass (1-3)", "type": "number", "default": 2.0, "minimum": 1, "maximum": 3, }, "minimum_lunar_distance": { "title": "Maximum Seeing [arcsec] (0-180)", "type": "number", "default": 30.0, "minimum": 0, "maximum": 180, }, "priority": { "title": "IPP (0-2)", "type": "number", "default": 1.0, "minimum": 0, "maximum": 2, }, }, "required": [ "start_date", "end_date", "maximum_airmass", "minimum_lunar_distance", "priority", ], } ui_json_schema = {}

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null

0

1

0

1

0

null

0

7

490a49a5edd6ecbbed644c8bc070e14c25355983

4,569

py

Python

pythonfuzz/main.py

MJ-SEO/py_fuzz

789fbfea21bf644ba4d00554fe4141694b0a190a

[ "Apache-2.0" ]

null

pythonfuzz/main.py

MJ-SEO/py_fuzz

789fbfea21bf644ba4d00554fe4141694b0a190a

[ "Apache-2.0" ]

null

pythonfuzz/main.py

MJ-SEO/py_fuzz

789fbfea21bf644ba4d00554fe4141694b0a190a

[ "Apache-2.0" ]

null

import argparse from re import S from pythonfuzz import fuzzer class PythonFuzz(object): def __init__(self, func): self.function = func def __call__(self, *args, **kwargs): parser = argparse.ArgumentParser(description='Coverage-guided fuzzer for python packages') parser.add_argument('dirs', type=str, nargs='*', help="one or more directories/files to use as seed corpus. the first directory will be used to save the generated test-cases") parser.add_argument('--exact-artifact-path', type=str, help='set exact artifact path for crashes/ooms') parser.add_argument('--regression', type=bool, default=False, help='run the fuzzer through set of files for regression or reproduction') parser.add_argument('--rss-limit-mb', type=int, default=4096, help='Memory usage in MB') parser.add_argument('--max-input-size', type=int, default=4096, help='Max input size in bytes') parser.add_argument('--dict', type=str, help='dictionary file') parser.add_argument('--close-fd-mask', type=int, default=0, help='Indicate output streams to close at startup') parser.add_argument('--runs', type=int, default=-1, help='Number of individual test runs, -1 (the default) to run indefinitely.') parser.add_argument('--timeout', type=int, default=5, help='If input takes longer then this timeout the process is treated as failure case') parser.add_argument('--inf-run', default=False, action='store_true', help='Decide the fuzzing wherter stop or keep runing after it finds a failure') # added parser.add_argument('--sched', type=str, default=None, help='Decide the schduler of fuzzing') # added args = parser.parse_args() f = fuzzer.Fuzzer(self.function, args.dirs, args.exact_artifact_path, args.rss_limit_mb, args.timeout, args.regression, args.max_input_size, args.close_fd_mask, args.runs, args.dict, args.inf_run, args.sched) #, self._fname) f.start() class PythonFuzzFile(object): def __init__(self, func): self.function = func self._fname = "tempfile" def _fuzzfile(self): return self._fname def __call__(self, *args, **kwargs): parser = argparse.ArgumentParser(description='Coverage-guided fuzzer for python packages') parser.add_argument('dirs', type=str, nargs='*', help="one or more directories/files to use as seed corpus. the first directory will be used to save the generated test-cases") parser.add_argument('--exact-artifact-path', type=str, help='set exact artifact path for crashes/ooms') parser.add_argument('--regression', type=bool, default=False, help='run the fuzzer through set of files for regression or reproduction') parser.add_argument('--rss-limit-mb', type=int, default=4096, help='Memory usage in MB') parser.add_argument('--max-input-size', type=int, default=4096, help='Max input size in bytes') parser.add_argument('--dict', type=str, help='dictionary file') parser.add_argument('--close-fd-mask', type=int, default=0, help='Indicate output streams to close at startup') parser.add_argument('--runs', type=int, default=-1, help='Number of individual test runs, -1 (the default) to run indefinitely.') parser.add_argument('--timeout', type=int, default=5, help='If input takes longer then this timeout the process is treated as failure case') parser.add_argument('--inf-run', default=False, action='store_true', help='Decide the fuzzing wherter stop or keep runing after it finds a failure') # added parser.add_argument('--fname', type=str, default=None, help='Specific file name for PythonfuzzFile driver') # added args = parser.parse_args() if args.fname: self._fname = args.fname print("MAIN fname: ", self._fname) f = fuzzer.Fuzzer(self.function, args.dirs, args.exact_artifact_path, args.rss_limit_mb, args.timeout, args.regression, args.max_input_size, args.close_fd_mask, args.runs, args.dict, args.inf_run, self._fname) f.start() ''' @PythonFuzz def PythonfuzzFile(): pass ''' if __name__ == '__main__': PythonFuzz()

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4.832203

0.235593

0.069449

0.131182

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0

0.006946

0.243817

4,569

82

165

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0.031746

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0.009383

0

1

0.079365

false

0

0.047619

0.015873

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0

null

0

1

0

1

0

null

0

7

491c98531bbdc0a047056b339097a2cbf3a02974

1,913

py

Python

aptlyweb/resources/package_search.py

istarion/aptly-web

6e782f7050b61e34d67a76fe1cc171d5db415d36

[ "Apache-2.0" ]

null

aptlyweb/resources/package_search.py

istarion/aptly-web

6e782f7050b61e34d67a76fe1cc171d5db415d36

[ "Apache-2.0" ]

1

2018-04-10T14:02:35.000Z

2018-04-14T17:30:27.000Z

aptlyweb/resources/package_search.py

istarion/aptly-web

6e782f7050b61e34d67a76fe1cc171d5db415d36

[ "Apache-2.0" ]

null

from flask_restful import Resource, reqparse from flask_restful import abort from aptlyweb.resources import pyptly_api from flask_security import login_required class PackageSearch(Resource): @staticmethod @login_required def get(query): result = [] for rep in pyptly_api.get_local_repos: repo_found = pyptly_api.show_repo_packages(rep["Name"], q="Name (~ .*{QUERY}.*)".format(QUERY=query)) if repo_found: result.append({ "container_type": "Repository", "container_name": rep["Name"], "packages": repo_found }) for snap in pyptly_api.get_snapshots(): snap_found = pyptly_api.show_snapshot_packages(snap["Name"], q="Name (~ .*{QUERY}.*)".format(QUERY=query)) if snap_found: result.append({ "container_type": "Snapshot", "container_name": snap["Name"], "packages": snap_found }) return result class PackageAdvancedSearch(Resource): @staticmethod @login_required def get(query): result = [] for rep in pyptly_api.get_local_repos: repo_found = pyptly_api.show_repo_packages(rep["Name"], q=query) if repo_found: result.append({ "container_type": "Repository", "container_name": rep["Name"], "packages": repo_found }) for snap in pyptly_api.get_snapshots(): snap_found = pyptly_api.show_snapshot_packages(snap["Name"], q=query) if snap_found: result.append({ "container_type": "Snapshot", "container_name": snap["Name"], "packages": snap_found }) return result

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118

0.544694

189

1,913

5.253968

0.227513

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0.04431

0.056395

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1,913

54

119

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false

0

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0

null

0

1

0

1

0

null

0

7

4927b145c69cb0354b732eeb78976ba4a56e3751

5,778

py

Python

tests/admm/test_tvl2.py

manvhah/sporco

9237d7fc37e75089a2a65ebfe02b7491410da7d4

[ "BSD-3-Clause" ]

1

2019-07-23T11:27:41.000Z

tests/admm/test_tvl2.py

wxwoods/sporco

7b0eefea8b6c720ab9a4998a7c55237445765738

[ "BSD-3-Clause" ]

null

tests/admm/test_tvl2.py

wxwoods/sporco

7b0eefea8b6c720ab9a4998a7c55237445765738

[ "BSD-3-Clause" ]

null

from __future__ import division from builtins import object from past.utils import old_div import numpy as np from sporco.admm import tvl2 import sporco.metric as sm class TestSet01(object): def setup_method(self, method): np.random.seed(12345) self.D = np.random.randn(16, 15) def test_01(self): lmbda = 3 try: b = tvl2.TVL2Denoise(self.D, lmbda) b.solve() except Exception as e: print(e) assert 0 def test_02(self): lmbda = 3 try: b = tvl2.TVL2Deconv(np.ones((1, 1)), self.D, lmbda) b.solve() except Exception as e: print(e) assert 0 def test_03(self): lmbda = 3 dt = np.float16 opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 20, 'AutoRho': {'Enabled': True}, 'DataType': dt}) b = tvl2.TVL2Denoise(self.D, lmbda, opt=opt) b.solve() assert b.X.dtype == dt assert b.Y.dtype == dt assert b.U.dtype == dt def test_04(self): lmbda = 3 dt = np.float32 opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 20, 'AutoRho': {'Enabled': True}, 'DataType': dt}) b = tvl2.TVL2Denoise(self.D, lmbda, opt=opt) b.solve() assert b.X.dtype == dt assert b.Y.dtype == dt assert b.U.dtype == dt def test_05(self): lmbda = 3 dt = np.float64 opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'MaxMainIter': 20, 'AutoRho': {'Enabled': True}, 'DataType': dt}) b = tvl2.TVL2Denoise(self.D, lmbda, opt=opt) b.solve() assert b.X.dtype == dt assert b.Y.dtype == dt assert b.U.dtype == dt def test_06(self): lmbda = 3 dt = np.float32 opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 20, 'AutoRho': {'Enabled': True}, 'DataType': dt}) b = tvl2.TVL2Deconv(np.ones((1, 1)), self.D, lmbda, opt=opt) b.solve() assert b.X.dtype == dt assert b.Y.dtype == dt assert b.U.dtype == dt def test_07(self): lmbda = 3 dt = np.float64 opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'MaxMainIter': 20, 'AutoRho': {'Enabled': True}, 'DataType': dt}) b = tvl2.TVL2Deconv(np.ones((1, 1)), self.D, lmbda, opt=opt) b.solve() assert b.X.dtype == dt assert b.Y.dtype == dt assert b.U.dtype == dt class TestSet02(object): def setup_method(self, method): np.random.seed(12345) N = 64 self.U = np.ones((N, N)) self.U[:, 0:(old_div(N, 2))] = -1 self.V = 1e-1 * np.random.randn(N, N) self.D = self.U + self.V def test_01(self): lmbda = 1e-1 opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'gEvalY': False, 'MaxMainIter': 300, 'rho': 75*lmbda}) b = tvl2.TVL2Denoise(self.D, lmbda, opt) X = b.solve() assert np.abs(b.itstat[-1].ObjFun - 32.875710674129564) < 1e-3 assert sm.mse(self.U, X) < 1e-3 def test_02(self): lmbda = 1e-1 opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'gEvalY': False, 'MaxMainIter': 250}) b = tvl2.TVL2Deconv(np.ones((1)), self.D, lmbda, opt) X = b.solve() assert np.abs(b.itstat[-1].ObjFun - 45.45958573088) < 1e-3 assert sm.mse(self.U, X) < 1e-3 class TestSet03(object): def setup_method(self, method): np.random.seed(12345) N = 32 self.U = np.ones((N, N, N)) self.U[:, 0:(old_div(N, 2)), :] = -1 self.V = 1e-1 * np.random.randn(N, N, N) self.D = self.U + self.V def test_01(self): lmbda = 1e-1 opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'gEvalY': False, 'MaxMainIter': 250, 'rho': 10*lmbda}) b = tvl2.TVL2Denoise(self.D, lmbda, opt, axes=(0, 1)) X = b.solve() assert np.abs(b.itstat[-1].ObjFun - 363.0802047) < 1e-3 assert sm.mse(self.U, X) < 1e-3 def test_02(self): lmbda = 1e-1 opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'gEvalY': False, 'MaxMainIter': 250}) b = tvl2.TVL2Deconv(np.ones((1)), self.D, lmbda, opt, axes=(0, 1)) X = b.solve() assert np.abs(b.itstat[-1].ObjFun - 564.1586542) < 1e-3 assert sm.mse(self.U, X) < 1e-3 class TestSet04(object): def setup_method(self, method): np.random.seed(12345) N = 32 self.U = np.ones((N, N, N)) self.U[:, 0:(old_div(N, 2)), :] = -1 self.V = 1e-1 * np.random.randn(N, N, N) self.D = self.U + self.V def test_01(self): lmbda = 1e-1 opt = tvl2.TVL2Denoise.Options({'Verbose': False, 'gEvalY': False, 'MaxMainIter': 250, 'rho': 10*lmbda}) b = tvl2.TVL2Denoise(self.D, lmbda, opt, axes=(0, 1, 2)) X = b.solve() assert np.abs(b.itstat[-1].ObjFun - 366.04267554965134) < 1e-3 assert sm.mse(self.U, X) < 1e-3 def test_02(self): lmbda = 1e-1 opt = tvl2.TVL2Deconv.Options({'Verbose': False, 'gEvalY': False, 'MaxMainIter': 250}) b = tvl2.TVL2Deconv(np.ones((1)), self.D, lmbda, opt, axes=(0, 1, 2)) X = b.solve() assert np.abs(b.itstat[-1].ObjFun - 567.72425227) < 1e-3 assert sm.mse(self.U, X) < 1e-3

29.479592

77

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781

5,778

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0.131882

0.02925

0.044735

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0.894357

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0

0.084012

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5,778

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78

29.630769

0.678918

0

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0

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0

0.040541

0

0.182432

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0

null

0

1

0

1

0

null

0

7

494c29b16772dd8e396d491f7bc2ec6f59d4bbad

1,643

py

Python

grr/server/grr_response_server/flows/general/registry_init.py

Onager/grr

646196bbfb332e4cb546b6d0fe1c09b57c675f7d

[ "Apache-2.0" ]

null

grr/server/grr_response_server/flows/general/registry_init.py

Onager/grr

646196bbfb332e4cb546b6d0fe1c09b57c675f7d

[ "Apache-2.0" ]

null

grr/server/grr_response_server/flows/general/registry_init.py

Onager/grr

646196bbfb332e4cb546b6d0fe1c09b57c675f7d

[ "Apache-2.0" ]

null

#!/usr/bin/env python """Load all flows so that they are visible in the registry. """ # pylint: disable=unused-import # These imports populate the Flow registry from grr.server.grr_response_server.flows.general import administrative from grr.server.grr_response_server.flows.general import artifact_fallbacks from grr.server.grr_response_server.flows.general import audit from grr.server.grr_response_server.flows.general import ca_enroller from grr.server.grr_response_server.flows.general import checks from grr.server.grr_response_server.flows.general import collectors from grr.server.grr_response_server.flows.general import discovery from grr.server.grr_response_server.flows.general import export from grr.server.grr_response_server.flows.general import file_finder from grr.server.grr_response_server.flows.general import filesystem from grr.server.grr_response_server.flows.general import filetypes from grr.server.grr_response_server.flows.general import find from grr.server.grr_response_server.flows.general import fingerprint from grr.server.grr_response_server.flows.general import hardware from grr.server.grr_response_server.flows.general import memory from grr.server.grr_response_server.flows.general import network from grr.server.grr_response_server.flows.general import processes from grr.server.grr_response_server.flows.general import registry from grr.server.grr_response_server.flows.general import transfer from grr.server.grr_response_server.flows.general import webhistory from grr.server.grr_response_server.flows.general import windows_vsc from grr.server.grr_response_server.flows.general import yara_flows

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49543776833869fefe4d774fed23db632f5a4f79

21,918

py

Python

hybridbackend/tensorflow/feature_column/dense_features_test.py

fuhailin/HybridBackend

113383c5870b7180fa67c194208a27f76bdbf3f0

[ "Apache-2.0" ]

38

2021-12-01T06:54:36.000Z

2022-03-23T11:23:21.000Z

hybridbackend/tensorflow/feature_column/dense_features_test.py

fuhailin/HybridBackend

113383c5870b7180fa67c194208a27f76bdbf3f0

[ "Apache-2.0" ]

15

2021-12-01T09:15:26.000Z

2022-03-28T02:49:21.000Z

hybridbackend/tensorflow/feature_column/dense_features_test.py

fuhailin/HybridBackend

113383c5870b7180fa67c194208a27f76bdbf3f0

[ "Apache-2.0" ]

8

2021-12-02T01:16:14.000Z

2022-01-28T04:51:16.000Z

# Copyright 2021 Alibaba Group Holding Limited. 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. # ============================================================================= r'''Tests for embedding columns. ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import os import hybridbackend.test as hbtest import unittest # pylint: disable=missing-docstring # pylint: disable=import-outside-toplevel def _test_get_dense_tensor(_): import tensorflow as tf import hybridbackend.tensorflow as hb # Inputs. vocabulary_size = 3 sparse_input = tf.SparseTensorValue( indices=((0, 0), (1, 0), (1, 4), (3, 0)), values=(2, 0, 1, 1), dense_shape=(4, 5)) # Embedding variable. embedding_dimension = 2 embedding_values = ( (1., 2.), # id 0 (3., 5.), # id 1 (7., 11.) # id 2 ) def _initializer(shape, dtype, partition_info): np.testing.assert_equal((vocabulary_size, embedding_dimension), shape) np.testing.assert_equal(tf.float32, dtype) np.testing.assert_equal(partition_info, None) return embedding_values with tf.Graph().as_default(): with hb.scope(): # Build columns. categorical_column = tf.feature_column.categorical_column_with_identity( key='aaa', num_buckets=vocabulary_size) emb_col = tf.feature_column.embedding_column( categorical_column, embedding_dimension, initializer=_initializer, combiner='mean') # Provide sparse input and get dense result. embedding_lookup = hb.keras.layers.DenseFeatures( [emb_col])({'aaa': sparse_input}) with hb.train.monitored_session() as sess: return sess.run(embedding_lookup) def _test_get_dense_tensor_sharded(rank): import tensorflow as tf import hybridbackend.tensorflow as hb # Inputs. vocabulary_size = 3 sparse_input = tf.SparseTensorValue( indices=((0, 0), (1, 0), (1, 4), (3, 0)), values=(2, 0, 1, 1), dense_shape=(4, 5)) # Embedding variable. embedding_dimension = 2 embedding_values_0 = ( (1., 2.), # id 0 (7., 11.), # id 2 ) embedding_values_1 = ( (3., 5.), # id 1 ) def _initializer_0(shape, dtype, partition_info): np.testing.assert_equal((2, embedding_dimension), shape) np.testing.assert_equal(tf.float32, dtype) np.testing.assert_equal(partition_info, None) return embedding_values_0 def _initializer_1(shape, dtype, partition_info): np.testing.assert_equal((1, embedding_dimension), shape) np.testing.assert_equal(tf.float32, dtype) np.testing.assert_equal(partition_info, None) return embedding_values_1 with tf.Graph().as_default(): with hb.scope(): categorical_column = tf.feature_column.categorical_column_with_identity( key='aaa', num_buckets=vocabulary_size) emb_col = tf.feature_column.embedding_column( categorical_column, embedding_dimension, initializer=_initializer_0 if rank == 0 else _initializer_1, combiner='mean') # Provide sparse input and get dense result. embedding_lookup = hb.keras.layers.DenseFeatures( [emb_col])({'aaa': sparse_input}) with hb.train.monitored_session() as sess: return sess.run(embedding_lookup) def _test_get_dense_tensor_with_varscope(rank): import tensorflow as tf import hybridbackend.tensorflow as hb # Inputs. vocabulary_size = 3 sparse_input = tf.SparseTensorValue( indices=((0, 0), (1, 0), (1, 4), (3, 0)), values=(2, 0, 1, 1), dense_shape=(4, 5)) # Embedding variable. embedding_dimension = 2 embedding_values_0 = ( (1., 2.), # id 0 (7., 11.), # id 2 ) embedding_values_1 = ( (3., 5.), # id 1 ) def _initializer_0(shape, dtype, partition_info): np.testing.assert_equal((1, embedding_dimension), shape) np.testing.assert_equal(tf.float32, dtype) return [embedding_values_0[partition_info.var_offset[0]]] def _initializer_1(shape, dtype, partition_info): del partition_info np.testing.assert_equal((1, embedding_dimension), shape) np.testing.assert_equal(tf.float32, dtype) return embedding_values_1 with tf.Graph().as_default(): with hb.scope(): partitioner = tf.min_max_variable_partitioner( max_partitions=2, min_slice_size=4) with tf.variable_scope('test', partitioner=partitioner): categorical_column = tf.feature_column.categorical_column_with_identity( key='aaa', num_buckets=vocabulary_size) emb_col = tf.feature_column.embedding_column( categorical_column, embedding_dimension, initializer=_initializer_0 if rank == 0 else _initializer_1, combiner='mean') # Provide sparse input and get dense result. embedding_lookup = hb.keras.layers.DenseFeatures( [emb_col])({'aaa': sparse_input}) with hb.train.monitored_session() as sess: return sess.run(embedding_lookup) def _test_embedding_column_with_optimizer(_, lr): import tensorflow as tf import hybridbackend.tensorflow as hb with tf.Graph().as_default(): with hb.scope(seed=42): columns = [ tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad1', num_buckets=10, default_value=0), dimension=30, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad2', num_buckets=10, default_value=0), dimension=40, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='user0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), ] features = { 'ad0': tf.constant([0, 1, 3, 2]), 'ad1': tf.constant([1, 5, 3, 4]), 'ad2': tf.constant([5, 2, 7, 4]), 'user0': tf.constant([2, 5, 4, 7]) } out_emb = hb.keras.layers.DenseFeatures( columns, num_groups=None)(features) loss = tf.reduce_mean(out_emb) opt = tf.train.AdamOptimizer(lr) step = tf.train.get_or_create_global_step() train_op = opt.minimize(loss, global_step=step) final_loss = None with hb.train.monitored_session( hooks=[ opt.make_session_run_hook(), tf.train.StopAtStepHook(last_step=100), tf.train.NanTensorHook(loss), tf.train.LoggingTensorHook( tensors={'loss': loss, 'step': step}, every_n_iter=20)]) as sess: while not sess.should_stop(): final_loss = sess.run(loss) sess.run(train_op) return final_loss def _test_get_dense_tensor_disable_concat(_): import tensorflow as tf import hybridbackend.tensorflow as hb with tf.Graph().as_default(): with hb.scope(emb_enable_concat=False): columns = [ tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='user0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), ] features = { 'ad0': tf.sparse.SparseTensor( values=[0, 1, 3, 2], indices=[[0, 0], [0, 1], [1, 0], [1, 1]], dense_shape=[2, 2]), 'user0': tf.constant([2, 5, 4, 7]) } embs = hb.keras.layers.DenseFeatures(columns)(features) with hb.train.monitored_session() as sess: return sess.run(embs) def _test_embedding_column_with_coalescing(_, lr): os.environ['HYBRIDBACKEND_DEFAULT_COMM'] = 'NCCL' import tensorflow as tf import hybridbackend.tensorflow as hb with tf.Graph().as_default(): with hb.scope(seed=42): columns = [ tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad1', num_buckets=10, default_value=0), dimension=30, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad2', num_buckets=10, default_value=0), dimension=40, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='user0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), ] features = { 'ad0': tf.constant([0, 1, 3, 2]), 'ad1': tf.constant([1, 5, 3, 4]), 'ad2': tf.constant([5, 2, 7, 4]), 'user0': tf.constant([2, 5, 4, 7]) } out_emb = hb.keras.layers.DenseFeatures(columns, num_groups=2)(features) loss = tf.reduce_mean(out_emb) opt = tf.train.AdamOptimizer(lr) step = tf.train.get_or_create_global_step() train_op = opt.minimize(loss, global_step=step) final_loss = None with hb.train.monitored_session( hooks=[ opt.make_session_run_hook(), tf.train.StopAtStepHook(last_step=100), tf.train.NanTensorHook(loss), tf.train.LoggingTensorHook( tensors={'loss': loss, 'step': step}, every_n_iter=20)]) as sess: while not sess.should_stop(): final_loss = sess.run(loss) sess.run(train_op) return final_loss def _test_embedding_column_with_function(_, lr): os.environ['HYBRIDBACKEND_DEFAULT_COMM'] = 'NCCL' import tensorflow as tf import hybridbackend.tensorflow as hb @hb.function(seed=42, emb_num_groups=2) def train_fn(): columns = [ tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad1', num_buckets=10, default_value=0), dimension=30, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad2', num_buckets=10, default_value=0), dimension=40, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='user0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), ] features = { 'ad0': tf.constant([0, 1, 3, 2]), 'ad1': tf.constant([1, 5, 3, 4]), 'ad2': tf.constant([5, 2, 7, 4]), 'user0': tf.constant([2, 5, 4, 7]) } out_emb = hb.keras.layers.DenseFeatures(columns)(features) loss = tf.reduce_mean(out_emb) opt = tf.train.AdamOptimizer(lr) step = tf.train.get_or_create_global_step() return loss, opt.minimize(loss, global_step=step) loss, train_op = train_fn() final_loss = None with hb.train.monitored_session( hooks=[ tf.train.StopAtStepHook(last_step=100), tf.train.NanTensorHook(loss), tf.train.LoggingTensorHook( tensors={'loss': loss}, every_n_iter=20)]) as sess: while not sess.should_stop(): final_loss = sess.run(loss) sess.run(train_op) return final_loss def _test_embedding_column_with_function_unique(_, lr): os.environ['HYBRIDBACKEND_DEFAULT_COMM'] = 'NCCL' import tensorflow as tf import hybridbackend.tensorflow as hb @hb.function(seed=42, emb_num_groups=2, emb_unique={'ad0': True}) def train_fn(): columns = [ tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad1', num_buckets=10, default_value=0), dimension=30, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad2', num_buckets=10, default_value=0), dimension=40, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='user0', num_buckets=10, default_value=0), dimension=20, initializer=tf.constant_initializer(0.5)), ] features = { 'ad0': tf.constant([0, 1, 3, 2]), 'ad1': tf.constant([1, 5, 3, 4]), 'ad2': tf.constant([5, 2, 7, 4]), 'user0': tf.constant([2, 5, 4, 7]) } out_emb = hb.keras.layers.DenseFeatures(columns)(features) loss = tf.reduce_mean(out_emb) opt = tf.train.AdamOptimizer(lr) step = tf.train.get_or_create_global_step() return loss, opt.minimize(loss, global_step=step) loss, train_op = train_fn() final_loss = None with hb.train.monitored_session( hooks=[ tf.train.StopAtStepHook(last_step=100), tf.train.NanTensorHook(loss), tf.train.LoggingTensorHook( tensors={'loss': loss}, every_n_iter=20)]) as sess: while not sess.should_stop(): final_loss = sess.run(loss) sess.run(train_op) return final_loss def _test_get_dense_tensor_with_segment_rank(rank): import tensorflow as tf import hybridbackend.tensorflow as hb # Inputs. vocabulary_size = 3 sparse_input = tf.SparseTensorValue( indices=((0, 1, 1), (0, 1, 2), (1, 1, 1), (1, 1, 2)), values=(2, 0, 1, 1), dense_shape=(4, 2, 3)) # Embedding variable. embedding_dimension = 2 embedding_values_0 = ( (1., 2.), # id 0 (7., 11.), # id 2 ) embedding_values_1 = ( (3., 5.), # id 1 ) def _initializer_0(shape, dtype, partition_info): np.testing.assert_equal((2, embedding_dimension), shape) np.testing.assert_equal(tf.float32, dtype) np.testing.assert_equal(partition_info, None) return embedding_values_0 def _initializer_1(shape, dtype, partition_info): np.testing.assert_equal((1, embedding_dimension), shape) np.testing.assert_equal(tf.float32, dtype) np.testing.assert_equal(partition_info, None) return embedding_values_1 @hb.function(emb_segment_rank={'aaa': 1}) def lookup_fn(): categorical_column = tf.feature_column.categorical_column_with_identity( key='aaa', num_buckets=vocabulary_size) emb_col = tf.feature_column.embedding_column( categorical_column, embedding_dimension, initializer=_initializer_0 if rank == 0 else _initializer_1, combiner='mean') return hb.keras.layers.DenseFeatures([emb_col])({'aaa': sparse_input}) with tf.Graph().as_default(): embs = lookup_fn() with hb.train.monitored_session() as sess: return sess.run(embs) def _test_shared_embedding_column(_, lr): import tensorflow as tf import hybridbackend.tensorflow as hb with tf.Graph().as_default(): with hb.scope(seed=42): columns = [ tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad1', num_buckets=10, default_value=0), dimension=30, initializer=tf.constant_initializer(0.5)), tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_identity( key='ad2', num_buckets=10, default_value=0), dimension=40, initializer=tf.constant_initializer(0.5)), ] columns += tf.feature_column.shared_embedding_columns( [ tf.feature_column.categorical_column_with_identity( key='ad0', num_buckets=10, default_value=0), tf.feature_column.categorical_column_with_identity( key='user0', num_buckets=10, default_value=0)], dimension=20, initializer=tf.constant_initializer(0.5)) features = { 'ad0': tf.constant([0, 1, 3, 2]), 'ad1': tf.constant([1, 5, 3, 4]), 'ad2': tf.constant([5, 2, 7, 4]), 'user0': tf.constant([2, 5, 4, 7]) } out_emb = hb.keras.layers.DenseFeatures(columns)(features) loss = tf.reduce_mean(out_emb) opt = tf.train.AdamOptimizer(lr) step = tf.train.get_or_create_global_step() train_op = opt.minimize(loss, global_step=step) final_loss = None with hb.train.monitored_session( hooks=[ opt.make_session_run_hook(), tf.train.StopAtStepHook(last_step=100), tf.train.NanTensorHook(loss), tf.train.LoggingTensorHook( tensors={'loss': loss, 'step': step}, every_n_iter=20)]) as sess: while not sess.should_stop(): final_loss = sess.run(loss) sess.run(train_op) return final_loss @unittest.skipUnless( os.getenv('HYBRIDBACKEND_WITH_CUDA') == 'ON', 'GPU required') class DenseFeaturesTest(unittest.TestCase): '''Tests for embedding columns. ''' def setUp(self): # pylint: disable=invalid-name os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' def test_get_dense_tensor(self): results = hbtest.Spawn()(_test_get_dense_tensor) np.testing.assert_allclose( results[0], [[7., 11.], [2., 3.5], [0., 0.], [3., 5.]], rtol=1e-6) def test_get_dense_tensor_sharded(self): results = hbtest.Spawn(2)(_test_get_dense_tensor_sharded) np.testing.assert_allclose( results[0], [[7., 11.], [2., 3.5], [0., 0.], [3., 5.]], rtol=1e-6) np.testing.assert_allclose( results[1], [[7., 11.], [2., 3.5], [0., 0.], [3., 5.]], rtol=1e-6) def test_get_dense_tensor_with_varscope(self): results = hbtest.Spawn(2)(_test_get_dense_tensor_with_varscope) np.testing.assert_allclose( results[0], [[7., 11.], [2., 3.5], [0., 0.], [3., 5.]], rtol=1e-6) np.testing.assert_allclose( results[1], [[7., 11.], [2., 3.5], [0., 0.], [3., 5.]], rtol=1e-6) def test_embedding_column_with_optimizer(self): results = hbtest.Spawn(2)( lambda rank: _test_embedding_column_with_optimizer(rank, 0.0001)) np.testing.assert_allclose(results[0], 0.490101, rtol=1e-6) np.testing.assert_allclose(results[1], 0.490101, rtol=1e-6) def test_get_dense_tensor_disable_concat(self): results = hbtest.Spawn()(_test_get_dense_tensor_disable_concat) np.testing.assert_equal(len(results[0]), 2) def test_embedding_column_with_coalescing(self): results = hbtest.Spawn(2)( lambda rank: _test_embedding_column_with_coalescing(rank, 0.0001)) np.testing.assert_allclose(results[0], 0.490101, rtol=1e-6) np.testing.assert_allclose(results[1], 0.490101, rtol=1e-6) def test_embedding_column_function(self): results = hbtest.Spawn(2)( lambda rank: _test_embedding_column_with_function(rank, 0.0001)) np.testing.assert_allclose(results[0], 0.490101, rtol=1e-6) np.testing.assert_allclose(results[1], 0.490101, rtol=1e-6) def test_embedding_column_function_unique(self): results = hbtest.Spawn(2)( lambda rank: _test_embedding_column_with_function_unique(rank, 0.0001)) np.testing.assert_allclose(results[0], 0.490101, rtol=1e-6) np.testing.assert_allclose(results[1], 0.490101, rtol=1e-6) def test_get_dense_tensor_with_segment_rank(self): results = hbtest.Spawn(2)(_test_get_dense_tensor_with_segment_rank) np.testing.assert_allclose( results[0], [[0., 0.], [4., 6.5], [0., 0.], [3., 5.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], rtol=1e-6) np.testing.assert_allclose( results[1], [[0., 0.], [4., 6.5], [0., 0.], [3., 5.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]], rtol=1e-6) def test_shared_embedding_column(self): results = hbtest.Spawn(2)( lambda rank: _test_shared_embedding_column(rank, 0.0001)) np.testing.assert_allclose(results[0], 0.490101, rtol=1e-6) np.testing.assert_allclose(results[1], 0.490101, rtol=1e-6) # pylint: enable=missing-docstring if __name__ == '__main__': hbtest.main(f'{__file__}.xml')

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7

49769e87e9073c01a246a5ab14f42dcea1f551d1

7,534

py

Python

model/model.py

Seraphir/gesture_recognition

06ab1a8e7601d52efed02303630abec2e15bad50

[ "MIT" ]

null

model/model.py

Seraphir/gesture_recognition

06ab1a8e7601d52efed02303630abec2e15bad50

[ "MIT" ]

null

model/model.py

Seraphir/gesture_recognition

06ab1a8e7601d52efed02303630abec2e15bad50

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from base import BaseModel from torchvision.models import resnet50 cfgs = { 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], } class C3DVGG(nn.Module): def __init__(self, num_classes=10, batch_norm=True, finetune=None): super().__init__() self.net2d = make_layers(cfgs['D'], batch_norm=batch_norm) self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) self.lc2d = nn.Sequential( nn.Linear(512 * 7 * 7, 2048), nn.ReLU(True) ) self.net3d = nn.Sequential( nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1)), ) self.lc3d = nn.Sequential( nn.Linear(8192, 2048), nn.ReLU(True) ) self.classifier = nn.Sequential( nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), ) self._initialize_weights() if finetune is not None: checkpoint = torch.load(finetune) self.load_state_dict(checkpoint['state_dict']) print("loaded {}".format(finetune)) def forward(self, x1, x2): x1 = self.net2d(x1) x1 = self.avgpool(x1) x1 = torch.flatten(x1, 1) x1 = self.lc2d(x1) x2 = self.net3d(x2) x2 = x2.view(-1, 8192) x2 = self.lc3d(x2) x = torch.cat((x1, x2), dim=1) x = self.classifier(x) # F.log_softmax(x, dim=1) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Conv3d): torch.nn.init.kaiming_normal_(m.weight) elif isinstance(m, nn.BatchNorm3d): m.weight.data.fill_(1) m.bias.data.zero_() class SimpleC3DVGG(nn.Module): def __init__(self, num_classes=10, batch_norm=True, finetune=None): super().__init__() self.net2d = make_layers(cfgs['D'], batch_norm=batch_norm) self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) self.net3d = nn.Sequential( nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)), nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.ReLU(), nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1)), ) self.classifier = nn.Sequential( nn.Linear(33280, 2048), nn.ReLU(True), nn.Dropout(0.85), nn.Linear(2048, num_classes), ) self._initialize_weights() if finetune is not None: checkpoint = torch.load(finetune) self.load_state_dict(checkpoint['state_dict']) print("loaded {}".format(finetune)) def forward(self, x1, x2): x1 = self.net2d(x1) x1 = self.avgpool(x1) x1 = x1.view(-1, 25088) x2 = self.net3d(x2) x2 = x2.view(-1, 8192) x = torch.cat((x1, x2), dim=1) x = self.classifier(x) return x def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Conv3d): torch.nn.init.kaiming_normal_(m.weight) elif isinstance(m, nn.BatchNorm3d): m.weight.data.fill_(1) m.bias.data.zero_() def make_layers(cfg, batch_norm=False): layers = [] in_channels = 3 for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers) # if __name__ == "__main__": # from torchviz import make_dot # # x1 = torch.rand(1, 3, 224, 224) # x2 = torch.rand(1, 3, 32, 112, 112) # net = MergeNet(num_classes=3) # print(net) # outputs = net.forward(x1, x2) # g = make_dot(outputs) # g.render('espnet_model', view=False) # print(outputs.size())

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1

0

null

0

7

498a8a99f576918ef7440ecf85e9de8ffa0b9137

6,786

py

Python

tests/test_main.py

VictorDavis/timetracker

4bbdcaa0c8e42457b4b8e96805f98f2d7a4edf07

[ "MIT" ]

null

tests/test_main.py

VictorDavis/timetracker

4bbdcaa0c8e42457b4b8e96805f98f2d7a4edf07

[ "MIT" ]

null

tests/test_main.py

VictorDavis/timetracker

4bbdcaa0c8e42457b4b8e96805f98f2d7a4edf07

[ "MIT" ]

null

from . import client def test_hello_world(): response = client.get("/") assert response.status_code == 200 record = response.json() assert record["message"] == "Hello, world!" def test_payer_crud(): # params payer_name = "Darwin" # payload payload = { "name": payer_name, } # create object response = client.post("/payers", json=payload) assert response.status_code == 200 record = response.json() payer_id = record["id"] assert record["name"] == payer_name # create twice (not ok) response = client.post("/payers", json=payload) assert response.status_code == 409 error = response.json() assert "Duplicate entry" in error["detail"] # get object response = client.get(f"/payers/{payer_id}", json=payload) assert response.status_code == 200 record = response.json() assert record["id"] == payer_id assert record["name"] == payer_name # delete object response = client.delete(f"/payers/{payer_id}", json=payload) assert response.status_code == 200 record = response.json() assert record["id"] == payer_id assert record["name"] == payer_name # delete twice (not ok) response = client.delete(f"/payers/{payer_id}", json=payload) assert response.status_code == 404 error = response.json() assert "Payer not found" in error["detail"] # get after delete (not ok) response = client.get(f"/payers/{payer_id}", json=payload) assert response.status_code == 404 error = response.json() assert "Payer not found" in error["detail"] def test_client_crud(): # params payer_name = "Payer1" client_name = "Wallace" # payload payload = { "payer": {"name": payer_name}, "name": client_name, } # create object response = client.post("/clients", json=payload) assert response.status_code == 200 record = response.json() client_id = record["id"] assert record["name"] == client_name # create twice (not ok) response = client.post("/clients", json=payload) assert response.status_code == 409 error = response.json() assert "Duplicate entry" in error["detail"] # get object response = client.get(f"/clients/{client_id}", json=payload) assert response.status_code == 200 record = response.json() assert record["id"] == client_id assert record["name"] == client_name # delete object response = client.delete(f"/clients/{client_id}", json=payload) assert response.status_code == 200 record = response.json() assert record["id"] == client_id assert record["name"] == client_name # delete twice (not ok) response = client.delete(f"/clients/{client_id}", json=payload) assert response.status_code == 404 error = response.json() assert "Client not found" in error["detail"] # get after delete (not ok) response = client.get(f"/clients/{client_id}", json=payload) assert response.status_code == 404 error = response.json() assert "Client not found" in error["detail"] def test_task_crud(): # params payer_name = "Payer1" client_name = "Client1" task_date = "2021-06-01" task_description = "I did a thing." task_hours = 2.5 # payload payload = { "client": {"payer": {"name": payer_name}, "name": client_name,}, "date": task_date, "description": task_description, "hours": task_hours, } # create object response = client.post("/tasks", json=payload) assert response.status_code == 200 record = response.json() task_id = record["id"] assert record["date"] == task_date assert record["description"] == task_description assert record["hours"] == task_hours # create twice (not ok) response = client.post("/tasks", json=payload) assert response.status_code == 409 error = response.json() assert "Duplicate entry" in error["detail"] # get object response = client.get(f"/tasks/{task_id}", json=payload) assert response.status_code == 200 record = response.json() assert record["id"] == task_id assert record["date"] == task_date assert record["description"] == task_description assert record["hours"] == task_hours # delete object response = client.delete(f"/tasks/{task_id}", json=payload) assert response.status_code == 200 record = response.json() assert record["id"] == task_id assert record["date"] == task_date assert record["description"] == task_description assert record["hours"] == task_hours # delete twice (not ok) response = client.delete(f"/tasks/{task_id}", json=payload) assert response.status_code == 404 error = response.json() assert "Task not found" in error["detail"] # get after delete (not ok) response = client.get(f"/tasks/{task_id}", json=payload) assert response.status_code == 404 error = response.json() assert "Task not found" in error["detail"] def test_paycheck_crud(): # params payer_name = "Payer1" paycheck_date = "2021-06-01" paycheck_amount = 2.5 # payload payload = { "payer": {"name": payer_name}, "date": paycheck_date, "amount": paycheck_amount, } # create object response = client.post("/paychecks", json=payload) assert response.status_code == 200 record = response.json() paycheck_id = record["id"] assert record["date"] == paycheck_date assert record["amount"] == paycheck_amount # create twice (not ok) response = client.post("/paychecks", json=payload) assert response.status_code == 409 error = response.json() assert "Duplicate entry" in error["detail"] # get object response = client.get(f"/paychecks/{paycheck_id}", json=payload) assert response.status_code == 200 record = response.json() assert record["id"] == paycheck_id assert record["date"] == paycheck_date assert record["amount"] == paycheck_amount # delete object response = client.delete(f"/paychecks/{paycheck_id}", json=payload) assert response.status_code == 200 record = response.json() assert record["id"] == paycheck_id assert record["date"] == paycheck_date assert record["amount"] == paycheck_amount # delete twice (not ok) response = client.delete(f"/paychecks/{paycheck_id}", json=payload) assert response.status_code == 404 error = response.json() assert "Paycheck not found" in error["detail"] # get after delete (not ok) response = client.get(f"/paychecks/{paycheck_id}", json=payload) assert response.status_code == 404 error = response.json() assert "Paycheck not found" in error["detail"]

29.376623

72

0.646331

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5.150421

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0.900234

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230

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1

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1

0

null

0

1

0

8

4999deba01f1ab799fc42cae9d5a2d565d92fcd1

12,086

py

Python

piwebasync/api/controllers/assetservers.py

newvicx/piwebasync

fc0d159aa4b99667777f428a090fe7a102481fea

[ "MIT" ]

null

piwebasync/api/controllers/assetservers.py

newvicx/piwebasync

fc0d159aa4b99667777f428a090fe7a102481fea

[ "MIT" ]

2

2022-03-02T17:42:21.000Z

2022-03-29T19:24:01.000Z

piwebasync/api/controllers/assetservers.py

newvicx/piwebasync

fc0d159aa4b99667777f428a090fe7a102481fea

[ "MIT" ]

null

from typing import List, Tuple, Union from ...types import APIRequestType, ControllerType, QueryStrType class AssetServers: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver.html """ CONTROLLER = "assetservers" def __init__(self, constructor: ControllerType) -> None: self._constructor = constructor def list( self, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/list.html """ action = None return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, selected_fields=selected_fields, web_id_type=web_id_type ) def get( self, webid: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/get.html """ action = None return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, selected_fields=selected_fields, web_id_type=web_id_type ) def get_by_path( self, path: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getbypath.html """ action = None return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, path=path, selected_fields=selected_fields, web_id_type=web_id_type ) def get_analysis_rule_plugins( self, webid: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getanalysisruleplugins.html """ action = "analysisruleplugins" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, selected_fields=selected_fields, web_id_type=web_id_type ) def get_by_name( self, name: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getbyname.html """ action = None return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, name=name, selected_fields=selected_fields, web_id_type=web_id_type ) def get_databases( self, webid: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getdatabases.html """ action = "assetdatabases" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, selected_fields=selected_fields, web_id_type=web_id_type ) def get_notification_contact_templates( self, webid: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getnotificationcontacttemplates.html """ action = "notificationcontacttemplates" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, selected_fields=selected_fields, web_id_type=web_id_type ) def get_notification_plugins( self, webid: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getnotificationplugins.html """ action = "notificationplugins" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, selected_fields=selected_fields, web_id_type=web_id_type ) def get_security( self, webid: str, security_item: Union[List[str], Tuple[str]] = None, user_identity: Union[List[str], Tuple[str]] = None, force_refresh: bool = None, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getsecurity.html """ action = "security" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, security_item_many=security_item, user_identity_many=user_identity, force_refresh=force_refresh, selected_fields=selected_fields, web_id_type=web_id_type ) def get_security_entries( self, webid: str, security_item: str = None, name_filter: QueryStrType = None, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getsecurityentries.html """ action = "securityentries" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, security_item=security_item, name_filter=name_filter, selected_fields=selected_fields, web_id_type=web_id_type, ) def get_security_entry_by_name( self, webid: str, name: str, security_item: str = None, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getsecurityentrybyname.html """ action = "securityentries" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, security_item=security_item, selected_fields=selected_fields, web_id_type=web_id_type, add_path = [name] ) def get_security_identities( self, webid: str, query: QueryStrType = None, field: str = None, sort_field: str = None, sort_order: str = None, max_count: int = None, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getsecurityidentities.html """ action = "securityidentities" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, query=query, field=field, sort_field=sort_field, sort_order=sort_order, max_count=max_count, selected_fields=selected_fields, web_id_type=web_id_type, ) def get_security_identities_for_user( self, webid: str, user_identity: str = None, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getsecurityidentitiesforuser.html """ action = "securityidentities" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, user_identity=user_identity, selected_fields=selected_fields, web_id_type=web_id_type, ) def get_security_mappings( self, webid: str, query: QueryStrType = None, field: str = None, sort_field: str = None, sort_order: str = None, max_count: int = None, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getsecuritymappings.html """ action = "securitymappings" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, query=query, field=field, sort_field=sort_field, sort_order=sort_order, max_count=max_count, selected_fields=selected_fields, web_id_type=web_id_type, ) def get_time_rule_plugins( self, webid: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/gettimeruleplugins.html """ action = "timeruleplugins" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, selected_fields=selected_fields, web_id_type=web_id_type ) def get_unit_classes( self, webid: str, selected_fields: Union[List[str], Tuple[str]] = None, web_id_type: str = None, ) -> APIRequestType: """ https://docs.osisoft.com/bundle/pi-web-api-reference/page/help/controllers/assetserver/actions/getunitclasses.html """ action = "unitclasses" return self._constructor._build_request( method="GET", protocol="HTTP", controller=self.CONTROLLER, action=action, webid=webid, selected_fields=selected_fields, web_id_type=web_id_type )

29.841975

139

0.579927

1,215

12,086

5.544856

0.08642

0.099748

0.064124

0.045421

0.844738

0.834348

0.827223

0

0.317309

12,086

404

140

29.915842

0.816507

0.162999

0

0.797297

0

0.032881

0.002877

0

1

0.057432

false

0

0.006757

0

0.125

0

null

0

1

0

1

0

null

0

7

49a2381a0358b7846a4f2f91b517df7aab7202f0

1,948

py

Python

system/migrations/0003_auto_20180608_0923.py

17621368758/tranpathPY

01cf371c260275811e3750de116fa5b95718bafe

[ "MIT" ]

1

2020-06-05T16:01:21.000Z

system/migrations/0003_auto_20180608_0923.py

17621368758/tranpathPY

01cf371c260275811e3750de116fa5b95718bafe

[ "MIT" ]

4

2020-02-11T23:27:37.000Z

2021-12-13T19:52:11.000Z

system/migrations/0003_auto_20180608_0923.py

17621368758/tranpathPY

01cf371c260275811e3750de116fa5b95718bafe

[ "MIT" ]

null

# Generated by Django 2.0.5 on 2018-06-08 09:23 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('system', '0002_auto_20180601_2250'), ] operations = [ migrations.AlterField( model_name='excel_import_file_fields_name', name='adder', field=models.ForeignKey(help_text='{"form":"F"}', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='excel_import_file_fields_name_adder', to='system.User', verbose_name='添加人（User.id）'), ), migrations.AlterField( model_name='excel_import_file_fields_name', name='excelImportFileMainId', field=models.ForeignKey(help_text='{"form":"F"}', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='excel_import_file_main_id', to='system.Excel_import_file_main', verbose_name='导入文件主表ID'), ), migrations.AlterField( model_name='excel_import_file_main', name='adder', field=models.ForeignKey(help_text='{"form":"F"}', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='excel_import_file_main_adder', to='system.User', verbose_name='添加人（User.id）'), ), migrations.AlterField( model_name='excel_import_file_main', name='importer', field=models.ForeignKey(help_text='{"form":"F"}', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='importer', to='system.User', verbose_name='设置字段名时间操作人（User.id）'), ), migrations.AlterField( model_name='excel_import_file_main', name='setFieldNamer', field=models.ForeignKey(help_text='{"form":"F"}', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='setFieldNamer', to='system.User', verbose_name='设置字段名时间操作人（User.id）'), ), ]

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b8f559ee80a214300a016d11ff8ed8c7049745f1

22,650

py

Python

ibutsu_client/api/run_api.py

ibutsu/ibutsu-client-python

8cd34d7fc8f9a2225195ae375a17200b992dde01

[ "MIT" ]

3

2020-07-02T14:48:08.000Z

2021-11-27T14:06:33.000Z

ibutsu_client/api/run_api.py

ibutsu/ibutsu-client-python

8cd34d7fc8f9a2225195ae375a17200b992dde01

[ "MIT" ]

6

2020-07-07T16:13:37.000Z

2021-11-10T17:02:59.000Z

ibutsu_client/api/run_api.py

ibutsu/ibutsu-client-python

8cd34d7fc8f9a2225195ae375a17200b992dde01

[ "MIT" ]

