lparkourer10/starcoder-python5b · Datasets at Hugging Face

794ab32b5932561453d6667cdf03981f5834b496

54

py

Python

bin/programmanager.py

pyDarkVOS/InarkOS

21b57a7f0e5a06d276e18e25ac8c30232eb0d8de

[ "Apache-2.0" ]

3

2022-02-20T17:22:18.000Z

2022-03-20T02:56:07.000Z

bin/programmanager.py

InarkVOS/InarkOS

21b57a7f0e5a06d276e18e25ac8c30232eb0d8de

[ "Apache-2.0" ]

null

bin/programmanager.py

InarkVOS/InarkOS

21b57a7f0e5a06d276e18e25ac8c30232eb0d8de

[ "Apache-2.0" ]

2

2022-03-11T13:36:37.000Z

2022-03-18T23:47:20.000Z

import os def run(p): os.system(f"python3 {p}")

10.8

27

0.592593

794ab40206460f242570eab47b2398553de8af36

7,985

py

Python

Collections-a-installer/community-general-2.4.0/plugins/modules/remote_management/redfish/idrac_redfish_facts.py

d-amien-b/simple-getwordpress

da90d515a0aa837b633d50db4d91d22b031c04a2

[ "MIT" ]

17

2017-06-07T23:15:01.000Z

2021-08-30T14:32:36.000Z

Collections-a-installer/community-general-2.4.0/plugins/modules/remote_management/redfish/idrac_redfish_facts.py

d-amien-b/simple-getwordpress

da90d515a0aa837b633d50db4d91d22b031c04a2

[ "MIT" ]

9

2017-06-25T03:31:52.000Z

2021-05-17T23:43:12.000Z

Collections-a-installer/community-general-2.4.0/plugins/modules/remote_management/redfish/idrac_redfish_facts.py

d-amien-b/simple-getwordpress

da90d515a0aa837b633d50db4d91d22b031c04a2

[ "MIT" ]

3

2018-05-26T21:31:22.000Z

2019-09-28T17:00:45.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # Copyright (c) 2019 Dell EMC Inc. # GNU General Public License v3.0+ (see LICENSE or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = ''' --- module: idrac_redfish_info short_description: Gather PowerEdge server information through iDRAC using Redfish APIs description: - Builds Redfish URIs locally and sends them to remote iDRAC controllers to get information back. - For use with Dell EMC iDRAC operations that require Redfish OEM extensions - This module was called C(idrac_redfish_facts) before Ansible 2.9, returning C(ansible_facts). Note that the M(community.general.idrac_redfish_info) module no longer returns C(ansible_facts)! options: category: required: true description: - Category to execute on iDRAC controller type: str command: required: true description: - List of commands to execute on iDRAC controller - C(GetManagerAttributes) returns the list of dicts containing iDRAC, LifecycleController and System attributes type: list baseuri: required: true description: - Base URI of iDRAC controller type: str username: required: true description: - User for authentication with iDRAC controller type: str password: required: true description: - Password for authentication with iDRAC controller type: str timeout: description: - Timeout in seconds for URL requests to OOB controller default: 10 type: int author: "Jose Delarosa (@jose-delarosa)" ''' EXAMPLES = ''' - name: Get Manager attributes with a default of 20 seconds community.general.idrac_redfish_info: category: Manager command: GetManagerAttributes baseuri: "{{ baseuri }}" username: "{{ username }}" password: "{{ password }}" timeout: 20 register: result # Examples to display the value of all or a single iDRAC attribute - name: Store iDRAC attributes as a fact variable ansible.builtin.set_fact: idrac_attributes: "{{ result.redfish_facts.entries | selectattr('Id', 'defined') | selectattr('Id', 'equalto', 'iDRACAttributes') | list | first }}" - name: Display all iDRAC attributes ansible.builtin.debug: var: idrac_attributes - name: Display the value of 'Syslog.1.SysLogEnable' iDRAC attribute ansible.builtin.debug: var: idrac_attributes['Syslog.1.SysLogEnable'] # Examples to display the value of all or a single LifecycleController attribute - name: Store LifecycleController attributes as a fact variable ansible.builtin.set_fact: lc_attributes: "{{ result.redfish_facts.entries | selectattr('Id', 'defined') | selectattr('Id', 'equalto', 'LCAttributes') | list | first }}" - name: Display LifecycleController attributes ansible.builtin.debug: var: lc_attributes - name: Display the value of 'CollectSystemInventoryOnRestart' attribute ansible.builtin.debug: var: lc_attributes['LCAttributes.1.CollectSystemInventoryOnRestart'] # Examples to display the value of all or a single System attribute - name: Store System attributes as a fact variable ansible.builtin.set_fact: system_attributes: "{{ result.redfish_facts.entries | selectattr('Id', 'defined') | selectattr('Id', 'equalto', 'SystemAttributes') | list | first }}" - name: Display System attributes ansible.builtin.debug: var: system_attributes - name: Display the value of 'PSRedPolicy' ansible.builtin.debug: var: system_attributes['ServerPwr.1.PSRedPolicy'] ''' RETURN = ''' msg: description: different results depending on task returned: always type: dict sample: List of Manager attributes ''' from ansible.module_utils.basic import AnsibleModule from ansible_collections.community.general.plugins.module_utils.redfish_utils import RedfishUtils from ansible.module_utils._text import to_native class IdracRedfishUtils(RedfishUtils): def get_manager_attributes(self): result = {} manager_attributes = [] properties = ['Attributes', 'Id'] response = self.get_request(self.root_uri + self.manager_uri) if response['ret'] is False: return response data = response['data'] # Manager attributes are supported as part of iDRAC OEM extension # Attributes are supported only on iDRAC9 try: for members in data[u'Links'][u'Oem'][u'Dell'][u'DellAttributes']: attributes_uri = members[u'@odata.id'] response = self.get_request(self.root_uri + attributes_uri) if response['ret'] is False: return response data = response['data'] attributes = {} for prop in properties: if prop in data: attributes[prop] = data.get(prop) if attributes: manager_attributes.append(attributes) result['ret'] = True except (AttributeError, KeyError) as e: result['ret'] = False result['msg'] = "Failed to find attribute/key: " + str(e) result["entries"] = manager_attributes return result CATEGORY_COMMANDS_ALL = { "Manager": ["GetManagerAttributes"] } def main(): result = {} module = AnsibleModule( argument_spec=dict( category=dict(required=True), command=dict(required=True, type='list'), baseuri=dict(required=True), username=dict(required=True), password=dict(required=True, no_log=True), timeout=dict(type='int', default=10) ), supports_check_mode=False ) is_old_facts = module._name in ('idrac_redfish_facts', 'community.general.idrac_redfish_facts') if is_old_facts: module.deprecate("The 'idrac_redfish_facts' module has been renamed to 'idrac_redfish_info', " "and the renamed one no longer returns ansible_facts", version='3.0.0', collection_name='community.general') # was Ansible 2.13 category = module.params['category'] command_list = module.params['command'] # admin credentials used for authentication creds = {'user': module.params['username'], 'pswd': module.params['password']} # timeout timeout = module.params['timeout'] # Build root URI root_uri = "https://" + module.params['baseuri'] rf_utils = IdracRedfishUtils(creds, root_uri, timeout, module) # Check that Category is valid if category not in CATEGORY_COMMANDS_ALL: module.fail_json(msg=to_native("Invalid Category '%s'. Valid Categories = %s" % (category, CATEGORY_COMMANDS_ALL.keys()))) # Check that all commands are valid for cmd in command_list: # Fail if even one command given is invalid if cmd not in CATEGORY_COMMANDS_ALL[category]: module.fail_json(msg=to_native("Invalid Command '%s'. Valid Commands = %s" % (cmd, CATEGORY_COMMANDS_ALL[category]))) # Organize by Categories / Commands if category == "Manager": # execute only if we find a Manager resource result = rf_utils._find_managers_resource() if result['ret'] is False: module.fail_json(msg=to_native(result['msg'])) for command in command_list: if command == "GetManagerAttributes": result = rf_utils.get_manager_attributes() # Return data back or fail with proper message if result['ret'] is True: del result['ret'] if is_old_facts: module.exit_json(ansible_facts=dict(redfish_facts=result)) else: module.exit_json(redfish_facts=result) else: module.fail_json(msg=to_native(result['msg'])) if __name__ == '__main__': main()

33.691983

156

0.665373

794ab4049b71b24e9ef1ef43cc06677b29a78388

2,995

py

Python

scripts/p600/main_p600_low_res.py

sakurakhadag/escp2-client

f8d58bdaedc4f7ca811769538586b759c37eb355

[ "MIT" ]

null

scripts/p600/main_p600_low_res.py

sakurakhadag/escp2-client

f8d58bdaedc4f7ca811769538586b759c37eb355

[ "MIT" ]

5

2019-10-10T13:53:48.000Z

2019-10-16T19:09:28.000Z

scripts/p600/main_p600_low_res.py

sakurakhadag/escp2-client

f8d58bdaedc4f7ca811769538586b759c37eb355

[ "MIT" ]

2

2019-10-11T17:56:31.000Z

2021-01-15T11:33:58.000Z

import sys import os sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))) import binascii import math from hex_functions import * from esc_functions import * from characters import * import numpy as np # cd to project base directory abspath = os.path.abspath(__file__) dname = os.path.dirname(abspath) os.chdir(os.path.join(dname, '..', '..')) # SPECIFY FILENAME, PRINTERNAME AND OUTPUTFOLDER filename = 'test_p600_low_res' # one of the printers for which the header and footer files are available in # the 'prns' folder printer = 'p600' outputfolder = 'output' # SET PARAMETERS # These parameters depend on the specific printer # printer units can be found by parsing the prn file # Same with color codes, print a file with all colors and parse it # other specs can be found by looking in spec sheet or service manual (if # available) # Shown parameters should work with R2400 / P600 / R3000 # unit parameters pmgmt = 720 vert = 720 hor = 720 mbase = 2880 nozzles = 180 # set nozzle row numbers (def black = 00) # Should work with R2400 and P600 black = b'\x00' lightBlack = b'\x10' lightLightBlack = b'\x30' cyan = b'\x02' lightCyan = b'\x12' magenta = b'\x01' lightMagenta = b'\x11' yellow = b'\x04' # select dot size d = b'\x10' # set page method ID esc_m = ESC_m(b'\x20') # set uni or bi directional mode unim = b'\x00' # 01 uni, 00 bi # CREATE THE RASTERDATA # initialize empty byte string containing the rasterdata raster = b'' # location of raster (in inches) x = 1 # one inch from left edge of paper y = 1 # one inch from top edge of paper # Create the matrix # width of the matrix (number of droplets in printhead travel direction) width = 100 matrix = np.zeros((nozzles, width)) # init the matrix as all zeros # set all rows of the matrix to 3's (large droplets), except for the last 2 # rows matrix[0:58, :] = 3 # Create the raster, # First set the x position of the printhead, # Print the matrix raster += ESC_dollar(hor, x) + ESC_i_matrix(black, matrix, spacing=0, fan=0) # First set the vertical position on the paper, then print the raster as # composed in the previous step, add a linefeed rasterdata = ESC_v(pmgmt, y) + raster + b'\x0c' # LOAD HEADER AND FOOTER FOR SELECTED PRINTER header = load_prn_file('prns/' + printer + '/' + printer + '-header.prn') footer = load_prn_file('prns/' + printer + '/' + printer + '-footer.prn') # COMPOSE BODY body = ESC_Graph() + ESC_Units(pmgmt, vert, hor, mbase) + ESC_Kmode() + \ ESC_imode(n=b'\x00') + ESC_Umode(unim) + ESC_edot(d) + \ ESC_Dras(v=240 / 3, h=120 / 3) + ESC_C(pmgmt) + ESC_c(pmgmt) + ESC_S(pmgmt) # + esc_m # COMBINE total = header + body + rasterdata + footer # CREATE OUTPUT DIR filename = outputfolder + '/' + filename + '.prn' # if not os.path.exists(outputfolder): # os.makedirs(outputfolder) # SAVE PRN FILE save_prn_file(total, filename) print('DONE!') print('path: ' + filename)

27.990654

90

0.691152

794ab41008d0ac086e0964a2a75758193146f1df

3,699

py

Python

lib/galaxy/managers/taggable.py

KyleL1998/galaxy

10be2cd8ac05680f8291eea7996f4d3fc76197de

[ "CC-BY-3.0" ]

null

lib/galaxy/managers/taggable.py

KyleL1998/galaxy

10be2cd8ac05680f8291eea7996f4d3fc76197de

[ "CC-BY-3.0" ]

null

lib/galaxy/managers/taggable.py

KyleL1998/galaxy

10be2cd8ac05680f8291eea7996f4d3fc76197de

[ "CC-BY-3.0" ]

null

""" Mixins for Taggable model managers and serializers. """ # from galaxy import exceptions as galaxy_exceptions import logging from galaxy.util import unicodify log = logging.getLogger(__name__) # TODO: work out the relation between serializers and managers and then fold these into the parent of the two def _tag_str_gen(item): # TODO: which user is this? all? for tag in item.tags: tag_str = tag.user_tname if tag.value is not None: tag_str += ":" + tag.user_value yield tag_str def _tags_to_strings(item): if not hasattr(item, 'tags'): return None return sorted(list(_tag_str_gen(item))) def _tags_from_strings(item, tag_handler, new_tags_list, user=None): # TODO: have to assume trans.user here... if not user: # raise galaxy_exceptions.RequestParameterMissingException( 'User required for tags on ' + str( item ) ) # TODO: this becomes a 'silent failure' - no tags are set. This is a questionable approach but # I haven't found a better one for anon users copying items with tags return # TODO: duped from tags manager - de-dupe when moved to taggable mixin tag_handler.delete_item_tags(user, item) new_tags_str = ','.join(new_tags_list) tag_handler.apply_item_tags(user, item, unicodify(new_tags_str, 'utf-8')) # TODO:!! does the creation of new_tags_list mean there are now more and more unused tag rows in the db? class TaggableManagerMixin(object): #: class of TagAssociation (e.g. HistoryTagAssociation) tag_assoc = None # TODO: most of this can be done by delegating to the GalaxyTagHandler? def get_tags(self, item): """ Return a list of tag strings. """ return _tags_to_strings(item) def set_tags(self, item, new_tags, user=None): """ Set an `item`'s tags from a list of strings. """ return _tags_from_strings(item, self.app.tag_handler, new_tags, user=user) # def tags_by_user( self, user, **kwargs ): # TODO: here or GalaxyTagHandler # pass class TaggableSerializerMixin(object): def add_serializers(self): self.serializers['tags'] = self.serialize_tags def serialize_tags(self, item, key, **context): """ Return tags as a list of strings. """ return _tags_to_strings(item) class TaggableDeserializerMixin(object): def add_deserializers(self): self.deserializers['tags'] = self.deserialize_tags def deserialize_tags(self, item, key, val, user=None, **context): """ Make sure `val` is a valid list of tag strings and assign them. Note: this will erase any previous tags. """ new_tags_list = self.validate.basestring_list(key, val) _tags_from_strings(item, self.app.tag_handler, new_tags_list, user=user) return item.tags class TaggableFilterMixin(object): def filter_has_partial_tag(self, item, val): """ Return True if any tag partially contains `val`. """ for tag_str in _tag_str_gen(item): if val in tag_str: return True return False def filter_has_tag(self, item, val): """ Return True if any tag exactly equals `val`. """ for tag_str in _tag_str_gen(item): if val == tag_str: return True return False def _add_parsers(self): self.fn_filter_parsers.update({ 'tag': { 'op': { 'eq' : self.filter_has_tag, 'has' : self.filter_has_partial_tag, } } })

30.073171

112

0.633685

794ab6d682d48d1fd11d8eb4e7b620152d9e3259

514

py

Python

load.py

TranslatorIIPrototypes/NodeNormalization

52461e0940e618984452b3fdf8cc13698ec71390

[ "MIT" ]

2

2021-01-12T19:34:38.000Z

2022-03-08T22:40:20.000Z

load.py

TranslatorSRI/NodeNormalization

3e8ca8440dd4bf831c901c048bc334661ca9aa5a

[ "MIT" ]

71

2020-10-02T12:35:49.000Z

2022-03-28T20:58:46.000Z

load.py

TranslatorSRI/NodeNormalization

3e8ca8440dd4bf831c901c048bc334661ca9aa5a

[ "MIT" ]

2

2020-10-06T16:01:50.000Z

2021-07-19T21:24:36.000Z

from node_normalizer.loader import NodeLoader import asyncio async def load_redis(): # instantiate the class that does all the work loader = NodeLoader() # call to load redis instances with normalized node data success: bool = await loader.load(1_000) # check the return if not success: loader.print_debug_msg(f'Failed to load node normalization data.', True) else: loader.print_debug_msg(f'Success', True) if __name__ == '__main__': asyncio.run(load_redis())

24.47619

80

0.70428

794ab75caaf75c071f6775f075f5c26ed0f8ddad

4,049

py

Python

src/robotide/application/releasenotes.py

veryl-technologies/t24-tests-ide

16cd803895916a785c0e1fec3f71f9388c21edc9

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

null

src/robotide/application/releasenotes.py

veryl-technologies/t24-tests-ide

16cd803895916a785c0e1fec3f71f9388c21edc9

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

null

src/robotide/application/releasenotes.py

veryl-technologies/t24-tests-ide

16cd803895916a785c0e1fec3f71f9388c21edc9

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

null

# Copyright 2008-2012 Nokia Siemens Networks Oyj # # 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 wx from wx.lib.ClickableHtmlWindow import PyClickableHtmlWindow from robotide.version import VERSION from robotide.pluginapi import ActionInfo class ReleaseNotes(object): """Shows release notes of the current version. The release notes tab will automatically be shown once per release. The user can also view them on demand by selecting "Release Notes" from the help menu. """ def __init__(self, application): self.application = application settings = application.settings self.version_shown = settings.get('version_shown', '') self._view = None self.enable() def enable(self): self.application.frame.actions.register_action(ActionInfo('Help', 'Release Notes', self.show, doc='Show the release notes')) self.show_if_updated() def show_if_updated(self): if self.version_shown != VERSION: self.show() self.application.settings['version_shown'] = VERSION def show(self, event=None): if not self._view: self._view = self._create_view() self.application.frame.notebook.AddPage(self._view, "Release Notes", select=False) self.application.frame.notebook.show_tab(self._view) def bring_to_front(self): if self._view: self.application.frame.notebook.show_tab(self._view) def _create_view(self): panel = wx.Panel(self.application.frame.notebook) html_win = PyClickableHtmlWindow(panel, -1) html_win.SetStandardFonts() html_win.SetPage(WELCOME_TEXT + RELEASE_NOTES) sizer = wx.BoxSizer(wx.VERTICAL) sizer.Add(html_win, 1, wx.EXPAND|wx.ALL, border=8) panel.SetSizer(sizer) return panel WELCOME_TEXT = """ <h2>Welcome to use RIDE version %s</h2> <p>Thank you for using the Robot Framework IDE (RIDE).</p> <p>Visit RIDE on the web:</p> <ul> <li><a href="https://github.com/robotframework/RIDE"> RIDE project page on github</a></li> <li><a href="https://github.com/robotframework/RIDE/wiki/Installation-Instructions"> Installation instructions</a></li> <li><a href="https://github.com/robotframework/RIDE/wiki/Release-notes"> Release notes</a></li> </ul> """ % VERSION # *** DO NOT EDIT THE CODE BELOW MANUALLY *** # Release notes are updated automatically by package.py script whenever # a numbered distribution is created. RELEASE_NOTES = """ <h2>Release notes for 1.2.3</h2> <table border="1"> <tr> <td><p><b>ID</b></p></td> <td><p><b>Type</b></p></td> <td><p><b>Priority</b></p></td> <td><p><b>Summary</b></p></td> </tr> <tr> <td><a href="http://code.google.com/p/robotframework-ride/issues/detail?id=1290">Issue 1290</a></td> <td>Defect</td> <td>Medium</td> <td>RIDE runs not selected (with checkboxes) tests</td> </tr> <tr> <td><a href="http://code.google.com/p/robotframework-ride/issues/detail?id=1306">Issue 1306</a></td> <td>Defect</td> <td>Medium</td> <td>[RIDE 1.2.2 running on Python 2.7.5.] - Unable to insert cell in RIDE, if the TC contains FOR loop.</td> </tr> <tr> <td><a href="http://code.google.com/p/robotframework-ride/issues/detail?id=1307">Issue 1307</a></td> <td>Enhancement</td> <td>Medium</td> <td>Possibility to create new folder with right click</td> </tr> </table> <p>Altogether 3 issues.</p> """

34.313559

108

0.663621

794aba662fded38d8df0dd71a2abc06dceceb757

106

py

Python

lbry/__init__.py

SNOmad1/lbry-sdk

268decd6556619625151c574890eba9f8dc2c90d

[ "MIT" ]

2

2021-12-24T18:29:49.000Z

2021-12-26T02:04:57.000Z

lbry/__init__.py

SNOmad1/lbry-sdk

268decd6556619625151c574890eba9f8dc2c90d

[ "MIT" ]

null

lbry/__init__.py

SNOmad1/lbry-sdk

268decd6556619625151c574890eba9f8dc2c90d

[ "MIT" ]

null

__version__ = "0.102.0" version = tuple(map(int, __version__.split('.'))) # pylint: disable=invalid-name

35.333333

81

0.698113

794aba680c0c281a148a75220cc54e9ca3b83457

1,056

py

Python

scripts/examples/OpenMV/14-WiFi-Shield/http_client_ssl.py

jiskra/openmv

a0f321836f77f94d8118910598dcdb79eb784d58

[ "MIT" ]

1,761

2015-07-10T23:14:17.000Z

2022-03-30T07:49:49.000Z

scripts/examples/OpenMV/14-WiFi-Shield/http_client_ssl.py

jiskra/openmv

a0f321836f77f94d8118910598dcdb79eb784d58

[ "MIT" ]

487

2015-07-07T23:21:20.000Z

2022-03-30T17:13:22.000Z

scripts/examples/OpenMV/14-WiFi-Shield/http_client_ssl.py

jiskra/openmv

a0f321836f77f94d8118910598dcdb79eb784d58

[ "MIT" ]

882

2015-08-01T08:34:19.000Z

2022-03-30T07:36:23.000Z

# Simple HTTPS client example. import network, usocket, ussl # AP info SSID="" # Network SSID KEY="" # Network key PORT = 443 HOST = "www.google.com" # Init wlan module and connect to network print("Trying to connect... (may take a while)...") wlan = network.WINC() wlan.connect(SSID, key=KEY, security=wlan.WPA_PSK) # We should have a valid IP now via DHCP print(wlan.ifconfig()) # Get addr info via DNS addr = usocket.getaddrinfo(HOST, PORT)[0][4] print(addr) # Create a new socket and connect to addr client = usocket.socket(usocket.AF_INET, usocket.SOCK_STREAM) client.connect(addr) # Set timeout client.settimeout(3.0) client = ussl.wrap_socket(client, server_hostname=HOST) # Send HTTP request and recv response request = "GET / HTTP/1.1\r\n" request += "HOST: %s\r\n" request += "User-Agent: Mozilla/5.0\r\n" request += "Connection: keep-alive\r\n\r\n" # Add more headers if needed. client.write(request%(HOST)+"\r\n") response = client.read(1024) for l in response.split(b"\r\n"): print(l.decode()) # Close socket client.close()

21.55102

61

0.708333

794abb083955bc5d92be1f7fc8a92b93ac3b8745

3,994

py

Python

src/python/dxpy/templating/templates/python/parallelized/src/code.py

yesimon/dx-toolkit

c13a16d570a55bde7778d6db9268f5c3fca81d0f

[ "Apache-2.0" ]

null

src/python/dxpy/templating/templates/python/parallelized/src/code.py

yesimon/dx-toolkit

c13a16d570a55bde7778d6db9268f5c3fca81d0f

[ "Apache-2.0" ]

null

src/python/dxpy/templating/templates/python/parallelized/src/code.py

yesimon/dx-toolkit

c13a16d570a55bde7778d6db9268f5c3fca81d0f

[ "Apache-2.0" ]

null

#!/usr/bin/env python # DX_APP_WIZARD_NAME DX_APP_WIZARD_VERSION # Generated by dx-app-wizard. # # Parallelized execution pattern: Your app will generate multiple jobs # to perform some computation in parallel, followed by a final # "postprocess" stage that will perform any additional computations as # necessary. # # See https://wiki.dnanexus.com/Developer-Portal for documentation and # tutorials on how to modify this file. # # DNAnexus Python Bindings (dxpy) documentation: # http://autodoc.dnanexus.com/bindings/python/current/ import os import dxpy @dxpy.entry_point("postprocess") def postprocess(process_outputs): # Change the following to process whatever input this stage # receives. You may also want to copy and paste the logic to download # and upload files here as well if this stage receives file input # and/or makes file output. for output in process_outputs: pass return { "answer": "placeholder value" } @dxpy.entry_point("process") def process(input1): # Change the following to process whatever input this stage # receives. You may also want to copy and paste the logic to download # and upload files here as well if this stage receives file input # and/or makes file output. print(input1) return { "output": "placeholder value" } @dxpy.entry_point("main") def main(DX_APP_WIZARD_INPUT_SIGNATURE): DX_APP_WIZARD_INITIALIZE_INPUTDX_APP_WIZARD_DOWNLOAD_ANY_FILES # Split your work into parallel tasks. As an example, the # following generates 10 subjobs running with the same dummy # input. subjobs = [] for i in range(10): subjob_input = { "input1": True } subjobs.append(dxpy.new_dxjob(subjob_input, "process")) # The following line creates the job that will perform the # "postprocess" step of your app. We've given it an input field # that is a list of job-based object references created from the # "process" jobs we just created. Assuming those jobs have an # output field called "output", these values will be passed to the # "postprocess" job. Because these values are not ready until the # "process" jobs finish, the "postprocess" job WILL NOT RUN until # all job-based object references have been resolved (i.e. the # jobs they reference have finished running). # # If you do not plan to have the "process" jobs create output that # the "postprocess" job will require, then you can explicitly list # the dependencies to wait for those jobs to finish by setting the # "depends_on" field to the list of subjobs to wait for (it # accepts either dxpy handlers or string IDs in the list). We've # included this parameter in the line below as well for # completeness, though it is unnecessary if you are providing # job-based object references in the input that refer to the same # set of jobs. postprocess_job = dxpy.new_dxjob(fn_input={ "process_outputs": [subjob.get_output_ref("output") for subjob in subjobs] }, fn_name="postprocess", depends_on=subjobs) DX_APP_WIZARD_UPLOAD_ANY_FILES # If you would like to include any of the output fields from the # postprocess_job as the output of your app, you should return it # here using a job-based object reference. If the output field in # the postprocess function is called "answer", you can pass that # on here as follows: # # return { "app_output_field": postprocess_job.get_output_ref("answer"), ...} # # Tip: you can include in your output at this point any open # objects (such as files) which will be closed by a job that # finishes later. The system will check to make sure that the # output object is closed and will attempt to clone it out as # output into the parent container only after all subjobs have # finished. output = {} DX_APP_WIZARD_OUTPUT return output dxpy.run()

40.755102

125

0.710566

794abb215412ad2a470d84ea519053f7e0e39ef7

4,248

py

Python

src/transformers/models/electra/__init__.py

kct22aws/transformers

04cddaf402591e9f5bdb5f116a111d829a0ce4f4

[ "Apache-2.0" ]

5

2020-10-30T13:07:02.000Z

2021-03-17T12:18:30.000Z

src/transformers/models/electra/__init__.py

guang7400613/transformers

28e091430eea9e0d40839e56fd0d57aec262f5f9

[ "Apache-2.0" ]

1

2022-01-17T03:24:35.000Z

src/transformers/models/electra/__init__.py

guang7400613/transformers

28e091430eea9e0d40839e56fd0d57aec262f5f9

[ "Apache-2.0" ]

1

2022-02-08T19:37:39.000Z

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...file_utils import _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available _import_structure = { "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig"], "tokenization_electra": ["ElectraTokenizer"], } if is_tokenizers_available(): _import_structure["tokenization_electra_fast"] = ["ElectraTokenizerFast"] if is_torch_available(): _import_structure["modeling_electra"] = [ "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "ElectraForCausalLM", "ElectraForMaskedLM", "ElectraForMultipleChoice", "ElectraForPreTraining", "ElectraForQuestionAnswering", "ElectraForSequenceClassification", "ElectraForTokenClassification", "ElectraModel", "ElectraPreTrainedModel", "load_tf_weights_in_electra", ] if is_tf_available(): _import_structure["modeling_tf_electra"] = [ "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFElectraForMaskedLM", "TFElectraForMultipleChoice", "TFElectraForPreTraining", "TFElectraForQuestionAnswering", "TFElectraForSequenceClassification", "TFElectraForTokenClassification", "TFElectraModel", "TFElectraPreTrainedModel", ] if is_flax_available(): _import_structure["modeling_flax_electra"] = [ "FlaxElectraForMaskedLM", "FlaxElectraForMultipleChoice", "FlaxElectraForPreTraining", "FlaxElectraForQuestionAnswering", "FlaxElectraForSequenceClassification", "FlaxElectraForTokenClassification", "FlaxElectraModel", "FlaxElectraPreTrainedModel", ] if TYPE_CHECKING: from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig from .tokenization_electra import ElectraTokenizer if is_tokenizers_available(): from .tokenization_electra_fast import ElectraTokenizerFast if is_torch_available(): from .modeling_electra import ( ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, ElectraForCausalLM, ElectraForMaskedLM, ElectraForMultipleChoice, ElectraForPreTraining, ElectraForQuestionAnswering, ElectraForSequenceClassification, ElectraForTokenClassification, ElectraModel, ElectraPreTrainedModel, load_tf_weights_in_electra, ) if is_tf_available(): from .modeling_tf_electra import ( TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, TFElectraForMaskedLM, TFElectraForMultipleChoice, TFElectraForPreTraining, TFElectraForQuestionAnswering, TFElectraForSequenceClassification, TFElectraForTokenClassification, TFElectraModel, TFElectraPreTrainedModel, ) if is_flax_available(): from .modeling_flax_electra import ( FlaxElectraForMaskedLM, FlaxElectraForMultipleChoice, FlaxElectraForPreTraining, FlaxElectraForQuestionAnswering, FlaxElectraForSequenceClassification, FlaxElectraForTokenClassification, FlaxElectraModel, FlaxElectraPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

34.258065

118

0.703154

794abb80a002be7f7bff494974c6d6c89f0ff17c

11,493

py

Python

tests/test_elements.py

gatling-nrl/scikit-fem

04730d80d612470b7e802eed4c21dd96b89cef61

[ "BSD-3-Clause" ]

1

2019-12-07T15:28:13.000Z

tests/test_elements.py

gatling-nrl/scikit-fem

04730d80d612470b7e802eed4c21dd96b89cef61

[ "BSD-3-Clause" ]

3

2022-01-07T00:56:47.000Z

2022-01-12T20:06:34.000Z

tests/test_elements.py

gdmcbain/scikit-fem

73890816c2142385abf4a9ffcd8d233e2d25e865

[ "BSD-3-Clause" ]

null

from unittest import TestCase, main import numpy as np from numpy.testing import assert_allclose, assert_array_equal import pytest from skfem.element import ( ElementHex1, ElementHexS2, ElementLineP0, ElementLineP1, ElementLineP2, ElementLinePp, ElementLineMini, ElementQuad0, ElementQuad1, ElementQuad2, ElementQuadP, ElementQuadRT0, ElementQuadS2, ElementTetMini, ElementTetP0, ElementTetP1, ElementTetP2, ElementTriMini, ElementTriP0, ElementTriP1, ElementTriP2, ElementTriP3, ElementTriP4, ElementTriRT0, ElementVectorH1, ElementHex2, ElementQuadBFS, ElementTriCR, ElementTriCCR, ElementTetCR, ElementTetCCR, ElementTriHermite, ElementTriMorley, ElementTriArgyris, ElementTriDG, ElementTetDG, ElementQuadDG, ElementQuadP, ElementHexDG, ElementWedge1, ) from skfem.mesh import MeshHex, MeshLine, MeshQuad, MeshTet, MeshTri from skfem.assembly import InteriorBasis, Functional from skfem.mapping import MappingAffine class TestNodality(TestCase): """Test for Element.doflocs.""" elems = [ ElementLineP0(), ElementLineP1(), ElementLineP2(), ElementLinePp(1), ElementLinePp(3), ElementLineMini(), ElementTriP0(), ElementTriP1(), ElementTriP2(), ElementTriP3(), ElementTriP4(), ElementTriMini(), ElementQuad0(), ElementQuad1(), ElementQuad2(), ElementQuadS2(), ElementQuadP(1), ElementQuadP(3), ElementTetP0(), ElementTetP1(), ElementTetP2(), ElementTetMini(), ElementHex1(), ElementHexS2(), ElementHex2(), ElementTetCR(), ElementTetCCR(), ElementTriCR(), ElementTriCCR(), ElementWedge1(), ] def runTest(self): for e in self.elems: N = e.doflocs.shape[0] Ih = np.zeros((N, N)) for itr in range(N): Ih[itr] = e.lbasis(e.doflocs.T, itr)[0] # Remove nan-rows: test nodality only on non-nan doflocs. # # Some elements, such as ElementTriMini might have a combination # of nodal dofs and non-nodal dofs. # # Nodal dof is defined so that there exists a point where the # corresponding basis function is one, and other basis functions # are zero. Non-nodal dof does not satisfy this property. ix = np.isnan(np.sum(Ih, axis=1)) Nnan = np.sum(ix) ixs = np.nonzero(~ix)[0] Ih = Ih[ixs].T[ixs].T assert_allclose(Ih, np.eye(N - Nnan), atol=1e-13, err_msg="{}".format(type(e))) class TestNodalityTriRT0(TestCase): elem = ElementTriRT0() def runTest(self): e = self.elem N = e.doflocs.shape[0] Ih = np.zeros((N, N)) normals = np.array([[0., -1.], [1 / np.sqrt(2), 1 / np.sqrt(2)], [-1., 0.]]).T for itr in range(N): # calculate integral of normal component over edge A = np.sum(e.lbasis(e.doflocs.T, itr)[0] * normals, axis=0) n = np.array([1., np.sqrt(2), 1.]) Ih[itr] = A * n assert_allclose(Ih, np.eye(N), err_msg="{}".format(type(e))) class TestNodalityQuadRT0(TestCase): elem = ElementQuadRT0() def runTest(self): e = self.elem N = e.doflocs.shape[0] Ih = np.zeros((N, N)) normals = np.array([[0., -1.], [1., 0.], [0., 1.], [-1., 0.]]).T for itr in range(N): # calculate integral of normal component over edge A = np.sum(e.lbasis(e.doflocs.T, itr)[0] * normals, axis=0) n = np.ones(4) Ih[itr] = A * n assert_allclose(Ih, np.eye(N), err_msg="{}".format(type(e))) class TestComposite(TestCase): def runTest(self): from skfem.element.element_composite import ElementComposite self.check_equivalence( ElementComposite(ElementTriP1(), ElementTriP1()), ElementVectorH1(ElementTriP1()) ) def check_equivalence(self, ec, ev): X = np.array([[0.125, 0.1111], [0.0555, 0.6]]) m = MeshTri.init_refdom() mapping = MappingAffine(m) for k in range(6): for i in [0, 1]: # accessing i'th component looks slightly different if ec.gbasis(mapping, X, k)[i].is_zero(): continue assert_array_equal( ev.gbasis(mapping, X, k)[0].value[i], ec.gbasis(mapping, X, k)[i].value ) for j in [0, 1]: assert_array_equal( ev.gbasis(mapping, X, k)[0].grad[i][j], ec.gbasis(mapping, X, k)[i].grad[j] ) class TestCompositeMul(TestComposite): def runTest(self): self.check_equivalence( ElementTriP1() * ElementTriP1(), ElementVectorH1(ElementTriP1()) ) class TestCompatibilityWarning(TestCase): meshes = [ MeshTet, MeshQuad, MeshHex, MeshLine, ] elem = ElementTriP1 def runTest(self): for m in self.meshes: def init_incompatible(): return InteriorBasis(m(), self.elem()) self.assertRaises(ValueError, init_incompatible) class TestDerivatives(TestCase): """Test values of derivatives.""" elems = [ ElementLineP0(), ElementLineP1(), ElementLineP2(), ElementLineMini(), ElementTriP0(), ElementTriP1(), ElementTriP2(), ElementTriP3(), ElementTriP4(), ElementTriMini(), ElementQuad0(), ElementQuad1(), ElementQuad2(), ElementQuadS2(), ElementTetP0(), ElementTetP1(), ElementTetP2(), ElementTetMini(), ElementHex1(), ElementHexS2(), ElementHex2(), ElementTriCR(), ElementTriCCR(), ElementTetCR(), ElementTetCCR(), ElementWedge1(), ] def runTest(self): for elem in self.elems: eps = 1e-6 for base in [0., .3, .6, .9]: if elem.dim == 1: y = np.array([[base, base + eps]]) elif elem.dim == 2: y = np.array([[base, base + eps, base, base], [base, base, base, base + eps]]) elif elem.dim == 3: y = np.array([[base, base + eps, base, base, base, base], [base, base, base, base + eps, base, base], [base, base, base, base, base, base + eps]]) i = 0 while True: try: out = elem.lbasis(y, i) except ValueError: break diff = (out[0][1] - out[0][0]) / eps errmsg = 'x-derivative for {}th bfun failed for {}' self.assertAlmostEqual(diff, out[1][0][0], delta=1e-3, msg=errmsg.format(i, elem)) if elem.dim > 1: diff = (out[0][3] - out[0][2]) / eps errmsg = 'y-derivative for {}th bfun failed for {}' self.assertAlmostEqual(diff, out[1][1][3], delta=1e-3, msg=errmsg.format(i, elem)) if elem.dim == 3: diff = (out[0][5] - out[0][4]) / eps errmsg = 'z-derivative for {}th bfun failed for {}' self.assertAlmostEqual(diff, out[1][2][4], delta=1e-3, msg=errmsg.format(i, elem)) i += 1 class TestPartitionofUnity(TestCase): """Test that elements form a partition of unity.""" elems = [ ElementLineP1(), ElementLineP2(), ElementTriP1(), ElementTriP2(), ElementTriP3(), ElementTriP4(), ElementQuad1(), ElementQuad2(), ElementQuadS2(), ElementTetP1(), ElementTetP2(), ElementHex1(), ElementHexS2(), ElementHex2(), ElementTetCR(), ElementTetCCR(), ElementTriCR(), ElementTriCCR(), ElementWedge1(), ] def runTest(self): for elem in self.elems: if elem.dim == 1: y = np.array([[.15]]) elif elem.dim == 2: y = np.array([[.15], [.15]]) elif elem.dim == 3: y = np.array([[.15], [.15], [.15]]) out = 0. for i in range(elem.doflocs.shape[0]): out += elem.lbasis(y, i)[0][0] self.assertAlmostEqual(out, 1, msg='failed for {}'.format(elem)) class TestElementLinePp(TestCase): def test_p_less_than_1_error(self): """Tests that exception is thrown when initializing with p < 1.""" with self.assertRaises(ValueError): ElementLinePp(0) class TestElementQuadBFS(TestCase): def test_throw_index_error(self): """Tests that exception is thrown when i % 4 not in (0, 1, 2, 3).""" element = ElementQuadBFS() with self.assertRaises(ValueError): element.gdof(0, 0, -1) with self.assertRaises(ValueError): element.gdof(0, 0, 16) @pytest.mark.parametrize( "m,e,edg", [ (MeshTri().refined(), ElementTriP1(), ElementTriDG), (MeshTri().refined(), ElementTriP2(), ElementTriDG), (MeshTet().refined(), ElementTetP1(), ElementTetDG), (MeshTet().refined(), ElementTetP2(), ElementTetDG), (MeshTri().refined(), ElementTriArgyris(), ElementTriDG), (MeshTri().refined(), ElementTriMorley(), ElementTriDG), (MeshTri().refined(), ElementTriHermite(), ElementTriDG), (MeshHex().refined(), ElementHex1(), ElementHexDG), (MeshQuad().refined(), ElementQuad1(), ElementQuadDG), ] ) def test_dg_element(m, e, edg): edg = edg(e) @Functional def square(w): return w['random'] ** 2 basis = InteriorBasis(m, e) basisdg = InteriorBasis(m, edg) assert_allclose( square.assemble( basis, random=basis.interpolate( basis.zeros() + 1)), square.assemble( basisdg, random=basisdg.interpolate( basisdg.zeros() + 1)), ) @pytest.mark.parametrize( "e,edg", [ (ElementTriP1(), ElementTriDG), (ElementTetP2(), ElementTetDG), (ElementTriArgyris(), ElementTriDG), (ElementQuad1(), ElementQuadDG), (ElementQuadP(4), ElementQuadDG), (ElementHex2(), ElementHexDG), ] ) def test_initialize_dg_composite_elements(e, edg): E = edg(e) * e

28.876884

78

0.507178

794abbe2fa47dc251d96ee54829be7ee1b916ab6

305

py

Python

30_of_codes/Day 3 - Intro to Conditional Statements/2-conditionalStatements.py

Kani712/Hacker_Rank

d208ef88ac33c056b89785688cf43d90275d00da

[ "MIT" ]

null

30_of_codes/Day 3 - Intro to Conditional Statements/2-conditionalStatements.py

Kani712/Hacker_Rank

d208ef88ac33c056b89785688cf43d90275d00da

[ "MIT" ]

null

30_of_codes/Day 3 - Intro to Conditional Statements/2-conditionalStatements.py

Kani712/Hacker_Rank

d208ef88ac33c056b89785688cf43d90275d00da

[ "MIT" ]

null

import math import os import random import re import sys if __name__ == '__main__': N = int(input()) if N % 2 != 0: print("Weird") if N % 2 == 0 and N in range(2, 6): print("Not Weird") if N % 2 == 0 and N in range(6, 21): print("Weird") if N % 2 == 0 and N > 20: print("Not Weird")

16.944444

36

0.567213

794abd0ba787033014c9661e708d6f864f34373b

655

py

Python

src/python-version/src/main.py

mopsfl/RLO-Jail-Time-Converter

0d5966eb77f702b44e2a8fd2c72aa63355595f10

[ "Apache-2.0" ]

1

2021-11-17T20:59:22.000Z

src/python-version/src/main.py

mopsfl/RLO-Jail-Time-Converter

0d5966eb77f702b44e2a8fd2c72aa63355595f10

[ "Apache-2.0" ]

null

src/python-version/src/main.py

mopsfl/RLO-Jail-Time-Converter

0d5966eb77f702b44e2a8fd2c72aa63355595f10

[ "Apache-2.0" ]

null

import os print("mopsfl - Real Life Online - Jail Time Convert - v.0.1\n") try: months = int(input("Enter the months: ")) method = input("Enter to what you want to convert it (hour, day): ") if method == "hour": print("\nResults: " + str(months/60) + " hour(s)") print("Results rounded: " + str(round(months/60)) + " hour(s)\n\n") elif method == "day": print("\nResults: " + str(months/1440) + " day(s)") print("Results rounded: " + str(round(months/1440)) + " day(s)\n\n") else: print("Invalid method") os.system('pause') except: print("Invalid input") os.system('pause')

27.291667

76

0.564885

794abd1670254d8b032ba59ef5f59d6dfd13f283

1,689

py

Python

google/cloud/essentialcontacts/v1/essentialcontacts-v1-py/google/cloud/essential_contacts_v1/__init__.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

7

2021-02-21T10:39:41.000Z

2021-12-07T07:31:28.000Z

google/cloud/essentialcontacts/v1/essentialcontacts-v1-py/google/cloud/essential_contacts_v1/__init__.py

googleapis/googleapis-gen

d84824c78563d59b0e58d5664bfaa430e9ad7e7a

[ "Apache-2.0" ]

6

2021-02-02T23:46:11.000Z

2021-11-15T01:46:02.000Z

google/cloud/essentialcontacts/v1/essentialcontacts-v1-py/google/cloud/essential_contacts_v1/__init__.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 .services.essential_contacts_service import EssentialContactsServiceClient from .services.essential_contacts_service import EssentialContactsServiceAsyncClient from .types.enums import NotificationCategory from .types.enums import ValidationState from .types.service import ComputeContactsRequest from .types.service import ComputeContactsResponse from .types.service import Contact from .types.service import CreateContactRequest from .types.service import DeleteContactRequest from .types.service import GetContactRequest from .types.service import ListContactsRequest from .types.service import ListContactsResponse from .types.service import SendTestMessageRequest from .types.service import UpdateContactRequest __all__ = ( 'EssentialContactsServiceAsyncClient', 'ComputeContactsRequest', 'ComputeContactsResponse', 'Contact', 'CreateContactRequest', 'DeleteContactRequest', 'EssentialContactsServiceClient', 'GetContactRequest', 'ListContactsRequest', 'ListContactsResponse', 'NotificationCategory', 'SendTestMessageRequest', 'UpdateContactRequest', 'ValidationState', )

34.469388

84

0.818828

794abe94168e7c232d9581f529ed568b88ececf3

491

py

Python

plotly/validators/layout/mapbox/layer/symbol/_icon.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/layout/mapbox/layer/symbol/_icon.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/layout/mapbox/layer/symbol/_icon.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

import _plotly_utils.basevalidators class IconValidator(_plotly_utils.basevalidators.StringValidator): def __init__( self, plotly_name='icon', parent_name='layout.mapbox.layer.symbol', **kwargs ): super(IconValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop('edit_type', 'plot'), role=kwargs.pop('role', 'style'), **kwargs )

25.842105

66

0.602851

794abec225ffcfad4f89aa5eab00f36dde0406ec

9,142

py

Python

salika/views/store_views.py

BarisSari/django_crud

ce9586c10da2f865d29d9a18e9ff5582abe5e3a0

[ "MIT" ]

null

salika/views/store_views.py

BarisSari/django_crud

ce9586c10da2f865d29d9a18e9ff5582abe5e3a0

[ "MIT" ]

null

salika/views/store_views.py

BarisSari/django_crud

ce9586c10da2f865d29d9a18e9ff5582abe5e3a0

[ "MIT" ]

null

from django.views.generic.detail import DetailView from django.views.generic.edit import CreateView, UpdateView, DeleteView from django.views.generic.list import ListView from ..models import Store from ..forms import StoreForm from django.urls import reverse_lazy from django.urls import reverse from django.http import Http404 class StoreListView(ListView): model = Store template_name = "salika/store_list.html" paginate_by = 20 context_object_name = "store_list" allow_empty = True page_kwarg = 'page' paginate_orphans = 0 def __init__(self, **kwargs): return super(StoreListView, self).__init__(**kwargs) def dispatch(self, *args, **kwargs): return super(StoreListView, self).dispatch(*args, **kwargs) def get(self, request, *args, **kwargs): return super(StoreListView, self).get(request, *args, **kwargs) def get_queryset(self): return super(StoreListView, self).get_queryset() def get_allow_empty(self): return super(StoreListView, self).get_allow_empty() def get_context_data(self, *args, **kwargs): ret = super(StoreListView, self).get_context_data(*args, **kwargs) return ret def get_paginate_by(self, queryset): return super(StoreListView, self).get_paginate_by(queryset) def get_context_object_name(self, object_list): return super(StoreListView, self).get_context_object_name(object_list) def paginate_queryset(self, queryset, page_size): return super(StoreListView, self).paginate_queryset(queryset, page_size) def get_paginator(self, queryset, per_page, orphans=0, allow_empty_first_page=True): return super(StoreListView, self).get_paginator(queryset, per_page, orphans=0, allow_empty_first_page=True) def render_to_response(self, context, **response_kwargs): return super(StoreListView, self).render_to_response(context, **response_kwargs) def get_template_names(self): return super(StoreListView, self).get_template_names() class StoreDetailView(DetailView): model = Store template_name = "salika/store_detail.html" context_object_name = "store" slug_field = 'slug' slug_url_kwarg = 'slug' pk_url_kwarg = 'pk' def __init__(self, **kwargs): return super(StoreDetailView, self).__init__(**kwargs) def dispatch(self, *args, **kwargs): return super(StoreDetailView, self).dispatch(*args, **kwargs) def get(self, request, *args, **kwargs): return super(StoreDetailView, self).get(request, *args, **kwargs) def get_object(self, queryset=None): return super(StoreDetailView, self).get_object(queryset) def get_queryset(self): return super(StoreDetailView, self).get_queryset() def get_slug_field(self): return super(StoreDetailView, self).get_slug_field() def get_context_data(self, **kwargs): ret = super(StoreDetailView, self).get_context_data(**kwargs) return ret def get_context_object_name(self, obj): return super(StoreDetailView, self).get_context_object_name(obj) def render_to_response(self, context, **response_kwargs): return super(StoreDetailView, self).render_to_response(context, **response_kwargs) def get_template_names(self): return super(StoreDetailView, self).get_template_names() class StoreCreateView(CreateView): model = Store form_class = StoreForm # fields = ['store_id', 'manager_staff', 'address', 'last_update'] template_name = "salika/store_create.html" success_url = reverse_lazy("store_list") def __init__(self, **kwargs): return super(StoreCreateView, self).__init__(**kwargs) def dispatch(self, request, *args, **kwargs): return super(StoreCreateView, self).dispatch(request, *args, **kwargs) def get(self, request, *args, **kwargs): return super(StoreCreateView, self).get(request, *args, **kwargs) def post(self, request, *args, **kwargs): return super(StoreCreateView, self).post(request, *args, **kwargs) def get_form_class(self): return super(StoreCreateView, self).get_form_class() def get_form(self, form_class=None): return super(StoreCreateView, self).get_form(form_class) def get_form_kwargs(self, **kwargs): return super(StoreCreateView, self).get_form_kwargs(**kwargs) def get_initial(self): return super(StoreCreateView, self).get_initial() def form_invalid(self, form): return super(StoreCreateView, self).form_invalid(form) def form_valid(self, form): obj = form.save(commit=False) obj.save() return super(StoreCreateView, self).form_valid(form) def get_context_data(self, **kwargs): ret = super(StoreCreateView, self).get_context_data(**kwargs) return ret def render_to_response(self, context, **response_kwargs): return super(StoreCreateView, self).render_to_response(context, **response_kwargs) def get_template_names(self): return super(StoreCreateView, self).get_template_names() def get_success_url(self): return reverse("salika:store_detail", args=(self.object.pk,)) class StoreUpdateView(UpdateView): model = Store form_class = StoreForm # fields = ['store_id', 'manager_staff', 'address', 'last_update'] template_name = "salika/store_update.html" initial = {} slug_field = 'slug' slug_url_kwarg = 'slug' pk_url_kwarg = 'pk' context_object_name = "store" def __init__(self, **kwargs): return super(StoreUpdateView, self).__init__(**kwargs) def dispatch(self, *args, **kwargs): return super(StoreUpdateView, self).dispatch(*args, **kwargs) def get(self, request, *args, **kwargs): return super(StoreUpdateView, self).get(request, *args, **kwargs) def post(self, request, *args, **kwargs): return super(StoreUpdateView, self).post(request, *args, **kwargs) def get_object(self, queryset=None): return super(StoreUpdateView, self).get_object(queryset) def get_queryset(self): return super(StoreUpdateView, self).get_queryset() def get_slug_field(self): return super(StoreUpdateView, self).get_slug_field() def get_form_class(self): return super(StoreUpdateView, self).get_form_class() def get_form(self, form_class=None): return super(StoreUpdateView, self).get_form(form_class) def get_form_kwargs(self, **kwargs): return super(StoreUpdateView, self).get_form_kwargs(**kwargs) def get_initial(self): return super(StoreUpdateView, self).get_initial() def form_invalid(self, form): return super(StoreUpdateView, self).form_invalid(form) def form_valid(self, form): obj = form.save(commit=False) obj.save() return super(StoreUpdateView, self).form_valid(form) def get_context_data(self, **kwargs): ret = super(StoreUpdateView, self).get_context_data(**kwargs) return ret def get_context_object_name(self, obj): return super(StoreUpdateView, self).get_context_object_name(obj) def render_to_response(self, context, **response_kwargs): return super(StoreUpdateView, self).render_to_response(context, **response_kwargs) def get_template_names(self): return super(StoreUpdateView, self).get_template_names() def get_success_url(self): return reverse("salika:store_detail", args=(self.object.pk,)) class StoreDeleteView(DeleteView): model = Store template_name = "salika/store_delete.html" slug_field = 'slug' slug_url_kwarg = 'slug' pk_url_kwarg = 'pk' context_object_name = "store" def __init__(self, **kwargs): return super(StoreDeleteView, self).__init__(**kwargs) def dispatch(self, *args, **kwargs): return super(StoreDeleteView, self).dispatch(*args, **kwargs) def get(self, request, *args, **kwargs): raise Http404 def post(self, request, *args, **kwargs): return super(StoreDeleteView, self).post(request, *args, **kwargs) def delete(self, request, *args, **kwargs): return super(StoreDeleteView, self).delete(request, *args, **kwargs) def get_object(self, queryset=None): return super(StoreDeleteView, self).get_object(queryset) def get_queryset(self): return super(StoreDeleteView, self).get_queryset() def get_slug_field(self): return super(StoreDeleteView, self).get_slug_field() def get_context_data(self, **kwargs): ret = super(StoreDeleteView, self).get_context_data(**kwargs) return ret def get_context_object_name(self, obj): return super(StoreDeleteView, self).get_context_object_name(obj) def render_to_response(self, context, **response_kwargs): return super(StoreDeleteView, self).render_to_response(context, **response_kwargs) def get_template_names(self): return super(StoreDeleteView, self).get_template_names() def get_success_url(self): return reverse("salika:store_list")

34.2397

115

0.698644

794abf88eb9add26c41f62c631e3b661446de974

16,233

py

Python

mindquantum/core/parameterresolver/parameterresolver.py

mindspore-ai/mindquantum

785150e6b44bb79b37f2fa4a3d86edc0ab3c83ce

[ "Apache-2.0" ]

13

2021-06-04T00:47:53.000Z

2022-03-20T14:30:38.000Z

mindquantum/core/parameterresolver/parameterresolver.py

mindspore-ai/mindquantum

785150e6b44bb79b37f2fa4a3d86edc0ab3c83ce

[ "Apache-2.0" ]

null

mindquantum/core/parameterresolver/parameterresolver.py

mindspore-ai/mindquantum

785150e6b44bb79b37f2fa4a3d86edc0ab3c83ce

[ "Apache-2.0" ]

4

2022-01-17T02:43:34.000Z

2022-02-20T16:03:44.000Z

# -*- coding: utf-8 -*- # Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Parameter resolver.""" from collections.abc import Iterable from copy import deepcopy import numpy as np import sympy as sp from mindquantum import mqbackend as mb from mindquantum.utils.type_value_check import _num_type class ParameterResolver(dict): """ A ParameterRsolver can set the parameter of parameterized quantum gate or parameterized quantum circuit. By specific which part of parameters needs to calculate gradient, the PQC operator can only calculate gradient of these parameters. Args: data (dict): initial parameter names and its values. Default: None. Examples: >>> from mindquantum.core import ParameterResolver >>> pr = ParameterResolver({'a': 0.3}) >>> pr['b'] = 0.5 >>> pr.no_grad_part('a') >>> pr *= 2 >>> pr {'a': 0.6, 'b': 1.0} >>> pr.no_grad_parameters {'a'} """ def __init__(self, data=None): if data is None: data = {} if not isinstance(data, (dict, ParameterResolver)): raise TypeError("Data require a dict or a ParameterResolver, but get {}!".format(type(data))) for k, v in data.items(): if not isinstance(k, str): raise TypeError("Parameter name should be a string, but get {}!".format(type(k))) if not isinstance(v, _num_type): raise TypeError("Require a number, but get {}, which is {}!".format(v, type(v))) super(ParameterResolver, self).__init__(data) self.no_grad_parameters = set() self.requires_grad_parameters = set(self.params_name) def get_cpp_obj(self): """Get cpp obj of this parameter resolver""" return mb.parameter_resolver(self, self.no_grad_parameters, self.requires_grad_parameters) def __setitem__(self, keys, values): """ Set parameter or as list of parameters of this parameter resolver. By default, the parameter you set requires gradient. Args: keys (Union[str, list[str]]): The name of parameters. values (Union[number, list[number]]): The value of parameters. Raises: TypeError: If the key that you set is not a string or a iterable of string. """ if isinstance(keys, str): if not isinstance(values, _num_type): raise TypeError("Parameter value should be a number, but get {}, which is {}!".format( values, type(values))) super().__setitem__(keys, values) self.requires_grad_parameters.add(keys) elif isinstance(keys, Iterable): if not isinstance(values, Iterable): raise ValueError("Values should be iterable.") if len(values) != len(keys): raise ValueError("Size of keys and values do not match.") for i, k in enumerate(keys): self.__setitem__(k, values[i]) else: raise TypeError("Parameter name should be a string, but get {}!".format(type(keys))) def __add__(self, pr): """ Add a parameter resolver with other parameter. Returns: ParameterResolver, parameter resolver after adding. Args: pr (ParameterResolver): The parameter resolver need to add. Examples: >>> from mindquantum import ParameterResolver >>> pr1 = ParameterResolver({'a': 1}) >>> pr2 = ParameterResolver({'a': 2, 'b': 3}) >>> (pr1 + pr2).expression() 3*a + 3*b """ if not isinstance(pr, ParameterResolver): raise ValueError('Require a parameter resolver, but get {}.'.format(type(pr))) res = self * 1 pr = pr * 1 for k, v in pr.items(): if k in res: res[k] += v pr[k] = res[k] res.update(pr) return res def __sub__(self, pr): """ Subtraction a parameter resolver with other parameter. Returns: :class:`mindquantum.core.parameterresolver.ParameterResolver` Args: pr (ParameterResolver): The parameter resolver need to subtract. Examples: >>> from mindquantum import ParameterResolver >>> pr1 = ParameterResolver({'a': 1}) >>> pr2 = ParameterResolver({'a': 2, 'b': 3}) >>> (pr1 - pr2).expression() -a - 3*b """ return self + (-1 * pr) def __neg__(self): """ Get the negative version of this parameter resolver. Returns: ParameterResolver, the negative version. Examples: >>> from mindquantum import ParameterResolver >>> pr1 = ParameterResolver({'a': 1}) >>> (-pr1).expression() -a """ return -1 * self def __imul__(self, num): """ Parameter support inplace multiply. Returns: :class:`mindquantum.core.parameterresolver.ParameterResolver` Args: num (number): Multiply factor. Examples: >>> from mindquantum import ParameterResolver >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr *= 2 >>> pr {'a': 2, 'b': 4} """ no_grad_parameters = deepcopy(self.no_grad_parameters) requires_grad_parameters = deepcopy(self.requires_grad_parameters) for k in self.keys(): self[k] = self[k] * num self.no_grad_parameters = no_grad_parameters self.requires_grad_parameters = requires_grad_parameters return self def __mul__(self, num): """ Multiply num with every value of parameter resolver. Returns: :class:`mindquantum.core.parameterresolver.ParameterResolver` Args: num (number): Multiply factor. Examples: >>> from mindquantum import ParameterResolver >>> pr1 = ParameterResolver({'a': 1, 'b': 2}) >>> pr2 = pr1 * 2 >>> pr2 {'a': 2, 'b': 4} """ no_grad_parameters = deepcopy(self.no_grad_parameters) requires_grad_parameters = deepcopy(self.requires_grad_parameters) out = deepcopy(self) out *= num out.no_grad_parameters = no_grad_parameters out.requires_grad_parameters = requires_grad_parameters return out def __rmul__(self, num): """ See :class:`mindquantum.core.parameterresolver.ParameterResolver.__mul__`. """ return self.__mul__(num) def __eq__(self, other): _check_pr_type(other) no_grad_eq = self.no_grad_parameters == other.no_grad_parameters requires_grad_eq = self.requires_grad_parameters == other.requires_grad_parameters return super().__eq__(other) and no_grad_eq and requires_grad_eq @property def params_name(self): """ Get the parameters name. Returns: list, a list of parameters name. Examples: >>> from mindquantum import ParameterResolver >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.params_name ['a', 'b'] """ return list(self.keys()) @property def para_value(self): """ Get the parameters value. Returns: list, a list of parameters value. Examples: >>> from mindquantum import ParameterResolver >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.para_value [1, 2] """ return list(self.values()) def requires_grad(self): """ Set all parameters of this parameter resolver to require gradient calculation. Inplace operation. Returns: ParameterResolver, the parameter resolver itself. Examples: >>> from mindquantum import ParameterResolver >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.no_grad_part('a') >>> pr.requires_grad() >>> pr.requires_grad_parameters {'a', 'b'} """ self.no_grad_parameters = set() self.requires_grad_parameters = set(self.params_name) return self def no_grad(self): """ Set all parameters to not require gradient calculation. Inplace operation. Returns: ParameterResolver, the parameter resolver itself. Examples: >>> from mindquantum import ParameterResolver >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.no_grad() >>> pr.requires_grad_parameters set() """ self.no_grad_parameters = set(self.params_name) self.requires_grad_parameters = set() return self def requires_grad_part(self, *names): """ Set part of parameters that requires grad. Inplace operation. Args: names (tuple[str]): Parameters that requires grad. Returns: ParameterResolver, the parameter resolver itself. Examples: >>> from mindquantum import ParameterResolver >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.no_grad() >>> pr.requires_grad_part('a') >>> pr.requires_grad_parameters {'a'} """ for name in names: if not isinstance(name, str): raise TypeError("name should be a string, but get {}!".format(type(name))) if name not in self: raise KeyError("Parameter {} not in this parameter resolver!".format(name)) while name in self.no_grad_parameters: self.no_grad_parameters.remove(name) while name not in self.requires_grad_parameters: self.requires_grad_parameters.add(name) return self def no_grad_part(self, *names): """ Set part of parameters that not requires grad. Args: names (tuple[str]): Parameters that not requires grad. Returns: ParameterResolver, the parameter resolver itself. Examples: >>> from mindquantum import ParameterResolver >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.no_grad_part('a') >>> pr.requires_grad_parameters {'b'} """ for name in names: if not isinstance(name, str): raise TypeError("name should be a string, but get {}!".format(type(name))) if name not in self: raise KeyError("Parameter {} not in this parameter resolver!".format(name)) while name not in self.no_grad_parameters: self.no_grad_parameters.add(name) while name in self.requires_grad_parameters: self.requires_grad_parameters.remove(name) return self def update(self, others): """ Update this parameter resolver with other parameter resolver. Args: others (ParameterResolver): other parameter resolver. Raises: ValueError: If some parameters require grad and not require grad in other parameter resolver and vice versa. Examples: >>> from mindquantum import ParameterResolver >>> pr1 = ParameterResolver({'a': 1}) >>> pr2 = ParameterResolver({'b': 2}) >>> pr2.no_grad() >>> pr1.update(pr2) >>> pr1 {'a': 1, 'b': 2} >>> pr1.no_grad_parameters {'b'} """ _check_pr_type(others) super().update(others) conflict = (self.no_grad_parameters & others.requires_grad_parameters) | (others.no_grad_parameters & self.requires_grad_parameters) if conflict: raise ValueError("Parameter conflict, {} require grad in some parameter \ resolver and not require grad in other parameter resolver ".format(conflict)) self.no_grad_parameters.update(others.no_grad_parameters) self.requires_grad_parameters.update(others.requires_grad_parameters) def expression(self): """ Get the expression of this parameter resolver. Returns: sympy.Expr, the symbol expression of this parameter resolver. Examples: >>> from mindquantum.core.parameterresolver import ParameterResolver as PR >>> pr = PR({'a' : 2, 'b' : 0.3}) >>> pr.expression() 2*a + 0.3*b """ res = 0 for k, v in self.items(): res += sp.Symbol(k) * v return res def conjugate(self): """ Get the conjugate of the parameter resolver. Returns: ParameterResolver, the conjugate version of this parameter resolver. Examples: >>> from mindquantum.core.parameterresolver import ParameterResolver as PR >>> pr = PR({'a' : 1, 'b': 1j}) >>> pr.conjugate().expression() a - 1.0*I*b """ out = 1 * self for k, v in out.items(): out[k] = np.conj(v) return out def combination(self, pr): """ Apply linear combination between this parameter resolver with input pr. Args: pr (Union[dict, ParameterResolver]): The parameter resolver you want to do linear combination. Returns: numbers.Number, the combination result. Examples: >>> from mindquantum import ParameterResolver >>> pr1 = ParameterResolver({'a': 1, 'b': 2}) >>> pr2 = ParameterResolver({'a': 2, 'b': 3}) >>> pr1.combination(pr2) 8 """ if not isinstance(pr, (ParameterResolver, dict)): raise ValueError('Require a parameter resolver or a dict, but get {}.'.format(type(pr))) res = 0 for k, v in self.items(): if k not in pr: raise KeyError('{} not in input parameter resolver'.format(k)) res += v * pr[k] return res @property def real(self): """ Get the real part of this parameter resolver Returns: ParameterResolver, the real part of this parameter resolver. Examples: >>> from mindquantum.core.parameterresolver import ParameterResolver as PR >>> pr = PR({'a': 1.2 + 1.3j}) >>> pr.real() {'a': 1.2} """ out = 1 * self for k, v in self.items(): out[k] = np.real(v) return out @property def imag(self): """ Get the real part of this parameter resolver Returns: ParameterResolver, the image part of this parameter resolver. Examples: >>> from mindquantum.core.parameterresolver import ParameterResolver as PR >>> pr = PR({'a': 1.2 + 1.3j}) >>> pr.imag() {'a': 1.3} """ out = 1 * self for k, v in self.items(): out[k] = np.imag(v) return out def _check_pr_type(pr): if not isinstance(pr, ParameterResolver): raise TypeError("Require a ParameterResolver, but get {}".format(type(pr)))

33.608696

114

0.562558

794abfa34738f78f99c260e408b150aa5d1b526d

974

py

Python

RotSite/polls/views.py

Mot93/Website-Rotaract-Valle-del-Savena

3d409f2d77978331cae5fde82616f47de2b8d59b

[ "MIT" ]

null

RotSite/polls/views.py

Mot93/Website-Rotaract-Valle-del-Savena

3d409f2d77978331cae5fde82616f47de2b8d59b

[ "MIT" ]

null

RotSite/polls/views.py

Mot93/Website-Rotaract-Valle-del-Savena

3d409f2d77978331cae5fde82616f47de2b8d59b

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse from django.template import loader from django.http import Http404 # Models I created from .models import Question def index(request): latest_question_list = Question.objects.order_by('-pub_date')[:5] template = loader.get_template('polls/index.html') context = { 'latest_question_list': latest_question_list, } return HttpResponse(template.render(context, request)) def detail(request, question_id): try: question = Question.objects.get(pk=question_id) except Question.DoesNotExist: raise Http404("Question does not exist") return render(request, 'polls/detail.html', {'question': question}) def results(request, question_id): response = "You're looking at the results of question %s." return HttpResponse(response % question_id) def vote(request, question_id): return HttpResponse("You're voting on question %s." % question_id)

34.785714

71

0.738193

794abfb9c8e1de418909af3b10b9c4e677cb140f

461

py

Python

python/test/test.py

mrondin1/StarSpace

fc2cd39472b3aadf00b6254edf00e099e13e598f

[ "MIT" ]

2,914

2017-10-04T21:55:41.000Z

2022-03-31T04:25:31.000Z

python/test/test.py

mrondin1/StarSpace

fc2cd39472b3aadf00b6254edf00e099e13e598f

[ "MIT" ]

215

2017-10-06T14:12:13.000Z

2022-03-18T06:56:27.000Z

python/test/test.py

mrondin1/StarSpace

fc2cd39472b3aadf00b6254edf00e099e13e598f

[ "MIT" ]

483

2017-10-07T20:09:47.000Z

2022-03-01T02:23:20.000Z

import starwrap as sw import numpy as np arg = sw.args() arg.trainFile = './input.txt' arg.testFile = './input.txt' arg.trainMode = 5 sp = sw.starSpace(arg) sp.init() sp.train() # sp.evaluate() sp.nearestNeighbor('some text', 10) sp.saveModel('model') sp.saveModelTsv('model.tsv') sp.initFromSavedModel('model') sp.initFromTsv('model.tsv') print(np.array(sp.getDocVector('this\tis\ttest', '\t'))) print(np.array(sp.getDocVector('this is test', ' ')))

19.208333

56

0.691974

794ac0413d710f7c3106974ec8a0798ef6422ab1

64,517

py

Python

tests/unit/pypyr/context_test.py

FooBarQuaxx/pypyr

ebe56b2200a53e2f38c78bbb42d466bb1556c37c

[ "Apache-2.0" ]

null

tests/unit/pypyr/context_test.py

FooBarQuaxx/pypyr

ebe56b2200a53e2f38c78bbb42d466bb1556c37c

[ "Apache-2.0" ]

null

tests/unit/pypyr/context_test.py

FooBarQuaxx/pypyr

ebe56b2200a53e2f38c78bbb42d466bb1556c37c

[ "Apache-2.0" ]

null

"""context.py unit tests.""" from collections.abc import MutableMapping from pypyr.context import Context, ContextItemInfo from pypyr.dsl import PyString, SicString from pypyr.errors import ( ContextError, KeyInContextHasNoValueError, KeyNotInContextError) import pytest import typing # ------------------- behaves like a dictionary-------------------------------# def test_context_is_dictionary_like(): """Context should behave like a dictionary.""" # initializes to empty d = Context() assert d is not None # len is not a given on custom implementations assert len(d) == 0 # dict ctor "just works" d = Context({'k1': 'v1', 'k2': 'v2'}) assert d assert len(d) == 2 assert d['k1'] == 'v1' assert d['k2'] == 'v2' # __set_item__ assignment add and update works d['k1'] = 'value 1' d['k2'] = 'value 2' d['k3'] = ['one list', 'two list', 'three list'] d['k4'] = {'kk1': 'vv1', 'kk2': 'vv2', 'kk3': 'vv3'} d['k5'] = True d['k6'] = ('thing', False, ['1', '2', '3'], 6) d['k7'] = 77 assert d['k5'] # isinstance resolves to dict - this test might become invalid if refactor # to a MutableMapping custom object assert isinstance(d, dict) assert isinstance(d, MutableMapping) assert len(d) == 7 # items() can iterate for k, v in d.items(): if k == 'k4': assert isinstance(v, dict) if k == 'k6': assert isinstance(v, tuple) # values() can iterate for v in d.values(): assert v # __get_item__ works assert d['k1'] == 'value 1' # update merging works mergedic = {'k1': 'NEWVALUE'} d.update(mergedic) assert d['k1'] == 'NEWVALUE' # del and clear original_length = len(d) del d['k1'] assert 'k1' not in d assert len(d) == original_length - 1 d.clear() assert len(d) == 0 def test_context_missing_override(): """Subclass of dict should override __missing__ on KeyNotFound.""" context = Context({'arbkey': 'arbvalue'}) with pytest.raises(KeyNotInContextError): context['notindict'] def test_context_missing_raise_key_error(): """Context should raise error compatible with dict KeyError.""" context = Context({'arbkey': 'arbvalue'}) with pytest.raises(KeyError): context['notindict'] # ------------------- behaves like a dictionary-------------------------------# # ------------------- asserts ------------------------------------------------# def test_assert_child_key_has_value_passes(): """Pass if [parent][child] has value.""" context = Context({ 'parent': { 'child': 1 } }) context.assert_child_key_has_value('parent', 'child', 'arb') def test_assert_child_key_has_value_raises_no_parent(): """Raise if [parent] doesn't exist.""" context = Context({ 'parent': { 'child': 1 } }) with pytest.raises(KeyNotInContextError): context.assert_child_key_has_value('XparentX', 'child', 'arb') def test_assert_child_key_has_value_raises_no_child(): """Raise if [parent][child] doesn't exist.""" context = Context({ 'parent': { 'child': 1 } }) with pytest.raises(KeyNotInContextError) as err: context.assert_child_key_has_value('parent', 'XchildX', 'arb') assert str(err.value) == ( "context['parent']['XchildX'] doesn't exist. It must exist for arb.") def test_assert_child_key_has_value_raises_child_none(): """Raise if [parent][child] is None.""" context = Context({ 'parent': { 'child': None } }) with pytest.raises(KeyInContextHasNoValueError) as err: context.assert_child_key_has_value('parent', 'child', 'arb') assert str(err.value) == ( "context['parent']['child'] must have a value for arb.") def test_assert_child_key_has_value_raises_parent_none(): """Raise if [parent] is None.""" context = Context({ 'parent': None }) with pytest.raises(KeyInContextHasNoValueError) as err: context.assert_child_key_has_value('parent', 'child', 'arb') assert str(err.value) == ("context['parent'] must have a value for arb.") def test_assert_child_key_has_value_raises_parent_not_iterable(): """Raise if [parent] is not iterable.""" context = Context({ 'parent': 1 }) with pytest.raises(ContextError) as err: context.assert_child_key_has_value('parent', 'child', 'arb') assert str(err.value) == ("context['parent'] must be iterable and contain " "'child' for arb. argument of type 'int' is not " "iterable") def test_assert_key_exists_raises(): """Raise KeyNotInContextError if key doesn't exist.""" context = Context({'key1': 'value1'}) with pytest.raises(KeyNotInContextError): context.assert_key_exists('notindict', None) def test_assert_key_exists_passes_value_none(): """assert_key_has_value passes if context dictionary key value is None.""" context = Context({'key1': None}) context.assert_key_exists('key1', None) def test_assert_key_exists_passes_string_values(): """assert_key_has_value passes if context dictionary key value is None.""" context = Context({'key1': 'something', 'key2': 'other', 'key3': False}) context.assert_key_exists('key2', None) context.assert_key_exists('key3', None) def test_assert_keys_exist_passes(): """Pass if list of keys all found in context dictionary.""" context = Context({'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}) context.assert_keys_exist(None, 'key1', 'key3') def test_assert_keys_exists_with_values_fails(): """Raise KeyNotInContextError if list of keys not all found in context.""" with pytest.raises(KeyNotInContextError): context = Context({'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}) context.assert_keys_exist(None, 'key1', 'key4', 'key2', ) def test_assert_key_has_value_fails_on_context_empty(): """Expect KeyNotInContextError if context empty.""" context = Context() with pytest.raises(KeyNotInContextError): context.assert_key_has_value('key', 'desc') def test_assert_key_has_value_fails_on_key_none(): """Expect AssertionError if assert key is None.""" context = Context({'key1': 'value1'}) with pytest.raises(AssertionError): context.assert_key_has_value(None, None) def test_assert_key_has_value_fails_key_not_found(): """Raise KeyNotInContextError if context doesn't have key on assert.""" context = Context({'key1': 'value1'}) with pytest.raises(KeyNotInContextError): context.assert_key_has_value('notindict', None) def test_assert_key_has_value__empty(): """No KeyNotInContextError if key exists but value empty (not None).""" context = Context({'key': ''}) # with pytest.raises(KeyNotInContextError): context.assert_key_has_value('key', None) def test_assert_key_has_value_fails_key_error_message(): """Raise KeyNotInContextError if missing key, assert message correct.""" context = Context({'key1': 'value1'}) with pytest.raises(KeyNotInContextError) as err_info: context.assert_key_has_value('notindict', 'mydesc') assert str(err_info.value) == ("context['notindict'] " "doesn't exist. It must exist for " "mydesc.") def test_assert_key_has_value_fails_key_empty(): """Raise KeyInContextHasNoValueError if context dict key value is None.""" context = Context({'key1': None}) with pytest.raises(KeyInContextHasNoValueError): context.assert_key_has_value('key1', None) def test_assert_key_has_value_passes(): """Pass if key_in_dict_has_value dictionary key has value.""" context = Context({'key1': 'value1'}) context.assert_key_has_value('key1', None) def test_assert_key_has_bool_true_passes(): """Pass if key_in_dict_has_value dictionary key has bool True value.""" context = Context({'key1': True}) context.assert_key_has_value('key1', None) def test_assert_key_has_bool_false_passes(): """Pass if key_in_dict_has_value dictionary key has bool False value.""" context = Context({'key1': False}) context.assert_key_has_value('key1', None) def test_assert_keys_have_values_passes(): """Pass if list of keys all found in context dictionary.""" context = Context({'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}) context.assert_keys_have_values(None, 'key1', 'key3') def test_assert_keys_have_values_fails(): """Raise KeyNotInContextError if list of keys don't all have values.""" with pytest.raises(KeyNotInContextError): context = Context({'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}) context.assert_keys_have_values(None, 'key1', 'key4', 'key2', ) def test_assert_key_type_value_passes(): """assert_key_type_value passes if key exists, has value and type right.""" info = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=True, has_value=True) Context().assert_key_type_value(info, None) def test_assert_key_type_value_no_key_raises(): """assert_key_type_value fails if key doesn't exist.""" info = ContextItemInfo(key='key1', key_in_context=False, expected_type=str, is_expected_type=True, has_value=True) with pytest.raises(KeyNotInContextError) as err_info: Context().assert_key_type_value(info, 'mydesc') assert str(err_info.value) == "mydesc couldn't find key1 in context." def test_assert_key_type_value_no_key_raises_extra_text(): """assert_key_type_value fails if key doesn't exist.""" info = ContextItemInfo(key='key1', key_in_context=False, expected_type=str, is_expected_type=True, has_value=True) with pytest.raises(KeyNotInContextError) as err_info: Context().assert_key_type_value(info, 'mydesc', 'extra text here') assert str(err_info.value) == ( "mydesc couldn't find key1 in context. extra text here") def test_assert_key_type_value_no_value_raises(): """assert_key_type_value fails if no value.""" info = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=True, has_value=False) with pytest.raises(KeyInContextHasNoValueError) as err_info: Context().assert_key_type_value(info, 'mydesc') assert str(err_info.value) == ("mydesc found key1 in context but it " "doesn\'t have a value.") def test_assert_key_type_value_no_value_raises_extra_text(): """assert_key_type_value fails if no value.""" info = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=True, has_value=False) with pytest.raises(KeyInContextHasNoValueError) as err_info: Context().assert_key_type_value(info, 'mydesc', 'extra text here') assert str(err_info.value) == ("mydesc found key1 in context but it " "doesn\'t have a value. extra text here") def test_assert_key_type_value_wrong_type_raises(): """assert_key_type_value fails if wrong type.""" info = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=False, has_value=True) with pytest.raises(KeyInContextHasNoValueError) as err_info: Context().assert_key_type_value(info, 'mydesc') assert str(err_info.value) == ("mydesc found key1 in context, but " "it\'s not a <class 'str'>.") def test_assert_key_type_value_wrong_type_raises_with_extra_error_text(): """assert_key_type_value fails if wrong type.""" info = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=False, has_value=True) with pytest.raises(KeyInContextHasNoValueError) as err_info: Context().assert_key_type_value(info, 'mydesc', 'extra text here') assert str(err_info.value) == ( "mydesc found key1 in context, but " "it\'s not a <class 'str'>. extra text here") def test_assert_keys_type_value_passes(): """assert_keys_type_value passes if all keys, types, values correct.""" info1 = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=True, has_value=True) info2 = ContextItemInfo(key='key2', key_in_context=True, expected_type=str, is_expected_type=True, has_value=True) info3 = ContextItemInfo(key='key3', key_in_context=True, expected_type=str, is_expected_type=True, has_value=True) Context().assert_keys_type_value(None, '', info1, info2, info3) def test_assert_keys_type_value_raises(): """assert_keys_type_value raises if issue with one in the middle.""" info1 = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=True, has_value=True) info2 = ContextItemInfo(key='key2', key_in_context=True, expected_type=str, is_expected_type=True, has_value=False) info3 = ContextItemInfo(key='key3', key_in_context=True, expected_type=str, is_expected_type=True, has_value=True) with pytest.raises(KeyInContextHasNoValueError) as err_info: Context().assert_keys_type_value('mydesc', None, info1, info2, info3) assert str(err_info.value) == ("mydesc found key2 in context but it " "doesn\'t have a value.") def test_assert_keys_type_value_raises_with_extra_error_text(): """assert_keys_type_value raises if issue with one in the middle.""" info1 = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=True, has_value=True) info2 = ContextItemInfo(key='key2', key_in_context=True, expected_type=str, is_expected_type=True, has_value=False) info3 = ContextItemInfo(key='key3', key_in_context=True, expected_type=str, is_expected_type=True, has_value=True) with pytest.raises(KeyInContextHasNoValueError) as err_info: Context().assert_keys_type_value('mydesc', 'extra text here', info1, info2, info3) assert str(err_info.value) == ("mydesc found key2 in context but it " "doesn\'t have a value. extra text here") # ------------------- asserts ------------------------------------------------# # ------------------- get_eval -----------------------------------------------# def test_get_eval_string_bool(): """Bool eval.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = 'key1 == \'down\'' output = context.get_eval_string(input_string) assert isinstance(output, bool) assert output def test_get_eval_string_builtins(): """Built-in on eval.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = 'len(key1)' assert context.get_eval_string(input_string) == 4 # ------------------- end get_eval--------------------------------------------# # ------------------- formats ------------------------------------------------# def test_string_interpolate_works(): """Interpolate works.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) context['input_string'] = 'Piping {key1} the {key2} wild' output = context.get_formatted('input_string') assert output == 'Piping down the valleys wild', ( "string interpolation incorrect") def test_string_interpolate_works_with_no_swaps(): """Interpolate no swap.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) context['input_string'] = 'Piping down the valleys wild' output = context.get_formatted('input_string') assert output == 'Piping down the valleys wild', ( "string interpolation incorrect") def test_string_interpolate_escapes_double_curly(): """Interpolate double curly escape.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) context['input_string'] = 'Piping {{ down the valleys wild' output = context.get_formatted('input_string') assert output == 'Piping { down the valleys wild', ( "string interpolation incorrect") def test_string_interpolate_escapes_double_curly_pair(): """Interpolate double double curly.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) context['input_string'] = 'Piping {{down}} the valleys wild' output = context.get_formatted('input_string') assert output == 'Piping {down} the valleys wild', ( "string interpolation incorrect") def test_string_interpolate_sic(): """Interpolate ignore sic.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) context['input_string'] = SicString("Piping {key1} the {key2} wild") output = context.get_formatted('input_string') assert output == 'Piping {key1} the {key2} wild', ( "string interpolation incorrect") def test_string_interpolate_py(): """Interpolate do py.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) context['input_string'] = PyString("len(key1) + len(key2)") output = context.get_formatted('input_string') assert output == 11, ( "string interpolation incorrect") def test_single_curly_should_throw(): """Interpolate single curly raise.""" with pytest.raises(ValueError): context = Context({'key1': 'value1'}) context['input_string'] = '{key1} this { is {key2} string' context.get_formatted('input_string') def test_tag_not_in_context_should_throw(): """Interpolate key not in context raises.""" with pytest.raises(KeyNotInContextError) as err: context = Context({'key1': 'value1'}) context['input_string'] = '{key1} this is {key2} string' context.get_formatted('input_string') assert str(err.value) == ( "Unable to format '{key1} this is " "{key2} string' at context['input_string'], because " "key2 not found in the pypyr context.") def test_context_item_not_a_string_should_return_as_is(): """Interpolate non-string.""" context = Context({'key1': 'value1'}) context['input_string'] = 77 val = context.get_formatted('input_string') assert val == 77 def test_context_item_list_should_iterate(): """Interpolate iterable.""" context = Context({'key1': 'value1'}) context['input_string'] = ['string1', '{key1}', 'string3'] val = context.get_formatted('input_string') assert val == ['string1', 'value1', 'string3'] def test_input_string_interpolate_works(): """Interpolate strings.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = 'Piping {key1} the {key2} wild' output = context.get_formatted_string(input_string) assert output == 'Piping down the valleys wild', ( "string interpolation incorrect") def test_input_string_tag_not_in_context_should_throw(): """Interpolate not in context.""" with pytest.raises(KeyNotInContextError) as err_info: context = Context({'key1': 'value1'}) input_string = '{key1} this is {key2} string' context.get_formatted_string(input_string) assert str(err_info.value) == ( "Unable to format '{key1} this is {key2} " "string' because key2 not found in the pypyr context.") def test_input_string_interpolate_sic(): """Interpolate sic.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = SicString("Piping {key1} the {key2} wild") output = context.get_formatted_string(input_string) assert output == "Piping {key1} the {key2} wild", ( "string interpolation incorrect") def test_input_string_interpolate_sic_singlequote(): """Interpolate sic with quotes.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = SicString('Piping {key1} the {key2} wild') output = context.get_formatted_string(input_string) assert output == "Piping {key1} the {key2} wild", ( "string interpolation incorrect") def test_input_string_interpolate_py_singlequote(): """Interpolate py single quotes.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = PyString('len(key1) * len(key2)') output = context.get_formatted_string(input_string) assert output == 28, ( "string interpolation incorrect") def test_input_string_not_a_string_throw(): """Interpolate takes string.""" with pytest.raises(TypeError) as err_info: context = Context({'key1': 'value1'}) input_string = 77 context.get_formatted_string(input_string) assert str(err_info.value) == ( "can only format on strings. 77 is a <class 'int'> instead.") def test_get_formatted_iterable_list(): """Simple list.""" input_obj = ['k1', 'k2', '{ctx3}', True, False, 44] context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3'}) output = context.get_formatted_iterable(input_obj) assert output is not input_obj assert output[0] == 'k1' assert output[1] == 'k2' assert output[2] == 'ctxvalue3' assert output[3] assert not output[4] assert output[5] == 44 def test_get_formatted_iterable_tuple(): """Simple tuple.""" input_obj = ('k1', 'k2', '{ctx3}', True, False, 44) context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3'}) output = context.get_formatted_iterable(input_obj) assert output is not input_obj assert output[0] == 'k1' assert output[1] == 'k2' assert output[2] == 'ctxvalue3' assert output[3] assert not output[4] assert output[5] == 44 def test_get_formatted_iterable_set(): """Simple set.""" input_obj = {'k1', 'k2', '{ctx3}', True, False, 44} context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3'}) output = context.get_formatted_iterable(input_obj) assert output is not input_obj assert len(output) == len(input_obj) diffs = output - input_obj assert len(diffs) == 1 assert 'ctxvalue3' in diffs def test_get_formatted_immutable_mapping(): """Simple read-only mapping test.""" class ReadOnlyMapping(typing.Mapping): def __init__(self, *args, **kwargs): self._data = dict(*args, **kwargs) def __getitem__(self, key): return self._data[key] def __len__(self): return len(self._data) def __iter__(self): return iter(self._data) input_obj = {'key': '{ctx}'} context = Context( {'ctx': ReadOnlyMapping({'arb': 1})}) output = context.get_formatted_iterable(input_obj) assert output is not input_obj assert isinstance(output['key'], ReadOnlyMapping) assert output['key'] == {'arb': 1} def test_get_formatted_iterable_nested(): """Straight deepish copy with no formatting.""" # dict containing dict, list, dict-list-dict, tuple, dict-tuple-list input_obj = {'k1': 'v1', 'k2': 'v2', 'k3': 'v3', 'k4': [ 1, 2, '3here', {'key4.1': 'value4.1', 'key4.2': 'value4.2', 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2value'}} ], 'k5': {'key5.1': 'value5.1', 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'sixend'), 'k7': 'simple string to close 7' } context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3'}) output = context.get_formatted_iterable(input_obj) assert output == input_obj assert output is not context # verify this was a deep copy - obj refs has to be different for nested assert id(output['k4']) != id(input_obj['k4']) assert id(output['k4'][3]['key4.3']) != id(input_obj['k4'][3]['key4.3']) assert id(output['k5']) != id(input_obj['k5']) assert id(output['k6']) != id(input_obj['k6']) assert id(output['k6'][2]) != id(input_obj['k6'][2]) assert id(output['k7']) == id(input_obj['k7']) # and proving the theory: mutating output does not touch input assert output['k4'][1] == 2 output['k4'][1] = 88 assert input_obj['k4'][1] == 2 assert output['k4'][1] == 88 def test_get_formatted_iterable_nested_with_formatting(): """Straight deepish copy with formatting.""" # dict containing dict, list, dict-list-dict, tuple, dict-tuple-list, bytes input_obj = {'k1': 'v1', 'k2': 'v2_{ctx1}', 'k3': bytes('v3{ctx1}', encoding='utf-8'), 'k4': [ 1, 2, '3_{ctx4}here', {'key4.1': 'value4.1', '{ctx2}_key4.2': 'value_{ctx3}_4.2', 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value'}} ], 'k5': {'key5.1': 'value5.1', 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'six_{ctx1}_end'), 'k7': 'simple string to close 7' } context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3', 'ctx4': 'ctxvalue4'}) output = context.get_formatted_iterable(input_obj) assert output != input_obj # verify formatted strings assert input_obj['k2'] == 'v2_{ctx1}' assert output['k2'] == 'v2_ctxvalue1' assert input_obj['k3'] == b'v3{ctx1}' assert output['k3'] == b'v3{ctx1}' assert input_obj['k4'][2] == '3_{ctx4}here' assert output['k4'][2] == '3_ctxvalue4here' assert input_obj['k4'][3]['{ctx2}_key4.2'] == 'value_{ctx3}_4.2' assert output['k4'][3]['ctxvalue2_key4.2'] == 'value_ctxvalue3_4.2' assert input_obj['k4'][3]['key4.3']['4.3.2'] == '4.3.2_{ctx1}_value' assert output['k4'][3]['key4.3']['4.3.2'] == '4.3.2_ctxvalue1_value' assert input_obj['k6'][4] == 'six_{ctx1}_end' assert output['k6'][4] == 'six_ctxvalue1_end' # verify this was a deep copy - obj refs has to be different for nested assert id(output['k4']) != id(input_obj['k4']) assert id(output['k4'][3]['key4.3']) != id(input_obj['k4'][3]['key4.3']) assert id(output['k5']) != id(input_obj['k5']) assert id(output['k6']) != id(input_obj['k6']) assert id(output['k6'][2]) != id(input_obj['k6'][2]) # strings are interned in python, so id is the same assert id(output['k7']) == id(input_obj['k7']) output['k7'] = 'mutate 7 on new' assert input_obj['k7'] == 'simple string to close 7' assert output['k7'] == 'mutate 7 on new' def test_get_formatted_iterable_nested_with_sic(): """Straight deepish copy with formatting.""" # dict containing dict, list, dict-list-dict, tuple, dict-tuple-list, bytes input_obj = {'k1': 'v1', 'k2': 'v2_{ctx1}', 'k3': bytes('v3{ctx1}', encoding='utf-8'), 'k4': [ 1, 2, '3_{ctx4}here', {'key4.1': 'value4.1', '{ctx2}_key4.2': SicString("value_{ctx3}_4.2"), 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value'}} ], 'k5': {'key5.1': 'value5.1', 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'six_{ctx1}_end'), 'k7': 'simple string to close 7' } context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3', 'ctx4': 'ctxvalue4'}) output = context.get_formatted_iterable(input_obj) assert output != input_obj # verify formatted strings assert input_obj['k2'] == 'v2_{ctx1}' assert output['k2'] == 'v2_ctxvalue1' assert input_obj['k3'] == b'v3{ctx1}' assert output['k3'] == b'v3{ctx1}' assert input_obj['k4'][2] == '3_{ctx4}here' assert output['k4'][2] == '3_ctxvalue4here' assert input_obj['k4'][3]['{ctx2}_key4.2'] == SicString("value_{ctx3}_4.2") assert output['k4'][3]['ctxvalue2_key4.2'] == 'value_{ctx3}_4.2' assert input_obj['k4'][3]['key4.3']['4.3.2'] == '4.3.2_{ctx1}_value' assert output['k4'][3]['key4.3']['4.3.2'] == '4.3.2_ctxvalue1_value' assert input_obj['k6'][4] == 'six_{ctx1}_end' assert output['k6'][4] == 'six_ctxvalue1_end' # verify this was a deep copy - obj refs has to be different for nested assert id(output['k4']) != id(input_obj['k4']) assert id(output['k4'][3]['key4.3']) != id(input_obj['k4'][3]['key4.3']) assert id(output['k5']) != id(input_obj['k5']) assert id(output['k6']) != id(input_obj['k6']) assert id(output['k6'][2]) != id(input_obj['k6'][2]) # strings are interned in python, so id is the same assert id(output['k7']) == id(input_obj['k7']) output['k7'] = 'mutate 7 on new' assert input_obj['k7'] == 'simple string to close 7' assert output['k7'] == 'mutate 7 on new' def test_get_formatted_iterable_non_string_key(): """Format context with non-strings in keys.""" input_obj = {'k1': 'v1', 'k2': 'v2_{ctx1}', 'k3': bytes('v3{ctx1}', encoding='utf-8'), 'k4': [ 1, 2, '3_{ctx4}here', {'key4.1': 'value4.1', '{ctx2}_key4.2': SicString("value_{ctx3}_4.2"), 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value', 7: '4.3.3_{ctx4}_value'}} ], 'k5': {'key5.1': 'value5.1', 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'six_{ctx1}_end'), 'k7': 'simple string to close 7', 6: {7, 89} } context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3', 'ctx4': 'ctxvalue4', 5: [1, 2, 3]}) output = context.get_formatted_iterable(input_obj) assert output != input_obj assert output == {'k1': 'v1', 'k2': 'v2_ctxvalue1', 'k3': bytes('v3{ctx1}', encoding='utf-8'), 'k4': [ 1, 2, '3_ctxvalue4here', {'key4.1': 'value4.1', 'ctxvalue2_key4.2': "value_{ctx3}_4.2", 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_ctxvalue1_value', 7: '4.3.3_ctxvalue4_value'}} ], 'k5': {'key5.1': 'value5.1', 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'six_ctxvalue1_end'), 'k7': 'simple string to close 7', 6: {7, 89} } def test_get_formatted_iterable_with_memo(): """Straight deepish copy with formatting.""" arb_dict = {'key4.1': 'value4.1', '{ctx2}_key4.2': 'value_{ctx3}_4.2', 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value'}} arb_list = [0, 1, 2] arb_string = 'arb string' arb_string_with_formatting = 'a {ctx1} string' input_obj = {'k1': arb_string, 'k2': 'v2_{ctx1}', 'k3': arb_list, 'k4': [ arb_dict, 2, '3_{ctx4}here', arb_dict ], 'k5': {'key5.1': arb_string, 'key5.2': arb_string_with_formatting}, 'k6': ('six6.1', False, arb_list, 77, 'six_{ctx1}_end'), 'k7': 'simple string to close 7', 'k8': arb_string_with_formatting } context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3', 'ctx4': 'ctxvalue4'}) output = context.get_formatted_iterable(input_obj) # same obj re-used at different levels of the hierarchy assert id(input_obj['k3']) == id(input_obj['k6'][2]) assert id(input_obj['k4'][0]) == id(input_obj['k4'][3]) assert output != input_obj # verify formatted strings assert input_obj['k2'] == 'v2_{ctx1}' assert output['k2'] == 'v2_ctxvalue1' assert input_obj['k4'][2] == '3_{ctx4}here' assert output['k4'][2] == '3_ctxvalue4here' assert input_obj['k4'][3]['{ctx2}_key4.2'] == 'value_{ctx3}_4.2' assert output['k4'][3]['ctxvalue2_key4.2'] == 'value_ctxvalue3_4.2' assert input_obj['k4'][3]['key4.3']['4.3.2'] == '4.3.2_{ctx1}_value' assert output['k4'][3]['key4.3']['4.3.2'] == '4.3.2_ctxvalue1_value' assert input_obj['k6'][4] == 'six_{ctx1}_end' assert output['k6'][4] == 'six_ctxvalue1_end' # verify this was a deep copy - obj refs has to be different for nested assert id(output['k4']) != id(input_obj['k4']) assert id(output['k4'][3]['key4.3']) != id(input_obj['k4'][3]['key4.3']) assert id(output['k5']) != id(input_obj['k5']) assert id(output['k6']) != id(input_obj['k6']) assert id(output['k6'][2]) != id(input_obj['k6'][2]) assert id(output['k7']) == id(input_obj['k7']) output['k7'] = 'mutate 7 on new' assert input_obj['k7'] == 'simple string to close 7' assert input_obj['k8'] == arb_string_with_formatting assert output['k8'] == 'a ctxvalue1 string' # memo did object re-use so same obj re-used at different levels of the # hierarchy assert id(output['k3']) == id(output['k6'][2]) assert id(output['k4']) != id(input_obj['k4']) assert id(output['k4'][0]) == id(output['k4'][3]) assert output['k5']['key5.1'] == input_obj['k5']['key5.1'] == arb_string assert id(output['k5']['key5.1']) == id( input_obj['k5']['key5.1']) == id(arb_string) assert id(output['k8']) == id(output['k5']['key5.2']) assert id(output['k8']) != id(arb_string_with_formatting) def test_iter_formatted(): """On iter_formatted yields a formatted string on each loop.""" context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3', 'ctx4': 'ctxvalue4'}) input_strings = [ "this {ctx1} is {ctx2} line 1", "this is {ctx3} line 2", "this is line 3", "this {ctx4} is line 4" ] output = list(context.iter_formatted_strings(input_strings)) assert output[0] == "this ctxvalue1 is ctxvalue2 line 1" assert output[1] == "this is ctxvalue3 line 2" assert output[2] == "this is line 3" assert output[3] == "this ctxvalue4 is line 4" def test_get_formatted_as_type_string_to_bool_no_subst(): """On get_formatted_as_type returns bool no formatting.""" context = Context() result = context.get_formatted_as_type('False', out_type=bool) assert isinstance(result, bool) assert not result def test_get_formatted_as_type_string_to_true_bool_no_subst(): """On get_formatted_as_type returns bool no formatting.""" context = Context() result = context.get_formatted_as_type('True', out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_false_no_subst(): """On get_formatted_as_type returns bool no formatting.""" context = Context() result = context.get_formatted_as_type(False, out_type=bool) assert isinstance(result, bool) assert not result def test_get_formatted_as_type_bool_true_no_subst(): """On get_formatted_as_type returns bool no formatting.""" context = Context() result = context.get_formatted_as_type(None, True, out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_false_with_subst(): """On get_formatted_as_type returns bool with formatting.""" context = Context({'k1': False}) result = context.get_formatted_as_type(None, '{k1}', out_type=bool) assert isinstance(result, bool) assert not result def test_get_formatted_as_type_bool_true_with_subst(): """On get_formatted_as_type returns bool with formatting.""" context = Context({'k1': True}) result = context.get_formatted_as_type(None, '{k1}', out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_true_with_list_input(): """On get_formatted_as_type returns bool True with arbitrary input.""" context = Context({'k1': True}) result = context.get_formatted_as_type([0, 1, 2], out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_false_with_empty_list_input(): """On get_formatted_as_type returns bool false with empty input.""" context = Context({'k1': True}) result = context.get_formatted_as_type([], out_type=bool) assert isinstance(result, bool) assert not result def test_get_formatted_as_type_bool_false_with_0_input(): """On get_formatted_as_type returns bool False with 0 input.""" context = Context({'k1': True}) result = context.get_formatted_as_type(0, out_type=bool) assert isinstance(result, bool) assert not result def test_get_formatted_as_type_bool_false_with_string_capital_false(): """On get_formatted_as_type returns bool False with string FALSE.""" context = Context({'k1': True}) result = context.get_formatted_as_type('FALSE', out_type=bool) assert isinstance(result, bool) assert not result def test_get_formatted_as_type_bool_true_with_1_input(): """On get_formatted_as_type returns bool True with int 1 input.""" context = Context({'k1': True}) result = context.get_formatted_as_type(1, out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_true_with_decimal_input(): """On get_formatted_as_type returns bool True with decimal input.""" context = Context({'k1': True}) result = context.get_formatted_as_type(1.1, out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_true_with_str_true(): """On get_formatted_as_type returns bool True with string true.""" context = Context({'k1': True}) result = context.get_formatted_as_type('true', out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_true_with_str_capital_true(): """On get_formatted_as_type returns bool True with string TRUE.""" context = Context({'k1': True}) result = context.get_formatted_as_type('TRUE', out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_true_with_str_1_true(): """On get_formatted_as_type returns bool True with string 1.""" context = Context({'k1': True}) result = context.get_formatted_as_type('1', out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_true_with_pystring_true(): """On get_formatted_as_type returns bool True with py string True.""" context = Context({'k1': True}) result = context.get_formatted_as_type(PyString('k1 and True'), out_type=bool) assert isinstance(result, bool) assert result def test_get_formatted_as_type_bool_false_with_pystring_false(): """On get_formatted_as_type returns bool True with py string True.""" context = Context({'k1': True}) result = context.get_formatted_as_type(PyString('not k1'), out_type=bool) assert isinstance(result, bool) assert not result def test_get_formatted_as_type_int_no_subst(): """On get_formatted_as_type returns int no formatting.""" context = Context() result = context.get_formatted_as_type('10', out_type=int) assert isinstance(result, int) assert result == 10 def test_get_formatted_as_type_int_with_subst(): """On get_formatted_as_type returns int no formatting.""" context = Context({'k1': 10}) result = context.get_formatted_as_type('{k1}', out_type=int) assert isinstance(result, int) assert result == 10 def test_get_formatted_as_type_float_no_subst(): """On get_formatted_as_type returns float no formatting.""" context = Context() result = context.get_formatted_as_type('10.1', out_type=float) assert isinstance(result, float) assert result == 10.1 def test_get_formatted_as_type_default_no_subst(): """On get_formatted_as_type returns default no formatting.""" context = Context() result = context.get_formatted_as_type(None, default=10, out_type=int) assert isinstance(result, int) assert result == 10 def test_get_formatted_as_type_default_with_subst(): """On get_formatted_as_type returns default with formatting.""" context = Context({'k1': 10}) result = context.get_formatted_as_type( None, default='{k1}', out_type=int) assert isinstance(result, int) assert result == 10 def test_get_formatted_as_type_default_with_subst_str(): """On get_formatted_as_type returns default with formatting.""" context = Context({'k1': 10}) result = context.get_formatted_as_type( None, default='xx{k1}xx') assert isinstance(result, str) assert result == 'xx10xx' def test_get_formatted_value_string(): """Format input strings.""" context = Context({'k1': 10}) assert context.get_formatted_value('{k1}') == 10 def test_get_formatted_value_int(): """Format input int.""" context = Context({'k1': 10}) assert context.get_formatted_value(11) == 11 def test_get_formatted_value_pystring(): """Format input pystring.""" context = Context({'k1': 10}) out = context.get_formatted_value(PyString('11')) assert out == 11 assert isinstance(out, int) def test_get_formatted_value_bool(): """Format input int.""" context = Context({'k1': 10}) assert not context.get_formatted_value(False) def test_get_formatted_value_dict(): """Format input dict.""" context = Context({'k1': 10}) assert context.get_formatted_value({'{k1}', 12}) == {10, 12} def test_get_formatted_value_list(): """Format input list.""" context = Context({'k1': 10}) assert context.get_formatted_value(['{k1}', 12, 13]) == [10, 12, 13] def test_get_processed_string_no_interpolation(): """On get_processed_string on plain string returns plain.""" context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3', 'ctx4': 'ctxvalue4'}) input_string = 'test string here' output = context.get_processed_string(input_string) assert input_string == output def test_get_processed_string_with_interpolation(): """Process string with interpolation.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = 'Piping {key1} the {key2} wild' output = context.get_processed_string(input_string) assert output == 'Piping down the valleys wild', ( "string interpolation incorrect") def test_get_processed_string_shorter_than_6_with_interpolation(): """Process string with interpolation.""" context = Context({'k': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = '{k}' output = context.get_processed_string(input_string) assert output == 'down', ( "string interpolation incorrect") def test_get_processed_string_shorter_than_6_no_interpolation(): """Process string with no interpolation.""" context = Context() input_string = 'k' output = context.get_processed_string(input_string) assert output == 'k', ( "string interpolation incorrect") def test_get_processed_string_sic_skips_interpolation(): """Process string with sic interpolation.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = SicString("Piping {key1} the {key2} wild") output = context.get_processed_string(input_string) assert output == 'Piping {key1} the {key2} wild', ( "string interpolation incorrect") def test_get_processed_string_pystring_double_quote(): """Process string with double quotes interpolation.""" context = Context({'key1': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = PyString("key1 == 'down'") output = context.get_processed_string(input_string) assert isinstance(output, bool) assert output def test_get_processed_string_pystring_single_quote(): """Process string with py string interpolation.""" context = Context({'key1': 2, 'key2': -3, 'key3': 'value3'}) input_string = PyString('abs(key1+key2)') output = context.get_processed_string(input_string) assert isinstance(output, int) assert output == 1 def test_get_processed_string_single_expression_keeps_type(): """Process string with interpolation honors type.""" context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': [0, 1, 3], 'ctx4': 'ctxvalue4'}) input_string = '{ctx3}' output = context.get_processed_string(input_string) assert output == [0, 1, 3] assert isinstance(output, list) def test_get_processed_string_single_expression_keeps_type_and_iterates(): """Process string with interpolation on iterable.""" context = Context( {'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': [0, {'s1': 'v1', '{ctx1}': '{ctx2}', 's3': [0, '{ctx4}']}, 3], 'ctx4': 'ctxvalue4'}) input_string = '{ctx3}' output = context.get_processed_string(input_string) assert output == [0, {'s1': 'v1', 'ctxvalue1': 'ctxvalue2', 's3': [0, 'ctxvalue4']}, 3] def test_get_processed_string_leading_literal(): """Process string with interpolation leading literal.""" context = Context({'k': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = 'leading literal{k}' output = context.get_processed_string(input_string) assert output == 'leading literaldown', ( "string interpolation incorrect") def test_get_processed_string_following_literal(): """Process string with interpolation literal end.""" context = Context({'k': 'down', 'key2': 'valleys', 'key3': 'value3'}) input_string = '{k}following literal' output = context.get_processed_string(input_string) assert output == 'downfollowing literal', ( "string interpolation incorrect") # ------------------- formats ------------------------------------------------# # ------------------- key info -----------------------------------------------# def test_key_in_context(): """Assert key in context.""" context = Context({'k1': 'v1', 'k2': False, 'k3': ['one', 'two']}) k1, = context.keys_exist('k1') assert k1 k1, k2, k3 = context.keys_exist('k1', 'k2', 'k3') assert k1 and k2 and k3 k4, k2, k1 = context.keys_exist('k4', 'k2', 'k1') assert k1 and k2 and not k4 def test_keys_of_type_exist_single(): """Assert key in context.""" """return a single tuple.""" context = Context({'k1': 'v1', 'k2': False, 'k3': ['one', 'two']}) k1, = context.keys_of_type_exist(('k1', str),) assert k1 assert k1.key == 'k1' assert k1.key_in_context assert k1.expected_type is str assert k1.is_expected_type assert k1.has_value def test_keys_of_type_exist_triple(): """Assert key in context.""" context = Context({'k1': 'v1', 'k2': False, 'k3': ['one', 'two']}) k3, k2, k1 = context.keys_of_type_exist( ('k3', list), ('k2', list), ('k1', str) ) assert k1 assert k1.key == 'k1' assert k1.key_in_context assert k1.expected_type is str assert k1.is_expected_type assert k1.has_value assert k2 assert k2.key == 'k2' assert k2.key_in_context assert k2.expected_type is list assert not k2.is_expected_type assert k2.has_value assert k3 assert k3.key == 'k3' assert k3.key_in_context assert k3.expected_type is list assert k3.is_expected_type assert k3.has_value def test_keys_none_exist(): """Assert key not in context.""" context = Context({'k1': 'v1', 'k2': False, 'k3': ['one', 'two']}) k4, = context.keys_of_type_exist( ('k4', list) ) k5, k6 = context.keys_of_type_exist( ('k5', bool), ('k6', list), ) assert k4 assert k4.key == 'k4' assert not k4.key_in_context assert k4.expected_type is list assert k4.is_expected_type is None assert not k4.has_value assert k5 assert k5.key == 'k5' assert not k5.key_in_context assert k5.expected_type is bool assert k5.is_expected_type is None assert not k5.has_value assert k6 assert k6.key == 'k6' assert not k6.key_in_context assert k6.expected_type is list assert k6.is_expected_type is None assert not k6.has_value # ------------------- key info -----------------------------------------------# # ------------------- merge --------------------------------------------------# def test_merge_pass_no_substitutions(): """Merge success case with no substitutions.""" context = Context({ 'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 6: 6 }) add_me = { 'key2': 'value4', 'key4': 'value5' } context.merge(add_me) assert context['key1'] == 'value1' assert context['key2'] == 'value4' assert context['key3'] == 'value3' assert context['key4'] == 'value5' assert context[6] == 6 def test_merge_pass_nested_with_substitutions(): """Merge success case with nested hierarchy and substitutions.""" context = Context({ 'key1': 'value1', 'key2': 'value2', 'key3': { 'k31': 'value31', 'k32': 'value32', }, 'key5': False, 15: 16 }) add_me = { 'key2': 'value4', 'key3': { 'k33': 'value33' }, 'key4': '444_{key1}_444', 'key5': { 'k51': PyString('key1') }, 13: 14, 15: 17 } context.merge(add_me) assert context == { 'key1': 'value1', 'key2': 'value4', 'key3': { 'k31': 'value31', 'k32': 'value32', 'k33': 'value33' }, 'key4': '444_value1_444', 'key5': { 'k51': 'value1' }, 13: 14, 15: 17 } def test_merge_pass_no_recognized_type(): """Merge success case where type not known mergable.""" arb_obj = TimeoutError('blah') context = Context({ 'key1': 'value1', 'key2': 'value2', 'key3': { 'k31': 'value31', 'k32': 'value32', }, 'key5': TimeoutError('boom')}) add_me = { 'key2': 'value4', 'key3': { 'k33': 'value33' }, 'key4': '444_{key1}_444', 'key5': arb_obj } context.merge(add_me) assert context == { 'key1': 'value1', 'key2': 'value4', 'key3': { 'k31': 'value31', 'k32': 'value32', 'k33': 'value33' }, 'key4': '444_value1_444', 'key5': arb_obj } def test_merge_pass_nested_with_types(): """Merge success case with nested hierarchy, substitutions, diff types.""" context = Context({ 'k1': 'v1', 'k2': 'v2_{ctx1}', 'k3': bytes('v3{ctx1}', encoding='utf-8'), 'k4': [ 1, 2, '3_{ctx4}here', {'key4.1': 'value4.1', '{ctx2}_key4.2': 'value_{ctx3}_4.2', 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value'}} ], 'k5': {'key5.1': {'kv511': 'value5.1'}, 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'six_{ctx1}_end'), 'k7': 'simple string to close 7', 'k8': ('tuple1', 'tuple2'), 'k9': {'set1', 'set2'}, 'k10': ( 1, 2, {'10.1': '10.1v', '10.2': '{10.2v}', }, 3), 'k11': { 'k11.1': '11.1v', 'k11.2': { 'k11.2.1': '11.2.1v', 'k11.2.2': { 'k11.2.2.1': '11.2.2.1v' }, }, }, 'k12': 'end' } ) add_me = { 'k4': [ 4.4, {'key4.3': { '4.3.1': 'merged value for 4.3.1' } } ], 'k5': { 'key5.1': { 'kv522': 'kv522 from merge {k1}' }}, 'k8': ('tuple3', ), 'k9': {'set3', }, 'k10': ({ '{k1}': [0, 1, 2, ( 'tuple in list in dict in tuple in dict', 'hello {k2}', {'k1': '{k1}'} ), [0, 1, 2, '{k1}', 3, (True, False), ['00', '{k1}']], 4] }, 4), 'k11': { 'k11.2': { 'k11.2.2': { 'add me': '{k1}' }, }, }, } context.merge(add_me) assert context == { 'k1': 'v1', 'k2': 'v2_{ctx1}', 'k3': bytes('v3{ctx1}', encoding='utf-8'), 'k4': [ 1, 2, '3_{ctx4}here', {'key4.1': 'value4.1', '{ctx2}_key4.2': 'value_{ctx3}_4.2', 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value'}}, 4.4, {'key4.3': { '4.3.1': 'merged value for 4.3.1' } } ], 'k5': { 'key5.1': { 'kv511': 'value5.1', 'kv522': 'kv522 from merge v1' }, 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'six_{ctx1}_end'), 'k7': 'simple string to close 7', 'k8': ('tuple1', 'tuple2', 'tuple3'), 'k9': {'set1', 'set2', 'set3'}, 'k10': ( 1, 2, {'10.1': '10.1v', '10.2': '{10.2v}', }, 3, {'v1': [0, 1, 2, ( 'tuple in list in dict in tuple in dict', 'hello v2_{ctx1}', {'k1': 'v1'} ), [0, 1, 2, 'v1', 3, (True, False), ['00', 'v1']], 4] }, 4 ), 'k11': { 'k11.1': '11.1v', 'k11.2': { 'k11.2.1': '11.2.1v', 'k11.2.2': { 'k11.2.2.1': '11.2.2.1v', 'add me': 'v1' }, }, }, 'k12': 'end' } def test_merge_interpolate_py(): """Merge with interpolate.""" context = Context() context.merge({"key": PyString("True")}) assert context["key"] is True def test_merge_replaced_by_interpolated_py_mapping(): """Merge with interpolate py string.""" context = Context({'key': {'b': 2}}) context.merge({"key": PyString("{'a': 1}")}) assert context["key"] == {'a': 1} def test_merge_interpolate_py_with_substitutions(): """Merge with interpolate substitutions.""" context = Context({"key": False}) context.merge({"key": PyString("5")}) assert context["key"] == 5 def test_merge_non_string_keys(): """Merge when key is not string.""" context = Context({1: False, 2: 'two', 3: '{two}'}) context.merge({2: 'merged'}) assert context == {1: False, 2: 'merged', 3: '{two}'} def test_merge_key_substitutions(): """Merge when keys substitute.""" context = Context({'k1': 'v1', 'k2': 'k1', 'k3': 'value3'}) context.merge({'{k2}': 'newvalue', '{k1}': 'k1merged', '{k3}': '3new'}) # notice that k1 resolves to newvalue because it evaluates after k2 merge. assert context == {'k1': 'newvalue', 'k2': 'k1', 'newvalue': 'k1merged', 'k3': 'value3', 'value3': '3new'} # ------------------- merge --------------------------------------------------# # ------------------- set_defaults -------------------------------------------# def test_set_defaults_pass_no_substitutions(): """Defaults success case with no substitutions.""" context = Context({ 'key1': 'value1', 'key2': 'value2', 'key3': 'value3', }) add_me = { 'key2': 'value4', 'key4': 'value5' } context.set_defaults(add_me) assert context['key1'] == 'value1' # since key2 exists already, shouldn't update assert context['key2'] == 'value2' assert context['key3'] == 'value3' assert context['key4'] == 'value5' def test_set_defaults_pass_nested_with_substitutions(): """Merge success case with nested hierarchy and substitutions.""" context = Context({ 'key1': 'value1', 'key2': 'value2', 'key3': { 'k31': 'value31', 'k32': 'value32', }}) add_me = { 'key2': 'value4', 'key3': { 'k33': 'value33' }, 'key4': '444_{key1}_444' } context.set_defaults(add_me) assert context == { 'key1': 'value1', 'key2': 'value2', 'key3': { 'k31': 'value31', 'k32': 'value32', 'k33': 'value33' }, 'key4': '444_value1_444' } def test_set_defaults_pass_nested_with_types(): """Defaults with nested hierarchy, substitutions, diff types.""" context = Context({ 'k1': 'v1', 'k2': 'v2_{ctx1}', 'k3': bytes('v3{ctx1}', encoding='utf-8'), 'k4': [ 1, 2, '3_{ctx4}here', {'key4.1': 'value4.1', '{ctx2}_key4.2': 'value_{ctx3}_4.2', 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value'}} ], 'k5': {'key5.1': {'kv511': 'value5.1'}, 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'six_{ctx1}_end'), 'k7': 'simple string to close 7', 'k8': ('tuple1', 'tuple2'), 'k9': {'set1', 'set2'}, 'k10': ( 1, 2, {'10.1': '10.1v', '10.2': '{10.2v}', }, 3), 'k11': { 'k11.1': '11.1v', 'k11.2': { 'k11.2.1': '11.2.1v', 'k11.2.2': { 'k11.2.2.1': '11.2.2.1v' }, }, }, 'k12': 'end' } ) add_me = { 'k4': [ 4.4, {'key4.3': { '4.3.1': 'merged value for 4.3.1' } } ], 'k5': { 'key5.1': { 'kv522': 'kv522 from merge {k1}' }}, 'k8': ('tuple3', ), 'k9': {'set3', }, 'k10': ({ '{k1}': [0, 1, 2, ( 'tuple in list in dict in tuple in dict', 'hello {k2}', {'k1': '{k1}'} ), [0, 1, 2, '{k1}', 3, (True, False), ['00', '{k1}']], 4] }, 4), 'k11': { 'k11.2': { 'k11.2.2': { 'add me': '{k1}' }, }, }, } context.set_defaults(add_me) assert context == { 'k1': 'v1', 'k2': 'v2_{ctx1}', 'k3': bytes('v3{ctx1}', encoding='utf-8'), 'k4': [ 1, 2, '3_{ctx4}here', {'key4.1': 'value4.1', '{ctx2}_key4.2': 'value_{ctx3}_4.2', 'key4.3': { '4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value'}} ], 'k5': {'key5.1': {'kv511': 'value5.1', 'kv522': 'kv522 from merge v1'}, 'key5.2': 'value5.2'}, 'k6': ('six6.1', False, [0, 1, 2], 77, 'six_{ctx1}_end'), 'k7': 'simple string to close 7', 'k8': ('tuple1', 'tuple2'), 'k9': {'set1', 'set2'}, 'k10': ( 1, 2, {'10.1': '10.1v', '10.2': '{10.2v}', }, 3), 'k11': { 'k11.1': '11.1v', 'k11.2': { 'k11.2.1': '11.2.1v', 'k11.2.2': { 'k11.2.2.1': '11.2.2.1v', 'add me': 'v1' }, }, }, 'k12': 'end' } # ------------------- set_defaults -------------------------------------------#