5

2020-07-02T18:13:03.000Z

2021-11-03T09:21:11.000Z

""" Ibutsu API A system to store and query test results # noqa: E501 The version of the OpenAPI document: 1.13.4 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from ibutsu_client.api_client import ApiClient, Endpoint as _Endpoint from ibutsu_client.model_utils import ( # noqa: F401 check_allowed_values, check_validations, date, datetime, file_type, none_type, validate_and_convert_types ) from ibutsu_client.model.run import Run from ibutsu_client.model.run_list import RunList from ibutsu_client.model.update_run import UpdateRun class RunApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client self.add_run_endpoint = _Endpoint( settings={ 'response_type': (Run,), 'auth': [ 'jwt' ], 'endpoint_path': '/run', 'operation_id': 'add_run', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'run', ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'run': (Run,), }, 'attribute_map': { }, 'location_map': { 'run': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.bulk_update_endpoint = _Endpoint( settings={ 'response_type': (RunList,), 'auth': [ 'jwt' ], 'endpoint_path': '/runs/bulk-update', 'operation_id': 'bulk_update', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'update_run', 'filter', 'page_size', ], 'required': [ 'update_run', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'update_run': (UpdateRun,), 'filter': ([str],), 'page_size': (int,), }, 'attribute_map': { 'filter': 'filter', 'page_size': 'pageSize', }, 'location_map': { 'update_run': 'body', 'filter': 'query', 'page_size': 'query', }, 'collection_format_map': { 'filter': 'multi', } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.get_run_endpoint = _Endpoint( settings={ 'response_type': (Run,), 'auth': [ 'jwt' ], 'endpoint_path': '/run/{id}', 'operation_id': 'get_run', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'id', ], 'required': [ 'id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'id': (str,), }, 'attribute_map': { 'id': 'id', }, 'location_map': { 'id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.get_run_list_endpoint = _Endpoint( settings={ 'response_type': (RunList,), 'auth': [ 'jwt' ], 'endpoint_path': '/run', 'operation_id': 'get_run_list', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'filter', 'estimate', 'page', 'page_size', ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'filter': ([str],), 'estimate': (bool,), 'page': (int,), 'page_size': (int,), }, 'attribute_map': { 'filter': 'filter', 'estimate': 'estimate', 'page': 'page', 'page_size': 'pageSize', }, 'location_map': { 'filter': 'query', 'estimate': 'query', 'page': 'query', 'page_size': 'query', }, 'collection_format_map': { 'filter': 'multi', } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.update_run_endpoint = _Endpoint( settings={ 'response_type': (Run,), 'auth': [ 'jwt' ], 'endpoint_path': '/run/{id}', 'operation_id': 'update_run', 'http_method': 'PUT', 'servers': None, }, params_map={ 'all': [ 'id', 'run', ], 'required': [ 'id', 'run', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'id': (str,), 'run': (Run,), }, 'attribute_map': { 'id': 'id', }, 'location_map': { 'id': 'path', 'run': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) def add_run( self, **kwargs ): """Create a run # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.add_run(async_req=True) >>> result = thread.get() Keyword Args: run (Run): Run item. [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: Run If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') return self.add_run_endpoint.call_with_http_info(**kwargs) def bulk_update( self, update_run, **kwargs ): """Update multiple runs with common metadata # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.bulk_update(update_run, async_req=True) >>> result = thread.get() Args: update_run (UpdateRun): The metadata to add to the test runs Keyword Args: filter ([str]): Fields to filter by. [optional] page_size (int): Set the number of items per page, defaults to 25. [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: RunList If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') kwargs['update_run'] = \ update_run return self.bulk_update_endpoint.call_with_http_info(**kwargs) def get_run( self, id, **kwargs ): """Get a single run by ID (uuid required) # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_run(id, async_req=True) >>> result = thread.get() Args: id (str): ID of test run Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: Run If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') kwargs['id'] = \ id return self.get_run_endpoint.call_with_http_info(**kwargs) def get_run_list( self, **kwargs ): """Get a list of the test runs # noqa: E501 The `filter` parameter takes a list of filters to apply in the form of: {name}{operator}{value} where: - `name` is any valid column in the database - `operator` is one of `=`, `!`, `＞`, `＜`, `)`, `(`, `~`, `*` - `value` is what you want to filter by Operators are simple correspondents to MongoDB's query selectors: - `=` becomes `$eq` - `!` becomes `$ne` - `＞` becomes `$gt` - `＜` becomes `$lt` - `)` becomes `$gte` - `(` becomes `$lte` - `~` becomes `$regex` - `*` becomes `$in` - `@` becomes `$exists` Notes: - For the `$exists` operator, \"true\", \"t\", \"yes\", \"y\" and `1` will all be considered true, all other values are considered false. Example queries: /run?filter=metadata.jenkins.job_name=jenkins_job /run?filter=summary.failures>0 # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_run_list(async_req=True) >>> result = thread.get() Keyword Args: filter ([str]): Fields to filter by. [optional] estimate (bool): Return an estimated count. [optional] page (int): Set the page of items to return, defaults to 1. [optional] page_size (int): Set the number of items per page, defaults to 25. [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: RunList If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') return self.get_run_list_endpoint.call_with_http_info(**kwargs) def update_run( self, id, run, **kwargs ): """Update a single run # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_run(id, run, async_req=True) >>> result = thread.get() Args: id (str): ID of the test run run (Run): The updated test run Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: Run If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_host_index'] = kwargs.get('_host_index') kwargs['id'] = \ id kwargs['run'] = \ run return self.update_run_endpoint.call_with_http_info(**kwargs)

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null

0

7

b8f5f93e29db7d1d7dcd402aeed83092a8cdd935

27,417

py

Python

metal_python/api/filesystemlayout_api.py

metal-stack/metal-python

cdf40fa86d2b2944f9818cef1c6723b1eecc506e

[ "MIT" ]

7

2020-12-21T05:24:24.000Z

2022-02-12T20:55:32.000Z

metal_python/api/filesystemlayout_api.py

metal-stack/metal-python

cdf40fa86d2b2944f9818cef1c6723b1eecc506e

[ "MIT" ]

6

2020-09-16T07:23:34.000Z

2022-01-18T12:05:30.000Z

metal_python/api/filesystemlayout_api.py

metal-stack/metal-python

cdf40fa86d2b2944f9818cef1c6723b1eecc506e

[ "MIT" ]

null

# coding: utf-8 """ metal-api API to manage and control plane resources like machines, switches, operating system images, machine sizes, networks, IP addresses and more # noqa: E501 OpenAPI spec version: v0.15.7 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from metal_python.api_client import ApiClient class FilesystemlayoutApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_filesystem_layout(self, body, **kwargs): # noqa: E501 """create a filesystemlayout. if the given ID already exists a conflict is returned # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_filesystem_layout(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1FilesystemLayoutCreateRequest body: (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_filesystem_layout_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.create_filesystem_layout_with_http_info(body, **kwargs) # noqa: E501 return data def create_filesystem_layout_with_http_info(self, body, **kwargs): # noqa: E501 """create a filesystemlayout. if the given ID already exists a conflict is returned # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_filesystem_layout_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1FilesystemLayoutCreateRequest body: (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_filesystem_layout" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `create_filesystem_layout`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['HMAC', 'jwt'] # noqa: E501 return self.api_client.call_api( '/v1/filesystemlayout', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1FilesystemLayoutResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_filesystem_layout(self, id, **kwargs): # noqa: E501 """deletes an filesystemlayout and returns the deleted entity # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_filesystem_layout(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: identifier of the filesystemlayout (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_filesystem_layout_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.delete_filesystem_layout_with_http_info(id, **kwargs) # noqa: E501 return data def delete_filesystem_layout_with_http_info(self, id, **kwargs): # noqa: E501 """deletes an filesystemlayout and returns the deleted entity # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_filesystem_layout_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: identifier of the filesystemlayout (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_filesystem_layout" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `delete_filesystem_layout`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['HMAC', 'jwt'] # noqa: E501 return self.api_client.call_api( '/v1/filesystemlayout/{id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1FilesystemLayoutResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_filesystem_layout(self, id, **kwargs): # noqa: E501 """get filesystemlayout by id # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_filesystem_layout(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: identifier of the filesystemlayout (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_filesystem_layout_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.get_filesystem_layout_with_http_info(id, **kwargs) # noqa: E501 return data def get_filesystem_layout_with_http_info(self, id, **kwargs): # noqa: E501 """get filesystemlayout by id # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_filesystem_layout_with_http_info(id, async_req=True) >>> result = thread.get() :param async_req bool :param str id: identifier of the filesystemlayout (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_filesystem_layout" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'id' is set if ('id' not in params or params['id'] is None): raise ValueError("Missing the required parameter `id` when calling `get_filesystem_layout`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in params: path_params['id'] = params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['HMAC', 'jwt'] # noqa: E501 return self.api_client.call_api( '/v1/filesystemlayout/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1FilesystemLayoutResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def list_filesystem_layouts(self, **kwargs): # noqa: E501 """get all filesystemlayouts # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_filesystem_layouts(async_req=True) >>> result = thread.get() :param async_req bool :return: list[V1FilesystemLayoutResponse] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.list_filesystem_layouts_with_http_info(**kwargs) # noqa: E501 else: (data) = self.list_filesystem_layouts_with_http_info(**kwargs) # noqa: E501 return data def list_filesystem_layouts_with_http_info(self, **kwargs): # noqa: E501 """get all filesystemlayouts # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.list_filesystem_layouts_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[V1FilesystemLayoutResponse] If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method list_filesystem_layouts" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['HMAC', 'jwt'] # noqa: E501 return self.api_client.call_api( '/v1/filesystemlayout', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[V1FilesystemLayoutResponse]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def match_filesystem_layout(self, body, **kwargs): # noqa: E501 """check if the given machine id satisfies the disk requirements of the filesystemlayout in question # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.match_filesystem_layout(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1FilesystemLayoutMatchRequest body: (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.match_filesystem_layout_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.match_filesystem_layout_with_http_info(body, **kwargs) # noqa: E501 return data def match_filesystem_layout_with_http_info(self, body, **kwargs): # noqa: E501 """check if the given machine id satisfies the disk requirements of the filesystemlayout in question # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.match_filesystem_layout_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1FilesystemLayoutMatchRequest body: (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method match_filesystem_layout" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `match_filesystem_layout`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['HMAC', 'jwt'] # noqa: E501 return self.api_client.call_api( '/v1/filesystemlayout/matches', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1FilesystemLayoutResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def try_filesystem_layout(self, body, **kwargs): # noqa: E501 """try to detect a filesystemlayout based on given size and image. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.try_filesystem_layout(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1FilesystemLayoutTryRequest body: (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.try_filesystem_layout_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.try_filesystem_layout_with_http_info(body, **kwargs) # noqa: E501 return data def try_filesystem_layout_with_http_info(self, body, **kwargs): # noqa: E501 """try to detect a filesystemlayout based on given size and image. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.try_filesystem_layout_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1FilesystemLayoutTryRequest body: (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method try_filesystem_layout" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `try_filesystem_layout`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['HMAC', 'jwt'] # noqa: E501 return self.api_client.call_api( '/v1/filesystemlayout/try', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1FilesystemLayoutResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_filesystem_layout(self, body, **kwargs): # noqa: E501 """updates a filesystemlayout. if the filesystemlayout was changed since this one was read, a conflict is returned # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_filesystem_layout(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1FilesystemLayoutUpdateRequest body: (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.update_filesystem_layout_with_http_info(body, **kwargs) # noqa: E501 else: (data) = self.update_filesystem_layout_with_http_info(body, **kwargs) # noqa: E501 return data def update_filesystem_layout_with_http_info(self, body, **kwargs): # noqa: E501 """updates a filesystemlayout. if the filesystemlayout was changed since this one was read, a conflict is returned # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update_filesystem_layout_with_http_info(body, async_req=True) >>> result = thread.get() :param async_req bool :param V1FilesystemLayoutUpdateRequest body: (required) :return: V1FilesystemLayoutResponse If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method update_filesystem_layout" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'body' is set if ('body' not in params or params['body'] is None): raise ValueError("Missing the required parameter `body` when calling `update_filesystem_layout`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['HMAC', 'jwt'] # noqa: E501 return self.api_client.call_api( '/v1/filesystemlayout', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='V1FilesystemLayoutResponse', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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b8fe4f97a68daea78c591a8bfef2d60f792348c8

64,579

py

Python

splunk_sdk/ingest/v1beta2/gen_models.py

ianlee4/splunk-cloud-sdk-python

d2870cd1e506d3844869d17becdcdf9d8d60a9a1

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

12

2019-08-01T06:16:17.000Z

2021-04-16T20:00:02.000Z

splunk_sdk/ingest/v1beta2/gen_models.py

ianlee4/splunk-cloud-sdk-python

d2870cd1e506d3844869d17becdcdf9d8d60a9a1

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

5

2020-09-27T12:03:24.000Z

2021-08-06T18:01:32.000Z

splunk_sdk/ingest/v1beta2/gen_models.py

ianlee4/splunk-cloud-sdk-python

d2870cd1e506d3844869d17becdcdf9d8d60a9a1

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

4

2019-08-20T17:49:27.000Z

2022-03-27T16:39:10.000Z

# Copyright © 2021 Splunk, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"): you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # [http://www.apache.org/licenses/LICENSE-2.0] # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. ############# This file is auto-generated. Do not edit! ############# """ SDC Service: Ingest API Use the Ingest service in Splunk Cloud Services to send event and metrics data, or upload a static file, to Splunk Cloud Services. OpenAPI spec version: v1beta2.32 (recommended default) Generated by: https://openapi-generator.tech """ from datetime import datetime from typing import List, Dict from splunk_sdk.common.sscmodel import SSCModel from splunk_sdk.base_client import dictify, inflate from enum import Enum class Error(SSCModel): @staticmethod def _from_dict(model: dict) -> "Error": instance = Error.__new__(Error) instance._attrs = model return instance def __init__(self, code: "str" = None, details: "object" = None, message: "str" = None, **extra): """Error""" self._attrs = dict() if code is not None: self._attrs["code"] = code if details is not None: self._attrs["details"] = details if message is not None: self._attrs["message"] = message for k, v in extra.items(): self._attrs[k] = v @property def code(self) -> "str": """ Gets the code of this Error. """ return self._attrs.get("code") @code.setter def code(self, code: "str"): """Sets the code of this Error. :param code: The code of this Error. :type: str """ self._attrs["code"] = code @property def details(self) -> "dict": """ Gets the details of this Error. """ return self._attrs.get("details") @details.setter def details(self, details: "dict"): """Sets the details of this Error. :param details: The details of this Error. :type: object """ self._attrs["details"] = details @property def message(self) -> "str": """ Gets the message of this Error. """ return self._attrs.get("message") @message.setter def message(self, message: "str"): """Sets the message of this Error. :param message: The message of this Error. :type: str """ self._attrs["message"] = message def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class Event(SSCModel): @staticmethod def _from_dict(model: dict) -> "Event": instance = Event.__new__(Event) instance._attrs = model return instance def __init__(self, body: "object", attributes: "Dict[str, object]" = None, host: "str" = None, id: "str" = None, nanos: "int" = None, source: "str" = None, sourcetype: "str" = None, timestamp: "int" = None, **extra): """Event""" self._attrs = dict() if body is not None: self._attrs["body"] = body if attributes is not None: self._attrs["attributes"] = attributes if host is not None: self._attrs["host"] = host if id is not None: self._attrs["id"] = id if nanos is not None: self._attrs["nanos"] = nanos if source is not None: self._attrs["source"] = source if sourcetype is not None: self._attrs["sourcetype"] = sourcetype if timestamp is not None: self._attrs["timestamp"] = timestamp for k, v in extra.items(): self._attrs[k] = v @property def body(self) -> "object": """ Gets the body of this Event. The raw event content. It can be a string, number, string array, number array, JSON object, map, list, a JSON array, or a byte array. """ return self._attrs.get("body") @body.setter def body(self, body: "object"): """Sets the body of this Event. The raw event content. It can be a string, number, string array, number array, JSON object, map, list, a JSON array, or a byte array. :param body: The body of this Event. :type: object """ if body is None: raise ValueError("Invalid value for `body`, must not be `None`") self._attrs["body"] = body @property def attributes(self) -> "dict": """ Gets the attributes of this Event. Specifies a JSON object that contains explicit custom fields to be defined at index time. """ return self._attrs.get("attributes") @attributes.setter def attributes(self, attributes: "dict"): """Sets the attributes of this Event. Specifies a JSON object that contains explicit custom fields to be defined at index time. :param attributes: The attributes of this Event. :type: Dict[str, object] """ self._attrs["attributes"] = attributes @property def host(self) -> "str": """ Gets the host of this Event. The host value assigned to the event data. Typically, this is the hostname of the client from which you are sending data. """ return self._attrs.get("host") @host.setter def host(self, host: "str"): """Sets the host of this Event. The host value assigned to the event data. Typically, this is the hostname of the client from which you are sending data. :param host: The host of this Event. :type: str """ self._attrs["host"] = host @property def id(self) -> "str": """ Gets the id of this Event. An optional ID that uniquely identifies the event data. It is used to deduplicate the data if same data is set multiple times. If ID is not specified, it will be assigned by the system. """ return self._attrs.get("id") @id.setter def id(self, id: "str"): """Sets the id of this Event. An optional ID that uniquely identifies the event data. It is used to deduplicate the data if same data is set multiple times. If ID is not specified, it will be assigned by the system. :param id: The id of this Event. :type: str """ self._attrs["id"] = id @property def nanos(self) -> "int": """ Gets the nanos of this Event. Optional nanoseconds part of the timestamp. """ return self._attrs.get("nanos") @nanos.setter def nanos(self, nanos: "int"): """Sets the nanos of this Event. Optional nanoseconds part of the timestamp. :param nanos: The nanos of this Event. :type: int """ self._attrs["nanos"] = nanos @property def source(self) -> "str": """ Gets the source of this Event. The source value to assign to the event data. For example, if you are sending data from an app that you are developing, set this key to the name of the app. """ return self._attrs.get("source") @source.setter def source(self, source: "str"): """Sets the source of this Event. The source value to assign to the event data. For example, if you are sending data from an app that you are developing, set this key to the name of the app. :param source: The source of this Event. :type: str """ self._attrs["source"] = source @property def sourcetype(self) -> "str": """ Gets the sourcetype of this Event. The sourcetype value assigned to the event data. """ return self._attrs.get("sourcetype") @sourcetype.setter def sourcetype(self, sourcetype: "str"): """Sets the sourcetype of this Event. The sourcetype value assigned to the event data. :param sourcetype: The sourcetype of this Event. :type: str """ self._attrs["sourcetype"] = sourcetype @property def timestamp(self) -> "int": """ Gets the timestamp of this Event. Epoch time in milliseconds. """ return self._attrs.get("timestamp") @timestamp.setter def timestamp(self, timestamp: "int"): """Sets the timestamp of this Event. Epoch time in milliseconds. :param timestamp: The timestamp of this Event. :type: int """ self._attrs["timestamp"] = timestamp def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class FileUploadDetails(SSCModel): @staticmethod def _from_dict(model: dict) -> "FileUploadDetails": instance = FileUploadDetails.__new__(FileUploadDetails) instance._attrs = model return instance def __init__(self, filename: "str" = None, **extra): """FileUploadDetails""" self._attrs = dict() if filename is not None: self._attrs["filename"] = filename for k, v in extra.items(): self._attrs[k] = v @property def filename(self) -> "str": """ Gets the filename of this FileUploadDetails. """ return self._attrs.get("filename") @filename.setter def filename(self, filename: "str"): """Sets the filename of this FileUploadDetails. :param filename: The filename of this FileUploadDetails. :type: str """ self._attrs["filename"] = filename def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class HECTokenAccessResponse(SSCModel): @staticmethod def _from_dict(model: dict) -> "HECTokenAccessResponse": instance = HECTokenAccessResponse.__new__(HECTokenAccessResponse) instance._attrs = model return instance def __init__(self, ack_enabled: "bool" = None, allow_query_string_auth: "bool" = None, created_at: "datetime" = None, created_by: "str" = None, description: "str" = None, disabled: "bool" = None, index: "str" = None, indexes: "List[str]" = None, last_modified_at: "datetime" = None, last_modified_by: "str" = None, name: "str" = None, source: "str" = None, sourcetype: "str" = None, tenant: "str" = None, **extra): """HECTokenAccessResponse""" self._attrs = dict() if ack_enabled is not None: self._attrs["ack_enabled"] = ack_enabled if allow_query_string_auth is not None: self._attrs["allow_query_string_auth"] = allow_query_string_auth if created_at is not None: self._attrs["created_at"] = created_at if created_by is not None: self._attrs["created_by"] = created_by if description is not None: self._attrs["description"] = description if disabled is not None: self._attrs["disabled"] = disabled if index is not None: self._attrs["index"] = index if indexes is not None: self._attrs["indexes"] = indexes if last_modified_at is not None: self._attrs["last_modified_at"] = last_modified_at if last_modified_by is not None: self._attrs["last_modified_by"] = last_modified_by if name is not None: self._attrs["name"] = name if source is not None: self._attrs["source"] = source if sourcetype is not None: self._attrs["sourcetype"] = sourcetype if tenant is not None: self._attrs["tenant"] = tenant for k, v in extra.items(): self._attrs[k] = v @property def ack_enabled(self) -> "bool": """ Gets the ack_enabled of this HECTokenAccessResponse. ack_enabled is set to true if events sent with the auth token should support indexer acknowledgement type: bool """ return self._attrs.get("ack_enabled") @ack_enabled.setter def ack_enabled(self, ack_enabled: "bool"): """Sets the ack_enabled of this HECTokenAccessResponse. ack_enabled is set to true if events sent with the auth token should support indexer acknowledgement type: bool :param ack_enabled: The ack_enabled of this HECTokenAccessResponse. :type: bool """ self._attrs["ack_enabled"] = ack_enabled @property def allow_query_string_auth(self) -> "bool": """ Gets the allow_query_string_auth of this HECTokenAccessResponse. allow_query_string_auth is set to true if this token can be passed into the ingest endpoint's query parameter for auth type: bool """ return self._attrs.get("allow_query_string_auth") @allow_query_string_auth.setter def allow_query_string_auth(self, allow_query_string_auth: "bool"): """Sets the allow_query_string_auth of this HECTokenAccessResponse. allow_query_string_auth is set to true if this token can be passed into the ingest endpoint's query parameter for auth type: bool :param allow_query_string_auth: The allow_query_string_auth of this HECTokenAccessResponse. :type: bool """ self._attrs["allow_query_string_auth"] = allow_query_string_auth @property def created_at(self) -> "datetime": """ Gets the created_at of this HECTokenAccessResponse. created_at is a timestamp that captures when this token was created. type: string format: date-time """ return self._attrs.get("created_at") @created_at.setter def created_at(self, created_at: "datetime"): """Sets the created_at of this HECTokenAccessResponse. created_at is a timestamp that captures when this token was created. type: string format: date-time :param created_at: The created_at of this HECTokenAccessResponse. :type: datetime """ self._attrs["created_at"] = created_at @property def created_by(self) -> "str": """ Gets the created_by of this HECTokenAccessResponse. created_by is the principal that created the token. type: string """ return self._attrs.get("created_by") @created_by.setter def created_by(self, created_by: "str"): """Sets the created_by of this HECTokenAccessResponse. created_by is the principal that created the token. type: string :param created_by: The created_by of this HECTokenAccessResponse. :type: str """ self._attrs["created_by"] = created_by @property def description(self) -> "str": """ Gets the description of this HECTokenAccessResponse. description is an optional description of the token. type: string """ return self._attrs.get("description") @description.setter def description(self, description: "str"): """Sets the description of this HECTokenAccessResponse. description is an optional description of the token. type: string :param description: The description of this HECTokenAccessResponse. :type: str """ self._attrs["description"] = description @property def disabled(self) -> "bool": """ Gets the disabled of this HECTokenAccessResponse. disabled is set to true if this auth token has been disabled and cannot be used to send events to HECv1 type: bool """ return self._attrs.get("disabled") @disabled.setter def disabled(self, disabled: "bool"): """Sets the disabled of this HECTokenAccessResponse. disabled is set to true if this auth token has been disabled and cannot be used to send events to HECv1 type: bool :param disabled: The disabled of this HECTokenAccessResponse. :type: bool """ self._attrs["disabled"] = disabled @property def index(self) -> "str": """ Gets the index of this HECTokenAccessResponse. index is the default value of the index field for records collected using this token. type: string """ return self._attrs.get("index") @index.setter def index(self, index: "str"): """Sets the index of this HECTokenAccessResponse. index is the default value of the index field for records collected using this token. type: string :param index: The index of this HECTokenAccessResponse. :type: str """ self._attrs["index"] = index @property def indexes(self) -> "List[str]": """ Gets the indexes of this HECTokenAccessResponse. indexes is a list of index names that this token is allowed to send events to type: []string """ return self._attrs.get("indexes") @indexes.setter def indexes(self, indexes: "List[str]"): """Sets the indexes of this HECTokenAccessResponse. indexes is a list of index names that this token is allowed to send events to type: []string :param indexes: The indexes of this HECTokenAccessResponse. :type: List[str] """ self._attrs["indexes"] = indexes @property def last_modified_at(self) -> "datetime": """ Gets the last_modified_at of this HECTokenAccessResponse. last_modified_at is a timestamp that captures when this token was last modified. type: string format: date-time """ return self._attrs.get("last_modified_at") @last_modified_at.setter def last_modified_at(self, last_modified_at: "datetime"): """Sets the last_modified_at of this HECTokenAccessResponse. last_modified_at is a timestamp that captures when this token was last modified. type: string format: date-time :param last_modified_at: The last_modified_at of this HECTokenAccessResponse. :type: datetime """ self._attrs["last_modified_at"] = last_modified_at @property def last_modified_by(self) -> "str": """ Gets the last_modified_by of this HECTokenAccessResponse. last_modified_by is the principal that last modified the token. type: string """ return self._attrs.get("last_modified_by") @last_modified_by.setter def last_modified_by(self, last_modified_by: "str"): """Sets the last_modified_by of this HECTokenAccessResponse. last_modified_by is the principal that last modified the token. type: string :param last_modified_by: The last_modified_by of this HECTokenAccessResponse. :type: str """ self._attrs["last_modified_by"] = last_modified_by @property def name(self) -> "str": """ Gets the name of this HECTokenAccessResponse. name is the name of the token (unique within the tenant that it belongs to). type: string """ return self._attrs.get("name") @name.setter def name(self, name: "str"): """Sets the name of this HECTokenAccessResponse. name is the name of the token (unique within the tenant that it belongs to). type: string :param name: The name of this HECTokenAccessResponse. :type: str """ self._attrs["name"] = name @property def source(self) -> "str": """ Gets the source of this HECTokenAccessResponse. source is the default value of the source field for records collected using this token. type: string """ return self._attrs.get("source") @source.setter def source(self, source: "str"): """Sets the source of this HECTokenAccessResponse. source is the default value of the source field for records collected using this token. type: string :param source: The source of this HECTokenAccessResponse. :type: str """ self._attrs["source"] = source @property def sourcetype(self) -> "str": """ Gets the sourcetype of this HECTokenAccessResponse. sourcetype is the default value of the sourcetype field for records collected using this token. type: string """ return self._attrs.get("sourcetype") @sourcetype.setter def sourcetype(self, sourcetype: "str"): """Sets the sourcetype of this HECTokenAccessResponse. sourcetype is the default value of the sourcetype field for records collected using this token. type: string :param sourcetype: The sourcetype of this HECTokenAccessResponse. :type: str """ self._attrs["sourcetype"] = sourcetype @property def tenant(self) -> "str": """ Gets the tenant of this HECTokenAccessResponse. tenant is the tenant that this token belongs to type: string """ return self._attrs.get("tenant") @tenant.setter def tenant(self, tenant: "str"): """Sets the tenant of this HECTokenAccessResponse. tenant is the tenant that this token belongs to type: string :param tenant: The tenant of this HECTokenAccessResponse. :type: str """ self._attrs["tenant"] = tenant def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class HECTokenCreateRequest(SSCModel): @staticmethod def _from_dict(model: dict) -> "HECTokenCreateRequest": instance = HECTokenCreateRequest.__new__(HECTokenCreateRequest) instance._attrs = model return instance def __init__(self, name: "str", ack_enabled: "bool" = None, allow_query_string_auth: "bool" = None, description: "str" = None, disabled: "bool" = None, index: "str" = None, indexes: "List[str]" = None, source: "str" = None, sourcetype: "str" = None, **extra): """HECTokenCreateRequest""" self._attrs = dict() if name is not None: self._attrs["name"] = name if ack_enabled is not None: self._attrs["ack_enabled"] = ack_enabled if allow_query_string_auth is not None: self._attrs["allow_query_string_auth"] = allow_query_string_auth if description is not None: self._attrs["description"] = description if disabled is not None: self._attrs["disabled"] = disabled if index is not None: self._attrs["index"] = index if indexes is not None: self._attrs["indexes"] = indexes if source is not None: self._attrs["source"] = source if sourcetype is not None: self._attrs["sourcetype"] = sourcetype for k, v in extra.items(): self._attrs[k] = v @property def name(self) -> "str": """ Gets the name of this HECTokenCreateRequest. name is the name of the token (unique within the tenant that it belongs to). type: string """ return self._attrs.get("name") @name.setter def name(self, name: "str"): """Sets the name of this HECTokenCreateRequest. name is the name of the token (unique within the tenant that it belongs to). type: string :param name: The name of this HECTokenCreateRequest. :type: str """ if name is None: raise ValueError("Invalid value for `name`, must not be `None`") self._attrs["name"] = name @property def ack_enabled(self) -> "bool": """ Gets the ack_enabled of this HECTokenCreateRequest. ack_enabled is set to true if events sent with the auth token should support indexer acknowledgement type: bool """ return self._attrs.get("ack_enabled") @ack_enabled.setter def ack_enabled(self, ack_enabled: "bool"): """Sets the ack_enabled of this HECTokenCreateRequest. ack_enabled is set to true if events sent with the auth token should support indexer acknowledgement type: bool :param ack_enabled: The ack_enabled of this HECTokenCreateRequest. :type: bool """ self._attrs["ack_enabled"] = ack_enabled @property def allow_query_string_auth(self) -> "bool": """ Gets the allow_query_string_auth of this HECTokenCreateRequest. allow_query_string_auth is set to true if this token can be passed into the ingest endpoint's query parameter for auth type: bool """ return self._attrs.get("allow_query_string_auth") @allow_query_string_auth.setter def allow_query_string_auth(self, allow_query_string_auth: "bool"): """Sets the allow_query_string_auth of this HECTokenCreateRequest. allow_query_string_auth is set to true if this token can be passed into the ingest endpoint's query parameter for auth type: bool :param allow_query_string_auth: The allow_query_string_auth of this HECTokenCreateRequest. :type: bool """ self._attrs["allow_query_string_auth"] = allow_query_string_auth @property def description(self) -> "str": """ Gets the description of this HECTokenCreateRequest. description is an optional description of the token. type: string """ return self._attrs.get("description") @description.setter def description(self, description: "str"): """Sets the description of this HECTokenCreateRequest. description is an optional description of the token. type: string :param description: The description of this HECTokenCreateRequest. :type: str """ self._attrs["description"] = description @property def disabled(self) -> "bool": """ Gets the disabled of this HECTokenCreateRequest. disabled is set to true if this auth token has been disabled and cannot be used to send events to HECv1 type: bool """ return self._attrs.get("disabled") @disabled.setter def disabled(self, disabled: "bool"): """Sets the disabled of this HECTokenCreateRequest. disabled is set to true if this auth token has been disabled and cannot be used to send events to HECv1 type: bool :param disabled: The disabled of this HECTokenCreateRequest. :type: bool """ self._attrs["disabled"] = disabled @property def index(self) -> "str": """ Gets the index of this HECTokenCreateRequest. index is the default value of the index field for records collected using this token. type: string """ return self._attrs.get("index") @index.setter def index(self, index: "str"): """Sets the index of this HECTokenCreateRequest. index is the default value of the index field for records collected using this token. type: string :param index: The index of this HECTokenCreateRequest. :type: str """ self._attrs["index"] = index @property def indexes(self) -> "List[str]": """ Gets the indexes of this HECTokenCreateRequest. indexes is a list of index names that this token is allowed to send events to type: []string """ return self._attrs.get("indexes") @indexes.setter def indexes(self, indexes: "List[str]"): """Sets the indexes of this HECTokenCreateRequest. indexes is a list of index names that this token is allowed to send events to type: []string :param indexes: The indexes of this HECTokenCreateRequest. :type: List[str] """ self._attrs["indexes"] = indexes @property def source(self) -> "str": """ Gets the source of this HECTokenCreateRequest. source is the default value of the source field for records collected using this token. type: string """ return self._attrs.get("source") @source.setter def source(self, source: "str"): """Sets the source of this HECTokenCreateRequest. source is the default value of the source field for records collected using this token. type: string :param source: The source of this HECTokenCreateRequest. :type: str """ self._attrs["source"] = source @property def sourcetype(self) -> "str": """ Gets the sourcetype of this HECTokenCreateRequest. sourcetype is the default value of the sourcetype field for records collected using this token. type: string """ return self._attrs.get("sourcetype") @sourcetype.setter def sourcetype(self, sourcetype: "str"): """Sets the sourcetype of this HECTokenCreateRequest. sourcetype is the default value of the sourcetype field for records collected using this token. type: string :param sourcetype: The sourcetype of this HECTokenCreateRequest. :type: str """ self._attrs["sourcetype"] = sourcetype def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class HECTokenCreateResponse(SSCModel): @staticmethod def _from_dict(model: dict) -> "HECTokenCreateResponse": instance = HECTokenCreateResponse.__new__(HECTokenCreateResponse) instance._attrs = model return instance def __init__(self, ack_enabled: "bool" = None, allow_query_string_auth: "bool" = None, created_at: "datetime" = None, created_by: "str" = None, description: "str" = None, disabled: "bool" = None, index: "str" = None, indexes: "List[str]" = None, last_modified_at: "datetime" = None, last_modified_by: "str" = None, name: "str" = None, source: "str" = None, sourcetype: "str" = None, tenant: "str" = None, token: "str" = None, **extra): """HECTokenCreateResponse""" self._attrs = dict() if ack_enabled is not None: self._attrs["ack_enabled"] = ack_enabled if allow_query_string_auth is not None: self._attrs["allow_query_string_auth"] = allow_query_string_auth if created_at is not None: self._attrs["created_at"] = created_at if created_by is not None: self._attrs["created_by"] = created_by if description is not None: self._attrs["description"] = description if disabled is not None: self._attrs["disabled"] = disabled if index is not None: self._attrs["index"] = index if indexes is not None: self._attrs["indexes"] = indexes if last_modified_at is not None: self._attrs["last_modified_at"] = last_modified_at if last_modified_by is not None: self._attrs["last_modified_by"] = last_modified_by if name is not None: self._attrs["name"] = name if source is not None: self._attrs["source"] = source if sourcetype is not None: self._attrs["sourcetype"] = sourcetype if tenant is not None: self._attrs["tenant"] = tenant if token is not None: self._attrs["token"] = token for k, v in extra.items(): self._attrs[k] = v @property def ack_enabled(self) -> "bool": """ Gets the ack_enabled of this HECTokenCreateResponse. ack_enabled is set to true if events sent with the auth token should support indexer acknowledgement type: bool """ return self._attrs.get("ack_enabled") @ack_enabled.setter def ack_enabled(self, ack_enabled: "bool"): """Sets the ack_enabled of this HECTokenCreateResponse. ack_enabled is set to true if events sent with the auth token should support indexer acknowledgement type: bool :param ack_enabled: The ack_enabled of this HECTokenCreateResponse. :type: bool """ self._attrs["ack_enabled"] = ack_enabled @property def allow_query_string_auth(self) -> "bool": """ Gets the allow_query_string_auth of this HECTokenCreateResponse. allow_query_string_auth is set to true if this token can be passed into the ingest endpoint's query parameter for auth type: bool """ return self._attrs.get("allow_query_string_auth") @allow_query_string_auth.setter def allow_query_string_auth(self, allow_query_string_auth: "bool"): """Sets the allow_query_string_auth of this HECTokenCreateResponse. allow_query_string_auth is set to true if this token can be passed into the ingest endpoint's query parameter for auth type: bool :param allow_query_string_auth: The allow_query_string_auth of this HECTokenCreateResponse. :type: bool """ self._attrs["allow_query_string_auth"] = allow_query_string_auth @property def created_at(self) -> "datetime": """ Gets the created_at of this HECTokenCreateResponse. created_at is a timestamp that captures when this token was created. type: string format: date-time """ return self._attrs.get("created_at") @created_at.setter def created_at(self, created_at: "datetime"): """Sets the created_at of this HECTokenCreateResponse. created_at is a timestamp that captures when this token was created. type: string format: date-time :param created_at: The created_at of this HECTokenCreateResponse. :type: datetime """ self._attrs["created_at"] = created_at @property def created_by(self) -> "str": """ Gets the created_by of this HECTokenCreateResponse. created_by is the principal that created the token. type: string """ return self._attrs.get("created_by") @created_by.setter def created_by(self, created_by: "str"): """Sets the created_by of this HECTokenCreateResponse. created_by is the principal that created the token. type: string :param created_by: The created_by of this HECTokenCreateResponse. :type: str """ self._attrs["created_by"] = created_by @property def description(self) -> "str": """ Gets the description of this HECTokenCreateResponse. description is an optional description of the token. type: string """ return self._attrs.get("description") @description.setter def description(self, description: "str"): """Sets the description of this HECTokenCreateResponse. description is an optional description of the token. type: string :param description: The description of this HECTokenCreateResponse. :type: str """ self._attrs["description"] = description @property def disabled(self) -> "bool": """ Gets the disabled of this HECTokenCreateResponse. disabled is set to true if this auth token has been disabled and cannot be used to send events to HECv1 type: bool """ return self._attrs.get("disabled") @disabled.setter def disabled(self, disabled: "bool"): """Sets the disabled of this HECTokenCreateResponse. disabled is set to true if this auth token has been disabled and cannot be used to send events to HECv1 type: bool :param disabled: The disabled of this HECTokenCreateResponse. :type: bool """ self._attrs["disabled"] = disabled @property def index(self) -> "str": """ Gets the index of this HECTokenCreateResponse. index is the default value of the index field for records collected using this token. type: string """ return self._attrs.get("index") @index.setter def index(self, index: "str"): """Sets the index of this HECTokenCreateResponse. index is the default value of the index field for records collected using this token. type: string :param index: The index of this HECTokenCreateResponse. :type: str """ self._attrs["index"] = index @property def indexes(self) -> "List[str]": """ Gets the indexes of this HECTokenCreateResponse. indexes is a list of index names that this token is allowed to send events to type: []string """ return self._attrs.get("indexes") @indexes.setter def indexes(self, indexes: "List[str]"): """Sets the indexes of this HECTokenCreateResponse. indexes is a list of index names that this token is allowed to send events to type: []string :param indexes: The indexes of this HECTokenCreateResponse. :type: List[str] """ self._attrs["indexes"] = indexes @property def last_modified_at(self) -> "datetime": """ Gets the last_modified_at of this HECTokenCreateResponse. last_modified_at is a timestamp that captures when this token was last modified. type: string format: date-time """ return self._attrs.get("last_modified_at") @last_modified_at.setter def last_modified_at(self, last_modified_at: "datetime"): """Sets the last_modified_at of this HECTokenCreateResponse. last_modified_at is a timestamp that captures when this token was last modified. type: string format: date-time :param last_modified_at: The last_modified_at of this HECTokenCreateResponse. :type: datetime """ self._attrs["last_modified_at"] = last_modified_at @property def last_modified_by(self) -> "str": """ Gets the last_modified_by of this HECTokenCreateResponse. last_modified_by is the principal that last modified the token. type: string """ return self._attrs.get("last_modified_by") @last_modified_by.setter def last_modified_by(self, last_modified_by: "str"): """Sets the last_modified_by of this HECTokenCreateResponse. last_modified_by is the principal that last modified the token. type: string :param last_modified_by: The last_modified_by of this HECTokenCreateResponse. :type: str """ self._attrs["last_modified_by"] = last_modified_by @property def name(self) -> "str": """ Gets the name of this HECTokenCreateResponse. name is the name of the token (unique within the tenant that it belongs to). type: string """ return self._attrs.get("name") @name.setter def name(self, name: "str"): """Sets the name of this HECTokenCreateResponse. name is the name of the token (unique within the tenant that it belongs to). type: string :param name: The name of this HECTokenCreateResponse. :type: str """ self._attrs["name"] = name @property def source(self) -> "str": """ Gets the source of this HECTokenCreateResponse. source is the default value of the source field for records collected using this token. type: string """ return self._attrs.get("source") @source.setter def source(self, source: "str"): """Sets the source of this HECTokenCreateResponse. source is the default value of the source field for records collected using this token. type: string :param source: The source of this HECTokenCreateResponse. :type: str """ self._attrs["source"] = source @property def sourcetype(self) -> "str": """ Gets the sourcetype of this HECTokenCreateResponse. sourcetype is the default value of the sourcetype field for records collected using this token. type: string """ return self._attrs.get("sourcetype") @sourcetype.setter def sourcetype(self, sourcetype: "str"): """Sets the sourcetype of this HECTokenCreateResponse. sourcetype is the default value of the sourcetype field for records collected using this token. type: string :param sourcetype: The sourcetype of this HECTokenCreateResponse. :type: str """ self._attrs["sourcetype"] = sourcetype @property def tenant(self) -> "str": """ Gets the tenant of this HECTokenCreateResponse. tenant is the tenant that this token belongs to. type: string """ return self._attrs.get("tenant") @tenant.setter def tenant(self, tenant: "str"): """Sets the tenant of this HECTokenCreateResponse. tenant is the tenant that this token belongs to. type: string :param tenant: The tenant of this HECTokenCreateResponse. :type: str """ self._attrs["tenant"] = tenant @property def token(self) -> "str": """ Gets the token of this HECTokenCreateResponse. token is the token value. type: string """ return self._attrs.get("token") @token.setter def token(self, token: "str"): """Sets the token of this HECTokenCreateResponse. token is the token value. type: string :param token: The token of this HECTokenCreateResponse. :type: str """ self._attrs["token"] = token def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class HECTokenUpdateRequest(SSCModel): @staticmethod def _from_dict(model: dict) -> "HECTokenUpdateRequest": instance = HECTokenUpdateRequest.__new__(HECTokenUpdateRequest) instance._attrs = model return instance def __init__(self, ack_enabled: "bool" = None, allow_query_string_auth: "bool" = None, description: "str" = None, disabled: "bool" = None, index: "str" = None, indexes: "List[str]" = None, source: "str" = None, sourcetype: "str" = None, **extra): """HECTokenUpdateRequest""" self._attrs = dict() if ack_enabled is not None: self._attrs["ack_enabled"] = ack_enabled if allow_query_string_auth is not None: self._attrs["allow_query_string_auth"] = allow_query_string_auth if description is not None: self._attrs["description"] = description if disabled is not None: self._attrs["disabled"] = disabled if index is not None: self._attrs["index"] = index if indexes is not None: self._attrs["indexes"] = indexes if source is not None: self._attrs["source"] = source if sourcetype is not None: self._attrs["sourcetype"] = sourcetype for k, v in extra.items(): self._attrs[k] = v @property def ack_enabled(self) -> "bool": """ Gets the ack_enabled of this HECTokenUpdateRequest. ack_enabled is set to true if events sent with the auth token should support indexer acknowledgement type: *bool """ return self._attrs.get("ack_enabled") @ack_enabled.setter def ack_enabled(self, ack_enabled: "bool"): """Sets the ack_enabled of this HECTokenUpdateRequest. ack_enabled is set to true if events sent with the auth token should support indexer acknowledgement type: *bool :param ack_enabled: The ack_enabled of this HECTokenUpdateRequest. :type: bool """ self._attrs["ack_enabled"] = ack_enabled @property def allow_query_string_auth(self) -> "bool": """ Gets the allow_query_string_auth of this HECTokenUpdateRequest. allow_query_string_auth is set to true if this token can be passed into the ingest endpoint's query parameter for auth type: *bool """ return self._attrs.get("allow_query_string_auth") @allow_query_string_auth.setter def allow_query_string_auth(self, allow_query_string_auth: "bool"): """Sets the allow_query_string_auth of this HECTokenUpdateRequest. allow_query_string_auth is set to true if this token can be passed into the ingest endpoint's query parameter for auth type: *bool :param allow_query_string_auth: The allow_query_string_auth of this HECTokenUpdateRequest. :type: bool """ self._attrs["allow_query_string_auth"] = allow_query_string_auth @property def description(self) -> "str": """ Gets the description of this HECTokenUpdateRequest. description is an optional description of the token. type: *string """ return self._attrs.get("description") @description.setter def description(self, description: "str"): """Sets the description of this HECTokenUpdateRequest. description is an optional description of the token. type: *string :param description: The description of this HECTokenUpdateRequest. :type: str """ self._attrs["description"] = description @property def disabled(self) -> "bool": """ Gets the disabled of this HECTokenUpdateRequest. disabled is set to true if this auth token has been disabled and cannot be used to send events to HECv1 type: *bool """ return self._attrs.get("disabled") @disabled.setter def disabled(self, disabled: "bool"): """Sets the disabled of this HECTokenUpdateRequest. disabled is set to true if this auth token has been disabled and cannot be used to send events to HECv1 type: *bool :param disabled: The disabled of this HECTokenUpdateRequest. :type: bool """ self._attrs["disabled"] = disabled @property def index(self) -> "str": """ Gets the index of this HECTokenUpdateRequest. index is the default value of the index field for records collected using this token type: *string """ return self._attrs.get("index") @index.setter def index(self, index: "str"): """Sets the index of this HECTokenUpdateRequest. index is the default value of the index field for records collected using this token type: *string :param index: The index of this HECTokenUpdateRequest. :type: str """ self._attrs["index"] = index @property def indexes(self) -> "List[str]": """ Gets the indexes of this HECTokenUpdateRequest. indexes is a list of index names that this token is allowed to send events to type: []string """ return self._attrs.get("indexes") @indexes.setter def indexes(self, indexes: "List[str]"): """Sets the indexes of this HECTokenUpdateRequest. indexes is a list of index names that this token is allowed to send events to type: []string :param indexes: The indexes of this HECTokenUpdateRequest. :type: List[str] """ self._attrs["indexes"] = indexes @property def source(self) -> "str": """ Gets the source of this HECTokenUpdateRequest. source is the default value of the source field for records collected using this token type: *string """ return self._attrs.get("source") @source.setter def source(self, source: "str"): """Sets the source of this HECTokenUpdateRequest. source is the default value of the source field for records collected using this token type: *string :param source: The source of this HECTokenUpdateRequest. :type: str """ self._attrs["source"] = source @property def sourcetype(self) -> "str": """ Gets the sourcetype of this HECTokenUpdateRequest. sourcetype is the default value of the sourcetype field for records collected using this token type: *string """ return self._attrs.get("sourcetype") @sourcetype.setter def sourcetype(self, sourcetype: "str"): """Sets the sourcetype of this HECTokenUpdateRequest. sourcetype is the default value of the sourcetype field for records collected using this token type: *string :param sourcetype: The sourcetype of this HECTokenUpdateRequest. :type: str """ self._attrs["sourcetype"] = sourcetype def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class HTTPResponse(SSCModel): @staticmethod def _from_dict(model: dict) -> "HTTPResponse": instance = HTTPResponse.__new__(HTTPResponse) instance._attrs = model return instance def __init__(self, code: "str" = None, details: "object" = None, message: "str" = None, **extra): """HTTPResponse""" self._attrs = dict() if code is not None: self._attrs["code"] = code if details is not None: self._attrs["details"] = details if message is not None: self._attrs["message"] = message for k, v in extra.items(): self._attrs[k] = v @property def code(self) -> "str": """ Gets the code of this HTTPResponse. """ return self._attrs.get("code") @code.setter def code(self, code: "str"): """Sets the code of this HTTPResponse. :param code: The code of this HTTPResponse. :type: str """ self._attrs["code"] = code @property def details(self) -> "dict": """ Gets the details of this HTTPResponse. """ return self._attrs.get("details") @details.setter def details(self, details: "dict"): """Sets the details of this HTTPResponse. :param details: The details of this HTTPResponse. :type: object """ self._attrs["details"] = details @property def message(self) -> "str": """ Gets the message of this HTTPResponse. """ return self._attrs.get("message") @message.setter def message(self, message: "str"): """Sets the message of this HTTPResponse. :param message: The message of this HTTPResponse. :type: str """ self._attrs["message"] = message def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class Metric(SSCModel): @staticmethod def _from_dict(model: dict) -> "Metric": instance = Metric.__new__(Metric) instance._attrs = model return instance def __init__(self, name: "str", dimensions: "Dict[str, str]" = None, type: "str" = None, unit: "str" = None, value: "float" = None, **extra): """Metric""" self._attrs = dict() if name is not None: self._attrs["name"] = name if dimensions is not None: self._attrs["dimensions"] = dimensions if type is not None: self._attrs["type"] = type if unit is not None: self._attrs["unit"] = unit if value is not None: self._attrs["value"] = value for k, v in extra.items(): self._attrs[k] = v @property def name(self) -> "str": """ Gets the name of this Metric. Name of the metric e.g. CPU, Memory etc. """ return self._attrs.get("name") @name.setter def name(self, name: "str"): """Sets the name of this Metric. Name of the metric e.g. CPU, Memory etc. :param name: The name of this Metric. :type: str """ if name is None: raise ValueError("Invalid value for `name`, must not be `None`") self._attrs["name"] = name @property def dimensions(self) -> "Dict[str, str]": """ Gets the dimensions of this Metric. Dimensions allow metrics to be classified e.g. {\"Server\":\"nginx\", \"Region\":\"us-west-1\", ...} """ return self._attrs.get("dimensions") @dimensions.setter def dimensions(self, dimensions: "Dict[str, str]"): """Sets the dimensions of this Metric. Dimensions allow metrics to be classified e.g. {\"Server\":\"nginx\", \"Region\":\"us-west-1\", ...} :param dimensions: The dimensions of this Metric. :type: Dict[str, str] """ self._attrs["dimensions"] = dimensions @property def type(self) -> "str": """ Gets the type of this Metric. Type of metric. Default is g for gauge. """ return self._attrs.get("type") @type.setter def type(self, type: "str"): """Sets the type of this Metric. Type of metric. Default is g for gauge. :param type: The type of this Metric. :type: str """ self._attrs["type"] = type @property def unit(self) -> "str": """ Gets the unit of this Metric. Unit of the metric e.g. percent, megabytes, seconds etc. """ return self._attrs.get("unit") @unit.setter def unit(self, unit: "str"): """Sets the unit of this Metric. Unit of the metric e.g. percent, megabytes, seconds etc. :param unit: The unit of this Metric. :type: str """ self._attrs["unit"] = unit @property def value(self) -> "float": """ Gets the value of this Metric. Value of the metric. If not specified, it will be defaulted to 0. """ return self._attrs.get("value") @value.setter def value(self, value: "float"): """Sets the value of this Metric. Value of the metric. If not specified, it will be defaulted to 0. :param value: The value of this Metric. :type: float """ self._attrs["value"] = value def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class MetricAttribute(SSCModel): @staticmethod def _from_dict(model: dict) -> "MetricAttribute": instance = MetricAttribute.__new__(MetricAttribute) instance._attrs = model return instance def __init__(self, default_dimensions: "Dict[str, str]" = None, default_type: "str" = None, default_unit: "str" = None, **extra): """MetricAttribute""" self._attrs = dict() if default_dimensions is not None: self._attrs["defaultDimensions"] = default_dimensions if default_type is not None: self._attrs["defaultType"] = default_type if default_unit is not None: self._attrs["defaultUnit"] = default_unit for k, v in extra.items(): self._attrs[k] = v @property def default_dimensions(self) -> "Dict[str, str]": """ Gets the default_dimensions of this MetricAttribute. Optional. If set, individual metrics inherit these dimensions and can override any and/or all of them. """ return self._attrs.get("defaultDimensions") @default_dimensions.setter def default_dimensions(self, default_dimensions: "Dict[str, str]"): """Sets the default_dimensions of this MetricAttribute. Optional. If set, individual metrics inherit these dimensions and can override any and/or all of them. :param default_dimensions: The default_dimensions of this MetricAttribute. :type: Dict[str, str] """ self._attrs["defaultDimensions"] = default_dimensions @property def default_type(self) -> "str": """ Gets the default_type of this MetricAttribute. Optional. If set, individual metrics inherit this type and can optionally override. """ return self._attrs.get("defaultType") @default_type.setter def default_type(self, default_type: "str"): """Sets the default_type of this MetricAttribute. Optional. If set, individual metrics inherit this type and can optionally override. :param default_type: The default_type of this MetricAttribute. :type: str """ self._attrs["defaultType"] = default_type @property def default_unit(self) -> "str": """ Gets the default_unit of this MetricAttribute. Optional. If set, individual metrics inherit this unit and can optionally override. """ return self._attrs.get("defaultUnit") @default_unit.setter def default_unit(self, default_unit: "str"): """Sets the default_unit of this MetricAttribute. Optional. If set, individual metrics inherit this unit and can optionally override. :param default_unit: The default_unit of this MetricAttribute. :type: str """ self._attrs["defaultUnit"] = default_unit def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class MetricEvent(SSCModel): @staticmethod def _from_dict(model: dict) -> "MetricEvent": instance = MetricEvent.__new__(MetricEvent) instance._attrs = model return instance def __init__(self, body: "List[Metric]", attributes: "MetricAttribute" = None, host: "str" = None, id: "str" = None, nanos: "int" = None, source: "str" = None, sourcetype: "str" = None, timestamp: "int" = None, **extra): """MetricEvent""" self._attrs = dict() if body is not None: self._attrs["body"] = body if attributes is not None: self._attrs["attributes"] = attributes.to_dict() if host is not None: self._attrs["host"] = host if id is not None: self._attrs["id"] = id if nanos is not None: self._attrs["nanos"] = nanos if source is not None: self._attrs["source"] = source if sourcetype is not None: self._attrs["sourcetype"] = sourcetype if timestamp is not None: self._attrs["timestamp"] = timestamp for k, v in extra.items(): self._attrs[k] = v @property def body(self) -> "List[Metric]": """ Gets the body of this MetricEvent. Specifies multiple related metrics e.g. Memory, CPU etc. """ return [Metric._from_dict(i) for i in self._attrs.get("body")] @body.setter def body(self, body: "List[Metric]"): """Sets the body of this MetricEvent. Specifies multiple related metrics e.g. Memory, CPU etc. :param body: The body of this MetricEvent. :type: List[Metric] """ if body is None: raise ValueError("Invalid value for `body`, must not be `None`") self._attrs["body"] = body @property def attributes(self) -> "MetricAttribute": """ Gets the attributes of this MetricEvent. """ return MetricAttribute._from_dict(self._attrs["attributes"]) @attributes.setter def attributes(self, attributes: "MetricAttribute"): """Sets the attributes of this MetricEvent. :param attributes: The attributes of this MetricEvent. :type: MetricAttribute """ self._attrs["attributes"] = attributes.to_dict() @property def host(self) -> "str": """ Gets the host of this MetricEvent. The host value assigned to the event data. Typically, this is the hostname of the client from which you are sending data. """ return self._attrs.get("host") @host.setter def host(self, host: "str"): """Sets the host of this MetricEvent. The host value assigned to the event data. Typically, this is the hostname of the client from which you are sending data. :param host: The host of this MetricEvent. :type: str """ self._attrs["host"] = host @property def id(self) -> "str": """ Gets the id of this MetricEvent. An optional ID that uniquely identifies the metric data. It is used to deduplicate the data if same data is set multiple times. If ID is not specified, it will be assigned by the system. """ return self._attrs.get("id") @id.setter def id(self, id: "str"): """Sets the id of this MetricEvent. An optional ID that uniquely identifies the metric data. It is used to deduplicate the data if same data is set multiple times. If ID is not specified, it will be assigned by the system. :param id: The id of this MetricEvent. :type: str """ self._attrs["id"] = id @property def nanos(self) -> "int": """ Gets the nanos of this MetricEvent. Optional nanoseconds part of the timestamp. """ return self._attrs.get("nanos") @nanos.setter def nanos(self, nanos: "int"): """Sets the nanos of this MetricEvent. Optional nanoseconds part of the timestamp. :param nanos: The nanos of this MetricEvent. :type: int """ self._attrs["nanos"] = nanos @property def source(self) -> "str": """ Gets the source of this MetricEvent. The source value to assign to the event data. For example, if you are sending data from an app that you are developing, set this key to the name of the app. """ return self._attrs.get("source") @source.setter def source(self, source: "str"): """Sets the source of this MetricEvent. The source value to assign to the event data. For example, if you are sending data from an app that you are developing, set this key to the name of the app. :param source: The source of this MetricEvent. :type: str """ self._attrs["source"] = source @property def sourcetype(self) -> "str": """ Gets the sourcetype of this MetricEvent. The sourcetype value assigned to the event data. """ return self._attrs.get("sourcetype") @sourcetype.setter def sourcetype(self, sourcetype: "str"): """Sets the sourcetype of this MetricEvent. The sourcetype value assigned to the event data. :param sourcetype: The sourcetype of this MetricEvent. :type: str """ self._attrs["sourcetype"] = sourcetype @property def timestamp(self) -> "int": """ Gets the timestamp of this MetricEvent. Epoch time in milliseconds. """ return self._attrs.get("timestamp") @timestamp.setter def timestamp(self, timestamp: "int"): """Sets the timestamp of this MetricEvent. Epoch time in milliseconds. :param timestamp: The timestamp of this MetricEvent. :type: int """ self._attrs["timestamp"] = timestamp def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None} class UploadSuccessResponse(SSCModel): @staticmethod def _from_dict(model: dict) -> "UploadSuccessResponse": instance = UploadSuccessResponse.__new__(UploadSuccessResponse) instance._attrs = model return instance def __init__(self, code: "str" = None, details: "FileUploadDetails" = None, message: "str" = None, **extra): """UploadSuccessResponse""" self._attrs = dict() if code is not None: self._attrs["code"] = code if details is not None: self._attrs["details"] = details.to_dict() if message is not None: self._attrs["message"] = message for k, v in extra.items(): self._attrs[k] = v @property def code(self) -> "str": """ Gets the code of this UploadSuccessResponse. """ return self._attrs.get("code") @code.setter def code(self, code: "str"): """Sets the code of this UploadSuccessResponse. :param code: The code of this UploadSuccessResponse. :type: str """ self._attrs["code"] = code @property def details(self) -> "FileUploadDetails": """ Gets the details of this UploadSuccessResponse. """ return FileUploadDetails._from_dict(self._attrs["details"]) @details.setter def details(self, details: "FileUploadDetails"): """Sets the details of this UploadSuccessResponse. :param details: The details of this UploadSuccessResponse. :type: FileUploadDetails """ self._attrs["details"] = details.to_dict() @property def message(self) -> "str": """ Gets the message of this UploadSuccessResponse. """ return self._attrs.get("message") @message.setter def message(self, message: "str"): """Sets the message of this UploadSuccessResponse. :param message: The message of this UploadSuccessResponse. :type: str """ self._attrs["message"] = message def to_dict(self): return {k: v for (k, v) in self._attrs.items() if v is not None}

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31,510

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Python

Account/operator_emulator/ui_emulator.py

fititnt/mydata-sdk

19d7a2ddbc3b5a05665539fbcc7f461c13793e03

[ "MIT" ]

null

Account/operator_emulator/ui_emulator.py

fititnt/mydata-sdk

19d7a2ddbc3b5a05665539fbcc7f461c13793e03

[ "MIT" ]

2

2018-04-20T23:07:01.000Z

2018-04-21T01:01:20.000Z

Account/operator_emulator/ui_emulator.py

fititnt/mydata-sdk--hiit

19d7a2ddbc3b5a05665539fbcc7f461c13793e03

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Minimum viable account - MyData Operator UI Emulator __author__ = "Jani Yli-Kantola" __copyright__ = "Digital Health Revolution (c) 2016" __credits__ = ["Harri Hirvonsalo", "Aleksi Palomäki"] __license__ = "MIT" __version__ = "0.0.1" __maintainer__ = "Jani Yli-Kantola" __contact__ = "https://github.com/HIIT/mydata-stack" __status__ = "Development" __date__ = 12.8.2016 """ from uuid import uuid4 import requests import time from requests.auth import HTTPBasicAuth import json request_statuses = [] account_ip = "http://127.0.0.1" account_port = "8080" account_host = account_ip+":"+account_port headers = {'Content-Type': 'application/json'} account_id = "" particular_id = "" contacts_id = "" predefined_account_username = "testUser" predefined_account_password = "Hello" username = "example_username-" + str(uuid4()) password = "example_password" account_template = { "data": { "type": "Account", "attributes": { 'firstName': 'ExampleFirstName', 'lastName': 'ExampleLastName', 'dateOfBirth': '2010-05-14', 'email': username + '@examlpe.org', 'username': username, 'password': password, 'acceptTermsOfService': 'True' } } } particular_template_for_patch = { "data": { "type": "Particular", "attributes": { 'lastname': 'NewExampleLastName' } } } contact_template = { "data": { "type": "Contact", "attributes": { 'address1': 'Example address 1', 'address2': 'Example address 2', 'postalCode': '97584', 'city': 'Example city', 'state': 'Example state', 'country': 'Example country', 'type': 'Personal', 'primary': 'True' } } } contact_template_for_patch = { "data": { "type": "Contact", "attributes": { 'address1': 'Example address 1', 'address2': 'Example address 2', 'postalCode': '65784', 'city': 'Example city', 'state': 'Example state', 'country': 'Example country', 'type': 'Personal', 'primary': 'False' } } } email_template = { "data": { "type": "Email", "attributes": { 'email': 'erkki@example.com', 'type': 'Personal', 'primary': 'True' } } } email_template_for_patch = { "data": { "type": "Email", "attributes": { 'email': 'pasi@example.org', 'type': 'School', 'primary': 'False' } } } telephone_template = { "data": { "type": "Telephone", "attributes": { 'tel': '0501234567', 'type': 'Personal', 'primary': 'True' } } } telephone_template_for_patch = { "data": { "type": "Telephone", "attributes": { 'tel': '+358 50 123 4567', 'type': 'School', 'primary': 'False' } } } setting_template = { "data": { "type": "Setting", "attributes": { 'key': 'lang', 'value': 'fi' } } } setting_template_for_patch = { "data": { "type": "Setting", "attributes": { 'key': 'lang', 'value': 'se' } } } def post(host=None, endpoint=None, headers=None, data=None): if host is None: raise AttributeError("Provide host as parameter") if endpoint is None: raise AttributeError("Provide endpoint as parameter") if headers is None: raise AttributeError("Provide headers as parameter") if data is None: raise AttributeError("Provide data as parameter") url = host + endpoint print("Endpoint: " + endpoint) print("Headers: " + json.dumps(headers)) print("Payload: " + json.dumps(data)) req = requests.post(url, headers=headers, json=data) status_code = str(req.status_code) print ("Response status: " + str(req.status_code)) try: response_data = json.loads(req.text) except Exception as exp: print(repr(exp)) print("req.text: " + repr(req.text)) response_data = repr(req.text) return status_code, response_data def patch(host=None, endpoint=None, headers=None, data=None): if host is None: raise AttributeError("Provide host as parameter") if endpoint is None: raise AttributeError("Provide endpoint as parameter") if headers is None: raise AttributeError("Provide headers as parameter") if data is None: raise AttributeError("Provide data as parameter") url = host + endpoint print("Endpoint: " + endpoint) print("Headers: " + json.dumps(headers)) print("Payload: " + json.dumps(data)) req = requests.patch(url, headers=headers, json=data) status_code = str(req.status_code) print ("Response status: " + str(req.status_code)) try: response_data = json.loads(req.text) except Exception as exp: print(repr(exp)) print("req.text: " + repr(req.text)) response_data = repr(req.text) return status_code, response_data def get(host=None, endpoint=None, headers=None, username=None, password=None): if host is None: raise AttributeError("Provide host as parameter") if endpoint is None: raise AttributeError("Provide endpoint as parameter") if headers is None: raise AttributeError("Provide headers as parameter") url = host + endpoint print("Endpoint: " + endpoint) print("Headers: " + json.dumps(headers)) if username is not None and password is not None: req = requests.get(url, headers=headers, auth=HTTPBasicAuth(username=username, password=password)) else: req = requests.get(url, headers=headers) status_code = str(req.status_code) print ("Response status: " + str(req.status_code)) try: response_data = json.loads(req.text) except Exception as exp: print(repr(exp)) print("req.text: " + repr(req.text)) response_data = repr(req.text) return status_code, response_data ######### Actions ################################## # Create Account and Authenticate ################################## label = "# \n# Create Account and Authenticate \n#################################" print(label) request_statuses.append(label) if not predefined_account_username and not predefined_account_password: # # Create Account title = "Create Account" print(title) try: account = post(host=account_host, endpoint="/api/accounts/", headers=headers, data=account_template) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + account[0] + ": " + json.dumps(account[1]) print('request_response: ' + request_response) request_statuses.append(request_response) account_id = str(account[1]['data'].get("id", "None")) print ("Response " + account[0] + ": " + json.dumps(account[1])) print ("Account ID: " + account_id) else: print("Using predefined account") username = predefined_account_username password = predefined_account_password # # Authenticate print ("------------------------------------") title = "Authenticate" print(title) try: api_auth = get(host=account_host, endpoint="/api/auth/user/", headers=headers, username=username, password=password) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + api_auth[0] + ": " + json.dumps(api_auth[1]) print('request_response: ' + request_response) request_statuses.append(request_response) apikey = str(api_auth[1].get("Api-Key", "None")) account_id = str(api_auth[1].get("account_id", "None")) headers['Api-Key'] = apikey print ("Response " + api_auth[0] + ": " + json.dumps(api_auth[1])) print ("apikey: " + apikey) # # ################################## # # PARTICULARS # ################################## label = "# \n# PARTICULARS \n#################################" print(label) request_statuses.append(label) title = "List Particulars" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/particulars/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) particular_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("particular_id: " + particular_id) print ("------------------------------------") title = "One Particular" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/particulars/" + particular_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entry[0] + ": " + json.dumps(entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("particular_id: " + str(entry[1]['data'].get("id", "None"))) print ("------------------------------------") title = "Patch Particular" print(title) try: particular_template_for_patch['data']['id'] = str(particular_id) updated_entry = patch(host=account_host, endpoint="/api/accounts/" + account_id + "/particulars/" + particular_id + "/", headers=headers, data=particular_template_for_patch) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + updated_entry[0] + ": " + json.dumps(updated_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + updated_entry[0] + ": " + json.dumps(updated_entry[1])) # ################################## # # CONTACTS # ################################## label = "# \n# CONTACTS \n#################################" print(label) request_statuses.append(label) title = "Add Contact" print(title) try: new_entry = post(host=account_host, endpoint="/api/accounts/" + account_id + "/contacts/", headers=headers, data=contact_template) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + new_entry[0] + ": " + json.dumps(new_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + new_entry[0] + ": " + json.dumps(new_entry[1])) print ("------------------------------------") title = "List Contacts" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/contacts/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) contacts_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("contacts_id: " + contacts_id) print ("------------------------------------") title = "One Contact" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/contacts/" + contacts_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("contacts_id: " + str(entry[1]['data'].get("id", "None"))) print ("------------------------------------") title = "Patch Contact" print(title) try: contact_template_for_patch['data']['id'] = str(contacts_id) updated_entry = patch(host=account_host, endpoint="/api/accounts/" + account_id + "/contacts/" + contacts_id + "/", headers=headers, data=contact_template_for_patch) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + updated_entry[0] + ": " + json.dumps(updated_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + updated_entry[0] + ": " + json.dumps(updated_entry[1])) # ################################## # # EMAIL # ################################## label = "# \n# EMAIL \n#################################" print(label) request_statuses.append(label) title = "Add Email" print(title) try: new_entry = post(host=account_host, endpoint="/api/accounts/" + account_id + "/emails/", headers=headers, data=email_template) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + new_entry[0] + ": " + json.dumps(new_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + new_entry[0] + ": " + json.dumps(new_entry[1])) print ("------------------------------------") title = "List Emails" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/emails/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) email_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("email_id: " + email_id) print ("------------------------------------") title = "One Email" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/emails/" + email_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("email_id: " + str(entry[1]['data'].get("id", "None"))) print ("------------------------------------") title = "Patch Email" print(title) try: email_template_for_patch['data']['id'] = str(email_id) updated_entry = patch(host=account_host, endpoint="/api/accounts/" + account_id + "/emails/" + email_id + "/", headers=headers, data=email_template_for_patch) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + updated_entry[0] + ": " + json.dumps(updated_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + updated_entry[0] + ": " + json.dumps(updated_entry[1])) # ################################## # # TELEPHONE # ################################## label = "# \n# TELEPHONE \n#################################" print(label) request_statuses.append(label) title = "Add Telephone" print(title) try: new_entry = post(host=account_host, endpoint="/api/accounts/" + account_id + "/telephones/", headers=headers, data=telephone_template) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + new_entry[0] + ": " + json.dumps(new_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + new_entry[0] + ": " + json.dumps(new_entry[1])) print ("------------------------------------") title = "List Telephones" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/telephones/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) telephones_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("telephones_id: " + telephones_id) print ("------------------------------------") title = "One Telephone" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/telephones/" + telephones_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("telephones_id: " + str(entry[1]['data'].get("id", "None"))) print ("------------------------------------") title = "Patch Telephone" print(title) try: telephone_template_for_patch['data']['id'] = str(telephones_id) updated_entry = patch(host=account_host, endpoint="/api/accounts/" + account_id + "/telephones/" + telephones_id + "/", headers=headers, data=telephone_template_for_patch) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + updated_entry[0] + ": " + json.dumps(updated_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + updated_entry[0] + ": " + json.dumps(updated_entry[1])) # ################################## # # SETTINGS # ################################## label = "# \n# SETTINGS \n#################################" print(label) request_statuses.append(label) title = "Add Setting" print(title) try: new_entry = post(host=account_host, endpoint="/api/accounts/" + account_id + "/settings/", headers=headers, data=setting_template) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + new_entry[0] + ": " + json.dumps(new_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + new_entry[0] + ": " + json.dumps(new_entry[1])) print ("------------------------------------") title = "List Settings" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/settings/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) settings_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("settings_id: " + settings_id) print ("------------------------------------") title = "One Setting" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/settings/" + settings_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("settings_id: " + str(entry[1]['data'].get("id", "None"))) print ("------------------------------------") title = "Patch Setting" print(title) try: setting_template_for_patch['data']['id'] = str(settings_id) updated_entry = patch(host=account_host, endpoint="/api/accounts/" + account_id + "/settings/" + settings_id + "/", headers=headers, data=setting_template_for_patch) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + updated_entry[0] + ": " + json.dumps(updated_entry[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + updated_entry[0] + ": " + json.dumps(updated_entry[1])) # ################################## # # EVENT LOGS # ################################## # # label = "# \n# EVENT LOGS \n#################################" # # print(label) # # request_statuses.append(label) # # # # print ("------------------------------------") # # title = "List Events" # # print(title) # # try: # # entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/logs/events/", headers=headers) # # except Exception as exp: # # print(title + ": " + repr(exp)) # # request_response = title + ": " + repr(exp) # # request_statuses.append(request_response) # # raise # # else: # # request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) # # print('request_response: ' + request_response) # # request_statuses.append(request_response) # # event_log_id = str(entries[1]['data'][0].get("id", "None")) # # print ("Response " + new_entry[0] + ": " + json.dumps(new_entry[1])) # # print ("event_log_id: " + event_log_id) # # # # # # print ("------------------------------------") # # title = "One Event" # # print(title) # # try: # # entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/logs/events/" + event_log_id + "/", headers=headers) # # except Exception as exp: # # print(title + ": " + repr(exp)) # # request_response = title + ": " + repr(exp) # # request_statuses.append(request_response) # # raise # # else: # # request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) # # print ("Response " + entry[0] + ": " + json.dumps(entry[1])) # # print ("event_log_id: " + str(entry[1]['data'].get("id", "None"))) # # # ################################## # # Service Link Records # ################################## label = "# \n# Service Link Records \n#################################" print(label) request_statuses.append(label) print ("------------------------------------") title = "Service Link Records" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/servicelinks/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) slr_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("slr_id: " + slr_id) print ("------------------------------------") title = "One Service Link Record" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/servicelinks/" + slr_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("slr_id: " + str(entry[1]['data'].get("id", "None"))) ################################## # Service Link Status Records ################################## label = "# \n# Service Link Status Records \n#################################" print(label) request_statuses.append(label) print ("------------------------------------") title = "Service Link Status Records" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/servicelinks/" + slr_id + "/statuses/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) slsr_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("slsr_id: " + slsr_id) print ("------------------------------------") title = "One Service Link Status Record" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/servicelinks/" + slr_id + "/statuses/" + slsr_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("slsr_id: " + str(entry[1]['data'].get("id", "None"))) ################################## # Consent Records ################################## label = "# \n# Consent Records \n#################################" print(label) request_statuses.append(label) print ("------------------------------------") title = "Consent Records" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/servicelinks/" + slr_id + "/consents/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) cr_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("cr_id: " + cr_id) print ("------------------------------------") title = "One Consent Record" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/servicelinks/" + slr_id + "/consents/" + cr_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("cr_id: " + str(entry[1]['data'].get("id", "None"))) ################################## # Consent Status Records ################################## label = "# \n# Consent Status Records \n#################################" print(label) request_statuses.append(label) print ("------------------------------------") title = "Consent Status Records" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/servicelinks/" + slr_id + "/consents/" + cr_id + "/statuses/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) csr_id = str(entries[1]['data'][0].get("id", "None")) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) print ("csr_id: " + csr_id) print ("------------------------------------") title = "One Consent Status Record" print(title) try: entry = get(host=account_host, endpoint="/api/accounts/" + account_id + "/servicelinks/" + slr_id + "/consents/" + cr_id + "/statuses/" + csr_id + "/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_statuses.append(title + ": " + entry[0] + ": " + json.dumps(entry[1])) request_statuses.append("csr_id: " + str(entry[1]['data'].get("id", "None"))) print ("Response " + entry[0] + ": " + json.dumps(entry[1])) print ("csr_id: " + str(entry[1]['data'].get("id", "None"))) ################################## # Export Account ################################## label = "# \n# Account Export \n#################################" print(label) request_statuses.append(label) print ("------------------------------------") title = "Account Export" print(title) try: entries = get(host=account_host, endpoint="/api/accounts/" + account_id + "/export/", headers=headers) except Exception as exp: print(title + ": " + repr(exp)) request_response = title + ": " + repr(exp) request_statuses.append(request_response) raise else: request_response = title + ": " + entries[0] + ": " + json.dumps(entries[1]) print('request_response: ' + request_response) request_statuses.append(request_response) print ("Response " + entries[0] + ": " + json.dumps(entries[1])) ################################# ################################# ################################# ################################# # REPORT # ################################# print ("=====================================") print("Request report") for request in request_statuses: print(request)

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0.804817

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0

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1

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null

0

7

625e76f051cc95f3c0cf8e882023e1bab40245d3

53

py

Python

module/__init__.py

abhinavg97/baseline_simpletransformers_classification

178881178b211e321683c1e338bb06e6e334d7fa

[ "Unlicense" ]

null

module/__init__.py

abhinavg97/baseline_simpletransformers_classification

178881178b211e321683c1e338bb06e6e334d7fa

[ "Unlicense" ]

null

module/__init__.py

abhinavg97/baseline_simpletransformers_classification

178881178b211e321683c1e338bb06e6e334d7fa

[ "Unlicense" ]

null

#from module import utils from module import metrics

17.666667

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null

0

1

0

1

0

1

0

7

6261216676c424621c685241922bd58b6cf51592

42

py

Python

lib/__init__.py

dato2003/Chat-1

29d07907eb94609e0f1c43ded45e08c3d82c4f39

[ "Apache-2.0" ]

null

lib/__init__.py

dato2003/Chat-1

29d07907eb94609e0f1c43ded45e08c3d82c4f39

[ "Apache-2.0" ]

null

lib/__init__.py

dato2003/Chat-1

29d07907eb94609e0f1c43ded45e08c3d82c4f39

[ "Apache-2.0" ]

1

2018-08-04T18:37:14.000Z

from . import database from . import types

21

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65cb238608ce766642b798e676267ace846dd7f0

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py

Python

modules/simple_wires/test_solver.py

Ao-Re/keep-typing-and-nobody-explodes

deff70c384b3271315acd49bcbfd62c05ed9a7ce

[ "MIT" ]

null

modules/simple_wires/test_solver.py

Ao-Re/keep-typing-and-nobody-explodes

deff70c384b3271315acd49bcbfd62c05ed9a7ce

[ "MIT" ]

null

modules/simple_wires/test_solver.py

Ao-Re/keep-typing-and-nobody-explodes

deff70c384b3271315acd49bcbfd62c05ed9a7ce

[ "MIT" ]

2

2020-10-04T17:04:31.000Z

2020-10-20T16:59:50.000Z

import unittest from .solver import solve_simple_wires class TestSimpleWireSolver(unittest.TestCase): def test_ThreeWires(self): input=['yellow', 'yellow', 'black'] self.assertEqual(solve_simple_wires(input, True), 'Cut the second wire') input=['yellow','red','white'] self.assertEqual(solve_simple_wires(input, True), 'Cut the last wire') input=['blue','red','blue'] self.assertEqual(solve_simple_wires(input, True), 'Cut the last blue wire') input=['white','white','red'] self.assertEqual(solve_simple_wires(input, True), 'Cut the last wire') def test_FourWires(self): input=['red', 'red', 'yellow', 'yellow'] self.assertEqual(solve_simple_wires(input, True), 'Cut the last red wire') input=['white','black','yellow','yellow'] self.assertEqual(solve_simple_wires(input, True), 'Cut the first wire') input=['white','black','yellow','blue'] self.assertEqual(solve_simple_wires(input, True), 'Cut the first wire') input=['white','yellow','yellow','white'] self.assertEqual(solve_simple_wires(input, True), 'Cut the last wire') input=['white','yellow','black','white'] self.assertEqual(solve_simple_wires(input, True), 'Cut the second wire') def test_FiveWires(self): input=['red','blue','yellow','red','black'] self.assertEqual(solve_simple_wires(input, True), 'Cut the fourth wire') input=['red','blue','yellow','blue','yellow'] self.assertEqual(solve_simple_wires(input, True), 'Cut the first wire') input=['red','blue','yellow','red','white'] self.assertEqual(solve_simple_wires(input, True), 'Cut the second wire') input=['black','blue','yellow','red','white'] self.assertEqual(solve_simple_wires(input, True), 'Cut the first wire') def test_SixWires(self): input=['red','blue','red','red','black','red'] self.assertEqual(solve_simple_wires(input, True), 'Cut the third wire') input=['red','white','yellow','white','black','red'] self.assertEqual(solve_simple_wires(input, True), 'Cut the fourth wire') input=['blue','blue','blue','white','black','white'] self.assertEqual(solve_simple_wires(input, False), 'Cut the last wire') input=['blue','blue','blue','red','black','white'] self.assertEqual(solve_simple_wires(input, False), 'Cut the fourth wire') def test_Invalid(self): input=['red'] self.assertEqual(solve_simple_wires(input, True), 'Invalid')

55.73913

83

0.646646

324

2,564

4.984568

0.108025

0.129412

0.188235

0.289783

0.780186

0.726316

0.713313

0.683591

0

0.178237

2,564

45

84

56.977778

0.766493

0

0.272727

0

0.2617

0

0.409091

1

0.113636

false

0

0.045455

0

0.181818

0

null

0

1

0

1

0

1

0

null

0

1

0

7

65eca2b219128d394778fff4d8481c6dbf03510d

383

py

Python

desafios/desafio030.py

genisyskernel/cursoemvideo-python

dec301e33933388c886fe78010f38adfb24dae82

[ "MIT" ]

1

2020-10-26T04:33:14.000Z

desafios/desafio030.py

genisyskernel/cursoemvideo-python

dec301e33933388c886fe78010f38adfb24dae82

[ "MIT" ]

null

desafios/desafio030.py

genisyskernel/cursoemvideo-python

dec301e33933388c886fe78010f38adfb24dae82

[ "MIT" ]

null

numero_inteiro = int(input("\033[1;35mDigite um numero inteiro: \033[m")) if numero_inteiro % 2 == 0: print("\033[1;36mO numero\033[m \033[1;35m{0}\033[m \033[1;36me\033[m \033[1;35mPAR\033[m\033[1;36m!\033[m".format(numero_inteiro)) else: print("\033[1;36mO numero\033[m \033[1;35m{0}\033[m \033[1;36me\033[m \033[1;35mIMPAR\033[m\033[1;36m!\033[m".format(numero_inteiro))

54.714286

137

0.684073

80

383

3.225

0.2625

0.170543

0.217054

0.248062

0.658915

0

0.294286

0.086162

383

6

138

63.833333

0.442857

0

0.4

0.631854

0.193211

0

1

0

false

0

0.4

0

null

0

1

0

1

0

1

0

1

0

1

0

1

0

null

0

8

65edf8942ed221dba6eb19240c5d997b4c53cabb

9,902

py

Python

torchreid/models/batch.py

Vill-Lab/IGOAS

42ca1d45e441f993c95b5e8f33c9f97ea3b916f3

[ "MIT" ]

8

2021-05-27T10:19:28.000Z

2021-10-15T12:38:04.000Z

torchreid/models/batch.py

Vill-Lab/IGOAS

42ca1d45e441f993c95b5e8f33c9f97ea3b916f3

[ "MIT" ]

3

2021-06-23T12:06:39.000Z

2021-09-12T08:40:44.000Z

torchreid/models/batch.py

Vill-Lab/IGOAS

42ca1d45e441f993c95b5e8f33c9f97ea3b916f3

[ "MIT" ]

6

2021-05-27T10:19:18.000Z

2021-11-13T12:02:17.000Z

import random from torch import nn import math class BatchCrop(nn.Module): def __init__(self, p = 0.5, sl=0.25, sh=0.75, r1=0.25, Threshold=1): super(BatchCrop, self).__init__() self.p = p self.sl = sl self.sh = sh self.r1 = r1 self.it = 0 self.Threshold = Threshold self.sx = None self.sy = None def forward(self, x): if self.training: # if random.uniform(0, 1) > self.p: # return x for attempt in range(100): h, w = x.size()[-2:] area = h * w target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1/self.r1) rh = int(round(math.sqrt(target_area * aspect_ratio))) rw = int(round(math.sqrt(target_area / aspect_ratio))) if rw < w and rh < h: if self.it % self.Threshold == 0: self.sx = random.randint(0, h - rh) self.sy = random.randint(0, w - rw) self.it += 1 x_crop = x[:, :, self.sx:self.sx + rh, self.sy:self.sy + rw] x = F.interpolate(x_crop, (384,128), mode='bilinear',align_corners= True) return x return x # class BatchDrop(nn.Module): # def __init__(self, h_ratio=0.3, w_ratio=1, Threshold=1): # super(BatchDrop, self).__init__() # self.h_ratio = h_ratio # self.w_ratio = w_ratio # self.it = 0 # self.Threshold = Threshold # self.sx = None # self.sy = None # def forward(self, x): # if self.training: # h, w = x.size()[-2:] # rh = round(self.h_ratio * h) # rw = round(self.w_ratio * w) # if self.it % self.Threshold == 0: # self.sx = random.randint(0, h - rh) # self.sy = random.randint(0, w - rw) # self.it += 1 # mask = x.new_ones(x.size()) # mask[:, :, self.sx:self.sx + rh, self.sy:self.sy + rw] = 0 # x = x * mask # return x # return x class BatchDrop(nn.Module): def __init__(self, sl=0.2, sh=0.5, r1=0.25, Threshold=1): super(BatchDrop, self).__init__() self.it = 0 self.Threshold = Threshold self.sx = None self.sy = None self.mean = mean self.sl = sl self.sh = sh self.r1 = r1 def forward(self, x): if self.training: for attempt in range(100): h, w = x.size()[-2:] area = h * w target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1/self.r1) rh = int(round(math.sqrt(target_area * aspect_ratio))) rw = int(round(math.sqrt(target_area / aspect_ratio))) if rw < w and rh < h: if self.it % self.Threshold == 0: self.sx = random.randint(0, h - rh) self.sy = random.randint(0, w - rw) self.it += 1 mask = x.new_ones(x.size()) mask[:, :, self.sx:self.sx + rh, self.sy:self.sy + rw] = 0 x = x * mask return x return x class BatchErasing(nn.Module): def __init__(self, sl=0.2, sh=0.5, r1=0.25, mean=[0.4914, 0.4822, 0.4465], Threshold=1): super(BatchErasing, self).__init__() self.it = 0 self.Threshold = Threshold self.sx = None self.sy = None self.mean = mean self.sl = sl self.sh = sh self.r1 = r1 def forward(self, x): if self.training: for attempt in range(100): h, w = x.size()[-2:] area = h * w target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1/self.r1) rh = int(round(math.sqrt(target_area * aspect_ratio))) rw = int(round(math.sqrt(target_area / aspect_ratio))) if rw < w and rh < h: if self.it % self.Threshold == 0: self.sx = random.randint(0, h - rh) self.sy = random.randint(0, w - rw) self.it += 1 x[:, 0, self.sx:self.sx + rh, self.sy:self.sy + rw] = self.mean[0] x[:, 1, self.sx:self.sx + rh, self.sy:self.sy + rw] = self.mean[1] x[:, 2, self.sx:self.sx + rh, self.sy:self.sy + rw] = self.mean[2] return x return x # class BatchChange(nn.Module): # def __init__(self, choose, sl=0.25, sh=0.75, r1=0.25, mean=[0.4914, 0.4822, 0.4465], Threshold=1): # super(BatchChange, self).__init__() # self.it = 0 # self.Threshold = Threshold # self.sx = None # self.sy = None # self.mean = mean # self.sl = sl # self.sh = sh # self.r1 = r1 # self.choose = choose # def forward(self, x): # if self.training: # h, w = x.size()[-2:] # area = h * w # target_area = random.uniform(self.sl, self.sh) * area # aspect_ratio = random.uniform(self.r1, 1/self.r1) # for attempt in range(100): # rh = int(round(math.sqrt(target_area * aspect_ratio))) # rw = int(round(math.sqrt(target_area / aspect_ratio))) # if rw < w and rh < h: # if self.it % self.Threshold == 0: # self.sx = random.randint(0, h - rh) # self.sy = random.randint(0, w - rw) # self.it += 1 # if self.choose == 0: # # print(self.choose) # mask = x.new_zeros(x.size()) # mask[:, :, self.sx:self.sx + rh, self.sy:self.sy + rw] = 1 # x = x * mask # return x # if self.choose == 1: # # print(self.choose) # x[:, 0, 0:self.sx , :] = self.mean[0] # x[:, 0, self.sx + rh:h , :] = self.mean[0] # x[:, 0, self.sx :self.sx+rh , 0:self.sy] = self.mean[0] # x[:, 0, self.sx :self.sx+rh , self.sy+rw:w] = self.mean[0] # x[:, 1, 0:self.sx , :] = self.mean[1] # x[:, 1, self.sx + rh:h , :] = self.mean[1] # x[:, 1, self.sx :self.sx+rh , 0:self.sy] = self.mean[1] # x[:, 1, self.sx :self.sx+rh , self.sy+rw:w] = self.mean[1] # x[:, 2, 0:self.sx , :] = self.mean[2] # x[:, 2, self.sx + rh:h , :] = self.mean[2] # x[:, 2, self.sx :self.sx+rh , 0:self.sy] = self.mean[2] # x[:, 2, self.sx :self.sx+rh , self.sy+rw:w] = self.mean[2] # return x # return x class RandomErasing(nn.Module): def __init__(self, sl=0.1, sh=0.4, r1=0.3, mean=[0.4914, 0.4822, 0.4465]): super(RandomErasing, self).__init__() # self.probability = probability self.mean = mean self.sl = sl self.sh = sh self.r1 = r1 # img 32,3,384,128 def forward(self, img): # if random.uniform(0, 1) > self.probability: # return img for i in range(img.size(0)): for attempt in range(100): area = img.size()[2] * img.size()[3] target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1 / self.r1) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if w < img.size()[3] and h < img.size()[2]: x1 = random.randint(0, img.size()[2] - h) y1 = random.randint(0, img.size()[3] - w) if img.size()[1] == 3: img[i, 0, x1:x1 + h, y1:y1 + w] = self.mean[0] img[i, 1, x1:x1 + h, y1:y1 + w] = self.mean[1] img[i, 2, x1:x1 + h, y1:y1 + w] = self.mean[2] # print(img[i, 0, x1:x1 + h, y1:y1 + w]) break return img class RandomDrop(nn.Module): def __init__(self, sl=0.1, sh=0.4, r1=0.3, mean=[0.4914, 0.4822, 0.4465]): super(RandomDrop, self).__init__() # self.probability = probability self.mean = mean self.sl = sl self.sh = sh self.r1 = r1 # img 32,3,384,128 def forward(self, img): # if random.uniform(0, 1) > self.probability: # return img for i in range(img.size(0)): for attempt in range(100): area = img.size()[2] * img.size()[3] target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.r1, 1 / self.r1) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if w < img.size()[3] and h < img.size()[2]: x1 = random.randint(0, img.size()[2] - h) y1 = random.randint(0, img.size()[3] - w) if img.size()[1] == 3: img[i, :, x1:x1 + h, y1:y1 + w] = 0 break return img

39.608

104

0.444961

1,305

9,902

3.295019

0.066667

0.058605

0.062791

0.036279

0.893721

0.870465

0.857674

0.83186

0.812326

0.799302

0

0.05568

0.414159

9,902

250

105

39.608

0.685571

0.354474

0

0.789855

0

0.001268

0

1

0.072464

false

0

0.021739

0

0.188406

0

null

0

1

0

1

0

null

0

7

02ba82b3c576487aff198bb2ea47f2e9f06e8f5d

152

py

Python

tests/sources/python-config-output.py

hugovk/python-versions

c27507421a8edf9cfe1817c0615054bf6c7211b6

[ "MIT" ]

92

2020-04-17T22:04:56.000Z

2022-03-11T19:19:45.000Z

tests/sources/python-config-output.py

Yuriy-Kukushkin/python-versions

ae216d3a0bc2b7e26696e35b476b4ef1e8e55b36

[ "MIT" ]

18

2020-04-27T06:17:15.000Z

2022-01-18T17:25:41.000Z

tests/sources/python-config-output.py

Yuriy-Kukushkin/python-versions

ae216d3a0bc2b7e26696e35b476b4ef1e8e55b36

[ "MIT" ]

77

2020-05-01T22:59:35.000Z

2022-03-20T08:38:58.000Z

import distutils.sysconfig import sysconfig from pprint import pprint pprint(sysconfig.get_config_vars()) pprint(distutils.sysconfig.get_config_vars())

25.333333

45

0.855263

20

152

6.3

0.4

0.285714

0.349206

0

0.065789

152

6

45

25.333333

0.887324

0

1

0

true

0

0.6

0

0.6

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

9

b86a88d608b66c039e37a7cbfed14bb59c971731

784

py

Python

test/test_timer.py

rakesh-padwal/sumologic-collectd-plugin

336f1e87fa1a27777f2cb668cee71f307e6d380a

[ "Apache-2.0" ]

null

test/test_timer.py

rakesh-padwal/sumologic-collectd-plugin

336f1e87fa1a27777f2cb668cee71f307e6d380a

[ "Apache-2.0" ]

null

test/test_timer.py

rakesh-padwal/sumologic-collectd-plugin

336f1e87fa1a27777f2cb668cee71f307e6d380a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from sumologic_collectd_metrics.timer import Timer def test_cancel_timer_normal(): timer = Timer(0.1, task) timer.start_timer() assert timer.timer is not None timer.cancel_timer() def test_cancel_timer_not_start(): timer = Timer(0.1, task) assert timer.timer is None timer.cancel_timer() def test_reset_timer_normal(): timer = Timer(0.1, task) timer.start_timer() assert timer.timer is not None timer.reset_timer() assert timer.timer is not None timer.cancel_timer() def test_reset_timer_not_start(): timer = Timer(0.1, task) assert timer.timer is None timer.reset_timer() assert timer.timer is not None timer.cancel_timer() def task(): print('Timer task ... ')

16

50

0.674745

115

784

4.391304

0.2

0.19802

0.190099

0.213861

0.867327

0.819802

0.764356

0

0.01473

0.220663

784

48

51

16.333333

0.811784

0.026786

0

0.72

0

0.019711

0

0.24

1

0.2

false

0

0.04

0

0.24

0.04

0

null

0

1

0

1

0

null

0

9

b889e166d1d975864c616ebbaea07770031c7f3f

7,392

py

Python

tests/plot/test_plot_io.py

virtualcell/Biosimulators_utils

1b34e1e0a9ace706d245e9d515d0fae1e55a248d

[ "MIT" ]

null

tests/plot/test_plot_io.py

virtualcell/Biosimulators_utils

1b34e1e0a9ace706d245e9d515d0fae1e55a248d

[ "MIT" ]

null

tests/plot/test_plot_io.py

virtualcell/Biosimulators_utils

1b34e1e0a9ace706d245e9d515d0fae1e55a248d

[ "MIT" ]

null

from biosimulators_utils.plot import io from biosimulators_utils.plot.data_model import PlotFormat from biosimulators_utils.report.data_model import DataGeneratorResults from biosimulators_utils.sedml.data_model import Plot2D, Curve, Plot3D, Surface, AxisScale, DataGenerator import numpy import os import shutil import tempfile import unittest class PlotIoTestCase(unittest.TestCase): def setUp(self): self.dirname = tempfile.mkdtemp() def tearDown(self): shutil.rmtree(self.dirname) def test_write_plot_2d_one_curve(self): time = DataGenerator(id='time') species_a = DataGenerator(id='species_a') plot = Plot2D( id='plot_1', curves=[ Curve( id='curve_1', name='Curve 1', x_data_generator=time, y_data_generator=species_a, x_scale=AxisScale.linear, y_scale=AxisScale.linear, ), ] ) data_gen_results = DataGeneratorResults() data_gen_results[time.id] = numpy.linspace(0., 10., 100 + 1) data_gen_results[species_a.id] = numpy.sin(data_gen_results[time.id]) base_path = self.dirname rel_path = 'path/to/sim.sedml/' + plot.id format = PlotFormat.pdf io.write_plot_2d(plot, data_gen_results, base_path, rel_path, format=format) self.assertTrue(os.path.isfile(os.path.join(base_path, 'path/to/sim.sedml/plot_1.pdf'))) def test_write_plot_2d_multiple_curves(self): time = DataGenerator(id='time') species_a = DataGenerator(id='species_a') species_b = DataGenerator(id='species_b') plot = Plot2D( id='plot_1', curves=[ Curve( id='curve_1', name='Curve 1', x_data_generator=time, y_data_generator=species_a, x_scale=AxisScale.linear, y_scale=AxisScale.linear, ), Curve( id='curve_2', name='Curve 2', x_data_generator=time, y_data_generator=species_b, x_scale=AxisScale.linear, y_scale=AxisScale.linear, ), ] ) data_gen_results = DataGeneratorResults() data_gen_results[time.id] = numpy.linspace(0., 10., 100 + 1) data_gen_results[species_a.id] = numpy.sin(data_gen_results[time.id]) data_gen_results[species_b.id] = numpy.cos(data_gen_results[time.id]) base_path = self.dirname rel_path = 'path/to/sim.sedml/' + plot.id format = PlotFormat.pdf io.write_plot_2d(plot, data_gen_results, base_path, rel_path, format=format) self.assertTrue(os.path.isfile(os.path.join(base_path, 'path/to/sim.sedml/plot_1.pdf'))) def test_write_plot_2d_mixed_axes(self): species_a = DataGenerator(id='species_a') species_b = DataGenerator(id='species_b') plot = Plot2D( id='plot_1', curves=[ Curve( id='curve_1', name='Curve 1', x_data_generator=species_a, y_data_generator=species_a, x_scale=AxisScale.linear, y_scale=AxisScale.log, ), Curve( id='curve_2', name='Curve 2', x_data_generator=species_b, y_data_generator=species_b, x_scale=AxisScale.log, y_scale=AxisScale.linear, ), ] ) data_gen_results = DataGeneratorResults() time = numpy.linspace(0., 10., 100 + 1) data_gen_results[species_a.id] = numpy.sin(time) data_gen_results[species_b.id] = numpy.cos(time) base_path = self.dirname rel_path = 'path/to/sim.sedml/' + plot.id format = PlotFormat.pdf io.write_plot_2d(plot, data_gen_results, base_path, rel_path, format=format) self.assertTrue(os.path.isfile(os.path.join(base_path, 'path/to/sim.sedml/plot_1.pdf'))) def test_write_plot_3d_one_surface(self): x = DataGenerator(id='x') y = DataGenerator(id='y') species_a = DataGenerator(id='species_a') plot = Plot3D( id='plot_1', surfaces=[ Surface( id='surface_1', name='Surface 1', x_data_generator=x, y_data_generator=y, z_data_generator=species_a, x_scale=AxisScale.linear, y_scale=AxisScale.linear, z_scale=AxisScale.linear, ), ] ) X = numpy.arange(-5, 5, 0.25) Y = numpy.arange(-5, 5, 0.25) X, Y = numpy.meshgrid(X, Y) Z = numpy.sin(numpy.sqrt(X**2 + Y**2)) data_gen_results = DataGeneratorResults() data_gen_results[x.id] = X data_gen_results[y.id] = Y data_gen_results[species_a.id] = Z base_path = self.dirname rel_path = 'path/to/sim.sedml/' + plot.id format = PlotFormat.pdf io.write_plot_3d(plot, data_gen_results, base_path, rel_path, format=format) self.assertTrue(os.path.isfile(os.path.join(base_path, 'path/to/sim.sedml/plot_1.pdf'))) def test_write_plot_3d_multiple_surfaces(self): x = DataGenerator(id='x') y = DataGenerator(id='y') species_a = DataGenerator(id='species_a') species_b = DataGenerator(id='species_b') plot = Plot3D( id='plot_1', surfaces=[ Surface( id='surface_1', name='Surface 1', x_data_generator=x, y_data_generator=y, z_data_generator=species_a, x_scale=AxisScale.linear, y_scale=AxisScale.linear, z_scale=AxisScale.linear, ), Surface( id='surface_2', name='Surface 2', x_data_generator=y, y_data_generator=x, z_data_generator=species_b, x_scale=AxisScale.log, y_scale=AxisScale.log, z_scale=AxisScale.log, ), ], ) X = numpy.arange(-5, 5, 0.25) Y = numpy.arange(-5, 5, 0.25) X, Y = numpy.meshgrid(X, Y) A = numpy.sin(numpy.sqrt(X**2 + Y**2)) B = numpy.cos(numpy.sqrt(X**2 + Y**2)) data_gen_results = DataGeneratorResults() data_gen_results[x.id] = X data_gen_results[y.id] = Y data_gen_results[species_a.id] = A data_gen_results[species_b.id] = B base_path = self.dirname rel_path = 'path/to/sim.sedml/' + plot.id format = PlotFormat.pdf io.write_plot_3d(plot, data_gen_results, base_path, rel_path, format=format) self.assertTrue(os.path.isfile(os.path.join(base_path, 'path/to/sim.sedml/plot_1.pdf')))

33.908257

105

0.540584

871

7,392

4.323766

0.092997

0.050186

0.100372

0.034519

0.842539

0.837759

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b898e068680cc5214a74098985fe039b4239d651