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Python

verilator/detect2600_verilator/check.py

tacertain/Tutorials_MiSTer

ffa0c2d01d7c40b2dce893470e9144d39644f321

[ "Apache-2.0" ]

50

2019-02-05T22:58:19.000Z

2022-03-15T05:15:23.000Z

verilator/detect2600_verilator/check.py

tacertain/Tutorials_MiSTer

ffa0c2d01d7c40b2dce893470e9144d39644f321

[ "Apache-2.0" ]

3

2021-05-03T08:18:06.000Z

2021-08-09T01:27:48.000Z

verilator/detect2600_verilator/check.py

tacertain/Tutorials_MiSTer

ffa0c2d01d7c40b2dce893470e9144d39644f321

[ "Apache-2.0" ]

15

2019-09-03T10:18:43.000Z

2022-01-19T06:09:41.000Z

import csv import subprocess size_table = { "00008400":"--","00006300":"--","00008000":"--","00010000":"--","00000800": "2K", "00001000" : "4K" } cart_data = [] md5_name = {} missing = [] match = [] incorrect = [] with open('romschecklist.chk') as csv_file: csv_reader = csv.reader(csv_file, delimiter=',') for row in csv_reader: md5_name[row[0]]=row[1]; #print(md5_name) with open('2600mapper.csv') as csv_file: csv_reader = csv.reader(csv_file, delimiter=',') line_count = 0 for row in csv_reader: if line_count!=0: the_cart = {} print(row) the_cart['MD5']=row[3] the_cart['MAPPER']=row[4] try: the_cart['NAME']=md5_name[row[3]] cart_data.append(the_cart) except: print("missing",row[0]) the_cart['NAME']=row[0] missing.append(the_cart) line_count=line_count+1; print(cart_data) for row in cart_data: cart='Roms-1/'+row['NAME'] print(cart) result=subprocess.check_output(['tmp/Vtop', cart]) print(result,row['MAPPER']) x = csv.reader(result.split('\n'),delimiter=',') mapper='' size='' line_count = 0 for y in x: if (line_count==0): # pull the size out size=y[3] sc=y[5] print(y) elif (line_count==1): print(y) mapper = y[1] line_count=line_count+1; if (mapper=="00"): mapper=size_table[size] if (sc=='1'): mapper = mapper +"SC" if (row['MAPPER']==mapper): print('FOUND') match.append(row) else: print('NOMATCH') row['INCORRECT']=mapper incorrect.append(row) with open('missing.txt', 'w') as f: for item in missing: f.write("%s\n" % item) with open('match.txt', 'w') as f: for item in match: f.write("%s\n" % item) with open('incorrect.txt', 'w') as f: for item in incorrect: f.write("%s\n" % item)

20.610526

116

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794ac36bc53f15d59f7ef701d279b3402d7bc92d

1,034

py

Python

_/Chapter 7/myshop/orders/views.py

paullewallencom/django-978-1-7872-8366-4

8677b798412cb28389ecf211b8af9692bb34bfcc

[ "Apache-2.0" ]

26

2019-10-04T20:37:43.000Z

2021-11-15T19:54:29.000Z

_/Chapter 7/myshop/orders/views.py

paullewallencom/django-978-1-7872-8366-4

8677b798412cb28389ecf211b8af9692bb34bfcc

[ "Apache-2.0" ]

1

2022-01-14T11:29:11.000Z

_/Chapter 7/myshop/orders/views.py

paullewallencom/django-978-1-7872-8366-4

8677b798412cb28389ecf211b8af9692bb34bfcc

[ "Apache-2.0" ]

29

2019-05-19T11:43:02.000Z

2021-11-16T13:05:30.000Z

from django.shortcuts import render from .models import OrderItem from .forms import OrderCreateForm from .tasks import order_created from cart.cart import Cart def order_create(request): cart = Cart(request) if request.method == 'POST': form = OrderCreateForm(request.POST) if form.is_valid(): order = form.save() for item in cart: OrderItem.objects.create(order=order, product=item['product'], price=item['price'], quantity=item['quantity']) # clear the cart cart.clear() # launch asynchronous task order_created.delay(order.id) return render(request, 'orders/order/created.html', {'order': order}) else: form = OrderCreateForm() return render(request, 'orders/order/create.html', {'cart': cart, 'form': form})

36.928571

81

0.526112

794ac4488173e551ff7ebec54fe253c853aa3281

35,062

py

Python

pyzoo/test/zoo/pipeline/nnframes/test_nn_classifier.py

jiaxinying/analytics-zoo

c3669b1736088df911c84b38fde3e90a571f51b7

[ "Apache-2.0" ]

4

2018-06-19T05:38:30.000Z

2020-06-22T14:26:26.000Z

pyzoo/test/zoo/pipeline/nnframes/test_nn_classifier.py

jiaxinying/analytics-zoo

c3669b1736088df911c84b38fde3e90a571f51b7

[ "Apache-2.0" ]

5

2021-06-08T23:28:18.000Z

2022-02-10T05:31:27.000Z

pyzoo/test/zoo/pipeline/nnframes/test_nn_classifier.py

jiaxinying/analytics-zoo

c3669b1736088df911c84b38fde3e90a571f51b7

[ "Apache-2.0" ]

1

2018-09-05T02:16:10.000Z

# # Copyright 2018 Analytics Zoo Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import errno import shutil import pytest from bigdl.nn.criterion import * from bigdl.nn.layer import * from bigdl.optim.optimizer import * from numpy.testing import assert_allclose from pyspark.ml import Pipeline, PipelineModel from pyspark.ml.feature import MinMaxScaler from pyspark.ml.tuning import ParamGridBuilder from pyspark.sql.types import * from zoo.common.nncontext import * from zoo.feature.common import * from zoo.feature.image import * from zoo.pipeline.api.keras import layers as ZLayer from zoo.pipeline.api.keras.models import Model as ZModel from zoo.pipeline.api.keras.optimizers import Adam as KAdam from zoo.pipeline.nnframes import * from zoo.util.tf import * class TestNNClassifer(): def setup_method(self, method): """ setup any state tied to the execution of the given method in a class. setup_method is invoked for every test method of a class. """ sparkConf = init_spark_conf().setMaster("local[1]").setAppName("testNNClassifer") self.sc = init_nncontext(sparkConf) self.sqlContext = SQLContext(self.sc) assert(self.sc.appName == "testNNClassifer") def teardown_method(self, method): """ teardown any state that was previously setup with a setup_method call. """ self.sc.stop() def get_estimator_df(self): data = self.sc.parallelize([ ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0)), ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0))]) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label", ArrayType(DoubleType(), False), False)]) df = self.sqlContext.createDataFrame(data, schema) return df def get_classifier_df(self): data = self.sc.parallelize([ ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0), ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0)]) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label", DoubleType(), False)]) df = self.sqlContext.createDataFrame(data, schema) return df def get_pipeline_df(self): data = self.sc.parallelize([ ((2.0, 1.0), (1.0, 2.0), 1.0), ((1.0, 2.0), (2.0, 1.0), 2.0), ((2.0, 1.0), (1.0, 2.0), 1.0), ((1.0, 2.0), (2.0, 1.0), 2.0)]) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label1", ArrayType(DoubleType(), False), False), StructField("label2", DoubleType(), False)]) df = self.sqlContext.createDataFrame(data, schema) return df def test_nnEstimator_construct_with_differnt_params(self): linear_model = Sequential().add(Linear(2, 2)) mse_criterion = MSECriterion() df = self.get_estimator_df() for e in [NNEstimator(linear_model, mse_criterion), NNEstimator(linear_model, mse_criterion, [2], [2]), NNEstimator(linear_model, mse_criterion, SeqToTensor([2]), SeqToTensor([2]))]: nnModel = e.setBatchSize(4).setMaxEpoch(1).fit(df) res = nnModel.transform(df) assert type(res).__name__ == 'DataFrame' def test_nnClassifier_construct_with_differnt_params(self): linear_model = Sequential().add(Linear(2, 2)) mse_criterion = MSECriterion() df = self.get_classifier_df() for e in [NNClassifier(linear_model, mse_criterion), NNClassifier(linear_model, mse_criterion, [2]), NNClassifier(linear_model, mse_criterion, SeqToTensor([2]))]: nnModel = e.setBatchSize(4).setMaxEpoch(1).fit(df) res = nnModel.transform(df) assert type(res).__name__ == 'DataFrame' def test_nnModel_construct_with_differnt_params(self): linear_model = Sequential().add(Linear(2, 2)) df = self.get_estimator_df() for e in [NNModel(linear_model), NNModel(linear_model, [2]), NNModel(linear_model, SeqToTensor([2]))]: res = e.transform(df) assert type(res).__name__ == 'DataFrame' assert e.getBatchSize() == 4 def test_nnClassiferModel_construct_with_differnt_params(self): linear_model = Sequential().add(Linear(2, 2)) df = self.get_classifier_df() for e in [NNClassifierModel(linear_model), NNClassifierModel(linear_model, [2]), NNClassifierModel(linear_model, SeqToTensor([2]))]: res = e.transform(df) assert type(res).__name__ == 'DataFrame' assert e.getBatchSize() == 4 def test_all_set_get_methods(self): linear_model = Sequential().add(Linear(2, 2)) mse_criterion = MSECriterion() estimator = NNEstimator(linear_model, mse_criterion, SeqToTensor([2]), SeqToTensor([2])) assert estimator.setBatchSize(30).getBatchSize() == 30 assert estimator.setMaxEpoch(40).getMaxEpoch() == 40 assert estimator.setLearningRate(1e-4).getLearningRate() == 1e-4 assert estimator.setFeaturesCol("abcd").getFeaturesCol() == "abcd" assert estimator.setLabelCol("xyz").getLabelCol() == "xyz" assert isinstance(estimator.setOptimMethod(Adam()).getOptimMethod(), Adam) nn_model = NNModel(linear_model, SeqToTensor([2])) assert nn_model.setBatchSize(20).getBatchSize() == 20 linear_model = Sequential().add(Linear(2, 2)) classNLL_criterion = ClassNLLCriterion() classifier = NNClassifier(linear_model, classNLL_criterion, SeqToTensor([2])) assert classifier.setBatchSize(20).getBatchSize() == 20 assert classifier.setMaxEpoch(50).getMaxEpoch() == 50 assert classifier.setLearningRate(1e-5).getLearningRate() == 1e-5 assert classifier.setLearningRateDecay(1e-9).getLearningRateDecay() == 1e-9 assert classifier.setCachingSample(False).isCachingSample() is False nn_classifier_model = NNClassifierModel(linear_model, SeqToTensor([2])) assert nn_classifier_model.setBatchSize((20)).getBatchSize() == 20 def test_nnEstimator_fit_nnmodel_transform(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion, SeqToTensor([2]), ArrayToTensor([2]))\ .setBatchSize(4).setLearningRate(0.2).setMaxEpoch(40) df = self.get_estimator_df() nnModel = estimator.fit(df) assert nnModel.getBatchSize() == 4 res = nnModel.transform(df) assert type(res).__name__ == 'DataFrame' res.registerTempTable("nnModelDF") # Compatible with spark 1.6 results = self.sqlContext.table("nnModelDF") count = results.rdd.count() data = results.rdd.collect() for i in range(count): row_label = data[i][1] row_prediction = data[i][2] assert_allclose(row_label[0], row_prediction[0], atol=0, rtol=1e-1) assert_allclose(row_label[1], row_prediction[1], atol=0, rtol=1e-1) def test_nnEstimator_fit_gradient_clipping(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion, SeqToTensor([2]), ArrayToTensor([2])) \ .setBatchSize(4).setLearningRate(0.2).setMaxEpoch(2)\ .setConstantGradientClipping(0.1, 0.2) df = self.get_estimator_df() estimator.fit(df) estimator.clearGradientClipping() estimator.fit(df) estimator.setGradientClippingByL2Norm(1.2) estimator.fit(df) def test_nnEstimator_fit_with_Cache_Disk(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion, SeqToTensor([2]), ArrayToTensor([2])) \ .setBatchSize(1).setLearningRate(0.2).setMaxEpoch(2) \ .setDataCacheLevel("DISK_AND_DRAM", 2) data = self.sc.parallelize([ ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0)), ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0)), ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0)), ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0))] * 10) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label", ArrayType(DoubleType(), False), False)]) df = self.sqlContext.createDataFrame(data, schema) estimator.fit(df) def test_nnEstimator_fit_with_non_default_featureCol(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion, SeqToTensor([2]), SeqToTensor([2]))\ .setBatchSize(4)\ .setLearningRate(0.01).setMaxEpoch(1) \ .setFeaturesCol("abcd").setLabelCol("xyz").setPredictionCol("tt") data = self.sc.parallelize([ ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0)), ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0))]) schema = StructType([ StructField("abcd", ArrayType(DoubleType(), False), False), StructField("xyz", ArrayType(DoubleType(), False), False)]) df = self.sqlContext.createDataFrame(data, schema) nnModel = estimator.fit(df) res = nnModel.transform(df) assert type(res).__name__ == 'DataFrame' assert res.select("abcd", "xyz", "tt").count() == 4 def test_nnEstimator_fit_with_different_OptimMethods(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion, SeqToTensor([2]), SeqToTensor([2]))\ .setBatchSize(4)\ .setLearningRate(0.01).setMaxEpoch(1) \ .setPredictionCol("tt") df = self.get_estimator_df() for opt in [SGD(learningrate=1e-3, learningrate_decay=0.0,), Adam(), LBFGS(), Adagrad(), Adadelta()]: nnModel = estimator.setOptimMethod(opt).fit(df) res = nnModel.transform(df) assert type(res).__name__ == 'DataFrame' assert res.select("features", "label", "tt").count() == 4 def test_nnEstimator_fit_with_adam_lr_schedile(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() df = self.get_estimator_df() nnModel = NNEstimator(model, criterion, SeqToTensor([2]), SeqToTensor([2])) \ .setBatchSize(4) \ .setLearningRate(0.01).setMaxEpoch(1) \ .setPredictionCol("tt") \ .setOptimMethod(KAdam( schedule=Plateau("Loss", factor=0.1, patience=2, mode="min", epsilon=0.01, cooldown=0, min_lr=1e-15))) \ .fit(df) res = nnModel.transform(df) assert type(res).__name__ == 'DataFrame' def test_nnEstimator_create_with_feature_size(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion, [2], [2])\ .setBatchSize(4).setLearningRate(0.2).setMaxEpoch(1) df = self.get_estimator_df() nnModel = estimator.fit(df) assert nnModel.getBatchSize() == 4 def test_nnEstimator_fit_with_train_val_summary(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() data = self.sc.parallelize([ ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0)), ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0))]) val_data = self.sc.parallelize([ ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0))]) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label", ArrayType(DoubleType(), False), False)]) df = self.sqlContext.createDataFrame(data, schema) val_df = self.sqlContext.createDataFrame(val_data, schema) tmp_dir = tempfile.mkdtemp() train_summary = TrainSummary(log_dir=tmp_dir, app_name="estTest") train_summary.set_summary_trigger("LearningRate", SeveralIteration(1)) val_summary = ValidationSummary(log_dir=tmp_dir, app_name="estTest") estimator = NNEstimator(model, criterion, SeqToTensor([2]), SeqToTensor([2]))\ .setBatchSize(4) \ .setMaxEpoch(5) \ .setTrainSummary(train_summary) assert (estimator.getValidation() is None) estimator.setValidation(EveryEpoch(), val_df, [MAE()], 2) \ .setValidationSummary(val_summary) assert (estimator.getValidation() is not None) nnModel = estimator.fit(df) res = nnModel.transform(df) lr_result = train_summary.read_scalar("LearningRate") mae_result = val_summary.read_scalar("MAE") assert isinstance(estimator.getTrainSummary(), TrainSummary) assert type(res).__name__ == 'DataFrame' assert len(lr_result) == 5 assert len(mae_result) == 4 def test_NNEstimator_checkpoint(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() df = self.get_estimator_df() try: tmp_dir = tempfile.mkdtemp() estimator = NNEstimator(model, criterion).setMaxEpoch(5)\ .setBatchSize(4)\ .setCheckpoint(tmp_dir, EveryEpoch(), False) checkpoint_config = estimator.getCheckpoint() assert checkpoint_config[0] == tmp_dir assert "EveryEpoch" in str(checkpoint_config) assert checkpoint_config[2] is False estimator.fit(df) assert len(os.listdir(tmp_dir)) > 0 finally: try: shutil.rmtree(tmp_dir) # delete directory except OSError as exc: if exc.errno != errno.ENOENT: # ENOENT - no such file or directory raise # re-raise exception def test_NNEstimator_multi_input(self): zx1 = ZLayer.Input(shape=(1, )) zx2 = ZLayer.Input(shape=(1, )) zz = ZLayer.merge([zx1, zx2], mode="concat") zy = ZLayer.Dense(2)(zz) zmodel = ZModel([zx1, zx2], zy) criterion = MSECriterion() df = self.get_estimator_df() estimator = NNEstimator(zmodel, criterion, [[1], [1]]).setMaxEpoch(5) \ .setBatchSize(4) nnmodel = estimator.fit(df) nnmodel.transform(df).collect() def test_NNEstimator_works_with_VectorAssembler_multi_input(self): if self.sc.version.startswith("2"): from pyspark.ml.linalg import Vectors from pyspark.ml.feature import VectorAssembler from pyspark.sql import SparkSession spark = SparkSession \ .builder \ .getOrCreate() df = spark.createDataFrame( [(1, 35, 109.0, Vectors.dense([2.0, 5.0, 0.5, 0.5]), 1.0), (2, 58, 2998.0, Vectors.dense([4.0, 10.0, 0.5, 0.5]), 2.0), (3, 18, 123.0, Vectors.dense([3.0, 15.0, 0.5, 0.5]), 1.0)], ["user", "age", "income", "history", "label"]) assembler = VectorAssembler( inputCols=["user", "age", "income", "history"], outputCol="features") df = assembler.transform(df) x1 = ZLayer.Input(shape=(1,)) x2 = ZLayer.Input(shape=(2,)) x3 = ZLayer.Input(shape=(2, 2,)) user_embedding = ZLayer.Embedding(5, 10)(x1) flatten = ZLayer.Flatten()(user_embedding) dense1 = ZLayer.Dense(2)(x2) gru = ZLayer.LSTM(4, input_shape=(2, 2))(x3) merged = ZLayer.merge([flatten, dense1, gru], mode="concat") zy = ZLayer.Dense(2)(merged) zmodel = ZModel([x1, x2, x3], zy) criterion = ClassNLLCriterion() classifier = NNClassifier(zmodel, criterion, [[1], [2], [2, 2]]) \ .setOptimMethod(Adam()) \ .setLearningRate(0.1) \ .setBatchSize(2) \ .setMaxEpoch(10) nnClassifierModel = classifier.fit(df) print(nnClassifierModel.getBatchSize()) res = nnClassifierModel.transform(df).collect() def test_NNModel_transform_with_nonDefault_featureCol(self): model = Sequential().add(Linear(2, 2)) nnModel = NNModel(model, SeqToTensor([2]))\ .setFeaturesCol("abcd").setPredictionCol("dcba") data = self.sc.parallelize([ ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0)), ((2.0, 1.0), (1.0, 2.0)), ((1.0, 2.0), (2.0, 1.0))]) schema = StructType([ StructField("abcd", ArrayType(DoubleType(), False), False), StructField("xyz", ArrayType(DoubleType(), False), False)]) df = self.sqlContext.createDataFrame(data, schema) res = nnModel.transform(df) assert type(res).__name__ == 'DataFrame' assert res.select("abcd", "dcba").count() == 4 def test_nnModel_set_Preprocessing(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion, [2], [2])\ .setBatchSize(4).setLearningRate(0.2).setMaxEpoch(1) df = self.get_estimator_df() nnModel = estimator.fit(df) newTransformer = ChainedPreprocessing([SeqToTensor([2]), TensorToSample()]) nnModel.setSamplePreprocessing(newTransformer) res = nnModel.transform(df) assert type(res).__name__ == 'DataFrame' assert res.count() == 4 def test_NNModel_save_load_BigDL_model(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion).setMaxEpoch(1).setBatchSize(4) df = self.get_estimator_df() nnModel = estimator.fit(df) try: tmp_dir = tempfile.mkdtemp() modelPath = os.path.join(tmp_dir, "model") nnModel.model.save(modelPath) loaded_model = Model.load(modelPath) resultDF = NNModel(loaded_model).transform(df) assert resultDF.count() == 4 finally: try: shutil.rmtree(tmp_dir) # delete directory except OSError as exc: if exc.errno != errno.ENOENT: # ENOENT - no such file or directory raise # re-raise exception def test_NNModel_save_load(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() estimator = NNEstimator(model, criterion).setMaxEpoch(1).setBatchSize(4) df = self.get_estimator_df() nnModel = estimator.fit(df) try: tmp_dir = tempfile.mkdtemp() modelPath = os.path.join(tmp_dir, "model") nnModel.save(modelPath) loaded_model = NNModel.load(modelPath) assert loaded_model.transform(df).count() == 4 finally: try: shutil.rmtree(tmp_dir) # delete directory except OSError as exc: if exc.errno != errno.ENOENT: # ENOENT - no such file or directory raise # re-raise exception def test_nnclassifier_fit_nnclassifiermodel_transform(self): model = Sequential().add(Linear(2, 2)) criterion = ClassNLLCriterion() classifier = NNClassifier(model, criterion, SeqToTensor([2])) \ .setBatchSize(4) \ .setLearningRate(0.2).setMaxEpoch(40) df = self.get_classifier_df() nnClassifierModel = classifier.fit(df) assert(isinstance(nnClassifierModel, NNClassifierModel)) res = nnClassifierModel.transform(df) assert type(res).__name__ == 'DataFrame' res.registerTempTable("nnClassifierModelDF") results = self.sqlContext.table("nnClassifierModelDF") count = results.rdd.count() data = results.rdd.collect() for i in range(count): row_label = data[i][1] row_prediction = data[i][2] assert row_label == row_prediction def test_nnclassifier_fit_with_Sigmoid(self): model = Sequential().add(Linear(2, 1)).add(Sigmoid()) criterion = BCECriterion() classifier = NNClassifier(model, criterion, SeqToTensor([2])) \ .setBatchSize(4) \ .setLearningRate(0.2).setMaxEpoch(40) data = self.sc.parallelize([ ((2.0, 1.0), 0.0), ((1.0, 2.0), 1.0), ((2.0, 1.0), 0.0), ((1.0, 2.0), 1.0)]) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label", DoubleType(), False)]) df = self.sqlContext.createDataFrame(data, schema) nnClassifierModel = classifier.fit(df) assert(isinstance(nnClassifierModel, NNClassifierModel)) res = nnClassifierModel.transform(df) res.registerTempTable("nnClassifierModelDF") results = self.sqlContext.table("nnClassifierModelDF") count = results.rdd.count() data = results.rdd.collect() for i in range(count): row_label = data[i][1] row_prediction = data[i][2] assert row_label == row_prediction def test_nnclassifierModel_set_Preprocessing(self): model = Sequential().add(Linear(2, 2)) criterion = ClassNLLCriterion() classifier = NNClassifier(model, criterion, SeqToTensor([2])) \ .setBatchSize(4) \ .setLearningRate(0.2).setMaxEpoch(1) df = self.get_classifier_df() nnClassifierModel = classifier.fit(df) newTransformer = ChainedPreprocessing([SeqToTensor([2]), TensorToSample()]) nnClassifierModel.setSamplePreprocessing(newTransformer) res = nnClassifierModel.transform(df) assert type(res).__name__ == 'DataFrame' assert res.count() == 4 def test_nnclassifier_create_with_size_fit_transform(self): model = Sequential().add(Linear(2, 2)) criterion = ClassNLLCriterion() classifier = NNClassifier(model, criterion, [2]) \ .setBatchSize(4) \ .setLearningRate(0.2).setMaxEpoch(40) df = self.get_classifier_df() nnClassifierModel = classifier.fit(df) res = nnClassifierModel.transform(df) assert type(res).__name__ == 'DataFrame' def test_nnclassifier_fit_different_optimMethods(self): model = Sequential().add(Linear(2, 2)) criterion = ClassNLLCriterion() classifier = NNClassifier(model, criterion, SeqToTensor([2]))\ .setBatchSize(4) \ .setLearningRate(0.2).setMaxEpoch(1) df = self.get_classifier_df() for opt in [Adam(), SGD(learningrate=1e-2, learningrate_decay=1e-6,), LBFGS(), Adagrad(), Adadelta()]: nnClassifierModel = classifier.setOptimMethod(opt).fit(df) res = nnClassifierModel.transform(df) res.collect() assert type(res).__name__ == 'DataFrame' def test_nnClassifier_fit_with_train_val_summary(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() data = self.sc.parallelize([ ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0), ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0)]) val_data = self.sc.parallelize([ ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0)]) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label", DoubleType(), False)]) df = self.sqlContext.createDataFrame(data, schema) val_df = self.sqlContext.createDataFrame(val_data, schema) tmp_dir = tempfile.mkdtemp() train_summary = TrainSummary(log_dir=tmp_dir, app_name="nnTest") train_summary.set_summary_trigger("LearningRate", SeveralIteration(1)) val_summary = ValidationSummary(log_dir=tmp_dir, app_name="nnTest") classfier = NNClassifier(model, criterion, SeqToTensor([2]))\ .setBatchSize(4) \ .setTrainSummary(train_summary).setMaxEpoch(5) \ .setValidation(EveryEpoch(), val_df, [Top1Accuracy()], 2) \ .setValidationSummary(val_summary) nnModel = classfier.fit(df) res = nnModel.transform(df) lr_result = train_summary.read_scalar("LearningRate") top1_result = val_summary.read_scalar("Top1Accuracy") assert isinstance(classfier.getTrainSummary(), TrainSummary) assert type(res).__name__ == 'DataFrame' assert len(lr_result) == 5 assert len(top1_result) == 4 def test_nnestimator_with_param_maps(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() data = self.sc.parallelize([ ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0), ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0)]) val_data = self.sc.parallelize([ ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0)]) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label", DoubleType(), False)]) df = self.sqlContext.createDataFrame(data, schema) val_df = self.sqlContext.createDataFrame(val_data, schema) classfier = NNEstimator(model, criterion, SeqToTensor([2]))\ .setBatchSize(4).setMaxEpoch(5) \ .setValidation(EveryEpoch(), val_df, [Top1Accuracy()], 2) param = ParamGridBuilder().addGrid(classfier.learningRate, [1e-3, 1.0]).build() print(param) models = classfier.fit(df, params=param) # print(models.model.get_weights()) assert len(models) == 2 w1 = models[0].model.get_weights() w2 = models[1].model.get_weights() for ww1, ww2 in zip(w1, w2): diff = np.sum((ww1 - ww2) ** 2) assert diff > 1e-2 def test_nnclassifier_with_param_maps(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() data = self.sc.parallelize([ ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0), ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0)]) val_data = self.sc.parallelize([ ((2.0, 1.0), 1.0), ((1.0, 2.0), 2.0)]) schema = StructType([ StructField("features", ArrayType(DoubleType(), False), False), StructField("label", DoubleType(), False)]) df = self.sqlContext.createDataFrame(data, schema) val_df = self.sqlContext.createDataFrame(val_data, schema) print(model.get_weights()) classfier = NNClassifier(model, criterion, SeqToTensor([2]))\ .setBatchSize(4).setMaxEpoch(5) \ .setValidation(EveryEpoch(), val_df, [Top1Accuracy()], 2) param = ParamGridBuilder().addGrid(classfier.learningRate, [1e-3, 1.0]).build() models = classfier.fit(df, params=param) assert len(models) == 2 w1 = models[0].model.get_weights() w2 = models[1].model.get_weights() for ww1, ww2 in zip(w1, w2): diff = np.sum((ww1 - ww2) ** 2) assert diff > 1e-2 def test_nnclassifier_in_pipeline(self): if self.sc.version.startswith("1"): from pyspark.mllib.linalg import Vectors df = self.sqlContext.createDataFrame( [(Vectors.dense([2.0, 1.0]), 1.0), (Vectors.dense([1.0, 2.0]), 2.0), (Vectors.dense([2.0, 1.0]), 1.0), (Vectors.dense([1.0, 2.0]), 2.0), ], ["features", "label"]) scaler = MinMaxScaler().setInputCol("features").setOutputCol("scaled") model = Sequential().add(Linear(2, 2)) criterion = ClassNLLCriterion() classifier = NNClassifier(model, criterion)\ .setBatchSize(4) \ .setLearningRate(0.01).setMaxEpoch(1).setFeaturesCol("scaled") pipeline = Pipeline(stages=[scaler, classifier]) pipelineModel = pipeline.fit(df) res = pipelineModel.transform(df) assert type(res).__name__ == 'DataFrame' # TODO: Add test for ML Vector once infra is ready. def test_NNClassifierModel_save_load_BigDL_model(self): model = Sequential().add(Linear(2, 2)) criterion = MSECriterion() classifier = NNClassifier(model, criterion).setMaxEpoch(1).setBatchSize(4) df = self.get_classifier_df() nnClassifierModel = classifier.fit(df) try: tmp_dir = tempfile.mkdtemp() modelPath = os.path.join(tmp_dir, "model") nnClassifierModel.model.save(modelPath) loaded_model = Model.load(modelPath) resultDF = NNClassifierModel(loaded_model).transform(df) assert resultDF.count() == 4 finally: try: shutil.rmtree(tmp_dir) # delete directory except OSError as exc: if exc.errno != errno.ENOENT: # ENOENT - no such file or directory raise # re-raise exception def test_NNClassifierModel_save_load(self): model = Sequential().add(Linear(2, 2)) criterion = ClassNLLCriterion() classifier = NNClassifier(model, criterion, [2]).setMaxEpoch(1).setBatchSize(4) df = self.get_classifier_df() nnClassifierModel = classifier.fit(df) try: tmp_dir = tempfile.mkdtemp() modelPath = os.path.join(tmp_dir, "model") nnClassifierModel.save(modelPath) loaded_model = NNClassifierModel.load(modelPath) assert (isinstance(loaded_model, NNClassifierModel)) assert loaded_model.transform(df).count() == 4 finally: try: shutil.rmtree(tmp_dir) # delete directory except OSError as exc: if exc.errno != errno.ENOENT: # ENOENT - no such file or directory raise # re-raise exception def test_NNModel_NNClassifier_pipeline_save_load(self): if self.sc.version.startswith("2.3") or self.sc.version.startswith("2.4"): from pyspark.ml.feature import MinMaxScaler from pyspark.ml.linalg import Vectors df = self.sqlContext.createDataFrame( [(Vectors.dense([2.0, 1.0]), 1.0), (Vectors.dense([1.0, 2.0]), 2.0), (Vectors.dense([2.0, 1.0]), 1.0), (Vectors.dense([1.0, 2.0]), 2.0), ], ["features", "label"]) scaler = MinMaxScaler().setInputCol("features").setOutputCol("scaled") model = Sequential().add(Linear(2, 2)) criterion = ClassNLLCriterion() classifier = NNClassifier(model, criterion)\ .setBatchSize(4) \ .setLearningRate(0.01).setMaxEpoch(1).setFeaturesCol("scaled") pipeline = Pipeline(stages=[scaler, classifier]) pipeline_model = pipeline.fit(df) try: tmp_dir = tempfile.mkdtemp() modelPath = os.path.join(tmp_dir, "model") pipeline_model.save(modelPath) loaded_model = PipelineModel.load(modelPath) df2 = self.sqlContext.createDataFrame( [(Vectors.dense([2.0, 1.0]), 1.0), (Vectors.dense([1.0, 2.0]), 2.0), (Vectors.dense([2.0, 1.0]), 1.0), (Vectors.dense([1.0, 2.0]), 2.0), ], ["features", "label"]) assert loaded_model.transform(df2).count() == 4 finally: try: shutil.rmtree(tmp_dir) # delete directory except OSError as exc: if exc.errno != errno.ENOENT: # ENOENT - no such file or directory raise # re-raise exception def test_input_node_of_tfnet_from_session(self): import tensorflow as tff input1 = tff.placeholder(dtype=tff.float32, shape=(None, 2)) input2 = tff.placeholder(dtype=tff.float32, shape=(None, 2)) hidden = tff.layers.dense(input1, 4) output = tff.layers.dense(hidden, 1) sess = tff.Session() sess.run(tff.global_variables_initializer()) tmp_dir = tempfile.mkdtemp() modelPath = os.path.join(tmp_dir, "model") raised_error = False try: export_tf(sess, modelPath, inputs=[input1, input2], outputs=[output]) except ValueError as v: assert (((str(v)).find((input2.name)[0:-2])) != -1) raised_error = True finally: try: shutil.rmtree(modelPath) # delete directory except OSError as exc: if exc.errno != errno.ENOENT: # ENOENT - no such file or directory raise # re-raise exception if not raised_error: raise ValueError("we do not find this error, test failed") def test_XGBClassifierModel_predict(self): from sys import platform if platform in ("darwin", "win32"): return resource_path = os.path.join(os.path.split(__file__)[0], "../../resources") path = os.path.join(resource_path, "xgbclassifier/") modelPath = path + "XGBClassifer.bin" filePath = path + "test.csv" model = XGBClassifierModel.loadModel(modelPath, 2) from pyspark.sql import SparkSession spark = SparkSession \ .builder \ .getOrCreate() df = spark.read.csv(filePath, sep=",", inferSchema=True, header=True) model.setFeaturesCol(["age", "gender", "jointime", "star"]) predict = model.transform(df) predict.count() if __name__ == "__main__": pytest.main()

40.628042

96

0.587331

794ac455a8adc30cfc215aae1c51c385a0ba1c07

8,621

py

Python

jobs/ztb_data_job.py

masling/stock

f7a0add2b7533ff43d1ed1a238ec14f55b39e488

[ "Apache-2.0" ]

null

jobs/ztb_data_job.py

masling/stock

f7a0add2b7533ff43d1ed1a238ec14f55b39e488

[ "Apache-2.0" ]

null

jobs/ztb_data_job.py

masling/stock

f7a0add2b7533ff43d1ed1a238ec14f55b39e488

[ "Apache-2.0" ]

null

#!/usr/local/bin/python # -*- coding: utf-8 -*- __author__ = 'Masling' # 每天的涨跌停数据来源金融界及东方财富网 import xlwt import pandas as pd import tushare as ts import datetime import libs.common as common import libs.stock_ztb as ztb ts.set_token('0d60b78ce095601b582c78f71b954e0e87a6e352b738a6a225d71649') # c代码 ,m 1沪 0深 ,n 名称,p 最新价格(里),zdp 涨跌幅, amount 成交额,ltsz 流通市值,tshare 总市值,hs 换手率,lbc 连板数,fbt 首次封板时间, # lbt 最后封板时间，fund 封板资金,zbc 炸版次数,hybk 所属行业 ,zttj 涨停统计 ,'zf 振幅，zs 涨速' 'ztf ,ztp 涨停价，nh 是否新高， # lb 量比,cc 入选理由,zz 涨速' east_data_index = {"c": "代码", "n": "名称", "p": "最新价", "zdp": "涨跌幅", "amount": "成交额", "ltsz": "流通市值", "tshare": "总市值", "hs": "换手率", "lbc": "连板数", "fbt": "首次封板时间", "lbt": "最后封板时间", "fund": "封单金额", "zbc": "打开次数", "hybk": "所属行业", "zttj": "涨停统计", "zf": "振幅", "zs": "涨速", "ztp": "涨停价", "nh": "是否新高"} jrj_data_index = [u'代码', u'名称', u'最新价', u'涨跌幅', u'封成比', u'封流比', u'封单金额', u'最后封板时间', u'首次封板时间', u'打开次数', u'振幅', u'涨停强度'] def save_excel(date, data_today, data_sum): # data is list type w = xlwt.Workbook(encoding='gbk') lb_st = w.add_sheet('连板股票') date_group = data_sum.groupby(['日期']).groups lb_group = data_sum.groupby(['连板数']).groups point_y = 0 point_x = 1 style_title = xlwt.XFStyle() style_title.font.bold = True style_title.font.height = 20 * 14 lb_list = list(lb_group.keys())[::-1] lb_dic = {} for lbs in lb_list: lb_st.write(point_x, point_y, lbs, style_title) lb_dic[point_x] = 1 point_x += 1 point_y = 1 point_x = 0 style = xlwt.XFStyle() style.alignment.wrap = 1 lb_st.col(0).width = 20 * 100 for df_date in date_group.keys(): lb_st.write(point_x, point_y, df_date.strftime('%Y-%m-%d'), style_title) lb_st.col(point_y).width = 256 * 20 point_i_x = 1 for lbs in lb_list: df = data_sum[(data_sum['日期'].astype(str) == df_date.strftime('%Y-%m-%d')) & (data_sum['连板数'] == lbs)] list_stock = df['名称'].values.tolist() lb_st.write(point_i_x, point_y, "\r\n".join(list_stock), style) lb_dic[point_i_x] = max(lb_dic[point_i_x], len(list_stock)) point_i_x += 1 point_y += 1 for key, value in lb_dic.items(): lb_st.row(key).height_mismatch = True lb_st.row(key).height = 20 * value * 15 # 20为基准数 ztb_st = w.add_sheet(date + '涨停板') excel_filename = date + ".xls" point_x = 0 point_y = 0 for columns in data_today.columns: ztb_st.write(point_x, point_y, columns, style_title) point_y += 1 point_x += 1 ztb_st.col(0).width = 150 * 20 ztb_st.col(7).width = 200 * 20 ztb_st.col(8).width = 200 * 20 ztb_st.col(9).width = 200 * 20 ztb_st.col(10).width = 200 * 20 ztb_st.col(13).width = 200 * 20 ztb_st.col(14).width = 200 * 20 style_title.font.bold = False data_today['涨停统计'] = data_today['涨停统计'].map(lambda x: eval(x)) for row in data_today.values: point_y = 0 for col in row: print(point_x, point_y, col) if isinstance(col, datetime.date): ztb_st.write(point_x, point_y, col.strftime('%Y-%m-%d')) else: ztb_st.write(point_x, point_y, col) point_y += 1 point_x += 1 w.save(excel_filename) def insert_or_update(data, primary_keys, tables='stock_quotations'): values = ['?'] * len(data.columns) update_sql = "" for column in data.columns: if column not in primary_keys: update_sql += "`%s`=?," % column if len(update_sql) > 0: update_sql = update_sql.rstrip(",") insert_sql = "insert into %s (%s) values (%s) on duplicate key UPDATE %s" % (tables, ",".join(data.columns), ",".join(values), update_sql) insert_sql = insert_sql.replace("?", "%s") data_copy = data.drop(primary_keys, axis=1) with common.conn() as db: for index, row in data.iterrows(): str_data = [str(x) for x in row.values.tolist()] str_data2 = [str(x) for x in data_copy.loc[index,].values.tolist()] params = tuple(str_data + str_data2) try: db.execute(insert_sql, params) except Exception as e: print("error :", e) def stat_all(date_time): # tmp_datetime db = common.engine() # today='2018-04-16' # 填补以前的数据 # x=pd.date_range('20170101','20180312') # date_list = [ for i in list(pd.date_range('20170401','20171231')) date = date_time.strftime('%Y%m%d') if common.is_holiday(date_time): common.logger.info('Holiday') return common.logger.info("start") obj = ztb.GetZDT() obj.today = date data_jrj = obj.get_jrj_data() # 读取金融届网站的涨停数据 data_jrj = pd.DataFrame(data_jrj, columns=jrj_data_index) data_jrj['日期'] = date # 去掉ST 新股退市股科创板 data_jrj = data_jrj[ ~(data_jrj['名称'].str.contains('ST') | data_jrj['名称'].str.startswith("N") | data_jrj['名称'].str.endswith("退") | data_jrj['代码'].str.startswith("68") )] data_jrj['封成比'] = data_jrj['封成比'].map(lambda x: round(x * 100, 3)) data_jrj['封流比'] = data_jrj['封流比'].map(lambda x: round(x * 100, 3)) insert_or_update(data_jrj, ['日期', '代码']) print(data_jrj) data_east = obj.get_east_data() # 读取金融届网站的涨停数据 df_ztb = pd.DataFrame.from_dict(data_east["ztb"], orient='columns') df_ztb = df_ztb.drop(['m'], axis=1) # 根据列名进行删除(去掉不需要的列) df_ztb = df_ztb.rename(columns=east_data_index) # 列名重命名 df_ztb['日期'] = date # 去掉ST 新股退市股科创板 df_ztb = df_ztb[ ~(df_ztb['名称'].str.contains('ST') | df_ztb['名称'].str.startswith("N") | df_ztb['名称'].str.endswith("退") | df_ztb['代码'].str.startswith("68") )] df_ztb['涨跌幅'] = df_ztb['涨跌幅'].map(lambda x: round(x, 3)) df_ztb['换手率'] = df_ztb['换手率'].map(lambda x: round(x, 3)) df_ztb['流通市值'] = df_ztb['流通市值'].map(lambda x: round(x, 3)) df_ztb['总市值'] = df_ztb['总市值'].map(lambda x: round(x, 3)) insert_or_update(df_ztb, ['日期', '代码']) print(df_ztb) sql_update_stock = "update stockInfo s INNER JOIN (select `代码`, `名称`,`所属行业`,`成交额`,`流通市值`,`连板数`,`涨停统计` " \ "from stock_quotations where `代码` in (select `代码` from stockInfo) and " \ "`日期` = (select max(`日期`) from stock_quotations)) q on s.`代码`=q.`代码` set " \ "s.`名称`=q.`名称`,s.`所属行业`=q.`所属行业`,s.`成交额`=q.`成交额`,s.`流通市值`=q.`流通市值`," \ "s.`连板数`=q.`连板数`,s.`涨停统计`=q.`涨停统计`" common.insert(sql_update_stock) sql_insert_stock = "insert into stockInfo (`代码`, `名称`,`所属行业`,`成交额`,`流通市值`,`连板数`,`涨停统计`) " \ "select `代码`, `名称`,`所属行业`,`成交额`,`流通市值`,`连板数`,`涨停统计` from stock_quotations " \ "where `代码` not in (select `代码` from stockInfo) and " \ "`日期` = (select max(`日期`) from stock_quotations) " common.insert(sql_insert_stock) sum_df = pd.read_sql("SELECT `日期`,CONCAT(名称,'[',代码,']') as 名称,连板数 from stock_quotations where `连板数`>1", db) today = pd.read_sql("select `日期`, `代码`, `名称`, `最新价`, `涨跌幅`, `封成比`, `封流比`, `封单金额`, `成交额`, `流通市值`," " `总市值`, `换手率`, `连板数`, `首次封板时间`, `最后封板时间`, `打开次数`, `涨停统计`, `涨停强度`, `振幅`, " " `所属行业` from stock_quotations where 日期='%s' order by `涨停强度` desc" % date, db) today['涨停统计'].astype(str) today['涨停统计'] = today['涨停统计'].map(lambda x: str("{ct}/{days}".format(**eval(x)))) save_excel(date, today, sum_df) # pro = ts.pro_api() # pd.read_sql("select * from stockInfo where ") # common.select() # final_data['concept'] = '' # for code in final_data['ts_code'].values: # concept = pro.concept_detail(ts_code=code) # # print(code) # # print(concept['concept_name'].values) # concept_str = '/'.join(cept for cept in concept['concept_name'].values) # # print(concept_str) # if len(concept_str) == 0: # concept_str = '暂无概念数据' # final_data.loc[final_data['ts_code'] == code, 'concept'] += concept_str # main函数入口 if __name__ == '__main__': # stat_all('20210409') # 使用方法传递。 tmp_datetime = common.run_with_args(stat_all)

41.647343

114

0.554808

794ac5320b0439c006ae983c177023eae28cf09d

1,001

py

Python

uamobile/data/cidr/ezweb.py

SandySalvatore/uamobile

51c637effc65b863c8f1897d971a13bb099bdb84

[ "MIT" ]

null

uamobile/data/cidr/ezweb.py

SandySalvatore/uamobile

51c637effc65b863c8f1897d971a13bb099bdb84

[ "MIT" ]

null

uamobile/data/cidr/ezweb.py

SandySalvatore/uamobile

51c637effc65b863c8f1897d971a13bb099bdb84

[ "MIT" ]

null

DATA = [ '111.107.116.64/26', '106.162.214.160/29', '111.107.116.192/28', '210.230.128.224/28', '219.108.158.0/27', '219.125.146.0/28', '61.117.2.32/29', '61.117.2.40/29', '219.108.158.40/29', '111.86.142.0/26', '111.86.141.64/26', '111.86.141.128/26', '111.86.141.192/26', '27.90.136.0/27', '27.90.136.32/27', '27.90.136.64/27', '27.90.136.96/27', '27.90.136.128/27', '27.90.136.160/27', '27.90.136.192/27', '27.90.137.192/27', '27.90.137.224/27', '27.90.136.224/27', '27.90.137.0/27', '27.90.137.32/27', '27.90.137.64/27', '27.90.137.96/27', '27.90.137.128/27', '27.90.137.160/27', '111.86.143.192/27', '111.86.143.224/27', '111.86.147.0/27', '111.86.142.128/27', '111.86.142.160/27', '111.86.142.192/27', '111.86.142.224/27', '111.86.143.0/27', '111.86.143.32/27', '111.86.147.32/27', '111.86.147.64/27', '111.86.147.96/27', '111.86.147.128/27', '111.86.147.160/27', '111.86.147.192/27', '111.86.147.224/27']

21.297872

29

0.546454

794ac57238462f0fc1ff2db1a817f51ccc2dab7b

64,766

py

Python

Lib/Plugin.py

iCH3F/ToonTime

7efeb94f748706df72b766b2ce5dade3dc171fed

[ "MIT" ]

2

2020-05-31T23:29:54.000Z

2022-01-11T18:11:07.000Z

Lib/Plugin.py

iCH3F/ToonTime

7efeb94f748706df72b766b2ce5dade3dc171fed

[ "MIT" ]

null

Lib/Plugin.py

iCH3F/ToonTime

7efeb94f748706df72b766b2ce5dade3dc171fed

[ "MIT" ]

1

2020-05-31T23:19:16.000Z

# -*- coding: utf-8 -*- import re import sys import requests from itertools import chain from base64 import b64decode from time import time, sleep from urlparse import parse_qsl from string import ascii_uppercase from urllib import quote_plus, urlencode import xbmc import xbmcgui import xbmcaddon import xbmcplugin from Lib.Common import * from Lib.SimpleTrakt import SimpleTrakt # Disable urllib3's "InsecureRequestWarning: Unverified HTTPS request is being made" warnings import requests from requests.packages.urllib3.exceptions import InsecureRequestWarning requests.packages.urllib3.disable_warnings(InsecureRequestWarning) PLUGIN_ID = int(sys.argv[1]) PLUGIN_URL = sys.argv[0] BASEURL = 'https://www.thewatchcartoononline.tv' # Due to a recent bug on the server end, the mobile URL is now only used on 'makeLatestCatalog()'. BASEURL_MOBILE = 'https://m.wcostream.com' # Mobile version of one of their domains (seems to be the only one). PROPERTY_CATALOG_PATH = 'wnt2.catalogPath' PROPERTY_CATALOG = 'wnt2.catalog' PROPERTY_EPISODE_LIST_URL = 'wnt2.listURL' PROPERTY_EPISODE_LIST_DATA = 'wnt2.listData' PROPERTY_LATEST_MOVIES = 'wnt2.latestMovies' PROPERTY_INFO_ITEMS = 'wnt2.infoItems' PROPERTY_SESSION_COOKIE = 'wnt2.cookie' ADDON = xbmcaddon.Addon() # Show catalog: whether to show the catalog categories or to go straight to the "ALL" section with all items visible. ADDON_SHOW_CATALOG = ADDON.getSetting('showCatalog') == 'true' # Use Latest Releases date: whether to sort the Latest Releases items by their date, or with a catalog. ADDON_LATEST_DATE = ADDON.getSetting('useLatestDate') == 'true' # Use Latest Releases thumbs: whether to show a little thumbnail available for the Latest Releases items only. ADDON_LATEST_THUMBS = ADDON.getSetting('showLatestThumbs') == 'true' # Use poster images for each catalog folder. Makes for a better experience on custom Kodi skins. ADDON_CATALOG_THUMBS = ADDON.getSetting('showCatalogThumbs') == 'true' ADDON_ICON = ADDON.getAddonInfo('icon') ADDON_ICON_DICT = {'icon': ADDON_ICON, 'thumb': ADDON_ICON, 'poster': ADDON_ICON} ADDON_TRAKT_ICON = 'special://home/addons/plugin.video.watchnixtoons2/resources/traktIcon.png' # To let the source website know it's this plugin. Also used inside "makeLatestCatalog()" and "actionResolve()". WNT2_USER_AGENT = 'Mozilla/5.0 (compatible; WatchNixtoons2/0.4.1; ' \ '+https://github.com/doko-desuka/plugin.video.watchnixtoons2)' MEDIA_HEADERS = None # Initialized in 'actionResolve()'. # Url paths: paths to parts of the website, to be added to the BASEURL url. # Also used to tell what kind of catalog is loaded in memory. # In case they change in the future it'll be easier to modify in here. URL_PATHS = { 'latest': 'latest', # No path used, 'makeLatestCatalog()' uses the homepage of the mobile website. 'popular': 'popular', # No path used, 'makePopularCatalog()' uses the hompage of the desktop website. 'dubbed': '/dubbed-anime-list', 'cartoons': '/cartoon-list', 'subbed': '/subbed-anime-list', 'movies': '/movie-list', 'latestmovies': '/anime/movies', 'ova': '/ova-list', 'search': '/search', 'genre': '/search-by-genre' } def actionMenu(params): def _menuItem(title, data, color): item = xbmcgui.ListItem('[B][COLOR ' + color + ']' + title + '[/COLOR][/B]', label2 = title) item.setArt(ADDON_ICON_DICT) item.setInfo('video', {'title': title, 'plot': title}) return (buildURL(data), item, True) xbmcplugin.addDirectoryItems( PLUGIN_ID, ( _menuItem('Latest Releases', {'action': 'actionCatalogMenu', 'path': URL_PATHS['latest']}, 'mediumaquamarine'), _menuItem( # Make the Latest Movies menu go straight to the item list, no catalog. 'Latest Movies', {'action': 'actionLatestMoviesMenu', 'path': URL_PATHS['latestmovies']}, 'mediumaquamarine' ), _menuItem('Popular & Ongoing Series', {'action': 'actionCatalogMenu', 'path': URL_PATHS['popular']}, 'mediumaquamarine'), _menuItem('Dubbed Anime', {'action': 'actionCatalogMenu', 'path': URL_PATHS['dubbed']}, 'lightgreen'), _menuItem('Cartoons', {'action': 'actionCatalogMenu', 'path': URL_PATHS['cartoons']}, 'lightgreen'), _menuItem('Subbed Anime', {'action': 'actionCatalogMenu', 'path': URL_PATHS['subbed']}, 'lightgreen'), _menuItem('Movies', {'action': 'actionCatalogMenu', 'path': URL_PATHS['movies']}, 'lightgreen'), _menuItem('OVA Series', {'action': 'actionCatalogMenu', 'path': URL_PATHS['ova']}, 'lightgreen'), _menuItem('Search', {'action': 'actionSearchMenu', 'path': 'search'}, 'lavender'), # Non-web path. _menuItem('Settings', {'action': 'actionShowSettings','path': 'settings'}, 'lavender') # Non-web path. ) ) xbmcplugin.endOfDirectory(PLUGIN_ID) def actionCatalogMenu(params): xbmcplugin.setContent(PLUGIN_ID, 'tvshows') catalog = getCatalogProperty(params) if ADDON_SHOW_CATALOG: def _catalogMenuItemsMake(): items = [ ] if ADDON_CATALOG_THUMBS: # The catalog folders will each get a letter image, taken from the web (this way # these images don't have to be distributed w/ the add-on, if they're not needed). # After they're downloaded, the images exist in Kodi's image cache folders. THUMBS_BASEURL = 'https://doko-desuka.github.io/128h/' artDict = {'thumb': None} miscItem = None for sectionName in sorted(catalog.iterkeys()): if catalog[sectionName]: item = xbmcgui.ListItem(sectionName) # Correct the address for the '#' (miscellaneous, non-letter) category. artDict['thumb'] = THUMBS_BASEURL + ('0' if sectionName == '#' else sectionName) + '.png' item.setArt(artDict) item.setInfo('video', {'plot': sectionName}) items.append( ( buildURL({'action': 'actionCatalogSection', 'path': params['path'], 'section': sectionName}), item, True ) ) else: items = [ ( buildURL({'action': 'actionCatalogSection', 'path': params['path'], 'section': sectionName}), xbmcgui.ListItem(sectionName), True ) for sectionName in sorted(catalog.iterkeys()) if len(catalog[sectionName]) ] # See if an "All" folder is necessary (when there's more than one folder in the catalog). if len(items) > 1: sectionAll = ( buildURL({'action': 'actionCatalogSection', 'path': params['path'], 'section': 'ALL'}), xbmcgui.ListItem('All'), True ) if ADDON_CATALOG_THUMBS: artDict['thumb'] = THUMBS_BASEURL + 'ALL.png' sectionAll[1].setArt(artDict) sectionAll[1].setInfo('video', {'plot': 'All'}) return [sectionAll] + items else: return items items = _catalogMenuItemsMake() if items: if len(items) > 1: xbmcplugin.addDirectoryItems(PLUGIN_ID, items) else: # Conveniency when a search leads to only 1 result, show it already without the catalog screen. params['section'] = 'ALL' actionCatalogSection(params) return else: xbmcplugin.addDirectoryItem(PLUGIN_ID, '', xbmcgui.ListItem('(No Results)'), isFolder=False) xbmcplugin.endOfDirectory(PLUGIN_ID) setViewMode() else: params['section'] = 'ALL' actionCatalogSection(params) def actionCatalogSection(params): catalog = getCatalogProperty(params) path = params['path'] # Set up a boolean indicating if the catalog items are already playable, instead of being folders # with more items inside. # This is true for the OVA, movies, latest-episodes, movie-search and episode-search catalogs. # Items in these catalogs link to the video player pages already. isSpecial = ( path in {URL_PATHS['ova'], URL_PATHS['movies'], URL_PATHS['latest']} or params.get('searchType', 'series') != 'series' # not series = movies or episodes search ) if isSpecial: action = 'actionResolve' isFolder = False else: action = 'actionEpisodesMenu' isFolder = True thumb = params.get('thumb', ADDON_ICON) if path != URL_PATHS['latest'] or not ADDON_LATEST_THUMBS: artDict = {'icon': thumb, 'thumb': thumb, 'poster': thumb} if thumb else None else: artDict = {'icon': thumb, 'thumb': 'DefaultVideo.png', 'poster': 'DefaultVideo.png'} if thumb else None # Persistent property with item metadata, used with the "Show Information" context menu. infoItems = getWindowProperty(PROPERTY_INFO_ITEMS) or { } if 'query' not in params and ADDON.getSetting('cleanupEpisodes') == 'true': listItemFunc = makeListItemClean else: listItemFunc = makeListItem if params['section'] == 'ALL': sectionItems = chain.from_iterable(catalog[sectionName] for sectionName in sorted(catalog)) else: sectionItems = catalog[params['section']] def _sectionItemsGen(): if ADDON_LATEST_THUMBS and path == URL_PATHS['latest']: # Special-case for the 'Latest Releases' catalog, which has some thumbnails available. # Each 'entry' is (URL, htmlTitle, thumb). NO_THUMB = '-120-72.jpg' # As seen on 2019-04-15. for entry in sectionItems: entryURL = entry[0] entryArt = ( artDict if entry[2].startswith(NO_THUMB) else {'icon':ADDON_ICON,'thumb':entry[2],'poster':entry[2]} ) # If there's metadata for this entry (requested by the user with "Show Information"), use it. if entryURL in infoItems: itemPlot, itemThumb = infoItems[entryURL] yield ( buildURL({'action': action, 'url': entryURL}), listItemFunc(entry[1], entryURL, entryArt, itemPlot, isFolder, isSpecial, None), isFolder ) else: yield ( buildURL({'action': action, 'url': entryURL}), listItemFunc(entry[1], entryURL, entryArt, '', isFolder, isSpecial, params), isFolder ) else: # Normal item listing, each 'entry' is (URL, htmlTitle). for entry in sectionItems: entryURL = entry[0] if entryURL in infoItems: itemPlot, itemThumb = infoItems[entryURL] entryArt = {'icon': ADDON_ICON, 'thumb': itemThumb, 'poster': itemThumb} yield ( buildURL({'action': action, 'url': entryURL}), listItemFunc(entry[1], entryURL, entryArt, itemPlot, isFolder, isSpecial, None), isFolder ) else: yield ( buildURL({'action': action, 'url': entryURL}), listItemFunc(entry[1], entryURL, artDict, '', isFolder, isSpecial, params), isFolder ) xbmcplugin.addDirectoryItems(PLUGIN_ID, tuple(_sectionItemsGen())) xbmcplugin.endOfDirectory(PLUGIN_ID) setViewMode() # Set the skin layout mode, if the option is enabled. def actionEpisodesMenu(params): xbmcplugin.setContent(PLUGIN_ID, 'episodes') # Memory-cache the last episode list, to help when the user goes back and forth while watching # multiple episodes of the same show. This way only one web request is needed for the same show. lastListURL = getRawWindowProperty(PROPERTY_EPISODE_LIST_URL) if lastListURL and lastListURL == params['url']: listData = getWindowProperty(PROPERTY_EPISODE_LIST_DATA) else: # New domain safety replace, in case the user is coming in from an old Kodi favorite item. url = params['url'].replace('watchcartoononline.io', 'thewatchcartoononline.tv', 1) r = requestHelper(url if url.startswith('http') else BASEURL + url) html = r.text plot, thumb = getPageMetadata(html) dataStartIndex = html.find('"sidebar_right3"') if dataStartIndex == -1: raise Exception('Episode list scrape fail: ' + url) # Episode list data: a tuple with the thumb, plot and an inner tuple of per-episode data. listData = ( thumb, plot, tuple( match.groups() for match in re.finditer( '''<a href="([^"]+).*?>([^<]+)''', html[dataStartIndex : html.find('"sidebar-all"')] ) ) ) setRawWindowProperty(PROPERTY_EPISODE_LIST_URL, params['url']) setWindowProperty(PROPERTY_EPISODE_LIST_DATA, listData) def _episodeItemsGen(): playlist = xbmc.PlayList(xbmc.PLAYLIST_VIDEO) playlist.clear() showURL = params['url'] thumb = listData[0] artDict = {'icon': thumb, 'thumb': thumb, 'poster': thumb} if thumb else None plot = listData[1] listItemFunc = makeListItemClean if ADDON.getSetting('cleanupEpisodes') == 'true' else makeListItem itemParams = {'action': 'actionResolve', 'url': None} listIter = iter(listData[2]) if ADDON.getSetting('reverseEpisodes') == 'true' else reversed(listData[2]) for URL, title in listIter: item = listItemFunc(title, URL, artDict, plot, isFolder=False, isSpecial=False, oldParams=None) itemParams['url'] = URL itemURL = buildURL(itemParams) playlist.add(itemURL, item) yield (itemURL, item, False) xbmcplugin.addDirectoryItems(PLUGIN_ID, tuple(_episodeItemsGen())) xbmcplugin.endOfDirectory(PLUGIN_ID) def actionLatestMoviesMenu(params): # Returns a list of links from a hidden "/anime/movies" area. # Since this page is very large (130 KB), we memory cache it after it's been requested. html = getRawWindowProperty(PROPERTY_LATEST_MOVIES) if not html: r = requestHelper(BASEURL + params['path']) html = r.text setRawWindowProperty(PROPERTY_LATEST_MOVIES, html) # Similar scraping logic to 'actionEpisodesMenu()'. dataStartIndex = html.find('"sidebar_right3"') if dataStartIndex == -1: raise Exception('Latest movies scrape fail: ' + url) # Persistent property with item metadata. infoItems = getWindowProperty(PROPERTY_INFO_ITEMS) or { } def _movieItemsGen(): artDict = {'icon': ADDON_ICON, 'thumb': ADDON_ICON, 'poster': ADDON_ICON} reIter = re.finditer( '''<a href="([^"]+).*?>([^<]+)''', html[dataStartIndex : html.find('"sidebar-all"')] ) # The page has like 6000 items going back to 2010, so we limit to only the latest 200. for x in range(200): entryURL, entryTitle = next(reIter).groups() if entryURL in infoItems: entryPlot, entryThumb = infoItems[entryURL] yield ( buildURL({'action': 'actionResolve', 'url': entryURL}), makeListItem( unescapeHTMLText(entryTitle), entryURL, {'icon': ADDON_ICON, 'thumb': entryThumb, 'poster': entryThumb}, entryPlot, isFolder = False, isSpecial = True, oldParams = None ), False ) else: yield ( buildURL({'action': 'actionResolve', 'url': entryURL}), makeListItem( unescapeHTMLText(entryTitle), entryURL, artDict, '', isFolder = False, isSpecial = True, oldParams = params ), False ) xbmcplugin.addDirectoryItems(PLUGIN_ID, tuple(_movieItemsGen())) xbmcplugin.endOfDirectory(PLUGIN_ID) setViewMode() # A sub menu, lists search options. def actionSearchMenu(params): def _modalKeyboard(heading): kb = xbmc.Keyboard('', heading) kb.doModal() return kb.getText() if kb.isConfirmed() else '' if 'searchType' in params: # Support for the 'actionShowInfo()' function reloading this route, sending it an already searched query. # This also supports external query calls, like from OpenMeta. if 'query' in params: query = params['query'] else: query = _modalKeyboard(params.get('searchTitle', 'Search')) if query: historyTypeIDs = {'series':'0', 'movies':'1', 'episodes':'2'} previousHistory = ADDON.getSetting('searchHistory') if previousHistory: # Limit search history to 40 items. if previousHistory.count('\n') == 40: previousHistory = previousHistory[:previousHistory.rfind('\n')] # Forget the oldest search result. ADDON.setSetting('searchHistory', historyTypeIDs[params['searchType']] + query + '\n' + previousHistory) else: ADDON.setSetting('searchHistory', historyTypeIDs[params['searchType']] + query) params['query'] = query params['section'] = 'ALL' # Force an uncategorized display (results are usually few). actionCatalogSection(params) # Send the search type and query for the catalog functions to use. return xbmcplugin.addDirectoryItems( PLUGIN_ID, ( ( buildURL({ 'action': 'actionSearchMenu', 'path': URL_PATHS['search'], # A special, non-web path used by 'getCatalogProperty()'. 'searchType': 'series', 'searchTitle': 'Search Cartoon/Anime Name' }), xbmcgui.ListItem('[COLOR lavender][B]Search Cartoon/Anime Name[/B][/COLOR]'), True ), ( buildURL({ 'action': 'actionSearchMenu', 'path': URL_PATHS['search'], 'searchType': 'movies', 'searchTitle': 'Search Movie Name' }), xbmcgui.ListItem('[COLOR lavender][B]Search Movie Name[/B][/COLOR]'), True ), ( buildURL({ 'action': 'actionSearchMenu', 'path': URL_PATHS['search'], 'searchType': 'episodes', 'searchTitle': 'Search Episode Name' }), xbmcgui.ListItem('[COLOR lavender][B]Search Episode Name[/B][/COLOR]'), True ), ( buildURL({'action': 'actionGenresMenu', 'path': URL_PATHS['genre']}), xbmcgui.ListItem('[COLOR lavender][B]Search by Genre[/B][/COLOR]'), True ), ( buildURL({'action': 'actionTraktMenu', 'path': 'trakt'}), xbmcgui.ListItem('[COLOR lavender][B]Search by Trakt List[/B][/COLOR]'), True ), ( buildURL({'action': 'actionSearchHistory', 'path': 'searchHistory'}), xbmcgui.ListItem('[COLOR lavender][B]Search History...[/B][/COLOR]'), True ) ) ) xbmcplugin.endOfDirectory(PLUGIN_ID) # A sub menu, lists all previous searches along with their categories. def actionSearchHistory(params): history = ADDON.getSetting('searchHistory').split('\n') # Non-UI setting, it's just a big string. # A blank string split creates a list with a blank string inside, so test if the first item is valid. if history[0]: # Use list indexes to map to 'searchType' and a label prefix. historyTypeNames = ['series', 'movies', 'episodes'] historyPrefixes = ['(Cartoon/Anime)', '(Movie)', '(Episode)'] searchPath = URL_PATHS['search'] historyItems = tuple( ( buildURL({ 'query': itemQuery, 'searchType': historyTypeNames[itemType], 'path': searchPath, 'section': 'ALL', 'action': 'actionCatalogSection' }), xbmcgui.ListItem('[B]%s[/B] "%s"' % (historyPrefixes[itemType], itemQuery)), True ) for itemType, itemQuery in ( (int(itemString[0]), itemString[1:]) for itemString in history ) ) clearHistoryItem = ( buildURL({'action': 'actionSearchHistoryClear'}), xbmcgui.ListItem('[B]Clear History...[/B]'), False ) xbmcplugin.addDirectoryItems(PLUGIN_ID, (clearHistoryItem,) + historyItems) else: xbmcplugin.addDirectoryItem(PLUGIN_ID, '', xbmcgui.ListItem('(No History)'), isFolder=False) xbmcplugin.endOfDirectory(PLUGIN_ID) def actionSearchHistoryClear(params): dialog = xbmcgui.Dialog() if dialog.yesno('Clear Search History', 'Are you sure?'): ADDON.setSetting('searchHistory', '') dialog.notification('WatchNixtoons2', 'Search history cleared', xbmcgui.NOTIFICATION_INFO, 3000, False) # Show the search menu afterwards. xbmc.executebuiltin('Container.Update(' + PLUGIN_URL + '?action=actionSearchMenu,replace)') # A sub menu, lists the genre categories in the genre search. def actionGenresMenu(params): r = requestHelper(BASEURL + URL_PATHS['genre']) html = r.text dataStartIndex = html.find(r'ddmcc">') if dataStartIndex == -1: raise Exception('Genres list scrape fail') xbmcplugin.addDirectoryItems( PLUGIN_ID, tuple( ( buildURL( { 'action': 'actionCatalogMenu', 'path': '/search-by-genre/' + match.group(1).rsplit('/', 1)[1], 'searchType': 'genres' } ), xbmcgui.ListItem(match.group(2)), True ) for match in re.finditer('''<a.*?"([^"]+).*?>(.*?)</''', html[dataStartIndex : html.find(r'</div></div>')]) ) ) xbmcplugin.endOfDirectory(PLUGIN_ID) def actionTraktMenu(params): instance = SimpleTrakt.getInstance() if instance.ensureAuthorized(ADDON): def _traktMenuItemsGen(): traktIconDict = {'icon': ADDON_TRAKT_ICON, 'thumb': ADDON_TRAKT_ICON, 'poster': ADDON_TRAKT_ICON} for listName, listURL, listDescription in instance.getUserLists(ADDON): item = xbmcgui.ListItem(listName) item.setArt(traktIconDict) item.setInfo('video', {'title': listName, 'plot': listDescription}) yield ( buildURL({'action': 'actionTraktList', 'listURL': listURL}), item, True ) xbmcplugin.addDirectoryItems(PLUGIN_ID, tuple(_traktMenuItemsGen())) xbmcplugin.endOfDirectory(PLUGIN_ID) # Only finish the directory if the user is authorized it. def actionTraktList(params): instance = SimpleTrakt.getInstance() if instance.ensureAuthorized(ADDON): def _traktListItemsGen(): traktIconDict = {'icon': ADDON_TRAKT_ICON, 'thumb': ADDON_TRAKT_ICON, 'poster': ADDON_TRAKT_ICON} for itemName, overview, searchType, query in sorted(instance.getListItems(params['listURL'], ADDON)): item = xbmcgui.ListItem(itemName) item.setInfo('video', {'title': itemName, 'plot': overview}) item.setArt(traktIconDict) yield ( # Trakt items will lead straight to a show name search. buildURL( { 'action': 'actionCatalogMenu', 'path': URL_PATHS['search'], 'query': query, 'searchType': searchType, } ), item, True ) xbmcplugin.addDirectoryItems(PLUGIN_ID, tuple(_traktListItemsGen())) xbmcplugin.endOfDirectory(PLUGIN_ID) def actionTraktAbout(params): xbmcgui.Dialog().ok( 'WatchNixtoons2', 'To search for items in your Trakt lists in WNT2, go to [B]Search > Search by Trakt List[/B] and pair your ' \ 'account. Searching for an item this way does a name search, same as if you went and searched for that ' \ 'name manually.' ) def actionClearTrakt(params): if 'watchnixtoons2' in xbmc.getInfoLabel('Container.PluginName'): xbmc.executebuiltin('Dialog.Close(all)') # Kinda buggy behavior. # Need to wait a bit and recreate the xbmcaddon.Addon() reference, otherwise the settings # don't seem to be changed. # See https://forum.kodi.tv/showthread.php?tid=290353&pid=2425543#pid2425543 global ADDON xbmc.sleep(500) if SimpleTrakt.clearTokens(ADDON): xbmcgui.Dialog().notification('WatchNixtoons2', 'Trakt tokens cleared', xbmcgui.NOTIFICATION_INFO, 3500, False) else: xbmcgui.Dialog().notification( 'WatchNixtoons2', 'Trakt tokens already cleared', xbmcgui.NOTIFICATION_INFO, 3500, False ) ADDON = xbmcaddon.Addon() def actionRestoreDatabase(params): if not xbmcgui.Dialog().yesno( 'WatchNixtoons2', 'This will update the Kodi database to remember any WatchNixtoons2 episodes that were already watched, ' \ 'but forgotten after an add-on update.\nProceed?', nolabel = 'Cancel', yeslabel = 'Ok' ): return # Action called from the settings dialog. # This will update all the WatchNixtoons2 'strFilename' columns of table 'files' of Kodi's MyVideos###.db # with the new BASEURL used by the add-on so that episodes are still considered as watched (playcount >= 1). import xbmcvfs try: import sqlite3 except: xbmcgui.Dialog().notification( 'WatchNixtoons2', 'sqlite3 not found', xbmcgui.NOTIFICATION_WARNING, 3000, True ) return # Find the 'MyVideos###.db' file. dirs, files = xbmcvfs.listdir('special://database') for file in files: if 'MyVideos' in file and file.endswith('.db'): path = xbmc.translatePath('special://database/' + file) break else: xbmcgui.Dialog().notification( 'WatchNixtoons2', 'MyVideos database file not found', xbmcgui.NOTIFICATION_WARNING, 3000, True ) return # Update the database. OLD_DOMAINS = getOldDomains() NEW_DOMAIN = BASEURL.replace('https://', '', 1) # Make sure to strip the scheme from the current address. replaceDomainFunc = lambda original, oldDomain: original.replace(oldDomain, NEW_DOMAIN) totalUpdates = 0 try: connection = sqlite3.connect(path) except Exception as e: xbmcDebug(e) xbmcgui.Dialog().notification( 'WatchNixtoons2', 'Unable to connect to MyVideos database', xbmcgui.NOTIFICATION_WARNING, 3000, True ) return getCursor = connection.cursor() setCursor = connection.cursor() pattern = 'plugin://plugin.video.watchnixtoons2/%actionResolve%' for idFile, strFilename in getCursor.execute( "SELECT idFile,strFilename FROM files WHERE strFilename LIKE '%s'" % pattern ): if any(oldDomain in strFilename for oldDomain in OLD_DOMAINS): strFilename = reduce(replaceDomainFunc, OLD_DOMAINS, strFilename) setCursor.execute("UPDATE files SET strFilename=? WHERE idFile=?", (strFilename, idFile)) totalUpdates += 1 try: if totalUpdates: connection.commit() # Only commit if needed. connection.close() except: xbmcgui.Dialog().notification( 'WatchNixtoons2', 'Unable to update the database (file permission error?)', xbmcgui.NOTIFICATION_WARNING, 3000, True ) return # Bring a notification before finishing. if totalUpdates: xbmcgui.Dialog().ok('WatchNixtoons2', 'Database update complete (%i items updated).' % totalUpdates) else: xbmcgui.Dialog().ok('WatchNixtoons2', 'Finished. No updates needed (0 items updated).') def actionUpdateFavourites(params): if not xbmcgui.Dialog().yesno( 'WatchNixtoons2', 'This will update any of your Kodi Favourites created with older versions of WatchNixtoons2 so they can point ' \ 'to the latest web address that the add-on uses.\nProceed?', nolabel = 'Cancel', yeslabel = 'Ok' ): return # Action called from the settings dialog. # This will update all the Kodi favourites that use WatchNixtoons2 so that they use the new BASEURL. import xbmcvfs FAVOURITES_PATH = 'special://userdata/favourites.xml' file = xbmcvfs.File(FAVOURITES_PATH) favoritesText = file.read() file.close() originalText = favoritesText[:] # Get a backup copy of the content. OLD_DOMAINS = getOldDomains() NEW_DOMAIN = BASEURL.replace('https://', '', 1) # Make sure to strip the scheme. replaceDomainFunc = lambda original, oldDomain: original.replace(oldDomain, NEW_DOMAIN) if any(oldDomain in originalText for oldDomain in OLD_DOMAINS): favoritesText = reduce(replaceDomainFunc, getOldDomains(), favoritesText) try: file = xbmcvfs.File(FAVOURITES_PATH, 'w') file.write(favoritesText) file.close() except: try: # Try again, in case this was some weird encoding error and not a write-permission error. file = xbmcvfs.File(FAVOURITES_PATH, 'w') file.write(originalText) file.close() detail = ' (original was restored)' except: detail = '' xbmcgui.Dialog().notification( 'WatchNixtoons2', 'Error while writing to file' + detail, xbmcgui.NOTIFICATION_WARNING, 3000, True ) return if 'watchnixtoons2' in xbmc.getInfoLabel('Container.PluginName'): xbmc.executebuiltin('Dialog.Close(all)') xbmcgui.Dialog().ok( 'WatchNixtoons2', 'One or more items updated succesfully. Kodi will now reload the Favourites file...' ) xbmc.executebuiltin('LoadProfile(%s)' % xbmc.getInfoLabel('System.ProfileName')) # Reloads 'favourites.xml'. else: xbmcgui.Dialog().ok('WatchNixtoons2', 'Finished. No old favorites found.') def actionShowSettings(params): # Modal dialog, so the program won't continue from this point until user closes\confirms it. ADDON.openSettings() # So right after it is a good time to update any settings globals. global ADDON_SHOW_CATALOG ADDON_SHOW_CATALOG = ADDON.getSetting('showCatalog') == 'true' global ADDON_LATEST_DATE # Set the catalog to be reloaded in case the user changed the "Order 'Latest Releases' By Date" setting. newLatestDate = ADDON.getSetting('useLatestDate') == 'true' if ADDON_LATEST_DATE != newLatestDate and URL_PATHS['latest'] in getRawWindowProperty(PROPERTY_CATALOG_PATH): setRawWindowProperty(PROPERTY_CATALOG_PATH, '') ADDON_LATEST_DATE = newLatestDate global ADDON_LATEST_THUMBS ADDON_LATEST_THUMBS = ADDON.getSetting('showLatestThumbs') == 'true' def getPageMetadata(html): # If we're on an episode or (old) movie page, see if there's a parent page with the actual metadata. stringStartIndex = html.find('"header-tag"') if stringStartIndex != -1: parentURL = re.search('href="([^"]+)', html[stringStartIndex:]).group(1) if '/anime/movies' not in parentURL: r = requestHelper(parentURL if parentURL.startswith('http') else BASEURL + parentURL) if r.ok: html = r.text # Thumbnail scraping. thumb = '' stringStartIndex = html.find('og:image" content="') if stringStartIndex != -1: thumbPath = html[stringStartIndex+19 : html.find('"', stringStartIndex+19)] # 19 = len('og:image" content="') if thumbPath: if thumbPath.startswith('http'): thumb = thumbPath + getThumbnailHeaders() elif thumbPath.startswith('/'): thumb = BASEURL + thumbPath + getThumbnailHeaders() # (Show) plot scraping. plot = '' stringStartIndex = html.find('Info:') if stringStartIndex != -1: match = re.search('</h3>\s*<p>(.*?)</p>', html[stringStartIndex:], re.DOTALL) plot = unescapeHTMLText(match.group(1).strip()) if match else '' return plot, thumb def actionShowInfo(params): xbmcgui.Dialog().notification('WatchNixtoons2', 'Requesting info...', ADDON_ICON, 2000, False) # Get the desktop page for the item, whatever it is. url = params['url'].replace('/m.', '/www.', 1) # Make sure the URL points to the desktop site. r = requestHelper(url if url.startswith('http') else BASEURL + url) html = r.text plot, thumb = getPageMetadata(html) # Use a persistent memory property holding a dictionary, and refresh the directory listing. if plot or thumb: infoItems = getWindowProperty(PROPERTY_INFO_ITEMS) or { } infoItems[url] = (plot, (thumb or 'DefaultVideo.png')) setWindowProperty(PROPERTY_INFO_ITEMS, infoItems) oldParams = dict(parse_qsl(params['oldParams'])) xbmc.executebuiltin('Container.Update(%s,replace)' % (PLUGIN_URL + '?' + params['oldParams'])) else: xbmcgui.Dialog().notification('WatchNixtoons2', 'No info found', ADDON_ICON, 1500, False) def unescapeHTMLText(text): text = text.encode('utf-8') if isinstance(text, unicode) else unicode(text, errors='ignore').encode('utf-8') # Unescape HTML entities. if r'&#' in text: # Strings found by regex-searching on all lists in the source website. It's very likely to only be these. return text.replace(r'‘', '‘').replace(r'”', '”').replace(r'–', '–').replace(r'&', '&')\ .replace(r'’', '’').replace(r'“', '“').replace(r'…', '…').replace(r' ', ' ')\ .replace(r'&', '&') else: return text.replace(r'&', '&') def getTitleInfo(unescapedTitle): # We need to interpret the full title of each episode's link's string # for information like episode number, season and show title. season = None episode = None multiPart = None showTitle = unescapedTitle episodeTitle = '' seasonIndex = unescapedTitle.find('Season ') # 7 characters long. if seasonIndex != -1: season = unescapedTitle[seasonIndex+7 : unescapedTitle.find(' ', seasonIndex+7)] if not season.isdigit(): # Handle inconsistently formatted episode title, with possibly ordinal season before or after # the word "Season" (case unknown, inconsistent). if season == 'Episode': # Find the word to the left of "Season ", separated by spaces (spaces not included in the result). season = unescapedTitle[unescapedTitle.rfind(' ', 0, seasonIndex-1) + 1 : seasonIndex-1] showTitle = unescapedTitle[:seasonIndex+7].strip(' -–:') # Include the "nth Season" term in the title. else: showTitle = unescapedTitle[:seasonIndex].strip(' -–:') season = {'second': '2', 'third': '3', 'fourth': '4', 'fifth': '5'}.get(season.lower(), '') else: showTitle = unescapedTitle[:seasonIndex].strip(' -–:') episodeIndex = unescapedTitle.find(' Episode ') # 9 characters long. if episodeIndex != -1: spaceIndex = unescapedTitle.find(' ', episodeIndex+9) if spaceIndex > episodeIndex: episodeSplit = unescapedTitle[episodeIndex+9 : spaceIndex].split('-') # For multipart episodes, like "42-43". episode = filter(str.isdigit, episodeSplit[0]) multiPart = filter(str.isdigit, episodeSplit[1]) if len(episodeSplit) > 1 else None # Get the episode title string (stripped of spaces, hyphens and en-dashes). englishIndex = unescapedTitle.rfind(' English', spaceIndex) if englishIndex != -1: episodeTitle = unescapedTitle[spaceIndex+1 : englishIndex].strip(' -–:') else: episodeTitle = unescapedTitle[spaceIndex+1:].strip(' -–:') # Safeguard for when season 1 is ocasionally omitted in the title. if not season: season = '1' if episode: return (showTitle[:episodeIndex].strip(' -'), season, episode, multiPart, episodeTitle.strip(' /')) else: englishIndex = unescapedTitle.rfind(' English') if englishIndex != -1: return (unescapedTitle[:englishIndex].strip(' -'), None, None, None, '') else: return (unescapedTitle.strip(' -'), None, None, None, '') def makeListItem(title, url, artDict, plot, isFolder, isSpecial, oldParams): unescapedTitle = unescapeHTMLText(title) item = xbmcgui.ListItem(unescapedTitle) isPlayable = False if not (isFolder or isSpecial): title, season, episode, multiPart, episodeTitle = getTitleInfo(unescapedTitle) # Playable content. isPlayable = True itemInfo = { 'mediatype': 'episode' if episode else 'tvshow', 'tvshowtitle': title, 'title': episodeTitle, 'plot': plot } if episode and episode.isdigit(): itemInfo['season'] = int(season) if season.isdigit() else -1 itemInfo['episode'] = int(episode) item.setInfo('video', itemInfo) elif isSpecial: isPlayable = True item.setInfo('video', {'mediatype': 'movie', 'title': unescapedTitle, 'plot': plot}) else: item.setInfo('video', {'mediatype': 'tvshow', 'title': unescapedTitle, 'plot': plot}) if artDict: item.setArt(artDict) # Add the context menu items, if necessary. contextMenuList = None if oldParams: contextMenuList = [ ( 'Nixtoons Information', 'RunPlugin('+PLUGIN_URL+'?action=actionShowInfo&url='+quote_plus(url)+'&oldParams='+quote_plus(urlencode(oldParams))+')' ) ] if isPlayable: item.setProperty('IsPlayable', 'true') # Allows the checkmark to be placed on watched episodes. playChaptersItem = ( 'Play Chapters', 'PlayMedia('+PLUGIN_URL+'?action=actionResolve&url='+quote_plus(url)+'&playChapters=1)' ) if contextMenuList: contextMenuList.append(playChaptersItem) else: contextMenuList = [playChaptersItem] if contextMenuList: item.addContextMenuItems(contextMenuList) return item # Variant of the 'makeListItem()' function that tries to format the item label using the season and episode. def makeListItemClean(title, url, artDict, plot, isFolder, isSpecial, oldParams): unescapedTitle = unescapeHTMLText(title) isPlayable = False if isFolder or isSpecial: item = xbmcgui.ListItem(unescapedTitle) if isSpecial: isPlayable = True item.setInfo('video', {'mediatype': 'video', 'title': unescapedTitle}) else: title, season, episode, multiPart, episodeTitle = getTitleInfo(unescapedTitle) if episode and episode.isdigit(): # The clean episode label will have this format: "SxEE Episode Name", with S and EE standing for digits. item = xbmcgui.ListItem( '[B]' + season + 'x' + episode.zfill(2) + ('-' + multiPart if multiPart else '') + '[/B] ' + (episodeTitle or title) ) itemInfo = { 'mediatype': 'episode', 'tvshowtitle': title, 'title': title, 'plot': plot, 'season': int(season) if season.isdigit() else -1, 'episode': int(episode) } else: item = xbmcgui.ListItem(title) itemInfo = {'mediatype': 'tvshow', 'tvshowtitle': title, 'title': title, 'plot': plot} item.setInfo('video', itemInfo) isPlayable = True if artDict: item.setArt(artDict) # Add the context menu items, if necessary. contextMenuList = None if oldParams: contextMenuList = [ ( 'Show Information', 'RunPlugin('+PLUGIN_URL+'?action=actionShowInfo&url='+quote_plus(url)+'&oldParams='+quote_plus(urlencode(oldParams))+')' ) ] if isPlayable: item.setProperty('IsPlayable', 'true') # Allows the checkmark to be placed on watched episodes. playChaptersItem = ( 'Play Chapters', 'PlayMedia('+PLUGIN_URL+'?action=actionResolve&url='+quote_plus(url)+'&playChapters=1)' ) if contextMenuList: contextMenuList.append(playChaptersItem) else: contextMenuList = [playChaptersItem] if contextMenuList: item.addContextMenuItems(contextMenuList) return item ''' (1. The catalog is a dictionary of lists, used to store data between add-on states to make xbmcgui.ListItems: { (2. Sections, as in alphabet sections of items, A, B, C, D, E, F etc., each section holds a list of items.) A: ( (item, item, item, ...) (3. Items, each item is a pair of <a> properties: (a.string, a['href']).) ) B: (...) C: (...) } ''' # Manually sorts items from an iterable into an alphabetised catalog. # Iterable contains (URL, name) pairs that might refer to a series, episode, ova or movie. def catalogFromIterable(iterable): catalog = {key: [ ] for key in ascii_uppercase} miscSection = catalog['#'] = [ ] for item in iterable: key = item[1][0].upper() if key in catalog: catalog[key].append(item) else: miscSection.append(item) return catalog def makeLatestCatalog(params): # Returns a list of links from the "Latest 50 Releases" area, but from their mobile site as it has lots of items. r = requestHelper(BASEURL_MOBILE) # Path unused, data is already on the homepage. html = r.text dataStartIndex = html.find('vList') if dataStartIndex == -1: raise Exception('(Mobile) Latest catalog scrape fail') thumbHeaders = getThumbnailHeaders() if ADDON_LATEST_DATE: # Make the catalog dict only have a single section, "LATEST", with items listed as they are. # This way the actionCatalogMenu() function will show this single section directly, with no alphabet categories. return { 'LATEST': tuple( (match.group(1), match.group(3), BASEURL_MOBILE + match.group(2) + thumbHeaders) for match in re.finditer( '''<a href="([^"]+).*?img src="([^"]+).*?div.*?div>(.*?)</div''', html[dataStartIndex : html.find('/ol')] ) ) } else: return catalogFromIterable( (match.group(1), match.group(3), BASEURL_MOBILE + match.group(2) + thumbHeaders) for match in re.finditer( '''<a href="([^"]+).*?img src="([^"]+).*?div.*?div>(.*?)</div''', html[dataStartIndex : html.find('/ol')] ) ) def makePopularCatalog(params): r = requestHelper(BASEURL) # We will scrape from the sidebar content on the homepage. html = r.text dataStartIndex = html.find('"sidebar-titles"') if dataStartIndex == -1: raise Exception('Popular catalog scrape fail: ' + params['path']) return catalogFromIterable( match.groups() for match in re.finditer( '''<a href="([^"]+).*?>([^<]+)''', html[dataStartIndex : html.find('</div>', dataStartIndex)] ) ) def makeSeriesSearchCatalog(params): r = requestHelper( BASEURL+'/search', data={'catara': params['query'], 'konuara': 'series'}, extraHeaders={'Referer': BASEURL+'/'}) html = r.text dataStartIndex = html.find('submit') if dataStartIndex == -1: raise Exception('Series search scrape fail: ' + params['query']) return catalogFromIterable( match.groups() for match in re.finditer( '''<a href="([^"]+)[^>]*>([^<]+)</a''', html[dataStartIndex : html.find('cizgiyazisi', dataStartIndex)] ) ) def makeMoviesSearchCatalog(params): # Try a movie category search (same code as in 'makeGenericCatalog()'). r = requestHelper(BASEURL + URL_PATHS['movies']) html = r.text dataStartIndex = html.find('"ddmcc"') if dataStartIndex == -1: raise Exception('Movies search scrape fail: ' + params['query']) lowerQuery = params['query'].lower() return catalogFromIterable( match.groups() for match in re.finditer( '''<a href="([^"]+).*?>([^<]+)''', html[dataStartIndex : html.find('/ul></ul', dataStartIndex)] ) if lowerQuery in match.group(2).lower() ) def makeEpisodesSearchCatalog(params): r = requestHelper( BASEURL+'/search', data={'catara': params['query'], 'konuara': 'episodes'}, extraHeaders={'Referer': BASEURL+'/'} ) html = r.text dataStartIndex = html.find('submit') if dataStartIndex == -1: raise Exception('Episode search scrape fail: ' + params['query']) return catalogFromIterable( match.groups() for match in re.finditer( '''<a href="([^"]+)[^>]*>([^<]+)</a''', html[dataStartIndex : html.find('cizgiyazisi', dataStartIndex)], re.DOTALL ) ) def makeSearchCatalog(params): searchType = params.get('searchType', 'series') if searchType == 'series': return makeSeriesSearchCatalog(params) elif searchType == 'movies': return makeMoviesSearchCatalog(params) else: return makeEpisodesSearchCatalog(params) def makeGenericCatalog(params): # The movies path is missing some items when scraped from BASEURL_MOBILE, so we use the BASEURL # (full website) in here. r = requestHelper(BASEURL + params['path']) html = r.text dataStartIndex = html.find('"ddmcc"') if dataStartIndex == -1: raise Exception('Generic catalog scrape fail: ' + params['path']) return catalogFromIterable( match.groups() for match in re.finditer( '''<li><a href="([^"]+).*?>([^<]+)''', html[dataStartIndex : html.find('</div>', dataStartIndex)] ) ) # Retrieves the catalog from a persistent XBMC window property between different add-on # directories, or recreates the catalog based on one of the catalog functions. def getCatalogProperty(params): path = params['path'] def _rebuildCatalog(): func = CATALOG_FUNCS.get(path, makeGenericCatalog) catalog = func(params) setWindowProperty(PROPERTY_CATALOG, catalog) if 'query' in params: # For searches, store the query and search type in the catalog path so we can identify # this particular search attempt. setRawWindowProperty(PROPERTY_CATALOG_PATH, path + params['query'] + params['searchType']) else: setRawWindowProperty(PROPERTY_CATALOG_PATH, path) setRawWindowProperty(PROPERTY_INFO_ITEMS, '') # Clear any previous info. return catalog # If these properties are empty (like when coming in from a favourites menu), or if # a different catalog (a different URL path) is stored in this property, then reload it. currentPath = getRawWindowProperty(PROPERTY_CATALOG_PATH) if ( # "If we're coming in from a search and the search query and type are different, or if we're not # coming in from a search and the paths are simply different, rebuild the catalog." ('query' in params and (params['query'] not in currentPath or params['searchType'] not in currentPath)) or ('query' not in params and currentPath != path) ): catalog = _rebuildCatalog() else: catalog = getWindowProperty(PROPERTY_CATALOG) if not catalog: catalog = _rebuildCatalog() return catalog def actionResolve(params): # Needs to be the BASEURL domain to get multiple video qualities. url = params['url'] # Sanitize the URL since on some occasions it's a path instead of full address. url = url if url.startswith('http') else (BASEURL + (url if url.startswith('/') else '/' + url)) r = requestHelper(url.replace('watchcartoononline.io', 'thewatchcartoononline.tv', 1)) # New domain safety replace. content = r.content def _decodeSource(subContent): chars = subContent[subContent.find('[') : subContent.find(']')] spread = int(re.search(r' - (\d+)\)\; }', subContent[subContent.find(' - '):]).group(1)) iframe = ''.join( chr( int(''.join(c for c in b64decode(char) if c.isdigit())) - spread ) for char in chars.replace('"', '').split(',') ) try: return BASEURL + re.search(r'src="([^"]+)', iframe).group(1) except: return None # Probably a temporary block, or change in embedded code. embedURL = None # On rare cases an episode might have several "chapters", which are video players on the page. embedURLPattern = b'onclick="myFunction' embedURLIndex = content.find(embedURLPattern) if 'playChapters' in params or ADDON.getSetting('chapterEpisodes') == 'true': # Multi-chapter episode found (that is, multiple embedURLPattern statements found). # Extract all chapters from the page. embedURLPatternLen = len(embedURLPattern) currentPlayerIndex = embedURLIndex dataIndices = [ ] while currentPlayerIndex != -1: dataIndices.append(currentPlayerIndex) currentPlayerIndex = content.find(embedURLPattern, currentPlayerIndex + embedURLPatternLen) # If more than one "embedURL" statement found, make a selection dialog and call them "chapters". if len(dataIndices) > 1: selectedIndex = xbmcgui.Dialog().select( 'Select Chapter', ['Chapter '+str(n) for n in xrange(1, len(dataIndices)+1)] ) else: selectedIndex = 0 if selectedIndex != -1: embedURL = _decodeSource(content[dataIndices[selectedIndex]:]) else: return # User cancelled the chapter selection. else: # Normal / single-chapter episode. embedURL = _decodeSource(content[embedURLIndex:]) # User asked to play multiple chapters, but only one chapter/video player found. if embedURL and 'playChapters' in params: xbmcgui.Dialog().notification('WatchNixtoons2', 'Only 1 chapter found...', ADDON_ICON, 2000, False) # Notify a failure in solving the player obfuscation. if not embedURL: xbmcgui.Dialog().ok('WatchNixtoons2', 'Unable to find a playable source') return # Request the embedded player page. r2 = requestHelper(unescapeHTMLText(embedURL)) # Sometimes a '&' symbol is present in this URL. html = r2.text # Notify about temporary blocks / failures. if 'high volume of requests' in html: xbmcgui.Dialog().ok( 'WatchNixtoons2 Fail (Server Response)', '"We are getting extremely high volume of requests on our video servers so that we temporarily block for free videos for free users. I apologize for the inconvenience."' ) return # Find the stream URLs. if 'getvid?evid' in html: # Query-style stream getting. sourceURL = re.search(b'"(/inc/embed/getvidlink[^"]+)', html, re.DOTALL).group(1) # Inline code similar to 'requestHelper()'. # The User-Agent for this next request is somehow encoded into the media tokens, so we make sure to use # the EXACT SAME value later, when playing the media, or else we get a HTTP 404 / 500 error. r3 = requestHelper( BASEURL + sourceURL, data = None, extraHeaders = { 'User-Agent': WNT2_USER_AGENT, 'Accept': '*/*', 'Referer': embedURL, 'X-Requested-With': 'XMLHttpRequest' } ) if not r3.ok: raise Exception('Sources XMLHttpRequest request failed') jsonData = r3.json() # Only two qualities are ever available: 480p ("SD") and 720p ("HD"). sourceURLs = [ ] sdToken = jsonData.get('enc', '') hdToken = jsonData.get('hd', '') sourceBaseURL = jsonData.get('server', '') + '/getvid?evid=' if sdToken: sourceURLs.append(('480 (SD)', sourceBaseURL + sdToken)) # Order the items as (LABEL, URL). if hdToken: sourceURLs.append(('720 (HD)', sourceBaseURL + hdToken)) # Use the same backup stream method as the source: cdn domain + SD stream. backupURL = jsonData.get('cdn', '') + '/getvid?evid=' + (sdToken or hdToken) else: # Alternative video player page, with plain stream links in the JWPlayer javascript. sourcesBlock = re.search('sources:\s*?\[(.*?)\]', html, re.DOTALL).group(1) streamPattern = re.compile('\{\s*?file:\s*?"(.*?)"(?:,\s*?label:\s*?"(.*?)")?') sourceURLs = [ # Order the items as (LABEL (or empty string), URL). (sourceMatch.group(2), sourceMatch.group(1)) for sourceMatch in streamPattern.finditer(sourcesBlock) ] # Use the backup link in the 'onError' handler of the 'jw' player. backupMatch = streamPattern.search(html[html.find(b'jw.onError'):]) backupURL = backupMatch.group(1) if backupMatch else '' mediaURL = None if len(sourceURLs) == 1: # Only one quality available. mediaURL = sourceURLs[0][1] elif len(sourceURLs) > 0: # Always force "select quality" for now. playbackMethod = ADDON.getSetting('playbackMethod') if playbackMethod == '0': # Select quality. selectedIndex = xbmcgui.Dialog().select( 'Select Quality', [(sourceItem[0] or '?') for sourceItem in sourceURLs] ) if selectedIndex != -1: mediaURL = sourceURLs[selectedIndex][1] else: # Auto-play user choice. sortedSources = sorted(sourceURLs) mediaURL = sortedSources[-1][1] if playbackMethod == '1' else sortedSources[0][1] if mediaURL: # Kodi headers for playing web streamed media. global MEDIA_HEADERS if not MEDIA_HEADERS: MEDIA_HEADERS = { 'User-Agent': WNT2_USER_AGENT, 'Accept': 'video/webm,video/ogg,video/*;q=0.9,application/ogg;q=0.7,audio/*;q=0.6,*/*;q=0.5', 'Connection': 'keep-alive', 'Referer': BASEURL + '/' } # Try to un-redirect the chosen media URL. # If it fails, try to un-resolve the backup URL. If not even the backup URL is working, abort playing. mediaHead = solveMediaRedirect(mediaURL, MEDIA_HEADERS) if not mediaHead: mediaHead = solveMediaRedirect(backupURL, MEDIA_HEADERS) if not mediaHead: return xbmcplugin.setResolvedUrl(PLUGIN_ID, False, xbmcgui.ListItem()) # Need to use the exact same ListItem name & infolabels when playing or else Kodi replaces that item # in the UI listing. item = xbmcgui.ListItem(xbmc.getInfoLabel('ListItem.Label')) item.setPath(mediaHead.url + '|' + '&'.join(key+'='+quote_plus(val) for key, val in MEDIA_HEADERS.iteritems())) item.setMimeType(mediaHead.headers.get('Content-Type', 'video/mp4')) # Avoids Kodi's MIME request. # When coming in from a Favourite item, there will be no metadata. Try to get at least a title. itemTitle = xbmc.getInfoLabel('ListItem.Title') if not itemTitle: match = re.search(b'<h1[^>]+>([^<]+)</h1', content) if match: itemTitle = match.group(1).replace(' English Subbed', '', 1).replace( 'English Dubbed', '', 1) else: itemTitle = '' episodeString = xbmc.getInfoLabel('ListItem.Episode') if episodeString != '' and episodeString != '-1': seasonInfoLabel = xbmc.getInfoLabel('ListItem.Season') item.setInfo('video', { 'tvshowtitle': xbmc.getInfoLabel('ListItem.TVShowTitle'), 'title': itemTitle, 'season': int(seasonInfoLabel) if seasonInfoLabel.isdigit() else -1, 'episode': int(episodeString), 'plot': xbmc.getInfoLabel('ListItem.Plot'), 'mediatype': 'episode' } ) else: item.setInfo('video', { 'title': itemTitle, 'plot': xbmc.getInfoLabel('ListItem.Plot'), 'mediatype': 'movie' } ) #xbmc.Player().play(listitem=item) # Alternative play method, lets you extend the Player class with your own. xbmcplugin.setResolvedUrl(PLUGIN_ID, True, item) else: # Failed. No source found, or the user didn't select one from the dialog. xbmcplugin.setResolvedUrl(PLUGIN_ID, False, xbmcgui.ListItem()) def buildURL(query): ''' Helper function to build a Kodi xbmcgui.ListItem URL. :param query: Dictionary of url parameters to put in the URL. :returns: A formatted and urlencoded URL string. ''' return (PLUGIN_URL + '?' + urlencode({k: v.encode('utf-8') if isinstance(v, unicode) else unicode(v, errors='ignore').encode('utf-8') for k, v in query.iteritems()})) def setViewMode(): if ADDON.getSetting('useViewMode') == 'true': viewModeID = ADDON.getSetting('viewModeID') if viewModeID.isdigit(): xbmc.executebuiltin('Container.SetViewMode(' + viewModeID + ')') def xbmcDebug(*args): xbmc.log('WATCHNIXTOONS2 > ' + ' '.join((val if isinstance(val, str) else repr(val)) for val in args), xbmc.LOGWARNING) def simpleRequest(url, requestFunc, headers): return requestFunc(url, headers=headers, verify=False, timeout=10) # Thumbnail HTTP headers for Kodi to use when grabbing thumbnail images. def getThumbnailHeaders(): # Original code: #return ( # '|User-Agent='+quote_plus(WNT2_USER_AGENT) # + '&Accept='+quote_plus('image/webp,*/*') # + '&Referer='+quote_plus(BASEURL+'/') #) cookieProperty = getRawWindowProperty(PROPERTY_SESSION_COOKIE) cookies = ('&Cookie=' + quote_plus(cookieProperty)) if cookieProperty else '' # Since it's a constant value, it can be precomputed. return '|User-Agent=Mozilla%2F5.0+%28compatible%3B+WatchNixtoons2%2F0.4.1%3B' \ '+%2Bhttps%3A%2F%2Fgithub.com%2Fdoko-desuka%2Fplugin.video.watchnixtoons2%29' \ '&Accept=image%2Fwebp%2C%2A%2F%2A&Referer=https%3A%2F%2Fwww.thewatchcartoononline.tv%2F' + cookies def getOldDomains(): # Old possible domains, in the order of likeliness. return ( 'www.wcostream.com', 'm.wcostream.com', 'www.watchcartoononline.io', 'm.watchcartoononline.io' ) def solveMediaRedirect(url, headers): # Use HEAD requests to fulfill possible 302 redirections. # Returns the final stream HEAD response. while True: try: mediaHead = simpleRequest(url, requests.head, headers) if 'Location' in mediaHead.headers: url = mediaHead.headers['Location'] # Change the URL to the redirected location. else: mediaHead.raise_for_status() return mediaHead # Return the response. except: return None # Return nothing on failure. def requestHelper(url, data=None, extraHeaders=None): myHeaders = { 'User-Agent': WNT2_USER_AGENT, 'Accept': 'text/html,application/xhtml+xml,application/xml,application/json;q=0.9,image/webp,*/*;q=0.8', 'Accept-Language': 'en-US,en;q=0.5', 'Cache-Control': 'no-cache', 'Pragma': 'no-cache', 'DNT': '1' } if extraHeaders: myHeaders.update(extraHeaders) # At the moment it's a single response cookie, "__cfduid". Other cookies are set w/ Javascript by ads. cookieProperty = getRawWindowProperty(PROPERTY_SESSION_COOKIE) if cookieProperty: cookieDict = dict(pair.split('=') for pair in cookieProperty.split('; ')) else: cookieDict = None startTime = time() if data: response = requests.post(url, data=data, headers=myHeaders, verify=False, cookies=cookieDict, timeout=10) else: response = requests.get(url, headers=myHeaders, verify=False, cookies=cookieDict, timeout=10) # Store the session cookie(s), if any. if not cookieProperty and response.cookies: setRawWindowProperty( PROPERTY_SESSION_COOKIE, '; '.join(pair[0]+'='+pair[1] for pair in response.cookies.get_dict().iteritems()) ) elapsed = time() - startTime if elapsed < 1.5: sleep(1.5 - elapsed) return response #def getRandomUserAgent(): # # Random user-agent logic. Thanks to http://edmundmartin.com/random-user-agent-requests-python/ # from random import choice # desktop_agents = ( # 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36', # 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36', # 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36', # 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14', # 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36', # 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36', # 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36', # 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36', # 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36', # 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0' # ) # return choice(desktop_agents) # Defined after all the functions exist. CATALOG_FUNCS = { URL_PATHS['latest']: makeLatestCatalog, URL_PATHS['popular']: makePopularCatalog, URL_PATHS['search']: makeSearchCatalog } def main(): ''' Main add-on routing function, calls a certain action (function). The 'action' parameter is the direct name of the function. ''' params = dict(parse_qsl(sys.argv[2][1:], keep_blank_values=True)) globals()[params.get('action', 'actionMenu')](params) # Defaults to 'actionMenu()'.