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Python

tests/typecode/test_contenttype.py

nicoddemus/scancode-toolkit

58dfec66faa2c8a90f1125861081266594a1e1d7

[ "Apache-2.0", "CC0-1.0" ]

null

tests/typecode/test_contenttype.py

nicoddemus/scancode-toolkit

58dfec66faa2c8a90f1125861081266594a1e1d7

[ "Apache-2.0", "CC0-1.0" ]

null

tests/typecode/test_contenttype.py

nicoddemus/scancode-toolkit

58dfec66faa2c8a90f1125861081266594a1e1d7

[ "Apache-2.0", "CC0-1.0" ]

null

# # Copyright (c) 2018 nexB Inc. and others. All rights reserved. # http://nexb.com and https://github.com/nexB/scancode-toolkit/ # The ScanCode software is licensed under the Apache License version 2.0. # Data generated with ScanCode require an acknowledgment. # ScanCode is a trademark of nexB Inc. # # You may not use this software except in compliance with the License. # You may obtain a copy of the License at: http://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. # # When you publish or redistribute any data created with ScanCode or any ScanCode # derivative work, you must accompany this data with the following acknowledgment: # # Generated with ScanCode and provided on an "AS IS" BASIS, WITHOUT WARRANTIES # OR CONDITIONS OF ANY KIND, either express or implied. No content created from # ScanCode should be considered or used as legal advice. Consult an Attorney # for any legal advice. # ScanCode is a free software code scanning tool from nexB Inc. and others. # Visit https://github.com/nexB/scancode-toolkit/ for support and download. from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import os from unittest.case import skipIf from unittest.case import expectedFailure from commoncode.testcase import FileBasedTesting from commoncode.system import on_linux from commoncode.system import on_mac from commoncode.system import on_windows from typecode.contenttype import get_filetype from typecode.contenttype import get_type from typecode.contenttype import get_pygments_lexer from typecode.contenttype import is_standard_include # aliases for testing get_mimetype_python = lambda l: get_type(l).mimetype_python get_filetype_pygment = lambda l: get_type(l).filetype_pygment get_filetype_file = lambda l: get_type(l).filetype_file get_mimetype_file = lambda l: get_type(l).mimetype_file is_text = lambda l: get_type(l).is_text is_archive = lambda l: get_type(l).is_archive is_compressed = lambda l: get_type(l).is_compressed is_media = lambda l: get_type(l).is_media is_winexe = lambda l: get_type(l).is_winexe is_source = lambda l: get_type(l).is_source is_script = lambda l: get_type(l).is_script is_special = lambda l: get_type(l).is_special is_pdf = lambda l: get_type(l).is_pdf is_pdf_with_text = lambda l: get_type(l).is_pdf_with_text is_binary = lambda l: get_type(l).is_binary is_c_source = lambda l: get_type(l).is_c_source is_stripped_elf = lambda l: get_type(l).is_stripped_elf is_elf = lambda l: get_type(l).is_elf elf_type = lambda l: get_type(l).elf_type get_link_target = lambda l: get_type(l).link_target is_link = lambda l: get_type(l).is_link is_broken_link = lambda l: get_type(l).is_broken_link size = lambda l: get_type(l).size contains_text = lambda l: get_type(l).contains_text is_data = lambda l: get_type(l).is_data is_js_map = lambda l: get_type(l).is_js_map class TestContentType(FileBasedTesting): test_data_dir = os.path.join(os.path.dirname(__file__), 'data') def test_size(self): test_dir = self.get_test_loc('contenttype/size') result = size(test_dir) assert 18 == result def test_filetype_file_on_unicode_file_name(self): test_zip = self.extract_test_zip('contenttype/unicode/unicode.zip') test_dir = os.path.join(test_zip, 'a') f = os.listdir(test_dir)[0] test_file = os.path.join(test_dir, f) assert os.path.exists(test_file) expected = 'PNG image data, 16 x 12, 8-bit/color RGBA, interlaced' if on_windows: # FIXME: this is a very short png file though expected = 'Non-ISO extended-ASCII text' assert expected == get_filetype_file(test_file) expected = 'image/png' if on_windows: # FIXME: this is a very short png file though expected = 'text/plain' assert expected == get_mimetype_file(test_file) @skipIf(not on_linux, 'Windows and macOS have some issues with some non-unicode paths') def test_filetype_file_on_unicode_file_name2(self): zip_file_name = 'contenttype/unicode/unicode2.zip' test_zip = self.extract_test_zip(zip_file_name.encode('utf-8')) test_dir = os.path.join(test_zip, 'a') f = [f for f in os.listdir(test_dir) if f.startswith('g')][0] test_file = os.path.join(test_dir, f) assert os.path.exists(test_file) expected = 'PNG image data, 16 x 12, 8-bit/color RGBA, interlaced' if on_windows: # FIXME: this is a very short png file though expected = 'Non-ISO extended-ASCII text' assert expected == get_filetype_file(test_file) expected = 'image/png' if on_windows: # FIXME: this is a very short png file though expected = 'text/plain' assert expected == get_mimetype_file(test_file) @skipIf(on_windows, 'Windows does not have (well supported) links.') def test_symbolink_links(self): test_dir = self.extract_test_tar('contenttype/links/links.tar.gz', verbatim=True) test_file1 = os.path.join(test_dir, 'prunedirs/targets/simlink_to_dir') assert is_link(test_file1) assert not is_broken_link(test_file1) assert '../sources/subdir' == get_link_target(test_file1) test_file2 = os.path.join(test_dir, 'prunedirs/targets/simlink_to_file') assert is_link(test_file2) assert not is_broken_link(test_file2) assert '../sources/a.txt' == get_link_target(test_file2) test_file3 = os.path.join(test_dir, 'prunedirs/targets/simlink_to_missing_file') assert is_link(test_file3) assert is_broken_link(test_file3) assert '../sources/temp.txt' == get_link_target(test_file3) test_file4 = os.path.join(test_dir, 'prunedirs/targets/simlink_to_missing_dir') assert is_link(test_file4) assert is_broken_link(test_file4) assert '../sources/tempdir' == get_link_target(test_file4) @skipIf(not on_windows, 'Hangs for now, for lack of proper sudo access on some test servers.') @skipIf(on_windows, 'Windows does not have fifos.') def test_contenttype_fifo(self): test_dir = self.get_temp_dir() myfifo = os.path.join(test_dir, 'myfifo') import subprocess if subprocess.call(['mkfifo', myfifo]) != 0: self.fail('Unable to create fifo') assert os.path.exists(myfifo) assert is_special(myfifo) assert 'FIFO pipe' == get_filetype(myfifo) def test_directory(self): test_file = self.get_test_loc('contenttype') assert not is_binary(test_file) assert not is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_archive_gnu_tar(self): test_file = self.get_test_loc('contenttype/archive/e.tar') assert 'posix tar archive (gnu)' == get_filetype(test_file) assert is_binary(test_file) assert is_archive(test_file) assert not is_compressed(test_file) assert contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_debian_package(self): test_file = self.get_test_loc('contenttype/package/libjama-dev_1.2.4-2_all.deb') assert 'debian binary package (format 2.0)' == get_filetype(test_file) assert is_binary(test_file) assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_package_json(self): test_file = self.get_test_loc('contenttype/package/package.json') assert 'ascii text, with very long lines' == get_filetype(test_file) assert not is_binary(test_file) assert '' == get_filetype_pygment(test_file) assert not is_source(test_file) def test_archive_gz(self): test_file = self.get_test_loc('contenttype/archive/file_4.26-1.diff.gz') assert get_filetype(test_file).startswith('gzip compressed data') assert is_binary(test_file) assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) @skipIf(on_windows, 'fails because of libmagic bug on windows.') def test_archive_squashfs_crashing(self): test_file = self.get_test_loc('contenttype/archive/crashing-squashfs') assert get_filetype_file(test_file).startswith('Squashfs filesystem, little endian, version 4.0') assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) @skipIf(on_windows, 'fails because of libmagic bug on windows.') def test_archive_squashfs_gz(self): test_file = self.get_test_loc('contenttype/archive/sqfs-gz.sqs') assert get_filetype_file(test_file).startswith('Squashfs filesystem, little endian, version 4.0') assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) @skipIf(on_windows, 'fails because of libmagic bug on windows.') def test_archive_squashfs_lzo(self): test_file = self.get_test_loc('contenttype/archive/sqfs-lzo.sqs') assert get_filetype_file(test_file).startswith('Squashfs filesystem, little endian, version 4.0') assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) @skipIf(on_windows, 'fails because of libmagic bug on windows.') def test_archive_squashfs_xz(self): test_file = self.get_test_loc('contenttype/archive/sqfs-xz.sqs') assert get_filetype_file(test_file).startswith('Squashfs filesystem, little endian, version 4.0') assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_archive_tar_bz2(self): test_file = self.get_test_loc('contenttype/archive/e.tar.bz2') assert is_binary(test_file) assert is_archive(test_file) assert 'bzip2 compressed data, block size = 900k' == get_filetype(test_file) assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_archive_tar_gz_1(self): test_file = self.get_test_loc('contenttype/archive/a.tar.gz') assert not is_source(test_file) assert not is_text(test_file) assert '' == get_filetype_pygment(test_file) assert 'application/x-gzip' == get_mimetype_file(test_file) assert get_filetype(test_file).startswith('gzip compressed data') assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_archive_tar_gz_3(self): test_file = self.get_test_loc('contenttype/archive/e.tar.gz') assert is_binary(test_file) assert is_archive(test_file) assert get_filetype(test_file).startswith('gzip compressed data') assert is_compressed(test_file) assert not contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_archive_tar_posix_not_compressed(self): test_file = self.get_test_loc('contenttype/archive/posixnotgnu.tar') assert is_binary(test_file) assert is_archive(test_file) assert 'posix tar archive' == get_filetype(test_file) assert not is_compressed(test_file) assert contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_ar_archive_win_library(self): test_file = self.get_test_loc('contenttype/archive/win-archive.lib') assert is_binary(test_file) assert is_archive(test_file) assert 'current ar archive' == get_filetype(test_file) assert not is_compressed(test_file) assert contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_win_dll(self): test_file = self.get_test_loc('contenttype/binary/windows.dll') assert is_binary(test_file) assert not is_archive(test_file) assert not is_compressed(test_file) assert contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_config_eclipse_data(self): test_file = self.get_test_loc('contenttype/config/eclipse_configuration_3u.cfs') assert is_binary(test_file) assert 'data' == get_filetype(test_file) assert contains_text(test_file) assert '' == get_filetype_pygment(test_file) def test_binary_data(self): test_file = self.get_test_loc('contenttype/binary/data.fdt') assert is_binary(test_file) assert 'data' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_binary_data_2(self): test_file = self.get_test_loc('contenttype/binary/dbase.fdt') assert 'data' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_binary_java_serialized_data(self): test_file = self.get_test_loc('contenttype/binary/jruby_time_zone_TimeOfDay.dat') assert is_binary(test_file) assert 'java serialization data, version 5' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_binary_random_data(self): assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_0')) assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_1')) assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_2')) assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_3')) assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_4')) assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_5')) assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_6')) assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_7')) assert 'data' == get_filetype(self.get_test_loc('contenttype/binary-random/binary_random_8')) assert '' == get_filetype_pygment(self.get_test_loc('contenttype/binary-random/binary_random_8')) def test_build_ant_build_xml(self): test_file = self.get_test_loc('contenttype/build/build.xml') assert not is_binary(test_file) assert 'exported sgml document, ascii text, with crlf line terminators' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) assert is_text(test_file) assert not is_source(test_file) assert not is_script(test_file) def test_build_makefile(self): test_file = self.get_test_loc('contenttype/build/Makefile') assert not is_source(test_file) assert not is_script(test_file) assert is_text(test_file) assert '' == get_filetype_pygment(test_file) assert 'ASCII text' == get_filetype_file(test_file) assert 'ascii text' == get_filetype(test_file) assert 'text/plain' == get_mimetype_file(test_file) def test_build_makefile_2(self): test_file = self.get_test_loc('contenttype/build/Makefile.inc') assert is_text(test_file) assert '' == get_filetype_pygment(test_file) assert 'makefile script, ascii text, with crlf line terminators' == get_filetype(test_file) assert 'text/x-makefile' == get_mimetype_file(test_file) assert 'makefile script, ASCII text, with CRLF line terminators' == get_filetype_file(test_file) assert not is_source(test_file) def test_build_ide_makefile(self): test_file = self.get_test_loc('contenttype/build/documentation.dsp') assert 'ascii text' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) assert not is_source(test_file) def test_build_java_maven_pom_pom(self): test_file = self.get_test_loc('contenttype/build/pom.pom') assert '' == get_filetype_pygment(test_file) assert 'xml document text' == get_filetype(test_file) assert not is_source(test_file) def test_build_java_maven_pom_xml(self): test_file = self.get_test_loc('contenttype/build/pom.xml') assert not is_source(test_file) assert 'exported sgml document, ascii text' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_certificate_rsa_eclipse(self): test_file = self.get_test_loc('contenttype/certificate/ECLIPSE.RSA') assert is_binary(test_file) assert 'data' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_certificate(self): test_file = self.get_test_loc('contenttype/certificate/CERTIFICATE') assert is_binary(test_file) assert 'data' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_code_assembly(self): test_file = self.get_test_loc('contenttype/code/assembly/bcopy.s') assert 'C source, ASCII text, with CRLF line terminators' == get_filetype_file(test_file) assert 'GAS' == get_filetype_pygment(test_file) assert 'text/x-c' == get_mimetype_file(test_file) assert is_source(test_file) assert is_text(test_file) def test_code_c_1(self): test_file = self.get_test_loc('contenttype/code/c/c_code.c') assert 'ti-xx graphing calculator (flash)' == get_filetype(test_file) assert 'C++' == get_filetype_pygment(test_file) assert is_source(test_file) assert is_text(test_file) def test_code_c_2(self): test_file = self.get_test_loc('contenttype/code/c/main.c') assert is_source(test_file) assert is_text(test_file) assert 'C++' == get_filetype_pygment(test_file) assert 'c source, ascii text' == get_filetype(test_file) assert 'C source, ASCII text' == get_filetype_file(test_file) assert 'text/x-c' == get_mimetype_file(test_file) def test_code_c_3(self): test_file = self.get_test_loc('contenttype/code/c/cpu.c') assert is_source(test_file) assert is_text(test_file) assert 'C++' == get_filetype_pygment(test_file) assert 'c source, ascii text' == get_filetype(test_file) assert 'text/x-c' == get_mimetype_file(test_file) def test_code_c_4(self): test_file = self.get_test_loc('contenttype/code/c/mm.c') assert is_source(test_file) assert is_text(test_file) assert 'C++' == get_filetype_pygment(test_file) assert 'c source, ascii text' == get_filetype(test_file) assert 'text/x-c' == get_mimetype_file(test_file) def test_code_c_5(self): test_file = self.get_test_loc('contenttype/code/c/pci.c') assert is_source(test_file) assert is_text(test_file) assert 'C source, ASCII text' == get_filetype_file(test_file) assert 'C++' == get_filetype_pygment(test_file) assert 'c source, ascii text' == get_filetype(test_file) assert 'text/x-c' == get_mimetype_file(test_file) def test_code_c_6(self): test_file = self.get_test_loc('contenttype/code/c/pci_v3.c') assert is_source(test_file) assert is_text(test_file) assert 'C source, ASCII text' == get_filetype_file(test_file) assert 'C++' == get_filetype_pygment(test_file) assert 'c source, ascii text' == get_filetype(test_file) assert 'text/x-c' == get_mimetype_file(test_file) def test_code_c_7(self): test_file = self.get_test_loc('contenttype/code/c/some.c') assert 'ti-xx graphing calculator (flash)' == get_filetype(test_file) assert is_source(test_file) assert 'C++' == get_filetype_pygment(test_file) def test_code_c_include(self): test_file = self.get_test_loc('contenttype/code/c/resource.h') assert 'ascii text, with crlf line terminators' == get_filetype(test_file) assert is_source(test_file) assert 'C++' == get_filetype_pygment(test_file) def test_code_c_include_2(self): test_file = self.get_test_loc('contenttype/code/c/netdb.h') assert 'very short file (no magic)' == get_filetype(test_file) assert is_source(test_file) assert 'C++' == get_filetype_pygment(test_file) def test_code_c_include_mixed_case_2(self): test_file = self.get_test_loc('contenttype/code/c/TEST_LOWERCASE.h') assert 'c source, ascii text' == get_filetype(test_file) assert 'C++' == get_filetype_pygment(test_file) def test_code_cpp_include_mixed_case(self): test_file = self.get_test_loc('contenttype/code/c/TEST.H') assert 'c source, ascii text' == get_filetype(test_file) assert 'C++' == get_filetype_pygment(test_file) def test_code_cpp_mixed_case(self): test_file = self.get_test_loc('contenttype/code/c/SIMPLE.C') assert 'c source, ascii text' == get_filetype(test_file) assert 'C++' == get_filetype_pygment(test_file) def test_code_cpp_mixed_case_2(self): test_file = self.get_test_loc('contenttype/code/cpp/string.CPP') expected = 'c source, ascii text' if on_mac: expected = 'c++ source, ascii text' assert expected == get_filetype(test_file) assert 'C++' == get_filetype_pygment(test_file) def test_code_cpp_1(self): test_file = self.get_test_loc('contenttype/code/cpp/stacktrace.cpp') assert is_source(test_file) assert is_text(test_file) assert 'C++' == get_filetype_pygment(test_file) assert 'c source, ascii text' == get_filetype(test_file) assert 'text/x-c' == get_mimetype_file(test_file) def test_code_cpp_non_ascii(self): test_file = self.get_test_loc('contenttype/code/cpp/non_ascii.cpp') assert is_source(test_file) assert is_text(test_file) assert 'application/octet-stream' == get_mimetype_file(test_file) assert 'C++' == get_filetype_pygment(test_file) assert 'data' == get_filetype(test_file) def test_code_cpp_stdafx(self): test_file = self.get_test_loc('contenttype/code/cpp/StdAfx.cpp') assert 'c source, ascii text' == get_filetype(test_file) assert 'C++' == get_filetype_pygment(test_file) def test_code_groff(self): test_file = self.get_test_loc(u'contenttype/code/groff/example.ms') assert not is_special(test_file) assert is_text(test_file) assert 'troff or preprocessor input, ascii text' == get_filetype(test_file) assert 'GAS' == get_filetype_pygment(test_file) # the Apache mimes do not have .ms in their types # but the type is still mysteriously returnedd on Windows assert 'text/troff' == get_mimetype_python(test_file) assert 'text/troff' == get_mimetype_file(test_file) assert get_filetype_file(test_file).startswith('troff or preprocessor input') def test_code_java_1(self): test_file = self.get_test_loc('contenttype/code/java/contenttype.java') assert not is_binary(test_file) assert 'ascii text' == get_filetype(test_file) assert 'Java' == get_filetype_pygment(test_file) def test_code_java_non_ascii(self): test_file = self.get_test_loc('contenttype/code/java/ChartTiming1.java') assert is_source(test_file) assert is_text(test_file) # FIXME: incorrect assert 'application/octet-stream' == get_mimetype_file(test_file) assert 'data' == get_filetype(test_file) assert 'Java' == get_filetype_pygment(test_file) def test_code_java_3(self): test_file = self.get_test_loc('contenttype/code/java/Appender.java') assert 'ascii text' == get_filetype(test_file) assert 'Java' == get_filetype_pygment(test_file) def test_code_java_jad(self): test_file = self.get_test_loc('contenttype/code/java/CommonViewerSiteFactory.jad') assert 'ascii text' == get_filetype(test_file) # FIXME: should this be Java code? assert 'Python' == get_filetype_pygment(test_file) def test_code_java_mixed_case(self): test_file = self.get_test_loc('contenttype/code/java/Logger.JAVA') assert 'ascii text' == get_filetype(test_file) assert 'Java' == get_filetype_pygment(test_file) def test_code_js(self): test_file = self.get_test_loc('contenttype/code/js/a.js') assert not is_media(test_file) assert 'ascii text, with crlf line terminators' == get_filetype(test_file) assert 'JavaScript' == get_filetype_pygment(test_file) def test_code_python_1(self): test_file = self.get_test_loc('contenttype/code/python/contenttype.py') assert not is_binary(test_file) assert 'Python' == get_pygments_lexer(test_file).name assert 'Python' == get_filetype_pygment(test_file) def test_code_python_2(self): test_file = self.get_test_loc('contenttype/code/python/extract.py') assert is_source(test_file) assert is_text(test_file) assert 'Python' == get_filetype_pygment(test_file) assert 'python script, ascii text executable' == get_filetype(test_file) assert 'text/x-python' == get_mimetype_file(test_file) assert get_filetype_file(test_file).startswith('Python script') def test_code_python_3(self): test_file = self.get_test_loc('contenttype/code/python/__init__.py') assert 'python script, ascii text executable' == get_filetype(test_file) assert 'Python' == get_filetype_pygment(test_file) def test_code_resource(self): test_file = self.get_test_loc('contenttype/code/c/CcccDevStudioAddIn.rc2') assert 'ascii text' == get_filetype(test_file) assert 'C' == get_filetype_pygment(test_file) def test_code_scala(self): test_file = self.get_test_loc('contenttype/code/scala/Applicative.scala') assert 'utf-8 unicode text' == get_filetype(test_file) assert 'Scala' == get_filetype_pygment(test_file) def test_compiled_elf_exe_32bits(self): test_file = self.get_test_loc('contenttype/compiled/linux/i686-shash') assert is_binary(test_file) assert 'elf 32-bit lsb executable, intel 80386, version 1 (sysv), dynamically linked, interpreter /lib/ld-linux.so.2, for gnu/linux 2.6.4, not stripped' == get_filetype(self.get_test_loc(u'contenttype/compiled/linux/i686-shash')) assert '' == get_filetype_pygment(test_file) def test_compiled_elf_exe_64bits(self): test_file = self.get_test_loc('contenttype/compiled/linux/x86_64-shash') assert is_binary(test_file) assert 'elf 64-bit lsb executable, x86-64, version 1 (sysv), dynamically linked, interpreter /lib64/ld-linux-x86-64.so.2, for gnu/linux 2.6.9, not stripped' == get_filetype(self.get_test_loc(u'contenttype/compiled/linux/x86_64-shash')) assert '' == get_filetype_pygment(test_file) def test_compiled_elf_so(self): test_file = self.get_test_loc(u'contenttype/compiled/linux/libssl.so.0.9.7') assert not is_special(test_file) assert not is_text(test_file) assert '' == get_filetype_pygment(test_file) assert 'application/x-sharedlib' == get_mimetype_file(test_file) assert 'elf 32-bit lsb shared object, intel 80386, version 1 (sysv), dynamically linked, stripped' == get_filetype(test_file) assert 'ELF 32-bit LSB shared object, Intel 80386, version 1 (SYSV), dynamically linked, stripped' == get_filetype_file(test_file) assert '' == get_filetype_pygment(test_file) def test_compiled_elf_so_2(self): test_file = self.get_test_loc('contenttype/compiled/linux/libnetsnmpagent.so.5') assert not is_source(test_file) assert 'elf 32-bit lsb shared object, intel 80386, version 1 (sysv), dynamically linked, not stripped' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_compiled_flash(self): test_file = self.get_test_loc('contenttype/compiled/flash/a.swf') assert is_binary(test_file) assert 'macromedia flash data, version 7' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_compiled_flash_2(self): test_file = self.get_test_loc('contenttype/compiled/flash/b.swf') assert is_binary(test_file) assert 'macromedia flash data, version 7' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_compiled_flash_swc(self): test_file = self.get_test_loc('contenttype/compiled/flash/flash-haloclassic.swc.incr') assert is_binary(test_file) assert 'data' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_compiled_java_classfile_1(self): test_file = self.get_test_loc('contenttype/compiled/java/CommonViewerSiteFactory.class') assert 'compiled java class data, version 46.0 (java 1.2)' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_compiled_java_classfile_2(self): test_file = self.get_test_loc('contenttype/compiled/java/old.class') assert is_binary(test_file) assert 'compiled java class data, version 46.0 (java 1.2)' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_compiled_python_1(self): test_dir = self.extract_test_zip('contenttype/compiled/python/compiled.zip') test_file = os.path.join(test_dir, 'command.pyc') assert 'python 2.5 byte-compiled' == get_filetype(test_file) assert not is_source(test_file) assert not is_text(test_file) assert 'application/octet-stream' == get_mimetype_file(test_file) assert '' == get_filetype_pygment(test_file) test_file2 = os.path.join(test_dir, 'contenttype.pyc') assert is_binary(test_file2) assert get_pygments_lexer(test_file2) is None test_file3 = os.path.join(test_dir, 'contenttype.pyo') assert is_binary(test_file3) assert get_pygments_lexer(test_file3) is None test_file4 = os.path.join(test_dir, 'extract.pyc') assert 'python 2.5 byte-compiled' == get_filetype(test_file4) assert not is_source(test_file4) assert not is_text(test_file4) assert 'application/octet-stream' == get_mimetype_file(test_file4) assert '' == get_filetype_pygment(test_file4) def test_compiled_win_dll(self): test_file = self.get_test_loc(u'contenttype/compiled/win/zlib1.dll') assert is_winexe(test_file) assert is_binary(self.get_test_loc('contenttype/compiled/win/zlib1.dll')) assert '' == get_filetype_pygment(test_file) def test_compiled_win_exe(self): test_file = self.get_test_loc(u'contenttype/compiled/win/file.exe') assert is_winexe(test_file) assert is_binary(self.get_test_loc('contenttype/compiled/win/file.exe')) assert '' == get_filetype_pygment(test_file) def test_config_conf(self): test_file = self.get_test_loc('contenttype/config/config.conf') assert 'ascii text, with crlf line terminators' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_config_linux_conf(self): test_file = self.get_test_loc('contenttype/config/defconfig-ar531x-jffs2') assert 'linux make config build file (old)' == get_filetype(test_file) assert not is_source(test_file) assert is_text(test_file) assert '' == get_filetype_pygment(test_file) assert 'linux make config build file (old)' == get_filetype(test_file) assert 'text/plain' == get_mimetype_file(test_file) def test_config_text_3(self): test_file = self.get_test_loc('contenttype/config/wrapper.conf') assert 'ascii text, with crlf line terminators' == get_filetype(test_file) assert 'ascii text, with crlf line terminators' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_debug_win_pdb(self): test_file = self.get_test_loc('contenttype/debug/QTMovieWin.pdb') assert is_binary(test_file) assert 'msvc program database ver \\004' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_doc_html(self): test_file = self.get_test_loc('contenttype/doc/html/contenttype.html') assert not is_binary(test_file) assert 'HTML' == get_pygments_lexer(test_file).name def test_doc_html_2(self): test_file = self.get_test_loc('contenttype/doc/html/allclasses-frame.html') assert is_source(test_file) assert is_text(test_file) assert 'HTML' == get_filetype_pygment(test_file) assert 'html document, ascii text' == get_filetype(test_file) assert 'text/html' == get_mimetype_file(test_file) assert 'HTML document, ASCII text' == get_filetype_file(test_file) def test_doc_html_3(self): test_file = self.get_test_loc('contenttype/doc/html/Label.html') assert is_source(test_file) assert is_text(test_file) assert 'HTML' == get_filetype_pygment(test_file) assert 'html document, ascii text, with very long lines' == get_filetype(test_file) assert 'text/html' == get_mimetype_file(test_file) assert 'HTML document, ASCII text, with very long lines' == get_filetype_file(test_file) def test_doc_html_4(self): test_file = self.get_test_loc('contenttype/doc/html/a.htm') assert not is_binary(test_file) assert not is_binary(test_file) assert 'HTML' == get_pygments_lexer(test_file).name def test_doc_office_word(self): test_file = self.get_test_loc('contenttype/doc/office/document') assert not is_archive(test_file) assert 'microsoft word 2007+' == get_filetype(test_file) def test_doc_office_word_2(self): test_file = self.get_test_loc('contenttype/doc/office/document.doc') assert not is_archive(test_file) assert 'microsoft word 2007+' == get_filetype(test_file) def test_doc_office_word_3(self): test_file = self.get_test_loc('contenttype/doc/office/word.doc') assert not is_special(test_file) assert '' == get_filetype_pygment(test_file) assert 'application/msword' == get_mimetype_file(test_file) assert get_filetype(test_file).startswith('composite document file v2 document') assert get_filetype_file(test_file).startswith('Composite Document File V2 Document') def test_docx_office_word(self): test_file = self.get_test_loc('contenttype/doc/office/word.docx') assert 'application/vnd.openxmlformats-officedocument.wordprocessingml.document' == get_mimetype_file(test_file) assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) def test_pptx_office_is_archive(self): test_file = self.get_test_loc('contenttype/doc/office/power.pptx') assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) def test_doc_office_excel_xlsx(self): test_file = self.get_test_loc('contenttype/doc/office/excel.xlsx') assert 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' == get_mimetype_file(test_file) assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) def test_doc_office_excel_xls(self): test_file = self.get_test_loc('contenttype/doc/office/excel.xls') assert 'application/vnd.ms-excel' == get_mimetype_file(test_file) def test_doc_office_powerpoint_pptx(self): test_file = self.get_test_loc('contenttype/doc/office/power.pptx') assert 'application/vnd.openxmlformats-officedocument.presentationml.presentation' == get_mimetype_file(test_file) assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) def test_doc_office_powerpoint_ppt(self): test_file = self.get_test_loc('contenttype/doc/office/power.ppt') assert 'application/vnd.ms-powerpoint' == get_mimetype_file(test_file) def test_doc_office_visio(self): test_file = self.get_test_loc('contenttype/doc/office/Glitch-ERD.vsd') assert 'application/vnd.ms-office' == get_mimetype_file(test_file) assert not is_text(test_file) assert is_binary(test_file) def test_doc_pdf_1(self): test_file = self.get_test_loc('contenttype/doc/pdf/a.pdf') assert is_pdf(test_file) assert is_pdf_with_text(test_file) assert 'pdf document, version 1.2' == get_filetype(test_file) assert not is_media(test_file) assert is_binary(test_file) def test_doc_pdf_2(self): test_file = self.get_test_loc('contenttype/doc/pdf/notpdf.pdf') assert not is_pdf_with_text(test_file) def test_doc_pdf_3(self): test_file = self.get_test_loc('contenttype/doc/pdf/pdf.pdf') assert is_pdf(test_file) assert is_pdf_with_text(test_file) assert 'pdf document, version 1.4' == get_filetype(test_file) def test_doc_postscript_1(self): test_file = self.get_test_loc('contenttype/doc/postscript/doc.ps') assert is_text(test_file) assert not is_binary(test_file) def test_doc_postscript_2(self): test_file = self.get_test_loc('contenttype/doc/postscript/a.ps') assert not is_binary(test_file) assert not is_media(test_file) def test_doc_postscript_eps(self): test_file = self.get_test_loc('contenttype/doc/postscript/Image1.eps') assert is_binary(test_file) assert 'application/octet-stream' == get_mimetype_file(test_file) assert get_filetype_file(test_file).startswith('DOS EPS Binary File Postscript') def test_doc_xml(self): test_file = self.get_test_loc('contenttype/doc/xml/simple.xml') assert not is_binary(test_file) assert 'ascii text' == get_filetype(test_file) def test_doc_xml_2(self): test_file = self.get_test_loc('contenttype/doc/xml/some.xml') assert not is_binary(test_file) assert 'xml document text' == get_filetype(test_file) def test_doc_xml_3(self): test_file = self.get_test_loc('contenttype/doc/xml/somespring.xml') assert not is_binary(test_file) assert 'xml document text' == get_filetype(test_file) def test_media_audio_aif(self): test_file = self.get_test_loc('contenttype/media/a.aif') assert is_media(test_file) assert is_binary(test_file) assert is_media(self.get_test_loc('contenttype/media/a.aiff')) assert is_binary(self.get_test_loc('contenttype/media/a.aiff')) def test_media_audio_au(self): test_file = self.get_test_loc('contenttype/media/a.au') assert is_media(test_file) assert is_binary(test_file) def test_media_audio_flac(self): test_file = self.get_test_loc('contenttype/media/a.flac') assert is_media(test_file) assert is_binary(test_file) def test_media_audio_mp3(self): test_file = self.get_test_loc('contenttype/media/a.mp3') assert is_media(test_file) assert is_binary(test_file) assert contains_text(test_file) def test_media_audio_wav(self): test_file = self.get_test_loc('contenttype/media/a.wav') assert is_media(test_file) assert is_binary(test_file) def test_media_image_bmp_1(self): test_file = self.get_test_loc('contenttype/media/Image1.bmp') assert is_media(test_file) assert is_binary(test_file) def test_media_image_bmp_2(self): test_file = self.get_test_loc('contenttype/media/TBarLrge.bmp') assert 'pc bitmap, windows 3.x format, 400 x 32 x 4' == get_filetype(test_file) def test_media_image_bmp_3(self): test_file = self.get_test_loc('contenttype/media/TBarMedm.bmp') assert 'pc bitmap, windows 3.x format, 210 x 16 x 4' == get_filetype(test_file) def test_media_image_dib(self): test_file = self.get_test_loc('contenttype/media/Image1.dib') assert is_media(test_file) assert is_binary(test_file) def test_media_image_gif(self): test_file = self.get_test_loc('contenttype/media/Image1.gif') assert is_media(test_file) assert is_binary(test_file) assert not contains_text(test_file) def test_media_image_ico(self): test_file = self.get_test_loc('contenttype/media/Image1.ico') assert is_media(test_file) assert is_binary(test_file) def test_media_image_iff(self): test_file = self.get_test_loc('contenttype/media/Image1.iff') assert is_media(test_file) assert is_binary(test_file) def test_media_image_img(self): # FIXME: .img files are more complex test_file = self.get_test_loc('contenttype/media/Image1.img') assert is_binary(test_file) assert get_filetype_file(test_file).startswith('GEM Image data') assert 'application/octet-stream' == get_mimetype_file(test_file) assert not get_mimetype_python(test_file) assert is_media(test_file) def test_media_image_jif(self): test_file = self.get_test_loc('contenttype/media/Image1.jif') assert is_media(test_file) assert is_binary(test_file) def test_media_image_jpeg(self): test_file = self.get_test_loc('contenttype/media/Image1.jpeg') assert is_media(test_file) assert is_binary(test_file) assert not contains_text(test_file) def test_media_image_jpg(self): test_file = self.get_test_loc('contenttype/media/Image1.jpg') assert is_media(test_file) assert is_binary(test_file) assert not contains_text(test_file) def test_media_image_pbm(self): test_file = self.get_test_loc('contenttype/media/Image1.pbm') assert is_media(test_file) assert not is_binary(test_file) def test_media_image_ppm(self): test_file = self.get_test_loc('contenttype/media/Image1.ppm') assert not is_binary(test_file) # this is text assert is_media(test_file) def test_media_image_pcx(self): test_file = self.get_test_loc('contenttype/media/Image1.pcx') assert is_media(test_file) assert is_binary(test_file) def test_media_image_photoshop(self): test_file = self.get_test_loc('contenttype/media/Image1.psd') assert is_media(test_file) assert is_binary(test_file) def test_media_image_png(self): test_file = self.get_test_loc('contenttype/media/a.png') assert is_media(test_file) assert is_binary(test_file) assert not contains_text(test_file) def test_media_image_psp(self): test_file = self.get_test_loc('contenttype/media/Image1.psp') assert is_media(test_file) assert is_binary(test_file) def test_media_image_ras(self): test_file = self.get_test_loc('contenttype/media/Image1.ras') assert is_media(test_file) assert is_binary(test_file) def test_media_image_svg(self): test_file = self.get_test_loc('contenttype/media/drawing.svg') assert not is_binary(test_file) assert is_media(test_file) assert '' == get_filetype_pygment(test_file) assert 'SVG Scalable Vector Graphics image' == get_filetype_file(test_file) assert not is_source(test_file) def test_media_image_tgg(self): test_file = self.get_test_loc('contenttype/media/Image1.tga') assert is_media(test_file) assert is_binary(test_file) def test_media_image_tif(self): test_file = self.get_test_loc('contenttype/media/Image1.tif') assert is_media(test_file) assert is_binary(test_file) def test_media_image_windows_metafile(self): test_file = self.get_test_loc('contenttype/media/Image1.emf') assert 'application/octet-stream' == get_mimetype_file(test_file) assert get_filetype_file(test_file).startswith('Windows Enhanced Metafile') assert not get_mimetype_python(test_file) assert is_media(test_file) assert is_binary(test_file) def test_media_video_mpeg(self): test_file = self.get_test_loc('contenttype/media/a4.mp4') assert is_media(test_file) assert is_binary(test_file) assert contains_text(test_file) def test_media_video_mpg(self): test_file = self.get_test_loc('contenttype/media/a4.mpg') assert is_media(test_file) assert is_binary(test_file) assert contains_text(test_file) def test_media_video_mp2(self): test_file = self.get_test_loc('contenttype/media/a.mp2') assert is_media(test_file) assert is_binary(test_file) assert contains_text(test_file) def test_media_video_msft_avi(self): test_file = self.get_test_loc('contenttype/media/a.avi') assert is_media(test_file) assert is_binary(test_file) def test_media_video_msft_wmf(self): test_file = self.get_test_loc('contenttype/media/Image1.wmf') assert is_media(test_file) assert is_binary(test_file) def test_media_video_msft_wmv(self): test_file = self.get_test_loc('contenttype/media/mov.wvm.wmv') assert is_media(test_file) assert is_binary(test_file) test_file = self.get_test_loc('contenttype/media/Movie.wmv') assert is_media(test_file) assert is_binary(test_file) test_file = self.get_test_loc('contenttype/media/Movie_0001.wmv') assert is_media(test_file) assert is_binary(test_file) test_file = self.get_test_loc('contenttype/media/Movie_0002.wmv') assert is_media(test_file) assert is_binary(test_file) def test_media_video_ogg(self): test_file = self.get_test_loc('contenttype/media/a.ogg') assert is_media(test_file) assert is_binary(test_file) def test_media_video_theora_ogg(self): test_file = self.get_test_loc('contenttype/media/a.theo.ogg') assert is_media(test_file) assert is_binary(test_file) def test_package_debian(self): test_file = self.get_test_loc('contenttype/package/wget-el_0.5.0-8_all.deb') assert 'debian binary package (format 2.0)' == get_filetype(test_file) assert is_binary(test_file) assert is_archive(test_file) assert not contains_text(test_file) def test_package_java_jar(self): test_file = self.get_test_loc('contenttype/package/ant-jsch-1.7.0.jar') assert 'java archive data (jar)' == get_filetype(test_file) assert is_binary(test_file) assert is_compressed(test_file) assert is_archive(test_file) assert not contains_text(test_file) def test_package_java_jar_as_zip(self): test_file = self.get_test_loc('contenttype/package/ant.zip') assert 'java archive data (jar)' == get_filetype(test_file) assert is_binary(test_file) assert is_compressed(test_file) assert is_archive(test_file) assert not contains_text(test_file) def test_package_java_war(self): test_file = self.get_test_loc('contenttype/package/c.war') assert 'zip archive data, at least v1.0 to extract' == get_filetype(test_file) assert is_binary(test_file) assert is_compressed(test_file) assert is_archive(test_file) assert not contains_text(test_file) def test_package_python_egg(self): test_file = self.get_test_loc('contenttype/package/TicketImport-0.7a-py2.5.egg') assert 'zip archive data, at least v2.0 to extract' == get_filetype(test_file) assert is_binary(test_file) assert is_compressed(test_file) assert is_archive(test_file) assert not contains_text(test_file) def test_package_rpm(self): test_file = self.get_test_loc('contenttype/package/wget-1.11.4-3.fc11.i586.rpm') assert 'rpm v3.0 bin i386/x86_64' == get_filetype(test_file) assert is_binary(test_file) assert is_archive(test_file) assert is_compressed(test_file) assert not contains_text(test_file) def test_package_rubygem(self): test_file = self.get_test_loc('contenttype/package/rubygems-update-1.4.1.gem') assert 'posix tar archive' == get_filetype(test_file) assert is_binary(test_file) assert is_compressed(test_file) assert is_archive(test_file) assert not contains_text(test_file) def test_script_bash(self): test_file = self.get_test_loc('contenttype/script/test.sh') assert 'posix shell script, ascii text executable' == get_filetype(test_file) assert 'Bash' == get_filetype_pygment(test_file) def test_script_bash_makelinks(self): test_file = self.get_test_loc('contenttype/script/makelinks') assert is_source(test_file) assert 'Bash' == get_filetype_pygment(test_file) def test_script_windows_bat(self): test_file = self.get_test_loc('contenttype/script/build_w32vc.bat') assert 'dos batch file, ascii text' == get_filetype(test_file) assert 'Batchfile' == get_filetype_pygment(test_file) def test_script_windows_bat_2(self): test_file = self.get_test_loc('contenttype/script/zip_src.bat') assert 'ascii text, with crlf line terminators' == get_filetype(test_file) assert 'Batchfile' == get_filetype_pygment(test_file) def test_script_install(self): test_file = self.get_test_loc('contenttype/script/install') assert 'ascii text' == get_filetype(test_file) assert '' == get_filetype_pygment(test_file) def test_text_crashing(self): # these used to make libmagic crash somehow test_file = self.get_test_loc('contenttype/text/crashing-a.txt') assert 'ASCII text' == get_filetype_file(test_file) test_file = self.get_test_loc('contenttype/text/crashing-z.txt') assert 'ASCII text' == get_filetype_file(test_file) assert '' == get_filetype_pygment(test_file) def test_text(self): test_file = self.get_test_loc('contenttype/text/x11-xconsortium_text.txt') assert not is_binary(test_file) assert not is_archive(test_file) assert '' == get_filetype_pygment(test_file) def test_text_license_copying(self): test_file = self.get_test_loc('contenttype/text/COPYING') assert 'ascii text' in get_filetype(test_file) assert not is_source(test_file) assert is_text(test_file) assert '' == get_filetype_pygment(test_file) assert 'text/plain' == get_mimetype_file(test_file) def test_text_license_credits(self): # FIXME test_file = self.get_test_loc('contenttype/text/CREDITS') assert 'iso-8859 text' == get_filetype(test_file) assert is_text(test_file) assert not is_source(test_file) assert '' == get_filetype_pygment(test_file) assert 'ISO-8859 text' == get_filetype_file(test_file) assert 'text/plain' == get_mimetype_file(test_file) def test_text_license_gpl(self): test_file = self.get_test_loc('contenttype/text/GPL.txt') assert not is_source(test_file) assert '' == get_filetype_pygment(test_file) def test_text_log(self): test_file = self.get_test_loc('contenttype/text/windowserver.log') assert not is_source(test_file) assert is_text(test_file) assert '' == get_filetype_pygment(test_file) assert 'ascii text' == get_filetype(test_file) assert 'ASCII text' == get_filetype_file(test_file) assert 'text/plain' == get_mimetype_file(test_file) assert '' == get_filetype_pygment(test_file) def test_is_standard_include(self): assert is_standard_include('<built-in>') assert is_standard_include('/usr/lib/this.h') assert is_standard_include('/usr/include/this.h') def test_text_iso_text_changelog_is_not_iso_cdrom(self): test_file = self.get_test_loc('contenttype/text/ChangeLog') assert 'Non-ISO extended-ASCII text' == get_filetype_file(test_file) assert '' == get_filetype_pygment(test_file) @expectedFailure def test_text_rsync_file_is_not_octet_stream(self): # this is a libmagic bug: http://bugs.gw.com/view.php?id=473 test_file = self.get_test_loc('contenttype/text/wildtest.txt') assert 'data' != get_filetype_file(test_file) assert 'octet' not in get_mimetype_file(test_file) def test_rgb_stream_is_binary(self): # this is a binaryornot bug: https://github.com/audreyr/binaryornot/issues/10 test_file = self.get_test_loc('contenttype/binary/pixelstream.rgb') assert 'data' == get_filetype_file(test_file) assert 'application/octet-stream' == get_mimetype_file(test_file) assert is_binary(test_file) def test_large_text_file_is_data(self): test_file = self.get_test_loc('contenttype/data/nulls.txt') assert is_data(test_file) assert '' == get_filetype_pygment(test_file) def test_is_js_map_for_css(self): test_file = self.get_test_loc('contenttype/build/ar-ER.css.map') assert is_js_map(test_file) assert '' == get_filetype_pygment(test_file) def test_is_js_map_for_js(self): test_file = self.get_test_loc('contenttype/build/ar-ER.js.map') assert is_js_map(test_file) assert '' == get_filetype_pygment(test_file) def test_test_is_js_map_for_binary(self): test_file = self.get_test_loc('contenttype/build/binary.js.map') assert not is_js_map(test_file) assert '' == get_filetype_pygment(test_file) def test_test_is_js_map_for_makefile(self): test_file = self.get_test_loc('contenttype/build/Makefile') assert not is_js_map(test_file) assert '' == get_filetype_pygment(test_file)