42.386126

181

0.605379

794ac688acc3387f549226d07d0fc9dfa6794ae4

1,155

py

Python

database/schemas/user.py

DiegoLing33/prestij.xyz-api

69a11a2c93dd98975f9becbc4b8f596e4941a05f

[ "MIT" ]

2

2020-10-28T14:00:05.000Z

2020-10-30T11:55:27.000Z

database/schemas/user.py

DiegoLing33/prestij.xyz-api

69a11a2c93dd98975f9becbc4b8f596e4941a05f

[ "MIT" ]

null

database/schemas/user.py

DiegoLing33/prestij.xyz-api

69a11a2c93dd98975f9becbc4b8f596e4941a05f

[ "MIT" ]

null

# ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # ██║░░░░░██║████╗░██║██╔════╝░░░░██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝ # ██║░░░░░██║██╔██╗██║██║░░██╗░░░░██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░ # ██║░░░░░██║██║╚████║██║░░╚██╗░░░██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░ # ███████╗██║██║░╚███║╚██████╔╝░░░██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗ # ╚══════╝╚═╝╚═╝░░╚══╝░╚═════╝░░░░╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝ # # Developed by Yakov V. Panov (C) Ling • Black 2020 # @site http://ling.black from datetime import datetime from typing import Optional, List from pydantic.main import BaseModel from .user_group import UserGroup from .user_meta import UserMeta from ..core.schemas import CoreSchema class UserBase(BaseModel): """ The base schema class """ login: str group_id: int class UserCreate(BaseModel): """ The create schema class """ login: str password: str class User(UserBase, CoreSchema): id: int created: datetime group: UserGroup meta: List[UserMeta] class Config: orm_mode = True arbitrary_types_allowed = True

25.108696

75

0.41039

794ac6a3389aee903d2040ab138922e0b6b2557f

10,730

py

Python

custom/world_vision/__init__.py

bglar/commcare-hq

972129fc26864c08c7bef07874bd2a7218550bff

[ "BSD-3-Clause" ]

1

2017-02-10T03:14:51.000Z

custom/world_vision/__init__.py

bglar/commcare-hq

972129fc26864c08c7bef07874bd2a7218550bff

[ "BSD-3-Clause" ]

null

custom/world_vision/__init__.py

bglar/commcare-hq

972129fc26864c08c7bef07874bd2a7218550bff

[ "BSD-3-Clause" ]

null

from custom.world_vision.reports.child_report import ChildTTCReport from custom.world_vision.reports.mixed_report import MixedTTCReport from custom.world_vision.reports.mother_report import MotherTTCReport from collections import OrderedDict DEFAULT_REPORT_CLASS = MixedTTCReport WORLD_VISION_DOMAINS = ('wvindia2', ) CUSTOM_REPORTS = ( ('TTC App Reports', ( MixedTTCReport, MotherTTCReport, ChildTTCReport )), ) REASON_FOR_CLOSURE_MAPPING = OrderedDict(( ('change_of_location', 'Migration'), ('end_of_pregnancy', 'End of care (Postpartum Completed)'), ('not_pregnant', 'Not Pregnant (mostly incorrect registrations)'), ('abortion', 'Abortion'), ('death', 'Death'), ('unknown', 'Unknown') )) CLOSED_CHILD_CASES_BREAKDOWN = { 'death': 'Death', 'change_of_location': 'Migration', 'end_of_care': 'End of care' } MOTHER_DEATH_MAPPING = { 'seizure': 'Seizure or fits', 'high_bp': 'High blood pressure', 'bleeding_postpartum': 'Excessive bleeding post-delivery', 'fever_or_infection_post_delivery': 'Fever or infection post-delivery', 'during_caeserian_surgery': 'During Caeserian Surgery', 'other': 'Other reason', } CHILD_DEATH_TYPE = { 'newborn_death': 'Newborn deaths (< 1 month)', 'infant_death': 'Infant deaths (< 1 year)', 'child_death': 'Child deaths (> 1yr)' } CHILD_CAUSE_OF_DEATH = OrderedDict(( ('ari', 'ARI'), ('fever', 'Fever'), ('dysentery', 'Dysentery or diarrhea'), ('injury', 'Injury or accident'), ('malnutrition', 'Malnutrition'), ('cholera', 'Cholera'), ('measles', 'Measles'), ('meningitis', 'Meningitis'), ('other', 'Other'), ('', 'Unknown') )) FAMILY_PLANNING_METHODS = { 'condom': 'Condom', 'iud': 'IUD', 'ocp': 'Contraceptive Pill', 'injection': 'Depo-provera injection or implant', 'permanent': 'Vasectomy or ligation', 'natural': 'Natural methods', 'other': 'Others', 'not_wish_to_disclose': 'Does not wish to disclose' } MOTHER_INDICATOR_TOOLTIPS = { "mother_registration_details": { "total": "Includes cases that were opened or closed within the date range, or remained open throughout " "the period", "total": "Total cases (both open and closed) irrespective of any date filters. Location filters " "still apply.", "no_date_opened": "Total open cases irrespective of any date filters. Location filters still apply.", "no_date_closed": "Total closed cases irrespective of any date filters. Location filters still apply.", "new_registrations": "Cases open between today and 30 days from today" }, "ante_natal_care_service_details": { "no_anc": "Pregnant mothers who didn't get a single ANC checkup", "anc_1": "Pregnant mothers who completed ANC1", "anc_2": "Pregnant mothers who completed ANC1 and ANC2", "anc_3": "Pregnant mothers who completed ANC1, ANC2 and ANC3", "anc_4": "Pregnant mothers who completed ANC1, ANC2, ANC3 and ANC4", "tt_1": "Pregnant mothers who got Tetanus 1 shot", "tt_2": "Pregnant mothers who got Tetanus 1 and Tetanus 2 shots", "tt_booster": "Pregnant mothers who got Tetanus Booster shot", "tt_completed": "Pregnant mothers who got Tetanus 2 or Tetanus Booster", "ifa_tablets": "Pregnant mothers who reported consuming IFA tablets currently", "100_tablets": "Mothers who completed 100 IFA tablets", "clinically_anemic": "Pregnant mothers who are currently identified as anemic by the Front Line Worker", "danger_signs": "Pregnant mothers who reported experiencing danger signs currently, " "hence referred to health center", "knows_closest_facility": "Pregnant mothers who reported they know their nearest health facility", "no_anc_eligible": "Mothers more than 2.75 months pregnant (end of 1st Trimester)", "anc_1_eligible": "Mothers more than 2.75 months pregnant (end of 1st Trimester)", "anc_2_eligible": "Mothers currently more than 5.5 months pregnant (2nd Trimester) and completed ANC1", "anc_3_eligible": "Mothers currently more than 7.3 months pregnant (3rd Trimester) " "and completed ANC1 and ANC2", "anc_4_eligible": "Mothers currently more than 8 months pregnant (end of 3rd Trimester) " "and completed ANC1, ANC2 and ANC3", "tt_1_eligible": "Pregnant women who did not get 2 tetanus shots in the last 5 years", "tt_2_eligible": "Pregnant women who got Tetatnus 1 shots", "tt_booster_eligible": "Pregnant women who got 2 tetanus shots during previous pregnancy " "in the last 5 years", "tt_completed_eligible": "Pregnant women eligible to get Tetanus 2 shot or Tetanus Booster shot", "ifa_tablets_eligible": "Women currently pregnant", "100_tablets_eligible": "Women who have delivered in the selected date range", "clinically_anemic_eligible": "Currently pregnant women", "danger_signs_eligible": "Currently pregnant women", "knows_closest_facility_eligible": "Currently pregnant women" }, "pregnant_women_breakdown_by_trimester": { "total_pregnant": "Currently pregnant women", "trimester_1": "Women less than 2.75 months pregnant", "trimester_2": "Women more than 2.75 months and less than 6.4 months pregnant", "trimester_3": "Women more than 6.4 months pregnant" }, "delivery_details": { "total_delivery": "Includes live births and still births", "trained_traditional_birth_attendant": "Deliveries at health center or done by trained birth attendant " "elsewhere", "institutional_deliveries": "Deliveries at health center or hospital", "home_deliveries": "Deliveries at home or on route", "abortions": "Number of reported abortions" }, "postnatal_care_details": { "pnc_1": "Mothers visited by Front Line Worker within 48 hours of delivery", "pnc_2": "Mothers visited by Front Line Worker within 2-4 days of delivery", "pnc_3": "Mothers visited by Front Line Worker within 5-7 days of delivery", "pnc_4": "Mothers visited by Front Line Worker within 21-42 days of delivery", "pnc_1_eligible": "Mothers who have delivered", "pnc_2_eligible": "Mothers who have delivered 2 or more days ago", "pnc_3_eligible": "Mothers who have delivered 5 or more days ago", "pnc_4_eligible": "Mothers who have delivered 21 or more days ago" } } CHILD_INDICATOR_TOOLTIPS = { "child_registration_details": { "total": "Includes cases that were opened or closed within the date range, or remained open " "throughout the period", "total": "Total cases (both open and closed) irrespective of any date filters. Location filters " "still apply.", "no_date_opened": "Total open cases irrespective of any date filters. Location filters still apply.", "no_date_closed": "Total closed cases irrespective of any date filters. Location filters still apply.", "new_registration": "Cases open between today and 30 days from today" }, "immunization_details": { "bcg_eligible": "All children in date range", "opv0_eligible": "All children in date range", "hep0_eligible": "All children in date range", "opv1_eligible": "Children more than 1.3 months old", "hep1_eligible": "Children more than 1.3 months old", "dpt1_eligible": "Children more than 1.3 months old", "opv2_eligible": "Children more than 2.5 months old", "hep2_eligible": "Children more than 2.5 months old", "dpt2_eligible": "Children more than 2.5 months old", "opv3_eligible": "Children more than 3.5 months old", "hep3_eligible": "Children more than 3.5 months old", "dpt3_eligible": "Children more than 3.5 months old", "measles_eligible": "Children more than 9 months old", "vita1_eligible": "Children more than 9 months old", "vita2_eligible": "Children more than 18 months old", "dpt_opv_booster_eligible": "Children more than 18 months old", "vita3_eligible": "Children more than 23 months old", "fully_immunized": "Children who received all vaccines from BCG to Measles", "fully_immunized_eligible": "Children more than 9 months old" }, "nutrition_details": { "colostrum_feeding": "Children who had colostrum milk within 1 hour of birth", "exclusive_breastfeeding": "Children currently less than 6 months old and exclusively breastfed", "complementary_feeding": "Children between 6-24 months old who are receiving complementary feeding", "supplementary_feeding": "Children currently less than 6 months old who are receiving supplementary " "feeding in addition to breast milk", "colostrum_feeding_total_eligible": "Children who reported about colostrum feeding (both yes and no)", "exclusive_breastfeeding_total_eligible": "Children currently less than 6 months old", "complementary_feeding_total_eligible": "Children currently between 6-24 months old", "supplementary_feeding_total_eligible": "Children currently less than 6 months old" }, "ebf_stopping_details": { "stopped_0_1": "Children currently less than 6 months old who stopped EBF when they were less than " "1 month old", "stopped_1_3": "Children currently less than 6 months old who stopped EBF when they were between " "1-3 months old", "stopped_3_5": "Children currently less than 6 months old who stopped EBF when they were between " "3-5 months old", "stopped_5_6": "Children currently less than 6 months old who stopped EBF when they were between " "5-6 months old" }, "child_health_indicators": { "ari_cases": "Children who reported Penumonia between the last two visits by Front Line Worker", "diarrhea_cases": "Children who reported Diarrhoea between the last two visits by Front Line Worker", "ors": "Children who reported having ORS when they had Diarrhoea the last time", "zinc": "Children who reported having Zinc when they had Diarrhoea the last time", "deworming": "Children who got deworming does in the last 6 months", "deworming_total_eligible": "Children more than 1 year old" } }

53.118812

112

0.67055

794ac918c0f31aced8b153bf76a35063d319b331

2,448

py

Python

maro/rl/models/torch/mlp_representation.py

zhawan/maro

d8c98deea4296cdcb90efd1fb59bc571cec3a2ef

[ "MIT" ]

null

maro/rl/models/torch/mlp_representation.py

zhawan/maro

d8c98deea4296cdcb90efd1fb59bc571cec3a2ef

[ "MIT" ]

null

maro/rl/models/torch/mlp_representation.py

zhawan/maro

d8c98deea4296cdcb90efd1fb59bc571cec3a2ef

[ "MIT" ]

null

# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. import torch import torch.nn as nn class MLPRepresentation(nn.Module): """ Deep Q network. Choose multi-layer full connection with dropout as the basic network architecture. """ def __init__(self, name: str, input_dim: int, hidden_dims: [int], output_dim: int, dropout_p: float): """ Init deep Q network. Args: name (str): Network name. input_dim (int): Network input dimension. hidden_dims ([int]): Network hiddenlayer dimension. The length of `hidden_dims` means the hidden layer number, which requires larger than 1. output_dim (int): Network output dimension. dropout_p (float): Dropout parameter. """ super().__init__() self._name = name self._dropout_p = dropout_p self._input_dim = input_dim self._hidden_dims = hidden_dims if hidden_dims is not None else [] self._output_dim = output_dim self._layers = self._build_layers([input_dim] + self._hidden_dims) if len(self._hidden_dims) == 0: self._head = nn.Linear(self._input_dim, self._output_dim) else: self._head = nn.Linear(hidden_dims[-1], self._output_dim) self._device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self._net = nn.Sequential(*self._layers, self._head).to(self._device) def forward(self, x): return self._net(x.to(self._device)).double() @property def input_dim(self): return self._input_dim @property def name(self): return self._name @property def output_dim(self): return self._output_dim def _build_basic_layer(self, input_dim, output_dim): """ Build basic layer. BN -> Linear -> LeakyReLU -> Dropout """ return nn.Sequential(nn.Linear(input_dim, output_dim), nn.LeakyReLU(), nn.Dropout(p=self._dropout_p)) def _build_layers(self, layer_dims: []): """ Build multi basic layer. BasicLayer1 -> BasicLayer2 -> ... """ layers = [] for input_dim, output_dim in zip(layer_dims, layer_dims[1:]): layers.append(self._build_basic_layer(input_dim, output_dim)) return layers

33.534247

105

0.602124

794ac919c3ec01a8602699480bddb8e6e313c533

5,482

py

Python

model/cpn/ade.cpn.R50_v1c.v37/train.py

akinoriosamura/TorchSeg-mirror

34033fe85fc24015bcef7a92aad39d2a25a001a5

[ "MIT" ]

null

model/cpn/ade.cpn.R50_v1c.v37/train.py

akinoriosamura/TorchSeg-mirror

34033fe85fc24015bcef7a92aad39d2a25a001a5

[ "MIT" ]

1

2021-06-08T20:36:43.000Z

model/cpn/ade.cpn.R50_v1c.v37/train.py

akinoriosamura/TorchSeg-mirror

34033fe85fc24015bcef7a92aad39d2a25a001a5

[ "MIT" ]

null

from __future__ import division import os.path as osp import sys import argparse from tqdm import tqdm import torch import torch.nn as nn import torch.nn.functional as F import torch.distributed as dist import torch.backends.cudnn as cudnn from config import config from dataloader import get_train_loader from network import CPNet from datasets import ADE from utils.init_func import init_weight, group_weight from engine.lr_policy import PolyLR from engine.logger import get_logger from engine.engine import Engine # from seg_opr.sync_bn import DataParallelModel, Reduce, BatchNorm2d from seg_opr.loss_opr import AutoOhemCrossEntropy2d try: from apex.parallel import SyncBatchNorm, DistributedDataParallel except ImportError: raise ImportError( "Please install apex from https://www.github.com/nvidia/apex .") logger = get_logger() torch.manual_seed(config.seed) if torch.cuda.is_available(): torch.cuda.manual_seed(config.seed) parser = argparse.ArgumentParser() with Engine(custom_parser=parser) as engine: args = parser.parse_args() cudnn.benchmark = True if engine.distributed: torch.cuda.set_device(engine.local_rank) # data loader train_loader, train_sampler = get_train_loader(engine, ADE) # config network and criterion criterion = AutoOhemCrossEntropy2d(reduction='mean', ignore_label=-1, drop_ratio=0.3) if engine.distributed: logger.info('Use the Multi-Process-SyncBatchNorm') BatchNorm2d = SyncBatchNorm # else: # BatchNorm2d = BatchNorm2d model = CPNet(config.num_classes, criterion=criterion, pretrained_model=config.pretrained_model, norm_layer=BatchNorm2d) init_weight(model.business_layer, nn.init.kaiming_normal_, BatchNorm2d, config.bn_eps, config.bn_momentum, mode='fan_in', nonlinearity='relu') # group weight and config optimizer base_lr = config.lr params_list = [] params_list = group_weight(params_list, model.backbone, BatchNorm2d, base_lr) for module in model.business_layer: params_list = group_weight(params_list, module, BatchNorm2d, base_lr * 10) # config lr policy total_iteration = config.nepochs * config.niters_per_epoch lr_policy = PolyLR(base_lr, config.lr_power, total_iteration) optimizer = torch.optim.SGD(params_list, lr=base_lr, momentum=config.momentum, weight_decay=config.weight_decay) if engine.distributed: if torch.cuda.is_available(): model.cuda() model = DistributedDataParallel(model) else: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # model = DataParallelModel(model, engine.devices) model.to(device) engine.register_state(dataloader=train_loader, model=model, optimizer=optimizer) if engine.continue_state_object: engine.restore_checkpoint() optimizer.zero_grad() model.train() for epoch in range(engine.state.epoch, config.nepochs): if engine.distributed: train_sampler.set_epoch(epoch) bar_format = '{desc}[{elapsed}<{remaining},{rate_fmt}]' pbar = tqdm(range(config.niters_per_epoch), file=sys.stdout, bar_format=bar_format) dataloader = iter(train_loader) for idx in pbar: engine.update_iteration(epoch, idx) minibatch = dataloader.next() imgs = minibatch['data'] gts = minibatch['label'] imgs = imgs.cuda(non_blocking=True) gts = gts.cuda(non_blocking=True) loss = model(imgs, gts) # reduce the whole loss over multi-gpu if engine.distributed: dist.all_reduce(loss, dist.ReduceOp.SUM) loss = loss / engine.world_size # else: # loss = Reduce.apply(*loss) / len(loss) optimizer.zero_grad() loss.backward() optimizer.step() current_idx = epoch * config.niters_per_epoch + idx lr = lr_policy.get_lr(current_idx) optimizer.param_groups[0]['lr'] = lr optimizer.param_groups[1]['lr'] = lr for i in range(2, len(optimizer.param_groups)): optimizer.param_groups[i]['lr'] = lr * 10 print_str = 'Epoch{}/{}'.format(epoch, config.nepochs) \ + ' Iter{}/{}:'.format(idx + 1, config.niters_per_epoch) \ + ' lr=%.2e' % lr \ + ' loss=%.2f' % loss.item() pbar.set_description(print_str, refresh=False) if (epoch >= config.nepochs - 20) or ( epoch % config.snapshot_iter == 0): if engine.distributed and (engine.local_rank == 0): engine.save_and_link_checkpoint(config.snapshot_dir, config.log_dir, config.log_dir_link) elif not engine.distributed: engine.save_and_link_checkpoint(config.snapshot_dir, config.log_dir, config.log_dir_link)

35.597403

82

0.609814

794ac9b5d77445bad9f987d710f17899a6976849

28,804

py

Python

patches/mario.py

unhold/game-and-watch-patch

dc33f2228d7c791a746502aef27a5331c0076503

[ "BSD-3-Clause" ]

48

2021-08-19T19:34:51.000Z

2022-03-29T02:02:35.000Z

patches/mario.py

unhold/game-and-watch-patch

dc33f2228d7c791a746502aef27a5331c0076503

[ "BSD-3-Clause" ]

5

2021-09-04T12:15:46.000Z

2022-01-21T07:47:06.000Z

patches/mario.py

unhold/game-and-watch-patch

dc33f2228d7c791a746502aef27a5331c0076503

[ "BSD-3-Clause" ]

11

2021-10-15T23:36:08.000Z

2022-03-05T12:38:23.000Z

from pathlib import Path from PIL import Image import patches from .compression import lzma_compress from .exception import BadImageError, InvalidStockRomError from .firmware import Device, ExtFirmware, Firmware, IntFirmware from .tileset import bytes_to_tilemap, decode_backdrop, tilemap_to_bytes from .utils import ( fds_remove_crc_gaps, printd, printe, printi, round_down_word, round_up_page, seconds_to_frames, ) build_dir = Path("build") # TODO: expose this properly or put in better location class MarioGnW(Device, name="mario"): class Int(IntFirmware): STOCK_ROM_SHA1_HASH = "efa04c387ad7b40549e15799b471a6e1cd234c76" # Note: this isn't the ACTUAL Stock ROM end, this is actually # pointing to where some rwdata is, but this data will be relocated # and compressed. This variable is used in the linker scripts as to # where to start putting novel code. STOCK_ROM_END = 0x18100 KEY_OFFSET = 0x106F4 NONCE_OFFSET = 0x106E4 RWDATA_OFFSET = 0x180A4 RWDATA_LEN = 36 RWDATA_ITCM_IDX = 0 RWDATA_DTCM_IDX = 1 class Ext(ExtFirmware): STOCK_ROM_SHA1_HASH = "eea70bb171afece163fb4b293c5364ddb90637ae" ENC_END = 0xF_E000 def _verify(self): h = self.hash(self[:-8192]) if h != self.STOCK_ROM_SHA1_HASH: raise InvalidStockRomError class FreeMemory(Firmware): FLASH_BASE = 0x240F2124 FLASH_LEN = 0x24100000 - FLASH_BASE def argparse(self, parser): group = parser.add_argument_group("Timeout patches") mgroup = group.add_mutually_exclusive_group() mgroup.add_argument( "--disable-sleep", action="store_true", help="Disables sleep timer" ) mgroup.add_argument( "--sleep-time", type=float, default=None, help="Go to sleep after this many seconds of inactivity.. " "Valid range: [1, 1092]", ) group.add_argument( "--hard-reset-time", type=float, default=None, help="Hold power button for this many seconds to perform hard reset.", ) group.add_argument( "--mario-song-time", type=float, default=None, help="Hold the A button for this many seconds on the time " "screen to launch the mario drawing song easter egg.", ) group = parser.add_argument_group("ROM Hacks and Graphical Mods") group.add_argument( "--smb1", type=Path, default="build/smb1.nes", help="Override SMB1 ROM with your own file.", ) mgroup = group.add_mutually_exclusive_group() mgroup.add_argument( "--smb1-graphics", nargs="*", default=[], type=Path, help="ROM hacks where just the graphical assets will be used.", ) mgroup.add_argument( "--smb1-graphics-glob", action="store_true", help='Add all IPS files from the "ips/" folder', ) mgroup = group.add_mutually_exclusive_group() mgroup.add_argument( "--clock-tileset", type=Path, default=None, help="Override the clock tileset", ) mgroup.add_argument( "--clock-tileset-index", type=Path, default=None, help="Override the clock tileset", ) # group.add_argument( # "--iconset", # type=Path, # default=Path("build/iconset.png"), # help="Override the iconset", # ) group = parser.add_argument_group("Low level flash savings flags") group.add_argument( "--no-save", action="store_true", help="Don't use up 2 pages (8192 bytes) of extflash for non-volatile saves. " "High scores and brightness/volume configurations will NOT survive homebrew launches.", ) group.add_argument("--no-smb2", action="store_true", help="Remove SMB2 rom.") group.add_argument( "--no-mario-song", action="store_true", help="Remove the mario song easter egg.", ) group.add_argument( "--no-sleep-images", action="store_true", help="Remove the 5 sleeping images.", ) group = parser.add_argument_group("High level flash savings flags") group.add_argument( "--slim", action="store_true", help="Remove mario song and sleeping images from extflash.", ) group.add_argument( "--clock-only", action="store_true", help="Everything in --slim plus remove SMB2.", ) group.add_argument( "--internal-only", action="store_true", help="Configuration so no external flash is used.", ) self.args = parser.parse_args() ############ # Validate # ############ if self.args.sleep_time and ( self.args.sleep_time < 1 or self.args.sleep_time > 1092 ): parser.error("--sleep-time must be in range [1, 1092]") if self.args.mario_song_time and ( self.args.mario_song_time < 1 or self.args.mario_song_time > 1092 ): parser.error("--mario_song-time must be in range [1, 1092]") if len(self.args.smb1_graphics) > 8: parser.error("A maximum of 8 SMB1 graphics mods can be specified.") if self.args.smb1_graphics_glob: ips_folder = Path("ips") self.args.smb1_graphics = list(ips_folder.glob("*.ips")) self.args.smb1_graphics.extend(list(ips_folder.glob("*.IPS"))) if self.args.internal_only: self.args.slim = True self.args.extended = True self.args.no_save = True if self.args.clock_only: self.args.slim = True self.args.no_smb2 = True if self.args.slim: self.args.no_mario_song = True self.args.no_sleep_images = True return self.args def patch(self): printi("Invoke custom bootloader prior to calling stock Reset_Handler.") self.internal.replace(0x4, "bootloader") printi("Intercept button presses for macros.") self.internal.bl(0x6B52, "read_buttons") printi("Mute clock audio on first boot.") self.internal.asm(0x49E0, "mov.w r1, #0x00000") if self.args.debug: # Override fault handlers for easier debugging via gdb. printi("Overriding handlers for debugging.") self.internal.replace(0x8, "NMI_Handler") self.internal.replace(0xC, "HardFault_Handler") if self.args.hard_reset_time: hard_reset_time_ms = int(round(self.args.hard_reset_time * 1000)) printi( f"Hold power button for {hard_reset_time_ms} milliseconds to perform hard reset." ) self.internal.asm(0x9CEE, f"movw r1, #{hard_reset_time_ms}") if self.args.sleep_time: printi(f"Setting sleep time to {self.args.sleep_time} seconds.") sleep_time_frames = seconds_to_frames(self.args.sleep_time) self.internal.asm(0x6C3C, f"movw r2, #{sleep_time_frames}") if self.args.disable_sleep: printi("Disable sleep timer") self.internal.replace(0x6C40, 0x91, size=1) if self.args.mario_song_time: printi(f"Setting Mario Song time to {self.args.mario_song_time} seconds.") mario_song_frames = seconds_to_frames(self.args.mario_song_time) self.internal.asm(0x6FC4, f"cmp.w r0, #{mario_song_frames}") if not self.args.encrypt: # Disable OTFDEC self.internal.nop(0x10688, 2) self.internal.nop(0x1068E, 1) # Dump the tileset tileset_addr, tileset_size = 0x9_8B84, 0x1_0000 palette_addr = 0xB_EC68 palette = self.external[palette_addr : palette_addr + 320] tileset_bytes = self.external[tileset_addr : tileset_addr + tileset_size] tileset = bytes_to_tilemap(tileset_bytes, palette=palette) tileset.save(build_dir / "tileset.png") tileset_index = bytes_to_tilemap(tileset_bytes) tileset_index.save(build_dir / "tileset_index.png") # Override tileset if self.args.clock_tileset: with Image.open(self.args.clock_tileset) as tileset: if tileset.height != 256 or tileset.width != 256: raise BadImageError( "Clock tileset image must have height=256, width=256" ) tileset = tileset.convert("RGB") if tileset.getpixel((255, 255))[:3] != (95, 115, 255): raise BadImageError( "Clock tileset image color is corrupt. Possibly due to some gamma issue." ) self.external[ tileset_addr : tileset_addr + tileset_size ] = tilemap_to_bytes(tileset, palette) # Dump the iconset iconset_addr, iconset_size = 0xAACE4, 0x3F00 palette_addr = 0xB_EC68 palette = self.external[palette_addr : palette_addr + 320] iconset = bytes_to_tilemap( self.external[iconset_addr : iconset_addr + iconset_size], palette=palette, bpp=4, ) iconset.save(build_dir / "iconset.png") # Override iconset # with Image.open(self.args.iconset) as iconset: # if iconset.height != 128 or iconset.width !=256: # raise BadImageError("Iconset image must have height=128, width=256") # iconset = iconset.convert("RGB") # if iconset.getpixel((255, 127))[:3] != (95, 115, 255): # raise BadImageError("Iconset image color is corrupt. Possibly due to some gamma issue.") # self.external[iconset_addr : iconset_addr + iconset_size] = \ # tilemap_to_bytes(iconset, palette, bpp=4)[:iconset_size] # Dump BALL logo # ball_logo_addr, ball_logo_size = 0x1_13CC, 768 # palette_addr = 0xB_EC68 # palette = self.external[palette_addr : palette_addr + 320] # ball_logo = bytes_to_tilemap( # self.external[ball_logo_addr : ball_logo_addr + ball_logo_size], # palette=palette, # width=128, # bpp=2, # ) # ball_logo.save(build_dir / "ball_logo.png") if self.args.smb1_graphics: printi("Intercept prepare_clock_rom") self.internal.bl(0x690E, "prepare_clock_rom") self.internal.nop(0x1_0EF0, 2) table = self.internal.address("SMB1_GRAPHIC_MODS", sub_base=True) for file_path in self.args.smb1_graphics: if file_path.suffix.lower() == ".nes": rom = file_path.read_bytes() if len(rom) == 40976: # Remove the NES header rom = rom[16:] assert len(rom) == 40960 graphics = rom[0x8000:0x9EC0] graphics_compressed = lzma_compress(graphics) loc = self.move_to_int( graphics_compressed, len(graphics_compressed), None ) loc += self.internal.FLASH_BASE elif file_path.suffix.lower() == ".ips": patch = file_path.read_bytes() patch = patches.ips.strip_header(patch) loc = self.move_to_int(patch, len(patch), None) loc += self.internal.FLASH_BASE else: raise ValueError( f"Don't know how to handle extension for {file_path}." ) # Update the SMB1_GRAPHIC_MODS table self.internal.replace(table, loc, size=4) table += 4 printd("Compressing and moving stuff stuff to internal firmware.") compressed_len = self.external.compress( 0x0, 7772 ) # Dst expects only 7772 bytes, not 7776 self.internal.bl(0x665C, "memcpy_inflate") self.move_ext(0x0, compressed_len, 0x7204) # Note: the 4 bytes between 7772 and 7776 is padding. self.ext_offset -= 7776 - round_down_word(compressed_len) # SMB1 ROM (plus loading custom ROM) printd("Compressing and moving SMB1 ROM to compressed_memory.") smb1_addr, smb1_size = 0x1E60, 40960 # Adding the header for patching convenience. (build_dir / "smb1.nes").write_bytes( b"NES\x1a\x02\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00" + self.external[smb1_addr : smb1_addr + smb1_size] ) smb1 = self.args.smb1.read_bytes() if len(smb1) == 40976: # Remove the NES header smb1 = smb1[16:] if len(smb1) != smb1_size: raise ValueError(f"Unknown length {len(smb1)} of file {self.args.smb1}") self.external[smb1_addr : smb1_addr + smb1_size] = smb1 patch_smb1_refr = self.internal.address("SMB1_ROM", sub_base=True) self.move_to_compressed_memory( smb1_addr, smb1_size, [0x7368, 0x10954, 0x7218, patch_smb1_refr] ) # I think these are all scenes for the clock, but not 100% sure. # The giant lookup table references all these self.move_to_compressed_memory(0xBE60, 11620, None) # Starting here are BALL references self.move_to_compressed_memory(0xEBC4, 528, 0x4154) self.rwdata_lookup(0xEBC4, 528) self.move_to_compressed_memory(0xEDD4, 100, 0x4570) references = { 0xEE38: 0x4514, 0xEE78: 0x4518, 0xEEB8: 0x4520, 0xEEF8: 0x4524, } for external, internal in references.items(): self.move_to_compressed_memory(external, 64, internal) references = [ 0x2AC, 0x2B0, 0x2B4, 0x2B8, 0x2BC, 0x2C0, 0x2C4, 0x2C8, 0x2CC, 0x2D0, ] self.move_to_compressed_memory(0xEF38, 128 * 10, references) self.move_to_compressed_memory(0xF438, 96, 0x456C) self.move_to_compressed_memory(0xF498, 180, 0x43F8) # This is the first thing passed into the drawing engine. self.move_to_compressed_memory(0xF54C, 1100, 0x43FC) self.move_to_compressed_memory(0xF998, 180, 0x4400) self.move_to_compressed_memory(0xFA4C, 1136, 0x4404) self.move_to_compressed_memory(0xFEBC, 864, 0x450C) self.move_to_compressed_memory(0x1_021C, 384, 0x4510) self.move_to_compressed_memory(0x1_039C, 384, 0x451C) self.move_to_compressed_memory(0x1_051C, 384, 0x4410) self.move_to_compressed_memory(0x1_069C, 384, 0x44F8) self.move_to_compressed_memory(0x1_081C, 384, 0x4500) self.move_to_compressed_memory(0x1_099C, 384, 0x4414) self.move_to_compressed_memory(0x1_0B1C, 384, 0x44FC) self.move_to_compressed_memory(0x1_0C9C, 384, 0x4504) self.move_to_compressed_memory(0x1_0E1C, 384, 0x440C) self.move_to_compressed_memory(0x1_0F9C, 384, 0x4408) self.move_to_compressed_memory(0x1_111C, 192, 0x44F4) self.move_to_compressed_memory(0x1_11DC, 192, 0x4508) self.move_to_compressed_memory(0x1_129C, 304, 0x458C) self.move_to_compressed_memory( 0x1_13CC, 768, 0x4584 ) # BALL logo tile idx tight self.move_to_compressed_memory(0x1_16CC, 1144, 0x4588) self.move_to_compressed_memory(0x1_1B44, 768, 0x4534) self.move_to_compressed_memory(0x1_1E44, 32, 0x455C) self.move_to_compressed_memory(0x1_1E64, 32, 0x4558) self.move_to_compressed_memory(0x1_1E84, 32, 0x4554) self.move_to_compressed_memory(0x1_1EA4, 32, 0x4560) self.move_to_compressed_memory(0x1_1EC4, 32, 0x4564) self.move_to_compressed_memory(0x1_1EE4, 64, 0x453C) self.move_to_compressed_memory(0x1_1F24, 64, 0x4530) self.move_to_compressed_memory(0x1_1F64, 64, 0x4540) self.move_to_compressed_memory(0x1_1FA4, 64, 0x4544) self.move_to_compressed_memory(0x1_1FE4, 64, 0x4548) self.move_to_compressed_memory(0x1_2024, 64, 0x454C) self.move_to_compressed_memory(0x1_2064, 64, 0x452C) self.move_to_compressed_memory(0x1_20A4, 64, 0x4550) self.move_to_compressed_memory(0x1_20E4, 21 * 96, 0x4574) self.move_to_compressed_memory(0x1_28C4, 192, 0x4578) self.move_to_compressed_memory(0x1_2984, 640, 0x457C) # This is a 320 byte palette used for BALL, but the last 160 bytes are empty self.move_to_compressed_memory(0x1_2C04, 320, 0x4538) mario_song_len = 0x85E40 # 548,416 bytes if self.args.no_mario_song: # This isn't really necessary, but we keep it here because its more explicit. printe("Erasing Mario Song") self.external.replace(0x1_2D44, b"\x00" * mario_song_len) self.rwdata_erase(0x1_2D44, mario_song_len) self.ext_offset -= mario_song_len self.internal.asm(0x6FC8, "b 0x1c") else: references = [ # Banners 0x11A00, 0x11A00 + 4, 0x11A00 + 8, 0x11A00 + 12, 0x11A00 + 16, 0x11A00 + 20, 0x11A00 + 24, # Audio 0x1199C, ] self.move_ext(0x1_2D44, mario_song_len, references) self.rwdata_lookup(0x1_2D44, mario_song_len) # Each tile is 16x16 pixels, stored as 256 bytes in row-major form. # These index into one of the palettes starting at 0xbec68. printe("Compressing clock graphics") compressed_len = self.external.compress(0x9_8B84, 0x1_0000) self.internal.bl(0x678E, "memcpy_inflate") printe("Moving clock graphics") self.move_ext(0x9_8B84, compressed_len, 0x7350) self.ext_offset -= 0x1_0000 - round_down_word(compressed_len) # Note: the clock uses a different palette; this palette only applies # to ingame Super Mario Bros 1 & 2 printe("Moving NES emulator palette.") self.move_to_compressed_memory(0xA_8B84, 192, 0xB720) # Note: UNKNOWN* represents a block of data that i haven't decoded # yet. If you know what the block of data is, please let me know! self.move_to_compressed_memory(0xA_8C44, 8352, 0xBC44) printe("Moving iconset.") # MODIFY THESE IF WE WANT CUSTOM GAME ICONS self.move_to_compressed_memory(0xA_ACE4, 16128, [0xCEA8, 0xD2F8]) printe("Moving menu stuff (icons? meta?)") references = [ 0x0_D010, 0x0_D004, 0x0_D2D8, 0x0_D2DC, 0x0_D2F4, 0x0_D2F0, ] self.move_to_compressed_memory(0xA_EBE4, 116, references) # Dump a playable version of SMB2 smb2_addr, smb2_size = 0xA_EC58, 0x1_0000 smb2_end = smb2_addr + smb2_size smb2 = self.external[smb2_addr:smb2_end].copy() smb2 = fds_remove_crc_gaps(smb2) (build_dir / "smb2.fds").write_bytes(smb2) if self.args.no_smb2: printe("Erasing SMB2 ROM") self.external.replace( smb2_addr, b"\x00" * smb2_size, ) self.ext_offset -= smb2_size else: printe("Compressing and moving SMB2 ROM.") compressed_len = self.external.compress(smb2_addr, smb2_size) self.internal.bl(0x6A12, "memcpy_inflate") self.move_to_compressed_memory(smb2_addr, compressed_len, 0x7374) self.ext_offset -= smb2_size - round_down_word( compressed_len ) # Move by the space savings. # Round to nearest page so that the length can be used as an imm compressed_len = round_up_page(compressed_len) # Update the length of the compressed data (doesn't matter if its too large) self.internal.asm(0x6A0A, f"mov.w r2, #{compressed_len}") self.internal.asm(0x6A1E, f"mov.w r3, #{compressed_len}") # Not sure what this data is self.move_to_compressed_memory(0xBEC58, 8 * 2, 0x10964) printe("Moving Palettes") # There are 80 colors, each in BGRA format, where A is always 0 # These are referenced by the scene table. self.move_to_compressed_memory(0xBEC68, 320, None) # Day palette [0600, 1700] self.move_to_compressed_memory(0xBEDA8, 320, None) # Night palette [1800, 0400) self.move_to_compressed_memory( 0xBEEE8, 320, None ) # Underwater palette (between 1200 and 2400 at XX:30) self.move_to_compressed_memory( 0xBF028, 320, None ) # Unknown palette. Maybe bowser castle? need to check... self.move_to_compressed_memory(0xBF168, 320, None) # Dawn palette [0500, 0600) # These are scene headers, each containing 2x uint32_t's. # They are MOSTLY [0x36, 0xF], but there are a few like [0x30, 0xF] and [0x20, 0xF], # Referenced by the scene table self.move_to_compressed_memory(0xBF2A8, 45 * 8, None) # IDK what this is. self.move_to_compressed_memory(0xBF410, 144, 0x1658C) # SCENE TABLE # Goes in chunks of 20 bytes (5 addresses) # Each scene is represented by 5 pointers: # 1. Pointer to a 2x uint32_t header (I think it's total tile (w, h) ) # The H is always 15, which would be 240 pixels tall. # The W is usually 54, which would be 864 pixels (probably the flag pole?) # 2. RLE something. Usually 32 bytes. # 3. RLE something # 4. RLE something # 5. Palette # # The RLE encoded data could be background tilemap, animation routine, etc. lookup_table_start = 0xB_F4A0 lookup_table_end = 0xB_F838 lookup_table_len = lookup_table_end - lookup_table_start # 46 * 5 * 4 = 920 for addr in range(lookup_table_start, lookup_table_end, 4): self.external.lookup(addr) # Now move the table self.move_to_compressed_memory(lookup_table_start, lookup_table_len, 0xDF88) # Not sure what this is references = [ 0xE8F8, 0xF4EC, 0xF4F8, 0x10098, 0x105B0, ] self.move_to_compressed_memory(0xBF838, 280, references) self.move_to_compressed_memory(0xBF950, 180, [0xE2E4, 0xF4FC]) self.move_to_compressed_memory(0xBFA04, 8, 0x1_6590) self.move_to_compressed_memory(0xBFA0C, 784, 0x1_0F9C) # MOVE EXTERNAL FUNCTIONS new_loc = self.move_ext(0xB_FD1C, 14244, None) references = [ # internal references to external functions 0x00D330, 0x00D310, 0x00D308, 0x00D338, 0x00D348, 0x00D360, 0x00D368, 0x00D388, 0x00D358, 0x00D320, 0x00D350, 0x00D380, 0x00D378, 0x00D318, 0x00D390, 0x00D370, 0x00D340, 0x00D398, 0x00D328, ] for reference in references: self.internal.lookup(reference) references = [ # external references to external functions 0xC_1174, 0xC_313C, 0xC_049C, 0xC_1178, 0xC_220C, 0xC_3490, 0xC_3498, ] for reference in references: reference = reference - 0xB_FD1C + new_loc try: self.internal.lookup(reference) except (IndexError, KeyError): self.external.lookup(reference) # BALL sound samples. self.move_to_compressed_memory(0xC34C0, 6168, 0x43EC) self.rwdata_lookup(0xC34C0, 6168) self.move_to_compressed_memory(0xC4CD8, 2984, 0x459C) self.move_to_compressed_memory(0xC5880, 120, 0x4594) total_image_length = 193_568 references = [ 0x1097C, 0x1097C + 4, 0x1097C + 8, 0x1097C + 12, 0x1097C + 16, ] for name, index in [ ("mario_sleeping", 0xC_58F8), ("mario_juggling", 0xC_D858), ("bowser_sleeping", 0xD_6C78), ("pizza", 0xE_16F8), ("minions_sleeping", 0xE_C318), ]: img, _ = decode_backdrop(self.external[index:]) img.save(build_dir / f"backdrop_{name}.png") if self.args.no_sleep_images: # Images Notes: # * In-between images are just zeros. # # start: 0x900C_58F8 end: 0x900C_D83F mario sleeping # start: 0x900C_D858 end: 0x900D_6C65 mario juggling # start: 0x900D_6C78 end: 0x900E_16E2 bowser sleeping # start: 0x900E_16F8 end: 0x900E_C301 mario and luigi eating pizza # start: 0x900E_C318 end: 0x900F_4D04 minions sleeping # zero_padded_end: 0x900f_4d18 # Total Image Length: 193_568 bytes printe("Deleting sleeping images.") self.external.replace(0xC58F8, b"\x00" * total_image_length) for reference in references: self.internal.replace(reference, b"\x00" * 4) # Erase image references self.ext_offset -= total_image_length else: self.move_ext(0xC58F8, total_image_length, references) # Definitely at least contains part of the TIME graphic on startup screen. self.move_to_compressed_memory(0xF4D18, 2880, 0x10960) # What is this data? # The memcpy to this address is all zero, so i guess its not used? self.external.replace(0xF5858, b"\x00" * 34728) # refence at internal 0x7210 self.ext_offset -= 34728 if self.compressed_memory_pos: # Compress and copy over compressed_memory self.internal.rwdata.append( self.compressed_memory[: self.compressed_memory_pos].copy(), self.compressed_memory.FLASH_BASE, ) # Compress, insert, and reference the modified rwdata self.int_pos += self.internal.rwdata.write_table_and_data( 0x17DB4, data_offset=self.int_pos ) # Shorten the external firmware # This rounds the negative self.ext_offset towards zero. self.ext_offset = round_up_page(self.ext_offset) if self.args.no_save: # Disable nvram loading for nop in [0x495E, 0x49A6, 0x49B2]: self.internal.nop(nop, 2) # self.internal.b(0x4988, 0x49be) # If you still want the first-startup "Press TIME Button" screen self.internal.b(0x4988, 0x49C0) # Skips Press TIME Button screen # Disable nvram saving # This just skips the body of the nvram_write_bank function self.internal.b(0x48BE, 0x4912) self.ext_offset -= 8192 else: printi("Update NVRAM read addresses") self.internal.asm( 0x4856, "ite ne; " f"movne.w r4, #{hex(0xff000 + self.ext_offset)}; " f"moveq.w r4, #{hex(0xfe000 + self.ext_offset)}", ) printi("Update NVRAM write addresses") self.internal.asm( 0x48C0, "ite ne; " f"movne.w r4, #{hex(0xff000 + self.ext_offset)}; " f"moveq.w r4, #{hex(0xfe000 + self.ext_offset)}", ) # Finally, shorten the firmware printi("Updating end of OTFDEC pointer") self.internal.add(0x1_06EC, self.ext_offset) self.external.shorten(self.ext_offset) internal_remaining_free = len(self.internal) - self.int_pos compressed_memory_free = ( len(self.compressed_memory) - self.compressed_memory_pos ) return internal_remaining_free, compressed_memory_free

39.242507

111

0.594119

794ac9f59a5ba7fdc2e4ed869476ced19ffbaf50

338

py

Python

import.py

muhdzakirahmat/unit6proj

5c1e260a2b5146f5e2a33e4e140404df74a80030

[ "Apache-2.0" ]

null

import.py

muhdzakirahmat/unit6proj

5c1e260a2b5146f5e2a33e4e140404df74a80030

[ "Apache-2.0" ]

null

import.py

muhdzakirahmat/unit6proj

5c1e260a2b5146f5e2a33e4e140404df74a80030

[ "Apache-2.0" ]

null

""" python import.py export.zip challenges,teams,both,metadata """ from Unit6 import create_app from Unit6.utils import import_ctf import sys app = create_app() with app.app_context(): if len(sys.argv) == 3: segments = sys.argv[2].split(',') else: segments = None import_ctf(sys.argv[1], segments=segments)

19.882353

58

0.677515

794aca4f26a8139ea16fbc6032d37ac6f93deced

9,117

py

Python

lib/datasets/imdb.py

alkymi-io/faster-rcnn.pytorch

2613b8d643d90ae16f9593a357ab4ba0de7f82a6

[ "MIT" ]

6

2018-11-28T08:09:40.000Z

2020-12-06T10:07:29.000Z

lib/datasets/imdb.py

alkymi-io/faster-rcnn.pytorch

2613b8d643d90ae16f9593a357ab4ba0de7f82a6

[ "MIT" ]

1

2020-09-24T19:21:24.000Z

lib/datasets/imdb.py

alkymi-io/faster-rcnn.pytorch

2613b8d643d90ae16f9593a357ab4ba0de7f82a6

[ "MIT" ]

1

2019-05-22T01:33:36.000Z

# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Xinlei Chen # -------------------------------------------------------- from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import os.path as osp import PIL from lib.model.utils.cython_bbox import bbox_overlaps import numpy as np import scipy.sparse from lib.model.utils.config import cfg import pdb ROOT_DIR = osp.join(osp.dirname(__file__), '..', '..') class imdb(object): """Image database.""" def __init__(self, name, classes=None): self._name = name self._num_classes = 0 if not classes: self._classes = [] else: self._classes = classes self._image_index = [] self._obj_proposer = 'gt' self._roidb = None self._roidb_handler = self.default_roidb # Use this dict for storing dataset specific config options self.config = {} @property def name(self): return self._name @property def num_classes(self): return len(self._classes) @property def classes(self): return self._classes @property def image_index(self): return self._image_index @property def roidb_handler(self): return self._roidb_handler @roidb_handler.setter def roidb_handler(self, val): self._roidb_handler = val def set_proposal_method(self, method): method = eval('self.' + method + '_roidb') self.roidb_handler = method @property def roidb(self): # A roidb is a list of dictionaries, each with the following keys: # boxes # gt_overlaps # gt_classes # flipped if self._roidb is not None: return self._roidb self._roidb = self.roidb_handler() return self._roidb @property def cache_path(self): cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache')) if not os.path.exists(cache_path): os.makedirs(cache_path) return cache_path @property def num_images(self): return len(self.image_index) def image_path_at(self, i): raise NotImplementedError def image_id_at(self, i): raise NotImplementedError def default_roidb(self): raise NotImplementedError def evaluate_detections(self, all_boxes, output_dir=None): """ all_boxes is a list of length number-of-classes. Each list element is a list of length number-of-images. Each of those list elements is either an empty list [] or a numpy array of detection. all_boxes[class][image] = [] or np.array of shape #dets x 5 """ raise NotImplementedError def _get_widths(self): return [PIL.Image.open(self.image_path_at(i)).size[0] for i in range(self.num_images)] def append_flipped_images(self): num_images = self.num_images widths = self._get_widths() for i in range(num_images): boxes = self.roidb[i]['boxes'].copy() oldx1 = boxes[:, 0].copy() oldx2 = boxes[:, 2].copy() boxes[:, 0] = widths[i] - oldx2 - 1 boxes[:, 2] = widths[i] - oldx1 - 1 assert (boxes[:, 2] >= boxes[:, 0]).all() entry = {'boxes': boxes, 'gt_overlaps': self.roidb[i]['gt_overlaps'], 'gt_classes': self.roidb[i]['gt_classes'], 'flipped': True} self.roidb.append(entry) self._image_index = self._image_index * 2 def evaluate_recall(self, candidate_boxes=None, thresholds=None, area='all', limit=None): """Evaluate detection proposal recall metrics. Returns: results: dictionary of results with keys 'ar': average recall 'recalls': vector recalls at each IoU overlap threshold 'thresholds': vector of IoU overlap thresholds 'gt_overlaps': vector of all ground-truth overlaps """ # Record max overlap value for each gt box # Return vector of overlap values areas = {'all': 0, 'small': 1, 'medium': 2, 'large': 3, '96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7} area_ranges = [[0 ** 2, 1e5 ** 2], # all [0 ** 2, 32 ** 2], # small [32 ** 2, 96 ** 2], # medium [96 ** 2, 1e5 ** 2], # large [96 ** 2, 128 ** 2], # 96-128 [128 ** 2, 256 ** 2], # 128-256 [256 ** 2, 512 ** 2], # 256-512 [512 ** 2, 1e5 ** 2], # 512-inf ] assert area in areas, 'unknown area range: {}'.format(area) area_range = area_ranges[areas[area]] gt_overlaps = np.zeros(0) num_pos = 0 for i in range(self.num_images): # Checking for max_overlaps == 1 avoids including crowd annotations # (...pretty hacking :/) max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1) gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) & (max_gt_overlaps == 1))[0] gt_boxes = self.roidb[i]['boxes'][gt_inds, :] gt_areas = self.roidb[i]['seg_areas'][gt_inds] valid_gt_inds = np.where((gt_areas >= area_range[0]) & (gt_areas <= area_range[1]))[0] gt_boxes = gt_boxes[valid_gt_inds, :] num_pos += len(valid_gt_inds) if candidate_boxes is None: # If candidate_boxes is not supplied, the default is to use the # non-ground-truth boxes from this roidb non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0] boxes = self.roidb[i]['boxes'][non_gt_inds, :] else: boxes = candidate_boxes[i] if boxes.shape[0] == 0: continue if limit is not None and boxes.shape[0] > limit: boxes = boxes[:limit, :] overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float)) _gt_overlaps = np.zeros((gt_boxes.shape[0])) for j in range(gt_boxes.shape[0]): # find which proposal box maximally covers each gt box argmax_overlaps = overlaps.argmax(axis=0) # and get the iou amount of coverage for each gt box max_overlaps = overlaps.max(axis=0) # find which gt box is 'best' covered (i.e. 'best' = most iou) gt_ind = max_overlaps.argmax() gt_ovr = max_overlaps.max() assert (gt_ovr >= 0) # find the proposal box that covers the best covered gt box box_ind = argmax_overlaps[gt_ind] # record the iou coverage of this gt box _gt_overlaps[j] = overlaps[box_ind, gt_ind] assert (_gt_overlaps[j] == gt_ovr) # mark the proposal box and the gt box as used overlaps[box_ind, :] = -1 overlaps[:, gt_ind] = -1 # append recorded iou coverage level gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps)) gt_overlaps = np.sort(gt_overlaps) if thresholds is None: step = 0.05 thresholds = np.arange(0.5, 0.95 + 1e-5, step) recalls = np.zeros_like(thresholds) # compute recall for each iou threshold for i, t in enumerate(thresholds): recalls[i] = (gt_overlaps >= t).sum() / float(num_pos) # ar = 2 * np.trapz(recalls, thresholds) ar = recalls.mean() return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds, 'gt_overlaps': gt_overlaps} def create_roidb_from_box_list(self, box_list, gt_roidb): assert len(box_list) == self.num_images, \ 'Number of boxes must match number of ground-truth images' roidb = [] for i in range(self.num_images): boxes = box_list[i] num_boxes = boxes.shape[0] overlaps = np.zeros((num_boxes, self.num_classes), dtype=np.float32) if gt_roidb is not None and gt_roidb[i]['boxes'].size > 0: gt_boxes = gt_roidb[i]['boxes'] gt_classes = gt_roidb[i]['gt_classes'] gt_overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float)) argmaxes = gt_overlaps.argmax(axis=1) maxes = gt_overlaps.max(axis=1) I = np.where(maxes > 0)[0] overlaps[I, gt_classes[argmaxes[I]]] = maxes[I] overlaps = scipy.sparse.csr_matrix(overlaps) roidb.append({ 'boxes': boxes, 'gt_classes': np.zeros((num_boxes,), dtype=np.int32), 'gt_overlaps': overlaps, 'flipped': False, 'seg_areas': np.zeros((num_boxes,), dtype=np.float32), }) return roidb @staticmethod def merge_roidbs(a, b): assert len(a) == len(b) for i in range(len(a)): a[i]['boxes'] = np.vstack((a[i]['boxes'], b[i]['boxes'])) a[i]['gt_classes'] = np.hstack((a[i]['gt_classes'], b[i]['gt_classes'])) a[i]['gt_overlaps'] = scipy.sparse.vstack([a[i]['gt_overlaps'], b[i]['gt_overlaps']]) a[i]['seg_areas'] = np.hstack((a[i]['seg_areas'], b[i]['seg_areas'])) return a def competition_mode(self, on): """Turn competition mode on or off.""" pass

34.274436

74

0.595591

794acb039946667442f68855501e53ee0b4b9987

46,094

py

Python

plenum/test/helper.py

cam-parra/indy-plenum

a891defac546488c6ec2f4a12d23894742d1427f

[ "Apache-2.0" ]

null

plenum/test/helper.py

cam-parra/indy-plenum

a891defac546488c6ec2f4a12d23894742d1427f

[ "Apache-2.0" ]

null

plenum/test/helper.py

cam-parra/indy-plenum

a891defac546488c6ec2f4a12d23894742d1427f

[ "Apache-2.0" ]

null

from datetime import datetime import itertools import os import random import string from _signal import SIGINT from contextlib import contextmanager from functools import partial from itertools import permutations, combinations from shutil import copyfile from sys import executable from time import sleep from typing import Tuple, Iterable, Dict, Optional, List, Any, Sequence, Union import base58 import pytest from indy.pool import set_protocol_version from common.serializers.serialization import invalid_index_serializer from plenum.common.signer_simple import SimpleSigner from plenum.common.timer import QueueTimer from plenum.config import Max3PCBatchWait from psutil import Popen import json import asyncio from indy.ledger import sign_and_submit_request, sign_request, submit_request, build_node_request, \ build_pool_config_request from indy.error import ErrorCode, IndyError from ledger.genesis_txn.genesis_txn_file_util import genesis_txn_file from plenum.common.constants import DOMAIN_LEDGER_ID, OP_FIELD_NAME, REPLY, REQNACK, REJECT, \ CURRENT_PROTOCOL_VERSION from plenum.common.exceptions import RequestNackedException, RequestRejectedException, CommonSdkIOException, \ PoolLedgerTimeoutException from plenum.common.messages.node_messages import Reply, PrePrepare, Prepare, Commit from plenum.common.txn_util import get_req_id, get_from from plenum.common.types import f, OPERATION from plenum.common.util import getNoInstances, get_utc_epoch from plenum.common.config_helper import PNodeConfigHelper from plenum.common.request import Request from plenum.server.node import Node from plenum.server.replica import Replica from plenum.test import waits from plenum.test.msgs import randomMsg from plenum.test.spy_helpers import getLastClientReqReceivedForNode, getAllArgs, getAllReturnVals, \ getAllMsgReceivedForNode from plenum.test.test_node import TestNode, TestReplica, \ getPrimaryReplica from stp_core.common.log import getlogger from stp_core.loop.eventually import eventuallyAll, eventually from stp_core.loop.looper import Looper from stp_core.network.util import checkPortAvailable logger = getlogger() # noinspection PyUnresolvedReferences def ordinal(n): return "%d%s" % ( n, "tsnrhtdd"[(n / 10 % 10 != 1) * (n % 10 < 4) * n % 10::4]) def random_string(length: int) -> str: return ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(length)) def send_reqs_batches_and_get_suff_replies( looper: Looper, txnPoolNodeSet, sdk_pool_handle, sdk_wallet_client, num_reqs: int, num_batches=1, **kwargs): # This method assumes that `num_reqs` <= num_batches*MaxbatchSize if num_batches == 1: return sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool_handle, sdk_wallet_client, num_reqs) else: requests = [] for _ in range(num_batches - 1): requests.extend( sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool_handle, sdk_wallet_client, num_reqs // num_batches)) rem = num_reqs % num_batches if rem == 0: rem = num_reqs // num_batches requests.extend( sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool_handle, sdk_wallet_client, rem)) return requests # noinspection PyIncorrectDocstring def checkResponseCorrectnessFromNodes(receivedMsgs: Iterable, reqId: int, fValue: int) -> bool: """ the client must get at least :math:`f+1` responses """ msgs = [(msg[f.RESULT.nm][f.REQ_ID.nm], msg[f.RESULT.nm][f.IDENTIFIER.nm]) for msg in getRepliesFromClientInbox(receivedMsgs, reqId)] groupedMsgs = {} for tpl in msgs: groupedMsgs[tpl] = groupedMsgs.get(tpl, 0) + 1 assert max(groupedMsgs.values()) >= fValue + 1 def getRepliesFromClientInbox(inbox, reqId) -> list: return list({_: msg for msg, _ in inbox if msg[OP_FIELD_NAME] == REPLY and msg[f.RESULT.nm] [f.REQ_ID.nm] == reqId}.values()) def checkLastClientReqForNode(node: TestNode, expectedRequest: Request): recvRequest = getLastClientReqReceivedForNode(node) assert recvRequest assert expectedRequest.as_dict == recvRequest.as_dict # noinspection PyIncorrectDocstring def assertLength(collection: Iterable[Any], expectedLength: int): assert len( collection) == expectedLength, "Observed length was {} but " \ "expected length was {}". \ format(len(collection), expectedLength) def assertEquality(observed: Any, expected: Any, details=None): assert observed == expected, "Observed value was {} but expected value " \ "was {}, details: {}".format(observed, expected, details) def randomOperation(): return { "type": "buy", "amount": random.randint(10, 100000) } def random_requests(count): return [randomOperation() for _ in range(count)] def random_request_objects(count, protocol_version): req_dicts = random_requests(count) return [Request(operation=op, protocolVersion=protocol_version) for op in req_dicts] def buildCompletedTxnFromReply(request, reply: Reply) -> Dict: txn = request.operation txn.update(reply) return txn async def msgAll(nodes): # test sending messages from every node to every other node # TODO split send and check so that the messages can be sent concurrently for p in permutations(nodes, 2): await sendMessageAndCheckDelivery(p[0], p[1]) def sendMessage(sender: Node, reciever: Node, msg: Optional[Tuple] = None): """ Sends message from one node to another :param nodes: :param sender: sender :param reciever: recepient :param msg: optional message - by default random one generated :return: """ logger.debug("Sending msg from {} to {}".format(sender.name, reciever.name)) msg = msg if msg else randomMsg() rid = sender.nodestack.getRemote(reciever.name).uid sender.nodestack.send(msg, rid) async def sendMessageAndCheckDelivery(sender: Node, reciever: Node, msg: Optional[Tuple] = None, method=None, customTimeout=None): """ Sends message from one node to another and checks that it was delivered :param sender: sender :param reciever: recepient :param msg: optional message - by default random one generated :param customTimeout: :return: """ logger.debug("Sending msg from {} to {}".format(sender.name, reciever.name)) msg = msg if msg else randomMsg() rid = sender.nodestack.getRemote(reciever.name).uid sender.nodestack.send(msg, rid) timeout = customTimeout or waits.expectedNodeToNodeMessageDeliveryTime() await eventually(checkMessageReceived, msg, reciever, method, retryWait=.1, timeout=timeout, ratchetSteps=10) def sendMessageToAll(nodes, sender: Node, msg: Optional[Tuple] = None): """ Sends message from one node to all others :param nodes: :param sender: sender :param msg: optional message - by default random one generated :return: """ for node in nodes: if node != sender: sendMessage(sender, node, msg) async def sendMessageAndCheckDeliveryToAll(nodes, sender: Node, msg: Optional[Tuple] = None, method=None, customTimeout=None): """ Sends message from one node to all other and checks that it was delivered :param nodes: :param sender: sender :param msg: optional message - by default random one generated :param customTimeout: :return: """ customTimeout = customTimeout or waits.expectedNodeToAllNodesMessageDeliveryTime( len(nodes)) for node in nodes: if node != sender: await sendMessageAndCheckDelivery(sender, node, msg, method, customTimeout) break def checkMessageReceived(msg, receiver, method: str = None): allMsgs = getAllMsgReceivedForNode(receiver, method) assert msg in allMsgs def addNodeBack(node_set, looper: Looper, node: Node, tconf, tdir) -> TestNode: config_helper = PNodeConfigHelper(node.name, tconf, chroot=tdir) restartedNode = TestNode(node.name, config_helper=config_helper, config=tconf, ha=node.nodestack.ha, cliha=node.clientstack.ha) for node in node_set: if node.name != restartedNode.name: node.nodestack.reconnectRemoteWithName(restartedNode.name) node_set.append(restartedNode) looper.add(restartedNode) return restartedNode def checkPropagateReqCountOfNode(node: TestNode, digest: str): assert digest in node.requests assert node.quorums.propagate.is_reached( len(node.requests[digest].propagates)) def requestReturnedToNode(node: TestNode, key: str, instId: int): params = getAllArgs(node, node.processOrdered) # Skipping the view no and time from each ordered request recvdOrderedReqs = [ (p['ordered'].instId, p['ordered'].valid_reqIdr[0]) for p in params] expected = (instId, key) return expected in recvdOrderedReqs def checkRequestReturnedToNode(node: TestNode, key: str, instId: int): assert requestReturnedToNode(node, key, instId) def checkRequestNotReturnedToNode(node: TestNode, key: str, instId: int): assert not requestReturnedToNode(node, key, instId) def check_request_is_not_returned_to_nodes(txnPoolNodeSet, request): instances = range(getNoInstances(len(txnPoolNodeSet))) for node, inst_id in itertools.product(txnPoolNodeSet, instances): checkRequestNotReturnedToNode(node, request.key, inst_id) def checkPrePrepareReqSent(replica: TestReplica, req: Request): prePreparesSent = getAllArgs(replica, replica.sendPrePrepare) expectedDigest = TestReplica.batchDigest([req]) assert expectedDigest in [p["ppReq"].digest for p in prePreparesSent] assert [req.digest, ] in \ [p["ppReq"].reqIdr for p in prePreparesSent] def checkPrePrepareReqRecvd(replicas: Iterable[TestReplica], expectedRequest: PrePrepare): for replica in replicas: params = getAllArgs(replica, replica._can_process_pre_prepare) assert expectedRequest.reqIdr in [p['pre_prepare'].reqIdr for p in params] def checkPrepareReqSent(replica: TestReplica, key: str, view_no: int): paramsList = getAllArgs(replica, replica.canPrepare) rv = getAllReturnVals(replica, replica.canPrepare) args = [p["ppReq"].reqIdr for p in paramsList if p["ppReq"].viewNo == view_no] assert [key] in args idx = args.index([key]) assert rv[idx] def checkSufficientPrepareReqRecvd(replica: TestReplica, viewNo: int, ppSeqNo: int): key = (viewNo, ppSeqNo) assert key in replica.prepares assert len(replica.prepares[key][1]) >= replica.quorums.prepare.value def checkSufficientCommitReqRecvd(replicas: Iterable[TestReplica], viewNo: int, ppSeqNo: int): for replica in replicas: key = (viewNo, ppSeqNo) assert key in replica.commits received = len(replica.commits[key][1]) minimum = replica.quorums.commit.value assert received > minimum def checkViewNoForNodes(nodes: Iterable[TestNode], expectedViewNo: int = None): """ Checks if all the given nodes have the expected view no :param nodes: The nodes to check for :param expectedViewNo: the view no that the nodes are expected to have :return: """ viewNos = set() for node in nodes: logger.debug("{}'s view no is {}".format(node, node.viewNo)) viewNos.add(node.viewNo) assert len(viewNos) == 1, 'Expected 1, but got {}. ' \ 'ViewNos: {}'.format(len(viewNos), [(n.name, n.viewNo) for n in nodes]) vNo, = viewNos if expectedViewNo is not None: assert vNo >= expectedViewNo, \ 'Expected at least {}, but got {}'.format(expectedViewNo, vNo) return vNo def waitForViewChange(looper, txnPoolNodeSet, expectedViewNo=None, customTimeout=None): """ Waits for nodes to come to same view. Raises exception when time is out """ timeout = customTimeout or waits.expectedPoolElectionTimeout(len(txnPoolNodeSet)) return looper.run(eventually(checkViewNoForNodes, txnPoolNodeSet, expectedViewNo, timeout=timeout)) def getNodeSuspicions(node: TestNode, code: int = None): params = getAllArgs(node, TestNode.reportSuspiciousNode) if params and code is not None: params = [param for param in params if 'code' in param and param['code'] == code] return params def checkDiscardMsg(processors, discardedMsg, reasonRegexp, *exclude): if not exclude: exclude = [] for p in filterNodeSet(processors, exclude): last = p.spylog.getLastParams(p.discard, required=False) assert last assert last['msg'] == discardedMsg assert reasonRegexp in last['reason'] def countDiscarded(processor, reasonPat): c = 0 for entry in processor.spylog.getAll(processor.discard): if 'reason' in entry.params and ( (isinstance( entry.params['reason'], str) and reasonPat in entry.params['reason']), (reasonPat in str( entry.params['reason']))): c += 1 return c def filterNodeSet(nodeSet, exclude: List[Union[str, Node]]): """ Return a set of nodes with the nodes in exclude removed. :param nodeSet: the set of nodes :param exclude: the list of nodes or node names to exclude :return: the filtered nodeSet """ return [n for n in nodeSet if n not in [nodeSet[x] if isinstance(x, str) else x for x in exclude]] def whitelistNode(toWhitelist: str, frm: Sequence[TestNode], *codes): for node in frm: node.whitelistNode(toWhitelist, *codes) def whitelistClient(toWhitelist: str, frm: Sequence[TestNode], *codes): for node in frm: node.whitelistClient(toWhitelist, *codes) def assertExp(condition): assert condition def assertFunc(func): assert func() def checkLedgerEquality(ledger1, ledger2): assertLength(ledger1, ledger2.size) assertEquality(ledger1.root_hash, ledger2.root_hash) assertEquality(ledger1.uncommitted_root_hash, ledger2.uncommitted_root_hash) def checkAllLedgersEqual(*ledgers): for l1, l2 in combinations(ledgers, 2): checkLedgerEquality(l1, l2) def checkStateEquality(state1, state2): if state1 is None: return state2 is None assertEquality(state1.as_dict, state2.as_dict) assertEquality(state1.committedHeadHash, state2.committedHeadHash) assertEquality(state1.committedHead, state2.committedHead) def check_seqno_db_equality(db1, db2): assert db1.size == db2.size, \ "{} != {}".format(db1.size, db2.size) assert {bytes(k): bytes(v) for k, v in db1._keyValueStorage.iterator()} == \ {bytes(k): bytes(v) for k, v in db2._keyValueStorage.iterator()} def check_primaries_equality(node1, node2): assert node1.primaries == node2.primaries, \ "{} != {}".format(node1.primaries, node2.primaries) def check_last_ordered_3pc(node1, node2): master_replica_1 = node1.master_replica master_replica_2 = node2.master_replica assert master_replica_1.last_ordered_3pc == master_replica_2.last_ordered_3pc, \ "{} != {}".format(master_replica_1.last_ordered_3pc, master_replica_2.last_ordered_3pc) return master_replica_1.last_ordered_3pc def check_last_ordered_3pc_backup(node1, node2): assert len(node1.replicas) == len(node2.replicas) for i in range(1, len(node1.replicas)): replica1 = node1.replicas[i] replica2 = node2.replicas[i] assert replica1.last_ordered_3pc == replica2.last_ordered_3pc, \ "{}: {} != {}: {}".format(replica1, replica1.last_ordered_3pc, replica2, replica2.last_ordered_3pc) def check_view_no(node1, node2): assert node1.viewNo == node2.viewNo, \ "{} != {}".format(node1.viewNo, node2.viewNo) def check_last_ordered_3pc_on_all_replicas(nodes, last_ordered_3pc): for n in nodes: for r in n.replicas.values(): assert r.last_ordered_3pc == last_ordered_3pc, \ "{} != {}".format(r.last_ordered_3pc, last_ordered_3pc) def check_last_ordered_3pc_on_master(nodes, last_ordered_3pc): for n in nodes: assert n.master_replica.last_ordered_3pc == last_ordered_3pc, \ "{} != {}".format(n.master_replica.last_ordered_3pc, last_ordered_3pc) def check_last_ordered_3pc_on_backup(nodes, last_ordered_3pc): for n in nodes: for i, r in n.replicas.items(): if i != 0: assert r.last_ordered_3pc == last_ordered_3pc, \ "{} != {}".format(r.last_ordered_3pc, last_ordered_3pc) def randomText(size): return ''.join(random.choice(string.ascii_letters) for _ in range(size)) def mockGetInstalledDistributions(packages): ret = [] for pkg in packages: obj = type('', (), {})() obj.key = pkg ret.append(obj) return ret def mockImportModule(moduleName): obj = type(moduleName, (), {})() obj.send_message = lambda *args: None return obj def initDirWithGenesisTxns( dirName, tconf, tdirWithPoolTxns=None, tdirWithDomainTxns=None, new_pool_txn_file=None, new_domain_txn_file=None): os.makedirs(dirName, exist_ok=True) if tdirWithPoolTxns: new_pool_txn_file = new_pool_txn_file or tconf.poolTransactionsFile copyfile( os.path.join( tdirWithPoolTxns, genesis_txn_file( tconf.poolTransactionsFile)), os.path.join( dirName, genesis_txn_file(new_pool_txn_file))) if tdirWithDomainTxns: new_domain_txn_file = new_domain_txn_file or tconf.domainTransactionsFile copyfile( os.path.join( tdirWithDomainTxns, genesis_txn_file( tconf.domainTransactionsFile)), os.path.join( dirName, genesis_txn_file(new_domain_txn_file))) def stopNodes(nodes: List[TestNode], looper=None, ensurePortsFreedUp=True): if ensurePortsFreedUp: assert looper, 'Need a looper to make sure ports are freed up' for node in nodes: node.stop() if ensurePortsFreedUp: ports = [[n.nodestack.ha[1], n.clientstack.ha[1]] for n in nodes] waitUntilPortIsAvailable(looper, ports) def waitUntilPortIsAvailable(looper, ports, timeout=5): ports = itertools.chain(*ports) def chk(): for port in ports: checkPortAvailable(("", port)) looper.run(eventually(chk, retryWait=.5, timeout=timeout)) def run_script(script, *args): s = os.path.join(os.path.dirname(__file__), '../../scripts/' + script) command = [executable, s] command.extend(args) with Popen([executable, s]) as p: sleep(4) p.send_signal(SIGINT) p.wait(timeout=1) assert p.poll() == 0, 'script failed' def viewNoForNodes(nodes): viewNos = {node.viewNo for node in nodes} assert 1 == len(viewNos) return next(iter(viewNos)) def primaryNodeNameForInstance(nodes, instanceId): primaryNames = {node.replicas[instanceId].primaryName for node in nodes} assert 1 == len(primaryNames) primaryReplicaName = next(iter(primaryNames)) return primaryReplicaName[:-2] def nodeByName(nodes, name): for node in nodes: if node.name == name: return node raise Exception("Node with the name '{}' has not been found.".format(name)) def send_pre_prepare(view_no, pp_seq_no, nodes, state_root=None, txn_root=None): pre_prepare = PrePrepare( 0, view_no, pp_seq_no, get_utc_epoch(), ["requests digest"], 0, "random digest", DOMAIN_LEDGER_ID, state_root or '0' * 44, txn_root or '0' * 44, 0, True ) primary_node = getPrimaryReplica(nodes).node non_primary_nodes = set(nodes) - {primary_node} sendMessageToAll(nodes, primary_node, pre_prepare) for non_primary_node in non_primary_nodes: sendMessageToAll(nodes, non_primary_node, pre_prepare) def send_prepare(view_no, pp_seq_no, nodes, state_root=None, txn_root=None): prepare = Prepare( 0, view_no, pp_seq_no, get_utc_epoch(), "random digest", state_root or '0' * 44, txn_root or '0' * 44 ) primary_node = getPrimaryReplica(nodes).node sendMessageToAll(nodes, primary_node, prepare) def send_commit(view_no, pp_seq_no, nodes): commit = Commit( 0, view_no, pp_seq_no) primary_node = getPrimaryReplica(nodes).node sendMessageToAll(nodes, primary_node, commit) def get_key_from_req(req: dict): return Request(identifier=req[f.IDENTIFIER.nm], reqId=req[f.REQ_ID.nm], operation=req[OPERATION], protocolVersion=req[f.PROTOCOL_VERSION.nm], signature=req[f.SIG.nm] if req.__contains__(f.SIG.nm) else None, ).key def chk_all_funcs(looper, funcs, acceptable_fails=0, retry_wait=None, timeout=None, override_eventually_timeout=False): # TODO: Move this logic to eventuallyAll def chk(): fails = 0 last_ex = None for func in funcs: try: func() except Exception as ex: fails += 1 if fails >= acceptable_fails: logger.debug('Too many fails, the last one: {}'.format(repr(ex))) last_ex = ex assert fails <= acceptable_fails, '{} out of {} failed. Last exception:' \ ' {}'.format(fails, len(funcs), last_ex) kwargs = {} if retry_wait: kwargs['retryWait'] = retry_wait if timeout: kwargs['timeout'] = timeout if override_eventually_timeout: kwargs['override_timeout_limit'] = override_eventually_timeout looper.run(eventually(chk, **kwargs)) def check_request_ordered(node, request: Request): # it's ok to iterate through all txns since this is a test for seq_no, txn in node.domainLedger.getAllTxn(): if get_req_id(txn) is None: continue if get_from(txn) is None: continue if get_req_id(txn) != request.reqId: continue if get_from(txn) != request.identifier: continue return True raise ValueError('{} request not ordered by node {}'.format(request, node.name)) def wait_for_requests_ordered(looper, nodes, requests): node_count = len(nodes) timeout_per_request = waits.expectedTransactionExecutionTime(node_count) total_timeout = (1 + len(requests) / 10) * timeout_per_request coros = [partial(check_request_ordered, node, request) for (node, request) in list(itertools.product(nodes, requests))] looper.run(eventuallyAll(*coros, retryWait=1, totalTimeout=total_timeout)) def create_new_test_node(test_node_class, node_config_helper_class, name, conf, tdir, plugin_paths, node_ha=None, client_ha=None): config_helper = node_config_helper_class(name, conf, chroot=tdir) return test_node_class(name, config_helper=config_helper, config=conf, pluginPaths=plugin_paths, ha=node_ha, cliha=client_ha) # ####### SDK def sdk_gen_request(operation, protocol_version=CURRENT_PROTOCOL_VERSION, identifier=None, **kwargs): # Question: Why this method is called sdk_gen_request? It does not use # the indy-sdk return Request(operation=operation, reqId=random.randint(10, 1000000000), protocolVersion=protocol_version, identifier=identifier, **kwargs) def sdk_gen_pool_request(looper, sdk_wallet_new_steward, node_alias, node_did): _, new_steward_did = sdk_wallet_new_steward node_ip = '{}.{}.{}.{}'.format( random.randint(1, 240), random.randint(1, 240), random.randint(1, 240), random.randint(1, 240)) data = { 'alias': node_alias, 'client_port': 50001, 'node_port': 50002, 'node_ip': node_ip, 'client_ip': node_ip, 'services': [] } req = looper.loop.run_until_complete( build_node_request(new_steward_did, node_did, json.dumps(data))) return Request(**json.loads(req)) def sdk_random_request_objects(count, protocol_version, identifier=None, **kwargs): ops = random_requests(count) return [sdk_gen_request(op, protocol_version=protocol_version, identifier=identifier, **kwargs) for op in ops] def sdk_sign_request_objects(looper, sdk_wallet, reqs: Sequence): wallet_h, did = sdk_wallet reqs_str = [json.dumps(req.as_dict) for req in reqs] reqs = [looper.loop.run_until_complete(sign_request(wallet_h, did, req)) for req in reqs_str] return reqs def sdk_sign_request_strings(looper, sdk_wallet, reqs: Sequence): wallet_h, did = sdk_wallet reqs_str = [json.dumps(req) for req in reqs] reqs = [looper.loop.run_until_complete(sign_request(wallet_h, did, req)) for req in reqs_str] return reqs def sdk_signed_random_requests(looper, sdk_wallet, count): _, did = sdk_wallet reqs_obj = sdk_random_request_objects(count, identifier=did, protocol_version=CURRENT_PROTOCOL_VERSION) return sdk_sign_request_objects(looper, sdk_wallet, reqs_obj) def sdk_send_signed_requests(pool_h, signed_reqs: Sequence): return [(json.loads(req), asyncio.ensure_future(submit_request(pool_h, req))) for req in signed_reqs] def sdk_send_random_requests(looper, pool_h, sdk_wallet, count: int): reqs = sdk_signed_random_requests(looper, sdk_wallet, count) return sdk_send_signed_requests(pool_h, reqs) def sdk_send_random_request(looper, pool_h, sdk_wallet): rets = sdk_send_random_requests(looper, pool_h, sdk_wallet, 1) return rets[0] def sdk_send_random_pool_requests(looper, pool_h, sdk_wallet_new_steward, count: int): node_alias = random_string(7) node_did = SimpleSigner(seed=random_string(32).encode()).identifier reqs = [sdk_gen_pool_request(looper, sdk_wallet_new_steward, node_alias, node_did) for _ in range(count)] return [sdk_sign_and_submit_req_obj(looper, pool_h, sdk_wallet_new_steward, req) for req in reqs] def sdk_send_random_pool_and_domain_requests(looper, pool_h, sdk_wallet_new_steward, count: int): node_alias = random_string(7) node_did = SimpleSigner(seed=random_string(32).encode()).identifier req_gens = [ lambda: sdk_gen_request(random_requests(1)[0], identifier=sdk_wallet_new_steward[1]), lambda: sdk_gen_pool_request(looper, sdk_wallet_new_steward, node_alias, node_did), ] res = [] for i in range(count): req = req_gens[i % len(req_gens)]() res.append(sdk_sign_and_submit_req_obj(looper, pool_h, sdk_wallet_new_steward, req)) looper.runFor(0.1) # Give nodes some time to start ordering, so that requests are really alternating return res def sdk_sign_and_submit_req(pool_handle, sdk_wallet, req): wallet_handle, sender_did = sdk_wallet return json.loads(req), asyncio.ensure_future( sign_and_submit_request(pool_handle, wallet_handle, sender_did, req)) def sdk_sign_and_submit_req_obj(looper, pool_handle, sdk_wallet, req_obj): s_req = sdk_sign_request_objects(looper, sdk_wallet, [req_obj])[0] return sdk_send_signed_requests(pool_handle, [s_req])[0] def sdk_sign_and_submit_op(looper, pool_handle, sdk_wallet, op): _, did = sdk_wallet req_obj = sdk_gen_request(op, protocol_version=CURRENT_PROTOCOL_VERSION, identifier=did) s_req = sdk_sign_request_objects(looper, sdk_wallet, [req_obj])[0] return sdk_send_signed_requests(pool_handle, [s_req])[0] def sdk_get_reply(looper, sdk_req_resp, timeout=None): req_json, resp_task = sdk_req_resp # TODO: change timeout evaluating logic, when sdk will can tuning timeout from outside if timeout is None: timeout = waits.expectedTransactionExecutionTime(7) try: resp = looper.run(asyncio.wait_for(resp_task, timeout=timeout)) resp = json.loads(resp) except IndyError as e: resp = e.error_code except TimeoutError as e: resp = ErrorCode.PoolLedgerTimeout return req_json, resp # TODO: Check places where sdk_get_replies used without sdk_check_reply # We need to be sure that test behaviour don't need to check response # validity def sdk_get_replies(looper, sdk_req_resp: Sequence, timeout=None): resp_tasks = [resp for _, resp in sdk_req_resp] # TODO: change timeout evaluating logic, when sdk will can tuning timeout from outside if timeout is None: timeout = waits.expectedTransactionExecutionTime(7) def get_res(task, done_list): if task in done_list: try: resp = json.loads(task.result()) except IndyError as e: resp = e.error_code else: resp = ErrorCode.PoolLedgerTimeout return resp done, pending = looper.run(asyncio.wait(resp_tasks, timeout=timeout)) if pending: for task in pending: task.cancel() ret = [(req, get_res(resp, done)) for req, resp in sdk_req_resp] return ret def sdk_check_reply(req_res): req, res = req_res if isinstance(res, ErrorCode): if res == ErrorCode.PoolLedgerTimeout: raise PoolLedgerTimeoutException('Got PoolLedgerTimeout for request {}' .format(req)) else: raise CommonSdkIOException('Got an error with code {} for request {}' .format(res, req)) if not isinstance(res, dict): raise CommonSdkIOException("Unexpected response format {}".format(res)) def _parse_op(res_dict): if res_dict['op'] == REQNACK: raise RequestNackedException('ReqNack of id {}. Reason: {}' .format(req['reqId'], res_dict['reason'])) if res_dict['op'] == REJECT: raise RequestRejectedException('Reject of id {}. Reason: {}' .format(req['reqId'], res_dict['reason'])) if 'op' in res: _parse_op(res) else: for resps in res.values(): if isinstance(resps, str): _parse_op(json.loads(resps)) elif isinstance(resps, dict): _parse_op(resps) else: raise CommonSdkIOException("Unexpected response format {}".format(res)) def sdk_get_and_check_replies(looper, sdk_req_resp: Sequence, timeout=None): rets = [] for req_res in sdk_get_replies(looper, sdk_req_resp, timeout): sdk_check_reply(req_res) rets.append(req_res) return rets def sdk_eval_timeout(req_count: int, node_count: int, customTimeoutPerReq: float = None, add_delay_to_timeout: float = 0): timeout_per_request = customTimeoutPerReq or waits.expectedTransactionExecutionTime(node_count) timeout_per_request += add_delay_to_timeout # here we try to take into account what timeout for execution # N request - total_timeout should be in # timeout_per_request < total_timeout < timeout_per_request * N # we cannot just take (timeout_per_request * N) because it is so huge. # (for timeout_per_request=5 and N=10, total_timeout=50sec) # lets start with some simple formula: return (1 + req_count / 10) * timeout_per_request def sdk_send_and_check(signed_reqs, looper, txnPoolNodeSet, pool_h, timeout=None): if not timeout: timeout = sdk_eval_timeout(len(signed_reqs), len(txnPoolNodeSet)) results = sdk_send_signed_requests(pool_h, signed_reqs) sdk_replies = sdk_get_replies(looper, results, timeout=timeout) for req_res in sdk_replies: sdk_check_reply(req_res) return sdk_replies def sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool, sdk_wallet, count, customTimeoutPerReq: float = None, add_delay_to_timeout: float = 0, override_timeout_limit=False, total_timeout=None): sdk_reqs = sdk_send_random_requests(looper, sdk_pool, sdk_wallet, count) if not total_timeout: total_timeout = sdk_eval_timeout(len(sdk_reqs), len(txnPoolNodeSet), customTimeoutPerReq=customTimeoutPerReq, add_delay_to_timeout=add_delay_to_timeout) sdk_replies = sdk_get_replies(looper, sdk_reqs, timeout=total_timeout) for req_res in sdk_replies: sdk_check_reply(req_res) return sdk_replies def sdk_send_batches_of_random_and_check(looper, txnPoolNodeSet, sdk_pool, sdk_wallet, num_reqs, num_batches=1, **kwargs): # This method assumes that `num_reqs` <= num_batches*MaxbatchSize if num_reqs < num_batches: raise BaseException( 'sdk_send_batches_of_random_and_check method assumes that `num_reqs` <= num_batches*MaxbatchSize') if num_batches == 1: return sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool, sdk_wallet, num_reqs, **kwargs) reqs_in_batch = num_reqs // num_batches reqs_in_last_batch = reqs_in_batch + num_reqs % num_batches sdk_replies = [] for _ in range(num_batches - 1): sdk_replies.extend(sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool, sdk_wallet, reqs_in_batch, **kwargs)) sdk_replies.extend(sdk_send_random_and_check(looper, txnPoolNodeSet, sdk_pool, sdk_wallet, reqs_in_last_batch, **kwargs)) return sdk_replies def sdk_send_batches_of_random(looper, txnPoolNodeSet, sdk_pool, sdk_wallet, num_reqs, num_batches=1, timeout=Max3PCBatchWait): if num_reqs < num_batches: raise BaseException( 'sdk_send_batches_of_random_and_check method assumes that `num_reqs` <= num_batches*MaxbatchSize') if num_batches == 1: sdk_reqs = sdk_send_random_requests(looper, sdk_pool, sdk_wallet, num_reqs) looper.runFor(timeout) return sdk_reqs reqs_in_batch = num_reqs // num_batches reqs_in_last_batch = reqs_in_batch + num_reqs % num_batches sdk_reqs = [] for _ in range(num_batches - 1): sdk_reqs.extend(sdk_send_random_requests(looper, sdk_pool, sdk_wallet, reqs_in_batch)) looper.runFor(timeout) sdk_reqs.extend(sdk_send_random_requests(looper, sdk_pool, sdk_wallet, reqs_in_last_batch)) looper.runFor(timeout) return sdk_reqs def sdk_sign_request_from_dict(looper, sdk_wallet, op, reqId=None): wallet_h, did = sdk_wallet reqId = reqId or random.randint(10, 100000) request = Request(operation=op, reqId=reqId, protocolVersion=CURRENT_PROTOCOL_VERSION, identifier=did) req_str = json.dumps(request.as_dict) resp = looper.loop.run_until_complete(sign_request(wallet_h, did, req_str)) return json.loads(resp) def sdk_check_request_is_not_returned_to_nodes(looper, nodeSet, request): instances = range(getNoInstances(len(nodeSet))) coros = [] for node, inst_id in itertools.product(nodeSet, instances): c = partial(checkRequestNotReturnedToNode, node=node, identifier=request['identifier'], reqId=request['reqId'], instId=inst_id ) coros.append(c) timeout = waits.expectedTransactionExecutionTime(len(nodeSet)) looper.run(eventuallyAll(*coros, retryWait=1, totalTimeout=timeout)) def sdk_json_to_request_object(json_req): return Request(identifier=json_req['identifier'], reqId=json_req['reqId'], operation=json_req['operation'], signature=json_req['signature'] if 'signature' in json_req else None, protocolVersion=json_req['protocolVersion'] if 'protocolVersion' in json_req else None) def sdk_json_couples_to_request_list(json_couples): req_list = [] for json_couple in json_couples: req_list.append(sdk_json_to_request_object(json_couple[0])) return req_list def sdk_get_bad_response(looper, reqs, exception, message): with pytest.raises(exception) as e: sdk_get_and_check_replies(looper, reqs) assert message in e._excinfo[1].args[0] def sdk_set_protocol_version(looper, version=CURRENT_PROTOCOL_VERSION): looper.loop.run_until_complete(set_protocol_version(version)) # Context managers to be used with tconf fixture @contextmanager def perf_monitor_disabled(tconf): old_unsafe = tconf.unsafe.copy() tconf.unsafe.add("disable_view_change") yield tconf tconf.unsafe = old_unsafe @contextmanager def view_change_timeout(tconf, vc_timeout, catchup_timeout=None, propose_timeout=None): old_catchup_timeout = tconf.MIN_TIMEOUT_CATCHUPS_DONE_DURING_VIEW_CHANGE old_view_change_timeout = tconf.VIEW_CHANGE_TIMEOUT old_propose_timeout = tconf.INITIAL_PROPOSE_VIEW_CHANGE_TIMEOUT old_propagate_request_delay = tconf.PROPAGATE_REQUEST_DELAY tconf.MIN_TIMEOUT_CATCHUPS_DONE_DURING_VIEW_CHANGE = \ 0.6 * vc_timeout if catchup_timeout is None else catchup_timeout tconf.VIEW_CHANGE_TIMEOUT = vc_timeout tconf.INITIAL_PROPOSE_VIEW_CHANGE_TIMEOUT = vc_timeout if propose_timeout is None else propose_timeout tconf.PROPAGATE_REQUEST_DELAY = 0 yield tconf tconf.MIN_TIMEOUT_CATCHUPS_DONE_DURING_VIEW_CHANGE = old_catchup_timeout tconf.VIEW_CHANGE_TIMEOUT = old_view_change_timeout tconf.INITIAL_PROPOSE_VIEW_CHANGE_TIMEOUT = old_propose_timeout tconf.PROPAGATE_REQUEST_DELAY = old_propagate_request_delay @contextmanager def max_3pc_batch_limits(tconf, size, wait=10000): old_size = tconf.Max3PCBatchSize old_wait = tconf.Max3PCBatchWait tconf.Max3PCBatchSize = size tconf.Max3PCBatchWait = wait yield tconf tconf.Max3PCBatchSize = old_size tconf.Max3PCBatchWait = old_wait @contextmanager def freshness(tconf, enabled, timeout): old_update_state = tconf.UPDATE_STATE_FRESHNESS old_timeout = tconf.STATE_FRESHNESS_UPDATE_INTERVAL tconf.UPDATE_STATE_FRESHNESS = enabled tconf.STATE_FRESHNESS_UPDATE_INTERVAL = timeout yield tconf tconf.UPDATE_STATE_FRESHNESS = old_update_state tconf.STATE_FRESHNESS_UPDATE_INTERVAL = old_timeout @contextmanager def primary_disconnection_time(tconf, value): old_tolarate_disconnection = tconf.ToleratePrimaryDisconnection tconf.ToleratePrimaryDisconnection = value yield tconf tconf.ToleratePrimaryDisconnection = old_tolarate_disconnection @contextmanager def acc_monitor(tconf, acc_monitor_enabled=True, acc_monitor_timeout=3, acc_monitor_delta=0): old_timeout = tconf.ACC_MONITOR_TIMEOUT old_delta = tconf.ACC_MONITOR_TXN_DELTA_K old_acc_monitor_enabled = tconf.ACC_MONITOR_ENABLED tconf.ACC_MONITOR_TIMEOUT = acc_monitor_timeout tconf.ACC_MONITOR_TXN_DELTA_K = acc_monitor_delta tconf.ACC_MONITOR_ENABLED = acc_monitor_enabled yield tconf tconf.ACC_MONITOR_TIMEOUT = old_timeout tconf.ACC_MONITOR_TXN_DELTA_K = old_delta tconf.ACC_MONITOR_ENABLED = old_acc_monitor_enabled def create_pre_prepare_params(state_root, ledger_id=DOMAIN_LEDGER_ID, txn_root=None, timestamp=None, bls_multi_sig=None, view_no=0, pool_state_root=None, pp_seq_no=0, inst_id=0, audit_txn_root=None, reqs=None): digest = Replica.batchDigest(reqs) if reqs is not None else "random digest" req_idrs = [req.key for req in reqs] if reqs is not None else ["random request"] params = [inst_id, view_no, pp_seq_no, timestamp or get_utc_epoch(), req_idrs, init_discarded(0), digest, ledger_id, state_root, txn_root or '1' * 32, 0, True, pool_state_root or generate_state_root(), audit_txn_root or generate_state_root()] if bls_multi_sig: params.append(bls_multi_sig.as_list()) return params def create_pre_prepare_no_bls(state_root, view_no=0, pool_state_root=None, pp_seq_no=0, inst_id=0, audit_txn_root=None): params = create_pre_prepare_params(state_root=state_root, view_no=view_no, pool_state_root=pool_state_root, pp_seq_no=pp_seq_no, inst_id=inst_id, audit_txn_root=audit_txn_root) return PrePrepare(*params) def create_commit_params(view_no, pp_seq_no, inst_id=0): return [inst_id, view_no, pp_seq_no] def create_commit_no_bls_sig(req_key, inst_id=0): view_no, pp_seq_no = req_key params = create_commit_params(view_no, pp_seq_no, inst_id=inst_id) return Commit(*params) def create_commit_with_bls_sig(req_key, bls_sig): view_no, pp_seq_no = req_key params = create_commit_params(view_no, pp_seq_no) params.append(bls_sig) return Commit(*params) def create_commit_bls_sig(bls_bft, req_key, pre_prepare): view_no, pp_seq_no = req_key params = create_commit_params(view_no, pp_seq_no) params = bls_bft.update_commit(params, pre_prepare) return Commit(*params) def create_prepare_params(view_no, pp_seq_no, state_root, inst_id=0): return [inst_id, view_no, pp_seq_no, get_utc_epoch(), "random digest", state_root, '1' * 32] def create_prepare_from_pre_prepare(pre_prepare): params = [pre_prepare.instId, pre_prepare.viewNo, pre_prepare.ppSeqNo, pre_prepare.ppTime, pre_prepare.digest, pre_prepare.stateRootHash, pre_prepare.txnRootHash, pre_prepare.auditTxnRootHash] return Prepare(*params) def create_prepare(req_key, state_root, inst_id=0): view_no, pp_seq_no = req_key params = create_prepare_params(view_no, pp_seq_no, state_root, inst_id=inst_id) return Prepare(*params) def generate_state_root(): return base58.b58encode(os.urandom(32)).decode("utf-8") def init_discarded(value=None): """init discarded field with value and return message like representation""" discarded = [] if value: discarded.append(value) return invalid_index_serializer.serialize(discarded, toBytes=False) def incoming_3pc_msgs_count(nodes_count: int = 4) -> int: pre_prepare = 1 # Message from Primary prepares = nodes_count - 2 # Messages from all nodes exclude primary and self node commits = nodes_count - 1 # Messages from all nodes exclude self node # The primary node receives the same number of messages. Doesn't get pre-prepare, # but gets one more prepare return pre_prepare + prepares + commits class MockTimestamp: def __init__(self, value=datetime.utcnow()): self.value = value def __call__(self): return self.value class MockTimer(QueueTimer): def __init__(self, get_current_time: Optional[MockTimestamp] = None): self._ts = get_current_time if get_current_time else MockTimestamp(0) QueueTimer.__init__(self, self._ts) def advance(self, seconds): self._ts.value += seconds self.service() def update_time(self, value): self._ts.value = value self.service()

35.484219

120

0.657374

794acb0d5f6018dbf7359cd8cf1afecb132a88a6

8,025

py

Python

lxmls/sequences/bak/forward_backward.py

SimonSuster/lxmls-toolkit

6a57884f8b7c98da816a60eb88593e0a1585d434

[ "MIT" ]