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Python

delicatessen/estimating_equations/dose_response.py

pzivich/Deli

761aa51c6949334b59fffb185be4266177454b6c

[ "MIT" ]

null

delicatessen/estimating_equations/dose_response.py

pzivich/Deli

761aa51c6949334b59fffb185be4266177454b6c

[ "MIT" ]

null

delicatessen/estimating_equations/dose_response.py

pzivich/Deli

761aa51c6949334b59fffb185be4266177454b6c

[ "MIT" ]

null

import numpy as np ################################################################# # Dose-Response Estimating Equations def ee_4p_logistic(theta, X, y): r"""Default stacked estimating equation estimating equations for the four parameter logistic model (4PL). 4PL is often used for dose-response and bioassay analyses. The estimating equations are .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 Here, theta is a 1-by-4 array, where 4 are the 4 parameters of the 4PL. The first theta corresponds to lower limit, the second corresponds to the effective dose (ED50), the third corresponds to the steepness of the curve, and the fourth corresponds to the upper limit. Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughtout, these user-defined functions are defined as ``psi``. Parameters ---------- theta : ndarray, list, vector Theta in this case consists of 4 values. In general, starting values ``>0`` are better choices for the 4PL model X : ndarray, list, vector 1-dimensional vector of n dose values. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n response values. No missing data should be included (missing data may cause unexpected behavior). Returns ------- array : Returns a 4-by-n NumPy array evaluated for the input theta, y, X Examples -------- Construction of a estimating equation(s) with ``ee_4p_logistic`` should be done similar to the following >>> from delicatessen import MEstimator >>> from delicatessen.data import load_inderjit >>> from delicatessen.estimating_equations import ee_4p_logistic For demonstration, we use dose-response data from Inderjit et al. (2002), which can be loaded from ``delicatessen`` directly. >>> d = load_inderjit() # Loading array of data >>> dose_data = d[:, 1] # Dose data >>> resp_data = d[:, 0] # Response data Defining psi, or the stacked estimating equations >>> def psi(theta): >>> return ee_4p_logistic(theta=theta, X=dose_data, y=resp_data) The 4PL model and others are harder to optimize compared to other estimating equations. Namely, the optimizer is not aware of implicit bounds on the parameters. To reduce non-convergence issues, we can give the root-finder good starting values. For the 4PL, the upper limit should *always* be greater than the lower limit. Second, the ED50 should be between the lower and upper limits. Third, the sign for the steepness depends on whether the response declines (positive) or the response increases (negative). Finally, some solvers may be better suited to the problem, so try a few different options. Here, we use some general starting values that should perform well in many cases. For the lower-bound, give the minimum response value as the initial. For ED50, give the mid-point between the maximum response and the minimum response. The initial value for steepness is more difficult. Ideally, we would give a starting value of zero, but that will fail in this example. Giving a small positive starting value works in this example. For the upper-bound, give the maximum response value as the initial. Finally, we use the ``lm`` solver. Note ---- To summarize the recommendations, be sure to examine your data (e.g., scatterplot). This will help to determine the initial starting values for the root-finding procedure. Otherwise, you may come across a convergence error. >>> estr = MEstimator(psi, init=[np.min(resp_data), >>> (np.max(resp_data)+np.min(resp_data)) / 2, >>> (np.max(resp_data)+np.min(resp_data)) / 2, >>> np.max(resp_data)]) >>> estr.estimate(solver='lm') Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Inspecting the parameter estimates >>> estr.theta[0] # lower limit >>> estr.theta[1] # ED(50) >>> estr.theta[2] # steepness >>> estr.theta[3] # upper limit References ---------- Ritz C, Baty F, Streibig JC, & Gerhard D. (2015). Dose-response analysis using R. *PloS One*, 10(12), e0146021. An H, Justin TL, Aubrey GB, Marron JS, & Dittmer DP. (2019). dr4pl: A Stable Convergence Algorithm for the 4 Parameter Logistic Model. *R J.*, 11(2), 171. Inderjit, Streibig JC, & Olofsdotter M. (2002). Joint action of phenolic acid mixtures and its significance in allelopathy research. *Physiologia Plantarum*, 114(3), 422-428. """ # Creating rho to cut down on typing rho = (X / theta[1]) ** theta[2] # Generalized 4PL model function for y-hat fx = theta[0] + (theta[3] - theta[0]) / (1 + rho) # Using a special implementatin of natural log here nested_log = np.log(X / theta[1], # ... to avoid dose=0 issues only take log where=0 < X) # ... where dose>0 (otherwise puts zero in place) # Calculate the derivatives for the gradient deriv = np.array((1 - 1/(1+rho), # Gradient for lower limit (theta[3]-theta[0])*theta[2]/theta[1]*rho/(1+rho)**2, # Gradient for steepness (theta[3] - theta[0]) * nested_log * rho / (1 + rho)**2, # Gradient for ED50 1 / (1 + rho)), ) # Gradient for upper limit # Compute gradient and return for each i return -2*(y-fx)*deriv def ee_3p_logistic(theta, X, y, lower): r"""Default stacked estimating equation estimating equations for the three parameter logistic model (3PL). 3PL is often used for dose-response and bioassay analyses. The estimating equations are .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 Here, theta is a 1-by-3 array, where 3 are the 3 parameters of the 3PL. The first theta corresponds to the effective dose (ED50), the second corresponds to the steepness of the curve, and the third corresponds to the upper limit. The lower limit is pre-specified by the user (and is no longer estimated) Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughtout, these user-defined functions are defined as ``psi``. Parameters ---------- theta : ndarray, list, vector Theta in this case consists of 3 values. In general, starting values ``>0`` are better choices for the 3PL model X : ndarray, list, vector 1-dimensional vector of n dose values. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n response values. No missing data should be included (missing data may cause unexpected behavior). lower : int, float Set value for the lower limit. Returns ------- array : Returns a 3-by-n NumPy array evaluated for the input theta, y, X Examples -------- Construction of a estimating equation(s) with ``ee_3p_logistic`` should be done similar to the following >>> from delicatessen import MEstimator >>> from delicatessen.data import load_inderjit >>> from delicatessen.estimating_equations import ee_3p_logistic For demonstration, we use dose-response data from Inderjit et al. (2002), which can be loaded from ``delicatessen`` directly. >>> d = load_inderjit() # Loading array of data >>> dose_data = d[:, 1] # Dose data >>> resp_data = d[:, 0] # Response data Since there is a natural lower-bound of 0 for root growth, we set ``lower=0``. Defining psi, or the stacked estimating equations >>> def psi(theta): >>> return ee_3p_logistic(theta=theta, X=dose_data, y=resp_data, >>> lower=0) The 3PL model and others are harder to optimize compared to other estimating equations. Namely, the optimizer is not aware of implicit bounds on the parameters. To reduce non-convergence issues, we can give the root-finder good starting values. For the 3PL, the upper limit should *always* be greater than the set lower limit. Second, the ED50 should be between the lower and upper limits. Third, the sign for the steepness depends on whether the response declines (positive) or the response increases (negative). Finally, some solvers may be better suited to the problem, so try a few different options. Here, we use some general starting values that should perform well in many cases. For ED50, give the mid-point between the maximum response and the minimum response. The initial value for steepness is more difficult. Ideally, we would give a starting value of zero, but that will fail in this example. Giving a small positive starting value works in this example. For the upper-bound, give the maximum response value as the initial. Finally, we use the ``lm`` solver. Note ---- To summarize the recommendations, be sure to examine your data (e.g., scatterplot). This will help to determine the initial starting values for the root-finding procedure. Otherwise, you may come across a convergence error. >>> estr = MEstimator(psi, init=[(np.max(resp_data)+np.min(resp_data)) / 2, >>> (np.max(resp_data)+np.min(resp_data)) / 2, >>> np.max(resp_data)]) >>> estr.estimate(solver='lm') Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Inspecting the parameter estimates >>> estr.theta[0] # ED(50) >>> estr.theta[1] # steepness >>> estr.theta[2] # upper limit References ---------- Ritz C, Baty F, Streibig JC, & Gerhard D. (2015). Dose-response analysis using R. *PloS One*, 10(12), e0146021. An H, Justin TL, Aubrey GB, Marron JS, & Dittmer DP. (2019). dr4pl: A Stable Convergence Algorithm for the 4 Parameter Logistic Model. *R J.*, 11(2), 171. Inderjit, Streibig JC, & Olofsdotter M. (2002). Joint action of phenolic acid mixtures and its significance in allelopathy research. *Physiologia Plantarum*, 114(3), 422-428. """ # Creating rho to cut down on typing rho = (X / theta[0])**theta[1] # Generalized 3PL model function for y-hat fx = lower + (theta[2] - lower) / (1 + rho) # Using a special implementation of natural log here nested_log = np.log(X / theta[0], # ... to avoid dose=0 issues only take log where=0 < X) # ... where dose>0 (otherwise puts zero in place) # Calculate the derivatives for the gradient deriv = np.array(((theta[2]-lower)*theta[1]/theta[0]*rho/(1+rho)**2, # Gradient for steepness (theta[2]-lower) * nested_log * rho / (1+rho)**2, # Gradient for ED50 1 / (1 + rho)), ) # Gradient for upper limit # Compute gradient and return for each i return -2*(y - fx)*deriv def ee_2p_logistic(theta, X, y, lower, upper): r"""Default stacked estimating equation estimating equations for the two parameter logistic model (2PL). 2PL is often used for dose-response and bioassay analyses. The estimating equations are .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 Here, theta is a 1-by-2 array, where 2 are the 2 parameters of the 2PL. The first theta corresponds to the effective dose (ED50), and the second corresponds to the steepness of the curve. Both the lower limit and upper limit are pre-specified by the user (and no longer estimated). Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughtout, these user-defined functions are defined as ``psi``. Parameters ---------- theta : ndarray, list, vector Theta in this case consists of 2 values. In general, starting values >0 are better choices for the 3PL model X : ndarray, list, vector 1-dimensional vector of n dose values. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n response values. No missing data should be included (missing data may cause unexpected behavior). lower : int, float Set value for the lower limit. upper : int, float Set value for the upper limit. Returns ------- array : Returns a 2-by-n NumPy array evaluated for the input theta, y, X Examples -------- Construction of a estimating equation(s) with ``ee_2p_logistic`` should be done similar to the following >>> from delicatessen import MEstimator >>> from delicatessen.data import load_inderjit >>> from delicatessen.estimating_equations import ee_2p_logistic For demonstration, we use dose-response data from Inderjit et al. (2002), which can be loaded from ``delicatessen`` directly. >>> d = load_inderjit() # Loading array of data >>> dose_data = d[:, 1] # Dose data >>> resp_data = d[:, 0] # Response data Since there is a natural lower-bound of 0 for root growth, we set ``lower=0``. While a natural upper bound does not exist for this example, we set ``upper=8`` for illustrative purposes. Defining psi, or the stacked estimating equations >>> def psi(theta): >>> return ee_2p_logistic(theta=theta, X=dose_data, y=resp_data, >>> lower=0, upper=8) The 2PL model and others are harder to optimize compared to other estimating equations. Namely, the optimizer is not aware of implicit bounds on the parameters. To reduce non-convergence issues, we can give the root-finder good starting values. First, the ED50 should be between the lower and upper limits. Second, the sign for the steepness depends on whether the response declines (positive) or the response increases (negative). Finally, some solvers may be better suited to the problem, so try a few different options. Here, we use some general starting values that should perform well in many cases. For ED50, give the mid-point between the maximum response and the minimum response. The initial value for steepness is more difficult. Ideally, we would give a starting value of zero, but that will fail in this example. Giving a small positive starting value works in this example. Finally, we use the ``lm`` solver. Note ---- To summarize the recommendations, be sure to examine your data (e.g., scatterplot). This will help to determine the initial starting values for the root-finding procedure. Otherwise, you may come across a convergence error. >>> estr = MEstimator(psi, init=[(np.max(resp_data)+np.min(resp_data)) / 2, >>> (np.max(resp_data)+np.min(resp_data)) / 2]) >>> estr.estimate(solver='lm') Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Inspecting the parameter estimates >>> estr.theta[0] # ED(50) >>> estr.theta[1] # steepness References ---------- Ritz C, Baty F, Streibig JC, & Gerhard D. (2015). Dose-response analysis using R. *PloS One*, 10(12), e0146021. An H, Justin TL, Aubrey GB, Marron JS, & Dittmer DP. (2019). dr4pl: A Stable Convergence Algorithm for the 4 Parameter Logistic Model. *R J.*, 11(2), 171. Inderjit, Streibig JC, & Olofsdotter M. (2002). Joint action of phenolic acid mixtures and its significance in allelopathy research. *Physiologia Plantarum*, 114(3), 422-428. """ # Creating rho to cut down on typing rho = (X / theta[0])**theta[1] # Generalized 3PL model function for y-hat fx = lower + (upper - lower) / (1 + rho) # Using a special implementatin of natural log here nested_log = np.log(X / theta[0], # ... to avoid dose=0 issues only take log where=0 < X) # ... where dose>0 (otherwise puts zero in place) # Calculate the derivatives for the gradient deriv = np.array(((upper-lower)*theta[1]/theta[0]*rho/(1+rho)**2, # Gradient for steepness (upper-lower) * nested_log * rho / (1+rho)**2), ) # Gradient for ED50 # Compute gradient and return for each i return -2*(y-fx)*deriv def ee_effective_dose_delta(theta, y, delta, steepness, ed50, lower, upper): r"""Default stacked estimating equation to pair with the 4 parameter logistic model for estimation of the :math:`delta` effective dose. The estimating equation is .. math:: \psi(Y_i, \theta) = \beta_1 + \frac{\beta_4 - \beta_1}{1 + (\theta / \beta_2)^{\beta_3}} - \beta_4(1-\delta) - \beta_1 \delta where theta is the :math:`ED(\delta)`, and the beta values are from a 4PL model (1: lower limit, 2: steepness, 3: ED(50), 4: upper limit). When lower or upper limits are place, the corresponding beta's are replaced by constants. For proper uncertainty estimation, this estimating equation should be stacked together with the correspond PL model. Note ---- This estimating equation is meant to be paired with the estimating equations for either the 4PL, 3PL, or 2PL models. Parameters ---------- theta : int, float Theta value corresponding to the ED(alpha). y : ndarray, list, vector 1-dimensional vector of n response values, used to construct correct shape for output. delta : float The effective dose level of interest, ED(alpha). steepness : float Estimated parameter for the steepness from the PL. ed50 : float Estimated parameter for the ED50, or ED(alpha=50), from the PL. lower : int, float Estimated parameter or pre-specified constant for the lower limit. This should be a pre-specified constant for both the 3PL and 2PL. upper : int, float Estimated parameter or pre-specified constant for the lower limit. This should be a pre-specified constant for the 2PL. Returns ------- array : Returns a 1-by-n NumPy array evaluated for the input theta Examples -------- Construction of a estimating equations for ED25 with ``ee_3p_logistic`` should be done similar to the following >>> from delicatessen import MEstimator >>> from delicatessen.data import load_inderjit >>> from delicatessen.estimating_equations import ee_2p_logistic, ee_effective_dose_delta For demonstration, we use dose-response data from Inderjit et al. (2002), which can be loaded from ``delicatessen`` directly. >>> d = load_inderjit() # Loading array of data >>> dose_data = d[:, 1] # Dose data >>> resp_data = d[:, 0] # Response data Since there is a natural lower-bound of 0 for root growth, we set ``lower=0``. While a natural upper bound does not exist for this example, we set ``upper=8`` for illustrative purposes. Defining psi, or the stacked estimating equations >>> def psi(theta): >>> pl_model = ee_3p_logistic(theta=theta, X=dose_data, y=resp_data, >>> lower=0) >>> ed_25 = ee_effective_dose_delta(theta[3], y=resp_data, delta=0.20, >>> steepness=theta[0], ed50=theta[1], >>> lower=0, upper=theta[2]) >>> # Returning stacked estimating equations >>> return np.vstack((pl_model, >>> ed_25,)) Notice that the estimating equations are stacked in the order of the parameters in ``theta`` (the first 3 belong to 3PL and the last belong to ED(25)). >>> estr = MEstimator(psi, init=[(np.max(resp_data)+np.min(resp_data)) / 2, >>> (np.max(resp_data)+np.min(resp_data)) / 2, >>> np.max(resp_data), >>> (np.max(resp_data)+np.min(resp_data)) / 2]) >>> estr.estimate(solver='lm') Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Inspecting the parameter estimates >>> estr.theta[0] # ED(50) >>> estr.theta[1] # steepness >>> estr.theta[2] # upper limit >>> estr.theta[3] # ED(25) References ---------- Ritz C, Baty F, Streibig JC, & Gerhard D. (2015). Dose-response analysis using R. *PloS One*, 10(12), e0146021. An H, Justin TL, Aubrey GB, Marron JS, & Dittmer DP. (2019). dr4pl: A Stable Convergence Algorithm for the 4 Parameter Logistic Model. *R J.*, 11(2), 171. Inderjit, Streibig JC, & Olofsdotter M. (2002). Joint action of phenolic acid mixtures and its significance in allelopathy research. *Physiologia Plantarum*, 114(3), 422-428. """ # Creating rho to cut down on typing rho = (theta / steepness)**ed50 # Theta is the corresponds ED(alpha) value # Calculating the predicted value for f(x,\theta), or y-hat fx = lower + (upper - lower) / (1 + rho) # Subtracting off (Upper*(1-delta) + Lower*delta) since theta should result in zeroing of quantity ed_delta = fx - upper*(1-delta) - lower*delta # Returning constructed 1-by-ndarray for stacked estimating equations return np.ones(np.asarray(y).shape[0])*ed_delta

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Python

Thesis/RNNs/to_create_2011.py

jmppmj/thesis_recurrent_neural_nets

adcb6ef11d8e4fb4099caac4ecb3edd5f623aa6c

[ "MIT" ]

null

Thesis/RNNs/to_create_2011.py

jmppmj/thesis_recurrent_neural_nets

adcb6ef11d8e4fb4099caac4ecb3edd5f623aa6c

[ "MIT" ]

null

Thesis/RNNs/to_create_2011.py

jmppmj/thesis_recurrent_neural_nets

adcb6ef11d8e4fb4099caac4ecb3edd5f623aa6c

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 #only run once~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! #creates csv with :::2011's::: feature data #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #:::hmi.sharp_720s from JSOC::: #http://jsoc.stanford.edu/doc/data/hmi/sharp/sharp.htm import pandas as pd import drms #https://pypi.org/project/drms/ #compile :::2011::: feature dataframe #multiple queries needed due to record return limit def get_2011_Features(): h = drms.Client() k = h.query('hmi.sharp_720s[][2011.01.01_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = k k = h.query('hmi.sharp_720s[][2011.01.08_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.01.15_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.01.22_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.01.29_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.02.05_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.02.12_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.02.19_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.02.26_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.03.05_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.03.12_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.03.19_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.03.26_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.04.02_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.04.09_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.04.16_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.04.23_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.04.30_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.05.07_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.05.14_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.05.21_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.05.28_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.06.04_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.06.11_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.06.18_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.06.25_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.07.02_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.07.09_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.07.16_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.07.23_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.07.30_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.08.06_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.08.13_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.08.20_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.08.27_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.09.03_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.09.10_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.09.17_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.09.24_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.10.01_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.10.08_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.10.15_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.10.22_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.10.29_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.11.05_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.11.12_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.11.19_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.11.26_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.12.03_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.12.10_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.12.17_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.12.24_TAI/7d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) k = h.query('hmi.sharp_720s[][2011.12.31_TAI/1d]', key='T_REC, HARPNUM, NOAA_AR, TOTUSJH, TOTUSJZ, SAVNCPP, USFLUX, ABSNJZH, TOTPOT, SIZE_ACR, NACR, MEANPOT, SIZE, MEANJZH, SHRGT45, MEANSHR, MEANJZD, MEANALP, MEANGBT, MEANGAM, MEANGBZ, MEANGBH, NPIX') f_dataframe = f_dataframe.append(k) f_dataframe.to_csv('create_2011_features.csv') return()

87.781421

252

0.728523

2,400

16,064

4.719167

0.048333

0.09359

0.057213

0.046795

0.969186

0.966979

0.964683

0

0.05344

0.112363

16,064

182

253

88.263736

0.740865

0.027764

0

0.464286

0

0.473214

0.775806

0.120395

0

1

0.008929

false

0

0.017857

0

0.026786

0

null

0

1

0

1

0

null

0

8

b22a8b48e7ad86f3238fd2c1c85183d6a42e903f

3,892

py

Python

read_log.py

NagisaZj/ac-teach

481811d5c80d0dbee54f16c063b4ea3262b82050

[ "MIT" ]

null

read_log.py

NagisaZj/ac-teach

481811d5c80d0dbee54f16c063b4ea3262b82050

[ "MIT" ]

null

read_log.py

NagisaZj/ac-teach

481811d5c80d0dbee54f16c063b4ea3262b82050

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt import csv import pandas def smooth(data, smooth_range): # print('hhhhhhh', type(data), len(data)) new_data = np.zeros_like(data) for i in range(0, data.shape[-1]): if i < smooth_range: new_data[:, i] = 1. * np.sum(data[:, :i + 1], axis=1) / (i + 1) else: new_data[:, i] = 1. * np.sum(data[:, i - smooth_range + 1:i + 1], axis=1) / smooth_range return new_data def read_csv(paths=[]): datas =[] for p in paths: with open(p,'r') as f: data=pandas.read_csv(f) print(data.keys()) w=[] w.append(smooth(data['r'][None,:],100)[0]) w.append(data['l']) w.append(data['t']) w.append(smooth(data['success'][None,:],100)[0]) datas.append(w) return datas # d1 = read_csv(['/data2/zj/ac-teach/logs/OneGoalPickPlaceDenseEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_0/gym_eval.monitor.csv']) #d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_2/gym_eval.monitor.csv']) # d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_6/monitor2.csv']) # d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_7/monitor2.csv']) # # d1 = read_csv(['/data2/zj/ac-teach/logs/OneGoalPickPlaceDenseEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_0/gym_eval.monitor.csv']) # d2 = read_csv(['/data2/zj/ac-teach/logs/OneGoalPickPlaceDenseEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_0/monitor.csv']) d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_10/monitor2.csv']) d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_10/monitor2.csv']) d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_15/gym_eval.monitor2.csv']) d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_18/gym_eval.monitor2.csv']) d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_18/monitor2.csv']) d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_20/gym_eval.monitor2.csv']) d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_20/monitor2.csv']) d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_22/gym_eval.monitor2.csv']) d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_22/monitor2.csv']) d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_20/gym_eval.monitor2.csv']) d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_22/gym_eval.monitor2.csv']) # d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_24/gym_eval.monitor2.csv']) d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_24/monitor2.csv']) # d1 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_31/gym_eval.monitor2.csv']) d2 = read_csv(['/data2/zj/ac-teach/logs/MetaWorldEnv-v0/efficiency_partial_complete_suboptimal_ours/seed_31/monitor2.csv']) plt.figure() # plt.plot(range(len(d1[0][0])),d1[0][0]) # plt.plot(range(len(d2[0][0])),d2[0][0],color='r') plt.plot(np.arange(d1[0][0].shape[0])*500,d1[0][0],label='Push, Push Source, Test') plt.plot(np.arange(d2[0][0].shape[0])*500,d2[0][0],color='r',label='Push, Push Source, Train') plt.legend() plt.show()

55.6

144

0.752312

616

3,892

4.512987

0.144481

0.057914

0.090647

0.105755

0.792806

0.777698

0.764029

0.763309

0

0.046395

0.080678

3,892

69

145

56.405797

0.730576

0.237667

0

0.044444

0

0.333333

0.572444

0.552471

0

1

0.044444

false

0

0.088889

0

0.177778

0.022222

0

null

0

1

0

1

0

1

null

0

7

b23343e83e6046a2c1f8a3f7cc9bc81900134114

220

py

Python

browser/Python site-packages/pyshark/__init__.py

lightnarcissus/TextWeb

7a67aede097a8e3a328edd539672cb0c777d1fde

[ "Apache-2.0" ]

null

browser/Python site-packages/pyshark/__init__.py

lightnarcissus/TextWeb

7a67aede097a8e3a328edd539672cb0c777d1fde

[ "Apache-2.0" ]

null

browser/Python site-packages/pyshark/__init__.py

lightnarcissus/TextWeb

7a67aede097a8e3a328edd539672cb0c777d1fde

[ "Apache-2.0" ]

1

2019-02-15T08:18:41.000Z

from pyshark.capture.live_capture import LiveCapture from pyshark.capture.file_capture import FileCapture from pyshark.capture.remote_capture import RemoteCapture from pyshark.capture.inmem_capture import InMemCapture

55

57

0.881818

28

220

6.785714

0.428571

0.231579

0.378947

0

0.081818

220

4

58

55

0.940594

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

b2760ec851740beb70f30248119233b3691d0adb

161

py

Python

tests/parser/aggregates.count.boundguards.1.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

tests/parser/aggregates.count.boundguards.1.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

tests/parser/aggregates.count.boundguards.1.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

input = """ a(0). b(1). b(2). b(3). okay(X) :- a(X), X < #count{V : b(V)}. """ output = """ a(0). b(1). b(2). b(3). okay(X) :- a(X), X < #count{V : b(V)}. """

9.470588

38

0.36646

36

161

1.638889

0.333333

0.067797

0.101695

0.135593

0.813559

0

0.062992

0.21118

161

16

39

10.0625

0.401575

0

0.857143

0

0.807453

0

1

0

false

0

1

null

0

1

0

1

0

1

0

1

0

null

0

10

a23d5910eab0f106406fe6814df7e124ecb7870e

1,968

py

Python

05_rotating_test.py

AlanCLo/python_logging_ref

8f032cfaa747a4907be92ca84b53ed47400d913c

[ "MIT" ]

null

05_rotating_test.py

AlanCLo/python_logging_ref

8f032cfaa747a4907be92ca84b53ed47400d913c

[ "MIT" ]

null

05_rotating_test.py

AlanCLo/python_logging_ref

8f032cfaa747a4907be92ca84b53ed47400d913c

[ "MIT" ]

null

#!/usr/bin/env python import sys import logging from logging.handlers import RotatingFileHandler log = logging.getLogger('test') log.setLevel(logging.DEBUG) # Max output # Ref: https://docs.python.org/2/library/logging.handlers.html#rotatingfilehandler rotating_handler = RotatingFileHandler('rotating.log', maxBytes=300, backupCount=3) rotating_handler.setFormatter(logging.Formatter('%(filename)s: %(message)s')) log.addHandler(rotating_handler) # Output 200 bytes log.info("1234567890123456789012345678") # This line is 50 bytes log.info("1234567890123456789012345678") # This line is 50 bytes log.info("1234567890123456789012345678") # This line is 50 bytes log.info("1234567890123456789012345678") # This line is 50 bytes # $ ./05_rotating_test.py && ls -lh rotating.log* # -rw-r--r-- 1 200B 28 Mar 20:59 rotating.log # $ ./05_rotating_test.py && ls -lh rotating.log* # -rw-r--r-- 1 150B 28 Mar 20:59 rotating.log # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.1 # $ ./05_rotating_test.py && ls -lh rotating.log* # -rw-r--r-- 1 100B 28 Mar 20:59 rotating.log # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.1 # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.2 # $ ./05_rotating_test.py && ls -lh rotating.log* # -rw-r--r-- 1 50B 28 Mar 20:59 rotating.log # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.1 # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.2 # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.3 # $ ./05_rotating_test.py && ls -lh rotating.log* # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.1 # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.2 # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.3 # $ ./05_rotating_test.py && ls -lh rotating.log* # -rw-r--r-- 1 200B 28 Mar 20:59 rotating.log # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.1 # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.2 # -rw-r--r-- 1 250B 28 Mar 20:59 rotating.log.3 #

35.781818

83

0.668191

349

1,968

3.724928

0.17192

0.211538

0.055385

0.069231

0.708462

0

0.202694

0.170224

1,968

54

84

36.444444

0.593386

0.707317

0

0.333333

0

0.283333

0.207407

0

1

0

false

0

0.25

0

0.25

0

null

1

0

1

0

1

0

null

0

7

a25680d07d4ad54482a0d27eab64404eaab1acb9

15,445

py

Python

google/area120/tables/v1alpha1/area120-tables-v1alpha1-py/google/area120/tables_v1alpha1/services/tables_service/pagers.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

7

2021-02-21T10:39:41.000Z

2021-12-07T07:31:28.000Z

google/area120/tables/v1alpha1/area120-tables-v1alpha1-py/google/area120/tables_v1alpha1/services/tables_service/pagers.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

6

2021-02-02T23:46:11.000Z

2021-11-15T01:46:02.000Z

google/area120/tables/v1alpha1/area120-tables-v1alpha1-py/google/area120/tables_v1alpha1/services/tables_service/pagers.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

4

2021-01-28T23:25:45.000Z

2021-08-30T01:55:16.000Z

# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from typing import Any, AsyncIterator, Awaitable, Callable, Sequence, Tuple, Optional, Iterator from google.area120.tables_v1alpha1.types import tables class ListTablesPager: """A pager for iterating through ``list_tables`` requests. This class thinly wraps an initial :class:`google.area120.tables_v1alpha1.types.ListTablesResponse` object, and provides an ``__iter__`` method to iterate through its ``tables`` field. If there are more pages, the ``__iter__`` method will make additional ``ListTables`` requests and continue to iterate through the ``tables`` field on the corresponding responses. All the usual :class:`google.area120.tables_v1alpha1.types.ListTablesResponse` attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup. """ def __init__(self, method: Callable[..., tables.ListTablesResponse], request: tables.ListTablesRequest, response: tables.ListTablesResponse, *, metadata: Sequence[Tuple[str, str]] = ()): """Instantiate the pager. Args: method (Callable): The method that was originally called, and which instantiated this pager. request (google.area120.tables_v1alpha1.types.ListTablesRequest): The initial request object. response (google.area120.tables_v1alpha1.types.ListTablesResponse): The initial response object. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ self._method = method self._request = tables.ListTablesRequest(request) self._response = response self._metadata = metadata def __getattr__(self, name: str) -> Any: return getattr(self._response, name) @property def pages(self) -> Iterator[tables.ListTablesResponse]: yield self._response while self._response.next_page_token: self._request.page_token = self._response.next_page_token self._response = self._method(self._request, metadata=self._metadata) yield self._response def __iter__(self) -> Iterator[tables.Table]: for page in self.pages: yield from page.tables def __repr__(self) -> str: return '{0}<{1!r}>'.format(self.__class__.__name__, self._response) class ListTablesAsyncPager: """A pager for iterating through ``list_tables`` requests. This class thinly wraps an initial :class:`google.area120.tables_v1alpha1.types.ListTablesResponse` object, and provides an ``__aiter__`` method to iterate through its ``tables`` field. If there are more pages, the ``__aiter__`` method will make additional ``ListTables`` requests and continue to iterate through the ``tables`` field on the corresponding responses. All the usual :class:`google.area120.tables_v1alpha1.types.ListTablesResponse` attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup. """ def __init__(self, method: Callable[..., Awaitable[tables.ListTablesResponse]], request: tables.ListTablesRequest, response: tables.ListTablesResponse, *, metadata: Sequence[Tuple[str, str]] = ()): """Instantiates the pager. Args: method (Callable): The method that was originally called, and which instantiated this pager. request (google.area120.tables_v1alpha1.types.ListTablesRequest): The initial request object. response (google.area120.tables_v1alpha1.types.ListTablesResponse): The initial response object. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ self._method = method self._request = tables.ListTablesRequest(request) self._response = response self._metadata = metadata def __getattr__(self, name: str) -> Any: return getattr(self._response, name) @property async def pages(self) -> AsyncIterator[tables.ListTablesResponse]: yield self._response while self._response.next_page_token: self._request.page_token = self._response.next_page_token self._response = await self._method(self._request, metadata=self._metadata) yield self._response def __aiter__(self) -> AsyncIterator[tables.Table]: async def async_generator(): async for page in self.pages: for response in page.tables: yield response return async_generator() def __repr__(self) -> str: return '{0}<{1!r}>'.format(self.__class__.__name__, self._response) class ListWorkspacesPager: """A pager for iterating through ``list_workspaces`` requests. This class thinly wraps an initial :class:`google.area120.tables_v1alpha1.types.ListWorkspacesResponse` object, and provides an ``__iter__`` method to iterate through its ``workspaces`` field. If there are more pages, the ``__iter__`` method will make additional ``ListWorkspaces`` requests and continue to iterate through the ``workspaces`` field on the corresponding responses. All the usual :class:`google.area120.tables_v1alpha1.types.ListWorkspacesResponse` attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup. """ def __init__(self, method: Callable[..., tables.ListWorkspacesResponse], request: tables.ListWorkspacesRequest, response: tables.ListWorkspacesResponse, *, metadata: Sequence[Tuple[str, str]] = ()): """Instantiate the pager. Args: method (Callable): The method that was originally called, and which instantiated this pager. request (google.area120.tables_v1alpha1.types.ListWorkspacesRequest): The initial request object. response (google.area120.tables_v1alpha1.types.ListWorkspacesResponse): The initial response object. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ self._method = method self._request = tables.ListWorkspacesRequest(request) self._response = response self._metadata = metadata def __getattr__(self, name: str) -> Any: return getattr(self._response, name) @property def pages(self) -> Iterator[tables.ListWorkspacesResponse]: yield self._response while self._response.next_page_token: self._request.page_token = self._response.next_page_token self._response = self._method(self._request, metadata=self._metadata) yield self._response def __iter__(self) -> Iterator[tables.Workspace]: for page in self.pages: yield from page.workspaces def __repr__(self) -> str: return '{0}<{1!r}>'.format(self.__class__.__name__, self._response) class ListWorkspacesAsyncPager: """A pager for iterating through ``list_workspaces`` requests. This class thinly wraps an initial :class:`google.area120.tables_v1alpha1.types.ListWorkspacesResponse` object, and provides an ``__aiter__`` method to iterate through its ``workspaces`` field. If there are more pages, the ``__aiter__`` method will make additional ``ListWorkspaces`` requests and continue to iterate through the ``workspaces`` field on the corresponding responses. All the usual :class:`google.area120.tables_v1alpha1.types.ListWorkspacesResponse` attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup. """ def __init__(self, method: Callable[..., Awaitable[tables.ListWorkspacesResponse]], request: tables.ListWorkspacesRequest, response: tables.ListWorkspacesResponse, *, metadata: Sequence[Tuple[str, str]] = ()): """Instantiates the pager. Args: method (Callable): The method that was originally called, and which instantiated this pager. request (google.area120.tables_v1alpha1.types.ListWorkspacesRequest): The initial request object. response (google.area120.tables_v1alpha1.types.ListWorkspacesResponse): The initial response object. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ self._method = method self._request = tables.ListWorkspacesRequest(request) self._response = response self._metadata = metadata def __getattr__(self, name: str) -> Any: return getattr(self._response, name) @property async def pages(self) -> AsyncIterator[tables.ListWorkspacesResponse]: yield self._response while self._response.next_page_token: self._request.page_token = self._response.next_page_token self._response = await self._method(self._request, metadata=self._metadata) yield self._response def __aiter__(self) -> AsyncIterator[tables.Workspace]: async def async_generator(): async for page in self.pages: for response in page.workspaces: yield response return async_generator() def __repr__(self) -> str: return '{0}<{1!r}>'.format(self.__class__.__name__, self._response) class ListRowsPager: """A pager for iterating through ``list_rows`` requests. This class thinly wraps an initial :class:`google.area120.tables_v1alpha1.types.ListRowsResponse` object, and provides an ``__iter__`` method to iterate through its ``rows`` field. If there are more pages, the ``__iter__`` method will make additional ``ListRows`` requests and continue to iterate through the ``rows`` field on the corresponding responses. All the usual :class:`google.area120.tables_v1alpha1.types.ListRowsResponse` attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup. """ def __init__(self, method: Callable[..., tables.ListRowsResponse], request: tables.ListRowsRequest, response: tables.ListRowsResponse, *, metadata: Sequence[Tuple[str, str]] = ()): """Instantiate the pager. Args: method (Callable): The method that was originally called, and which instantiated this pager. request (google.area120.tables_v1alpha1.types.ListRowsRequest): The initial request object. response (google.area120.tables_v1alpha1.types.ListRowsResponse): The initial response object. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ self._method = method self._request = tables.ListRowsRequest(request) self._response = response self._metadata = metadata def __getattr__(self, name: str) -> Any: return getattr(self._response, name) @property def pages(self) -> Iterator[tables.ListRowsResponse]: yield self._response while self._response.next_page_token: self._request.page_token = self._response.next_page_token self._response = self._method(self._request, metadata=self._metadata) yield self._response def __iter__(self) -> Iterator[tables.Row]: for page in self.pages: yield from page.rows def __repr__(self) -> str: return '{0}<{1!r}>'.format(self.__class__.__name__, self._response) class ListRowsAsyncPager: """A pager for iterating through ``list_rows`` requests. This class thinly wraps an initial :class:`google.area120.tables_v1alpha1.types.ListRowsResponse` object, and provides an ``__aiter__`` method to iterate through its ``rows`` field. If there are more pages, the ``__aiter__`` method will make additional ``ListRows`` requests and continue to iterate through the ``rows`` field on the corresponding responses. All the usual :class:`google.area120.tables_v1alpha1.types.ListRowsResponse` attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup. """ def __init__(self, method: Callable[..., Awaitable[tables.ListRowsResponse]], request: tables.ListRowsRequest, response: tables.ListRowsResponse, *, metadata: Sequence[Tuple[str, str]] = ()): """Instantiates the pager. Args: method (Callable): The method that was originally called, and which instantiated this pager. request (google.area120.tables_v1alpha1.types.ListRowsRequest): The initial request object. response (google.area120.tables_v1alpha1.types.ListRowsResponse): The initial response object. metadata (Sequence[Tuple[str, str]]): Strings which should be sent along with the request as metadata. """ self._method = method self._request = tables.ListRowsRequest(request) self._response = response self._metadata = metadata def __getattr__(self, name: str) -> Any: return getattr(self._response, name) @property async def pages(self) -> AsyncIterator[tables.ListRowsResponse]: yield self._response while self._response.next_page_token: self._request.page_token = self._response.next_page_token self._response = await self._method(self._request, metadata=self._metadata) yield self._response def __aiter__(self) -> AsyncIterator[tables.Row]: async def async_generator(): async for page in self.pages: for response in page.rows: yield response return async_generator() def __repr__(self) -> str: return '{0}<{1!r}>'.format(self.__class__.__name__, self._response)

40.116883

95

0.662933

1,702

15,445

5.810811

0.10517

0.058241

0.048028

0.068251

0.926188

0.922952

0.913549

0

0.012634

0.251797

15,445

384

96

40.221354

0.843198

0.473098

0

0.801242

0

0.008238

0

1

0.167702

false

0

0.012422

0.074534

0.310559

0

null

0

1

0

1

0

null

0

7

a27ede8c67c9624a2abddd0e9520dc0d9d8818bc

83

py

Python

Python/Tests/TestData/Grammar/VarAnnotationIllegal.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

695

2019-05-06T23:49:37.000Z

2022-03-30T01:56:00.000Z

Python/Tests/TestData/Grammar/VarAnnotationIllegal.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

1,672

2019-05-06T21:09:38.000Z

2022-03-31T23:16:04.000Z

Python/Tests/TestData/Grammar/VarAnnotationIllegal.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

186

2019-05-13T03:17:37.000Z

2022-03-31T16:24:05.000Z

fob, oar: baz fob: oar, baz fob, oar: baz = 1 fob: oar, baz = 1 fob: oar = baz = 1

13.833333

18

0.578313

18

83

2.666667

0.222222

0.625

0.9375

0.625

1

0.625

0

0.04918

0.26506

83

5

19

16.6

0.737705

0

null

0

null

0

1

0

null

1

0

1

0

null

0

1

0

8

a28aefb5a0a2f7b18bd05f5a3c63864e001ccd02

206

py

Python

Nkechi Esomonu/Phase 1/Python Basic 1/Day 1/Day2/task 1.py

nkem1010/python-challenge-solutions

203cedc691094a83b110fc75764aac51dbbc1a03

[ "MIT" ]

null

Nkechi Esomonu/Phase 1/Python Basic 1/Day 1/Day2/task 1.py

nkem1010/python-challenge-solutions

203cedc691094a83b110fc75764aac51dbbc1a03

[ "MIT" ]

null

Nkechi Esomonu/Phase 1/Python Basic 1/Day 1/Day2/task 1.py

nkem1010/python-challenge-solutions

203cedc691094a83b110fc75764aac51dbbc1a03

[ "MIT" ]

null

text="Twinkle, twinkle, little star,\n\tHow I wonder what you are!\n\t\tUp above the world so high,\n\t\tLike a diamond in the sky.\nTwinkle, twinkle, little star,\n\tHow I wonder what you are" print(text)

103

193

0.742718

41

206

3.731707

0.609756

0.169935

0.222222

0.235294

0.509804

0

0.135922

206

2

194

103

0.859551

0

0.5

0.898551

0

1

0

false

0

0.5

0

null

0

1

0

1

0

1

0

1

0

null

0

1

0

7

a2c47c43655e5763046f3ed729b82a6f10e74966

45

py

Python

services/validators/__init__.py

rfukui/orign

7d0c22d5f006727ec33fa57efec75c7e762decc5

[ "Unlicense" ]

null

services/validators/__init__.py

rfukui/orign

7d0c22d5f006727ec33fa57efec75c7e762decc5

[ "Unlicense" ]

null

services/validators/__init__.py

rfukui/orign

7d0c22d5f006727ec33fa57efec75c7e762decc5

[ "Unlicense" ]

1

2020-11-09T15:21:51.000Z

from .input_validator import input_validator

22.5

44

0.888889

6

45

6.333333

0.666667

0.736842

0

0.088889

45

1

45

0.926829

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

a2d00de25998c8e4c3213d35dd273d9891b55336

19,304

py

Python

src/server/db/ProjectMapper.py

muenstermannmarius/ElectionSystem

a6e60d9147423787e869587b808def4771f89cb7

[ "RSA-MD" ]

null

src/server/db/ProjectMapper.py

muenstermannmarius/ElectionSystem

a6e60d9147423787e869587b808def4771f89cb7

[ "RSA-MD" ]

null

src/server/db/ProjectMapper.py

muenstermannmarius/ElectionSystem

a6e60d9147423787e869587b808def4771f89cb7

[ "RSA-MD" ]

null

from server.bo.Project import Project from server.db.Mapper import Mapper class ProjectMapper(Mapper): """Mapper class that maps project objects to a relational database. database. For this a set of methods is made available, with methods, which can be used to search for, create, modify and delete objects. can be deleted. The mapping is bidirectional. I.e., objects can be be converted into DB structures and DB structures into objects. """ def __init__(self): super().__init__() def find_all(self): """Read out all projects. :return A collection of projects objects that all projects represent.""" result = [] cursor = self._connection.cursor() cursor.execute("SELECT * FROM Project") tuples = cursor.fetchall() for (id, creation_date, name, short_description, special_room, room_desired, num_blockdays_prior_lecture, date_blockdays_during_lecture, num_blockdays_during_lecture, num_blockdays_in_exam, weekly, num_spots, language, external_partner, edv_number, projecttype_id, module_id, professor_id, add_professor_id, current_state_id) in tuples: project = Project() project.set_id(id) project.set_date(creation_date) project.set_name(name) project.set_short_description(short_description) project.set_special_room(special_room) project.set_room_desired(room_desired) project.set_num_blockdays_prior_lecture(num_blockdays_prior_lecture) project.set_date_blockdays_during_lecture(date_blockdays_during_lecture) project.set_num_blockdays_during_lecture(num_blockdays_during_lecture) project.set_num_blockdays_in_exam(num_blockdays_in_exam) project.set_weekly(weekly) project.set_num_spots(num_spots) project.set_language(language) project.set_external_partner(external_partner) project.set_edv_number(edv_number) project.set_projecttype_id(projecttype_id) project.set_module_id(module_id) project.set_professor_id(professor_id) project.set_add_professor_id(add_professor_id) project.set_state(current_state_id) result.append(project) self._connection.commit() cursor.close() return result def find_by_id(self, id): """Read out the project based on their id. : param project_id of the associated project. : return a project object with the id number.""" result = None cursor = self._connection.cursor() command = "SELECT * FROM Project WHERE id={}".format(id) cursor.execute(command) tuples = cursor.fetchall() for (id, creation_date, name, short_description, special_room, room_desired, num_blockdays_prior_lecture, date_blockdays_during_lecture, num_blockdays_during_lecture, num_blockdays_in_exam, weekly, num_spots, language, external_partner, edv_number, projecttype_id, module_id, professor_id, add_professor_id, current_state_id) in tuples: project = Project() project.set_id(id) project.set_date(creation_date) project.set_name(name) project.set_short_description(short_description) project.set_special_room(special_room) project.set_room_desired(room_desired) project.set_num_blockdays_prior_lecture(num_blockdays_prior_lecture) project.set_date_blockdays_during_lecture(date_blockdays_during_lecture) project.set_num_blockdays_during_lecture(num_blockdays_during_lecture) project.set_num_blockdays_in_exam(num_blockdays_in_exam) project.set_weekly(weekly) project.set_num_spots(num_spots) project.set_language(language) project.set_external_partner(external_partner) project.set_edv_number(edv_number) project.set_projecttype_id(projecttype_id) project.set_module_id(module_id) project.set_professor_id(professor_id) project.set_add_professor_id(add_professor_id) project.set_state(current_state_id) result = project self._connection.commit() cursor.close() return result def find_project_by_name(self, name): """Read out all projects based on their name. :return A collection of projects objects that all represent all projects by name.""" result = [] cursor = self._connection.cursor() cursor.execute("SELECT * FROM Project WHERE name LIKE '{}' ORDER BY name".format(name)) tuples = cursor.fetchall() for (id, creation_date, name, short_description, special_room, room_desired, num_blockdays_prior_lecture, date_blockdays_during_lecture, num_blockdays_during_lecture, num_blockdays_in_exam, weekly, num_spots, language, external_partner, edv_number, projecttype_id, module_id, professor_id, add_professor_id, current_state_id) in tuples: project = Project() project.set_id(id) project.set_date(creation_date) project.set_name(name) project.set_short_description(short_description) project.set_special_room(special_room) project.set_room_desired(room_desired) project.set_num_blockdays_prior_lecture(num_blockdays_prior_lecture) project.set_date_blockdays_during_lecture(date_blockdays_during_lecture) project.set_num_blockdays_during_lecture(num_blockdays_during_lecture) project.set_num_blockdays_in_exam(num_blockdays_in_exam) project.set_weekly(weekly) project.set_num_spots(num_spots) project.set_language(language) project.set_external_partner(external_partner) project.set_edv_number(edv_number) project.set_projecttype_id(projecttype_id) project.set_module_id(module_id) project.set_professor_id(professor_id) project.set_add_professor_id(add_professor_id) project.set_state(current_state_id) result.append(project) self._connection.commit() cursor.close() return result def insert(self, project): """Insertion of a project object into the database. The primary key of the transferred object is also checked and if necessary corrected. : param project the object to be saved : return the object that has already been transferred, but with a possibly corrected ID. """ cursor = self._connection.cursor() cursor.execute("SELECT MAX(id) AS maxid FROM Project ") tuples = cursor.fetchall() for (maxid) in tuples: if maxid[0] is not None: """ If we determine a central ID we use this by 1 and assign this value as the ID to the project object. """ project.set_id(maxid[0] + 1) else: """If we CAN'T find a maximum ID, let's assume that the table is empty and that we can start with ID 1. """ project.set_id(1) command = "INSERT INTO Project (id, creation_date, name, short_description, special_room, room_desired, num_blockdays_prior_lecture, \ date_blockdays_during_lecture, num_blockdays_during_lecture, num_blockdays_in_exam, weekly, num_spots, language, external_partner, edv_number, \ projecttype_id, module_id, professor_id, add_professor_id, current_state_id) \ VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)" data = (project.get_id(), project.get_date(), project.get_name(), project.get_short_description(), project.get_special_room(), project.get_room_desired(), project.get_num_blockdays_prior_lecture(), project.get_date_blockdays_during_lecture(), project.get_num_blockdays_during_lecture(), project.get_num_blockdays_in_exam(), project.get_weekly(), project.get_num_spots(), project.get_language(), project.get_external_partner(), project.get_edv_number(), project.get_projecttype_id(), project.get_module_id(), project.get_professor_id(), project.get_add_professor_id(), project.get_state()) cursor.execute(command, data) self._connection.commit() cursor.close() return project def delete(self, project): """Deleting the data of a project object from the database. : param project the "object" to be deleted from the DB """ cursor = self._connection.cursor() command = "DELETE FROM Project WHERE id={}".format(project.get_id()) cursor.execute(command) self._connection.commit() cursor.close() return project def update(self, project): """Repeated writing of an project object to the database. : param project the object to be written into the DB """ cursor = self._connection.cursor() command = "UPDATE Project " + "SET name=%s, short_description=%s, special_room=%s, room_desired=%s, num_blockdays_prior_lecture=%s, \ date_blockdays_during_lecture=%s, num_blockdays_during_lecture=%s, num_blockdays_in_exam=%s, weekly=%s, num_spots=%s, \ language=%s, external_partner=%s, edv_number=%s, projecttype_id=%s, module_id=%s, professor_id=%s, add_professor_id=%s, current_state_id=%s WHERE id=%s" data = (project.get_name(), project.get_short_description(), project.get_special_room(), project.get_room_desired(), project.get_num_blockdays_prior_lecture(), project.get_date_blockdays_during_lecture(), project.get_num_blockdays_during_lecture(), project.get_num_blockdays_in_exam(), project.get_weekly(), project.get_num_spots(), project.get_language(), project.get_external_partner(), project.get_edv_number(), project.get_projecttype_id(), project.get_module_id(), project.get_professor_id(), project.get_add_professor_id(), project.get_state(), project.get_id()) cursor.execute(command, data) self._connection.commit() cursor.close() def find_project_by_professor_id(self, professor_id): result = [] cursor = self._connection.cursor() command = "SELECT * FROM Project WHERE professor_id={}".format(professor_id) cursor.execute(command) tuples = cursor.fetchall() for (id, creation_date, name, short_description, special_room, room_desired, num_blockdays_prior_lecture, date_blockdays_during_lecture, num_blockdays_during_lecture, num_blockdays_in_exam, weekly, num_spots, language, external_partner, edv_number, projecttype_id, module_id, professor_id, add_professor_id, current_state_id) in tuples: project = Project() project.set_id(id) project.set_date(creation_date) project.set_name(name) project.set_short_description(short_description) project.set_special_room(special_room) project.set_room_desired(room_desired) project.set_num_blockdays_prior_lecture(num_blockdays_prior_lecture) project.set_date_blockdays_during_lecture(date_blockdays_during_lecture) project.set_num_blockdays_during_lecture(num_blockdays_during_lecture) project.set_num_blockdays_in_exam(num_blockdays_in_exam) project.set_weekly(weekly) project.set_num_spots(num_spots) project.set_language(language) project.set_external_partner(external_partner) project.set_edv_number(edv_number) project.set_projecttype_id(projecttype_id) project.set_module_id(module_id) project.set_professor_id(professor_id) project.set_add_professor_id(add_professor_id) project.set_state(current_state_id) result.append(project) self._connection.commit() cursor.close() return result def find_project_by_projecttype_id(self, projecttype_id): result = [] cursor = self._connection.cursor() command = "SELECT * FROM Project WHERE projecttype_id={}".format(projecttype_id) cursor.execute(command) tuples = cursor.fetchall() for (id, creation_date, name, short_description, special_room, room_desired, num_blockdays_prior_lecture, date_blockdays_during_lecture, num_blockdays_during_lecture, num_blockdays_in_exam, weekly, num_spots, language, external_partner, edv_number, projecttype_id, module_id, professor_id, add_professor_id, current_state_id) in tuples: project = Project() project.set_id(id) project.set_date(creation_date) project.set_name(name) project.set_short_description(short_description) project.set_special_room(special_room) project.set_room_desired(room_desired) project.set_num_blockdays_prior_lecture(num_blockdays_prior_lecture) project.set_date_blockdays_during_lecture(date_blockdays_during_lecture) project.set_num_blockdays_during_lecture(num_blockdays_during_lecture) project.set_num_blockdays_in_exam(num_blockdays_in_exam) project.set_weekly(weekly) project.set_num_spots(num_spots) project.set_language(language) project.set_external_partner(external_partner) project.set_edv_number(edv_number) project.set_projecttype_id(projecttype_id) project.set_module_id(module_id) project.set_professor_id(professor_id) project.set_add_professor_id(add_professor_id) project.set_state(current_state_id) result.append(project) self._connection.commit() cursor.close() return result def find_project_by_state(self, state): """Read out all projects based on their state. :return A collection of projects objects that all represent all projects by state.""" result = [] cursor = self._connection.cursor() cursor.execute("SELECT * FROM Project WHERE current_state_id={}".format(state)) tuples = cursor.fetchall() for (id, creation_date, name, short_description, special_room, room_desired, num_blockdays_prior_lecture, date_blockdays_during_lecture, num_blockdays_during_lecture, num_blockdays_in_exam, weekly, num_spots, language, external_partner, edv_number, projecttype_id, module_id, professor_id, add_professor_id, current_state_id) in tuples: project = Project() project.set_id(id) project.set_date(creation_date) project.set_name(name) project.set_short_description(short_description) project.set_special_room(special_room) project.set_room_desired(room_desired) project.set_num_blockdays_prior_lecture(num_blockdays_prior_lecture) project.set_date_blockdays_during_lecture(date_blockdays_during_lecture) project.set_num_blockdays_during_lecture(num_blockdays_during_lecture) project.set_num_blockdays_in_exam(num_blockdays_in_exam) project.set_weekly(weekly) project.set_num_spots(num_spots) project.set_language(language) project.set_external_partner(external_partner) project.set_edv_number(edv_number) project.set_projecttype_id(projecttype_id) project.set_module_id(module_id) project.set_professor_id(professor_id) project.set_add_professor_id(add_professor_id) project.set_state(current_state_id) result.append(project) self._connection.commit() cursor.close() return result def get_project_by_module(self, module_id): """Read out all projects based on their module. :return A collection of projects objects that all represent all projects by module.""" result = [] cursor = self._connection.cursor() command = "SELECT * FROM Project WHERE module_id={}".format(module_id) cursor.execute(command) tuples = cursor.fetchall() for (id, creation_date, name, short_description, special_room, room_desired, num_blockdays_prior_lecture, date_blockdays_during_lecture, num_blockdays_during_lecture, num_blockdays_in_exam, weekly, num_spots, language, external_partner, edv_number, projecttype_id, module_id, professor_id, add_professor_id, current_state_id) in tuples: project = Project() project.set_id(id) project.set_date(creation_date) project.set_name(name) project.set_short_description(short_description) project.set_special_room(special_room) project.set_room_desired(room_desired) project.set_num_blockdays_prior_lecture(num_blockdays_prior_lecture) project.set_date_blockdays_during_lecture(date_blockdays_during_lecture) project.set_num_blockdays_during_lecture(num_blockdays_during_lecture) project.set_num_blockdays_in_exam(num_blockdays_in_exam) project.set_weekly(weekly) project.set_num_spots(num_spots) project.set_language(language) project.set_external_partner(external_partner) project.set_edv_number(edv_number) project.set_projecttype_id(projecttype_id) project.set_module_id(module_id) project.set_professor_id(professor_id) project.set_add_professor_id(add_professor_id) project.set_state(current_state_id) result.append(project) self._connection.commit() cursor.close() return result

45.314554

173

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Python

titus/titus/inspector/jsongadget.py

jmilleralpine/hadrian

6a438e0370487bbbac5e64a4d6d7a2728902d153

[ "Apache-2.0" ]

127

2015-08-05T17:08:35.000Z

2019-10-17T07:07:08.000Z

titus/titus/inspector/jsongadget.py

jmilleralpine/hadrian

6a438e0370487bbbac5e64a4d6d7a2728902d153

[ "Apache-2.0" ]

54

2015-11-20T02:21:29.000Z

2019-11-23T20:17:23.000Z

titus/titus/inspector/jsongadget.py

jmilleralpine/hadrian

6a438e0370487bbbac5e64a4d6d7a2728902d153

[ "Apache-2.0" ]

58

2015-05-27T18:19:29.000Z

2019-05-23T12:37:17.000Z

#!/usr/bin/env python # Copyright (C) 2014 Open Data ("Open Data" refers to # one or more of the following companies: Open Data Partners LLC, # Open Data Research LLC, or Open Data Capital LLC.) # # This file is part of Hadrian. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import json import StringIO import titus.producer.tools as t from titus.inspector.defs import * def depthGreaterThan(obj, target): """Helper function for determining if an object's depth is greater than a given target. A string, number, boolean, or null has depth 0, a list or dict of such objects has depth 1, etc. :type obj: Pythonized JSON :param obj: object to inspect :type target: non-negative integer :param target: target depth :rtype: bool :return: ``True`` if the depth of ``obj`` is greater than ``target``; ``False`` otherwise. """ if isinstance(obj, dict): return any(depthGreaterThan(x, target - 1) for x in obj.values()) or \ (len(obj) == 0 and target <= 0) elif isinstance(obj, (list, tuple)): return any(depthGreaterThan(x, target - 1) for x in obj) or \ (len(obj) == 0 and target <= 0) else: return target < 0 class LookCommand(Command): """The 'json look' command in pfainspector.""" def __init__(self, mode): self.name = "look" self.syntax = "look <name> [maxDepth=8] [indexWidth=30]" self.help = "look at a named PFA document or subexpression in memory\n " + self.syntax self.mode = mode def complete(self, established, active): """Handle tab-complete for this command's arguments. :type established: string :param established: part of the text that has been established :type active: string :param active: part of the text to be completed :rtype: list of strings :return: potential completions """ options = ["maxDepth=", "indexWidth="] words = getcomplete(established) if len(words) == 0: if active in self.mode.pfaFiles: return [active + "["] else: return sorted(x for x in self.mode.pfaFiles if x.startswith(active)) elif len(words) == 1 and isinstance(words[0], parser.Extract) and words[0].partial: if words[0].text in self.mode.pfaFiles: return [x for x in extcomplete(self.mode.pfaFiles[words[0].text].obj, words[0].items) if x.startswith(active)] else: return [] elif not words[-1].partial: return [x for x in options if x.startswith(active)] else: return [] def action(self, args): """Perform the action associated with this command. :type args: list of titus.inspector.parser.Ast :param args: arguments passed to the command :rtype: ``None`` :return: nothing; results must be printed to the screen """ if len(args) == 1 and args[0] == parser.Word("help"): print self.help else: options = {"maxDepth": 8, "indexWidth": 30} while len(args) > 0 and isinstance(args[-1], parser.Option): opt = args.pop() if opt.word.text in ["maxDepth", "indexWidth"]: try: options[opt.word.text] = opt.value.value() except TypeError: raise InspectorError("illegal value for {0}".format(opt.word.text)) else: raise InspectorError("option {0} unrecognized".format(opt.word.text)) if not isinstance(options["maxDepth"], (int, long)) or options["maxDepth"] <= 0: raise InspectorError("maxDepth must be a positive integer") if not isinstance(options["indexWidth"], (int, long)) or options["indexWidth"] <= 0: raise InspectorError("indexWidth must be a positive integer") if len(args) == 1 and isinstance(args[0], parser.Word): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) node = self.mode.pfaFiles[args[0].text].obj elif len(args) == 1 and isinstance(args[0], parser.Extract): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) node = self.mode.pfaFiles[args[0].text].obj items = args[0].items node = extaction(args[0], node, items) else: self.syntaxError() if not depthGreaterThan(node, 0): print json.dumps(node) else: content = StringIO.StringIO() if not depthGreaterThan(node, 1): t.look(node, maxDepth=options["maxDepth"], indexWidth=options["indexWidth"], inlineDepth=0, stream=content) elif not depthGreaterThan(node, 2): t.look(node, maxDepth=options["maxDepth"], indexWidth=options["indexWidth"], inlineDepth=1, stream=content) else: t.look(node, maxDepth=options["maxDepth"], indexWidth=options["indexWidth"], inlineDepth=2, stream=content) content = content.getvalue() if content.count("\n") <= 100: print content else: proc = pipe("less") try: proc.stdin.write(content) except IOError as err: if str(err) != "[Errno 32] Broken pipe": raise pipewait(proc) class CountCommand(Command): """The 'json count' command in pfainspector.""" def __init__(self, mode): self.name = "count" self.syntax = "count <name> <pattern>" self.help = "count instances in a PFA document or subexpression that match a regular expression\n " + self.syntax self.mode = mode def complete(self, established, active): """Handle tab-complete for this command's arguments. :type established: string :param established: part of the text that has been established :type active: string :param active: part of the text to be completed :rtype: list of strings :return: potential completions """ words = getcomplete(established) if len(words) == 0: if active in self.mode.pfaFiles: return [active + "["] else: return sorted(x for x in self.mode.pfaFiles.keys() if x.startswith(active)) elif len(words) == 1 and isinstance(words[0], parser.Extract) and words[0].partial: if words[0].text in self.mode.pfaFiles: return [x for x in extcomplete(self.mode.pfaFiles[words[0].text].obj, words[0].items) if x.startswith(active)] else: return [] else: return [] def action(self, args): """Perform the action associated with this command. :type args: list of titus.inspector.parser.Ast :param args: arguments passed to the command :rtype: ``None`` :return: nothing; results must be printed to the screen """ if len(args) == 1 and args[0] == parser.Word("help"): print self.help else: if len(args) == 2 and isinstance(args[0], parser.Word): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) node = self.mode.pfaFiles[args[0].text].obj elif len(args) == 2 and isinstance(args[0], parser.Extract): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) node = self.mode.pfaFiles[args[0].text].obj items = args[0].items node = extaction(args[0], node, items) else: self.syntaxError() regex = args[-1].regex() print "{0} matches".format(t.count(regex, node)) class IndexCommand(Command): """The 'json index' command in pfainspector.""" def __init__(self, mode): self.name = "index" self.syntax = "index <name> <pattern>" self.help = "list indexes of a PFA document or subexpression that match a regular expression\n " + self.syntax self.mode = mode def complete(self, established, active): """Handle tab-complete for this command's arguments. :type established: string :param established: part of the text that has been established :type active: string :param active: part of the text to be completed :rtype: list of strings :return: potential completions """ words = getcomplete(established) if len(words) == 0: if active in self.mode.pfaFiles: return [active + "["] else: return sorted(x for x in self.mode.pfaFiles.keys() if x.startswith(active)) elif len(words) == 1 and isinstance(words[0], parser.Extract) and words[0].partial: if words[0].text in self.mode.pfaFiles: return [x for x in extcomplete(self.mode.pfaFiles[words[0].text].obj, words[0].items) if x.startswith(active)] else: return [] else: return [] def action(self, args): """Perform the action associated with this command. :type args: list of titus.inspector.parser.Ast :param args: arguments passed to the command :rtype: ``None`` :return: nothing; results must be printed to the screen """ if len(args) == 1 and args[0] == parser.Word("help"): print self.help else: if len(args) == 2 and isinstance(args[0], parser.Word): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) node = self.mode.pfaFiles[args[0].text].obj elif len(args) == 2 and isinstance(args[0], parser.Extract): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) node = self.mode.pfaFiles[args[0].text].obj items = args[0].items node = extaction(args[0], node, items) else: self.syntaxError() regex = args[-1].regex() def display(i): if isinstance(i, basestring): if " " in i: return json.dumps(i) else: return i else: return str(i) print "Indexes that match the pattern:" count = 0 for index in t.indexes(regex, node): print " [" + ", ".join(display(i) for i in index) + "]" count += 1 if count == 0: print " (none)" class FindCommand(Command): """The 'json find' command in pfainspector.""" def __init__(self, mode): self.name = "find" self.syntax = "find <name> <pattern> [maxDepth=3] [indexWidth=30]" self.help = "show all matches of a regular expression in a PFA document or subexpression\n " + self.syntax self.mode = mode def complete(self, established, active): """Handle tab-complete for this command's arguments. :type established: string :param established: part of the text that has been established :type active: string :param active: part of the text to be completed :rtype: list of strings :return: potential completions """ options = ["maxDepth=", "indexWidth="] words = getcomplete(established) if len(words) == 0: if active in self.mode.pfaFiles: return [active + "["] else: return sorted(x for x in self.mode.pfaFiles if x.startswith(active)) elif len(words) == 1 and isinstance(words[0], parser.Extract) and words[0].partial: if words[0].text in self.mode.pfaFiles: return [x for x in extcomplete(self.mode.pfaFiles[words[0].text].obj, words[0].items) if x.startswith(active)] else: return [] elif not words[-1].partial: return [x for x in options if x.startswith(active)] else: return [] def action(self, args): """Perform the action associated with this command. :type args: list of titus.inspector.parser.Ast :param args: arguments passed to the command :rtype: ``None`` :return: nothing; results must be printed to the screen """ if len(args) == 1 and args[0] == parser.Word("help"): print self.help else: options = {"maxDepth": 3, "indexWidth": 30} while len(args) > 0 and isinstance(args[-1], parser.Option): opt = args.pop() if opt.word.text in ["maxDepth", "indexWidth"]: try: options[opt.word.text] = opt.value.value() except TypeError: raise InspectorError("illegal value for {0}".format(opt.word.text)) else: raise InspectorError("option {1} unrecognized".format(opt.word.text)) if not isinstance(options["maxDepth"], (int, long)) or options["maxDepth"] <= 0: raise InspectorError("maxDepth must be a positive integer") if not isinstance(options["indexWidth"], (int, long)) or options["indexWidth"] <= 0: raise InspectorError("indexWidth must be a positive integer") if len(args) == 2 and isinstance(args[0], parser.Word): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) node = self.mode.pfaFiles[args[0].text].obj elif len(args) == 2 and isinstance(args[0], parser.Extract): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) node = self.mode.pfaFiles[args[0].text].obj items = args[0].items node = extaction(args[0], node, items) else: self.syntaxError() regex = args[-1].regex() def display(i): if isinstance(i, basestring): if " " in i: return json.dumps(i) else: return i else: return str(i) content = StringIO.StringIO() count = 0 for index in t.indexes(regex, node): content.write("At index [" + ", ".join(display(i) for i in index) + "]:\n") matched = t.get(node, index) if not depthGreaterThan(matched, 0): content.write(json.dumps(matched) + "\n") elif not depthGreaterThan(matched, 1): t.look(matched, maxDepth=options["maxDepth"], indexWidth=options["indexWidth"], inlineDepth=0, stream=content) elif not depthGreaterThan(matched, 2): t.look(matched, maxDepth=options["maxDepth"], indexWidth=options["indexWidth"], inlineDepth=1, stream=content) else: t.look(matched, maxDepth=options["maxDepth"], indexWidth=options["indexWidth"], inlineDepth=2, stream=content) content.write("\n") count += 1 if count == 0: print " (none)" content = content.getvalue() if content.count("\n") <= 100: print content else: proc = pipe("less") try: proc.stdin.write(content) except IOError as err: if str(err) != "[Errno 32] Broken pipe": raise pipewait(proc) class ChangeCommand(Command): """The 'json change' command in pfainspector.""" def __init__(self, mode): self.name = "change" self.syntax = "change <name> <pattern> to <replacement>" self.help = "replace instances in a PFA document or subexpression that match a regular expression\n " + self.syntax self.mode = mode def complete(self, established, active): """Handle tab-complete for this command's arguments. :type established: string :param established: part of the text that has been established :type active: string :param active: part of the text to be completed :rtype: list of strings :return: potential completions """ words = getcomplete(established) if len(words) == 0: if active in self.mode.pfaFiles: return [active + "["] else: return sorted(x for x in self.mode.pfaFiles.keys() if x.startswith(active)) elif len(words) == 1 and isinstance(words[0], parser.Extract) and words[0].partial: if words[0].text in self.mode.pfaFiles: return [x for x in extcomplete(self.mode.pfaFiles[words[0].text].obj, words[0].items) if x.startswith(active)] else: return [] elif len(words) == 2 and isinstance(words[0], (parser.Word, parser.Extract)) and words[0].text in self.mode.pfaFiles: return ["to "] else: return [] def action(self, args): """Perform the action associated with this command. :type args: list of titus.inspector.parser.Ast :param args: arguments passed to the command :rtype: ``None`` :return: nothing; results must be printed to the screen """ if len(args) == 1 and args[0] == parser.Word("help"): print self.help else: if len(args) == 4 and isinstance(args[0], parser.Word): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) model = self.mode.pfaFiles[args[0].text] node = model.obj safecopy = copy.deepcopy(node) def rollback(): self.mode.pfaFiles[args[0].text].obj = safecopy regex = args[1].regex() replacement = args[3].replacement() elif len(args) == 4 and isinstance(args[0], parser.Extract): if args[0].text not in self.mode.pfaFiles: raise InspectorError("no PFA document named \"{0}\" in memory (try 'load <file> as {1}')".format(args[0].text, args[0].text)) model = self.mode.pfaFiles[args[0].text] node = model.obj safecopy = copy.deepcopy(node) def rollback(): self.mode.pfaFiles[args[0].text].obj = safecopy items = args[0].items node = extaction(args[0], node, items) regex = args[1].regex() replacement = args[3].replacement() else: self.syntaxError() def display(i): if isinstance(i, basestring): if " " in i: return json.dumps(i) else: return i else: return str(i) def replace(value, groups): if isinstance(value, parser.Replacement): try: return groups[value.name] except KeyError: raise InspectorError("group ({0}) not found in regular expression".format(value.name)) elif isinstance(value, dict): return dict((k, replace(v, groups)) for k, v in value.items()) elif isinstance(value, (list, tuple)): return [replace(x, groups) for x in value] else: return value def removeAts(obj): if isinstance(obj, dict): return dict((k, removeAts(v)) for k, v in obj.items() if k != "@") elif isinstance(obj, (list, tuple)): return [removeAts(x) for x in obj] else: return obj ask = True self.mode.pause() try: for index, match in t.search(regex, node): replacedReplacement = replace(replacement, match.groups) if ask: print "At index [" + ", ".join(display(i) for i in index) + "]:" print "Original: " + json.dumps(removeAts(t.get(node, index))) print "Change to: " + json.dumps(replacedReplacement) action = None while action is None: response = raw_input("(Y/n/all/stop/revert): ") normalized = response.strip().lower() if normalized in ("", "y", "yes"): action = "yes" elif normalized in ("n", "no"): action = "no" elif normalized == "all": action = "all" elif normalized == "stop": action = "stop" elif normalized == "revert": action = "revert" print else: action = "yes" if action == "yes": t.assign(node, index, replacedReplacement) elif action == "all": t.assign(node, index, replacedReplacement) ask = False elif action == "stop": break elif action == "revert": rollback() break except: rollback() self.mode.resume() raise else: model.reset() self.mode.resume() class JsonGadget(Gadget): """The 'json' gadget in pfainspector.""" def __init__(self, mode): self.commandGroup = CommandGroup("json", [ LookCommand(mode), CountCommand(mode), IndexCommand(mode), FindCommand(mode), ChangeCommand(mode) ])

40.136738

145

0.531092

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4.65873

0.106061

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0c412899c03b81c1f041a7664177a8afb9f2893e

1,198

py

Python

cart_venv/Lib/site-packages/tensorflow_core/_api/v1/compat/v1/nn/rnn_cell/__init__.py

juice1000/Synchronous-vs-Asynchronous-Learning-Tensorflow-

654be60f7986ac9bb7ce1d080ddee377c3389f93

[ "MIT" ]

2

2019-08-04T20:28:14.000Z

2019-10-27T23:26:42.000Z

cart_venv/Lib/site-packages/tensorflow_core/_api/v1/compat/v1/nn/rnn_cell/__init__.py

juice1000/Synchronous-vs-Asynchronous-Learning-Tensorflow-

654be60f7986ac9bb7ce1d080ddee377c3389f93

[ "MIT" ]

null

cart_venv/Lib/site-packages/tensorflow_core/_api/v1/compat/v1/nn/rnn_cell/__init__.py

juice1000/Synchronous-vs-Asynchronous-Learning-Tensorflow-

654be60f7986ac9bb7ce1d080ddee377c3389f93

[ "MIT" ]

1

2020-11-04T03:16:29.000Z

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Module for constructing RNN Cells. """ from __future__ import print_function as _print_function import sys as _sys from tensorflow.python.ops.rnn_cell_impl import BasicLSTMCell from tensorflow.python.ops.rnn_cell_impl import BasicRNNCell from tensorflow.python.ops.rnn_cell_impl import DeviceWrapper from tensorflow.python.ops.rnn_cell_impl import DropoutWrapper from tensorflow.python.ops.rnn_cell_impl import GRUCell from tensorflow.python.ops.rnn_cell_impl import LSTMCell from tensorflow.python.ops.rnn_cell_impl import LSTMStateTuple from tensorflow.python.ops.rnn_cell_impl import MultiRNNCell from tensorflow.python.ops.rnn_cell_impl import RNNCell from tensorflow.python.ops.rnn_cell_impl import ResidualWrapper del _print_function from tensorflow.python.util import module_wrapper as _module_wrapper if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper): _sys.modules[__name__] = _module_wrapper.TFModuleWrapper( _sys.modules[__name__], "compat.v1.nn.rnn_cell", public_apis=None, deprecation=False, has_lite=False)

41.310345

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1,198

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1

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1

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7

a773b4d66c44bb9a3985707203b0e93bb7afdbc8

29,268

py

Python

sdk/python/pulumi_kong/service.py

pulumi/pulumi-kong

775c17e4eac38934252410ed3dcdc6fc3bd40c5c

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

4

2020-02-23T10:05:20.000Z

2020-05-15T14:22:10.000Z

sdk/python/pulumi_kong/service.py

pulumi/pulumi-kong

775c17e4eac38934252410ed3dcdc6fc3bd40c5c

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

41

2020-04-21T22:04:23.000Z

2022-03-31T15:29:53.000Z

sdk/python/pulumi_kong/service.py

pulumi/pulumi-kong

775c17e4eac38934252410ed3dcdc6fc3bd40c5c

[ "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! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['ServiceArgs', 'Service'] @pulumi.input_type class ServiceArgs: def __init__(__self__, *, protocol: pulumi.Input[str], ca_certificate_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, client_certificate_id: Optional[pulumi.Input[str]] = None, connect_timeout: Optional[pulumi.Input[int]] = None, host: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, path: Optional[pulumi.Input[str]] = None, port: Optional[pulumi.Input[int]] = None, read_timeout: Optional[pulumi.Input[int]] = None, retries: Optional[pulumi.Input[int]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, tls_verify: Optional[pulumi.Input[bool]] = None, tls_verify_depth: Optional[pulumi.Input[int]] = None, write_timeout: Optional[pulumi.Input[int]] = None): """ The set of arguments for constructing a Service resource. """ pulumi.set(__self__, "protocol", protocol) if ca_certificate_ids is not None: pulumi.set(__self__, "ca_certificate_ids", ca_certificate_ids) if client_certificate_id is not None: pulumi.set(__self__, "client_certificate_id", client_certificate_id) if connect_timeout is not None: pulumi.set(__self__, "connect_timeout", connect_timeout) if host is not None: pulumi.set(__self__, "host", host) if name is not None: pulumi.set(__self__, "name", name) if path is not None: pulumi.set(__self__, "path", path) if port is not None: pulumi.set(__self__, "port", port) if read_timeout is not None: pulumi.set(__self__, "read_timeout", read_timeout) if retries is not None: pulumi.set(__self__, "retries", retries) if tags is not None: pulumi.set(__self__, "tags", tags) if tls_verify is not None: pulumi.set(__self__, "tls_verify", tls_verify) if tls_verify_depth is not None: pulumi.set(__self__, "tls_verify_depth", tls_verify_depth) if write_timeout is not None: pulumi.set(__self__, "write_timeout", write_timeout) @property @pulumi.getter def protocol(self) -> pulumi.Input[str]: return pulumi.get(self, "protocol") @protocol.setter def protocol(self, value: pulumi.Input[str]): pulumi.set(self, "protocol", value) @property @pulumi.getter(name="caCertificateIds") def ca_certificate_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: return pulumi.get(self, "ca_certificate_ids") @ca_certificate_ids.setter def ca_certificate_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "ca_certificate_ids", value) @property @pulumi.getter(name="clientCertificateId") def client_certificate_id(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "client_certificate_id") @client_certificate_id.setter def client_certificate_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "client_certificate_id", value) @property @pulumi.getter(name="connectTimeout") def connect_timeout(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "connect_timeout") @connect_timeout.setter def connect_timeout(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "connect_timeout", value) @property @pulumi.getter def host(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "host") @host.setter def host(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "host", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def path(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "path") @path.setter def path(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "path", value) @property @pulumi.getter def port(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "port") @port.setter def port(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "port", value) @property @pulumi.getter(name="readTimeout") def read_timeout(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "read_timeout") @read_timeout.setter def read_timeout(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "read_timeout", value) @property @pulumi.getter def retries(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "retries") @retries.setter def retries(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "retries", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="tlsVerify") def tls_verify(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "tls_verify") @tls_verify.setter def tls_verify(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "tls_verify", value) @property @pulumi.getter(name="tlsVerifyDepth") def tls_verify_depth(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "tls_verify_depth") @tls_verify_depth.setter def tls_verify_depth(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "tls_verify_depth", value) @property @pulumi.getter(name="writeTimeout") def write_timeout(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "write_timeout") @write_timeout.setter def write_timeout(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "write_timeout", value) @pulumi.input_type class _ServiceState: def __init__(__self__, *, ca_certificate_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, client_certificate_id: Optional[pulumi.Input[str]] = None, connect_timeout: Optional[pulumi.Input[int]] = None, host: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, path: Optional[pulumi.Input[str]] = None, port: Optional[pulumi.Input[int]] = None, protocol: Optional[pulumi.Input[str]] = None, read_timeout: Optional[pulumi.Input[int]] = None, retries: Optional[pulumi.Input[int]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, tls_verify: Optional[pulumi.Input[bool]] = None, tls_verify_depth: Optional[pulumi.Input[int]] = None, write_timeout: Optional[pulumi.Input[int]] = None): """ Input properties used for looking up and filtering Service resources. """ if ca_certificate_ids is not None: pulumi.set(__self__, "ca_certificate_ids", ca_certificate_ids) if client_certificate_id is not None: pulumi.set(__self__, "client_certificate_id", client_certificate_id) if connect_timeout is not None: pulumi.set(__self__, "connect_timeout", connect_timeout) if host is not None: pulumi.set(__self__, "host", host) if name is not None: pulumi.set(__self__, "name", name) if path is not None: pulumi.set(__self__, "path", path) if port is not None: pulumi.set(__self__, "port", port) if protocol is not None: pulumi.set(__self__, "protocol", protocol) if read_timeout is not None: pulumi.set(__self__, "read_timeout", read_timeout) if retries is not None: pulumi.set(__self__, "retries", retries) if tags is not None: pulumi.set(__self__, "tags", tags) if tls_verify is not None: pulumi.set(__self__, "tls_verify", tls_verify) if tls_verify_depth is not None: pulumi.set(__self__, "tls_verify_depth", tls_verify_depth) if write_timeout is not None: pulumi.set(__self__, "write_timeout", write_timeout) @property @pulumi.getter(name="caCertificateIds") def ca_certificate_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: return pulumi.get(self, "ca_certificate_ids") @ca_certificate_ids.setter def ca_certificate_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "ca_certificate_ids", value) @property @pulumi.getter(name="clientCertificateId") def client_certificate_id(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "client_certificate_id") @client_certificate_id.setter def client_certificate_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "client_certificate_id", value) @property @pulumi.getter(name="connectTimeout") def connect_timeout(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "connect_timeout") @connect_timeout.setter def connect_timeout(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "connect_timeout", value) @property @pulumi.getter def host(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "host") @host.setter def host(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "host", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def path(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "path") @path.setter def path(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "path", value) @property @pulumi.getter def port(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "port") @port.setter def port(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "port", value) @property @pulumi.getter def protocol(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "protocol") @protocol.setter def protocol(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "protocol", value) @property @pulumi.getter(name="readTimeout") def read_timeout(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "read_timeout") @read_timeout.setter def read_timeout(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "read_timeout", value) @property @pulumi.getter def retries(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "retries") @retries.setter def retries(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "retries", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]: return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="tlsVerify") def tls_verify(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "tls_verify") @tls_verify.setter def tls_verify(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "tls_verify", value) @property @pulumi.getter(name="tlsVerifyDepth") def tls_verify_depth(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "tls_verify_depth") @tls_verify_depth.setter def tls_verify_depth(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "tls_verify_depth", value) @property @pulumi.getter(name="writeTimeout") def write_timeout(self) -> Optional[pulumi.Input[int]]: return pulumi.get(self, "write_timeout") @write_timeout.setter def write_timeout(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "write_timeout", value) class Service(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, ca_certificate_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, client_certificate_id: Optional[pulumi.Input[str]] = None, connect_timeout: Optional[pulumi.Input[int]] = None, host: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, path: Optional[pulumi.Input[str]] = None, port: Optional[pulumi.Input[int]] = None, protocol: Optional[pulumi.Input[str]] = None, read_timeout: Optional[pulumi.Input[int]] = None, retries: Optional[pulumi.Input[int]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, tls_verify: Optional[pulumi.Input[bool]] = None, tls_verify_depth: Optional[pulumi.Input[int]] = None, write_timeout: Optional[pulumi.Input[int]] = None, __props__=None): """ ## # Service The service resource maps directly onto the json for the service endpoint in Kong. For more information on the parameters [see the Kong Service create documentation](https://docs.konghq.com/gateway-oss/2.5.x/admin-api/#service-object). ## Example Usage ```python import pulumi import pulumi_kong as kong service = kong.Service("service", connect_timeout=1000, host="test.org", path="/mypath", port=8080, protocol="http", read_timeout=3000, retries=5, write_timeout=2000) ``` To use a client certificate and ca certificates combine with certificate resource (note protocol must be `https`): ```python import pulumi import pulumi_kong as kong certificate = kong.Certificate("certificate", certificate=\"\"\" -----BEGIN CERTIFICATE----- ...... -----END CERTIFICATE----- \"\"\", private_key=\"\"\" -----BEGIN PRIVATE KEY----- ..... -----END PRIVATE KEY----- \"\"\", snis=["foo.com"]) ca = kong.Certificate("ca", certificate=\"\"\" -----BEGIN CERTIFICATE----- ...... -----END CERTIFICATE----- \"\"\", private_key=\"\"\" -----BEGIN PRIVATE KEY----- ..... -----END PRIVATE KEY----- \"\"\", snis=["ca.com"]) service = kong.Service("service", protocol="https", host="test.org", tls_verify=True, tls_verify_depth=2, client_certificate_id=certificate.id, ca_certificate_ids=[ca.id]) ``` ## Argument reference * `name` - (Required) Service name * `protocol` - (Required) Protocol to use * `host` - (Optional) Host to map to * `port` - (Optional, int) Port to map to. Default: 80 * `path` - (Optional) Path to map to * `retries` - (Optional, int) Number of retries. Default: 5 * `connect_timeout` - (Optional, int) Connection timeout. Default(ms): 60000 * `write_timeout` - (Optional, int) Write timout. Default(ms): 60000 * `read_timeout` - (Optional, int) Read timeout. Default(ms): 60000 * `tags` - (Optional) A list of strings associated with the Service for grouping and filtering. * `client_certificate_id` - (Optional) ID of Certificate to be used as client certificate while TLS handshaking to the upstream server. Use ID from `Certificate` resource * `tls_verify` - (Optional) Whether to enable verification of upstream server TLS certificate. If not set then the nginx default is respected. * `tls_verify_depth` - (Optional) Maximum depth of chain while verifying Upstream server’s TLS certificate. * `ca_certificate_ids` - (Optional) A of CA Certificate IDs (created from the certificate resource). that are used to build the trust store while verifying upstream server’s TLS certificate. ## Import To import a service ```sh $ pulumi import kong:index/service:Service <service_identifier> <service_id> ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. """ ... @overload def __init__(__self__, resource_name: str, args: ServiceArgs, opts: Optional[pulumi.ResourceOptions] = None): """ ## # Service The service resource maps directly onto the json for the service endpoint in Kong. For more information on the parameters [see the Kong Service create documentation](https://docs.konghq.com/gateway-oss/2.5.x/admin-api/#service-object). ## Example Usage ```python import pulumi import pulumi_kong as kong service = kong.Service("service", connect_timeout=1000, host="test.org", path="/mypath", port=8080, protocol="http", read_timeout=3000, retries=5, write_timeout=2000) ``` To use a client certificate and ca certificates combine with certificate resource (note protocol must be `https`): ```python import pulumi import pulumi_kong as kong certificate = kong.Certificate("certificate", certificate=\"\"\" -----BEGIN CERTIFICATE----- ...... -----END CERTIFICATE----- \"\"\", private_key=\"\"\" -----BEGIN PRIVATE KEY----- ..... -----END PRIVATE KEY----- \"\"\", snis=["foo.com"]) ca = kong.Certificate("ca", certificate=\"\"\" -----BEGIN CERTIFICATE----- ...... -----END CERTIFICATE----- \"\"\", private_key=\"\"\" -----BEGIN PRIVATE KEY----- ..... -----END PRIVATE KEY----- \"\"\", snis=["ca.com"]) service = kong.Service("service", protocol="https", host="test.org", tls_verify=True, tls_verify_depth=2, client_certificate_id=certificate.id, ca_certificate_ids=[ca.id]) ``` ## Argument reference * `name` - (Required) Service name * `protocol` - (Required) Protocol to use * `host` - (Optional) Host to map to * `port` - (Optional, int) Port to map to. Default: 80 * `path` - (Optional) Path to map to * `retries` - (Optional, int) Number of retries. Default: 5 * `connect_timeout` - (Optional, int) Connection timeout. Default(ms): 60000 * `write_timeout` - (Optional, int) Write timout. Default(ms): 60000 * `read_timeout` - (Optional, int) Read timeout. Default(ms): 60000 * `tags` - (Optional) A list of strings associated with the Service for grouping and filtering. * `client_certificate_id` - (Optional) ID of Certificate to be used as client certificate while TLS handshaking to the upstream server. Use ID from `Certificate` resource * `tls_verify` - (Optional) Whether to enable verification of upstream server TLS certificate. If not set then the nginx default is respected. * `tls_verify_depth` - (Optional) Maximum depth of chain while verifying Upstream server’s TLS certificate. * `ca_certificate_ids` - (Optional) A of CA Certificate IDs (created from the certificate resource). that are used to build the trust store while verifying upstream server’s TLS certificate. ## Import To import a service ```sh $ pulumi import kong:index/service:Service <service_identifier> <service_id> ``` :param str resource_name: The name of the resource. :param ServiceArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(ServiceArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, ca_certificate_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, client_certificate_id: Optional[pulumi.Input[str]] = None, connect_timeout: Optional[pulumi.Input[int]] = None, host: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, path: Optional[pulumi.Input[str]] = None, port: Optional[pulumi.Input[int]] = None, protocol: Optional[pulumi.Input[str]] = None, read_timeout: Optional[pulumi.Input[int]] = None, retries: Optional[pulumi.Input[int]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, tls_verify: Optional[pulumi.Input[bool]] = None, tls_verify_depth: Optional[pulumi.Input[int]] = None, write_timeout: Optional[pulumi.Input[int]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = ServiceArgs.__new__(ServiceArgs) __props__.__dict__["ca_certificate_ids"] = ca_certificate_ids __props__.__dict__["client_certificate_id"] = client_certificate_id __props__.__dict__["connect_timeout"] = connect_timeout __props__.__dict__["host"] = host __props__.__dict__["name"] = name __props__.__dict__["path"] = path __props__.__dict__["port"] = port if protocol is None and not opts.urn: raise TypeError("Missing required property 'protocol'") __props__.__dict__["protocol"] = protocol __props__.__dict__["read_timeout"] = read_timeout __props__.__dict__["retries"] = retries __props__.__dict__["tags"] = tags __props__.__dict__["tls_verify"] = tls_verify __props__.__dict__["tls_verify_depth"] = tls_verify_depth __props__.__dict__["write_timeout"] = write_timeout super(Service, __self__).__init__( 'kong:index/service:Service', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, ca_certificate_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, client_certificate_id: Optional[pulumi.Input[str]] = None, connect_timeout: Optional[pulumi.Input[int]] = None, host: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, path: Optional[pulumi.Input[str]] = None, port: Optional[pulumi.Input[int]] = None, protocol: Optional[pulumi.Input[str]] = None, read_timeout: Optional[pulumi.Input[int]] = None, retries: Optional[pulumi.Input[int]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None, tls_verify: Optional[pulumi.Input[bool]] = None, tls_verify_depth: Optional[pulumi.Input[int]] = None, write_timeout: Optional[pulumi.Input[int]] = None) -> 'Service': """ Get an existing Service resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _ServiceState.__new__(_ServiceState) __props__.__dict__["ca_certificate_ids"] = ca_certificate_ids __props__.__dict__["client_certificate_id"] = client_certificate_id __props__.__dict__["connect_timeout"] = connect_timeout __props__.__dict__["host"] = host __props__.__dict__["name"] = name __props__.__dict__["path"] = path __props__.__dict__["port"] = port __props__.__dict__["protocol"] = protocol __props__.__dict__["read_timeout"] = read_timeout __props__.__dict__["retries"] = retries __props__.__dict__["tags"] = tags __props__.__dict__["tls_verify"] = tls_verify __props__.__dict__["tls_verify_depth"] = tls_verify_depth __props__.__dict__["write_timeout"] = write_timeout return Service(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="caCertificateIds") def ca_certificate_ids(self) -> pulumi.Output[Optional[Sequence[str]]]: return pulumi.get(self, "ca_certificate_ids") @property @pulumi.getter(name="clientCertificateId") def client_certificate_id(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "client_certificate_id") @property @pulumi.getter(name="connectTimeout") def connect_timeout(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "connect_timeout") @property @pulumi.getter def host(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "host") @property @pulumi.getter def name(self) -> pulumi.Output[str]: return pulumi.get(self, "name") @property @pulumi.getter def path(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "path") @property @pulumi.getter def port(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "port") @property @pulumi.getter def protocol(self) -> pulumi.Output[str]: return pulumi.get(self, "protocol") @property @pulumi.getter(name="readTimeout") def read_timeout(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "read_timeout") @property @pulumi.getter def retries(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "retries") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Sequence[str]]]: return pulumi.get(self, "tags") @property @pulumi.getter(name="tlsVerify") def tls_verify(self) -> pulumi.Output[Optional[bool]]: return pulumi.get(self, "tls_verify") @property @pulumi.getter(name="tlsVerifyDepth") def tls_verify_depth(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "tls_verify_depth") @property @pulumi.getter(name="writeTimeout") def write_timeout(self) -> pulumi.Output[Optional[int]]: return pulumi.get(self, "write_timeout")

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a7aa90080b2951abd0d6d93a271b85572a8280fe

3,990

py

Python

tests/longest_palindrome_substr_test.py

gradeawarrior/python-interview-problems

ede738df98f979c45b6657aa6147f0fd5cbfc3dc

[ "Apache-2.0" ]

null

tests/longest_palindrome_substr_test.py

gradeawarrior/python-interview-problems

ede738df98f979c45b6657aa6147f0fd5cbfc3dc

[ "Apache-2.0" ]

null

tests/longest_palindrome_substr_test.py

gradeawarrior/python-interview-problems

ede738df98f979c45b6657aa6147f0fd5cbfc3dc

[ "Apache-2.0" ]

null

""" Given a string s, find the longest palindromic substring in s. You may assume that the maximum length of s is 1000. Example: Input: "babad" Output: "bab" Note: "aba" is also a valid answer. Example: Input: "cbbd" Output: "bb" """ import pytest from project.longest_palindrome_substr import Solution from time import time @pytest.mark.parametrize("test_input, expected", [ ("babad", "bab"), ("cbbd", "bb"), ("", ""), ("a", "a"), ("ab", "a"), ("abc", "a"), ("abcdd", "dd"), ("aaabaaaa", "aaabaaa"), ("salas", "salas"), ("psalas", "salas"), ("peter salas", "salas"), ("abcdefghijklmnopqrstuvwxyz", "a"), ("abcdefghijklmmnopqrstuvwxyz", "mm"), ("abcdefghijklmnopqrstuvwxyzz", "zz"), ("abcdefghijklmnopqrstuvwxyzzz", "zzz"), ("kztakrekvefgchersuoiuatzlmwynzjhdqqftjcqmntoyckqfawikkdrnfgbwtdpbkymvwoumurjdzygyzsbmwzpcxcdmmpwzmeibligwiiqbecxwyxigikoewwrczkanwwqukszsbjukzumzladrvjefpegyicsgctdvldetuegxwihdtitqrdmygdrsweahfrepdcudvyvrggbkthztxwicyzazjyeztytwiyybqdsczozvtegodacdokczfmwqfmyuixbeeqluqcqwxpyrkpfcdosttzooykpvdykfxulttvvwnzftndvhsvpgrgdzsvfxdtzztdiswgwxzvbpsjlizlfrlgvlnwbjwbujafjaedivvgnbgwcdbzbdbprqrflfhahsvlcekeyqueyxjfetkxpapbeejoxwxlgepmxzowldsmqllpzeymakcshfzkvyykwljeltutdmrhxcbzizihzinywggzjctzasvefcxmhnusdvlderconvaisaetcdldeveeemhugipfzbhrwidcjpfrumshbdofchpgcsbkvaexfmenpsuodatxjavoszcitjewflejjmsuvyuyrkumednsfkbgvbqxfphfqeqozcnabmtedffvzwbgbzbfydiyaevoqtfmzxaujdydtjftapkpdhnbmrylcibzuqqynvnsihmyxdcrfftkuoymzoxpnashaderlosnkxbhamkkxfhwjsyehkmblhppbyspmcwuoguptliashefdklokjpggfiixozsrlwmeksmzdcvipgkwxwynzsvxnqtchgwwadqybkguscfyrbyxudzrxacoplmcqcsmkraimfwbauvytkxdnglwfuvehpxd", "dtzztd"), ("iptmykvjanwiihepqhzupneckpzomgvzmyoybzfynybpfybngttozprjbupciuinpzryritfmyxyppxigitnemanreexcpwscvcwddnfjswgprabdggbgcillisyoskdodzlpbltefiz", "illi"), ]) def test_palindrome_brute_force(test_input, expected): threshold = 800 start = time() assert Solution().longestPalindromeBruteForce(test_input) == expected duration = (time() - start) * 1000 # Duration in ms assert duration < threshold, "Expecting duration to be < %s ms" % threshold @pytest.mark.parametrize("test_input, expected", [ ("babad", "bab"), ("cbbd", "bb"), ("", ""), ("a", "a"), ("ab", "a"), ("abc", "a"), ("abcdd", "dd"), ("aaabaaaa", "aaabaaa"), ("salas", "salas"), ("psalas", "salas"), ("peter salas", "salas"), ("abcdefghijklmnopqrstuvwxyz", "a"), ("abcdefghijklmmnopqrstuvwxyz", "mm"), ("abcdefghijklmnopqrstuvwxyzz", "zz"), ("abcdefghijklmnopqrstuvwxyzzz", "zzz"), ("kztakrekvefgchersuoiuatzlmwynzjhdqqftjcqmntoyckqfawikkdrnfgbwtdpbkymvwoumurjdzygyzsbmwzpcxcdmmpwzmeibligwiiqbecxwyxigikoewwrczkanwwqukszsbjukzumzladrvjefpegyicsgctdvldetuegxwihdtitqrdmygdrsweahfrepdcudvyvrggbkthztxwicyzazjyeztytwiyybqdsczozvtegodacdokczfmwqfmyuixbeeqluqcqwxpyrkpfcdosttzooykpvdykfxulttvvwnzftndvhsvpgrgdzsvfxdtzztdiswgwxzvbpsjlizlfrlgvlnwbjwbujafjaedivvgnbgwcdbzbdbprqrflfhahsvlcekeyqueyxjfetkxpapbeejoxwxlgepmxzowldsmqllpzeymakcshfzkvyykwljeltutdmrhxcbzizihzinywggzjctzasvefcxmhnusdvlderconvaisaetcdldeveeemhugipfzbhrwidcjpfrumshbdofchpgcsbkvaexfmenpsuodatxjavoszcitjewflejjmsuvyuyrkumednsfkbgvbqxfphfqeqozcnabmtedffvzwbgbzbfydiyaevoqtfmzxaujdydtjftapkpdhnbmrylcibzuqqynvnsihmyxdcrfftkuoymzoxpnashaderlosnkxbhamkkxfhwjsyehkmblhppbyspmcwuoguptliashefdklokjpggfiixozsrlwmeksmzdcvipgkwxwynzsvxnqtchgwwadqybkguscfyrbyxudzrxacoplmcqcsmkraimfwbauvytkxdnglwfuvehpxd", "dtzztd"), ("iptmykvjanwiihepqhzupneckpzomgvzmyoybzfynybpfybngttozprjbupciuinpzryritfmyxyppxigitnemanreexcpwscvcwddnfjswgprabdggbgcillisyoskdodzlpbltefiz", "illi"), ]) def test_palindrome_optimized(test_input, expected): threshold = 5 start = time() assert Solution().longestPalindrome(test_input) == expected duration = (time() - start) * 1000 # Duration in ms assert duration < threshold, "Expecting duration to be < %s ms" % threshold

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null

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11

38f00c5ed31455c33a91eb5aaddc041d5e21a125

292

py

Python

emmet-core/emmet/core/vasp/calc_types/__init__.py

nwinner/emmet

6bd779ba785a84f57b61954c88d1ed0dfa95b8cb

[ "BSD-3-Clause-LBNL" ]

null

emmet-core/emmet/core/vasp/calc_types/__init__.py

nwinner/emmet

6bd779ba785a84f57b61954c88d1ed0dfa95b8cb

[ "BSD-3-Clause-LBNL" ]

null

emmet-core/emmet/core/vasp/calc_types/__init__.py

nwinner/emmet

6bd779ba785a84f57b61954c88d1ed0dfa95b8cb

[ "BSD-3-Clause-LBNL" ]

null

from pathlib import Path try: import emmet.core.vasp.calc_types.enums except ImportError: import emmet.core.vasp.calc_types.generate from emmet.core.vasp.calc_types.enums import RunType, TaskType, CalcType from emmet.core.vasp.calc_types.utils import run_type, task_type, calc_type

29.2

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null

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7

ac40e55b44d5dfcbb72a2094c91a0f12f8219621

1,892

py

Python

build/geographic_info/geographic_msgs/cmake/geographic_msgs-genmsg-context.py

Jam-cpu/Masters-Project---Final

0b266b1f117a579b96507249f0a128d0e3cc082a

[ "BSD-3-Clause-Clear" ]

null

build/geographic_info/geographic_msgs/cmake/geographic_msgs-genmsg-context.py

Jam-cpu/Masters-Project---Final

0b266b1f117a579b96507249f0a128d0e3cc082a

[ "BSD-3-Clause-Clear" ]

null

build/geographic_info/geographic_msgs/cmake/geographic_msgs-genmsg-context.py

Jam-cpu/Masters-Project---Final

0b266b1f117a579b96507249f0a128d0e3cc082a

[ "BSD-3-Clause-Clear" ]

null

# generated from genmsg/cmake/pkg-genmsg.context.in messages_str = "/workspace/src/geographic_info/geographic_msgs/msg/BoundingBox.msg;/workspace/src/geographic_info/geographic_msgs/msg/GeographicMapChanges.msg;/workspace/src/geographic_info/geographic_msgs/msg/GeographicMap.msg;/workspace/src/geographic_info/geographic_msgs/msg/GeoPath.msg;/workspace/src/geographic_info/geographic_msgs/msg/GeoPoint.msg;/workspace/src/geographic_info/geographic_msgs/msg/GeoPointStamped.msg;/workspace/src/geographic_info/geographic_msgs/msg/GeoPose.msg;/workspace/src/geographic_info/geographic_msgs/msg/GeoPoseStamped.msg;/workspace/src/geographic_info/geographic_msgs/msg/KeyValue.msg;/workspace/src/geographic_info/geographic_msgs/msg/MapFeature.msg;/workspace/src/geographic_info/geographic_msgs/msg/RouteNetwork.msg;/workspace/src/geographic_info/geographic_msgs/msg/RoutePath.msg;/workspace/src/geographic_info/geographic_msgs/msg/RouteSegment.msg;/workspace/src/geographic_info/geographic_msgs/msg/WayPoint.msg" services_str = "/workspace/src/geographic_info/geographic_msgs/srv/GetGeographicMap.srv;/workspace/src/geographic_info/geographic_msgs/srv/GetGeoPath.srv;/workspace/src/geographic_info/geographic_msgs/srv/GetRoutePlan.srv;/workspace/src/geographic_info/geographic_msgs/srv/UpdateGeographicMap.srv" pkg_name = "geographic_msgs" dependencies_str = "geometry_msgs;std_msgs;uuid_msgs" langs = "gencpp;geneus;genlisp;gennodejs;genpy" dep_include_paths_str = "geographic_msgs;/workspace/src/geographic_info/geographic_msgs/msg;geometry_msgs;/opt/ros/melodic/share/geometry_msgs/cmake/../msg;std_msgs;/opt/ros/melodic/share/std_msgs/cmake/../msg;uuid_msgs;/workspace/src/unique_identifier/uuid_msgs/msg" PYTHON_EXECUTABLE = "/usr/bin/python2" package_has_static_sources = '' == 'TRUE' genmsg_check_deps_script = "/opt/ros/melodic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"

157.666667

954

0.853066

260

1,892

5.953846

0.269231

0.189922

0.270026

0.319121

0.591085

0.562662

0.475452

0

0.000538

0.01797

1,892

11

955

172

0.832616

0.025899

0

1

0.333333

0.889734

0.870722

0

1

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false

0

null

0

1

0

1

0

1

null

0

7

ac74490696d806dc0200c067ef165426b936706f

1,524

py

Python

tests/endpoints/test_base_endpoints.py

Elias-Wilde/my-projekt

74993684aaf9806fa20d67dc83fd103cff492b2a

[ "MIT" ]

null

tests/endpoints/test_base_endpoints.py

Elias-Wilde/my-projekt

74993684aaf9806fa20d67dc83fd103cff492b2a

[ "MIT" ]

null

tests/endpoints/test_base_endpoints.py

Elias-Wilde/my-projekt

74993684aaf9806fa20d67dc83fd103cff492b2a

[ "MIT" ]

null

def test_landing_page(client, captured_templates): """ GIVEN a Flask application configured for testing (client) WHEN the '/' route is requested (GET) THEN there should be the correct `status_code`, `template.name`, and the correct `page_title` in the context """ # mimic a browser: 'GET /', as if you visit the site response = client.get("/") # check that the HTTP response is a success assert response.status_code == 200 # check that the rendered template is the correct one assert len(captured_templates) == 1 template, context = captured_templates[0] assert template.name == "landing_page.html" assert "page_title" in context assert context["page_title"] == "Help & Help" def test_get_started(client, captured_templates): """ GIVEN a Flask application configured for testing (client) WHEN the '/get_started' route is requested (GET) THEN there should be the correct `status_code`, `template.name`, and the correct `page_title` in the context """ # mimic a browser: 'GET /', as if you visit the site response = client.get("/get_started") # check that the HTTP response is a success assert response.status_code == 200 # check that the rendered template is the correct one assert len(captured_templates) == 1 template, context = captured_templates[0] assert template.name == "get_started.html" assert "page_title" in context assert context["page_title"] == "Get Started"

29.882353

68

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206

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1,524

50

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false

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0.125

0

null

0

1

0

null

0

1

0

7

ac8a2776e7429687f1f16206dcb2f574e7140266

34,454

py

Python

build-files/server-code/IoT/image_maps.py

emerginganalytics/ualr-cyber-gym

1156bc2c85c17af02da048f40b2be875f89db0ce

[ "MIT" ]

3

2020-09-02T19:18:03.000Z

2021-04-29T20:23:01.000Z

build-files/server-code/IoT/image_maps.py

emerginganalytics/ualr-cyber-gym

1156bc2c85c17af02da048f40b2be875f89db0ce

[ "MIT" ]

26

2021-12-23T19:37:27.000Z

2022-03-28T04:03:41.000Z

build-files/server-code/IoT/image_maps.py

emerginganalytics/cyberarena

311d179a30017285571f65752eaa91b78c7097aa

[ "MIT" ]