1

2015-09-20T05:16:38.000Z

lxmls/sequences/bak/forward_backward.py

daviddao/LxMLS-labs-solution

78413c1ee61752ca33988c454e3b2c27326e7063

[ "MIT" ]

null

lxmls/sequences/bak/forward_backward.py

daviddao/LxMLS-labs-solution

78413c1ee61752ca33988c454e3b2c27326e7063

[ "MIT" ]

null

import numpy as np ###### # Computes the forward backward trellis for a given sequence. # N - Length of sequence # H - Number of hidden states # Receives: # Node potentials (N,H) vector # Edge potentials (N-1,H,H) # # Emission probabilities: (length, num_states) array # Initial probabilities: (num_states) array # Transition probabilities: (length, num_states+1, num_states) array # # OR # # Transition probabilities: (length-1, num_states, num_states) array # Final probabilities: (num_states) array ###### def run_forward(initial_scores, transition_scores, final_scores, emission_scores): length = np.size(emission_scores, 0) # Length of the sequence. num_states = np.size(initial_scores) # Number of states. # Forward variables. forward = np.zeros([length, num_states]) # Initialization. forward[0,:] = emission_scores[0,:] * initial_scores # Forward loop. for pos in xrange(1,length): for current_state in xrange(num_states): forward[pos, current_state] = \ np.sum(forward[pos-1, :] * transition_scores[pos-1, current_state, :]) forward[pos, current_state] *= emission_scores[pos, current_state] # Termination. likelihood = sum(forward[length-1,:] * final_scores) # print 'Likelihood =', likelihood return likelihood, forward def run_backward(initial_scores, transition_scores, final_scores, emission_scores): length = np.size(emission_scores, 0) # Length of the sequence. num_states = np.size(initial_scores) # Number of states. # Backward variables. backward = np.zeros([length, num_states]) # Initialization. backward[length-1,:] = final_scores # Backward loop. for pos in xrange(length-2,-1,-1): for current_state in xrange(num_states): backward[pos, current_state] = \ sum(backward[pos+1, :] * transition_scores[pos, :, current_state] * emission_scores[pos+1, :]) # prob = 0.0 # for next_state in xrange(num_states): # back = backward[pos+1, next_state] # trans = transition_scores[pos, next_state, current_state]; # observation = emission_scores[pos+1, next_state]; # prob += trans * observation * back; # backward[pos, current_state] = prob # backward[0,:] *= initial_scores #sanity_check_forward_backward(forward,backward) # Termination. likelihood = sum(backward[0,:] * initial_scores * emission_scores[0,:]) # print 'Likelihood =', likelihood return likelihood, backward def forward_backward(initial_scores, transition_scores, final_scores, emission_scores): likelihood, forward = run_forward(initial_scores, transition_scores, final_scores, emission_scores) print 'Likelihood =', likelihood likelihood, backward = run_backward(initial_scores, transition_scores, final_scores, emission_scores) print 'Likelihood =', likelihood # length = np.size(emission_scores, 0) # Length of the sequence. # num_states = np.size(initial_scores) # Number of states. # # forward = np.zeros([length, num_states]) # backward = np.zeros([length, num_states]) # # forward[0,:] = emission_scores[0,:] * initial_scores # ## Forward loop. # for pos in xrange(1,length): # for current_state in xrange(num_states): # for prev_state in xrange(num_states): # forward_v = forward[pos-1, prev_state] # trans_v = transition_scores[pos-1, current_state, prev_state] # prob = forward_v*trans_v # forward[pos, current_state] += prob # forward[pos, current_state] *= emission_scores[pos, current_state] ## forward[length-1,:] *= final_scores # print 'Likelihood =', sum(forward[length-1,:] * final_scores) # # ## Backward loop. ## backward[length-1,:] = final_scores # backward[length-1,:] = final_scores #1.0 # for pos in xrange(length-2,-1,-1): # for current_state in xrange(num_states): # prob = 0.0 # for next_state in xrange(num_states): # back = backward[pos+1, next_state] # trans = transition_scores[pos, next_state, current_state]; # observation = emission_scores[pos+1, next_state]; # prob += trans * observation * back; # backward[pos, current_state] = prob ## backward[0,:] *= initial_scores # #sanity_check_forward_backward(forward,backward) # print 'Likelihood =', sum(backward[0,:] * initial_scores * emission_scores[0,:]) return forward,backward ###### # Computes the forward backward trellis for a given sequence and node and edge potentials # N - Length of sequence # H - Number of hidden states # Receives: # Node potentials (N,H) vector # Edge potentials (N-1,H,H) ###### #def forward_backward(node_potentials,edge_potentials): # H,N = node_potentials.shape # forward = np.zeros([H,N],dtype=float) # backward = np.zeros([H,N],dtype=float) # forward[:,0] = node_potentials[:,0] # ## Forward loop # for pos in xrange(1,N): # for current_state in xrange(H): # for prev_state in xrange(H): # forward_v = forward[prev_state,pos-1] # trans_v = edge_potentials[prev_state,current_state,pos-1] # prob = forward_v*trans_v # forward[current_state,pos] += prob # forward[current_state,pos] *= node_potentials[current_state,pos] # ## Backward loop # backward[:,N-1] = 1 # for pos in xrange(N-2,-1,-1): # for current_state in xrange(H): # prob = 0 # for next_state in xrange(H): # back = backward[next_state,pos+1] # trans = edge_potentials[current_state,next_state,pos]; # observation = node_potentials[next_state,pos+1]; # prob += trans * observation * back; # backward[current_state,pos] = prob # #sanity_check_forward_backward(forward,backward) # return forward,backward # def forward_backward_trans_probs(node_potentials,transitions_probs): # H,N = node_potentials.shape # forward = np.zeros([H,N],dtype=float) # backward = np.zeros([H,N],dtype=float) # forward[:,0] = node_potentials[:,0] # ## Forward loop # for pos in xrange(1,N): # for current_state in xrange(H): # for prev_state in xrange(H): # forward_v = forward[prev_state,pos-1] # trans_v = transitions_probs[current_state,prev_state] # prob = forward_v*trans_v # forward[current_state,pos] += prob # forward[current_state,pos] *= node_potentials[current_state,pos] # ## Backward loop # backward[:,N-1] = 1 # for pos in xrange(N-2,-1,-1): # for current_state in xrange(H): # prob = 0 # for next_state in xrange(H): # back = backward[next_state,pos+1] # trans = transition_probs[next_state,current_state]; # observation = node_potentials[next_state,pos+1]; # prob += trans * observation * back; # backward[current_state,pos] = prob # #sanity_check_forward_backward(forward,backward) # return forward,backward ######### ## For every position - pos the sum_states forward(pos,state)*backward(pos,state) = Likelihood ######### def sanity_check_forward_backward(forward,backward): N,H = forward.shape likelihood = np.zeros([N,1]) for pos in xrange(N): aux = 0 for current_state in xrange(H): aux += forward[pos,current_state]*backward[pos,current_state] likelihood[pos] = aux for i in xrange(pos): if abs(aux - likelihood[i]) > 0.001: print "Likelihood for pos %i and pos %i mismatch: %f - %f"%(i,pos,likelihood[i],aux) return False print likelihood return True

38.214286

105

0.62866

794acc9e2d5260575008284823c3db2dec34914a

5,404

py

Python

tmp.py

pandezhao/Paperwork

7a8a82eb2d85949c467b15ea634806269b0f902c

[ "MIT" ]

null

tmp.py

pandezhao/Paperwork

7a8a82eb2d85949c467b15ea634806269b0f902c

[ "MIT" ]

null

tmp.py

pandezhao/Paperwork

7a8a82eb2d85949c467b15ea634806269b0f902c

[ "MIT" ]

null

import torch from torchvision import datasets, transforms from torch import nn, optim from torch.nn import init, functional from torch.nn.utils import clip_grad_norm from torch.autograd import Variable from torch.utils.data import DataLoader import matplotlib.pyplot as plt from norm import show_data import torch.nn.functional as F class rnn(nn.Module): def __init__(self, input_size, hidden_size, num_layers, bias, batch_first, dropout): super(rnn, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.bias = bias self.batch_first = batch_first self.dropout = dropout self.LSTM = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bias=bias, batch_first=batch_first,dropout=dropout) # self.linear1 = nn.Linear(in_features=hidden_size, out_features=hidden_size) self.linear2 = nn.Linear(in_features=hidden_size, out_features=10) self.tanh = nn.Tanh() self.relu = nn.ReLU() self.batch_norm = nn.BatchNorm1d(hidden_size) def forward(self, input_, hx): hx = torch.stack(hx, 0) hx = [hx, hx] _, (out, _) = self.LSTM(input_, hx) output = self.batch_norm(out[-1]) output = self.tanh(output) # activation function can not be relu, must be tanh output = self.linear2(output) return output data_path = "CNN dataset" save_dir = "CNN saved" use_gpu = True epochs = 2 batch_size = 32 hidden_size = 100 def transform_flatten(tensor): return tensor.view(-1,1).contiguous() train_set = datasets.MNIST(root=data_path, train=True, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) , download=True) test_set = datasets.MNIST(root=data_path, train=False, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) , download=True) train_loader = DataLoader(train_set, batch_size = batch_size, shuffle = False) test_loader = DataLoader(test_set, batch_size = batch_size, shuffle = False) model = rnn(input_size=28, hidden_size=hidden_size, num_layers=2, bias=True, batch_first=False, dropout=0.0) Loss = nn.CrossEntropyLoss() optimizer = optim.RMSprop(params=model.parameters(), lr=1e-3, momentum=0.9) train_Accuracy = [] train_Loss = [] test_Accuracy = [] test_Loss = [] if use_gpu: model.cuda() def compute_loss(data, label): hx = torch.Tensor(batch_size, hidden_size).normal_(0, 0.001) # 这里的1的意思是input size，比如对于这里，由于每次输入一个像素,所以input size = 1. 所以是1 if use_gpu: hx = hx.cuda() hx = (hx, hx) # 所以input size = 1. 所以是1 output = model(input_=data,hx=hx) # output = model(x=data) loss = Loss(output, label) accuracy = (output.max(1)[1] == label).float().mean() return loss, accuracy for epoch in range(epochs): count = 0 for data, label in train_loader: # data = data + torch.FloatTensor(0.0001 * numpy.random.randn(data.size(0),784,1)) model.train(True) data = data.permute(2, 0, 3, 1) data = Variable(data.view(28, batch_size, 28)) # print(data) # data = Variable(data.reshape(batch_size,1,28,28)) # data = Variable(data) label = Variable(label) if use_gpu: data = data.cuda() label = label.cuda() # model.zero_grad() optimizer.zero_grad() # loss, accuracy = compute_loss(data=data, label=label) Train_loss, Train_accuracy = compute_loss(data, label) # print(output) # output = model(x=data) Train_loss.backward() clip_grad_norm(parameters = model.parameters(), max_norm=1) optimizer.step() count += 1 if count % 20 == 0: train_Accuracy.append(Train_accuracy) train_Loss.append(Train_loss) print('Epoch:{},iteration:{},train_loss:{},train_accuracy:{},'.format(epoch, count, Train_loss, Train_accuracy)) if count % 20 == 1: with torch.no_grad(): model.train(False) Loss_sum = [] Accuracy_sum = [] count_tmp = 0 for test_data, test_label in test_loader: test_data = test_data.permute(2, 0, 3, 1) test_data = Variable(test_data.view(28, batch_size, 28)) test_label = Variable(test_label) if use_gpu: test_data = test_data.cuda() test_label = test_label.cuda() Tes_Loss, Tes_Accuracy = compute_loss(test_data, test_label) Loss_sum.append(Tes_Loss) Accuracy_sum.append(Tes_Accuracy) count_tmp += 1 if count_tmp == 100: break test_Loss.append(sum(Loss_sum)/len(Loss_sum)) test_Accuracy.append(sum(Accuracy_sum)/len(Accuracy_sum)) show_data(train_Accuracy, train_Loss, test_Loss, test_Accuracy, scatter=False)

37.79021

129

0.599556

794accbe8738a4937be52abd3771a290cff29729

2,165

py

Python

VGG-19/vgg-19/tensornet/layers/linear.py

zfgao66/deeplearning-mpo-tensorflow

c345b9fea79e16f98f9b50e0b4e0bcaf4ed4c8e6

[ "MIT" ]

24

2019-04-30T14:59:43.000Z

2021-11-16T03:47:38.000Z

VGG-19/vgg-19/tensornet/layers/linear.py

HC1022/deeplearning-mpo

c345b9fea79e16f98f9b50e0b4e0bcaf4ed4c8e6

[ "MIT" ]

null

VGG-19/vgg-19/tensornet/layers/linear.py

HC1022/deeplearning-mpo

c345b9fea79e16f98f9b50e0b4e0bcaf4ed4c8e6

[ "MIT" ]

9

2019-08-14T10:50:37.000Z

2022-03-15T14:41:52.000Z

import tensorflow as tf from .auxx import get_var_wrap def linear(inp, out_size, weights_initializer=tf.contrib.layers.xavier_initializer(uniform=False), weights_regularizer=None, biases_initializer=tf.zeros_initializer, biases_regularizer=None, trainable=True, cpu_variables=False, scope=None): """ linear layer Args: inp: input tensor, float - [batch_size, inp_size] out_size: layer units count, int weights_initializer: weights init function weights_regularizer: weights regularizer function biases_initializer: biases init function (if None then no biases will be used) biases_regularizer: biases regularizer function trainable: trainable variables flag, bool cpu_variables: cpu variables flag, bool scope: layer variable scope name, string Returns: out: output tensor, float - [batch_size, out_size] """ with tf.variable_scope(scope): shape = inp.get_shape().as_list() assert len(shape) == 2, 'Not 2D input tensor' inp_size = shape[-1] weights = get_var_wrap('weights', shape=[inp_size, out_size], initializer=weights_initializer, regularizer=weights_regularizer, trainable=trainable, cpu_variable=cpu_variables) if biases_initializer is not None: biases = get_var_wrap('biases', shape=[out_size], initializer=biases_initializer, regularizer=biases_regularizer, trainable=trainable, cpu_variable=cpu_variables) out = tf.add(tf.matmul(inp, weights, name='matmul'), biases, name='out') else: out = tf.matmul(inp, weights, name='out') return out

42.45098

94

0.53903

794acd0e6f327be540f49fbb4c25674a0a16c051

3,441

py

Python

experiments/vgg16/VGG16_qDCA.py

petrapoklukar/DCA

e5b3f3481433306a4b33e712272f8bbf5e9d05ce

[ "MIT" ]

2

2022-02-14T15:54:22.000Z

2022-02-15T18:43:36.000Z

experiments/vgg16/VGG16_qDCA.py

petrapoklukar/DCA

e5b3f3481433306a4b33e712272f8bbf5e9d05ce

[ "MIT" ]

null

experiments/vgg16/VGG16_qDCA.py

petrapoklukar/DCA

e5b3f3481433306a4b33e712272f8bbf5e9d05ce

[ "MIT" ]

null

import os import pickle from dca.DCA import DCA from dca.schemes import ( DCALoggers, DelaunayGraphParams, ExperimentDirs, GeomCAParams, HDBSCANParams, QueryData, REData, ) import typer from VGG16_utils import _analyze_query_point_assignment app = typer.Typer() @app.command() def vgg16_qDCA(version_id: str, run_DCA: int = 1, run_qDCA: int = 1, cleanup: int = 1): repr_level = "feat_lin1" experiment_path = "output/vgg16_qDCA/" experiment_id = version_id # Set parameters path_to_dataset = f"representations/vgg16/{version_id}" path_to_Rfeatures = os.path.join(path_to_dataset, "sampled_Rfeatures.pkl") if os.path.isfile(path_to_Rfeatures): with open(path_to_Rfeatures, "rb") as f: Rdata = pickle.load(f) else: raise ValueError(f"Input file {path_to_Rfeatures} not found.") path_to_Efeatures = os.path.join(path_to_dataset, "sampled_Efeatures.pkl") if os.path.isfile(path_to_Efeatures): with open(path_to_Efeatures, "rb") as f: Edata = pickle.load(f) else: raise ValueError(f"Input file {path_to_Efeatures} not found.") R = Rdata[repr_level] E = Edata[repr_level] init_data_config = REData(R=R, E=E) experiment_config = ExperimentDirs( experiment_dir=experiment_path, experiment_id=experiment_id, ) graph_config = DelaunayGraphParams( filtered_edges_dir=os.path.join(experiment_id, "logs"), ) hdbscan_config = HDBSCANParams( clusterer_dir=os.path.join(experiment_id, "logs"), ) geomCA_config = GeomCAParams() exp_loggers = DCALoggers(experiment_config.logs_dir) output = [] if run_DCA: dca = DCA( experiment_config, graph_config, hdbscan_config, geomCA_config, loggers=exp_loggers, ) dca_scores = dca.fit( init_data_config ) # Do not call cleanup, output files are needed for qDCA output.append(dca_scores) if run_qDCA: path_to_Qfeatures = os.path.join(path_to_dataset, "query_features.pkl") if os.path.isfile(path_to_Qfeatures): with open(path_to_Qfeatures, "rb") as f: query_data = pickle.load(f) else: raise ValueError(f"Input file {path_to_Qfeatures} not found.") Q = query_data[repr_level] query_data_config = QueryData( Q=Q, query_input_array_files_dir=os.path.join(experiment_id, "logs"), query_input_array_comp_assignment_filename=f"query_data_comp_assignment.npy", ) dca = DCA( experiment_config, graph_config, hdbscan_config, geomCA_config, exp_loggers, ) query_points_to_RE_assignment = dca.process_query_points( init_data_config, query_data_config, assign_to_RE=True ) output.append(query_points_to_RE_assignment) accuracy = _analyze_query_point_assignment( query_data, Rdata, Edata, init_data_config.num_R, query_points_to_RE_assignment, experiment_config.DCA_dir, ) print("Accuracy: %s", accuracy) output.append(accuracy) if cleanup: dca.cleanup() return output if __name__ == "__main__": typer.run(vgg16_qDCA)

28.675

89

0.637315

794acd1a438c20fa8cd3bdda0d24a850066f9baf

206

py

Python

file_FindString.py

karus4226/ulti_code

55c10ca4bd4210c7d784efcb6276d288f253bb40

[ "MIT" ]

null

file_FindString.py

karus4226/ulti_code

55c10ca4bd4210c7d784efcb6276d288f253bb40

[ "MIT" ]

null

file_FindString.py

karus4226/ulti_code

55c10ca4bd4210c7d784efcb6276d288f253bb40

[ "MIT" ]

null

import mmap def ListFile(input_str, input_file): with open(input_file) as f: s = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) if s.find(input_str) != -1: print('true')

25.75

61

0.606796

794acf35b66827ee323d19ae7acdda01babc7973

64,941

py

Python

nova/tests/unit/objects/test_request_spec.py

nfvri/nova

2ce5a440c44eb512f07adacd313304e226bb56a0

[ "Apache-2.0" ]

1

2021-12-27T00:47:30.000Z

nova/tests/unit/objects/test_request_spec.py

nfvri/nova

2ce5a440c44eb512f07adacd313304e226bb56a0

[ "Apache-2.0" ]

null

nova/tests/unit/objects/test_request_spec.py

nfvri/nova

2ce5a440c44eb512f07adacd313304e226bb56a0

[ "Apache-2.0" ]

null

# Copyright 2015 Red Hat, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import collections import mock from oslo_serialization import jsonutils from oslo_utils.fixture import uuidsentinel as uuids from oslo_utils import uuidutils from oslo_versionedobjects import base as ovo_base from nova import context from nova import exception from nova.network import model as network_model from nova import objects from nova.objects import base from nova.objects import request_spec from nova import test from nova.tests.unit.api.openstack import fakes from nova.tests.unit import fake_flavor from nova.tests.unit import fake_instance from nova.tests.unit import fake_network_cache_model from nova.tests.unit import fake_request_spec from nova.tests.unit.objects import test_objects class _TestRequestSpecObject(object): def test_image_meta_from_image_as_object(self): # Just isolating the test for the from_dict() method image_meta = objects.ImageMeta(name='foo') spec = objects.RequestSpec() spec._image_meta_from_image(image_meta) self.assertEqual(image_meta, spec.image) @mock.patch.object(objects.ImageMeta, 'from_dict') def test_image_meta_from_image_as_dict(self, from_dict): # Just isolating the test for the from_dict() method image_meta = objects.ImageMeta(name='foo') from_dict.return_value = image_meta spec = objects.RequestSpec() spec._image_meta_from_image({'name': 'foo'}) self.assertEqual(image_meta, spec.image) def test_image_meta_from_image_as_none(self): # just add a dumb check to have a full coverage spec = objects.RequestSpec() spec._image_meta_from_image(None) self.assertIsNone(spec.image) @mock.patch.object(base, 'obj_to_primitive') def test_to_legacy_image(self, obj_to_primitive): spec = objects.RequestSpec(image=objects.ImageMeta()) fake_dict = mock.Mock() obj_to_primitive.return_value = fake_dict self.assertEqual(fake_dict, spec._to_legacy_image()) obj_to_primitive.assert_called_once_with(spec.image) @mock.patch.object(base, 'obj_to_primitive') def test_to_legacy_image_with_none(self, obj_to_primitive): spec = objects.RequestSpec(image=None) self.assertEqual({}, spec._to_legacy_image()) self.assertFalse(obj_to_primitive.called) def test_from_instance_as_object(self): instance = objects.Instance() instance.uuid = uuidutils.generate_uuid() instance.numa_topology = None instance.pci_requests = None instance.project_id = fakes.FAKE_PROJECT_ID instance.user_id = fakes.FAKE_USER_ID instance.availability_zone = 'nova' spec = objects.RequestSpec() spec._from_instance(instance) instance_fields = ['numa_topology', 'pci_requests', 'uuid', 'project_id', 'user_id', 'availability_zone'] for field in instance_fields: if field == 'uuid': self.assertEqual(getattr(instance, field), getattr(spec, 'instance_uuid')) else: self.assertEqual(getattr(instance, field), getattr(spec, field)) def test_from_instance_as_dict(self): instance = dict(uuid=uuidutils.generate_uuid(), numa_topology=None, pci_requests=None, project_id=fakes.FAKE_PROJECT_ID, user_id=fakes.FAKE_USER_ID, availability_zone='nova') spec = objects.RequestSpec() spec._from_instance(instance) instance_fields = ['numa_topology', 'pci_requests', 'uuid', 'project_id', 'user_id', 'availability_zone'] for field in instance_fields: if field == 'uuid': self.assertEqual(instance.get(field), getattr(spec, 'instance_uuid')) else: self.assertEqual(instance.get(field), getattr(spec, field)) @mock.patch.object(objects.InstancePCIRequests, 'from_request_spec_instance_props') def test_from_instance_with_pci_requests(self, pci_from_spec): fake_pci_requests = objects.InstancePCIRequests() pci_from_spec.return_value = fake_pci_requests instance = dict( uuid=uuidutils.generate_uuid(), root_gb=10, ephemeral_gb=0, memory_mb=10, vcpus=1, numa_topology=None, project_id=fakes.FAKE_PROJECT_ID, user_id=fakes.FAKE_USER_ID, availability_zone='nova', pci_requests={ 'instance_uuid': 'fakeid', 'requests': [{'count': 1, 'spec': [{'vendor_id': '8086'}]}]}) spec = objects.RequestSpec() spec._from_instance(instance) pci_from_spec.assert_called_once_with(instance['pci_requests']) self.assertEqual(fake_pci_requests, spec.pci_requests) def test_from_instance_with_numa_stuff(self): instance = dict( uuid=uuidutils.generate_uuid(), root_gb=10, ephemeral_gb=0, memory_mb=10, vcpus=1, project_id=fakes.FAKE_PROJECT_ID, user_id=fakes.FAKE_USER_ID, availability_zone='nova', pci_requests=None, numa_topology={'cells': [{'id': 1, 'cpuset': ['1'], 'memory': 8192, 'pagesize': None, 'cpu_topology': None, 'cpu_pinning_raw': None}]}) spec = objects.RequestSpec() spec._from_instance(instance) self.assertIsInstance(spec.numa_topology, objects.InstanceNUMATopology) cells = spec.numa_topology.cells self.assertEqual(1, len(cells)) self.assertIsInstance(cells[0], objects.InstanceNUMACell) def test_from_flavor_as_object(self): flavor = objects.Flavor() spec = objects.RequestSpec() spec._from_flavor(flavor) self.assertEqual(flavor, spec.flavor) def test_from_flavor_as_dict(self): flavor_dict = dict(id=1) ctxt = context.RequestContext('fake', 'fake') spec = objects.RequestSpec(ctxt) spec._from_flavor(flavor_dict) self.assertIsInstance(spec.flavor, objects.Flavor) self.assertEqual({'id': 1}, spec.flavor.obj_get_changes()) def test_to_legacy_instance(self): spec = objects.RequestSpec() spec.flavor = objects.Flavor(root_gb=10, ephemeral_gb=0, memory_mb=10, vcpus=1) spec.numa_topology = None spec.pci_requests = None spec.project_id = fakes.FAKE_PROJECT_ID spec.user_id = fakes.FAKE_USER_ID spec.availability_zone = 'nova' instance = spec._to_legacy_instance() self.assertEqual({'root_gb': 10, 'ephemeral_gb': 0, 'memory_mb': 10, 'vcpus': 1, 'numa_topology': None, 'pci_requests': None, 'project_id': fakes.FAKE_PROJECT_ID, 'user_id': fakes.FAKE_USER_ID, 'availability_zone': 'nova'}, instance) def test_to_legacy_instance_with_unset_values(self): spec = objects.RequestSpec() self.assertEqual({}, spec._to_legacy_instance()) def test_from_retry(self): retry_dict = {'num_attempts': 1, 'hosts': [['fake1', 'node1']]} ctxt = context.RequestContext('fake', 'fake') spec = objects.RequestSpec(ctxt) spec._from_retry(retry_dict) self.assertIsInstance(spec.retry, objects.SchedulerRetries) self.assertEqual(1, spec.retry.num_attempts) self.assertIsInstance(spec.retry.hosts, objects.ComputeNodeList) self.assertEqual(1, len(spec.retry.hosts)) self.assertEqual('fake1', spec.retry.hosts[0].host) self.assertEqual('node1', spec.retry.hosts[0].hypervisor_hostname) def test_from_retry_missing_values(self): retry_dict = {} ctxt = context.RequestContext('fake', 'fake') spec = objects.RequestSpec(ctxt) spec._from_retry(retry_dict) self.assertIsNone(spec.retry) def test_populate_group_info(self): filt_props = {} filt_props['group_updated'] = True filt_props['group_policies'] = set(['affinity']) filt_props['group_hosts'] = set(['fake1']) filt_props['group_members'] = set(['fake-instance1']) # Make sure it can handle group uuid not being present. for group_uuid in (None, uuids.group_uuid): if group_uuid: filt_props['group_uuid'] = group_uuid spec = objects.RequestSpec() spec._populate_group_info(filt_props) self.assertIsInstance(spec.instance_group, objects.InstanceGroup) self.assertEqual('affinity', spec.instance_group.policy) self.assertEqual(['fake1'], spec.instance_group.hosts) self.assertEqual(['fake-instance1'], spec.instance_group.members) if group_uuid: self.assertEqual(uuids.group_uuid, spec.instance_group.uuid) def test_populate_group_info_missing_values(self): filt_props = {} spec = objects.RequestSpec() spec._populate_group_info(filt_props) self.assertIsNone(spec.instance_group) def test_from_limits(self): limits_dict = {'numa_topology': None, 'vcpu': 1.0, 'disk_gb': 1.0, 'memory_mb': 1.0} spec = objects.RequestSpec() spec._from_limits(limits_dict) self.assertIsInstance(spec.limits, objects.SchedulerLimits) self.assertIsNone(spec.limits.numa_topology) self.assertEqual(1, spec.limits.vcpu) self.assertEqual(1, spec.limits.disk_gb) self.assertEqual(1, spec.limits.memory_mb) def test_from_limits_missing_values(self): limits_dict = {} spec = objects.RequestSpec() spec._from_limits(limits_dict) self.assertIsInstance(spec.limits, objects.SchedulerLimits) self.assertIsNone(spec.limits.numa_topology) self.assertIsNone(spec.limits.vcpu) self.assertIsNone(spec.limits.disk_gb) self.assertIsNone(spec.limits.memory_mb) def test_from_hints(self): hints_dict = {'foo_str': '1', 'bar_list': ['2']} spec = objects.RequestSpec() spec._from_hints(hints_dict) expected = {'foo_str': ['1'], 'bar_list': ['2']} self.assertEqual(expected, spec.scheduler_hints) def test_from_hints_with_no_hints(self): spec = objects.RequestSpec() spec._from_hints(None) self.assertIsNone(spec.scheduler_hints) @mock.patch.object(objects.SchedulerLimits, 'from_dict') def test_from_primitives(self, mock_limits): spec_dict = {'instance_type': objects.Flavor(), 'instance_properties': objects.Instance( uuid=uuidutils.generate_uuid(), numa_topology=None, pci_requests=None, project_id=1, user_id=2, availability_zone='nova')} filt_props = {} # We seriously don't care about the return values, we just want to make # sure that all the fields are set mock_limits.return_value = None ctxt = context.RequestContext('fake', 'fake') spec = objects.RequestSpec.from_primitives(ctxt, spec_dict, filt_props) mock_limits.assert_called_once_with({}) # Make sure that all fields are set using that helper method skip = ['id', 'security_groups', 'network_metadata', 'is_bfv'] for field in [f for f in spec.obj_fields if f not in skip]: self.assertTrue(spec.obj_attr_is_set(field), 'Field: %s is not set' % field) # just making sure that the context is set by the method self.assertEqual(ctxt, spec._context) def test_from_primitives_with_requested_destination(self): destination = objects.Destination(host='foo') spec_dict = {} filt_props = {'requested_destination': destination} ctxt = context.RequestContext('fake', 'fake') spec = objects.RequestSpec.from_primitives(ctxt, spec_dict, filt_props) self.assertEqual(destination, spec.requested_destination) def test_from_components(self): ctxt = context.RequestContext('fake-user', 'fake-project') destination = objects.Destination(host='foo') instance = fake_instance.fake_instance_obj(ctxt) image = {'id': uuids.image_id, 'properties': {'mappings': []}, 'status': 'fake-status', 'location': 'far-away'} flavor = fake_flavor.fake_flavor_obj(ctxt) filter_properties = {'requested_destination': destination} instance_group = None spec = objects.RequestSpec.from_components(ctxt, instance.uuid, image, flavor, instance.numa_topology, instance.pci_requests, filter_properties, instance_group, instance.availability_zone, objects.SecurityGroupList()) # Make sure that all fields are set using that helper method skip = ['id', 'network_metadata', 'is_bfv'] for field in [f for f in spec.obj_fields if f not in skip]: self.assertTrue(spec.obj_attr_is_set(field), 'Field: %s is not set' % field) # just making sure that the context is set by the method self.assertEqual(ctxt, spec._context) self.assertEqual(destination, spec.requested_destination) @mock.patch('nova.objects.RequestSpec._populate_group_info') def test_from_components_with_instance_group(self, mock_pgi): # This test makes sure that we don't overwrite instance group passed # to from_components ctxt = context.RequestContext('fake-user', 'fake-project') instance = fake_instance.fake_instance_obj(ctxt) image = {'id': uuids.image_id, 'properties': {'mappings': []}, 'status': 'fake-status', 'location': 'far-away'} flavor = fake_flavor.fake_flavor_obj(ctxt) filter_properties = {'fake': 'property'} instance_group = objects.InstanceGroup() objects.RequestSpec.from_components(ctxt, instance.uuid, image, flavor, instance.numa_topology, instance.pci_requests, filter_properties, instance_group, instance.availability_zone) self.assertFalse(mock_pgi.called) @mock.patch('nova.objects.RequestSpec._populate_group_info') def test_from_components_without_instance_group(self, mock_pgi): # This test makes sure that we populate instance group if not # present ctxt = context.RequestContext(fakes.FAKE_USER_ID, fakes.FAKE_PROJECT_ID) instance = fake_instance.fake_instance_obj(ctxt) image = {'id': uuids.image_id, 'properties': {'mappings': []}, 'status': 'fake-status', 'location': 'far-away'} flavor = fake_flavor.fake_flavor_obj(ctxt) filter_properties = {'fake': 'property'} objects.RequestSpec.from_components(ctxt, instance.uuid, image, flavor, instance.numa_topology, instance.pci_requests, filter_properties, None, instance.availability_zone) mock_pgi.assert_called_once_with(filter_properties) @mock.patch('nova.objects.RequestSpec._populate_group_info') def test_from_components_without_security_groups(self, mock_pgi): # This test makes sure that we populate instance group if not # present ctxt = context.RequestContext(fakes.FAKE_USER_ID, fakes.FAKE_PROJECT_ID) instance = fake_instance.fake_instance_obj(ctxt) image = {'id': uuids.image_id, 'properties': {'mappings': []}, 'status': 'fake-status', 'location': 'far-away'} flavor = fake_flavor.fake_flavor_obj(ctxt) filter_properties = {'fake': 'property'} spec = objects.RequestSpec.from_components(ctxt, instance.uuid, image, flavor, instance.numa_topology, instance.pci_requests, filter_properties, None, instance.availability_zone) self.assertNotIn('security_groups', spec) def test_from_components_with_port_resource_request(self, ): ctxt = context.RequestContext(fakes.FAKE_USER_ID, fakes.FAKE_PROJECT_ID) instance = fake_instance.fake_instance_obj(ctxt) image = {'id': uuids.image_id, 'properties': {'mappings': []}, 'status': 'fake-status', 'location': 'far-away'} flavor = fake_flavor.fake_flavor_obj(ctxt) filter_properties = {'fake': 'property'} rg = request_spec.RequestGroup() spec = objects.RequestSpec.from_components(ctxt, instance.uuid, image, flavor, instance.numa_topology, instance.pci_requests, filter_properties, None, instance.availability_zone, port_resource_requests=[rg]) self.assertListEqual([rg], spec.requested_resources) def test_get_scheduler_hint(self): spec_obj = objects.RequestSpec(scheduler_hints={'foo_single': ['1'], 'foo_mul': ['1', '2']}) self.assertEqual('1', spec_obj.get_scheduler_hint('foo_single')) self.assertEqual(['1', '2'], spec_obj.get_scheduler_hint('foo_mul')) self.assertIsNone(spec_obj.get_scheduler_hint('oops')) self.assertEqual('bar', spec_obj.get_scheduler_hint('oops', default='bar')) def test_get_scheduler_hint_with_no_hints(self): spec_obj = objects.RequestSpec() self.assertEqual('bar', spec_obj.get_scheduler_hint('oops', default='bar')) @mock.patch.object(objects.RequestSpec, '_to_legacy_instance') @mock.patch.object(base, 'obj_to_primitive') def test_to_legacy_request_spec_dict(self, image_to_primitive, spec_to_legacy_instance): fake_image_dict = mock.Mock() image_to_primitive.return_value = fake_image_dict fake_instance = {'root_gb': 1.0, 'ephemeral_gb': 1.0, 'memory_mb': 1.0, 'vcpus': 1, 'numa_topology': None, 'pci_requests': None, 'project_id': fakes.FAKE_PROJECT_ID, 'availability_zone': 'nova', 'uuid': '1'} spec_to_legacy_instance.return_value = fake_instance fake_flavor = objects.Flavor(root_gb=10, ephemeral_gb=0, memory_mb=512, vcpus=1) spec = objects.RequestSpec(num_instances=1, image=objects.ImageMeta(), # instance properties numa_topology=None, pci_requests=None, project_id=1, availability_zone='nova', instance_uuid=uuids.instance, flavor=fake_flavor) spec_dict = spec.to_legacy_request_spec_dict() expected = {'num_instances': 1, 'image': fake_image_dict, 'instance_properties': fake_instance, 'instance_type': fake_flavor} self.assertEqual(expected, spec_dict) def test_to_legacy_request_spec_dict_with_unset_values(self): spec = objects.RequestSpec() self.assertEqual({'num_instances': 1, 'image': {}, 'instance_properties': {}, 'instance_type': {}}, spec.to_legacy_request_spec_dict()) def test_to_legacy_filter_properties_dict(self): fake_numa_limits = objects.NUMATopologyLimits() fake_computes_obj = objects.ComputeNodeList( objects=[objects.ComputeNode(host='fake1', hypervisor_hostname='node1')]) fake_dest = objects.Destination(host='fakehost') spec = objects.RequestSpec( ignore_hosts=['ignoredhost'], force_hosts=['fakehost'], force_nodes=['fakenode'], retry=objects.SchedulerRetries(num_attempts=1, hosts=fake_computes_obj), limits=objects.SchedulerLimits(numa_topology=fake_numa_limits, vcpu=1.0, disk_gb=10.0, memory_mb=8192.0), instance_group=objects.InstanceGroup(hosts=['fake1'], policy='affinity', members=['inst1', 'inst2'], uuid=uuids.group_uuid), scheduler_hints={'foo': ['bar']}, requested_destination=fake_dest) expected = {'ignore_hosts': ['ignoredhost'], 'force_hosts': ['fakehost'], 'force_nodes': ['fakenode'], 'retry': {'num_attempts': 1, 'hosts': [['fake1', 'node1']]}, 'limits': {'numa_topology': fake_numa_limits, 'vcpu': 1.0, 'disk_gb': 10.0, 'memory_mb': 8192.0}, 'group_updated': True, 'group_hosts': set(['fake1']), 'group_policies': set(['affinity']), 'group_members': set(['inst1', 'inst2']), 'group_uuid': uuids.group_uuid, 'scheduler_hints': {'foo': 'bar'}, 'requested_destination': fake_dest} self.assertEqual(expected, spec.to_legacy_filter_properties_dict()) def test_to_legacy_filter_properties_dict_with_nullable_values(self): spec = objects.RequestSpec(force_hosts=None, force_nodes=None, retry=None, limits=None, instance_group=None, scheduler_hints=None) self.assertEqual({}, spec.to_legacy_filter_properties_dict()) def test_to_legacy_filter_properties_dict_with_unset_values(self): spec = objects.RequestSpec() self.assertEqual({}, spec.to_legacy_filter_properties_dict()) def test_ensure_network_metadata(self): network_a = fake_network_cache_model.new_network({ 'physical_network': 'foo', 'tunneled': False}) vif_a = fake_network_cache_model.new_vif({'network': network_a}) network_b = fake_network_cache_model.new_network({ 'physical_network': 'foo', 'tunneled': False}) vif_b = fake_network_cache_model.new_vif({'network': network_b}) network_c = fake_network_cache_model.new_network({ 'physical_network': 'bar', 'tunneled': False}) vif_c = fake_network_cache_model.new_vif({'network': network_c}) network_d = fake_network_cache_model.new_network({ 'physical_network': None, 'tunneled': True}) vif_d = fake_network_cache_model.new_vif({'network': network_d}) nw_info = network_model.NetworkInfo([vif_a, vif_b, vif_c, vif_d]) info_cache = objects.InstanceInfoCache(network_info=nw_info, instance_uuid=uuids.instance) instance = objects.Instance(id=3, uuid=uuids.instance, info_cache=info_cache) spec = objects.RequestSpec() self.assertNotIn('network_metadata', spec) spec.ensure_network_metadata(instance) self.assertIn('network_metadata', spec) self.assertIsInstance(spec.network_metadata, objects.NetworkMetadata) self.assertEqual(spec.network_metadata.physnets, set(['foo', 'bar'])) self.assertTrue(spec.network_metadata.tunneled) def test_ensure_network_metadata_missing(self): nw_info = network_model.NetworkInfo([]) info_cache = objects.InstanceInfoCache(network_info=nw_info, instance_uuid=uuids.instance) instance = objects.Instance(id=3, uuid=uuids.instance, info_cache=info_cache) spec = objects.RequestSpec() self.assertNotIn('network_metadata', spec) spec.ensure_network_metadata(instance) self.assertNotIn('network_metadata', spec) @mock.patch.object(request_spec.RequestSpec, '_get_by_instance_uuid_from_db') @mock.patch('nova.objects.InstanceGroup.get_by_uuid') def test_get_by_instance_uuid(self, mock_get_ig, get_by_uuid): fake_spec = fake_request_spec.fake_db_spec() get_by_uuid.return_value = fake_spec mock_get_ig.return_value = objects.InstanceGroup(name='fresh') req_obj = request_spec.RequestSpec.get_by_instance_uuid(self.context, fake_spec['instance_uuid']) self.assertEqual(1, req_obj.num_instances) # ignore_hosts is not persisted self.assertIsNone(req_obj.ignore_hosts) self.assertEqual('fake', req_obj.project_id) self.assertEqual({'hint': ['over-there']}, req_obj.scheduler_hints) self.assertEqual(['host1', 'host3'], req_obj.force_hosts) self.assertIsNone(req_obj.availability_zone) self.assertEqual(['node1', 'node2'], req_obj.force_nodes) self.assertIsInstance(req_obj.image, objects.ImageMeta) self.assertIsInstance(req_obj.numa_topology, objects.InstanceNUMATopology) self.assertIsInstance(req_obj.pci_requests, objects.InstancePCIRequests) self.assertIsInstance(req_obj.flavor, objects.Flavor) # The 'retry' field is not persistent. self.assertIsNone(req_obj.retry) self.assertIsInstance(req_obj.limits, objects.SchedulerLimits) self.assertIsInstance(req_obj.instance_group, objects.InstanceGroup) self.assertEqual('fresh', req_obj.instance_group.name) def _check_update_primitive(self, req_obj, changes): self.assertEqual(req_obj.instance_uuid, changes['instance_uuid']) serialized_obj = objects.RequestSpec.obj_from_primitive( jsonutils.loads(changes['spec'])) # primitive fields for field in ['instance_uuid', 'num_instances', 'project_id', 'scheduler_hints', 'force_hosts', 'availability_zone', 'force_nodes']: self.assertEqual(getattr(req_obj, field), getattr(serialized_obj, field)) # object fields for field in ['image', 'numa_topology', 'pci_requests', 'flavor', 'limits', 'network_metadata']: self.assertEqual( getattr(req_obj, field).obj_to_primitive(), getattr(serialized_obj, field).obj_to_primitive()) self.assertIsNone(serialized_obj.instance_group.members) self.assertIsNone(serialized_obj.instance_group.hosts) self.assertIsNone(serialized_obj.retry) self.assertIsNone(serialized_obj.requested_destination) self.assertIsNone(serialized_obj.ignore_hosts) def test_create(self): req_obj = fake_request_spec.fake_spec_obj(remove_id=True) def _test_create_args(self2, context, changes): self._check_update_primitive(req_obj, changes) # DB creation would have set an id changes['id'] = 42 return changes with mock.patch.object(request_spec.RequestSpec, '_create_in_db', _test_create_args): req_obj.create() def test_create_id_set(self): req_obj = request_spec.RequestSpec(self.context) req_obj.id = 3 self.assertRaises(exception.ObjectActionError, req_obj.create) def test_create_does_not_persist_requested_fields(self): req_obj = fake_request_spec.fake_spec_obj(remove_id=True) expected_network_metadata = objects.NetworkMetadata( physnets=set(['foo', 'bar']), tunneled=True) req_obj.network_metadata = expected_network_metadata expected_destination = request_spec.Destination(host='sample-host') req_obj.requested_destination = expected_destination rg = request_spec.RequestGroup(resources={'fake-rc': 13}) req_obj.requested_resources = [rg] expected_retry = objects.SchedulerRetries( num_attempts=2, hosts=objects.ComputeNodeList(objects=[ objects.ComputeNode(host='host1', hypervisor_hostname='node1'), objects.ComputeNode(host='host2', hypervisor_hostname='node2'), ])) req_obj.retry = expected_retry orig_create_in_db = request_spec.RequestSpec._create_in_db with mock.patch.object(request_spec.RequestSpec, '_create_in_db') \ as mock_create_in_db: mock_create_in_db.side_effect = orig_create_in_db req_obj.create() mock_create_in_db.assert_called_once() updates = mock_create_in_db.mock_calls[0][1][1] # assert that the following fields are not stored in the db # 1. network_metadata # 2. requested_destination # 3. requested_resources # 4. retry data = jsonutils.loads(updates['spec'])['nova_object.data'] self.assertNotIn('network_metadata', data) self.assertIsNone(data['requested_destination']) self.assertIsNone(data['requested_resources']) self.assertIsNone(data['retry']) self.assertIsNotNone(data['instance_uuid']) # also we expect that the following fields are not reset after create # 1. network_metadata # 2. requested_destination # 3. requested_resources # 4. retry self.assertIsNotNone(req_obj.network_metadata) self.assertJsonEqual(expected_network_metadata.obj_to_primitive(), req_obj.network_metadata.obj_to_primitive()) self.assertIsNotNone(req_obj.requested_destination) self.assertJsonEqual(expected_destination.obj_to_primitive(), req_obj.requested_destination.obj_to_primitive()) self.assertIsNotNone(req_obj.requested_resources) self.assertEqual( 13, req_obj.requested_resources[0].resources['fake-rc']) self.assertIsNotNone(req_obj.retry) self.assertJsonEqual(expected_retry.obj_to_primitive(), req_obj.retry.obj_to_primitive()) def test_save_does_not_persist_requested_fields(self): req_obj = fake_request_spec.fake_spec_obj(remove_id=True) req_obj.create() # change something to make sure _save_in_db is called expected_network_metadata = objects.NetworkMetadata( physnets=set(['foo', 'bar']), tunneled=True) req_obj.network_metadata = expected_network_metadata expected_destination = request_spec.Destination(host='sample-host') req_obj.requested_destination = expected_destination rg = request_spec.RequestGroup(resources={'fake-rc': 13}) req_obj.requested_resources = [rg] expected_retry = objects.SchedulerRetries( num_attempts=2, hosts=objects.ComputeNodeList(objects=[ objects.ComputeNode(host='host1', hypervisor_hostname='node1'), objects.ComputeNode(host='host2', hypervisor_hostname='node2'), ])) req_obj.retry = expected_retry req_obj.num_instances = 2 req_obj.ignore_hosts = [uuids.ignored_host] orig_save_in_db = request_spec.RequestSpec._save_in_db with mock.patch.object(request_spec.RequestSpec, '_save_in_db') \ as mock_save_in_db: mock_save_in_db.side_effect = orig_save_in_db req_obj.save() mock_save_in_db.assert_called_once() updates = mock_save_in_db.mock_calls[0][1][2] # assert that the following fields are not stored in the db # 1. network_metadata # 2. requested_destination # 3. requested_resources # 4. retry # 5. ignore_hosts data = jsonutils.loads(updates['spec'])['nova_object.data'] self.assertNotIn('network_metadata', data) self.assertIsNone(data['requested_destination']) self.assertIsNone(data['requested_resources']) self.assertIsNone(data['retry']) self.assertIsNone(data['ignore_hosts']) self.assertIsNotNone(data['instance_uuid']) # also we expect that the following fields are not reset after save # 1. network_metadata # 2. requested_destination # 3. requested_resources # 4. retry # 5. ignore_hosts self.assertIsNotNone(req_obj.network_metadata) self.assertJsonEqual(expected_network_metadata.obj_to_primitive(), req_obj.network_metadata.obj_to_primitive()) self.assertIsNotNone(req_obj.requested_destination) self.assertJsonEqual(expected_destination.obj_to_primitive(), req_obj.requested_destination.obj_to_primitive()) self.assertIsNotNone(req_obj.requested_resources) self.assertEqual(13, req_obj.requested_resources[0].resources ['fake-rc']) self.assertIsNotNone(req_obj.retry) self.assertJsonEqual(expected_retry.obj_to_primitive(), req_obj.retry.obj_to_primitive()) self.assertIsNotNone(req_obj.ignore_hosts) self.assertEqual([uuids.ignored_host], req_obj.ignore_hosts) def test_save(self): req_obj = fake_request_spec.fake_spec_obj() # Make sure the requested_destination is not persisted since it is # only valid per request/operation. req_obj.requested_destination = objects.Destination(host='fake') def _test_save_args(self2, context, instance_uuid, changes): self._check_update_primitive(req_obj, changes) # DB creation would have set an id changes['id'] = 42 return changes with mock.patch.object(request_spec.RequestSpec, '_save_in_db', _test_save_args): req_obj.save() @mock.patch.object(request_spec.RequestSpec, '_destroy_in_db') def test_destroy(self, destroy_in_db): req_obj = fake_request_spec.fake_spec_obj() req_obj.destroy() destroy_in_db.assert_called_once_with(req_obj._context, req_obj.instance_uuid) @mock.patch.object(request_spec.RequestSpec, '_destroy_bulk_in_db') def test_destroy_bulk(self, destroy_bulk_in_db): uuids_to_be_deleted = [] for i in range(0, 5): uuid = uuidutils.generate_uuid() uuids_to_be_deleted.append(uuid) destroy_bulk_in_db.return_value = 5 result = objects.RequestSpec.destroy_bulk(self.context, uuids_to_be_deleted) destroy_bulk_in_db.assert_called_once_with(self.context, uuids_to_be_deleted) self.assertEqual(5, result) def test_reset_forced_destinations(self): req_obj = fake_request_spec.fake_spec_obj() # Making sure the fake object has forced hosts and nodes self.assertIsNotNone(req_obj.force_hosts) self.assertIsNotNone(req_obj.force_nodes) with mock.patch.object(req_obj, 'obj_reset_changes') as mock_reset: req_obj.reset_forced_destinations() self.assertIsNone(req_obj.force_hosts) self.assertIsNone(req_obj.force_nodes) mock_reset.assert_called_once_with(['force_hosts', 'force_nodes']) def test_compat_requested_destination(self): req_obj = objects.RequestSpec( requested_destination=objects.Destination()) versions = ovo_base.obj_tree_get_versions('RequestSpec') primitive = req_obj.obj_to_primitive(target_version='1.5', version_manifest=versions) self.assertNotIn( 'requested_destination', primitive['nova_object.data']) primitive = req_obj.obj_to_primitive(target_version='1.6', version_manifest=versions) self.assertIn('requested_destination', primitive['nova_object.data']) def test_compat_security_groups(self): sgl = objects.SecurityGroupList(objects=[]) req_obj = objects.RequestSpec(security_groups=sgl) versions = ovo_base.obj_tree_get_versions('RequestSpec') primitive = req_obj.obj_to_primitive(target_version='1.7', version_manifest=versions) self.assertNotIn('security_groups', primitive['nova_object.data']) primitive = req_obj.obj_to_primitive(target_version='1.8', version_manifest=versions) self.assertIn('security_groups', primitive['nova_object.data']) def test_compat_user_id(self): req_obj = objects.RequestSpec(project_id=fakes.FAKE_PROJECT_ID, user_id=fakes.FAKE_USER_ID) versions = ovo_base.obj_tree_get_versions('RequestSpec') primitive = req_obj.obj_to_primitive(target_version='1.8', version_manifest=versions) primitive = primitive['nova_object.data'] self.assertNotIn('user_id', primitive) self.assertIn('project_id', primitive) def test_compat_network_metadata(self): network_metadata = objects.NetworkMetadata(physnets=set(), tunneled=False) req_obj = objects.RequestSpec(network_metadata=network_metadata, user_id=fakes.FAKE_USER_ID) versions = ovo_base.obj_tree_get_versions('RequestSpec') primitive = req_obj.obj_to_primitive(target_version='1.9', version_manifest=versions) primitive = primitive['nova_object.data'] self.assertNotIn('network_metadata', primitive) self.assertIn('user_id', primitive) def test_compat_requested_resources(self): req_obj = objects.RequestSpec(requested_resources=[], instance_uuid=uuids.instance) versions = ovo_base.obj_tree_get_versions('RequestSpec') primitive = req_obj.obj_to_primitive(target_version='1.11', version_manifest=versions) primitive = primitive['nova_object.data'] self.assertNotIn('requested_resources', primitive) self.assertIn('instance_uuid', primitive) def test_default_requested_destination(self): req_obj = objects.RequestSpec() self.assertIsNone(req_obj.requested_destination) def test_security_groups_load(self): req_obj = objects.RequestSpec() self.assertNotIn('security_groups', req_obj) self.assertIsInstance(req_obj.security_groups, objects.SecurityGroupList) self.assertIn('security_groups', req_obj) def test_network_requests_load(self): req_obj = objects.RequestSpec() self.assertNotIn('network_metadata', req_obj) self.assertIsInstance(req_obj.network_metadata, objects.NetworkMetadata) self.assertIn('network_metadata', req_obj) def test_destination_aggregates_default(self): destination = objects.Destination() self.assertIsNone(destination.aggregates) def test_destination_require_aggregates(self): destination = objects.Destination() destination.require_aggregates(['foo', 'bar']) destination.require_aggregates(['baz']) self.assertEqual(['foo,bar', 'baz'], destination.aggregates) def test_destination_1dotoh(self): destination = objects.Destination(aggregates=['foo']) primitive = destination.obj_to_primitive(target_version='1.0') self.assertNotIn('aggregates', primitive['nova_object.data']) def test_create_raises_on_unchanged_object(self): ctxt = context.RequestContext(uuids.user_id, uuids.project_id) req_obj = request_spec.RequestSpec(context=ctxt) self.assertRaises(exception.ObjectActionError, req_obj.create) def test_save_can_be_called_on_unchanged_object(self): req_obj = fake_request_spec.fake_spec_obj(remove_id=True) req_obj.create() req_obj.save() class TestRequestSpecObject(test_objects._LocalTest, _TestRequestSpecObject): pass class TestRemoteRequestSpecObject(test_objects._RemoteTest, _TestRequestSpecObject): pass class TestRequestGroupObject(test.TestCase): def setUp(self): super(TestRequestGroupObject, self).setUp() self.user_id = uuids.user_id self.project_id = uuids.project_id self.context = context.RequestContext(uuids.user_id, uuids.project_id) def test_fields_defaulted_at_create(self): rg = request_spec.RequestGroup(self.context) self.assertTrue(rg.use_same_provider) self.assertEqual({}, rg.resources) self.assertEqual(set(), rg.required_traits) self.assertEqual(set(), rg.forbidden_traits) self.assertEqual([], rg.aggregates) self.assertIsNone(rg.requester_id) self.assertEqual([], rg.provider_uuids) self.assertIsNone(rg.in_tree) def test_from_port_request(self): port_resource_request = { "resources": { "NET_BW_IGR_KILOBIT_PER_SEC": 1000, "NET_BW_EGR_KILOBIT_PER_SEC": 1000}, "required": ["CUSTOM_PHYSNET_2", "CUSTOM_VNIC_TYPE_NORMAL"] } rg = request_spec.RequestGroup.from_port_request( self.context, uuids.port_id, port_resource_request) self.assertTrue(rg.use_same_provider) self.assertEqual( {"NET_BW_IGR_KILOBIT_PER_SEC": 1000, "NET_BW_EGR_KILOBIT_PER_SEC": 1000}, rg.resources) self.assertEqual({"CUSTOM_PHYSNET_2", "CUSTOM_VNIC_TYPE_NORMAL"}, rg.required_traits) self.assertEqual(uuids.port_id, rg.requester_id) # and the rest is defaulted self.assertEqual(set(), rg.forbidden_traits) self.assertEqual([], rg.aggregates) self.assertEqual([], rg.provider_uuids) def test_from_port_request_without_traits(self): port_resource_request = { "resources": { "NET_BW_IGR_KILOBIT_PER_SEC": 1000, "NET_BW_EGR_KILOBIT_PER_SEC": 1000}} rg = request_spec.RequestGroup.from_port_request( self.context, uuids.port_id, port_resource_request) self.assertTrue(rg.use_same_provider) self.assertEqual( {"NET_BW_IGR_KILOBIT_PER_SEC": 1000, "NET_BW_EGR_KILOBIT_PER_SEC": 1000}, rg.resources) self.assertEqual(uuids.port_id, rg.requester_id) # and the rest is defaulted self.assertEqual(set(), rg.required_traits) self.assertEqual(set(), rg.forbidden_traits) self.assertEqual([], rg.aggregates) self.assertEqual([], rg.provider_uuids) def test_compat_requester_and_provider(self): req_obj = objects.RequestGroup( requester_id=uuids.requester, provider_uuids=[uuids.rp1], required_traits=set(['CUSTOM_PHYSNET_2'])) versions = ovo_base.obj_tree_get_versions('RequestGroup') primitive = req_obj.obj_to_primitive( target_version='1.2', version_manifest=versions)['nova_object.data'] self.assertIn('in_tree', primitive) self.assertIn('requester_id', primitive) self.assertIn('provider_uuids', primitive) self.assertIn('required_traits', primitive) primitive = req_obj.obj_to_primitive( target_version='1.1', version_manifest=versions)['nova_object.data'] self.assertNotIn('in_tree', primitive) self.assertIn('requester_id', primitive) self.assertIn('provider_uuids', primitive) self.assertIn('required_traits', primitive) primitive = req_obj.obj_to_primitive( target_version='1.0', version_manifest=versions)['nova_object.data'] self.assertNotIn('in_tree', primitive) self.assertNotIn('requester_id', primitive) self.assertNotIn('provider_uuids', primitive) self.assertIn('required_traits', primitive) class TestMappingRequestGroupsToProviders(test.NoDBTestCase): def setUp(self): super(TestMappingRequestGroupsToProviders, self).setUp() self.spec = request_spec.RequestSpec() def test_no_groups(self): allocations = None provider_traits = {} self.spec.map_requested_resources_to_providers( allocations, provider_traits) # we cannot assert much, at least we see that the above call doesn't # blow self.assertIsNone(self.spec.requested_resources) def test_unnumbered_group_not_supported(self): allocations = {} provider_traits = {} group1 = request_spec.RequestGroup( use_same_provider=False) self.spec.requested_resources = [group1] self.assertRaises( NotImplementedError, self.spec.map_requested_resources_to_providers, allocations, provider_traits) def test_forbidden_traits_not_supported(self): allocations = {} provider_traits = {} group1 = request_spec.RequestGroup( forbidden_traits={'STORAGE_DISK_HDD'}) self.spec.requested_resources = [group1] self.assertRaises( NotImplementedError, self.spec.map_requested_resources_to_providers, allocations, provider_traits) def test_aggregates_not_supported(self): allocations = {} provider_traits = {} group1 = request_spec.RequestGroup( aggregates=[[uuids.agg1]]) self.spec.requested_resources = [group1] self.assertRaises( NotImplementedError, self.spec.map_requested_resources_to_providers, allocations, provider_traits) def test_one_group(self): allocations = { uuids.compute1_rp: { "resources": { 'VCPU': 1 } }, uuids.net_dev1_rp: { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, 'NET_BW_EGR_KILOBIT_PER_SEC': 1, } } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], } group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1] self.spec.map_requested_resources_to_providers( allocations, provider_traits) self.assertEqual([uuids.net_dev1_rp], group1.provider_uuids) def test_one_group_no_matching_allocation(self): # NOTE(gibi): This negative test scenario should not happen in real # end to end test as we assume that placement only returns candidates # that are valid. But still we want to cover the error case in our # implementation allocations = { uuids.compute1_rp: { "resources": { 'VCPU': 1 } }, uuids.net_dev1_rp: { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, } } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], } group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1] self.assertRaises( ValueError, self.spec.map_requested_resources_to_providers, allocations, provider_traits) def test_one_group_no_matching_trait(self): # NOTE(gibi): This negative test scenario should not happen in real # end to end test as we assume that placement only returns candidates # that are valid. But still we want to cover the error case in our # implementation allocations = { uuids.compute1_rp: { "resources": { 'VCPU': 1 } }, uuids.net_dev1_rp: { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, 'NET_BW_EGR_KILOBIT_PER_SEC': 1, } } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_rp: [ 'CUSTOM_PHYSNET_PHYSNET1', 'CUSTOM_VNIC_TYPE_NORMAL' ], } group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1] self.assertRaises( ValueError, self.spec.map_requested_resources_to_providers, allocations, provider_traits) def test_two_groups_same_provider(self): allocations = { uuids.compute1_rp: { "resources": { 'VCPU': 1 } }, uuids.net_dev1_rp: { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 3, 'NET_BW_EGR_KILOBIT_PER_SEC': 3, } } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], } group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) group2 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 2, "NET_BW_EGR_KILOBIT_PER_SEC": 2, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1, group2] self.spec.map_requested_resources_to_providers( allocations, provider_traits) self.assertEqual([uuids.net_dev1_rp], group1.provider_uuids) self.assertEqual([uuids.net_dev1_rp], group2.provider_uuids) def test_two_groups_different_providers(self): # NOTE(gibi): we use OrderedDict here to make the test deterministic allocations = collections.OrderedDict() allocations[uuids.compute1_rp] = { "resources": { 'VCPU': 1 } } allocations[uuids.net_dev1_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 2, 'NET_BW_EGR_KILOBIT_PER_SEC': 2, } } allocations[uuids.net_dev2_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, 'NET_BW_EGR_KILOBIT_PER_SEC': 1, } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], uuids.net_dev2_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], } group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) group2 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 2, "NET_BW_EGR_KILOBIT_PER_SEC": 2, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1, group2] self.spec.map_requested_resources_to_providers( allocations, provider_traits) self.assertEqual([uuids.net_dev2_rp], group1.provider_uuids) self.assertEqual([uuids.net_dev1_rp], group2.provider_uuids) def test_two_groups_different_providers_reverse(self): """Similar as test_two_groups_different_providers but reorder the groups to exercises another code path """ # NOTE(gibi): we use OrderedDict here to make the test deterministic allocations = collections.OrderedDict() allocations[uuids.compute1_rp] = { "resources": { 'VCPU': 1 } } allocations[uuids.net_dev1_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 2, 'NET_BW_EGR_KILOBIT_PER_SEC': 2, } } allocations[uuids.net_dev2_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, 'NET_BW_EGR_KILOBIT_PER_SEC': 1, } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], uuids.net_dev2_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], } group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 2, "NET_BW_EGR_KILOBIT_PER_SEC": 2, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) group2 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1, group2] self.spec.map_requested_resources_to_providers( allocations, provider_traits) self.assertEqual([uuids.net_dev1_rp], group1.provider_uuids) self.assertEqual([uuids.net_dev2_rp], group2.provider_uuids) def test_two_groups_different_providers_different_traits(self): allocations = collections.OrderedDict() allocations[uuids.compute1_rp] = { "resources": { 'VCPU': 1 } } allocations[uuids.net_dev1_physnet1_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, 'NET_BW_EGR_KILOBIT_PER_SEC': 1, } } allocations[uuids.net_dev2_physnet0_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, 'NET_BW_EGR_KILOBIT_PER_SEC': 1, } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_physnet1_rp: [ 'CUSTOM_PHYSNET_PHYSNET1', 'CUSTOM_VNIC_TYPE_NORMAL' ], uuids.net_dev2_physnet0_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], } group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) group2 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET1", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1, group2] self.spec.map_requested_resources_to_providers( allocations, provider_traits) self.assertEqual([uuids.net_dev2_physnet0_rp], group1.provider_uuids) self.assertEqual([uuids.net_dev1_physnet1_rp], group2.provider_uuids) def test_three_groups(self): """A complex example where a lot of mappings are tried before the solution is found. """ allocations = collections.OrderedDict() allocations[uuids.compute1_rp] = { "resources": { 'VCPU': 1 } } allocations[uuids.net_dev1_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 3, 'NET_BW_EGR_KILOBIT_PER_SEC': 3, } } allocations[uuids.net_dev2_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 2, 'NET_BW_EGR_KILOBIT_PER_SEC': 2, } } allocations[uuids.net_dev3_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, 'NET_BW_EGR_KILOBIT_PER_SEC': 3, } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], uuids.net_dev2_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], uuids.net_dev3_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], } # this fits to 2 RPs group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 3, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) # this fits to 2 RPs group2 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 2, "NET_BW_EGR_KILOBIT_PER_SEC": 2, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) # this fits to only one RPs group3 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 3, "NET_BW_EGR_KILOBIT_PER_SEC": 3, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1, group2, group3] orig_validator = self.spec._is_valid_group_rp_mapping with mock.patch.object( self.spec, '_is_valid_group_rp_mapping', side_effect=orig_validator ) as mock_validator: self.spec.map_requested_resources_to_providers( allocations, provider_traits) self.assertEqual([uuids.net_dev3_rp], group1.provider_uuids) self.assertEqual([uuids.net_dev2_rp], group2.provider_uuids) self.assertEqual([uuids.net_dev1_rp], group3.provider_uuids) # the algorithm tried out many possible mappings before found the # the solution self.assertEqual(58, mock_validator.call_count) @mock.patch.object(request_spec.LOG, 'debug') def test_two_groups_matches_but_allocation_leftover(self, mock_debug): # NOTE(gibi): This negative test scenario should not happen in real # end to end test as we assume that placement only returns candidates # that are valid and this candidate is not valid as it provides more # resources than the ports are requesting. Still we want to cover the # error case in our implementation allocations = collections.OrderedDict() allocations[uuids.compute1_rp] = { "resources": { 'VCPU': 1 } } allocations[uuids.net_dev1_physnet0_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 2, 'NET_BW_EGR_KILOBIT_PER_SEC': 2, } } allocations[uuids.net_dev2_physnet0_rp] = { "resources": { 'NET_BW_IGR_KILOBIT_PER_SEC': 1, 'NET_BW_EGR_KILOBIT_PER_SEC': 1, } } provider_traits = { uuids.compute1_rp: [], uuids.net_dev1_physnet0_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], uuids.net_dev2_physnet0_rp: [ 'CUSTOM_PHYSNET_PHYSNET0', 'CUSTOM_VNIC_TYPE_NORMAL' ], } group1 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) group2 = request_spec.RequestGroup( resources={ "NET_BW_IGR_KILOBIT_PER_SEC": 1, "NET_BW_EGR_KILOBIT_PER_SEC": 1, }, required_traits={ "CUSTOM_PHYSNET_PHYSNET0", "CUSTOM_VNIC_TYPE_NORMAL", }) self.spec.requested_resources = [group1, group2] self.assertRaises( ValueError, self.spec.map_requested_resources_to_providers, allocations, provider_traits) self.assertIn('allocations leftover', mock_debug.mock_calls[3][1][0])

41.870406

79

0.601962

794acf808a7856e2c4b9f9889650b521c10f2b89

28,233

py

Python

torch/testing/_internal/jit_utils.py

vulcantron/pytorch

bbbf00a8a83879e687fb7e96e31a619388b6b54e

[ "Intel" ]

null

torch/testing/_internal/jit_utils.py

vulcantron/pytorch

bbbf00a8a83879e687fb7e96e31a619388b6b54e

[ "Intel" ]

null

torch/testing/_internal/jit_utils.py

vulcantron/pytorch

bbbf00a8a83879e687fb7e96e31a619388b6b54e

[ "Intel" ]

null

# Torch from torch.autograd import Variable from torch.autograd.function import _nested_map from torch.jit.annotations import BroadcastingList2, BroadcastingList3 # noqa: F401 from torch.onnx import OperatorExportTypes import torch import torch.cuda import torch.jit import torch.jit._logging import torch.jit.frontend import torch.jit.quantized import zipfile import functools # Testing utils from torch.testing import FileCheck from torch.testing._internal.common_utils import IS_WINDOWS, \ freeze_rng_state, enable_profiling_mode_for_profiling_tests, ProfilingMode, TEST_BAILOUTS from torch.testing._internal.common_jit import JitCommonTestCase from torch.testing._internal.common_utils import enable_profiling_mode # noqa: F401 # Standard library from contextlib import contextmanager from functools import reduce from io import StringIO from collections import defaultdict import importlib.util import inspect import io import math import os import pickle import sys import tempfile import textwrap from importlib.abc import Loader from typing import Any, Dict, List RUN_CUDA = torch.cuda.is_available() RUN_CUDA_MULTI_GPU = RUN_CUDA and torch.cuda.device_count() > 1 RUN_CUDA_HALF = RUN_CUDA # HIP supports half, no version check necessary if torch.cuda.is_available() and not torch.version.hip: CUDA_VERSION = torch._C._cuda_getCompiledVersion() for d in range(torch.cuda.device_count()): major = torch.cuda.get_device_capability(d)[0] if (major < 6): RUN_CUDA_HALF = False def execWrapper(code, glob, loc): exec(code, glob, loc) def do_input_map(fn, input): return _nested_map(lambda t: isinstance(t, torch.Tensor), fn)(input) def clear_class_registry(): torch._C._jit_clear_class_registry() torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore() torch.jit._state._clear_class_state() def get_execution_plan(graph_executor_state): execution_plans = list(graph_executor_state.execution_plans.values()) num_plans = len(execution_plans) if num_plans != 1: raise RuntimeError('This test assumes this GraphExecutor should ' 'only have one execution plan, got: {}'.format(num_plans)) return execution_plans[0] class _AssertRaisesRegexWithHighlightContext(object): """ A context manager that is useful for checking that error messages highlight the correct part of the source code. """ def __init__(self, test_case, exception, regex, highlight): self.test_case = test_case self.exception_type = exception self.regex = regex self.highlight = highlight def __enter__(self): return self def __exit__(self, type, value, traceback): with self.test_case.assertRaisesRegex(self.exception_type, self.regex): if type: raise value if self.highlight: FileCheck().check_source_highlighted(self.highlight).run(str(value)) return True FUSION_GROUP = "prim::TensorExprGroup" class JitTestCase(JitCommonTestCase): _do_cuda_memory_leak_check = True _restored_warnings = False class capture_stdout(list): """ Replace sys.stdout with a temporary StringIO """ def __enter__(self): self.sys_stdout = sys.stdout self.stringio = StringIO() sys.stdout = self.stringio return self def __exit__(self, *args): self.append(str(self.stringio.getvalue())) del self.stringio sys.stdout = self.sys_stdout class capture_stderr(list): """ Replace sys.stderr with a temporary StringIO """ def __enter__(self): self.sys_stderr = sys.stderr self.stringio = StringIO() sys.stderr = self.stringio return self def __exit__(self, *args): self.append(str(self.stringio.getvalue())) del self.stringio sys.stderr = self.sys_stderr def setHooks(self): torch._C._jit_set_emit_hooks(self.emitModuleHook, self.emitFunctionHook) def clearHooks(self): torch._C._jit_set_emit_hooks(None, None) def setUp(self): super().setUp() # unittest overrides all warning filters and forces all of them to show up # after we install our own to silence those coming from inside PyTorch. # This will ensure that our filter still takes precedence. if not JitTestCase._restored_warnings: torch.jit.TracerWarning.ignore_lib_warnings() JitTestCase._restored_warnings = True self.setHooks() def tearDown(self): super().tearDown() # needs to be cleared because python might be unloaded before # the callback gets destucted self.clearHooks() clear_class_registry() def assertAllFused(self, graph, except_for=()): # note this helper collects nodes on 'fast path' only # i.e. the true blocks of specialized checks def get_nodes_and_parents_recursively(block, kind, acc): for node in block.nodes(): if node.kind() == kind: acc[block].append(node) elif node.kind() == 'prim::DifferentiableGraph': get_nodes_and_parents_recursively(node.g('Subgraph'), kind, acc) elif node.kind() == 'prim::If' and (node.inputs().__next__().node().kind() == 'aten::all' or node.inputs().__next__().node().kind() == 'prim::TypeCheck' or node.inputs().__next__().node().kind() == 'prim::RequiresGradCheck'): get_nodes_and_parents_recursively(node.blocks().__next__(), kind, acc) else: for inner_block in node.blocks(): get_nodes_and_parents_recursively(inner_block, kind, acc) allowed_nodes = {'prim::Constant', FUSION_GROUP, 'prim::BailoutTemplate', 'prim::TupleConstruct', 'prim::If', 'prim::TypeCheck', 'prim::RequiresGradCheck'} | set(except_for) fusion_groups : Dict[torch._C.Block, List[torch._C.Node]] = defaultdict(list) get_nodes_and_parents_recursively(graph, FUSION_GROUP, fusion_groups) self.assertTrue(len(fusion_groups) == 1, 'got {}'.format(graph)) (graph, fusion_nodes) = list(fusion_groups.items())[0] # the block contains one FUSION_GROUP and the rest of nodes are `allowed_nodes` self.assertTrue(len(fusion_nodes) == 1, 'got {}'.format(graph)) self.assertTrue(all(node.kind() in allowed_nodes for node in graph.nodes()), 'got {}'.format(graph)) def _isHookExceptionOk(self, e): se = str(e) allowed = ("Could not export Python function", "closures are not exportable") for a in allowed: if a in se: return True return False def _compared_saved_loaded(self, m): def extract_files(buffer): # crack open the zip format to get at the main module code archive = zipfile.ZipFile(buffer) # check that we have no duplicate names self.assertEqual(len(set(archive.namelist())), len(archive.namelist())) files = list(filter(lambda x: x.startswith('archive/code/'), archive.namelist())) # unwrap all the code files into strings code_files_str = filter(lambda x: x.endswith('.py'), files) code_files_stream = (archive.open(f) for f in code_files_str) code_files = ("".join([line.decode() for line in file]) for file in code_files_stream) # unpickled all the debug files debug_files_str = filter(lambda f: f.endswith('.debug_pkl'), files) debug_files_stream = (archive.open(f) for f in debug_files_str) debug_files = (pickle.load(f) for f in debug_files_stream) return code_files, debug_files # disable the hook while we parse code, otherwise we will re-enter the hook with torch._jit_internal._disable_emit_hooks(): try: # short-circuit if this is an empty function or module if len(m.code) == 0: return if isinstance(m, torch._C.ScriptModule): if len(m._method_names()) == 0: return # save the module to a buffer buffer = io.BytesIO() torch.jit.save(m, buffer) # copy the data in the buffer so we can restore it later. This # is because py2 and py3 have different semantics with zipfile # and it's easier to just work with a fresh copy each time. buffer_copy = buffer.getvalue() code_files, debug_files = extract_files(buffer) except RuntimeError as e: if not self._isHookExceptionOk(e): raise else: return # import the model again (from a the copy we made of the original) buffer2 = io.BytesIO(buffer_copy) imported = torch.jit.load(buffer2) # save it again saved_module_buffer_2 = io.BytesIO() torch.jit.save(imported, saved_module_buffer_2) saved_module_buffer_2.seek(0) code_files_2, debug_files_2 = extract_files(saved_module_buffer_2) for a, b in zip(code_files, code_files_2): self.assertMultiLineEqual(a, b) if isinstance(m, torch._C.ScriptModule): self.assertTrue(torch._C._ivalue_tags_match(m, imported._c)) def emitFunctionHook(self, func): # func has invalid names for export, skip the jitter check if func.name == "<lambda>" or "aten::" in func.name: return self._compared_saved_loaded(func) def emitModuleHook(self, module): self._compared_saved_loaded(module) def assertGraphContains(self, graph, kind): self.assertTrue(any(n.kind() == kind for n in graph.nodes())) def assertGraphContainsExactly(self, graph, kind, num_kind_nodes, consider_subgraphs=False): def perform_assert(graph, kind, actual, expected, consider_subgraphs): if actual == expected: return subgraph = 'including' if consider_subgraphs else 'excluding' raise AssertionError( '{}\nError: graph contains {} {} nodes ({} subgraphs) but expected {}'.format( graph, actual, kind, subgraph, expected)) if consider_subgraphs: strgraph = str(graph) count = strgraph.count(kind) - strgraph.count('with {}'.format(kind)) perform_assert(graph, kind, count, num_kind_nodes, consider_subgraphs) return def nodes(block): out = [] for node in block.nodes(): if node.kind() == kind: out.append(node) for block in node.blocks(): out += nodes(block) return out out_nodes = nodes(graph) perform_assert(graph, kind, len(out_nodes), num_kind_nodes, consider_subgraphs) def assertExpectedONNXGraph(self, g, *args, **kwargs): g = torch.onnx._optimize_trace(g, operator_export_type=OperatorExportTypes.ONNX) self.assertExpectedGraph(g, *args, **kwargs) def assertExpectedGraph(self, trace, *args, **kwargs): if isinstance(trace, torch._C.Graph): graph = trace else: graph = trace.graph() torch._C._jit_pass_lint(graph) torch._C._jit_pass_dce(graph) torch._C._jit_pass_lint(graph) graph = torch._C._jit_pass_canonicalize(graph) torch._C._jit_pass_lint(graph) self.assertExpected(str(graph), *args, **kwargs) def run_pass(self, name, trace): if isinstance(trace, torch._C.Graph): graph = trace set_graph = False else: set_graph = True graph = trace.graph() torch._C._jit_pass_lint(graph) result = getattr(torch._C, '_jit_pass_' + name)(graph) if result is not None and not isinstance(result, bool): graph = result torch._C._jit_pass_lint(graph) if set_graph: trace.set_graph(graph) return graph def get_frame_vars(self, frames_up): frame = inspect.currentframe() if not frame: raise RuntimeError("failed to inspect frame") i = 0 while i < frames_up + 1: frame = frame.f_back if not frame: raise RuntimeError("failed to get frame") i += 1 defined_vars: Dict[str, Any] = {} defined_vars.update(frame.f_locals) defined_vars.update(frame.f_globals) return defined_vars def assertRaisesRegexWithHighlight(self, exception, regex, highlight): return _AssertRaisesRegexWithHighlightContext(self, exception, regex, highlight) def checkScriptRaisesRegex(self, script, inputs, exception, regex, name=None, outputs=None, capture_output=False, frames_up=1, profiling=ProfilingMode.PROFILING): """ Checks that a given function will throw the correct exception, when executed with normal python, the string frontend, and the AST frontend. Logic taken from `checkScript` (see comments there for details) """ with enable_profiling_mode_for_profiling_tests(): # Normal Python with self.assertRaisesRegex(exception, regex): if isinstance(script, str): frame = self.get_frame_vars(frames_up) the_locals: Dict[str, Any] = {} execWrapper(script, glob=frame, loc=the_locals) frame.update(the_locals) python_fn = frame[name] else: python_fn = script python_fn(*inputs) # String frontend with self.assertRaisesRegex(exception, regex): if isinstance(script, str): cu = torch.jit.CompilationUnit(script, _frames_up=frames_up) string_frontend = getattr(cu, name) else: source = textwrap.dedent(inspect.getsource(script)) cu = torch.jit.CompilationUnit(source, _frames_up=frames_up) string_frontend = getattr(cu, script.__name__) with self.assertRaisesRegex(exception, regex): string_frontend(*inputs) # optimized run string_frontend(*inputs) # Python AST frontend if not isinstance(script, str): with self.assertRaisesRegex(exception, regex): ge = torch.jit.script(python_fn) # profiling run with self.assertRaisesRegex(exception, regex): ge(*inputs) # optimized run ge(*inputs) def checkBailouts(self, model, inputs, expected): state = model.get_debug_state() plan = get_execution_plan(state) num_bailouts = plan.code.num_bailouts() for i in range(0, num_bailouts): plan.code.request_bailout(i) bailout_outputs = model(*inputs) self.assertEqual(bailout_outputs, expected) def checkScript(self, script, inputs, name='func', optimize=True, inputs_requires_grad=False, capture_output=False, frames_up=1, profiling=ProfilingMode.PROFILING, atol=None, rtol=None): """ Checks that a given script generates the same output as the Python version using the given inputs. """ with torch.jit.optimized_execution(optimize): with enable_profiling_mode_for_profiling_tests(): extra_profile_runs = any(isinstance(x, torch.Tensor) and x.requires_grad for x in inputs) if isinstance(script, str): # Compile the string to a Script function # with enable_profiling_mode(): cu = torch.jit.CompilationUnit(script, _frames_up=frames_up) # Execute the Python function so we can run it later and get its # outputs frame = self.get_frame_vars(frames_up) the_locals: Dict[str, Any] = {} execWrapper(script, glob=frame, loc=the_locals) frame.update(the_locals) python_fn = frame[name] scripted_fn = getattr(cu, name) else: # Check the string frontend first source = textwrap.dedent(inspect.getsource(script)) self.checkScript( source, inputs, script.__name__, optimize=optimize, inputs_requires_grad=inputs_requires_grad, capture_output=capture_output, profiling=profiling, frames_up=2) # Continue checking the Python frontend scripted_fn = torch.jit.script(script, _frames_up=1) python_fn = script if inputs_requires_grad: recording_inputs = do_input_map(lambda t: t.detach().requires_grad_(), inputs) else: recording_inputs = inputs if capture_output: with self.capture_stdout() as script_stdout: script_outputs = scripted_fn(*recording_inputs) with self.capture_stdout() as opt_script_stdout: opt_script_outputs = scripted_fn(*recording_inputs) with self.capture_stdout() as _python_stdout: python_outputs = python_fn(*inputs) if not IS_WINDOWS: self.assertExpected(script_stdout[0], subname='stdout') self.assertEqual(python_outputs, opt_script_outputs, atol=atol, rtol=rtol) else: # profiling run script_outputs = scripted_fn(*recording_inputs) if inputs_requires_grad or extra_profile_runs: opt_script_outputs = scripted_fn(*recording_inputs) # optimized run opt_script_outputs = scripted_fn(*recording_inputs) if TEST_BAILOUTS: self.checkBailouts(scripted_fn, inputs, opt_script_outputs) python_outputs = python_fn(*inputs) self.assertEqual(python_outputs, script_outputs, atol=atol, rtol=rtol) self.assertEqual(script_outputs, opt_script_outputs, atol=atol, rtol=rtol) return scripted_fn def checkTrace(self, func, reference_tensors, input_tensors=None, drop=None, allow_unused=False, verbose=False, inputs_require_grads=True, check_tolerance=1e-5, export_import=True, _force_outplace=False): # TODO: check gradients for parameters, not just inputs def allSum(vs): # drop allows us to remove some values from ever being used # to test unused outputs if drop is not None: vs = vs[:-drop] # we don't want all the grad for all the outputs to be the same # so we multiply each by a constant return sum(math.log(i + 2) * v.sum() for i, v in enumerate(vs) if v is not None) if input_tensors is None: input_tensors = reference_tensors def flatten_inputs(inputs): def input_reduce(input, fn, acc): if isinstance(input, torch.Tensor): fn(input, acc) elif isinstance(input, dict): reduce(lambda acc, key: input_reduce(input[key], fn, acc), input, acc) else: reduce(lambda acc, val: input_reduce(val, fn, acc), input, acc) return acc return tuple(input_reduce(recording_inputs, lambda t, acc: acc.append(t), [])) nograd_inputs = reference_tensors if inputs_require_grads: recording_inputs = do_input_map(lambda t: t.clone().requires_grad_(), reference_tensors) flattened_recording_inputs = flatten_inputs(recording_inputs) else: recording_inputs = reference_tensors # `check_trace` is set to False because check_trace is run with @no_grad # Also, `checkTrace` already does all the checks # against python function ge = torch.jit.trace(func, input_tensors, check_tolerance=check_tolerance, _force_outplace=_force_outplace, check_trace=False) if export_import: ge = self.getExportImportCopy(ge) if verbose: print(ge.graph) # test no gradients case outputs = func(*nograd_inputs) outputs_ge = ge(*nograd_inputs) self.assertEqual(outputs, outputs_ge) # test gradients case outputs = func(*recording_inputs) if inputs_require_grads: grads = torch.autograd.grad(allSum(outputs), flattened_recording_inputs, allow_unused=allow_unused) outputs_ge = ge(*recording_inputs) if inputs_require_grads: grads_ge = torch.autograd.grad(allSum(outputs_ge), flattened_recording_inputs, allow_unused=allow_unused) self.assertEqual(outputs, outputs_ge) if inputs_require_grads: self.assertEqual(grads, grads_ge) self.assertEqual(outputs, outputs_ge) if inputs_require_grads: self.assertEqual(grads, grads_ge) # test the grad grad case outputs = func(*recording_inputs) l1 = allSum(outputs) if inputs_require_grads: grads = torch.autograd.grad(l1, flattened_recording_inputs, create_graph=True, allow_unused=allow_unused) if inputs_require_grads: l2 = (allSum(grads) * l1) grads2 = torch.autograd.grad(l2, flattened_recording_inputs, allow_unused=allow_unused) if inputs_require_grads: recording_inputs = do_input_map(lambda t: Variable(t, requires_grad=True), reference_tensors) flattened_recording_inputs = flatten_inputs(recording_inputs) outputs_ge = ge(*recording_inputs) l1_ge = allSum(outputs_ge) if inputs_require_grads: grads_ge = torch.autograd.grad( l1_ge, flattened_recording_inputs, create_graph=True, allow_unused=allow_unused) if inputs_require_grads: l2_ge = (allSum(grads_ge) * l1_ge) grads2_ge = torch.autograd.grad(l2_ge, flattened_recording_inputs, allow_unused=allow_unused) self.assertEqual(outputs, outputs_ge) if inputs_require_grads: self.assertEqual(grads, grads_ge) for g2, g2_ge in zip(grads2, grads2_ge): if g2 is None and g2_ge is None: continue self.assertEqual(g2, g2_ge, atol=8e-4, rtol=8e-4) return ge def checkModule(self, nn_module, args): """ Check that a nn.Module's results in Script mode match eager and that it can be exported """ sm = torch.jit.script(nn_module) with freeze_rng_state(): eager_out = nn_module(*args) with freeze_rng_state(): script_out = sm(*args) self.assertEqual(eager_out, script_out) self.assertExportImportModule(sm, args) return sm @contextmanager def inline_everything_mode(should_inline): old = torch._C._jit_get_inline_everything_mode() torch._C._jit_set_inline_everything_mode(should_inline) try: yield finally: torch._C._jit_set_inline_everything_mode(old) @contextmanager def set_fusion_group_inlining(inlining): old = torch._C._debug_get_fusion_group_inlining() torch._C._debug_set_fusion_group_inlining(inlining) try: yield finally: torch._C._debug_set_fusion_group_inlining(old) # note: not re-entrant, use unnested only @contextmanager def disable_autodiff_subgraph_inlining(enabled=True): torch._C._debug_set_autodiff_subgraph_inlining(not enabled) try: yield finally: torch._C._debug_set_autodiff_subgraph_inlining(True) def _inline_everything(fn): @functools.wraps(fn) def wrapper(*args, **kwargs): with inline_everything_mode(True): fn(*args, **kwargs) return wrapper # this exists for forward compatibility reasons temporarily. # TODO(suo) remove def _tmp_donotuse_dont_inline_everything(fn): @functools.wraps(fn) def wrapper(*args, **kwargs): with inline_everything_mode(False): fn(*args, **kwargs) return wrapper # make it easy to quicky define/trace a function for these tests def _trace(*args, **kwargs): def wrapper(func): return torch.jit.trace(func, args, **kwargs) return wrapper def enable_cpu_fuser(fn): def wrapper(*args, **kwargs): torch._C._jit_override_can_fuse_on_cpu_legacy(True) torch._C._jit_override_can_fuse_on_cpu(True) torch._C._jit_set_te_must_use_llvm_cpu(False) try: fn(*args, **kwargs) finally: torch._C._jit_override_can_fuse_on_cpu_legacy(False) torch._C._jit_override_can_fuse_on_cpu(False) torch._C._jit_set_te_must_use_llvm_cpu(True) return wrapper def enable_cpu_fuser_if(cond): if cond: return enable_cpu_fuser else: def noop_fuser(fn): def wrapper(*args, **kwargs): return fn(*args, **kwargs) return wrapper return noop_fuser def get_forward(c): return c._get_method('forward') def get_forward_graph(c): return c._get_method('forward').graph def get_module_method(m, module, method): return m._c.getattr(module)._get_method(method) def attrs_with_prefix(module, prefix): return [x for x, _ in module._modules._c.items() if x.startswith(prefix)] def warmup_backward(f, *args): profiling_count = 3 results = [] for i in range(profiling_count): if len(args) > 0: r = torch.autograd.grad(f, *args) results.append(r) else: f.backward(retain_graph=True) return results # TODO: Remove me once https://bugs.python.org/issue42666 is resolved def make_global(*args): for arg in args: setattr(sys.modules[arg.__module__], arg.__name__, arg) # Helper function to eval Python3 code without causing a syntax error for # this file under py2 def _get_py3_code(code, fn_name): with tempfile.TemporaryDirectory() as tmp_dir: script_path = os.path.join(tmp_dir, 'script.py') with open(script_path, 'w') as f: f.write(code) spec = importlib.util.spec_from_file_location(fn_name, script_path) module = importlib.util.module_from_spec(spec) loader = spec.loader assert isinstance(loader, Loader) # Assert type to meet MyPy requriement loader.exec_module(module) fn = getattr(module, fn_name) return fn

38.675342

124

0.609145

794acfb4facf81e0d36a23bcb12e938b97d763c8

3,285

py

Python

nodemcu/usocketio/client.py

adrianalin/bme680_nodemcu_socketio

1d2264b593bf70c5248c3db8fbbaa34d8f15f50e

[ "MIT" ]

1

2021-06-16T21:38:13.000Z

nodemcu/usocketio/client.py

juergs/bme680_nodemcu_socketio

1d2264b593bf70c5248c3db8fbbaa34d8f15f50e

[ "MIT" ]

null

nodemcu/usocketio/client.py

juergs/bme680_nodemcu_socketio

1d2264b593bf70c5248c3db8fbbaa34d8f15f50e

[ "MIT" ]

2

2019-09-08T08:35:58.000Z

2022-03-29T07:22:06.000Z

""" Micropython Socket.IO client. """ import ure as re import ujson as json import usocket as socket from ucollections import namedtuple from .protocol import * from .transport import SocketIO URL_RE = re.compile(r'http://([A-Za-z0-9\-\.]+)(?:\:([0-9]+))?(/.+)?') URI = namedtuple('URI', ('hostname', 'port', 'path')) def urlparse(uri): """Parse http:// URLs""" match = URL_RE.match(uri) if match: return URI(match.group(1), int(match.group(2)), match.group(3)) def _connect_http(hostname, port, path): """Stage 1 do the HTTP connection to get our SID""" try: sock = socket.socket() addr = socket.getaddrinfo(hostname, port) sock.connect(addr[0][4]) def send_header(header, *args): if __debug__: print(str(header), *args) sock.write(header % args + '\r\n') send_header(b'GET %s HTTP/1.1', path) send_header(b'Host: %s:%s', hostname, port) send_header(b'') header = sock.readline()[:-2] assert header == b'HTTP/1.1 200 OK', header length = None while header: header = sock.readline()[:-2] if not header: break header, value = header.split(b': ') header = header.lower() if header == b'content-type': assert value == b'application/octet-stream' elif header == b'content-length': length = int(value) assert length data = sock.read(length) return decode_payload(data) finally: sock.close() def connect(uri): """Connect to a socket IO server.""" uri = urlparse(uri) assert uri path = uri.path or '/' + 'socket.io/?EIO=3' # Start a HTTP connection, which will give us an SID to use to upgrade # the websockets connection packets = _connect_http(uri.hostname, uri.port, path) # The first packet should open the connection, # following packets might be initialisation messages for us packet_type, params = next(packets) assert packet_type == PACKET_OPEN params = json.loads(params) print("Websocket parameters = {}".format(params)) assert 'websocket' in params['upgrades'] sid = params['sid'] path += '&sid={}'.format(sid) if __debug__: print("Connecting to websocket SID {}".format(sid)) # Start a websocket and send a probe on it ws_uri = 'ws://{hostname}:{port}{path}&transport=websocket'.format( hostname=uri.hostname, port=uri.port, path=path) socketio = SocketIO(ws_uri, **params) # handle rest of the packets once we're in the main loop @socketio.on('connection') def on_connect(data): for packet_type, data in packets: socketio._handle_packet(packet_type, data) socketio._send_packet(PACKET_PING, 'probe') # Send a follow-up poll # _connect_http(uri.hostname, uri.port, path + '&transport=polling') # We should receive an answer to our probe packet = socketio._recv() assert packet == (PACKET_PONG, 'probe') # Upgrade the connection socketio._send_packet(PACKET_UPGRADE) packet = socketio._recv() assert packet == (PACKET_NOOP, '') return socketio

26.071429

74

0.606697

794acfbb01d70027e24fcc220ee04f9d32d4c84b

1,638

py

Python

symphony/cli/pyinventory/graphql/users_query.py

marosmars/magma

51177a6ad7e66216184693a7b3d1dc58f901cd0e

[ "BSD-3-Clause" ]

2

2020-11-05T18:58:26.000Z

2021-02-09T06:42:49.000Z

symphony/cli/pyinventory/graphql/users_query.py

marosmars/magma

51177a6ad7e66216184693a7b3d1dc58f901cd0e

[ "BSD-3-Clause" ]

2

2021-03-31T19:41:55.000Z

2021-12-13T20:39:15.000Z

symphony/cli/pyinventory/graphql/users_query.py

marosmars/magma

51177a6ad7e66216184693a7b3d1dc58f901cd0e

[ "BSD-3-Clause" ]

1

2021-04-16T02:19:25.000Z

#!/usr/bin/env python3 # @generated AUTOGENERATED file. Do not Change! from dataclasses import dataclass from datetime import datetime from gql.gql.datetime_utils import DATETIME_FIELD from gql.gql.graphql_client import GraphqlClient from functools import partial from numbers import Number from typing import Any, Callable, List, Mapping, Optional from dataclasses_json import DataClassJsonMixin from gql.gql.enum_utils import enum_field from .user_role_enum import UserRole from .user_status_enum import UserStatus QUERY: List[str] = [""" query UsersQuery { users { edges { node { id authID email status role } } } } """] @dataclass class UsersQuery(DataClassJsonMixin): @dataclass class UsersQueryData(DataClassJsonMixin): @dataclass class UserConnection(DataClassJsonMixin): @dataclass class UserEdge(DataClassJsonMixin): @dataclass class User(DataClassJsonMixin): id: str authID: str email: str status: UserStatus = enum_field(UserStatus) role: UserRole = enum_field(UserRole) node: Optional[User] edges: List[UserEdge] users: Optional[UserConnection] data: UsersQueryData @classmethod # fmt: off def execute(cls, client: GraphqlClient) -> UsersQueryData: # fmt: off variables = {} response_text = client.call(''.join(set(QUERY)), variables=variables) return cls.from_json(response_text).data

24.447761

77

0.637973

794ad26db5e0be67bb9b35936c8558a8df02c219

4,752

py

Python

sandbox/optimal_args_hashbits.py

wltrimbl/khmer

ff95776eabee96420f1ae43d0eff562682cbb17b

[ "CNRI-Python" ]

null

sandbox/optimal_args_hashbits.py

wltrimbl/khmer

ff95776eabee96420f1ae43d0eff562682cbb17b

[ "CNRI-Python" ]

null

sandbox/optimal_args_hashbits.py

wltrimbl/khmer

ff95776eabee96420f1ae43d0eff562682cbb17b

[ "CNRI-Python" ]

null

#! /usr/bin/env python # This file is part of khmer, https://github.com/dib-lab/khmer/, and is # Copyright (C) 2015, Michigan State University. # Copyright (C) 2015, The Regents of the University of California. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # # * Neither the name of the Michigan State University nor the names # of its contributors may be used to endorse or promote products # derived from this software without specific prior written # permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # Contact: khmer-project@idyll.org # pylint: disable=invalid-name,missing-docstring """ Estimate optimal arguments using nodegraph counting. % python sandbox/optimal_args_nodegraph.py <data1> [ <data2> <...> ] Use '-h' for parameter help. """ from __future__ import print_function import sys import math import threading import khmer from khmer.khmer_args import (report_on_config, info, add_threading_args, build_nodegraph_args) from khmer.kfile import check_input_files, check_space from khmer.kfile import check_space from khmer.khmer_args import graphsize_args_report def get_parser(): parser = build_nodegraph_args(descr="Load sequences into the compressible " "graph format plus optional tagset.") add_threading_args(parser) parser.add_argument('input_filenames', metavar='input_sequence_filename', nargs='+', help='input FAST[AQ] sequence filename') return parser def main(): info('optimal_args_nodegraph.py', ['graph', 'SeqAn']) args = get_parser().parse_args() report_on_config(args, graphtype='nodegraph') filenames = args.input_filenames base = filenames[0] for _ in args.input_filenames: check_input_files(_, False) check_space(args.input_filenames, False) print('Counting kmers from sequences in %s' % repr(filenames), file=sys.stderr) htable = khmer.new_nodegraph(args.ksize, args.max_tablesize, args.n_tables) target_method = htable.consume_seqfile_with_reads_parser for _, filename in enumerate(filenames): rparser = khmer.ReadParser(filename) threads = [] print('consuming input', filename, file=sys.stderr) for num in xrange(args.threads): cur_thread = threading.Thread( target=target_method, args=(rparser,)) threads.append(cur_thread) cur_thread.start() for thread in threads: thread.join() unique_kmers = htable.n_unique_kmers() print('Total number of unique k-mers: {0}'.format(unique_kmers), file=sys.stderr) info_optimal = open(base + '.optimal_args', 'w') fp_rate = khmer.calc_expected_collisions(htable) print('fp rate estimated to be %1.3f' % fp_rate, file=sys.stderr) if fp_rate > 0.15: # 0.18 is ACTUAL MAX. Do not change. print("**", file=sys.stderr) print("** ERROR: the graph structure is too small for this data set." "Increase table size/# tables.", file=sys.stderr) print("**", file=sys.stderr) if not False: sys.exit(1) to_print = graphsize_args_report(unique_kmers, fp_rate) print(to_print, file=info_optimal) print('optimal arguments were written to', base + '.optimal_args', file=sys.stderr) if __name__ == '__main__': main() # vim: set filetype=python tabstop=4 softtabstop=4 shiftwidth=4 expandtab: # vim: set textwidth=79:

37.417323

79

0.700547

794ad46945130273d4a03ed2144fbd3d9d8d6cac

355

py

Python

app/src/resources/remove_files.py

gerardovitale/covid-project

b4e28e8ee095070f2a2433f61725fd8c0374365e

[ "MIT" ]

null

app/src/resources/remove_files.py

gerardovitale/covid-project

b4e28e8ee095070f2a2433f61725fd8c0374365e

[ "MIT" ]

null

app/src/resources/remove_files.py

gerardovitale/covid-project

b4e28e8ee095070f2a2433f61725fd8c0374365e

[ "MIT" ]

null

import os from shutil import rmtree from typing import List def remove_files(files: List[str]) -> None: for file in files: if os.path.isfile(file): os.remove(file) print(f'[INFO] {file} has been removed') elif os.path.isdir(file): rmtree(file) print(f'[INFO] {file} has been removed')

25.357143

52

0.591549

794ad46e1921906f5f20516f312868538270e0f5

2,525

py

Python

heppy/Response.py

bladeroot/heppy

b597916ff80890ca057b17cdd156e90bbbd9a87a

[ "BSD-3-Clause" ]

null

heppy/Response.py

bladeroot/heppy

b597916ff80890ca057b17cdd156e90bbbd9a87a

[ "BSD-3-Clause" ]

null

heppy/Response.py

bladeroot/heppy

b597916ff80890ca057b17cdd156e90bbbd9a87a

[ "BSD-3-Clause" ]

null

import xml.etree.ElementTree as ET from Doc import Doc class Response(Doc): def __init__(self, root): self.data = {} self.root = root self.parse(self.root[0]) def find(self, tag, name): return tag.find(name, namespaces=self.nsmap) def findall(self, tag, name): return tag.findall(name, self.nsmap) def find_text(self, parent, name): tag = self.find(parent, name) if tag is not None: return tag.text.strip() def _put_attr(self, data, tag, attr): attr_value = tag.attrib.get(attr) if attr_value: data[attr] = attr_value def put_tag_data(self, dest, root, tag_name, attrs=[]): if '@' in tag_name: tag_name, key = tag_name.split('@') elif ':' in tag_name: key = tag_name.split(':')[1] else: key = tag_name tag = self.find(root, tag_name) if tag is None: return dest[key] = tag.text.strip() for attr in attrs: self._put_attr(dest, tag, attr) def put_extension_block(self, response, command, root_tag, tags_data): data = dict() data['command'] = command module_name = command.split(':')[0] for tag_name, attrs in tags_data.iteritems(): response.put_tag_data(data, root_tag, module_name + ':' + tag_name, attrs) response.put_to_list('extensions', data) def put_to_dict(self, name, values): if name not in self.data: self.data[name] = {} for k, v in values.iteritems(): self.data[name][k] = v def put_to_list(self, name, value=[]): if name not in self.data: self.data[name] = [] if type(value) in [list, tuple]: self.data[name].extend(value) else: self.data[name].append(value) def parse(self, tag): ns = tag.tag.split('}')[0][1:] name = tag.tag.split('}')[1] module = self.get_module(ns) if module is None: return if name in module.opmap: name = module.opmap[name] method = 'parse_' + name if not hasattr(module, method): raise Exception('unknown tag', ns + ':' + name) getattr(module, method)(self, tag) @staticmethod def parsexml(xml): root = ET.fromstring(xml) return Response(root) @staticmethod def build(name, start): type = globals()[name] return type(start)

29.360465

86

0.556436

794ad4a65ab33a25f44fbb339c7380a6d133a15f

461

py

Python

pythonnest/tests/__init__.py

d9pouces/PythonNest

53ad0c53f5c1b411a2af630099869e55a3549d22

[ "CECILL-B" ]

1

2017-05-01T20:00:14.000Z

pythonnest/tests/__init__.py

d9pouces/PythonNest

53ad0c53f5c1b411a2af630099869e55a3549d22

[ "CECILL-B" ]

null

pythonnest/tests/__init__.py

d9pouces/PythonNest

53ad0c53f5c1b411a2af630099869e55a3549d22

[ "CECILL-B" ]

2

2015-07-30T18:14:50.000Z

2019-11-02T10:06:59.000Z

""" Package gathering all unitary tests for pythonnest. Module names must start with `test_` to be taken into account. You should consider to install :mod:`Distribute` to run all tests with:: $ python setup.py test """ import unittest __author__ = 'Matthieu Gallet' # __copyright__ = "Copyright 2013, 19pouces.net" # __credits__ = "flanker" # __maintainer__ = "flanker" # __email__ = "flanker@19pouces.net" if __name__ == '__main__': unittest.main()

25.611111

72

0.73102

794ad50f5cb8e2917881acdf35f43b5e911cec4d

224

py

Python

sw/2.0/ostur_frontend/src/profile-flask.py

alvarop/ostur

6e56a3f53ac3ca09c12586cd35d0b829dd3d6e78

[ "MIT" ]

7

2017-11-30T20:22:02.000Z

2021-01-03T02:22:52.000Z

sw/2.0/ostur_frontend/src/profile-flask.py

alvarop/ostur

6e56a3f53ac3ca09c12586cd35d0b829dd3d6e78

[ "MIT" ]

null

sw/2.0/ostur_frontend/src/profile-flask.py

alvarop/ostur

6e56a3f53ac3ca09c12586cd35d0b829dd3d6e78

[ "MIT" ]

1

2018-02-26T02:53:14.000Z

#!flask/bin/python from werkzeug.contrib.profiler import ProfilerMiddleware from ostur_frontend import app app.config["PROFILE"] = True app.wsgi_app = ProfilerMiddleware(app.wsgi_app, restrictions=[30]) app.run(debug=True)

28

66

0.808036

794ad53a08efe2d55a8b2cb4c1848400a2c212dc

6,126

py

Python

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/program_enrollments/management/commands/tests/test_migrate_saml_uids.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

3

2021-12-15T04:58:18.000Z

2022-02-06T12:15:37.000Z

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/program_enrollments/management/commands/tests/test_migrate_saml_uids.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

null

Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/program_enrollments/management/commands/tests/test_migrate_saml_uids.py

osoco/better-ways-of-thinking-about-software

83e70d23c873509e22362a09a10d3510e10f6992

[ "MIT" ]

1

2019-01-02T14:38:50.000Z

""" Tests for the migrate_saml_uids management command. """ from unittest.mock import mock_open, patch from django.core.management import call_command from django.test import TestCase from social_django.models import UserSocialAuth from common.djangoapps.student.tests.factories import UserFactory from lms.djangoapps.program_enrollments.management.commands import migrate_saml_uids from lms.djangoapps.program_enrollments.management.commands.tests.utils import UserSocialAuthFactory _COMMAND_PATH = 'lms.djangoapps.program_enrollments.management.commands.migrate_saml_uids' class TestMigrateSamlUids(TestCase): """ Test migrate_saml_uids command. """ provider_slug = 'gatech' @classmethod def setUpClass(cls): super().setUpClass() cls.command = migrate_saml_uids.Command() def _format_email_uid_pair(self, email, uid): return f'{{"email":"{email}","student_key":"{uid}"}}' def _format_single_email_uid_pair_json(self, email, uid): return '[{obj}]'.format( obj=self._format_email_uid_pair(email, uid) ) def _call_command(self, data): """ Call management command with `data` as contents of input file. """ with patch( _COMMAND_PATH + '.py3_open', mock_open(read_data=data) ) as _: call_command( self.command, uid_mapping='./foo.json', saml_provider_slug=self.provider_slug ) def _format_slug_urn_pair(self, slug, urn): return f'{slug}:{urn}' def test_single_mapping(self): new_urn = '9001' auth = UserSocialAuthFactory.create(slug=self.provider_slug) email = auth.user.email old_uid = auth.uid self._call_command(self._format_single_email_uid_pair_json(email, new_urn)) auth.refresh_from_db() assert auth.uid == self._format_slug_urn_pair(self.provider_slug, new_urn) assert not auth.uid == old_uid def test_post_save_occurs(self): """ Test the signals downstream of this update are called with appropriate arguments """ auth = UserSocialAuthFactory.create(slug=self.provider_slug) new_urn = '9001' email = auth.user.email with patch('lms.djangoapps.program_enrollments.signals.matriculate_learner') as signal_handler_mock: self._call_command(self._format_single_email_uid_pair_json(email, new_urn)) assert signal_handler_mock.called # first positional arg matches the user whose auth was updated assert signal_handler_mock.call_args[0][0].id == auth.user.id # second positional arg matches the urn we changed assert signal_handler_mock.call_args[0][1] == self._format_slug_urn_pair(self.provider_slug, new_urn) def test_multiple_social_auth_records(self): """ Test we only alter one UserSocialAuth record if a learner has two """ auth1 = UserSocialAuthFactory.create(slug=self.provider_slug) auth2 = UserSocialAuthFactory.create( slug=self.provider_slug, user=auth1.user ) new_urn = '9001' email = auth1.user.email assert email == auth2.user.email self._call_command(self._format_single_email_uid_pair_json(email, new_urn)) auths = UserSocialAuth.objects.filter( user__email=email, uid=self._format_slug_urn_pair(self.provider_slug, new_urn) ) assert auths.count() == 1 @patch(_COMMAND_PATH + '.log') def test_learner_without_social_auth_records(self, mock_log): user = UserFactory() email = user.email new_urn = '9001' mock_info = mock_log.info self._call_command(self._format_single_email_uid_pair_json(email, new_urn)) mock_info.assert_any_call( 'Number of users identified in the mapping file without' ' {slug} UserSocialAuth records: 1'.format( slug=self.provider_slug ) ) @patch(_COMMAND_PATH + '.log') def test_learner_missed_by_mapping_file(self, mock_log): auth = UserSocialAuthFactory() # pylint disable required b/c this lint rule is confused about subfactories email = auth.user.email new_urn = '9001' mock_info = mock_log.info self._call_command(self._format_single_email_uid_pair_json('different' + email, new_urn)) mock_info.assert_any_call( 'Number of users with {slug} UserSocialAuth records ' 'for which there was no mapping in the provided file: 1'.format( slug=self.provider_slug ) ) @patch(_COMMAND_PATH + '.log') def test_several_learners(self, mock_log): auths = [UserSocialAuthFactory() for _ in range(5)] new_urn = '9001' mock_info = mock_log.info self._call_command('[{}]'.format( ','.join( [ self._format_email_uid_pair( auth.user.email, new_urn + str(ind) ) for ind, auth in enumerate(auths) ] ) )) for ind, auth in enumerate(auths): auth.refresh_from_db() assert auth.uid == self._format_slug_urn_pair(self.provider_slug, new_urn + str(ind)) mock_info.assert_any_call('Number of mappings in the mapping file updated: 5') @patch(_COMMAND_PATH + '.log') def test_learner_duplicated_in_mapping(self, mock_log): auth = UserSocialAuthFactory() email = auth.user.email new_urn = '9001' mock_info = mock_log.info self._call_command('[{}]'.format( ','.join([self._format_email_uid_pair(email, new_urn) for _ in range(5)]) )) mock_info.assert_any_call('Number of mappings in the mapping file where the ' 'identified user has already been processed: 4')

35.410405

113

0.635162

794ad6a233ff93f8c755e160c3aaf3ff416cd4e1

39,853

py

Python

examples/benchmark/utils/bert_modeling.py

Ezra-H/autodist

b5ab28d0d867c22742daa3c1d324fe20c1852bd7

[ "Apache-2.0" ]

127

2020-07-16T16:33:10.000Z

2022-03-25T09:58:50.000Z

examples/benchmark/utils/bert_modeling.py

Ezra-H/autodist

b5ab28d0d867c22742daa3c1d324fe20c1852bd7

[ "Apache-2.0" ]

17

2020-07-16T20:03:44.000Z

2021-02-24T19:53:12.000Z

examples/benchmark/utils/bert_modeling.py

Ezra-H/autodist

b5ab28d0d867c22742daa3c1d324fe20c1852bd7

[ "Apache-2.0" ]

26

2020-07-21T01:23:55.000Z

2022-02-24T03:43:08.000Z

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """The main BERT model and related functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import copy import json import math import six import tensorflow as tf from utils import tf_utils class BertConfig(object): """Configuration for `BertModel`.""" def __init__(self, vocab_size, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, initializer_range=0.02, backward_compatible=True): """Constructs BertConfig. Args: vocab_size: Vocabulary size of `inputs_ids` in `BertModel`. hidden_size: Size of the encoder layers and the pooler layer. num_hidden_layers: Number of hidden layers in the Transformer encoder. num_attention_heads: Number of attention heads for each attention layer in the Transformer encoder. intermediate_size: The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act: The non-linear activation function (function or string) in the encoder and pooler. hidden_dropout_prob: The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob: The dropout ratio for the attention probabilities. max_position_embeddings: The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size: The vocabulary size of the `token_type_ids` passed into `BertModel`. initializer_range: The stdev of the truncated_normal_initializer for initializing all weight matrices. backward_compatible: Boolean, whether the variables shape are compatible with checkpoints converted from TF 1.x BERT. """ self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.backward_compatible = backward_compatible @classmethod def from_dict(cls, json_object): """Constructs a `BertConfig` from a Python dictionary of parameters.""" config = BertConfig(vocab_size=None) for (key, value) in six.iteritems(json_object): config.__dict__[key] = value return config @classmethod def from_json_file(cls, json_file): """Constructs a `BertConfig` from a json file of parameters.""" with tf.io.gfile.GFile(json_file, "r") as reader: text = reader.read() return cls.from_dict(json.loads(text)) def to_dict(self): """Serializes this instance to a Python dictionary.""" output = copy.deepcopy(self.__dict__) return output def to_json_string(self): """Serializes this instance to a JSON string.""" return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" def get_bert_model(input_word_ids, input_mask, input_type_ids, config=None, name=None, float_type=tf.float32): """Wraps the core BERT model as a keras.Model.""" bert_model_layer = BertModel( config=config, float_type=float_type, name=name) pooled_output, sequence_output = bert_model_layer( input_word_ids, input_mask, input_type_ids) bert_model = tf.keras.Model( inputs=[input_word_ids, input_mask, input_type_ids], outputs=[pooled_output, sequence_output]) return bert_model class BertModel(tf.keras.layers.Layer): """BERT model ("Bidirectional Encoder Representations from Transformers"). Example usage: ```python # Already been converted into WordPiece token ids input_word_ids = tf.constant([[31, 51, 99], [15, 5, 0]]) input_mask = tf.constant([[1, 1, 1], [1, 1, 0]]) input_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]]) config = modeling.BertConfig(vocab_size=32000, hidden_size=512, num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024) pooled_output, sequence_output = modeling.BertModel(config=config)( input_word_ids=input_word_ids, input_mask=input_mask, input_type_ids=input_type_ids) ... ``` """ def __init__(self, config, float_type=tf.float32, **kwargs): super(BertModel, self).__init__(**kwargs) self.config = ( BertConfig.from_dict(config) if isinstance(config, dict) else copy.deepcopy(config)) self.float_type = float_type def build(self, unused_input_shapes): """Implements build() for the layer.""" self.embedding_lookup = EmbeddingLookup( vocab_size=self.config.vocab_size, embedding_size=self.config.hidden_size, initializer_range=self.config.initializer_range, dtype=tf.float32, name="word_embeddings") self.embedding_postprocessor = EmbeddingPostprocessor( use_type_embeddings=True, token_type_vocab_size=self.config.type_vocab_size, use_position_embeddings=True, max_position_embeddings=self.config.max_position_embeddings, dropout_prob=self.config.hidden_dropout_prob, initializer_range=self.config.initializer_range, dtype=tf.float32, name="embedding_postprocessor") self.encoder = Transformer( num_hidden_layers=self.config.num_hidden_layers, hidden_size=self.config.hidden_size, num_attention_heads=self.config.num_attention_heads, intermediate_size=self.config.intermediate_size, intermediate_activation=self.config.hidden_act, hidden_dropout_prob=self.config.hidden_dropout_prob, attention_probs_dropout_prob=self.config.attention_probs_dropout_prob, initializer_range=self.config.initializer_range, backward_compatible=self.config.backward_compatible, float_type=self.float_type, name="encoder") self.pooler_transform = tf.keras.layers.Dense( units=self.config.hidden_size, activation="tanh", kernel_initializer=get_initializer(self.config.initializer_range), name="pooler_transform") super(BertModel, self).build(unused_input_shapes) def __call__(self, input_word_ids, input_mask=None, input_type_ids=None, **kwargs): inputs = tf_utils.pack_inputs( [input_word_ids, input_mask, input_type_ids]) return super(BertModel, self).__call__(inputs, **kwargs) def call(self, inputs, mode="bert"): """Implements call() for the layer. Args: inputs: packed input tensors. mode: string, `bert` or `encoder`. Returns: Output tensor of the last layer for BERT training (mode=`bert`) which is a float Tensor of shape [batch_size, seq_length, hidden_size] or a list of output tensors for encoder usage (mode=`encoder`). """ unpacked_inputs = tf_utils.unpack_inputs(inputs) input_word_ids = unpacked_inputs[0] input_mask = unpacked_inputs[1] input_type_ids = unpacked_inputs[2] word_embeddings = self.embedding_lookup(input_word_ids) embedding_tensor = self.embedding_postprocessor( word_embeddings=word_embeddings, token_type_ids=input_type_ids) if self.float_type == tf.float16: embedding_tensor = tf.cast(embedding_tensor, tf.float16) attention_mask = None if input_mask is not None: attention_mask = create_attention_mask_from_input_mask( input_word_ids, input_mask) if mode == "encoder": return self.encoder( embedding_tensor, attention_mask, return_all_layers=True) sequence_output = self.encoder(embedding_tensor, attention_mask) first_token_tensor = tf.squeeze(sequence_output[:, 0:1, :], axis=1) pooled_output = self.pooler_transform(first_token_tensor) return (pooled_output, sequence_output) def get_config(self): config = {"config": self.config.to_dict()} base_config = super(BertModel, self).get_config() return dict(list(base_config.items()) + list(config.items())) class EmbeddingLookup(tf.keras.layers.Layer): """Looks up words embeddings for id tensor.""" def __init__(self, vocab_size, embedding_size=768, initializer_range=0.02, **kwargs): super(EmbeddingLookup, self).__init__(**kwargs) self.vocab_size = vocab_size self.embedding_size = embedding_size self.initializer_range = initializer_range def build(self, unused_input_shapes): """Implements build() for the layer.""" self.embeddings = self.add_weight( "embeddings", shape=[self.vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range), dtype=self.dtype) super(EmbeddingLookup, self).build(unused_input_shapes) def call(self, inputs): """Implements call() for the layer.""" input_shape = tf_utils.get_shape_list(inputs) flat_input = tf.reshape(inputs, [-1]) output = tf.gather(self.embeddings, flat_input) output = tf.reshape(output, input_shape + [self.embedding_size]) return output class EmbeddingPostprocessor(tf.keras.layers.Layer): """Performs various post-processing on a word embedding tensor.""" def __init__(self, use_type_embeddings=False, token_type_vocab_size=None, use_position_embeddings=True, max_position_embeddings=512, dropout_prob=0.0, initializer_range=0.02, initializer=None, **kwargs): super(EmbeddingPostprocessor, self).__init__(**kwargs) self.use_type_embeddings = use_type_embeddings self.token_type_vocab_size = token_type_vocab_size self.use_position_embeddings = use_position_embeddings self.max_position_embeddings = max_position_embeddings self.dropout_prob = dropout_prob self.initializer_range = initializer_range if not initializer: self.initializer = get_initializer(self.initializer_range) else: self.initializer = initializer if self.use_type_embeddings and not self.token_type_vocab_size: raise ValueError("If `use_type_embeddings` is True, then " "`token_type_vocab_size` must be specified.") def build(self, input_shapes): """Implements build() for the layer.""" (word_embeddings_shape, _) = input_shapes width = word_embeddings_shape.as_list()[-1] self.type_embeddings = None if self.use_type_embeddings: self.type_embeddings = self.add_weight( "type_embeddings", shape=[self.token_type_vocab_size, width], initializer=get_initializer(self.initializer_range), dtype=self.dtype) self.position_embeddings = None if self.use_position_embeddings: self.position_embeddings = self.add_weight( "position_embeddings", shape=[self.max_position_embeddings, width], initializer=get_initializer(self.initializer_range), dtype=self.dtype) self.output_layer_norm = tf.keras.layers.LayerNormalization( name="layer_norm", axis=-1, epsilon=1e-12, dtype=tf.float32) self.output_dropout = tf.keras.layers.Dropout(rate=self.dropout_prob, dtype=tf.float32) super(EmbeddingPostprocessor, self).build(input_shapes) def __call__(self, word_embeddings, token_type_ids=None, **kwargs): inputs = tf_utils.pack_inputs([word_embeddings, token_type_ids]) return super(EmbeddingPostprocessor, self).__call__(inputs, **kwargs) def call(self, inputs): """Implements call() for the layer.""" unpacked_inputs = tf_utils.unpack_inputs(inputs) word_embeddings = unpacked_inputs[0] token_type_ids = unpacked_inputs[1] input_shape = tf_utils.get_shape_list(word_embeddings, expected_rank=3) batch_size = input_shape[0] seq_length = input_shape[1] width = input_shape[2] output = word_embeddings if self.use_type_embeddings: flat_token_type_ids = tf.reshape(token_type_ids, [-1]) token_type_embeddings = tf.gather(self.type_embeddings, flat_token_type_ids) token_type_embeddings = tf.reshape(token_type_embeddings, [batch_size, seq_length, width]) output += token_type_embeddings if self.use_position_embeddings: position_embeddings = tf.expand_dims( tf.slice( self.position_embeddings, [ 0, 0], [ seq_length, width]), axis=0) output += position_embeddings output = self.output_layer_norm(output) output = self.output_dropout(output) return output class Attention(tf.keras.layers.Layer): """Performs multi-headed attention from `from_tensor` to `to_tensor`. This is an implementation of multi-headed attention based on "Attention is all you Need". If `from_tensor` and `to_tensor` are the same, then this is self-attention. Each timestep in `from_tensor` attends to the corresponding sequence in `to_tensor`, and returns a fixed-with vector. This function first projects `from_tensor` into a "query" tensor and `to_tensor` into "key" and "value" tensors. These are (effectively) a list of tensors of length `num_attention_heads`, where each tensor is of shape [batch_size, seq_length, size_per_head]. Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor and returned. In practice, the multi-headed attention are done with tf.einsum as follows: Input_tensor: [BFD] Wq, Wk, Wv: [DNH] Q:[BFNH] = einsum('BFD,DNH->BFNH', Input_tensor, Wq) K:[BTNH] = einsum('BTD,DNH->BTNH', Input_tensor, Wk) V:[BTNH] = einsum('BTD,DNH->BTNH', Input_tensor, Wv) attention_scores:[BNFT] = einsum('BTNH,BFNH->BNFT', K, Q) / sqrt(H) attention_probs:[BNFT] = softmax(attention_scores) context_layer:[BFNH] = einsum('BNFT,BTNH->BFNH', attention_probs, V) Wout:[DNH] Output:[BFD] = einsum('BFNH,DNH>BFD', context_layer, Wout) """ def __init__(self, num_attention_heads=12, size_per_head=64, attention_probs_dropout_prob=0.0, initializer_range=0.02, backward_compatible=False, **kwargs): super(Attention, self).__init__(**kwargs) self.num_attention_heads = num_attention_heads self.size_per_head = size_per_head self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.backward_compatible = backward_compatible def build(self, unused_input_shapes): """Implements build() for the layer.""" self.query_dense = self._projection_dense_layer("query") self.key_dense = self._projection_dense_layer("key") self.value_dense = self._projection_dense_layer("value") self.attention_probs_dropout = tf.keras.layers.Dropout( rate=self.attention_probs_dropout_prob) super(Attention, self).build(unused_input_shapes) def reshape_to_matrix(self, input_tensor): """Reshape N > 2 rank tensor to rank 2 tensor for performance.""" ndims = input_tensor.shape.ndims if ndims < 2: raise ValueError("Input tensor must have at least rank 2." "Shape = %s" % (input_tensor.shape)) if ndims == 2: return input_tensor width = input_tensor.shape[-1] output_tensor = tf.reshape(input_tensor, [-1, width]) return output_tensor def __call__(self, from_tensor, to_tensor, attention_mask=None, **kwargs): inputs = tf_utils.pack_inputs([from_tensor, to_tensor, attention_mask]) return super(Attention, self).__call__(inputs, **kwargs) def call(self, inputs): """Implements call() for the layer.""" (from_tensor, to_tensor, attention_mask) = tf_utils.unpack_inputs(inputs) # Scalar dimensions referenced here: # B = batch size (number of sequences) # F = `from_tensor` sequence length # T = `to_tensor` sequence length # N = `num_attention_heads` # H = `size_per_head` # `query_tensor` = [B, F, N ,H] query_tensor = self.query_dense(from_tensor) # `key_tensor` = [B, T, N, H] key_tensor = self.key_dense(to_tensor) # `value_tensor` = [B, T, N, H] value_tensor = self.value_dense(to_tensor) # Take the dot product between "query" and "key" to get the raw # attention scores. attention_scores = tf.einsum( "BTNH,BFNH->BNFT", key_tensor, query_tensor) attention_scores = tf.multiply( attention_scores, 1.0 / math.sqrt( float( self.size_per_head))) if attention_mask is not None: # `attention_mask` = [B, 1, F, T] attention_mask = tf.expand_dims(attention_mask, axis=[1]) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. adder = (1.0 - tf.cast(attention_mask, attention_scores.dtype)) * -10000.0 # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. attention_scores += adder # Normalize the attention scores to probabilities. # `attention_probs` = [B, N, F, T] attention_probs = tf.nn.softmax(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.attention_probs_dropout(attention_probs) # `context_layer` = [B, F, N, H] context_tensor = tf.einsum( "BNFT,BTNH->BFNH", attention_probs, value_tensor) return context_tensor def _projection_dense_layer(self, name): """A helper to define a projection layer.""" return Dense3D( num_attention_heads=self.num_attention_heads, size_per_head=self.size_per_head, kernel_initializer=get_initializer(self.initializer_range), output_projection=False, backward_compatible=self.backward_compatible, name=name) class Dense3D(tf.keras.layers.Layer): """A Dense Layer using 3D kernel with tf.einsum implementation. Attributes: num_attention_heads: An integer, number of attention heads for each multihead attention layer. size_per_head: An integer, hidden size per attention head. hidden_size: An integer, dimension of the hidden layer. kernel_initializer: An initializer for the kernel weight. bias_initializer: An initializer for the bias. activation: An activation function to use. If nothing is specified, no activation is applied. use_bias: A bool, whether the layer uses a bias. output_projection: A bool, whether the Dense3D layer is used for output linear projection. backward_compatible: A bool, whether the variables shape are compatible with checkpoints converted from TF 1.x. """ def __init__(self, num_attention_heads=12, size_per_head=72, kernel_initializer=None, bias_initializer="zeros", activation=None, use_bias=True, output_projection=False, backward_compatible=False, **kwargs): """Inits Dense3D.""" super(Dense3D, self).__init__(**kwargs) self.num_attention_heads = num_attention_heads self.size_per_head = size_per_head self.hidden_size = num_attention_heads * size_per_head self.kernel_initializer = kernel_initializer self.bias_initializer = bias_initializer self.activation = activation self.use_bias = use_bias self.output_projection = output_projection self.backward_compatible = backward_compatible @property def compatible_kernel_shape(self): if self.output_projection: return [self.hidden_size, self.hidden_size] return [self.last_dim, self.hidden_size] @property def compatible_bias_shape(self): return [self.hidden_size] @property def kernel_shape(self): if self.output_projection: return [ self.num_attention_heads, self.size_per_head, self.hidden_size] return [self.last_dim, self.num_attention_heads, self.size_per_head] @property def bias_shape(self): if self.output_projection: return [self.hidden_size] return [self.num_attention_heads, self.size_per_head] def build(self, input_shape): """Implements build() for the layer.""" dtype = tf.as_dtype(self.dtype or tf.keras.backend.floatx()) if not (dtype.is_floating or dtype.is_complex): raise TypeError( "Unable to build `Dense3D` layer with non-floating " "point (and non-complex) dtype %s" % (dtype,)) input_shape = tf.TensorShape(input_shape) if tf.compat.dimension_value(input_shape[-1]) is None: raise ValueError("The last dimension of the inputs to `Dense3D` " "should be defined. Found `None`.") self.last_dim = tf.compat.dimension_value(input_shape[-1]) self.input_spec = tf.keras.layers.InputSpec( min_ndim=3, axes={-1: self.last_dim}) # Determines variable shapes. if self.backward_compatible: kernel_shape = self.compatible_kernel_shape bias_shape = self.compatible_bias_shape else: kernel_shape = self.kernel_shape bias_shape = self.bias_shape self.kernel = self.add_weight( "kernel", shape=kernel_shape, initializer=self.kernel_initializer, dtype=self.dtype, trainable=True) if self.use_bias: self.bias = self.add_weight( "bias", shape=bias_shape, initializer=self.bias_initializer, dtype=self.dtype, trainable=True) else: self.bias = None super(Dense3D, self).build(input_shape) def call(self, inputs): """Implements ``call()`` for Dense3D. Args: inputs: A float tensor of shape [batch_size, sequence_length, hidden_size] when output_projection is False, otherwise a float tensor of shape [batch_size, sequence_length, num_heads, dim_per_head]. Returns: The projected tensor with shape [batch_size, sequence_length, num_heads, dim_per_head] when output_projection is False, otherwise [batch_size, sequence_length, hidden_size]. """ if self.backward_compatible: kernel = tf.keras.backend.reshape(self.kernel, self.kernel_shape) bias = (tf.keras.backend.reshape(self.bias, self.bias_shape) if self.use_bias else None) else: kernel = self.kernel bias = self.bias if self.output_projection: ret = tf.einsum("abcd,cde->abe", inputs, kernel) else: ret = tf.einsum("abc,cde->abde", inputs, kernel) if self.use_bias: ret += bias if self.activation is not None: return self.activation(ret) return ret class Dense2DProjection(tf.keras.layers.Layer): """A 2D projection layer with tf.einsum implementation.""" def __init__(self, output_size, kernel_initializer=None, bias_initializer="zeros", activation=None, fp32_activation=False, **kwargs): super(Dense2DProjection, self).__init__(**kwargs) self.output_size = output_size self.kernel_initializer = kernel_initializer self.bias_initializer = bias_initializer self.activation = activation self.fp32_activation = fp32_activation def build(self, input_shape): """Implements build() for the layer.""" dtype = tf.as_dtype(self.dtype or tf.keras.backend.floatx()) if not (dtype.is_floating or dtype.is_complex): raise TypeError("Unable to build `Dense2DProjection` layer with " "non-floating point (and non-complex) " "dtype %s" % (dtype,)) input_shape = tf.TensorShape(input_shape) if tf.compat.dimension_value(input_shape[-1]) is None: raise ValueError("The last dimension of the inputs to " "`Dense2DProjection` should be defined. " "Found `None`.") last_dim = tf.compat.dimension_value(input_shape[-1]) self.input_spec = tf.keras.layers.InputSpec( min_ndim=3, axes={-1: last_dim}) self.kernel = self.add_weight( "kernel", shape=[last_dim, self.output_size], initializer=self.kernel_initializer, dtype=self.dtype, trainable=True) self.bias = self.add_weight( "bias", shape=[self.output_size], initializer=self.bias_initializer, dtype=self.dtype, trainable=True) super(Dense2DProjection, self).build(input_shape) def call(self, inputs): """Implements call() for Dense2DProjection. Args: inputs: float Tensor of shape [batch, from_seq_length, num_attention_heads, size_per_head]. Returns: A 3D Tensor. """ ret = tf.einsum("abc,cd->abd", inputs, self.kernel) ret += self.bias if self.activation is not None: if self.dtype == tf.float16 and self.fp32_activation: ret = tf.cast(ret, tf.float32) return self.activation(ret) return ret class TransformerBlock(tf.keras.layers.Layer): """Single transformer layer. It has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a positionwise fully connected feed-forward network. """ def __init__(self, hidden_size=768, num_attention_heads=12, intermediate_size=3072, intermediate_activation="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, backward_compatible=False, float_type=tf.float32, **kwargs): super(TransformerBlock, self).__init__(**kwargs) self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.intermediate_activation = tf_utils.get_activation( intermediate_activation) self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.backward_compatible = backward_compatible self.float_type = float_type if self.hidden_size % self.num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (self.hidden_size, self.num_attention_heads)) self.attention_head_size = int( self.hidden_size / self.num_attention_heads) def build(self, unused_input_shapes): """Implements build() for the layer.""" self.attention_layer = Attention( num_attention_heads=self.num_attention_heads, size_per_head=self.attention_head_size, attention_probs_dropout_prob=self.attention_probs_dropout_prob, initializer_range=self.initializer_range, backward_compatible=self.backward_compatible, name="self_attention") self.attention_output_dense = Dense3D( num_attention_heads=self.num_attention_heads, size_per_head=int(self.hidden_size / self.num_attention_heads), kernel_initializer=get_initializer(self.initializer_range), output_projection=True, backward_compatible=self.backward_compatible, name="self_attention_output") self.attention_dropout = tf.keras.layers.Dropout( rate=self.hidden_dropout_prob) self.attention_layer_norm = ( tf.keras.layers.LayerNormalization( name="self_attention_layer_norm", axis=-1, epsilon=1e-12, # We do layer norm in float32 for numeric stability. dtype=tf.float32)) self.intermediate_dense = Dense2DProjection( output_size=self.intermediate_size, kernel_initializer=get_initializer(self.initializer_range), activation=self.intermediate_activation, # Uses float32 so that gelu activation is done in float32. fp32_activation=True, name="intermediate") self.output_dense = Dense2DProjection( output_size=self.hidden_size, kernel_initializer=get_initializer(self.initializer_range), name="output") self.output_dropout = tf.keras.layers.Dropout( rate=self.hidden_dropout_prob) self.output_layer_norm = tf.keras.layers.LayerNormalization( name="output_layer_norm", axis=-1, epsilon=1e-12, dtype=tf.float32) super(TransformerBlock, self).build(unused_input_shapes) def common_layers(self): """Explicitly gets all layer objects inside a Transformer encoder block.""" return [ self.attention_layer, self.attention_output_dense, self.attention_dropout, self.attention_layer_norm, self.intermediate_dense, self.output_dense, self.output_dropout, self.output_layer_norm ] def __call__(self, input_tensor, attention_mask=None, **kwargs): inputs = tf_utils.pack_inputs([input_tensor, attention_mask]) return super(TransformerBlock, self).__call__(inputs, **kwargs) def call(self, inputs): """Implements call() for the layer.""" (input_tensor, attention_mask) = tf_utils.unpack_inputs(inputs) attention_output = self.attention_layer( from_tensor=input_tensor, to_tensor=input_tensor, attention_mask=attention_mask) attention_output = self.attention_output_dense(attention_output) attention_output = self.attention_dropout(attention_output) # Use float32 in keras layer norm and the gelu activation in the # intermediate dense layer for numeric stability attention_output = self.attention_layer_norm(input_tensor + attention_output) if self.float_type == tf.float16: attention_output = tf.cast(attention_output, tf.float16) intermediate_output = self.intermediate_dense(attention_output) if self.float_type == tf.float16: intermediate_output = tf.cast(intermediate_output, tf.float16) layer_output = self.output_dense(intermediate_output) layer_output = self.output_dropout(layer_output) # Use float32 in keras layer norm for numeric stability layer_output = self.output_layer_norm(layer_output + attention_output) if self.float_type == tf.float16: layer_output = tf.cast(layer_output, tf.float16) return layer_output class Transformer(tf.keras.layers.Layer): """Multi-headed, multi-layer Transformer from "Attention is All You Need". This is almost an exact implementation of the original Transformer encoder. See the original paper: https://arxiv.org/abs/1706.03762 Also see: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py """ def __init__(self, num_hidden_layers=12, hidden_size=768, num_attention_heads=12, intermediate_size=3072, intermediate_activation="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, backward_compatible=False, float_type=tf.float32, **kwargs): super(Transformer, self).__init__(**kwargs) self.num_hidden_layers = num_hidden_layers self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.intermediate_activation = tf_utils.get_activation( intermediate_activation) self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.backward_compatible = backward_compatible self.float_type = float_type def build(self, unused_input_shapes): """Implements build() for the layer.""" self.layers = [] for i in range(self.num_hidden_layers): self.layers.append( TransformerBlock( hidden_size=self.hidden_size, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, intermediate_activation=self.intermediate_activation, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, initializer_range=self.initializer_range, backward_compatible=self.backward_compatible, float_type=self.float_type, name=( "layer_%d" % i))) super(Transformer, self).build(unused_input_shapes) def __call__(self, input_tensor, attention_mask=None, **kwargs): inputs = tf_utils.pack_inputs([input_tensor, attention_mask]) return super(Transformer, self).__call__(inputs=inputs, **kwargs) def call(self, inputs, return_all_layers=False): """Implements call() for the layer. Args: inputs: packed inputs. return_all_layers: bool, whether to return outputs of all layers inside encoders. Returns: Output tensor of the last layer or a list of output tensors. """ unpacked_inputs = tf_utils.unpack_inputs(inputs) input_tensor = unpacked_inputs[0] attention_mask = unpacked_inputs[1] output_tensor = input_tensor all_layer_outputs = [] for layer in self.layers: output_tensor = layer(output_tensor, attention_mask) all_layer_outputs.append(output_tensor) if return_all_layers: return all_layer_outputs return all_layer_outputs[-1] def get_initializer(initializer_range=0.02): """Creates a `tf.initializers.truncated_normal` with the given range. Args: initializer_range: float, initializer range for stddev. Returns: TruncatedNormal initializer with stddev = `initializer_range`. """ return tf.keras.initializers.TruncatedNormal(stddev=initializer_range) def create_attention_mask_from_input_mask(from_tensor, to_mask): """Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. """ from_shape = tf_utils.get_shape_list(from_tensor, expected_rank=[2, 3]) batch_size = from_shape[0] from_seq_length = from_shape[1] to_shape = tf_utils.get_shape_list(to_mask, expected_rank=2) to_seq_length = to_shape[1] to_mask = tf.cast( tf.reshape(to_mask, [batch_size, 1, to_seq_length]), dtype=from_tensor.dtype) # We don't assume that `from_tensor` is a mask (although it could be). We # don't actually care if we attend *from* padding tokens (only *to* padding) # tokens so we create a tensor of all ones. # # `broadcast_ones` = [batch_size, from_seq_length, 1] broadcast_ones = tf.ones( shape=[batch_size, from_seq_length, 1], dtype=from_tensor.dtype) # Here we broadcast along two dimensions to create the mask. mask = broadcast_ones * to_mask return mask

41.341286

95

0.63935

794ad7811fc6d0e26a30cf119ba413cf8840674c

23,057

py

Python

python_scripts/02_basic_preprocessing.py

lucyleeow/euroscipy-2019-scikit-learn-tutorial

81ec6483c5529af4655bf64ba0513f3f28cf565e

[ "CC0-1.0" ]

27

2019-07-24T15:14:23.000Z

2021-12-02T10:13:47.000Z

python_scripts/02_basic_preprocessing.py

lucyleeow/euroscipy-2019-scikit-learn-tutorial

81ec6483c5529af4655bf64ba0513f3f28cf565e

[ "CC0-1.0" ]

6

2019-08-07T13:07:10.000Z

2019-11-27T14:57:57.000Z

python_scripts/02_basic_preprocessing.py

lucyleeow/euroscipy-2019-scikit-learn-tutorial

81ec6483c5529af4655bf64ba0513f3f28cf565e

[ "CC0-1.0" ]

16

2019-07-24T09:13:08.000Z

2021-11-04T19:24:42.000Z

# -*- coding: utf-8 -*- # --- # jupyter: # jupytext: # formats: notebooks//ipynb,python_scripts//py:percent # text_representation: # extension: .py # format_name: percent # format_version: '1.2' # jupytext_version: 1.2.2 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %% [markdown] # # Introduction to scikit-learn # # ## Basic preprocessing and model fitting # # In this notebook, we present how to build predictive models on tabular # datasets. # # In particular we will highlight: # * the difference between numerical and categorical variables; # * the importance of scaling numerical variables; # * typical ways to deal categorical variables; # * train predictive models on different kinds of data; # * evaluate the performance of a model via cross-validation. # # ## Introducing the dataset # # To this aim, we will use data from the 1994 Census bureau database. The goal # with this data is to regress wages from heterogeneous data such as age, # employment, education, family information, etc. # # Let's first load the data located in the `datasets` folder. # %% import pandas as pd df = pd.read_csv("https://www.openml.org/data/get_csv/1595261/adult-census.csv") # Or use the local copy: # df = pd.read_csv('../datasets/adult-census.csv') # %% [markdown] # Let's have a look at the first records of this data frame: # %% df.head() # %% [markdown] # The target variable in our study will be the "class" column while we will use # the other columns as input variables for our model. This target column divides # the samples (also known as records) into two groups: high income (>50K) vs low # income (<=50K). The resulting prediction problem is therefore a binary # classification problem. # # For simplicity, we will ignore the "fnlwgt" (final weight) column that was # crafted by the creators of the dataset when sampling the dataset to be # representative of the full census database. # %% target_name = "class" target = df[target_name].to_numpy() target # %% data = df.drop(columns=[target_name, "fnlwgt"]) data.head() # %% [markdown] # We can check the number of samples and the number of features available in # the dataset: # %% print( f"The dataset contains {data.shape[0]} samples and {data.shape[1]} " "features" ) # %% [markdown] # ## Working with numerical data # # The numerical data is the most natural type of data used in machine learning # and can (almost) directly be fed to predictive models. We can quickly have a # look at such data by selecting the subset of columns from the original data. # # We will use this subset of data to fit a linear classification model to # predict the income class. # %% data.columns # %% data.dtypes # %% numerical_columns = [c for c in data.columns if data[c].dtype.kind in ["i", "f"]] numerical_columns # %% data_numeric = data[numerical_columns] data_numeric.head() # %% [markdown] # When building a machine learning model, it is important to leave out a # subset of the data which we can use later to evaluate the trained model. # The data used to fit a model a called training data while the one used to # assess a model are called testing data. # # Scikit-learn provides an helper function `train_test_split` which will # split the dataset into a training and a testing set. It will ensure that # the data are shuffled randomly before splitting the data. # %% from sklearn.model_selection import train_test_split data_train, data_test, target_train, target_test = train_test_split( data_numeric, target, random_state=42 ) print( f"The training dataset contains {data_train.shape[0]} samples and " f"{data_train.shape[1]} features" ) print( f"The testing dataset contains {data_test.shape[0]} samples and " f"{data_test.shape[1]} features" ) # %% [markdown] # We will build a linear classification model called "Logistic Regression". The # `fit` method is called to train the model from the input and target data. Only # the training data should be given for this purpose. # # In addition, when checking the time required to train the model and internally # check the number of iterations done by the solver to find a solution. # %% from sklearn.linear_model import LogisticRegression import time model = LogisticRegression(solver='lbfgs') start = time.time() model.fit(data_train, target_train) elapsed_time = time.time() - start print( f"The model {model.__class__.__name__} was trained in " f"{elapsed_time:.3f} seconds for {model.n_iter_} iterations" ) # %% [markdown] # Let's ignore the convergence warning for now and instead let's try # to use our model to make some predictions on the first three records # of the held out test set: # %% target_predicted = model.predict(data_test) target_predicted[:5] # %% target_test[:5] # %% predictions = data_test.copy() predictions['predicted-class'] = target_predicted predictions['expected-class'] = target_test predictions['correct'] = target_predicted == target_test predictions.head() # %% [markdown] # To quantitatively evaluate our model, we can use the method `score`. It will # compute the classification accuracy when dealing with a classificiation # problem. # %% print( f"The test accuracy using a {model.__class__.__name__} is " f"{model.score(data_test, target_test):.3f}" ) # %% [markdown] # This is mathematically equivalent as computing the average number of time # the model makes a correct prediction on the test set: # %% (target_test == target_predicted).mean() # %% [markdown] # ## Exercise 1 # # - What would be the score of a model that always predicts `' >50K'`? # - What would be the score of a model that always predicts `' <= 50K'`? # - Is 81% or 82% accuracy a good score for this problem? # # Hint: You can compute the cross-validated of a [DummyClassifier](https://scikit-learn.org/stable/modules/model_evaluation.html#dummy-estimators) the performance of such baselines. # # Use the dedicated notebook to do this exercise. # %% [markdown] # Let's now consider the `ConvergenceWarning` message that was raised previously # when calling the `fit` method to train our model. This warning informs us that # our model stopped learning becaused it reached the maximum number of # iterations allowed by the user. This could potentially be detrimental for the # model accuracy. We can follow the (bad) advice given in the warning message # and increase the maximum number of iterations allowed. # %% model = LogisticRegression(solver='lbfgs', max_iter=50000) start = time.time() model.fit(data_train, target_train) elapsed_time = time.time() - start # %% print( f"The accuracy using a {model.__class__.__name__} is " f"{model.score(data_test, target_test):.3f} with a fitting time of " f"{elapsed_time:.3f} seconds in {model.n_iter_} iterations" ) # %% [markdown] # We can observe now a longer training time but not significant improvement in # the predictive performance. Instead of increasing the number of iterations, we # can try to help fit the model faster by scaling the data first. A range of # preprocessing algorithms in scikit-learn allows to transform the input data # before training a model. We can easily combine these sequential operation with # a scikit-learn `Pipeline` which will chain the operations and can be used as # any other classifier or regressor. The helper function `make_pipeline` will # create a `Pipeline` by giving the successive transformations to perform. # # In our case, we will standardize the data and then train a new logistic # regression model on that new version of the dataset set. # %% data_train.describe() # %% from sklearn.preprocessing import StandardScaler scaler = StandardScaler() data_train_scaled = scaler.fit_transform(data_train) data_train_scaled # %% data_train_scaled = pd.DataFrame(data_train_scaled, columns=data_train.columns) data_train_scaled.describe() # %% from sklearn.pipeline import make_pipeline model = make_pipeline(StandardScaler(), LogisticRegression(solver='lbfgs')) start = time.time() model.fit(data_train, target_train) elapsed_time = time.time() - start # %% print( f"The accuracy using a {model.__class__.__name__} is " f"{model.score(data_test, target_test):.3f} with a fitting time of " f"{elapsed_time:.3f} seconds in {model[-1].n_iter_} iterations" ) # %% [markdown] # We can see that the training time and the number of iterations is much shorter # while the predictive performance (accuracy) stays the same. # # In the previous example, we split the original data into a training set and a # testing set. This strategy has several issues: in the setting where the amount # of data is limited, the subset of data used to train or test will be small; # and the splitting was done in a random manner and we have no information # regarding the confidence of the results obtained. # # Instead, we can use what cross-validation. Cross-validation consists in # repeating this random splitting into training and testing sets and aggregate # the model performance. By repeating the experiment, one can get an estimate of # the variabilty of the model performance. # # The function `cross_val_score` allows for such experimental protocol by giving # the model, the data and the target. Since there exists several # cross-validation strategies, `cross_val_score` takes a parameter `cv` which # defines the splitting strategy. # # # # # # # # %% from sklearn.model_selection import cross_val_score scores = cross_val_score(model, data_numeric, target, cv=5) print(f"The different scores obtained are: \n{scores}") # %% print(f"The mean cross-validation accuracy is: " f"{scores.mean():.3f} +/- {scores.std():.3f}") # %% [markdown] # Note that by computing the standard-deviation of the cross-validation scores # we can get an idea of the uncertainty of our estimation of the predictive # performance of the model: in the above results, only the first 2 decimals seem # to be trustworthy. Using a single train / test split would not allow us to # know anything about the level of uncertainty of the accuracy of the model. # # Setting `cv=5` created 5 distinct splits to get 5 variations for the training # and testing sets. Each training set is used to fit one model which is then # scored on the matching test set. This strategy is called K-fold # cross-validation where `K` corresponds to the number of splits. # # The following matplotlib code helps visualize how the datasets is partitionned # between train and test samples at each iteration of the cross-validation # procedure: # %% # %matplotlib inline from sklearn.model_selection import KFold import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Patch cmap_cv = plt.cm.coolwarm def plot_cv_indices(cv, X, y, ax, lw=20): """Create a sample plot for indices of a cross-validation object.""" splits = list(cv.split(X=X, y=y)) n_splits = len(splits) # Generate the training/testing visualizations for each CV split for ii, (train, test) in enumerate(splits): # Fill in indices with the training/test groups indices = np.zeros(shape=X.shape[0], dtype=np.int32) indices[train] = 1 # Visualize the results ax.scatter(range(len(indices)), [ii + .5] * len(indices), c=indices, marker='_', lw=lw, cmap=cmap_cv, vmin=-.2, vmax=1.2) # Formatting yticklabels = list(range(n_splits)) ax.set(yticks=np.arange(n_splits+2) + .5, yticklabels=yticklabels, xlabel='Sample index', ylabel="CV iteration", ylim=[n_splits + .2, -.2], xlim=[0, 100]) ax.set_title('{}'.format(type(cv).__name__), fontsize=15) return ax # %% # Some random data points n_points = 100 X = np.random.randn(n_points, 10) y = np.random.randn(n_points) fig, ax = plt.subplots(figsize=(10, 6)) cv = KFold(5) plot_cv_indices(cv, X, y, ax); # %% [markdown] # ## Working with categorical variables # # As we have seen in the previous section, a numerical variable is a continuous # quantity represented by a real or integer number. Those variables can be # naturally handled by machine learning algorithms that typically composed of # a sequence of arithmetic instructions such as additions and multiplications. # # By opposition, categorical variables have discrete values typically represented # by string labels taken in a finite list of possible choices. For instance, the # variable `native-country` in our dataset is a categorical variable because it # encodes the data using a finite list of possible countries (along with the `?` # marker when this information is missing): # %% data["native-country"].value_counts() # %% [markdown] # In the remainder of this section, we will present different strategies to # encode categorical data into numerical data which can be used by a # machine-learning algorithm. # %% data.dtypes # %% categorical_columns = [c for c in data.columns if data[c].dtype.kind not in ["i", "f"]] categorical_columns # %% data_categorical = data[categorical_columns] data_categorical.head() # %% print(f"The datasets is composed of {data_categorical.shape[1]} features") # %% [markdown] # ### Encoding ordinal categories # # The most intuitive strategy is to encode each category with a number. # The `OrdinalEncoder` will transform the data in such manner. # %% from sklearn.preprocessing import OrdinalEncoder encoder = OrdinalEncoder() data_encoded = encoder.fit_transform(data_categorical) print(f"The dataset encoded contains {data_encoded.shape[1]} features") data_encoded[:5] # %% [markdown] # We can see that all categories have been encoded for each feature # independently. We can also notice that the number of features before and after # the encoding is the same. # # However, one has to be careful when using this encoding strategy. Using this # integer representation can lead the downstream models to make the assumption # that the categories are ordered: 0 is smaller than 1 which is smaller than 2, # etc. # # By default, `OrdinalEncoder` uses a lexicographical strategy to map string # category labels to integers. This strategy is completely arbitrary and often be # meaningless. For instance suppose the dataset has a categorical variable named # "size" with categories such as "S", "M", "L", "XL". We would like the integer # representation to respect the meaning of the sizes by mapping them to increasing # integers such as 0, 1, 2, 3. However lexicographical strategy used by default # would map the labels "S", "M", "L", "XL" to 2, 1, 0, 3. # # The `OrdinalEncoder` class accepts a "categories" constructor argument to pass # an the correct ordering explicitly. # # If a categorical variable does not carry any meaningful order information then # this encoding might be misleading to downstream statistical models and you might # consider using one-hot encoding instead (see below). # # Note however that the impact a violation of this ordering assumption is really # dependent on the downstream models (for instance linear models are much more # sensitive than models built from a ensemble of decision trees). # # ### Encoding nominal categories (without assuming any order) # # `OneHotEncoder` is an alternative encoder that can prevent the dowstream # models to make a false assumption about the ordering of categories. For a # given feature, it will create as many new columns as there are possible # categories. For a given sample, the value of the column corresponding to the # category will be set to `1` while all the columns of the other categories will # be set to `0`. # %% print(f"The dataset is composed of {data_categorical.shape[1]} features") data_categorical.head() # %% from sklearn.preprocessing import OneHotEncoder encoder = OneHotEncoder(sparse=False) data_encoded = encoder.fit_transform(data_categorical) print(f"The dataset encoded contains {data_encoded.shape[1]} features") data_encoded # %% [markdown] # Let's wrap this numpy array in a dataframe with informative column names as provided by the encoder object: # %% columns_encoded = encoder.get_feature_names(data_categorical.columns) pd.DataFrame(data_encoded, columns=columns_encoded).head() # %% [markdown] # Look at how the workclass variable of the first 3 records has been encoded and compare this to the original string representation. # # The number of features after the encoding is than 10 times larger than in the # original data because some variables such as `occupation` and `native-country` # have many possible categories. # # We can now integrate this encoder inside a machine learning pipeline as in the # case with numerical data: let's train a linear classifier on # the encoded data and check the performance of this machine learning pipeline # using cross-validation. # %% model = make_pipeline( OneHotEncoder(handle_unknown='ignore'), LogisticRegression(solver='lbfgs', max_iter=1000) ) scores = cross_val_score(model, data_categorical, target) print(f"The different scores obtained are: \n{scores}") # %% print(f"The accuracy is: {scores.mean():.3f} +/- {scores.std():.3f}") # %% [markdown] # As you can see, this representation of the categorical variables of the data is slightly more predictive of the revenue than the numerical variables that we used previously. # %% [markdown] # ## Exercise 2: # # - Try to fit a logistic regression model on categorical data transformed by # the OrdinalEncoder instead. What do you observe? # # Use the dedicated notebook to do this exercise. # %% [markdown] # ## Using numerical and categorical variables together # # In the previous sections, we saw that we need to treat data specifically # depending of their nature (i.e. numerical or categorical). # # Scikit-learn provides a `ColumnTransformer` class which will dispatch some # specific columns to a specific transformer making it easy to fit a single # predictive model on a dataset that combines both kinds of variables together # (heterogeneously typed tabular data). # # We can first define the columns depending on their data type: # * **binary encoding** will be applied to categorical columns with only too # possible values (e.g. sex=male or sex=female in this example). Each binary # categorical columns will be mapped to one numerical columns with 0 or 1 # values. # * **one-hot encoding** will be applied to categorical columns with more that # two possible categories. This encoding will create one additional column for # each possible categorical value. # * **numerical scaling** numerical features which will be standardized. # # # # # # # # %% binary_encoding_columns = ['sex'] one_hot_encoding_columns = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'native-country'] scaling_columns = ['age', 'education-num', 'hours-per-week', 'capital-gain', 'capital-loss'] # %% [markdown] # We can now create our `ColumnTransfomer` by specifying a list of triplet # (preprocessor name, transformer, columns). Finally, we can define a pipeline # to stack this "preprocessor" with our classifier (logistic regression). # %% from sklearn.compose import ColumnTransformer preprocessor = ColumnTransformer([ ('binary-encoder', OrdinalEncoder(), binary_encoding_columns), ('one-hot-encoder', OneHotEncoder(handle_unknown='ignore'), one_hot_encoding_columns), ('standard-scaler', StandardScaler(), scaling_columns) ]) model = make_pipeline( preprocessor, LogisticRegression(solver='lbfgs', max_iter=1000) ) # %% [markdown] # The final model is more complex than the previous models but still follows the # same API: # - the `fit` method is called to preprocess the data then train the classifier; # - the `predict` method can make predictions on new data; # - the `score` method is used to predict on the test data and compare the # predictions to the expected test labels to compute the accuracy. # %% data_train, data_test, target_train, target_test = train_test_split( data, target, random_state=42 ) model.fit(data_train, target_train) model.predict(data_test)[:5] # %% target_test[:5] # %% data_test.head() # %% model.score(data_test, target_test) # %% [markdown] # This model can also be cross-validated as usual (instead of using a single # train-test split): # %% scores = cross_val_score(model, data, target, cv=5) print(f"The different scores obtained are: \n{scores}") # %% print(f"The accuracy is: {scores.mean():.3f} +- {scores.std():.3f}") # %% [markdown] # The compound model has a higher predictive accuracy than the # two models that used numerical and categorical variables in # isolation. # %% [markdown] # # Fitting a more powerful model # # Linear models are very nice because they are usually very cheap to train, # small to deploy, fast to predict and give a good baseline. # # However it is often useful to check whether more complex models such as # ensemble of decision trees can lead to higher predictive performance. # # In the following we try a scalable implementation of the Gradient Boosting # Machine algorithm. For this class of models, we know that contrary to linear # models, it is useless to scale the numerical features and furthermore it is # both safe and significantly more computationally efficient use an arbitrary # integer encoding for the categorical variable even if the ordering is # arbitrary. Therefore we adapt the preprocessing pipeline as follows: # %% from sklearn.experimental import enable_hist_gradient_boosting from sklearn.ensemble import HistGradientBoostingClassifier # For each categorical column, extract the list of all possible categories # in some arbritrary order. categories = [data[column].unique() for column in data[categorical_columns]] preprocessor = ColumnTransformer([ ('categorical', OrdinalEncoder(categories=categories), categorical_columns), ], remainder="passthrough") model = make_pipeline(preprocessor, HistGradientBoostingClassifier()) model.fit(data_train, target_train) print(model.score(data_test, target_test)) # %% [markdown] # We can observe that we get significantly higher accuracies with the Gradient # Boosting model. This is often what we observe whenever the dataset has a large # number of samples and limited number of informative features (e.g. less than # 1000) with a mix of numerical and categorical variables. # # This explains why Gradient Boosted Machines are very popular among datascience # practitioners who work with tabular data. # # # # # # # # %% [markdown] # ## Exercise 3: # # - Check that scaling the numerical features does not impact the speed or # accuracy of HistGradientBoostingClassifier # - Check that one-hot encoding the categorical variable does not improve the # accuracy of HistGradientBoostingClassifier but slows down the training. # # Use the dedicated notebook to do this exercise.

33.127874

181

0.742421

794ad91e6b4b902022a35fa59f5d0d92a0decdfd

802

py

Python

python/batch-compute-with-step-functions/workshop/stack/cicdpipeline_stack.py

hy714335634/aws-cdk-examples

c66198642985e68e0541a4777d2a8e9acd222b7d

[ "Apache-2.0" ]

null

python/batch-compute-with-step-functions/workshop/stack/cicdpipeline_stack.py

hy714335634/aws-cdk-examples

c66198642985e68e0541a4777d2a8e9acd222b7d

[ "Apache-2.0" ]

null

python/batch-compute-with-step-functions/workshop/stack/cicdpipeline_stack.py

hy714335634/aws-cdk-examples

c66198642985e68e0541a4777d2a8e9acd222b7d

[ "Apache-2.0" ]

null

from aws_cdk import ( core ) from construct.cicdpipeline.cicd_batch import CICDBatch from construct.cicdpipeline.cicd_web import CICDWeb class CICDPipelineStack(core.Stack): def __init__(self, scope: core.Construct, id: str, UserName="default", EmailAddress="default", BatchRepo="default", WebRepo="default", WebService="default", **kwargs ) -> None: super().__init__(scope, id, **kwargs) self.My_CICDBatch = CICDBatch(self, "CICDBatch-" + UserName, UserName=UserName, Repo=BatchRepo ) self.My_CICDWeb = CICDWeb(self, "CICDWeb-" + UserName, UserName=UserName, Repo=WebRepo, WebService=WebService )

25.870968

55

0.5798

794ad95513d571a04f14ed62d1a3d09cc27d8657

1,813

py

Python

python-the-hard-way/21-functions-can-return-something.py

Valka7a/python-playground

f08d4374f2cec2e8b1afec3753854b1ec10ff480

[ "MIT" ]

null

python-the-hard-way/21-functions-can-return-something.py

Valka7a/python-playground

f08d4374f2cec2e8b1afec3753854b1ec10ff480

[ "MIT" ]

null

python-the-hard-way/21-functions-can-return-something.py

Valka7a/python-playground

f08d4374f2cec2e8b1afec3753854b1ec10ff480

[ "MIT" ]

null

# Exercise 21: Functions Can Return Something def add(a, b): print "ADDING %d + %d" % (a, b) return a + b def subtract(a, b): print "SUBTRACTING %d - %d" % (a, b) return a - b def multiply(a, b): print "MULTIPLYING %d * %d" % (a, b) return a * b def divide(a, b): print "DIVIDING %d / %d" % (a, b) return a / b print "Let's do some math with just functions!" # Drill 3 age = add(40, 15) height = subtract(68, 7) weight = multiply(94, 5) iq = divide(101, 3) print "Age: %d, Height: %d, Weight: %d, IQ: %d" % (age, height, weight, iq) # A puzzle for the extra credit, type it in anyway. print "Here is a puzzle." what = add(age, subtract(height, multiply(weight, divide(iq, 2)))) print "That becomes: ", what, "Can you do it by hand?" # Study Drills # 1.If you aren't really sure what 'return' does, try writing a # few of your own functions and have them return some values. # You can return anything that you can put to the right of an '='. # 2. At the end of the scrpt is a puzzle. I'm taking the return # value of one function and using it as the argument of another # function. I'm doing this in a chain so that I'm kind of # creating a formula using the functions. It looks really weird, # but if you run the script you can see the results. What you # should do is try to figure out the normal formula that would # recreate this same set of operations. # 3. Once you have the formula worked out for the puzzle, get # in there and see what happens when you modify the parts of # the functions. Try to change it on purpose to make another # value. # 4. Do the inverse. Write a simple formula and use the # functions in the same way to calculate it. # Drill 4 next = multiply(age, add(height, subtract(weight, divide(iq, 3)))) print "This becomes from mine formula: ", next, "Can you do it?"

31.807018

75

0.688913

794ad96dcb9388fb4d6e61885714b7abe06bf22d

2,347

py

Python

imageapp/models.py

Edwin-Karanu-Muiruri/django-gallery-karanu

06f456189ce1a3a164df1d6ce0971b684159ccfb

[ "MIT" ]

null

imageapp/models.py

Edwin-Karanu-Muiruri/django-gallery-karanu

06f456189ce1a3a164df1d6ce0971b684159ccfb

[ "MIT" ]

8

2020-05-26T08:37:17.000Z

2022-01-13T02:46:44.000Z

imageapp/models.py

Edwin-Karanu-Muiruri/django-gallery-karanu

06f456189ce1a3a164df1d6ce0971b684159ccfb

[ "MIT" ]

null

from django.db import models from cloudinary.models import CloudinaryField # Create your models here. class Category(models.Model): name = models.CharField(max_length = 50) def __str__(self): return self.name def save_category(self): self.save() def delete_category(self): self.delete() @classmethod def update_category(cls,id,value): cls.objects.filter(id = id).update(name = value) class Location(models.Model): name = models.CharField(max_length = 50) def __str__(self): return self.name def save_location(self): self.save() def delete_location(self): self.delete() @classmethod def update_location(cls,id,value): cls.objects.filter(id = id).update(name = value) @classmethod def display_all_locations(cls): return cls.objects.all() class Image(models.Model): image_name = models.CharField(max_length = 50) image = CloudinaryField('image') description = models.TextField() category = models.ForeignKey(Category , on_delete = models.CASCADE,default = 'category') location = models.ForeignKey(Location, on_delete = models.CASCADE, default = 'location') def __str__(self): return self.image_name def save_image(self): self.save() def delete_image(): self.delete() @classmethod def get_image_by_id(cls,id): image = cls.objects.get(id = id) return image @classmethod def search_image(cls,category_search): try: searched_category = Category.objects.get(name__icontains = category_search) images = Image.objects.filter(category = searched_category.id) return images except Exception: return "No images matched that category. Please try another eg. Family, Friends or Places" @classmethod def filter_by_location(cls,location_search): searched_location = Location.objects.get(name = location_search) images = Image.objects.filter(location = searched_location.id) return images @classmethod def display_all_images(cls): return cls.objects.all() @classmethod def update_image_description(cls,id,value): cls.objects.filter(id = id).update(description = value)

27.940476

102

0.659139

794ad97eb8176cc86f403a372f2d42b4b097b84b

1,021

py

Python

molecool/tests/test_measure.py

ywang40/molecool

4ec6af6d894c3152b1cd9d616e44521b3c6a5a46

[ "BSD-3-Clause" ]

null

molecool/tests/test_measure.py

ywang40/molecool

4ec6af6d894c3152b1cd9d616e44521b3c6a5a46

[ "BSD-3-Clause" ]

1

2020-12-18T19:24:59.000Z

molecool/tests/test_measure.py

ywang40/molecool

4ec6af6d894c3152b1cd9d616e44521b3c6a5a46

[ "BSD-3-Clause" ]

null

""" Tests for the measure module. """ import molecool import numpy as np import pytest def test_calculate_distance(): r1 = np.array([0, 0, 0]) r2 = np.array([0, 1, 0]) expected_distance = 1 calculated_diatance = molecool.calculate_distance(r1, r2) assert expected_distance == calculated_diatance def test_calculate_angle(): r1 = np.array([0, 0, -1]) r2 = np.array([0, 0, 0]) r3 = np.array([1, 0, 0]) expect_angle = 90 calculated_angle = molecool.calculate_angle(r1, r2, r3, degrees=True) assert expect_angle == calculated_angle @pytest.mark.parametrize("p1, p2, p3, expected_angle", [ (np.array([np.sqrt(2)/2, np.sqrt(2)/2, 0]), np.array([0, 0, 0]), np.array([1, 0, 0]), 45), (np.array([0, 0, -1]), np.array([0, 1, 0]), np.array([1, 0, 0]), 60), ]) def test_calculate_angle_many(p1, p2, p3, expected_angle): calculated_angle = molecool.calculate_angle(p1, p2, p3, degrees=True) assert expected_angle == pytest.approx(calculated_angle)

24.902439

94

0.644466

794ada23c90178a7c63699908b299622fc11437c

8,977

py

Python

libqtile/dgroups.py

luanfagu/pyBox

a91308a4131dbe244555521f3dd248ff53d20588

[ "MIT" ]

null

libqtile/dgroups.py

luanfagu/pyBox

a91308a4131dbe244555521f3dd248ff53d20588

[ "MIT" ]

null

libqtile/dgroups.py

luanfagu/pyBox

a91308a4131dbe244555521f3dd248ff53d20588

[ "MIT" ]

null

# Copyright (c) 2011-2012 Florian Mounier # Copyright (c) 2012-2014 roger # Copyright (c) 2012 Craig Barnes # Copyright (c) 2012-2014 Tycho Andersen # Copyright (c) 2013 Tao Sauvage # Copyright (c) 2014 ramnes # Copyright (c) 2014 Sebastian Kricner # Copyright (c) 2014 Sean Vig # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import collections import libqtile.hook from libqtile.backend.base import Static from libqtile.command import lazy from libqtile.config import Group, Key, Match, Rule from libqtile.log_utils import logger def simple_key_binder(mod, keynames=None): """Bind keys to mod+group position or to the keys specified as second argument""" def func(dgroup): # unbind all for key in dgroup.keys[:]: dgroup.qtile.ungrab_key(key) dgroup.keys.remove(key) if keynames: keys = keynames else: # keys 1 to 9 and 0 keys = list(map(str, list(range(1, 10)) + [0])) # bind all keys for keyname, group in zip(keys, dgroup.qtile.groups): name = group.name key = Key([mod], keyname, lazy.group[name].toscreen()) key_s = Key([mod, "shift"], keyname, lazy.window.togroup(name)) key_c = Key( [mod, "control"], keyname, lazy.group.switch_groups(name) ) dgroup.keys.append(key) dgroup.keys.append(key_s) dgroup.keys.append(key_c) dgroup.qtile.grab_key(key) dgroup.qtile.grab_key(key_s) dgroup.qtile.grab_key(key_c) return func class DGroups: """Dynamic Groups""" def __init__(self, qtile, dgroups, key_binder=None, delay=1): self.qtile = qtile self.groups = dgroups self.groups_map = {} self.rules = [] self.rules_map = {} self.last_rule_id = 0 for rule in getattr(qtile.config, 'dgroups_app_rules', []): self.add_rule(rule) self.keys = [] self.key_binder = key_binder self._setup_hooks() self._setup_groups() self.delay = delay self.timeout = {} def add_rule(self, rule, last=True): rule_id = self.last_rule_id self.rules_map[rule_id] = rule if last: self.rules.append(rule) else: self.rules.insert(0, rule) self.last_rule_id += 1 return rule_id def remove_rule(self, rule_id): rule = self.rules_map.get(rule_id) if rule: self.rules.remove(rule) del self.rules_map[rule_id] else: logger.warning('Rule "%s" not found', rule_id) def add_dgroup(self, group, start=False): self.groups_map[group.name] = group rule = Rule(group.matches, group=group.name) self.rules.append(rule) if start: self.qtile.add_group(group.name, group.layout, group.layouts, group.label) def _setup_groups(self): for group in self.groups: self.add_dgroup(group, group.init) if group.spawn and not self.qtile.no_spawn: if isinstance(group.spawn, str): spawns = [group.spawn] else: spawns = group.spawn for spawn in spawns: pid = self.qtile.cmd_spawn(spawn) self.add_rule(Rule(Match(net_wm_pid=pid), group.name)) def _setup_hooks(self): libqtile.hook.subscribe.addgroup(self._addgroup) libqtile.hook.subscribe.client_new(self._add) libqtile.hook.subscribe.client_killed(self._del) if self.key_binder: libqtile.hook.subscribe.setgroup( lambda: self.key_binder(self) ) libqtile.hook.subscribe.changegroup( lambda: self.key_binder(self) ) def _addgroup(self, group_name): if group_name not in self.groups_map: self.add_dgroup(Group(group_name, persist=False)) def _add(self, client): if client in self.timeout: logger.info('Remove dgroup source') self.timeout.pop(client).cancel() # ignore static windows if isinstance(client, Static): return # ignore windows whose groups is already set (e.g. from another hook or # when it was set on state restore) if client.group is not None: return group_set = False intrusive = False for rule in self.rules: # Matching Rules if rule.matches(client): if rule.group: if rule.group in self.groups_map: layout = self.groups_map[rule.group].layout layouts = self.groups_map[rule.group].layouts label = self.groups_map[rule.group].label else: layout = None layouts = None label = None group_added = self.qtile.add_group(rule.group, layout, layouts, label) client.togroup(rule.group) group_set = True group_obj = self.qtile.groups_map[rule.group] group = self.groups_map.get(rule.group) if group and group_added: for k, v in list(group.layout_opts.items()): if isinstance(v, collections.Callable): v(group_obj.layout) else: setattr(group_obj.layout, k, v) affinity = group.screen_affinity if affinity and len(self.qtile.screens) > affinity: self.qtile.screens[affinity].set_group(group_obj) if rule.float: client.enablefloating() if rule.intrusive: intrusive = rule.intrusive if rule.break_on_match: break # If app doesn't have a group if not group_set: current_group = self.qtile.current_group.name if current_group in self.groups_map and \ self.groups_map[current_group].exclusive and \ not intrusive: wm_class = client.get_wm_class() if wm_class: if len(wm_class) > 1: wm_class = wm_class[1] else: wm_class = wm_class[0] group_name = wm_class else: group_name = client.name or 'Unnamed' self.add_dgroup(Group(group_name, persist=False), start=True) client.togroup(group_name) self.sort_groups() def sort_groups(self): grps = self.qtile.groups sorted_grps = sorted(grps, key=lambda g: self.groups_map[g.name].position) if grps != sorted_grps: self.qtile.groups = sorted_grps libqtile.hook.fire("changegroup") def _del(self, client): # ignore static windows if isinstance(client, Static): return group = client.group def delete_client(): # Delete group if empty and don't persist if group and group.name in self.groups_map and \ not self.groups_map[group.name].persist and \ len(group.windows) <= 0: self.qtile.delete_group(group.name) self.sort_groups() del self.timeout[client] # Wait the delay until really delete the group logger.info('Add dgroup timer with delay {}s'.format(self.delay)) self.timeout[client] = self.qtile.call_later( self.delay, delete_client )

34.929961

90

0.574134

794adae09b3013480d9b5a52fdd962a0bc51d495

4,982

py

Python

TimeSformer/timesformer/utils/c2_model_loading.py

balaganeshmohan/Emotion-recognition

ad4816226598155c273d99fa4a4ca80953adcaa1

[ "MIT" ]

null

TimeSformer/timesformer/utils/c2_model_loading.py

balaganeshmohan/Emotion-recognition

ad4816226598155c273d99fa4a4ca80953adcaa1

[ "MIT" ]

null

TimeSformer/timesformer/utils/c2_model_loading.py

balaganeshmohan/Emotion-recognition

ad4816226598155c273d99fa4a4ca80953adcaa1

[ "MIT" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. """Caffe2 to PyTorch checkpoint name converting utility.""" import re def get_name_convert_func(): """ Get the function to convert Caffe2 layer names to PyTorch layer names. Returns: (func): function to convert parameter name from Caffe2 format to PyTorch format. """ pairs = [ # ------------------------------------------------------------ # 'nonlocal_conv3_1_theta_w' -> 's3.pathway0_nonlocal3.conv_g.weight' [ r"^nonlocal_conv([0-9]+)_([0-9]+)_(.*)", r"s\1.pathway0_nonlocal\2_\3", ], # 'theta' -> 'conv_theta' [r"^(.*)_nonlocal([0-9]+)_(theta)(.*)", r"\1_nonlocal\2.conv_\3\4"], # 'g' -> 'conv_g' [r"^(.*)_nonlocal([0-9]+)_(g)(.*)", r"\1_nonlocal\2.conv_\3\4"], # 'phi' -> 'conv_phi' [r"^(.*)_nonlocal([0-9]+)_(phi)(.*)", r"\1_nonlocal\2.conv_\3\4"], # 'out' -> 'conv_out' [r"^(.*)_nonlocal([0-9]+)_(out)(.*)", r"\1_nonlocal\2.conv_\3\4"], # 'nonlocal_conv4_5_bn_s' -> 's4.pathway0_nonlocal3.bn.weight' [r"^(.*)_nonlocal([0-9]+)_(bn)_(.*)", r"\1_nonlocal\2.\3.\4"], # ------------------------------------------------------------ # 't_pool1_subsample_bn' -> 's1_fuse.conv_f2s.bn.running_mean' [r"^t_pool1_subsample_bn_(.*)", r"s1_fuse.bn.\1"], # 't_pool1_subsample' -> 's1_fuse.conv_f2s' [r"^t_pool1_subsample_(.*)", r"s1_fuse.conv_f2s.\1"], # 't_res4_5_branch2c_bn_subsample_bn_rm' -> 's4_fuse.conv_f2s.bias' [ r"^t_res([0-9]+)_([0-9]+)_branch2c_bn_subsample_bn_(.*)", r"s\1_fuse.bn.\3", ], # 't_pool1_subsample' -> 's1_fuse.conv_f2s' [ r"^t_res([0-9]+)_([0-9]+)_branch2c_bn_subsample_(.*)", r"s\1_fuse.conv_f2s.\3", ], # ------------------------------------------------------------ # 'res4_4_branch_2c_bn_b' -> 's4.pathway0_res4.branch2.c_bn_b' [ r"^res([0-9]+)_([0-9]+)_branch([0-9]+)([a-z])_(.*)", r"s\1.pathway0_res\2.branch\3.\4_\5", ], # 'res_conv1_bn_' -> 's1.pathway0_stem.bn.' [r"^res_conv1_bn_(.*)", r"s1.pathway0_stem.bn.\1"], # 'conv1_xy_w_momentum' -> 's1.pathway0_stem.conv_xy.' [r"^conv1_xy(.*)", r"s1.pathway0_stem.conv_xy\1"], # 'conv1_w_momentum' -> 's1.pathway0_stem.conv.' [r"^conv1_(.*)", r"s1.pathway0_stem.conv.\1"], # 'res4_0_branch1_w' -> 'S4.pathway0_res0.branch1.weight' [ r"^res([0-9]+)_([0-9]+)_branch([0-9]+)_(.*)", r"s\1.pathway0_res\2.branch\3_\4", ], # 'res_conv1_' -> 's1.pathway0_stem.conv.' [r"^res_conv1_(.*)", r"s1.pathway0_stem.conv.\1"], # ------------------------------------------------------------ # 'res4_4_branch_2c_bn_b' -> 's4.pathway0_res4.branch2.c_bn_b' [ r"^t_res([0-9]+)_([0-9]+)_branch([0-9]+)([a-z])_(.*)", r"s\1.pathway1_res\2.branch\3.\4_\5", ], # 'res_conv1_bn_' -> 's1.pathway0_stem.bn.' [r"^t_res_conv1_bn_(.*)", r"s1.pathway1_stem.bn.\1"], # 'conv1_w_momentum' -> 's1.pathway0_stem.conv.' [r"^t_conv1_(.*)", r"s1.pathway1_stem.conv.\1"], # 'res4_0_branch1_w' -> 'S4.pathway0_res0.branch1.weight' [ r"^t_res([0-9]+)_([0-9]+)_branch([0-9]+)_(.*)", r"s\1.pathway1_res\2.branch\3_\4", ], # 'res_conv1_' -> 's1.pathway0_stem.conv.' [r"^t_res_conv1_(.*)", r"s1.pathway1_stem.conv.\1"], # ------------------------------------------------------------ # pred_ -> head.projection. [r"pred_(.*)", r"head.projection.\1"], # '.b_bn_fc' -> '.se.fc' [r"(.*)b_bn_fc(.*)", r"\1se.fc\2"], # conv_5 -> head.conv_5. [r"conv_5(.*)", r"head.conv_5\1"], # conv_5 -> head.conv_5. [r"lin_5(.*)", r"head.lin_5\1"], # '.bn_b' -> '.weight' [r"(.*)bn.b\Z", r"\1bn.bias"], # '.bn_s' -> '.weight' [r"(.*)bn.s\Z", r"\1bn.weight"], # '_bn_rm' -> '.running_mean' [r"(.*)bn.rm\Z", r"\1bn.running_mean"], # '_bn_riv' -> '.running_var' [r"(.*)bn.riv\Z", r"\1bn.running_var"], # '_b' -> '.bias' [r"(.*)[\._]b\Z", r"\1.bias"], # '_w' -> '.weight' [r"(.*)[\._]w\Z", r"\1.weight"], ] def convert_caffe2_name_to_pytorch(caffe2_layer_name): """ Convert the caffe2_layer_name to pytorch format by apply the list of regular expressions. Args: caffe2_layer_name (str): caffe2 layer name. Returns: (str): pytorch layer name. """ for source, dest in pairs: caffe2_layer_name = re.sub(source, dest, caffe2_layer_name) return caffe2_layer_name return convert_caffe2_name_to_pytorch

41.516667

80

0.483541

794adae9d3325381cb36e9c7ba403c9ad78226a0

4,017

py

Python

venv/Lib/site-packages/ldap3/extend/microsoft/dirSync.py

pileofscraps/wordcloud_backend

2b2feff5e58de2bbdb0393d78d703f21ee1cf3ba

[ "MIT" ]

4

2021-01-31T20:30:40.000Z

2022-02-19T08:56:28.000Z

venv/Lib/site-packages/ldap3/extend/microsoft/dirSync.py

pileofscraps/wordcloud_backend

2b2feff5e58de2bbdb0393d78d703f21ee1cf3ba

[ "MIT" ]

null

venv/Lib/site-packages/ldap3/extend/microsoft/dirSync.py

pileofscraps/wordcloud_backend

2b2feff5e58de2bbdb0393d78d703f21ee1cf3ba

[ "MIT" ]

6

2021-08-24T19:28:52.000Z

2022-02-20T18:21:34.000Z

""" """ # Created on 2015.10.21 # # Author: Giovanni Cannata # # Copyright 2015 - 2018 Giovanni Cannata # # This file is part of ldap3. # # ldap3 is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published # by the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # ldap3 is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public License # along with ldap3 in the COPYING and COPYING.LESSER files. # If not, see <http://www.gnu.org/licenses/>. from ...core.exceptions import LDAPExtensionError from ...protocol.microsoft import dir_sync_control, extended_dn_control, show_deleted_control from ... import SUBTREE, DEREF_NEVER from ...utils.dn import safe_dn class DirSync(object): def __init__(self, connection, sync_base, sync_filter, attributes, cookie, object_security, ancestors_first, public_data_only, incremental_values, max_length, hex_guid ): self.connection = connection if self.connection.check_names and sync_base: self. base = safe_dn(sync_base) else: self.base = sync_base self.filter = sync_filter self.attributes = attributes self.cookie = cookie self.object_security = object_security self.ancestors_first = ancestors_first self.public_data_only = public_data_only self.incremental_values = incremental_values self.max_length = max_length self.hex_guid = hex_guid self.more_results = True def loop(self): result = self.connection.search(search_base=self.base, search_filter=self.filter, search_scope=SUBTREE, attributes=self.attributes, dereference_aliases=DEREF_NEVER, controls=[dir_sync_control(criticality=True, object_security=self.object_security, ancestors_first=self.ancestors_first, public_data_only=self.public_data_only, incremental_values=self.incremental_values, max_length=self.max_length, cookie=self.cookie), extended_dn_control(criticality=False, hex_format=self.hex_guid), show_deleted_control(criticality=False)] ) if not self.connection.strategy.sync: response, result = self.connection.get_response(result) else: response = self.connection.response result = self.connection.result if result['description'] == 'success' and 'controls' in result and '1.2.840.113556.1.4.841' in result['controls']: self.more_results = result['controls']['1.2.840.113556.1.4.841']['value']['more_results'] self.cookie = result['controls']['1.2.840.113556.1.4.841']['value']['cookie'] return response elif 'controls' in result: raise LDAPExtensionError('Missing DirSync control in response from server') else: raise LDAPExtensionError('error %r in DirSync' % result)

43.663043

122

0.564849

794adbf916a38d41b39eefd1c1009715f429f0a1

3,522

py

Python

saleor/graphql/checkout/tests/deprecated/test_checkout_line_delete.py

SlashKing/saleor

bdd78044d542ef5650af7f5c0fd177001661c5b2

[ "CC-BY-4.0" ]

1

2022-02-21T07:17:08.000Z

saleor/graphql/checkout/tests/deprecated/test_checkout_line_delete.py

SlashKing/saleor

bdd78044d542ef5650af7f5c0fd177001661c5b2

[ "CC-BY-4.0" ]

81

2021-10-11T04:26:07.000Z

2022-03-28T04:46:43.000Z

saleor/graphql/checkout/tests/deprecated/test_checkout_line_delete.py

SlashKing/saleor

bdd78044d542ef5650af7f5c0fd177001661c5b2

[ "CC-BY-4.0" ]

1

2022-02-16T22:00:59.000Z

from unittest import mock import graphene from .....checkout.error_codes import CheckoutErrorCode from .....checkout.fetch import fetch_checkout_info, fetch_checkout_lines from .....checkout.utils import calculate_checkout_quantity from .....plugins.manager import get_plugins_manager from ....tests.utils import get_graphql_content from ...mutations import update_checkout_shipping_method_if_invalid MUTATION_CHECKOUT_LINES_DELETE = """ mutation checkoutLineDelete($checkoutId: ID, $token: UUID, $lineId: ID!) { checkoutLineDelete(checkoutId: $checkoutId, token: $token lineId: $lineId) { checkout { token lines { quantity variant { id } } } errors { field message code } } } """ @mock.patch( "saleor.graphql.checkout.mutations.update_checkout_shipping_method_if_invalid", wraps=update_checkout_shipping_method_if_invalid, ) def test_checkout_line_delete_by_id( mocked_update_shipping_method, user_api_client, checkout_with_item ): checkout = checkout_with_item lines, _ = fetch_checkout_lines(checkout) assert calculate_checkout_quantity(lines) == 3 assert checkout.lines.count() == 1 line = checkout.lines.first() assert line.quantity == 3 checkout_id = graphene.Node.to_global_id("Checkout", checkout.pk) line_id = graphene.Node.to_global_id("CheckoutLine", line.pk) variables = {"checkoutId": checkout_id, "lineId": line_id} response = user_api_client.post_graphql(MUTATION_CHECKOUT_LINES_DELETE, variables) content = get_graphql_content(response) data = content["data"]["checkoutLineDelete"] assert not data["errors"] checkout.refresh_from_db() lines, _ = fetch_checkout_lines(checkout) assert checkout.lines.count() == 0 assert calculate_checkout_quantity(lines) == 0 manager = get_plugins_manager() checkout_info = fetch_checkout_info(checkout, lines, [], manager) mocked_update_shipping_method.assert_called_once_with(checkout_info, lines) def test_checkout_line_delete_neither_token_and_id_given( user_api_client, checkout_with_item ): checkout = checkout_with_item line = checkout.lines.first() line_id = graphene.Node.to_global_id("CheckoutLine", line.pk) variables = {"lineId": line_id} response = user_api_client.post_graphql(MUTATION_CHECKOUT_LINES_DELETE, variables) content = get_graphql_content(response) data = content["data"]["checkoutLineDelete"] assert len(data["errors"]) == 1 assert not data["checkout"] assert data["errors"][0]["code"] == CheckoutErrorCode.GRAPHQL_ERROR.name def test_checkout_line_delete_both_token_and_id_given( user_api_client, checkout_with_item ): checkout = checkout_with_item line = checkout.lines.first() checkout_id = graphene.Node.to_global_id("Checkout", checkout.pk) line_id = graphene.Node.to_global_id("CheckoutLine", line.pk) variables = {"checkoutId": checkout_id, "token": checkout.token, "lineId": line_id} response = user_api_client.post_graphql(MUTATION_CHECKOUT_LINES_DELETE, variables) content = get_graphql_content(response) data = content["data"]["checkoutLineDelete"] assert len(data["errors"]) == 1 assert not data["checkout"] assert data["errors"][0]["code"] == CheckoutErrorCode.GRAPHQL_ERROR.name

34.871287

87

0.703861

794adc05261b372410d8a043aa6918e595e6bfe2

631

py

Python

setup.py

frcl/gitdir

65d85ca01adf506374a75a6b56d7b985c0d7bd0b

[ "MIT" ]

null

setup.py

frcl/gitdir

65d85ca01adf506374a75a6b56d7b985c0d7bd0b

[ "MIT" ]

null

setup.py

frcl/gitdir

65d85ca01adf506374a75a6b56d7b985c0d7bd0b

[ "MIT" ]

1

2021-04-03T13:41:58.000Z

import setuptools with open('README.md', 'r') as fh: long_description = fh.read() setuptools.setup( name='gitdir', version='1.2.4', author='Siddharth Dushantha', author_email='siddharth.dushantha@gmail.com', description='Download a single directory/folder from a GitHub repo', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/sdushantha/gitdir', packages=setuptools.find_packages(), entry_points={ 'console_scripts': [ 'gitdir = gitdir.gitdir:main', ] }, install_requires=['colorama~=0.4'] )

27.434783

72

0.671949

794adc5e5f593aae0f60d1cb70d8bff5a5df7877

3,092

py

Python

pontoon/base/urls.py

rhencke/pontoon

d530830acd4e03f3e29cae3273a5fede9f246499

[ "BSD-3-Clause" ]

null

pontoon/base/urls.py

rhencke/pontoon

d530830acd4e03f3e29cae3273a5fede9f246499

[ "BSD-3-Clause" ]

null

pontoon/base/urls.py

rhencke/pontoon

d530830acd4e03f3e29cae3273a5fede9f246499

[ "BSD-3-Clause" ]

null

from django.conf.urls import url from django.views.generic import RedirectView, TemplateView import views urlpatterns = [ # Home url(r'^$', views.home, name='pontoon.home'), # Terms url(r'^terms/$', TemplateView.as_view(template_name='terms.html'), name='pontoon.terms'), # TRANSLATE URLs # Legacy: Translate project's page url(r'^locale/(?P<locale>[A-Za-z0-9\-\@\.]+)/project/(?P<slug>.+)' + '/page/(?P<page>.+)/$', RedirectView.as_view(url="/%(locale)s/%(slug)s/%(page)s/", permanent=True)), # Legacy: Translate project url(r'^locale/(?P<locale>[A-Za-z0-9\-\@\.]+)/project/(?P<slug>.+)/$', RedirectView.as_view(url="/%(locale)s/%(slug)s/", permanent=True)), # AJAX: Get locale details url(r'^teams/(?P<locale>[A-Za-z0-9\-\@\.]+)/projects/$', views.locale_projects, name='pontoon.locale.projects'), # AJAX: Get locale stats used in All Resources part url(r'^teams/(?P<locale>[A-Za-z0-9\-\@\.]+)/stats/$', views.locale_stats, name='pontoon.locale.stats'), # AJAX: Get locale-project pages/paths with stats url(r'^(?P<locale>[A-Za-z0-9\-\@\.]+)/(?P<slug>[\w-]+)/parts/$', views.locale_project_parts, name='pontoon.locale.project.parts'), # AJAX: Get authors and time range data url(r'^(?P<locale>[A-Za-z0-9\-\@\.]+)/(?P<slug>[\w-]+)/(?P<part>.+)/authors-and-time-range/$', views.authors_and_time_range, name='pontoon.authors.and.time.range'), # Locale-agnostic links url(r'^projects/(?P<slug>[\w-]+)/(?P<part>.+)/$', views.translate_locale_agnostic, name='pontoon.translate.locale.agnostic'), # Translate project url(r'^(?P<locale>[A-Za-z0-9\-\@\.]+)/(?P<slug>[\w-]+)/(?P<part>.+)/$', views.translate, name='pontoon.translate'), # Download translation memory url(r'^(?P<locale>[A-Za-z0-9\-\@\.]+)/(?P<slug>[\w-]+)/(?P<filename>.+)\.tmx$', views.download_translation_memory, name='pontoon.download_tmx'), # AJAX url(r'^get-entities/', views.entities, name='pontoon.entities'), url(r'^update/', views.update_translation, name='pontoon.update'), url(r'^perform-checks/', views.perform_checks, name='pontoon.perform.checks'), url(r'^get-history/', views.get_translation_history, name='pontoon.get_history'), url(r'^unapprove-translation/', views.unapprove_translation, name='pontoon.unapprove_translation'), url(r'^reject-translation/', views.reject_translation, name='pontoon.reject_translation'), url(r'^unreject-translation/', views.unreject_translation, name='pontoon.unreject_translation'), url(r'^other-locales/', views.get_translations_from_other_locales, name='pontoon.other_locales'), url(r'^download/', views.download, name='pontoon.download'), url(r'^upload/', views.upload, name='pontoon.upload'), url(r'^update-tour-status/', views.update_tour_status, name='pontoon.update_tour_status'), ]

36.376471

98

0.611902

794adc63730a7a9f443e4fd343d4d02a17fe901b

1,159

py

Python

examples/cutensor/elementwise_binary.py

Dahlia-Chehata/cupy

1005f55075f89aa17e60074aaa6494ff8d033251

[ "MIT" ]

null

examples/cutensor/elementwise_binary.py

Dahlia-Chehata/cupy

1005f55075f89aa17e60074aaa6494ff8d033251

[ "MIT" ]

null

examples/cutensor/elementwise_binary.py

Dahlia-Chehata/cupy

1005f55075f89aa17e60074aaa6494ff8d033251

[ "MIT" ]

null

# # D_{x,y,z} = alpha * A_{z,y,x} + gamma * C_{x,y,z} # import numpy import cupy from cupy import cutensor import cupyx.time dtype = numpy.float32 mode_a = ('z', 'y', 'x') mode_c = ('x', 'y', 'z') extent = {'x': 400, 'y': 200, 'z': 300} a = cupy.random.random([extent[i] for i in mode_a]) c = cupy.random.random([extent[i] for i in mode_c]) a = a.astype(dtype) c = c.astype(dtype) desc_a = cutensor.create_tensor_descriptor(a) desc_c = cutensor.create_tensor_descriptor(c) mode_a = cutensor.create_mode(*mode_a) mode_c = cutensor.create_mode(*mode_c) alpha = numpy.array(1.1, dtype) gamma = numpy.array(1.3, dtype) perf = cupyx.time.repeat( cutensor.elementwise_binary, (alpha, a, desc_a, mode_a, gamma, c, desc_c, mode_c), n_warmup=1, n_repeat=5) itemsize = numpy.dtype(dtype).itemsize transfer_byte = a.size * itemsize if alpha.item() != 0.0: transfer_byte += a.size * itemsize if gamma.item() != 0.0: transfer_byte += c.size * itemsize elapsed = perf.gpu_times.mean() gbs = transfer_byte / elapsed / 1e9 print('dtype: {}'.format(numpy.dtype(dtype).name)) print(perf) print('effective memory bandwidth (GB/s): {}'.format(gbs))

24.659574

58

0.679034

794adcfb959d52f6b697bbd2b7040e3254c6436c

820

py

Python

shop/shop_backend/api/migrations/0010_profile.py

CSchool/lksh-web-services

2558bd7a07f8cb501634e5eb1f37345a38d34f2e

[ "Apache-2.0" ]

null

shop/shop_backend/api/migrations/0010_profile.py

CSchool/lksh-web-services

2558bd7a07f8cb501634e5eb1f37345a38d34f2e

[ "Apache-2.0" ]

null

shop/shop_backend/api/migrations/0010_profile.py

CSchool/lksh-web-services

2558bd7a07f8cb501634e5eb1f37345a38d34f2e

[ "Apache-2.0" ]

null

# Generated by Django 3.2.6 on 2021-08-05 10:26 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('api', '0009_auto_20210805_1005'), ] operations = [ migrations.CreateModel( name='Profile', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('tokens', models.IntegerField(default=0, verbose_name='tokens')), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='userprofile', to=settings.AUTH_USER_MODEL)), ], ), ]

32.8

149

0.65122

794addabf568faf10fc1c581e5bfbf36d9e7fc3c

23,857

py

Python

adbForTest/Utils/adbUtils.py

LiuTianen/PackManage

4b067954cc223baa14569a6f1517954b9cdb968f

[ "MIT" ]

null

adbForTest/Utils/adbUtils.py

LiuTianen/PackManage

4b067954cc223baa14569a6f1517954b9cdb968f

[ "MIT" ]

null

adbForTest/Utils/adbUtils.py

LiuTianen/PackManage

4b067954cc223baa14569a6f1517954b9cdb968f

[ "MIT" ]

null

#!/usr/bin/env python #coding=utf-8 import os import platform import re import time import subprocess class AdbTools(object): def __init__(self, device_id=''): self.__system = platform.system() self.__find = '' self.__command = '' self.__device_id = device_id self.__get_find() self.__check_adb() self.__connection_devices() def __get_find(self): """ 判断系统类型，windows使用findstr，linux使用grep :return: """ if self.__system is "Windows": self.__find = "findstr" else: self.__find = "grep" def __check_adb(self): """ 检查adb 判断是否设置环境变量ANDROID_HOME :return: """ if "ANDROID_HOME" in os.environ: if self.__system == "Windows": path = os.path.join(os.environ["ANDROID_HOME"], "platform-tools", "adb.exe") if os.path.exists(path): self.__command = path else: raise EnvironmentError( "Adb not found in $ANDROID_HOME path: %s." % os.environ["ANDROID_HOME"]) else: path = os.path.join(os.environ["ANDROID_HOME"], "platform-tools", "adb") if os.path.exists(path): self.__command = path else: raise EnvironmentError( "Adb not found in $ANDROID_HOME path: %s." % os.environ["ANDROID_HOME"]) else: raise EnvironmentError( "Adb not found in $ANDROID_HOME path: %s." % os.environ["ANDROID_HOME"]) def __connection_devices(self): """ 连接指定设备，单个设备可不传device_id :return: """ if self.__device_id == "": return self.__device_id = "-s %s" % self.__device_id def adb(self, args): """ 执行adb命令 :param args:参数 :return: """ cmd = "%s %s %s" % (self.__command, self.__device_id, str(args)) return os.popen(cmd) def shell(self, args): """ 执行adb shell命令 :param args:参数 :return: """ cmd = "%s %s shell %s" % (self.__command, self.__device_id, str(args)) return os.popen(cmd) def getOnlineDevices(self): devices = str(self.adb("devices").read()) online = re.findall(r'(.*?)\s+device\s', devices) return online def get_current_application(self): """ 获取当前运行的应用信息 :return: """ return self.shell('dumpsys window w | %s \/ | %s name=' % (self.__find, self.__find)).read() def get_current_package(self): """ 获取当前运行app包名 :return: """ reg = re.compile(r'name=(.+?)/') return re.findall(reg, self.get_current_application())[0] def get_current_activity(self): """ 获取当前运行activity :return: package/activity """ reg = re.compile(r'name=(.+?)\)') return re.findall(reg, self.get_current_application())[0] def __get_process(self, package_name): """ 获取进程信息 :param package_name: :return: """ if self.__system is "Windows": pid_command = self.shell("ps | %s %s$" % (self.__find, package_name)).read() else: pid_command = self.shell("ps | %s -w %s" % (self.__find, package_name)).read() return pid_command def process_exists(self, package_name): """ 返回进程是否存在 :param package_name: :return: """ process = self.__get_process(package_name) return package_name in process def get_pid(self, package_name): """ 获取pid :return: """ pid_command = self.__get_process(package_name) if pid_command == '': print("The process doesn't exist.") return pid_command req = re.compile(r"\d+") result = str(pid_command).split() result.remove(result[0]) return req.findall(" ".join(result))[0] def get_uid(self, pid): """ 获取uid :param pid: :return: """ result = self.shell("cat /proc/%s/status" % pid).readlines() for i in result: if 'uid' in i.lower(): return i.split()[1] def get_flow_data_tcp(self, uid): """ 获取应用tcp流量 :return:(接收, 发送) """ tcp_rcv = self.shell("cat proc/uid_stat/%s/tcp_rcv" % uid).read().split()[0] tcp_snd = self.shell("cat proc/uid_stat/%s/tcp_snd" % uid).read().split()[0] return tcp_rcv, tcp_snd def get_flow_data_all(self, uid): """ 获取应用流量全部数据包含该应用多个进程的所有数据 tcp udp等 (rx_bytes, tx_bytes) >> (接收, 发送) :param uid: :return:list(dict) """ all_data = [] d = {} data = self.shell("cat /proc/net/xt_qtaguid/stats | %s %s" % (self.__find, uid)).readlines() for i in data: if not i.startswith('\n'): item = i.strip().split() d['idx'] = item[0] d['iface'] = item[1] d['acct_tag_hex'] = item[2] d['uid_tag_int'] = item[3] d['cnt_set'] = item[4] d['rx_bytes'] = item[5] d['rx_packets'] = item[6] d['tx_bytes'] = item[7] d['tx_packets'] = item[8] d['rx_tcp_bytes'] = item[9] d['rx_tcp_packets'] = item[10] d['rx_udp_bytes'] = item[11] d['rx_udp_packets'] = item[12] d['rx_other_bytes'] = item[13] d['rx_other_packets'] = item[14] d['tx_tcp_bytes'] = item[15] d['tx_tcp_packets'] = item[16] d['tx_udp_bytes'] = item[17] d['tx_udp_packets'] = item[18] d['tx_other_bytes'] = item[19] d['tx_other_packets'] = item[20] all_data.append(d) d = {} return all_data def dump_apk_launch(self, path): """ dump apk文件 :param path: apk路径 :return: """ # 检查build-tools是否添加到环境变量中 # 需要用到里面的aapt命令 l = os.environ['PATH'].split(';') build_tools = False for i in l: if 'build-tools' in i: build_tools = True if not build_tools: raise EnvironmentError("ANDROID_HOME BUILD-TOOLS COMMAND NOT FOUND.\nPlease set the environment variable.") cmd = ('aapt dump badging %s' % (path,)) + (' | %s launchable ' %(self.__find)) result = "" p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE, shell=True) (output, err) = p.communicate() output = str(output, encoding='utf8') if output != "": result = output.split("'")[1] return result @staticmethod def dump_apk_name(path): """ dump apk文件 :param path: apk路径 :return: """ # 检查build-tools是否添加到环境变量中 # 需要用到里面的aapt命令 l = os.environ['PATH'].split(';') build_tools = False for i in l: if 'build-tools' in i: build_tools = True if not build_tools: raise EnvironmentError("ANDROID_HOME BUILD-TOOLS COMMAND NOT FOUND.\nPlease set the environment variable.") cmd = ('aapt dump badging %s' % (path,)) + " | findstr package:" result = "" p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE, shell=True) (output, err) = p.communicate() output = str(output, encoding='utf8') if output != "": result = output.split("'")[1] return result @staticmethod def dump_xml(path, filename): """ dump apk xml文件 :return: """ return os.popen('aapt dump xmlstrings %s %s' % (path, filename)) def uiautomator_dump(self): """ 获取屏幕uiautomator xml文件 :return: """ return self.shell('uiautomator dump').read().split()[-1] def pull(self, source, target): """ 从手机端拉取文件到电脑端 :return: """ self.adb('pull %s %s' % (source, target)) def push(self, source, target): """ 从电脑端推送文件到手机端 :param source: :param target: :return: """ self.adb('push %s %s' % (source, target)) def remove(self, path): """ 从手机端删除文件 :return: """ self.shell('rm %s' % (path,)) def clear_app_data(self, package): """ 清理应用数据 :return: """ self.shell('pm clear %s' % (package,)) def install(self, path): """ 安装apk文件 :return: """ # adb install 安装错误常见列表 errors = {'INSTALL_FAILED_ALREADY_EXISTS': '程序已经存在', 'INSTALL_DEVICES_NOT_FOUND': '找不到设备', 'INSTALL_FAILED_DEVICE_OFFLINE': '设备离线', 'INSTALL_FAILED_INVALID_APK': '无效的APK', 'INSTALL_FAILED_INVALID_URI': '无效的链接', 'INSTALL_FAILED_INSUFFICIENT_STORAGE': '没有足够的存储空间', 'INSTALL_FAILED_DUPLICATE_PACKAGE': '已存在同名程序', 'INSTALL_FAILED_NO_SHARED_USER': '要求的共享用户不存在', 'INSTALL_FAILED_UPDATE_INCOMPATIBLE': '版本不能共存', 'INSTALL_FAILED_SHARED_USER_INCOMPATIBLE': '需求的共享用户签名错误', 'INSTALL_FAILED_MISSING_SHARED_LIBRARY': '需求的共享库已丢失', 'INSTALL_FAILED_REPLACE_COULDNT_DELETE': '需求的共享库无效', 'INSTALL_FAILED_DEXOPT': 'dex优化验证失败', 'INSTALL_FAILED_DEVICE_NOSPACE': '手机存储空间不足导致apk拷贝失败', 'INSTALL_FAILED_DEVICE_COPY_FAILED': '文件拷贝失败', 'INSTALL_FAILED_OLDER_SDK': '系统版本过旧', 'INSTALL_FAILED_CONFLICTING_PROVIDER': '存在同名的内容提供者', 'INSTALL_FAILED_NEWER_SDK': '系统版本过新', 'INSTALL_FAILED_TEST_ONLY': '调用者不被允许测试的测试程序', 'INSTALL_FAILED_CPU_ABI_INCOMPATIBLE': '包含的本机代码不兼容', 'CPU_ABIINSTALL_FAILED_MISSING_FEATURE': '使用了一个无效的特性', 'INSTALL_FAILED_CONTAINER_ERROR': 'SD卡访问失败', 'INSTALL_FAILED_INVALID_INSTALL_LOCATION': '无效的安装路径', 'INSTALL_FAILED_MEDIA_UNAVAILABLE': 'SD卡不存在', 'INSTALL_FAILED_INTERNAL_ERROR': '系统问题导致安装失败', 'INSTALL_PARSE_FAILED_NO_CERTIFICATES': '文件未通过认证 >> 设置开启未知来源', 'INSTALL_PARSE_FAILED_INCONSISTENT_CERTIFICATES': '文件认证不一致 >> 先卸载原来的再安装', 'INSTALL_FAILED_INVALID_ZIP_FILE': '非法的zip文件 >> 先卸载原来的再安装', 'INSTALL_CANCELED_BY_USER': '需要用户确认才可进行安装', 'INSTALL_FAILED_VERIFICATION_FAILURE': '验证失败 >> 尝试重启手机', 'DEFAULT': '未知错误' } print('Installing...') l = self.adb('install -r %s' % (path,)).read() if 'Success' in l: print('Install Success') if 'Failure' in l: reg = re.compile('\\[(.+?)\\]') key = re.findall(reg, l)[0] try: print('Install Failure >> %s' % errors[key]) except KeyError: print('Install Failure >> %s' % key) return l def uninstall(self, package): """ 卸载apk :param package: 包名 :return: """ print('Uninstalling...') l = self.adb('uninstall %s' % (package,)).read() print(l) def screenshot(self, target_path=''): """ 手机截图 :param target_path: 目标路径 :return: """ format_time = time.strftime('%Y%m%d%H%M%S') self.shell('screencap -p /sdcard/%s.png' % (format_time,)) time.sleep(1) if target_path == '': self.pull('/sdcard/%s.png' % (format_time,), os.path.expanduser('~')) else: self.pull('/sdcard/%s.png' % (format_time,), target_path) self.remove('/sdcard/%s.png' % (format_time,)) def get_cache_logcat(self): """ 导出缓存日志 :return: """ return self.adb('logcat -v time -d') def get_logcat_tag(self,logTag): """ 导出缓存日志 :return: """ return self.adb('logcat %s' %(logTag)).read().strip() def get_crash_logcat(self): """ 导出崩溃日志 :return: """ return self.adb('logcat -v time -d | %s AndroidRuntime' % (self.__find,)) def clear_cache_logcat(self): """ 清理缓存区日志 :return: """ self.adb('logcat -c') def get_top(self): """ 获取运行中的APP进程信息 :return: """ return self.shell('top -m 10').read().strip() def get_device_time(self): """ 获取设备时间 :return: """ return self.shell('date').read().strip() def ls(self, command): """ shell ls命令 :return: """ return self.shell('ls %s' % (command,)).readlines() def file_exists(self, target): """ 判断文件在目标路径是否存在 :return: """ l = self.ls(target) for i in l: if i.strip() == target: return True return False def is_install(self, target_app): """ 判断目标app在设备上是否已安装 :param target_app: 目标app包名 :return: bool """ return target_app in self.shell('pm list packages %s' % (target_app,)).read() def get_device_model(self): """ 获取设备型号 :return: """ return self.shell('getprop ro.product.model').read().strip() def get_device_id(self): """ 获取设备id :return: """ return self.adb('get-serialno').read().strip() def get_device_android_version(self): """ 获取设备Android版本 :return: """ return self.shell('getprop ro.build.version.release').read().strip() def get_device_sdk_version(self): """ 获取设备SDK版本 :return: """ return self.shell('getprop ro.build.version.sdk').read().strip() def get_device_mac_address(self): """ 获取设备MAC地址 :return: """ return self.shell('cat /sys/class/net/wlan0/address').read().strip() def get_device_ip_address(self): """ 获取设备IP地址 pass: 适用WIFI 蜂窝数据 :return: """ if not self.get_wifi_state() and not self.get_data_state(): return l = self.shell('ip addr | %s global' % self.__find).read() reg = re.compile('\d+\.\d+\.\d+\.\d+') return re.findall(reg, l)[0] def get_device_imei(self): """ 获取设备IMEI :return: """ sdk = self.get_device_sdk_version() # Android 5.0以下方法 if int(sdk) < 21: l = self.shell('dumpsys iphonesubinfo').read() reg = re.compile('[0-9]{15}') return re.findall(reg, l)[0] elif self.root(): l = self.shell('service call iphonesubinfo 1').read() print(l) print(re.findall(re.compile("'.+?'"), l)) imei = '' for i in re.findall(re.compile("'.+?'"), l): imei += i.replace('.', '').replace("'", '').replace(' ', '') return imei else: print('The device not root.') return '' def check_sim_card(self): """ 检查设备SIM卡 :return: """ return len(self.shell('getprop | %s gsm.operator.alpha]' % self.__find).read().strip().split()[-1]) > 2 def get_device_operators(self): """ 获取运营商 :return: """ return self.shell('getprop | %s gsm.operator.alpha]' % self.__find).read().strip().split()[-1] def get_device_state(self): """ 获取设备状态 :return: """ return self.adb('get-state').read().strip() def get_display_state(self): """ 获取屏幕状态 :return: 亮屏/灭屏 """ l = self.shell('dumpsys power').readlines() for i in l: if 'mScreenOn=' in i: return i.split()[-1] == 'mScreenOn=true' if 'Display Power' in i: return 'ON' in i.split('=')[-1].upper() def get_screen_normal_size(self): """ 获取设备屏幕分辨率 >> 标配 :return: """ return self.shell('wm size').read().strip().split()[-1].split('x') def get_screen_reality_size(self): """ 获取设备屏幕分辨率 >> 实际分辨率 :return: """ x = 0 y = 0 l = self.shell(r'getevent -p | %s -e "0"' % self.__find).readlines() for n in l: if len(n.split()) > 0: if n.split()[0] == '0035': x = int(n.split()[7].split(',')[0]) elif n.split()[0] == '0036': y = int(n.split()[7].split(',')[0]) return x, y def get_device_interior_sdcard(self): """ 获取内部SD卡空间 :return: (path,total,used,free,block) """ return self.shell('df | %s \/mnt\/shell\/emulated' % self.__find).read().strip().split() def get_device_external_sdcard(self): """ 获取外部SD卡空间 :return: (path,total,used,free,block) """ return self.shell('df | %s \/storage' % self.__find).read().strip().split() def __fill_rom(self, path, stream, count): """ 填充数据 :param path: 填充地址 :param stream: 填充流大小 :param count: 填充次数 :return: """ self.shell('dd if=/dev/zero of=%s bs=%s count=%s' % (path, stream, count)).read().strip() def fill_interior_sdcard(self, filename, size): """ 填充内置SD卡 :param filename: 文件名 :param size: 填充大小，单位byte :return: """ if size > 10485760: # 10m self.__fill_rom('sdcard/%s' % filename, 10485760, size / 10485760) else: self.__fill_rom('sdcard/%s' % filename, size, 1) def fill_external_sdcard(self, filename, size): """ 填充外置SD卡 :param filename: 文件名 :param size: 填充大小，单位byte :return: """ path = self.get_device_external_sdcard()[0] if size > 10485760: # 10m self.__fill_rom('%s/%s' % (path, filename), 10485760, size / 10485760) else: self.__fill_rom('%s/%s' % (path, filename), size, 1) def kill_process(self, pid): """ 杀死进程 pass: 一般需要权限不推荐使用 :return: """ return self.shell('kill %s' % pid).read().strip() def quit_app(self, package): """ 退出应用 :return: """ return self.shell('am force-stop %s' % package).read().strip() def reboot(self): """ 重启设备 :return: """ self.adb('reboot') def recovery(self): """ 重启设备并进入recovery模式 :return: """ self.adb('reboot recovery') def fastboot(self): """ 重启设备并进入fastboot模式 :return: """ self.adb('reboot bootloader') def root(self): """ 获取root状态 :return: """ return 'not found' not in self.shell('su -c ls -l /data/').read().strip() def wifi(self, power): """ 开启/关闭wifi pass: 需要root权限 :return: """ if not self.root(): print('The device not root.') return if power: self.shell('su -c svc wifi enable').read().strip() else: self.shell('su -c svc wifi disable').read().strip() def data(self, power): """ 开启/关闭蜂窝数据 pass: 需要root权限 :return: """ if not self.root(): print('The device not root.') return if power: self.shell('su -c svc data enable').read().strip() else: self.shell('su -c svc data disable').read().strip() def get_wifi_state(self): """ 获取WiFi连接状态 :return: """ return 'enabled' in self.shell('dumpsys wifi | %s ^Wi-Fi' % self.__find).read().strip() def get_data_state(self): """ 获取移动网络连接状态 :return: """ return '2' in self.shell('dumpsys telephony.registry | %s mDataConnectionState' % self.__find).read().strip() def get_network_state(self): """ 设备是否连上互联网 :return: """ return 'unknown host' not in self.shell('ping -w 1 www.baidu.com').read().strip() def get_wifi_password_list(self): """ 获取WIFI密码列表 :return: """ if not self.root(): print('The device not root.') return [] l = re.findall(re.compile('ssid=".+?"\s{3}psk=".+?"'), self.shell('su -c cat /data/misc/wifi/*.conf').read()) return [re.findall(re.compile('".+?"'), i) for i in l] def call(self, number): """ 拨打电话 :param number: :return: """ self.shell('am start -a android.intent.action.CALL -d tel:%s' % number) def open_url(self, url): """ 打开网页 :return: """ self.shell('am start -a android.intent.action.VIEW -d %s' % url) def start_application(self, component): """ 启动一个应用 e.g: com.android.settings/com.android.settings.Settings """ self.shell("am start -n %s" % component) def send_keyevent(self, keycode): """ 发送一个按键事件 https://developer.android.com/reference/android/view/KeyEvent.html :return: """ self.shell('input keyevent %s' % keycode) def rotation_screen(self, param): """ 旋转屏幕 :param param: 0 >> 纵向，禁止自动旋转; 1 >> 自动旋转 :return: """ self.shell('/system/bin/content insert --uri content://settings/system --bind ' 'name:s:accelerometer_rotation --bind value:i:%s' % param) def instrument(self, command): """ 启动instrument app :param command: 命令 :return: """ return self.shell('am instrument %s' % command).read() def export_apk(self, package, target_path='', timeout=5000): """ 从设备导出应用 :param timeout: 超时时间 :param target_path: 导出后apk存储路径 :param package: 包名 :return: """ num = 0 if target_path == '': self.adb('pull /data/app/%s-1/base.apk %s' % (package, os.path.expanduser('~'))) while 1: num += 1 if num <= timeout: if os.path.exists(os.path.join(os.path.expanduser('~'), 'base.apk')): os.rename(os.path.join(os.path.expanduser('~'), 'base.apk'), os.path.join(os.path.expanduser('~'), '%s.apk' % package)) else: self.adb('pull /data/app/%s-1/base.apk %s' % (package, target_path)) while 1: num += 1 if num <= timeout: if os.path.exists(os.path.join(os.path.expanduser('~'), 'base.apk')): os.rename(os.path.join(os.path.expanduser('~'), 'base.apk'), os.path.join(os.path.expanduser('~'), '%s.apk' % package)) if __name__ == '__main__': print(AdbTools().getOnlineDevices())