4

2020-11-20T20:38:49.000Z

2021-04-29T20:23:12.000Z

ImageMaps = {'heart': [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [1, 0, 1], [0, 0, 0], [120, 7, 120], [232, 25, 232], [0, 0, 0], [0, 0, 0], [232, 25, 232], [120, 7, 120], [0, 0, 0], [232, 25, 232], [232, 25, 232], [194, 29, 194], [232, 25, 232], [232, 25, 232], [194, 29, 194], [232, 25, 232], [233, 25, 232], [120, 7, 120], [194, 29, 194], [232, 25, 232], [232, 24, 233], [232, 25, 232], [232, 25, 232], [194, 29, 194], [120, 7, 121], [0, 0, 0], [120, 7, 120], [194, 29, 194], [232, 24, 232], [232, 25, 232], [194, 29, 194], [120, 7, 120], [0, 1, 0], [0, 0, 0], [0, 0, 0], [120, 7, 120], [194, 29, 194], [194, 29, 194], [120, 7, 120], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 0], [232, 24, 232], [232, 25, 232], [0, 0, 0], [0, 0, 0], [0, 1, 1], [0, 0, 0], [1, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 1]]], 'heartrate': [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [219, 24, 96], [0, 0, 0], [219, 24, 96], [219, 24, 96], [219, 24, 96], [219, 24, 96], [219, 24, 96], [219, 24, 96], [0, 0, 0], [219, 24, 96], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [219, 24, 96], [0, 0, 0], [0, 0, 0], [219, 24, 96], [219, 24, 96], [219, 24, 96], [219, 24, 96], [219, 24, 96], [0, 0, 0], [219, 24, 96], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [237, 104, 155], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [219, 24, 96], [0, 0, 0], [0, 0, 0], [0, 0, 0], [219, 24, 96], [219, 24, 96], [219, 24, 96], [219, 24, 96], [0, 0, 0], [219, 24, 96], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [219, 24, 96], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 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3bd32dbb3e1eb8a4655fe2db4db2635d37f9c2b7

26,250

py

Python

test/test_numeric.py

hirzel/jsonsubschema

411b3b1fa0cbdc6e74e0d7975ef17ee99d79b175

[ "Apache-2.0" ]

16

2020-05-29T09:21:25.000Z

2022-01-12T09:03:29.000Z

test/test_numeric.py

hirzel/jsonsubschema

411b3b1fa0cbdc6e74e0d7975ef17ee99d79b175

[ "Apache-2.0" ]

9

2019-11-10T18:32:44.000Z

2022-02-18T00:47:14.000Z

test/test_numeric.py

hirzel/jsonsubschema

411b3b1fa0cbdc6e74e0d7975ef17ee99d79b175

[ "Apache-2.0" ]

9

2019-11-02T06:52:57.000Z

2022-01-03T08:35:24.000Z

''' Created on May 30, 2019 @author: Andrew Habib ''' import unittest from jsonschema.exceptions import SchemaError from jsonsubschema import isSubschema class TestIntegerSubtype(unittest.TestCase): def test_identity(self): s1 = {"type": "integer"} s2 = s1 self.assertTrue(isSubschema(s1, s2)) def test_min_min(self): s1 = {"type": "integer", "minimum": 5} s2 = {"type": "integer", "minimum": 1} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_max_max(self): s1 = {"type": "integer", "maximum": 10} s2 = {"type": "integer", "maximum": 5} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_max_min(self): s1 = {"type": "integer", "maximum": 10} s2 = {"type": "integer", "minimum": 5} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max(self): s1 = {"type": "integer", "minimum": 10} s2 = {"type": "integer", "maximum": 20} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max_min_max1(self): s1 = {"type": "integer", "minimum": 5, "maximum": 10} s2 = {"type": "integer", "minimum": 1, "maximum": 20} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max_min_max2(self): s1 = {"type": "integer", "minimum": 5, "maximum": 20} s2 = {"type": "integer", "minimum": 10, "maximum": 20} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_min_max_min_max3(self): s1 = {"type": "integer", "minimum": 5, "maximum": 20} s2 = {"type": "integer", "minimum": 40, "maximum": 100} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_xmin_max_min_max(self): s1 = {"type": "integer", "minimum": 5, "exclusiveMinimum": True, "maximum": 20} s2 = {"type": "integer", "minimum": 5, "maximum": 20} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_xmin_max_min_xmax(self): s1 = {"type": "integer", "minimum": 5, "exclusiveMinimum": True, "maximum": 20} s2 = {"type": "integer", "minimum": 5, "maximum": 20, "exclusiveMaximum": True} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_xmin_xmax_min_max(self): s1 = {"type": "integer", "minimum": 5, "exclusiveMinimum": True, "maximum": 20, "exclusiveMaximum": True} s2 = {"type": "integer", "minimum": 5, "maximum": 20} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max_xmin_xmax1(self): s1 = {"type": "integer", "minimum": 5, "exclusiveMinimum": True, "maximum": 20, "exclusiveMaximum": True} s2 = {"type": "integer", "minimum": 6, "maximum": 19} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_min_max_xmin_xmax2(self): s1 = {"type": "integer", "minimum": 5, "exclusiveMinimum": True, "maximum": 20, "exclusiveMaximum": True} s2 = {"type": "integer", "minimum": 6, "maximum": 20} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_xmin_xmax_xmin_xmax(self): s1 = {"type": "integer", "minimum": 5, "exclusiveMinimum": False, "maximum": 20, "exclusiveMaximum": True} s2 = {"type": "integer", "minimum": 5, "exclusiveMinimum": True, "maximum": 20, "exclusiveMaximum": True} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_mulOf1(self): s1 = {"type": "integer", "multipleOf": 10} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf2(self): s1 = {"type": "integer", "multipleOf": 10} s2 = {"type": "integer", "multipleOf": 5} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf3(self): s1 = {"type": "integer", "multipleOf": 10} s2 = {"type": "integer", "multipleOf": 98} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_min(self): s1 = {"type": "integer", "multipleOf": 10} s2 = {"type": "integer", "minimum": 5} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_min_min(self): s1 = {"type": "integer", "multipleOf": 10, "minimum": 10} s2 = {"type": "integer", "minimum": 5} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_min_min_max(self): s1 = {"type": "integer", "multipleOf": 10, "minimum": 10} s2 = {"type": "integer", "minimum": 5, "maximum": 500} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max_mul(self): s1 = {"type": "integer", "minimum": 5, "maximum": 10, "multipleOf": 15} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_join1(self): s1 = {"anyOf": [{"type": "integer", "minimum": 5, "maximum": 10}, {"type": "integer", }]} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_join2(self): s1 = {"anyOf": [{"type": "integer", "minimum": 5, "maximum": 10}, {"type": "integer", "minimum": 0}]} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_join3(self): s1 = {"anyOf": [{"type": "integer", "minimum": 5, "maximum": 10}, {"type": "integer", "minimum": 0, "maximum": 3}]} s2 = {"type": "integer", "minimum": -1} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_join4(self): s1 = {"anyOf": [{"type": "integer", "minimum": 5, "maximum": 10}, {"type": "integer", "minimum": 0, "maximum": 4}]} s2 = {"type": "integer", "minimum": 1, "maximum": 8} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_join5(self): s1 = {"anyOf": [{"type": "integer", "minimum": 5, "exclusiveMinimum": True, "maximum": 10}, {"type": "integer", "minimum": 0, "maximum": 4}]} s2 = {"type": "integer", "minimum": 1, "maximum": 8} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_join6(self): s1 = {"anyOf": [{"type": "integer", "minimum": 0, "maximum": 10}, {"type": "integer", "minimum": 11}]} s2 = {"type": "integer", "minimum": 0} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_join_mulof1(self): s1 = {"anyOf": [{"type": "integer", "multipleOf": 5}, {"type": "integer"}]} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_join_mulof2(self): s1 = {"anyOf": [{"type": "integer", "multipleOf": 5}, {"type": "integer", "multipleOf": 7}]} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_join_mulof3(self): s1 = {"anyOf": [{"type": "integer", "multipleOf": 5}, {"type": "integer", "multipleOf": 7}]} s2 = {"type": "integer", "multipleOf": 35} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_join_mulof4(self): s1 = {"anyOf": [{"type": "integer", "multipleOf": 5}, {"type": "integer", "multipleOf": 7}]} s2 = {"type": "integer", "multipleOf": 5} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_join_mulof5(self): s1 = {"anyOf": [{"type": "integer", "multipleOf": 3}, {"type": "integer", "multipleOf": 6}]} s2 = {"type": "integer", "multipleOf": 3} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_join_mulof6(self): s1 = {"anyOf": [{"type": "integer", "multipleOf": 12}, {"type": "integer", "multipleOf": 9}]} s2 = {"type": "integer", "multipleOf": 3} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_join_mulof7(self): s1 = {"anyOf": [{"type": "integer", "multipleOf": 3, "maximum": 10}, {"type": "integer", "multipleOf": 5}]} s2 = {"type": "integer", "multipleOf": 3} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_join_mulof8(self): s1 = {"anyOf": [{"type": "integer", "minimum": 5, "maximum": 15, "multipleOf": 5}, {"type": "integer", "minimum": 5, "maximum": 15, "multipleOf": 3}]} s2 = {"anyOf": [{"type": "integer", "minimum": 0, "maximum": 12, "multipleOf": 3}, {"type": "integer", "minimum": 1, "maximum": 20, "multipleOf": 5}]} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_join_mulof9(self): s1 = {"type": "integer", "minimum": -4, "maximum": 10, "multipleOf": 5} s2 = {"anyOf": [{"type": "integer", "minimum": 0, "maximum": 20, "multipleOf": 10}, {"type": "integer", "minimum": 1, "maximum": 10, "multipleOf": 5}]} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) # @unittest.skip("Corner case of multipleOf") # check canonicalization/rewrite_enum def test_join_mulof10(self): s1 = {"enum": [1, 3, 5, 7, 9, 10]} s2 = {"anyOf": [{"type": "integer", "minimum": 0, "maximum": 20, "multipleOf": 10}, { "type": "integer", "minimum": 1, "maximum": 10, "multipleOf": 5}, {"enum": [1, 3, 7, 9]}]} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) class TestNumberSubtype(unittest.TestCase): def test_identity(self): s1 = {"type": "number"} s2 = s1 self.assertTrue(isSubschema(s1, s2)) def test_min_min(self): s1 = {"type": "number", "minimum": 5} s2 = {"type": "number", "minimum": 1} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_max_max(self): s1 = {"type": "number", "maximum": 10} s2 = {"type": "number", "maximum": 5} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_max_min(self): s1 = {"type": "number", "maximum": 10} s2 = {"type": "number", "minimum": 5} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max(self): s1 = {"type": "number", "minimum": 10} s2 = {"type": "number", "maximum": 20} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max_min_max1(self): s1 = {"type": "number", "minimum": 5, "maximum": 10} s2 = {"type": "number", "minimum": 1, "maximum": 20} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max_min_max2(self): s1 = {"type": "number", "minimum": 5, "maximum": 20} s2 = {"type": "number", "minimum": 10, "maximum": 20} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_min_max_min_max3(self): s1 = {"type": "number", "minimum": 5, "maximum": 20} s2 = {"type": "number", "minimum": 40, "maximum": 100} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_xmin_max_min_max(self): s1 = {"type": "number", "minimum": 5, "exclusiveMinimum": True, "maximum": 20} s2 = {"type": "number", "minimum": 5, "maximum": 20} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_xmin_max_min_xmax(self): s1 = {"type": "number", "minimum": 5, "exclusiveMinimum": True, "maximum": 20} s2 = {"type": "number", "minimum": 5, "maximum": 20, "exclusiveMaximum": True} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_xmin_xmax_min_max(self): s1 = {"type": "number", "minimum": 5, "exclusiveMinimum": True, "maximum": 20, "exclusiveMaximum": True} s2 = {"type": "number", "minimum": 5, "maximum": 20} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_max_xmin_xmax1(self): s1 = {"type": "number", "minimum": 5, "exclusiveMinimum": True, "maximum": 20, "exclusiveMaximum": True} s2 = {"type": "number", "minimum": 6, "maximum": 19} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_min_max_xmin_xmax2(self): s1 = {"type": "number", "minimum": 5, "exclusiveMinimum": True, "maximum": 20, "exclusiveMaximum": True} s2 = {"type": "number", "minimum": 6, "maximum": 20} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_xmin_xmax_xmin_xmax(self): s1 = {"type": "number", "minimum": 5, "exclusiveMinimum": False, "maximum": 20, "exclusiveMaximum": True} s2 = {"type": "number", "minimum": 5, "exclusiveMinimum": True, "maximum": 20, "exclusiveMaximum": True} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_mulOf1(self): s1 = {"type": "number", "multipleOf": 10.5} s2 = {"type": "number"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf2(self): s1 = {"type": "number", "multipleOf": 1.5} s2 = {"type": "number", "multipleOf": 6} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_mulOf3(self): s1 = {"type": "number", "multipleOf": .5} s2 = {"type": "number", "multipleOf": -.5} self.assertRaises(SchemaError, isSubschema, s1, s2) def test_mulOf4(self): s1 = {"type": "number", "multipleOf": 1} s2 = {"type": "number"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_min(self): s1 = {"type": "number", "multipleOf": 10} s2 = {"type": "number", "minimum": 5} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_min_min(self): s1 = {"type": "number", "multipleOf": 10, "minimum": 10} s2 = {"type": "number", "minimum": 5} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_min_min_max(self): s1 = {"type": "number", "multipleOf": 10, "minimum": 10} s2 = {"type": "number", "minimum": 5, "maximum": 500} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) class TestNumericSubtype(unittest.TestCase): def test_int_num(self): s1 = {"type": "integer"} s2 = {"type": "number"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_min_num_int(self): s1 = {"type": "number", "minimum": 1.5} s2 = {"type": "integer", "minimum": 1} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_num_min_int(self): s1 = {"type": "number", "multipleOf": 10} s2 = {"type": "integer", "minimum": 5} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_num_int(self): s1 = {"type": "number", "multipleOf": 10} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_mulOf_num_int2(self): s1 = {"type": "number", "multipleOf": 1} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_decimal1(self): s1 = {'maximum': 10.} s2 = {'maximum': 10} with self.subTest('LHS < RHS'): self.assertTrue(isSubschema(s1, s2)) with self.subTest('RHS > LHS'): self.assertTrue(isSubschema(s2, s1)) def test_not1(self): s1 = {'not': {'type': 'integer', 'minimum': 10, 'maximum': 20}} s2 = {'not': {'minimum': 10, 'maximum': 20}} s1_ = {'anyOf': [{'type': 'boolean'}, {'type': 'object'}, {'type': 'null'}, {'type': 'array'}, {'type': 'string'}, {'maximum': 9, 'type': 'integer'}, {'minimum': 21, 'type': 'integer'}, { 'type': 'number', 'maximum': 9}, {'type': 'number', 'minimum': 21}, {'allOf': [{'type': 'number', 'minimum': 10, 'maximum': 20}, {'not': {'type': 'integer'}}]}]} # with self.subTest('LHS < RHS'): # self.assertFalse(isSubschema(s1, s1)) # with self.subTest('RHS > LHS'): # self.assertTrue(isSubschema(s2, s1)) class TestCompositeNumericSubtype(unittest.TestCase): def test_invalid_schema(self): s1 = {"type": "integer"} s2 = {"type": "number", "allOf": [""]} with self.subTest(): self.assertRaises(SchemaError, isSubschema, s1, s2) with self.subTest(): self.assertRaises(SchemaError, isSubschema, s2, s1) def test_int_int_num1(self): s1 = {"type": "integer"} s2 = {"type": "number", "allOf": [{"type": "integer"}, {"type": "number", "minimum": 10}]} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_int_int_num2(self): s1 = {"type": "integer", "multipleOf": 5} s2 = {"type": "number", "allOf": [{"type": "integer"}, {"type": "number", "minimum": 10}]} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_int_mul_mul1(self): s1 = {"type": "integer", "multipleOf": 5} s2 = {"type": "number", "multipleOF": 3, "allOf": [{"type": "integer"}, {"type": "number", "multipleOf": 3}]} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_int_mul_mul2(self): s1 = {"type": "integer", "multipleOf": 15} s2 = {"type": "number", "multipleOf": 3, "allOf": [{"type": "integer"}, {"type": "number", "multipleOf": 5}]} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_all_all_1(self): s1 = {"type": "integer", "allOf": [{"multipleOf": 3}, {"minimum": 5}]} # 6, 9, 12, 15, 18, ... s2 = {"type": "number", "multipleOf": 3, "allOf": [{"type": "integer"}, {"type": "number", "multipleOf": 5}]} # ..., -30, -15, 15, 30, 45, .. with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_all_all_2(self): s1 = {"type": "integer", "allOf": [{"multipleOf": 3}]} s2 = {"type": "number", "multipleOf": 3, "allOf": [{"type": "integer"}, {"type": "number", "multipleOf": 3}]} # ..., -30, -15, 15, 30, 45, .. with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_all_all_3(self): s1 = {"type": "number", "allOf": [{"multipleOf": .3}]} s2 = {"type": "number", "multipleOf": 3, "allOf": [{"type": "integer"}, {"type": "number", "multipleOf": 3}]} # ..., -30, -15, 15, 30, 45, .. with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_enum1(self): s1 = {"enum": [1, 2, 3]} s2 = {"type": "number"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_enum2(self): s1 = {"enum": [1.0, 2, 3]} s2 = {"enum": [1, 2.0]} with self.subTest(): self.assertFalse(isSubschema(s1, s2)) with self.subTest(): self.assertTrue(isSubschema(s2, s1)) def test_enum3(self): s1 = {"enum": [1, 2, 3]} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1)) def test_enum4(self): s1 = {"enum": [1, 2.0, 3]} s2 = {"type": "integer"} with self.subTest(): self.assertTrue(isSubschema(s1, s2)) with self.subTest(): self.assertFalse(isSubschema(s2, s1))

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0

8

ce2a5210be26ac16ba0265bf5eec10423ac7ecf8

7,713

py

Python

regions/forms.py

aliibsamohammed/django_country_location

88eb48d1a8b2375bbd239752b3bee6d5ce1274fc

[ "MIT" ]

null

regions/forms.py

aliibsamohammed/django_country_location

88eb48d1a8b2375bbd239752b3bee6d5ce1274fc

[ "MIT" ]

null

regions/forms.py

aliibsamohammed/django_country_location

88eb48d1a8b2375bbd239752b3bee6d5ce1274fc

[ "MIT" ]

null

from django import forms from .models import Continent, SubContinent, Country, State, City, Region, TimeZone class TimeZoneCreateForm(forms.ModelForm): class Meta: model = TimeZone fields = '__all__' class ContinentCreateForm(forms.ModelForm): class Meta: model = Continent fields = '__all__' class SubContinentCreateForm(forms.ModelForm): class Meta: model = SubContinent fields = '__all__' class CountryCreateForm(forms.ModelForm): class Meta: model = Country fields = '__all__' class StateCreateForm(forms.ModelForm): class Meta: model = State fields = '__all__' class CityCreateForm(forms.ModelForm): class Meta: model = City fields = '__all__' def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fields['state'].queryset = State.objects.none() if 'country' in self.data: try: country_id = int(self.data.get('country')) self.fields['state'].queryset = State.objects.filter(country_id=country_id).order_by('state_name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['state'].queryset = self.instance.country.state_set.order_by('state_name') class RegionCreateForm(forms.ModelForm): class Meta: model = Region fields = '__all__' def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fields['sub_continent'].queryset = SubContinent.objects.none() self.fields['country'].queryset = Country.objects.none() self.fields['state'].queryset = State.objects.none() self.fields['city'].queryset = City.objects.none() if 'continent' in self.data: try: continent_id = int(self.data.get('continent')) self.fields['subcontinent'].queryset = SubContinent.objects.filter(continent_id=continent_id).order_by('name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['subcontinent'].queryset = self.instance.continent.subcontinent_set.order_by('name') if 'subcontinent' in self.data: try: subcontinent_id = int(self.data.get('subcontinent')) self.fields['country'].queryset = Country.objects.filter(subcontinent_id=subcontinent_id).order_by('name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['country'].queryset = self.instance.subcontinent.country_set.order_by('name') if 'country' in self.data: try: country_id = int(self.data.get('country')) self.fields['state'].queryset = State.objects.filter(country_id=country_id).order_by('state_name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['state'].queryset = self.instance.country.state_set.order_by('state_name') if 'state' in self.data: try: state_id = int(self.data.get('state')) self.fields['city'].queryset = City.objects.filter(state_id=state_id).order_by('city_name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['city'].queryset = self.instance.state.city_set.order_by('city_name') class TimeZoneUpdateForm(forms.ModelForm): class Meta: model = TimeZone fields = '__all__' class ContinentUpdateForm(forms.ModelForm): class Meta: model = Continent fields = '__all__' class SubContinentUpdateForm(forms.ModelForm): class Meta: model = SubContinent fields = '__all__' class CountryUpdateForm(forms.ModelForm): class Meta: model = Country fields = '__all__' class StateUpdateForm(forms.ModelForm): class Meta: model = State fields = '__all__' class CityUpdateForm(forms.ModelForm): class Meta: model = City fields = '__all__' """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fields['state'].queryset = State.objects.none() if 'country' in self.data: try: country_id = int(self.data.get('country')) self.fields['state'].queryset = State.objects.filter(country_id=country_id).order_by('name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['state'].queryset = self.instance.country.state_set.order_by('name') """ class RegionUpdateForm(forms.ModelForm): class Meta: model = Region fields = '__all__' def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fields['sub_continent'].queryset = SubContinent.objects.none() self.fields['country'].queryset = Country.objects.none() self.fields['state'].queryset = State.objects.none() self.fields['city'].queryset = City.objects.none() if 'continent' in self.data: try: continent_id = int(self.data.get('continent')) self.fields['subcontinent'].queryset = SubContinent.objects.filter(continent_id=continent_id).order_by('name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['subcontinent'].queryset = self.instance.continent.subcontinent_set.order_by('name') if 'subcontinent' in self.data: try: subcontinent_id = int(self.data.get('subcontinent')) self.fields['country'].queryset = Country.objects.filter(subcontinent_id=subcontinent_id).order_by('name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['country'].queryset = self.instance.subcontinent.country_set.order_by('name') if 'country' in self.data: try: country_id = int(self.data.get('country')) self.fields['state'].queryset = State.objects.filter(country_id=country_id).order_by('state_name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['state'].queryset = self.instance.country.state_set.order_by('state_name') if 'state' in self.data: try: state_id = int(self.data.get('state')) self.fields['city'].queryset = City.objects.filter(state_id=state_id).order_by('city_name') except (ValueError, TypeError): pass # invalid input from the client; ignore and fallback to empty City queryset elif self.instance.pk: self.fields['city'].queryset = self.instance.state.city_set.order_by('city_name')

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null

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1

0

1

0

null

0

1

0

8

ce3fd7c4623396b15cfc3a1242c0b0ad4cb50c90

7,777

py

Python

tests/test_get_volume_for_home.py

ifxit/nidho

7d49bb7d879d0f3d444df50f2c18c2cdf883216c

[ "MIT" ]

11

2016-06-09T12:07:14.000Z

2018-01-18T08:01:08.000Z

tests/test_get_volume_for_home.py

ifxit/nidho

7d49bb7d879d0f3d444df50f2c18c2cdf883216c

[ "MIT" ]

4

2016-07-06T11:06:34.000Z

2020-01-02T10:11:48.000Z

tests/test_get_volume_for_home.py

ifxit/nidhogg

7d49bb7d879d0f3d444df50f2c18c2cdf883216c

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals import pytest from nidhogg.sevenmode import SevenMode from nidhogg.clustermode import ClusterMode cluster_ret_value = { 'attributes-list': { 'volume-attributes': [{ 'volume-id-attributes': { 'name': "name11", 'type': "dp", }, 'volume-state-attributes': {'state': "online"}, 'volume-space-attributes': { "size-total": "120259084288", "size-used": "9575825408", "size-available": "110683258880", }, 'volume-inode-attributes': { "files-total": "4358138", "files-used": "14024", } }, { 'volume-id-attributes': { 'name': "name12", 'type': "rw", }, 'volume-state-attributes': {'state': "online"}, 'volume-space-attributes': { "size-total": "1202590842", "size-used": "95758254", "size-available": "1106832588", }, 'volume-inode-attributes': { "files-total": "4358138", "files-used": "14024", } }, { 'volume-id-attributes': { 'name': "name13", 'type': "rw", }, 'volume-state-attributes': {'state': "online"}, 'volume-space-attributes': { "size-total": "1202590842", "size-used": "95758334", "size-available": "1106832508", }, 'volume-inode-attributes': { "files-total": "4358138", "files-used": "14024", } }] }, 'num-records': '3' } seven_ret_value = { 'volumes': { 'volume-info': [{ 'name': "name11", 'state': "online", "size-total": "120259084288", "size-used": "9575825408", "size-available": "110683258880", "files-total": "4358138", "files-used": "14024", "raid-status": "snapmirror", }, { 'name': "name12", 'state': "online", "size-total": "1202590842", "size-used": "95758254", "size-available": "1106832588", "files-total": "4358138", "files-used": "14024", "raid-status": "ok", }, { 'name': "name13", 'state': "online", "size-total": "1202590842", "size-used": "95758334", "size-available": "1106832508", "files-total": "4358138", "files-used": "14024", "raid-status": "ok", }] } } @pytest.mark.parametrize('mode', [ (ClusterMode, cluster_ret_value), (SevenMode, seven_ret_value) ], indirect=True) def test_get_volume_for_project(mode, monkeypatch): def get_quota_size(*args, **kwargs): return 12345.0 monkeypatch.setattr("nidhogg.core.Nidhogg.get_allocated_quota_size", get_quota_size) def get_quota_ratio(*args, **kwargs): return 0.1 monkeypatch.setattr("nidhogg.core.Nidhogg.get_allocated_quota_ratio", get_quota_ratio) project_volumes = mode.get_volumes_with_quota_info(filter_volume_names=[]) assert project_volumes == [ # only online volumes with state = rw are returned # { # 'filer': u'my.url.to.filer', # 'files_total': 4358138.0, # 'files_used': 14024.0, # 'name': 'name11', # 'size_available': 110683258880.0, # 'size_total': 120259084288.0, # 'size_used': 9575825408.0, # 'state': 'offline', # 'snapable': False, # 'quota_ratio': 0.1, # 'quota_size': 12345.0, # }, { 'filer': u'my.url.to.filer', 'files_total': 4358138.0, 'files_used': 14024.0, 'name': 'name12', 'size_available': 1106832588.0, 'size_total': 1202590842.0, 'size_used': 95758254.0, 'state': 'online', 'snapable': True, 'quota_ratio': 0.1, 'quota_size': 12345.0, }, { 'filer': u'my.url.to.filer', 'files_total': 4358138.0, 'files_used': 14024.0, 'name': 'name13', 'size_available': 1106832508.0, 'size_total': 1202590842.0, 'size_used': 95758334.0, 'state': 'online', 'snapable': True, 'quota_ratio': 0.1, 'quota_size': 12345.0, }] @pytest.mark.parametrize('mode', [ (ClusterMode, cluster_ret_value), (SevenMode, seven_ret_value) ], indirect=True) def test_get_volume_for_project_with_filter(mode, monkeypatch): def get_quota_size(*args, **kwargs): return 12345.0 monkeypatch.setattr("nidhogg.core.Nidhogg.get_allocated_quota_size", get_quota_size) def get_quota_ratio(*args, **kwargs): return 0.1 monkeypatch.setattr("nidhogg.core.Nidhogg.get_allocated_quota_ratio", get_quota_ratio) project_volumes = mode.get_volumes_with_quota_info(filter_volume_names=["name12"]) assert project_volumes == \ [{ 'filer': u'my.url.to.filer', 'files_total': 4358138.0, 'files_used': 14024.0, 'name': 'name12', 'size_available': 1106832588.0, 'size_total': 1202590842.0, 'size_used': 95758254.0, 'state': 'online', 'snapable': True, 'quota_ratio': 0.1, 'quota_size': 12345.0, }] @pytest.mark.parametrize('mode', [ (ClusterMode, cluster_ret_value), (SevenMode, seven_ret_value) ], indirect=True) def test_get_volume_for_user(mode): home_volumes = mode.get_volumes(filter_volume_names=[]) assert home_volumes == [ # only online volumes with state = rw are returned # { # 'filer': u'my.url.to.filer', # 'files_total': 4358138.0, # 'files_used': 14024.0, # 'name': 'name11', # 'size_available': 110683258880.0, # 'size_total': 120259084288.0, # 'size_used': 9575825408.0, # 'state': 'offline', # 'snapable': False, # }, { 'filer': u'my.url.to.filer', 'files_total': 4358138.0, 'files_used': 14024.0, 'name': 'name12', 'size_available': 1106832588.0, 'size_total': 1202590842.0, 'size_used': 95758254.0, 'state': 'online', 'snapable': True, }, { 'filer': u'my.url.to.filer', 'files_total': 4358138.0, 'files_used': 14024.0, 'name': 'name13', 'size_available': 1106832508.0, 'size_total': 1202590842.0, 'size_used': 95758334.0, 'state': 'online', 'snapable': True, }] @pytest.mark.parametrize('mode', [ (ClusterMode, cluster_ret_value), (SevenMode, seven_ret_value) ], indirect=True) def test_get_volume_for_user_with_filter(mode): home_volumes = mode.get_volumes(filter_volume_names=['name12']) assert home_volumes == \ [{ 'filer': u'my.url.to.filer', 'files_total': 4358138.0, 'files_used': 14024.0, 'name': 'name12', 'size_available': 1106832588.0, 'size_total': 1202590842.0, 'size_used': 95758254.0, 'state': 'online', 'snapable': True, }]

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7

020ea8033963d2ba84debc0b7df4adade608b0d0

7,867

py

Python

models/sed_encoders.py

thomeou/General-network-architecture-for-sound-event-localization-and-detection

03b3aaccf3c87dd8fb857960e765ae768ad36625

[ "MIT" ]

4

2020-12-04T11:57:20.000Z

2022-03-12T15:23:03.000Z

models/sed_encoders.py

mammothb/General-network-architecture-for-sound-event-localization-and-detection

03b3aaccf3c87dd8fb857960e765ae768ad36625

[ "MIT" ]

3

2021-08-02T09:16:17.000Z

2021-12-15T13:24:13.000Z

models/sed_encoders.py

mammothb/General-network-architecture-for-sound-event-localization-and-detection

03b3aaccf3c87dd8fb857960e765ae768ad36625

[ "MIT" ]

4

2021-01-23T10:18:46.000Z

2021-11-09T15:01:51.000Z

import logging import torch import torch.nn as nn import torch.nn.functional as F import torchvision.models as models from models.model_utils import ConvBlock, init_layer, _ResNet3, _ResNet, _ResnetBasicBlock class PannCnn14L6(nn.Module): """ Derived from PANN CNN14 network. PannCnn14L6 has 6 CNN layers (3 convblock) """ def __init__(self, n_input_channels: int = 1, p_dropout: float = 0.2, pretrained: bool = False, **kwargs): """ :param n_input_channels: Number of input channels. :param p_dropout: Dropout probability. :param pretrained: If True, load pretrained model. """ super().__init__() self.n_input_channels = n_input_channels self.p_dropout = p_dropout self.n_output_channels = 256 self.time_downsample_ratio = 8 self.freq_downsample_ratio = 8 self.conv_block1 = ConvBlock(in_channels=n_input_channels, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) # Load pretrained model self.load_pretrained_weight(pretrained=pretrained) def load_pretrained_weight(self, pretrained: bool = False): logger = logging.getLogger('lightning') pretrained_path = '../pretrained_models/Cnn14_DecisionLevelAtt_mAP=0.425.pth' if pretrained: checkpoint = torch.load(pretrained_path, map_location=lambda storage, loc: storage) try: self.load_state_dict(checkpoint['model'], strict=False) logger.info('Load pretrained weights from checkpoint {}.'.format(pretrained_path)) except: logger.info('WARNING: Coud not load pretrained weights from checkpoint {}.'.format(pretrained_path)) def forward(self, x): """ Input x: (batch_size, n_channels, n_timesteps/n_frames, n_features/n_freqs) """ x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) return x @property def count_number_of_params(self): n_params = sum([param.numel() for param in self.parameters()]) n_trainable_params = sum(param.numel() for param in self.parameters() if param.requires_grad) return n_params, n_trainable_params class PannCnn14L6F64(nn.Module): """ Derived from PANN CNN14 network. PannCnn14L6 has 6 CNN layers (3 convblock) """ def __init__(self, n_input_channels: int = 1, p_dropout: float = 0.2, pretrained: bool = False, **kwargs): """ :param n_input_channels: Number of input channels. :param p_dropout: Dropout probability. :param pretrained: If True, load pretrained model. """ super().__init__() self.n_input_channels = n_input_channels self.p_dropout = p_dropout self.n_output_channels = 256 self.time_downsample_ratio = 8 self.freq_downsample_ratio = 64 self.conv_block1 = ConvBlock(in_channels=n_input_channels, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) # Load pretrained model self.load_pretrained_weight(pretrained=pretrained) def load_pretrained_weight(self, pretrained: bool = False): logger = logging.getLogger('lightning') pretrained_path = '../pretrained_models/Cnn14_DecisionLevelAtt_mAP=0.425.pth' if pretrained: checkpoint = torch.load(pretrained_path, map_location=lambda storage, loc: storage) try: self.load_state_dict(checkpoint['model'], strict=False) logger.info('Load pretrained weights from checkpoint {}.'.format(pretrained_path)) except: logger.info('WARNING: Coud not load pretrained weights from checkpoint {}.'.format(pretrained_path)) def forward(self, x): """ Input x: (batch_size, n_channels, n_timesteps/n_frames, n_features/n_freqs) """ x = self.conv_block1(x, pool_size=(2, 4), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) x = self.conv_block2(x, pool_size=(2, 4), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) x = self.conv_block3(x, pool_size=(2, 4), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) return x @property def count_number_of_params(self): n_params = sum([param.numel() for param in self.parameters()]) n_trainable_params = sum(param.numel() for param in self.parameters() if param.requires_grad) return n_params, n_trainable_params class PannCnn14L8(nn.Module): """ Derived from PANN CNN14 network. PannCnn14L8 has 8 CNN layers (4 convblock) """ def __init__(self, n_input_channels: int = 1, p_dropout: float = 0.2, pretrained: bool = False, **kwargs): """ :param n_input_channels: Number of input channels. :param p_dropout: Dropout probability. :param pretrained: If True, load pretrained model. """ super().__init__() self.n_input_channels = n_input_channels self.p_dropout = p_dropout self.n_output_channels = 512 self.time_downsample_ratio = 16 self.freq_downsample_ratio = 16 self.conv_block1 = ConvBlock(in_channels=n_input_channels, out_channels=64) self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) # Load pretrained model self.load_pretrained_weight(pretrained=pretrained) def load_pretrained_weight(self, pretrained: bool = False): logger = logging.getLogger('lightning') pretrained_path = '../pretrained_models/Cnn14_DecisionLevelAtt_mAP=0.425.pth' if pretrained: checkpoint = torch.load(pretrained_path, map_location=lambda storage, loc: storage) try: self.load_state_dict(checkpoint['model'], strict=False) logger.info('Load pretrained weights from checkpoint {}.'.format(pretrained_path)) except: logger.info('WARNING: Coud not load pretrained weights from checkpoint {}.'.format(pretrained_path)) def forward(self, x): """ Input x: (batch_size, n_channels, n_timesteps/n_frames, n_features/n_freqs) """ x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') x = F.dropout(x, p=self.p_dropout, training=self.training) return x @property def count_number_of_params(self): n_params = sum([param.numel() for param in self.parameters()]) n_trainable_params = sum(param.numel() for param in self.parameters() if param.requires_grad) return n_params, n_trainable_params if __name__ == '__main__': encoder = PannCnn14L8() print(encoder.count_number_of_params) print(encoder)

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py

Python

tests/markets/test_price.py

overlay-market/v1-core

e18fabd242f21c243a555712d3f08ca059941f41

[ "MIT" ]

3

2022-02-17T16:11:39.000Z

2022-03-10T23:46:19.000Z

tests/markets/test_price.py

overlay-market/v1-core

e18fabd242f21c243a555712d3f08ca059941f41

[ "MIT" ]

10

2022-01-25T21:49:20.000Z

2022-03-31T00:32:29.000Z

tests/markets/test_price.py

overlay-market/v1-core

e18fabd242f21c243a555712d3f08ca059941f41

[ "MIT" ]

2

2022-01-21T01:04:54.000Z

2022-02-23T08:38:20.000Z

from brownie import reverts from brownie.test import given, strategy from decimal import Decimal from math import exp from pytest import approx from .utils import RiskParameter def test_bid_adds_static_spread(market, rando): # params idx for delta param idx = RiskParameter.DELTA.value # get the price data from call to update. update tests in test_update.py tx = market.update({"from": rando}) data = tx.return_value _, _, _, price_micro, price_macro, _, _, _ = data delta = Decimal(market.params(idx) / 1e18) # use zero volume so no market impact volume = 0 # bids get the lower of micro/macro prices (worse price), multiplied # by additional spread e^(-delta) expect_bid = int(min(price_micro, price_macro) * exp(-delta)) actual_bid = int(market.bid(data, volume)) assert actual_bid == approx(expect_bid) @given( volume=strategy('decimal', min_value='0.0001', max_value='1.0000', places=4)) def test_bid_adds_market_impact(market, volume, rando): # params idx for delta, lmbda params idx_delta = RiskParameter.DELTA.value idx_lmbda = RiskParameter.LMBDA.value # get the price data from call to update. update tests in test_update.py tx = market.update({"from": rando}) data = tx.return_value _, _, _, price_micro, price_macro, _, _, _ = data delta = Decimal(market.params(idx_delta) / 1e18) lmbda = Decimal(market.params(idx_lmbda) / 1e18) # use volume anywhere from 0.1% to 100% of the cap input_volume = volume * Decimal(1e18) # bids get the lower of micro/macro prices (worse price), multiplied # by additional spread e^(-delta-lmbda*volume) expect_bid = int(min(price_micro, price_macro) * exp(-delta-lmbda*volume)) actual_bid = int(market.bid(data, input_volume)) assert actual_bid == approx(expect_bid) def test_bid_reverts_when_slippage_greater_than_max(market, rando): # params idx for delta, lmbda params idx_delta = RiskParameter.DELTA.value idx_lmbda = RiskParameter.LMBDA.value # get the price data from call to update. update tests in test_update.py tx = market.update({"from": rando}) data = tx.return_value _, _, _, price_micro, price_macro, _, _, _ = data delta = Decimal(market.params(idx_delta) / 1e18) lmbda = Decimal(market.params(idx_lmbda) / 1e18) # use volume greater than max slippage tol = 1e-4 # tolerance put at +/- 1bps max_pow = 20 max_volume = (max_pow - delta) / lmbda # check reverts when volume produces slippage greater than max volume = Decimal(max_volume) * Decimal(1 + tol) input_volume = volume * Decimal(1e18) with reverts("OVLV1:slippage>max"): market.bid(data, input_volume) # check does not revert when volume produces slippage about equal to max volume = Decimal(max_volume) * Decimal(1 - tol) input_volume = volume * Decimal(1e18) _ = market.bid(data, input_volume) def test_ask_adds_static_spread(market, rando): # params idx for delta param idx = RiskParameter.DELTA.value # get the price data from call to update. update tests in test_update.py tx = market.update({"from": rando}) data = tx.return_value _, _, _, price_micro, price_macro, _, _, _ = data delta = Decimal(market.params(idx) / 1e18) # use zero volume so no market impact volume = 0 # asks get the higher of micro/macro prices (worse price), multiplied # by additional spread e^(+delta) expect_ask = int(max(price_micro, price_macro) * exp(delta)) actual_ask = int(market.ask(data, volume)) assert actual_ask == approx(expect_ask) @given( volume=strategy('decimal', min_value='0.0001', max_value='1.0000', places=4)) def test_ask_adds_market_impact(market, volume, rando): # params idx for delta, lmbda params idx_delta = RiskParameter.DELTA.value idx_lmbda = RiskParameter.LMBDA.value # get the price data from call to update. update tests in test_update.py tx = market.update({"from": rando}) data = tx.return_value _, _, _, price_micro, price_macro, _, _, _ = data delta = Decimal(market.params(idx_delta) / 1e18) lmbda = Decimal(market.params(idx_lmbda) / 1e18) # use volume anywhere from 0.1% to 100% of the cap input_volume = volume * Decimal(1e18) # asks get the higher of micro/macro prices (worse price), multiplied # by additional spread e^(delta+lmbda*volume) expect_ask = int(max(price_micro, price_macro) * exp(delta+lmbda*volume)) actual_ask = int(market.ask(data, input_volume)) assert actual_ask == approx(expect_ask) def test_ask_reverts_when_impact_greater_than_max_slippage(market, rando): # params idx for delta, lmbda params idx_delta = RiskParameter.DELTA.value idx_lmbda = RiskParameter.LMBDA.value # get the price data from call to update. update tests in test_update.py tx = market.update({"from": rando}) data = tx.return_value _, _, _, price_micro, price_macro, _, _, _ = data delta = Decimal(market.params(idx_delta) / 1e18) lmbda = Decimal(market.params(idx_lmbda) / 1e18) # use volume greater than max slippage tol = 1e-4 # tolerance put at +/- 1bps max_pow = 20 max_volume = (max_pow - delta) / lmbda # check reverts when volume produces slippage greater than max volume = Decimal(max_volume) * Decimal(1 + tol) input_volume = volume * Decimal(1e18) with reverts("OVLV1:slippage>max"): market.ask(data, input_volume) # check does not revert when volume produces slippage about equal to max volume = Decimal(max_volume) * Decimal(1 - tol) input_volume = volume * Decimal(1e18) _ = market.ask(data, input_volume)

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0

7

02845a53a449853263d1c4027f9f764717630cb4

84

py

Python

Exp_upload/setup.py

Jwy-Leo/Tool

bc02a2c1b450d41a2505d61551e9959359d8640b

[ "MIT" ]

5

2018-04-24T11:44:53.000Z

2020-01-02T05:58:30.000Z

Exp_upload/setup.py

Jwy-Leo/Tool

bc02a2c1b450d41a2505d61551e9959359d8640b

[ "MIT" ]

null

Exp_upload/setup.py

Jwy-Leo/Tool

bc02a2c1b450d41a2505d61551e9959359d8640b

[ "MIT" ]

null

import os os.system('pip install gspread') os.system('pip install oauth2client')

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7

65ef882c93b299f38ff422cc6fe44f8f13737371

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py

Python

examples/pipeline/hetero_feature_binning/common_tools.py

rubenlozanoaht3m/DataDogm

cd605e8072cca31e8418830c3300657ae2fa5b16

[ "Apache-2.0" ]

715

2019-01-24T10:52:03.000Z

2019-10-31T12:19:22.000Z

examples/pipeline/hetero_feature_binning/common_tools.py

rubenlozanoaht3m/DataDogm

cd605e8072cca31e8418830c3300657ae2fa5b16

[ "Apache-2.0" ]

270

2019-02-11T02:57:36.000Z

2019-08-29T11:22:33.000Z

examples/pipeline/hetero_feature_binning/common_tools.py

rubenlozanoaht3m/DataDogm

cd605e8072cca31e8418830c3300657ae2fa5b16

[ "Apache-2.0" ]

200

2019-01-26T14:21:35.000Z

2019-11-01T01:14:36.000Z

# # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json from pipeline.backend.pipeline import PipeLine from pipeline.component import DataTransform from pipeline.component import HeteroFeatureBinning from pipeline.component import Intersection from pipeline.component import OneHotEncoder from pipeline.component import Reader from pipeline.interface import Data from pipeline.interface import Model def prettify(response, verbose=True): if verbose: print(json.dumps(response, indent=4, ensure_ascii=False)) print() return response def make_add_one_hot_dsl(config, namespace, bin_param, is_multi_host=False): parties = config.parties guest = parties.guest[0] hosts = parties.host guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"} host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"} guest_eval_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"} host_eval_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"} # initialize pipeline pipeline = PipeLine() # set job initiator pipeline.set_initiator(role='guest', party_id=guest) # set participants information if is_multi_host: pipeline.set_roles(guest=guest, host=hosts) else: pipeline.set_roles(guest=guest, host=hosts[0]) # define Reader components to read in data reader_0 = Reader(name="reader_0") # configure Reader for guest reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data) # configure Reader for host reader_0.get_party_instance(role='host', party_id=hosts[0]).component_param(table=host_train_data) if is_multi_host: reader_0.get_party_instance(role='host', party_id=hosts[1]).component_param(table=host_train_data) reader_1 = Reader(name="reader_1") reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_eval_data) reader_1.get_party_instance(role='host', party_id=hosts[0]).component_param(table=host_eval_data) if is_multi_host: reader_1.get_party_instance(role='host', party_id=hosts[1]).component_param(table=host_eval_data) # define DataTransform components data_transform_0 = DataTransform(name="data_transform_0") # start component numbering at 0 data_transform_1 = DataTransform(name="data_transform_1") # get DataTransform party instance of guest data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest) # configure DataTransform for guest data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense") # get and configure DataTransform party instance of host data_transform_0.get_party_instance(role='host', party_id=hosts[0]).component_param(with_label=False) if is_multi_host: data_transform_0.get_party_instance(role='host', party_id=hosts[1]).component_param(with_label=False) # define Intersection components intersection_0 = Intersection(name="intersection_0") intersection_1 = Intersection(name="intersection_1") hetero_feature_binning_0 = HeteroFeatureBinning(**bin_param) hetero_feature_binning_1 = HeteroFeatureBinning(name='hetero_feature_binning_1') one_hot_encoder_0 = OneHotEncoder(name='one_hot_encoder_0', transform_col_indexes=-1, transform_col_names=None, need_run=True) # add components to pipeline, in order of task execution pipeline.add_component(reader_0) pipeline.add_component(reader_1) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) # set data_transform_1 to replicate model from data_transform_0 pipeline.add_component( data_transform_1, data=Data( data=reader_1.output.data), model=Model( data_transform_0.output.model)) # set data input sources of intersection components pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data)) pipeline.add_component(intersection_1, data=Data(data=data_transform_1.output.data)) # set train & validate data of hetero_lr_0 component pipeline.add_component(hetero_feature_binning_0, data=Data(data=intersection_0.output.data)) pipeline.add_component(hetero_feature_binning_1, data=Data(data=intersection_1.output.data), model=Model(hetero_feature_binning_0.output.model)) pipeline.add_component(one_hot_encoder_0, data=Data(data=hetero_feature_binning_0.output.data)) # compile pipeline once finished adding modules, this step will form conf and dsl files for running job pipeline.compile() # pipeline.fit(work_mode=work_mode) return pipeline def make_normal_dsl(config, namespace, bin_param, dataset='breast', is_multi_host=False, host_dense_output=True): parties = config.parties guest = parties.guest[0] hosts = parties.host if dataset == 'breast': guest_table_name = 'breast_hetero_guest' host_table_name = 'breast_hetero_host' elif dataset == 'default_credit': guest_table_name = 'default_credit_hetero_guest' host_table_name = 'default_credit_hetero_host' else: raise ValueError(f"dataset: {dataset} cannot be recognized") guest_train_data = {"name": guest_table_name, "namespace": f"experiment{namespace}"} host_train_data = {"name": host_table_name, "namespace": f"experiment{namespace}"} # initialize pipeline pipeline = PipeLine() # set job initiator pipeline.set_initiator(role='guest', party_id=guest) # set participants information if is_multi_host: pipeline.set_roles(guest=guest, host=hosts) else: pipeline.set_roles(guest=guest, host=hosts[0]) # define Reader components to read in data reader_0 = Reader(name="reader_0") # configure Reader for guest reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data) # configure Reader for host reader_0.get_party_instance(role='host', party_id=hosts[0]).component_param(table=host_train_data) if is_multi_host: reader_0.get_party_instance(role='host', party_id=hosts[1]).component_param(table=host_train_data) # define DataTransform components data_transform_0 = DataTransform(name="data_transform_0") # start component numbering at 0 # get DataTransform party instance of guest data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest) # configure DataTransform for guest data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense") # get and configure DataTransform party instance of host if host_dense_output: output_format = 'dense' else: output_format = 'sparse' if is_multi_host: data_transform_0.get_party_instance(role='host', party_id=hosts). \ component_param(with_label=False, output_format=output_format) else: data_transform_0.get_party_instance(role='host', party_id=hosts[0]). \ component_param(with_label=False, output_format=output_format) # define Intersection components intersection_0 = Intersection(name="intersection_0") hetero_feature_binning_0 = HeteroFeatureBinning(**bin_param) # add components to pipeline, in order of task execution pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) # set data input sources of intersection components pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data)) # set train & validate data of hetero_lr_0 component pipeline.add_component(hetero_feature_binning_0, data=Data(data=intersection_0.output.data)) # compile pipeline once finished adding modules, this step will form conf and dsl files for running job pipeline.compile() # fit model # pipeline.fit(work_mode=work_mode) return pipeline def make_asymmetric_dsl(config, namespace, guest_param, host_param, dataset='breast', is_multi_host=False, host_dense_output=True): parties = config.parties guest = parties.guest[0] hosts = parties.host if dataset == 'breast': guest_table_name = 'breast_hetero_guest' host_table_name = 'breast_hetero_host' elif dataset == 'default_credit': guest_table_name = 'default_credit_hetero_guest' host_table_name = 'default_credit_hetero_host' else: raise ValueError(f"dataset: {dataset} cannot be recognized") guest_train_data = {"name": guest_table_name, "namespace": f"experiment{namespace}"} host_train_data = {"name": host_table_name, "namespace": f"experiment{namespace}"} # initialize pipeline pipeline = PipeLine() # set job initiator pipeline.set_initiator(role='guest', party_id=guest) # set participants information if is_multi_host: pipeline.set_roles(guest=guest, host=hosts) else: pipeline.set_roles(guest=guest, host=hosts[0]) # define Reader components to read in data reader_0 = Reader(name="reader_0") # configure Reader for guest reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data) # configure Reader for host reader_0.get_party_instance(role='host', party_id=hosts[0]).component_param(table=host_train_data) if is_multi_host: reader_0.get_party_instance(role='host', party_id=hosts[1]).component_param(table=host_train_data) # define DataTransform components data_transform_0 = DataTransform(name="data_transform_0") # start component numbering at 0 # get DataTransform party instance of guest data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest) # configure DataTransform for guest data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense") # get and configure DataTransform party instance of host if host_dense_output: output_format = 'dense' else: output_format = 'sparse' if is_multi_host: data_transform_0.get_party_instance(role='host', party_id=hosts). \ component_param(with_label=False, output_format=output_format) else: data_transform_0.get_party_instance(role='host', party_id=hosts[0]). \ component_param(with_label=False, output_format=output_format) # define Intersection components intersection_0 = Intersection(name="intersection_0") hetero_feature_binning_0 = HeteroFeatureBinning(name="hetero_feature_binning_0") hetero_feature_binning_0.get_party_instance(role='guest', party_id=guest).component_param(**guest_param) if is_multi_host: hetero_feature_binning_0.get_party_instance(role='host', party_id=hosts).component_param(**host_param) else: hetero_feature_binning_0.get_party_instance(role='host', party_id=hosts[0]).component_param(**host_param) # add components to pipeline, in order of task execution pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) # set data input sources of intersection components pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data)) # set train & validate data of hetero_lr_0 component pipeline.add_component(hetero_feature_binning_0, data=Data(data=intersection_0.output.data)) # compile pipeline once finished adding modules, this step will form conf and dsl files for running job pipeline.compile() # fit model # pipeline.fit(work_mode=work_mode) return pipeline