29.636025

119

0.499476

794addb6a7b397814132af4b6a926938a9a2864e

10,349

py

Python

configs/common/Options.py

dspencer001/usu_gem5

ff3150a999cb141908e134304811714cfc2500e4

[ "BSD-3-Clause" ]

null

configs/common/Options.py

dspencer001/usu_gem5

ff3150a999cb141908e134304811714cfc2500e4

[ "BSD-3-Clause" ]

null

configs/common/Options.py

dspencer001/usu_gem5

ff3150a999cb141908e134304811714cfc2500e4

[ "BSD-3-Clause" ]

null

# Copyright (c) 2006-2008 The Regents of The University of Michigan # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer; # redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution; # neither the name of the copyright holders nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # Authors: Lisa Hsu import m5 from m5.defines import buildEnv from m5.objects import * from Benchmarks import * def addCommonOptions(parser): # system options parser.add_option("--cpu-type", type="choice", default="atomic", choices = ["atomic", "timing", "detailed", "inorder", "arm_detailed"], help = "type of cpu to run with") parser.add_option("--checker", action="store_true"); parser.add_option("-n", "--num-cpus", type="int", default=1) parser.add_option("--caches", action="store_true") parser.add_option("--l2cache", action="store_true") parser.add_option("--fastmem", action="store_true") parser.add_option("--clock", action="store", type="string", default='2GHz') parser.add_option("--num-dirs", type="int", default=1) parser.add_option("--num-l2caches", type="int", default=1) parser.add_option("--num-l3caches", type="int", default=1) parser.add_option("--l1d_size", type="string", default="64kB") parser.add_option("--l1i_size", type="string", default="32kB") parser.add_option("--l2_size", type="string", default="2MB") parser.add_option("--l3_size", type="string", default="16MB") parser.add_option("--l1d_assoc", type="int", default=2) parser.add_option("--l1i_assoc", type="int", default=2) parser.add_option("--l2_assoc", type="int", default=8) parser.add_option("--l3_assoc", type="int", default=16) parser.add_option("--cacheline_size", type="int", default=64) parser.add_option("--ruby", action="store_true") parser.add_option("--smt", action="store_true", default=False, help = """ Only used if multiple programs are specified. If true, then the number of threads per cpu is same as the number of programs.""") # Run duration options parser.add_option("-m", "--maxtick", type="int", default=m5.MaxTick, metavar="T", help="Stop after T ticks") parser.add_option("--maxtime", type="float") parser.add_option("-I", "--maxinsts", action="store", type="int", default=None, help="""Total number of instructions to simulate (default: run forever)""") parser.add_option("--work-item-id", action="store", type="int", help="the specific work id for exit & checkpointing") parser.add_option("--work-begin-cpu-id-exit", action="store", type="int", help="exit when work starts on the specified cpu") parser.add_option("--work-end-exit-count", action="store", type="int", help="exit at specified work end count") parser.add_option("--work-begin-exit-count", action="store", type="int", help="exit at specified work begin count") parser.add_option("--init-param", action="store", type="int", default=0, help="""Parameter available in simulation with m5 initparam""") # Checkpointing options ###Note that performing checkpointing via python script files will override ###checkpoint instructions built into binaries. parser.add_option("--take-checkpoints", action="store", type="string", help="<M,N> take checkpoints at tick M and every N ticks thereafter") parser.add_option("--max-checkpoints", action="store", type="int", help="the maximum number of checkpoints to drop", default=5) parser.add_option("--checkpoint-dir", action="store", type="string", help="Place all checkpoints in this absolute directory") parser.add_option("-r", "--checkpoint-restore", action="store", type="int", help="restore from checkpoint <N>") parser.add_option("--checkpoint-at-end", action="store_true", help="take a checkpoint at end of run") parser.add_option("--work-begin-checkpoint-count", action="store", type="int", help="checkpoint at specified work begin count") parser.add_option("--work-end-checkpoint-count", action="store", type="int", help="checkpoint at specified work end count") parser.add_option("--work-cpus-checkpoint-count", action="store", type="int", help="checkpoint and exit when active cpu count is reached") parser.add_option("--restore-with-cpu", action="store", type="choice", default="atomic", choices = ["atomic", "timing", "detailed", "inorder"], help = "cpu type for restoring from a checkpoint") # CPU Switching - default switch model goes from a checkpoint # to a timing simple CPU with caches to warm up, then to detailed CPU for # data measurement parser.add_option("--repeat-switch", action="store", type="int", default=None, help="switch back and forth between CPUs with period <N>") parser.add_option("-s", "--standard-switch", action="store", type="int", default=None, help="switch from timing to Detailed CPU after warmup period of <N>") parser.add_option("-p", "--prog-interval", type="int", help="CPU Progress Interval") # Fastforwarding and simpoint related materials parser.add_option("-W", "--warmup-insts", action="store", type="int", default=None, help="Warmup period in total instructions (requires --standard-switch)") parser.add_option("--bench", action="store", type="string", default=None, help="base names for --take-checkpoint and --checkpoint-restore") parser.add_option("-F", "--fast-forward", action="store", type="string", default=None, help="Number of instructions to fast forward before switching") parser.add_option("-S", "--simpoint", action="store_true", default=False, help="""Use workload simpoints as an instruction offset for --checkpoint-restore or --take-checkpoint.""") parser.add_option("--at-instruction", action="store_true", default=False, help="""Treat value of --checkpoint-restore or --take-checkpoint as a number of instructions.""") def addSEOptions(parser): # Benchmark options parser.add_option("-c", "--cmd", default="", help="The binary to run in syscall emulation mode.") parser.add_option("-o", "--options", default="", help="""The options to pass to the binary, use " " around the entire string""") parser.add_option("-i", "--input", default="", help="Read stdin from a file.") parser.add_option("--output", default="", help="Redirect stdout to a file.") parser.add_option("--errout", default="", help="Redirect stderr to a file.") def addFSOptions(parser): # Simulation options parser.add_option("--timesync", action="store_true", help="Prevent simulated time from getting ahead of real time") # System options parser.add_option("--kernel", action="store", type="string") parser.add_option("--script", action="store", type="string") parser.add_option("--frame-capture", action="store_true", help="Stores changed frame buffers from the VNC server to compressed "\ "files in the gem5 output directory") if buildEnv['TARGET_ISA'] == "arm": parser.add_option("--bare-metal", action="store_true", help="Provide the raw system without the linux specific bits") parser.add_option("--machine-type", action="store", type="choice", choices=ArmMachineType.map.keys(), default="RealView_PBX") # Benchmark options parser.add_option("--dual", action="store_true", help="Simulate two systems attached with an ethernet link") parser.add_option("-b", "--benchmark", action="store", type="string", dest="benchmark", help="Specify the benchmark to run. Available benchmarks: %s"\ % DefinedBenchmarks) # Metafile options parser.add_option("--etherdump", action="store", type="string", dest="etherdump", help="Specify the filename to dump a pcap capture of the" \ "ethernet traffic") # Disk Image Options parser.add_option("--disk-image", action="store", type="string", default=None, help="Path to the disk image to use.") # Memory Size Options parser.add_option("--mem-size", action="store", type="string", default=None, help="Specify the physical memory size (single memory)")

55.047872

85

0.638516

794ade2213d5045c673eb601fdac23fd2e6a8d14

300

py

Python

tests/asserts/attribute.py

FilippoBoido/py2puml

9f25ca7ea0bcb2f9e7b25751bcda680a0e8f7d90

[ "MIT" ]

59

2020-06-04T11:32:10.000Z

2022-03-22T18:37:28.000Z

tests/asserts/attribute.py

mayshukla/py2puml

9f25ca7ea0bcb2f9e7b25751bcda680a0e8f7d90

[ "MIT" ]

21

2020-05-14T14:31:23.000Z

2022-03-25T02:44:05.000Z

tests/asserts/attribute.py

mayshukla/py2puml

9f25ca7ea0bcb2f9e7b25751bcda680a0e8f7d90

[ "MIT" ]

11

2021-01-07T04:11:47.000Z

2022-03-01T21:32:32.000Z

from py2puml.domain.umlclass import UmlAttribute def assert_attribute(attribute: UmlAttribute, expected_name: str, expected_type: str, expected_staticity: bool): assert attribute.name == expected_name assert attribute.type == expected_type assert attribute.static == expected_staticity

37.5

112

0.8

794adec29f9a6d57ea58fa35c13c4a673cd69d9c

3,408

py

Python

galsendev_demo/settings.py

PapiHack/galsendev-demo-docker

f3563fc7756dc14b88a3710d26f91937de94a2ea

[ "MIT" ]

2

2021-06-27T19:35:26.000Z

2021-07-03T13:19:28.000Z

galsendev_demo/settings.py

PapiHack/galsendev-demo-docker

f3563fc7756dc14b88a3710d26f91937de94a2ea

[ "MIT" ]

null

galsendev_demo/settings.py

PapiHack/galsendev-demo-docker

f3563fc7756dc14b88a3710d26f91937de94a2ea

[ "MIT" ]

null

""" Django settings for galsendev_demo project. Generated by 'django-admin startproject' using Django 3.2.4. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-bihq%hc(s$)4&j8kltsyj&5etkj02mj&8m9n9p0pbtxvl2rkyn' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'galsendev_demo.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ BASE_DIR / 'templates' ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'galsendev_demo.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': 'galsendev_demo', 'USER': 'galsendev', 'PASSWORD': 'dev4life', 'HOST': 'db', 'PORT': 5432, } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'fr-fr' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

25.818182

91

0.692782

794adf08540e2d5096120f2a51c3122fa639a650

492

py

Python

plotly/validators/mesh3d/hoverlabel/font/_sizesrc.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/mesh3d/hoverlabel/font/_sizesrc.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/mesh3d/hoverlabel/font/_sizesrc.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

import _plotly_utils.basevalidators class SizesrcValidator(_plotly_utils.basevalidators.SrcValidator): def __init__( self, plotly_name='sizesrc', parent_name='mesh3d.hoverlabel.font', **kwargs ): super(SizesrcValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop('edit_type', 'none'), role=kwargs.pop('role', 'info'), **kwargs )

25.894737

66

0.605691

794adf2f271a0eca3be9e230b094681670cdb629

3,891

py

Python

software/glasgow/platform/ice40.py

emilazy/Glasgow

4575ad07ccce76b0b92d29a76fc18a3700a68823

[ "Apache-2.0", "0BSD" ]

null

software/glasgow/platform/ice40.py

emilazy/Glasgow

4575ad07ccce76b0b92d29a76fc18a3700a68823

[ "Apache-2.0", "0BSD" ]

null

software/glasgow/platform/ice40.py

emilazy/Glasgow

4575ad07ccce76b0b92d29a76fc18a3700a68823

[ "Apache-2.0", "0BSD" ]

null

import asyncio from nmigen import * from nmigen.vendor.lattice_ice40 import * from ..device.hardware import * from ..gateware import GatewareBuildError __all__ = ["GlasgowPlatformICE40"] class GlasgowPlatformICE40(LatticeICE40Platform): def toolchain_program(self, products, name): bitstream = products.get("{}.bin".format(name)) loop = asyncio.get_event_loop() loop.run_until_complete(GlasgowHardwareDevice().download_bitstream(bitstream)) def get_pll(self, pll, simple_feedback=True): if not 10e6 <= pll.f_in <= 133e6: pll.logger.error("PLL: f_in (%.3f MHz) must be between 10 and 133 MHz", pll.f_in / 1e6) raise GatewareBuildError("PLL f_in out of range") if not 16e6 <= pll.f_out <= 275e6: pll.logger.error("PLL: f_out (%.3f MHz) must be between 16 and 275 MHz", pll.f_out / 1e6) raise GatewareBuildError("PLL f_out out of range") # The documentation in the iCE40 PLL Usage Guide incorrectly lists the # maximum value of DIVF as 63, when it is only limited to 63 when using # feedback modes other that SIMPLE. if simple_feedback: divf_max = 128 else: divf_max = 64 variants = [] for divr in range(0, 16): f_pfd = pll.f_in / (divr + 1) if not 10e6 <= f_pfd <= 133e6: continue for divf in range(0, divf_max): if simple_feedback: f_vco = f_pfd * (divf + 1) if not 533e6 <= f_vco <= 1066e6: continue for divq in range(1, 7): f_out = f_vco * (2 ** -divq) variants.append((divr, divf, divq, f_pfd, f_out)) else: for divq in range(1, 7): f_vco = f_pfd * (divf + 1) * (2 ** divq) if not 533e6 <= f_vco <= 1066e6: continue f_out = f_vco * (2 ** -divq) variants.append((divr, divf, divq, f_pfd, f_out)) if not variants: pll.logger.error("PLL: f_in (%.3f MHz) to f_out (%.3f) constraints not satisfiable", pll.f_in / 1e6, pll.f_out / 1e6) raise GatewareBuildError("PLL f_in/f_out out of range") def f_out_diff(variant): *_, f_out = variant return abs(f_out - pll.f_out) divr, divf, divq, f_pfd, f_out = min(variants, key=f_out_diff) if f_pfd < 17: filter_range = 1 elif f_pfd < 26: filter_range = 2 elif f_pfd < 44: filter_range = 3 elif f_pfd < 66: filter_range = 4 elif f_pfd < 101: filter_range = 5 else: filter_range = 6 if simple_feedback: feedback_path = "SIMPLE" else: feedback_path = "NON_SIMPLE" ppm = abs(pll.f_out - f_out) / pll.f_out * 1e6 pll.logger.debug("PLL: f_in=%.3f f_out(req)=%.3f f_out(act)=%.3f [MHz] ppm=%d", pll.f_in / 1e6, pll.f_out / 1e6, f_out / 1e6, ppm) pll.logger.trace("iCE40 PLL: feedback_path=%s divr=%d divf=%d divq=%d filter_range=%d", feedback_path, divr, divf, divq, filter_range) return Instance("SB_PLL40_CORE", p_FEEDBACK_PATH=feedback_path, p_PLLOUT_SELECT="GENCLK", p_DIVR=divr, p_DIVF=divf, p_DIVQ=divq, p_FILTER_RANGE=filter_range, i_REFERENCECLK=ClockSignal(pll.idomain), o_PLLOUTCORE=ClockSignal(pll.odomain), i_RESETB=~ResetSignal(pll.idomain), i_BYPASS=Const(0), )

35.697248

96

0.52917

794ae01501c75f2cf1e708dd9e72e109f8078f69

6,998

py

Python

rev-sim-recommender-veto/VeTo.py

schatzopoulos/VeTo-workloads

1475c4d1638b9897c9e52c9192d3a6723bb1bdc4

[ "Apache-2.0" ]

null

rev-sim-recommender-veto/VeTo.py

schatzopoulos/VeTo-workloads

1475c4d1638b9897c9e52c9192d3a6723bb1bdc4

[ "Apache-2.0" ]

null

rev-sim-recommender-veto/VeTo.py

schatzopoulos/VeTo-workloads

1475c4d1638b9897c9e52c9192d3a6723bb1bdc4

[ "Apache-2.0" ]

null

import sys import csv import os # Define CSV dialect to be used. csv.register_dialect( 'exp_dialect', delimiter = '\t' ) class VeTo: def score(self, coeff, method, rrf_k, topk_thr, lines_to_read, sim_score): if method == 'borda': return coeff * lines_to_read elif method == 'rrf': return coeff * (1.0 / (rrf_k + (topk_thr - lines_to_read))) elif method == 'sum': return coeff * float(sim_score) def run(self, method, basic_path, kfold, topk_thr, alpha, beta, rrf_k): experiment_path = basic_path+"experiments/"+str(kfold)+"-fold/" suggestion_path = experiment_path+"suggestions/" hin_wp_avg_precision = [] hin_wp_avg_recall = [] test_size_strict = 0 try: # Run once for each pair of train/test sets. for fold in range(0,kfold): if fold == (kfold-1): fold_path = experiment_path+"folds/fold"+str(fold)+"+/" else: fold_path = experiment_path+"folds/fold"+str(fold)+"/" # Create a dictionary with all items in train set train_set = dict() with open(fold_path+"train.csv",'r') as train_file: train_entries = csv.reader(train_file,dialect='exp_dialect') for entry in train_entries: train_set[entry[0]] = 1 train_file.close() # Create a list with all items in the test set test_set = [] with open(fold_path+"test.csv",'r') as test_file: test_entries_csv = csv.reader(test_file,dialect='exp_dialect') test_size = 0 for entry in test_entries_csv: test_set.append(entry[0]) test_size += 1 test_file.close() if fold == 0: test_size_strict = test_size #to be used in case the size of last partition is larger hin_wp_avg_precision = [0]*test_size_strict hin_wp_avg_recall = [0]*test_size_strict # Get suggestions based on HIN hin_wp_sugg = dict() for entry in train_set: try: with open(basic_path+"input/author_sim/HIN-APT/"+entry+".csv",'r') as auth_sim1_file: sim1_authors = csv.reader(auth_sim1_file,dialect='exp_dialect') lines_to_read = topk_thr for auth in sim1_authors: if auth[1] in train_set: #do not consider anyone in the training set continue lines_to_read -= 1 if lines_to_read == -1: break if auth[1] in hin_wp_sugg: hin_wp_sugg[auth[1]] += self.score(alpha, method, rrf_k, topk_thr, lines_to_read, auth[2]) #pow(lines_to_read,3) #* float(auth[2]) #get borda-count score else: hin_wp_sugg[auth[1]] = self.score(alpha, method, rrf_k, topk_thr, lines_to_read, auth[2]) # #* float(auth[2]) #get borda-count score auth_sim1_file.close() except FileNotFoundError: # print("NOT FOUND: " + basic_path+"input/author_sim/HIN-APT/"+entry+".csv") pass try: with open(basic_path+"input/author_sim/HIN-APV/"+entry+".csv",'r') as auth_sim2_file: sim2_authors = csv.reader(auth_sim2_file,dialect='exp_dialect') lines_to_read = topk_thr for auth in sim2_authors: if auth[1] in train_set: #do not consider anyone in the training set continue lines_to_read -= 1 if lines_to_read == -1: break if auth[1] in hin_wp_sugg: hin_wp_sugg[auth[1]] += self.score(beta, method, rrf_k, topk_thr, lines_to_read, auth[2]) #pow(lines_to_read,3) #* float(auth[2]) #get borda-count score else: hin_wp_sugg[auth[1]] = self.score(beta, method, rrf_k, topk_thr, lines_to_read, auth[2]) #pow(lines_to_read,3) #* float(auth[2]) #get borda-count score auth_sim2_file.close() except FileNotFoundError: # print("NOT FOUND: " + basic_path+"input/author_sim/HIN-APV/"+entry+".csv") pass hin_wp_sugg_list = sorted(hin_wp_sugg,key=hin_wp_sugg.get, reverse=True) #sort suggestions based on borda count hin_wp_sugg_list = hin_wp_sugg_list[0:test_size] #keep as many as in the test size # Calculate top-k precision & recall for different k values for k in range(1,test_size_strict): #print("- Calculating precision & recall for fold #"+str(fold)+" at top-"+str(k)+":") #debug #consider first k elements for each method hin_wp_sugg_list_topk = hin_wp_sugg_list[0:k] hin_wp_found = set(test_set).intersection(hin_wp_sugg_list_topk) hin_wp_found_cnt = len(hin_wp_found) hin_wp_precision = hin_wp_found_cnt/k hin_wp_recall = hin_wp_found_cnt/test_size_strict hin_wp_avg_precision[k] += hin_wp_precision hin_wp_avg_recall[k] += hin_wp_recall value = [] hin_wp_f1_measures = [0]*test_size_strict for k in range(1,test_size_strict): # hin_wp_avg_precision[k] = hin_wp_avg_precision[k]/kfold hin_wp_avg_recall[k] = hin_wp_avg_recall[k]/kfold if (hin_wp_avg_precision[k]+hin_wp_avg_recall[k]) !=0: hin_wp_f1_measures[k] = 2*hin_wp_avg_precision[k]*hin_wp_avg_recall[k]/(hin_wp_avg_precision[k]+hin_wp_avg_recall[k]) else: hin_wp_f1_measures[k] = 0 # print([k,hin_wp_avg_precision[k]]) # print([k,hin_wp_avg_recall[k]]) # print([hin_wp_avg_recall[k],hin_wp_avg_precision[k]]) # print([k,hin_wp_f1_measures[k]]) value.append(hin_wp_f1_measures[k]) return value; except IOError as e: print("=> ERROR: Cannot open file...") print(e)

48.262069

191

0.508574

794ae06e34a11f05cd8176aae3b10696070f3e89

187,839

py

Python

locations/spiders/carrefour.py

cmecklenborg/alltheplaces

e62b59fb0071b6e289c4622d368fdb203a28347e

[ "MIT" ]

null

locations/spiders/carrefour.py

cmecklenborg/alltheplaces

e62b59fb0071b6e289c4622d368fdb203a28347e

[ "MIT" ]

null

locations/spiders/carrefour.py

cmecklenborg/alltheplaces

e62b59fb0071b6e289c4622d368fdb203a28347e

[ "MIT" ]

null

# -*- coding: utf-8 -*- import scrapy import re CITIES = [ { "zip": 3636, "name": "POZO CERCADO (EL CHORRO (F), DPTO. RIVADAVIA (S))", "state": "SALTA", "lat": -23.4933, "lon": -61.9267, }, { "zip": 4123, "name": "LAS SALADAS", "state": "SALTA", "lat": -25.7833, "lon": -64.5, }, { "zip": 4126, "name": "EL BRETE", "state": "SALTA", "lat": -26.0667, "lon": -65.3667, }, { "zip": 4141, "name": "LA CIENEGUITA", "state": "SALTA", "lat": -26.4367, "lon": -65.97, }, { "zip": 4190, "name": "AGUAS CALIENTES", "state": "SALTA", "lat": -25.8, "lon": -65.0333, }, { "zip": 4191, "name": "SAN PEDRO DE LOS CORRALES", "state": "SALTA", "lat": -25.9689, "lon": -64.6583, }, { "zip": 4193, "name": "PUENTE DE PLATA", "state": "SALTA", "lat": -25.95, "lon": -64.7167, }, { "zip": 4198, "name": "CAMARA (ARENAL, DPTO. 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GONZALEZ", "state": "FORMOSA", "lat": -24.25, "lon": -61.25, }, { "zip": 8300, "name": "NEUQUEN", "state": "NEUQUEN", "lat": -38.95, "lon": -68.0667, }, { "zip": 8301, "name": "PLANICIE BANDERITA", "state": "NEUQUEN", "lat": -38.8083, "lon": -68.0833, }, {"zip": 8305, "name": "TRATAYEN", "state": "NEUQUEN", "lat": -38.4, "lon": -68.6}, { "zip": 8309, "name": "VISTA ALEGRE NORTE", "state": "NEUQUEN", "lat": -38.8, "lon": -68.1333, }, { "zip": 8311, "name": "VILLA EL CHOCON", "state": "NEUQUEN", "lat": -39.2333, "lon": -68.75, }, { "zip": 8313, "name": "LIMAY CENTRO", "state": "NEUQUEN", "lat": -39.2278, "lon": -68.8056, }, { "zip": 8315, "name": "BAJADA COLORADA", "state": "NEUQUEN", "lat": -39.85, "lon": -69.7333, }, { "zip": 8316, "name": "CHINA MUERTA", "state": "NEUQUEN", "lat": -38.9833, "lon": -68.325, }, { "zip": 8318, "name": "PLAZA HUINCUL", "state": "NEUQUEN", "lat": -38.9167, "lon": -69.15, }, { "zip": 8319, "name": "CAMPAMENTO SOL", "state": "NEUQUEN", "lat": -39.4467, "lon": -69.3489, }, { "zip": 8322, "name": "BARRIO PELIGROSO", "state": "NEUQUEN", "lat": -38.9333, "lon": -69.2333, }, { "zip": 8340, "name": "AGUADA FLORENCIO", "state": "NEUQUEN", "lat": -38.7167, "lon": -70.1583, }, { "zip": 8341, "name": "CATAN LIL", "state": "NEUQUEN", "lat": -39.75, "lon": -70.6167, }, { "zip": 8345, "name": "HARAS PATRIA", "state": "NEUQUEN", "lat": -39.0667, "lon": -70.8333, }, { "zip": 8347, "name": "PINO SOLO", "state": "NEUQUEN", "lat": -38.2867, "lon": -70.49, }, { "zip": 8349, "name": "CERRO DE LA PARVA", "state": "NEUQUEN", "lat": -37.8061, "lon": -70.6485, }, { "zip": 8351, "name": "TRAHUNCURA", "state": "NEUQUEN", "lat": -38.15, "lon": -70.1139, }, { "zip": 8353, "name": "CHACAY MELEHUE", "state": "NEUQUEN", "lat": -37.2333, "lon": -70.3667, }, ] from locations.items import GeojsonPointItem DAYS = ["Mo", "Tu", "We", "Th", "Fr", "Sa", "Su"] class CarrefourSpider(scrapy.Spider): name = "carrefour" item_attributes = {"brand": "Carrefour"} allowed_domains = ["carrefour.com.ar"] start_urls = ("https://www.carrefour.com.ar/storelocator/index/",) def get_string(self, stri): day_parts = ( stri.replace("horas", "") .replace("hrs.", "") .replace("hrs", "") .replace("de ", "") .split(" y ") ) res = "" for days in day_parts: time_parts = days.split(" a ") if len(time_parts) > 1: res += time_parts[0].strip() + "-" + time_parts[1].strip() + " and " return res.rstrip(" and ") def store_hours(self, store_hours): lastday = DAYS[0] lasttime = self.get_string(store_hours[0]) opening_hours = lastday for day in range(1, 7): # loop by days if day == len(store_hours): break str_curr = self.get_string(store_hours[day]) if str_curr != lasttime: if lastday == DAYS[day - 1]: opening_hours += " " + lasttime + ";" + DAYS[day] else: opening_hours += ( "-" + DAYS[day - 1] + " " + lasttime + ";" + DAYS[day] ) lasttime = str_curr lastday = DAYS[day] if lasttime != "": if lastday == DAYS[day]: opening_hours += " " + str_curr else: opening_hours += "-" + DAYS[6] + " " + str_curr else: opening_hours = opening_hours.rstrip(DAYS[6]) return opening_hours.rstrip(";").strip() def phone_normalize(self, phone): r = re.search( r"\+?(\s+)*(\d{1})?(\s|$)*(\d{3})(\s+|$)*(\d{3})(\s+|-)?(\d{2})(\s+|-)?(\d{2})", phone, ) return ( ( "(" + r.group(4) + ") " + r.group(6) + "-" + r.group(8) + "-" + r.group(10) ) if r else phone ) def parse(self, response): # high-level list of states for city in CITIES: formdata = { "search[address]": city["name"], "search[geocode]": str(city["lat"]) + "," + str(city["lon"]), } yield scrapy.FormRequest( "https://www.carrefour.com.ar/storelocator/index/search/", formdata=formdata, callback=self.parse_shops, ) def parse_shops(self, response): # high-level list of states shops = response.xpath('//div[@class="storelocator_item"]') for shop in shops: if not shop.xpath('./div[@class="moreData"]/div/text()'): return address = shop.xpath('./div[@class="moreData"]/div/text()').extract() id_num = shop.xpath('./div[@class="id"]/text()').extract_first() time_data = response.xpath('//div[@id="store-detail-' + id_num + '"]') dates = time_data.xpath( './div[@class="timetable"]//td[@class="hour"]/text()' ).extract() yield GeojsonPointItem( lat=float( shop.xpath('./div[@class="geodata"]/@data-lat').extract_first() ), lon=float( shop.xpath('./div[@class="geodata"]/@data-lng').extract_first() ), phone=time_data.xpath('./div/div[@class="tel"]/text()') .extract_first() .strip(), # website='https://www.carrefour.com.ar/storelocator/index/', ref=id_num, opening_hours="" if not dates else self.store_hours(dates), addr_full=address[0], city=address[1], state="", postcode="", country="Argentina", )

21.933559

94

0.379075

794ae2122656da6525de9364c5f00519abc36bf9

1,384

py

Python

cogs/Twitch.py

loukayl/virtualv-Managmentbot

246eb2057a1d9cd772a3115de40436bf1e46ffb6

[ "Apache-2.0", "MIT" ]

null

cogs/Twitch.py

loukayl/virtualv-Managmentbot

246eb2057a1d9cd772a3115de40436bf1e46ffb6

[ "Apache-2.0", "MIT" ]

null

cogs/Twitch.py

loukayl/virtualv-Managmentbot

246eb2057a1d9cd772a3115de40436bf1e46ffb6

[ "Apache-2.0", "MIT" ]

null

from TwitchApiPy import TwitchApiPy from discord.ext import commands import settings from TwitchApiPy import TwitchApiPy from discord.ext import commands import settings import discord import os, sys, discord, platform, random, aiohttp, json from discord.ext import commands if not os.path.isfile("config.py"): sys.exit("'config.py' not found! Please add it and try again.") else: import config # This Cog is not being use for now due to an issue class Twitch(commands.Cog, TwitchApiPy): def __init__(self, bot): self.bot = bot self._last_member = None api = TwitchApiPy() api.ClientID = settings.ClientID api.OAuth = settings.OAuth @commands.command(pass_context=True) async def TwitchFollower(self, ctx, name): Count = self.api.GetFollowerCount(name) await ctx.send("{}{} : {}".format("Follower count of ", name, Count)) @commands.command(pass_context=True) async def TwitchChannelInfo(self, ctx, name): info = self.api.GetChannelInfo(name) info2 = self.api.GetChannelStatus(name) text = ( "Channel name : {} , Last Played Game : {} , Last Streams Title : {} , Is Live : {} , Language : {}").format( info["name"], info["game"], info["title"], info2["islive"], info2["language"]) await ctx.send(text) def setup(bot): bot.add_cog(Twitch(bot))

30.755556

121

0.668353

794ae22d978bd846053688382be0abb5d4da53d7

66

py

Python

libs/p3270/__init__.py

rocketbot-cl/Terminal_emulator

34f2e50f23908fb645f93e58aa579f03ad8e02d9

[ "MIT" ]

null

libs/p3270/__init__.py

rocketbot-cl/Terminal_emulator

34f2e50f23908fb645f93e58aa579f03ad8e02d9

[ "MIT" ]

null

libs/p3270/__init__.py

rocketbot-cl/Terminal_emulator

34f2e50f23908fb645f93e58aa579f03ad8e02d9

[ "MIT" ]

2

2022-03-17T20:50:04.000Z

2022-03-30T12:26:10.000Z

from p3270.p3270 import S3270, P3270Client, Config, StatusMessage

33

65

0.833333

794ae462681c2df26e0a30de9b67fc61d3a7c27c

5,140

py

Python

esmvalcore/_recipe_checks.py

Peter9192/ESMValCore

febd96a39480cc837afbf4e1f5b0ef61571af76a

[ "Apache-2.0" ]

null

esmvalcore/_recipe_checks.py

Peter9192/ESMValCore

febd96a39480cc837afbf4e1f5b0ef61571af76a

[ "Apache-2.0" ]

null

esmvalcore/_recipe_checks.py

Peter9192/ESMValCore

febd96a39480cc837afbf4e1f5b0ef61571af76a

[ "Apache-2.0" ]

null

"""Module with functions to check a recipe.""" import logging import os import subprocess import yamale from ._data_finder import get_start_end_year from ._task import get_flattened_tasks, which from .preprocessor import PreprocessingTask logger = logging.getLogger(__name__) class RecipeError(Exception): """Recipe contains an error.""" def ncl_version(): """Check the NCL version.""" ncl = which('ncl') if not ncl: raise RecipeError("Recipe contains NCL scripts, but cannot find " "an NCL installation.") try: cmd = [ncl, '-V'] version = subprocess.check_output(cmd, universal_newlines=True) except subprocess.CalledProcessError: logger.error("Failed to execute '%s'", ' '.join(' '.join(cmd))) raise RecipeError("Recipe contains NCL scripts, but your NCL " "installation appears to be broken.") version = version.strip() logger.info("Found NCL version %s", version) major, minor = (int(i) for i in version.split('.')[:2]) if major < 6 or (major == 6 and minor < 4): raise RecipeError("NCL version 6.4 or higher is required to run " "a recipe containing NCL scripts.") def recipe_with_schema(filename): """Check if the recipe content matches schema.""" schema_file = os.path.join(os.path.dirname(__file__), 'recipe_schema.yml') logger.debug("Checking recipe against schema %s", schema_file) recipe = yamale.make_data(filename) schema = yamale.make_schema(schema_file) yamale.validate(schema, recipe) def diagnostics(diags): """Check diagnostics in recipe.""" for name, diagnostic in diags.items(): if 'scripts' not in diagnostic: raise RecipeError( "Missing scripts section in diagnostic {}".format(name)) variable_names = tuple(diagnostic.get('variables', {})) scripts = diagnostic.get('scripts') if scripts is None: scripts = {} for script_name, script in scripts.items(): if script_name in variable_names: raise RecipeError( "Invalid script name {} encountered in diagnostic {}: " "scripts cannot have the same name as variables.".format( script_name, name)) if not script.get('script'): raise RecipeError( "No script defined for script {} in diagnostic {}".format( script_name, name)) def duplicate_datasets(datasets): """Check for duplicate datasets.""" checked_datasets_ = [] for dataset in datasets: if dataset in checked_datasets_: raise RecipeError( "Duplicate dataset {} in datasets section".format(dataset)) checked_datasets_.append(dataset) def variable(var, required_keys): """Check variables as derived from recipe.""" required = set(required_keys) missing = required - set(var) if missing: raise RecipeError( "Missing keys {} from variable {} in diagnostic {}".format( missing, var.get('short_name'), var.get('diagnostic'))) def data_availability(input_files, var): """Check if the required input data is available.""" if not input_files: raise RecipeError("No input files found for variable {}".format(var)) required_years = set(range(var['start_year'], var['end_year'] + 1)) available_years = set() for filename in input_files: start, end = get_start_end_year(filename) available_years.update(range(start, end + 1)) missing_years = required_years - available_years if missing_years: raise RecipeError( "No input data available for years {} in files {}".format( ", ".join(str(year) for year in missing_years), input_files)) def tasks_valid(tasks): """Check that tasks are consistent.""" filenames = set() msg = "Duplicate preprocessor filename {}, please file a bug report." for task in get_flattened_tasks(tasks): if isinstance(task, PreprocessingTask): for product in task.products: if product.filename in filenames: raise ValueError(msg.format(product.filename)) filenames.add(product.filename) def extract_shape(settings): """Check that `extract_shape` arguments are valid.""" shapefile = settings.get('shapefile', '') if not os.path.exists(shapefile): raise RecipeError("In preprocessor function `extract_shape`: " f"Unable to find 'shapefile: {shapefile}'") valid = { 'method': {'contains', 'representative'}, 'crop': {True, False}, } for key in valid: value = settings.get(key) if not (value is None or value in valid[key]): raise RecipeError( f"In preprocessor function `extract_shape`: Invalid value " f"'{value}' for argument '{key}', choose from " "{}".format(', '.join(f"'{k}'".lower() for k in valid[key])))

36.453901

78

0.620039

794ae5f8c94523c844f42ca9005708562d7e11d5

124

py

Python

run.py

DoubleDoorDevelopment/MultiStream

2cf789d03c2206f56b0d861dd8f8c3943dc7e061

[ "BSD-3-Clause" ]

null

run.py

DoubleDoorDevelopment/MultiStream

2cf789d03c2206f56b0d861dd8f8c3943dc7e061

[ "BSD-3-Clause" ]

null

run.py

DoubleDoorDevelopment/MultiStream

2cf789d03c2206f56b0d861dd8f8c3943dc7e061

[ "BSD-3-Clause" ]

null

# Run this file to start the app during development from app import app if __name__ == '__main__': app.run(debug=True)

20.666667

51

0.725806

794ae610beaa167c547d84e22f613f439c8c1948

57,776

py

Python

sympy/core/basic.py

torsknod/sympy-torsknod

19425c8d2d876710413987eaa6e69ff9d47a0380

[ "BSD-3-Clause" ]

1

2020-03-12T02:52:16.000Z

sympy/core/basic.py

torsknod/sympy-torsknod

19425c8d2d876710413987eaa6e69ff9d47a0380

[ "BSD-3-Clause" ]

null

sympy/core/basic.py

torsknod/sympy-torsknod

19425c8d2d876710413987eaa6e69ff9d47a0380

[ "BSD-3-Clause" ]

null

"""Base class for all the objects in SymPy""" from __future__ import print_function, division from sympy.core.assumptions import ManagedProperties from sympy.core.cache import cacheit from sympy.core.core import BasicType, C from sympy.core.sympify import _sympify, sympify, SympifyError from sympy.core.compatibility import (reduce, iterable, Iterator, ordered, string_types, with_metaclass) from sympy.core.decorators import deprecated from sympy.core.singleton import S class Basic(with_metaclass(ManagedProperties)): """ Base class for all objects in SymPy. Conventions: 1) Always use ``.args``, when accessing parameters of some instance: >>> from sympy import cot >>> from sympy.abc import x, y >>> cot(x).args (x,) >>> cot(x).args[0] x >>> (x*y).args (x, y) >>> (x*y).args[1] y 2) Never use internal methods or variables (the ones prefixed with ``_``): >>> cot(x)._args # do not use this, use cot(x).args instead (x,) """ __slots__ = ['_mhash', # hash value '_args', # arguments '_assumptions' ] # To be overridden with True in the appropriate subclasses is_Atom = False is_Symbol = False is_Dummy = False is_Wild = False is_Function = False is_Add = False is_Mul = False is_Pow = False is_Number = False is_Float = False is_Rational = False is_Integer = False is_NumberSymbol = False is_Order = False is_Derivative = False is_Piecewise = False is_Poly = False is_AlgebraicNumber = False is_Relational = False is_Equality = False is_Boolean = False is_Not = False is_Matrix = False @property @deprecated(useinstead="is_Float", issue=1721, deprecated_since_version="0.7.0") def is_Real(self): # pragma: no cover """Deprecated alias for ``is_Float``""" # When this is removed, remove the piece of code disabling the warning # from test_pickling.py return self.is_Float def __new__(cls, *args): obj = object.__new__(cls) obj._assumptions = cls.default_assumptions obj._mhash = None # will be set by __hash__ method. obj._args = args # all items in args must be Basic objects return obj def copy(self): return self.func(*self.args) def __reduce_ex__(self, proto): """ Pickling support.""" return type(self), self.__getnewargs__(), self.__getstate__() def __getnewargs__(self): return self.args def __getstate__(self): return {} def __setstate__(self, state): for k, v in state.items(): setattr(self, k, v) def __hash__(self): # hash cannot be cached using cache_it because infinite recurrence # occurs as hash is needed for setting cache dictionary keys h = self._mhash if h is None: h = hash((type(self).__name__,) + self._hashable_content()) self._mhash = h return h def _hashable_content(self): """Return a tuple of information about self that can be used to compute the hash. If a class defines additional attributes, like ``name`` in Symbol, then this method should be updated accordingly to return such relevent attributes. Defining more than _hashable_content is necessary if __eq__ has been defined by a class. See note about this in Basic.__eq__.""" return self._args @property def assumptions0(self): """ Return object `type` assumptions. For example: Symbol('x', real=True) Symbol('x', integer=True) are different objects. In other words, besides Python type (Symbol in this case), the initial assumptions are also forming their typeinfo. Examples ======== >>> from sympy import Symbol >>> from sympy.abc import x >>> x.assumptions0 {'commutative': True} >>> x = Symbol("x", positive=True) >>> x.assumptions0 {'commutative': True, 'complex': True, 'hermitian': True, 'imaginary': False, 'negative': False, 'nonnegative': True, 'nonpositive': False, 'nonzero': True, 'positive': True, 'real': True, 'zero': False} """ return {} def compare(self, other): """ Return -1, 0, 1 if the object is smaller, equal, or greater than other. Not in the mathematical sense. If the object is of a different type from the "other" then their classes are ordered according to the sorted_classes list. Examples ======== >>> from sympy.abc import x, y >>> x.compare(y) -1 >>> x.compare(x) 0 >>> y.compare(x) 1 """ # all redefinitions of __cmp__ method should start with the # following lines: if self is other: return 0 n1 = self.__class__ n2 = other.__class__ c = (n1 > n2) - (n1 < n2) if c: return c # st = self._hashable_content() ot = other._hashable_content() c = (len(st) > len(ot)) - (len(st) < len(ot)) if c: return c for l, r in zip(st, ot): if isinstance(l, Basic): c = l.compare(r) elif isinstance(l, frozenset): c = 0 else: c = (l > r) - (l < r) if c: return c return 0 @staticmethod def _compare_pretty(a, b): from sympy.series.order import Order if isinstance(a, Order) and not isinstance(b, Order): return 1 if not isinstance(a, Order) and isinstance(b, Order): return -1 if a.is_Rational and b.is_Rational: l = a.p * b.q r = b.p * a.q return (l > r) - (l < r) else: from sympy.core.symbol import Wild p1, p2, p3 = Wild("p1"), Wild("p2"), Wild("p3") r_a = a.match(p1 * p2**p3) if r_a and p3 in r_a: a3 = r_a[p3] r_b = b.match(p1 * p2**p3) if r_b and p3 in r_b: b3 = r_b[p3] c = Basic.compare(a3, b3) if c != 0: return c return Basic.compare(a, b) @staticmethod @deprecated(useinstead="default_sort_key", issue=1491, deprecated_since_version="0.7.2") def compare_pretty(a, b): """ Is a > b in the sense of ordering in printing? THIS FUNCTION IS DEPRECATED. Use ``default_sort_key`` instead. :: yes ..... return 1 no ...... return -1 equal ... return 0 Strategy: It uses Basic.compare as a fallback, but improves it in many cases, like ``x**3``, ``x**4``, ``O(x**3)`` etc. In those simple cases, it just parses the expression and returns the "sane" ordering such as:: 1 < x < x**2 < x**3 < O(x**4) etc. Examples ======== >>> from sympy.abc import x >>> from sympy import Basic, Number >>> Basic._compare_pretty(x, x**2) -1 >>> Basic._compare_pretty(x**2, x**2) 0 >>> Basic._compare_pretty(x**3, x**2) 1 >>> Basic._compare_pretty(Number(1, 2), Number(1, 3)) 1 >>> Basic._compare_pretty(Number(0), Number(-1)) 1 """ try: a = _sympify(a) except SympifyError: pass try: b = _sympify(b) except SympifyError: pass if not isinstance(b, Basic): return +1 # sympy > other # now both objects are from SymPy, so we can proceed to usual comparison a = a.sort_key() b = b.sort_key() return (a > b) - (a < b) @classmethod def fromiter(cls, args, **assumptions): """ Create a new object from an iterable. This is a convenience function that allows one to create objects from any iterable, without having to convert to a list or tuple first. Examples ======== >>> from sympy import Tuple >>> Tuple.fromiter(i for i in range(5)) (0, 1, 2, 3, 4) """ return cls(*tuple(args), **assumptions) @classmethod def class_key(cls): """Nice order of classes. """ return 5, 0, cls.__name__ @cacheit def sort_key(self, order=None): """ Return a sort key. Examples ======== >>> from sympy.core import S, I >>> sorted([S(1)/2, I, -I], key=lambda x: x.sort_key()) [1/2, -I, I] >>> S("[x, 1/x, 1/x**2, x**2, x**(1/2), x**(1/4), x**(3/2)]") [x, 1/x, x**(-2), x**2, sqrt(x), x**(1/4), x**(3/2)] >>> sorted(_, key=lambda x: x.sort_key()) [x**(-2), 1/x, x**(1/4), sqrt(x), x, x**(3/2), x**2] """ # XXX: remove this when issue #2070 is fixed def inner_key(arg): if isinstance(arg, Basic): return arg.sort_key(order) else: return arg args = self._sorted_args args = len(args), tuple([ inner_key(arg) for arg in args ]) return self.class_key(), args, S.One.sort_key(), S.One def __eq__(self, other): """Return a boolean indicating whether a == b on the basis of their symbolic trees. This is the same as a.compare(b) == 0 but faster. Notes ===== If a class that overrides __eq__() needs to retain the implementation of __hash__() from a parent class, the interpreter must be told this explicitly by setting __hash__ = <ParentClass>.__hash__. Otherwise the inheritance of __hash__() will be blocked, just as if __hash__ had been explicitly set to None. References ========== from http://docs.python.org/dev/reference/datamodel.html#object.__hash__ """ if type(self) is not type(other): # issue 3001 a**1.0 == a like a**2.0 == a**2 while isinstance(self, C.Pow) and self.exp == 1: self = self.base while isinstance(other, C.Pow) and other.exp == 1: other = other.base try: other = _sympify(other) except SympifyError: return False # sympy != other if type(self) is not type(other): return False return self._hashable_content() == other._hashable_content() def __ne__(self, other): """a != b -> Compare two symbolic trees and see whether they are different this is the same as: a.compare(b) != 0 but faster """ if type(self) is not type(other): try: other = _sympify(other) except SympifyError: return True # sympy != other if type(self) is not type(other): return True return self._hashable_content() != other._hashable_content() def dummy_eq(self, other, symbol=None): """ Compare two expressions and handle dummy symbols. Examples ======== >>> from sympy import Dummy >>> from sympy.abc import x, y >>> u = Dummy('u') >>> (u**2 + 1).dummy_eq(x**2 + 1) True >>> (u**2 + 1) == (x**2 + 1) False >>> (u**2 + y).dummy_eq(x**2 + y, x) True >>> (u**2 + y).dummy_eq(x**2 + y, y) False """ dummy_symbols = [ s for s in self.free_symbols if s.is_Dummy ] if not dummy_symbols: return self == other elif len(dummy_symbols) == 1: dummy = dummy_symbols.pop() else: raise ValueError( "only one dummy symbol allowed on the left-hand side") if symbol is None: symbols = other.free_symbols if not symbols: return self == other elif len(symbols) == 1: symbol = symbols.pop() else: raise ValueError("specify a symbol in which expressions should be compared") tmp = dummy.__class__() return self.subs(dummy, tmp) == other.subs(symbol, tmp) # Note, we always use the default ordering (lex) in __str__ and __repr__, # regardless of the global setting. See issue 2388. def __repr__(self): from sympy.printing import sstr return sstr(self, order=None) def __str__(self): from sympy.printing import sstr return sstr(self, order=None) def atoms(self, *types): """Returns the atoms that form the current object. By default, only objects that are truly atomic and can't be divided into smaller pieces are returned: symbols, numbers, and number symbols like I and pi. It is possible to request atoms of any type, however, as demonstrated below. Examples ======== >>> from sympy import I, pi, sin >>> from sympy.abc import x, y >>> (1 + x + 2*sin(y + I*pi)).atoms() set([1, 2, I, pi, x, y]) If one or more types are given, the results will contain only those types of atoms. Examples ======== >>> from sympy import Number, NumberSymbol, Symbol >>> (1 + x + 2*sin(y + I*pi)).atoms(Symbol) set([x, y]) >>> (1 + x + 2*sin(y + I*pi)).atoms(Number) set([1, 2]) >>> (1 + x + 2*sin(y + I*pi)).atoms(Number, NumberSymbol) set([1, 2, pi]) >>> (1 + x + 2*sin(y + I*pi)).atoms(Number, NumberSymbol, I) set([1, 2, I, pi]) Note that I (imaginary unit) and zoo (complex infinity) are special types of number symbols and are not part of the NumberSymbol class. The type can be given implicitly, too: >>> (1 + x + 2*sin(y + I*pi)).atoms(x) # x is a Symbol set([x, y]) Be careful to check your assumptions when using the implicit option since ``S(1).is_Integer = True`` but ``type(S(1))`` is ``One``, a special type of sympy atom, while ``type(S(2))`` is type ``Integer`` and will find all integers in an expression: >>> from sympy import S >>> (1 + x + 2*sin(y + I*pi)).atoms(S(1)) set([1]) >>> (1 + x + 2*sin(y + I*pi)).atoms(S(2)) set([1, 2]) Finally, arguments to atoms() can select more than atomic atoms: any sympy type (loaded in core/__init__.py) can be listed as an argument and those types of "atoms" as found in scanning the arguments of the expression recursively: >>> from sympy import Function, Mul >>> from sympy.core.function import AppliedUndef >>> f = Function('f') >>> (1 + f(x) + 2*sin(y + I*pi)).atoms(Function) set([f(x), sin(y + I*pi)]) >>> (1 + f(x) + 2*sin(y + I*pi)).atoms(AppliedUndef) set([f(x)]) >>> (1 + x + 2*sin(y + I*pi)).atoms(Mul) set([I*pi, 2*sin(y + I*pi)]) """ if types: types = tuple( [t if isinstance(t, type) else type(t) for t in types]) else: types = (Atom,) result = set() for expr in preorder_traversal(self): if isinstance(expr, types): result.add(expr) return result @property def free_symbols(self): """Return from the atoms of self those which are free symbols. For most expressions, all symbols are free symbols. For some classes this is not true. e.g. Integrals use Symbols for the dummy variables which are bound variables, so Integral has a method to return all symbols except those. Derivative keeps track of symbols with respect to which it will perform a derivative; those are bound variables, too, so it has its own symbols method. Any other method that uses bound variables should implement a symbols method.""" union = set.union return reduce(union, [arg.free_symbols for arg in self.args], set()) @property def canonical_variables(self): """Return a dictionary mapping any variable defined in ``self.variables`` as underscore-suffixed numbers corresponding to their position in ``self.variables``. Enough underscores are added to ensure that there will be no clash with existing free symbols. Examples ======== >>> from sympy import Lambda >>> from sympy.abc import x >>> Lambda(x, 2*x).canonical_variables {x: 0_} """ if not hasattr(self, 'variables'): return {} u = "_" while any(s.name.endswith(u) for s in self.free_symbols): u += "_" name = '%%i%s' % u V = self.variables return dict(list(zip(V, [C.Symbol(name % i, **v.assumptions0) for i, v in enumerate(V)]))) def is_hypergeometric(self, k): from sympy.simplify import hypersimp return hypersimp(self, k) is not None @property def is_number(self): """Returns ``True`` if 'self' contains no free symbols. See Also ======== is_comparable sympy.core.expr.is_number """ # should be overriden by subclasses return False @property def is_comparable(self): """Return True if self can be computed to a real number with precision, else False. Examples ======== >>> from sympy import exp_polar, pi, I >>> (I*exp_polar(I*pi/2)).is_comparable True >>> (I*exp_polar(I*pi*2)).is_comparable False """ is_real = self.is_real if is_real is False: return False is_number = self.is_number if is_number is False: return False if is_real and is_number: return True n, i = [p.evalf(2) for p in self.as_real_imag()] if not i.is_Number or not n.is_Number: return False if i: # if _prec = 1 we can't decide and if not, # the answer is False so return False return False else: return n._prec != 1 @property def func(self): """ The top-level function in an expression. The following should hold for all objects:: >> x == x.func(*x.args) Examples ======== >>> from sympy.abc import x >>> a = 2*x >>> a.func <class 'sympy.core.mul.Mul'> >>> a.args (2, x) >>> a.func(*a.args) 2*x >>> a == a.func(*a.args) True """ return self.__class__ @property def args(self): """Returns a tuple of arguments of 'self'. Examples ======== >>> from sympy import cot >>> from sympy.abc import x, y >>> cot(x).args (x,) >>> cot(x).args[0] x >>> (x*y).args (x, y) >>> (x*y).args[1] y Notes ===== Never use self._args, always use self.args. Only use _args in __new__ when creating a new function. Don't override .args() from Basic (so that it's easy to change the interface in the future if needed). """ return self._args @property def _sorted_args(self): """ The same as ``args``. Derived classes which don't fix an order on their arguments should override this method to produce the sorted representation. """ return self.args def iter_basic_args(self): """ Iterates arguments of ``self``. Examples ======== >>> from sympy.abc import x >>> a = 2*x >>> a.iter_basic_args() <...iterator object at 0x...> >>> list(a.iter_basic_args()) [2, x] """ return iter(self.args) def as_poly(self, *gens, **args): """Converts ``self`` to a polynomial or returns ``None``. >>> from sympy import sin >>> from sympy.abc import x, y >>> print((x**2 + x*y).as_poly()) Poly(x**2 + x*y, x, y, domain='ZZ') >>> print((x**2 + x*y).as_poly(x, y)) Poly(x**2 + x*y, x, y, domain='ZZ') >>> print((x**2 + sin(y)).as_poly(x, y)) None """ from sympy.polys import Poly, PolynomialError try: poly = Poly(self, *gens, **args) if not poly.is_Poly: return None else: return poly except PolynomialError: return None def as_content_primitive(self, radical=False): """A stub to allow Basic args (like Tuple) to be skipped when computing the content and primitive components of an expression. See docstring of Expr.as_content_primitive """ return S.One, self def subs(self, *args, **kwargs): """ Substitutes old for new in an expression after sympifying args. `args` is either: - two arguments, e.g. foo.subs(old, new) - one iterable argument, e.g. foo.subs(iterable). The iterable may be o an iterable container with (old, new) pairs. In this case the replacements are processed in the order given with successive patterns possibly affecting replacements already made. o a dict or set whose key/value items correspond to old/new pairs. In this case the old/new pairs will be sorted by op count and in case of a tie, by number of args and the default_sort_key. The resulting sorted list is then processed as an iterable container (see previous). If the keyword ``simultaneous`` is True, the subexpressions will not be evaluated until all the substitutions have been made. Examples ======== >>> from sympy import pi, exp >>> from sympy.abc import x, y >>> (1 + x*y).subs(x, pi) pi*y + 1 >>> (1 + x*y).subs({x:pi, y:2}) 1 + 2*pi >>> (1 + x*y).subs([(x, pi), (y, 2)]) 1 + 2*pi >>> reps = [(y, x**2), (x, 2)] >>> (x + y).subs(reps) 6 >>> (x + y).subs(reversed(reps)) x**2 + 2 >>> (x**2 + x**4).subs(x**2, y) y**2 + y To replace only the x**2 but not the x**4, use xreplace: >>> (x**2 + x**4).xreplace({x**2: y}) x**4 + y To delay evaluation until all substitutions have been made, set the keyword ``simultaneous`` to True: >>> (x/y).subs([(x, 0), (y, 0)]) 0 >>> (x/y).subs([(x, 0), (y, 0)], simultaneous=True) nan This has the added feature of not allowing subsequent substitutions to affect those already made: >>> ((x + y)/y).subs({x + y: y, y: x + y}) 1 >>> ((x + y)/y).subs({x + y: y, y: x + y}, simultaneous=True) y/(x + y) In order to obtain a canonical result, unordered iterables are sorted by count_op length, number of arguments and by the default_sort_key to break any ties. All other iterables are left unsorted. >>> from sympy import sqrt, sin, cos >>> from sympy.abc import a, b, c, d, e >>> A = (sqrt(sin(2*x)), a) >>> B = (sin(2*x), b) >>> C = (cos(2*x), c) >>> D = (x, d) >>> E = (exp(x), e) >>> expr = sqrt(sin(2*x))*sin(exp(x)*x)*cos(2*x) + sin(2*x) >>> expr.subs(dict([A,B,C,D,E])) a*c*sin(d*e) + b See Also ======== replace: replacement capable of doing wildcard-like matching, parsing of match, and conditional replacements xreplace: exact node replacement in expr tree; also capable of using matching rules """ from sympy.core.containers import Dict from sympy.utilities import default_sort_key unordered = False if len(args) == 1: sequence = args[0] if isinstance(sequence, set): unordered = True elif isinstance(sequence, (Dict, dict)): unordered = True sequence = sequence.items() elif not iterable(sequence): from sympy.utilities.misc import filldedent raise ValueError(filldedent(""" When a single argument is passed to subs it should be a dictionary of old: new pairs or an iterable of (old, new) tuples.""")) elif len(args) == 2: sequence = [args] else: raise ValueError("subs accepts either 1 or 2 arguments") sequence = list(sequence) for i in range(len(sequence)): o, n = sequence[i] so, sn = sympify(o), sympify(n) if not isinstance(so, Basic): if type(o) is str: so = C.Symbol(o) sequence[i] = (so, sn) if _aresame(so, sn): sequence[i] = None continue sequence = list(filter(None, sequence)) if unordered: sequence = dict(sequence) if not all(k.is_Atom for k in sequence): d = {} for o, n in sequence.items(): try: ops = o.count_ops(), len(o.args) except TypeError: ops = (0, 0) d.setdefault(ops, []).append((o, n)) newseq = [] for k in sorted(d.keys(), reverse=True): newseq.extend( sorted([v[0] for v in d[k]], key=default_sort_key)) sequence = [(k, sequence[k]) for k in newseq] del newseq, d else: sequence = sorted([(k, v) for (k, v) in sequence.items()], key=default_sort_key) if kwargs.pop('simultaneous', False): # XXX should this be the default for dict subs? reps = {} rv = self for old, new in sequence: d = C.Dummy() rv = rv._subs(old, d, **kwargs) reps[d] = new if not isinstance(rv, Basic): break return rv.xreplace(reps) else: rv = self for old, new in sequence: rv = rv._subs(old, new, **kwargs) if not isinstance(rv, Basic): break return rv @cacheit def _subs(self, old, new, **hints): """Substitutes an expression old -> new. If self is not equal to old then _eval_subs is called. If _eval_subs doesn't want to make any special replacement then a None is received which indicates that the fallback should be applied wherein a search for replacements is made amongst the arguments of self. >>> from sympy import Add >>> from sympy.abc import x, y, z Examples ======== Add's _eval_subs knows how to target x + y in the following so it makes the change: >>> (x + y + z).subs(x + y, 1) z + 1 Add's _eval_subs doesn't need to know how to find x + y in the following: >>> Add._eval_subs(z*(x + y) + 3, x + y, 1) is None True The returned None will cause the fallback routine to traverse the args and pass the z*(x + y) arg to Mul where the change will take place and the substitution will succeed: >>> (z*(x + y) + 3).subs(x + y, 1) z + 3 ** Developers Notes ** An _eval_subs routine for a class should be written if: 1) any arguments are not instances of Basic (e.g. bool, tuple); 2) some arguments should not be targeted (as in integration variables); 3) if there is something other than a literal replacement that should be attempted (as in Piecewise where the condition may be updated without doing a replacement). If it is overridden, here are some special cases that might arise: 1) If it turns out that no special change was made and all the original sub-arguments should be checked for replacements then None should be returned. 2) If it is necessary to do substitutions on a portion of the expression then _subs should be called. _subs will handle the case of any sub-expression being equal to old (which usually would not be the case) while its fallback will handle the recursion into the sub-arguments. For example, after Add's _eval_subs removes some matching terms it must process the remaining terms so it calls _subs on each of the un-matched terms and then adds them onto the terms previously obtained. 3) If the initial expression should remain unchanged then the original expression should be returned. (Whenever an expression is returned, modified or not, no further substitution of old -> new is attempted.) Sum's _eval_subs routine uses this strategy when a substitution is attempted on any of its summation variables. """ def fallback(self, old, new): """ Try to replace old with new in any of self's arguments. """ hit = False args = list(self.args) for i, arg in enumerate(args): if not hasattr(arg, '_eval_subs'): continue arg = arg._subs(old, new, **hints) if arg is not args[i]: hit = True args[i] = arg if hit: rv = self.func(*args) hack2 = hints.get('hack2', False) if hack2 and self.is_Mul and not rv.is_Mul: # 2-arg hack coeff = S.One nonnumber = [] for i in args: if i.is_Number: coeff *= i else: nonnumber.append(i) nonnumber = self.func(*nonnumber) if coeff is S.One: return nonnumber else: return self.func(coeff, nonnumber, evaluate=False) return rv return self if _aresame(self, old): return new rv = self._eval_subs(old, new) if rv is None: rv = fallback(self, old, new) return rv def _eval_subs(self, old, new): """Override this stub if you want to do anything more than attempt a replacement of old with new in the arguments of self. See also: _subs """ return None def xreplace(self, rule): """ Replace occurrences of objects within the expression. Parameters ========== rule : dict-like Expresses a replacement rule Returns ======= xreplace : the result of the replacement Examples ======== >>> from sympy import symbols, pi, exp >>> x, y, z = symbols('x y z') >>> (1 + x*y).xreplace({x: pi}) pi*y + 1 >>> (1 + x*y).xreplace({x:pi, y:2}) 1 + 2*pi Replacements occur only if an entire node in the expression tree is matched: >>> (x*y + z).xreplace({x*y: pi}) z + pi >>> (x*y*z).xreplace({x*y: pi}) x*y*z >>> (2*x).xreplace({2*x: y, x: z}) y >>> (2*2*x).xreplace({2*x: y, x: z}) 4*z >>> (x + y + 2).xreplace({x + y: 2}) x + y + 2 >>> (x + 2 + exp(x + 2)).xreplace({x + 2: y}) x + exp(y) + 2 xreplace doesn't differentiate between free and bound symbols. In the following, subs(x, y) would not change x since it is a bound symbol, but xreplace does: >>> from sympy import Integral >>> Integral(x, (x, 1, 2*x)).xreplace({x: y}) Integral(y, (y, 1, 2*y)) Trying to replace x with an expression raises an error: >>> Integral(x, (x, 1, 2*x)).xreplace({x: 2*y}) #doctest: +SKIP ValueError: Invalid limits given: ((2*y, 1, 4*y),) See Also ======== replace: replacement capable of doing wildcard-like matching, parsing of match, and conditional replacements subs: substitution of subexpressions as defined by the objects themselves. """ if self in rule: return rule[self] elif rule: args = [] for a in self.args: try: args.append(a.xreplace(rule)) except AttributeError: args.append(a) args = tuple(args) if not _aresame(args, self.args): return self.func(*args) return self @deprecated(useinstead="has", issue=2389, deprecated_since_version="0.7.2") def __contains__(self, obj): if self == obj: return True for arg in self.args: try: if obj in arg: return True except TypeError: if obj == arg: return True return False @cacheit def has(self, *patterns): """ Test whether any subexpression matches any of the patterns. Examples ======== >>> from sympy import sin >>> from sympy.abc import x, y, z >>> (x**2 + sin(x*y)).has(z) False >>> (x**2 + sin(x*y)).has(x, y, z) True >>> x.has(x) True Note that ``expr.has(*patterns)`` is exactly equivalent to ``any(expr.has(p) for p in patterns)``. In particular, ``False`` is returned when the list of patterns is empty. >>> x.has() False """ return any(self._has(pattern) for pattern in patterns) def _has(self, pattern): """Helper for .has()""" from sympy.core.function import UndefinedFunction, Function if isinstance(pattern, UndefinedFunction): return any(f.func == pattern or f == pattern for f in self.atoms(Function, UndefinedFunction)) pattern = sympify(pattern) if isinstance(pattern, BasicType): return any(isinstance(arg, pattern) for arg in preorder_traversal(self)) try: match = pattern._has_matcher() return any(match(arg) for arg in preorder_traversal(self)) except AttributeError: return any(arg == pattern for arg in preorder_traversal(self)) def _has_matcher(self): """Helper for .has()""" return self.__eq__ def replace(self, query, value, map=False, simultaneous=True, exact=False): """ Replace matching subexpressions of ``self`` with ``value``. If ``map = True`` then also return the mapping {old: new} where ``old`` was a sub-expression found with query and ``new`` is the replacement value for it. If the expression itself doesn't match the query, then the returned value will be ``self.xreplace(map)`` otherwise it should be ``self.subs(ordered(map.items()))``. Traverses an expression tree and performs replacement of matching subexpressions from the bottom to the top of the tree. The default approach is to do the replacement in a simultaneous fashion so changes made are targeted only once. If this is not desired or causes problems, ``simultaneous`` can be set to False. In addition, if an expression containing more than one Wild symbol is being used to match subexpressions and the ``exact`` flag is True, then the match will only succeed if non-zero values are received for each Wild that appears in the match pattern. The list of possible combinations of queries and replacement values is listed below: Examples ======== Initial setup >>> from sympy import log, sin, cos, tan, Wild, Mul, Add >>> from sympy.abc import x, y >>> f = log(sin(x)) + tan(sin(x**2)) 1.1. type -> type obj.replace(type, newtype) When object of type ``type`` is found, replace it with the result of passing its argument(s) to ``newtype``. >>> f.replace(sin, cos) log(cos(x)) + tan(cos(x**2)) >>> sin(x).replace(sin, cos, map=True) (cos(x), {sin(x): cos(x)}) >>> (x*y).replace(Mul, Add) x + y 1.2. type -> func obj.replace(type, func) When object of type ``type`` is found, apply ``func`` to its argument(s). ``func`` must be written to handle the number of arguments of ``type``. >>> f.replace(sin, lambda arg: sin(2*arg)) log(sin(2*x)) + tan(sin(2*x**2)) >>> (x*y).replace(Mul, lambda *args: sin(2*Mul(*args))) sin(2*x*y) 2.1. pattern -> expr obj.replace(pattern(wild), expr(wild)) Replace subexpressions matching ``pattern`` with the expression written in terms of the Wild symbols in ``pattern``. >>> a = Wild('a') >>> f.replace(sin(a), tan(a)) log(tan(x)) + tan(tan(x**2)) >>> f.replace(sin(a), tan(a/2)) log(tan(x/2)) + tan(tan(x**2/2)) >>> f.replace(sin(a), a) log(x) + tan(x**2) >>> (x*y).replace(a*x, a) y When the default value of False is used with patterns that have more than one Wild symbol, non-intuitive results may be obtained: >>> b = Wild('b') >>> (2*x).replace(a*x + b, b - a) 2/x For this reason, the ``exact`` option can be used to make the replacement only when the match gives non-zero values for all Wild symbols: >>> (2*x + y).replace(a*x + b, b - a, exact=True) y - 2 >>> (2*x).replace(a*x + b, b - a, exact=True) 2*x 2.2. pattern -> func obj.replace(pattern(wild), lambda wild: expr(wild)) All behavior is the same as in 2.1 but now a function in terms of pattern variables is used rather than an expression: >>> f.replace(sin(a), lambda a: sin(2*a)) log(sin(2*x)) + tan(sin(2*x**2)) 3.1. func -> func obj.replace(filter, func) Replace subexpression ``e`` with ``func(e)`` if ``filter(e)`` is True. >>> g = 2*sin(x**3) >>> g.replace(lambda expr: expr.is_Number, lambda expr: expr**2) 4*sin(x**9) The expression itself is also targeted by the query but is done in such a fashion that changes are not made twice. >>> e = x*(x*y + 1) >>> e.replace(lambda x: x.is_Mul, lambda x: 2*x) 2*x*(2*x*y + 1) See Also ======== subs: substitution of subexpressions as defined by the objects themselves. xreplace: exact node replacement in expr tree; also capable of using matching rules """ from sympy.core.symbol import Dummy from sympy.simplify.simplify import bottom_up try: query = sympify(query) except SympifyError: pass try: value = sympify(value) except SympifyError: pass if isinstance(query, type): _query = lambda expr: isinstance(expr, query) if isinstance(value, type): _value = lambda expr, result: value(*expr.args) elif callable(value): _value = lambda expr, result: value(*expr.args) else: raise TypeError( "given a type, replace() expects another " "type or a callable") elif isinstance(query, Basic): _query = lambda expr: expr.match(query) # XXX remove the exact flag and make multi-symbol # patterns use exact=True semantics; to do this the query must # be tested to find out how many Wild symbols are present. # See https://groups.google.com/forum/ # ?fromgroups=#!topic/sympy/zPzo5FtRiqI # for a method of inspecting a function to know how many # parameters it has. if isinstance(value, Basic): if exact: _value = lambda expr, result: (value.subs(result) if all(val for val in result.values()) else expr) else: _value = lambda expr, result: value.subs(result) elif callable(value): # match dictionary keys get the trailing underscore stripped # from them and are then passed as keywords to the callable; # if ``exact`` is True, only accept match if there are no null # values amongst those matched. if exact: _value = lambda expr, result: (value(**dict([ ( str(key)[:-1], val) for key, val in result.items()])) if all(val for val in result.values()) else expr) else: _value = lambda expr, result: value(**dict([ ( str(key)[:-1], val) for key, val in result.items()])) else: raise TypeError( "given an expression, replace() expects " "another expression or a callable") elif callable(query): _query = query if callable(value): _value = lambda expr, result: value(expr) else: raise TypeError( "given a callable, replace() expects " "another callable") else: raise TypeError( "first argument to replace() must be a " "type, an expression or a callable") mapping = {} # changes that took place mask = [] # the dummies that were used as change placeholders def rec_replace(expr): result = _query(expr) if result or result == {}: new = _value(expr, result) if new is not None and new != expr: mapping[expr] = new if simultaneous: # don't let this expression be changed during rebuilding d = Dummy() mask.append((d, new)) expr = d else: expr = new return expr rv = bottom_up(self, rec_replace, atoms=True) # restore original expressions for Dummy symbols if simultaneous: mask = list(reversed(mask)) for o, n in mask: r = {o: n} rv = rv.xreplace(r) if not map: return rv else: if simultaneous: # restore subexpressions in mapping for o, n in mask: r = {o: n} mapping = dict([(k.xreplace(r), v.xreplace(r)) for k, v in mapping.items()]) return rv, mapping def find(self, query, group=False): """Find all subexpressions matching a query. """ query = _make_find_query(query) results = list(filter(query, preorder_traversal(self))) if not group: return set(results) else: groups = {} for result in results: if result in groups: groups[result] += 1 else: groups[result] = 1 return groups def count(self, query): """Count the number of matching subexpressions. """ query = _make_find_query(query) return sum(bool(query(sub)) for sub in preorder_traversal(self)) def matches(self, expr, repl_dict={}, old=False): """ Helper method for match() that looks for a match between Wild symbols in self and expressions in expr. Examples ======== >>> from sympy import symbols, Wild, Basic >>> a, b, c = symbols('a b c') >>> x = Wild('x') >>> Basic(a + x, x).matches(Basic(a + b, c)) is None True >>> Basic(a + x, x).matches(Basic(a + b + c, b + c)) {x_: b + c} """ expr = sympify(expr) if not isinstance(expr, self.__class__): return None if self == expr: return repl_dict if len(self.args) != len(expr.args): return None d = repl_dict.copy() for arg, other_arg in zip(self.args, expr.args): if arg == other_arg: continue d = arg.xreplace(d).matches(other_arg, d, old=old) if d is None: return None return d def match(self, pattern, old=False): """ Pattern matching. Wild symbols match all. Return ``None`` when expression (self) does not match with pattern. Otherwise return a dictionary such that:: pattern.xreplace(self.match(pattern)) == self Examples ======== >>> from sympy import Wild >>> from sympy.abc import x, y >>> p = Wild("p") >>> q = Wild("q") >>> r = Wild("r") >>> e = (x+y)**(x+y) >>> e.match(p**p) {p_: x + y} >>> e.match(p**q) {p_: x + y, q_: x + y} >>> e = (2*x)**2 >>> e.match(p*q**r) {p_: 4, q_: x, r_: 2} >>> (p*q**r).xreplace(e.match(p*q**r)) 4*x**2 The ``old`` flag will give the old-style pattern matching where expressions and patterns are essentially solved to give the match. Both of the following give None unless ``old=True``: >>> (x - 2).match(p - x, old=True) {p_: 2*x - 2} >>> (2/x).match(p*x, old=True) {p_: 2/x**2} """ from sympy import signsimp pattern = sympify(pattern) s = signsimp(self) p = signsimp(pattern) # if we still have the same relationship between the types of # input, then use the sign simplified forms if (pattern.func == self.func) and (s.func == p.func): rv = p.matches(s, old=old) else: rv = pattern.matches(self, old=old) return rv def count_ops(self, visual=None): """wrapper for count_ops that returns the operation count.""" from sympy import count_ops return count_ops(self, visual) def doit(self, **hints): """Evaluate objects that are not evaluated by default like limits, integrals, sums and products. All objects of this kind will be evaluated recursively, unless some species were excluded via 'hints' or unless the 'deep' hint was set to 'False'. >>> from sympy import Integral >>> from sympy.abc import x >>> 2*Integral(x, x) 2*Integral(x, x) >>> (2*Integral(x, x)).doit() x**2 >>> (2*Integral(x, x)).doit(deep = False) 2*Integral(x, x) """ if hints.get('deep', True): terms = [ term.doit(**hints) if isinstance(term, Basic) else term for term in self.args ] return self.func(*terms) else: return self def _eval_rewrite(self, pattern, rule, **hints): if self.is_Atom: if hasattr(self, rule): return getattr(self, rule)() return self sargs = self.args terms = [ t._eval_rewrite(pattern, rule, **hints) if isinstance(t, Basic) else t for t in sargs ] return self.func(*terms) def rewrite(self, *args, **hints): """ Rewrite functions in terms of other functions. Rewrites expression containing applications of functions of one kind in terms of functions of different kind. For example you can rewrite trigonometric functions as complex exponentials or combinatorial functions as gamma function. As a pattern this function accepts a list of functions to to rewrite (instances of DefinedFunction class). As rule you can use string or a destination function instance (in this case rewrite() will use the str() function). There is also possibility to pass hints on how to rewrite the given expressions. For now there is only one such hint defined called 'deep'. When 'deep' is set to False it will forbid functions to rewrite their contents. Examples ======== >>> from sympy import sin, exp >>> from sympy.abc import x Unspecified pattern: >>> sin(x).rewrite(exp) -I*(exp(I*x) - exp(-I*x))/2 Pattern as a single function: >>> sin(x).rewrite(sin, exp) -I*(exp(I*x) - exp(-I*x))/2 Pattern as a list of functions: >>> sin(x).rewrite([sin, ], exp) -I*(exp(I*x) - exp(-I*x))/2 """ if not args: return self else: pattern = args[:-1] if isinstance(args[-1], string_types): rule = '_eval_rewrite_as_' + args[-1] else: rule = '_eval_rewrite_as_' + args[-1].__name__ if not pattern: return self._eval_rewrite(None, rule, **hints) else: if iterable(pattern[0]): pattern = pattern[0] pattern = [ p.__class__ for p in pattern if self.has(p) ] if pattern: return self._eval_rewrite(tuple(pattern), rule, **hints) else: return self class Atom(Basic): """ A parent class for atomic things. An atom is an expression with no subexpressions. Examples ======== Symbol, Number, Rational, Integer, ... But not: Add, Mul, Pow, ... """ is_Atom = True __slots__ = [] def matches(self, expr, repl_dict={}, old=False): if self == expr: return repl_dict def xreplace(self, rule, hack2=False): return rule.get(self, self) def doit(self, **hints): return self @classmethod def class_key(cls): return 2, 0, cls.__name__ @cacheit def sort_key(self, order=None): from sympy.core import S return self.class_key(), (1, (str(self),)), S.One.sort_key(), S.One def _eval_simplify(self, ratio, measure): return self @property def _sorted_args(self): # this is here as a safeguard against accidentally using _sorted_args # on Atoms -- they cannot be rebuilt as atom.func(*atom._sorted_args) # since there are no args. So the calling routine should be checking # to see that this property is not called for Atoms. raise AttributeError('Atoms have no args. It might be necessary' ' to make a check for Atoms in the calling code.') def _aresame(a, b): """Return True if a and b are structurally the same, else False. Examples ======== To SymPy, 2.0 == 2: >>> from sympy import S >>> 2.0 == S(2) True Since a simple 'same or not' result is sometimes useful, this routine was written to provide that query: >>> from sympy.core.basic import _aresame >>> _aresame(S(2.0), S(2)) False """ for i, j in zip(preorder_traversal(a), preorder_traversal(b)): if i != j or type(i) != type(j): return False else: return True def _atomic(e): """Return atom-like quantities as far as substitution is concerned: Derivatives, Functions and Symbols. Don't return any 'atoms' that are inside such quantities unless they also appear outside, too. Examples ======== >>> from sympy import Derivative, Function, cos >>> from sympy.abc import x, y >>> from sympy.core.basic import _atomic >>> f = Function('f') >>> _atomic(x + y) set([x, y]) >>> _atomic(x + f(y)) set([x, f(y)]) >>> _atomic(Derivative(f(x), x) + cos(x) + y) set([y, cos(x), Derivative(f(x), x)]) """ from sympy import Derivative, Function, Symbol pot = preorder_traversal(e) seen = set() try: free = e.free_symbols except AttributeError: return set([e]) atoms = set() for p in pot: if p in seen: pot.skip() continue seen.add(p) if isinstance(p, Symbol) and p in free: atoms.add(p) elif isinstance(p, (Derivative, Function)): pot.skip() atoms.add(p) return atoms class preorder_traversal(Iterator): """ Do a pre-order traversal of a tree. This iterator recursively yields nodes that it has visited in a pre-order fashion. That is, it yields the current node then descends through the tree breadth-first to yield all of a node's children's pre-order traversal. For an expression, the order of the traversal depends on the order of .args, which in many cases can be arbitrary. Parameters ========== node : sympy expression The expression to traverse. keys : (default None) sort key(s) The key(s) used to sort args of Basic objects. When None, args of Basic objects are processed in arbitrary order. If key is defined, it will be passed along to ordered() as the only key(s) to use to sort the arguments; if ``key`` is simply True then the default keys of ordered will be used. Yields ====== subtree : sympy expression All of the subtrees in the tree. Examples ======== >>> from sympy import symbols >>> from sympy.core.basic import preorder_traversal >>> x, y, z = symbols('x y z') The nodes are returned in the order that they are encountered unless key is given; simply passing key=True will guarantee that the traversal is unique. >>> list(preorder_traversal((x + y)*z, keys=None)) # doctest: +SKIP [z*(x + y), z, x + y, y, x] >>> list(preorder_traversal((x + y)*z, keys=True)) [z*(x + y), z, x + y, x, y] """ def __init__(self, node, keys=None): self._skip_flag = False self._pt = self._preorder_traversal(node, keys) def _preorder_traversal(self, node, keys): yield node if self._skip_flag: self._skip_flag = False return if isinstance(node, Basic): args = node.args if keys: if keys != True: args = ordered(args, keys, default=False) else: args = ordered(args) for arg in args: for subtree in self._preorder_traversal(arg, keys): yield subtree elif iterable(node): for item in node: for subtree in self._preorder_traversal(item, keys): yield subtree def skip(self): """ Skip yielding current node's (last yielded node's) subtrees. Examples -------- >>> from sympy.core import symbols >>> from sympy.core.basic import preorder_traversal >>> x, y, z = symbols('x y z') >>> pt = preorder_traversal((x+y*z)*z) >>> for i in pt: ... print(i) ... if i == x+y*z: ... pt.skip() z*(x + y*z) z x + y*z """ self._skip_flag = True def __next__(self): return next(self._pt) def __iter__(self): return self def _make_find_query(query): """Convert the argument of Basic.find() into a callable""" try: query = sympify(query) except SympifyError: pass if isinstance(query, type): return lambda expr: isinstance(expr, query) elif isinstance(query, Basic): return lambda expr: expr.match(query) is not None return query

31.606127

94

0.526966

794ae66ce4d1e65b2a0a4063feaace439b5480eb

6,445

py

Python

config/train.py

prismformore/SDSEN

815d1afcf8091eed4c3b35e8a3d56b28b7f3979d

[ "MIT" ]

18

2019-09-16T10:27:45.000Z

2021-02-22T13:52:03.000Z

config/train.py

prismformore/SDSEN

815d1afcf8091eed4c3b35e8a3d56b28b7f3979d

[ "MIT" ]

null

config/train.py

prismformore/SDSEN

815d1afcf8091eed4c3b35e8a3d56b28b7f3979d

[ "MIT" ]

5

2019-09-16T14:04:56.000Z

2022-03-22T12:59:01.000Z

import os import sys import cv2 import argparse import numpy as np import torch from torch import nn from torch.nn import MSELoss from torch.optim import Adam from torch.optim.lr_scheduler import MultiStepLR from torch.autograd import Variable from torch.utils.data import DataLoader from tensorboardX import SummaryWriter import settings from dataset import TrainValDataset from model import SDSEN from cal_ssim import SSIM logger = settings.logger torch.cuda.manual_seed_all(66) torch.manual_seed(66) torch.cuda.set_device(settings.device_id) def ensure_dir(dir_path): if not os.path.isdir(dir_path): os.makedirs(dir_path) class Session: def __init__(self): self.log_dir = settings.log_dir self.model_dir = settings.model_dir ensure_dir(settings.log_dir) ensure_dir(settings.model_dir) logger.info('set log dir as %s' % settings.log_dir) logger.info('set model dir as %s' % settings.model_dir) self.net = SDSEN().cuda() self.crit = MSELoss().cuda() self.ssim = SSIM().cuda() self.step = 0 self.save_steps = settings.save_steps self.num_workers = settings.num_workers self.batch_size = settings.batch_size self.writers = {} self.dataloaders = {} self.opt = Adam(self.net.parameters(), lr=settings.lr) self.sche = MultiStepLR(self.opt, milestones=[15000, 17500], gamma=0.1) def tensorboard(self, name): self.writers[name] = SummaryWriter(os.path.join(self.log_dir, name + '.events')) return self.writers[name] def write(self, name, out): for k, v in out.items(): self.writers[name].add_scalar(k, v, self.step) out['lr'] = self.opt.param_groups[0]['lr'] out['step'] = self.step outputs = [ "{}:{:.4g}".format(k, v) for k, v in out.items() ] logger.info(name + '--' + ' '.join(outputs)) def get_dataloader(self, dataset_name): dataset = TrainValDataset(dataset_name) if not dataset_name in self.dataloaders: self.dataloaders[dataset_name] = \ DataLoader(dataset, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, drop_last=True) return iter(self.dataloaders[dataset_name]) def save_checkpoints(self, name): ckp_path = os.path.join(self.model_dir, name) obj = { 'net': self.net.state_dict(), 'clock': self.step, 'opt': self.opt.state_dict(), } torch.save(obj, ckp_path) def load_checkpoints(self, name): ckp_path = os.path.join(self.model_dir, name) try: obj = torch.load(ckp_path) logger.info('Load checkpoint %s' % ckp_path) except FileNotFoundError: logger.info('No checkpoint %s!!' % ckp_path) return self.net.load_state_dict(obj['net']) self.opt.load_state_dict(obj['opt']) self.step = obj['clock'] self.sche.last_epoch = self.step def inf_batch(self, name, batch): O, B = batch['O'].cuda(), batch['B'].cuda() O, B = Variable(O, requires_grad=False), Variable(B, requires_grad=False) R = O - B O_Rs = self.net(O) loss_list = [self.crit(O_R, R) for O_R in O_Rs] ssim_list = [self.ssim(O - O_R, O - R) for O_R in O_Rs] if name == 'train': self.net.zero_grad() sum(loss_list).backward() self.opt.step() losses = { 'loss%d' % i: loss.item() for i, loss in enumerate(loss_list) } ssimes = { 'ssim%d' % i: ssim.item() for i, ssim in enumerate(ssim_list) } losses.update(ssimes) self.write(name, losses) return O - O_Rs[-1] def save_image(self, name, img_lists): data, pred, label = img_lists pred = pred.cpu().data data, label, pred = data * 255, label * 255, pred * 255 pred = np.clip(pred, 0, 255) h, w = pred.shape[-2:] gen_num = (6, 2) img = np.zeros((gen_num[0] * h, gen_num[1] * 3 * w, 3)) for img_list in img_lists: for i in range(gen_num[0]): row = i * h for j in range(gen_num[1]): idx = i * gen_num[1] + j tmp_list = [data[idx], pred[idx], label[idx]] for k in range(3): col = (j * 3 + k) * w tmp = np.transpose(tmp_list[k], (1, 2, 0)) img[row: row+h, col: col+w] = tmp img_file = os.path.join(self.log_dir, '%d_%s.jpg' % (self.step, name)) cv2.imwrite(img_file, img) def run_train_val(ckp_name='latest'): sess = Session() sess.load_checkpoints(ckp_name) sess.tensorboard('train') sess.tensorboard('val') dt_train = sess.get_dataloader('train') dt_val = sess.get_dataloader('val') while sess.step < 20000: sess.sche.step() sess.net.train() try: batch_t = next(dt_train) except StopIteration: dt_train = sess.get_dataloader('train') batch_t = next(dt_train) pred_t = sess.inf_batch('train', batch_t) if sess.step % 4 == 0: sess.net.eval() try: batch_v = next(dt_val) except StopIteration: dt_val = sess.get_dataloader('val') batch_v = next(dt_val) pred_v = sess.inf_batch('val', batch_v) if sess.step % int(sess.save_steps / 16) == 0: sess.save_checkpoints('latest') if sess.step % int(sess.save_steps / 2) == 0: sess.save_image('train', [batch_t['O'], pred_t, batch_t['B']]) if sess.step % 4 == 0: sess.save_image('val', [batch_v['O'], pred_v, batch_v['B']]) logger.info('save image as step_%d' % sess.step) if sess.step % sess.save_steps == 0: sess.save_checkpoints('step_%d' % sess.step) logger.info('save model as step_%d' % sess.step) sess.step += 1 if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-m', '--model', default='latest') args = parser.parse_args(sys.argv[1:]) run_train_val(args.model)

31.593137

88

0.569123

794ae68b2348153a632fd1c7b30fc597877a3414

9,934

py

Python

src/m7_loops_within_loops_graphics.py

wilsonta/20-Exam3Practice

840c3bf0cf73c05db83c412f71771d05d5baafab

[ "MIT" ]

null

src/m7_loops_within_loops_graphics.py

wilsonta/20-Exam3Practice

840c3bf0cf73c05db83c412f71771d05d5baafab

[ "MIT" ]

null

src/m7_loops_within_loops_graphics.py

wilsonta/20-Exam3Practice

840c3bf0cf73c05db83c412f71771d05d5baafab

[ "MIT" ]

null

""" PRACTICE Exam 3. This problem provides practice at: *** LOOPS WITHIN LOOPS in 2D GRAPHICS problems. *** Authors: David Mutchler, Vibha Alangar, Matt Boutell, Dave Fisher, Mark Hays, Amanda Stouder, Aaron Wilkin, their colleagues, and Tim Wilson. """ # DONE: 1. PUT YOUR NAME IN THE ABOVE LINE. ############################################################################### # Students: # # These problems have DIFFICULTY and TIME ratings: # DIFFICULTY rating: 1 to 10, where: # 1 is very easy # 3 is an "easy" Test 2 question. # 5 is a "typical" Test 2 question. # 7 is a "hard" Test 2 question. # 10 is an EXTREMELY hard problem (too hard for a Test 2 question) # # TIME ratings: A ROUGH estimate of the number of minutes that we # would expect a well-prepared student to take on the problem. # # IMPORTANT: For ALL the problems in this module, # if you reach the time estimate and are NOT close to a solution, # STOP working on that problem and ASK YOUR INSTRUCTOR FOR HELP # on it, in class or via Piazza. ############################################################################### import rosegraphics as rg def main(): """ Calls the TEST functions in this module. """ #run_test_hourglass() run_test_many_hourglasses() def run_test_hourglass(): """ Tests the hourglass function. """ print() print('--------------------------------------------------') print('Testing the hourglass function:') print('--------------------------------------------------') test1 = '(n = 3, radius = 40, blue)' test2 = '(n = 8, radius = 15, green)' title1 = 'Hourglass, two tests: {} and {}'.format(test1, test2) window1 = rg.RoseWindow(600, 500, title1) hourglass(window1, 3, rg.Point(150, 200), 40, 'blue') hourglass(window1, 8, rg.Point(450, 250), 15, 'green') window1.close_on_mouse_click() test3 = '(n = 6, radius = 30, red)' title2 = 'Hourglass, one more test: {}'.format(test3) window2 = rg.RoseWindow(400, 700, title2) hourglass(window2, 6, rg.Point(200, 350), 30, 'red') window2.close_on_mouse_click() def hourglass(window, n, point, radius, color): """ See hourglass_picture.pdf in this project for pictures that may help you better understand the following specification: Displays an "hourglass" shape of circles in the given window. -- Each circle has the given radius and given color. -- Each circle has a horizontal line drawn through it. -- The middlemost of the circles is centered at the given point. -- There is a single circle in that middlemost row. -- There are n rows (including the middlemost row) of circles going UP from the middlemost circle. -- There are n rows (including the middlemost row) of circles going DOWN from the middlemost circle. -- Each circle barely touches its neighbor circles. Preconditions: :type window: rg.RoseWindow :type n: int :type point: rg.Point :type radius: int :type color: str where n and radius are positive and color is a string that denotes a color that rosegraphics understands. """ for k in range(n): centery=point.y-k*2*radius centerx = point.x - k * radius for j in range(k+1): circlex=centerx+j*2*radius circle=rg.Circle(rg.Point(circlex,centery),radius) circle.fill_color=color line=rg.Line(rg.Point(circlex-radius,centery),rg.Point(circlex+radius,centery)) circle.attach_to(window) line.attach_to(window) window.render() for m in range(n-1): centery=point.y+2*radius+m*2*radius centerx=point.x-radius-m*radius for j in range(m+2): circlex = centerx + j * 2 * radius circle = rg.Circle(rg.Point(circlex, centery), radius) circle.fill_color = color line2 = rg.Line(rg.Point(circlex - radius, centery), rg.Point(circlex + radius, centery)) circle.attach_to(window) line2.attach_to(window) window.render() # ------------------------------------------------------------------------- # DONE: 2. Implement and test this function. # We provided some tests for you (above). # ------------------------------------------------------------------------- ########################################################################### # BONUS: Avoid replicated code if you can. Hint: You are allowed # to define an additional function(s) if you wish. ########################################################################### # ------------------------------------------------------------------------- # DIFFICULTY AND TIME RATINGS (see top of this file for explanation) # DIFFICULTY: 8 # TIME ESTIMATE: 25 minutes (warning: this problem is challenging) # ------------------------------------------------------------------------- def run_test_many_hourglasses(): """ Tests the many_hourglasses function. """ print() print('--------------------------------------------------') print('Testing the many_hourglasses function:') print('--------------------------------------------------') test1 = '(n = 4, radius = 30, red-blue-black-green)' test2 = '(n = 3, radius = 70, brown-cyan-yellow-green)' title1 = 'Many hourglasses, two tests: {} and {}'.format(test1, test2) window1 = rg.RoseWindow(800, 400, title1) square1 = rg.Square(rg.Point(50, 150), 30) square2 = rg.Square(rg.Point(400, 200), 70) many_hourglasses(window1, square1, 4, ('red', 'blue', 'black', 'green')) many_hourglasses(window1, square2, 3, ('brown', 'cyan', 'yellow', 'green')) window1.close_on_mouse_click() test3 = '(n = 7, radius = 40, red-black-blue)' title2 = 'Many hourglasses, one more test: {}'.format(test3) window2 = rg.RoseWindow(1200, 500, title2) square3 = rg.Square(rg.Point(50, 250), 40) many_hourglasses(window2, square3, 7, ('red', 'black', 'blue')) window2.close_on_mouse_click() def many_hourglasses(window, square, m, colors): """ See many_hourglasses_picture.pdf in this project for pictures that may help you better understand the following specification: Displays m rectangles, where: -- Each rectangle has an hourglass of circles inside it, per the hourglass function above. -- The circles in the hourglasses are all the same size. -- The leftmost rectangle is the given square, and it contains an hourglass with a single circle that fills the square. -- Each successive rectangle is immediately to the right of the previous rectangle, and each contains an hourglass with the hourglass' n being one greater than the n used for the previous rectangle. -- The colors for the hourglass figures use the given sequence of colors, "wrapping" if m exceeds the length of the sequence. Preconditions: :type window: rg.RoseWindow :type square: rg.Square :type m: int :type colors: (list | tuple) of str where m is positive and colors is a sequence of strings, each of which denotes a color that rosegraphics understands. """ square.attach_to(window) window.render() hourglass(window,1,square.center,square.length_of_each_side/2,colors[0]) corner1=rg.Point(square.center.x - square.length_of_each_side / 2, square.center.y - square.length_of_each_side / 2) corner2=rg.Point(square.center.x + square.length_of_each_side / 2, square.center.y + square.length_of_each_side / 2) newcorner1=corner1 newcorner2=corner2 i=1 for k in range(1,m): corneraddition1=k*square.length_of_each_side corneraddition2=k*2*square.length_of_each_side newcorner1=rg.Point(newcorner1.x+k*square.length_of_each_side,corner1.y-corneraddition1) newcorner2=rg.Point(newcorner2.x+(k+1)*square.length_of_each_side,corner2.y+corneraddition1) rect=rg.Rectangle(newcorner1,newcorner2) center=rg.Point(newcorner1.x+(newcorner2.x-newcorner1.x)/2,newcorner1.y+(newcorner2.y-newcorner1.y)/2) rect.attach_to(window) window.render() if i==len(colors): i=0 hourglass(window,k+1,center,square.length_of_each_side/2,colors[i]) i=i+1 # ------------------------------------------------------------------------- # DONE: 3. Implement and test this function. # We provided some tests for you (above). # ------------------------------------------------------------------------- ########################################################################### # IMPORTANT: # 1. Partial credit if you draw JUST the rectangles. # 2. No additional credit unless you CALL the hourglass function # in the PREVIOUS problem appropriately # to draw the hourglass figures. ########################################################################### # ------------------------------------------------------------------------- # DIFFICULTY AND TIME RATINGS (see top of this file for explanation) # DIFFICULTY: 7 (assuming that you already have # a correct "hourglass" function above) # TIME ESTIMATE: 20 minutes (warning: this problem is challenging) # ------------------------------------------------------------------------- # ----------------------------------------------------------------------------- # Calls main to start the ball rolling. # ----------------------------------------------------------------------------- main()

42.452991

120

0.54993

794ae6ee782c9df7ff718b035f612193ad85b487

7,438

py

Python

tests/unit/ops/test_ops_schema.py

davidxia/NVTabular

97b05ac74204d4e21fa31d522d0f84fb37cf94a9

[ "Apache-2.0" ]

null

tests/unit/ops/test_ops_schema.py

davidxia/NVTabular

97b05ac74204d4e21fa31d522d0f84fb37cf94a9

[ "Apache-2.0" ]

null

tests/unit/ops/test_ops_schema.py

davidxia/NVTabular

97b05ac74204d4e21fa31d522d0f84fb37cf94a9

[ "Apache-2.0" ]

null

import numpy as np import pytest import nvtabular as nvt from nvtabular import ColumnSchema, ColumnSelector, Schema, dispatch, ops @pytest.mark.parametrize("properties", [{}, {"p1": "1"}]) @pytest.mark.parametrize("tags", [[], ["TAG1", "TAG2"]]) @pytest.mark.parametrize( "op", [ ops.Bucketize([1]), ops.Rename(postfix="_trim"), ops.Categorify(), ops.Categorify(encode_type="combo"), ops.Clip(0), ops.DifferenceLag("1"), ops.FillMissing(), ops.Groupby(["1"]), ops.HashBucket(1), ops.HashedCross(1), ops.JoinGroupby(["1"]), ops.ListSlice(0), ops.LogOp(), ops.Normalize(), ops.TargetEncoding(["1"]), ops.AddMetadata(tags=["excellent"], properties={"domain": {"min": 0, "max": 20}}), ops.ValueCount(), ], ) @pytest.mark.parametrize("selection", [["1"], ["2", "3"], ["1", "2", "3", "4"]]) def test_schema_out(tags, properties, selection, op): # Create columnSchemas column_schemas = [] all_cols = [] for x in range(5): all_cols.append(str(x)) column_schemas.append(ColumnSchema(str(x), tags=tags, properties=properties)) # Turn to Schema schema = Schema(column_schemas) # run schema through op selector = ColumnSelector(selection) new_schema = op.compute_output_schema(schema, selector) # should have dtype float for col_name in selector.names: names_group = [name for name in new_schema.column_schemas if col_name in name] if names_group: for name in names_group: schema1 = new_schema.column_schemas[name] # should not be exactly the same name, having gone through operator assert schema1.dtype == op.output_dtype() if name in selector.names: assert ( schema1.properties == op._add_properties(schema.column_schemas[schema1.name]).properties ) all_tags = op.output_tags() + tags assert len(schema1.tags) == len(all_tags) else: assert set(op.output_tags()).issubset(schema1.tags) not_used = [col for col in all_cols if col not in selector.names] for col_name in not_used: assert col_name not in new_schema.column_schemas @pytest.mark.parametrize("properties", [{"p1": "1"}]) @pytest.mark.parametrize("tags", [["TAG1", "TAG2"]]) @pytest.mark.parametrize( "op_routine", [ [ops.Categorify()], [ops.Clip(min_value=10), ops.Categorify()], [ops.Categorify(), ops.Rename(postfix="_test")], [ops.Clip(min_value=10), ops.Categorify(), ops.Rename(postfix="_test")], ], ) def test_categorify_schema_properties(properties, tags, op_routine): run_op_full(properties, tags, op_routine) @pytest.mark.parametrize("properties", [{}]) @pytest.mark.parametrize("tags", [[]]) @pytest.mark.parametrize( "op_routine", [ [ops.Categorify()], [ops.Clip(min_value=10), ops.Categorify()], [ops.Categorify(), ops.Rename(postfix="_test")], [ops.Clip(min_value=10), ops.Categorify(), ops.Rename(postfix="_test")], ], ) def test_categorify_schema_properties_blank(properties, tags, op_routine): run_op_full(properties, tags, op_routine) @pytest.mark.parametrize("properties", [{}]) @pytest.mark.parametrize("tags", [["TAG1", "TAG2"]]) @pytest.mark.parametrize( "op_routine", [ [ops.Categorify()], [ops.Clip(min_value=10), ops.Categorify()], [ops.Categorify(), ops.Rename(postfix="_test")], [ops.Clip(min_value=10), ops.Categorify(), ops.Rename(postfix="_test")], ], ) def test_categorify_schema_properties_tag(properties, tags, op_routine): run_op_full(properties, tags, op_routine) @pytest.mark.parametrize("properties", [{"p1": "1"}]) @pytest.mark.parametrize("tags", [[]]) @pytest.mark.parametrize( "op_routine", [ [ops.Categorify()], [ops.Clip(min_value=10), ops.Categorify()], [ops.Categorify(), ops.Rename(postfix="_test")], [ops.Clip(min_value=10), ops.Categorify(), ops.Rename(postfix="_test")], ], ) def test_categorify_schema_properties_props(properties, tags, op_routine): run_op_full(properties, tags, op_routine) def run_op_full(properties, tags, op_routine): column_schemas = [] all_cols = [] for x in range(5): all_cols.append(str(x)) column_schemas.append(ColumnSchema(str(x), tags=tags, properties=properties)) # Turn to Schema schema = Schema(column_schemas) df_dict = {} num_rows = 10000 for column_name in schema.column_names: df_dict[column_name] = np.random.randint(1, 1000, num_rows) df = dispatch._make_df(df_dict) dataset = nvt.Dataset(df) test_node = ColumnSelector(schema.column_names) >> op_routine[0] for op in op_routine[1:]: test_node = test_node >> op processor = nvt.Workflow(test_node) processor.fit(dataset) new_gdf = processor.transform(dataset).to_ddf().compute() workflow_schema_out = processor.output_node.output_schema for column_name in workflow_schema_out.column_names: schema1 = workflow_schema_out.column_schemas[column_name] assert "domain" in schema1.properties embeddings_info = schema1.properties["domain"] # should always exist, represents unkown assert embeddings_info["min"] == 0 assert embeddings_info["max"] == new_gdf[column_name].max() + 1 @pytest.mark.parametrize("properties", [{"p1": "1"}]) @pytest.mark.parametrize("tags", [[]]) @pytest.mark.parametrize( "op_routine", [ [ops.Categorify(), ops.Rename(postfix="_test"), ops.ValueCount()], ], ) def test_ops_list_vc(properties, tags, op_routine): column_schemas = [] all_cols = [] for x in range(5): all_cols.append(str(x)) column_schemas.append(ColumnSchema(str(x), tags=tags, properties=properties)) # Turn to Schema schema = Schema(column_schemas) df_dict = {} num_rows = 10000 for column_name in schema.column_names: df_dict[column_name] = np.random.randint(1, 1000, num_rows) df_dict[column_name] = [[x] * np.random.randint(1, 10) for x in df_dict[column_name]] df = dispatch._make_df(df_dict) dataset = nvt.Dataset(df) test_node = ColumnSelector(schema.column_names) >> op_routine[0] for op in op_routine[1:]: test_node = test_node >> op processor = nvt.Workflow(test_node) processor.fit(dataset) new_gdf = processor.transform(dataset).to_ddf().compute() workflow_schema_out = processor.output_node.output_schema for column_name in workflow_schema_out.column_names: schema1 = workflow_schema_out.column_schemas[column_name] assert "domain" in schema1.properties embeddings_info = schema1.properties["domain"] # should always exist, represents unkown assert embeddings_info["min"] == 0 assert embeddings_info["max"] == new_gdf[column_name]._column.elements.max() + 1 assert "value_count" in schema1.properties val_c = schema1.properties["value_count"] assert val_c["min"] == op_routine[-1].stats[column_name]["value_count"]["min"] assert val_c["max"] == op_routine[-1].stats[column_name]["value_count"]["max"]

35.932367

93

0.642915

794ae76ed74e0e253b874598d9df1653ac0b7641

6,578

py

Python

Wrapping/Generators/Python/Tests/extras.py

gift-surg/ITK_NiftyMIC

26415ac2e6197de7b07ffcb0c3f740aa937ba7e9

[ "Apache-2.0" ]

3

2019-11-19T09:47:25.000Z

2022-02-24T00:32:31.000Z

Wrapping/Generators/Python/Tests/extras.py

gift-surg/ITK_NiftyMIC

26415ac2e6197de7b07ffcb0c3f740aa937ba7e9

[ "Apache-2.0" ]

1

2019-03-18T14:19:49.000Z

2020-01-11T13:54:33.000Z

Wrapping/Generators/Python/Tests/extras.py

gift-surg/ITK_NiftyMIC

26415ac2e6197de7b07ffcb0c3f740aa937ba7e9

[ "Apache-2.0" ]

1

2022-02-24T00:32:36.000Z

#========================================================================== # # Copyright Insight Software Consortium # # 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.txt # # 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. # #==========================================================================*/ # also test the import callback feature from __future__ import print_function def custom_callback(name, progress): if progress == 0: print("Loading %s..." % name, file=sys.stderr) if progress == 1: print("done", file=sys.stderr) import itkConfig itkConfig.ImportCallback = custom_callback import itk import sys # test the force load function itk.force_load() fileName = sys.argv[1] PixelType = itk.UC dim = 2 ImageType = itk.Image[PixelType, dim] ReaderType = itk.ImageFileReader[ImageType] reader = ReaderType.New(FileName=fileName) # test echo itk.echo(reader) itk.echo(reader, sys.stdout) # test class_ assert itk.class_(reader) == ReaderType assert itk.class_("dummy") == str # test template assert itk.template(ReaderType) == (itk.ImageFileReader, (ImageType,)) assert itk.template(reader) == (itk.ImageFileReader, (ImageType,)) try: itk.template(str) raise Exception("unknown class should send an exception") except KeyError: pass # test ctype assert itk.ctype("unsigned short") == itk.US assert itk.ctype(" unsigned \n short \t ") == itk.US assert itk.ctype("signed short") == itk.SS assert itk.ctype("short") == itk.SS try: itk.ctype("dummy") raise Exception("unknown C type should send an exception") except KeyError: pass # test output assert itk.output(reader) == reader.GetOutput() assert itk.output(1) == 1 # test the deprecated image assert itk.image(reader) == reader.GetOutput() assert itk.image(1) == 1 # test size s = itk.size(reader) assert s[0] == s[1] == 256 s = itk.size(reader.GetOutput()) assert s[0] == s[1] == 256 # test physical size s = itk.physical_size(reader) assert s[0] == s[1] == 256.0 s = itk.physical_size(reader.GetOutput()) assert s[0] == s[1] == 256.0 # test spacing s = itk.spacing(reader) assert s[0] == s[1] == 1.0 s = itk.spacing(reader.GetOutput()) assert s[0] == s[1] == 1.0 # test origin s = itk.origin(reader) assert s[0] == s[1] == 0.0 s = itk.origin(reader.GetOutput()) assert s[0] == s[1] == 0.0 # test index s = itk.index(reader) assert s[0] == s[1] == 0 s = itk.index(reader.GetOutput()) assert s[0] == s[1] == 0 # test region s = itk.region(reader) assert s.GetIndex()[0] == s.GetIndex()[1] == 0 assert s.GetSize()[0] == s.GetSize()[1] == 256 s = itk.region(reader.GetOutput()) assert s.GetIndex()[0] == s.GetIndex()[1] == 0 assert s.GetSize()[0] == s.GetSize()[1] == 256 # test range assert itk.range(reader) == (0, 255) assert itk.range(reader.GetOutput()) == (0, 255) # test write itk.imwrite(reader, sys.argv[2]) itk.write(reader, sys.argv[2]) itk.imwrite(reader, sys.argv[2], True) # test read image=itk.imread(fileName) assert type(image) == itk.Image[itk.RGBPixel[itk.UC],2] image=itk.imread(fileName, itk.F) assert type(image) == itk.Image[itk.F,2] # test search res = itk.search("Index") assert res[0] == "Index" assert res[1] == "index" assert "ContinuousIndex" in res res = itk.search("index", True) assert "Index" not in res # test down_cast obj = itk.Object.cast(reader) # be sure that the reader is casted to itk::Object assert obj.__class__ == itk.Object down_casted = itk.down_cast(obj) assert down_casted == reader assert down_casted.__class__ == ReaderType # pipeline, auto_pipeline and templated class are tested in other files # BridgeNumPy try: # Images import numpy as np image = itk.imread(fileName) arr = itk.GetArrayFromImage(image) arr.fill(1) assert np.any(arr != itk.GetArrayFromImage(image)) view = itk.GetArrayViewFromImage(image) view.fill(1) assert np.all(view == itk.GetArrayFromImage(image)) image = itk.GetImageFromArray(arr) image.FillBuffer(2) assert np.any(arr != itk.GetArrayFromImage(image)) image = itk.GetImageViewFromArray(arr) image.FillBuffer(2) assert np.all(arr == itk.GetArrayFromImage(image)) image = itk.GetImageFromArray(arr, isVector=True) assert image.GetImageDimension() == 2 image = itk.GetImageViewFromArray(arr, isVector=True) assert image.GetImageDimension() == 2 arr = np.array([[1,2,3],[4,5,6]]).astype(np.uint8) assert arr.shape[0] == 2 assert arr.shape[1] == 3 assert arr[1,1] == 5 image = itk.GetImageFromArray(arr) arrKeepAxes = itk.GetArrayFromImage(image, keepAxes=True) assert arrKeepAxes.shape[0] == 3 assert arrKeepAxes.shape[1] == 2 assert arrKeepAxes[1,1] == 4 arr = itk.GetArrayFromImage(image, keepAxes=False) assert arr.shape[0] == 2 assert arr.shape[1] == 3 assert arr[1,1] == 5 arrKeepAxes = itk.GetArrayViewFromImage(image, keepAxes=True) assert arrKeepAxes.shape[0] == 3 assert arrKeepAxes.shape[1] == 2 assert arrKeepAxes[1,1] == 4 arr = itk.GetArrayViewFromImage(image, keepAxes=False) assert arr.shape[0] == 2 assert arr.shape[1] == 3 assert arr[1,1] == 5 # VNL Vectors v1 = itk.vnl_vector.D(2) v1.fill(1) v_np = itk.GetArrayFromVnlVector(v1) assert v1.get(0) == v_np[0] v_np[0] = 0 assert v1.get(0) != v_np[0] view = itk.GetArrayViewFromVnlVector(v1) assert v1.get(0) == view[0] view[0] = 0 assert v1.get(0) == view[0] # VNL Matrices m1 = itk.vnl_matrix.D(2,2) m1.fill(1) m_np = itk.GetArrayFromVnlMatrix(m1) assert m1.get(0,0) == m_np[0,0] m_np[0,0] = 0 assert m1.get(0,0) != m_np[0,0] view = itk.GetArrayViewFromVnlMatrix(m1) assert m1.get(0,0) == view[0,0] view[0,0] = 0 assert m1.get(0,0) == view[0,0] arr = np.zeros([3,3]) m_vnl = itk.GetVnlMatrixFromArray(arr) assert m_vnl(0,0) == 0 m_vnl.put(0,0,3) assert m_vnl(0,0) == 3 assert arr[0,0] == 0 except ImportError: print("NumPy not imported. Skipping BridgeNumPy tests") # Numpy is not available, do not run the Bridge NumPy tests pass

28.23176

77

0.656887

794ae7d5f07e8d7af583cb71d8f547e92e1d5561

3,880

py

Python

nipype/interfaces/fsl/tests/test_auto_FNIRT.py

dPys/nipype

75030b29297808e7c9a9e91b411b685154dff60b

[ "Apache-2.0" ]

1

2019-03-25T14:11:18.000Z

nipype/interfaces/fsl/tests/test_auto_FNIRT.py

dPys/nipype

75030b29297808e7c9a9e91b411b685154dff60b

[ "Apache-2.0" ]

1

2017-01-05T01:24:33.000Z

nipype/interfaces/fsl/tests/test_auto_FNIRT.py

wtriplett/nipype

388f140fceaf55438a987e9cdfa2a8e995428afd

[ "Apache-2.0" ]

1

2020-12-16T16:36:48.000Z

# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT from ..preprocess import FNIRT def test_FNIRT_inputs(): input_map = dict( affine_file=dict(argstr="--aff=%s", extensions=None,), apply_inmask=dict(argstr="--applyinmask=%s", sep=",", xor=["skip_inmask"],), apply_intensity_mapping=dict( argstr="--estint=%s", sep=",", xor=["skip_intensity_mapping"], ), apply_refmask=dict(argstr="--applyrefmask=%s", sep=",", xor=["skip_refmask"],), args=dict(argstr="%s",), bias_regularization_lambda=dict(argstr="--biaslambda=%f",), biasfield_resolution=dict(argstr="--biasres=%d,%d,%d",), config_file=dict(argstr="--config=%s",), derive_from_ref=dict(argstr="--refderiv",), environ=dict(nohash=True, usedefault=True,), field_file=dict(argstr="--fout=%s", hash_files=False,), fieldcoeff_file=dict(argstr="--cout=%s",), hessian_precision=dict(argstr="--numprec=%s",), in_file=dict(argstr="--in=%s", extensions=None, mandatory=True,), in_fwhm=dict(argstr="--infwhm=%s", sep=",",), in_intensitymap_file=dict(argstr="--intin=%s", copyfile=False,), inmask_file=dict(argstr="--inmask=%s", extensions=None,), inmask_val=dict(argstr="--impinval=%f",), intensity_mapping_model=dict(argstr="--intmod=%s",), intensity_mapping_order=dict(argstr="--intorder=%d",), inwarp_file=dict(argstr="--inwarp=%s", extensions=None,), jacobian_file=dict(argstr="--jout=%s", hash_files=False,), jacobian_range=dict(argstr="--jacrange=%f,%f",), log_file=dict( argstr="--logout=%s", extensions=None, genfile=True, hash_files=False, ), max_nonlin_iter=dict(argstr="--miter=%s", sep=",",), modulatedref_file=dict(argstr="--refout=%s", hash_files=False,), out_intensitymap_file=dict(argstr="--intout=%s", hash_files=False,), output_type=dict(), ref_file=dict(argstr="--ref=%s", extensions=None, mandatory=True,), ref_fwhm=dict(argstr="--reffwhm=%s", sep=",",), refmask_file=dict(argstr="--refmask=%s", extensions=None,), refmask_val=dict(argstr="--imprefval=%f",), regularization_lambda=dict(argstr="--lambda=%s", sep=",",), regularization_model=dict(argstr="--regmod=%s",), skip_implicit_in_masking=dict(argstr="--impinm=0",), skip_implicit_ref_masking=dict(argstr="--imprefm=0",), skip_inmask=dict(argstr="--applyinmask=0", xor=["apply_inmask"],), skip_intensity_mapping=dict( argstr="--estint=0", xor=["apply_intensity_mapping"], ), skip_lambda_ssq=dict(argstr="--ssqlambda=0",), skip_refmask=dict(argstr="--applyrefmask=0", xor=["apply_refmask"],), spline_order=dict(argstr="--splineorder=%d",), subsampling_scheme=dict(argstr="--subsamp=%s", sep=",",), warp_resolution=dict(argstr="--warpres=%d,%d,%d",), warped_file=dict( argstr="--iout=%s", extensions=None, genfile=True, hash_files=False, ), ) inputs = FNIRT.input_spec() for key, metadata in list(input_map.items()): for metakey, value in list(metadata.items()): assert getattr(inputs.traits()[key], metakey) == value def test_FNIRT_outputs(): output_map = dict( field_file=dict(extensions=None,), fieldcoeff_file=dict(extensions=None,), jacobian_file=dict(extensions=None,), log_file=dict(extensions=None,), modulatedref_file=dict(extensions=None,), out_intensitymap_file=dict(), warped_file=dict(extensions=None,), ) outputs = FNIRT.output_spec() for key, metadata in list(output_map.items()): for metakey, value in list(metadata.items()): assert getattr(outputs.traits()[key], metakey) == value

47.317073

87

0.622938

794ae7dd093c1a9464ff107010330d1f8903aa13

15,924

py

Python

main.py

SIRAJULHUDA/samples

2aff7aa9b1e2a5250260b2459e0ab9084c7b3f94

[ "MIT" ]

null

main.py

SIRAJULHUDA/samples

2aff7aa9b1e2a5250260b2459e0ab9084c7b3f94

[ "MIT" ]

null

main.py

SIRAJULHUDA/samples

2aff7aa9b1e2a5250260b2459e0ab9084c7b3f94

[ "MIT" ]

null

import sys, io, os from PyQt4 import QtCore, QtGui, uic from PyQt4.QtGui import QPainter, QColor, QFont from os.path import expanduser import subprocess as sp import numpy from PIL import Image, ImageDraw, ImageFont from PIL.ImageQt import ImageQt import atexit from queue import Queue from PyQt4.QtCore import QSettings import signal import preview_thread, core, video_thread class Command(QtCore.QObject): videoTask = QtCore.pyqtSignal(str, str, QFont, int, int, int, int, tuple, tuple, str, str) def __init__(self): QtCore.QObject.__init__(self) print("2") import argparse self.parser = argparse.ArgumentParser(description='Create a visualization for an audio file') self.parser.add_argument('-i', '--input', dest='input', help='input audio file', required=True) self.parser.add_argument('-o', '--output', dest='output', help='output video file', required=True) self.parser.add_argument('-b', '--background', dest='bgimage', help='background image file', required=True) self.parser.add_argument('-t', '--text', dest='text', help='title text', required=True) self.parser.add_argument('-f', '--font', dest='font', help='title font', required=False) self.parser.add_argument('-s', '--fontsize', dest='fontsize', help='title font size', required=False) self.parser.add_argument('-c', '--textcolor', dest='textcolor', help='title text color in r,g,b format', required=False) self.parser.add_argument('-C', '--viscolor', dest='viscolor', help='visualization color in r,g,b format', required=False) self.parser.add_argument('-x', '--xposition', dest='xposition', help='x position', required=False) self.parser.add_argument('-y', '--yposition', dest='yposition', help='y position', required=False) self.parser.add_argument('-a', '--alignment', dest='alignment', help='title alignment', required=False, type=int, choices=[0, 1, 2]) self.args = self.parser.parse_args() self.settings = QSettings('settings.ini', QSettings.IniFormat) # load colours as tuples from comma-separated strings self.textColor = core.Core.RGBFromString(self.settings.value("textColor", '255, 255, 255')) self.visColor = core.Core.RGBFromString(self.settings.value("visColor", '255, 255, 255')) if self.args.textcolor: self.textColor = core.Core.RGBFromString(self.args.textcolor) if self.args.viscolor: self.visColor = core.Core.RGBFromString(self.args.viscolor) # font settings if self.args.font: self.font = QFont(self.args.font) else: self.font = QFont(self.settings.value("titleFont", QFont())) if self.args.fontsize: self.fontsize = int(self.args.fontsize) else: self.fontsize = int(self.settings.value("fontSize", 35)) if self.args.alignment: self.alignment = int(self.args.alignment) else: self.alignment = int(self.settings.value("alignment", 0)) if self.args.xposition: self.textX = int(self.args.xposition) else: self.textX = int(self.settings.value("xPosition", 70)) if self.args.yposition: self.textY = int(self.args.yposition) else: self.textY = int(self.settings.value("yPosition", 375)) ffmpeg_cmd = self.settings.value("ffmpeg_cmd", expanduser("~")) self.videoThread = QtCore.QThread(self) self.videoWorker = video_thread.Worker(self) self.videoWorker.moveToThread(self.videoThread) self.videoWorker.videoCreated.connect(self.videoCreated) self.videoThread.start() self.videoTask.emit(self.args.bgimage, self.args.text, self.font, self.fontsize, self.alignment, self.textX, self.textY, self.textColor, self.visColor, self.args.input, self.args.output) def videoCreated(self): self.videoThread.quit() self.videoThread.wait() self.cleanUp() def cleanUp(self): self.settings.setValue("titleFont", self.font.toString()) self.settings.setValue("alignment", str(self.alignment)) self.settings.setValue("fontSize", str(self.fontsize)) self.settings.setValue("xPosition", str(self.textX)) self.settings.setValue("yPosition", str(self.textY)) self.settings.setValue("visColor", '%s,%s,%s' % self.visColor) self.settings.setValue("textColor", '%s,%s,%s' % self.textColor) sys.exit(0) class Main(QtCore.QObject): newTask = QtCore.pyqtSignal(str, str, QFont, int, int, int, int, tuple, tuple) processTask = QtCore.pyqtSignal() videoTask = QtCore.pyqtSignal(str, str, QFont, int, int, int, int, tuple, tuple, str, str) def __init__(self, window): QtCore.QObject.__init__(self) # print('main thread id: {}'.format(QtCore.QThread.currentThreadId())) self.window = window self.core = core.Core() self.settings = QSettings('settings.ini', QSettings.IniFormat) # load colors as tuples from a comma-separated string self.textColor = core.Core.RGBFromString(self.settings.value("textColor", '255, 255, 255')) self.visColor = core.Core.RGBFromString(self.settings.value("visColor", '255, 255, 255')) self.previewQueue = Queue() self.previewThread = QtCore.QThread(self) self.previewWorker = preview_thread.Worker(self, self.previewQueue) self.previewWorker.moveToThread(self.previewThread) self.previewWorker.imageCreated.connect(self.showPreviewImage) self.previewThread.start() self.timer = QtCore.QTimer(self) self.timer.timeout.connect(self.processTask.emit) self.timer.start(500) #window.pushButton_selectInput.clicked.connect(self.openInputFileDialog) #window.pushButton_selectOutput.clicked.connect(self.openOutputFileDialog) #window.pushButton_createVideo.clicked.connect(self.createAudioVisualisation) #window.pushButton_selectBackground.clicked.connect(self.openBackgroundFileDialog) print("5") fileName = "siraj.mp3" if not fileName == "": #self.settings.setValue("inputDir", os.path.dirname(fileName)) self.window.label_input.setText(fileName) fileName = "siraj.mkv" if not fileName == "": #self.settings.setValue("outputDir", os.path.dirname(fileName)) self.window.label_output.setText(fileName) fileName = "siraj.jpg" if not fileName == "": #self.settings.setValue("backgroundDir", os.path.dirname(fileName)) self.window.label_background.setText(fileName) #window.pushButton_createVideo.clicked.connect(self.createAudioVisualisation) window.progressBar_create.setValue(0) window.setWindowTitle("Audio Visualizer") window.pushButton_selectInput.setText("Select Input Music File") window.pushButton_selectOutput.setText("Select Output Video File") window.pushButton_selectBackground.setText("Select Background Image") window.label_font.setText("Title Font") window.label_alignment.setText("Title Options") window.label_colorOptions.setText("Colors") window.label_fontsize.setText("Fontsize") window.label_title.setText("Title Text") window.label_textColor.setText("Text:") window.label_visColor.setText("Visualizer:") #window.pushButton_createVideo.setText("Create Video") window.groupBox_create.setTitle("Create") window.groupBox_settings.setTitle("Settings") window.groupBox_preview.setTitle("Preview") window.alignmentComboBox.addItem("Left") window.alignmentComboBox.addItem("Middle") window.alignmentComboBox.addItem("Right") window.fontsizeSpinBox.setValue(35) window.textXSpinBox.setValue(70) window.textYSpinBox.setValue(375) window.lineEdit_textColor.setText('%s,%s,%s' % self.textColor) window.lineEdit_visColor.setText('%s,%s,%s' % self.visColor) window.pushButton_textColor.clicked.connect(lambda: self.pickColor('text')) window.pushButton_visColor.clicked.connect(lambda: self.pickColor('vis')) btnStyle = "QPushButton { background-color : %s; outline: none; }" % QColor(*self.textColor).name() window.pushButton_textColor.setStyleSheet(btnStyle) btnStyle = "QPushButton { background-color : %s; outline: none; }" % QColor(*self.visColor).name() window.pushButton_visColor.setStyleSheet(btnStyle) titleFont = self.settings.value("titleFont") if not titleFont == None: window.fontComboBox.setCurrentFont(QFont(titleFont)) alignment = self.settings.value("alignment") if not alignment == None: window.alignmentComboBox.setCurrentIndex(int(alignment)) fontSize = self.settings.value("fontSize") if not fontSize == None: window.fontsizeSpinBox.setValue(int(fontSize)) xPosition = self.settings.value("xPosition") if not xPosition == None: window.textXSpinBox.setValue(int(xPosition)) yPosition = self.settings.value("yPosition") if not yPosition == None: window.textYSpinBox.setValue(int(yPosition)) window.fontComboBox.currentFontChanged.connect(self.drawPreview) window.lineEdit_title.textChanged.connect(self.drawPreview) window.alignmentComboBox.currentIndexChanged.connect(self.drawPreview) window.textXSpinBox.valueChanged.connect(self.drawPreview) window.textYSpinBox.valueChanged.connect(self.drawPreview) window.fontsizeSpinBox.valueChanged.connect(self.drawPreview) window.lineEdit_textColor.textChanged.connect(self.drawPreview) window.lineEdit_visColor.textChanged.connect(self.drawPreview) #self.drawPreview() ffmpeg_cmd = self.settings.value("ffmpeg_cmd", expanduser("~")) self.videoThread = QtCore.QThread(self) self.videoWorker = video_thread.Worker(self) self.videoWorker.moveToThread(self.videoThread) self.videoWorker.videoCreated.connect(self.videoCreated) self.videoWorker.progressBarUpdate.connect(self.progressBarUpdated) self.videoWorker.progressBarSetText.connect(self.progressBarSetText) self.videoThread.start() self.videoTask.emit(self.window.label_background.text(), self.window.lineEdit_title.text(), self.window.fontComboBox.currentFont(), self.window.fontsizeSpinBox.value(), self.window.alignmentComboBox.currentIndex(), self.window.textXSpinBox.value(), self.window.textYSpinBox.value(), core.Core.RGBFromString(self.window.lineEdit_textColor.text()), core.Core.RGBFromString(self.window.lineEdit_visColor.text()), self.window.label_input.text(), self.window.label_output.text()) #window.show() def cleanUp(self): self.timer.stop() self.previewThread.quit() self.previewThread.wait() self.settings.setValue("titleFont", self.window.fontComboBox.currentFont().toString()) self.settings.setValue("alignment", str(self.window.alignmentComboBox.currentIndex())) self.settings.setValue("fontSize", str(self.window.fontsizeSpinBox.value())) self.settings.setValue("xPosition", str(self.window.textXSpinBox.value())) self.settings.setValue("yPosition", str(self.window.textYSpinBox.value())) self.settings.setValue("visColor", self.window.lineEdit_visColor.text()) self.settings.setValue("textColor", self.window.lineEdit_textColor.text()) def openInputFileDialog(self): inputDir = self.settings.value("inputDir", expanduser("~")) #fileName = QtGui.QFileDialog.getOpenFileName(self.window, # "Open Music File", inputDir, "Music Files (*.mp3 *.wav *.ogg *.flac)"); fileName = r"C:\Users\MSHK\Downloads\المصطفى ﷺ _ مشاري راشد العفاسي وابنه محمد (192 kbps).mp3" if not fileName == "": self.settings.setValue("inputDir", os.path.dirname(fileName)) self.window.label_input.setText(fileName) def openOutputFileDialog(self): outputDir = self.settings.value("outputDir", expanduser("~")) #fileName = QtGui.QFileDialog.getSaveFileName(self.window, # "Set Output Video File", outputDir, "Video Files (*.mkv)"); fileName = r"C:\Users\MSHK\Pictures\tesing.mkv" if not fileName == "": self.settings.setValue("outputDir", os.path.dirname(fileName)) self.window.label_output.setText(fileName) def openBackgroundFileDialog(self): backgroundDir = self.settings.value("backgroundDir", expanduser("~")) #fileName = QtGui.QFileDialog.getOpenFileName(self.window, #"Open Background Image", backgroundDir, "Image Files (*.jpg *.png);; Video Files (*.mp4)"); fileName = r"C:\Users\MSHK\Pictures\butterfly - Copy - Copy.jpg" if not fileName == "": self.settings.setValue("backgroundDir", os.path.dirname(fileName)) self.window.label_background.setText(fileName) self.drawPreview() def createAudioVisualisation(self): ffmpeg_cmd = self.settings.value("ffmpeg_cmd", expanduser("~")) self.videoThread = QtCore.QThread(self) self.videoWorker = video_thread.Worker(self) self.videoWorker.moveToThread(self.videoThread) self.videoWorker.videoCreated.connect(self.videoCreated) self.videoWorker.progressBarUpdate.connect(self.progressBarUpdated) self.videoWorker.progressBarSetText.connect(self.progressBarSetText) self.videoThread.start() self.videoTask.emit(self.window.label_background.text(), self.window.lineEdit_title.text(), self.window.fontComboBox.currentFont(), self.window.fontsizeSpinBox.value(), self.window.alignmentComboBox.currentIndex(), self.window.textXSpinBox.value(), self.window.textYSpinBox.value(), core.Core.RGBFromString(self.window.lineEdit_textColor.text()), core.Core.RGBFromString(self.window.lineEdit_visColor.text()), self.window.label_input.text(), self.window.label_output.text()) def progressBarUpdated(self, value): self.window.progressBar_create.setValue(value) def progressBarSetText(self, value): self.window.progressBar_create.setFormat(value) def videoCreated(self): self.videoThread.quit() self.videoThread.wait() def drawPreview(self): self.newTask.emit(self.window.label_background.text(), self.window.lineEdit_title.text(), self.window.fontComboBox.currentFont(), self.window.fontsizeSpinBox.value(), self.window.alignmentComboBox.currentIndex(), self.window.textXSpinBox.value(), self.window.textYSpinBox.value(), core.Core.RGBFromString(self.window.lineEdit_textColor.text()), core.Core.RGBFromString(self.window.lineEdit_visColor.text())) # self.processTask.emit() def showPreviewImage(self, image): self._scaledPreviewImage = image self._previewPixmap = QtGui.QPixmap.fromImage(self._scaledPreviewImage) self.window.label_preview.setPixmap(self._previewPixmap) def pickColor(self, colorTarget): color = QtGui.QColorDialog.getColor() if color.isValid(): RGBstring = '%s,%s,%s' % (str(color.red()), str(color.green()), str(color.blue())) btnStyle = "QPushButton { background-color : %s; outline: none; }" % color.name() if colorTarget == 'text': self.window.lineEdit_textColor.setText(RGBstring) window.pushButton_textColor.setStyleSheet(btnStyle) elif colorTarget == 'vis': self.window.lineEdit_visColor.setText(RGBstring) window.pushButton_visColor.setStyleSheet(btnStyle) if len(sys.argv) > 1: # command line mode app = QtGui.QApplication(sys.argv, False) command = Command() signal.signal(signal.SIGINT, command.cleanUp) sys.exit(app.exec_()) else: # gui mode if __name__ == "__main__": app = QtGui.QApplication(sys.argv) window = uic.loadUi("main.ui") # window.adjustSize() desc = QtGui.QDesktopWidget() dpi = desc.physicalDpiX() topMargin = 0 if (dpi == 96) else int(10 * (dpi / 96)) window.resize(window.width() * (dpi / 96), window.height() * (dpi / 96)) window.verticalLayout_2.setContentsMargins(0, topMargin, 0, 0) print("1") main = Main(window) signal.signal(signal.SIGINT, main.cleanUp) atexit.register(main.cleanUp) sys.exit(app.exec_())

41.795276

136

0.716152

794ae9d47cd348e73a34ba5bd696f2b537a2718a

26,037

py

Python

hops/application_4_alignment.py

ExoWorldsSpies/hops

a33e434befe17318c064210a289b453c6f91b44f

[ "MIT" ]

5

2020-02-22T13:51:47.000Z

2021-12-10T20:24:11.000Z

hops/application_4_alignment.py

ExoWorldsSpies/hops

a33e434befe17318c064210a289b453c6f91b44f

[ "MIT" ]

6

2020-02-24T16:29:11.000Z

2021-11-27T22:57:19.000Z

hops/application_4_alignment.py

ExoWorldsSpies/hops

a33e434befe17318c064210a289b453c6f91b44f

[ "MIT" ]

2

2020-04-04T17:33:05.000Z

2021-03-04T20:10:23.000Z

import os import time import numpy as np import matplotlib.patches as mpatches import hops.pylightcurve3 as plc from astropy.io import fits as pf from hops.application_windows import MainWindow class AlignmentWindow(MainWindow): def __init__(self, log): MainWindow.__init__(self, log, name='HOPS - Alignment', position=2) # set variables, create and place widgets self.bin_fits = self.log.get_param('bin_fits') self.burn_limit = int(1.1 * self.log.get_param('burn_limit')) * self.bin_fits * self.bin_fits self.shift_tolerance_p = self.log.get_param('shift_tolerance_p') self.rotation_tolerance = self.log.get_param('rotation_tolerance') self.min_calibration_stars_number = int(self.log.get_param('min_calibration_stars_number')) self.all_frames = plc.open_dict(self.log.all_frames) self.science_files = [] for science_file in self.all_frames: if not self.all_frames[science_file][self.log.skip_key]: self.science_files.append([self.all_frames[science_file][self.log.time_key], science_file]) else: self.all_frames[science_file][self.log.align_x0_key] = False self.all_frames[science_file][self.log.align_y0_key] = False self.all_frames[science_file][self.log.align_u0_key] = False fits = pf.open(os.path.join(self.log.reduction_directory, science_file), mode='update') fits[1].header.set(self.log.align_x0_key, False) fits[1].header.set(self.log.align_y0_key, False) fits[1].header.set(self.log.align_u0_key, False) fits.flush() fits.close() self.science_files.sort() self.science_files = [ff[1] for ff in self.science_files] self.skip_time = 0 self.science_counter = 0 self.test_level = None self.redraw = None self.stars = None self.science_file = None self.fits = None self.std = None self.mean = None self.star_std = None self.int_psf = None self.stars_detected = None self.rotation_detected = None self.check_num = None self.check_num_snr = None self.x0 = None self.y0 = None self.u0 = None self.f0 = None self.comparisons = None self.comparisons_snr = None self.small_angles = None self.large_angles = None self.circle = None self.settings_to_check = None self.comparisons_to_check = None # common definitions for all images fits = plc.open_fits(os.path.join(self.log.reduction_directory, self.science_files[0])) self.shift_tolerance = int(max(len(fits[1].data), len(fits[1].data[0])) * (self.shift_tolerance_p / 100.0)) self.y_length, self.x_length = fits[1].data.shape self.circles_diameter = 0.02 * max(self.y_length, self.x_length) # progress window y_scale = (self.root.winfo_screenheight() - 500) / self.root.winfo_screenheight() self.progress_figure = self.FitsWindow(figsize=(0.5, y_scale, 10, 10, len(fits[1].data[0]) / len(fits[1].data))) self.progress_figure.load_fits(fits[1], input_name=self.science_files[0]) self.progress_all_stars = self.Label(text='') self.progress_alignment = self.Progressbar(task="Aligning frames") # self.progress_all_frames = self.Progressbar(task="Aligning all stars in all frames") self.setup_window([ [[self.progress_figure, 0, 2]], [[self.progress_all_stars, 0, 2]], [[self.progress_alignment, 0, 2]], [[self.Button(text='STOP ALIGNMENT & RETURN TO MAIN MENU', command=self.trigger_exit), 0, 2]], [] ]) self.set_close_button_function(self.trigger_exit) def run_alignment(self): self.close = self.trigger_exit if self.log.get_param('alignment_complete'): if self.askyesno('Overwrite files', 'Alignment has been completed, do you want to run again?'): self.log.set_param('alignment_complete', False) self.log.save_local_log() else: self.log.set_param('proceed', True) self.show() if not self.log.get_param('alignment_complete'): self.progress_all_stars.set('Analysing first frame...') self.after(self.find_all_stars) else: self.def_close() def alignment_log(self, *text): # print(*text) pass def find_all_stars(self): if self.exit: self.after(self.align) else: fits = plc.open_fits(os.path.join(self.log.reduction_directory, self.science_files[0])) metadata = self.all_frames[self.science_files[0]] self.progress_figure.load_fits(fits[1], self.science_files[0]) stars, psf = plc.find_all_stars(fits[1].data, mean=metadata[self.log.mean_key], std=metadata[self.log.std_key], std_limit=3, burn_limit=self.burn_limit, star_std=metadata[self.log.psf_key], progressbar=self.progress_all_stars, progress_window=self, verbose=True ) if self.exit: self.after(self.choose_calibartion_stars) stars = np.array(stars) self.log.save_local_log() all_stars_dict = {'all_stars': stars} plc.save_dict(all_stars_dict, 'all_stars.pickle') self.progress_all_stars.set('Choosing calibrating stars...') self.after(self.choose_calibartion_stars) def choose_calibartion_stars(self): if self.exit: self.after(self.align) else: all_stars_dict = plc.open_dict('all_stars.pickle') stars = np.array(all_stars_dict['all_stars']) fits = pf.open(os.path.join(self.log.reduction_directory, self.science_files[0]), memmap=False) metadata = self.all_frames[self.science_files[0]] frame_mean = metadata[self.log.mean_key] frame_std = metadata[self.log.std_key] frame_star_psf = metadata[self.log.psf_key] bright_stars = [] std_limit = 30 while len(bright_stars) < 100 and std_limit >= 5.0: bright_stars = [] for star in stars: if star[2] + star[3] < 2.0 * self.burn_limit / 3.0: if star[-1] > (2 * np.pi * (std_limit * frame_std) * frame_star_psf * frame_star_psf): self.alignment_log(star[0], star[1], star[-1], 2 * np.pi * (frame_mean + std_limit * frame_std) * (frame_star_psf ** 2)) bright_stars.append(star) std_limit -= 5 if len(bright_stars) < self.min_calibration_stars_number: bright_stars = [] std_limit = 30 while len(bright_stars) < 100 and std_limit >= 5.0: bright_stars = [] for star in stars: if star[-1] > (2 * np.pi * (std_limit * frame_std) * frame_star_psf * frame_star_psf): self.alignment_log(star[0], star[1], star[-1], 2 * np.pi * (frame_mean + std_limit * frame_std) * (frame_star_psf ** 2)) bright_stars.append(star) std_limit -= 5 stars = sorted(bright_stars, key=lambda x: -x[-1] / (x[-2] ** 3)) x_ref_position = stars[0][0] y_ref_position = stars[0][1] f_ref = stars[0][-1] del stars[0] # take the rest as calibration stars and calculate their polar coordinates relatively to the first calibration_stars_polar = [] for star in stars: r_position, u_position = plc.cartesian_to_polar(star[0], star[1], x_ref_position, y_ref_position) if r_position > 5 * frame_star_psf: calibration_stars_polar.append([r_position, u_position]) stars = sorted(stars, key=lambda x: -x[-1]) calibration_stars_polar_snr = [] for star in stars: r_position, u_position = plc.cartesian_to_polar(star[0], star[1], x_ref_position, y_ref_position) if r_position > 5 * frame_star_psf: calibration_stars_polar_snr.append([r_position, u_position]) if len(calibration_stars_polar) <= self.min_calibration_stars_number: self.check_num = len(calibration_stars_polar) - 0.5 self.check_num_snr = len(calibration_stars_polar) - 0.5 else: self.check_num = max(self.min_calibration_stars_number - 0.5, len(calibration_stars_polar) / 10.0 - 0.5) self.check_num_snr = max(self.min_calibration_stars_number - 0.5, len(calibration_stars_polar) / 20.0 - 0.5) self.x0 = x_ref_position self.y0 = y_ref_position self.u0 = 0 self.f0 = f_ref self.comparisons = calibration_stars_polar self.comparisons_snr = calibration_stars_polar_snr fits.close() ustep = np.arcsin(float(frame_star_psf) / self.comparisons[int(len(self.comparisons) / 2)][0]) self.small_angles = np.append(np.arange(-self.rotation_tolerance, self.rotation_tolerance, ustep), np.arange(-self.rotation_tolerance, self.rotation_tolerance, ustep) + np.pi) self.large_angles = np.array([np.pi, 0]) for ff in range(1, int(np.pi / ustep) + 1): self.large_angles = np.append(self.large_angles, np.pi - ff * ustep) self.large_angles = np.append(self.large_angles, np.pi + ff * ustep) self.large_angles = np.append(self.large_angles, 0 - ff * ustep) self.large_angles = np.append(self.large_angles, 0 + ff * ustep) # set the looking window and angular step self.progress_all_stars.set(' ') self.after(self.align) def align(self): if self.exit: self.after(self.plot_current) else: if self.science_counter == 0: self.progress_alignment.initiate(len(self.science_files)) self.stars = None self.science_file = self.science_files[self.science_counter] self.fits = pf.open(os.path.join(self.log.reduction_directory, self.science_file), memmap=False, mode='update') self.std = self.fits[1].header[self.log.std_key] self.mean = self.fits[1].header[self.log.mean_key] self.star_std = self.fits[1].header[self.log.psf_key] self.int_psf = int(max(1, round(self.fits[1].header[self.log.psf_key]))) self.stars_detected = False self.rotation_detected = False self.progress_alignment.show_message(' ') self.progress_figure.load_fits(self.fits[1], self.science_file, draw=False) self.circle = mpatches.Circle((self.x0, self.y0), self.circles_diameter, ec='r', fill=False) self.progress_figure.ax.add_patch(self.circle) self.test_level = 1 self.redraw = 0 self.skip_time = 0 self.after(self.detect_stars) def detect_stars(self): if self.exit: self.after(self.plot_current) else: if self.test_level == 1: self.stars = plc.find_single_star(self.fits[1].data, self.x0, self.y0, mean=self.mean, std=self.std, burn_limit=self.burn_limit, star_std=self.star_std) if self.stars: self.stars.append(2 * np.pi * self.stars[2] * self.stars[4] * self.stars[5]) self.stars = [self.stars] elif self.test_level == 2: self.skip_time = time.time() self.stars = plc.find_all_stars(self.fits[1].data, x_low=self.x0 - self.shift_tolerance, x_upper=self.x0 + self.shift_tolerance, y_low=self.y0 - self.shift_tolerance, y_upper=self.y0 + self.shift_tolerance, x_centre=self.x0, y_centre=self.y0, mean=self.mean, std=self.std, burn_limit=self.burn_limit, star_std=self.star_std, verbose=True, order_by_distance_and_flux=self.f0)[0] self.progress_all_stars.set(' ') elif self.test_level == 4: if self.askyesno('HOPS - Alignment', 'Stars not found close to their previous positions.\n' 'Do you want to skip this frame?'): self.after(self.plot_current) self.stars = plc.find_all_stars(self.fits[1].data, mean=self.mean, std=self.std, burn_limit=self.burn_limit, star_std=self.star_std, order_by_flux=self.f0, verbose=True)[0] self.progress_all_stars.set(' ') if self.stars: self.settings_to_check = [] if self.test_level == 1: self.settings_to_check.append([self.stars[0][0], self.stars[0][1], self.u0, self.stars[0]]) self.setting_checking = 0 self.comparisons_to_check = self.comparisons elif self.test_level == 2: for star in self.stars: self.settings_to_check.append([star[0], star[1], self.u0, star]) self.comparisons_to_check = self.comparisons_snr elif self.test_level == 3: for star in self.stars: for rotation in self.small_angles: self.settings_to_check.append([star[0], star[1], rotation, star]) elif self.test_level == 4: for star in self.stars: self.settings_to_check.append([star[0], star[1], self.u0, star]) elif self.test_level == 5: for star in self.stars: for rotation in self.large_angles: self.settings_to_check.append([star[0], star[1], rotation, star]) self.setting_checking = 0 self.after(self.check_star) else: if self.test_level == 1: self.test_level = 2 self.progress_figure.draw() self.progress_all_stars.set('Analysing frame...') self.progress_alignment.show_message('Testing small shift...') self.after(self.detect_stars) elif self.test_level == 2: self.test_level = 3 self.progress_alignment.show_message('Testing small shift & rotation...') self.after(self.detect_stars) elif self.test_level == 3: self.test_level = 4 self.progress_all_stars.set('Analysing frame...') self.progress_alignment.show_message('Testing large shift...') self.after(self.detect_stars) elif self.test_level == 4: self.test_level = 5 self.progress_alignment.show_message('Testing large shift & rotation...') self.after(self.detect_stars) else: self.after(self.plot_current) def check_star(self): if self.exit: self.after(self.plot_current) else: x, y, u, star = self.settings_to_check[self.setting_checking] self.alignment_log('Checking star at: ', x, y, ', with rotation:', u) if self.redraw >= 1: self.circle.set_center((x, y)) self.progress_figure.draw() self.redraw = 0 else: self.redraw += 0.01 test = 0 for comp in self.comparisons_to_check: check_x = int(x + comp[0] * np.cos(u + comp[1])) check_y = int(y + comp[0] * np.sin(u + comp[1])) if 0 < check_x < self.x_length and 0 < check_y < self.y_length: check_sum = np.sum(self.fits[1].data[check_y - self.int_psf:check_y + self.int_psf + 1, check_x - self.int_psf:check_x + self.int_psf + 1]) check_lim = (self.fits[1].header[self.log.mean_key] + 3 * self.fits[1].header[self.log.std_key]) * ((2 * self.int_psf + 1) ** 2) if check_sum > check_lim: test += 1 else: test -= 1 self.alignment_log('Check ref. star at: ', check_x, check_y, ', Test: ', test) if abs(test) > self.check_num: break if test >= self.check_num: self.stars_detected = True if self.test_level > 1: self.rotation_detected = True self.x0 = x self.y0 = y self.u0 = u self.f0 = star[-1] self.after(self.plot_current) else: self.setting_checking += 1 if self.setting_checking < len(self.settings_to_check): self.after(self.check_star) else: if self.test_level == 1: self.test_level = 2 self.progress_figure.draw() self.progress_all_stars.set('Analysing frame...') self.progress_alignment.show_message('Testing small shift...') self.after(self.detect_stars) elif self.test_level == 2: self.test_level = 3 self.progress_alignment.show_message('Testing small shift & rotation...') self.after(self.detect_stars) elif self.test_level == 3: self.test_level = 4 self.progress_all_stars.set('Analysing frame...') self.progress_alignment.show_message('Testing large shift...') self.after(self.detect_stars) elif self.test_level == 4: self.test_level = 5 self.progress_alignment.show_message('Testing large shift & rotation...') self.after(self.detect_stars) else: self.after(self.plot_current) def plot_current(self): if self.exit: self.after(self.save) else: if self.stars_detected: if self.rotation_detected: test_u0 = [] test_cos = [] test_sin = [] for ii in self.comparisons[:int(self.check_num + 0.5)]: check_x = self.x0 + ii[0] * np.cos(self.u0 + ii[1]) check_y = self.y0 + ii[0] * np.sin(self.u0 + ii[1]) star = plc.find_single_star(self.fits[1].data, check_x, check_y, mean=self.mean, std=self.std, burn_limit=self.burn_limit, star_std=self.star_std) if star: diff = plc.cartesian_to_polar(star[0], star[1], self.x0, self.y0)[1] - ii[1] if diff < 0: diff += 2 * np.pi test_u0.append(diff) test_cos.append(np.cos(diff)) test_sin.append(np.sin(diff)) if len(test_u0) > 0: test_cos = np.median(test_cos) test_sin = np.median(test_sin) self.u0 = np.arccos(test_cos) if test_sin < 0: self.u0 = np.pi + (np.pi - self.u0) self.fits[1].header.set(self.log.align_x0_key, self.x0) self.fits[1].header.set(self.log.align_y0_key, self.y0) self.fits[1].header.set(self.log.align_u0_key, self.u0) self.circle.set_center((self.x0, self.y0)) for ii in self.comparisons[:int(self.check_num + 0.5)]: circle = mpatches.Circle((self.x0 + ii[0] * np.cos(self.u0 + ii[1]), self.y0 + ii[0] * np.sin(self.u0 + ii[1])), self.circles_diameter, ec='w', fill=False) self.progress_figure.ax.add_patch(circle) else: self.fits[1].header.set(self.log.align_x0_key, False) self.fits[1].header.set(self.log.align_y0_key, False) self.fits[1].header.set(self.log.align_u0_key, False) self.all_frames[self.science_file][self.log.align_x0_key] = self.fits[1].header[self.log.align_x0_key] self.all_frames[self.science_file][self.log.align_y0_key] = self.fits[1].header[self.log.align_y0_key] self.all_frames[self.science_file][self.log.align_u0_key] = self.fits[1].header[self.log.align_u0_key] if not self.fits[1].header[self.log.align_x0_key]: self.all_frames[self.science_file][self.log.skip_key] = True self.fits.flush() self.fits.close() self.progress_figure.draw() if self.skip_time == 0: self.progress_alignment.update() else: self.progress_alignment.update(skip=time.time() - self.skip_time) self.skip_time = 0 self.science_counter += 1 if self.science_counter >= len(self.science_files): self.progress_all_stars.set('Aligning all stars in all frames...') self.after(self.save) else: self.after(self.align) def save(self): if self.exit: self.after(self.check_visibility) else: plc.save_dict(self.all_frames, self.log.all_frames) self.after(self.check_visibility) def check_visibility(self): if self.exit: self.def_close() else: all_stars_dict = plc.open_dict('all_stars.pickle') stars = np.array(all_stars_dict['all_stars']) fits = plc.open_fits(os.path.join(self.log.reduction_directory, self.science_files[0])) polar_coords = [] for star in all_stars_dict['all_stars']: polar_coords.append(plc.cartesian_to_polar(star[0], star[1], self.all_frames[self.science_files[0]][ self.log.align_x0_key], self.all_frames[self.science_files[0]][ self.log.align_y0_key])) in_fov = np.ones(len(polar_coords)) for science_file in self.science_files: metadata = self.all_frames[science_file] if self.exit: self.def_close() ref_x_position = metadata[self.log.align_x0_key] ref_y_position = metadata[self.log.align_y0_key] ref_u_position = metadata[self.log.align_u0_key] star_std = metadata[self.log.psf_key] if ref_x_position: in_fov_single = [] for star in polar_coords: cartesian_x = ref_x_position + star[0] * np.cos(ref_u_position + star[1]) cartesian_y = ref_y_position + star[0] * np.sin(ref_u_position + star[1]) if (3 * star_std < cartesian_x < len(fits[1].data[0]) - 3 * star_std and 3 * star_std < cartesian_y < len(fits[1].data) - 3 * star_std): in_fov_single.append(1) else: in_fov_single.append(0) in_fov *= in_fov_single all_stars_dict['in_fov'] = np.array(in_fov) visible_fov_x_min = np.min(stars[np.where(in_fov), 0]) - 3 * star_std visible_fov_x_max = np.max(stars[np.where(in_fov), 0]) + 3 * star_std visible_fov_y_min = np.min(stars[np.where(in_fov), 1]) - 3 * star_std visible_fov_y_max = np.max(stars[np.where(in_fov), 1]) + 3 * star_std self.log.set_param('min_x', float(visible_fov_x_min)) self.log.set_param('min_y', float(visible_fov_y_min)) self.log.set_param('max_x', float(visible_fov_x_max)) self.log.set_param('max_y', float(visible_fov_y_max)) plc.save_dict(all_stars_dict, 'all_stars.pickle') self.log.set_param('alignment_complete', True) self.log.set_param('alignment_version', self.log.version) self.log.save_local_log() self.log.set_param('proceed', True) self.def_close()

43.395

124

0.53616

794aeb145ac4f80ebe094c058e7ec3173c5a14a9

3,161

py

Python

mdanalysis/runConfGen.py

otayfuroglu/mdutils

481148316d9347c1136c22f581a3668da4192168

[ "MIT" ]

null

mdanalysis/runConfGen.py

otayfuroglu/mdutils

481148316d9347c1136c22f581a3668da4192168

[ "MIT" ]

null

mdanalysis/runConfGen.py

otayfuroglu/mdutils

481148316d9347c1136c22f581a3668da4192168

[ "MIT" ]

null

#! /home/omert/miniconda3/bin/python from gmx_md_utils import * import sys, os, shutil from rdkit import Chem from rdkit.Chem import AllChem, TorsionFingerprints from rdkit.ML.Cluster import Butina def mainCalcRMS(mols_path, ref_path): ref_mol = Chem.MolFromMolFile(ref_path) suppl = Chem.SDMolSupplier(mols_path) for i, mol in enumerate(suppl): print("RMSD for %d. mol" %i, calcRMS(mol, ref_mol)) def mainGenConf(): mol_path = "Fabienne_project/Aa12b12BCD/ZnPc_SCH3_Aa1a2b1b2BCD_1.mol2" fileBase = mol_path.split("/")[-1].replace(".mol", "") print(fileBase) numConfs = 500 maxAttempts = 5000 pruneRmsThresh = 0.3 clusterMethod = "RMSD" clusterThreshold = 2.0 minimizeIterations = 0 # suppl = Chem.ForwardSDMolSupplier(input_file) print(mol_path) # suppl = Chem.MolFromMolFile(mol_path) suppl = Chem.MolFromMol2File(mol_path) i=0 xyzDIR = "xyz" if os.path.exists(xyzDIR): shutil.rmtree(xyzDIR) os.mkdir(xyzDIR) sdfDIR = "sdf" if os.path.exists(sdfDIR): shutil.rmtree(sdfDIR) os.mkdir(sdfDIR) for mol in [suppl]: i = i+1 if mol is None: continue m = Chem.AddHs(mol, addCoords=True) # generate the confomers conformerIds = genGonformers(m, numConfs, maxAttempts, pruneRmsThresh, True, True, True) # align conformers # AllChem.AlignMolConformers(m, conformerIds) conformerPropsDict = {} for j, conformerId in enumerate(conformerIds): conf_file_base = fileBase + "_conf_" + str(j) writeConf2sdf(m, "%s/%s.sdf" % (sdfDIR, conf_file_base), conformerId) mol = read("%s/%s.sdf" % (sdfDIR, conf_file_base)) write("%s/%s.xyz" % (xyzDIR, conf_file_base), mol) # energy minimise (optional) and energy calculation # props = calcEnergy(m, conformerId, minimizeIterations) # conformerPropsDict[conformerId] = props # # cluster the conformers # rmsClusters = getClusterConf(m, clusterMethod, clusterThreshold) # print("Molecule", i, ": generated", len(conformerIds), "conformers and", len(rmsClusters), "clusters") # rmsClustersPerCluster = [] # clusterNumber = 0 # minEnergy = 9999999999999 # for cluster in rmsClusters: # clusterNumber = clusterNumber+1 # rmsWithinCluster = alignConfs(m, cluster) # for conformerId in cluster: # e = props["energy_abs"] # if e < minEnergy: # minEnergy = e # props = conformerPropsDict[conformerId] # props["cluster_no"] = clusterNumber # props["cluster_centroid"] = cluster[0] + 1 # idx = cluster.index(conformerId) # if idx > 0: # props["rms_to_centroid"] = rmsWithinCluster[idx-1] # else: # props["rms_to_centroid"] = 0.0 # writeMinEConf2sdf(m, "target_conf_" + str(i) + ".sdf", rmsClusters, conformerPropsDict, minEnergy) mainGenConf()

36.755814

113

0.608035

794aeb15e34e805bb4cb518de4515271d3f2f13f

360

py

Python

pmu-tools-master/ucevent/ucmsg.py

patinnc/60secs

45ad68e4359e0dfd506f9e3a898c216ed38e7fd0

[ "MIT" ]

null

pmu-tools-master/ucevent/ucmsg.py

patinnc/60secs

45ad68e4359e0dfd506f9e3a898c216ed38e7fd0

[ "MIT" ]

null

pmu-tools-master/ucevent/ucmsg.py

patinnc/60secs

45ad68e4359e0dfd506f9e3a898c216ed38e7fd0

[ "MIT" ]

1

2021-03-22T20:38:10.000Z

# Handle warnings and errors # Separate module to avoid circular imports import sys import fnmatch quiet = False debug = None def debug_msg(x, y): if debug and any(map(lambda p: fnmatch.fnmatch(x, p), debug.split(","))): print >>sys.stderr, "debug:", x + ": " + str(y) def warning(x): if not quiet: print >>sys.stderr, "WARNING:", x

22.5

77

0.638889

794aeb1606271051d05972f7162bf659643d7dc4

7,547

py

Python

python/netsnmp/client.py

aristanetworks/net-snmp

49efeb8092af1d6be8b7535a7556ac24b25d2b6d

[ "Net-SNMP" ]

1

2015-07-08T20:43:18.000Z

python/netsnmp/client.py

aristanetworks/net-snmp

49efeb8092af1d6be8b7535a7556ac24b25d2b6d

[ "Net-SNMP" ]

1

2016-11-14T16:42:51.000Z

2016-11-14T19:55:59.000Z

python/netsnmp/client.py

aristanetworks/net-snmp

49efeb8092af1d6be8b7535a7556ac24b25d2b6d

[ "Net-SNMP" ]

null

import client_intf import string import re import types from sys import stderr from client_intf import read_module, read_mib # control verbosity of error output verbose = 1 secLevelMap = { 'noAuthNoPriv':1, 'authNoPriv':2, 'authPriv':3 } def _parse_session_args(kargs): sessArgs = { 'Version':3, 'DestHost':'localhost', 'Community':'public', 'Timeout':1000000, 'Retries':3, 'RemotePort':161, 'LocalPort':0, 'SecLevel':'noAuthNoPriv', 'SecName':'initial', 'PrivProto':'DEFAULT', 'PrivPass':'', 'AuthProto':'DEFAULT', 'AuthPass':'', 'ContextEngineId':'', 'SecEngineId':'', 'Context':'', 'Engineboots':0, 'Enginetime':0, 'UseNumeric':0, 'OurIdentity':'', 'TheirIdentity':'', 'TheirHostname':'', 'TrustCert':'' } keys = kargs.keys() for key in keys: if sessArgs.has_key(key): sessArgs[key] = kargs[key] else: print >>stderr, "ERROR: unknown key", key return sessArgs def STR(obj): if obj != None: obj = str(obj) return obj class Varbind(object): def __init__(self, tag=None, iid=None, val=None, type=None): self.tag = STR(tag) self.iid = STR(iid) self.val = STR(val) self.type = STR(type) # parse iid out of tag if needed if iid == None and tag != None: regex = re.compile(r'^((?:\.\d+)+|(?:\w+(?:[-:]*\w+)+))\.?(.*)$') match = regex.match(tag) if match: (self.tag, self.iid) = match.group(1,2) def __setattr__(self, name, val): self.__dict__[name] = val def print_str(self): return self.tag, self.iid, self.val, self.type class VarList(object): def __init__(self, *vs): self.varbinds = [] for var in vs: if isinstance(var, netsnmp.client.Varbind): self.varbinds.append(var) else: self.varbinds.append(Varbind(var)) def __len__(self): return len(self.varbinds) def __getitem__(self, index): return self.varbinds[index] def __setitem__(self, index, val): if isinstance(val, netsnmp.client.Varbind): self.varbinds[index] = val else: raise TypeError def __iter__(self): return iter(self.varbinds) def __delitem__(self, index): del self.varbinds[index] def __repr__(self): return repr(self.varbinds) def __getslice__(self, i, j): return self.varbinds[i:j] def append(self, *vars): for var in vars: if isinstance(var, netsnmp.client.Varbind): self.varbinds.append(var) else: raise TypeError class Session(object): def __init__(self, **args): self.sess_ptr = None self.UseLongNames = 0 self.UseNumeric = 0 self.UseSprintValue = 0 self.UseEnums = 0 self.BestGuess = 0 self.RetryNoSuch = 0 self.ErrorStr = '' self.ErrorNum = 0 self.ErrorInd = 0 sess_args = _parse_session_args(args) for k,v in sess_args.items(): self.__dict__[k] = v # check for transports that may be tunneled transportCheck = re.compile('^(tls|dtls|ssh)'); match = transportCheck.match(sess_args['DestHost']) if match: self.sess_ptr = client_intf.session_tunneled( sess_args['Version'], sess_args['DestHost'], sess_args['LocalPort'], sess_args['Retries'], sess_args['Timeout'], sess_args['SecName'], secLevelMap[sess_args['SecLevel']], sess_args['ContextEngineId'], sess_args['Context'], sess_args['OurIdentity'], sess_args['TheirIdentity'], sess_args['TheirHostname'], sess_args['TrustCert'], ); elif sess_args['Version'] == 3: self.sess_ptr = client_intf.session_v3( sess_args['Version'], sess_args['DestHost'], sess_args['LocalPort'], sess_args['Retries'], sess_args['Timeout'], sess_args['SecName'], secLevelMap[sess_args['SecLevel']], sess_args['SecEngineId'], sess_args['ContextEngineId'], sess_args['Context'], sess_args['AuthProto'], sess_args['AuthPass'], sess_args['PrivProto'], sess_args['PrivPass'], sess_args['Engineboots'], sess_args['Enginetime']) else: self.sess_ptr = client_intf.session( sess_args['Version'], sess_args['Community'], sess_args['DestHost'], sess_args['LocalPort'], sess_args['Retries'], sess_args['Timeout']) def get(self, varlist): res = client_intf.get(self, varlist) return res def set(self, varlist): res = client_intf.set(self, varlist) return res def getnext(self, varlist): res = client_intf.getnext(self, varlist) return res def getbulk(self, nonrepeaters, maxrepetitions, varlist): if self.Version == 1: return None res = client_intf.getbulk(self, nonrepeaters, maxrepetitions, varlist) return res def walk(self, varlist): res = client_intf.walk(self, varlist) return res def __del__(self): res = client_intf.delete_session(self) return res import netsnmp def snmpget(*args, **kargs): sess = Session(**kargs) var_list = VarList() for arg in args: if isinstance(arg, netsnmp.client.Varbind): var_list.append(arg) else: var_list.append(Varbind(arg)) res = sess.get(var_list) return res def snmpset(*args, **kargs): sess = Session(**kargs) var_list = VarList() for arg in args: if isinstance(arg, netsnmp.client.Varbind): var_list.append(arg) else: var_list.append(Varbind(arg)) res = sess.set(var_list) return res def snmpgetnext(*args, **kargs): sess = Session(**kargs) var_list = VarList() for arg in args: if isinstance(arg, netsnmp.client.Varbind): var_list.append(arg) else: var_list.append(Varbind(arg)) res = sess.getnext(var_list) return res def snmpgetbulk(nonrepeaters, maxrepetitions,*args, **kargs): sess = Session(**kargs) var_list = VarList() for arg in args: if isinstance(arg, netsnmp.client.Varbind): var_list.append(arg) else: var_list.append(Varbind(arg)) res = sess.getbulk(nonrepeaters, maxrepetitions, var_list) return res def snmpwalk(*args, **kargs): sess = Session(**kargs) if isinstance(args[0], netsnmp.client.VarList): var_list = args[0] else: var_list = VarList() for arg in args: if isinstance(arg, netsnmp.client.Varbind): var_list.append(arg) else: var_list.append(Varbind(arg)) res = sess.walk(var_list) return res

28.055762

78

0.543395

794aeb575b765228142bcdec088214db472bf427

2,311

py

Python

venv/Lib/site-packages/win32comext/shell/demos/viewstate.py

ajayiagbebaku/NFL-Model

afcc67a85ca7138c58c3334d45988ada2da158ed

[ "MIT" ]

150

2021-11-02T05:31:51.000Z

2022-03-24T06:22:22.000Z

venv/Lib/site-packages/win32comext/shell/demos/viewstate.py

ajayiagbebaku/NFL-Model

afcc67a85ca7138c58c3334d45988ada2da158ed

[ "MIT" ]

4

2021-12-01T11:55:58.000Z

2022-02-24T16:14:37.000Z

venv/Lib/site-packages/win32comext/shell/demos/viewstate.py

ajayiagbebaku/NFL-Model

afcc67a85ca7138c58c3334d45988ada2da158ed

[ "MIT" ]

33

2021-11-03T00:29:41.000Z

2022-03-15T13:15:56.000Z

""" Demonstrates how to propagate a folder's view state to all its subfolders The format of the ColInfo stream is apparently undocumented, but it can be read raw from one folder and copied to another's view state. """ from win32com.shell import shell, shellcon import pythoncom import os, sys template_folder = os.path.split(sys.executable)[0] print("Template folder:", template_folder) template_pidl = shell.SHILCreateFromPath(template_folder, 0)[0] template_pb = shell.SHGetViewStatePropertyBag( template_pidl, "Shell", shellcon.SHGVSPB_FOLDERNODEFAULTS, pythoncom.IID_IPropertyBag, ) # Column info has to be read as a stream # This may blow up if folder has never been opened in Explorer and has no ColInfo yet template_iunk = template_pb.Read("ColInfo", pythoncom.VT_UNKNOWN) template_stream = template_iunk.QueryInterface(pythoncom.IID_IStream) streamsize = template_stream.Stat()[2] template_colinfo = template_stream.Read(streamsize) def update_colinfo(not_used, dir_name, fnames): for fname in fnames: full_fname = os.path.join(dir_name, fname) if os.path.isdir(full_fname): print(full_fname) pidl = shell.SHILCreateFromPath(full_fname, 0)[0] pb = shell.SHGetViewStatePropertyBag( pidl, "Shell", shellcon.SHGVSPB_FOLDERNODEFAULTS, pythoncom.IID_IPropertyBag, ) ## not all folders already have column info, and we're replacing it anyway pb.Write("ColInfo", template_stream) iunk = pb.Read("ColInfo", pythoncom.VT_UNKNOWN) s = iunk.QueryInterface(pythoncom.IID_IStream) s.Write(template_colinfo) s = None ## attribute names read from registry, can't find any way to enumerate IPropertyBag for attr in ( "Address", "Buttons", "Col", "Vid", "WFlags", "FFlags", "Sort", "SortDir", "ShowCmd", "FolderType", "Mode", "Rev", ): pb.Write(attr, template_pb.Read(attr)) pb = None os.path.walk(template_folder, update_colinfo, None)

34.492537

95

0.621809

794aeb8d7f0ab1256b43c35ea4fb64c0820c38ee

1,940

py

Python

nowcasting_dataset/square.py

lenassero/nowcasting_dataset

deaf098c4d318f3ef532bac73f9cc4fa2858479b

[ "MIT" ]

null

nowcasting_dataset/square.py

lenassero/nowcasting_dataset

deaf098c4d318f3ef532bac73f9cc4fa2858479b

[ "MIT" ]

null

nowcasting_dataset/square.py

lenassero/nowcasting_dataset

deaf098c4d318f3ef532bac73f9cc4fa2858479b

[ "MIT" ]

null

""" Square objects """ from numbers import Number from typing import NamedTuple, Union from nowcasting_dataset.consts import Array class BoundingBox(NamedTuple): """Bounding box tuple""" top: Union[Number, float] bottom: Union[Number, float] left: Union[Number, float] right: Union[Number, float] class Square: """Class for computing bounding box for satellite imagery.""" def __init__(self, size_pixels: int, meters_per_pixel: Number): """ Init Args: size_pixels: number of pixels meters_per_pixel: how many meters for each pixel """ self.size_pixels = size_pixels size_meters = size_pixels * meters_per_pixel self._half_size_meters = size_meters / 2 def bounding_box_centered_on( self, x_meters_center: Number, y_meters_center: Number ) -> BoundingBox: """ Get bounding box from a centre Args: x_meters_center: x center of the bounding box y_meters_center: y center of the bounding box Returns: Bounding box """ return BoundingBox( top=y_meters_center + self._half_size_meters, bottom=y_meters_center - self._half_size_meters, left=x_meters_center - self._half_size_meters, right=x_meters_center + self._half_size_meters, ) def get_bounding_box_mask(bounding_box: BoundingBox, x: Array, y: Array) -> Array: """ Get boundary box mask from x and y locations. I.e are the x,y coords in the boundaring box Args: bounding_box: Bounding box x: x coordinates y: y coordinates Returns: list of booleans if the x and y coordinates are in the bounding box """ mask = ( (x >= bounding_box.left) & (x <= bounding_box.right) & (y >= bounding_box.bottom) & (y <= bounding_box.top) ) return mask

26.944444

94

0.63299

794aeba8187a07b167c1891ce4e55acf97fbc78e

2,812

py

Python

test/helpers.py

DalavanCloud/hpc-container-maker

555093c0a5c98bd2b0114831b8c676c0c3c50dd7

[ "Apache-2.0" ]

1

2019-02-25T22:54:31.000Z

test/helpers.py

DalavanCloud/hpc-container-maker

555093c0a5c98bd2b0114831b8c676c0c3c50dd7

[ "Apache-2.0" ]

null

test/helpers.py

DalavanCloud/hpc-container-maker

555093c0a5c98bd2b0114831b8c676c0c3c50dd7

[ "Apache-2.0" ]

null

# Copyright (c) 2018, NVIDIA CORPORATION. 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. # pylint: disable=invalid-name, too-few-public-methods """Unit testing helpers""" from __future__ import unicode_literals from __future__ import print_function from distutils.version import StrictVersion import logging # pylint: disable=unused-import import hpccm.config from hpccm.common import container_type, linux_distro def centos(function): """Decorator to set the Linux distribution to CentOS 7""" def wrapper(*args, **kwargs): hpccm.config.g_linux_distro = linux_distro.CENTOS hpccm.config.g_linux_version = StrictVersion('7.0') return function(*args, **kwargs) return wrapper def docker(function): """Decorator to set the global container type to docker""" def wrapper(*args, **kwargs): hpccm.config.g_ctype = container_type.DOCKER return function(*args, **kwargs) return wrapper def invalid_ctype(function): """Decorator to set the global container type to an invalid value""" def wrapper(*args, **kwargs): hpccm.config.g_ctype = None return function(*args, **kwargs) return wrapper def invalid_distro(function): """Decorator to set the global Linux distribution to an invalid value""" def wrapper(*args, **kwargs): hpccm.config.g_linux_distro = None return function(*args, **kwargs) return wrapper def singularity(function): """Decorator to set the global container type to singularity""" def wrapper(*args, **kwargs): hpccm.config.g_ctype = container_type.SINGULARITY return function(*args, **kwargs) return wrapper def ubuntu(function): """Decorator to set the Linux distribution to Ubuntu 16.04""" def wrapper(*args, **kwargs): hpccm.config.g_linux_distro = linux_distro.UBUNTU hpccm.config.g_linux_version = StrictVersion('16.04') return function(*args, **kwargs) return wrapper def ubuntu18(function): """Decorator to set the Linux distribution to Ubuntu 18.04""" def wrapper(*args, **kwargs): hpccm.config.g_linux_distro = linux_distro.UBUNTU hpccm.config.g_linux_version = StrictVersion('18.04') return function(*args, **kwargs) return wrapper

32.321839

76

0.711238

794aec20bcb82f4b0a81082338dbfaee65785904

7,477

py

Python

infiltrate/models/deck.py

Qazzquimby/eternalCardEvaluator

ef8640ed819a89e5198f8aedf0861a29c57c5720

[ "MIT" ]

4

2019-04-08T09:30:10.000Z

2020-09-15T19:25:30.000Z

infiltrate/models/deck.py

Qazzquimby/eternalCardEvaluator

ef8640ed819a89e5198f8aedf0861a29c57c5720

[ "MIT" ]

19

2019-04-09T19:02:14.000Z

2020-12-25T05:22:45.000Z

infiltrate/models/deck.py

Qazzquimby/eternalCardEvaluator

ef8640ed819a89e5198f8aedf0861a29c57c5720

[ "MIT" ]

null

"""The Deck model and related utilities""" import enum import logging import typing as t import urllib.error from datetime import datetime import tqdm import infiltrate.browsers as browsers import infiltrate.global_data as global_data import infiltrate.models.card as card # todo replace application with config injection from infiltrate import application, db class DeckHasCard(db.Model): """A table showing how many copies of a card a deck has.""" deck_id = db.Column( "deck_id", db.String(length=100), db.ForeignKey("decks.id"), primary_key=True ) set_num = db.Column("set_num", db.Integer, primary_key=True) card_num = db.Column("card_num", db.Integer, primary_key=True) num_played = db.Column("num_played", db.Integer, nullable=False) __table_args__ = ( db.ForeignKeyConstraint( [set_num, card_num], [card.Card.set_num, card.Card.card_num] ), {}, ) def to_card_id(self) -> card.CardId: return card.CardId(set_num=self.set_num, card_num=self.card_num) class DeckType(enum.Enum): """Enum for deck types matching Warcry""" unknown = 0 standard = 1 throne = 1 draft = 2 gauntlet = 3 forge = 4 campaign = 5 event = 6 _ = 7 expedition = 8 other = 9 class Archetype(enum.Enum): """Enum for deck archetypes matching Warcry""" unknown = 0 aggro = 1 midrange = 2 combo = 3 control = 4 tempo = 5 aggro_control = 6 aggro_combo = 7 aggro_midrange = 8 control_combo = 9 control_midrange = 10 tempo_combo = 11 tempo_control = 12 combo_midrange = 13 class Deck(db.Model): """Model representing an Eternal Deck from Warcry""" __tablename__ = "decks" id = db.Column("id", db.String(length=100), primary_key=True) archetype = db.Column("archetype", db.Enum(Archetype), nullable=True) date_added = db.Column("date_added", db.DateTime) date_updated = db.Column("date_updated", db.DateTime) deck_type = db.Column("deck_type", db.Enum(DeckType)) description = db.Column("description", db.Text, nullable=True) patch = db.Column("patch", db.String(length=10)) username = db.Column("username", db.String(length=30)) views = db.Column("views", db.Integer) rating = db.Column("rating", db.Integer) cards = db.relationship("DeckHasCard") @classmethod def get_from_id(cls, deck_id: str): """Gets the deck matching the deck id.""" return Deck.query.filter_by(id=deck_id).first() # noinspection PyMissingOrEmptyDocstring class _WarcryNewIdGetter: ITEMS_PER_PAGE = 50 def get_new_ids(self, max_decks=None): if max_decks is not None: max_pages = max_decks / self.ITEMS_PER_PAGE else: max_pages = None logging.info("Getting new deck ids") new_ids = [] page = 0 while True: ids_on_page = self.get_ids_from_page(page) new_ids_on_page = self.remove_old_ids(ids_on_page) new_ids += new_ids_on_page if not new_ids_on_page or max_pages is not None and page >= max_pages: # todo this may need testing. break page += 1 logging.info(f"Pages of deck ids ready: {page}") return new_ids def get_ids_from_page(self, page: int): url = ( "https://api.eternalwarcry.com/v1/decks/SearchDecks" + f"?starting={self.ITEMS_PER_PAGE * page}" + f"&perpage={self.ITEMS_PER_PAGE}" + f"&key={application.config['WARCRY_KEY']}" ) page_json = browsers.get_json_from_url(url) ids = self.get_ids_from_page_json(page_json) return ids @staticmethod def get_ids_from_page_json(page_json: t.Dict): decks = page_json["decks"] ids = [deck["deck_id"] for deck in decks] return ids @staticmethod def remove_old_ids(ids: t.List[str]) -> t.List[str]: new_ids = [] for deck_id in ids: if not Deck.get_from_id(deck_id): new_ids.append(deck_id) else: break return new_ids def get_new_warcry_ids(max_decks=1_000): """Return all Warcry deck IDs newer than any in the database.""" id_getter = _WarcryNewIdGetter() ids = id_getter.get_new_ids(max_decks=max_decks) return ids def update_decks(): """Updates the database with all new Warcry decks""" # noinspection PyMissingOrEmptyDocstring class _WarcyDeckUpdater: def run(self): ids = get_new_warcry_ids(1_000) for deck_id in tqdm.tqdm(ids, desc="Updating decks"): self.update_deck(deck_id) def update_deck(self, deck_id: str): url = ( "https://api.eternalwarcry.com/v1/decks/details" + f"?key={application.config['WARCRY_KEY']}" + f"&deck_id={deck_id}" ) try: page_json = browsers.get_json_from_url(url) except (ConnectionError, urllib.error.HTTPError): return self.make_deck_from_details_json(page_json) def make_deck_from_details_json(self, page_json: t.Dict): archetype = Archetype[page_json["archetype"].lower().replace(" ", "_")] try: deck_type = DeckType.__dict__[ page_json["deck_type"].lower().replace(" ", "_") ] except KeyError: # not sure this is the right exception deck_type = DeckType(int(page_json["deck_type"])) deck = Deck( id=page_json["deck_id"], archetype=archetype, date_added=datetime.strptime( page_json["date_added_full"][:19], "%Y-%m-%dT%H:%M:%S" ), date_updated=datetime.strptime( page_json["date_updated_full"][:19], "%Y-%m-%dT%H:%M:%S" ), deck_type=deck_type, description=page_json["description"].encode("ascii", errors="ignore"), patch=page_json["patch"], username=page_json["username"], views=page_json["views"], rating=page_json["rating"], ) self.add_cards_to_deck(deck, page_json) db.session.merge(deck) db.session.commit() @staticmethod def add_cards_to_deck(deck: Deck, page_json: t.Dict): cards_json = ( page_json["deck_cards"] + page_json["sideboard_cards"] + page_json["market_cards"] ) for card_json in cards_json: set_num = card_json["set_number"] card_num = card_json["eternal_id"] card_id = card.CardId(set_num, card_num) # todo better to pass all_cards to this than use the global if global_data.all_cards.card_exists(card_id): deck_has_card = DeckHasCard( deck_id=page_json["deck_id"], set_num=set_num, card_num=card_num, num_played=card_json["count"], ) deck.cards.append(deck_has_card) logging.info("Updating decks") updater = _WarcyDeckUpdater() updater.run()

31.284519

86

0.589408

794aec21d21c068ce8a50d5b32a032b7b1b4ebf1

874

py

Python

libralli/circcuitpython/adafruit-circuitpython-bundle-7.x-mpy-20211225/examples/tmp117_limits_test.py

Yarik9008/SoftAcademic

118c9dc4620ca444c1557edd141a838820577202

[ "MIT" ]

1

2021-04-24T05:25:43.000Z

libralli/circcuitpython/adafruit-circuitpython-bundle-7.x-mpy-20211225/examples/tmp117_limits_test.py

Yarik9008/SoftAcademic

118c9dc4620ca444c1557edd141a838820577202

[ "MIT" ]

4

2021-01-07T17:25:13.000Z

2021-12-14T20:23:00.000Z

libralli/circcuitpython/adafruit-circuitpython-bundle-7.x-mpy-20211225/examples/tmp117_limits_test.py

Yarik9008/SoftAcademic

118c9dc4620ca444c1557edd141a838820577202

[ "MIT" ]

6

2021-01-07T07:18:13.000Z

2021-11-20T06:23:14.000Z

# SPDX-FileCopyrightText: 2020 Bryan Siepert, written for Adafruit Industries # # SPDX-License-Identifier: Unlicense import time import board from adafruit_tmp117 import TMP117, AlertMode i2c = board.I2C() # uses board.SCL and board.SDA tmp117 = TMP117(i2c) tmp117.high_limit = 25 tmp117.low_limit = 10 print("\nHigh limit", tmp117.high_limit) print("Low limit", tmp117.low_limit) # Try changing `alert_mode` to see how it modifies the behavior of the alerts. # tmp117.alert_mode = AlertMode.WINDOW #default # tmp117.alert_mode = AlertMode.HYSTERESIS print("Alert mode:", AlertMode.string[tmp117.alert_mode]) print("\n\n") while True: print("Temperature: %.2f degrees C" % tmp117.temperature) alert_status = tmp117.alert_status print("High alert:", alert_status.high_alert) print("Low alert:", alert_status.low_alert) print("") time.sleep(1)

28.193548

79

0.745995

794aec60e5f189a7da5a8f7df61d20197417bf21

108,580

py

Python

databricks/koalas/tests/test_groupby.py

AishwaryaKalloli/koalas

8d35a74508c1319996c8c27e2a5e24af52b9ee31

[ "Apache-2.0" ]

null

databricks/koalas/tests/test_groupby.py

AishwaryaKalloli/koalas

8d35a74508c1319996c8c27e2a5e24af52b9ee31

[ "Apache-2.0" ]

null

databricks/koalas/tests/test_groupby.py

AishwaryaKalloli/koalas

8d35a74508c1319996c8c27e2a5e24af52b9ee31

[ "Apache-2.0" ]