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core/__init__.py

dokzlo13/c.nord_task

08428f093329d72ae2e9a79223b0d6a9e9bb78c3

[ "Unlicense" ]

null

core/__init__.py

dokzlo13/c.nord_task

08428f093329d72ae2e9a79223b0d6a9e9bb78c3

[ "Unlicense" ]

null

core/__init__.py

dokzlo13/c.nord_task

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Python

src/config/api-server/vnc_cfg_api_server/tests/resources/test_virtual_network.py

atsgen/tf-controller

9321889cdd3d7108980cc88937b2e82956502cc5

[ "Apache-2.0" ]

null

src/config/api-server/vnc_cfg_api_server/tests/resources/test_virtual_network.py

atsgen/tf-controller

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[ "Apache-2.0" ]

null

src/config/api-server/vnc_cfg_api_server/tests/resources/test_virtual_network.py

atsgen/tf-controller

9321889cdd3d7108980cc88937b2e82956502cc5

[ "Apache-2.0" ]

null

# # Copyright (c) 2017 Juniper Networks, Inc. All rights reserved. # import logging from cfgm_common import get_bgp_rtgt_min_id from cfgm_common import VNID_MIN_ALLOC from cfgm_common.exceptions import BadRequest from cfgm_common.exceptions import HttpError from cfgm_common.exceptions import PermissionDenied from cfgm_common.exceptions import RefsExistError from cfgm_common.tests import test_common from testtools import ExpectedException from vnc_api.vnc_api import GlobalSystemConfig from vnc_api.vnc_api import Project from vnc_api.vnc_api import ProviderDetails from vnc_api.vnc_api import RouteTargetList from vnc_api.vnc_api import VirtualMachineInterface from vnc_api.vnc_api import VirtualNetwork from vnc_api.vnc_api import VirtualNetworkType from vnc_cfg_api_server.tests import test_case logger = logging.getLogger(__name__) class TestVirtualNetwork(test_case.ApiServerTestCase): @classmethod def setUpClass(cls, *args, **kwargs): cls.console_handler = logging.StreamHandler() cls.console_handler.setLevel(logging.DEBUG) logger.addHandler(cls.console_handler) super(TestVirtualNetwork, cls).setUpClass(*args, **kwargs) @classmethod def tearDownClass(cls, *args, **kwargs): logger.removeHandler(cls.console_handler) super(TestVirtualNetwork, cls).tearDownClass(*args, **kwargs) @property def api(self): return self._vnc_lib def test_allocate_vn_id(self): mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) self.api.virtual_network_create(vn_obj) vn_obj = self.api.virtual_network_read(id=vn_obj.uuid) vn_id = vn_obj.virtual_network_network_id self.assertEqual(vn_obj.get_fq_name_str(), mock_zk.get_vn_from_id(vn_id)) self.assertGreaterEqual(vn_id, VNID_MIN_ALLOC) def test_deallocate_vn_id(self): mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) self.api.virtual_network_create(vn_obj) vn_obj = self.api.virtual_network_read(id=vn_obj.uuid) vn_id = vn_obj.virtual_network_network_id self.api.virtual_network_delete(id=vn_obj.uuid) self.assertNotEqual(mock_zk.get_vn_from_id(vn_id), vn_obj.get_fq_name_str()) def test_not_deallocate_vn_id_if_fq_name_does_not_correspond(self): mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) self.api.virtual_network_create(vn_obj) vn_obj = self.api.virtual_network_read(id=vn_obj.uuid) vn_id = vn_obj.virtual_network_network_id fake_fq_name = "fake fq_name" mock_zk._vn_id_allocator.delete(vn_id - VNID_MIN_ALLOC) mock_zk._vn_id_allocator.reserve(vn_id - VNID_MIN_ALLOC, fake_fq_name) self.api.virtual_network_delete(id=vn_obj.uuid) self.assertIsNotNone(mock_zk.get_vn_from_id(vn_id)) self.assertEqual(fake_fq_name, mock_zk.get_vn_from_id(vn_id)) def test_cannot_set_vn_id(self): vn_obj = VirtualNetwork('%s-vn' % self.id()) vn_obj.set_virtual_network_network_id(42) with ExpectedException(PermissionDenied): self.api.virtual_network_create(vn_obj) def test_cannot_update_vn_id(self): vn_obj = VirtualNetwork('%s-vn' % self.id()) self.api.virtual_network_create(vn_obj) vn_obj = self.api.virtual_network_read(id=vn_obj.uuid) vn_obj.set_virtual_network_network_id(42) with ExpectedException(PermissionDenied): self.api.virtual_network_update(vn_obj) # test can update with same value, needed internally # TODO(ethuleau): not sure why it's needed vn_obj = self.api.virtual_network_read(id=vn_obj.uuid) vn_obj.set_virtual_network_network_id( vn_obj.virtual_network_network_id) self.api.virtual_network_update(vn_obj) def test_create_vn_with_configured_rt_in_system_range(self): gsc = self.api.global_system_config_read(GlobalSystemConfig().fq_name) vn = VirtualNetwork('%s-vn' % self.id()) rt_name = 'target:%d:%d' % (gsc.autonomous_system, get_bgp_rtgt_min_id( gsc.autonomous_system) + 1000) vn.set_route_target_list(RouteTargetList([rt_name])) self.assertRaises(BadRequest, self.api.virtual_network_create, vn) def test_update_vn_with_configured_rt_in_system_range(self): gsc = self.api.global_system_config_read(GlobalSystemConfig().fq_name) vn = VirtualNetwork('%s-vn' % self.id()) self.api.virtual_network_create(vn) rt_name = 'target:%d:%d' % (gsc.autonomous_system, get_bgp_rtgt_min_id( gsc.autonomous_system) + 1000) vn.set_route_target_list(RouteTargetList([rt_name])) self.assertRaises(BadRequest, self.api.virtual_network_update, vn) def test_allocate_vxlan_id(self): # enable vxlan routing on project proj = self._vnc_lib.project_read( fq_name=["default-domain", "default-project"]) proj.set_vxlan_routing(True) self._vnc_lib.project_update(proj) mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) vn_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn_obj_properties.set_vxlan_network_identifier(6000) vn_obj.set_virtual_network_properties(vn_obj_properties) self.api.virtual_network_create(vn_obj) # VN created, now read back the VN data to check if vxlan_id is set vn_obj = self.api.virtual_network_read(id=vn_obj.uuid) vn_obj_properties = vn_obj.get_virtual_network_properties() if not vn_obj_properties: self.fail("VN properties are not set") vxlan_id = vn_obj_properties.get_vxlan_network_identifier() self.assertEqual(vxlan_id, 6000) self.assertEqual(vn_obj.get_fq_name_str() + "_vxlan", mock_zk.get_vn_from_id(vxlan_id)) self.assertGreaterEqual(vxlan_id, VNID_MIN_ALLOC) self.api.virtual_network_delete(id=vn_obj.uuid) logger.debug('PASS - test_allocate_vxlan_id') def test_cannot_allocate_vxlan_id(self): # enable vxlan routing on project proj = self._vnc_lib.project_read( fq_name=["default-domain", "default-project"]) proj.set_vxlan_routing(True) self._vnc_lib.project_update(proj) mock_zk = self._api_server._db_conn._zk_db vn1_obj = VirtualNetwork('%s-vn' % self.id()) vn1_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn1_obj_properties.set_vxlan_network_identifier(6001) vn1_obj_properties.set_forwarding_mode('l2_l3') vn1_obj.set_virtual_network_properties(vn1_obj_properties) self.api.virtual_network_create(vn1_obj) # VN created, now read back the VN data to check if vxlan_id is set vn1_obj = self.api.virtual_network_read(id=vn1_obj.uuid) vn1_obj_properties = vn1_obj.get_virtual_network_properties() if not vn1_obj_properties: self.fail("VN properties are not set") vxlan_id = vn1_obj_properties.get_vxlan_network_identifier() self.assertEqual(vxlan_id, 6001) # Verified vxlan_id for VN1, now create VN2 with same vxlan_id vn2_obj = VirtualNetwork('%s-vn2' % self.id()) vn2_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn2_obj_properties.set_vxlan_network_identifier(6001) vn2_obj_properties.set_forwarding_mode('l2_l3') vn2_obj.set_virtual_network_properties(vn2_obj_properties) with ExpectedException(BadRequest): self.api.virtual_network_create(vn2_obj) self.assertEqual(vn1_obj.get_fq_name_str() + "_vxlan", mock_zk.get_vn_from_id(vxlan_id)) self.assertGreaterEqual(vxlan_id, VNID_MIN_ALLOC) self.api.virtual_network_delete(id=vn1_obj.uuid) logger.debug('PASS - test_cannot_allocate_vxlan_id') def test_deallocate_vxlan_id(self): # enable vxlan routing on project proj = self._vnc_lib.project_read( fq_name=["default-domain", "default-project"]) proj.set_vxlan_routing(True) self._vnc_lib.project_update(proj) mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) vn_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn_obj_properties.set_vxlan_network_identifier(6002) vn_obj.set_virtual_network_properties(vn_obj_properties) self.api.virtual_network_create(vn_obj) # VN created, now read back the VN data to check if vxlan_id is set vn_obj = self.api.virtual_network_read(id=vn_obj.uuid) vn_obj_properties = vn_obj.get_virtual_network_properties() if not vn_obj_properties: self.fail("VN properties are not set") vxlan_id = vn_obj_properties.get_vxlan_network_identifier() self.assertEqual(vxlan_id, 6002) self.api.virtual_network_delete(id=vn_obj.uuid) self.assertNotEqual(vn_obj.get_fq_name_str() + "_vxlan", mock_zk.get_vn_from_id(vxlan_id)) logger.debug('PASS - test_deallocate_vxlan_id') def test_update_vxlan_id(self): # enable vxlan routing on project proj = self._vnc_lib.project_read( fq_name=["default-domain", "default-project"]) proj.set_vxlan_routing(True) self._vnc_lib.project_update(proj) vn_obj = VirtualNetwork('%s-vn' % self.id()) vn_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn_obj_properties.set_vxlan_network_identifier(6003) vn_obj_properties.set_forwarding_mode('l2_l3') vn_obj.set_virtual_network_properties(vn_obj_properties) self.api.virtual_network_create(vn_obj) # VN created, now read back the VN data to check if vxlan_id is set vn_obj_read = self.api.virtual_network_read(id=vn_obj.uuid) vn_obj_properties_read = vn_obj_read.get_virtual_network_properties() if not vn_obj_properties_read: self.fail("VN properties are not set") vxlan_id = vn_obj_properties_read.get_vxlan_network_identifier() self.assertEqual(vxlan_id, 6003) # Created VN. Now Update it with a different vxlan_id vn_obj_properties.set_vxlan_network_identifier(6004) vn_obj.set_virtual_network_properties(vn_obj_properties) self.api.virtual_network_update(vn_obj) vn_obj_read = self.api.virtual_network_read(id=vn_obj.uuid) vn_obj_properties_read = vn_obj_read.get_virtual_network_properties() if not vn_obj_properties_read: self.fail("VN properties are not set") vxlan_id = vn_obj_properties_read.get_vxlan_network_identifier() self.assertEqual(vxlan_id, 6004) self.api.virtual_network_delete(id=vn_obj.uuid) logger.debug('PASS - test_update_vxlan_id') def test_cannot_update_vxlan_id(self): # enable vxlan routing on project proj = self._vnc_lib.project_read( fq_name=["default-domain", "default-project"]) proj.set_vxlan_routing(True) self._vnc_lib.project_update(proj) vn1_obj = VirtualNetwork('%s-vn1' % self.id()) vn1_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn1_obj_properties.set_vxlan_network_identifier(6005) vn1_obj_properties.set_forwarding_mode('l2_l3') vn1_obj.set_virtual_network_properties(vn1_obj_properties) self.api.virtual_network_create(vn1_obj) # VN created, create second VN with different vxlan_id vn2_obj = VirtualNetwork('%s-vn2' % self.id()) vn2_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn2_obj_properties.set_vxlan_network_identifier(6006) vn2_obj_properties.set_forwarding_mode('l2_l3') vn2_obj.set_virtual_network_properties(vn2_obj_properties) self.api.virtual_network_create(vn2_obj) # Created Two VNs. Now Update it second VN with 1st VNs VXLAN_ID vn2_obj_properties.set_vxlan_network_identifier(6005) vn2_obj.set_virtual_network_properties(vn2_obj_properties) with ExpectedException(BadRequest): self.api.virtual_network_update(vn2_obj) vn_obj_read = self.api.virtual_network_read(id=vn2_obj.uuid) vn_obj_properties_read = vn_obj_read.get_virtual_network_properties() if not vn_obj_properties_read: self.fail("VN properties are not set") vxlan_id = vn_obj_properties_read.get_vxlan_network_identifier() self.assertEqual(vxlan_id, 6006) self.api.virtual_network_delete(id=vn2_obj.uuid) self.api.virtual_network_delete(id=vn1_obj.uuid) logger.debug('PASS - test_cannot_update_vxlan_id') def test_update_auto_vxlan_id_with_the_same_value(self): """ Test case. 1. Set VxLAN identifier mode to 'automatic'. 2. Create new VirtualNetwork. 3. Set VxLAN identifier mode to 'configured'. 4. Update VirtualNetwork with vxlan network identifier equal to network id. """ gvc_fq_name = ['default-global-system-config', 'default-global-vrouter-config'] vxlan_id_mode = {'auto': 'automatic', 'user': 'configured'} # Set VxLAN identifier mode to 'automatic' gvc = self.api.global_vrouter_config_read(fq_name=gvc_fq_name) gvc.set_vxlan_network_identifier_mode(vxlan_id_mode['auto']) self.api.global_vrouter_config_update(gvc) gvc = self.api.global_vrouter_config_read(fq_name=gvc_fq_name) # verify vxlan id mode has been set self.assertEqual(gvc.vxlan_network_identifier_mode, vxlan_id_mode['auto']) # Create new VirtualNetwork vn = VirtualNetwork('%s-vn' % self.id()) self.api.virtual_network_create(vn) vn = self.api.virtual_network_read(fq_name=vn.fq_name) # verify vn_network_id has been set vn_network_id = vn.get_virtual_network_network_id() self.assertTrue(vn_network_id > 0) # Set VxLAN identifier mode to 'configured' (user defined) gvc.set_vxlan_network_identifier_mode(vxlan_id_mode['user']) self.api.global_vrouter_config_update(gvc) gvc = self.api.global_vrouter_config_read(fq_name=gvc_fq_name) # verify vxlan id mode has been set self.assertEqual(gvc.vxlan_network_identifier_mode, vxlan_id_mode['user']) # Update VirtualNetwork with vxlan network identifier # equal to network id vn_properties = VirtualNetworkType() vn_properties.set_vxlan_network_identifier(vn_network_id) vn.set_virtual_network_properties(vn_properties) self.api.virtual_network_update(vn) # verify vn_network_id is the same as vxlan_network_id vn = self.api.virtual_network_read(fq_name=vn.fq_name) vxlan_id = vn.get_virtual_network_properties() \ .get_vxlan_network_identifier() self.assertEqual(vn_network_id, vxlan_id) def test_context_undo_fail_db_create(self): mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) zk_alloc_count_start = mock_zk._vn_id_allocator.get_alloc_count() def stub(*args, **kwargs): return (False, (500, "Fake error")) with ExpectedException(HttpError): with test_common.flexmocks( [(self._api_server._db_conn, 'dbe_create', stub)]): self.api.virtual_network_create(vn_obj) zk_alloc_count_current = mock_zk._vn_id_allocator.get_alloc_count() self.assertEqual(zk_alloc_count_start, zk_alloc_count_current) def test_context_undo_vxlan_id_fail_db_create(self): # enable vxlan routing on project proj = self._vnc_lib.project_read( fq_name=["default-domain", "default-project"]) proj.set_vxlan_routing(True) self._vnc_lib.project_update(proj) mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) vn_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn_obj_properties.set_vxlan_network_identifier(6000) vn_obj.set_virtual_network_properties(vn_obj_properties) def stub(*args, **kwargs): return (False, (500, "Fake error")) zk_alloc_count_start = mock_zk._vn_id_allocator.get_alloc_count() with ExpectedException(HttpError): with test_common.flexmocks( [(self._api_server._db_conn, 'dbe_create', stub)]): self.api.virtual_network_create(vn_obj) # make sure allocation counter stays the same zk_alloc_count_current = mock_zk._vn_id_allocator.get_alloc_count() self.assertEqual(zk_alloc_count_start, zk_alloc_count_current) def test_context_undo_fail_db_delete(self): vn_obj = self.create_virtual_network('vn-l2-%s' % self.id()) vn_ipam_refs = vn_obj.get_network_ipam_refs() mock_zk = self._api_server._db_conn._zk_db zk_alloc_count_start = mock_zk._vn_id_allocator.get_alloc_count() def stub(*args, **kwargs): return (False, (500, "Fake error")) with ExpectedException(HttpError): with test_common.flexmocks( [(self._api_server._db_conn, 'dbe_delete', stub)]): self.api.virtual_network_delete(id=vn_obj.uuid) # Make sure ipam refs still present (undo action recreated it) vn_obj = self.api.virtual_network_read(id=vn_obj.uuid) vn_ipam_refs_after_delete_fail = vn_obj.get_network_ipam_refs() self.assertEqual(vn_ipam_refs[0]['to'], vn_ipam_refs_after_delete_fail[0]['to']) self.assertEqual(vn_ipam_refs[0]['uuid'], vn_ipam_refs_after_delete_fail[0]['uuid']) self.assertEqual(vn_ipam_refs[0]['attr'].ipam_subnets[0].subnet_uuid, vn_ipam_refs_after_delete_fail[0][ 'attr'].ipam_subnets[0].subnet_uuid) # Make sure allocation counter stays the same zk_alloc_count_current = mock_zk._vn_id_allocator.get_alloc_count() self.assertEqual(zk_alloc_count_start, zk_alloc_count_current) def test_context_undo_vxlan_id_fail_db_update(self): # enable vxlan routing on project proj = self._vnc_lib.project_read( fq_name=["default-domain", "default-project"]) proj.set_vxlan_routing(True) self._vnc_lib.project_update(proj) mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) # Create vxlan vxlan_id = 6000 vn_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn_obj_properties.set_vxlan_network_identifier(vxlan_id) vn_obj_properties.set_forwarding_mode('l2_l3') vn_obj.set_virtual_network_properties(vn_obj_properties) self.api.virtual_network_create(vn_obj) vxlan_fqname = mock_zk.get_vn_from_id(vxlan_id) # Update vxlan id (will fail) new_vxlan_id = 6005 vn_obj_properties.set_vxlan_network_identifier(new_vxlan_id) vn_obj.set_virtual_network_properties(vn_obj_properties) def stub(*args, **kwargs): return (False, (500, "Fake error")) zk_alloc_count_start = mock_zk._vn_id_allocator.get_alloc_count() with ExpectedException(HttpError): with test_common.flexmocks( [(self._api_server._db_conn, 'dbe_update', stub)]): self.api.virtual_network_update(vn_obj) # Make sure vxlan_id is still allocated with same name new_vxlan_fqname = mock_zk.get_vn_from_id(vxlan_id) self.assertEqual(new_vxlan_fqname, vxlan_fqname) # Make sure new_vxlan_id is deallocated update_vxlan_fqname = mock_zk.get_vn_from_id(new_vxlan_id) self.assertEqual(update_vxlan_fqname, None) # Make sure allocation counter stays the same zk_alloc_count_current = mock_zk._vn_id_allocator.get_alloc_count() self.assertEqual(zk_alloc_count_start, zk_alloc_count_current) def test_context_undo_vn_to_vxlan_id_fail_db_update(self): # Enable vxlan routing on project proj = self._vnc_lib.project_read( fq_name=["default-domain", "default-project"]) proj.set_vxlan_routing(True) self._vnc_lib.project_update(proj) mock_zk = self._api_server._db_conn._zk_db vn_obj = VirtualNetwork('%s-vn' % self.id()) self.api.virtual_network_create(vn_obj) vn_fqname = mock_zk.get_vn_from_id(vn_obj.virtual_network_network_id) vn_id = vn_obj.virtual_network_network_id # Change vn to vxlan type vxlan_id = 6000 vn_obj_properties = VirtualNetworkType(forwarding_mode='l3') vn_obj_properties.set_vxlan_network_identifier(vxlan_id) vn_obj_properties.set_forwarding_mode('l2_l3') vn_obj.set_virtual_network_properties(vn_obj_properties) def stub(*args, **kwargs): return (False, (500, "Fake error")) zk_alloc_count_start = mock_zk._vn_id_allocator.get_alloc_count() with ExpectedException(HttpError): with test_common.flexmocks( [(self._api_server._db_conn, 'dbe_update', stub)]): self.api.virtual_network_update(vn_obj) # Make sure vxlan_id was dealocated new_vxlan_fqname = mock_zk.get_vn_from_id(vxlan_id) self.assertEqual(new_vxlan_fqname, None) # Make sure vn id is the same new_vn_id = vn_obj.virtual_network_network_id self.assertEqual(vn_id, new_vn_id) # Make sure fqname is the same fot vn_id update_vn_fqname = mock_zk.get_vn_from_id( vn_obj.virtual_network_network_id) self.assertEqual(vn_fqname, update_vn_fqname) # Make sure allocation counter stays the same zk_alloc_count_current = mock_zk._vn_id_allocator.get_alloc_count() self.assertEqual(zk_alloc_count_start, zk_alloc_count_current) def test_create_provider_vn(self): project = Project('%s-project' % self.id()) project_uuid = self.api.project_create(project) project = self.api.project_read(id=project_uuid) vn = VirtualNetwork('%s-vn' % self.id(), parent_obj=project) vn.set_is_provider_network(True) vn.set_provider_properties( ProviderDetails( params_dict={"segmentation_id": 100, "physical_network": "physnet1"})) vn_uuid = self.api.virtual_network_create(vn) is_provider_network = (self .api.virtual_network_read(id=vn_uuid) .get_is_provider_network()) self.assertTrue(is_provider_network) # end test_create_provider_vn def test_create_provider_vn_without_provider_details(self): project = Project('%s-project' % self.id()) project_uuid = self.api.project_create(project) project = self.api.project_read(id=project_uuid) vn = VirtualNetwork('%s-vn' % self.id(), parent_obj=project) vn.set_is_provider_network(True) vn_uuid = self.api.virtual_network_create(vn) is_provider_network = (self .api.virtual_network_read(id=vn_uuid) .get_is_provider_network()) self.assertTrue(is_provider_network) # end test_create_provider_vn_without_provider_details def test_update_not_in_use_non_provider_vn_to_provider(self): project = Project('%s-project' % self.id()) project_uuid = self.api.project_create(project) project = self.api.project_read(id=project_uuid) vn = VirtualNetwork('%s-vn' % self.id(), parent_obj=project) vn_uuid = self.api.virtual_network_create(vn) vn = self.api.virtual_network_read(id=vn_uuid) is_provider_network = vn.get_is_provider_network() self.assertFalse(is_provider_network) vn.set_is_provider_network(True) vn.set_provider_properties( ProviderDetails( params_dict={"segmentation_id": 100, "physical_network": "physnet1"})) self.api.virtual_network_update(vn) vn = self.api.virtual_network_read(id=vn_uuid) is_provider_network = vn.get_is_provider_network() self.assertTrue(is_provider_network) updated_provider_properties = vn.get_provider_properties() segmentation_id = updated_provider_properties.get_segmentation_id() physical_network = updated_provider_properties.get_physical_network() self.assertEqual((100, "physnet1"), (segmentation_id, physical_network)) # end test_update_non_provider_vn_to_provider def test_update_non_provider_vn_to_provider_without_provider_details(self): project = Project('%s-project' % self.id()) project_uuid = self.api.project_create(project) project = self.api.project_read(id=project_uuid) vn = VirtualNetwork('%s-vn' % self.id(), parent_obj=project) vn_uuid = self.api.virtual_network_create(vn) vn = self.api.virtual_network_read(id=vn_uuid) is_provider_network = vn.get_is_provider_network() self.assertFalse(is_provider_network) vn.set_is_provider_network(True) self.api.virtual_network_update(vn) vn = self.api.virtual_network_read(id=vn_uuid) is_provider_network = vn.get_is_provider_network() self.assertTrue(is_provider_network) # end test_update_non_provider_vn_to_provider_without_provider_details def test_update_in_use_vn_to_provider_vn(self): project = Project('%s-project' % self.id()) project_uuid = self.api.project_create(project) project = self.api.project_read(id=project_uuid) vn = VirtualNetwork('%s-vn' % self.id(), parent_obj=project) vn_uuid = self.api.virtual_network_create(vn) vmi = VirtualMachineInterface('%s-vmi' % self.id(), parent_obj=project) vmi.set_virtual_network(vn) self.api.virtual_machine_interface_create(vmi) vn = self.api.virtual_network_read(id=vn_uuid) vn.set_is_provider_network(True) vn.set_provider_properties( ProviderDetails( params_dict={"segmentation_id": 100, "physical_network": "physnet1"})) self.api.virtual_network_update(vn) updated_provider_properties = (self .api.virtual_network_read(id=vn.uuid) .get_provider_properties()) segmentation_id = updated_provider_properties.get_segmentation_id() physical_network = updated_provider_properties.get_physical_network() self.assertEqual((100, "physnet1"), (segmentation_id, physical_network)) # end test_update_in_use_vn_to_provider_vn def test_update_in_use_vn_to_provider_vn_without_physnet_label(self): project = Project('%s-project' % self.id()) project_uuid = self.api.project_create(project) project = self.api.project_read(id=project_uuid) vn = VirtualNetwork('%s-vn' % self.id(), parent_obj=project) vn_uuid = self.api.virtual_network_create(vn) vmi = VirtualMachineInterface('%s-vmi' % self.id(), parent_obj=project) vmi.set_virtual_network(vn) self.api.virtual_machine_interface_create(vmi) vn = self.api.virtual_network_read(id=vn_uuid) vn.set_is_provider_network(True) vn.set_provider_properties( ProviderDetails( params_dict={"segmentation_id": 100})) with ExpectedException(RefsExistError): self.api.virtual_network_update(vn) updated_provider_properties = (self .api.virtual_network_read(id=vn.uuid) .get_provider_properties()) self.assertEqual(None, updated_provider_properties) # end test_update_in_use_vn_to_provider_vn_without_physnet_label def test_update_in_use_vn_to_provider_vn_without_segmentation(self): project = Project('%s-project' % self.id()) project_uuid = self.api.project_create(project) project = self.api.project_read(id=project_uuid) vn = VirtualNetwork('%s-vn' % self.id(), parent_obj=project) vn_uuid = self.api.virtual_network_create(vn) vmi = VirtualMachineInterface('%s-vmi' % self.id(), parent_obj=project) vmi.set_virtual_network(vn) self.api.virtual_machine_interface_create(vmi) vn = self.api.virtual_network_read(id=vn_uuid) vn.set_is_provider_network(True) vn.set_provider_properties( ProviderDetails( params_dict={"physical_network": "physnet1"})) with ExpectedException(RefsExistError): self.api.virtual_network_update(vn) updated_provider_properties = (self .api.virtual_network_read(id=vn.uuid) .get_provider_properties()) self.assertEqual(None, updated_provider_properties) # end test_update_in_use_vn_to_provider_vn_without_segmentation def test_update_in_use_provider_vn(self): project = Project('%s-project' % self.id()) project_uuid = self.api.project_create(project) project = self.api.project_read(id=project_uuid) vn = VirtualNetwork('%s-vn' % self.id(), parent_obj=project) vn.set_is_provider_network(True) vn.set_provider_properties( ProviderDetails( params_dict={"segmentation_id": 100, "physical_network": "physnet1"})) vn_uuid = self.api.virtual_network_create(vn) vmi = VirtualMachineInterface('%s-vmi' % self.id(), parent_obj=project) vmi.set_virtual_network(vn) self.api.virtual_machine_interface_create(vmi) vn = self.api.virtual_network_read(id=vn_uuid) vn.set_provider_properties( ProviderDetails( params_dict={"segmentation_id": 200, "physical_network": "physnet2"})) with ExpectedException(RefsExistError): self.api.virtual_network_update(vn) updated_provider_properties = (self .api.virtual_network_read(id=vn.uuid) .get_provider_properties()) segmentation_id = updated_provider_properties.get_segmentation_id() physical_network = updated_provider_properties.get_physical_network() self.assertEqual((100, "physnet1"), (segmentation_id, physical_network)) # end test_update_in_use_provider_vn

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5a49e16c79e7766c893de993c2b4108841560d63

4,824

py

Python

tests/func/test_pkg.py

amisev/dvc

025de9aeb509a539d5560f82caf47e851162f4a2

[ "Apache-2.0" ]

null

tests/func/test_pkg.py

amisev/dvc

025de9aeb509a539d5560f82caf47e851162f4a2

[ "Apache-2.0" ]

null

tests/func/test_pkg.py

amisev/dvc

025de9aeb509a539d5560f82caf47e851162f4a2

[ "Apache-2.0" ]

null

import os import git import filecmp from dvc.pkg import PkgManager from tests.utils import trees_equal def test_install_and_uninstall(repo_dir, dvc_repo, pkg): name = os.path.basename(pkg.root_dir) pkg_dir = os.path.join(repo_dir.root_dir, ".dvc", "pkg") mypkg_dir = os.path.join(pkg_dir, name) dvc_repo.pkg.install(pkg.root_dir) assert os.path.exists(pkg_dir) assert os.path.isdir(pkg_dir) assert os.path.exists(mypkg_dir) assert os.path.isdir(mypkg_dir) assert os.path.isdir(os.path.join(mypkg_dir, ".git")) dvc_repo.pkg.install(pkg.root_dir) assert os.path.exists(pkg_dir) assert os.path.isdir(pkg_dir) assert os.path.exists(mypkg_dir) assert os.path.isdir(mypkg_dir) assert os.path.isdir(os.path.join(mypkg_dir, ".git")) git_repo = git.Repo(mypkg_dir) assert git_repo.active_branch.name == "master" dvc_repo.pkg.uninstall(name) assert not os.path.exists(mypkg_dir) dvc_repo.pkg.uninstall(name) assert not os.path.exists(mypkg_dir) def test_uninstall_corrupted(repo_dir, dvc_repo): name = os.path.basename("mypkg") pkg_dir = os.path.join(repo_dir.root_dir, ".dvc", "pkg") mypkg_dir = os.path.join(pkg_dir, name) os.makedirs(mypkg_dir) dvc_repo.pkg.uninstall(name) assert not os.path.exists(mypkg_dir) def test_force_install(repo_dir, dvc_repo, pkg): name = os.path.basename(pkg.root_dir) pkg_dir = os.path.join(repo_dir.root_dir, ".dvc", "pkg") mypkg_dir = os.path.join(pkg_dir, name) os.makedirs(mypkg_dir) dvc_repo.pkg.install(pkg.root_dir) assert not os.listdir(mypkg_dir) dvc_repo.pkg.install(pkg.root_dir, force=True) assert os.path.exists(pkg_dir) assert os.path.isdir(pkg_dir) assert os.path.exists(mypkg_dir) assert os.path.isdir(mypkg_dir) assert os.path.isdir(os.path.join(mypkg_dir, ".git")) def test_install_version(repo_dir, dvc_repo, pkg): name = os.path.basename(pkg.root_dir) pkg_dir = os.path.join(repo_dir.root_dir, ".dvc", "pkg") mypkg_dir = os.path.join(pkg_dir, name) dvc_repo.pkg.install(pkg.root_dir, version="branch") assert os.path.exists(pkg_dir) assert os.path.isdir(pkg_dir) assert os.path.exists(mypkg_dir) assert os.path.isdir(mypkg_dir) assert os.path.isdir(os.path.join(mypkg_dir, ".git")) git_repo = git.Repo(mypkg_dir) assert git_repo.active_branch.name == "branch" def test_import(repo_dir, dvc_repo, pkg): name = os.path.basename(pkg.root_dir) src = pkg.FOO dst = pkg.FOO + "_imported" dvc_repo.pkg.install(pkg.root_dir) dvc_repo.pkg.imp(name, src, dst) assert os.path.exists(dst) assert os.path.isfile(dst) assert filecmp.cmp(repo_dir.FOO, dst, shallow=False) def test_import_dir(repo_dir, dvc_repo, pkg): name = os.path.basename(pkg.root_dir) src = pkg.DATA_DIR dst = pkg.DATA_DIR + "_imported" dvc_repo.pkg.install(pkg.root_dir) dvc_repo.pkg.imp(name, src, dst) assert os.path.exists(dst) assert os.path.isdir(dst) trees_equal(src, dst) def test_import_url(repo_dir, dvc_repo, pkg): name = os.path.basename(pkg.root_dir) pkg_dir = os.path.join(repo_dir.root_dir, ".dvc", "pkg") mypkg_dir = os.path.join(pkg_dir, name) src = pkg.FOO dst = pkg.FOO + "_imported" dvc_repo.pkg.imp(pkg.root_dir, src, dst) assert os.path.exists(pkg_dir) assert os.path.isdir(pkg_dir) assert os.path.exists(mypkg_dir) assert os.path.isdir(mypkg_dir) assert os.path.isdir(os.path.join(mypkg_dir, ".git")) assert os.path.exists(dst) assert os.path.isfile(dst) assert filecmp.cmp(repo_dir.FOO, dst, shallow=False) def test_import_url_version(repo_dir, dvc_repo, pkg): name = os.path.basename(pkg.root_dir) pkg_dir = os.path.join(repo_dir.root_dir, ".dvc", "pkg") mypkg_dir = os.path.join(pkg_dir, name) src = "version" dst = src dvc_repo.pkg.imp(pkg.root_dir, src, dst, version="branch") assert os.path.exists(pkg_dir) assert os.path.isdir(pkg_dir) assert os.path.exists(mypkg_dir) assert os.path.isdir(mypkg_dir) assert os.path.isdir(os.path.join(mypkg_dir, ".git")) assert os.path.exists(dst) assert os.path.isfile(dst) with open(dst, "r+") as fobj: assert fobj.read() == "branch" def test_get_file(repo_dir, dvc_repo, pkg): src = pkg.FOO dst = pkg.FOO + "_imported" PkgManager.get(pkg.root_dir, src, dst) assert os.path.exists(dst) assert os.path.isfile(dst) assert filecmp.cmp(repo_dir.FOO, dst, shallow=False) def test_get_dir(repo_dir, dvc_repo, pkg): src = pkg.DATA_DIR dst = pkg.DATA_DIR + "_imported" PkgManager.get(pkg.root_dir, src, dst) assert os.path.exists(dst) assert os.path.isdir(dst) trees_equal(src, dst)

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5a69bc34e6d7a55844c7d9ffc3db6aae966673bd

161

py

Python

lasier/adapters/caches/__init__.py

luizalabs/lasier

edb0e850cb630fb55ce83c255bbb1e7fca08b21f

[ "MIT" ]

61

2019-12-13T20:08:30.000Z

2022-03-22T11:51:04.000Z

lasier/adapters/caches/__init__.py

jairhenrique/lasier

29bf96cb888493d369a22400bec6acffe345d168

[ "MIT" ]

25

2019-12-13T17:14:46.000Z

2022-03-17T18:49:34.000Z

lasier/adapters/caches/__init__.py

jairhenrique/lasier

29bf96cb888493d369a22400bec6acffe345d168

[ "MIT" ]

6

2020-04-02T21:10:08.000Z

2022-03-17T15:31:15.000Z

from .aiocache import Adapter as AiocacheAdapter # noqa from .django import Adapter as DjangoAdapter # noqa from .redis import Adapter as RedisAdapter # noqa

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ce56027bba1dafd0566245479a05ec73c1dc496b

2,603

py

Python

ValAgents.py

Mister-SOSA/ValAgents

06fcc47c24dc1ded6b8d79710f485c24d726ca76

[ "MIT" ]

null

ValAgents.py

Mister-SOSA/ValAgents

06fcc47c24dc1ded6b8d79710f485c24d726ca76

[ "MIT" ]

null

ValAgents.py

Mister-SOSA/ValAgents

06fcc47c24dc1ded6b8d79710f485c24d726ca76

[ "MIT" ]

null

""" Dictionaries of all VALORANT agents with their respective UUIDs, categorized by role. """ list_all_agents = { "Jett" : "add6443a-41bd-e414-f6ad-e58d267f4e95", "Reyna" : "a3bfb853-43b2-7238-a4f1-ad90e9e46bcc", "Raze" : "f94c3b30-42be-e959-889c-5aa313dba261", "Yoru" : "7f94d92c-4234-0a36-9646-3a87eb8b5c89", "Phoenix" : "eb93336a-449b-9c1b-0a54-a891f7921d69", "Neon" : "bb2a4828-46eb-8cd1-e765-15848195d751", "Breach" : "5f8d3a7f-467b-97f3-062c-13acf203c006", "Skye" : "6f2a04ca-43e0-be17-7f36-b3908627744d", "Sova" : "320b2a48-4d9b-a075-30f1-1f93a9b638fa", "KAY/O" : "601dbbe7-43ce-be57-2a40-4abd24953621", "Killjoy" : "1e58de9c-4950-5125-93e9-a0aee9f98746", "Cypher" : "117ed9e3-49f3-6512-3ccf-0cada7e3823b", "Sage" : "569fdd95-4d10-43ab-ca70-79becc718b46", "Chamber" : "22697a3d-45bf-8dd7-4fec-84a9e28c69d7", "Omen" : "8e253930-4c05-31dd-1b6c-968525494517", "Brimstone" : "9f0d8ba9-4140-b941-57d3-a7ad57c6b417", "Astra" : "41fb69c1-4189-7b37-f117-bcaf1e96f1bf", "Viper" : "707eab51-4836-f488-046a-cda6bf494859" } list_deulists = { "Jett" : "add6443a-41bd-e414-f6ad-e58d267f4e95", "Reyna" : "a3bfb853-43b2-7238-a4f1-ad90e9e46bcc", "Raze" : "f94c3b30-42be-e959-889c-5aa313dba261", "Yoru" : "7f94d92c-4234-0a36-9646-3a87eb8b5c89", "Phoenix" : "eb93336a-449b-9c1b-0a54-a891f7921d69", "Neon" : "bb2a4828-46eb-8cd1-e765-15848195d751" } list_initiators = { "Breach" : "5f8d3a7f-467b-97f3-062c-13acf203c006", "Skye" : "6f2a04ca-43e0-be17-7f36-b3908627744d", "Sova" : "320b2a48-4d9b-a075-30f1-1f93a9b638fa", "KAY/O" : "601dbbe7-43ce-be57-2a40-4abd24953621" } list_sentinels = { "Killjoy" : "1e58de9c-4950-5125-93e9-a0aee9f98746", "Cypher" : "117ed9e3-49f3-6512-3ccf-0cada7e3823b", "Sage" : "569fdd95-4d10-43ab-ca70-79becc718b46", "Chamber" : "22697a3d-45bf-8dd7-4fec-84a9e28c69d7" } list_controllers = { "Omen" : "8e253930-4c05-31dd-1b6c-968525494517", "Brimstone" : "9f0d8ba9-4140-b941-57d3-a7ad57c6b417", "Astra" : "41fb69c1-4189-7b37-f117-bcaf1e96f1bf", "Viper" : "707eab51-4836-f488-046a-cda6bf494859" } def deulists(): return list_deulists def initiators(): return list_initiators def sentinels(): return list_sentinels def controllers(): return list_controllers def returnAgents(role): if (role == 'deulists'): return list_deulists if (role == 'initiators'): return list_initiators if (role == 'sentinels'): return list_sentinels if (role == 'controllers'): return list_controllers

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9

ce805bf1b4c52a5e9fb1250d1cb0b232e3484b16

9,126

py

Python

tests/integration/Test_Verbs.py

Drewlark/whitakers_words

c0bf18d06215eb1e585413e5d426c9426b30c85a

[ "MIT" ]

null

tests/integration/Test_Verbs.py

Drewlark/whitakers_words

c0bf18d06215eb1e585413e5d426c9426b30c85a

[ "MIT" ]

null

tests/integration/Test_Verbs.py

Drewlark/whitakers_words

c0bf18d06215eb1e585413e5d426c9426b30c85a

[ "MIT" ]

null

import unittest from whitakers_words.enums import Mood, Number, Person, Tense, Voice, WordType from whitakers_words.parser import Parser class VerbTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.par = Parser() def test_amat(self): result = self.par.parse("amat") self.assertEqual(len(result.forms), 1) self.assertEqual(len(result.forms[0].analyses), 1) for analysis in result.forms[0].analyses.values(): self.assertEqual(analysis.lexeme.roots[0], "am") self.assertEqual(analysis.lexeme.wordType, WordType.V) self.assertEqual(len(analysis.inflections), 1) expected_features = { "Mood": Mood.IND, "Number": Number.S, "Person": Person["3"], "Tense": Tense.PRES, "Voice": Voice.ACTIVE, } self.assertEqual(analysis.inflections[0].stem, "am") self.assertEqual(analysis.inflections[0].affix, "at") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertEqual(analysis.inflections[0].features, expected_features) def test_quaero(self): result = self.par.parse("quaerebar") # response syntax and basics self.assertEqual(len(result.forms), 1) self.assertEqual(len(result.forms[0].analyses), 1) for analysis in result.forms[0].analyses.values(): self.assertEqual(analysis.lexeme.roots[0], "quaer") self.assertEqual(analysis.lexeme.wordType, WordType.V) self.assertEqual(len(analysis.inflections), 1) expected_features = { "Mood": Mood.IND, "Number": Number.S, "Person": Person["1"], "Tense": Tense.IMPF, "Voice": Voice.PASSIVE, } self.assertEqual(analysis.inflections[0].stem, "quaer") self.assertEqual(analysis.inflections[0].affix, "ebar") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertEqual(analysis.inflections[0].features, expected_features) def test_tulisti(self): result = self.par.parse("tulisti") # response syntax and basics self.assertEqual(len(result.forms), 1) self.assertEqual(len(result.forms[0].analyses), 1) for analysis in result.forms[0].analyses.values(): self.assertEqual(analysis.lexeme.roots[0], "fer") self.assertEqual(analysis.lexeme.wordType, WordType.V) self.assertEqual(len(analysis.inflections), 1) expected_features = { "Mood": Mood.IND, "Number": Number.S, "Person": Person["2"], "Tense": Tense.PERF, "Voice": Voice.ACTIVE, } self.assertEqual(analysis.inflections[0].stem, "tul") self.assertEqual(analysis.inflections[0].affix, "isti") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertEqual(analysis.inflections[0].features, expected_features) def test_amavisse(self): result = self.par.parse("amavisse") # response syntax and basics self.assertEqual(len(result.forms), 1) self.assertEqual(len(result.forms[0].analyses), 1) for analysis in result.forms[0].analyses.values(): self.assertEqual(analysis.lexeme.roots[0], "am") self.assertEqual(analysis.lexeme.wordType, WordType.V) self.assertEqual(len(analysis.inflections), 1) expected_features = { "Mood": Mood.INF, "Number": Number.X, "Person": Person["0"], "Tense": Tense.PERF, "Voice": Voice.ACTIVE, } self.assertEqual(analysis.inflections[0].stem, "amav") self.assertEqual(analysis.inflections[0].affix, "isse") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertEqual(analysis.inflections[0].features, expected_features) def test_abiri(self): result = self.par.parse("abiri") # response syntax and basics self.assertEqual(len(result.forms), 1) self.assertEqual(len(result.forms[0].analyses), 1) for analysis in result.forms[0].analyses.values(): self.assertEqual(analysis.lexeme.roots[0], "abe") self.assertEqual(analysis.lexeme.wordType, WordType.V) self.assertEqual(len(analysis.inflections), 1) expected_features = { "Mood": Mood.INF, "Number": Number.X, "Person": Person["0"], "Tense": Tense.PRES, "Voice": Voice.PASSIVE, } self.assertEqual(analysis.inflections[0].stem, "abi") self.assertEqual(analysis.inflections[0].affix, "ri") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertEqual(analysis.inflections[0].features, expected_features) def test_decet(self): result = self.par.parse("decet") # response syntax and basics self.assertEqual(len(result.forms), 1) self.assertEqual(len(result.forms[0].analyses), 1) for analysis in result.forms[0].analyses.values(): self.assertEqual(analysis.lexeme.roots[0], "dec") self.assertEqual(analysis.lexeme.wordType, WordType.V) self.assertEqual(len(analysis.inflections), 1) expected_features = { "Mood": Mood.IND, "Number": Number.S, "Person": Person["3"], "Tense": Tense.PRES, "Voice": Voice.ACTIVE, } self.assertEqual(analysis.inflections[0].stem, "dec") self.assertEqual(analysis.inflections[0].affix, "et") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertEqual(analysis.inflections[0].features, expected_features) def test_alit(self): result = self.par.parse("alit") # response syntax and basics self.assertEqual(len(result.forms), 1) self.assertEqual(len(result.forms[0].analyses), 2) for analysis in result.forms[0].analyses.values(): self.assertEqual(analysis.lexeme.roots[0], "al") self.assertEqual(analysis.lexeme.wordType, WordType.V) self.assertEqual(len(analysis.inflections), 1) expected_features = { "Mood": Mood.IND, "Number": Number.S, "Person": Person["3"], "Tense": Tense.PRES, "Voice": Voice.ACTIVE, } self.assertEqual(analysis.inflections[0].stem, "al") self.assertEqual(analysis.inflections[0].affix, "it") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertEqual(analysis.inflections[0].features, expected_features) def test_venit(self): result = self.par.parse("venit") # response syntax and basics self.assertEqual(len(result.forms), 1) self.assertEqual(len(result.forms[0].analyses), 2) for analysis in result.forms[0].analyses.values(): self.assertEqual(analysis.lexeme.wordType, WordType.V) if analysis.lexeme.roots[0] == "vene": # venere, venio = to be sold as a slave self.assertEqual(len(analysis.inflections), 1) expected_features = { "Mood": Mood.IND, "Number": Number.S, "Person": Person["3"], "Tense": Tense.PRES, "Voice": Voice.ACTIVE, } self.assertEqual(analysis.inflections[0].stem, "veni") self.assertEqual(analysis.inflections[0].affix, "t") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertEqual(analysis.inflections[0].features, expected_features) elif analysis.lexeme.roots[0] == "veni": self.assertEqual(len(analysis.inflections), 2) for inflection in analysis.inflections: self.assertEqual(analysis.inflections[0].stem, "ven") self.assertEqual(analysis.inflections[0].affix, "it") self.assertEqual(analysis.inflections[0].wordType, WordType.V) self.assertTrue(inflection.has_feature(Mood.IND)) self.assertTrue(inflection.has_feature(Number.S)) self.assertTrue(inflection.has_feature(Person["3"])) self.assertTrue(inflection.has_feature(Voice.ACTIVE)) other_features = [x.features["Tense"] for x in analysis.inflections] self.assertTrue(Tense.PRES in other_features) self.assertTrue(Tense.PERF in other_features) else: self.fail("Invalid root")

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9,126

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false

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0

null

1

0

null

0

1

0

10

0b735f175b94591aedb43cb089d7a0f138df40ce

204

py

Python

pygns3/__init__.py

mvdwoord/PyGNS3

e44f57be97f01e5a41fd1847860a5a78b5754790

[ "MIT" ]

9

2017-08-11T09:31:42.000Z

2020-03-31T12:59:16.000Z

pygns3/__init__.py

mvdwoord/PyGNS3

e44f57be97f01e5a41fd1847860a5a78b5754790

[ "MIT" ]

3

2019-02-22T13:28:34.000Z

2019-09-09T16:15:20.000Z

pygns3/__init__.py

mvdwoord/PyGNS3

e44f57be97f01e5a41fd1847860a5a78b5754790

[ "MIT" ]

7

2017-10-05T18:25:13.000Z

2021-06-28T10:23:18.000Z

from .controller import GNS3API, GNS3Compute, GNS3Controller, GNS3Project, GNS3VM from requests.auth import HTTPBasicAuth __all__ = ['GNS3API', 'GNS3Compute', 'GNS3Controller', 'GNS3Project', 'GNS3VM']

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null

0

1

0

1

0

7

0b82614e97ecb670905d909e626237b61a660a2e

86

py

Python

processor/__init__.py

RichardDominik/AICITY2021_Track2_DMT

50f27363532ae712868ff1ceaf128a3bbec426ac

[ "MIT" ]

74

2021-04-19T03:09:45.000Z

2022-03-29T06:32:08.000Z

processor/__init__.py

RichardDominik/AICITY2021_Track2_DMT

50f27363532ae712868ff1ceaf128a3bbec426ac

[ "MIT" ]

16

2021-05-14T06:09:26.000Z

2022-02-23T20:08:27.000Z

processor/__init__.py

RichardDominik/AICITY2021_Track2_DMT

50f27363532ae712868ff1ceaf128a3bbec426ac

[ "MIT" ]

18

2021-05-10T02:17:01.000Z

2022-03-27T05:18:55.000Z

from .processor import do_train, do_inference from .uda_processor import do_uda_train

28.666667

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