null

# # Copyright (C) 2019 Databricks, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import unittest import inspect from distutils.version import LooseVersion from itertools import product import numpy as np import pandas as pd from databricks import koalas as ks from databricks.koalas.config import option_context from databricks.koalas.exceptions import PandasNotImplementedError, DataError from databricks.koalas.missing.groupby import ( MissingPandasLikeDataFrameGroupBy, MissingPandasLikeSeriesGroupBy, ) from databricks.koalas.testing.utils import ReusedSQLTestCase, TestUtils from databricks.koalas.groupby import is_multi_agg_with_relabel class GroupByTest(ReusedSQLTestCase, TestUtils): def test_groupby_simple(self): pdf = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7], "b": [4, 2, 7, 3, 3, 1, 1, 1, 2], "c": [4, 2, 7, 3, None, 1, 1, 1, 2], "d": list("abcdefght"), }, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) kdf = ks.from_pandas(pdf) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values("a").reset_index(drop=True) self.assert_eq( sort(kdf.groupby("a", as_index=as_index).sum()), sort(pdf.groupby("a", as_index=as_index).sum()), ) self.assert_eq( sort(kdf.groupby("a", as_index=as_index).b.sum()), sort(pdf.groupby("a", as_index=as_index).b.sum()), ) self.assert_eq( sort(kdf.groupby("a", as_index=as_index)["b"].sum()), sort(pdf.groupby("a", as_index=as_index)["b"].sum()), ) self.assert_eq( sort(kdf.groupby("a", as_index=as_index)[["b", "c"]].sum()), sort(pdf.groupby("a", as_index=as_index)[["b", "c"]].sum()), ) self.assert_eq( sort(kdf.groupby("a", as_index=as_index)[[]].sum()), sort(pdf.groupby("a", as_index=as_index)[[]].sum()), ) self.assert_eq( sort(kdf.groupby("a", as_index=as_index)["c"].sum()), sort(pdf.groupby("a", as_index=as_index)["c"].sum()), ) self.assert_eq(kdf.groupby("a").a.sum().sort_index(), pdf.groupby("a").a.sum().sort_index()) self.assert_eq( kdf.groupby("a")["a"].sum().sort_index(), pdf.groupby("a")["a"].sum().sort_index() ) self.assert_eq( kdf.groupby("a")[["a"]].sum().sort_index(), pdf.groupby("a")[["a"]].sum().sort_index() ) self.assert_eq( kdf.groupby("a")[["a", "c"]].sum().sort_index(), pdf.groupby("a")[["a", "c"]].sum().sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b).sum().sort_index(), pdf.a.groupby(pdf.b).sum().sort_index() ) for axis in [0, "index"]: self.assert_eq( kdf.groupby("a", axis=axis).a.sum().sort_index(), pdf.groupby("a", axis=axis).a.sum().sort_index(), ) self.assert_eq( kdf.groupby("a", axis=axis)["a"].sum().sort_index(), pdf.groupby("a", axis=axis)["a"].sum().sort_index(), ) self.assert_eq( kdf.groupby("a", axis=axis)[["a"]].sum().sort_index(), pdf.groupby("a", axis=axis)[["a"]].sum().sort_index(), ) self.assert_eq( kdf.groupby("a", axis=axis)[["a", "c"]].sum().sort_index(), pdf.groupby("a", axis=axis)[["a", "c"]].sum().sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b, axis=axis).sum().sort_index(), pdf.a.groupby(pdf.b, axis=axis).sum().sort_index(), ) self.assertRaises(ValueError, lambda: kdf.groupby("a", as_index=False).a) self.assertRaises(ValueError, lambda: kdf.groupby("a", as_index=False)["a"]) self.assertRaises(ValueError, lambda: kdf.groupby("a", as_index=False)[["a"]]) self.assertRaises(ValueError, lambda: kdf.groupby("a", as_index=False)[["a", "c"]]) self.assertRaises(KeyError, lambda: kdf.groupby("z", as_index=False)[["a", "c"]]) self.assertRaises(KeyError, lambda: kdf.groupby(["z"], as_index=False)[["a", "c"]]) self.assertRaises(TypeError, lambda: kdf.a.groupby(kdf.b, as_index=False)) self.assertRaises(NotImplementedError, lambda: kdf.groupby("a", axis=1)) self.assertRaises(NotImplementedError, lambda: kdf.groupby("a", axis="columns")) self.assertRaises(ValueError, lambda: kdf.groupby("a", "b")) self.assertRaises(TypeError, lambda: kdf.a.groupby(kdf.a, kdf.b)) # we can't use column name/names as a parameter `by` for `SeriesGroupBy`. self.assertRaises(KeyError, lambda: kdf.a.groupby(by="a")) self.assertRaises(KeyError, lambda: kdf.a.groupby(by=["a", "b"])) self.assertRaises(KeyError, lambda: kdf.a.groupby(by=("a", "b"))) # we can't use DataFrame as a parameter `by` for `DataFrameGroupBy`/`SeriesGroupBy`. self.assertRaises(ValueError, lambda: kdf.groupby(kdf)) self.assertRaises(ValueError, lambda: kdf.a.groupby(kdf)) self.assertRaises(ValueError, lambda: kdf.a.groupby((kdf,))) # non-string names pdf = pd.DataFrame( { 10: [1, 2, 6, 4, 4, 6, 4, 3, 7], 20: [4, 2, 7, 3, 3, 1, 1, 1, 2], 30: [4, 2, 7, 3, None, 1, 1, 1, 2], 40: list("abcdefght"), }, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) kdf = ks.from_pandas(pdf) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(10).reset_index(drop=True) self.assert_eq( sort(kdf.groupby(10, as_index=as_index).sum()), sort(pdf.groupby(10, as_index=as_index).sum()), ) self.assert_eq( sort(kdf.groupby(10, as_index=as_index)[20].sum()), sort(pdf.groupby(10, as_index=as_index)[20].sum()), ) self.assert_eq( sort(kdf.groupby(10, as_index=as_index)[[20, 30]].sum()), sort(pdf.groupby(10, as_index=as_index)[[20, 30]].sum()), ) def test_groupby_multiindex_columns(self): pdf = pd.DataFrame( { (10, "a"): [1, 2, 6, 4, 4, 6, 4, 3, 7], (10, "b"): [4, 2, 7, 3, 3, 1, 1, 1, 2], (20, "c"): [4, 2, 7, 3, None, 1, 1, 1, 2], (30, "d"): list("abcdefght"), }, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby((10, "a")).sum().sort_index(), pdf.groupby((10, "a")).sum().sort_index() ) self.assert_eq( kdf.groupby((10, "a"), as_index=False) .sum() .sort_values((10, "a")) .reset_index(drop=True), pdf.groupby((10, "a"), as_index=False) .sum() .sort_values((10, "a")) .reset_index(drop=True), ) self.assert_eq( kdf.groupby((10, "a"))[[(20, "c")]].sum().sort_index(), pdf.groupby((10, "a"))[[(20, "c")]].sum().sort_index(), ) # TODO: a pandas bug? # expected = pdf.groupby((10, "a"))[(20, "c")].sum().sort_index() expected = pd.Series( [4.0, 2.0, 1.0, 4.0, 8.0, 2.0], name=(20, "c"), index=pd.Index([1, 2, 3, 4, 6, 7], name=(10, "a")), ) self.assert_eq(kdf.groupby((10, "a"))[(20, "c")].sum().sort_index(), expected) if LooseVersion(pd.__version__) < LooseVersion("1.1.3"): self.assert_eq( kdf[(20, "c")].groupby(kdf[(10, "a")]).sum().sort_index(), pdf[(20, "c")].groupby(pdf[(10, "a")]).sum().sort_index(), ) else: # seems like a pandas bug introduced in pandas 1.1.3. self.assert_eq(kdf[(20, "c")].groupby(kdf[(10, "a")]).sum().sort_index(), expected) def test_split_apply_combine_on_series(self): pdf = pd.DataFrame( { "a": [1, 2, 6, 4, 4, 6, 4, 3, 7], "b": [4, 2, 7, 3, 3, 1, 1, 1, 2], "c": [4, 2, 7, 3, None, 1, 1, 1, 2], "d": list("abcdefght"), }, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) kdf = ks.from_pandas(pdf) funcs = [ ((True, False), ["sum", "min", "max", "count", "first", "last"]), ((True, True), ["mean"]), ((False, False), ["var", "std"]), ] funcs = [(check_exact, almost, f) for (check_exact, almost), fs in funcs for f in fs] for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(list(df.columns)).reset_index(drop=True) for check_exact, almost, func in funcs: for kkey, pkey in [("b", "b"), (kdf.b, pdf.b)]: with self.subTest(as_index=as_index, func=func, key=pkey): if as_index is True or func != "std": self.assert_eq( sort(getattr(kdf.groupby(kkey, as_index=as_index).a, func)()), sort(getattr(pdf.groupby(pkey, as_index=as_index).a, func)()), check_exact=check_exact, almost=almost, ) self.assert_eq( sort(getattr(kdf.groupby(kkey, as_index=as_index), func)()), sort(getattr(pdf.groupby(pkey, as_index=as_index), func)()), check_exact=check_exact, almost=almost, ) else: # seems like a pandas' bug for as_index=False and func == "std"? self.assert_eq( sort(getattr(kdf.groupby(kkey, as_index=as_index).a, func)()), sort(pdf.groupby(pkey, as_index=True).a.std().reset_index()), check_exact=check_exact, almost=almost, ) self.assert_eq( sort(getattr(kdf.groupby(kkey, as_index=as_index), func)()), sort(pdf.groupby(pkey, as_index=True).std().reset_index()), check_exact=check_exact, almost=almost, ) for kkey, pkey in [(kdf.b + 1, pdf.b + 1), (kdf.copy().b, pdf.copy().b)]: with self.subTest(as_index=as_index, func=func, key=pkey): self.assert_eq( sort(getattr(kdf.groupby(kkey, as_index=as_index).a, func)()), sort(getattr(pdf.groupby(pkey, as_index=as_index).a, func)()), check_exact=check_exact, almost=almost, ) self.assert_eq( sort(getattr(kdf.groupby(kkey, as_index=as_index), func)()), sort(getattr(pdf.groupby(pkey, as_index=as_index), func)()), check_exact=check_exact, almost=almost, ) for check_exact, almost, func in funcs: for i in [0, 4, 7]: with self.subTest(as_index=as_index, func=func, i=i): self.assert_eq( sort(getattr(kdf.groupby(kdf.b > i, as_index=as_index).a, func)()), sort(getattr(pdf.groupby(pdf.b > i, as_index=as_index).a, func)()), check_exact=check_exact, almost=almost, ) self.assert_eq( sort(getattr(kdf.groupby(kdf.b > i, as_index=as_index), func)()), sort(getattr(pdf.groupby(pdf.b > i, as_index=as_index), func)()), check_exact=check_exact, almost=almost, ) for check_exact, almost, func in funcs: for kkey, pkey in [ (kdf.b, pdf.b), (kdf.b + 1, pdf.b + 1), (kdf.copy().b, pdf.copy().b), (kdf.b.rename(), pdf.b.rename()), ]: with self.subTest(func=func, key=pkey): self.assert_eq( getattr(kdf.a.groupby(kkey), func)().sort_index(), getattr(pdf.a.groupby(pkey), func)().sort_index(), check_exact=check_exact, almost=almost, ) self.assert_eq( getattr((kdf.a + 1).groupby(kkey), func)().sort_index(), getattr((pdf.a + 1).groupby(pkey), func)().sort_index(), check_exact=check_exact, almost=almost, ) self.assert_eq( getattr((kdf.b + 1).groupby(kkey), func)().sort_index(), getattr((pdf.b + 1).groupby(pkey), func)().sort_index(), check_exact=check_exact, almost=almost, ) self.assert_eq( getattr(kdf.a.rename().groupby(kkey), func)().sort_index(), getattr(pdf.a.rename().groupby(pkey), func)().sort_index(), check_exact=check_exact, almost=almost, ) def test_aggregate(self): pdf = pd.DataFrame( {"A": [1, 1, 2, 2], "B": [1, 2, 3, 4], "C": [0.362, 0.227, 1.267, -0.562]} ) kdf = ks.from_pandas(pdf) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(list(df.columns)).reset_index(drop=True) for kkey, pkey in [("A", "A"), (kdf.A, pdf.A)]: with self.subTest(as_index=as_index, key=pkey): self.assert_eq( sort(kdf.groupby(kkey, as_index=as_index).agg("sum")), sort(pdf.groupby(pkey, as_index=as_index).agg("sum")), ) self.assert_eq( sort(kdf.groupby(kkey, as_index=as_index).agg({"B": "min", "C": "sum"})), sort(pdf.groupby(pkey, as_index=as_index).agg({"B": "min", "C": "sum"})), ) self.assert_eq( sort( kdf.groupby(kkey, as_index=as_index).agg( {"B": ["min", "max"], "C": "sum"} ) ), sort( pdf.groupby(pkey, as_index=as_index).agg( {"B": ["min", "max"], "C": "sum"} ) ), ) if as_index: self.assert_eq( sort(kdf.groupby(kkey, as_index=as_index).agg(["sum"])), sort(pdf.groupby(pkey, as_index=as_index).agg(["sum"])), ) else: # seems like a pandas' bug for as_index=False and func_or_funcs is list? self.assert_eq( sort(kdf.groupby(kkey, as_index=as_index).agg(["sum"])), sort(pdf.groupby(pkey, as_index=True).agg(["sum"]).reset_index()), ) for kkey, pkey in [(kdf.A + 1, pdf.A + 1), (kdf.copy().A, pdf.copy().A)]: with self.subTest(as_index=as_index, key=pkey): self.assert_eq( sort(kdf.groupby(kkey, as_index=as_index).agg("sum")), sort(pdf.groupby(pkey, as_index=as_index).agg("sum")), ) self.assert_eq( sort(kdf.groupby(kkey, as_index=as_index).agg({"B": "min", "C": "sum"})), sort(pdf.groupby(pkey, as_index=as_index).agg({"B": "min", "C": "sum"})), ) self.assert_eq( sort( kdf.groupby(kkey, as_index=as_index).agg( {"B": ["min", "max"], "C": "sum"} ) ), sort( pdf.groupby(pkey, as_index=as_index).agg( {"B": ["min", "max"], "C": "sum"} ) ), ) self.assert_eq( sort(kdf.groupby(kkey, as_index=as_index).agg(["sum"])), sort(pdf.groupby(pkey, as_index=as_index).agg(["sum"])), ) expected_error_message = ( r"aggs must be a dict mapping from column name to aggregate functions " r"$string or list of strings$." ) with self.assertRaisesRegex(ValueError, expected_error_message): kdf.groupby("A", as_index=as_index).agg(0) # multi-index columns columns = pd.MultiIndex.from_tuples([(10, "A"), (10, "B"), (20, "C")]) pdf.columns = columns kdf.columns = columns for as_index in [True, False]: stats_kdf = kdf.groupby((10, "A"), as_index=as_index).agg( {(10, "B"): "min", (20, "C"): "sum"} ) stats_pdf = pdf.groupby((10, "A"), as_index=as_index).agg( {(10, "B"): "min", (20, "C"): "sum"} ) self.assert_eq( stats_kdf.sort_values(by=[(10, "B"), (20, "C")]).reset_index(drop=True), stats_pdf.sort_values(by=[(10, "B"), (20, "C")]).reset_index(drop=True), ) stats_kdf = kdf.groupby((10, "A")).agg({(10, "B"): ["min", "max"], (20, "C"): "sum"}) stats_pdf = pdf.groupby((10, "A")).agg({(10, "B"): ["min", "max"], (20, "C"): "sum"}) self.assert_eq( stats_kdf.sort_values( by=[(10, "B", "min"), (10, "B", "max"), (20, "C", "sum")] ).reset_index(drop=True), stats_pdf.sort_values( by=[(10, "B", "min"), (10, "B", "max"), (20, "C", "sum")] ).reset_index(drop=True), ) # non-string names pdf.columns = [10, 20, 30] kdf.columns = [10, 20, 30] for as_index in [True, False]: stats_kdf = kdf.groupby(10, as_index=as_index).agg({20: "min", 30: "sum"}) stats_pdf = pdf.groupby(10, as_index=as_index).agg({20: "min", 30: "sum"}) self.assert_eq( stats_kdf.sort_values(by=[20, 30]).reset_index(drop=True), stats_pdf.sort_values(by=[20, 30]).reset_index(drop=True), ) stats_kdf = kdf.groupby(10).agg({20: ["min", "max"], 30: "sum"}) stats_pdf = pdf.groupby(10).agg({20: ["min", "max"], 30: "sum"}) self.assert_eq( stats_kdf.sort_values(by=[(20, "min"), (20, "max"), (30, "sum")]).reset_index( drop=True ), stats_pdf.sort_values(by=[(20, "min"), (20, "max"), (30, "sum")]).reset_index( drop=True ), ) def test_aggregate_func_str_list(self): # this is test for cases where only string or list is assigned pdf = pd.DataFrame( { "kind": ["cat", "dog", "cat", "dog"], "height": [9.1, 6.0, 9.5, 34.0], "weight": [7.9, 7.5, 9.9, 198.0], } ) kdf = ks.from_pandas(pdf) agg_funcs = ["max", "min", ["min", "max"]] for aggfunc in agg_funcs: # Since in Koalas groupby, the order of rows might be different # so sort on index to ensure they have same output sorted_agg_kdf = kdf.groupby("kind").agg(aggfunc).sort_index() sorted_agg_pdf = pdf.groupby("kind").agg(aggfunc).sort_index() self.assert_eq(sorted_agg_kdf, sorted_agg_pdf) # test on multi index column case pdf = pd.DataFrame( {"A": [1, 1, 2, 2], "B": [1, 2, 3, 4], "C": [0.362, 0.227, 1.267, -0.562]} ) kdf = ks.from_pandas(pdf) columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")]) pdf.columns = columns kdf.columns = columns for aggfunc in agg_funcs: sorted_agg_kdf = kdf.groupby(("X", "A")).agg(aggfunc).sort_index() sorted_agg_pdf = pdf.groupby(("X", "A")).agg(aggfunc).sort_index() self.assert_eq(sorted_agg_kdf, sorted_agg_pdf) @unittest.skipIf(pd.__version__ < "0.25.0", "not supported before pandas 0.25.0") def test_aggregate_relabel(self): # this is to test named aggregation in groupby pdf = pd.DataFrame({"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}) kdf = ks.from_pandas(pdf) # different agg column, same function agg_pdf = pdf.groupby("group").agg(a_max=("A", "max"), b_max=("B", "max")).sort_index() agg_kdf = kdf.groupby("group").agg(a_max=("A", "max"), b_max=("B", "max")).sort_index() self.assert_eq(agg_pdf, agg_kdf) # same agg column, different functions agg_pdf = pdf.groupby("group").agg(b_max=("B", "max"), b_min=("B", "min")).sort_index() agg_kdf = kdf.groupby("group").agg(b_max=("B", "max"), b_min=("B", "min")).sort_index() self.assert_eq(agg_pdf, agg_kdf) # test on NamedAgg agg_pdf = ( pdf.groupby("group").agg(b_max=pd.NamedAgg(column="B", aggfunc="max")).sort_index() ) agg_kdf = ( kdf.groupby("group").agg(b_max=ks.NamedAgg(column="B", aggfunc="max")).sort_index() ) self.assert_eq(agg_kdf, agg_pdf) # test on NamedAgg multi columns aggregation agg_pdf = ( pdf.groupby("group") .agg( b_max=pd.NamedAgg(column="B", aggfunc="max"), b_min=pd.NamedAgg(column="B", aggfunc="min"), ) .sort_index() ) agg_kdf = ( kdf.groupby("group") .agg( b_max=ks.NamedAgg(column="B", aggfunc="max"), b_min=ks.NamedAgg(column="B", aggfunc="min"), ) .sort_index() ) self.assert_eq(agg_kdf, agg_pdf) def test_dropna(self): pdf = pd.DataFrame( {"A": [None, 1, None, 1, 2], "B": [1, 2, 3, None, None], "C": [4, 5, 6, 7, None]} ) kdf = ks.from_pandas(pdf) # pd.DataFrame.groupby with dropna parameter is implemented since pandas 1.1.0 if LooseVersion(pd.__version__) >= LooseVersion("1.1.0"): for dropna in [True, False]: for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values("A").reset_index(drop=True) self.assert_eq( sort(kdf.groupby("A", as_index=as_index, dropna=dropna).std()), sort(pdf.groupby("A", as_index=as_index, dropna=dropna).std()), ) self.assert_eq( sort(kdf.groupby("A", as_index=as_index, dropna=dropna).B.std()), sort(pdf.groupby("A", as_index=as_index, dropna=dropna).B.std()), ) self.assert_eq( sort(kdf.groupby("A", as_index=as_index, dropna=dropna)["B"].std()), sort(pdf.groupby("A", as_index=as_index, dropna=dropna)["B"].std()), ) self.assert_eq( sort( kdf.groupby("A", as_index=as_index, dropna=dropna).agg( {"B": "min", "C": "std"} ) ), sort( pdf.groupby("A", as_index=as_index, dropna=dropna).agg( {"B": "min", "C": "std"} ) ), ) for dropna in [True, False]: for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(["A", "B"]).reset_index(drop=True) self.assert_eq( sort( kdf.groupby(["A", "B"], as_index=as_index, dropna=dropna).agg( {"C": ["min", "std"]} ) ), sort( pdf.groupby(["A", "B"], as_index=as_index, dropna=dropna).agg( {"C": ["min", "std"]} ) ), almost=True, ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C")]) pdf.columns = columns kdf.columns = columns for dropna in [True, False]: for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(("X", "A")).reset_index(drop=True) sorted_stats_kdf = sort( kdf.groupby(("X", "A"), as_index=as_index, dropna=dropna).agg( {("X", "B"): "min", ("Y", "C"): "std"} ) ) sorted_stats_pdf = sort( pdf.groupby(("X", "A"), as_index=as_index, dropna=dropna).agg( {("X", "B"): "min", ("Y", "C"): "std"} ) ) self.assert_eq(sorted_stats_kdf, sorted_stats_pdf) else: # Testing dropna=True (pandas default behavior) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values("A").reset_index(drop=True) self.assert_eq( sort(kdf.groupby("A", as_index=as_index, dropna=True)["B"].min()), sort(pdf.groupby("A", as_index=as_index)["B"].min()), ) if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(["A", "B"]).reset_index(drop=True) self.assert_eq( sort( kdf.groupby(["A", "B"], as_index=as_index, dropna=True).agg( {"C": ["min", "std"]} ) ), sort(pdf.groupby(["A", "B"], as_index=as_index).agg({"C": ["min", "std"]})), almost=True, ) # Testing dropna=False index = pd.Index([1.0, 2.0, np.nan], name="A") expected = pd.Series([2.0, np.nan, 1.0], index=index, name="B") result = kdf.groupby("A", as_index=True, dropna=False)["B"].min().sort_index() self.assert_eq(expected, result) expected = pd.DataFrame({"A": [1.0, 2.0, np.nan], "B": [2.0, np.nan, 1.0]}) result = ( kdf.groupby("A", as_index=False, dropna=False)["B"] .min() .sort_values("A") .reset_index(drop=True) ) self.assert_eq(expected, result) index = pd.MultiIndex.from_tuples( [(1.0, 2.0), (1.0, None), (2.0, None), (None, 1.0), (None, 3.0)], names=["A", "B"] ) expected = pd.DataFrame( { ("C", "min"): [5.0, 7.0, np.nan, 4.0, 6.0], ("C", "std"): [np.nan, np.nan, np.nan, np.nan, np.nan], }, index=index, ) result = ( kdf.groupby(["A", "B"], as_index=True, dropna=False) .agg({"C": ["min", "std"]}) .sort_index() ) self.assert_eq(expected, result) expected = pd.DataFrame( { ("A", ""): [1.0, 1.0, 2.0, np.nan, np.nan], ("B", ""): [2.0, np.nan, np.nan, 1.0, 3.0], ("C", "min"): [5.0, 7.0, np.nan, 4.0, 6.0], ("C", "std"): [np.nan, np.nan, np.nan, np.nan, np.nan], } ) result = ( kdf.groupby(["A", "B"], as_index=False, dropna=False) .agg({"C": ["min", "std"]}) .sort_values(["A", "B"]) .reset_index(drop=True) ) self.assert_eq(expected, result) def test_describe(self): # support for numeric type, not support for string type yet datas = [] datas.append({"a": [1, 1, 3], "b": [4, 5, 6], "c": [7, 8, 9]}) datas.append({"a": [-1, -1, -3], "b": [-4, -5, -6], "c": [-7, -8, -9]}) datas.append({"a": [0, 0, 0], "b": [0, 0, 0], "c": [0, 8, 0]}) # it is okay if string type column as a group key datas.append({"a": ["a", "a", "c"], "b": [4, 5, 6], "c": [7, 8, 9]}) percentiles = [0.25, 0.5, 0.75] formatted_percentiles = ["25%", "50%", "75%"] non_percentile_stats = ["count", "mean", "std", "min", "max"] for data in datas: pdf = pd.DataFrame(data) kdf = ks.from_pandas(pdf) describe_pdf = pdf.groupby("a").describe().sort_index() describe_kdf = kdf.groupby("a").describe().sort_index() # since the result of percentile columns are slightly difference from pandas, # we should check them separately: non-percentile columns & percentile columns # 1. Check that non-percentile columns are equal. agg_cols = [col.name for col in kdf.groupby("a")._agg_columns] self.assert_eq( describe_kdf.drop(list(product(agg_cols, formatted_percentiles))), describe_pdf.drop(columns=formatted_percentiles, level=1), check_exact=False, ) # 2. Check that percentile columns are equal. # The interpolation argument is yet to be implemented in Koalas. quantile_pdf = pdf.groupby("a").quantile(percentiles, interpolation="nearest") quantile_pdf = quantile_pdf.unstack(level=1).astype(float) self.assert_eq( describe_kdf.drop(list(product(agg_cols, non_percentile_stats))), quantile_pdf.rename(columns="{:.0%}".format, level=1), ) # not support for string type yet datas = [] datas.append({"a": ["a", "a", "c"], "b": ["d", "e", "f"], "c": ["g", "h", "i"]}) datas.append({"a": ["a", "a", "c"], "b": [4, 0, 1], "c": ["g", "h", "i"]}) for data in datas: pdf = pd.DataFrame(data) kdf = ks.from_pandas(pdf) self.assertRaises(NotImplementedError, lambda: kdf.groupby("a").describe().sort_index()) # multi-index columns pdf = pd.DataFrame({("x", "a"): [1, 1, 3], ("x", "b"): [4, 5, 6], ("y", "c"): [7, 8, 9]}) kdf = ks.from_pandas(pdf) describe_pdf = pdf.groupby(("x", "a")).describe().sort_index() describe_kdf = kdf.groupby(("x", "a")).describe().sort_index() # 1. Check that non-percentile columns are equal. agg_column_labels = [col._column_label for col in kdf.groupby(("x", "a"))._agg_columns] self.assert_eq( describe_kdf.drop( [ tuple(list(label) + [s]) for label, s in product(agg_column_labels, formatted_percentiles) ] ), describe_pdf.drop(columns=formatted_percentiles, level=2), check_exact=False, ) # 2. Check that percentile columns are equal. # The interpolation argument is yet to be implemented in Koalas. quantile_pdf = pdf.groupby(("x", "a")).quantile(percentiles, interpolation="nearest") quantile_pdf = quantile_pdf.unstack(level=1).astype(float) self.assert_eq( describe_kdf.drop( [ tuple(list(label) + [s]) for label, s in product(agg_column_labels, non_percentile_stats) ] ), quantile_pdf.rename(columns="{:.0%}".format, level=2), ) def test_aggregate_relabel_multiindex(self): pdf = pd.DataFrame({"A": [0, 1, 2, 3], "B": [5, 6, 7, 8], "group": ["a", "a", "b", "b"]}) pdf.columns = pd.MultiIndex.from_tuples([("y", "A"), ("y", "B"), ("x", "group")]) kdf = ks.from_pandas(pdf) if LooseVersion(pd.__version__) < LooseVersion("1.0.0"): agg_pdf = pd.DataFrame( {"a_max": [1, 3]}, index=pd.Index(["a", "b"], name=("x", "group")) ) elif LooseVersion(pd.__version__) >= LooseVersion("1.0.0"): agg_pdf = pdf.groupby(("x", "group")).agg(a_max=(("y", "A"), "max")).sort_index() agg_kdf = kdf.groupby(("x", "group")).agg(a_max=(("y", "A"), "max")).sort_index() self.assert_eq(agg_pdf, agg_kdf) # same column, different methods if LooseVersion(pd.__version__) < LooseVersion("1.0.0"): agg_pdf = pd.DataFrame( {"a_max": [1, 3], "a_min": [0, 2]}, index=pd.Index(["a", "b"], name=("x", "group")) ) elif LooseVersion(pd.__version__) >= LooseVersion("1.0.0"): agg_pdf = ( pdf.groupby(("x", "group")) .agg(a_max=(("y", "A"), "max"), a_min=(("y", "A"), "min")) .sort_index() ) agg_kdf = ( kdf.groupby(("x", "group")) .agg(a_max=(("y", "A"), "max"), a_min=(("y", "A"), "min")) .sort_index() ) self.assert_eq(agg_pdf, agg_kdf) # different column, different methods if LooseVersion(pd.__version__) < LooseVersion("1.0.0"): agg_pdf = pd.DataFrame( {"a_max": [6, 8], "a_min": [0, 2]}, index=pd.Index(["a", "b"], name=("x", "group")) ) elif LooseVersion(pd.__version__) >= LooseVersion("1.0.0"): agg_pdf = ( pdf.groupby(("x", "group")) .agg(a_max=(("y", "B"), "max"), a_min=(("y", "A"), "min")) .sort_index() ) agg_kdf = ( kdf.groupby(("x", "group")) .agg(a_max=(("y", "B"), "max"), a_min=(("y", "A"), "min")) .sort_index() ) self.assert_eq(agg_pdf, agg_kdf) def test_all_any(self): pdf = pd.DataFrame( { "A": [1, 1, 2, 2, 3, 3, 4, 4, 5, 5], "B": [True, True, True, False, False, False, None, True, None, False], } ) kdf = ks.from_pandas(pdf) for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values("A").reset_index(drop=True) self.assert_eq( sort(kdf.groupby("A", as_index=as_index).all()), sort(pdf.groupby("A", as_index=as_index).all()), ) self.assert_eq( sort(kdf.groupby("A", as_index=as_index).any()), sort(pdf.groupby("A", as_index=as_index).any()), ) self.assert_eq( sort(kdf.groupby("A", as_index=as_index).all()).B, sort(pdf.groupby("A", as_index=as_index).all()).B, ) self.assert_eq( sort(kdf.groupby("A", as_index=as_index).any()).B, sort(pdf.groupby("A", as_index=as_index).any()).B, ) self.assert_eq( kdf.B.groupby(kdf.A).all().sort_index(), pdf.B.groupby(pdf.A).all().sort_index() ) self.assert_eq( kdf.B.groupby(kdf.A).any().sort_index(), pdf.B.groupby(pdf.A).any().sort_index() ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("Y", "B")]) pdf.columns = columns kdf.columns = columns for as_index in [True, False]: if as_index: sort = lambda df: df.sort_index() else: sort = lambda df: df.sort_values(("X", "A")).reset_index(drop=True) self.assert_eq( sort(kdf.groupby(("X", "A"), as_index=as_index).all()), sort(pdf.groupby(("X", "A"), as_index=as_index).all()), ) self.assert_eq( sort(kdf.groupby(("X", "A"), as_index=as_index).any()), sort(pdf.groupby(("X", "A"), as_index=as_index).any()), ) def test_raises(self): kdf = ks.DataFrame( {"a": [1, 2, 6, 4, 4, 6, 4, 3, 7], "b": [4, 2, 7, 3, 3, 1, 1, 1, 2]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) # test raises with incorrect key self.assertRaises(ValueError, lambda: kdf.groupby([])) self.assertRaises(KeyError, lambda: kdf.groupby("x")) self.assertRaises(KeyError, lambda: kdf.groupby(["a", "x"])) self.assertRaises(KeyError, lambda: kdf.groupby("a")["x"]) self.assertRaises(KeyError, lambda: kdf.groupby("a")["b", "x"]) self.assertRaises(KeyError, lambda: kdf.groupby("a")[["b", "x"]]) def test_nunique(self): pdf = pd.DataFrame( {"a": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], "b": [2, 2, 2, 3, 3, 4, 4, 5, 5, 5]} ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("a").agg({"b": "nunique"}).sort_index(), pdf.groupby("a").agg({"b": "nunique"}).sort_index(), ) if LooseVersion(pd.__version__) < LooseVersion("1.1.0"): expected = ks.DataFrame({"b": [2, 2]}, index=pd.Index([0, 1], name="a")) self.assert_eq(kdf.groupby("a").nunique().sort_index(), expected) self.assert_eq( kdf.groupby("a").nunique(dropna=False).sort_index(), expected, ) else: self.assert_eq( kdf.groupby("a").nunique().sort_index(), pdf.groupby("a").nunique().sort_index() ) self.assert_eq( kdf.groupby("a").nunique(dropna=False).sort_index(), pdf.groupby("a").nunique(dropna=False).sort_index(), ) self.assert_eq( kdf.groupby("a")["b"].nunique().sort_index(), pdf.groupby("a")["b"].nunique().sort_index(), ) self.assert_eq( kdf.groupby("a")["b"].nunique(dropna=False).sort_index(), pdf.groupby("a")["b"].nunique(dropna=False).sort_index(), ) nunique_kdf = kdf.groupby("a", as_index=False).agg({"b": "nunique"}) nunique_pdf = pdf.groupby("a", as_index=False).agg({"b": "nunique"}) self.assert_eq( nunique_kdf.sort_values(["a", "b"]).reset_index(drop=True), nunique_pdf.sort_values(["a", "b"]).reset_index(drop=True), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("y", "b")]) pdf.columns = columns kdf.columns = columns if LooseVersion(pd.__version__) < LooseVersion("1.1.0"): expected = ks.DataFrame({("y", "b"): [2, 2]}, index=pd.Index([0, 1], name=("x", "a"))) self.assert_eq( kdf.groupby(("x", "a")).nunique().sort_index(), expected, ) self.assert_eq( kdf.groupby(("x", "a")).nunique(dropna=False).sort_index(), expected, ) else: self.assert_eq( kdf.groupby(("x", "a")).nunique().sort_index(), pdf.groupby(("x", "a")).nunique().sort_index(), ) self.assert_eq( kdf.groupby(("x", "a")).nunique(dropna=False).sort_index(), pdf.groupby(("x", "a")).nunique(dropna=False).sort_index(), ) def test_unique(self): for pdf in [ pd.DataFrame( {"a": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], "b": [2, 2, 2, 3, 3, 4, 4, 5, 5, 5]} ), pd.DataFrame( { "a": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0], "b": ["w", "w", "w", "x", "x", "y", "y", "z", "z", "z"], } ), ]: with self.subTest(pdf=pdf): kdf = ks.from_pandas(pdf) actual = kdf.groupby("a")["b"].unique().sort_index().to_pandas() expect = pdf.groupby("a")["b"].unique().sort_index() self.assert_eq(len(actual), len(expect)) for act, exp in zip(actual, expect): self.assertTrue(sorted(act) == sorted(exp)) def test_value_counts(self): pdf = pd.DataFrame({"A": [1, 2, 2, 3, 3, 3], "B": [1, 1, 2, 3, 3, 3]}, columns=["A", "B"]) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("A")["B"].value_counts().sort_index(), pdf.groupby("A")["B"].value_counts().sort_index(), ) self.assert_eq( kdf.groupby("A")["B"].value_counts(sort=True, ascending=False).sort_index(), pdf.groupby("A")["B"].value_counts(sort=True, ascending=False).sort_index(), ) self.assert_eq( kdf.groupby("A")["B"].value_counts(sort=True, ascending=True).sort_index(), pdf.groupby("A")["B"].value_counts(sort=True, ascending=True).sort_index(), ) self.assert_eq( kdf.B.rename().groupby(kdf.A).value_counts().sort_index(), pdf.B.rename().groupby(pdf.A).value_counts().sort_index(), ) self.assert_eq( kdf.B.groupby(kdf.A.rename()).value_counts().sort_index(), pdf.B.groupby(pdf.A.rename()).value_counts().sort_index(), ) self.assert_eq( kdf.B.rename().groupby(kdf.A.rename()).value_counts().sort_index(), pdf.B.rename().groupby(pdf.A.rename()).value_counts().sort_index(), ) def test_size(self): pdf = pd.DataFrame({"A": [1, 2, 2, 3, 3, 3], "B": [1, 1, 2, 3, 3, 3]}) kdf = ks.from_pandas(pdf) self.assert_eq(kdf.groupby("A").size().sort_index(), pdf.groupby("A").size().sort_index()) self.assert_eq( kdf.groupby("A")["B"].size().sort_index(), pdf.groupby("A")["B"].size().sort_index() ) self.assert_eq( kdf.groupby("A")[["B"]].size().sort_index(), pdf.groupby("A")[["B"]].size().sort_index() ) self.assert_eq( kdf.groupby(["A", "B"]).size().sort_index(), pdf.groupby(["A", "B"]).size().sort_index() ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("Y", "B")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("X", "A")).size().sort_index(), pdf.groupby(("X", "A")).size().sort_index() ) self.assert_eq( kdf.groupby([("X", "A"), ("Y", "B")]).size().sort_index(), pdf.groupby([("X", "A"), ("Y", "B")]).size().sort_index(), ) def test_diff(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, } ) kdf = ks.from_pandas(pdf) self.assert_eq(kdf.groupby("b").diff().sort_index(), pdf.groupby("b").diff().sort_index()) self.assert_eq( kdf.groupby(["a", "b"]).diff().sort_index(), pdf.groupby(["a", "b"]).diff().sort_index() ) self.assert_eq( kdf.groupby(["b"])["a"].diff().sort_index(), pdf.groupby(["b"])["a"].diff().sort_index() ) self.assert_eq( kdf.groupby(["b"])[["a", "b"]].diff().sort_index(), pdf.groupby(["b"])[["a", "b"]].diff().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).diff().sort_index(), pdf.groupby(pdf.b // 5).diff().sort_index() ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].diff().sort_index(), pdf.groupby(pdf.b // 5)["a"].diff().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).diff().sort_index(), pdf.groupby(("x", "b")).diff().sort_index() ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).diff().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).diff().sort_index(), ) def test_rank(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq(kdf.groupby("b").rank().sort_index(), pdf.groupby("b").rank().sort_index()) self.assert_eq( kdf.groupby(["a", "b"]).rank().sort_index(), pdf.groupby(["a", "b"]).rank().sort_index() ) self.assert_eq( kdf.groupby(["b"])["a"].rank().sort_index(), pdf.groupby(["b"])["a"].rank().sort_index() ) self.assert_eq( kdf.groupby(["b"])[["a", "c"]].rank().sort_index(), pdf.groupby(["b"])[["a", "c"]].rank().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).rank().sort_index(), pdf.groupby(pdf.b // 5).rank().sort_index() ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].rank().sort_index(), pdf.groupby(pdf.b // 5)["a"].rank().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).rank().sort_index(), pdf.groupby(("x", "b")).rank().sort_index() ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).rank().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).rank().sort_index(), ) def test_cumcount(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) kdf = ks.from_pandas(pdf) for ascending in [True, False]: self.assert_eq( kdf.groupby("b").cumcount(ascending=ascending).sort_index(), pdf.groupby("b").cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.groupby(["a", "b"]).cumcount(ascending=ascending).sort_index(), pdf.groupby(["a", "b"]).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.groupby(["b"])["a"].cumcount(ascending=ascending).sort_index(), pdf.groupby(["b"])["a"].cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.groupby(["b"])[["a", "c"]].cumcount(ascending=ascending).sort_index(), pdf.groupby(["b"])[["a", "c"]].cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).cumcount(ascending=ascending).sort_index(), pdf.groupby(pdf.b // 5).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].cumcount(ascending=ascending).sort_index(), pdf.groupby(pdf.b // 5)["a"].cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.groupby("b").cumcount(ascending=ascending).sum(), pdf.groupby("b").cumcount(ascending=ascending).sum(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b).cumcount(ascending=ascending).sort_index(), pdf.a.rename().groupby(pdf.b).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).cumcount(ascending=ascending).sort_index(), pdf.a.groupby(pdf.b.rename()).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).cumcount(ascending=ascending).sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cumcount(ascending=ascending).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns for ascending in [True, False]: self.assert_eq( kdf.groupby(("x", "b")).cumcount(ascending=ascending).sort_index(), pdf.groupby(("x", "b")).cumcount(ascending=ascending).sort_index(), ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).cumcount(ascending=ascending).sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cumcount(ascending=ascending).sort_index(), ) def test_cummin(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("b").cummin().sort_index(), pdf.groupby("b").cummin().sort_index() ) self.assert_eq( kdf.groupby(["a", "b"]).cummin().sort_index(), pdf.groupby(["a", "b"]).cummin().sort_index(), ) self.assert_eq( kdf.groupby(["b"])["a"].cummin().sort_index(), pdf.groupby(["b"])["a"].cummin().sort_index(), ) self.assert_eq( kdf.groupby(["b"])[["a", "c"]].cummin().sort_index(), pdf.groupby(["b"])[["a", "c"]].cummin().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).cummin().sort_index(), pdf.groupby(pdf.b // 5).cummin().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].cummin().sort_index(), pdf.groupby(pdf.b // 5)["a"].cummin().sort_index(), ) self.assert_eq( kdf.groupby("b").cummin().sum().sort_index(), pdf.groupby("b").cummin().sum().sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b).cummin().sort_index(), pdf.a.rename().groupby(pdf.b).cummin().sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).cummin().sort_index(), pdf.a.groupby(pdf.b.rename()).cummin().sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).cummin().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cummin().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).cummin().sort_index(), pdf.groupby(("x", "b")).cummin().sort_index(), ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).cummin().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cummin().sort_index(), ) kdf = ks.DataFrame([["a"], ["b"], ["c"]], columns=["A"]) self.assertRaises(DataError, lambda: kdf.groupby(["A"]).cummin()) kdf = ks.DataFrame([[1, "a"], [2, "b"], [3, "c"]], columns=["A", "B"]) self.assertRaises(DataError, lambda: kdf.groupby(["A"])["B"].cummin()) def test_cummax(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("b").cummax().sort_index(), pdf.groupby("b").cummax().sort_index() ) self.assert_eq( kdf.groupby(["a", "b"]).cummax().sort_index(), pdf.groupby(["a", "b"]).cummax().sort_index(), ) self.assert_eq( kdf.groupby(["b"])["a"].cummax().sort_index(), pdf.groupby(["b"])["a"].cummax().sort_index(), ) self.assert_eq( kdf.groupby(["b"])[["a", "c"]].cummax().sort_index(), pdf.groupby(["b"])[["a", "c"]].cummax().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).cummax().sort_index(), pdf.groupby(pdf.b // 5).cummax().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].cummax().sort_index(), pdf.groupby(pdf.b // 5)["a"].cummax().sort_index(), ) self.assert_eq( kdf.groupby("b").cummax().sum().sort_index(), pdf.groupby("b").cummax().sum().sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b).cummax().sort_index(), pdf.a.rename().groupby(pdf.b).cummax().sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).cummax().sort_index(), pdf.a.groupby(pdf.b.rename()).cummax().sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).cummax().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cummax().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).cummax().sort_index(), pdf.groupby(("x", "b")).cummax().sort_index(), ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).cummax().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cummax().sort_index(), ) kdf = ks.DataFrame([["a"], ["b"], ["c"]], columns=["A"]) self.assertRaises(DataError, lambda: kdf.groupby(["A"]).cummax()) kdf = ks.DataFrame([[1, "a"], [2, "b"], [3, "c"]], columns=["A", "B"]) self.assertRaises(DataError, lambda: kdf.groupby(["A"])["B"].cummax()) def test_cumsum(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("b").cumsum().sort_index(), pdf.groupby("b").cumsum().sort_index() ) self.assert_eq( kdf.groupby(["a", "b"]).cumsum().sort_index(), pdf.groupby(["a", "b"]).cumsum().sort_index(), ) self.assert_eq( kdf.groupby(["b"])["a"].cumsum().sort_index(), pdf.groupby(["b"])["a"].cumsum().sort_index(), ) self.assert_eq( kdf.groupby(["b"])[["a", "c"]].cumsum().sort_index(), pdf.groupby(["b"])[["a", "c"]].cumsum().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).cumsum().sort_index(), pdf.groupby(pdf.b // 5).cumsum().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].cumsum().sort_index(), pdf.groupby(pdf.b // 5)["a"].cumsum().sort_index(), ) self.assert_eq( kdf.groupby("b").cumsum().sum().sort_index(), pdf.groupby("b").cumsum().sum().sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b).cumsum().sort_index(), pdf.a.rename().groupby(pdf.b).cumsum().sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).cumsum().sort_index(), pdf.a.groupby(pdf.b.rename()).cumsum().sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).cumsum().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cumsum().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).cumsum().sort_index(), pdf.groupby(("x", "b")).cumsum().sort_index(), ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).cumsum().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cumsum().sort_index(), ) kdf = ks.DataFrame([["a"], ["b"], ["c"]], columns=["A"]) self.assertRaises(DataError, lambda: kdf.groupby(["A"]).cumsum()) kdf = ks.DataFrame([[1, "a"], [2, "b"], [3, "c"]], columns=["A", "B"]) self.assertRaises(DataError, lambda: kdf.groupby(["A"])["B"].cumsum()) def test_cumprod(self): pdf = pd.DataFrame( { "a": [1, 2, 3, 4, 5, 6] * 3, "b": [1, 1, 2, 3, 5, 8] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("b").cumprod().sort_index(), pdf.groupby("b").cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.groupby(["a", "b"]).cumprod().sort_index(), pdf.groupby(["a", "b"]).cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.groupby(["b"])["a"].cumprod().sort_index(), pdf.groupby(["b"])["a"].cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.groupby(["b"])[["a", "c"]].cumprod().sort_index(), pdf.groupby(["b"])[["a", "c"]].cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.groupby(kdf.b // 3).cumprod().sort_index(), pdf.groupby(pdf.b // 3).cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.groupby(kdf.b // 3)["a"].cumprod().sort_index(), pdf.groupby(pdf.b // 3)["a"].cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.groupby("b").cumprod().sum().sort_index(), pdf.groupby("b").cumprod().sum().sort_index(), almost=True, ) self.assert_eq( kdf.a.rename().groupby(kdf.b).cumprod().sort_index(), pdf.a.rename().groupby(pdf.b).cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).cumprod().sort_index(), pdf.a.groupby(pdf.b.rename()).cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).cumprod().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).cumprod().sort_index(), almost=True, ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).cumprod().sort_index(), pdf.groupby(("x", "b")).cumprod().sort_index(), almost=True, ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).cumprod().sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).cumprod().sort_index(), almost=True, ) kdf = ks.DataFrame([["a"], ["b"], ["c"]], columns=["A"]) self.assertRaises(DataError, lambda: kdf.groupby(["A"]).cumprod()) kdf = ks.DataFrame([[1, "a"], [2, "b"], [3, "c"]], columns=["A", "B"]) self.assertRaises(DataError, lambda: kdf.groupby(["A"])["B"].cumprod()) def test_nsmallest(self): pdf = pd.DataFrame( { "a": [1, 1, 1, 2, 2, 2, 3, 3, 3] * 3, "b": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, "c": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, "d": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, }, index=np.random.rand(9 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby(["a"])["b"].nsmallest(1).sort_values(), pdf.groupby(["a"])["b"].nsmallest(1).sort_values(), ) self.assert_eq( kdf.groupby(["a"])["b"].nsmallest(2).sort_index(), pdf.groupby(["a"])["b"].nsmallest(2).sort_index(), ) self.assert_eq( (kdf.b * 10).groupby(kdf.a).nsmallest(2).sort_index(), (pdf.b * 10).groupby(pdf.a).nsmallest(2).sort_index(), ) self.assert_eq( kdf.b.rename().groupby(kdf.a).nsmallest(2).sort_index(), pdf.b.rename().groupby(pdf.a).nsmallest(2).sort_index(), ) self.assert_eq( kdf.b.groupby(kdf.a.rename()).nsmallest(2).sort_index(), pdf.b.groupby(pdf.a.rename()).nsmallest(2).sort_index(), ) self.assert_eq( kdf.b.rename().groupby(kdf.a.rename()).nsmallest(2).sort_index(), pdf.b.rename().groupby(pdf.a.rename()).nsmallest(2).sort_index(), ) with self.assertRaisesRegex(ValueError, "nsmallest do not support multi-index now"): kdf.set_index(["a", "b"]).groupby(["c"])["d"].nsmallest(1) def test_nlargest(self): pdf = pd.DataFrame( { "a": [1, 1, 1, 2, 2, 2, 3, 3, 3] * 3, "b": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, "c": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, "d": [1, 2, 2, 2, 3, 3, 3, 4, 4] * 3, }, index=np.random.rand(9 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby(["a"])["b"].nlargest(1).sort_values(), pdf.groupby(["a"])["b"].nlargest(1).sort_values(), ) self.assert_eq( kdf.groupby(["a"])["b"].nlargest(2).sort_index(), pdf.groupby(["a"])["b"].nlargest(2).sort_index(), ) self.assert_eq( (kdf.b * 10).groupby(kdf.a).nlargest(2).sort_index(), (pdf.b * 10).groupby(pdf.a).nlargest(2).sort_index(), ) self.assert_eq( kdf.b.rename().groupby(kdf.a).nlargest(2).sort_index(), pdf.b.rename().groupby(pdf.a).nlargest(2).sort_index(), ) self.assert_eq( kdf.b.groupby(kdf.a.rename()).nlargest(2).sort_index(), pdf.b.groupby(pdf.a.rename()).nlargest(2).sort_index(), ) self.assert_eq( kdf.b.rename().groupby(kdf.a.rename()).nlargest(2).sort_index(), pdf.b.rename().groupby(pdf.a.rename()).nlargest(2).sort_index(), ) with self.assertRaisesRegex(ValueError, "nlargest do not support multi-index now"): kdf.set_index(["a", "b"]).groupby(["c"])["d"].nlargest(1) def test_fillna(self): pdf = pd.DataFrame( { "A": [1, 1, 2, 2] * 3, "B": [2, 4, None, 3] * 3, "C": [None, None, None, 1] * 3, "D": [0, 1, 5, 4] * 3, } ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("A").fillna(0).sort_index(), pdf.groupby("A").fillna(0).sort_index() ) self.assert_eq( kdf.groupby("A")["C"].fillna(0).sort_index(), pdf.groupby("A")["C"].fillna(0).sort_index(), ) self.assert_eq( kdf.groupby("A")[["C"]].fillna(0).sort_index(), pdf.groupby("A")[["C"]].fillna(0).sort_index(), ) self.assert_eq( kdf.groupby("A").fillna(method="bfill").sort_index(), pdf.groupby("A").fillna(method="bfill").sort_index(), ) self.assert_eq( kdf.groupby("A")["C"].fillna(method="bfill").sort_index(), pdf.groupby("A")["C"].fillna(method="bfill").sort_index(), ) self.assert_eq( kdf.groupby("A")[["C"]].fillna(method="bfill").sort_index(), pdf.groupby("A")[["C"]].fillna(method="bfill").sort_index(), ) self.assert_eq( kdf.groupby("A").fillna(method="ffill").sort_index(), pdf.groupby("A").fillna(method="ffill").sort_index(), ) self.assert_eq( kdf.groupby("A")["C"].fillna(method="ffill").sort_index(), pdf.groupby("A")["C"].fillna(method="ffill").sort_index(), ) self.assert_eq( kdf.groupby("A")[["C"]].fillna(method="ffill").sort_index(), pdf.groupby("A")[["C"]].fillna(method="ffill").sort_index(), ) self.assert_eq( kdf.groupby(kdf.A // 5).fillna(method="bfill").sort_index(), pdf.groupby(pdf.A // 5).fillna(method="bfill").sort_index(), ) self.assert_eq( kdf.groupby(kdf.A // 5)["C"].fillna(method="bfill").sort_index(), pdf.groupby(pdf.A // 5)["C"].fillna(method="bfill").sort_index(), ) self.assert_eq( kdf.groupby(kdf.A // 5)[["C"]].fillna(method="bfill").sort_index(), pdf.groupby(pdf.A // 5)[["C"]].fillna(method="bfill").sort_index(), ) self.assert_eq( kdf.groupby(kdf.A // 5).fillna(method="ffill").sort_index(), pdf.groupby(pdf.A // 5).fillna(method="ffill").sort_index(), ) self.assert_eq( kdf.groupby(kdf.A // 5)["C"].fillna(method="ffill").sort_index(), pdf.groupby(pdf.A // 5)["C"].fillna(method="ffill").sort_index(), ) self.assert_eq( kdf.groupby(kdf.A // 5)[["C"]].fillna(method="ffill").sort_index(), pdf.groupby(pdf.A // 5)[["C"]].fillna(method="ffill").sort_index(), ) self.assert_eq( kdf.C.rename().groupby(kdf.A).fillna(0).sort_index(), pdf.C.rename().groupby(pdf.A).fillna(0).sort_index(), ) self.assert_eq( kdf.C.groupby(kdf.A.rename()).fillna(0).sort_index(), pdf.C.groupby(pdf.A.rename()).fillna(0).sort_index(), ) self.assert_eq( kdf.C.rename().groupby(kdf.A.rename()).fillna(0).sort_index(), pdf.C.rename().groupby(pdf.A.rename()).fillna(0).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Z", "D")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("X", "A")).fillna(0).sort_index(), pdf.groupby(("X", "A")).fillna(0).sort_index(), ) self.assert_eq( kdf.groupby(("X", "A")).fillna(method="bfill").sort_index(), pdf.groupby(("X", "A")).fillna(method="bfill").sort_index(), ) self.assert_eq( kdf.groupby(("X", "A")).fillna(method="ffill").sort_index(), pdf.groupby(("X", "A")).fillna(method="ffill").sort_index(), ) def test_ffill(self): idx = np.random.rand(4 * 3) pdf = pd.DataFrame( { "A": [1, 1, 2, 2] * 3, "B": [2, 4, None, 3] * 3, "C": [None, None, None, 1] * 3, "D": [0, 1, 5, 4] * 3, }, index=idx, ) kdf = ks.from_pandas(pdf) if LooseVersion(pd.__version__) <= LooseVersion("0.24.2"): self.assert_eq( kdf.groupby("A").ffill().sort_index(), pdf.groupby("A").ffill().sort_index().drop("A", 1), ) self.assert_eq( kdf.groupby("A")[["B"]].ffill().sort_index(), pdf.groupby("A")[["B"]].ffill().sort_index().drop("A", 1), ) else: self.assert_eq( kdf.groupby("A").ffill().sort_index(), pdf.groupby("A").ffill().sort_index() ) self.assert_eq( kdf.groupby("A")[["B"]].ffill().sort_index(), pdf.groupby("A")[["B"]].ffill().sort_index(), ) self.assert_eq( kdf.groupby("A")["B"].ffill().sort_index(), pdf.groupby("A")["B"].ffill().sort_index() ) self.assert_eq(kdf.groupby("A")["B"].ffill()[idx[6]], pdf.groupby("A")["B"].ffill()[idx[6]]) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Z", "D")]) pdf.columns = columns kdf.columns = columns if LooseVersion(pd.__version__) <= LooseVersion("0.24.2"): self.assert_eq( kdf.groupby(("X", "A")).ffill().sort_index(), pdf.groupby(("X", "A")).ffill().sort_index().drop(("X", "A"), 1), ) else: self.assert_eq( kdf.groupby(("X", "A")).ffill().sort_index(), pdf.groupby(("X", "A")).ffill().sort_index(), ) def test_bfill(self): idx = np.random.rand(4 * 3) pdf = pd.DataFrame( { "A": [1, 1, 2, 2] * 3, "B": [2, 4, None, 3] * 3, "C": [None, None, None, 1] * 3, "D": [0, 1, 5, 4] * 3, }, index=idx, ) kdf = ks.from_pandas(pdf) if LooseVersion(pd.__version__) <= LooseVersion("0.24.2"): self.assert_eq( kdf.groupby("A").bfill().sort_index(), pdf.groupby("A").bfill().sort_index().drop("A", 1), ) self.assert_eq( kdf.groupby("A")[["B"]].bfill().sort_index(), pdf.groupby("A")[["B"]].bfill().sort_index().drop("A", 1), ) else: self.assert_eq( kdf.groupby("A").bfill().sort_index(), pdf.groupby("A").bfill().sort_index() ) self.assert_eq( kdf.groupby("A")[["B"]].bfill().sort_index(), pdf.groupby("A")[["B"]].bfill().sort_index(), ) self.assert_eq( kdf.groupby("A")["B"].bfill().sort_index(), pdf.groupby("A")["B"].bfill().sort_index(), ) self.assert_eq(kdf.groupby("A")["B"].bfill()[idx[6]], pdf.groupby("A")["B"].bfill()[idx[6]]) # multi-index columns columns = pd.MultiIndex.from_tuples([("X", "A"), ("X", "B"), ("Y", "C"), ("Z", "D")]) pdf.columns = columns kdf.columns = columns if LooseVersion(pd.__version__) <= LooseVersion("0.24.2"): self.assert_eq( kdf.groupby(("X", "A")).bfill().sort_index(), pdf.groupby(("X", "A")).bfill().sort_index().drop(("X", "A"), 1), ) else: self.assert_eq( kdf.groupby(("X", "A")).bfill().sort_index(), pdf.groupby(("X", "A")).bfill().sort_index(), ) @unittest.skipIf(pd.__version__ < "0.24.0", "not supported before pandas 0.24.0") def test_shift(self): pdf = pd.DataFrame( { "a": [1, 1, 2, 2, 3, 3] * 3, "b": [1, 1, 2, 2, 3, 4] * 3, "c": [1, 4, 9, 16, 25, 36] * 3, }, index=np.random.rand(6 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq(kdf.groupby("a").shift().sort_index(), pdf.groupby("a").shift().sort_index()) # TODO: seems like a pandas' bug when fill_value is not None? # self.assert_eq(kdf.groupby(['a', 'b']).shift(periods=-1, fill_value=0).sort_index(), # pdf.groupby(['a', 'b']).shift(periods=-1, fill_value=0).sort_index()) self.assert_eq( kdf.groupby(["b"])["a"].shift().sort_index(), pdf.groupby(["b"])["a"].shift().sort_index(), ) self.assert_eq( kdf.groupby(["a", "b"])["c"].shift().sort_index(), pdf.groupby(["a", "b"])["c"].shift().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).shift().sort_index(), pdf.groupby(pdf.b // 5).shift().sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].shift().sort_index(), pdf.groupby(pdf.b // 5)["a"].shift().sort_index(), ) # TODO: known pandas' bug when fill_value is not None pandas>=1.0.0 # https://github.com/pandas-dev/pandas/issues/31971#issue-565171762 if LooseVersion(pd.__version__) < LooseVersion("1.0.0"): self.assert_eq( kdf.groupby(["b"])[["a", "c"]].shift(periods=-1, fill_value=0).sort_index(), pdf.groupby(["b"])[["a", "c"]].shift(periods=-1, fill_value=0).sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b).shift().sort_index(), pdf.a.rename().groupby(pdf.b).shift().sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).shift().sort_index(), pdf.a.groupby(pdf.b.rename()).shift().sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).shift().sort_index(), pdf.a.rename().groupby(pdf.b.rename()).shift().sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "a")).shift().sort_index(), pdf.groupby(("x", "a")).shift().sort_index(), ) # TODO: seems like a pandas' bug when fill_value is not None? # self.assert_eq(kdf.groupby([('x', 'a'), ('x', 'b')]).shift(periods=-1, # fill_value=0).sort_index(), # pdf.groupby([('x', 'a'), ('x', 'b')]).shift(periods=-1, # fill_value=0).sort_index()) def test_apply(self): pdf = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9, 16, 25, 36]}, columns=["a", "b", "c"], ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("b").apply(lambda x: x + x.min()).sort_index(), pdf.groupby("b").apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby("b").apply(len).sort_index(), pdf.groupby("b").apply(len).sort_index(), ) self.assert_eq( kdf.groupby("b")["a"].apply(lambda x, y, z: x + x.min() + y * z, 10, z=20).sort_index(), pdf.groupby("b")["a"].apply(lambda x, y, z: x + x.min() + y * z, 10, z=20).sort_index(), ) self.assert_eq( kdf.groupby("b")[["a"]].apply(lambda x: x + x.min()).sort_index(), pdf.groupby("b")[["a"]].apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(["a", "b"]).apply(lambda x, y, z: x + x.min() + y + z, 1, z=2).sort_index(), pdf.groupby(["a", "b"]).apply(lambda x, y, z: x + x.min() + y + z, 1, z=2).sort_index(), ) self.assert_eq( kdf.groupby(["b"])["c"].apply(lambda x: 1).sort_index(), pdf.groupby(["b"])["c"].apply(lambda x: 1).sort_index(), ) self.assert_eq( kdf.groupby(["b"])["c"].apply(len).sort_index(), pdf.groupby(["b"])["c"].apply(len).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).apply(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5).apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].apply(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5)["a"].apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)[["a"]].apply(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5)[["a"]].apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)[["a"]].apply(len).sort_index(), pdf.groupby(pdf.b // 5)[["a"]].apply(len).sort_index(), almost=True, ) self.assert_eq( kdf.a.rename().groupby(kdf.b).apply(lambda x: x + x.min()).sort_index(), pdf.a.rename().groupby(pdf.b).apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).apply(lambda x: x + x.min()).sort_index(), pdf.a.groupby(pdf.b.rename()).apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).apply(lambda x: x + x.min()).sort_index(), pdf.a.rename().groupby(pdf.b.rename()).apply(lambda x: x + x.min()).sort_index(), ) with self.assertRaisesRegex(TypeError, "int object is not callable"): kdf.groupby("b").apply(1) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).apply(lambda x: 1).sort_index(), pdf.groupby(("x", "b")).apply(lambda x: 1).sort_index(), ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).apply(lambda x: x + x.min()).sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).apply(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(("x", "b")).apply(len).sort_index(), pdf.groupby(("x", "b")).apply(len).sort_index(), ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).apply(len).sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).apply(len).sort_index(), ) def test_apply_without_shortcut(self): with option_context("compute.shortcut_limit", 0): self.test_apply() def test_apply_negative(self): def func(_) -> ks.Series[int]: return pd.Series([1]) with self.assertRaisesRegex(TypeError, "Series as a return type hint at frame groupby"): ks.range(10).groupby("id").apply(func) def test_apply_with_new_dataframe(self): pdf = pd.DataFrame( {"timestamp": [0.0, 0.5, 1.0, 0.0, 0.5], "car_id": ["A", "A", "A", "B", "B"]} ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("car_id").apply(lambda _: pd.DataFrame({"column": [0.0]})).sort_index(), pdf.groupby("car_id").apply(lambda _: pd.DataFrame({"column": [0.0]})).sort_index(), ) self.assert_eq( kdf.groupby("car_id") .apply(lambda df: pd.DataFrame({"mean": [df["timestamp"].mean()]})) .sort_index(), pdf.groupby("car_id") .apply(lambda df: pd.DataFrame({"mean": [df["timestamp"].mean()]})) .sort_index(), ) # dataframe with 1000+ records pdf = pd.DataFrame( { "timestamp": [0.0, 0.5, 1.0, 0.0, 0.5] * 300, "car_id": ["A", "A", "A", "B", "B"] * 300, } ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("car_id").apply(lambda _: pd.DataFrame({"column": [0.0]})).sort_index(), pdf.groupby("car_id").apply(lambda _: pd.DataFrame({"column": [0.0]})).sort_index(), ) self.assert_eq( kdf.groupby("car_id") .apply(lambda df: pd.DataFrame({"mean": [df["timestamp"].mean()]})) .sort_index(), pdf.groupby("car_id") .apply(lambda df: pd.DataFrame({"mean": [df["timestamp"].mean()]})) .sort_index(), ) def test_apply_with_new_dataframe_without_shortcut(self): with option_context("compute.shortcut_limit", 0): self.test_apply_with_new_dataframe() def test_apply_key_handling(self): pdf = pd.DataFrame( {"d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], "v": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]} ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("d").apply(sum).sort_index(), pdf.groupby("d").apply(sum).sort_index() ) with ks.option_context("compute.shortcut_limit", 1): self.assert_eq( kdf.groupby("d").apply(sum).sort_index(), pdf.groupby("d").apply(sum).sort_index() ) def test_apply_with_side_effect(self): pdf = pd.DataFrame( {"d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0], "v": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]} ) kdf = ks.from_pandas(pdf) acc = ks.utils.default_session().sparkContext.accumulator(0) def sum_with_acc_frame(x) -> ks.DataFrame[np.float64, np.float64]: nonlocal acc acc += 1 return np.sum(x) actual = kdf.groupby("d").apply(sum_with_acc_frame).sort_index() actual.columns = ["d", "v"] self.assert_eq(actual, pdf.groupby("d").apply(sum).sort_index().reset_index(drop=True)) self.assert_eq(acc.value, 2) def sum_with_acc_series(x) -> np.float64: nonlocal acc acc += 1 return np.sum(x) self.assert_eq( kdf.groupby("d")["v"].apply(sum_with_acc_series).sort_index(), pdf.groupby("d")["v"].apply(sum).sort_index().reset_index(drop=True), ) self.assert_eq(acc.value, 4) def test_transform(self): pdf = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9, 16, 25, 36]}, columns=["a", "b", "c"], ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("b").transform(lambda x: x + x.min()).sort_index(), pdf.groupby("b").transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby("b")["a"].transform(lambda x: x + x.min()).sort_index(), pdf.groupby("b")["a"].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby("b")[["a"]].transform(lambda x: x + x.min()).sort_index(), pdf.groupby("b")[["a"]].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(["a", "b"]).transform(lambda x: x + x.min()).sort_index(), pdf.groupby(["a", "b"]).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(["b"])["c"].transform(lambda x: x + x.min()).sort_index(), pdf.groupby(["b"])["c"].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5).transform(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)["a"].transform(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5)["a"].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby(kdf.b // 5)[["a"]].transform(lambda x: x + x.min()).sort_index(), pdf.groupby(pdf.b // 5)[["a"]].transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b).transform(lambda x: x + x.min()).sort_index(), pdf.a.rename().groupby(pdf.b).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).transform(lambda x: x + x.min()).sort_index(), pdf.a.groupby(pdf.b.rename()).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).transform(lambda x: x + x.min()).sort_index(), pdf.a.rename().groupby(pdf.b.rename()).transform(lambda x: x + x.min()).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).transform(lambda x: x + x.min()).sort_index(), pdf.groupby(("x", "b")).transform(lambda x: x + x.min()).sort_index(), ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]).transform(lambda x: x + x.min()).sort_index(), pdf.groupby([("x", "a"), ("x", "b")]).transform(lambda x: x + x.min()).sort_index(), ) def test_transform_without_shortcut(self): with option_context("compute.shortcut_limit", 0): self.test_transform() def test_filter(self): pdf = pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6], "b": [1, 1, 2, 3, 5, 8], "c": [1, 4, 9, 16, 25, 36]}, columns=["a", "b", "c"], ) kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("b").filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby("b").filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( kdf.groupby("b")["a"].filter(lambda x: any(x == 2)).sort_index(), pdf.groupby("b")["a"].filter(lambda x: any(x == 2)).sort_index(), ) self.assert_eq( kdf.groupby("b")[["a"]].filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby("b")[["a"]].filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( kdf.groupby(["a", "b"]).filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby(["a", "b"]).filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( kdf.groupby(kdf["b"] // 5).filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby(pdf["b"] // 5).filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( kdf.groupby(kdf["b"] // 5)["a"].filter(lambda x: any(x == 2)).sort_index(), pdf.groupby(pdf["b"] // 5)["a"].filter(lambda x: any(x == 2)).sort_index(), ) self.assert_eq( kdf.groupby(kdf["b"] // 5)[["a"]].filter(lambda x: any(x.a == 2)).sort_index(), pdf.groupby(pdf["b"] // 5)[["a"]].filter(lambda x: any(x.a == 2)).sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b).filter(lambda x: any(x == 2)).sort_index(), pdf.a.rename().groupby(pdf.b).filter(lambda x: any(x == 2)).sort_index(), ) self.assert_eq( kdf.a.groupby(kdf.b.rename()).filter(lambda x: any(x == 2)).sort_index(), pdf.a.groupby(pdf.b.rename()).filter(lambda x: any(x == 2)).sort_index(), ) self.assert_eq( kdf.a.rename().groupby(kdf.b.rename()).filter(lambda x: any(x == 2)).sort_index(), pdf.a.rename().groupby(pdf.b.rename()).filter(lambda x: any(x == 2)).sort_index(), ) with self.assertRaisesRegex(TypeError, "int object is not callable"): kdf.groupby("b").filter(1) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( kdf.groupby(("x", "b")).filter(lambda x: any(x[("x", "a")] == 2)).sort_index(), pdf.groupby(("x", "b")).filter(lambda x: any(x[("x", "a")] == 2)).sort_index(), ) self.assert_eq( kdf.groupby([("x", "a"), ("x", "b")]) .filter(lambda x: any(x[("x", "a")] == 2)) .sort_index(), pdf.groupby([("x", "a"), ("x", "b")]) .filter(lambda x: any(x[("x", "a")] == 2)) .sort_index(), ) def test_idxmax(self): pdf = pd.DataFrame( {"a": [1, 1, 2, 2, 3] * 3, "b": [1, 2, 3, 4, 5] * 3, "c": [5, 4, 3, 2, 1] * 3} ) kdf = ks.from_pandas(pdf) self.assert_eq( pdf.groupby(["a"]).idxmax().sort_index(), kdf.groupby(["a"]).idxmax().sort_index() ) self.assert_eq( pdf.groupby(["a"]).idxmax(skipna=False).sort_index(), kdf.groupby(["a"]).idxmax(skipna=False).sort_index(), ) self.assert_eq( pdf.groupby(["a"])["b"].idxmax().sort_index(), kdf.groupby(["a"])["b"].idxmax().sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a).idxmax().sort_index(), kdf.b.rename().groupby(kdf.a).idxmax().sort_index(), ) self.assert_eq( pdf.b.groupby(pdf.a.rename()).idxmax().sort_index(), kdf.b.groupby(kdf.a.rename()).idxmax().sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a.rename()).idxmax().sort_index(), kdf.b.rename().groupby(kdf.a.rename()).idxmax().sort_index(), ) with self.assertRaisesRegex(ValueError, "idxmax only support one-level index now"): kdf.set_index(["a", "b"]).groupby(["c"]).idxmax() # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( pdf.groupby(("x", "a")).idxmax().sort_index(), kdf.groupby(("x", "a")).idxmax().sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).idxmax(skipna=False).sort_index(), kdf.groupby(("x", "a")).idxmax(skipna=False).sort_index(), ) def test_idxmin(self): pdf = pd.DataFrame( {"a": [1, 1, 2, 2, 3] * 3, "b": [1, 2, 3, 4, 5] * 3, "c": [5, 4, 3, 2, 1] * 3} ) kdf = ks.from_pandas(pdf) self.assert_eq( pdf.groupby(["a"]).idxmin().sort_index(), kdf.groupby(["a"]).idxmin().sort_index() ) self.assert_eq( pdf.groupby(["a"]).idxmin(skipna=False).sort_index(), kdf.groupby(["a"]).idxmin(skipna=False).sort_index(), ) self.assert_eq( pdf.groupby(["a"])["b"].idxmin().sort_index(), kdf.groupby(["a"])["b"].idxmin().sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a).idxmin().sort_index(), kdf.b.rename().groupby(kdf.a).idxmin().sort_index(), ) self.assert_eq( pdf.b.groupby(pdf.a.rename()).idxmin().sort_index(), kdf.b.groupby(kdf.a.rename()).idxmin().sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a.rename()).idxmin().sort_index(), kdf.b.rename().groupby(kdf.a.rename()).idxmin().sort_index(), ) with self.assertRaisesRegex(ValueError, "idxmin only support one-level index now"): kdf.set_index(["a", "b"]).groupby(["c"]).idxmin() # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( pdf.groupby(("x", "a")).idxmin().sort_index(), kdf.groupby(("x", "a")).idxmin().sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).idxmin(skipna=False).sort_index(), kdf.groupby(("x", "a")).idxmin(skipna=False).sort_index(), ) def test_head(self): pdf = pd.DataFrame( { "a": [1, 1, 1, 1, 2, 2, 2, 3, 3, 3] * 3, "b": [2, 3, 1, 4, 6, 9, 8, 10, 7, 5] * 3, "c": [3, 5, 2, 5, 1, 2, 6, 4, 3, 6] * 3, }, index=np.random.rand(10 * 3), ) kdf = ks.from_pandas(pdf) self.assert_eq(pdf.groupby("a").head(2).sort_index(), kdf.groupby("a").head(2).sort_index()) self.assert_eq( pdf.groupby("a").head(-2).sort_index(), kdf.groupby("a").head(-2).sort_index() ) self.assert_eq( pdf.groupby("a").head(100000).sort_index(), kdf.groupby("a").head(100000).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(2).sort_index(), kdf.groupby("a")["b"].head(2).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(-2).sort_index(), kdf.groupby("a")["b"].head(-2).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(100000).sort_index(), kdf.groupby("a")["b"].head(100000).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].head(2).sort_index(), kdf.groupby("a")[["b"]].head(2).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].head(-2).sort_index(), kdf.groupby("a")[["b"]].head(-2).sort_index(), ) self.assert_eq( pdf.groupby("a")[["b"]].head(100000).sort_index(), kdf.groupby("a")[["b"]].head(100000).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2).head(2).sort_index(), kdf.groupby(kdf.a // 2).head(2).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2)["b"].head(2).sort_index(), kdf.groupby(kdf.a // 2)["b"].head(2).sort_index(), ) self.assert_eq( pdf.groupby(pdf.a // 2)[["b"]].head(2).sort_index(), kdf.groupby(kdf.a // 2)[["b"]].head(2).sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a).head(2).sort_index(), kdf.b.rename().groupby(kdf.a).head(2).sort_index(), ) self.assert_eq( pdf.b.groupby(pdf.a.rename()).head(2).sort_index(), kdf.b.groupby(kdf.a.rename()).head(2).sort_index(), ) self.assert_eq( pdf.b.rename().groupby(pdf.a.rename()).head(2).sort_index(), kdf.b.rename().groupby(kdf.a.rename()).head(2).sort_index(), ) # multi-index midx = pd.MultiIndex( [["x", "y"], ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"]], [[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]], ) pdf = pd.DataFrame( { "a": [1, 1, 1, 1, 2, 2, 2, 3, 3, 3], "b": [2, 3, 1, 4, 6, 9, 8, 10, 7, 5], "c": [3, 5, 2, 5, 1, 2, 6, 4, 3, 6], }, columns=["a", "b", "c"], index=midx, ) kdf = ks.from_pandas(pdf) self.assert_eq(pdf.groupby("a").head(2).sort_index(), kdf.groupby("a").head(2).sort_index()) self.assert_eq( pdf.groupby("a").head(-2).sort_index(), kdf.groupby("a").head(-2).sort_index() ) self.assert_eq( pdf.groupby("a").head(100000).sort_index(), kdf.groupby("a").head(100000).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(2).sort_index(), kdf.groupby("a")["b"].head(2).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(-2).sort_index(), kdf.groupby("a")["b"].head(-2).sort_index() ) self.assert_eq( pdf.groupby("a")["b"].head(100000).sort_index(), kdf.groupby("a")["b"].head(100000).sort_index(), ) # multi-index columns columns = pd.MultiIndex.from_tuples([("x", "a"), ("x", "b"), ("y", "c")]) pdf.columns = columns kdf.columns = columns self.assert_eq( pdf.groupby(("x", "a")).head(2).sort_index(), kdf.groupby(("x", "a")).head(2).sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).head(-2).sort_index(), kdf.groupby(("x", "a")).head(-2).sort_index(), ) self.assert_eq( pdf.groupby(("x", "a")).head(100000).sort_index(), kdf.groupby(("x", "a")).head(100000).sort_index(), ) def test_missing(self): kdf = ks.DataFrame({"a": [1, 2, 3, 4, 5, 6, 7, 8, 9]}) # DataFrameGroupBy functions missing_functions = inspect.getmembers( MissingPandasLikeDataFrameGroupBy, inspect.isfunction ) unsupported_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "unsupported_function" ] for name in unsupported_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*GroupBy.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(kdf.groupby("a"), name)() deprecated_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "deprecated_function" ] for name in deprecated_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*GroupBy.*{}.*is deprecated".format(name) ): getattr(kdf.groupby("a"), name)() # SeriesGroupBy functions missing_functions = inspect.getmembers(MissingPandasLikeSeriesGroupBy, inspect.isfunction) unsupported_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "unsupported_function" ] for name in unsupported_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*GroupBy.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(kdf.a.groupby(kdf.a), name)() deprecated_functions = [ name for (name, type_) in missing_functions if type_.__name__ == "deprecated_function" ] for name in deprecated_functions: with self.assertRaisesRegex( PandasNotImplementedError, "method.*GroupBy.*{}.*is deprecated".format(name) ): getattr(kdf.a.groupby(kdf.a), name)() # DataFrameGroupBy properties missing_properties = inspect.getmembers( MissingPandasLikeDataFrameGroupBy, lambda o: isinstance(o, property) ) unsupported_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "unsupported_property" ] for name in unsupported_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*GroupBy.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(kdf.groupby("a"), name) deprecated_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "deprecated_property" ] for name in deprecated_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*GroupBy.*{}.*is deprecated".format(name) ): getattr(kdf.groupby("a"), name) # SeriesGroupBy properties missing_properties = inspect.getmembers( MissingPandasLikeSeriesGroupBy, lambda o: isinstance(o, property) ) unsupported_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "unsupported_property" ] for name in unsupported_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*GroupBy.*{}.*not implemented( yet\\.|\\. .+)".format(name), ): getattr(kdf.a.groupby(kdf.a), name) deprecated_properties = [ name for (name, type_) in missing_properties if type_.fget.__name__ == "deprecated_property" ] for name in deprecated_properties: with self.assertRaisesRegex( PandasNotImplementedError, "property.*GroupBy.*{}.*is deprecated".format(name) ): getattr(kdf.a.groupby(kdf.a), name) @staticmethod def test_is_multi_agg_with_relabel(): assert is_multi_agg_with_relabel(a="max") is False assert is_multi_agg_with_relabel(a_min=("a", "max"), a_max=("a", "min")) is True def test_get_group(self): pdf = pd.DataFrame( [ ("falcon", "bird", 389.0), ("parrot", "bird", 24.0), ("lion", "mammal", 80.5), ("monkey", "mammal", np.nan), ], columns=["name", "class", "max_speed"], index=[0, 2, 3, 1], ) pdf.columns.name = "Koalas" kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby("class").get_group("bird"), pdf.groupby("class").get_group("bird"), ) self.assert_eq( kdf.groupby("class")["name"].get_group("mammal"), pdf.groupby("class")["name"].get_group("mammal"), ) self.assert_eq( kdf.groupby("class")[["name"]].get_group("mammal"), pdf.groupby("class")[["name"]].get_group("mammal"), ) self.assert_eq( kdf.groupby(["class", "name"]).get_group(("mammal", "lion")), pdf.groupby(["class", "name"]).get_group(("mammal", "lion")), ) self.assert_eq( kdf.groupby(["class", "name"])["max_speed"].get_group(("mammal", "lion")), pdf.groupby(["class", "name"])["max_speed"].get_group(("mammal", "lion")), ) self.assert_eq( kdf.groupby(["class", "name"])[["max_speed"]].get_group(("mammal", "lion")), pdf.groupby(["class", "name"])[["max_speed"]].get_group(("mammal", "lion")), ) self.assert_eq( (kdf.max_speed + 1).groupby(kdf["class"]).get_group("mammal"), (pdf.max_speed + 1).groupby(pdf["class"]).get_group("mammal"), ) self.assert_eq( kdf.groupby("max_speed").get_group(80.5), pdf.groupby("max_speed").get_group(80.5), ) self.assertRaises(KeyError, lambda: kdf.groupby("class").get_group("fish")) self.assertRaises(TypeError, lambda: kdf.groupby("class").get_group(["bird", "mammal"])) self.assertRaises(KeyError, lambda: kdf.groupby("class")["name"].get_group("fish")) self.assertRaises( TypeError, lambda: kdf.groupby("class")["name"].get_group(["bird", "mammal"]) ) self.assertRaises( KeyError, lambda: kdf.groupby(["class", "name"]).get_group(("lion", "mammal")) ) self.assertRaises(ValueError, lambda: kdf.groupby(["class", "name"]).get_group(("lion",))) self.assertRaises(ValueError, lambda: kdf.groupby(["class", "name"]).get_group(("mammal",))) self.assertRaises(ValueError, lambda: kdf.groupby(["class", "name"]).get_group("mammal")) # MultiIndex columns pdf.columns = pd.MultiIndex.from_tuples([("A", "name"), ("B", "class"), ("C", "max_speed")]) pdf.columns.names = ["Hello", "Koalas"] kdf = ks.from_pandas(pdf) self.assert_eq( kdf.groupby(("B", "class")).get_group("bird"), pdf.groupby(("B", "class")).get_group("bird"), ) self.assert_eq( kdf.groupby(("B", "class"))[[("A", "name")]].get_group("mammal"), pdf.groupby(("B", "class"))[[("A", "name")]].get_group("mammal"), ) self.assert_eq( kdf.groupby([("B", "class"), ("A", "name")]).get_group(("mammal", "lion")), pdf.groupby([("B", "class"), ("A", "name")]).get_group(("mammal", "lion")), ) self.assert_eq( kdf.groupby([("B", "class"), ("A", "name")])[[("C", "max_speed")]].get_group( ("mammal", "lion") ), pdf.groupby([("B", "class"), ("A", "name")])[[("C", "max_speed")]].get_group( ("mammal", "lion") ), ) self.assert_eq( (kdf[("C", "max_speed")] + 1).groupby(kdf[("B", "class")]).get_group("mammal"), (pdf[("C", "max_speed")] + 1).groupby(pdf[("B", "class")]).get_group("mammal"), ) self.assert_eq( kdf.groupby(("C", "max_speed")).get_group(80.5), pdf.groupby(("C", "max_speed")).get_group(80.5), ) self.assertRaises(KeyError, lambda: kdf.groupby(("B", "class")).get_group("fish")) self.assertRaises( TypeError, lambda: kdf.groupby(("B", "class")).get_group(["bird", "mammal"]) ) self.assertRaises( KeyError, lambda: kdf.groupby(("B", "class"))[("A", "name")].get_group("fish") ) self.assertRaises( KeyError, lambda: kdf.groupby([("B", "class"), ("A", "name")]).get_group(("lion", "mammal")), ) self.assertRaises( ValueError, lambda: kdf.groupby([("B", "class"), ("A", "name")]).get_group(("lion",)), ) self.assertRaises( ValueError, lambda: kdf.groupby([("B", "class"), ("A", "name")]).get_group(("mammal",)) ) self.assertRaises( ValueError, lambda: kdf.groupby([("B", "class"), ("A", "name")]).get_group("mammal") )

41.585599

100

0.479085

794af019c7a36bc1aedf7b78af40e3210237544e

2,558

py

Python

python_modules/dagster/dagster/core/storage/output_manager.py

ericct/dagster

dd2c9f05751e1bae212a30dbc54381167a14f6c5

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/core/storage/output_manager.py

ericct/dagster

dd2c9f05751e1bae212a30dbc54381167a14f6c5

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/core/storage/output_manager.py

ericct/dagster

dd2c9f05751e1bae212a30dbc54381167a14f6c5

[ "Apache-2.0" ]

null

from abc import ABC, abstractmethod, abstractproperty from dagster import check from dagster.core.definitions.definition_config_schema import ( convert_user_facing_definition_config_schema, ) from dagster.core.definitions.resource import ResourceDefinition class IOutputManagerDefinition: @abstractproperty def output_config_schema(self): """The schema for per-output configuration for outputs that are managed by this manager""" class OutputManagerDefinition(ResourceDefinition, IOutputManagerDefinition): """Definition of an output manager resource. An OutputManagerDefinition is a :py:class:`ResourceDefinition` whose resource_fn returns an :py:class:`OutputManager`. OutputManagers are used to handle the outputs of solids. """ def __init__( self, resource_fn=None, config_schema=None, description=None, output_config_schema=None, required_resource_keys=None, version=None, ): self._output_config_schema = convert_user_facing_definition_config_schema( output_config_schema ) super(OutputManagerDefinition, self).__init__( resource_fn=resource_fn, config_schema=config_schema, description=description, required_resource_keys=required_resource_keys, version=version, ) @property def output_config_schema(self): return self._output_config_schema def copy_for_configured(self, name, description, config_schema, _): check.invariant(name is None, "ResourceDefintions do not have names") return OutputManagerDefinition( config_schema=config_schema, description=description or self.description, resource_fn=self.resource_fn, required_resource_keys=self.required_resource_keys, output_config_schema=self.output_config_schema, ) class OutputManager(ABC): """Base class for user-provided output managers. OutputManagers are used to handle the outputs of solids. The easiest way to define an OutputManager is with the :py:function:`output_manager` decorator. """ @abstractmethod def handle_output(self, context, obj): """Handles an output produced by a solid. Usually, this means materializing it to persistent storage. Args: context (OutputContext): The context of the step output that produces this object. obj (Any): The data object to be handled. """

34.106667

100

0.702893

794af0436dd7ce6ca83a21bb8970cd7e6f2ca1e4

19,542

py

Python

boxes/secnotes/psexec.py

jasonperhaps/HTB

85c2b6ea551b8f20b72dad0f1941278e10be015d

[ "MIT" ]

null

boxes/secnotes/psexec.py

jasonperhaps/HTB

85c2b6ea551b8f20b72dad0f1941278e10be015d

[ "MIT" ]

null

boxes/secnotes/psexec.py

jasonperhaps/HTB

85c2b6ea551b8f20b72dad0f1941278e10be015d

[ "MIT" ]

null

#!/usr/bin/env python # SECUREAUTH LABS. Copyright 2018 SecureAuth Corporation. All rights reserved. # # This software is provided under under a slightly modified version # of the Apache Software License. See the accompanying LICENSE file # for more information. # # PSEXEC like functionality example using RemComSvc (https://github.com/kavika13/RemCom) # # Author: # beto (@agsolino) # # Reference for: # DCE/RPC and SMB. import sys import os import cmd import logging from threading import Thread, Lock import argparse import random import string import time from impacket.examples import logger from impacket import version, smb from impacket.smbconnection import SMBConnection from impacket.dcerpc.v5 import transport from impacket.structure import Structure from impacket.examples import remcomsvc, serviceinstall class RemComMessage(Structure): structure = ( ('Command','4096s=""'), ('WorkingDir','260s=""'), ('Priority','<L=0x20'), ('ProcessID','<L=0x01'), ('Machine','260s=""'), ('NoWait','<L=0'), ) class RemComResponse(Structure): structure = ( ('ErrorCode','<L=0'), ('ReturnCode','<L=0'), ) RemComSTDOUT = "RemCom_stdout" RemComSTDIN = "RemCom_stdin" RemComSTDERR = "RemCom_stderr" lock = Lock() class PSEXEC: def __init__(self, command, path, exeFile, copyFile, port=445, username='', password='', domain='', hashes=None, aesKey=None, doKerberos=False, kdcHost=None, serviceName=None): self.__username = username self.__password = password self.__port = port self.__command = command self.__path = path self.__domain = domain self.__lmhash = '' self.__nthash = '' self.__aesKey = aesKey self.__exeFile = exeFile self.__copyFile = copyFile self.__doKerberos = doKerberos self.__kdcHost = kdcHost self.__serviceName = serviceName if hashes is not None: self.__lmhash, self.__nthash = hashes.split(':') def run(self, remoteName, remoteHost): stringbinding = 'ncacn_np:%s[\pipe\svcctl]' % remoteName logging.debug('StringBinding %s'%stringbinding) rpctransport = transport.DCERPCTransportFactory(stringbinding) rpctransport.set_dport(self.__port) rpctransport.setRemoteHost(remoteHost) if hasattr(rpctransport, 'set_credentials'): # This method exists only for selected protocol sequences. rpctransport.set_credentials(self.__username, self.__password, self.__domain, self.__lmhash, self.__nthash, self.__aesKey) rpctransport.set_kerberos(self.__doKerberos, self.__kdcHost) self.doStuff(rpctransport) def openPipe(self, s, tid, pipe, accessMask): pipeReady = False tries = 50 while pipeReady is False and tries > 0: try: s.waitNamedPipe(tid,pipe) pipeReady = True except: tries -= 1 time.sleep(2) pass if tries == 0: raise Exception('Pipe not ready, aborting') fid = s.openFile(tid,pipe,accessMask, creationOption = 0x40, fileAttributes = 0x80) return fid def doStuff(self, rpctransport): dce = rpctransport.get_dce_rpc() try: dce.connect() except Exception, e: if logging.getLogger().level == logging.DEBUG: import traceback traceback.print_exc() logging.critical(str(e)) sys.exit(1) global dialect dialect = rpctransport.get_smb_connection().getDialect() try: unInstalled = False s = rpctransport.get_smb_connection() # We don't wanna deal with timeouts from now on. s.setTimeout(100000) if self.__exeFile is None: installService = serviceinstall.ServiceInstall(rpctransport.get_smb_connection(), remcomsvc.RemComSvc(), self.__serviceName) else: try: f = open(self.__exeFile) except Exception, e: logging.critical(str(e)) sys.exit(1) installService = serviceinstall.ServiceInstall(rpctransport.get_smb_connection(), f) if installService.install() is False: return if self.__exeFile is not None: f.close() # Check if we need to copy a file for execution if self.__copyFile is not None: installService.copy_file(self.__copyFile, installService.getShare(), os.path.basename(self.__copyFile)) # And we change the command to be executed to this filename self.__command = os.path.basename(self.__copyFile) + ' ' + self.__command tid = s.connectTree('IPC$') fid_main = self.openPipe(s,tid,'\RemCom_communicaton',0x12019f) packet = RemComMessage() pid = os.getpid() packet['Machine'] = ''.join([random.choice(string.letters) for _ in range(4)]) if self.__path is not None: packet['WorkingDir'] = self.__path packet['Command'] = self.__command packet['ProcessID'] = pid s.writeNamedPipe(tid, fid_main, str(packet)) # Here we'll store the command we type so we don't print it back ;) # ( I know.. globals are nasty :P ) global LastDataSent LastDataSent = '' # Create the pipes threads stdin_pipe = RemoteStdInPipe(rpctransport, '\%s%s%d' % (RemComSTDIN, packet['Machine'], packet['ProcessID']), smb.FILE_WRITE_DATA | smb.FILE_APPEND_DATA, installService.getShare()) stdin_pipe.start() stdout_pipe = RemoteStdOutPipe(rpctransport, '\%s%s%d' % (RemComSTDOUT, packet['Machine'], packet['ProcessID']), smb.FILE_READ_DATA) stdout_pipe.start() stderr_pipe = RemoteStdErrPipe(rpctransport, '\%s%s%d' % (RemComSTDERR, packet['Machine'], packet['ProcessID']), smb.FILE_READ_DATA) stderr_pipe.start() # And we stay here till the end ans = s.readNamedPipe(tid,fid_main,8) if len(ans): retCode = RemComResponse(ans) logging.info("Process %s finished with ErrorCode: %d, ReturnCode: %d" % ( self.__command, retCode['ErrorCode'], retCode['ReturnCode'])) installService.uninstall() if self.__copyFile is not None: # We copied a file for execution, let's remove it s.deleteFile(installService.getShare(), os.path.basename(self.__copyFile)) unInstalled = True sys.exit(retCode['ErrorCode']) except SystemExit: raise except Exception as e: if logging.getLogger().level == logging.DEBUG: import traceback traceback.print_exc() logging.debug(str(e)) if unInstalled is False: installService.uninstall() if self.__copyFile is not None: s.deleteFile(installService.getShare(), os.path.basename(self.__copyFile)) sys.stdout.flush() sys.exit(1) class Pipes(Thread): def __init__(self, transport, pipe, permissions, share=None): Thread.__init__(self) self.server = 0 self.transport = transport self.credentials = transport.get_credentials() self.tid = 0 self.fid = 0 self.share = share self.port = transport.get_dport() self.pipe = pipe self.permissions = permissions self.daemon = True def connectPipe(self): try: lock.acquire() global dialect #self.server = SMBConnection('*SMBSERVER', self.transport.get_smb_connection().getRemoteHost(), sess_port = self.port, preferredDialect = SMB_DIALECT) self.server = SMBConnection(self.transport.get_smb_connection().getRemoteName(), self.transport.get_smb_connection().getRemoteHost(), sess_port=self.port, preferredDialect=dialect) user, passwd, domain, lm, nt, aesKey, TGT, TGS = self.credentials if self.transport.get_kerberos() is True: self.server.kerberosLogin(user, passwd, domain, lm, nt, aesKey, kdcHost=self.transport.get_kdcHost(), TGT=TGT, TGS=TGS) else: self.server.login(user, passwd, domain, lm, nt) lock.release() self.tid = self.server.connectTree('IPC$') self.server.waitNamedPipe(self.tid, self.pipe) self.fid = self.server.openFile(self.tid,self.pipe,self.permissions, creationOption = 0x40, fileAttributes = 0x80) self.server.setTimeout(1000000) except: if logging.getLogger().level == logging.DEBUG: import traceback traceback.print_exc() logging.error("Something wen't wrong connecting the pipes(%s), try again" % self.__class__) class RemoteStdOutPipe(Pipes): def __init__(self, transport, pipe, permisssions): Pipes.__init__(self, transport, pipe, permisssions) def run(self): self.connectPipe() while True: try: ans = self.server.readFile(self.tid,self.fid, 0, 1024) except: pass else: try: global LastDataSent if ans != LastDataSent: sys.stdout.write(ans.decode('cp437')) sys.stdout.flush() else: # Don't echo what I sent, and clear it up LastDataSent = '' # Just in case this got out of sync, i'm cleaning it up if there are more than 10 chars, # it will give false positives tho.. we should find a better way to handle this. if LastDataSent > 10: LastDataSent = '' except: pass class RemoteStdErrPipe(Pipes): def __init__(self, transport, pipe, permisssions): Pipes.__init__(self, transport, pipe, permisssions) def run(self): self.connectPipe() while True: try: ans = self.server.readFile(self.tid,self.fid, 0, 1024) except: pass else: try: sys.stderr.write(str(ans)) sys.stderr.flush() except: pass class RemoteShell(cmd.Cmd): def __init__(self, server, port, credentials, tid, fid, share, transport): cmd.Cmd.__init__(self, False) self.prompt = '\x08' self.server = server self.transferClient = None self.tid = tid self.fid = fid self.credentials = credentials self.share = share self.port = port self.transport = transport self.intro = '[!] Press help for extra shell commands' def connect_transferClient(self): #self.transferClient = SMBConnection('*SMBSERVER', self.server.getRemoteHost(), sess_port = self.port, preferredDialect = SMB_DIALECT) self.transferClient = SMBConnection('*SMBSERVER', self.server.getRemoteHost(), sess_port=self.port, preferredDialect=dialect) user, passwd, domain, lm, nt, aesKey, TGT, TGS = self.credentials if self.transport.get_kerberos() is True: self.transferClient.kerberosLogin(user, passwd, domain, lm, nt, aesKey, kdcHost=self.transport.get_kdcHost(), TGT=TGT, TGS=TGS) else: self.transferClient.login(user, passwd, domain, lm, nt) def do_help(self, line): print """ lcd {path} - changes the current local directory to {path} exit - terminates the server process (and this session) put {src_file, dst_path} - uploads a local file to the dst_path RELATIVE to the connected share (%s) get {file} - downloads pathname RELATIVE to the connected share (%s) to the current local dir ! {cmd} - executes a local shell cmd """ % (self.share, self.share) self.send_data('\r\n', False) def do_shell(self, s): os.system(s) self.send_data('\r\n') def do_get(self, src_path): try: if self.transferClient is None: self.connect_transferClient() import ntpath filename = ntpath.basename(src_path) fh = open(filename,'wb') logging.info("Downloading %s\%s" % (self.share, src_path)) self.transferClient.getFile(self.share, src_path, fh.write) fh.close() except Exception, e: logging.critical(str(e)) pass self.send_data('\r\n') def do_put(self, s): try: if self.transferClient is None: self.connect_transferClient() params = s.split(' ') if len(params) > 1: src_path = params[0] dst_path = params[1] elif len(params) == 1: src_path = params[0] dst_path = '/' src_file = os.path.basename(src_path) fh = open(src_path, 'rb') f = dst_path + '/' + src_file pathname = string.replace(f,'/','\\') logging.info("Uploading %s to %s\%s" % (src_file, self.share, dst_path)) self.transferClient.putFile(self.share, pathname.decode(sys.stdin.encoding), fh.read) fh.close() except Exception, e: logging.error(str(e)) pass self.send_data('\r\n') def do_lcd(self, s): if s == '': print os.getcwd() else: os.chdir(s) self.send_data('\r\n') def emptyline(self): self.send_data('\r\n') return def default(self, line): self.send_data(line.decode(sys.stdin.encoding).encode('cp437')+'\r\n') def send_data(self, data, hideOutput = True): if hideOutput is True: global LastDataSent LastDataSent = data else: LastDataSent = '' self.server.writeFile(self.tid, self.fid, data) class RemoteStdInPipe(Pipes): def __init__(self, transport, pipe, permisssions, share=None): self.shell = None Pipes.__init__(self, transport, pipe, permisssions, share) def run(self): self.connectPipe() self.shell = RemoteShell(self.server, self.port, self.credentials, self.tid, self.fid, self.share, self.transport) self.shell.cmdloop() # Process command-line arguments. if __name__ == '__main__': # Init the example's logger theme logger.init() print version.BANNER parser = argparse.ArgumentParser(add_help = True, description = "PSEXEC like functionality example using RemComSvc.") parser.add_argument('target', action='store', help='[[domain/]username[:password]@]<targetName or address>') parser.add_argument('command', nargs='*', default = ' ', help='command (or arguments if -c is used) to execute at ' 'the target (w/o path) - (default:cmd.exe)') parser.add_argument('-c', action='store',metavar = "pathname", help='copy the filename for later execution, ' 'arguments are passed in the command option') parser.add_argument('-path', action='store', help='path of the command to execute') parser.add_argument('-file', action='store', help="alternative RemCom binary (be sure it doesn't require CRT)") parser.add_argument('-debug', action='store_true', help='Turn DEBUG output ON') group = parser.add_argument_group('authentication') group.add_argument('-hashes', action="store", metavar = "LMHASH:NTHASH", help='NTLM hashes, format is LMHASH:NTHASH') group.add_argument('-no-pass', action="store_true", help='don\'t ask for password (useful for -k)') group.add_argument('-k', action="store_true", help='Use Kerberos authentication. Grabs credentials from ccache file ' '(KRB5CCNAME) based on target parameters. If valid credentials cannot be found, it will use the ' 'ones specified in the command line') group.add_argument('-aesKey', action="store", metavar = "hex key", help='AES key to use for Kerberos Authentication ' '(128 or 256 bits)') group = parser.add_argument_group('connection') group.add_argument('-dc-ip', action='store', metavar="ip address", help='IP Address of the domain controller. If omitted it will use the domain part (FQDN) specified in ' 'the target parameter') group.add_argument('-target-ip', action='store', metavar="ip address", help='IP Address of the target machine. If omitted it will use whatever was specified as target. ' 'This is useful when target is the NetBIOS name and you cannot resolve it') group.add_argument('-port', choices=['139', '445'], nargs='?', default='445', metavar="destination port", help='Destination port to connect to SMB Server') group.add_argument('-service-name', action='store', metavar="service name", default = '', help='This will be the name of the service') if len(sys.argv)==1: parser.print_help() sys.exit(1) options = parser.parse_args() if options.debug is True: logging.getLogger().setLevel(logging.DEBUG) else: logging.getLogger().setLevel(logging.INFO) import re domain, username, password, remoteName = re.compile('(?:(?:([^/@:]*)/)?([^@:]*)(?::([^@]*))?@)?(.*)').match( options.target).groups('') #In case the password contains '@' if '@' in remoteName: password = password + '@' + remoteName.rpartition('@')[0] remoteName = remoteName.rpartition('@')[2] if domain is None: domain = '' if options.target_ip is None: options.target_ip = remoteName if password == '' and username != '' and options.hashes is None and options.no_pass is False and options.aesKey is None: from getpass import getpass password = getpass("Password:") if options.aesKey is not None: options.k = True command = ' '.join(options.command) if command == ' ': command = 'cmd.exe' executer = PSEXEC(command, options.path, options.file, options.c, int(options.port), username, password, domain, options.hashes, options.aesKey, options.k, options.dc_ip, options.service_name) executer.run(remoteName, options.target_ip)

39.638945

162

0.579623

794af0b36086db972b14e2b4ada89a0b64e84ce0

3,100

py

Python

source/scripts/python/frontend/source/frontend/settings.py

Tabzz98/core

02ddfe5e0f7ecaa833a8c36dbc059a968479d8ce

[ "Apache-2.0" ]

1

2022-02-08T17:56:53.000Z

source/scripts/python/frontend/source/frontend/settings.py

Tabzz98/core

02ddfe5e0f7ecaa833a8c36dbc059a968479d8ce

[ "Apache-2.0" ]

null

source/scripts/python/frontend/source/frontend/settings.py

Tabzz98/core

02ddfe5e0f7ecaa833a8c36dbc059a968479d8ce

[ "Apache-2.0" ]

null

""" Django settings for frontend project. Generated by 'django-admin startproject' using Django 1.10. For more information on this file, see https://docs.djangoproject.com/en/1.10/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.10/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.10/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '+rr0rprnfxfg_bi3f4mhn9=t)5kw2wzk7ya$0gj0b2jdz+*cz^' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'frontend.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'frontend.wsgi.application' # Database # https://docs.djangoproject.com/en/1.10/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.10/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.10/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.10/howto/static-files/ STATIC_URL = '/static/'

25.619835

91

0.699032

794af12c7d480e2d45fcc4cb8504bacffdbc7f2c

4,211

py

Python

tests/layers/graph/test_usage.py

FrostByte266/neupy

4b7127e5e4178b0cce023ba36542f5ad3f1d798c

[ "MIT" ]

801

2015-09-23T09:24:47.000Z

2022-03-29T19:19:03.000Z

tests/layers/graph/test_usage.py

FrostByte266/neupy

4b7127e5e4178b0cce023ba36542f5ad3f1d798c

[ "MIT" ]

277

2015-09-22T19:48:50.000Z

2022-03-11T23:25:32.000Z

tests/layers/graph/test_usage.py

FrostByte266/neupy

4b7127e5e4178b0cce023ba36542f5ad3f1d798c

[ "MIT" ]

194

2015-09-23T15:03:57.000Z

2022-03-31T13:54:46.000Z

import numpy as np from neupy import layers from neupy.utils import asfloat from base import BaseTestCase class UsageTestCase(BaseTestCase): def test_network_wrong_number_of_input_values(self): network = layers.join( layers.Input(2), layers.Relu(10), layers.Relu(1), ) input_value_1 = asfloat(np.random.random((10, 2))) input_value_2 = asfloat(np.random.random((10, 2))) with self.assertRaisesRegexp(ValueError, "but 2 inputs was provided"): network.output(input_value_1, input_value_2) def test_multi_outputs_propagation(self): network = layers.join( layers.Input(4), layers.parallel( layers.Linear(2), layers.Linear(3), layers.Linear(4), ) ) x = asfloat(np.random.random((7, 4))) out1, out2, out3 = self.eval(network.output(x)) self.assertEqual((7, 2), out1.shape) self.assertEqual((7, 3), out2.shape) self.assertEqual((7, 4), out3.shape) def test_multi_inputs_propagation(self): network = layers.join( layers.parallel( layers.Input(10, name='input-1'), layers.Input(4, name='input-2'), ), layers.Concatenate(), ) x1 = asfloat(np.random.random((3, 10))) x2 = asfloat(np.random.random((3, 4))) out1 = self.eval(network.output(x1, x2)) out2 = self.eval(network.output({'input-2': x2, 'input-1': x1})) self.assertEqual((3, 14), out1.shape) np.testing.assert_array_almost_equal(out1, out2) def test_different_input_types(self): input_layer = layers.Input(10, name='input') network = layers.join( input_layer, layers.Sigmoid(5), layers.Sigmoid(4), ) x_matrix = asfloat(np.random.random((3, 10))) out1 = self.eval(network.output(x_matrix)) self.assertEqual((3, 4), out1.shape) out2 = self.eval(network.output({input_layer: x_matrix})) np.testing.assert_array_almost_equal(out1, out2) out3 = self.eval(network.output({'input': x_matrix})) np.testing.assert_array_almost_equal(out2, out3) unknown_layer = layers.Input(5, name='unk') message = "The `unk` layer doesn't appear in the network" with self.assertRaisesRegexp(ValueError, message): network.output({unknown_layer: x_matrix}) def test_not_an_input_layer_exception(self): network = layers.join( layers.Input(10), layers.Sigmoid(2, name='sigmoid-2'), layers.Sigmoid(10), ) x_test = asfloat(np.ones((7, 5))) with self.assertRaisesRegexp(ValueError, "is not an input layer"): network.output({'sigmoid-2': x_test}) def test_if_layer_in_the_graph(self): network = layers.join( layers.Input(10), layers.Relu(2), ) final_layer = layers.Sigmoid(1) self.assertNotIn(final_layer, network) network_2 = layers.join(network, final_layer) self.assertIn(final_layer, network_2) def test_graph_length(self): network = layers.join( layers.Input(10), layers.Relu(3), ) self.assertEqual(2, len(network)) network_2 = layers.join( network, layers.parallel( layers.Relu(1), layers.Relu(2), ), ) self.assertEqual(2, len(network)) self.assertEqual(4, len(network_2)) def test_graph_predictions(self): network = layers.join( layers.Input(10), layers.Relu(5), layers.Relu(3), ) input = np.random.random((100, 10)) output = network.predict(input, verbose=False) self.assertEqual(output.shape, (100, 3)) output = network.predict(input, batch_size=10, verbose=False) self.assertEqual(output.shape, (100, 3)) with self.assertRaisesRegexp(TypeError, "Unknown arguments"): network.predict(input, batchsize=10)

31.425373

78

0.580622

794af1d47040ea5ff6943c00215c0fcc0668353b

2,902

py

Python

scripts/cross_f1.py

JD-AI-Research-NLP/RoR

201a7cc08c8b2911204d0dd966039fe99cce15a4

[ "Apache-2.0" ]

15

2021-11-18T10:10:32.000Z

2022-03-16T07:58:06.000Z

scripts/cross_f1.py

JD-AI-Research-NLP/RoR

201a7cc08c8b2911204d0dd966039fe99cce15a4

[ "Apache-2.0" ]

2

2021-11-30T12:15:49.000Z

2022-01-19T09:21:48.000Z

scripts/cross_f1.py

JD-AI-Research-NLP/RoR

201a7cc08c8b2911204d0dd966039fe99cce15a4

[ "Apache-2.0" ]

2

2021-12-03T01:27:17.000Z

2021-12-22T02:44:16.000Z

import json import numpy as np import re import string from collections import Counter import argparse def add_arguments(parser): parser.add_argument("--regional_answer", help="path to regional answer", required=True) parser.add_argument("--global_answer", help="path to global answer", required=True) parser.add_argument("--output_file", help="path to output file", required=True) def normalize_answer(s): """Lower text and remove punctuation, articles and extra whitespace.""" def remove_articles(text): return re.sub(r'\b(a|an|the)\b', ' ', text) def white_space_fix(text): return ' '.join(text.split()) def remove_punc(text): exclude = set(string.punctuation) return ''.join(ch for ch in text if ch not in exclude) def lower(text): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(s)))) def f1_score(prediction, ground_truth): prediction_tokens = normalize_answer(prediction).split() ground_truth_tokens = normalize_answer(ground_truth).split() common = Counter(prediction_tokens) & Counter(ground_truth_tokens) num_same = sum(common.values()) if num_same == 0: return 0 precision = 1.0 * num_same / len(prediction_tokens) recall = 1.0 * num_same / len(ground_truth_tokens) f1 = (2 * precision * recall) / (precision + recall) return f1 def cross_f1_max(predictions): cross_f1_max = [] for i in range(len(predictions)): index = list(range(len(predictions))) index.pop(i) cross_f1_max.append(max([f1_score(predictions[i], predictions[j]) for j in index])) return cross_f1_max def cross_f1_mean(predictions): cross_f1_mean = [] for i in range(len(predictions)): index = list(range(len(predictions))) index.pop(i) cross_f1_mean.append(sum([f1_score(predictions[i], predictions[j]) for j in index])/len(index)) return cross_f1_mean if __name__ == "__main__": parser = argparse.ArgumentParser() add_arguments(parser) args = parser.parse_args() with open(args.regional_answer,'r') as f: regional_answer = json.load(f) with open(args.global_answer,'r') as f: global_answer = json.load(f) cross_answer = {} delta = 0.1 gamma = 0.8 for (qid, answer),(_, g_answer) in zip(regional_answer.items(),global_answer.items()): score = [i['score']*gamma for i in answer][:10] text = [i['text'] for i in answer][:10] score1 = [i['score']*(1-gamma) for i in g_answer][:10] text1 = [i['text'] for i in g_answer][:10] score = score + score1 text = text + text1 cross_f1 = cross_f1_mean(text) score_list = [delta*i + (1-delta) *j for i,j in zip(score,cross_f1)] max_idx = np.argmax(score_list) cross_answer[qid] = text[max_idx] with open(args.output_file,'w') as f: json.dump(cross_answer,f)

34.963855

103

0.670917

794af2056466bf6f25fe5d53468c520fed4c79eb

22,921

py

Python

google/estimators/importance_sampling_ci.py

SnowflyLXF/FedDICE

a63a3233037e37ae27d6c130f37ffc4b92190d5e

[ "Apache-2.0" ]

null

google/estimators/importance_sampling_ci.py

SnowflyLXF/FedDICE

a63a3233037e37ae27d6c130f37ffc4b92190d5e

[ "Apache-2.0" ]

null

google/estimators/importance_sampling_ci.py

SnowflyLXF/FedDICE

a63a3233037e37ae27d6c130f37ffc4b92190d5e

[ "Apache-2.0" ]

null

# 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 __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from scipy import stats as stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tf_agents.specs import tensor_spec from tf_agents.policies import tf_policy from tf_agents.utils import common as tfagents_common from typing import Any, Callable, Iterable, Optional, Sequence, Text, Tuple, Union import dice_rl.data.dataset as dataset_lib import dice_rl.utils.common as common_lib class ImportanceSamplingCI(object): """Approximate average reward of policy using importance sampling.""" def __init__(self, dataset_spec, policy_optimizer, policy_network, mode, ci_method, delta_tail, gamma: Union[float, tf.Tensor], reward_fn: Callable = None, clipping: Optional[float] = 2000., policy_regularizer: float = 0., q_network=None, q_optimizer=None, target_update_tau: Union[float, tf.Tensor] = 0.01, target_update_period: int = 1, num_samples: Optional[int] = None): """Initializes the importance sampling estimator. Args: dataset_spec: The spec of the dataset that will be given. policy_optimizer: The optimizer to use for learning policy. policy_network: The policy NN network. mode: Importance sampling estimator (e.g., "weighted-step-wise"). ci_method: Method for constructing confidence intervals (e.g., "CH" for Chernoff-Hoeffding). delta_tail: Total probability quantile threshold (will be halved in code for 2-tail) gamma: The discount factor to use. reward_fn: A function that takes in an EnvStep and returns the reward for that step. If not specified, defaults to just EnvStep.reward. clipping: Threshold for clipping IS factor. policy_regularizer: float on policy regularizer. q_network: A function that returns the values for each observation and action. If specified, the Q-values are learned and used for doubly-robust estimation. q_optimizer: TF optimizer for q_network. target_update_tau: Rate at which to set target network parameters. target_update_period: Rate at which to set target network parameters. num_samples: Number of samples to take from policy to estimate average next state value. If actions are discrete, this defaults to computing average explicitly. If actions are not discrete, this defaults to using a single sample. """ self._dataset_spec = dataset_spec self._policy_optimizer = policy_optimizer self._policy_network = policy_network if self._policy_network is not None: self._policy_network.create_variables() self._mode = mode self._ci_method = ci_method self._delta_tail = delta_tail self._gamma = gamma if reward_fn is None: reward_fn = lambda env_step: env_step.reward self._reward_fn = reward_fn self._clipping = clipping self._policy_regularizer = policy_regularizer self._q_network = q_network if self._q_network is not None: self._q_network.create_variables() self._target_network = self._q_network.copy(name='TargetQNetwork') self._target_network.create_variables() self._target_update_tau = target_update_tau self._target_update_period = target_update_period self._update_targets = self._get_target_updater( tau=self._target_update_tau, period=self._target_update_period) self._q_optimizer = q_optimizer self._initialize() self._num_samples = num_samples self._categorical_action = common_lib.is_categorical_spec(self._dataset_spec.action) if not self._categorical_action and self._num_samples is None: self._num_samples = 1 def _get_target_updater(self, tau=1.0, period=1): def update(): return tfagents_common.soft_variables_update( self._q_network.variables, self._target_network.variables, tau, tau_non_trainable=1.0) return tfagents_common.Periodically(update, period, 'update_targets') def _initialize(self): tfagents_common.soft_variables_update( self._q_network.variables, self._target_network.variables, tau=1.0) def _orthogonal_regularization(self, network): reg = 0 for layer in network.layers: if isinstance(layer, tf.keras.layers.Dense): prod = tf.matmul(tf.transpose(layer.kernel), layer.kernel) reg += tf.reduce_sum(tf.math.square(prod * (1 - tf.eye(prod.shape[0])))) return reg def _get_q_value(self, env_step): if self._q_network is None: return tf.zeros_like(env_step.reward) return self._q_network((env_step.observation, env_step.action))[0] def _get_v_value(self, env_step, policy): return self._get_average_value(self._q_network, env_step, policy) def _get_target_value(self, env_step, policy): return self._get_average_value(self._target_network, env_step, policy) def _get_average_value(self, network, env_step, policy): if self._q_network is None: return tf.zeros_like(env_step.reward) tfagents_step = dataset_lib.convert_to_tfagents_timestep(env_step) if self._categorical_action and self._num_samples is None: action_weights = policy.distribution( tfagents_step).action.probs_parameter() action_dtype = self._dataset_spec.action.dtype batch_size = tf.shape(action_weights)[0] num_actions = tf.shape(action_weights)[-1] actions = ( # Broadcast actions tf.ones([batch_size, 1], dtype=action_dtype) * tf.range(num_actions, dtype=action_dtype)[None, :]) else: batch_size = tf.shape(env_step.observation)[0] num_actions = self._num_samples action_weights = tf.ones([batch_size, num_actions]) / num_actions actions = tf.stack( [policy.action(tfagents_step).action for _ in range(num_actions)], axis=1) flat_actions = tf.reshape( actions, tf.concat([[batch_size * num_actions], tf.shape(actions)[2:]], axis=0)) flat_observations = tf.reshape( tf.tile(env_step.observation[:, None, ...], [1, num_actions] + [1] * len(env_step.observation.shape[1:])), tf.concat([[batch_size * num_actions], tf.shape(env_step.observation)[1:]], axis=0)) flat_values, _ = network((flat_observations, flat_actions)) values = tf.reshape( flat_values, tf.concat([[batch_size, num_actions], tf.shape(flat_values)[1:]], axis=0)) return tf.reduce_sum(values * action_weights, axis=1) def _get_log_prob(self, policy_network, env_step): # TODO(ofirnachum): env_step.action is shaped [B] but network's action_spec # is BoundedTensorSpec(shape=[1], ...); which leads network to use a # MVNDiag distribution here with event_shape=[1]. MVNDiag expects inputs of # shape [B, 1]. return policy_network(env_step.observation)[0].log_prob( env_step.action[..., tf.newaxis]) def clip_is_factor(self, is_factor): return tf.minimum(self._clipping, tf.maximum(-self._clipping, is_factor)) def clip_log_factor(self, log_factor): return tf.minimum(tf.math.log(self._clipping), tf.maximum(-tf.math.log(self._clipping), log_factor)) def get_is_weighted_reward_samples(self, dataset: dataset_lib.OffpolicyDataset, target_policy: tf_policy.TFPolicy, episode_limit: Optional[int] = None, eps: Optional[float] = 1e-8): """Get the IS weighted reweard samples.""" episodes, valid_steps = dataset.get_all_episodes(limit=episode_limit) total_num_steps_per_episode = tf.shape(valid_steps)[1] - 1 num_episodes = tf.shape(valid_steps)[0] num_samples = num_episodes * total_num_steps_per_episode init_env_step = tf.nest.map_structure( lambda t: t[:, 0, ...], episodes) env_step = tf.nest.map_structure( lambda t: tf.squeeze( tf.reshape(t[:, 0:total_num_steps_per_episode, ...], [num_samples, -1])), episodes) next_env_step = tf.nest.map_structure( lambda t: tf.squeeze( tf.reshape(t[:, 1:1 + total_num_steps_per_episode, ...], [num_samples, -1])), episodes) tfagents_env_step = dataset_lib.convert_to_tfagents_timestep(env_step) gamma_weights = tf.reshape( tf.pow(self._gamma, tf.cast(env_step.step_num, tf.float32)), [num_episodes, total_num_steps_per_episode]) rewards = (-self._get_q_value(env_step) + self._reward_fn(env_step) + self._gamma * next_env_step.discount * self._get_v_value(next_env_step, target_policy)) rewards = tf.reshape(rewards, [num_episodes, total_num_steps_per_episode]) init_values = self._get_v_value(init_env_step, target_policy) init_offset = (1 - self._gamma) * init_values target_log_probabilities = target_policy.distribution( tfagents_env_step).action.log_prob(env_step.action) if tf.rank(target_log_probabilities) > 1: target_log_probabilities = tf.reduce_sum(target_log_probabilities, -1) if self._policy_network is not None: baseline_policy_log_probability = self._get_log_prob( self._policy_network, env_step) if tf.rank(baseline_policy_log_probability) > 1: baseline_policy_log_probability = tf.reduce_sum( baseline_policy_log_probability, -1) policy_log_ratios = tf.reshape( tf.maximum(-1.0 / eps, target_log_probabilities - baseline_policy_log_probability), [num_episodes, total_num_steps_per_episode]) else: policy_log_ratios = tf.reshape( tf.maximum(-1.0 / eps, target_log_probabilities - env_step.get_log_probability()), [num_episodes, total_num_steps_per_episode]) valid_steps_in = valid_steps[:, 0:total_num_steps_per_episode] mask = tf.cast( tf.logical_and(valid_steps_in, episodes.discount[:, :-1] > 0.), tf.float32) masked_rewards = tf.where(mask > 0, rewards, tf.zeros_like(rewards)) clipped_policy_log_ratios = mask * self.clip_log_factor(policy_log_ratios) if self._mode in ['trajectory-wise', 'weighted-trajectory-wise']: trajectory_avg_rewards = tf.reduce_sum( masked_rewards * gamma_weights, axis=1) / tf.reduce_sum( gamma_weights, axis=1) trajectory_log_ratios = tf.reduce_sum(clipped_policy_log_ratios, axis=1) if self._mode == 'trajectory-wise': trajectory_avg_rewards *= tf.exp(trajectory_log_ratios) return init_offset + trajectory_avg_rewards else: offset = tf.reduce_max(trajectory_log_ratios) normalized_clipped_ratios = tf.exp(trajectory_log_ratios - offset) normalized_clipped_ratios /= tf.maximum( eps, tf.reduce_mean(normalized_clipped_ratios)) trajectory_avg_rewards *= normalized_clipped_ratios return init_offset + trajectory_avg_rewards elif self._mode in ['step-wise', 'weighted-step-wise']: trajectory_log_ratios = mask * tf.cumsum(policy_log_ratios, axis=1) if self._mode == 'step-wise': trajectory_avg_rewards = tf.reduce_sum( masked_rewards * gamma_weights * tf.exp(trajectory_log_ratios), axis=1) / tf.reduce_sum( gamma_weights, axis=1) return init_offset + trajectory_avg_rewards else: # Average over data, for each time step. offset = tf.reduce_max(trajectory_log_ratios, axis=0) # TODO: Handle mask. normalized_imp_weights = tf.exp(trajectory_log_ratios - offset) normalized_imp_weights /= tf.maximum( eps, tf.reduce_sum(mask * normalized_imp_weights, axis=0) / tf.maximum(eps, tf.reduce_sum(mask, axis=0)))[None, :] trajectory_avg_rewards = tf.reduce_sum( masked_rewards * gamma_weights * normalized_imp_weights, axis=1) / tf.reduce_sum( gamma_weights, axis=1) return init_offset + trajectory_avg_rewards else: ValueError('Estimator is not implemented!') def estimate_average_reward(self, dataset: dataset_lib.OffpolicyDataset, target_policy: tf_policy.TFPolicy, episode_limit: Optional[int] = None): is_weighted_reward_samples = self.get_is_weighted_reward_samples( dataset, target_policy, episode_limit) return tf.reduce_mean(is_weighted_reward_samples) def estimate_reward_ci(self, dataset: dataset_lib.OffpolicyDataset, target_policy: tf_policy.TFPolicy, episode_limit: Optional[int] = None, num_grid: Optional[int] = 100, eps: Optional[float] = 1e-6, num_bootstraps: Optional[int] = 10000, num_bootstrap_samples: Optional[int] = 10000): """Estimate the confidence interval of reward.""" is_weighted_reward_samples = self.get_is_weighted_reward_samples( dataset, target_policy, episode_limit) episodes, valid_steps = dataset.get_all_episodes(limit=episode_limit) num_episodes = tf.shape(valid_steps)[0] max_abs_reward = tf.reduce_max( tf.where(valid_steps, tf.abs(self._reward_fn(episodes)), 0.)) # mean estimate center = self.estimate_average_reward(dataset, target_policy) delta_tail_half = self._delta_tail / 2.0 num_episodes_float = tf.cast(num_episodes, tf.float32) if self._ci_method == 'CH': # Chernoff-Hoeffding width = max_abs_reward * tf.math.sqrt( tf.math.log(1.0 / delta_tail_half) / num_episodes_float) lb = center - width ub = center + width elif self._ci_method == 'BE': # Empirical Bernstein constant_term = 7 * max_abs_reward * tf.math.log( 2.0 / delta_tail_half) / (3 * (num_episodes_float - 1)) variance_term = tf.reduce_sum( tf.square(is_weighted_reward_samples - center)) variance_term *= tf.math.log(2.0 / delta_tail_half) / ( num_episodes_float - 1) width = constant_term + tf.math.sqrt(variance_term) / num_episodes_float lb = center - width ub = center + width elif self._ci_method == 'C-BE': # Clipped empirical Bernstein # need to learn c def compute_center_width(c_const): """Compute the center and width of CI.""" c_vec = c_const * tf.ones_like(is_weighted_reward_samples) c_is_weighted_reward_samples = tf.minimum(is_weighted_reward_samples, c_vec) / c_vec constant_term = 7 * num_episodes_float * tf.math.log( 2.0 / delta_tail_half) / (3 * (num_episodes_float - 1)) center = tf.reduce_sum(c_is_weighted_reward_samples) / tf.reduce_sum( 1.0 / c_vec) variance_term = tf.reduce_sum( tf.square(c_is_weighted_reward_samples - center)) variance_term *= tf.math.log(2.0 / delta_tail_half) / ( num_episodes_float - 1) width = (constant_term + tf.math.sqrt(variance_term)) / tf.reduce_sum( 1.0 / c_vec) return center, width def compute_bdd(c_const): center, width = compute_center_width(c_const) return center - width, center + width def compute_obj(c_const, obj='width'): center, width = compute_center_width(c_const) if obj == 'lb': return center - width elif obj == 'ub': # minimize ub return -(center + width) elif obj == 'width': return width elif obj == 'lb_ub': return -2 * width else: ValueError('Objective is not implemented') c_grid = tf.linspace(eps, max_abs_reward, num_grid) objs = tf.map_fn(compute_obj, c_grid, dtype=tf.float32) star_index = tf.argmax(objs) c_star = tf.gather(c_grid, star_index) lb, ub = compute_bdd(c_star) elif self._ci_method == 'TT': # Student-t test # Two-tailed confidence intervals t_statistic_quantile = stats.t.ppf(1 - delta_tail_half, num_episodes_float - 1) std_term = tf.math.sqrt( tf.reduce_sum(tf.square(is_weighted_reward_samples - center)) / (num_episodes_float - 1)) width = t_statistic_quantile * std_term / tf.math.sqrt(num_episodes_float) lb = center - width ub = center + width elif self._ci_method == 'BCa': # Bootstrap # see references # https://faculty.washington.edu/heagerty/Courses/b572/public/GregImholte-1.pdf # http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf gaussian_rv = tfp.distributions.Normal(loc=0, scale=1) def _compute_bootstrap_lb_ub(reward_samples): """Compute Efron's bootstrap lb.""" sample_mean = tf.reduce_mean(reward_samples) # Step 1, sample with replacement and compute subsampled mean uniform_log_prob = tf.tile( tf.expand_dims(tf.zeros(num_episodes), 0), [num_bootstraps, 1]) ind = tf.random.categorical(uniform_log_prob, num_bootstrap_samples) bootstrap_subsamples = tf.gather(reward_samples, ind) subsample_means = tf.reduce_mean(bootstrap_subsamples, axis=1) # Step 2, sort subsample means, compute y, z_0, and a sorted_subsample_means = tf.sort( subsample_means, axis=0, direction='ASCENDING') # bias factor z_0 = gaussian_rv.quantile( tf.reduce_sum( tf.cast( tf.greater(sample_mean, sorted_subsample_means), tf.float32)) / float(num_bootstraps)) # y is the leave-one-out, jackknife sample mean mask_matrix = tf.ones([num_episodes, num_episodes ]) - tf.eye(num_episodes) leave_one_out_subsample_sums = tf.einsum('j,jk->k', reward_samples, mask_matrix) ys = leave_one_out_subsample_sums / (num_episodes_float - 1) # average of jackknife estimate y_bar = tf.reduce_mean(ys) # acceleration factor d_ys = y_bar - ys a = tf.reduce_sum(tf.pow(d_ys, 3.0)) / tf.maximum( eps, 6.0 * tf.pow(tf.reduce_sum(tf.pow(d_ys, 2.0)), 1.5)) # Step 3, compute z_scores for lb and ub z_score_delta_tail = gaussian_rv.quantile(delta_tail_half) z_score_1_delta_tail = gaussian_rv.quantile(1.0 - delta_tail_half) z_lb = z_0 + (z_score_delta_tail + z_0) / tf.maximum( eps, 1 - a * (z_score_delta_tail + z_0)) z_ub = z_0 + (z_score_1_delta_tail + z_0) / tf.maximum( eps, 1 - a * (z_score_1_delta_tail + z_0)) # Step 4, compute corresponding quantiles and get bootstrap intervals lb_index = tf.cast( tf.maximum( tf.minimum( tf.floor(num_bootstraps * gaussian_rv.cdf(z_lb)), num_bootstraps - 1), 1), tf.int64) ub_index = tf.cast( tf.maximum( tf.minimum( tf.floor(num_bootstraps * gaussian_rv.cdf(z_ub)), num_bootstraps - 1), 1), tf.int64) lb = tf.gather(sorted_subsample_means, lb_index) ub = tf.gather(sorted_subsample_means, ub_index) return lb, ub lb, ub = _compute_bootstrap_lb_ub(is_weighted_reward_samples) else: ValueError('Confidence interval is not implemented!') return [lb, ub] @tf.function def train_step(self, experience: dataset_lib.EnvStep, target_policy: tf_policy.TFPolicy): """Performs a single training step based on batch and MLE. Args: experience: A batch of transitions. Elements must have shape [batch_size, 2, ...]. target_policy: The policy whose value we want to estimate. Returns: The losses and the train op. """ env_step = tf.nest.map_structure(lambda t: t[:, 0, ...], experience) next_env_step = tf.nest.map_structure(lambda t: t[:, 1, ...], experience) if self._policy_network is not None: assert self._policy_optimizer is not None with tf.GradientTape( watch_accessed_variables=False, persistent=True) as tape: tape.watch(self._policy_network.variables) policy_loss = self.compute_policy_loss(env_step) policy_loss += self._policy_regularizer * self._orthogonal_regularization( self._policy_network) policy_grads = tape.gradient(policy_loss, self._policy_network.variables) policy_grad_op = self._policy_optimizer.apply_gradients( zip(policy_grads, self._policy_network.variables)) else: policy_loss = 0.0 if self._q_network is not None: assert self._q_optimizer is not None with tf.GradientTape( watch_accessed_variables=False, persistent=True) as tape: tape.watch(self._q_network.variables) q_loss = self.compute_q_loss(env_step, next_env_step, target_policy) q_grads = tape.gradient(q_loss, self._q_network.variables) q_grad_op = self._q_optimizer.apply_gradients( zip(q_grads, self._q_network.variables)) update_op = self._update_targets() else: q_loss = 0.0 return (tf.reduce_mean(policy_loss), tf.reduce_mean(q_loss)) def compute_policy_loss(self, env_step): policy_loss = -tf.reduce_mean( self._get_log_prob(self._policy_network, env_step)) return policy_loss def compute_q_loss(self, env_step, next_env_step, target_policy): q_value = self._get_q_value(env_step) target_value = tf.stop_gradient( self._get_target_value(next_env_step, target_policy)) reward = self._reward_fn(env_step) td_error = (-q_value + reward + self._gamma * next_env_step.discount * target_value) return tf.math.square(td_error)

42.76306

92

0.66306

794af20f72d7184e76e6ca2f4cf13acda37ebcfe

1,811

py

Python

fwtest/comms/grbl_comms.py

firmware-testing/fwtest

4ab5303ebe94efd224ee21370200dd4f3e1fa997

[ "MIT" ]

null

fwtest/comms/grbl_comms.py

firmware-testing/fwtest

4ab5303ebe94efd224ee21370200dd4f3e1fa997

[ "MIT" ]

null

fwtest/comms/grbl_comms.py

firmware-testing/fwtest

4ab5303ebe94efd224ee21370200dd4f3e1fa997

[ "MIT" ]

null

import time import serial class GrblComms: def __init__(self, port: str) -> None: # This ought to either look at the usb tree, or trigger a re-enumeration # and watch dmesg... for now, require hardcoding the port. self.port = port self.serial = serial.Serial(self.port, timeout=0.1, xonxoff=1, baudrate=115200) def communicate(self, line: str) -> None: print("dump") while self.chat(b''): pass # Restore "defaults" for the build print("reset settings") self.chat(b"\n$RST=$\n") self.wait_for_idle(alarm_ok=True) print("ctrl-x") self.chat(b"\x18") # Ctrl-X to reset self.wait_for_idle(alarm_ok=True) print("unlock") self.chat(b"$X\n") self.wait_for_idle() self.chat(b"$$\n") print("write") self.chat(line) # can be multiple lines print("waiting") self.wait_for_idle() def configure( self, x_steps_per_mm=400, x_axis_max_feed=1000, x_axis_accel=45, max_jerk=0.5 ): return f"$100={x_steps_per_mm}\n$110={x_axis_max_feed}\n$120={x_axis_accel}\n".encode() def chat(self, data): print(">>", data) self.serial.write(data) self.serial.flush() tmp = self.serial.read(1024) print("<<", tmp) return tmp def wait_for_idle(self, alarm_ok=False): # TODO: check timeout instead for i in range(100): data = self.chat(b"?\n") if data.startswith(b"ok\r\n"): data = data[len(b"ok\r\n"):] if data.startswith(b"<Idle"): break elif alarm_ok and data.startswith(b"<Alarm"): break else: raise Exception("Timeout")

28.746032

95

0.556599

794af36ab2e0b0dc3e9178242c3cf74bf0d467da

8,417

py

Python

greenglacier.py

TobyAbel/greenglacier

602fdd59ddb7d6bbc34d1ca38504c7d3d7bfe404

[ "Apache-2.0" ]

null

greenglacier.py

TobyAbel/greenglacier

602fdd59ddb7d6bbc34d1ca38504c7d3d7bfe404

[ "Apache-2.0" ]

null

greenglacier.py

TobyAbel/greenglacier

602fdd59ddb7d6bbc34d1ca38504c7d3d7bfe404

[ "Apache-2.0" ]

null

#!/usr/bin/env python2.7 from __future__ import print_function import os import hashlib import math import binascii import gevent import gevent.pool import gevent.queue import gevent.monkey import pprint gevent.monkey.patch_socket() gevent.monkey.patch_ssl() gevent.monkey.patch_os() from retrying import retry # the following helper functions are (temporarily) shamelessly stolen from boto.glacier.utils _MEGABYTE = 1024 * 1024 DEFAULT_PART_SIZE = 4 * _MEGABYTE MAXIMUM_NUMBER_OF_PARTS = 10000 # This is in USD and is correct for eu-west-1 at the time of writing # CHECK THIS FOR YOURSELF! PRICE_PER_THOUSAND_REQUESTS = 0.055 STORAGE_PRICE_PER_GB_MONTH = 0.004 RETRIEVAL_PRICE_PER_THOUSAND_REQUESTS = 0.055 RETRIEVAL_PRICE_PER_GB = 0.01 def tree_hash(fo): """ Given a hash of each 1MB chunk (from chunk_hashes) this will hash together adjacent hashes until it ends up with one big one. So a tree of hashes. """ hashes = [] hashes.extend(fo) while len(hashes) > 1: new_hashes = [] while True: if len(hashes) > 1: first = hashes.pop(0) second = hashes.pop(0) new_hashes.append(hashlib.sha256(first + second).digest()) elif len(hashes) == 1: only = hashes.pop(0) new_hashes.append(only) else: break hashes.extend(new_hashes) return hashes[0] def chunk_hashes(bytestring, chunk_size=_MEGABYTE): chunk_count = int(math.ceil(len(bytestring) / float(chunk_size))) hashes = [] for i in range(chunk_count): start = i * chunk_size end = (i + 1) * chunk_size hashes.append(hashlib.sha256(bytestring[start:end]).digest()) if not hashes: return [hashlib.sha256(b'').digest()] return hashes def bytes_to_hex(str_as_bytes): return binascii.hexlify(str_as_bytes) def minimum_part_size(size_in_bytes, default_part_size=DEFAULT_PART_SIZE): """Calculate the minimum part size needed for a multipart upload. Glacier allows a maximum of 10,000 parts per upload. It also states that the maximum archive size is 10,000 * 4 GB, which means the part size can range from 1MB to 4GB (provided it is one 1MB multiplied by a power of 2). This function will compute what the minimum part size must be in order to upload a file of size ``size_in_bytes``. It will first check if ``default_part_size`` is sufficient for a part size given the ``size_in_bytes``. If this is not the case, then the smallest part size than can accomodate a file of size ``size_in_bytes`` will be returned. If the file size is greater than the maximum allowed archive size of 10,000 * 4GB, a ``ValueError`` will be raised. """ # The default part size (4 MB) will be too small for a very large # archive, as there is a limit of 10,000 parts in a multipart upload. # This puts the maximum allowed archive size with the default part size # at 40,000 MB. We need to do a sanity check on the part size, and find # one that works if the default is too small. part_size = _MEGABYTE if (default_part_size * MAXIMUM_NUMBER_OF_PARTS) < size_in_bytes: if size_in_bytes > (4096 * _MEGABYTE * 10000): raise ValueError("File size too large: %s" % size_in_bytes) min_part_size = size_in_bytes / 10000 power = 3 while part_size < min_part_size: part_size = math.ldexp(_MEGABYTE, power) power += 1 part_size = int(part_size) else: part_size = default_part_size return part_size # TODO: progress callbacks using basesubscriber class MultipartUploadPart(object): """ Represent a part - have a part number, the upload, etc. self.upload - does what you'd expect this should be the first phase in subclassing below to handle S3 """ pass class MultipartPartUploader(gevent.Greenlet): def __init__(self, upload, work, callback=None, retries=8): gevent.Greenlet.__init__(self) self.upload = upload self.work = work self.retries = retries if callback: self.link(callback) def _run(self): filename, offset, size = self.work print('Loading chunk %s' % offset) chunk = self.readfile(filename, offset, size) return self.upload_part(chunk, offset, size) def readfile(self, filename, offset, size): filesize = os.stat(filename).st_size print('Reading bytes %s to %s (or less, if we run out of file to read) of %s' % (offset * size, offset * size + size, filesize)) with open(filename, 'rb') as fileobj: fileobj.seek(offset * size) return fileobj.read(size) def upload_part(self, chunk, offset, size): @retry(stop_max_attempt_number=self.retries) def retry_upload(range, checksum, body): print('Uploading chunk %s - hashstring %s - range %s' % (offset, checksum, range)) self.upload.upload_part(range=range, checksum=str(checksum), body=body) hashbytes = tree_hash(chunk_hashes(chunk)) hashstring = bytes_to_hex(hashbytes) first_byte = offset * size last_byte = first_byte + len(chunk) - 1 rangestr = 'bytes %d-%d/*' % (first_byte, last_byte) retry_upload(rangestr, hashstring, chunk) return offset, hashbytes class GreenGlacierUploader(object): class UploadFailedException(Exception): pass def __init__(self, vault, concurrent_uploads=10, part_size=4194304): self.vault = vault self.part_size = part_size # will be overridden on upload self.concurrent_uploads = concurrent_uploads def prepare(self, filename, description=None): """ Allows you to check the vital stats (including cost) of an upload before you commit to it. """ self.filename = filename self.description = description or filename self.filesize = os.stat(self.filename).st_size self.minimum = minimum_part_size(self.filesize) self.part_size = max(self.part_size, self.minimum) if self.part_size else self.minimum self.total_parts = int((self.filesize / self.part_size) + 1) print('Preparing to upload %s with %s %s-sized parts' % (filename, self.total_parts, self.part_size)) print('This is expected to cost $%s in request fees, transfer is free' % (PRICE_PER_THOUSAND_REQUESTS * self.total_parts / 1000)) print('Storing this archive will cost $%s per month' % (STORAGE_PRICE_PER_GB_MONTH * self.filesize / 1000000000)) print('Retrieving this archive will cost $%s in request fees, and $%s in transfer fees' % (RETRIEVAL_PRICE_PER_THOUSAND_REQUESTS / 1000, RETRIEVAL_PRICE_PER_GB * self.filesize / 1000000000)) def upload(self, filename=None, description=None): if filename and filename != self.filename: self.prepare(filename, description) else: self.description = description or self.description work_queue = gevent.queue.Queue() print('Uploading %s with %s %s-sized parts...' % (self.filename, self.total_parts, self.part_size)) self.res = [None] * self.total_parts multipart_upload = self.vault.initiate_multipart_upload(archiveDescription=self.description, partSize=str(self.part_size)) for part in range(self.total_parts): work_queue.put((self.filename, part, self.part_size)) active = gevent.pool.Pool(self.concurrent_uploads, MultipartPartUploader) while not work_queue.empty(): # TODO: replace with list e.g. if work: spawn(m, work.pop()) work = work_queue.get() active.spawn(multipart_upload, work, self.callback) active.join() # wait for final chunks to upload.. print('Completing uploading with total size %s' % (self.filesize)) final_checksum = bytes_to_hex(tree_hash(self.res)) multipart_upload.complete(archiveSize=str(self.filesize), checksum=final_checksum) def callback(self, g): print('greenlet finished, saving value') try: part_num, chunk_hash = g.get() self.res[part_num] = chunk_hash except: g.upload.abort() raise

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